conservation agriculture: its effects on crop and soil in ... · conservation agriculture: its...

Conservation Agriculture: Its effects on crop and soil

in rice-based cropping systems in Bangladesh

By

Md. Ariful Islam

MS (Agronomy)

This thesis is presented for the degree of

Doctor of Philosophy

Of

Murdoch University

2016

II

Declaration

I declare that this thesis is my own account of my research and contains as its main

content work which has not previously been submitted for a degree at any tertiary

institution.

Md. Ariful Islam

III

Abstract

Intensive rice-based cropping systems in the Eastern Indo-Gangetic Plains (Eastern India

and Bangladesh) have played a pivotal role in increasing food security in that region but

sustainability of these cropping systems is under threat. Conservation agriculture (CA) ‒

cropping systems based on minimum soil disturbance, crop residue retention and suitable

crop rotations ‒ has been proposed to address these challenges but there has been

limited research on its effects on crop productivity and soil properties in Bangladesh. This

thesis examines the effects of implementing minimum soil disturbance and increased crop

residue retention on soil properties and crop performance over three years in two rice-

based rotations. Two field trials were conducted during 2010-2013 in contrasting triple-

cropping rotations (with crop number in parentheses): 1. Legume-dominated rotation ‒

lentil (1, 4 and 7)-mungbean (2 and 5)-monsoon rice (3 and 6) in an Alluvial soil region;

and 2. Cereal-dominated rotation ‒ wheat (1, 4 and 7)-mungbean (2 and 5)-monsoon rice

(3 and 6) in the High Barind Tract (HBT) region of north-west Bangladesh. There were

three tillage treatments in main plots ‒ strip tillage (ST), bed planting (BP) and

conventional tillage (CT). Sub-plots comprised two levels of residue ‒ high residue (HR)

and low residue (LR). Puddled transplanted rice was applied in CT and unpuddled

transplanted rice in ST and BP. This thesis focuses on soil properties and the growth and

yield of the cool-dry season crops in each year, namely lentil on the Alluvial soil and wheat

on the HBT soil.

During the first two growing seasons treatment effects on soil properties and crop

performance were marginal but became clearly apparent in the third year. In the legume-

dominated system, grain yield of lentil was 15 % higher in HR than LR averaged across all

tillage types in Year 2. In Year 3, the yield of lentil was higher by 23 % in ST and 18 % in BP

compared with CT. In the cereal-dominated system, grain yield was not affected by tillage

and residue treatments in Year 1. However, in Year 2, grain yield of wheat was depressed

by 39 % in BP due to poor crop establishment. In Year 3, the yield of wheat was greater by

9 % in ST and 7 % in BP than CT; wheat yield of HR was 3 % higher compared to LR.

IV

The soil water content (SWC) increased and bulk density (BD) and penetration resistance

(PR) decreased in surface soil (0-5 cm) with ST and at 5-10 cm and 10-15 cm soil depth

with BP, compared to CT. The retention of more intact residue left between the plant

rows conserved more SWC and lowered the soil BD and PR of surface soil under ST.

Implementation of ST and BP with HR treatment gradually improved soil physical

properties and alleviated puddling effects that characterise current practices (CT and LR)

in rice-based systems. Such improvements are probably due to increases in soil organic

carbon (SOC) and total nitrogen (TN) with ST and BP. Greater root growth under BP was

not associated with increased grain yield. However, the overall improvement in soil

surface conditions and greater root growth at depth may have allowed extraction of water

and nutrients from a larger soil volume in ST resulting in a gradual increase in crop

productivity over time.

After 2.5 years in both legume- and cereal-dominated rotations, the SOC concentrations,

SOC-stocks and labile C fraction (water soluble carbon ‒ WSC) at 0-7.5 cm soil depth were

greater in ST than CT. By contrast, the SOC concentrations and storage, and WSC

increased at 7.5-15 cm soil depth in BP compared to CT and ST. Soil C losses through the

emission of CO2 were greater in CT than ST and BP. The relative efficacy of tillage in

storing SOC was in the order of ST>BP>CT. High residue retention increased SOC

concentrations, SOC storage, WSC and CO2 emission from soil. In the cereal-dominated

rotation, ST sequestered 0.44-0.20 Mg C/ha annually while CT caused 0.41-0.66 Mg C/ha

loss at 0-15 cm soil depth. In contrast to the legume-dominated rotation, neither CT nor

ST sequestered SOC but ST reduced the loss by 0.40 Mg C/ha annually compared to CT.

Based on the C balance, it is estimated that annual carbon inputs of 3.8 Mg C/ha under ST

and 6 Mg C/ha under CT condition in the legume-dominated system, and 1.0 Mg C/ha

under ST and 7.7 Mg C/ha under CT condition in the cereal-dominated system, would be

required to maintain SOC at the antecedent level.

V

In the present study, ST and HR treatment increased TN, N-stocks, total soluble nitrogen

and potentially mineralizable nitrogen (PMN) in the surface soil (0-7.5 cm) as compared to

CT and LR at the end of Crop 7. In ST and HR, the lower mineral N (NH₄-N and NO₃-N) and

larger PMN indicated the greater immobilization or less mineralization of N, or both, and

restricted the potential losses of N. Retention of HR resulted in positive N balance while LR

caused a negative N balance. Regardless of treatment variation, the soil TN, N-stocks and

available N were greater in the cereal-dominated cropping system than in the legume-

dominated system, probably due to carry-over of higher N fertilizer rates applied to the

cereal crop, and greater above- and below-ground biomass. The changes of soil TN due to

residue were only apparent in legume-dominated system. The greater input derived from

nitrogenous residue of mungbean and lentil may account for the positive effects of HR in

the legume-dominated system.

Application of ST and HR has potential for increasing carbon sequestration and N

accumulation while reducing N losses, hence improving soil properties and thereby crop

growth and yields, within 2-3 years in rice-based systems of Bangladesh. However, further

studies are required over a longer time period to evaluate the performance of unpuddled

rice rotated with ST non-rice crops with a range of residue retention levels under different

soil, climatic, and socio-economic conditions in the eastern Indo-Gangetic Plains.

VI

Table of Contents

Declaration ................................................................................................. II

Abstract ..................................................................................................... III

Table of Contents ....................................................................................... VI

List of Tables .......................................................................................... XVIII

List of Figures ......................................................................................... XXIV

Appendices ......................................................................................... XXXVII

List of Abbreviations .......................................................................... XXXVIII

List of Botanical Names ........................................................................... XLIII

Acknowledgements ............................................................................... XLIV

1 Literature Review ..................................................................................... 1

1.1 Introduction ......................................................................................................... 1

1.2 Conventional agriculture: basic concepts .............................................................. 6

1.2.1 Concept of tillage ................................................................................................... 6

1.2.2 Crop residue management options under conventional system .......................... 9

1.3 Concept of conservation agriculture ..................................................................... 9

1.4 The key components of Conservation Agriculture ............................................... 10

1.4.1 Minimum soil disturbance ................................................................................... 10

1.4.1.1 Minimum tillage ............................................................................................ 10

1.4.1.2 No-tillage ....................................................................................................... 10

1.4.1.3 Strip tillage .................................................................................................... 11

1.4.1.4 Permanent raised planting ........................................................................... 11

VII

1.4.2 Permanent ground cover: residue management ................................................ 12

1.4.3 Crop rotation........................................................................................................ 13

1.4.3.1 Crop diversification in rice-based systems.................................................... 13

1.4.3.2 Inclusion of legumes in rice-based systems .................................................. 15

1.5 Development of conservation agriculture............................................................ 16

1.6 Conservation agriculture adoption worldwide ..................................................... 17

1.7 Constraints of conservation agriculture ............................................................... 19

1.8 Effect of conservation agriculture on crop performance and system productivity 21

1.9 Influence of conservation agriculture on soil properties ...................................... 25

1.9.1 Soil physical properties ........................................................................................ 25

1.9.1.1 Soil structure and aggregation ...................................................................... 26

1.9.1.2 Soil bulk density and porosity ....................................................................... 26

1.9.1.3 Soil penetration resistance ........................................................................... 27

1.9.1.4 Soil water content ......................................................................................... 28

1.9.2 Effects of conservation agriculture on soil organic carbon and its fractions ...... 29

1.9.2.1 Soil organic carbon ........................................................................................ 29

1.9.2.2 Soil organic carbon turnover ......................................................................... 30

1.9.2.2.1 Water soluble carbon ............................................................................. 30

1.9.2.2.2 Carbon dioxide (mineralization, root and microbial respiration) .......... 31

1.9.3 Effects of conservation agriculture on soil nitrogen dynamics ........................... 32

1.9.3.1 Total soil nitrogen ......................................................................................... 33

1.9.3.2 Mineral nitrogen ........................................................................................... 33

1.9.3.3 Potentially mineralizable nitrogen ................................................................ 35

1.10 Research gaps and objectives ............................................................................ 35

VIII

2 Effects of tillage and residue management on yield and yield attributes of

winter crops in rice-based systems in Bangladesh ......................................38

2.1 Introduction ....................................................................................................... 38

2.2 Materials and Methods ...................................................................................... 40

2.2.1 Climate and weather ........................................................................................... 41

2.2.2 Experimental design and treatments .................................................................. 43

2.2.3 Residue management protocols.......................................................................... 44

2.2.4 Agronomy of legume-dominant system .............................................................. 46

2.2.4.1 Nutrient management .................................................................................. 47

2.2.4.2 Disease, weed and pest management of lentil ............................................ 48

2.2.4.3 Agronomic measurements of lentil .............................................................. 48

2.2.5 Agronomy of cereal-dominated systems ............................................................ 50

2.2.5.1 Crop husbandry of wheat ............................................................................. 50

2.2.5.2 Agronomic measurements of wheat ............................................................ 52

2.2.6 Yield measurements for lentil and wheat ........................................................... 52

2.2.7 Statistical analysis ................................................................................................ 52

2.3 Results ............................................................................................................... 53

2.3.1 Weather ............................................................................................................... 53

2.3.2 Tillage and residue effects on crop performance of legume-dominated system 54

2.3.2.1 Seed and straw yield of lentil ........................................................................ 54

2.3.2.2 Yield components of lentil ............................................................................ 56

2.3.2.3 Correlation and regression of yield and yield components of lentil ............ 60

2.3.2.4. Yield performance of rice and mungbean in legume-dominated system ... 62

2.3.3 Tillage and residue effects on crop performance of cereal-dominated system . 63

2.3.3.1 Grain and straw yield of wheat ..................................................................... 63

IX

2.3.3.2 Yield components of wheat .......................................................................... 65

2.3.3.3 Correlation and regression of yield and yield components of wheat........... 70

2.3.3.4 Yield performance of rice and mungbean in cereal-dominated system ...... 72

2.4 Discussion ........................................................................................................... 73

2.4.1 Lentil .................................................................................................................... 74

2.4.2 Wheat .................................................................................................................. 77

2.4.3 Cropping system productivity .............................................................................. 79

2.5 Conclusions ........................................................................................................ 81

3 Effects of tillage and residue management on soil strength, soil water and

crop root growth in rice-based systems on silty loam soil in Bangladesh ... 82

3.1 Introduction ....................................................................................................... 82

3.2 Materials and method ........................................................................................ 83

3.2.1 Treatment details................................................................................................. 83

3.2.2 Measurement of soil water content and penetration resistance ....................... 84

3.2.3 Root sampling of lentil ......................................................................................... 84

3.2.4 Nodulation of lentil .............................................................................................. 85

3.2.5 Root sampling of wheat ....................................................................................... 85

3.2.6 Measurement of root parameters ....................................................................... 86

3.2.7 Statistical analysis ................................................................................................ 86

3.3 Results ................................................................................................................ 87

3.3.1 Soil physical properties during root assessment of lentil at Alipur ..................... 87

3.3.1.1 Volumetric soil water content ...................................................................... 87


3.3.2 Root characteristics of lentil ................................................................................ 89

3.3.3 Root and shoot growth and their ratio for lentil ................................................. 93

X

3.3.4 Nodulation of lentil .............................................................................................. 94

3.3.5 Soil physical properties during root assessment of wheat at Digram ................ 96

3.3.5.1 Volumetric soil water content ...................................................................... 96


3.3.6 Root characteristics of wheat .............................................................................. 97

3.3.7 Root and shoot growth, and their ratio of wheat ............................................. 103

3.4 Discussion .........................................................................................................104

3.4.1 Soil penetration resistance and soil water content .......................................... 104

3.4.2 Root distribution as affected by tillage and residue over time ......................... 106

3.4.3 Rooting patterns of wheat and lentil ................................................................ 108

3.4.4 Root distribution related to soil water content and penetration resistance .... 109

3.4.5 Above-ground shoot growth and yield influenced by rooting patterns ........... 110

3.5 Conclusion ........................................................................................................110

4 Effects of tillage and residue management on soil physical properties in

rice-based cropping systems in Bangladesh ............................................. 112

4.1 Introduction ......................................................................................................112

4.2 Materials and methods .....................................................................................112

4.2.1 Treatments and crop management ................................................................... 112

4.2.2 Soil bulk density ................................................................................................. 113

4.2.3 Soil temperature ................................................................................................ 113

4.2.4 Volumetric soil water content ........................................................................... 114

4.2.5 Soil penetration resistance ................................................................................ 115

4.2.6 Sampling time and location ............................................................................... 116

4.2.7 Statistical analysis .............................................................................................. 117

4.3 Results ..............................................................................................................117

4.3.1 Alipur ................................................................................................................. 117

XI

4.3.1.1 Soil bulk density .......................................................................................... 118

4.3.1.1.1 Tillage effects ........................................................................................ 118

4.3.1.1.2 Residue effects ..................................................................................... 119

4.3.1.2. Volumetric soil water content and penetration resistance ....................... 122

4.3.1.2.1 Tillage effects ........................................................................................ 122

4.3.1.2.2 Residue effects ..................................................................................... 123

4.3.1.3 Trends of volumetric soil water content and penetration resistance

following planting of lentil ...................................................................................... 125

4.3.1.3.1 Tillage effects ........................................................................................ 125

4.3.1.3.2 Residue effects ..................................................................................... 126

4.3.1.4 Relationship between soil physical properties ........................................... 128

4.3.1.4.1 Relation between soil bulk density and penetration resistance .......... 128

4.3.1.5 Depth distribution of soil physical parameters at bed planting and strip

tillage system in Alipur ............................................................................................ 129

4.3.1.5.1 Distribution of soil bulk density ............................................................ 129

4.3.1.5.2 Distribution of volumetric soil water content ...................................... 130

4.3.1.5.3 Distribution of soil penetration resistance ........................................... 130

4.3.1.6 Soil temperature at Alipur .......................................................................... 131

4.3.2 Digram ................................................................................................................ 133

4.3.2.1 Soil bulk density at different depth............................................................. 133

4.3.2.1.1 Tillage effects ........................................................................................ 133

4.3.2.1.2 Residue effects ..................................................................................... 134

4.3.2.2 Volumetric soil water content and penetration resistance ........................ 135

4.3.2.2.1 Tillage effects ........................................................................................ 136

XII

4.3.2.2.2 Residue effects ..................................................................................... 137

4.3.2.3 Trends of volumetric soil water content and penetration resistance

following planting of wheat .................................................................................... 140

4.3.2.3.1 Tillage effects ....................................................................................... 140

4.3.2.3.2 Residue effects ..................................................................................... 141

4.3.2.4 Relationship between soil physical properties ........................................... 143

4.3.2.4.1 Relation between soil bulk density and penetration resistance.......... 143

4.3.2.5 Depth distribution of soil physical parameter at bed planting and strip tillage

system in Digram .................................................................................................... 144

4.3.2.5.1 Distribution of soil bulk density ........................................................... 144

4.3.2.5.2 Distribution of volumetric soil water content ...................................... 145

4.3.2.5.3 Distribution of soil penetration resistance .......................................... 145

4.3.2.6 Soil temperature at Digram ........................................................................ 146

4.4 Discussion .........................................................................................................147

4.4.1 Soil bulk density and penetration resistance .................................................... 147

4.4.2 Volumetric soil water content ........................................................................... 149

4.4.3 System differences ............................................................................................ 152

4.5 Conclusion ........................................................................................................152

5 Short-medium term effects of conservation management practices on soil

organic carbon pools in rice-based systems in Bangladesh ....................... 154

5.1 Introduction ......................................................................................................154

5.2 Materials and Methods .....................................................................................157

5.2.1 Experimental site and treatment details ........................................................... 157

5.2.2 Quality assurance and quality control procedures ........................................... 157

XIII

5.2.3 Estimation of annual C inputs ............................................................................ 158

5.2.4 Soil sampling and analytical methods ............................................................... 158

5.2.5 Bulk density ........................................................................................................ 159

5.2.6 Soil organic carbon, SOC-stocks and stratification ratio ................................... 159

5.2.7 Soil carbon sequestration and C build-up or C losses (%) ................................. 160

5.2.8 Water soluble organic carbon ........................................................................... 160

5.2.9 Measurement of soil carbon dioxide emission ................................................. 161

5.2.10 Statistical analysis ............................................................................................ 162

5.3 Results .............................................................................................................. 162

5.3.1 Alipur .................................................................................................................. 162

5.3.1.1 Soil organic carbon concentrations ............................................................. 162

5.3.1.2 Distribution and stratification of SOC concentrations at strip tillage system

in Alipur ................................................................................................................... 164

5.3.1.3 Distribution and stratification of SOC concentrations at bed planting system

in Alipur ................................................................................................................... 164

5.3.1.4 Temporal variation of soil organic carbon concentrations ......................... 165

5.3.1.5 Soil organic carbon stocks and sequestration ............................................ 166

5.3.1.6 Water soluble carbon .................................................................................. 167

5.3.1.7 Carbon dioxide-carbon (CO₂-C) emission .................................................... 168

5.3.1.8 Correlation among different organic carbon pools .................................... 170

5.3.1.9 Carbon balances .......................................................................................... 171

5.3.2 Digram ................................................................................................................ 173

5.3.2.1 Soil organic carbon concentrations ............................................................. 173

5.3.2.2 Distribution and stratification of SOC concentrations at strip tillage system

in Digram ................................................................................................................. 175

XIV


in Digram ................................................................................................................. 175

5.3.2.4 Temporal variation of SOC .......................................................................... 176

5.3.2.5 Soil organic carbon stocks and sequestration ............................................ 177

5.3.2.6 Water soluble carbon ................................................................................. 178

5.3.2.7 Carbon dioxide-carbon (CO₂-C) emission .................................................... 179

5.3.2.8 Correlation among different organic carbon pools .................................... 181

5.3.2.9 Carbon balances .......................................................................................... 182

5.4 Discussion .........................................................................................................184

5.4.1 Tillage effects ..................................................................................................... 184

5.4.2 Residue effects .................................................................................................. 187

5.4.3 Dynamics of soil organic carbon concentrations .............................................. 189

5.4.4 Distribution and stratification of soil organic carbon concentrations .............. 191

5.4.5 Cropping system differences ............................................................................. 193

5.5 Conclusions .......................................................................................................195

6 Effects of tillage and residue on N cycling and dynamics in two paddy soils

in Bangladesh .......................................................................................... 197

6.1 Introduction ......................................................................................................197

6.2 Materials and methods .....................................................................................200

6.2.1 Site description and field management ............................................................ 200

6.2.2 Plant measurements.......................................................................................... 200

6.2.3 Soil measurements ............................................................................................ 200

6.2.3.1 Soil sampling procedures ............................................................................ 200

6.2.3.2 Bulk density ................................................................................................. 201

6.2.3.3 Total soil N and N-stocks............................................................................. 201

XV

6.2.3.4 Soil N accumulation ..................................................................................... 202

6.2.3.5 Nitrogen uptake .......................................................................................... 202

6.2.3.6 Mineral N pools ........................................................................................... 202

6.2.3.7 Anaerobic potentially mineralizable N ........................................................ 202

6.2.3.8 Total soluble N ............................................................................................ 203

6.2.3.9 Nitrogen balance calculations ..................................................................... 203

6.2.4 Statistical analysis .............................................................................................. 204

6.3 Results .............................................................................................................. 204

6.3.1 Alipur .................................................................................................................. 204

6.3.1.1 Total soil N concentrations ......................................................................... 204

6.3.1.2 Distribution and stratification of total soil N concentrations at strip tillage

system in Alipur ....................................................................................................... 206

6.3.1.3 Distribution and stratification of total soil N concentrations at bed planting

system in Alipur ....................................................................................................... 206

6.3.1.4 Temporal variation of soil total nitrogen concentrations ........................... 207

6.3.1.5 N-stocks ....................................................................................................... 208

6.3.1.6 Nitrogen accumulation................................................................................ 209

6.3.1.7 Nitrogen uptake by lentil plants.................................................................. 209

6.3.1.8 Mineral N pools (NH₄-N plus NO₃-N) .......................................................... 210

6.3.1.9 Anaerobic potentially mineralizable N ........................................................ 212

6.3.1.10 Total soluble N .......................................................................................... 213

6.3.1.11 Plant N concentrations of lentil ................................................................ 214

6.3.1.12 Relationships among TN, N-stocks, plant N and the available indices of N

................................................................................................................................. 215

XVI

6.3.1.13 Nitrogen balances ..................................................................................... 218

6.3.2 Digram ............................................................................................................... 220

6.3.2.1 Total soil N concentrations ......................................................................... 220


system in Digram .................................................................................................... 221


system in Digram .................................................................................................... 222

6.3.2.4 C: N ratio ..................................................................................................... 223

6.3.2.5 N-stocks ....................................................................................................... 224

6.3.2.6 N uptake by wheat plants ........................................................................... 225

6.3.2.7 Mineral N pools (NH₄-N plus NO₃-N) .......................................................... 226

6.3.2.8 Anaerobic potentially mineralizable N ....................................................... 228

6.3.2.9 Total soluble N ............................................................................................ 229

6.3.2.10 Plant N concentrations in wheat .............................................................. 230


................................................................................................................................. 231

6.3.2.12 Nitrogen balances at Digram .................................................................... 234

6.4 Discussion .........................................................................................................236

6.4.1 Soil total N concentrations and N-stocks .......................................................... 236

6.4.2 Nitrogen balance ............................................................................................... 239

6.4.3 Nitrogen turnover and cycling ........................................................................... 240

6.4.4 Responses of plant growth and plant N to N supply ......................................... 243

6.4.5 Cropping system differences ............................................................................. 244

6.4.6 Optimum N management under CA system ..................................................... 245

6.5 Conclusions .......................................................................................................245

XVII

7 General discussion and conclusions ...................................................... 247

7.1 Tillage and residue management effects on crop performance and soil properties

.............................................................................................................................. 247

7.2 Non-treatment factors affecting crop growth and yield ..................................... 252

7.3 Constraints of different treatments and their potential solution ........................ 254

7.4 Prospects and future research directions ........................................................... 256

7.5 Conclusion ........................................................................................................ 258

8 References ............................................................................................ 260

XVIII

List of Tables

Table 1.1. Area of arable crop land under conservation agriculture by region in

2013

19

Table 1.2. Summary of major constraints of conservation agriculture systems 20

Table 1.3. Constraints to cropping systems in the Indo-Gangetic Plains 22

Table 2.1. Site characteristics of two different experiments under different

cropping systems

41

Table 2.2. Basic soil properties and nutrient status of study sites at Alipur and

Digram

43

Table 2.3. Details of three tillage treatments at Alipur and Digram 44

Table 2.4. Details of residue management protocols of the lentil-mungbean-

monsoon rice cropping sequence at Alipur in 2010-13

45

Table 2.5. Details of residue management protocols of wheat-mungbean-

monsoon rice cropping sequence at Digram in 2010-13

46

Table 2.6. Details of crop, variety, seed rate or seedlings/hill, row spacing,

sowing and harvesting date of lentil-mungbean-monsoon rice

cropping sequence during 2010-2013 at Alipur

47

Table 2.7. Details of disease, insects and weeds in lentil and their management

practices

49

Table 2.8. Details of crop, variety, seed rate or seedlings/hill, row spacing,,

sowing and harvesting date of wheat-mungbean-monsoon rice

cropping sequence at Digram during 2010-2013

50

Table 2.9. Tillage and residue effects on plant population and branching of lentil 56

Table 2.10. Tillage and residue effects on plant population (%) affected by foot

and collar rot diseases of lentil in 2012-13

57

Table 2.11. Tillage and residue effects on plant height, pods/plant and seeds/plant

of lentil

58

Table 2.12 Tillage and residue effects on 1000-seed weight and harvest index 60

Table 2.13. Correlation matrix of important yield attributes and yields of lentil 61

XIX

Table 2.14. Tillage and residue effects on grain and straw yield of rice and

mungbean of lentil-mungbean-monsoon rice cropping system in

Alipur. Note: no yield results are available for Crop 2 (mungbean) due

to crop damage by heavy rainfall

63

Table 2.15. Tillage and residue effects on plant population and plant height (cm)

of wheat

65

Table 2.16. Tillage and residue effects on tillers and effective tillers per plant of

wheat

67

Table 2.17. Tillage and residue effects on spikes/m², spike length (cm) and

spikelets/spike of wheat

68

Table 2.18. Tillage and residue effects on grains/spike, 1000-seed weight and

harvest index (%) of wheat

69

Table 2.19. Correlation matrix of important yield attributes and yields of wheat 71

Table 2.20. Tillage and residue effects on grain and straw yield of rice and

mungbean of wheat-mungbean-monsoon rice cropping system in

Digram. Note; no yield results are available for Crop 2 (mungbean) due

to crop damage by heavy rainfall

73

Table 3.1. Total root dry weight, shoot dry weight and root to shoot ratio (g/g) of

five lentil plants under different tillage and residue management at

Alipur

93

Table 3.2. Tillage and residue effects on nodulation of lentil in legume-

dominated rice-based system

95

Table 3.3. Total root dry weight, shoot dry weight and root to shoot ratio (g/g) of

five wheat plants under different tillage and residue management at

Digram

103

Table 4.1. Soil bulk density (g/cc) at three different depths (0-5 cm, 5-10 cm and

10-15 cm) under tillage and residue after different crop in legume-

dominated system in Alipur

121

XX

Table 4.2. Soil penetration resistance (MPa) at three different depths (0-5 cm, 5-

10 cm and 10-15 cm) under tillage and residue after different crop in

cereal-dominated system in Digram

139

Table 5.1. Carbon dioxide measurements at different crop growth stages during

2010-13 of rice-based system

161

Table 5.2. Tillage and residue effects on soil organic carbon concentrations and

stratification ratio of SOC concentrations during 2.5 years of legume-

dominated rice-based system at Alipur

163

Table 5.3. Tillage and residue effects on soil organic carbon stocks (Mg C/ha) and

sequestration (Mg C/ha/yr) of legume-dominated rice-based system

at Alipur

167

Table 5.4. Tillage and residue effects on water soluble carbon (mg/kg) of legume-

dominated rice-based system at Alipur

168

Table 5.5. Correlation among soil organic carbon forms of legume-dominated

rice-based system at Alipur in 2012-13 (n = 96)

170

Table 5.6. Estimated carbon balance for the legume-dominated rice-based

rotation at Alipur considering residue of eight consecutive crops in

2010-2013. STHR = strip tillage-high residue; STLR = strip tillage-low

residue; CTHR = conventional tillage-high residue; CTLR = conventional

tillage-low residue

172


stratification ratio of SOC concentrations during 2.5 years of legume-

dominated rice-based system at Digram

174

Table 5.8. Tillage and residue effects on soil SOC-stocks (Mg C/ha) and

sequestration (Mg C/ha/yr) of cereal-dominated rice-based system at

Digram

178

Table 5.9. Tillage and residue effects on water soluble carbon (mg/kg) of cereal-

dominated rice-based system at Digram

179

Table 5.10. Tillage and residue effects on CO₂-emission (g CO2 m-2day-1) at 180

XXI

different growth stages of wheat at Digram in 2010-11

Table 5.11. Correlation among soil organic carbon forms of cereal-dominated rice-

based system at Digram in 2012-13 (n = 96)

181

Table 5.12. Estimated carbon balance for the cereal-dominated rice-based

rotation at Digram considering residue of eight consecutive crops in



tillage-low residue

183

Table 6.1. Tillage and residue effects on total soil N concentrations and

stratification ratio of TN concentrations during 2.5 years of the

legume-dominated rice-based cropping system at Alipur

205

Table 6.2. Tillage and residue effects on N-stocks (Mg N/ha) and N-accumulation

rate during 2.5 years of the legume-dominated rice-based cropping

system at Alipur

209

Table 6.3. Tillage and residue effects on N uptake by lentil plants in 2010-11,

2011-12 and 2012-13

210

Table 6.4. Tillage and residue effects on mineral N (mg N/kg) at 0-15 cm depth at

Alipur in 2011-12

211

Table 6.5. Tillage and residue effects on mineral N (mg N/kg) at 0-7.5 and 7.5-15

cm soil depth at Alipur in 2012-13

212

Table 6.6. Tillage and residue effects on potentially mineralizable N (PMN) at 0-

7.5 and 7.5-15 cm soil depth at Alipur in 2012-13

213

Table 6.7. Tillage and residue effects on total soluble N in legume-dominated

rice-based cropping system at Alipur in 2011-13

214

Table 6.8. Tillage and residue effects on plant N concentrations of lentil in 2010-

11, 2011-12 and 2012-13

215

Table 6.9. Correlation matrix for the relationships among soil total N (TN), N-

stocks, lentil plant N and the indices of N availability at 0-15 cm at

Alipur in 2012-13 (n = 24)

217

XXII

Table 6.10. Estimated nitrogen balance for the legume-dominated rice-based

rotation at Alipur considering residue of eight consecutive crops in



tillage-low residue

219

Table 6.11. Tillage and residue effects on total soil N (TN) concentrations and

stratification ratio (SR) of TN concentrations during 2.5 years of a

cereal-dominated rice-based cropping system at Digram

220

Table 6.12. Tillage and residue effects on C-N ratio during 2.5 years of a cereal-

dominated rice-based cropping system at Digram

223

Table 6.13. Tillage and residue effects on N-stocks (Mg N/ha) and N accumulation

rates of the cereal-dominated rice-based cropping system in 2010-13

at Digram

224

Table 6.14. Tillage and residue effects on N uptake by wheat plants in 2010-11,

2011-12 and 2012-13

225

Table 6.15. Tillage and residue effects on mineral N (mg N/kg) at 0-15 cm soil

depth in 2011-12 at Digram

226

Table 6.16. Tillage and residue effects on mineral N (mg N/kg) at 0-7.5 and 7.5-15

cm soil depth in 2012-13 at Digram

227

Table 6.17. Tillage and residue effects on anaerobic potentially mineralizable N

(PMN) at Digram in 2012-13

229

Table 6.18. Tillage and residue effects on total soluble N in legume-dominated

rice-based system at Digram in 2011-13

230

Table 6.19. Tillage and residue effects on plant N concentrations of wheat in

2010-11, 2011-12 and 2012-13

231

Table 6.20. Correlation matrix for the relationships among TN, N-stocks, plant N

and the available indices of N at Digram at 0-15 cm in 2012-13 (n = 24)

233

Table 6.21. Estimated nitrogen balance for the cereal-dominated rice-based

rotation at Digram considering residue of eight consecutive crops in

235

XXIII



tillage-low residue

XXIV

List of Figures

Figure 1.1. Schematic diagram of rice-dryland ecosystem showing conventional

and conservation management. Adapted from Zhou et al.(2014)

2

Figure. 1.2. Potential benefits of conservation agriculture at eco-system level.

Adapted from Srinivasarao et al. (2015)

6

Figure 1.3. The activities of farmer’s cultivation techniques, a) puddling for rice

cultivation, b) conventional cropping based on intensive tillage, c)

broadcast seed and fertilizer, d) levelling following tillage and e)

crop residue burning

7

Figure. 1.4. Problems associated with conventional agriculture systems in rice-

based system in Indo-Gangetic Plains. Modified from Devkota

(2011)

8

Figure 1.5. A Versatile Multi-crop Planter is using for strip tillage 11

Figure 1.6. A Versatile Multi-crop Planter is using for reshaping permanent

raised bed

12

Figure 1.7. Extent of global area of conservation agriculture over time.

(Source: above the respective bar)

18

Figure 1.8. The soil nitrogen cycle. Adapted from Hofman and Cleemput (2004) 32

Figure 2.1. General soil map of Bangladesh showing field study sites (A); High

Barind Tract, Digram, Godagari, Rajshahi (red circle) in figure (B);

and; Alipur, Durgapur, Rajshahi (yellow circle) in figure (C)

42

Figure 2.2. Monthly and annual rainfall, mean maximum and minimum

temperatures over the 33-months period of 2010-2013 at the

experimental site

54

Figure 2.3. Effects of tillage and residue retention on lentil seed yield (Figure

a1-c1) and straw yield (Figure a2–c2) over three growing seasons.

ST — strip tillage, BP — bed planting, CT — conventional tillage; HR

— high residue, LR — low residue. Values are means of four

replicates ± standard error of mean and the floating error bar on

55

XXV

each figure represents the least significant difference (LSD) for

significant effects at P≤0.05

Figure 2.4. Regression of a) plant population and seed yield, b) branches/plant

and seed yield and c) pods/plant and seed yield for three years of

results (2010-13)

62

Figure 2.5. Effects of tillage and residue on wheat grain yield (Figure a1-c1) and

straw yield (Fig a2–c2). ST — strip tillage, BP — bed planting, CT —

conventional tillage; HR — high residue, LR — low residue. Values

are means of four replicates, ± standard error of mean and the

floating error bar on each figure represents the least significant

difference (LSD) for significant effects only at P≤0.05

64

Figure 2.6. Regression of a) plant population and grain yield, b) spikes/m² and

grain yield and c) spikelets/spike and grain yield for three years of

results (2010-13)

72

Figure 3.1. Tillage and residue effects on mean volumetric soil water content

(%) (a1-a3) and mean penetration resistance (MPa) (b1-b3) at three

soil depths (0-5 cm, 5-10 cm and 10-15 cm) at Alipur during 2010-

11 to 2012-13. The floating error bars indicate the average least

significant difference (LSD) at P≤0.05 for significant treatment and

depth difference

88

Figure 3.2. Tillage and residue effects on lentil root distribution at 0-15 cm soil

depth during the 2010-11 growing season. Root parameters

measured are a) Root volume (cm³), b) Root dry weight (g), c) Root

length (m), d) Root length density-RLD (cm/cm³) and e) Specific

root length-SRL (m/g). Error bars indicate ± 1 standard error of the

mean

90

Figure 3.3. Tillage and residue effects on lentil root distribution at 0-10 cm and

10-20 cm soil depth during the 2011-12 growing season. Root

parameters measured are a) Root volume (cm³), b) Root dry weight

91

XXVI

(g), c) Root length (m), d) Root length density-RLD (cm/cm³) and e)

Specific root length-SRL (m/g). Error bars indicate ± 1 standard

error of the mean

Figure 3.4. Tillage and residue effects on lentil root distribution at 0-10 cm and

10-20 cm soil depth during the 2012-13 growing season. Root

parameters measured are a) Root volume (cm³), b) Root dry weight

(g), c) Root length (m), d) Root length density-RLD (cm/cm³) and e)

Specific root length-SRL (m/g). Error bars indicate ± 1 standard

error of the mean

92

Figure 3.5. Tillage and residue effects on mean volumetric soil water content

(%) (a1-a3) and mean penetration resistance (MPa) (b1-b3) at three

soil depths (0-5 cm, 5-10 cm and 10-15 cm) at Digram during 2010-

11 to 2012-13. The floating error bars indicate the average least

significant difference (LSD) at P≤0.05 for significant treatment and

depth difference

96

Figure 3.6. Tillage and residue effects on wheat root distribution at 0-50 cm

soil depth (10 cm increments of five soil depths) during the 2010-11

growing season. Root parameters measured are a) Root volume

(cm3), b) Root dry weight (g), c) Root length (m), d) Root length

density-RLD (cm/cm3) and e) Specific root length-SRL (m/g). Error

bars indicate ± 1 standard error of the mean

98


soil depth (10 cm increments of six soil depths) during the 2011-12

growing season. Root parameters measured are a) Root volume




100


soil depth (10 cm increments of seven soil depths) during the 2012-

102

XXVII

13 growing season. Root parameters measured are a) Root volume




Figure 4.1. Relationship between volumetric water content (SWC) (%)

(calculated from the gravimetric soil water content) and MP406

volumetric water content (θprobe) (%) for the data collected at 5

cm increments down the soil profile collected after 7 crops at Alipur

(○,‐ ‐) and Digram (●,—) in 2012-13. The soil profile depth was to 15

cm. Symbols are data points and the line represents the regression

equation shown above

114

Figure 4.2. Schematic diagram of strip tillage plot showing the location of

measurements of soil water content and penetration resistance in

between the strips (closed black circle) and in the strip (open black

circle) in a strip-tillage plot

115

Figure 4.3. Schematic diagram of the newly formed bed. The blue circles

indicates the sampling spot of centre of the bed (closed symbol)

and furrow of the bed (open symbol) for soil moisture, soil

penetration resistance and bulk density measurements

117

Figure 4.4. Tillage effects on soil bulk density over cropping cycles-initially and

after Crops 1, 3, 4, 6 and 7 at 0-5 cm, 5-10 cm and 10-15 cm soil

depths in Alipur. Values are means across residue levels. The error

bars for each data point represents ± 1 standard error. The floating

error bars on the figure at each depth represent the least

significant difference (LSD) at P≤0.05 for tillage after each crop (T)

and interaction between tillage and cropping cycle (TXCC)

119

Figure 4.5. Residue effects on soil bulk density after different cropping cycles ̶

initially and after Crops 1, 3, 4, 6 and 7 at 0-5 cm, 5-10 cm and 10-

15 cm soil depths in Alipur. Values are means across tillage

120

XXVIII

treatments. The error bars for each data point represents ± 1

standard error. The floating error bars on the figure at each depth

represent the least significant difference (LSD) at P≤0.05 for residue

after each crop

Figure 4.6. Dynamic changes of volumetric soil water content (%) and

penetration resistance (MPa) due to tillage after Crops 1, 3, 4, 6 and

7 at 0-5 cm, 5-10 cm and 10-15 cm soil depths in Alipur. Values are

means across residue levels. Error bars were ± 1 standard error of

the mean and floating bar indicates significant difference at P≤0.05

level between treatments on that time of measurement

123


penetration resistance (MPa) due to residue after Crops 1, 3, 4, 6

and 7 at 0-5 cm, 5-10 cm and 10-15 cm soil depths in Digram.

Values are means across tillage treatments. Error bars were ± 1

standard error of the mean and floating error bars indicate

significant difference at P≤0.05 level between treatments on that

time of measurement

124

Figure 4.8. The volumetric soil water content (%) (a1-a3) and soil penetration

resistance (b1-b3) at 0-5 cm, 5-10 cm and 10-15 cm soil depths for

different tillage treatments at 5 days after sowing (DAS) to 35 DAS

during lentil planting in 2013 in Alipur. Values are means across

residue levels. Floating error bars indicate the least significant

difference (LSD) at P≤0.05, for the effects of tillage on that dates of

measurement and error bars indicate ± 1 standard error of the

mean

126



different residue treatments at 5 days after sowing (DAS) to 35

DAS during lentil planting in 2013 in Alipur. Values are means

127

XXIX

across tillage treatments. Floating error bars indicate the least

significant difference (LSD) at P≤0.05, for the effects of tillage on

that date of measurement and error bars indicates ± 1 standard

error of the mean

Figure 4.10. Relationship between soil penetration resistance (MPa) and bulk

density (g/cc) after different crop determined: a) after Crop 1; b)

after Crop 3; c) after Crop 4; d) after Crop 6; e) after Crop 7 during

2010-13 in Alipur. Values are for all three depths (0-5 cm, 5-10 cm

and 10-15 cm). The line represents the regression equation shown

above in the graph

128

Figure 4.11. Variation of soil bulk density after Crop 7 in Alipur relative to depth

from the bed top (BT) and from the level of the bed top in the

furrow (BF) of the bed planting system (a); and in the strip (IS) and

off-the strip (OS) of strip tillage system (b). For comparison, initial

values (before starting the experiment) are also shown. Floating

error bars indicate the least significant difference (LSD) at P≤0.05,

for the effects of sampling positions

129

Figure 4.12. Variation of volumetric soil water content (%) after Crop 7 in Alipur

relative to depth from the bed top (BT) and from the level of the

bed top in the furrow (BF) of the bed planting system (a); and in the

strip (IS) and off-the strip (OS) of strip tillage system (b). For

comparison, initial values (before starting the experiment) are also

shown. Floating error bars indicate the least significant difference

(LSD) at P≤0.05, for the effects of sampling positions

130

Figure 4.13. Variation of soil penetration resistance (MPa) after Crop 7 in Alipur

relative to depth from the bed top (BT) and from the level of the

bed top in the furrow (BF) of the bed planting system (a); and in the

strip (IS) and off-the strip (OS) of strip tillage system (b). For


131

XXX



Figure 4.14. The variation of mean soil day (a) and night soil temperature (°C)

(b) due to different treatments during wheat growing season at

Alipur in 2012-13. Values are means of seven day intervals

132

Figure 4.15. Tillage effects on soil bulk density over cropping cycles - initially and

after Crops 1, 3, 4 and 6 at 0-5 cm, 5-10 cm and 10-15 cm soil

depths in Digram. Values are means across residue levels. The error

bars for each data point represents ± 1 standard error. The floating

error bars on figure at each depth represent the least significant

difference (LSD) at P≤0.05 for tillage after each crop (T) and

interaction between tillage and cropping cycles (TXCC)

134


initially and after Crops 1, 3, 4 and 6 at 0-5 cm, 5-10 cm and 10-15

cm soil depths in Digram. Values are means across tillage

treatments. The error bars for each data point represents ± 1

standard error. The floating error bars on figure at each depth

represent the least significant difference (LSD) at P≤0.05 for residue

after each crop

135


penetration resistance (MPa) due to tillage after Crops 1, 3, 4, 6 and

7 at 0-5 cm, 5-10 cm and 10-15 cm soil depths in Digram. Values are

means across residue levels. Error bars were ± 1 standard error of

the mean and floating error bar indicates significant difference at

P≤0.05 level between treatments on that time of measurement

137


penetration resistance (MPa) due to residue after Crops 1, 3, 4, 6

and 7 at 0-5 cm, 5-10 cm and 10-15 cm soil depths in Digram.

Values are means across tillage treatments. Error bars were ± 1

138

XXXI

standard error of the mean and floating error bar indicates

significant difference at P≤0.05 level between treatments on that

time of measurement



different tillage treatments at 5 days after sowing (DAS) to 35 DAS

during wheat planting in 2013 in Digram. Values are means across

residue levels. Floating error bars indicate the least significant

difference (LSD) at P≤0.05, for the effects of tillage on that date of

measurement and error bars indicates ± 1 standard error of the

mean

141



different residue treatments at 5 days after sowing (DAS) up to 35

DAS during wheat planting in 2013 in Digram. Values are means

across tillage treatments. Floating error bars indicate the least

significant difference (LSD) at P≤0.05, for the effects of tillage on

that dates of measurement and error bars indicate ± 1 standard

error of the mean

142

Figure 4.21. Relationship between soil penetration resistance (MPa) and bulk

density (g/cc) after different crop determined: a) after Crop 1; b)

after Crop 3; c) after Crop 4; d) after Crop 6 during 2010-13 in

Digram. Values are for all three depths (0-5 cm, 5-10 cm and 10-15

cm). The line represents the regression equation shown above in

the graph

143

Figure 4.22. Variation of soil bulk density after Crop 6 in Digram relative to

depth from the bed top (BT) and from the level of the bed top in

the furrow (BF) of the bed planting system (a); and in the strip (IS)

and off-the strip (OS) of strip tillage system (b). For comparison,

144

XXXII

initial values (before starting the experiment) are also shown.

Floating error bars indicate the least significant difference (LSD) at

P≤0.05, for the effects of sampling positions

Figure 4.23. Variation of volumetric soil water content (%) after Crop 7 in

Digram relative to depth from the bed top (BT) and from the level

of the bed top in the furrow (BF) of the bed planting system (a); and

in the strip (IS) and off-the strip (OS) of strip tillage system (b). For




145

Figure 4.24. Variation of soil penetration resistance (MPa) after Crop 7 in

Digram relative to depth from the bed top (BT) and from the level

of the bed top in the furrow (BF) of the bed planting system (a); and

in the strip (IS) and off-the strip (OS) of strip tillage system (b). For




146

Figure 4.25. The variation of mean soil day (a) and night soil temperature (°C)

(b) due to different treatments during wheat growing season at

Digram in 2012-13. Values are means of seven day intervals

147

Figure 5.1. Schematic representation of CO₂ production processes in soil.

Those processes are root respiration, rhizosphere respiration, litter

decomposition, and oxidation of SOM. Adapted from Luo and Zhou

(2006)

156

Figure 5.2. Variation of soil organic carbon concentrations at different

cropping seasons in Alipur relative to depth (at 0-15 cm soil depth

before starting of the experiment — Initial, after Crop 1 and after

Crop 4, and at 0-7.5 cm and 7.5-15 cm soil depth after Crop 7) in

the strip (IS) and off-the strip (OS) of strip tillage system (ST).

164

XXXIII


P≤0.05, for the effects of sampling location of strip tillage system


cropping seasons in Alipur relative to depth (at 0-15 cm soil depth


Crop 4, and at 0-7.5 cm and 7.5-15 cm soil depth after Crop 7) from

the bed top (BT) and from the level of the bed top in the furrow

(BF) of the bed planting system (BP). Floating error bars indicate

the least significant difference (LSD) at P≤0.05, for the effects of

sampling location of bed planting system

165

Figure 5.4. Temporal variation of soil organic carbon concentrations at Alipur.

The floating error bar indicates the average least significant

difference (LSD) at P≤0.05 for the different cropping cycles and

tillage. Values are means across residue levels

166

Figure 5.5. Tillage effects on CO₂ flux (g CO₂ m²/day) at different growth stages

of lentil in Alipur in 2011-12 and 2012-13. The floating error bar on

each figure represents the least significant difference (LSD) at

P≤0.05, for the different crop growth stages where they were

significantly different. Values are means across residue levels

169

Figure 5.6. Residue effects on CO₂ flux (g CO₂ m²/day) at different growth

stages of lentil in Alipur in 2011-12 and 2012-13. The floating error

bar on each figure represents the least significant difference (LSD)

at P≤0.05, for the different crop growth stages where there were

significant treatment differences. Values are means across tillage

treatments

170

Figure 5.7. Relationship between cumulative C input and SOC sequestration

during 2.5 years under ST and CT conditions of legume-dominated

rice–based system at Alipur

173

Figure 5.8. Variation of soil organic carbon concentrations at different 175

XXXIV

cropping seasons in Digram relative to depth (at 0-15 cm soil depth


Crop 4, and at 0-7.5 cm and 7.5-15 cm soil depth after Crop 7) in

the strip (IS) and off-the strip (OS) of strip tillage system (ST).


P≤0.05, for the effects of sampling location of strip tillage system


cropping seasons in Digram relative to depth (at 0-15 cm soil depth


Crop 4, and at 0-7.5 cm and 7.5-15 cm soil depth after Crop 7) from

the bed top (BT) and from the level of the bed top in the furrow

(BF) of the bed planting system (BP). Floating error bars indicate

the least significant difference (LSD) at P≤0.05, for the effects of

sampling location of bed planting system

176

Figure 5.10. Temporal variation of soil organic carbon (SOC) at Digram. The

floating error bar indicates the average least significant difference

(LSD) at P≤0.05 for the different cropping cycles where they were

significantly different. Values are means across residue levels

177

Figure 5.11. Tillage effects on CO₂ flux (g CO₂ m²/day) at different growth stages

of wheat in Digram. The floating error bar on each figure represents

the least significant difference (LSD) for significant effects at

P≤0.05. Values are means across residue levels

180

Figure 5.12. Residue effects on CO₂ flux (g CO₂ m²/day) at different growth

stages of wheat in Digram. The floating error bars represent the

least significant difference (LSD) for significant effects at P≤0.05 for

each sampling time. Values are means across treatments

181

Figure 5.13. Relationship between cumulative C input and SOC sequestration

during 2.5 years under ST and CT conditions of cereal-dominated

rice–based system at Digram

184

XXXV

Figure 6.1. The conceptual model of N cycling of conservation agriculture

system

198

Figure 6.2. Variation of soil total nitrogen concentrations at different cropping

seasons in Alipur relative to depth (at 0-15 cm soil depth before

starting of the experiment — Initial, after Crop 1 and after Crop 4,

and at 0-7.5 cm and 7.5-15 cm soil depth after Crop 7) in the strip

(IS) and off-the strip (OS) of strip tillage system (ST). Floating error

bars indicate the least significant difference (LSD) at P≤0.05, for the

effects of sampling location of strip tillage system

206


seasons in Alipur relative to depth (at 0-15 cm soil depth before


and at 0-7.5 cm and 7.5-15 cm soil depth after Crop 7) from the bed

top (BT) and from the level of the bed top in the furrow (BF) of the

bed planting system (BP). Floating error bars indicate the least

significant difference (LSD) at P≤0.05, for the effects of sampling

location of bed planting system

207

Figure 6.4. Temporal variation of total soil nitrogen concentrations at Alipur.

The floating error bar indicates the average least significant

difference (LSD) at P≤0.05 for the different cropping cycles and

tillage. Values are means across residue levels

208


seasons in Digram relative to depth (at 0-15 cm soil depth before


and at 0-7.5 cm and 7.5-15 cm soil depth after Crop 7) in the strip

(IS) and off-the strip (OS) of strip tillage system

221


seasons in Digram relative to depth (at 0-15 cm soil depth before


222

XXXVI

and at 0-7.5 cm and 7.5-15 cm soil depth after Crop 7) from the bed

top (BT) and from the level of the bed top in the furrow (BF) of the

bed planting system (BP). Floating error bars indicate the least

significant difference (LSD) at P≤0.05, for the effects of sampling

location of bed planting system

Figure 6.7. Influences of crop residue on inorganic nitrogen transformation

process. Modified from Chen (2014)

243

XXXVII

Appendices

Appendix 1 The soil organic carbon concentrations at different depths in

furrow of the bed

313

Appendix 2 Tillage and residue effects on C-N ratio in legume-dominated

rice-based system at Alipur in 2011-13

315

XXXVIII

List of Abbreviations

1000-seed weight TSW

2-wheel tractor 2-WT

4-wheel tractor 4-WT

Agro-ecological zone AEZ

Ammonia nitrogen NH₃-N

Ammonium nitrogen NH₄-N

Analysis of variance ANOVA

Approximately ~

Arsenic As

Australian Centre for International Agricultural Research ACIAR

Bangladesh Agricultural Research Council BARC

Bangladesh Agricultural Research Institute BARI

Bangladesh Agricultural University BAU

Bangladesh Bureau of Statistics BBS

Bangladesh Economic Review BER

Bangladesh Institute of Development Studies BIDS

Bangladesh Rice Research Institute BRRI

Bed planting system BP

Biological nitrogen fixation BNF

Carbon C

Carbon dioxide CO₂

Carbon dioxide-carbon CO₂-C

Carbon-nitrogen ratio C:N ratio

Cation exchange capacity CEC

Centimetre cm

Centimoles of charge per kilogram cmol/kg

Coefficient of variation CV

Conservation agriculture CA

XXXIX

Conventional flat CTF

Conventional till with and without residue CWR/CNR

Conventional tillage CT

Conventional tillage with residue burned CTB

Conventional tillage with residue incorporated CTS

Cropping cycles CC

Days after sowing DAS

DeciSiemens per metre dS/m

Degree celsius °C

Di-ammonium phosphate DAP

Dinitrogen gas N₂

Direct-seeded rice DSR

Dry weight DW

Dry weight DW

Duncan's multiple range test DMRT

Eastern Indo-Gangetic Plains EIGP

Food and Agriculture Organization of the United Nations FAO

Geometric mean diameter GMD

Gram g

Gram per cubic centimetre g/cm3

Gram per kilogram g/kg

Greater than >

Greater than equal ≥

Greenhouse gasses GHGs

Harvest index HI

Hectare ha

High Barind Tract HBT

High residue retention HR

In the strip IS

XL

Indo-Gangetic Plains IGP

International Centre for Agricultural Research in the Dry Areas ICARDA

International Maize and Wheat Improvement Center CIMMYT

International Rice Research Institute IRRI

Kilogram kg

Kilogram per hectare kg/ha

Least significant difference LSD

Less than <

Low residue retention LR

Mean weight diameter MWD

Megagram per hectare per year Mg/ha/yr

Megapascal MPa

Megaram per hectare Mg/ha

Methane CH4

Metre m

Mid-season MS

Millilitre mL

Miligram per kilogram mg/kg

Milligram nitrogen per gram mg N/g

Milligram per gram mg/g

Millimetre mm

Million hectares M ha

Minimum tillage MT

Molar M

Nitrate nitrogen NO₃-N

Nitrogen N

Nitrogen accumulation Nacc

Nitrogen storage N storage

Nitrogen use efficiency NUE

XLI

Nitrogen-stocks N-stocks

Nitrous oxide N₂O

Not significant ns

No-till flat NTF

No-tillage NT

Off the strip OS

Per cent %

Permanent raised beds PRB

Phosphorus P

Plant population PP

Polyvinyl chloride PVC

Potassium chloride KCl

Potentially mineralizable nitrogen PMN

Power tiller operated seeder PTOS

Probability P

Pulses Research Centre PRC

Reduced tillage RT

Regression coefficient r²

Residue R

Revolutions per minute rpm

Root dry weight RDW

Root length RL

Root length density RLD

Root volume RV

Soil bulk density BD

Soil organic carbon SOC

Soil organic carbon stocks SOC-stocks

Soil organic matter SOM

Soil penetration resistance PR

XLII

Soil total nitrogen TN

Soil water content SWC

Specific root length SRL

Square metre m2

Standard deviation SD

Standard error SE

Stemphylium leaf blight SLB

Stratification ratio SR

Strip tillage ST

Sulphur S

Tillage T

Tonnes per hectare t/ha

Total soil nitrogen TN

Total soluble nitrogen TSN

Transplanted aman rice T. aman rice

United States Department of Agriculture USDA

United States of America USA

Versatile Multi-crop Planter VMP

Water soluble carbon WSC

Weight per volume W/V

Wheat Research Centre WRC

Year Yr

Years Yrs

Zero tillage ZT

Zero-till with and without residue ZWR/ZNR

XLIII

List of Botanical Names

barley (Hordeum vulgare L.)

black gram (Vigna mungo L. Hepper)

chickpea (Cicer arietinum L.)

cotton (Gossypium hirsutum L.)

lentil (Lens culinaris Medikus)

maize (Zea mays L.)

mungbean (Vigna radiata L. R. Wilczek)

mustard (Brassica campestris L.)

pigeonpea (Cajanus cajan L.)

rice (Oryza sativa L.)

sorghum (Sorghum bicolor L. Moench)

soybean (Glycine max L. Merr.)

wheat (Triticum aestivum L.)

potato (Solanum tuberosum L.)

chilli (Capsicum annum L.)

pearl millet (Pennisetum glaucum L.)

jute (Corchorus olitorius L.)

XLIV

Acknowledgements

I thank to the omniscient, omnipotent and omnipresent Almighty “Allah”, the supreme

ruler of the Universe, who enabled me to make my dream a reality, a successful

completion of the research and submission of this thesis.

It is my profound privilege to express my immense gratitude, sincere appreciation and

heartfelt indebtedness to my honorable supervisor who changed my views and uncovered

my eyes to see the nature deeply, Professor Richard W Bell, School of Veterinary and Life

Sciences, Murdoch University for his constant and intellectual guidance, affectionate

feelings, cordial support, constant encouragement, effective suggestions and constructive

criticisms from the beginning of my PhD. Without his support, I may not have reached at

this point of my career. I am incredibly lucky to have such a great supervisor. I believe

everything he taught me will serve in future.

I’m also very much grateful to my co-supervisor Adjunct Professor Dr Chris Johansen,

University of Western Australia, and Consultant in Agricultural Research and Development

for all kind of advice toward making a clear story for the discussion, tireless review, critical

evaluation and criticisms, scholastic guidance, fruitful discussion and all round help and

co-operation for successful completion of this thesis.

I am thankful to my in-country co-supervisor Professor M. Jahiruddin, Soil Science Division,

Bangladesh Agricultural University, Mymensingh for his continuous encouragement, all

kind of advice and affectionate behavior for successful completion of this thesis.

I would like to thank Dr Wendy Vance, Research Officer, School of Veterinary and Life

Sciences, Murdoch University for her endless co-operation throughout my PhD study.

XLV

I am also thankful to Dr Md. Enamul Haque, Adjunct Associate Professor, Murdoch

University who look after my experiments in my absence, when I was in Australia. I

appreciate his help.

I’m also very much grateful to the farmers of Alipur and Digram villages for their co-

operation and help during my field work. I must also thank to Abdul Kuddus Gazi and Neaz

Mehedi Phillips for their support.

I would like to thank many peoples of Land management group in Murdoch University I

came across; with whom I shared some good facts, feelings and ideas. Among them few

names I cannot leave without mentioning: Enamul Kabir, Sarith Hin, Sitaram Panta, Alice,

Singaravel, Karthika Krishnasamy, Fariba Mokhtari, Asha Abegunawardana, Khairul Alam,

Nurul Hasan Mahmud, Truc, Thin, Dr Qifu Ma, Dr Surrender Mann, Stan.

I am grateful to the Australian Centre for International Agricultural Research (ACIAR),

Australia for providing financial assistance in the form of International Fellowship and

Bangladesh Agricultural Research Institute (BARI) for providing study leave without which

it could not have been possible to pursue my PhD at this prestigious Australian University.

I would like to thank all of you whom I remember for bringing me a smile on my face at

some point of my stay in Australia and Bangladesh. I wish you all the best! Special thanks

to Masuka Rahman and Dr Shahidul for their inspiration during my last days in Australia.

I am overwhelmed with sincere feelings of indebtedness to my beloved parents for their

patience, sacrifice and encouragement throughout my life and my PhD. I’m thankful to my

beloved brothers and sisters for their abundant love and affection which inspired me to

complete this journey. I extremely grateful to my elder brother Dr Md Shafiqul Islam for

his affection, guidance, care, mental support and encouragement throughout my life and

PhD.

XLVI

Md Rafsan Islam, my son has made a lot of sacrifices since his birth as his father was a PhD

student. In my monotonous and painful time, he inspired and powered me by his sweet

smiling and talking. Every minute of my hard time I spent with him was precious.

A special thanks and appreciation to the world best tea maker, my wife Umme Rubayet

Rimi for her endless support and strength she provided me at every step of this journey.

Above all, she shouldered all the household responsibilities and kept me free for

concentrating on my studies.

XLVII

Dedicated

to

the small holder farmers of Bangladesh

1

1 Literature Review

1.1 Introduction

Food production for a growing world population, while conserving natural resources,

now faces a greater challenge than ever before (Lobell et al., 2008; Foley et al., 2011;

Gathala et al., 2011b; Pittelkow et al., 2015a). An example of the challenge is

Bangladesh, a densely populated country in South Asia, with a per capita agricultural

land allocation of 506 m2 which continues to decline at the rate of 1 % per year

(Quasem, 2011). The government of Bangladesh has been importing large amounts of

food every year to meet the domestic demand (Bangladesh Economic Review, 2011).

Bangladesh has no alternative but to increase its crop production per unit area to

minimize the import costs yet meet the food demand for an increasing population

(Bakr et al., 2011). Under such a situation cropping intensity in Bangladesh has been

increasing dramatically, it is now over 198 %, to maintain food security on the available

amount of agricultural land (Jahiruddin & Satter, 2010). Rice-dryland cropping patterns

play the major role in producing food, hence this system is an unavoidable lifeline for

about 160 million people in Bangladesh. This system has so far effectively maintained

the balance between food production and population growth (Gathala et al., 2011b).

However, the current cultivation practice of the rice-based system is input intensive,

damages soil health, pollutes environments and is not very profitable for farmers

(Gathala et al., 2013). As a consequence, the long-term sustainability of the rice-based

system is being hampered by stagnating or declining yield and productivity (Hobbs &

Morris, 1996; Sharma, 1997; Bajpai & Tripathi, 2000), degrading soil and water

resources (Timsina & Connor, 2001), declining soil organic carbon (SOC) and soil total

nitrogen (TN) and delays in sowing (Ladha et al., 2003a). Moreover, the deteriorating

soil physical properties and soil fertility have been implicated in the decline in crop

yield over the long-term in rice-based systems (Sharma, 1997). As for example, the

productivity of rice-wheat system in the Indo-Gangetic Plains (IGP) is stagnating or

even declining and thereby the system is threatened also by degradation of the

environment, increasing water and labour scarcity, and changes of socio-economic

status (Rijsberman, 2006; Erenstein et al., 2007; Gathala et al., 2011b).

2

In intensive rice-based systems, rice and non-rice (dryland crop) crops are grown in a

sequence with frequent cycling of wetting and drying under anaerobic and aerobic

conditions (Zhou et al., 2014). The contrasting environments alter the soil C and N

cycles, soil chemical speciation and soil biological properties through the diversity of

soil organisms (Zhou et al., 2014) (Figure1.1 ).

Figure 1.1. Schematic diagram of rice-dryland ecosystem showing conventional and

conservation management. Adapted from Zhou et al. (2014).

In intensive rice-based systems, rice is mainly grown in puddled soil with intensive

tillage which is followed by residue removal for the cultivation of the succeeding non-

rice crop in Bangladesh. The rotation associated with contrasting growing

environments and conventional cultivation leads to deterioration of soil chemical and

physical properties (Dwivedi et al., 2003; Singh et al., 2005a). Puddling is broadly

practiced for lowland rice cultivation for a range of reasons throughout the IGP

(Sharma et al., 2005). For example, Humphreys et al. (2005) reported that puddling in

the IGP is practiced for rapid rice transplanting through softening the soil, reducing

percolation loss of water and nutrients, and controlling weed incidence. However it

3

causes aggregate breakdown, macropore destruction and formation of subsurface

compaction (Sharma & De Datta, 1986; Sharma et al., 2005), which adversely affects

the succeeding dryland crop (Bajpai & Tripathi, 2000; Sharma et al., 2005). Subsurface

compaction restricted root growth, water and nutrient uptake, and resulted in

lowering yield of the succeeding dryland crop (Bajpai & Tripathi, 2000; Pagliai et al.,

2004). However, several researchers have reported that rice transplanting into soil

without puddling did not result in a yield penalty (Gathala et al., 2011a; Jat et al., 2013;

Haque et al., 2016). Before sowing of arable crops, previously puddled soils take more

time to dry and form cracks, and as a consequence form hard and large clods that

provide poor seedbed and seed-soil contact upon dry tillage. Hence, extra tillage is

required to prepare a suitable seedbed for succeeding dryland crops, which diminishes

farm profits (Sharma et al., 1988; Sharma et al., 2005). In addition, simultaneous use of

puddling for rice and intensive tillage for dryland arable non-rice crops over a longer

period caused the degradation of soil structure and accelerated soil organic matter

(SOM) decomposition resulting in a decline in SOC (Dalal & Mayer, 1986b; Six et al.,

2004; Shibu et al., 2010) and TN concentrations (Dalal & Mayer, 1986a).

In a long-term study on the Loess Plateau in the south-central Shanxi province, China,

He et al. (2009) reported that conventional tillage (CT) based on intensive tillage and

residue removal or burning reduced soil water content (SWC), macro-porosity, macro-

aggregates and increased soil bulk density (BD), thereby reducing plant available water

and nutrient availability. Moreover, Chivenge et al. (2007) reported that tillage

disrupted soil nutrient storage, accelerated SOM mineralization, and losses of SOC and

TN from the soil. From a study in semi-arid and tropical India, Manna et al. (2013)

found that intensive farm management practices have led to gradual depletion of soil

nutrients and exacerbated soil degradation. The alterations in microbial composition,

nutrient depletion and structural degradation might have collectively contributed to

the decline in crop productivity (Manna et al., 2013). Several previous studies indicate

that intensive soil tillage resulted in the degradation of agricultural soils, with

decreases in SOC and loss of soil structure, adversely affecting soil functioning and

causing a long term threat to future yields (Lal, 1994; Ladha et al., 2003c; Pagliai et al.,

2004; D’Haene et al., 2008).

4

In large parts of the developed and developing world, soil tillage by plough or hoe is

the main cause of land degradation which leads to stagnating or even declining

production levels and increasing production costs (Garcia-Torres et al., 2003). It also

causes water runoff, soil erosion, and increased soil compaction (Garcia-Torres et al.,

2003). This further leads to more severe droughts and losses in soil fertility but with

less responsiveness to fertilizer. Thus it is clear that increased food production must be

accompanied by concerted action to reduce the degradation of agricultural soils in

Bangladesh, as is generally the case globally.

In rice-based systems, crop residues are the vital source of organic C input that is

necessary to maintain or increase SOC concentrations, and improve the soil physical

properties and hydrothermal regime (Singh et al., 2005b; Jat et al., 2009). However,

crop residues are burnt or removed from the field for livestock feed, bedding, roofing

and fencing material in rice-based systems in the IGP (Timsina & Connor, 2001). As a

result, there is a rapid decline in SOM due to trivial return of residue inputs (Timsina &

Connor, 2001). It has been shown that soils undergoing continuous cropping with

removal of crop residues and repeated tillage are declining in SOC (Hossain, 2001),

which may cause yield decline (Ladha et al., 2003a). On the other hand, residues left

on the topsoil of zero tillage (ZT) act as a barrier to protect the soil from runoff and

intercepting rain drops while over time preventing surface soil crust formation (Naresh

et al., 2013). In addition, residues with ZT reduce evaporation, and buffer temperature

and moisture fluctuations (Blevins & Frye, 1993).

The agricultural soils of Bangladesh are now low in organic matter; 60 % of arable soils

have fallen below 1.5 % organic matter whereas a productive mineral soil should have

at least 2.5 % organic matter (Rijpma & Jahiruddin, 2004). In a soil survey, Karim et al.

(2004) showed that the organic matter depletion ranged from 9-62 % in different agro-

ecological zones of Bangladesh during the period 1969 to 2000. It is estimated that at

least 2 million metric tonnes of nutrients are annually removed from Bangladesh soils.

One estimate puts the cost of land degradation as 3 % of crop output or 1 % of crop

GDP every year in Bangladesh (Bangladesh Institute of Development Studies, 2004).

5

Conservation agriculture (CA) based on minimum tillage, residue retention and crop

rotation, compared to the conventional system, has been proposed as a potential

approach to alleviate a range of agricultural problems in small holder farming systems

in the tropics (Hobbs et al., 2008; Foley et al., 2011). Conservation agriculture aims to

maximize crop yields while maintaining ecosystem health, unlike conventional systems

that aim to maximize yields with less regard for environmental considerations

(Dumanski et al., 2006; Naresh et al., 2016). The impacts of CA have been generally

positive in agricultural, environmental, economic and social terms (Garcia-Torres et al.,

2003). Conservation agriculture has a wide range of benefits including improvement in

soil fertility, carbon sequestration while minimizing greenhouse emissions (Reicosky &

Saxton, 2007). Several CA-based experiments have been evaluated as an alternative to

conventional practices, and positive benefits in terms of increase yield, productivity,

economic return and the efficiency of resources have been reported in rice-based

systems in the IGP (Kumar & Ladha, 2011; Gathala et al., 2013; Laik et al., 2014; Alam

et al., 2015; Gathala et al., 2015). Figure 1.2 shows the potential benefits of CA at eco-

system level for achieving food security and sustainability (Srinivasarao et al., 2015).

6

Figure 1.2. Potential benefits of conservation agriculture at eco-system level.

Adapted from Srinivasarao et al. (2015).

1.2 Conventional agriculture: basic concepts

Conventional agriculture in South Asia can be generally described as follows.

1.2.1 Concept of tillage

Tillage can be defined as any action involving soil disturbance for the purpose of crop

production (Boone, 1988). Generally farmers till their land to invert soil, release

nutrients through the oxidation of organic matter, make planting easier and to control

weeds, pests and diseases (Hobbs & Govaerts, 2010). It involves soil physical, chemical

or biological manipulation to optimize conditions for germination, seedling

establishment and crop growth (Lal, 1979; Lal, 1983). The tillage practice was

intensified with the advent of mechanical power and tractors. However, later it was

clearly shown that rigorous tillage resulted in various negative effects on soil and

environment. Tillage pulverizes the surface layer making it more prone to erosion and

7

oxidization of SOM. In addition, tractors used for tillage compact the subsoil. The

activities of farmer’s cultivation techniques are presented in Figure 1.3a-e.

Figure 1.3. The activities of farmer’s cultivation techniques, a) puddling for rice

cultivation, b) conventional cropping based on intensive tillage, c) broadcast seed

and fertilizer, d) levelling following tillage and e) crop residue burning, f) residue

remove

Generally tillage practices are done with an expense of energy, cost, time, water and

fuel for tilling land for crop production in Bangladesh (Islam et al., 2012; Gathala et al.,

8

2013; Kumar et al., 2013). Briefly, the consequences of conventional agriculture are

presented below (Figure 1.4).

Figure. 1.4. Problems associated with conventional agriculture systems in rice-based

system in Indo-Gangetic Plains. Modified from Devkota (2011).

Problems

Conventional agriculture practices of rice-based system

Cropping systems Tillage Residue

management

• Rice-rice-rice

• Wheat-fallow-rice

• Rice-maize-rice

• Rice-potato-rice

• Rice-mustard-rice

• Puddling for rice (wet cultivation)

• Multiple passes of intensive tillage for 2-3 non-rice crop

• Planking/Laddering

• Residue removal

• Residue burning

• Limited residue retention

Impact

s

• Declining soil fertility

• Crop yield stagnation/depression

• Depletion of soil SOC and nutrient

• Deteriorate soil physical properties

• Decrease turnaround time

• Pests and diseases outbreaks Increase production cost –farm economics

• Shortage of time, energy,labour, water

• More times required to dry after rice harvest and form cracks

• Poor seed bed and seed-soil contact

• Increase water logging

• Increase soil compaction

• Decrease root growth

• Extra tillage for good seed bed preparation

• Destruction of soil structure and soil aggregation

• Decline in SOC and TN

• Induced drought

• Increased water, energy and labour requirements

• Promotes erosion and run-off

• Increase evaporation

• Pollutes environment

• Depletion of soil fertility

• Reduced soil water content

• Reduced nutrient availability

• Losses of SOC and other nutrients

• Induced drought

• De-nitrification, volatilization, run-off and leaching loss

• Low fertilizer use efficiency

• Low water use efficiency

• Decreased ground water table

Poor agricultural

productivity and soil

degradation

Declining soil

fertility

Unsustainable rice-

based system

9

1.2.2 Crop residue management options under conventional system

Farmers in South Asia can manage crop residue in a number of ways including: removal

from the field, burning in situ, composting, or retention for succeeding crops.

However, usually little residue is recycled in the field—it is normally either harvested

for fuel, animal feed, or bedding or burned in the field (Singh et al., 2008). The two

common practices of residue management are as follows:

Residue burning

Crop residues, particularly rice straw, that are not used as animal feed are burnt in the

western IGP (Singh et al., 2005b). Residues are burnt due to logistic constraints and

lack of proper technologies for in situ recycling of crop residues (Jat et al., 2004).

Burning is a low cost method and helps to reduce pest and disease transmission in the

straw biomass (Kirkby, 1999). However, residue burning not only leads to the loss of a

considerable amount of nutrients and organic matter but also contributes to the global

greenhouse gasses (GHGs) through N₂O and CO₂ emissions (Grace et al., 2002; Samra

et al., 2003).

Residue incorporation

Incorporation of crop residues into the soil and allowing them to decompose returns

almost all the nutrients in the straw to the soil (Singh et al., 2005b). Traditionally this

practice was considered useful in returning organic matter to the soil and protecting

the soil from erosion. In tropical soils, incorporated rice residue and continuous

flooding has become common through intensification of rice cropping practices

(Cassman & Pingali, 1995).

1.3 Concept of conservation agriculture

Dumanski et al. (2006) considered that: Conservation agriculture is “a holistic idea

designed to optimize yields and profits while achieving a balance of agricultural,

economic and environmental benefits. It can be defined as a sequence of principles

and practices that are promoted in application of modern agricultural technologies to

improve production while simultaneously protecting and enhancing the land resources

on which production depends”. Conservation agriculture, a valid tool for sustainable

10

land management, is based on three key principles: minimal soil disturbance,

permanent soil cover and crop rotation (Hobbs, 2007b). Although it could fit in all sizes

of farm and agro-ecological systems, its adoption is urgently required in degrading

environments and the regions of acute labour and energy shortage during the cropping

season (Food and Agriculture Organization, 2016b). The details of individual CA-based

crop management technologies are described below.

1.4 The key components of Conservation Agriculture

1.4.1 Minimum soil disturbance

The term conservation tillage can cover several related terms, zero-tillage (ZT), no-

tillage (NT), direct-drilling, strip or zone tillage, minimum-tillage and/or ridge-tillage,

and point to the fact that it has a conservation goal. Commonly used terms to describe

conservation tillage are elaborated as follows.

1.4.1.1 Minimum tillage

Minimum tillage (MT) refers to the minimum soil manipulation necessary for seed and

fertilizer placement in the soil. A reduction in tractor passes and thus reduction in soil

compaction may maintain or improve soil structure and stability, maintain SOM

content and increase soil moisture retention, biological properties and buffer soil

temperature as well as prevent the establishment of some weeds (NLWRA, 2001).

Some examples of minimum tillage are described as follows.

1.4.1.2 No-tillage

According to Lal (1983), NT systems eliminate all pre-planting mechanical seedbed

preparation except for the opening of a narrow (2-3 cm wide) strip or small hole in the

ground for seed placement to ensure adequate seed/soil contact. Soane et al. (2012)

defined no-till (also known as direct drilling and zero tillage, ZT) as a system in which

crops are sown without any prior loosening of the soil by cultivation other than the

very shallow disturbance (<5 cm) which may arise by the passage of the drill coulters

or narrow tynes and after which usually 30-100 % of the surface remains covered with

plant residues.

11

1.4.1.3 Strip tillage

The concept of strip tillage (ST) is described by Lal (1983). The seedbed is divided into a

seedling zone and a soil management zone (Figure 1.5). The seedling zone (varied

seeding depth and width, 4-5 cm

width and 5-7 cm depth) is

mechanically tilled to optimize the

soil and micro-climate environment

for germination and seedling

establishment. The inter-row or soil

management zone (e.g. 20 cm) is left

undisturbed and protected by mulch.

Strip tillage can also be achieved by

chiselling in the row zone to assist

water infiltration and root

proliferation. This tillage has the potential of combining the benefits of CT and NT by

disturbing the seeding row and leaving the inter-row with complete residue cover (Vyn

& Raimbault, 1993). Strip-tillage is a mode of conservation tillage involving seed bed

tilled in strips, leaving the no-till zone with at least 30 % crop residue retention (Trevini

et al., 2013). According to Food and Agriculture Organization (2016a), provided the

disturbed area is less than 15 cm wide or 25 % cropped area, ST qualifies as a form of

conservation agriculture.

1.4.1.4 Permanent raised planting

Permanent raised bed planting is a form of reduced tillage but there is substantial soil

disturbance during formation of new beds. Raised beds are formed by moving soil

laterally from the furrows to form a raised bed (Naresh et al., 2014b). There are two

parts in a in a bed planting (BP) system - centre of the bed and furrow of the bed

(Figure 1.6). The furrows are used for irrigation channels, drains and traffic lanes.

Generally, two to six rows are planted on the top of each bed (Naresh et al., 2011). In

the permanent raised bed (PRB) technique, once developed, the bed is not destroyed

or displaced but is only renovated each season (Gathala et al., 2015). According to

Sayre and Moreno (1997), the beds and furrows need to be kept permanently in the

Figure 1.5. A Versatile Multi-crop Planter

is using for strip tillage.

12

same position and reshaped as necessary from crop to crop in PRB. Dimensions and

configurations of raised beds may vary with soil conditions, field slope, available

machinery, crop type and irrigation technique. The PRB is generally constructed with

medium soil disturbance and

maximum residue retention where

equipment wheels and irrigation

channels are restricted to

permanent furrows and by planting

crops on the edges of beds

(Govaerts et al., 2007). While it

involves reduced tillage, PRB

involves more soil disturbance that

permitted in FAO CA guidelines. The

PRB has a beneficial effect on soil

properties and crop performance globally. As for example, the long-term effects of PRB

significantly improved soil chemical and biological properties, compared with

conventionally tilled beds in Northwest Mexico (Govaerts et al., 2007). In addition, PRB

was effective in improvement of plant-available soil water and aggregate stability

compared with CT (Verhulst et al., 2011). Singh et al. (2010) also found that the grain

yield increased in PRB as a result of improved soil properties and reduced waterlogging

in the Indian Punjab.

1.4.2 Permanent ground cover: residue management

Residue retention is one of the major principles of CA whose goal is maintenance of

surface soil cover to protect the soil from sunlight and direct raindrop impacts (Busari

et al., 2015). Residue cover also protects the soil surface from wind and water erosion,

while retaining C at the soil surface. In South Asia, the amount of residue being

returned to the soil is inadequate due to its uses for different purposes (Mohanty et

al., 2007). Gangwar et al. (2006) reported that mulch can increase yield, water use

efficiency and profitability, while decreasing weed pressure. In order to avoid serious

adverse impacts on soil, crop and environment, it remains to be determined where,

when, and how much of crop residue can be removed from soil (Wilhelm et al., 2007).

Figure 1.6. A Versatile Multi-crop Planter is

using for reshaping permanent raised bed.

13

According to Graham et al. (2007), the threshold levels of crop residue removal must

be established based on the amount of residue needed to: (i) conserve soil and water,

(ii) maintain or increase crop production, (iii) increase SOM pools, (iv) reduce net GHG

emissions, and (v) minimize non-point source pollution. The cutting height at harvest

and also the spread pattern of the residues are the main management options at

harvest (Anderson, 2009). When deciding on the cutter height of mechanical

harvesters it is important to know the height of the lowest obstruction under the

seeding bar of the subsequent seed drill. Standing wheat stubble is much easier to

seed into than stubble that has been flattened by machinery (Anderson, 2009). With

the emergence of a range of planters for 2-WT (e.g. Haque et al. 2011; Haque et al.

2016) there is a need to determine the optimal residue retention for CA in rice-based

cropping systems.

1.4.3 Crop rotation

Crop rotation involves growing different crops in planned succession on the same field

and it is one of the key pillars of CA. However, selection of appropriate crops and

cropping systems is an important factor for maintaining soil fertility, productivity and

profitability. As an example, in Bangladesh mungbean is a short duration legume crop

which can be grown in early summer (March to May) and could fill the gap in rice-

based cropping systems between the winter and rainy season rice crops. Therefore,

suitable crop selection in the system is important for a CA system to be successful. The

appropriate crop selections in rice-based systems are discussed below:

1.4.3.1 Crop diversification in rice-based systems

Rice is the dominant crop and occupies about 81 % of the total cropped area in

Bangladesh (Bangladesh Bureau of Statistics, 2010). Further, rice is grown in three

distinct seasons, namely aman rice (monsoon rice), aus rice (pre-monsoon rice) and

boro rice (dry season rice). As a result, most cropping systems are dominated by rice in

Bangladesh. However, repeated growing of monoculture rice for longer periods could

face a number of problems, such as decline of soil fertility (Singh & Singh, 1995; Manna

et al., 2003; Jat et al., 2012a), deterioration soil physical properties (Sharma et al.,

2003), reduction in water table, and pest and disease outbreaks etc, resulting in a

14

serious threat to agricultural sustainability (Jat et al., 2012a). In Bangladesh, the dry

season rice (boro rice) contributes over 60 % of total rice production (Bangladesh

Bureau of Statistics, 2015). As a dry season crop, boro rice is grown under irrigated

conditions. Hence, continuously using ground water for boro rice cultivation under

ponded condition leads to severe ground water depletion (Karim et al., 2014).

Rahman and Mondal (2010) predicted that water availability for the cultivation of boro

rice will be drastically reduced in the future due to global climate change and ground

water depletion in the High Barind Tract (HBT) of Bangladesh. Also, arsenic (As)

contamination of groundwater is widely prevalent in Bangladesh, caused by

groundwater depletion (Brammer, 2009). Further, rice is typically established by

transplanting seedlings into puddled soil in Bangladesh. This requires a huge amount of

water and labour which are becoming increasingly scarce and expensive, making rice

production less profitable and unsustainable. In addition, continuous flooded rice

cultivation reduces the N availability owing to slow and incomplete decomposition of

retained residue (Olk & Cassman, 1995). Under such conditions, inclusion of a dryland

crop in a rice-based system could hasten the decomposition of organic matter through

providing aerobic soil conditions (Jat et al., 2012a). In addition, higher production cost

and lower market value of rice encourages diversification of rice-rice systems with

higher valued crops which can provide more income and improved nutrition. Campbell

et al. (1990) reported that yield potential can be increased under diversified crop

rotations by favorably altering plant diseases, root distribution, weeds, moisture

conservation, and nutrient availability. Wheat among other crops can add diversity,

requires reduced water, and results in higher profit while sustaining the productivity of

the rice-wheat system compared to rice-rice systems (Halvorson et al., 2002). In

addition, rice-wheat rotations have been considered as a potential rotation to

sequester SOC due to slow decomposition of carbon resulting from anaerobic rice

cultivation, and addition of greater carbon input through higher biomass of rice and

wheat than other rice-dryland cropping systems (Kukal et al., 2009; Sahrawat, 2012).

http://click.thesaurus.com/click/nn1ov4?clkpage=the&clksite=thes&clkld=0&clkdest=http%3A%2F%2Fwww.thesaurus.com%2Fbrowse%2Funprofitable

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15

1.4.3.2 Inclusion of legumes in rice-based systems

The rice-wheat rotation is a dominant and desirable cropping system in Northwest

Bangladesh for ensuring food security. However, the sustainability of rice-wheat

system in the IGP is under threat (Ghosh et al., 2012; Bhatt et al., 2016). Rice and

wheat are heavy feeders of nutrients (Chauhan et al., 2012a) and continuous cereal

cultivation is leading to the degradation of SOM, soil structure and the depletion of

plant nutrients which are also major causes of yield decline in intensive cereal-based

cropping systems in South Asia (Ladha et al., 2003a; Mulvaney et al., 2009). In addition,

Zhou et al. (2014) reported that rice and wheat rotation using conventional practices is

leading to stagnated or reduced yields through the deterioration of soil physical

properties and decreased water and fertilizer use efficiencies. Therefore, some form of

crop diversification is necessary to sustain the agricultural production system. There is

some evidence that inclusion of leguminous crops in a cropping sequence reverses the

degradation process, increases yield, and improves soil fertility by fixing of

atmospheric nitrogen (N) in their root nodules, which in turn supplies residual N to the

succeeding crop (Kumbhar et al., 2007; Ghosh et al., 2012). In addition, legume-based

cropping sequences reduce water and nutrient requirement compared to cereal-based

systems. Further, legume crops leave more unused SWC in the soil profile which would

benefit deep rooting crops grown after the shallow rooting legume crops (Cutforth et

al., 2013). Legume-based cropping sequences can accumulate SOC, increase soil N

content, and improve soil aggregation which can be attributed to symbiotic fixation of

N, return of leaf litter and N-rich roots to the soil (Bhattacharyya et al., 2009b; Ghosh

et al., 2012) which leads to residual benefits for the following crop. Kumar Rao et al.

(1998) reported that a grain legume can supply 20-60 kg N/ha to the succeeding crop.

In addition, the potential NO₃-N losses can be minimized by growing legumes crops

during the dry-wet transition periods after monsoon rice. Sarker (2005) found that

incorporation of mungbean residue was effective in increasing the growth and yield of

succeeding monsoon rice. Further, inclusion of mungbean in the rotation could

increase system productivity and economic returns (Gathala et al., 2013). In addition,

increased grain legume cultivation is critical for providing essential protein, minerals

and vitamins to humans and livestock (Lauren et al., 2001). Therefore, it is necessary to

16

further examine prospects for legume-based rotations under CA as information on

legumes in rice-based cropping systems in Bangladesh is relatively limited.

1.5 Development of conservation agriculture

Soil tillage in fragile ecosystems was questioned in the 1930s , when dustbowls

devastated wide areas of the mid-west of the United States of America (Friedrich et al.,

2009). It was observed that water and wind-driven soil erosion were greatly

diminished by conservation tillage (Cline & Hendershot). Therefore, the concept of

conservation tillage was introduced for protecting the soil by reduced tillage (RT) and

residue retention. In the 1940s, advances in machinery design to seed directly without

any tillage and CA principles were described by Edward Faulkner in his book

“Ploughman’s Folly” (Faulkner, 1945). But the practical application of conservation-

tillage did not occur until the 1960s. In the 1970s, farmers and scientists transformed

the CA technology in Brazil, and at the same time research on NT with mulching was

started in West Africa (Lal, 1976). The concept of MT was promoted by increasing

concerns about soil erosion aggravated by intensive tillage (Thomas et al., 2007b).

Later the development of inexpensive weed control with herbicides accelerated the

spread of conservation tillage (Blevins & Frye, 1993). With the beginning of widespread

use of herbicides, tillage practices were supposed to be unnecessary, at least for weed

management. Experiments with MT started in North America and UK with the

availability of herbicides and then it spread to commercial farming in South America

and Australia (Johansen et al., 2012). For the implementation of MT, it became

necessary to develop a planter that can effectively deliver seed and fertilizer into

undisturbed soils. The other two pillars of CA, permanent soil cover and diverse crop

rotation became viable with the development of herbicides and minimum tillage

planters. During the 1970s, increased fuel prices encouraged farmers to change to CA

farming and hence commercial farmers widely adopted CA for saving fuel and to

protect the soil from erosion (Haggblade & Tembo, 2003). In Brazil and West Africa, NT

direct seeding and mulching appeared during the early 1970s (Lal, 1976). But it took

over 20 years to reach a significant adoption in South America (Haggblade & Tembo,

2003). In Zimbabwe, about 30 % of the commercial farmers using high-power traction

had adopted CA by 1998 (Nyagumbo, 1998). As the high-power traction was not

17

available for the small holder farmers, it was necessary to develop alternative

machinery to fit into small farms. In Bangladesh, the International Maize and Wheat

Improvement Center (CIMMYT) introduced a power tiller operated seeder (PTOS) from

China for timely sowing of wheat compared to sowing under CT by animal draught

power after monsoon rice in 1995 (Roy et al., 2004). In 2003-04, two-wheel tractor (2-

WT) operated no-till seeders were introduced with the collaboration of Food and

Agriculture Organization (FAO), CIMMYT and Bangladesh Agricultural Research

Institute (BARI) (Hossain et al., 2015a). Later, an Australian Centre for International

Agricultural Research (ACIAR)-funded project improved the 2-WT operated no-till

seeder for planting seeds of a range of crops and to manage residue properly (Hossain

et al., 2009). Currently, around 450,000 to 700,000 2-WT are used by small holders

accounting for 85 % of primary tillage in Bangladesh (Krupnik et al., 2013; Hossain et

al., 2015a). However, even though a range of planters for minimum soil disturbance

planting have been developed in recent years, there is still limited adoption of CA by

smallholders in rice-based cropping in Bangladesh or the Eastern Indo-Gangetic Plains

(Johansen et al., 2012). In part, this can be attributed to the limited number of medium

to long term studies on CA in farmers’ fields to demonstrate its performance in rice-

based cropping systems, using machinery suitable for small farms or fields.

1.6 Conservation agriculture adoption worldwide

Farmers’ commitment and mutual support of all linked stakeholders are required for

the rapid adoption and spread of CA (Kassam et al., 2014). Over the last three decades,

CA has been practiced continuously and has spread widely (Kassam et al., 2009). The

adoption of CA for arable cropping systems by region is given in Table 1.1. The

different components of CA are now being practiced from the Arctic Circle (e.g.,

Finland) across the tropics (e.g., Kenya, Uganda), to about 50° latitude south (e.g.,

Malvinas/Falkland Islands) (Derpsch et al., 2010). Conservation agriculture is practiced

on all kinds of farm sizes from a half hectare (e.g. China, Zambia) to hundreds of

hectares in many countries of the world, and to thousands of hectares in countries like

Australia, Brazil, USA or Kazakhstan (Kassam et al., 2009).

https://www.google.com.bd/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0CBwQFjAA&url=http%3A%2F%2Fwww.fao.org%2F&ei=gvyYVczVENCxuASuwIPoBQ&usg=AFQjCNFN0FJRtsVrfnxh2u66Un8onLMaSw&sig2=OIFanpJ8l8BorVfwgiD91g&bvm=bv.96952980,d.cWw

https://www.google.com.bd/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0CBwQFjAA&url=http%3A%2F%2Fwww.fao.org%2F&ei=gvyYVczVENCxuASuwIPoBQ&usg=AFQjCNFN0FJRtsVrfnxh2u66Un8onLMaSw&sig2=OIFanpJ8l8BorVfwgiD91g&bvm=bv.96952980,d.cWw

18

Figure 1.7. Extent of global area of conservation agriculture over time (Source: above

the respective bar).

Although currently there has been continued and rapid spread of CA systems across

the world, the total area of CA at present is only 9 % (about 125 M ha) of the total

cropped area (Friedrich et al., 2012). The current database of an ongoing collaboration

between FAO’s Conservation Agriculture and AQUASTAT programmes in 2016 shows

that globally the spread of CA is about 156 M ha while in 1973/74 it was only on 2.8 M

ha (Figure 1.7). The area of CA in the world is currently spreading at a rate of 10 M ha

per year and rapid expansion is mainly on large farmers’ land (Kassam et al., 2014).

However, adoption has been limited in small holder farms and in intensive rice-based

cropping systems (Food and Agriculture Organization, 2013a). There are several

constraints that impede widespread uptake of CA in small holder farms, such as lack of

extension programs, traditional mindset, lack of technical knowledge, weak

institutional support, unavailable affordable CA equipment and machinery and lack of

suitable herbicide (Friedrich et al., 2012).

19

Table 1.1. Area of arable crop land under conservation agriculture by region in 2013.

Region Area (M ha) Percent of global total Percent of arable land

South America 64 41.4 60.0

North America 54 34.8 24.0

Australia and New Zealand 17.9 11.5 35.9

Asia 10.3 6.6 3.0

Russia and Ukraine 5.2 3.4 3.3

Europe 2.1 1.4 2.8

Africa 1.2 0.8 0.9

Global total 155 100 10.9

Adapted from Kassam et al. (2014).

1.7 Constraints of conservation agriculture

Although CA has many beneficial effects on soil, environment and crop there are also

constraints to adoption of CA practices. Due to resource constraints and trade-offs

with other farm activities, especially with regard to the availability of crop residues,

seeds, land, labor, cash or credit it has been reported that small holder farmers rarely

adopt all three CA principles together (Wall, 2007; Kassam et al., 2009). Moreover,

Giller et al. (2009) identified some important constraints such as limited mechanization

within the small holder system, lack of suitable implements, lack of proper fertility

management options, weed control problems, limited access to credit, lack of

appropriate technical information, blanket recommendations that ignore the resource

status of rural households, competition for crop residues in mixed crop-livestock

systems, and limited availability of household labour. Some other constraints are

summarized in Table 1.2.

20

Table 1.2. Summary of major constraints of conservation agriculture systems.

Constraint Major finding References

Risk of lower crop

yields

Without concurrent implementation of residue retention

and crop rotation, NT alone tends to cause yield losses

Pittelkow et al. (2015a)

Grain yields of wheat reduced under ZT during the initial

years of CA adoption

Govaerts et al. (2005)

During the first few years lower yields under NT compared

to ploughing.

Guto et al. (2012)

Adverse effects of waterlogging in CA decreased grain

yield of maize

Thierfelder and Wall (2010)

Slow adoption and

extent

Capital- and labour-constrained small holder farmers

often reluctant to adopt CA because of concerns such as

risk of yield loss, increased labour demand if herbicides

unavailable, unavailable crop residue due to use for

household purposes, lack of knowledge and skills on CA

Giller et al. (2009)

Weeds and

herbicides

Increased recruitment of small-seeded weeds in minimum

and NT systems.

Chauhan et al. (2006a)

Herbicides are relied on as the main means of weed

control in conservation tillage systems

Anderson (2009)

Greater dependence on herbicides Lafond et al. (2009)

During initial years of CA adoption, weed control is often

laborious and more costly with a greater requirement for

herbicides

Wall (2007)

High costs to import herbicide during initial 4-5 years

causes reluctance to adopt CA in many developing

countries

Machado and Silva (2001)

Constant use of herbicide in conservation tillage systems

resulted in the development of herbicide resistance; and

heavy use may badly affect succeeding crops and the

chemical runoff can lead to water pollution

D'Emden and Llewellyn

(2006); and Hinkle (1983)

Heavy and continuous use of herbicides may adversely

affect the environment

Hinkle (1983)

Continuous use of herbicide reduces its efficacy Chauhan et al. (2006b)

New machinery and

operating skills

required

May require additional machinery Hulugalle and Scott (2008)

Specialized equipment is important for successful

adoption of CA

Hobbs et al. (2008)

CA is a knowledge intensive process Umar et al. (2011)

Other uses of crop

residue

Use of crop residue for different purposes such as livestock feeding, fuel and burning are the major constraints for the adoption of CA

Bhan and Behera (2014)

Nutrient

immobilization

High amounts of cereal residues with a high C:N ratio

causes temporary immobilization of soil mineral N

Abiven and Recous (2007)

21

Constraint Major finding References

Greater immobilization occurs under ZT with residue

retention

Bradford and Peterson

(2000)

Carryover of insect

pests and diseases

30 % higher pesticides required in conservation tillage

over CT to protect from enhanced insect, pests and

diseases

Hinkle (1983)

Rice cultivation under CA was more affected by

Laodelphax striatellus because it was difficult to apply

insecticide or herbicides under the layer of straw and

stubble in CA systems

Mousques and Friedrich

(2007)

1.8 Effect of conservation agriculture on crop performance and system productivity

In the IGP, there are numerous constraints to crop production in rice-based systems.

Since this thesis deals with the development of CA for two intensive rice-based

rotations in the Eastern IGP Bangladesh, some of the major constraints and their

possibilities for alleviation are summarized in Table 1.3.

22

Table 1.3. Constraints to cropping systems in the Indo-Gangetic Plains.

Constraint Cropping

system

Cause Consequence Solution

Reference

Unsustainable

production

system

Rice-wheat Low yield and

farm income;

environmental

constraints and

weather

variability

Plateauing and

reducing

agronomic

productivity

and

profitability

Adaptation of

CA based

systems

(Bhushan et al., 2007;

Hobbs, 2007a; Kumar &

Ladha, 2011; Raman et

al., 2011; Gathala et al.,

2013; Jat et al., 2014;

Laik et al., 2014)

Decreasing

crop

productivity

Rice-

wheat;

Cotton-

wheat

Degradation of

soil physical

properties

Decline in crop

productivity

Application of

CA-based

management

system -

minimum

or ZT and crop

residue

retention

(Mishra et al., 2015)

Lower system

productivity

Rice-wheat Puddling use for

rice cultivation

Deterioration

of soil

structure,

failure of

seedling

emergence

and yield loss

of next crop

after rice

Direct seeded

unpuddled

rice,

permanent

raised bed

(Kukal & Aggarwal,

2003a; Mohanty et al.,

2006)

Soil organic

carbon

depletion

Rice-wheat Intensive tillage

and removal of

crop residue

Reduces

productivity

and causes

environmental

degradation

Residue

retention and

ZT system

(Ghimire et al., 2011)

Rice-wheat Reduces

sustainability

Integrated

nutrient

management

(Yadav et al., 2000;

Nayak et al., 2012)

Adverse

environmental

impacts and

unsustainable

productivity

ZT and residue

retention

(Bhattacharyya et al.,

2006b; Bhattacharyya

et al., 2012b; Das et al.,

2013; Das et al., 2014)

23

Constraint Cropping

system

Cause Consequence Solution

Reference

Total Soil N

depletion

Cotton-

wheat

Intensive tillage

and removal of

crop residue

Unsustainable

and lower crop

productivity

ZT under BP

system and

residue

retention

(Bhattacharyya et al.,

2013)

In the IGP, the productivity of rice-based systems has plateaued or started diminishing

due to mismanagement of natural resources (Ladha et al., 2003c). Traditional crop

establishment methods in rice-based systems such as puddling for transplanted rice

and intensive tillage for wheat planting require large amounts of water, energy and

labour, which are becoming increasingly scarce and expensive (Mishra & Singh, 2012).

Moreover, conventional agronomic practices are no longer able to maintain the gains

in productivity during the past few decades (Chauhan et al., 2012a). Conservation

agriculture as a paradigm shift is proposed for enhancing the system's productivity and

sustainability in South Asia (Jat et al., 2011). Laik et al. (2014) concluded that CA

comprising ZT with full residue retention enhanced the productivity and economic

returns over farmers’ practices involving intensive tillage for wheat cultivation, and

puddling for rice cultivation with residue removal. Kumar et al. (2013) demonstrated

from five wheat establishment methods (CT, reduced-tillage, rotovator tillage, raised

bed and zero-tillage) that ZT improved the operational field capacity of machinery by

81 %, and decreased specific energy (energy required to produce per kg of grain) by 17

% and increased the energy usage efficiency by 13 % compared to CT in a Typic

Ustochrept alluvial sandy loam soil in the IGP. Erenstein and Laxmi (2008) concluded

from a comprehensive review of ZT impacts on wheat in the Indian IGP that ZT wheat

is suitable for rice-wheat systems in the IGP by allowing earlier wheat planting,

facilitating weed control, reducing production costs and saving water. A different study

of rice-wheat systems in the IGP showed that the resource conserving CA technologies

saved water consumption and negative environmental impacts and increased crop

production (Gupta & Seth, 2007). A survey in the IGP showed that even resource-poor

small holders have started to benefit from this technology by using contractors to

direct-drill their crops (Hobbs & Gupta, 2002). Hobbs and Gupta (2003) also showed

that wheat yields were greater when it was planted with ZT after unpuddled rice. In

24

another 2-year study, yield of dry direct-seeding rice and wheat under NT performed

the same as with conventional practice, but under NT conditions the water savings and

labor use were significant (Bhushan et al., 2007).

Wang et al. (2012) reported that yields with RT were higher by 13-16 % in spring maize

and 9-37 % in winter wheat, whereas those with NT were comparable to conventional

methods in China. Balwinder et al. (2011) reported that mulch residue improved crop

performance when water was limiting, and occasionally increased yield. Some other

researchers have demonstrated that NT and residue mulching is effective in increasing

crop yields (Naudin et al., 2010). However, Zheng et al. (2014) showed from a meta-

analysis in China that NT without straw retention increased the risk of yield loss,

although CA effects on crop yield differ due to the variation of regional, climate and

crop types.

Although some studies demonstrated no advantage of PRB, there is increasing

evidence that this procedure is advantageous to system productivity. Permanent

raised beds are increasingly used in many developed and developing countries and

have been introduced in Bangladesh with the aim of improving system productivity

(Talukder et al., 2002). Singh et al. (2010) studied the effects of PRB on soil fertility,

yield, and water and nutrient use efficiencies in a pigeon pea–wheat system in India.

They concluded that PRB produced greater yield of pigeonpea and higher system

productivity but lower wheat yield as compared to conventional flat bed. Hossain et al.

(2008) evaluated system productivity, fertility and N-use efficiency under N

fertilization, straw retention and tillage options in a rice-wheat-mungbean cropping

system. They concluded that PRB with straw retention produced the highest

productivity for all three crops in the sequence. Within each N rate the total system

productivity was the greatest with residue on PRB and least in conventional traditional

planting with no straw retention. Wheat performed better with BP in terms of spike

number, spike length, grain yield as well as N absorption and also this planting method

reduced the level of plant lodging even when N application was high (Hossain et al.,

2006). Therefore, the combination of PRB with N and residues retained appears to be a

very promising technology for sustainable intensification of rice-wheat systems in

25

Bangladesh. Khaleque et al. (2008) demonstrated from an experiment of BP and N

application on wheat yield and N-use efficiency that plants take up more N and

thereby increased wheat yield in newly formed BP compared to the conventional

planting system. Talukder et al. (2004) reported from a three-year study of the rice-

wheat-maize+mungbean cropping system in Bangladesh that 50 % previous crop

residue increased maize yield by 31.6 % and rice yield by 19.3 %. Moreover, in a

different study of the rice-wheat system in Bangladesh, Hossain et al. (2008) concluded

that wheat root length density and root diameter were increased with raised beds,

straw mulch and N application. In a rice-wheat system at Jabalpur, Madhya Pradesh,

India Gathala et al. (2015) evaluated four tillage methods (direct seeding in dry fields,

direct seeding of sprouted seeds in a puddled field by drum seeder, manual

transplanting, and mechanical transplanting) for rice and four tillage methods (CT, ZT,

ST and BP) for wheat. They found that direct seeding of sprouted seeds of rice

following ST of wheat resulted in higher yield and water productivity by ensuring

timely and low-cost sowing. However, Islam et al. (2014) did not find from their three

years experiment on the rice-maize rotation in Bangladesh significant yield differences

due to different tillage (CT, single pass wet tillage in rice, BP and ST) and residue

retention (0, 50 and 100 %). In a 4-year study of rice-maize rotations in Bangladesh,

Gathala et al. (2015) evaluated CA-based tillage (RT, ST, fresh beds and permanent

beds) productivity and profitability relative to current practice – CT (puddled)

transplanted rice on flat followed by conventional-tilled maize on flat. They concluded

that although there was no yield difference due to different CA-based tillage options,

PRB and ST resulted in higher net income and benefit cost ratio compared to CT for

both rice and maize.

1.9 Influence of conservation agriculture on soil properties

1.9.1 Soil physical properties

The contrasting soil environment and management condition in rice-based cropping

systems exacerbates soil physical problems, particularly for the non-rice crop.

Therefore, it is of particular interest to evaluate the extent to which CA can alleviate

soil physical constraints in rice-based systems.

26

1.9.1.1 Soil structure and aggregation

Soil aggregate stability is one of the key indicators for soil quality in agro-ecosystems

(Paul et al., 2013). Soil structural stability is the ability of aggregates to remain intact

when exposed to different stresses (Kay et al., 1988). In general, CT reduces soil

aggregation and particulate organic matter by accelerating the turnover of aggregate-

associated SOM (Six et al., 1999). In maize-wheat cropping systems on a Cumulic

Phaeozem soil of Central Mexico, ZT with residue retention increased aggregate

distribution and stability compared to CT and thereby reduced top layer slaking

(Govaerts et al., 2009). More stable soil aggregate structure is present under ZT,

compared to CT (Limon-Ortega et al., 2002). Govaerts et al. (2007) found higher

aggregate stability and mean weight diameter (MWD) in PRB with full residue

retention compared to residue removal in Mexico. Shaver et al. (2003) reported that

macro-aggregation has a linear relationship with the C content of the aggregates

whereby each extra g of organic C/kg in the macro-aggregates increased the macro-

aggregates by 4.4 %. From an 11-year long-term experiment of the Chinese Loess

Plateau, He et al. (2011) found that macro-aggregates (>0.25 mm) and macroporosity

(>60 µm) with NT increased by 8.1 % and 43.3 % compared to CT in the 0-30 cm soil

layer. Also Chen et al. (2009a) reported that the portion of 0.25-2.00 mm aggregates,

MWD and geometric mean diameter (GMD) of aggregates under conservation tillage

were larger than CT at both 0-15 cm and 15-30 cm soil depths in the rainfed areas of

northern China. Residue retention with low quality SOM can increase soil aggregate

stability more than high quality organic resources but N fertilizer application negates

these effects (Chivenge et al., 2011).

1.9.1.2 Soil bulk density and porosity

The soil bulk density (BD) varies with crop management as well as with inherent soil

qualities (Singh & Kaur, 2012). From a 22-year long-term field trial in Central Ohio it

was found that soil BD and penetration resistance were lower under no-till than

ploughed soil. Machado and Silva (2001) reported that the soil BD of soils tended to be

lower with soybean-wheat/ hairy vetch-maize under NT than with CT. Gill and Aulakh

(1990) also reported that soil BD decreased and grain yield of wheat increased under

NT compared to CT. By contrast, soil BD decreased significantly with CT compared to

27

ZT after both rice and wheat crops at 0-15 and 15-30 cm soil depths in a rice-wheat

cropping system in India (Bhattacharyya et al., 2006b). Similarly, Gangwar et al. (2006)

also found that soil BD decreased significantly with CT compared to ZT in a sandy loam

soil of the IGP. However, in a drought prone area of Northwest Bangladesh, soil BD at

0-7.5 cm and 7.5 -15 cm depth did not significantly change due to application of either

minimum tillage or CT practices (Islam et al., 2012).

Retention of previous crop residue usually significantly decreases soil BD (Blanco-

Canqui & Lal, 2009). Addition of each tonne crop residue per hectare over a 12-year

period reduced soil BD by 0.01 g/cm³ and increased effective porosity by 0.3 % in the

surface 2.5 cm soil depth in wheat-fallow, wheat-corn-fallow and continuous cropping

(Shaver et al., 2003). Application of mulch of fodder radish decreased the soil BD and

increased transmission pores in the 0-10 cm soil layer (Glab & Kulig, 2008). Besides, the

method of residue retention also significantly influences the soil BD. Soil BD was lower

when crop residue was incorporated compared to when it was retained on the soil

surface as mulch (Acharya et al., 1998). Bhattacharyya et al. (2006a) reported that soil

BD was significantly lower in a CT system compared to ZT due to the incorporation of

crop residues in surface soil of CT in the Indian Himalayas. They also demonstrated

that the BD was significantly lower with soybean-pea and soybean-lentil rotations than

a soybean-wheat rotation at this location.

1.9.1.3 Soil penetration resistance

Soil penetration resistance (PR) is a commonly used indicator of soil strength and soil

compaction. High PR is correlated with poor aeration, poor drainage and restricted

root growth (Celik, 2011). Tillage and residue management influences soil PR as a

result of altering the soil structure and hence soil pore size distribution. In tillage

studies, soil BD and PR are two interrelated variables to assess the soil pore size

distribution but individual use of PR or BD may give misleading information (Campbell

& Henshall, 1991). The relation between soil PR and soil BD is positive and in compact

soil, soil PR strongly increases while increases in soil BD are small (Allbrook, 1986). In a

study on sandy soil it was shown that wheel traffic increased soil PR about 35 % while

BD increased less than 3 %; that is, soil PR was ten times more sensitive than BD as an

28

indicator of soil compaction (Vazquez et al., 1991). For showing tillage effects, Ferreras

et al. (2000) found greater PR in the upper 10 cm soil under NT than CT. Also, Franzen

et al. (1994) observed significantly lower soil PR with NT below 10 cm soil depth due to

addition of mulch. It was also shown in other studies that soil PR was significantly

higher with CT compared to ZT (Carman, 1997). Schwartz et al. (2003) found that the

PR increased with NT practices as compared to CT and RT. Limon-Ortega et al. (2002)

found that PR decreased as the amount of crop residues applied for each tillage-straw

treatment increased in Northwest Mexico. Increasing crop residues can increase SOM

content and thereby improve SWC which in turn lowers soil PR (Shaver et al., 2003).

Complete stover removal increased soil PR in a sloping silt loam from 0.9 to 1.2 MPa

and in a nearly level silt loam from 0.8 to 1.1 MPa (Blanco-Canqui & Lal, 2007).

1.9.1.4 Soil water content

Soil moisture conservation is a critical issue for crop production in most rainfed

cropping areas around the world. It is widely recognized that puddling of soil during

rice cultivation degrades the soil physical conditions and results in lower yields of

dryland crops in rice-based systems (McDonald et al., 2006). After rice, the surface soil

should be sufficiently dry to allow entry of machinery for establishing the dryland crop.

Puddled soil, however, may require several days following rice harvest to reach an

appropriate moisture content for tillage (Flinn & Khokhar, 1989). Several studies in the

IGP have demonstrated yield reductions (1-1.5 %) of wheat for every day delay in

planting after the optimum sowing date (Hobbs & Morris, 1996). Further, when soil

moisture level permits initiation of tillage, primary tillage produces massive structure

and clods in previously puddled soil, and hence extra tillage is needed to prepare a fine

seed bed (Timsina & Connor, 2001). Intensive tillage can disrupt soil pores and thereby

decrease water infiltration (Shukla et al., 2003). In contrast, the positive influence of ZT

on soil structure, pore geometry may increase SWC and its transmission (Azooz et al.,

1996). Conservation agriculture practices such as ZT and residue retention are

important tools for conserving soil and water resources (Reeves, 1994). In northeast

China, Liu et al. (2013) reported that SWC under NT was higher than in CT at 0-30 cm

soil depth. In another study of North Cameroon, NT or RT improved SWC and corn

yield compared to CT (Naudin et al., 2010). Pagliai et al. (2004) found that lower SWC

29

under CT soil reduced root growth of a wheat crop following rice. The SWC is also

sensitive to crop residue removal and after removal the exposed soils quickly lose

moisture (Blanco-Canqui & Lal, 2009). Mulch cover explained 84 % of variations in SWC

under NT in a silty clay loam soil (Wilhelm et al., 1986). Crop residue retention

improves SWC in three different ways: 1) increasing infiltration rate and decreasing

runoff losses, 2) reducing evaporation and abrupt fluctuations in soil surface

temperature, and 3) increasing SOM concentrations, which increases water retention

capacity of the soil (Blanco-Canqui & Lal, 2009).

1.9.2 Effects of conservation agriculture on soil organic carbon and its fractions

1.9.2.1 Soil organic carbon

Soil organic matter and carbonate minerals are the sources of SOC. The SOM is formed

by various organic compounds which are processed by living and non-living organisms

(Franzluebbers, 2010). The SOC is the main component and makes up a significant

portion (50-58 %) of SOM (Franzluebbers, 2010). The SOC is a quantifiable component

and different to SOM as it refers only to the C content of organic compounds.

Generally, laboratories measure SOC and convert to SOM by a conversion factor of

1.72, i.e. SOM (%) = SOC (%) X 1.72. Soil organic carbon is a key indicator of soil quality

and sustainability as it is inextricably linked to physical, chemical, and biological soil

quality indicators (Reeves, 1997). Therefore, maintenance of SOC is essential for

sustainable agro-ecosystems. However, SOC is greatly influenced by different tillage

and residue management practices. In rice-based systems, crop residues are the main

source of organic C which improves soil physical properties and the hydrothermal

regime (Yadvinder-Singh et al., 2005). In rice-based systems of the IGP, the

conventional production practices involving intensive tillage along with removal of

almost all crop residue resulted in loss of SOC and other nutrients (Beri et al., 2003).

Dolan et al. (2006) studied the effects of tillage, residue and N management on SOC

and N in a Minnesota soil and concluded that 30 % more SOC was obtained with NT

than mouldboard plough and chisel plough tillage in the surface soil (0-20 cm). This

trend was reversed at 20-25 cm soil depths, where significantly greater SOC and total

N were found in ploughed treatments than in NT, possibly due to residues buried by

30

inversion. Similarly, other researchers reported that tillage practice can influence the

distribution of SOC in the soil profile with higher SOC content in surface soil with ZT

than with CT, but a higher content of SOC in the deeper soil layers of tilled plots where

residue is incorporated through tillage (Gal et al., 2007; Thomas et al., 2007a). In an

Oxic Paleustalf at Wagga Wagga, New South Wales, SOC levels were significantly

higher at 0-20 cm soil depth with direct drilling compared to CT (Chan et al., 2002).

Also, Castellanos-Navarrete et al. (2012) reported that crop residue retention along

with ZT and crop rotation increased SOC concentrations only at 0-5 cm soil depth

compared to CT. Soils under RT increased SOC by 7.3 % compared to plough-till at 0-20

cm soil depth (Chen et al., 2009b).

1.9.2.2 Soil organic carbon turnover

Labile SOC fractions in the soil surface layers are sensitive to effects of CA (Li et al.,

2012). Dou et al. (2008) found SOC and the labile SOC pools significantly increased

under NT and intensified cropping at 0-5 cm depth while they decreased with CT with

the effects gradually decreasing with depth. In the rainfed areas of northern China,

Chen et al. (2009a) found that SOC fractions such as particulate organic C,

permanganate oxidizable C, hot-water extractable C, microbial biomass C and

dissolved organic C were all significantly higher in NT and ST than in CT in the upper 15

cm.

1.9.2.2.1 Water soluble carbon

The water soluble carbon (WSC) is a small portion (<5 %) of the total SOC content (Tao

& Lin, 2000; Ohno et al., 2007; Scaglia & Adani, 2009). However, it plays an important

role in many biogeochemical processes of soil and is considered as the most mobile

and reactive soil carbon source (Lu et al., 2011), altering a number of physical,

chemical and biological processes in both aquatic and terrestrial environments

(Schnabel et al., 2002; Marschner & Kalbitz, 2003). Water soluble carbon is one of the

sensitive early indicators of effects of soil management practices on soil quality (Blair

et al., 1995). In a study of northern China, Liu et al. (2014b) obtained 232 % higher

WSC at 0-5 cm and 123 % greater at 5-10 cm soil depth under NT as compared to CT

after 17 years. However, treatments were not significantly different below 10 cm soil

31

depth. Li et al. (2012) found that WSC under double NT (rice with NT-rape with NT)

plus crop residue treatment were 1-1.3 times higher than with residue removal and CT

in a 3-year experiment of rice-rape rotation in central China.

1.9.2.2.2 Carbon dioxide (mineralization, root and microbial respiration)

It is well known that tillage exposes the protected SOM and stimulates CO₂ efflux from

soil (Reicosky et al., 1997; Rochette & Angers, 1999). Conventional tillage improves soil

aeration along with incorporating soil residue and hastens SOC oxidation which leads

to increased CO₂ emissions (Himes, 1998; West & Post, 2002). By contrast, minimum

tillage and crop residue retention can reduce CO₂ emissions from the soil surface,

resulting in increased C sequestration in the soil compared to intensive tillage and

residue removal (Reicosky, 2001). From research on a calcareous Hypogleyic Luvisol,

Buragiene et al. (2011) showed that the emission of CO₂ was greater after intensive

ploughing and lowest in NT soils. Almaraz et al. (2009) examined tillage (CT and NT)

and N₂-fixing soybean (Glycine max) residue effects on greenhouse gas (CO₂ and N₂O)

emissions and concluded that CT with incorporation of soybean residue induced

greater CO₂ emission than the NT system.

The conventional rice-wheat rotation is a considerable source of GHG emissions as

puddled rice contributes to methane (CH₄) emissions and dryland crop (wheat)

production contributes to N₂O and CO₂ emissions (Pathak et al., 2011). On the other

hand, paddy soils have potential for increased SOC storage as compared to dryland

soils with proper soil management (Xu et al., 2013). Emission of CO₂ from soil can be

mitigated by sequestration of SOC (Lal, 2004b; Das et al., 2013). Thus, SOC

sequestration is an effective strategy for restoring the degraded soils, enhancing soil

fertility and reducing the atmospheric CO₂ emission and thereby mitigating climate

change (Wang et al., 2010). Adopting CA has been recognized as an important strategy

to reduce greenhouse gasses through sequestering SOC, as well as through minimizing

use of fuel and fertilizer (Pathak et al., 2012; Dendooven et al., 2012a; Alam et al.,

2016).

32

1.9.3 Effects of conservation agriculture on soil nitrogen dynamics

Nitrogen is the most important yield limiting nutrient in intensive irrigated rice-

systems (De Datta et al., 1998; Ali et al., 2007a; Devkota et al., 2013). In most

ecosystems, N regulates net plant primary production (Lambers et al., 1998). Nitrogen

undergoes various transformation processes (Figure 1.8). There are three major

pathways of N loss, firstly leaching (mainly NO₃-N and intermittently NH₄-N and soluble

organic N), secondly by de-nitrification (emission of N₂O, NO and N₂ gases) and thirdly

ammonia (NH₃-N) volatilization (Ladha et al., 2005). The resultant leachate and gases

go to water bodies and the atmosphere and can pollute the environment (Ladha et al.,

2005). Consequently, N management plays a vital role in improving crop yield and

quality, environmental quality, and economics of crop production (Campbell et al.,

1995). A mechanistic knowledge of the soil N cycle is critical in understanding the

behaviour of ecosystems and their responses to natural and anthropogenic mediated

change (Jones et al., 2004).

Figure 1.8. The soil nitrogen cycle. Adapted from Hofman and Cleemput (2004).

Nitrogen dynamics in intensive rice-dryland crop rotations (anaerobic-anaerobic) can

be greatly altered by changing the crop establishment method from a conventional to

a CA system (Devkota et al., 2013). Nitrogen dynamics under conventional cultivation

techniques in lowland paddy soil have been extensively studied (Buresh & De Datta,

1991; Tripathi et al., 1997; Ali et al., 2007a). However, there are limited studies

33

available that report changes for CA cultivation of different crops especially for dryland

in intensive rice-based system in Bangladesh.

1.9.3.1 Total soil nitrogen

Conservation agriculture effects on soil TN content generally mirror those on total SOC

as the N cycle is inextricably linked to the C cycle (Bradford & Peterson, 2000).

Nitrogen dynamics can also be affected by a change from conventional ploughing to

conservation tillage (Van den Putte et al., 2012). A significantly higher TN was observed

both under ZT and PRB compared to CT in the highlands of Central Mexico (Govaerts et

al., 2007). Sainju et al. (2007) reported that improving soil and crop management

practices such as RT and increased cropping intensity increase soil TN and its fractions

to a depth of 20 cm compared to conventional practice in dryland conditions. Residue

retention improved SOC, TN and other essential nutrients, and increased thereby crop

yields compared to residue removal (Das et al., 2013; Bhattacharyya, 2013; Das et al.,

2014). On the other hand, Sainju et al. (2008) found from a 10-year experiment in USA

that there were no effects of tillage and cropping system on SOC and soil TN. Tillage

may affect surface residue and N fractions, e.g. the surface residue, NH₄-N and NO₃-N

at 5-10 and 10-20 cm, TN and PMN at 0-5 cm were greater in ST compared to CT

(Sainju et al., 2013). There are several labile active fractions of N described below

which may be more responsive early indicators for change in soil N turnover under

minimum tillage than total N.

1.9.3.2 Mineral nitrogen

The organic forms of N are generally not important for growth of crops. The organically

bound N is generally only available for crop or microbial growth through N

mineralization during the decomposition of crop residues (Lupwayi et al., 2006; Van

Den Bossche et al., 2009). In a rice-based system, NH₄-N is the major available form of

N for rice in anaerobic soils (Tripathi et al., 1997; Devkota et al., 2013). The

accumulated NH₄-N during the rice season undergoes nitrification to NO₃-N which is

facilitated by dry conditions and intensive cultivation for growing arable crops after

rice (Tripathi et al., 1997). However, this NO₃-N is prone to losses through leaching or

34

de-nitrification to N₂ and N₂O upon soil flooding and heavy rains (George et al., 1993;

Tripathi et al., 1997).

Changes in cultivation techniques, i.e. from conventional to conservation systems, may

alter N transformation in intensive rice-based system (Arora et al., 2010; Devkota et

al., 2013). Conservation systems largely influence the soil physical environment such as

soil temperature, water filled pore space and soil strength which in turn affects the N

transformation processes (Linn & Doran, 1984; Devkota et al., 2013). Conservation

systems such as ZT and residue retention reduce N mineralization through decreasing

the decomposition of SOM and increase the immobilization of N (Drinkwater et al.,

2000; Yadvinder-Singh et al., 2005). In contrast, CT based on intensive tillage hastens N

mineralization of soil organic N and increases NO₃-N in the soil profile (Sainju & Singh,

2001; Al-Kaisi & Licht, 2004).

The impact of minimum tillage and residue retention on N mineralization is still

inconclusive (Verhulst et al., 2010). The N availability for plant uptake is dependent on

the rate of C mineralization. Intensive tillage increases the mineralization of soil TN

(Schomberg & Jones, 1999). By contrast, Schoenau and Campbell (1996) reported that

CA enhanced greater initial immobilization which led to greater initial N fertilizer

requirements but requirements decreased over time because of the build-up a larger

pool of readily mineralizable organic N. Deep placement of N under NT increased N use

efficiency in rice and wheat (Ladha et al., 2003b). Rao and Dao (1996) reported that

the yield and NUE in wheat increased under ZT condition due to increased availability

of applied N and reduced loss. During the transition period from CT to CA,

immobilization of N takes place as a result of slow turnover of SOM (Pekrun et al.,

2003). Sainju et al. (2013) found that surface soil residue and N storage increased in ST

while there was increased microbial activity and N mineralization in CT because of

residue incorporation to a greater depth. In Mexico, Govaerts et al. (2007) found that

tillage and residue management effects did not significantly affect the concentrations

of NH₄-N and NO₃-N in surface soil (0-5 cm) while the highest concentrations of NO₃-N

was found in CT at 5-20 cm soil layer compared to PRB. Practicing NT in wheat-maize

cropping system for 11 years significantly increased available N at the top 10 cm by 31

35

% as compared to CT treatment in North China Plain (He et al., 2011). In most previous

studies, the soil NO₃-N and NH₄-N has been determined at the end of different field

experiments. There are limited studies on changes in NO₃-N and NH₄-N, both during

(when plant demand of NO₃-N and NH₄-N is high, at time of greater decomposition of

residue) and after harvest of the dryland crop grown following rice. The measurement

of soil NO₃-N and NH₄-N a number of times during crop growth may be necessary to

understand the dynamics of N availability and their implication for crop N uptake.

1.9.3.3 Potentially mineralizable nitrogen

The potentially mineralizable nitrogen (PMN) is the amount of N that will mineralize in

infinite time at optimum temperature and moisture (Curtin & Campbell, 2007). An

accurate estimate of potentially mineralizable N (PMN) in soil is useful to predict

optimum crop yield and quality and also to minimize N loss to the environment that

may result from overuse of fertilizers (Bordoloi et al., 2013). Tillage and residue

management can greatly affect PMN (Mikha et al., 2006). Doran (1987) reported that

the PMN was greater in the 0-7.5 cm soil layer under ZT, which might be due to either

greater immobilization, less mineralization or both as compared to CT. From a series of

four long-term tillage trials in Canada, Sharifi et al. (2008) found greater PMN under NT

than under CT at three of the four sites. Franzluebbers et al. (1995) also reported that

PNM was greater in NT than in CT at 0-5 cm soil depth after 9 years in south-central

Texas.

1.10 Research gaps and objectives

Rice-based cropping systems in the Eastern IGP are among the most intensive cropping

systems in the world. However, the annual alternation between anaerobic, puddled

and aerobic soil conditions inevitably results in soil degradation. This is compounded,

at least in traditional systems in Asia, by residue removal and intensive tillage for both

rice and aerobic crops. Plough pan development and degraded soil physical conditions

restrict root growth and deplete SOM and nutrients. Conservation agriculture

methods, including minimum tillage and residue retention, show promise in alleviating

this soil degradation. However, most such studies have been conducted in the western

and central IGP, but little is known of the effects of minimum tillage and residue

36

retention in the rice-based cropping systems of the Eastern IGP that includes

Bangladesh. Here, the specific research needs include:

• Assessment of the impacts of minimum tillage and residue retention on

productivity of upland crops and rice and on soil conditions of rice-based

cropping systems.

• Investigation of the extent to which minimum tillage and residue retention can

increase yield through conserving soil moisture and improving soil properties.

This needs to be studied in the Bangladesh context where most farmers

remove much of the crop residues for use as livestock feed, building materials

and fuel.

• Although there have been short-term studies of growing crops on raised beds

or permanent raised beds and their effects on wheat performance in

Bangladesh, the effects of ST as implemented with 2-WT have not yet been

comprehensively evaluated. Thus there is a need to understand effects of ST on

crop production, soil properties and nutrient pools in contrasting soil types in

intensive rice-based systems. There is also a need to compare performance of

crops on raised beds and strip tillage. While many single-season crop

comparisons have been done, there is a shortage of studies that have

implemented these tillage options and residue retention in all crops in the

rotation and continued these for more than two crop cycles.

In this thesis, different tillage practices, namely ST, BP and CT; and residue

management, namely retention of HR and LR on soil properties and crop performance

of two intensive rice-based systems under contrasting environments were

investigated. One system was a legume-dominated (lentil-mungbean-monsoon rice)

system where two legume crops were grown in the annual rotation in an alluvium

area. Another system was a cereal-dominated rotation (wheat-mungbean-monsoon

rice) where two cereal crops were grown in the system in a drought prone area, the

High Barind Tract (HBT).

37

The objectives of this thesis are therefore, in contrasting rice-based cropping rotations

of Bangladesh, to evaluate the effects of soil disturbance and residue management

levels on:

• crop biomass production and yield over a three-year period;

• soil physical conditions and rooting habit of the cool dry season crop;

• soil organic carbon and its turnover; and the C budget;

• soil N pools, turnover, and N balance and nitrogen use efficiency.

38

2 Effects of tillage and residue management on yield and yield attributes

of winter crops in rice-based systems in Bangladesh

2.1 Introduction

The cropping sector in Bangladesh is heavily dominated by rice which is grown on

more than three-fourths of the total cultivable land throughout the year in three

distinct cropping seasons (Hoque, 2001). However, rice-based cropping systems are

necessarily complex as rice and post-rice (upland) crops are grown under different soil

conditions. Rice cultivation in South Asia is characterized by puddling of soil before rice

establishment and removal or burning of crop residues. Although puddling has some

benefit for weed control, seedling transplanting and reduced deep percolation of the

standing water (Ringrose-Voase et al., 2000), it destroys soil aggregates, and degrades

other soil physical properties to the detriment of the following upland crop (Sharma &

De Datta, 1986). Field preparation for a following upland crop is impeded owing to

drying of the soil and the formation of hard cracked soil blocks (Aggarwal et al., 1995).

Consequently, extra tillage and irrigation are necessary to prepare a good seedbed

after rice, which causes delayed planting and ultimately results in lower yield potential

(Hobbs, 2001).

It is argued that crop diversification is essential for sustaining farming systems through

the improvement of soil fertility, reduction of production risk (Hoque, 2001), and

weakening of the plough pan which forms under intensive rice cultivation (Salam et al.,

2014). Wheat is the second most important cereal crop in Bangladesh on the basis of

harvested area (Food and Agriculture Organization, 2013b). However, continuous

cereal cropping in the rice–wheat system and intensive cultivation may further

exacerbate declining soil physical properties and there are reports of declining crop

yields in intensive rice-based cropping of Bangladesh (Ladha et al., 2003a; Mollah et al.,

2007). Rice-wheat cropping systems remove on average 278, 53 and 287 kg/ha of N, P

and K annually from crop land in the Indo-Gangetic Plains (IGP) (Singh & Singh, 2001).

Inclusion of legumes in the cropping system is a possible form of crop diversification

39

that also improves fertility status of soil (Kumar Rao et al., 1998; Porpavai et al., 2011)

and increases system productivity and economic returns (Bhushan et al., 2007).

To produce more food from less land, crop intensification is necessary however it

needs to be a sustainable intensification. Increasing intensity of land cultivation by

raising three crops in a year instead of two may reduce yield of individual crops by

degrading soil physical, chemical and biological properties (Bhattacharyya et al., 2008;

Johansen et al., 2012; Singh & Kaur, 2012). Possible intensification approaches to

addressing this problem include conservation agriculture (no-till farming), cover-

cropping and integrated nutrient management (Lal, 2013). Legume-based cropping

sequences and conservation agriculture (CA) have the potential to enhance soil organic

carbon (SOC) and soil total nitrogen (TN) and improve soil aggregation (Bhattacharyya

et al., 2009b), which should lead to their positive residual effects on succeeding crops.

Conservation agriculture has potential application in diverse agro-ecological zones, and

has been advocated for enhancing food security for millions of smallholders in the

developing world (Derpsch & Friedrich, 2009). It is proposed as a panacea to

agricultural problems in smallholder farming systems, although its applicability for

particular agro-environments needs to be properly demonstrated (Chivenge et al.,

2007). Pittelkow et al. (2015a) showed from a meta-analysis of 610 studies worldwide

involving 5,463 paired comparisons that zero-tillage (ZT) alone decreased yields and

even with rotations and residue retention there was a small overall decrease in crop

yields. By contrast, in dry climates, the overall effect of ZT with and without residue

retention and rotation of crop was to increase yields. Conservation agriculture is based

on the three key principles, namely minimal soil disturbance, and permanent soil cover

combined with diverse crop rotations (Hobbs, 2007a). Many positive benefits are

claimed for CA such as increased crop yield (Bhushan et al., 2007; Farooq et al., 2011;

Saha & Ghosh, 2013; Choudhury et al., 2014), improved production and reduced cost

(Erenstein & Laxmi, 2008). Compared to conventional tillage, CA practices generally

result in improved crop yield and productivity. For example, Ghuman and Sur (2001)

showed in a 5-year field experiment of the subtropical climate of Northwest Punjab,

India that minimum tillage and crop residue retention improved soil properties and

40

sustained crop production. By contrast, Powlson et al. (2014) concluded from a meta-

analysis of 43 studies that the net benefits of CA were small and often overstated.

However, the rice-based intensive cropping systems of the Eastern Indo-Gangetic

Plains (EIGP) are poorly represented in the studies of Powlson et al. (2014) and

Pittlekow et al. (2015). Hence questions remain about the effects of CA

implementation on SOC and crop yields in this system.

In the western and central IGP of India and Pakistan, CA based on 4-WT has been well

researched during the last two decades. However, CA based on 2-WT is under

development and there is limited information on crop performance particularly in

intensive rice-based systems in the EIGP. Due to small and scattered fields in

Bangladesh, 4-wheel tractors (4-WT) are not suitable for mechanization (Roy & Singh,

2008; Sarkar et al., 2012). Under these circumstances, the Versatile Multi-crop Planter

(VMP) mounted on a 2-wheel tractor (2-WT) has been developed for CA practices on

small farms, to handle diverse cropping systems and multiple planting modes (single-

pass shallow-tillage; strip tillage; zero tillage; bed planting, and conventional tillage)

with a wide range of crops (Haque et al., 2011). This machine is also capable of placing

seed and fertilizer in rows when driven by 12-16 hp 2-WT.

This study evaluates the CA options of minimum tillage and residue retention in two

representative cropping systems in Bangladesh, i.e. rice-wheat-mungbean in silty clay

upland soil in the High Barind Tract (HBT) and rice-lentil-mungbean on an alluvial loam

soil. In this Chapter, the aim was to evaluate the effect of three types of tillage (strip

tillage-ST, bed planting-BP and conventional tillage-CT) and two residue levels (high

residue-HR and low residue-LR) on lentil and wheat growth and their yield

performance under different rice-based cropping systems in two regions of Bangladesh

in a 3-year (2010-11, 2011-12 and 2012-13) field experiment.

2.2 Materials and Methods

Two experiments were conducted over three years (2010-2013) in rice-based systems

(legume-dominant and cereal-dominant rotations) at two locations in Bangladesh. Site

41

and climatic conditions are described in Table 2.1 and Figure 2.1, and baseline soil

properties are described in Table 2.2.

Table 2.1. Site characteristics of two different experiments under different cropping

systems.

Characteristics Legume-dominant system Cereal-dominant system

Location (Figure 2.1) Alipur, Durgapur, Rajshahi, Bangladesh Digram, Godagari, Rajshahi, Bangladesh

Latitude, longitude 24°28՜ N, 88°46՜ E 24°31՜ N, 88°22՜ E

Elevation above sea level 20 m 40 m

Agro-ecological zone (AEZ) AEZ-11 (High Ganges river flood plain) AEZ-26 (High Barind Tract)

Crop rotation tested lentil-mungbean-rice

(Legume-dominated system)

wheat-mungbean-rice

(Cereal-dominated system)

General site physiography Alluvial plain Drought-prone uplifted and undulating

ancient alluvial area

Taxonomic soil classification (Huq & Shoaib, 2013)

Soil tracts Gangetic alluvium High Barind Tract

Subgroup (USDA) Typic Haplaquepts Aeric Albaquepts

Soil series Arial/Sara Atahar

Physiographic unit Ganges river flood plain Barind Tract

Parent material types Ganges river alluvium Madhupur Clay

USDA - United States Department of Agriculture; m - metre

2.2.1 Climate and weather

Climatic conditions of both experimental sites are characterized by hot and humid

summers, and cool winters with an average annual rainfall of 1134 mm at the weather

station representative of both experimental sites, most of which is received from June

to August. During 2010-2013, monthly mean temperature was lowest (10 °C) in

January and highest (36 °C) in April-May (Figure 2.2).

42

Figure 2.1. General soil map of Bangladesh showing field study sites (A); High Barind

Tract, Digram, Godagari, Rajshahi (red circle) in figure (B); and; Alipur, Durgapur,

Rajshahi (yellow circle) in figure (C) .

Daily temperature and rainfall data were collected by the weather station at

Shyampur, Rajshahi, Bangladesh. The weather station is approximately 10 km from

Alipur and 25 km from Digram.

(C)

(B)

(A)

43

Table 2.2. Basic soil properties and nutrient status of study sites at Alipur and

Digram.

Soil properties

Soil

depth

(cm)

Sites Protocol Reference

Alipur Digram

pH (1:5 H2O) 0-15 7.81 6.30 Glass electrode Thomas (1996)

Electrical Conductivity

(dS/m) (1:2.5 H2O)

0.36 0.26 Electrical conductivity

meter

Organic Carbon (mg/g) 6.12 7.31 Walkley and Black Rayment and

Higginson (1992)

Total N (mg N/g) 0.74 0.95 Kjeldahl method O'Neill and Webb

(1970)

C:N ratio 8.3 7.7

Cation Exchange

Capacity (cmol/kg)

26.8 29.2 Ammonium acetate

extraction

Scholenberger and

Simon (1945)

Textural class Silty loam Silty

loam

Hydrometer method Bouyoucos (1962)

Sand (g/kg) 324 164

Silt (g/kg) 520 660

Clay (g/kg) 156 176

Bulk density (g/cm3) 0-5 1.54 1.41 Core sampler method

Black and Hartge

(1986)

5-10 1.58 1.44

10-15 1.70 1.52

2.2.2 Experimental design and treatments

The experiment included four replicates of each treatment in a split-plot design. The

main plots were assigned to three types of tillage, namely strip tillage (ST), bed

planting (BP) (as defined in Chapter 1) and a conventional tillage system (CT). In CT,

intensive tillage is used for non-rice crop and puddling (wet tillage) used for the

cultivation of rice crops (Table 2.3). A VMP was used for planting non-rice crops under

ST and BP while unpuddled rice was transplanted following strip tillage (Haque et al.,

2016). Two previous crop residue treatments were assigned in the sub-plots —

retention of high residue (100 % legume residue + 50 % cereal residue) and retention

of low residue (0 % legume residue + 20 % cereal residue) (Table 2.4). These

treatments were repeated in the same plots for each crop over the three years. At

Alipur, main plot size was 7.5 m long x 14 m wide and sub-plot was 7.5 m long x 7 m

44

wide and; the main plot was 8.5 m long x 14 m wide and sub-plot was 8.5 m long x 7 m

wide at Digram.

Table 2.3. Details of three tillage treatments at Alipur and Digram.

Tillage Details

Strip-tillage — ST

(Figure 4.2)

• Versatile Multi-crop Planter (VMP) used to form strip

• row to row distance — 20 cm for both wheat and lentil

• tillage and seed placement — 5-7 cm deep

• strip width — 4-5 cm

Bed planting system

(Permanent beds were

reshaped during the sowing

of each non-rice crop) — BP

(Figure 4.3)

• VMP used to form and reshape bed for every non-rice crop

• mid-furrow to mid-furrow distance — 55 cm

• usually 2 rows of rice or non-rice crop per bed with a row spacing on the

beds of 20 cm (cereal crop)/20-25 cm (legume crop)

• head to head width (Bed) — 30 cm

• base to base width (Bed) — 45 cm

• base to base width (furrow) — 10 cm

• height of the bed — 11 cm (from base of the furrow to the level of the

bed top)

• reshaped beds before sowing of each crop is a mode of reduced tillage

while newly formed beds (i.e. for Crop 1) involve a high levels of soil

disturbance

Conventional tillage — CT

(Control)

• Three times full rotary tillage by 2-WT, tillage to depths of about 6 to 9

cm depth, incorporating residue, followed by one time land leveling

• broadcast seed sowing before final rotary tillage operation

2.2.3 Residue management protocols

The high and low residue covers were retained based on the average height of residue

across all experimental plots. Afterwards the retained residue for the specific height

was cut in quadrats, dried and weight; and converted to tonne per hectare for each

height. There were two types of residue retained in the present trials: 1) anchored

residue-standing residue retained, and 2) loose residue-residue chopped and placed on

the soil surface. The details of residue management protocols of the cropping

sequence at Alipur in 2010-13 are presented in Table 2.4.

45

Table 2.4. Details of residue management protocols of the lentil-mungbean-monsoon

rice cropping sequence at Alipur in 2010-13.

Crop #.

Crop residue Year

(and residue

type)

Residue weight (t/ha) and

height (cm, in parentheses)

for high residue plot



for low residue plot

Previous rice

residue

2010

(Loose)

1ST - 5, 1BP - 5, 1CT - 5 ST - 2, BP - 2, CT - 2

1 Lentil

residue

2010 - 11

(Loose)

100 % residue returned to the

same plot after mungbean

sowing

ST - 2.1, BP - 1.6, CT - 1.9

No above ground residue

returned to plot

2 Mungbean

residue

2011

(Anchored)

100 % residue retained except

pods (estimated residue weight

from the succeeding years)

ST - 2.38 (74.6 cm), BP - 2.91

(72.2 cm), CT - 2.44 (76.9 cm)


retained

3 Rice residue 2011

(Anchored)

ST - 4.11 (60 cm), BP - 3.50 (60

cm), CT - 3.96 (60 cm)

ST - 1.90 (24 cm), BP - 1.59 (24

cm), CT - 1.77 (24 cm)

4 Lentil 2011 - 12

(Loose)


same plot after sowing

ST - 1.93, BP - 1.83, CT - 2.05


returned to plot

5 Mungbean

residue

2012

(Anchored)


husk

ST - 2.38 (60.7 cm), BP - 2.91

(60.1 cm), CT - 2.44 (60.1 cm)


returned to plot

6 Rice residue 2012

(Anchored)

ST - 4.30 (61 cm), BP - 3.98 (61

cm), CT - 4.81 (61 cm)

ST - 2.31 (24 cm), BP - 1.95 (24

cm), CT - 2.05 (24 cm)

7 Lentil 2012 - 13

(Loose)


same plot after sowing of

mungbean

ST - 1.85, BP - 1.80, CT - 1.34


Total amount of residue (t/ha) deposited

during 7 successive crops

ST - 24.05, BP - 23.53, CT - 23.94 ST - 6.21, BP - 5.54, CT - 5.82

1ST - strip tillage; BP - bed planting; CT - conventional tillage; Crop # - Crop number in

the sequence

The residue management protocols of the wheat-mungbean-monsoon rice cropping

system at Digram in 2010-13 are presented in Table 2.5.

46

Table 2.5. Details of residue management protocols of wheat-mungbean-monsoon

rice cropping sequence at Digram in 2010-13.

Crop #.

Crop residue Year

(and residue

type)



for high residue plot

Residue weight (t/ha) and height

(cm, in parentheses)

for low residue plot

Previous rice

residue

2010

(Anchored)

1ST - 3.28, 1BP - 3.28, 1CT - 3.28 ST - 1.73, BP - 1.73, CT - 1.73

1 Wheat

residue

2010 - 11

(Anchored)

ST - 2.73 (47 cm), BP - 2.64 (47

cm), CT - 2.46 (47 cm)

ST - 1.45 (19 cm), BP - 1.61 (19

cm), CT - 1.32 (19 cm)

2 Mungbean

residue

2011

(Anchored)


pods (estimated from the

following years)

ST - 3.86 (51.6 cm), BP - 3.53

(50.2 cm), CT - 3.83 (54.1 cm)

No above ground residue retained

3 Rice residue 2011

(Anchored)

ST - 5.46 (62.5 cm), BP - 5.42

(62.5 cm), CT - 5.43 (62.5 cm)

ST - 2.47 (25 cm), BP - 2.26 (25

cm), CT - 2.48 (25 cm)

4 Wheat

residue

2011 - 12

(Anchored)

ST - 3.58 (50 cm), BP - 3.20 (50

cm), CT - 3.33 (50 cm)

ST - 1.80 (20 cm), BP - 1.53 (20

cm), CT - 1.83 (20 cm)

5 Mungbean

residue

2012

(Anchored)


husk

ST - 3.86 (54.1 cm), BP - 3.53

(50.2 cm), CT - 3.83 (51.6 cm)

No above ground residue retained

6 Rice residue 2012

(Anchored)

ST - 5.39 (55 cm), BP - 4.73 (55

cm), CT - 5.38 (55 cm)

ST - 2.33 (22 cm), BP - 2.67 (22

cm), CT - 3.22 (22 cm)

7 Wheat

residue

2012 - 13

(Anchored)

ST - 3.99 (52 cm), BP - 3.67 (52

cm), CT - 3.51 (52 cm)

ST - 1.98 (21 cm), BP - 1.73 (21

cm), CT - 1.68 (21 cm)

Total amount of residue retained (t/ha)

during 7 successive crops

ST - 32.2, BP - 30.0, CT - 31.1 ST - 11.8, BP - 11.5, CT - 12.3

1ST - strip tillage; BP - bed planting; CT - conventional tillage; Crop # - Crop number in

the sequence

2.2.4 Agronomy of legume-dominant system

The field trials at Alipur were initiated with winter lentil (Lens culinaris Medik.) in 2010-

11 (Nov-Mar); followed by mungbean (Vigna radiata (L.) R. Wilczek) in the early wet

season of 2011 (Mar-May) and then transplanted rice (Oryza sativa L.) in the main wet

season of 2011 (Jul-Oct). This sequence was continued until Crop 7. Lentil crops were

studied in detail but only grain and biomass data of rice and mungbean in the system

47

are presented. Table 2.6 outlines the details of the production technology of the lentil-

mungbean-monsoon rice system.

Table 2.6. Details of crop, variety, seed rate or seedlings/hill, row spacing, sowing

and harvesting date of lentil-mungbean-monsoon rice cropping sequence during

2010-2013 at Alipur.

Year Crop Variety Seed rate

(kg/ha) or

seedlings of

rice/hill

Date of

sowing

Date of

harvesting

Row spacing

(cm)

2010-11 Lentil BARI Masur 6 34 10-11 Nov,

2010

8 Mar, 2011 20 cm

2011 Mungbean BARI Mung 6 37.5 21 Mar,

2011

28 May-10

Jun, 2011

ST - 30 cm,

BP - 30 cm

2011 Monsoon rice Hybrid rice Tej

(Bayer crop

Science)

Two

seedlings/hill

8 Jul, 2011 24 Oct, 2011

ST and BP - 20

cm


2011

5 Mar, 2012 ST - 25 cm,

BP - 25 cm

2012 Mungbean BARI Mung 6 37.5 23 Mar,

2012

28 May,

2012

ST - 25 cm,

BP - 20 cm

2012 Monsoon rice Hybrid rice

(ACI-1)

Two

seedlings/hill

Seeding-6

Jun, 2012

Planting-5

Jul, 2012

9 Oct, 2012

ST and BP - 20

cm


2012

11 Mar,

2013

ST and BP - 20

cm

Note: Transplanting of monsoon rice in unpuddled soil for ST and BP; puddled soil for

CT

2.2.4.1 Nutrient management

Both lentil and mungbean were fertilized during final land preparation at the rate of

20, 20, 20 and 1 kg/ha of nitrogen (N), phosphorus (P) in the form of DAP and

potassium (K) and boron (B) in the form of muriate of potash (MP) and boric acid,

respectively, as recommended by the Pulses Research Centre, Bangladesh. The BINA

(Bangladesh Institute of Nuclear Agriculture)-LT-18 Rhizobium inoculum for lentil and

BINA-MB-1 Bradyrhizobium inoculum for mungbean were applied at the rate of 50

48

g/kg seed. Only DAP fertilizer, a source of N and P, was drilled with seed by VMP while

other fertilizers were broadcast in ST and BP plots and all fertilizers were broadcast in

CT plots. Both puddled and unpuddled monsoon rice in the rotation was fertilized by

broadcast application of 90, 10, 35, 12 and 1 kg/ha of N, P, K, S and Zn, respectively.

2.2.4.2 Disease, weed and pest management of lentil

Prior to seeding of lentil, weeds were suppressed using pre-plant application of

glyphosate (Table 2.7) and during the season by one hand weeding at 25-30 days after

sowing (DAS) in every year. The plants were monitored regularly to detect any diseases

and insects on lentil plants. In 2012-13, foot and collar rot disease of lentil, caused by

Sclerotium rolfsii, were scored visually for each plot as a percentage of total plant

population. Selective fungicides were applied before or at first appearance of fungal

diseases (collar rot, a soil-borne disease and stemphylium blight, a foliar disease of

lentil). Insecticides were applied to control insects especially for aphids (Aphis

craccivora) at their first appearance. The details of weed, pest, diseases and their

management practices of lentil are presented in Table 2.7.

2.2.4.3 Agronomic measurements of lentil

Plant density at 30 DAS and at harvest was determined from three randomly located

pre-selected (after seeding) quadrats of 0.5 m² each. The average heights of 10

randomly selected plants in each plot were measured from ground level up to the tip

of the uppermost leaf. Total number of pods was counted from 10 randomly selected

plants per plot. Seeds per pod were calculated from 20 randomly selected pods from

the same 10 plants. Five hundred seeds were counted to derive 1000-seed wt. Dry

weights of plant parts are reported after oven drying at 65 °C to constant weight.

49

Table 2.7. Details of disease, insects and weeds in lentil and their management

practices.

Year Name Frequency and date of

application

Name of fungicide, insecticide and herbicide and

their functions

2010-11 Fungicide Sprayed on canopy twice - 1

and 11 Dec, 2010

Bavistin® (common name: Carbendazim) @ 2 g/L

water to protect against foot and collar rot and foot

and root rot diseases (caused by Sclerotium rolfsii,

Fusarium avenaceum, Fusarium solani, Rhizoctonia

solani, Pythium sp.)

Three times - 6 and 25 Jan and

9 Feb, 2011

Rovral® (common name: iprodione) @ 2 g/L sprayed

against stemphylium blight diseases (Causal

pathogen: Stemphylium sp.)

Insecticide Twice - 20 and 28 Jan, 2011 Malathion® (common name: O,O-dimethyl

phosphorodithioate of diethyl mercapto-succinate)

@ 1 ml/L of water to control aphids

Herbicide Once - 8 Nov, 2010 Roundup® (common name: Isopropylamine salt of

N-(phosphonomethyl) glycine) @ 250 ml/15L H2O

applied 48 hours before lentil sowing

2011-12 Fungicide Three times -30 Nov, 8 and 20

Dec, 2011


water (as above)

Three times-6 and 15 Jan, 15

Feb, 2012

Rovral® (common name: iprodione) @ 2 g/L (as

above)

Insecticide Twice-6 Jan, 16 Feb, 2012 Malathion® (common name: O,O-dimethyl


@ 1 ml/L of water (as above)

Herbicide Once -10 Nov, 2011 Roundup® (common name: Isopropylamine salt of

N-(phosphonomethyl) glycine) 250 ml/15 L H2O (as

above)

2012-13 Fungicide Three times - 2,18 and 29 Dec,

2012


water (as above)

Once - 12 Dec, 2012 Provex® (common name: carboxin) @ 2 g/L water

Three times - 16 Jan, 5 Feb, 20

Feb, 2013

Rovral® (common name: iprodione) @ 2 g/L (as

above)

Insecticide Twice - 2-17 Jan, 2013 Malathion® (common name: O,O-dimethyl


@ 1 ml/L of water (as above)

Herbicide Twice -14-16 Nov, 2013 Roundup® (common name: Isopropylamine salt of

N-(phosphonomethyl) glycine) 250 ml/15 L H2O (as

above)

Weeding Hand weeding at 25-30 DAS in every year

50

2.2.5 Agronomy of cereal-dominated systems

The Digram field trial was initiated with winter wheat (Triticum aestivum L.) in 2010-11

(Nov-Mar); followed by mungbean in the early wet season of 2011 (Mar-May) and then

transplanted rice in the main wet season of 2011 (Jul-Oct). Wheat crops were studied

in detail but only grain and biomass data of rice and mungbean in the system are

presented. This system was continued for 7 crops following the same sequence of

crops. Table 2.8 outlines the details of crop, variety, seed rate or seedlings/hill, line to

line distance, sowing and harvesting date of wheat-mungbean-monsoon rice cropping

sequence during 2010-2013.

Table 2.8. Details of crop, variety, seed rate or seedlings/hill, row spacing, sowing

and harvesting date of wheat-mungbean-monsoon rice cropping sequence at Digram

during 2010-2013.

Year Crop Variety Seed rate

(kg/ha)

/rice seedlings

/hill

Row

spacing

(cm)

Date of sowing

(and or

transplanting

of monsoon

rice)

Date of harvesting

2010-11 Wheat BARI Gom 24

(Prodip)

120 20 13 Dec, 2010 3 Apr, 2011

2011 Mungbean BARI Mung 6 37.5 20 12 Apr, 2011 20 Jun, 2011

2011 Monsoon

rice

Local variety

Swarna

Two seedlings

/hill

20-25/15 12 Jul, 2011 21 Nov, 2011


(Prodip)

120 20 23-24 Dec,

2011

29 Mar, 2012

2012 Mungbean BARI Mung 6 37.5 20 17 Apr, 2012 17-30 Jun, 2012

2012 Monsoon

rice

BRRI Dhan 51

Two

seedlings/hill

20-25 13 Jul, 2012 15 Nov, 2012


(Prodip)

120 20 3-4 Dec, 2012 29 Mar, 2013

Note: Transplanting of monsoon rice in unpuddled soil for ST and BP; puddled soil for

CT

2.2.5.1 Crop husbandry of wheat

Although the irrigation amount was not precisely measured in this experiment, the

same amount (each application ~ 3 cm) was applied to all the treatments following the

51

same procedure for all irrigations. Generally two to three irrigations, depending on

rainfall during wheat season are necessary for growing wheat (Mojid et al., 2013). Two

flood irrigations at 27 and 72 DAS were applied in 2010-11. In 2011-12, three

irrigations were applied at 32, 64 and 92 DAS. In 2012-13, two irrigations at 32 and 60

DAS were applied and a third irrigation was not applied as a downpour occurred

before hand. The seeds of wheat were treated with the fungicide Provex® at the rate of

3 g/kg seed just before seed sowing in the field for controlling the major seed and soil

borne diseases.

Nutrient management

Fertilizers were applied for wheat at the rate of 120-30-55-20 kg/ha of N, P, K and S in

the form of urea, DAP, MP and gypsum, respectively, as recommended by the Wheat

Research Centre, Bangladesh (Wheat Research Centre, 2004). Two-thirds of urea and

all of DAP, MP and gypsum were applied before final land preparation. The remaining

one-third of urea was applied as a top dressing after the first irrigation. The DAP was

drilled with seed by VMP while other fertilizers were broadcast in ST and BP plots.

However, for conventional tilled plots, all fertilizers were broadcast. Mungbean was

fertilized and supplied with Rhizobium inoculants as described above. Monsoon rice in

the rotation was fertilized by broadcasting at the rate of 90, 10, 50, 8 and 1 kg/ha of N,

P and K, S and Zn.

Weed, insect and bird management

Intensive care was taken to control bird damage to seed and to seedlings up to 25 days

after sowing (DAS). One hand weeding was done at 30-35 DAS when the soil drained to

field capacity in the first and second growing season and in the third growing season

Affinity® herbicide (carfentrazone) @ 2.5 g/litre water was applied after the first

irrigation. In 2010-11 and 2011-12, soap solution was applied to control aphids which

appeared sporadically in the experimental field. Aphid infestation was severe in 2012-

13 and Malathion 57 EC® @ 3 ml/L of H2O was applied to control these aphids.

52

2.2.5.2 Agronomic measurements of wheat

Initial plant population and plant density at harvest were assessed from three pre-

selected quadrats of 0.5 m² each. At harvest, the average heights of 10 plants selected

randomly in each plot were measured from ground level up to the tip of the longest

leaf blade or spike awn. The number of spikelets/spike and number of grains per spike

were calculated from 10 randomly selected spikes per plot. All the grains of the 10

sampled plants were separated from the spikes and oven dried thoroughly to constant

moisture content. Thereafter, 1000 grains were counted and their weight was

determined.

2.2.6 Yield measurements for lentil and wheat

The crops were harvested from the central 3 m x 2 m area of each plot when the pods

(lentil) or spikes (wheat) turned straw colour and the yields were converted to t/ha.

The harvested bunches were threshed, cleaned, and sun dried. Grain and straw yield

was determined after sun drying from which a 100 g sub-sample from each treatment

was taken for oven drying and finally plot dry weights were converted to t/ha.

Biological yield (kg/ha) and harvest index (%) were determined as follows-

• Biological yield (t/ha) = grain + straw yield

• Harvest index (%) (HI) = grain yield/biological yield x 100

2.2.7 Statistical analysis

Data were analysed separately for lentil and wheat each year using GenStat 15th

Edition (VSN International Ltd, United Kingdom). Mean values were calculated for each

set of measurements, and analysis of variance (ANOVA) for split-plot design was

performed to assess treatment effects on the measured variables. When the F-test

was significant, treatment means were separated by least significant difference (LSD)

at P≤0.05. A correlation matrix of different yield and yield attributes were based on

Pearson correlation coefficients (P≤0.01 and P≤0.05).

53

2.3 Results

2.3.1 Weather

The weather conditions at the experimental locations were quite variable during the

three years of experimentation. The monthly rainfall and minimum/maximum

temperatures for the years 2010 to 2013 are shown in Figure 2.2. The rainfall was

highest in 2011-12 (1108 mm) followed by 2010-11 (1092 mm) and 2012-13 (909 mm)

up to the lentil harvest (Mar 13). Most rain was received during the summer season,

during May-September. During lentil and wheat growing seasons (Nov-Mar), the

amount of rainfall was highest in 2012-13 (136 mm), less in 2010-11 (59 mm) and

negligible in 2011-12 (14 mm).

In 2010-11, it rained on 8-9 Dec at the start of the growing season. There was no

rainfall except drizzle during the second lentil growing season (Nov-Mar). In the third

lentil growing season, 101.6 mm rainfall occurred in November (4-7 Nov, 2012) before

crop sowing. In the middle of the growing season on 20 Jan, 2013 there was drizzle and

22 mm rainfall occurred in February (17-18 Feb, 2013).

54

Figure 2.2. Monthly and annual rainfall, mean maximum and minimum temperatures

over the 33-months period of 2010-2013 at the experimental site.

The mean monthly minimum and maximum temperatures were similar across the

years. The daily minimum temperature in these years reached 3-4 °C and foggy

weather persisted continuously from 8 to 24 Dec in the second growing season. April

and May were the hottest months with mean monthly maximum temperatures of 38

°C in May, 2012. December and January were the coldest months with mean monthly

minimum temperature of 9-12 °C in 2012.

2.3.2 Tillage and residue effects on crop performance of legume-dominated system

2.3.2.1 Seed and straw yield of lentil

In the legume-dominated system, the seed yield of lentil (1.74-1.92 t/ha) under CTHR,

CTLR, STHR and STLR was higher while lower under BPHR (1.58 t/ha) and BPLR (1.37

t/ha) in Year 1 (Figure 2.3a1). Although depressed yield was measured with BP there

was no yield variation between ST and CT in Year 1 (Figure 2.3a1). In Year 2, the seed

0

5

10

15

20

25

30

35

40

45

0

50

100

150

200

250

300

350

400

450

500

Jul-

10

Au

g-1

0

Se

p-1

0

Oct

-10

No

v-1

0

De

c-1

0

Jan

-11

Fe

b-1

1

Ma

r-1

1

Ap

r-1

1

Ma

y-1

1

Jun

-11

Me

an

mo

thly

te

mp

era

ture

(°C

)

Mo

nth

ly r

ain

fall

(m

m)

Months

2010-11 Rainfall Max. Temp. Min. Temp.

Total rainfall: 1092 mm

Rice Mung Lentil/wheat

0

5

10

15

20

25

30

35

40

45

0

50

100

150

200

250

300

350

400

450

500

Jul-

11

Au

g-1

1

Se

p-1

1

Oct

-11

No

v-1

1

De

c-1

1

Jan

-12

Feb

-12

Ma

r-1

2

Ap

r-1

2

Ma

y-1

2

Jun

-12

Me

an

mo

thly

te

mp

era

ture

(°C

)

Mo

nth

ly r

ain

fall

(m

m)

Months



0

5

10

15

20

25

30

35

40

45

0

50

100

150

200

250

300

350

400

450

500

Jul-

12

Au

g-1

2

Se

p-1

2

Oct

-12

No

v-1

2

De

c-1

2

Jan

-13

Fe

b-1

3

Ma

r-1

3

Ap

r-1

3

Ma

y-1

3

Jun

-13

Me

an

mo

thly

te

mp

era

ture

(°C

) Month

Mo

nth

ly r

ain

fall

(m

m)



55

yield of lentil was 15 % higher in HR than LR (Figure 2.3a2). In Year 3, compared to CT,

the seed yield of lentil was higher by 23 % in ST and 18 % in BP (Figure 2.3a3).

Figure 2.3. Effects of tillage and residue retention on lentil seed yield (Figure a1-c1)

and straw yield (Figure a2–c2) over three growing seasons. ST — strip tillage, BP —

bed planting, CT — conventional tillage; HR — high residue, LR — low residue. Values

are means of four replicates ± standard error of mean and the floating error bar on

each figure represents the least significant difference (LSD) for significant effects at

P≤0.05.

In Year 1, the straw yield of lentil was not significantly affected either by tillage or

residue or their interaction (Figure 2.3a2). In Year 2, the straw yield of lentil with HR

was 22 % higher than LR (Figure 2.3b2). However, in Year 3, the straw yield of lentil

with ST and BP were 28 % and 25 % higher over CT (Figure 2.3c2).

See

d y

ield

(t/

ha)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

ST BP CT

a1) TillageXResidue Tillage

2010-11

BP-HR ST-LR BP-LR CT-HR CT-LR ST-HR

0.0

0.5

1.0

1.5

2.0

2.5

3.0

ST BP CTTillage treatment

b1) Residue 2011-12

0.0

0.5

1.0

1.5

2.0

2.5

3.0

ST BP CT

Tillage and residue treatment

c1)

Tillage

2012-13

Stra

w y

ield

(t/

ha)

0.0

0.5

1.0

1.5

2.0

2.5

3.0


a2)


2010-11

0.0

0.5

1.0

1.5

2.0

2.5

3.0


b2) Residue 2011-12

0.0

0.5

1.0

1.5

2.0

2.5

3.0

ST BP CT


C2) Tillage

2012-13

56

2.3.2.2 Yield components of lentil

Plant population and branching of lentil

The overall plant establishment in all treatments was satisfactory for all the study

years. There were no treatment effects on plant population either at 30 DAS or at

harvest in the first two years (Table 2.9). In 2012-13, there was a significant tillage and

residue interaction on plant population at both 30 DAS and at harvest. In 2012-13, the

plant populations (214 and 214 plants/m²) of STHR were higher than that of CTHR (144

and 143 plants/m²) at 30 DAS and harvest. In ST and BP, HR increased plant population

while it decreased in CT (Table 2.9).

Table 2.9. Tillage and residue effects on plant population and branching of lentil.

Tillage

treatment1

2010-11 2011-12 2012-13

HR1 LR Mean HR LR Mean HR LR Mean

Plant population/m² at 30 DAS

ST 160 173 167 148 144 146 214 202 208

BP 149 163 156 138 136 137 176 157 167

CT 124 142 133 141 146 143 144 171 157

Mean 144 160 142 142 178 177

LSD20.05

Tillage (T) ns ns 32.8*

Residue (R) ns ns ns

TxR ns ns 35.3*

Plant population/m² at harvest

ST 154 156 155 138 143 141 214 204 209

BP 139 150 145 123 131 127 170 151 161

CT 122 136 129 136 140 138 143 169 156

Mean 139 148 133 138 176 175

LSD0.05



TxR ns ns 44.0*

Branches/plant

ST 32.4 30.9 32.0 33.7 27.8 30.7 15.5 15.0 15.3

BP 33.5 31.2 32 .3 35.8 33.5 34.7 18.3 17.3 17.8

CT 56.9 54.7 55.8 36.8 32.1 34.4 17.1 15.1 16.1

Mean 40.9 38.9 35.4 31.1 17.0 15.8

57

LSD0.05

Tillage (T) 16.4* ns 2.04*

Residue (R) ns 2.8** 1.04*

TxR ns ns ns

1HR - high residue; LR - low residue, ST - strip tillage; BP - bed planting; CT -

conventional tillage; 2the least significant difference (LSD) at the P≤0.05, ns - not

significant, * - significant at P≤0.05 and ** - significant at P≤0.01.

Several plants were affected by foot and collar rot diseases in the first few days after

sowing in every year but the infestation was higher in third growing season with BP

(Table 2.10). At flowering to podding stage, generally stemphylium leaf blight (SLB)

disease was more visible in the CT plot in all the growing seasons but no quantitative

data on these differences were gathered. Some plants were affected by aphids during

the vegetative and flowering stages. Apart from these pests and diseases, over the

whole growing period the crop grew well with good canopy development and

maintained similar plant density until harvest.

Table 2.10. Tillage and residue effects on plant population (%) affected by foot and

collar rot diseases of lentil in 2012-13.

Tillage

treatment1

14 December, 12 23 December, 12 26 December, 12

Residue treatment1 Residue treatment Residue treatment


ST 0.0 0.1 0.1 0.3 0.4 0.3 0.6 0.7 0.7

BP 4.0 2.7 3.4 7.5 4.8 6.1 10.0 6.0 8.0

CT 0.1 0.0 0.1 0.6 0.4 0.5 1.1 0.6 0.9

Mean 1.4 1.0 2.8 1.9 3.9 2.4

LSD20.05

Tillage (T) 2.8* 4.9* 6.5*


TxR ns ns ns




58

In the 2010-11 season, the highest number of branches (55.8) was in plants grown in

CT (Table 2.9). In 2011-12, HR increased branch number. In 2012-13, the highest

number of branches (35.4) was obtained in BP compared to other tillage treatments. In

2012-13, when branch numbers were less than half those of the previous two years,

HR increased number of branches per plant from 15.8 to 17 per plant.

Plant height, pods/plant, seeds/plant, 1000-seed weight and harvest index

The HR treatments significantly increased plant height by 1-4 cm in all the study years

but tillage had no effects on plant height (Table 2.11).

Table 2.11. Tillage and residue effects on plant height, pods/plant and seeds/plant of

lentil.

Tillage

treatment1

2010-11 2011-12 2012-13


Plant height (cm)

ST 33.9 32.3 33.1 39.2 34.3 36.7 35.7 34.0 34.8

BP 36.2 33.8 35.0 40.3 37.2 38.8 37.6 36.2 36.9

CT 34.7 33.5 34.1 41.1 36.6 38.9 36.8 35.8 36.3

Mean 34.9 33.2 40.2 36.0 36.7 35.3

LSD20.05

Tillage (T) ns ns ns

Residue (R) 0.71** 1.30** 0.91**

TxR ns ns ns

Pods/plant

ST 111 120 115 102 93 98 83 79 81

BP 127 109 118 124 108 116 93 87 90

CT 172 165 168 108 93 100 83 77 80

Mean 137 131 112 98 86 81

LSD0.05

Tillage (T) 31.2** 10.8** ns

Residue (R) ns 10.0** ns

TxR ns ns ns

Seeds/pod

ST 1.83 1.78 1.80 1.76 1.71 1.74 1.63 1.62 1.63

BP 1.90 1.83 1.86 1.78 1.76 1.77 1.60 1.71 1.66

CT 1.90 1.80 1.85 1.71 1.76 1.74 1.66 1.67 1.67

Mean 1.88 1.80 1.75 1.75 1.63 1.67

59

LSD0.05

Tillage (T) 0.055* ns ns

Residue (R) 0.058* ns ns

TxR ns 0.05* ns




In 2010-11, there were significantly higher number of pods/plant in CT treatment (168)

than that of ST (115) and BP (118) treatments (Table 2.11). In 2011-12, the pods/plant

of BP (116) was higher than that of CT (100) and ST (98). In 2011-12, HR increased

pods/plant (112) relative to LR (98). In 2012-13, the pods/plant was not affected either

by tillage or residue treatments.

In 2010-11, the seeds/pod of BP (1.86) was higher than that of CT (1.85) and ST (1.80)

(Table 2.11). In 2011-12, the significantly highest number of seeds/pod (1.78) was

counted in BPHR and the lowest in CTHR (1.71) (Table 2.11). Treatments had no effect

on 1000-seed weight except in 2011-12, when the 1000-seed weight of STLR (19.3) was

higher than that of STHR (17.4) (Table 2.12). The harvest index was significantly higher

with HR (48.9) than LR treatment (47.6) in 2010-11; but in 2011-12, the harvest index

was higher in LR (53.4) than HR (51.2) (Table 2.12). The harvest index was not affected

by tillage and residue retention in 2012-13 (Table 2.12).

60

Table 2.12. Tillage and residue effects on 1000-seed weight and harvest index.

Tillage

treatment1

2010-11 2011-12 2012-13


1000-seed weight

ST 16.5 17.0 16.8 17.4 19.3 18.3 20.0 20.8 20.4

BP 16.3 16.4 16.3 17.9 17.5 17.7 20.7 20.9 20.8

CT 16.4 16.8 16.6 17.5 18.5 18.0 20.9 20.7 20.8

Mean 16.4 16.7 17.6 18.4 20.5 20.8

LSD20.05



TxR ns 0.80** ns

Harvest index (%)

ST 46.2 46.4 46.3 50.6 54.2 52.4 57.6 56.4 57.0

BP 50.2 48.0 49.1 51.9 52.7 52.3 55.9 56.6 56.3

CT 50.3 48.7 49.5 51.1 53.2 52.2 57.9 58.8 58.3

Mean 48.9 47.6 51.2 53.4 57.1 57.2

LSD0.05


Residue (R) 1.06* 1.42** ns

TxR ns ns ns




2.3.2.3 Correlation and regression of yield and yield components of lentil

In 2010-11, number of branches/plant and pods/plant were positively correlated with

seed yield (Table 2.13) and both parameters were correlated to each other. There was

a significant positive correlation between lentil seed yield and both plant population

and plant height in 2011-12. Straw yield also showed a significant correlation with

plant population, plant height and seed yield.

61

Table 2.13. Correlation matrix of important yield attributes and yields of lentil.

2010-11

Plant

population

Plant

height

Branches

/plant

Pods

Seeds Seed yield Straw

yield

Plant population 1

Plant height -0.17 1

Branches/plant -0.31 0.14 1

Pods -0.17 0.19 0.59* 1

Seeds 0.01 0.67** 0.25 0.19 1

Seed yield 0.22 -0.09 0.52** 0.49* 0.27 1

Straw yield 0.01 -0.30 0.15 -0.08 -0.05 0.39 1

2011-12

Plant

population

Plant

height

Branches

/plant

Pods

Seeds

Seed yield Straw

yield

Plant population 1

Plant height 0.11 1

Branches/plant -0.12 0.43* 1

Pods 0.02 0.46* 0.79** 1

Seeds -0.05 0.08 -0.25 0 1

Seed yield 0.44* 0.80** 0.28 0.21 0.13 1

Straw yield 0.43* 0.83** 0.38 0.32 0.1 0.95* 1

2012-13

Plant

population

Plant

height

Branches

/plant

Pods

Seeds

Seed yield Straw

yield

Plant population 1


Branches/plant -0.26 0.35 1

Pods -0.15 0.40* 0.54** 1

Seeds 0.07 -0.26 -0.07 -0.24 1

Seed yield 0.71** 0.11 0.00 0.2 0.07 1

Straw yield 0.68** 0.15 0.05 0.31 -0.09 0.95** 1

* - significant at 5% level and ** - significant at 1% level

Pod number increased with increasing plant height and branch number as their

relationships were significantly positive. In 2012-13, plant population was positively

correlated with seed and straw yield of lentil. The number of pods significantly

increased with increasing plant height and branches.

62

The relationship between plant population/m² and yield across all three years (2010-

13) was positive (R² = 0.31) and linear (Figure 2.4a-c) but yield was not correlated with

branches/plant, or pods/plant (R² = 0.023, R² = 0.012, respectively).

Figure 2.4. Regression of a) plant population and seed yield, b) branches/plant and

seed yield and c) pods/plant and seed yield for three years of results (2010-13).

2.3.2.4. Yield performance of rice and mungbean in legume-dominated system

There were no treatment effects on yield of rice as Crop 3 in the system, however by

Crop 6, grain yield of rice with ST (8.3 t/ha) was higher than with BP (7.0 t/ha) and

similar with CT (8.1 t/ha) treatments (Table 2.14). All pods of mungbean as Crop 2

were damaged due to heavy rainfall, hence there was no seed harvested. The yield of

mungbean as Crop 5 in the rotation was greater with CT (1.5 t/ha) or BP (1.4 t/ha) than

ST (0.9 t/ha); and compared to LR (1.1 t/ha), HR (1.4 t/ha) increased mungbean yield

(Table 2.14). Straw yield responses mirrored those of seed yield.

y = 0.006x + 1.05 R² = 0.31

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0 50 100 150 200 250 300

Se

ed

yie

ld (

t/h

a)

Plant population per m2

a) y = -0.004x + 2.02

R² = 0.024

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0 20 40 60 80 100

Se

ed

yie

ld (

t/h

a)

Branches/plant

b)

y = -0.001x + 2.04 R² = 0.013

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0 50 100 150 200 250

Se

ed

yie

ld (

t/h

a)

Pods/ plant

c)

63

Table 2.14. Tillage and residue effects on grain and straw yield of rice and mungbean

of lentil-mungbean-monsoon rice cropping system in Alipur. Note: no yield results

are available for Crop 2 (mungbean) due to crop damage by heavy rainfall.

Tillage

treatment1

Rice Mungbean Rice

2011 (Crop 3) 2012 (Crop 5) 2012 (Crop 6)


Grain or seed yield (t/ha)

ST 5.1 5.9 5.5 1.0 0.8 0.9 8.3 8.3 8.3

BP 4.4 5.0 4.7 1.6 1.1 1.4 7.0 7.0 7.0

CT 5.7 5.1 5.4 1.6 1.4 1.5 8.5 7.7 8.1

Mean 5.1 5.3 1.4 1.1 7.9 7.7

LSD20.05

Tillage (T) ns 0.39* 1.03*


TxR ns ns ns

Straw yield (t/ha)

ST 5.4 6.2 5.8 2.4 2.3 2.3 10.9 8.9 9.9

BP 5.2 5.3 5.2 2.9 2.7 2.8 8.4 6.5 7.5

CT 6.3 6.0 6.2 2.4 2.3 2.4 12.0 7.9 9.9

Mean 5.7 5.8 2.6 2.5 10.4 7.8

LSD0.05

Tillage (T) ns 0.28** 1.37**

Residue (R) ns ns 0.74**

TxR ns ns 1.50*




2.3.3 Tillage and residue effects on crop performance of cereal-dominated system

2.3.3.1 Grain and straw yield of wheat

Grain yield of wheat was not affected by tillage and residue treatments in 2010-11

(Figure 2.5a1). In 2011-12, grain yield of wheat was greater by 39 % in CT and 33 % in

ST than BP (Figure 2.5b1). In 2012-13, the yield of wheat was 9 % higher in ST and 7 %

greater in BP than CT, and compared to LR, yield was 3 % higher in HR (Figure 2.5c1).

64

In 2010-11, the straw yield of wheat with HR was 5 % higher than LR treatment (Figure

2.5a2). In 2011-12, the straw yield of wheat was higher by 19 % in ST and 27 % in CT

than BP; HR had 11 % greater straw yield than LR treatment (Figure 2.5b2). In 2012-13,

the straw yield of wheat with ST was higher by 11 % and 8 % than CT and BP,

respectively; the straw yield of wheat was 5 % higher with HR than LR treatment

(Figure 2.5c2).

Figure 2.5. Effects of tillage and residue on wheat grain yield (Figure a1-c1) and straw

yield (Fig a2–c2). ST — strip tillage, BP — bed planting, CT — conventional tillage; HR

— high residue, LR — low residue. Values are means of four replicates, ± standard

error of mean and the floating error bar on each figure represents the least

significant difference (LSD) for significant effects only at P≤0.05.

Gra

in y

ield

(t/

ha)

0.0

1.0

2.0

3.0

4.0

5.0

6.0


a1) BP-HR ST-LR BP-LR CT-HR CT-LR ST-HR

2010-11

0.0

1.0

2.0

3.0

4.0

5.0

6.0

ST BP CT

b1) Tillage 2011-12

0.0

1.0

2.0

3.0

4.0

5.0

6.0

ST BP CTTillage and residue treatment

c1) Tillage Residue

2012-13

Stra

w y

ield

(t/

ha)

0.0

1.5

3.0

4.5

6.0

7.5

9.0

ST BP CT

Tillage treatement

a2) Residue

2010-11


0.0

1.5

3.0

4.5

6.0

7.5

9.0

ST BP CT

Tillage treatment

b2) Residue Tillage

2011-12

0.0

1.5

3.0

4.5

6.0

7.5

9.0

ST BP CT


c2) Tillage Residue 2012-13

65

2.3.3.2 Yield components of wheat

Plant population and plant height

In 2010-11, the highest population was obtained with BP, especially with LR, but in ST

and CT where plant population was reduced, there was no effect of residue level

(Table 2.15). Overall, ST produced a lower plant population than other tillage types at

30 DAS in 2010-11. By harvest, there were no effects of tillage or residue on plant

populations due to the decline in plant numbers in BP and CT.

Table 2.15. Tillage and residue effects on plant population and plant height (cm) of

wheat.

Tillage

treatment1

2010-11 2011-12 2012-13


Plant population/m² at 30 DAS

ST 87 93 90 74 60 67 144 153 149

BP 147 118 132 27 29 28 120 122 121

CT 121 135 128 103 104 103 111 142 127

Mean 118 115 68 64 125 139

LSD20.05

Tillage (T) 16.0** 25.3** ns

Residue (R) ns ns 12.7*

TxR 23.0* ns ns

Plant population/m² at harvest

ST 88 86 87 70 57 64 137 142 139

BP 90 83 86 26 27 27 114 116 115

CT 101 100 100 100 100 100 106 137 121

Mean 93 90 66 61.4 119 131

LSD0.05

Tillage (T) ns 24.9** ns


TxR ns ns 26.0*

Plant height (cm)

ST 98.1 95.7 96.9 103 102 102 107 106 107

BP 96.2 94.4 95.3 102 99.9 101 109 109 109

CT 93.0 92.6 92.7 106 105 105 106 104 105

Mean 95.7 94.2 103 102 108 107

LSD0.05

Tillage (T) ns ns 2.5**

66

Residue (R) ns 1.3* ns

TxR ns ns ns




In 2011-12, the population was 73 % lower in BP and 35 % lower in ST, compared to CT

(Table 2.15). In 2012-13, the plant population at both sampling times under ST was

higher than in the previous 2 years and was significantly higher with LR (Table 2.15).

There were no treatment effects on plant height at harvest in 2010-11. In 2011-12,

plants grew significantly taller with HR than LR. In 2012-13, the tallest plants were

recorded in BP (Table 2.15).

Tillers/plant

In 2010-11, the highest tiller number was in BP (8.3) vs 6.6 in CT and 7.6 in ST; HR had

higher tillers/plant (8.0) than LR (7.3) (Table 2.16). In 2011-12, the highest tillers/plant

was again obtained with BP (13.0) followed by ST (9.8) and CT (8.6) (Table 2.16). The

numbers of tillers were lower in 2012-13 than in the previous years, but tillage

treatment had no effect on total tillers/plant while significantly higher numbers of

effective tillers/plant were obtained with HR.

67

Table 2.16. Tillage and residue effects on tillers and effective tillers per plant of

wheat.

Tillage

treatment1

2010-11 2011-12 2012-13


Total tillers/plant

ST 7.9 7.4 7.6 9.8 9.9 9.8 5.2 4.7 5.0

BP 8.6 8.0 8.3 12.8 13.2 13.0 5.3 5.1 5.2

CT 6.6 6.5 6.6 8.5 8.6 8.6 4.9 4.6 4.7

Mean 8.0 7.3 10.4 10.5 5.1 4.8

LSD20.05

Tillage (T) 0.80** 2.1* ns

Residue(R) 0.44* ns ns

TxR ns ns ns

Effective tillers/plant

ST 6.6 6.3 6.4 8.4 8.7 8.6 4.5 4.1 4.3

BP 7.1 6.7 6.9 11.3 12.1 11.7 4.8 4.5 4.7

CT 5.4 5.4 5.4 7.3 7.3 7.3 4.4 4.1 4.2

Mean 6.3 6.1 9.0 9.4 4.6 4.2

LSD0.05

Tillage (T) 0.98* 1.5** ns


TxR ns ns ns




Spikes/m², spike length and spikelets/spike

In 2010-11, the spikes/m2 was not affected either by tillage or residue retention

treatments (Table 2.17). In 2011-12, the highest spike/m² (266) was counted with CT

and lowest with BP (152) (Table 2.17). In 2012-13, the higher spike/m² was obtained

with ST (301) and BP (295) than CT (270); HR had higher spike/m² (295) than LR (283).

Across three years, the spikes/m² was remarkably similar in CT and most variable with

BP.

68

Table 2.17. Tillage and residue effects on spikes/m², spike length (cm) and

spikelets/spike of wheat.

Tillage

treatment1

2010-11 2011-12 2012-13


Spikes/m²

ST 234 231 232 249 208 228 311 291 301

BP 256 215 235 151 153 152 303 287 295

CT 270 262 266 267 265 266 271 269 270

Mean 253 236 222 209 295 283

LSD20.05

Tillage (T) ns 59.8** 25.6*


TxR ns ns ns

Spike length (cm)

ST 17.1 16.9 17.0 19.2 18.0 18.6 18.1 17.8 17.9

BP 16.8 16.4 16.6 19.7 18.7 19.2 18.6 18.7 18.6

CT 16.8 15.6 16.2 18.8 17.8 18.3 18.6 17.7 18.1

Mean 16.9 16.3 19.2 18.2 18.4 18.1

LSD0.05

Tillage (T) 0.48** ns 0.37**

Residue (R) ns 0.48** 0.26**

TxR ns ns 0.44*

Spikelets/spike

ST 21.0 21.4 21.2 20.5 19.9 20.2 22.6 21.6 22.1

BP 21.0 20.8 20.9 20.7 20.2 20.4 21.9 21.7 21.8

CT 20.8 21.0 20.9 19.8 18.9 19.3 21.5 20.1 20.8

Mean 20.9 21.0 20.3 19.7 22.0 21.1

LSD0.05



TxR ns ns ns




In 2010-11, the higher spike length was obtained with ST (17 cm) than CT (16.2 cm)

(Table 2.17). In 2011-12, HR significantly increased spike length from 18.2 to 19.2 cm

(Table 2.17). In 2012-13, the spike length of BPHR and CTHR (18.6 cm) were higher

69

than that of CTLR (17.7 cm) (Table 2.17). The spikelets/spike was unaffected by

treatments in Year 1 and 2 (Table 2.17). In Year 3, the spikelets/spike of ST (22.1) and

BP (21.8) were higher than that of CT (20.8), HR had higher spikelets/spike (22.0) than

LR (21.1) (Table 2.17).

Grains/spike, 1000-grain weight and harvest index

The grains/spike and 1000-grain weight were not affected by tillage and residue or

their combination in all the study years (Table 2.18). In 2010-11, HI was greater with BP

(43.1) and in 2011-12, the greater HI (43.3-43.7) was found with ST or CT than BP

(38.8) (Table 2.18).

Table 2.18. Tillage and residue effects on grains/spike, 1000-seed weight and harvest

index (%) of wheat.

Tillage

treatment1

2010-11 2011-12 2012-13

HR LR Mean HR LR Mean HR LR Mean

Grains/spike

ST 48.1 50.4 49.2 53.3 52.7 53.0 51.0 50.2 50.6

BP 52.7 50.3 51.5 55.0 53.4 54.2 52.0 52.0 52.0

CT 49.5 49.9 49.7 53.2 53.6 53.4 50.1 49.0 50.0

Mean 50.1 50.2 53.8 53.2 51.0 50.4

LSD20.05



TxR ns ns ns

1000-grain weight (g)

ST 44.2 44.9 44.5 44.9 43.8 44.3 46.4 46.9 46.6

BP 45.3 45.0 45.2 44.2 43.7 43.9 45.9 44.1 45.0

CT 44.9 44.2 44.5 44.4 45.3 44.8 46.8 47.0 46.9

Mean 44.8 44.7 44.5 44.2 46.4 46.0

LSD0.05



TxR ns ns ns

Harvest index (HI)

ST 40.0 42.1 41.0 42.7 44.6 43.7 39.5 39.6 39.6

BP 43.2 43.0 43.1 36.2 41.4 38.8 40.5 41.5 41.0

CT 42.0 42.5 42.2 43.2 43.4 43.3 39.8 40.1 39.9

70

Mean 41.7 42.5 40.7 43.2 39.9 40.4

LSD0.05

Tillage (T) 1.5* 2.3** ns


TxR ns ns ns




2.3.3.3 Correlation and regression of yield and yield components of wheat

The contribution of different yield attributes to yield varied from year to year (Table

2.19). Grain yield had a significant positive correlation with plant height and spikes/m²

in 2010-11. The correlation of straw yield with plant height, spikes/m²and grain yield

was highly significant. Plant height and spikelets/spike were also correlated. There was

a significant positive correlation of plant population/m², plant height, spikes/m² and

1000-seed weight with grain and straw yield of wheat in 2011-12. Among these yield

attributes, grain yield had strong positive correlation with plant population/m² and

spikes/m². Plant population, plant height and spikes/m² were correlated with one

another. In 2012-13, there was a significant positive correlation of plant height,

spikes/m²andspikelets/spike with grain yield of wheat. Straw yield also had a

significant correlation with spikelets/spike and grain yield of wheat. Spikes/m² and

spikelets/spike were strongly correlated.

71

Table 2.19. Correlation matrix of important yield attributes and yields of wheat.

2010-11

Plant

population

Plant

height

Spikes

/m²

Spikelets

/spike

TSW Grain

yield

Straw

yield

Plant population 1


Spikes/m² 0.30 0.24 1

Spikelets/spike -0.49* 0.49* 0.27 1

TSW -0.01 -0.14 0.20 -0.15 1

Grain yield -0.03 0.60** 0.49* 0.37 0.02 1

Straw yield 0.04 0.67** 0.53** 0.26 0.00 0.62** 1

2011-12

Plant

population

Plant

height

Spikes

/m²

Spikelets

/spike

TSW Grain

yield

Straw

yield

Plant population 1

Plant height 0.70** 1

Spikes/m² 0.88** 0.69** 1

Spikelets/spike -0.41 -0.21 -0.16 1

TSW 0.33 0.28 0.34 0.08 1

Grain yield 0.82** 0.49* 0.77** -0.23 0.48* 1

Straw yield 0.77** 0.70** 0.74** 0.00 0.41* 0.79** 1

2012-13

Plant

population

Plant

height

Spikes

/m²

Spikelets

/spike

TSW Grain

yield

Straw

yield

Plant population 1


Spikes/m² -0.05 0.22 1

Spikelets/spike -0.21 0.37 0.58** 1

TSW 0.06 -0.21 -0.01 -0.10 1

Grain yield 0.08 0.41* 0.43* 0.55** -0.17 1

Straw yield 0.20 0.28 0.39 0.58** 0.17 0.63** 1

* - significant at 5% level and ** - significant at 1% level; TSW - 1000-seed weight

Considering the three years results, the correlations of plant population/m² and

spikes/m² with yield were positive (R² = 0.34, R² = 0.45, R² = 0.027, respectively) and

linear while spikelets/spike was not correlated with yield (Figure 2.6a-c).

72

Figure 2.6. Regression of a) plant population and grain yield, b) spikes/m² and grain

yield and c) spikelets/spike and grain yield for three years of results (2010-13).

2.3.3.4 Yield performance of rice and mungbean in cereal-dominated system

In the cereal-dominated system, the tillage and residue retention showed no

significant effect on the grain and straw yields of rice (Crop 3 and Crop 6) and

mungbean (Crop 5) (Table 2.20). Heavy rainfall damaged all pods of Crop 2

(mungbean), therefore no seed yield data could be collected.

y = 0.013x + 2.80 R² = 0.34

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0 50 100 150 200

Gra

in y

ield

(t/

ha

)

Plant population per m2

a) y = 0.01x + 1.48

R² = 0.45

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0 100 200 300 400

Gra

in y

ield

(t/

ha

)

Spikes/m2

b)

c)

73

Table 2.20. Tillage and residue effects on grain and straw yield of rice and mungbean

of wheat-mungbean-monsoon rice cropping system in Digram. Note; no yield results

are available for Crop 2 (mungbean) due to crop damage by heavy rainfall.

Tillage

treatment1

Rice Mungbean Rice

2011 (Crop 3) 2012 (Crop 5) 2012 (Crop 6)

HR1 LR1 Mean HR LR Mean HR LR Mean

Grain yield (t/ha)

ST 5.9 5.7 5.8 0.7 0.8 0.8 6.0 6.6 6.3

BP 5.9 5.2 5.6 0.7 0.7 0.7 5.9 6.4 6.1

CT 5.1 6.2 5.7 0.7 0.8 0.7 6.7 7.0 6.9

Mean 5.6 5.7 0.7 0.8 6.2 6.7

LSD20.05



TxR ns ns ns

Straw yield (t/ha)

ST 10.7 10.3 10.5 3.9 3.4 3.6 7.1 7.1 7.1

BP 9.4 8.4 8.9 3.5 3.4 3.5 6.6 7.3 6.9

CT 9.7 10.9 10.3 3.8 3.5 3.7 7.9 9.2 8.5

Mean 9.9 9.9 3.7 3.4 7.2 7.9

LSD0.05



TxR ns ns ns




2.4 Discussion

Although there were some operational problems of VMP in implementing CA

techniques initially (e.g. for wheat on BP in Year 2), yields of both lentil and wheat

were comparable between ST and CT in the first two years. By the third year the yield

advantage of both ST and BP over CT, and HR over LR, had become apparent,

suggesting the feasibility of adopting CA practices in these rice-based cropping systems

in Bangladesh.

74

2.4.1 Lentil

In CT with LR, which is the current form of tillage and residue retention for lentil after

rice, yields were ~1.8-2 t/ha and consistent across the three years, despite some

differences in rainfall and temperature during those years. Such yields of lentil are

consistent with levels achieved by researchers and leading farmers using

recommended inputs for lentil. For example, the potential yield of lentil (BARI Masur

6) in Bangladesh is 2.25 t/ha (Uddin, 2008). Strip tillage with LR achieved comparable

yields to CT with LR in Year 1, but levels dropped in Year 2 while in Year 3 they

exceeded those of CT with LR. By contrast, ST along with HR achieved comparable

yields to CT LR (the current practice) in Year 1 and 2 but in Year 3 significantly

exceeded the conventional yield (2.5 vs 2 t/ha). Indeed the yield in ST HR increased

progressively from 1.7 t/ha in Year 1 to 2 t/ha in Year 2 and 2.5 t/ha in Year 3. Hence, it

was observed that the response to HR and ST was dynamic. It has been shown in the

present study that the yield of lentil was comparable or higher under ST than under CT

in the first year, by the third year after one year transition the yield advantage of both

ST and BP; and HR over CT and LR. A study conducted in a loam soil at Grafton NSW,

Australia by So et al. (2009) showed that the yield of soybean under NT system were

less or equal during the first five years and outperformed compared to CT after one

year transition in a 14 years soybean-oat rotation. However, Govaerts et al. (2005)

reported that conversion from a conventional system to conservation systems requires

several crop cycles before potential advantages become apparent. Also it was shown in

the review of Giller et al. (2009) that the short-term effects of conservation tillage

practices on crop yield could be variable (positive, negative or neutral). The depressed

lentil yield under ST and LR in Year 2 may reflect a negative transitional phase between

full tillage and minimum tillage, possibly associated with reduced mineralization of N,

although as a legume, lentil growth should be relatively independent of soil mineral N

supply (see Chapter 6 for further discussion of soil N dynamics).

In Year 1, the lentil yield under BP was depressed relative to the current practice (i.e.

CT with LR). This depressed yield was correlated with reduced number of

branches/plant and pods/plant (see Tables 2.9 and 2.11) and with lower surface soil

water (see Chapter 4). By Year 2, the yields were comparable across treatments and in

75

Year 3, BP with or without increased residue retention increased lentil yield relative to

the current practice of CT and LR. Both Talukder et al. (2008) and Lauren et al. (2008)

found that retention of crop residue significantly increased crop yields on permanent

beds after only 1-2 cropping cycles in rice-wheat-maize and rice-wheat-mung cropping

systems in Bangladesh. During the initial year, crop yields may be reduced due to the

increased net N immobilization by microorganisms through decomposition of rice

residue with high C:N ratio (Yadvinder-Singh. et al., 2004). However, the results of the

present experiment for the second growing season showed no yield differences

between BP and the current practice while in Year 3, BP out performed CT in yield. This

corresponds with the timeframe for significant SOC and TN increases (see Chapters 5

and 6) which might be contributing to higher yield over time with ST and BP treatment

in combination with HR. In the present study, HR significantly increased the yield of

lentil in the second growing season while in third growing season the yield was not

affected by residue management. In the second growing season after Crop 3, soil

water content was greater with HR than LR which might contribute to a significant

yield increase with HR in Crop 4 (see Chapter 4). A plausible reason for a positive effect

of surface residue retention is the decrease in soil water loss through evaporation and

increase in the amount of moisture stored in the root zone that is available to the plant

(Govaerts et al., 2009). However in Year 3, regardless of treatments the higher stored

soil moisture content at sowing and throughout the growing period may have negated

the beneficial effects of residue retention on soil water availability.

When the data were pooled across the three years, lentil yield was positively

correlated with plant population. This suggests that part of the response to tillage and

residue retention may be related to plant emergence and survival. Implementing ST

and BP; and residue retention over the first two years of the study did not significantly

affect plant population. Overall the plant population density was less in the first two

years than in Year 3. However, the population density in the third growing season was

satisfactory and influenced by tillage and residue. The higher plant population in the

third growing season might be attributed to higher moisture availability at germination

(see Chapter 4). A significantly higher plant population (20-25 %) was obtained in ST

than CT in 2012-13 which might be due to better seed placement into moist soil with

76

machine sowing that increased emergence of lentil. In addition, better crop

establishment due to high germination rates has been linked to proper placement of

seed in the strip producing improved seed-soil contact. Licht and Al-Kaisi (2005a) also

reported that strip tillage promoted corn (Zea mays L.) emergence in wet soils when

compared to no-tillage and chisel plough. In addition, over time improvement of

planter performance and operator skills with residue management for seeding in the

rice-based system may have resulted in increased plant population under ST.

Furthermore, improved soil physical properties could facilitate better plant emergence

and survival under ST (see Chapters 3 and 4). Higher branching and podding of lentil in

HR with BP and CT treatment from second growing season might be related to lower

plant density (Table 2.9 and 2.11). Moosavi et al. (2014) similarly reported that the

number of pods/plant decreased with increasing plant density of lentil. Moreover,

Khourgami et al. (2012) recorded the higher number of branches/plant of lentil was

obtained from lower plant density. In Year 1, yield components such as branches/plant

and number of pods per plant were higher as a result of lower plant population in CT.

The greater plant height and higher number of pods/plant might be contributed to

increase yield of lentil under HR in Year 2 (Table 2.11 and Figure 2.3).

During the growing season lentil plants were affected by foot and collar rot disease

(Sclerotium rolfsii). Early in the third growing season, the seed zone of subsurface

layers remained wet due to heavy rainfall for a few days but it resulted in foot and

collar rot disease of lentil only in BP. This might be due to residue buried in the seeding

zone and compaction of the soil surface layer on the top of beds by the bed shaper and

pressing roller of the VMP: poor aeration favours Pythium disease incidence. Further

the fungal pathogen might infect roots due to close contact of seed and residue in the

seed zone of BP especially when the soil was wet. In addition, planting seeds of all

crops in rotation in the same row on BP system may vulnerable to certain root diseases

and their spread. Consequently, the mean plant population was decreased at harvest

relative to emergence under BP with HR. However, finally the lower plant population

did not decrease the yield due to recovery growth and compensation by other yield

components in the BP treatment. Similarly, Govaerts et al. (2006a) observed higher

root rot diseases in zero tillage with residue retention than in the traditional

77

agricultural system but disease did not depress crop yield of wheat or maize in wheat-

wheat and wheat-maize rotations.

In summary, the higher seed yield of lentil under ST and BP; and HR retention

compared to CT treatment in the third growing season can be attributed in part to

higher plant population (Table 2.9). Further reasons for higher yield in the third

growing season under ST and HR treatment might be due to greater soil water content

(see Chapter 4), and increased organic carbon and nutrient levels (see Chapters 5 and

6).

2.4.2 Wheat

In the cereal-dominated system, overall the wheat yield in Year 1 was lower (3.3-3.5

t/ha) than the potential yield 3.5-5.1 t/ha (Bangladesh Agricultural Research Institute,

2014) due to sowing after the optimal time for Northwest Bangladesh. Delayed sowing

of wheat after the optimum time (mid-November) results in 0.04 Mg/ha yield loss per

day delay in the Indo-Gangetic Plains (Regmi et al., 2002). In the present trial, grain

yields were not significantly affected by tillage and residue retention during the initial

year. A similar result was found by Das et al. (2014) who reported that in Year 1 of the

experiments, wheat grain yield was not affected by tillage and residue treatment in an

irrigated cotton-wheat system. During the initial years of experimentation, Jat et al.

(2014) also found poor grain yield of wheat under a permanent bed planting system in

a rice-wheat rotation of the Eastern Gangetic Plains of South Asia. With timely planting

in Year 2, the yield substantially increased under ST (4.1-4.4 t/ha) and CT (4.6-4.8 t/ha)

but declined in BP (2.8-2.9 t/ha), which was attributed to poor plant establishment and

a lower plant population. The poor yield performance on BP in Year 2 was attributed to

lack of experience of the VMP operator seeding on bed which resulted in shallow

sowing depth of seeds on top of the bed where soils dried rapidly after sowing

(especially with HR). Poor plant establishment limited plant population and thereby

limited crop yield in BP. In Year 3, the overall yield was satisfactory (4.3-4.9 t/ha) and

greater yield (4.6-4.9 t/ha) with ST and BP was probably due to sowing at the optimum

time in moist soil. Wheat yields with ST and BP increased by 8-10 % over CT possibly

because of efficient use of fertilizer and more effective weed control between the row

78

of ST and BP plot in Year 3. High residue increased yield by 3 % relative to LR in Year 3.

Over time improvement of machine performance and operator skill in sowing seeds

under such conditions may have improved the placement of seed in moist soil to

improve seed germination.

In the present study, it took three years to get the full benefits of ST and BP with high

residue retention. After a transition period, wheat grain yields under ST and BP were

significantly greater compared to CT in Year 3. Govaerts et al. (2005) reported that it

could take some time (roughly 5 years) to get clear benefits but these conclusions are

based on one crop per year grown in a low rainfall, low temperature and semi-arid

environment while there were three crops per year grown in the present study under

irrigation in a moderate to high temperature zone. However, Usman et al. (2010)

showed from an experiment of tillage impacts on wheat yield under the rice-wheat

system in western Pakistan that ZT and reduced tillage increased wheat yield over CT

in the second year. Gangwar et al. (2006) also concluded that wheat yield in ST with

residue retention was greater than CT and ZT with residue burning in sandy loam soils

in a rice-wheat system in the IGP during the third year.

In Year 1, although the overall plant population of wheat was not satisfactory, crops

maintained similar plant density from sowing to harvest. In Year 2, overall the plant

population was low in all treatments and unsatisfactory in BP. The lower plant

population at BP was attributed to poor seed-soil contact as result of seeding on

residue and insufficient soil moisture in the seeding zone for seed germination during

crop establishment (moisture was not measured but visually observed in seeding zone

of BP treatments). This lower plant population led to lower plant height and spikes/m²

and finally contributed to depressed yield in BP in the second growing season. Jat et al.

(2013) reported that fewer spikes of wheat under BP may lead to poor wheat

performance compared to ST and CT. In case of BP, Yadvinder-Singh et al. (2009) also

reported that soil moisture at the time of planting is a critical factor for determining

tilth of beds on medium to fine-textured soils. With optimum moisture condition in the

third growing season (see Chapter 4), plant establishment was better than in the

previous two years. Most of the yield contributing characters in the third growing

79

season, namely plant height, effective tiller number, spike number, spike length and

spikelets, were higher in ST and BP compared to CT. These important yield-related

characters were positively correlated with the yield of wheat (Table 2.19). The greater

plants/m², and increased spike/m² had positive linear relationships with grain yield of

wheat (see Figure 2.6).

High residue retention increased the straw yield of wheat from the first growing

season and continued to do so in the succeeding years, which was associated with

improved moisture availability with HR (see Figure 4.19, 4.21 in Chapter 4). However,

the plant population of wheat dropped with HR in third growing season (Table 2.15).

Perhaps the effects of denser mulching from rice residues impeded germination or

crop establishment. In addition, heavy residue recycled from the previous six crops

may hamper seed-soil contact. This suggestion is supported by Rieger et al. (2008) who

reported that increased residue retention accounts for poor winter wheat (Triticum

aestivum L.) crop establishment. In the third growing season, increased residue

retention also increased grain yield of wheat. Significantly higher grains/spike and

1000-grain weight under HR retention might have accounted for higher grain yield of

wheat (Kamkar et al., 2014). Prasad and Power (1991) reported that increased residue

retention produced higher grain yield in a wide variety of crops. Crop residues

generally improve soil moisture retention, SOC, N and other nutrient levels as they are

a direct source of organic C and nutrients (Das et al., 2013). After three years, the

improvement of soil physical properties (see Chapter 4) and the accumulation of SOC

and TN levels (see Chapters 5 and 6) may be contributing to the increased yield of

wheat with increased residue retention (see Chapters 5 and 6).

2.4.3 Cropping system productivity

In the legume-dominated system, although the yield of first rice crop (Crop 3) was not

significantly affected by treatment, the yield of second mungbean (Crop 5) crop was

greater under BP or CT. This might be attributed to the dry conditions and drying out

of the soil which led to greater soil penetration resistance in the ST for mungbean at

extreme dry conditions of summer. After two years, the yield of the second rice crop

(Crop 6) in the system under ST and unpuddled transplanting was greater or similar to

80

CT (puddled) which might be attributed to improved soil conditions over time with

minimal soil disturbance under ST (see Chapter 4, 5 and 6). In addition, this might be

attributed to the soil not drying out with higher rainfall and better soil water content in

the small cultivated area of ST. The findings of the present study of similar rice yield

under unpuddled transplanted rice as compared to puddled transplanted rice for the

initial years are in agreement with some findings in India (Bajpai & Tripathi, 2000).

However, the poor yield of rice (Crop 6) under BP and unpuddled transplanting might

be due to compaction and settling of bed soil as a result of submerged soils. In the

cereal-dominated system, there were no yield differences of rice between puddled and

unpuddled systems (see Table 2.20). By contrast, several authors reported lower yield

of unpuddled rice as compared to puddled transplanted rice in the initial years (Kumar

& Ladha, 2011; Jat et al., 2014). However, in both cereal- and legume-dominant

rotations, the benefits of CA over the conventional system started to emerge from

Crop 7 (third growing season), with the winter crop after rice. It has been

demonstrated from the previous studies in the IGP that over time unpuddled

transplanted rice followed by ST and BP to establish subsequent cool dry season crops

alleviated puddling effects on soil and thereby favoring better plant establishment,

root proliferation and increased crop ability to utilize sub-soil water, and nutrients that

led to higher yield (Gangwar et al., 2006; Gathala et al., 2011b). Jat et al. (2014)

concluded from seven years of a rice-wheat experiment in the Eastern IGP that the

yield benefits of wheat under CA were immediate (from the first year) and the yield

benefit of the cropping system began from the second year with an increasing trend

over time though appreciable yield benefit of CA practices for rice crops after 3-4

years. In a study conducted by Adhikari et al. (2007) in the IGP of Nepal, the grain yield

of rice in its’ second year was significantly affected by tillage, where ZT transplanted

rice produced higher grain yield compared to full tillage. The trend of rice yield over

two years and third year (Haque et al. 2016) in the present research; and the evidence

from above mentioned studies in IGP suggests that unpuddled rice transplanting and

ST and BP may also increase rice yield gradually and likely could be superior in the long

run.

81

2.5 Conclusions

This Chapter has evaluated the effects of CA practices (i.e. minimum or reduced tillage

in the form of ST or BP, respectively, and high residue retention) relative to

conventional practices (CT and LR) on crop yield performance during three years in

intensive rice-based cropping systems in Northwest Bangladesh. In both legume-

dominated (lentil-mungbean-rice) and cereal-dominated (wheat-mungbean-rice)

rotations on different soil types (calcareous alluvial and HBT, respectively), CA in

intensive rice-based cropping sequences seems feasible. In the cool-dry season crops

(lentil and wheat), yield under ST and BP was initially similar to CT in HBT and in alluvial

areas, but by Crop 7, yields with ST and BP exceeded those with CT. These results

suggest that ST and BP; and high residue can enhance crop yield of cool dry season

crops in the intensive rice-based cropping systems but it takes 2-3 years (equivalent to

4-7 consecutive crops) before the yield benefit of CA practices can be clearly seen. The

positive linear relationship between crop yield and plant population for both wheat

and lentil suggested that the higher plant populations established with ST and BP; and

HR contributed to higher yield.

In the cereal-dominated system, the rice grain yields were equivalent in CA (ST and HR)

and the conventional system (CT and LR). Although the mungbean yield was lower with

ST, lentil yields were increased in ST while rice yield (Crop 3) was not significantly

different to CT in legume-dominated system. However, the rice yield (Crop 6) was

dropped in BP in legume-dominated system.

Although CA techniques have started to show positive benefits on cool dry season crop

after three years, ongoing studies are needed to confirm the long-term benefits that

accrue from ST or BP and increased residue retention. Moreover, there is a need to

understand the nature of the soil changes under ST or BP and HR that have contributed

to increased crop yields. These are discussed in Chapters 3-6.

82

3 Effects of tillage and residue management on soil strength, soil water

and crop root growth in rice-based systems on silty loam soil in

Bangladesh

3.1 Introduction

Though puddling is commonly practiced for weed control, water retention and ease of

transplanting for rice, the yield of the next crop after rice has been reported to

decrease due to the deterioration of soil structure caused by puddling (Sharma et al.,

2003; Mohanty et al., 2006). Adverse effects of puddling for rice on root growth of the

following wheat crop have been shown (Kukal & Aggarwal, 2003a; Balwinder et al.,

2011; Kumar & Ladha, 2011). Puddling results in breakdown of soil aggregates,

destruction of macrospores, and formation of hard plough pan at shallow depths of

soil (Gathala et al., 2011a). The soil strength of compacted soil layers below the

puddled zone rapidly increases as the soil dries, and limits the depth of root

exploitation in subsequent crops (International Rice Research Institute, 1986). As a

result, drought may be induced in post-rice crops by restricting the root depth (Kukal &

Aggarwal, 2003b). Sadras and Calvino (2001) calculated a 0.4 % wheat yield decline for

each centimeter reduction of rooting depth.

Components of conservation agriculture (CA) such as minimum tillage with residue

retention have been recommended as a key measure to minimize the degradation of

soil and increase water availability for crops (Huang et al., 2012; Bhatt et al., 2016). The

CA systems may have advantages over conventional systems due to reduced soil

disturbance and the protective effect of crop residues cover of the soil (Blanco-Canqui

& Lal, 2009; Choudhury et al., 2014) and soil water conservation on dryland soils

(Pittelkow et al. 2014). Further, CA improved the availability of soil water (Bescansa et

al., 2006) and increased the number of soil biopores (Francis & Knight, 1993) that may

facilitate root growth (Martino & Shaykewich, 1994).

Wheat and lentil are contrasting crops commonly grown after rice in Northwest

Bangladesh. The root systems of these crops may respond differently to the degraded

83

soil structure and the presence of plough pans in paddy fields. Conversely, they may

respond differently to the short term and cumulative effects of minimum tillage and

residue retention of previous crops in rice-based systems.

There have been numerous studies on rice, maize and wheat root systems (Aggarwal

et al., 2006; Martinez et al., 2008) but intensive study of legume root systems has been

less common (Gregory, 1988), particularly on lentil root systems in relation to grain

yield (Gahoonia et al., 2005). Ali et al. (2007b) studied the root systems of six different

crops – wheat, lentil, chickpea, barley, linseed and mustard – after monsoon rice under

water stress condition to identify alternative crops to deep rooting chickpea in the

High Barind Tract of Bangladesh. They concluded that thin rooted barley is the most

suitable alternative crop which could fit in this area due to its satisfactory yield and

greater soil moisture extraction habit. However, to date, there has been no

comprehensive study of the growth and distribution of crop root systems and their

response to variation of soil penetration resistance (PR) and volumetric soil water

content (%) (SWC) in rice-based systems under CA practices in Bangladesh. Two field

experiments, described in Chapter 2, were used to examine the effects of different

tillage and residue management on soil PR and water content and root distribution of

the cool dry season crops, lentil and wheat.

3.2 Materials and method

Two experiments were established in the rabi season (cool dry-winter season) during

2010-11, 2011-12 and 2012-13. The lentil-mungbean-monsoon rice experiment was

conducted at Alipur, Durgapur, Rajshahi and the wheat-mungbean-monsoon rice

experiment at HBT, Digram, Rajshahi. Weather and site details were described in

Chapter 2.

3.2.1 Treatment details

Details of the treatments were described in Chapter 2. Briefly, tillage treatments

consisted of strip tillage (ST), bed planting (BP) for non-rice crop, and unpuddled

transplanting for the rice crop; and puddled transplanting for rice crop and

84

conventional tillage (CT) for the non-rice crop; and there were two levels of residue:

high residue (HR) and low residue (LR) retention.

3.2.2 Measurement of soil water content and penetration resistance

One day before root sampling, the SWC was measured with a MP406 capacitance

sensor (ICT International, Armidale, NSW) at three random spots in each plot at 5 cm

increments to 15 cm. After measuring SWC of surface 5 cm soil depth excavated to the

surface 5 cm soil and measured the next depth (5-10 cm) and afterwards 10-15 cm soil

depth. At the same time, the soil PR was measured with a field hand-held

penetrometer (Eijkelkamp, the Netherlands) at five random locations in each plot at 5

cm increments to 15 cm. Based on soil strength, cone number 1 (diameter: 11.28 mm;

base area: 1 cm²) or 2 (diameter: 15.96 mm; base area: 2 cm²) of the penetrometer

were used and calculated soil PR as per manual of Eijkelkamp penetrometer. The

measurements of SWC and PR were in the tilled strip (IS) as well as the inter-row space

between the strips or off the strip (OS) of ST, in the furrow and on centre of the bed for

BP and between the plants in the CT plot.

3.2.3 Root sampling of lentil

Five randomly pre-selected plants were sampled for root distribution at flower

initiation [first year (2010-11) at 57-60 days after sowing (DAS), second year (2011-12)

at 58-61 DAS and third year (2012-13) at 60-62 DAS]. Soil was sampled under

representative sections of plant rows from ST and BP plots and representative sections

of plants under CT. Samples taken from a corner of each plot in order to minimize the

soil disturbance of rest of the plot. Shoots were detached from the collar region of the

plant and above-ground biomass was determined. Blocks of soil, 15 cm long (along the

row) and 20 cm wide (across the row) was excavated manually. In Year 1, only one

block was sampled down the soil profile at 0-15 cm as roots could not be found below

12-13 cm soil depth. As roots were found up to 18-19 cm in the succeeding two years,

two blocks per plot were sampled down the soil profile at 0-10 and 10-20 cm. The

dimensions of each block were 15 x 20 x 15 cm³ in the first year and 15 x 20 x 10 cm³ in

the following two years. Therefore, all the root and shoot characters of lentil were

calculated and presented considering 300 cm² surface area. Extracted soil was soaked

85

in water in plastic buckets for 2 to 3 hours. The slurry was washed over a fine sieve (0.5

mm) and roots were collected by hand with the non-root material and organic debris

picked out or washed off carefully, and the selected root mass was stored in a

refrigerator until further assessment. The shoot and root samples were oven dried at

72 °C for three days (until constant weight) to determine the shoot and root dry

weight.

3.2.4 Nodulation of lentil

For all the study years, nodulation was assessed just prior to flowering or at first

flowering. For nodulation ranking, eight representative plants were selected per plot.

In 2010-11, the nodulation was ranked on a 0-5 scale based on the crown and effective

nodules (pink colour nodule) following the procedure of (Rupela, 1990). However, this

ranking system was found to be too insensitive for lentil nodulation rating therefore in

2011-12 and 2012-13, the total as well as effective nodule (pink coloration inside)

numbers/plant was counted separately. The average nodule ranking, nodule and

effective nodule numbers of 5 randomly selected plants in each plot were counted.

Also fresh weight and dry weight of 1000-effective nodule were measured in 2012-13.

3.2.5 Root sampling of wheat

Five randomly pre-selected plants were sampled for root distribution at booting to

heading stage (at 65-70 DAS) in the first year (2010-11) and at grain filling stage in the

following two years [at 87-92 DAS in second year (2011-12) and 89-93 DAS in third year

(2012-13)]. The first year results on root development patterns suggested that some

information might be missing when sampling at 65-70 DAS (booting stage). Thus

samplings were done at the maturity stage to get correct estimates of root growth of

wheat in the final two years. Soil was sampled under representative sections of plant

rows of ST and BP plots; and a representative section of plants of CT. Above-ground

biomass was carefully excised at the soil surface. A block of soil was 10 cm deep, 20 cm

long (along the row) and 20 cm wide (across the row) were excavated manually. Based

on rooting depth, the digging continued to a depth of 40 cm in 10 cm increments in the

first year, to a depth of 50 cm and 70 cm in the second year and third year. The

86

dimensions of each block were 20 x 20 x 10 cm³. Therefore, all the root and shoot

characters of wheat were calculated and presented considering 400 cm² surface area.

All other operations were performed as for lentil.

3.2.6 Measurement of root parameters

Root volume (RV) was recorded by water displacement from a volumetric cylinder and

root dry weights were recorded after oven drying at 70 °C to a constant weight. Root

length was measured by grid method of Newman (1966) modified by Tennant (1975).

The root length was calculated as follows:

Root length = 11/14 x number of intercepts x grid unit (cm)

Root length density (RLD, root length/soil volume) and specific root length (SRL, root

length/oven dry weight) were calculated for each sample.

The above-ground shoots were dried in an oven at 70 °C to a constant weight. The root

to shoot ratio was calculated by dividing the total weight of roots by that of shoots.


Data on SWC, PR, root volume, root length, RLD, root dry weight and SRL were

analysed separately for lentil and wheat each year using GenStat 15th Edition (VSN

International Ltd, United Kingdom). Mean values were calculated for each set of

measurements, and analysis of variance (ANOVA) for split-plot design was performed

to assess treatment effects on the measured variables. On an average ~80-90 % roots

were confined to top 10 cm soil profile and the remaining ~10-20 % to 10-20 cm for

lentil and 10-70 cm for wheat. Tillage was assigned in main-plots and residue retention

levels in sub-plots while soil depth for the measurement of SWC and soil PR in sub-sub

plots. Root for each depth were analysed separately for assessing root growth under

tillage and residue retention. When the F-test was significant, treatment means were

separated by least significant difference (LSD) at P≤0.05. A correlation matrix of

different properties was based on Pearson correlation coefficients (P≤0.01 and

P≤0.05).

87

3.3 Results

3.3.1 Soil physical properties during root assessment of lentil at Alipur

3.3.1.1 Volumetric soil water content

Volumetric soil water content (SWC) was significantly affected by tillage, depth and the

interaction of tillage and depth in 2010-11 (Figure 3.1a1). The SWC increased with

increasing soil depth. The greater SWC was measured with CT and the lower SWC was

in BP at all soil depths (0-5 cm, 5-10 cm and 10-15 cm). In 2011-12, the SWC was

higher in BP and the lower SWC in ST at surface soil layer (0-5 cm) (Figure 3.1a2).

Retention of high residue conserved higher SWC than low residue retention at all soil

depths. In 2012-13, the SWC was significantly affected by tillage, residue, depth and

the interaction of tillage and depth (Figure 3.1a3). High residue retention increased

SWC as compared to low residue retention. The SWC was significantly lower under CT

than under ST and BP at all soil depths.

88

Figure 3.1. Tillage and residue effects on mean volumetric soil water content (%) (a1-

a3) and mean penetration resistance (MPa) (b1-b3) at three soil depths (0-5 cm, 5-10

cm and 10-15 cm) at Alipur during 2010-11 to 2012-13. The floating error bars

indicate the average least significant difference (LSD) at P≤0.05 for significant

treatment and depth difference.


In 2010-11, soil PR was significantly affected by tillage, residue, depth and the

interaction of tillage and depth (Figure 3.1b1). Regardless of treatments, PR increased

with increasing soil depth but retention of high residue decreased PR. The lowest PR

was in BP and the highest PR was in CT at all the study depths (0-5 cm, 5-10 cm and 10-

Vo

lum

etr

ic s

oil

wat

er

con

ten

t (%

)

0.0

7.0

14.0

21.0

28.0

35.0

HR LR HR LR HR LR

0-5 cm 5-10 cm 10-15 cm

Depth Tillage x Depth Tillage


a1)2010-11

0.0

7.0

14.0

21.0

28.0

35.0

HR LR HR LR HR LR

0-5 cm 5-10 cm 10-15 cm

Tillage x Depth Residue

a2)2011-12

0.0

7.0

14.0

21.0

28.0

35.0

HR LR HR LR HR LR

0-5 cm 5-10 cm 10-15 cm

Treatment

Depth TillageX Depth Residue

a3)2012-13

Soil

pe

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re

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ance

(M

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0.0

1.5

3.0

4.5

6.0

7.5

9.0

HR LR HR LR HR LR

0-5 cm 5-10 cm 10-15 cm

Depth Tillage x Depth Tillage Residue

b1)2010-11


0.0

1.5

3.0

4.5

6.0

7.5

9.0

HR LR HR LR HR LR

0-5 cm 5-10 cm 10-15 cm

Depth

TillageX Depth

Tillage Residue

b2)2011-12

0.0

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3.0

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6.0

7.5

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HR LR HR LR HR LR

0-5 cm 5-10 cm 10-15 cm

Treatment

Depth TillageX Depth Tillage Residue

b3)2012-13

89

15 cm). In 2011-12, the soil PR was significantly affected by tillage, residue, depth and

the interaction of tillage and depth (Figure 3.1b2). Irrespective of treatments, soil PR

increased with increasing soil depth but retention of high residue again decreased soil

PR. In BP, though there was no significant tillage effect on PR at 0-5 cm, it was lower at

5-10 cm and 10-15 cm soil depths. In 2012-13, the soil PR was significantly affected by

tillage, residue, depth and the interaction of tillage and depth (Figure 3.1b3).

Regardless of treatment, PR increased with increasing soil depth but retention of high

residue decreased PR. Penetration resistance was not significantly affected by tillage in

the surface layer (0-5 cm depth) but was lowest at 5-10 cm and 10-15 cm depths.

3.3.2 Root characteristics of lentil

In 2010-11, the important rooting characteristics namely root volume and root length

were greater at 0-15 cm soil depth with BP as compared to CT (Figure 3.2a and Figure

3.2c); and HR enhanced root volume and RLD relative to LR (Figure 3.2a and Figure

3.2d). However, the RDW, SRL, shoot weight and root shoot ratio in the first growing

season was not significantly affected by different treatments.

90

Figure 3.2. Tillage and residue effects on lentil root distribution at 0-15 cm soil depth

during the 2010-11 growing season. Root parameters measured are a) Root volume

(cm³), b) Root dry weight (g), c) Root length (m), d) Root length density-RLD (cm/cm³)

and e) Specific root length-SRL (m/g). Error bars indicate ± 1 standard error of the

mean.

0.0 10.0 20.0 30.0 40.0 50.0

0-15 cm

SRL (m/g) in 2010-11

at 0-15 cm Tns; Rns; TxRns

0.0 0.2 0.4 0.6 0.8

0-15 cm

RLD (cm/cm3) in 2010-11

at 0-15 cm Tns; R*; TxRns

0.0 10.0 20.0 30.0 40.0

0-15 cm

Root length (m) in 2010-11

at 0-15 cm T*; Rns; TxRns

0.0 5.0 10.0 15.0 20.0

0-15 cm

Root volume (cm³) in 2010-11

ST-HRST-LRBP-HRBP-LRCT-HRCT-LR

at 0-15 cm Tns; R**; TxR**

Soil

de

pth

(cm

)

a)

0.0 0.2 0.4 0.6 0.8

0-15 cm

Root dry wt. (g) in 2010-11


b)

c)

d)

e)

91

In 2011-12, the root volume, root dry weight, RLD and root length at 0-10 cm depth

were not affected by tillage; and compare to LR, root growth was higher with HR

treatment (Figure 3.3a-d). At 10-20 cm depth, greater root volume, root dry weight,

root length and RLD were obtained in BP while the lowest was in CT (Figure 3.3a-d).

Figure 3.3. Tillage and residue effects on lentil root distribution at 0-10 cm and 10-20

cm soil depth during the 2011-12 growing season. Root parameters measured are a)

Root volume (cm³), b) Root dry weight (g), c) Root length (m), d) Root length density-

RLD (cm/cm³) and e) Specific root length-SRL (m/g). Error bars indicate ± 1 standard

error of the mean.

Soil

de

pth

(cm

)

0.0 0.2 0.4 0.6 0.8

0-10 cm

10-20 cm

RLD (cm/cm3) in 2011-12

at 0-10 cm Tns; R**; TxRns


d)

0.0 2.0 4.0 6.0 8.0

0-10 cm

10-20 cm

Root volume (cm3) in 2011-12

ST-HR

ST-LR

BP-HR

BP-LR

CT-HR

CT-LR



a)

0.0 0.2 0.4 0.6 0.8

0-10 cm

10-20 cm

Root dry wt.(g) in 2011-12

at 0-10 cm Tns; R*; TxRns

at 10-20 cm T**; Rns; TxRns

b)

0.0 5.0 10.0 15.0 20.0 25.0

0-10 cm

10-20 cm




c)

0.0 10.0 20.0 30.0 40.0

0-20 cm

SRL (m/g) in 2011-12


e)

92

In 2012-13, significantly higher root volume, root dry weight, root length, and RLD

were measured in the surface layer (0-10 cm depth) under BP and HR relative to CT

and LR, respectively (Figure 3.4a-d). At 10-20 cm depth, the greater root volume, root

dry weight, root length, and RLD were measured under BP than other tillage

treatments. Below 10 cm soil depth, residue effects had disappeared (Figure 3.4a-d).

Figure 3.4. Tillage and residue effects on lentil root distribution at 0-10 cm and 10-20

cm soil depth during the 2012-13 growing season. Root parameters measured are a)

Soil

de

pth

(cm

)

0.0 2.0 4.0 6.0 8.0

0-10 cm

10-20 cm


ST-HRST-LRBP-HRBP-LRCT-HRCT-LR

at 0-10 cm T**; R**; TxRns


a)

0.0 0.2 0.4 0.6 0.8

0-10 cm

10-20 cm




b)

0.0 5.0 10.0 15.0 20.0 25.0 30.0

0-10 cm

10-20 cm


at 0-10 cm T**; R**; TxR**


c)

0.0 0.2 0.4 0.6 0.8 1.0

0-10 cm

10-20 cm

RLD (cm/cm3) in 2012-13

at 0-10 cm T**; R**; TxR**


d)

0.0 10.0 20.0 30.0 40.0 50.0 60.0

0-10 cm

10-20 cm

SRL (m/g) in 2012-13

at 0-10 cm Tns; R**; TxR*


e)

93

Root volume (cm³), b) Root dry weight (g), c) Root length (m), d) Root length density-

RLD (cm/cm³) and e) Specific root length-SRL (m/g). Error bars indicate ± 1 standard

error of the mean.

3.3.3 Root and shoot growth and their ratio for lentil

The total root dry weights in 2011-12 and 2012-13 were greater with HR compare to

LR. In 2012-13, the root dry weight was higher under BP relative to that under other

tillage treatments (Table 3.1). Although treatment differences in shoot growth and

root to shoot ratio were not significant in the first two years, HR enhanced shoot

growth and root to shoot ratio in BP relative to ST in 2012-13 (Table 3.1).

Table 3.1. Total root dry weight, shoot dry weight and root to shoot ratio (g/g) of five

lentil plants under different tillage and residue management at Alipur.

Tillage

treatment1

2010-11 2011-12 2012-13


Root dry wt. (g/0.03 m²)

ST 0.50 0.53 0.51 0.63 0.50 0.57 0.50 0.44 0.47

BP 0.65 0.50 0.58 0.67 0.62 0.64 0.74 0.64 0.69

CT 0.55 0.43 0.49 0.66 0.58 0.62 0.51 0.44 0.48

Mean 0.57 0.48 0.65 0.57 0.58 0.51

LSD20.05


Residue (R) ns 0.085* 0.041**

T x R ns ns ns

Shoot dry wt. (g/0.03 m²)

ST 2.12 2.52 2.32 3.77 2.65 3.21 2.68 2.60 2.64

BP 2.80 2.10 2.45 3.25 3.01 3.13 3.08 2.63 2.85

CT 3.05 2.85 2.95 3.85 3.46 3.66 2.60 1.98 2.29

Mean 2.66 2.49 3.63 3.04 2.78 2.40

LSD0.05



T x R ns ns ns

Root to shoot ratio (g/g)

ST 0.26 0.21 0.23 0.18 0.19 0.18 0.19 0.17 0.18

BP 0.23 0.24 0.24 0.21 0.21 0.21 0.24 0.24 0.24

CT 0.18 0.16 0.17 0.18 0.18 0.18 0.20 0.22 0.21

94

Mean 0.23 0.20 0.19 0.19 0.21 0.21

LSD0.05



T x R ns ns ns

1HR - high residue; LR - low residue, ST - strip tillage; BP - bed planting; CT - conventional

tillage; 2the least significant difference (LSD) at the P≤0.05, ns - not significant, * - significant at

P≤0.05 and ** - significant at P≤0.01.

3.3.4 Nodulation of lentil

In 2010-11, there was no significant effect of treatment on nodule scores (Table 3.2).

In 2011-12, total and effective nodule numbers were higher in CT and BP, but

significantly lower numbers in ST. Retention of high residue increased the total nodule

number (Table 3.2).

95

Table 3.2.Tillage and residue effects on nodulation of lentil in legume-dominated

rice-based system.

Tillage

treatment1

Residue treatment1 Mean Residue treatment1 Mean

HR LR HR LR

2010-11

Nodule score/plant

ST 1.10 1.25 1.18

BP 1.35 1.18 1.26

CT 1.30 1.43 1.36

Mean 1.25 1.28

LSD0.05

Tillage (T) ns

Residue (R) ns

T x R ns

2011-12

Total nodule number/plant Effective nodule number/plant

ST 186.0 163.4 174.7 34.3 28.2 31.2

BP 240.3 179.7 210.0 50.0 42.0 46.0

CT 230.4 214.5 222.5 45.2 50.2 48.0

Mean 218.9 185.9 43.1 40.1

LSD0.05

Tillage (T) 39.0* 10.3*

Residue (R) 22.4** ns

T x R ns ns

2012-13

Total nodule number/plant Effective nodule number/plant

ST 122.8 113.2 118.0 41.2 38.9 40.1

BP 208.1 180.1 193.9 65.1 53.4 59.3

CT 124.0 120.0 121.8 44.4 35.4 39.9

Mean 151.6 137.5 50.2 42.6

LSD0.05

Tillage (T) 15.7** 8.3**

Residue (R) 11.7* 5.4*

T x R ns ns




96

In 2012-13, significantly higher total and effective nodule numbers were found in BP

than the other tillage treatments (Table 3.2). Compared to LR, higher numbers of total

and effective nodules were found with HR.

3.3.5 Soil physical properties during root assessment of wheat at Digram

3.3.5.1 Volumetric soil water content In 2010-11, irrespective of treatments, the SWC increased with increasing soil depth

(Figure 3.5a1). The SWC at 0-5 cm was lower in BP than in other tillage treatments.

However, the SWC contents at 5-10 cm and 10-15 cm depths were lower with CT than

ST and BP.

Figure 3.5. Tillage and residue effects on mean volumetric soil water content (%) (a1-

a3) and mean penetration resistance (MPa) (b1-b3) at three soil depths (0-5 cm, 5-10

cm and 10-15 cm) at Digram during 2010-11 to 2012-13. The floating error bars

Soil

pe

ne

trat

ion

re

sist

ance

(M

Pa)

0.0

7.0

14.0

21.0

28.0

35.0

HR LR HR LR HR LR

0-5 cm 5-10 cm 10-15 cm

Depth TillageX Depth

a1)2010-11


Vo

lum

etr

ic s

oil

wat

er

con

ten

t (%

)

0.0

7.0

14.0

21.0

28.0

35.0

HR LR HR LR HR LR

0-5 cm 5-10 cm 10-15 cm

Depth Tillage x Depth Tillage

a2)2011-12

0.0

7.0

14.0

21.0

28.0

35.0

HR LR HR LR HR LR

0-5 cm 5-10 cm 10-15 cm

Treatment

Depth TillageX Depth Residue

a3)2012-13

0.0

1.5

3.0

4.5

6.0

7.5

9.0

HR LR HR LR HR LR

0-5 cm 5-10 cm 10-15 cm

Depth Tillage

b1)2010-11


0.0

1.5

3.0

4.5

6.0

7.5

9.0

HR LR HR LR HR LR

0-5 cm 5-10 cm 10-15 cm

Depth

TillageX Depth

Tillage Residue

b2)2011-12

0.0

1.5

3.0

4.5

6.0

7.5

9.0

HR LR HR LR HR LR

0-5 cm 5-10 cm 10-15 cm

Treatment

Depth Tillage Residue

b3)2012-13

97

indicate the average least significant difference (LSD) at P≤0.05 for significant

treatment and depth difference.

In 2011-12, the SWC was significantly affected by tillage, depth and the interaction of

tillage and depth (Figure 3.5a2). Irrespective of treatments, the SWC increased with

increasing soil depth. The SWC at all depths were higher in BP than in other tillage

treatments (Figure 3.5a2). In 2012-13, regardless of treatments, the SWC increased

with increasing soil depth and HR conserved higher SWC than LR (Figure 3.5a3). In BP,

the SWC at 0-5 cm depth was lower while it was greater at 10-15 cm depth (Figure

3.5a3).


The soil PR increased with soil depth irrespective of treatments in all the study years

(Figure 3.5b1-b3). In 2010-11, soil PR was significantly affected by tillage and depth

(Figure 3.5b1). Soil PR at 0-10 cm depth was lower under BP while it was greater under

CT. In 2011-12, regardless of treatments, soil PR decreased with HR. As compared to

CT, soil PR was lower in BP at all depths (0-5, 5-10 and 10-15 cm). In 2012-13, CT had

significantly higher soil PR while BP had lower soil PR at 0-5 cm and 5-10 cm depths

(Figure 3.5b3). High residue retention significantly decreased soil PR compared to LR.

Below 10 cm depth, the soil was too hard to measure the soil PR (Figure 3.5b3).

3.3.6 Root characteristics of wheat

In 2010-11, the root volume, root dry weight, root length, RLD and SRL at all soil

depths (0-10 cm, 10-20 cm, 20-30 cm, 30-40 cm and 40-50 cm) were not affected

either by tillage or residue (Figure 3.6a-e). The root growth gradually decreased with

increasing soil depth across all tillage and residue treatments. About 80 % of root

length and mass was found in 0-10 cm depth and remaining 20 % at 10-50 cm depth

(Figure 3.6c).

98

Figure 3.6. Tillage and residue effects on wheat root distribution at 0-50 cm soil

depth (10 cm increments of five soil depths) during the 2010-11 growing season.

Root parameters measured are a) Root volume (cm3), b) Root dry weight (g), c) Root

length (m), d) Root length density-RLD (cm/cm3) and e) Specific root length-SRL

(m/g). Error bars indicate ± 1 standard error of the mean.

Soil

de

pth

(cm

)

0.0 2.0 4.0 6.0 8.0 10.0 12.0

0-10 cm

10-20 cm

20-30 cm

30-40 cm

40-50 cm


ST-HR

ST-LR

BP-HR

BP-LR

CT-HR

CT-LR

at 0-50 cm (10 cm increments of five soil depths) Tns; Rns; TxRns

a)

0.0 0.5 1.0 1.5

0-10 cm

10-20 cm

20-30 cm

30-40 cm

40-50 cm

Root dry wt (g) in 2010-11


b)

0.0 20.0 40.0 60.0 80.0 100.0

0-10 cm

10-20 cm

20-30 cm

30-40 cm

40-50 cm



c)

0.0 1.0 2.0 3.0

0-10 cm

10-20 cm

20-30 cm

30-40 cm

40-50 cm

RLD (cm/cm3) in 2010-11


d)

0.0 50.0 100.0 150.0

0-10 cm

10-20 cm

20-30 cm

30-40 cm

40-50 cm

SRL (m/g) in 2010-11


e)

99

In 2011-12, the root volume, root dry weight, root length and RLD at 0-10 cm depth

were greater with BP than other tillage treatments (Figure 3.7a-d). Compared with LR,

HR significantly increased root volume, root dry weight, root length and RLD (Figure

3.7a-d). At 10-20 cm depth, BP enhanced root growth by increasing all root parameters

compared to other tillage treatments (Figure 3.7a-d). Regardless of treatments, root

growth gradually decreased with increasing soil depth and the treatment effect on

root growth disappeared below 20 cm depth. About 80 % of wheat roots were

recorded in top 10 cm of soil (Figure 3.7a-d).

100


depth (10 cm increments of six soil depths) during the 2011-12 growing season. Root

parameters measured are a) Root volume (cm3), b) Root dry weight (g), c) Root



Soil

de

pth

(cm

)

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0

0-10 cm

10-20 cm

20-30 cm

30-40 cm

40-50 cm

50-60 cm


ST-HR

ST-LR

BP-HR

BP-LR

CT-HR

CT-LR




a)

0.0 0.5 1.0 1.5 2.0 2.5 3.0

0-10 cm

10-20 cm

20-30 cm

30-40 cm

40-50 cm





b)

0.0 20.0 40.0 60.0 80.0 100.0 120.0 140.0

0-10 cm

10-20 cm

20-30 cm

30-40 cm

40-50 cm

50-60 cm





c)

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

0-10 cm

10-20 cm

20-30 cm

30-40 cm

40-50 cm

50-60 cm

RLD (cm/cm3) in 2011-12



at 20-70 cm(10 cm increments of five soil depths) Tns; Rns; TxRns

d)

0.0 20.0 40.0 60.0 80.0 100.0 120.0 140.0 160.0

0-10 cm

10-20 cm

20-30 cm

30-40 cm

40-50 cm

SRL (m/g) in 2011-12


at 0-50 cm except 20-30 cm (10 cm increments of five soil depths) Tns; Rns; TxRns

e)

101

In 2012-13, all root parameters of wheat at 0-10 cm depth were greater in BP than

other tillage treatments (Figure 3.8a-d). High residue retention significantly increased

all root parameters of wheat at 0-10 cm depth (Figure 3.8a-d). At 10-20 cm depth, all

root parameters were greater with BP than in other tillage treatments but root growth

beyond 10 cm depth was not influenced by different residue levels (Figure 3.8a-d).

Treatment effects below 20 cm soil depth were not significant. Regardless of

treatments, root growth gradually decreased with increasing soil depth: as in previous

years, about 80 % of root growth was limited to the top 10 cm depth (Figure 3.8a-d).

102


depth (10 cm increments of seven soil depths) during the 2012-13 growing season.

Root parameters measured are a) Root volume (cm3), b) Root dry weight (g), c) Root



0.0 20.0 40.0 60.0 80.0 100.0 120.0 140.0

0-10 cm

10-20 cm

20-30 cm

30-40 cm

40-50 cm

50-60 cm

60-70 cm





Soil

de

pth

(cm

)

0.0 0.5 1.0 1.5 2.0

0-10 cm

10-20 cm

20-30 cm

30-40 cm

40-50 cm

50-60 cm


at 0-10 cm T*; R*; TxRns



b)

c)

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

0-10 cm

10-20 cm

20-30 cm

30-40 cm

40-50 cm

50-60 cm

60-70 cm

RLD (cm/cm3) in 2012-13

at 0-10 cm T*; R**; TxRns



d)

0.0 20.0 40.0 60.0 80.0 100.0 120.0 140.0 160.0 180.0

0-10 cm

10-20 cm

20-30 cm

30-40 cm

40-50 cm

SRL (m/g) in 2012-13




e)

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0

0-10 cm

10-20 cm

20-30 cm

30-40 cm

40-50 cm

50-60 cm

60-70 cm


ST-HR

ST-LR

BP-HR

BP-LR

CT-HR

CT-LR

at 0-10 cm T*; R*; TXRns



a)

103

3.3.7 Root and shoot growth, and their ratio of wheat

Neither root nor shoot weight nor their ratio was significantly affected by treatments

in 2010-11 (Table 3.3). However, in 2011-12, the root and shoot weights were greater

with BP than other tillage treatments. Further, HR increased root weight, shoot weight

and root to shoot ratio as compared to LR treatment (Table 3.3). In 2012-13, the root

dry weight was greater in BP than in other tillage treatments, with HR increased root

dry weight compared to LR (Table 3.3).

Table 3.3. Total root dry weight, shoot dry weight and root to shoot ratio (g/g) of five

wheat plants under different tillage and residue management at Digram.

Tillage

treatment1

2010-11 2011-12 2012-13

HR LR Mean HR LR Mean HR LR Mean

Root dry wt.(g/0.04 m²)

ST 1.25 1.32 1.28 2.21 1.84 2.03 1.68 1.49 1.58

BP 1.38 1.31 1.34 2.93 2.44 2.69 1.99 1.84 1.92

CT 1.30 1.37 1.34 2.27 1.91 2.09 1.57 1.26 1.41

Mean 1.31 1.33 2.47 2.06 1.75 1.53

LSD20.05

Tillage (T) ns 0.13** 0.29**

Residue (R) ns 0.23** 0.17**

T x R ns ns ns

Shoot dry wt. (g/0.04 m²)

ST 25.1 24.8 25.0 66.3 58.0 62.2 55.3 52.3 53.8

BP 27.2 27.3 27.3 84.0 75.7 79.9 57.5 56.7 57.1

CT 27.1 22.5 24.8 71.0 60.7 65.9 53.9 51.1 52.5

Mean 26.5 24.9 73.8 64.8 55.6 53.4

LSD0.05



T x R ns ns ns

Root to shoot ratio (g/g)

ST 0.050 0.055 0.053 0.033 0.032 0.033 0.030 0.028 0.029

BP 0.051 0.050 0.050 0.035 0.032 0.034 0.035 0.033 0.034

CT 0.050 0.061 0.055 0.032 0.032 0.032 0.029 0.025 0.027

Mean 0.050 0.055 0.034 0.032 0.031 0.029

LSD0.05



104

T x R ns ns ns




3.4 Discussion

The changes of SWC and soil PR due to tillage and residue at the time of root

assessment and their potential connection with root growth are presented in the

current Chapter. However, the detailed effects of tillage and residue on SWC and soil

PR, and other important soil physical properties, throughout the cropping cycle are

presented in Chapter 4.

3.4.1 Soil penetration resistance and soil water content

The soil PR was reduced (P≤0.05) by BP and HR throughout the study at all depths

measured. The similar findings of lower values of soil PR in different soil layers of

effective root zone (0-50 cm) under permanent BP and ZT compared to CT were also

reported by Parihar et al. (2016). The decrease in soil PR under BP could be attributed

to the construction of beds by heaping of pulverized top soil (in Year 1) and confining

wheel traffic to the furrow of the bed. Naresh et al. (2014a) also found the lowest soil

PR in BP compared to CT and ZT.

Reduced soil PR also became apparent in the treatment with unpuddled rice followed

by ST with HR for the non-rice crop. In the rainfed legume-dominated site, the soil PR

with unpuddled rice followed by ST with HR decreased by 14-17 % at 5-15 cm depth

and in the irrigated cereal-dominated site by 16-18 % at 0-10 cm depth, compared to

puddled rice followed by CT with LR. These results are consistent with those of Singh et

al. (2016) and Saha et al. (2010), who reported that soil PR decreased under ZT

compared to CT at 0-15 cm and 15-30 cm depths. The findings of the present study

indicated that over time unpuddled rice and ST with HR gradually improved soil

physical properties such as increasing SWC and lowering soil PR while diminishing

existed plough pan as a consequence of puddled rice cultivation. However, in case of

the cereal-dominated system the soil PR of the surface soil was also lower in ST. By

105

contrast in CT, repeated tillage and puddling contributed to increased soil compaction

and thereby enhanced soil PR (Kukal & Aggarwal, 2003b; Parihar et al., 2016).

Retention of HR decreased the soil PR compared to LR and soil PR increased with

increasing soil depth throughout the years at all soil depths in the present study.

Similar results were reported by Singh et al. (2016), who found that residue retention

reduced soil PR compared to removal of rice and maize residue. They also found that

the soil PR increased with increasing soil depth. Over time retention of HR gradually

decreased soil PR which should facilitate improved root growth over LR. Rahman et al.

(2005) and Chakraborty et al. (2008) also found that residue retention decreased soil

PR and thereby increased root growth.

In the present study, the SWC decreased in BP more than in ST and CT at Alipur, a

rainfed legume-dominated zone in Year 1. The raised and pulverized soil of the new

bed might have caused this. The SWCs however were not significantly different at

Digram, an irrigated site, except in Year 2. Probably equal amount of irrigation

application and greater transpiration during vegetative stage of wheat resulted in non-

significant differences in SWC due to tillage at Digram. In Year 2, the SWC was

significantly higher for BP compared to ST and CT at all depths. The reduced uptake of

soil water by the lower plant population in Year 2 (see table 2.15 in Chapter 2) might

have contributed to this. The higher SWC also could promote root growth of existing

wheat plant in BP. In Year 3, with increasing soil depth, the SWC increased in ST and

BP while it decreased in CT. The greater SWC at deeper soil profile of ST and BP might

be associated with improved infiltration rate with time. The improvement of soil

aggregate stability with time and preservation of water conducting pores as a result of

minimal soil disturbance increased the infiltration rate in soil under ZT and with

residue retention (Dwivedi et al., 2012; Singh et al., 2016). Further, residue retention

on ST and BP protected the surface soil from evaporation and increased the infiltration

rate. By contrast, constant rice puddling and intensive tillage for cultivation of dryland

crop disrupted soil structure and thereby decreased infiltration rate under CT,

consistent with observations by Singh et al. (2016). In addition to lower infiltration

rate, high evaporation as a result of bare and pulverized soil led to decreased SWC in

106

CT. Several researchers of India also reported that the infiltration rate increased with

ZT and residue retention than with CT and residue removal (Gathala et al., 2011b; Jat

et al., 2013). The SWC was higher under HR compared to LR in progressive years,

probably due to the protection from evaporation and increased infiltration rate.

3.4.2 Root distribution as affected by tillage and residue over time

Both legume- and cereal-dominated trials were carried out in silty loam soil with low

levels of soil organic carbon (SOC) (0.61 % at Alipur and 0. 73 % at Digram) (see

Chapter 2) compared to a good agricultural soil (~1.16 % SOC) (Bangladesh Agricultural

Research Council, 2012). Puddling is used for rice cultivation followed by intensive

tillage for the establishment of succeeding cool dry season crops in both areas. Silty

loam soils are prone to soil physical changes due to puddling for rice cultivation (Hobbs

et al., 1994) which can impair root growth of cool dry season crops after rice.

All the given measurements of roots such as root volume, RDW, root length, RLD and

SRL were similarly affected by tillage and residue. However, most commonly RLD is

used to measure root distribution in the soil profile (Qin et al., 2004; Chopart et al.,

2008). Hence the following discussion focuses on RLD.

Root growth of lentil and wheat in the surface soil (0-10 and 10-20 cm) was highest in

BP and with HR throughout the study. Although there were no clear differences

between ST and CT at surface 10 cm depth in all years, the root growth at 10-20 cm

depth in ST started to increase over CT from the Year 2 onwards. The raised bed

planting system offers a favorable condition for root growth. Initial tillage operations

to form the bed loosen the soil and reduce the soil PR. The present results are in good

agreement with other studies (Aggarwal et al., 2006; Hossain et al., 2008; Singh et al.,

2013), which also found higher root growth in bed planting systems. Over time (after

three years) accumulation of high residue in BP and ST systems increased SWC and

decreased PR through induced cracks and bio-pores (old root channels) in the

undisturbed mid-row space of beds and thus favoured better root growth in BP and ST

than CT. Singh et al. (2014b) and Bonfil et al. (1999) reported that the improved

balance between micro- and macro-porosity and residue retention under ZT accounted

107

for better root growth of wheat over CT. In the present study, BP and HR also provided

a favourable environment for better nodule formation in roots compared to other

treatments.

A favourable rhizosphere environment enhanced nodulation in BP (Kumar et al., 2015).

Pramanik et al. (2009) reported that better drainage and quick re-aeration of the root

zone after irrigation under BP increased nodulation. Nodule formation is sensitive to

unfavourable soil moisture condition (Kulathunga et al., 2008). In the current study,

higher SWC in BP with HR may explain the increased nodulation. Likewise, the lower

soil BD or PR in BP with HR also might be conducive to greater nodulation, consistent

with observations by Aggarwal and Goswami (2003). However, in the present study,

root sampling in BP was only at the centre of the bed, which is equivalent to 62 % area

of bed planting plot, while there was no sampling in the remaining 35-38 % of the

furrow portion of the BP plot. This sampling bias may influence the results of root

growth in BP. The explanation of root sampling method could able to clear the above

statement. During sampling soil for the assessment of root growth, a sample block of

soil was selected with the surface dimensions 15 cm long (along the row) X 20 cm wide

(across the row) X 10 cm deep for lentil and 20 cm long (along the row) X 20 cm (across

the row) X 10 cm deep wide for wheat for all treatments. Sample block was collected

from the middle of inter-row to the middle of the next inter-row (20 cm wide) for ST.

In case of BP system, row spacing was 20 cm at the centre of the bed and sampling

location was 10 cm in either direction from one of the rows (Figure 4.3). Since bed is

trapezoidal with 30 cm wide at the bed top, hence it was 15 cm (from the middle of

inter-row to the edge of the bed) of 20 cm sampling wide on bed top. There was

remaining 5 cm from edge of the bed to slope of the bed, which was covered by some

part of furrow (of 0-5 cm furrow). However, the total furrow of the BP was not

considered for the assessment of root growth which led to biased sampling in favour of

BP system. However, by scaling the value of root growth to the furrow dimensions of

the bed (55 cm — furrow-furrow distance) or to one hectare, the root growth of BP

system likely to be equal or even less than other treatments. Since the root growth of

furrow was not counted in the present study, hence the root growth was not scaled to

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the furrow dimensions of the bed and only presented the root growth on BP which is

biased.

The RLD was similar in CT and ST in Years 1 and 2, however higher RLD was found in ST

than CT in Year 3. The reason behind the increase of RLD in ST after three years might

be due to the improvement of SOC as a result of greater deposition of higher organic

input in ST (see Chapter 5 for SOC results). The restricted root growth at depth was

associated with a pre-existing plough pan. In the experimental field, farmers had

regularly cultivated puddled monsoon rice followed by intensive tillage with residue

removal for two other dryland crops. Over time implementation of unpuddled rice

followed by ST for cool dry season crop together with HR tended to decrease the hard

pan, as reflected by lower soil PR at 10-15 cm depth (Table 3.1) and at 5-10 cm depth

(Table 3.6) and thereby enhanced root growth in Year 3. However, both BP and ST

systems took three years to overcome the detrimental effects of puddling and rigorous

tillage practices of the non-rice crop. After three years, the RLD improved significantly

compared to the first two years. These results are consistent with those of Pearson et

al. (1991) who reported that tillage effects on root growth of wheat was less or similar

in the first three years, however the growth was greater in successive years under

minimum tillage compared to CT.

3.4.3 Rooting patterns of wheat and lentil

Lentil and wheat have contrasting root systems that may result in varied responses to

tillage and residue retention. The root system of lentil consists of a slender tap root

with a mass of fibrous lateral roots at shallow depth (Saxena, 2009). The root system of

wheat is fibrous, denser and penetrates deeper than that of lentil. Although root

growth of lentil extended up to 20 cm depth, ~90 % of roots were concentrated in the

surface 10 cm depth. The root growth of lentil and wheat below 20 cm depth was not

significantly different under different tillage and residue treatments. Though the root

growth of wheat reached 70 cm depth, ~80-90 % roots were distributed in the top 10

cm profile. The distribution of lentil roots at 0-10 cm and 10-20 cm depths due to

tillage treatments was significantly different (P<0.01) both in Years 2 and 3. In Year 2,

about 90 % lentil roots in ST, 89 % in BP and 95 % in CT were restricted to the surface

10 cm of soil. In Year 3, there were 86 % of roots in ST and 90 % in BP while 92 % of

109

lentil roots were confined to the surface 10 cm in CT. Like lentil, the vertical rooting

depth of wheat roots was in the following order: ST>BP>CT. The results of the vertical

distribution of root under different tillage treatments suggested that least roots were

distributed in the deeper soil profile under CT. Greater root growth deeper in the soil

profile under ST and BP may allow greater extraction of water and nutrients from a

greater soil volume. Similar results have been reported by Singh et al. (2014b) who

found maximum wheat roots (about 96 %) in the surface 0-15 cm depth with CT

compared to 87 % with ZT in a rice-wheat system of the IGP.

3.4.4 Root distribution related to soil water content and penetration resistance

The RLD was associated with the changes of soil PR and SWC due to different tillage

and residue treatments in all years. The soil PR might be the major influential factor for

enhancing root growth of cool dry season crops after rice in the current study. The

critical values of soil PR that limits root growth for most of the field crops is 2-3 MPa

(Aggarwal et al., 2006). During root assessment in Year 1 in the rainfed legume-

dominated system, the SWC and PR in BP were significantly (P≤0.05) lower than those

in CT and ST at all soil depths (0-5, 5-10 and 10-15 cm). Repeated loosening of surface

soil on centre of the bed and the greater accumulation of SOC (see Chapter 5) and N

(see Chapter 6) was associated with a decreased soil PR at top of the BP in Year 2 and

Year 3.

The increased SWC under HR resulted in decreased soil PR in Year 3 as compared to LR.

Retention of HR on the soil surface led to greater accumulation of SOC and total soil N

(see Chapters 5 and 6) and improvement of soil physical condition such as increased

SWC and decreased soil PR in Year 3. This favourable condition under HR possibly led

to improvement in root growth and thus increased yield of lentil and wheat in Year 3

(see Chapter 2). Similarly in the North China Plain, Mu et al. (2016) also found that

crop residue retention compared to residue removal resulted in improvement of SWC

and N status, and reduction in soil PR, leading to increased root mass density and crop

yield.

110

3.4.5 Above-ground shoot growth and yield influenced by rooting patterns

Although the root growth under ST and BP, with HR, was significantly greater

compared to CT and LR, the shoot growth was only higher in later years with BP and

HR. The better root growth deeper in the soil profile under ST was not associated with

shoot growth, however gradual improvement of root growth in ST increased yield in

Year 3 (see Chapter 2). The shoot growth is largely controlled by other limiting factors

such as differences in plant density, and other limitations on shoot growth that may

have masked the root response. For example, Busscher and Bauer (2003) and Moreno

et al. (1996) reported that root growth was reduced without affecting shoot growth

and yield in compacted soil. Guan et al. (2014) found that root growth was reduced in

compacted soil without affecting water and nutrient uptake.

3.5 Conclusion

The results of the three year’s study of these rice-based systems demonstrated that

over time improvement of root growth of cool dry season crops under ST and HR might

be associated with the gradual improvement of soil physical properties such as soil PR

and SWC. Further, over time unpuddled rice and ST with high residue cover gradually

reduced sub-soil compaction and improved SWC which enhanced root growth at

deeper soil profile compared to CT and LR. Hence, establishing rice by transplanting

into unpuddled soil resulted in positive effects on root growth of subsequent cool dry

season crops at deeper soil layers. The gradual improvement of soil physical properties

and root growth in the deeper soil profile probably resulted in increased crop yield. In

contrast, existing hard pan beneath the tilled layer at 10-15 cm soil depth as a result of

consistent use of puddling rice cultivation followed by CT restricted the root growth

relative to ST.

In the current study, there was better root growth in BP compared to other

treatments. This is probably associated with the root sampling only on the bed top

where loosened top soil was heaped thereby enhancing root growth. The bed top has

a unique opportunity to enhance root growth through the loosening and pulverizing of

soil in the seeding zone from the beginning of the experiment. However, increased

111

shoot growth in BP was not associated with improved root growth, probably other

limiting factors controlled shoot growth in the present study.

Although the results of these experiments are from an initial three years study, further

improvements of root growth with CA-based management, especially unpuddled rice

followed by ST with HR, are possible through ongoing changes in soil physical

properties, SOC and N. In this study, although the tillage and residue effects on root

growth of lentil and wheat disappeared beyond 20 cm soil depth, greater relative root

growth occurred in the deeper soil profile under ST which could facilitate greater

absorption of nutrients and water from the larger volume of soil profile. Therefore,

more detailed assessment of maximum rooting depth is important for assessing the

benefits of minimum tillage and increased residue on cool dry season crops in the long

term (~5-6 years) in rice-based systems on silty loam soils. Also, further long-term

experiments in rice-based system under different soil and environment conditions are

needed to evaluate the performance of ST and BP, and retention of HR, on root growth

of cool dry season crops following rice.

112

4 Effects of tillage and residue management on soil physical properties in

rice-based cropping systems in Bangladesh

4.1 Introduction

In Chapter 3, root growth of cool-dry season crops (lentil and wheat) under different

tillage and residue treatments was related to soil penetration resistance (PR), bulk

density (BD) and soil water content (SWC). The root growth data and related soil

physical properties such as the soil PR and SWC were assessed only at one growth

stage, near flowering. In brief, the main findings were as follows. The soil PR was lower

in BP and with HR compared to ST and CT, and with LR treatments at all depths of

measurement. The soil PR in Crop 7 clearly declined in ST and HR relative to CT and LR,

which suggests an improvement of soil structure and a weakening of the plough pan

over time under ST and HR treatment. Similarly by Crop 7, the SWC increased with

increasing soil depth with ST and BP, and with HR compared to CT and LR, likely due to

the improvement of infiltration rate and reduce evaporation under ST and BP, and HR.

The objective of the present Chapter was to investigate the temporal effects of

different tillage and residue management on several soil physical properties during

lentil and wheat crops within the two rice-based cropping systems.

4.2 Materials and methods

4.2.1 Treatments and crop management

Complete details of treatments and crop management procedure are given in Chapter

2. Briefly, tillage treatments consisted of strip tillage (ST), bed planting (BP) and

conventional tillage (CT) with two levels of residue applied – high (HR) and low residue

(LR) retained. The two rice-based cropping systems were studied during the cropping

seasons of 2010-11, 2011-12 and 2012-13, viz. lentil-mungbean-monsoon rice at

Alipur, Durgapur, Rajshahi and wheat-mungbean-monsoon rice at HBT, Digram,

Godagari, Rajshahi. Site and weather details are described in Chapter 2. During rice

establishment, soil was puddled in CT while unpuddled transplanting of rice (Haque et

113

al., 2016) was followed in ST and BP. In the cropping sequence, the crop numbers 1, 4

and 7 were lentil or wheat (winter crop), the crop numbers 2 and 5 were mungbean

and the crop numbers 3 and 6 were monsoon rice.

4.2.2 Soil bulk density

Soil bulk density at 0-5, 5-10 and 10-15 cm layers was determined using soil cores

(Black & Hartge, 1986). Briefly, three spots identified randomly per plot and then the

samples for each depth taken one above the other. The sample of BD were taken once

before crop sowing and again after harvest of cool dry season crops using a stainless

steel core sampler of volume 61.8 cm³. The soil BD was collected from: in between the

rows and in the rows of ST plots; at centre of the bed and in the furrow of BP; and

between the plants of the CT plots. In order to compare seed bed or field condition

under different treatments, ST — average value of in the strip (IS) and off the strip (OS)

were compared with centre of the bed and CT treatment in the present study.

However, the difference between OS vs IS and centre of the bed vs furrow of the bed

were also examined. The collected soil cores were trimmed to the exact volume of the

cylinder. Each soil core sample sealed in the aluminum box, weighed wet and then

dried in an oven at 110 °C for about 72 hours until constant weight; and then re-

weighed to determine the gravimetric SWC and the mass of dry soil per unit volume of

soil core to calculate the BD.

4.2.3 Soil temperature

Soil temperature was measured using Maxim’s i-Button temperature sensors (Haight,

2009). For each site, six i-Buttons (one for each treatment) were used for only

Replication-2. The soil temperature of the experimental site was most likely to be

homogenous for as the soils under a soil series are characterized by homogenous

number and kinds of soil horizons that have similar characteristics (Huq & Shoaib,

2013). Hence, the representative data from one replication was considered to be

sufficient to understand the treatment effects. Soil temperatures were recorded

continuously throughout the third growing season of the cool dry season crop, from

planting to harvesting, at two hours intervals at 5 cm soil depth. Temperature was

114

measured in between the plant row at center of the bed in BP, in the inter-row space

of ST and between the plants in CT plot.

4.2.4 Volumetric soil water content

Calibration of MP406 soil water probe meter for Alipur and Digram

A MP406 soil water probe (θprobe) (ICT international, Australia) was used to measure

the volumetric SWC. The SWC was measured at three random spots across the plot at

5 cm soil depth increments to 15 cm at both sites. The MP406 measures the soil

dielectric constant by frequency domain reflectometry (Vance 2013). The regression

equations for each site are:

Alipur, y = 1.49x- 25.2, r² = 0.58

Digram, y = 0.80x+2.01, r² = 0.78

Figure 4.1. Relationship between volumetric water content (SWC) (%) (calculated

from the gravimetric soil water content) and MP406 volumetric water content

(θprobe) (%) for the data collected at 5 cm increments down the soil profile collected

after 7 crops at Alipur (○,‐ ‐) and Digram (●,—) in 2012-13. The soil profile depth was

to 15 cm. Symbols are data points and the line represents the regression equation

shown above.

The dielectric constant is shown in millivolts (mV), and converted to SWC using an in-

built calibration. The gravimetric soil water contents were converted to SWC using BD

15

20

25

30

35

40

45

20 25 30 35 40 45

Gra

vim

etri

cally

cal

cula

ted

Volu

met

ric

wat

er c

onte

nt (%

)

MP406Volumetric water content (%)

115

(Cresswell & Hamilton, 2002). The pairs of data (n =72) comprising calculated SWC and

volumetric soil water content from the MP406 (θprobe) were used to construct

calibration curves, specific to the Alipur and Digram soils (Figure 4.1).

4.2.5 Soil penetration resistance

At the same time as volumetric soil water content (%) (SWC) measurement, soil PR was

measured with a field hand-held penetrometer (Eijkelkamp, the Netherlands). Five

measurements per plot were made at each depth (5 cm increments to 15 cm) for

computing the average soil PR. Based on soil strength, cone number 1 (diameter: 11.28

mm; base area: 1 cm²) or 2 (diameter: 15.96 mm; base area: 2 cm²) of the

penetrometer were used and calculated soil PR as per manual of Eijkelkamp

penetrometer. In the ST treatment, the measurements of soil BD, SWC and soil PR

were in the tilled strip (IS) as well as the inter-row space between the strip or off the

strip (OS) of ST (Figure 4.2). Measurements were between the plants in the CT plot and

in the furrow and on centre of the bed of BP (Figure 4.3).

Figure 4.2. Schematic diagram of strip tillage plot showing the location of

measurements of soil water content and penetration resistance in between the

strips (closed black circle) and in the strip (open black circle) in a strip-tillage plot.

116

4.2.6 Sampling time and location

The SWC, soil PR and soil BD samples were collected every year at the end of the rice

season (when the soil was nearly at field capacity condition) and again after harvest of

the winter crop (lentil/wheat) during 2010-11, 2011-12 and 2012-13. These

measurements were also taken before starting of the experiment in 2010 as baseline

information and after the first, third, fourth, sixth and seventh cropping seasons in the

sequence (after lentil/wheat and rice). The SWC and soil PR was measured at dry

condition after the dryland crop (Crop 1 and 4). After the rice crop (Initial, Crop 3 and

6), the wet soil was drained until it reached field capacity before the measurement of

SWC and soil PR. After Crop 7, the soil was pre-wetted, and then drained until it

reached field capacity before taking SWC and PR measurements. Also the trend of SWC

and soil PR after planting of winter crops — lentil and wheat, were measured at five

days interval until 35 days after sowing (DAS) in the third growing season. In order to

compare seed bed conditions under different treatments, average values of in the strip

(IS) and off the strip (OS) for ST were compared with centre of the bed and CT.

However, the comparisons between OS (off-the strip) and IS (in the strip); and

between BT (from the bed top) and BF (from the level of the bed top in the furrow) of

the bed planting (BP) system were also examined. In case of BP system, soil samples

were taken at 5 cm increments to a depth of 15 cm considering the bed top as 0 (zero)

cm when sampling in either bed or furrow. Thus there was no value of 0-5 cm

increment sampled in the furrow as the furrow is 5 cm deep. Mention that since the

formation of bed, the bed height and furrow depth lessened over time after all crops

and it is noteworthy after rice crop. Finally after rice and non-rice crop when the soil

was settled on bed, the actual depth of furrow was about 5 cm from the level of the

bed top. Therefore, the top level of furrow from the level of the bed top was

considered as 0-5 cm depth, which is gap.

The above procedures in relation to sampling location of ST and BP also followed for

the measurement of soil carbon concentrations (see Chapter 5) and soil total nitrogen

concentrations (see Chapter 6).

117

Figure 4.3. Schematic diagram of the newly formed bed. The blue circles indicates the

sampling spot of centre of the bed (closed symbol) and furrow of the bed (open

symbol) for soil moisture, soil penetration resistance and bulk density

measurements.


Data were analysed separately for lentil and wheat each year using GenStat 15th

Edition. Mean values were calculated for each set of measurements at each depth, and

analysis of variance (ANOVA) for a split-plot (main plot: tillage and sub-plot: residue)

and split-split-plot (main plot: tillage, sub-plot: residue and sub-sub-plot: cropping

cycle) were employed to assess treatment effects on the measured variables. When

the F-test was significant, treatment means were separated by least significant

difference (LSD) at P≤0.05.

4.3 Results

The main effects of tillage and residue levels on SWC, soil BD and PR are presented in

this chapter. As the interaction effects of tillage and residue on SWC, soil PR and BD

were not significant for most of the times of measurement, the significant interaction

effects of tillage and residue are presented.

4.3.1 Alipur

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4.3.1.1 Soil bulk density

4.3.1.1.1 Tillage effects

The soil BD was measured at three different depths (0-5 cm, 5-10 cm and 10-15 cm) at

different times during the study period including initial (before starting of the

experiment), after rice (after Crop 3 and 6) and after the non-rice winter crop (after

Crop 1, 4 and 7) (Figure 4.4). Across all sampling dates, the soil BD increased with

increasing soil depth (Figure 4.4). The tillage impact on soil BD at 0-5 cm depth became

apparent after Crops 4 and 6. The soil BD of ST (1.36 g/cc) and CT (1.37 g/cc) were

significantly lower than that of BP (1.45 g/cc) after Crop 4. After Crop 6, the BD of ST

(1.37 g/cc) was significantly lower than that of CT and BP (1.44 g/cc). At 5-10 cm soil

depth, ST and CT (1.52 g/cc) had greater BD than BP (1.48 g/cc). At 10-15 cm soil

depth, the soil BD of ST (1.71, 1.76, 1.64 and 1.64 g/cc) and CT (1.76, 1.74, 1.64 and

1.65 g/cc) were significantly higher than that of BP (1.64, 1.61, 1.55 and 1.54 g/cc)

after Crop 1, 3, 6 and 7, respectively (Figure 4.4).

At 0-5 cm soil depth, the interaction of tillage and cropping cycles on BD was

significant (P≤0.05, LSD 0.039), whilst it was not significant at 5-10 cm and 10-15 cm

soil depth (Figure 4.4). At 0-5 cm soil depth, the soil BD of ST and BP (1.36 g/cc) was

lower after Crop 7 than the initial BD value (1.54 g/cc). However, the soil BD of BP

treatment fluctuated between formation of the new bed (initial), the permanent bed

(after rice, after Crop 3 and 6) and after reshaping of the bed (after Crop 4 and 7)

(Figure 4.4).

119

Figure 4.4. Tillage effects on soil bulk density over cropping cycles-initially and after

Crops 1, 3, 4, 6 and 7 at 0-5 cm, 5-10 cm and 10-15 cm soil depths in Alipur. Values

are means across residue levels. The error bars for each data point represents ± 1

standard error. The floating error bars on the figure at each depth represent the least

significant difference (LSD) at P≤0.05 for tillage after each crop (T) and interaction

between tillage and cropping cycle (TXCC).

4.3.1.1.2 Residue effects

At 0-5 cm soil depth, the soil BD under HR (1.43, 1.41 and 1.35 g/cc) was lower than

under LR (1.45, 1.43 and 1.38 g/cc) after Crop 3, 6 and 7, respectively (Figure 4.5). At 5-

Bu

lk d

en

sity

(g/

cc)

1.20

1.35

1.50

1.65

1.80

0 1 2 3 4 5 6 7 8

Cropping Cycle

0-5 cm ST BP CT

After 1 C After 3 C After 4 C After 6 C After 7 CInitial

T TT X CC

1.20

1.35

1.50

1.65

1.80

0 1 2 3 4 5 6 7 8

5-10 cmT

1.20

1.35

1.50

1.65

1.80

0 1 2 3 4 5 6 7 8

Cropping Cycle

10-15 cm


TT TT

120

10 cm soil depth, the soil BD under HR (1.50 g/cc) was lower than under LR (1.52 g/cc)

only after Crop 7. At 10-15 cm soil depth, residue effects on soil BD were not apparent

across all treatments after all crops (Figure 4.5).

Figure 4.5. Residue effects on soil bulk density after different cropping cycles ̶ initially

and after Crops 1, 3, 4, 6 and 7 at 0-5 cm, 5-10 cm and 10-15 cm soil depths in Alipur.

Values are means across tillage treatments. The error bars for each data point

represents ± 1 standard error. The floating error bars on the figure at each depth

represent the least significant difference (LSD) at P≤0.05 for residue after each crop.

1.20

1.35

1.50

1.65

1.80

0 1 2 3 4 5 6 7 8

Cropping Cycle

0-5 cm HR LR


Bu

lk d

en

sity

(g/

cc)

1.20

1.35

1.50

1.65

1.80

0 1 2 3 4 5 6 7 8

Cropping Cycle

5-10 cm


1.20

1.35

1.50

1.65

1.80

0 1 2 3 4 5 6 7 8

Cropping Cycle

10-15 cm HR LR


121

The interaction effects of tillage and residue management were not significant across

all depth of measurements except at 10-15 cm soil depth (Table 4.1).

Table 4.1. Soil bulk density (g/cc) at three different depths (0-5 cm, 5-10 cm and 10-

15 cm) under tillage and residue after different crop in legume-dominated system in

Alipur.

Tillage1

Residue1 Cropping cycles LSD20.05

Initial After

Crop 1

After

Crop 3

After

Crop 4

After

Crop 6

After

Crop 7

Tillage

(T)

Residue

(R)

TXR

Soil depth (0-5 cm)

ST HR

1.54

1.45 1.40 1.35 1.37 1.35

ns 0.01** ns

LR 1.42 1.40 1.37 1.38 1.37

BP HR 1.34 1.45 1.46 1.43 1.35

LR 1.40 1.48 1.44 1.45 1.38

CT HR 1.42 1.43 1.36 1.43 1.36

LR 1.44 1.48 1.37 1.46 1.40

Soil depth (5-10 cm)

ST HR

1.58

1.61 1.57 1.53 1.52 1.51

ns ns ns

LR 1.57 1.56 1.52 1.52 1.53

BP HR 1.51 1.53 1.48 1.49 1.47

LR 1.60 1.53 1.49 1.50 1.49

CT HR 1.56 1.53 1.51 1.51 1.51

LR 1.64 1.58 1.54 1.51 1.53

Soil depth (10-15 cm)

ST HR

1.70

1.72 1.80 1.68 1.64 1.65

LR 1.71 1.73 1.67 1.64 1.64

BP HR 1.59 1.62 1.60 1.53 1.54 0.1** ns 0.1*

LR 1.69 1.59 1.57 1.56 1.54

CT HR 1.73 1.72 1.65 1.63 1.64

LR 1.80 1.77 1.74 1.65 1.66




After Crop 1 and 3, the soil BD was higher with ST and CT (1.7-1.8 g/cc) and lower with BP

after Crop 4, 6 and 7 (1.5-1.6 g/cc) (Table 4.1).

122

4.3.1.2. Volumetric soil water content and penetration resistance


The effects of tillage on soil water content (SWC) and penetration resistance (PR) after

different crops are presented in Figure 4.6. The SWC was not affected due to tillage

after Crop 3 to 6 at 0-5 cm soil depth and from Crop 1 to 4 at 5-10 cm and 10-15 cm

soil depth (Figure 4.6). After Crop 1, the SWC at 0-5 cm soil depth of BP (11.1 %) was

significantly (P≤0.05) lower than that of CT (13.6 %) and ST (13.2 %) (Figure 4.6).

Similarly, the SWC at 0-5 cm soil depth of BP (33.9 %) was significantly (P≤0.05) lower

than that of CT (35.7 %) and ST (34.6 %) after Crop 7 (Figure 4.6). By contrast, the SWC

at 5-10 cm soil depth of BP (33 and 34 %) was higher than that of ST (31 and 33 %) and

CT (32 and 33 %) after Crop 6 and 7, respectively (Figure 4.6). Similarly, the SWC at 10-

15 cm soil depth of BP (31.7 % and 33.8 %) was higher than that of CT (29.2 and 31.5

%) and ST (29.0 and 32.3 %) after Crop 6 and 7, respectively (Figure 4.6).

After Crop 1, the soil PR at 0-5 cm soil depth of BP (1.2 MPa) was significantly (P≤0.05)

lower than that of CT (2.9 MPa) and ST (2.4 MPa) (Figure 4.6). After Crop 7, however

the soil PR at 0-5 cm soil depth of BP (0.46 MPa) and CT (0.45 MPa) were significantly

(P≤0.05) lower than that of ST (0.51 MPa) (Figure 4.6). At 5-10 cm soil depth, the soil

PR of BP (3.1, 0.7 and 1.0 MPa) was lower than that of CT (7.0, 1.2 and 1.3 MPa) and ST

(5.1, 1.3 and 1.3 MPa) after Crop 1, 6 and 7, respectively (Figure 4.6). Similarly, the soil

PRs measured at 10-15 cm depth were 1.9 and 1.8 MPa in BP, which was significantly

(P≤0.05) lower than 2.5 and 2.4 MPa in CT and 2.4 and 2.1 MPa in ST after Crop 6 and

7, respectively (Figure 4.6).

123

Figure 4.6. Dynamic changes of volumetric soil water content (%) and penetration

resistance (MPa) due to tillage after Crops 1, 3, 4, 6 and 7 at 0-5 cm, 5-10 cm and 10-

15 cm soil depths in Alipur. Values are means across residue levels. Error bars were ±

1 standard error of the mean and floating bar indicates significant difference at

P≤0.05 level between treatments on that time of measurement.


The effects of residue on SWC and soil PR after different crops are presented in Figure

4.7. The SWC at 0-5 cm soil depth was 36.9, 36.1 and 35.0 % in HR treatments which

were significantly (P≤0.05) higher than 35.7, 35.3 and 34.5 % in LR treatments after

0

9

18

27

36

45

After Crop 1 After Crop 3 After Crop 4 After Crop 6

0-5 cm ST BP CT

0

9

18

27

36

45

After Crop 1 After Crop 3 After Crop 4 After Crop 6 After Crop 7

5-10 cm

0

9

18

27

36

45


Cropping Cycle

10-15 cm

After 1 C After 3 C After 4 C After 6 C After 7 C

Vo

lum

etr

ic s

oil

wat

er

con

ten

t (%

)

0

2

4

6

8

10


0-5 cm ST BP CT

0

2

4

6

8

10


5-10 cm

0

2

4

6

8

10


Cropping cycle

10-15 cm


Soil

pe

ne

trat

ion

re

sist

ance

(M

Pa

)

124

Crop 1, 6 and 7, respectively (Figure 4.7). The SWC at 5-10 cm soil and 10-15 cm soil

depth with HR (14.7 and 18.0 %) was greater compared to LR (12.7 and 16.2 %) after

Crop 4 (Figure 4.7).

The soil PR at 0-5 cm soil depth of HR (0.5, 4.5, 0.4 MPa) was lower than that of LR (0.6,

5.3 and 0.5 MPa) after Crop 3, 4 and 7, respectively (Figure 4.7). At 5-10 cm soil depth,

the soil PR under HR (7.0 and 1.1 MPa) was lower than under LR (8.0 and 1.2 MPa)

after Crop 4 and 7, respectively (Figure 4.7). The soil was too dry to insert the

penetrometer at 10-15 cm soil depth, hence the measurement of soil PR were not

taken after Crop 1 and 4 (Figure 4.7). However, the soil PR with HR was lower (2.0

MPa) as compared to LR (2.2 MPa) after Crop 7 at 10-15 cm soil depth (Figure 4.7).


resistance (MPa) due to residue after Crops 1, 3, 4, 6 and 7 at 0-5 cm, 5-10 cm and 10-

Soil

pe

ne

trat

ion

re

sist

ance

(M

Pa

)

0

2

4

6

8

10


0-5 cm HR LR

0

2

4

6

8

10


5-10 cm

0

2

4

6

8

10


Cropping cycle

10-15 cm


Vo

lum

etr

ic s

oil

wat

er

con

ten

t (%

)

0

9

18

27

36

45


0-5 cm HR LR

0

9

18

27

36

45


5-10 cm

0

9

18

27

36

45


Cropping Cycle

10-15 cm


125

15 cm soil depths in Digram. Values are means across tillage treatments. Error bars

were ± 1 standard error of the mean and floating error bars indicate significant

difference at P≤0.05 level between treatments on that time of measurement.

4.3.1.3 Trends of volumetric soil water content and penetration resistance following

planting of lentil


Across all treatments, the SWC continued to decrease and soil PR increase at all depths

of measurement from sowing to 35 DAS in Crop 7 (Figure 4.8a1-a3 and Figure 4.8b1-

b3). The BP and ST had significantly higher surface SWC (0-5 cm) than that of CT at all

depths of measurement (Figure 4.8a1). At 5-10 cm depth, there was no effect of tillage

treatments on SWC except at 35 DAS, which was the last reading taken. The SWC of BP

(24 %) and ST (25 %) were significantly (P≤0.01) lower than that of CT (26 %) at 35 DAS

(Figure 4.8a2). At 10-15 cm soil depth, the SWC of BP and ST (30 %) was significantly

higher than that of CT (29 %) at 5 DAS and 10 DAS (Figure 4.8a3). After 10 DAS when

10-15 cm soil depth was monitored, no differences of SWC were seen for tillage

treatments (Figure 4.8a3).

The soil PR values at 0-5 cm soil depth of different tillage types followed the order of

ST = BP>CT while the order was CT>ST>BP at 5-10 cm and 10-15 cm soil depths (Figure

4.8b1-b3). The soil PR of CT (1.3, 1.3 and 1.6 MPa) was lower compared to BP (1.5, 1.5

and 1.8 MPa) and ST (1.6, 1.6 and 1.8 MPa) at 20, 25 and 30 DAS, respectively at

surface soil (0-5 cm depth) (Figure 4.8b1). However, the soil PR was consistently and

significantly greater with CT while lower with ST and BP in all days of measurement at

5-10 cm and 10-15 cm soil depth (Figure 4.8b2-b3).

126


resistance (b1-b3) at 0-5 cm, 5-10 cm and 10-15 cm soil depths for different tillage

treatments at 5 days after sowing (DAS) to 35 DAS during lentil planting in 2013 in

Alipur. Values are means across residue levels. Floating error bars indicate the least

significant difference (LSD) at P≤0.05, for the effects of tillage on that dates of

measurement and error bars indicate ± 1 standard error of the mean.


Following sowing of lentil (Crop 7), the SWC continued to decrease while soil PR

increase irrespective of tillage treatments (Figure 4.9). The SWC of HR was consistently

and significantly greater than that of LR at 0-5 cm soil depth at all dates of

Soil

pe

ne

trat

ion

re

sist

ance

(M

Pa

)

0.0

1.5

3.0

4.5

6.0

5 10 15 20 25 30 35

b1) 0-5 cm ST BP CT

0.0

1.5

3.0

4.5

6.0

5 10 15 20 25 30 35

b2) 5-10 cm

0.0

1.5

3.0

4.5

6.0

5 10 15 20 25 30 35

Days after sowing

b3) 10-15 cm

Vo

lum

etr

ic s

oil

wat

er

con

ten

t (%

)

0

8

16

24

32

40

5 10 15 20 25 30 35

a1) 0-5 cm ST BP CT

0

8

16

24

32

40

5 10 15 20 25 30 35

a2) 5-10 cm

0

8

16

24

32

40

5 10 15 20 25 30 35

Days after sowing

a3) 10-15 cm

127

measurement except at 35 DAS (Figure 4.9a1). Similarly, the SWC at 5-10 cm soil depth

under HR was significantly greater than under LR at all dates of measurement except at

30 and 35 DAS (Figure 4.9a2). At 10-15 cm soil depth, the SWC under HR was also

greater compared to SWC under LR only at 5, 20, 30 and 35 DAS but treatment

differences were absent for all other measurements (Figure 4.9a3).

At 0-5 and 5-10 cm soil depth, the soil PR under HR was significantly lower than under

LR at all dates of measurement except at 35 DAS (Figure 4.9b1 and b2). Similarly, at 10-

15 cm soil depth, except at 10 DAS, the soil PR under HR was significantly lower than

under LR at all dates of measurement (Figure 4.9b3).


resistance (b1-b3) at 0-5 cm, 5-10 cm and 10-15 cm soil depths for different residue

treatments at 5 days after sowing (DAS) to 35 DAS during lentil planting in 2013 in

Soil

pe

ne

trat

ion

re

sist

ance

(M

Pa

)

0.0

1.5

3.0

4.5

6.0

5 10 15 20 25 30 35

b1) 0-5 cm HR LR

0.0

1.5

3.0

4.5

6.0

5 10 15 20 25 30 35

b2) 5-10 cm

0.0

1.5

3.0

4.5

6.0

5 10 15 20 25 30 35

Days after sowing

b3) 10-15 cm

Vo

lum

etr

ic s

oil

wat

er

con

ten

t (%

)

0

8

16

24

32

40

5 10 15 20 25 30 35

a1) 0-5 cm HR LR

0

8

16

24

32

40

5 10 15 20 25 30 35

a2) 5-10 cm

0

8

16

24

32

40

5 10 15 20 25 30 35

Days after sowing

a3) 10-15 cm

128

Alipur. Values are means across tillage treatments. Floating error bars indicate the

least significant difference (LSD) at P≤0.05, for the effects of tillage on that date of

measurement and error bars indicates ± 1 standard error of the mean.

4.3.1.4 Relationship between soil physical properties

4.3.1.4.1 Relation between soil bulk density and penetration resistance

The soil PR increased with an increase in soil BD after all crops in Alipur (Figure 4.10a-

e.).

Figure 4.10. Relationship between soil penetration resistance (MPa) and bulk density

(g/cc) after different crop determined: a) after Crop 1; b) after Crop 3; c) after Crop 4;

y = 13.67x - 16.85 R² = 0.52

0

2

4

6

8

10

1.0 1.2 1.4 1.6 1.8 2.0

Pe

ne

tra

tio

n r

esi

sta

nce

(M

Pa

)

Bulk density (g/cc)

a) y = 6.11x - 8.16

R² = 0.74

0

2

4

6

8

10

1.0 1.2 1.4 1.6 1.8 2.0

Pe

ne

tra

tio

n r

esi

sta

nce

(M

Pa

)

Bulk density (g/cc)

b)

y = 12.72x - 12.26 R² = 0.48

0

2

4

6

8

10

1.0 1.2 1.4 1.6 1.8 2.0

Pe

ne

tra

tio

n r

esi

sta

nce

(M

Pa

)

Bulk density (g/cc)

c) y = 5.40x - 6.85

R² = 0.60

0

2

4

6

8

10

1.0 1.2 1.4 1.6 1.8 2.0

Pe

ne

tra

tio

n r

esi

sta

nce

(M

Pa

)

Bulk density (g/cc)

d)

y = 6.05x - 7.80 R² = 0.88

0

2

4

6

8

10

1.0 1.2 1.4 1.6 1.8 2.0

Pe

ne

tra

tio

n r

esi

sta

nce

(M

Pa

)

Bulk density (g/cc)

e)

129

d) after Crop 6; e) after Crop 7 during 2010-13 in Alipur. Values are for all three

depths (0-5 cm, 5-10 cm and 10-15 cm). The line represents the regression equation

shown above in the graph.

4.3.1.5 Depth distribution of soil physical parameters at bed planting and strip tillage

system in Alipur

4.3.1.5.1 Distribution of soil bulk density

The soil BD was not significantly different due to the sampling position from bed top

(BT) and from the level of the bed top in the furrow (BF) of the BP except at 5-10 cm

soil depth, where the soil BD of BT was higher than that of BF (Figure 4.11a).

Figure 4.11.Variation of soil bulk density after Crop 7 in Alipur relative to depth from

the bed top (BT) and from the level of the bed top in the furrow (BF) of the bed

planting system (a); and in the strip (IS) and off-the strip (OS) of strip tillage system

(b). For comparison, initial values (before starting the experiment) are also shown.

Floating error bars indicate the least significant difference (LSD) at P≤0.05, for the

effects of sampling positions.

The soil BD was not significantly different between measurements taken in the strip

(IS) and off-the strip (OS) except at 10-15 cm soil depth, where the IS value was higher

than that of OS of ST (Figure 4.11b).

0.0

0.4

0.8

1.2

1.6

2.0

0-5 cm 5-10 cm 10-15 cm

Bu

lk d

en

sit

y (

g/cc)

Sampling position and depth

Initial BT BF

BT vs BF

a)

0.0

0.4

0.8

1.2

1.6

2.0

0-5 cm 5-10 cm 10-15 cm

Bu

lk d

en

sit

y (

g/cc)


Initial OS IS

OS vs IS

b)

130

4.3.1.5.2 Distribution of volumetric soil water content

After Crop 7, the variation of SWC found similar between BT and BF at all depths of

study (Figure 4.12a). The SWC for IS and OS in the ST exhibited significant difference

only in the depth of 5-10 cm, where the SWC of OS was higher than IS (Figure 4.12b).

Figure 4.12. Variation of volumetric soil water content (%) after Crop 7 in Alipur

relative to depth from the bed top (BT) and from the level of the bed top in the

furrow (BF) of the bed planting system (a); and in the strip (IS) and off-the strip (OS)

of strip tillage system (b). For comparison, initial values (before starting the

experiment) are also shown. Floating error bars indicate the least significant

difference (LSD) at P≤0.05, for the effects of sampling positions.

4.3.1.5.3 Distribution of soil penetration resistance

Regardless of treatment, the soil PR increased with increasing soil depth for all

measurements (Figure 4.13a and 4.13b). The soil PR of BT was higher than that of BF in

5-10 cm and 10-15 cm soil depths (Figure 4.13a). The differences of soil PR due to

sampling position of ST were not significant in 0-5 cm and 10-15 cm soil depths. The

soil PR of OS was significantly lower than that of IS in the 5-10 cm depth (Figure 4.13b).

0

8

16

24

32

40

0-5 cm 5-10 cm 10-15 cm

Vo

lum

etric

so

il w

ate

r c

on

te

nt (

%)


Initial BT BFa)

0

8

16

24

32

40

0-5 cm 5-10 cm 10-15 cm

Vo

lum

etric

so

il w

ate

r c

on

te

nt (

%)


Initial OS IS

OS vs IS b)

131

Figure 4.13.Variation of soil penetration resistance (MPa) after Crop 7 in Alipur






4.3.1.6 Soil temperature at Alipur

The trend in soil temperature with time at 0-5 cm soil depth was little affected by

tillage and residue management during the lentil season in 2012-13 (Figure 4.14a and

4.14b). Generally, the soil temperature decreased with days after sowing irrespective

of treatments and minimum values were measured at 56-63 DAS during both day and

night (Figure 4.14a and 4.14b). Afterwards, the temperatures tended to rise and

reached a peak at 120-127 DAS, the end of measurement. The maximum soil

temperature at day and minimum at night varied between 25.8 and 24.0 °C and 12.6 to

13.7 °C, respectively. The day soil temperature was generally higher in CT and BPLR

and lower in BPHR than other treatments at all dates of measurement except at 88-95

and 96-103 DAS (Figure 4.14a). When the temperature was monitored at 88-95 and

96-103 DAS, the maximum soil temperature was recorded in STLR. Afterwards when

the atmospheric temperature started to warm the soil temperature remained lower

with STHR up to 127 DAS (Figure 4.14a). However, the maximum (23.3 °C) and

0

1

2

3

4

5

0-5 cm 5-10 cm 10-15 cm

So

il p

en

etra

tio

n r

esis

ta

nce

(M

Pa

)


Initial OS IS

OS vs IS b)

0

1

2

3

4

5

0-5 cm 5-10 cm 10-15 cm

So

il p

en

etra

tio

n r

esis

ta

nce

(M

Pa

)


Initial BT BF

BT vs BF BT vs BF

a)

132

minimum night soil temperature (13.7 °C) of BPLR was higher at all dates of

measurement. While the night soil temperature of BPLR was higher than other

treatments from beginning to 103 DAS (Figure 4.14b). When the soil temperature

reached at peak across all treatments at 112-119 DAS (19.9 °C) and 120-127 DAS (22.3

°C), the lowest soil temperatures were recorded from STHR treatment (19.9 °C and

22.3 °C) compared to other treatments (Figure 4.14b).

Figure 4.14. The variation of mean soil day (a) and night soil temperature (°C) (b) due

to different treatments during wheat growing season at Alipur in 2012-13. Values are

means of seven day intervals.

10

15

20

25

30

1-7

8-1

5

16

-23

24

-31

32

-39

40

-47

48

-55

56

-63

64

-71

72

-79

80

-87

88

-95

96

-10

3

10

4-1

11

11

2-1

19

12

0-1

27

Days after sowing

Day temperature (°C)

ST HR BP HR CT HR

ST LR BP LR CT LR

10

15

20

25

30

1-7

8-1

5

16

-23

24

-31

32

-39

40

-47

48

-55

56

-63

64

-71

72

-79

80

-87

88

-95

96

-10

3

10

4-1

11

11

2-1

19

12

0-1

27

Days after sowing

Night temperature (°C)

Me

an s

oil

tem

pe

ratu

re (

°C)

b)

a)

133

4.3.2 Digram

4.3.2.1 Soil bulk density at different depth


The soil BD increased with increasing soil depth across all sampling dates (Figure 4.15).

The tillage impact on soil BD became visible after Crop 1 and tillage effects were

significantly different after Crop 1, 4 and 6 at 0-5 cm depth (Figure 4.15). At 0-5 cm soil

depth, the soil BD of ST (1.36 g/cc) was significantly lower than that of CT (1.43 g/cc)

and BP (1.40 g/cc) after Crop 1. The soil BD of ST (1.23 g/cc) was significantly lower

than that of BP (1.35 g/cc) and CT (1.29 g/cc) after Crop 4. After Crop 6, the soil BD of

ST (1.23 g/cc) was significantly lower than that of BP (1.29 g/cc) and CT (1.30 g/cc)

(Figure 4.15). At 5-10 cm depth, the soil BD (1.46 and 1.36 g/cc) of BP was lower than

CT (1.51 and 1.42 g/cc) and ST (1.50 and 1.40 g/cc) after Crop 1 and 6, respectively

(Figure 4.15). At 10-15 cm soil depth, the soil BD of BP (1.57, 1.51, 1.46 and 1.43 g/cc)

was significantly (P≤0.05) lower than the BD of CT (1.69, 1.59, 1.59 and 1.59 g/cc) and

ST (1.63, 1.60, 1.58 and 1.54 g/cc) (Figure 4.15).

The interaction between tillage and cropping cycles on soil BD was significant at 0-5 cm

soil depth (P≤0.05, LSD 0.029) and at 10-15 cm soil depth (P≤0.05, LSD 0.065) (Figure

4.15). At 0-5 cm soil depth, the soil BD of ST was lowest after Crop 6 (1.23 g/cc) while

the highest soil BD was measured after Crop 1 with CT (1.43 g/cc). At 10-15 cm soil

depth, the lowest soil BD was measured in BP (1.43 g/cc) after Crop 6 and the highest

BD measured after Crop 1 in CT (1.69 g/cc) (Figure 4.15).

134

Figure 4.15. Tillage effects on soil bulk density over cropping cycles - initially and

after Crops 1, 3, 4 and 6 at 0-5 cm, 5-10 cm and 10-15 cm soil depths in Digram.

Values are means across residue levels. The error bars for each data point represents

± 1 standard error. The floating error bars on figure at each depth represent the least

significant difference (LSD) at P≤0.05 for tillage after each crop (T) and interaction

between tillage and cropping cycles (TXCC).


The soil BD under HR (1.25 and 1.38 g/cc) was lower than that under LR (1.30 and 1.40

g/cc) at 0-5 cm and 5-10 cm soil depth, respectively (Figure 4.16). However, the

1.20

1.35

1.50

1.65

1.80

0 1 2 3 4 5 6 7

0-5 cm ST BP CT

T X CCT T T

1.20

1.35

1.50

1.65

1.80

0 1 2 3 4 5 6 7

5-10 cm

T T

1.20

1.35

1.50

1.65

1.80

0 1 2 3 4 5 6 7

Cropping Cycle

10-15 cm

After 1 C After 3 C After 4 C After 6 CInitial

T TXCC TT T

Bu

lk d

en

sity

(g/

cc)

135

residue effects on soil BD were absent after all crops at 10-15 cm soil depth (Figure

4.16).


initially and after Crops 1, 3, 4 and 6 at 0-5 cm, 5-10 cm and 10-15 cm soil depths in

Digram. Values are means across tillage treatments. The error bars for each data

point represents ± 1 standard error. The floating error bars on figure at each depth

represent the least significant difference (LSD) at P≤0.05 for residue after each crop.

4.3.2.2 Volumetric soil water content and penetration resistance

1.20

1.35

1.50

1.65

1.80

0 1 2 3 4 5 6 7

0-5 cm HR LR

1.20

1.35

1.50

1.65

1.80

0 1 2 3 4 5 6 7

5-10 cm

1.20

1.35

1.50

1.65

1.80

0 1 2 3 4 5 6 7

Cropping Cycle

10-15 cm

After 1 C After 3 C After 4 C After 6 CInitial

Bu

lk d

en

sity

(g/

cc)

136


Figure 4.17 shows the dynamic nature of SWC and soil PR at 0-5, 5-10, 10-15 cm soil

depth under different tillage systems after different crops. When the soil depth of 0-5

cm was monitored, no differences in SWC were seen for tillage treatments over time

(Figure 4.17). At 5-10 cm, the SWC of BP (21 % and 17 %) was significantly (P≤0.05)

lower than that of CT (25 % and 20 %) and ST (23 % and 19 %) after Crop 1 and 4

(Figure 4.17), whilst after Crop 6 and 7, the SWC of BP (36 % and 33 %) was

significantly higher than that of CT (33 % and 31 %) and ST (34 % and 30 %) (Figure

4.17). At 10-15 cm soil depth, the SWC of BP (31.6 % and 32.5 %) and ST (30.0 % and

30.3 %) was higher than that of CT (28.4 % and 29.8 %) after Crop 6 and 7 (Figure 4.17).

At 0-5 cm soil depth, the soil PR of BP (0.7 MPa) was significantly lower than that of ST

(1.3 MPa) and CT (1.6 MPa) after Crop 1 (Figure 4.17). However, the soil PR of BP (2.0

MPa) was significantly higher than that of ST (1.6 MPa) and CT (1.5 MPa) after Crop 4

(Figure 4.17). After Crop 7, the soil PR of BP (0.6 MPa) and ST (0.7 MPa) was lower than

that of CT (0.9 MPa) at 0-5 cm soil depth (Figure 4.17). At 5-10 and 10-15 cm soil

depths, the soil PR of BP was consistently lower than that of CT and ST after all crops

and the order of soil PR was CT>ST>BP (Figure 4.17). At 5-10 soil depth, the soil PR was

lower with BP (1.5, 2.3, 3.7, 0.6 and 0.8 MPa) and higher with CT (4.4, 3.9, 5.3, 1.4 and

1.8 MPa) after Crop 1, 3, 4, 6 and 7, respectively (Figure 4.17). The soil PR of ST (2.6,

0.9 and 1.5 MPa) was lower than that of CT (4.4, 1.4 and 1.8 MPa) after Crop 1, 6 and 7

(Figure 4.17). Similarly, at 10-15 cm soil depth, the soil PR of BP (5.9, 6.4, 6.9, 2.1 and

1.8 MPa) was lower than that of CT (10.0, 8.3, 7.8, 4.1 and 3.2 MPa) and ST (8.3, 7.4,

7.3, 2.6 and 2.7 MPa) (Figure 4.17). The soil PR of ST was also significantly lower than

that of CT at 10-15 cm soil depths (Figure 4.17).

137


resistance (MPa) due to tillage after Crops 1, 3, 4, 6 and 7 at 0-5 cm, 5-10 cm and 10-

15 cm soil depths in Digram. Values are means across residue levels. Error bars were

± 1 standard error of the mean and floating error bar indicates significant difference

at P≤0.05 level between treatments on that time of measurement.


At 0-5 cm soil depth, the SWC of HR (19, 41 and 33 %) was 1-2 % higher than that of LR

(17, 40 and 31 %) after Crop 4, 6 and 7, respectively (Figure 4.18). At 5-10 cm soil

depth, the SWC of HR (23.3 and 32.0 %) was higher as compared to LR (22.8 and 30.9

%) after Crop 1 and 7 (Figure 4.18). At 10-15 cm soil depth, there were no differences

in SWC due to residue treatments after all crops (Figure 4.18).

Soil

pe

ne

trat

ion

re

sist

ance

(M

Pa)

0

2

4

6

8

10


0-5 cm ST BP CT

0

2

4

6

8

10


5-10 cm

0

2

4

6

8

10


Cropping cycle

10-15 cm


Vo

lum

etr

ic s

oil

wat

er

con

ten

t (%

)

0

9

18

27

36

45

After Crop 1 After Crop 3 After Crop 4 After Crop 6

0-5 cm ST BP CT

0

9

18

27

36

45


5-10 cm

0

9

18

27

36

45


Cropping Cycle

10-15 cm


138


resistance (MPa) due to residue after Crops 1, 3, 4, 6 and 7 at 0-5 cm, 5-10 cm and 10-

15 cm soil depths in Digram. Values are means across tillage treatments. Error bars

were ± 1 standard error of the mean and floating error bar indicates significant

difference at P≤0.05 level between treatments on that time of measurement.

At 0-5 cm soil depth, the soil PR of HR (1.4 MPa) was significantly (P≤0.05) higher than

that of LR (1.0 MPa) after Crop 1 but the soil PR of HR (0.7 MPa) was lower than that of

LR (0.8 MPa) after Crop 7 (Figure 4.18). At 5-10 cm soil depth, the soil PR of HR (3.2

MPa) was significantly (P≤0.05) higher than that of LR (2.5 MPa) after Crop 1 (Figure

4.18). At 10-15 cm soil depth, residue effects on soil PR disappeared at all sampling

times (Figure 4.18).

0

2

4

6

8

10


0-5 cm HR LR

0

2

4

6

8

10


5-10 cm

0

2

4

6

8

10


Cropping cycle

10-15 cm


Soil

pe

ne

trat

ion

re

sist

ance

(M

Pa)

0

9

18

27

36

45


0-5 cm HR LR

0

9

18

27

36

45


5-10 cm

0

9

18

27

36

45


Cropping Cycle

10-15 cm


Vo

lum

etr

ic s

oil

wat

er

con

ten

t (%

)

139

Table 4.2. Soil penetration resistance (MPa) at three different depths (0-5 cm, 5-10

cm and 10-15 cm) under tillage and residue after different crop in cereal-dominated

system in Digram.

Tillage

Residue Soil penetration resistance (MPa)

Initial After Crop 1 After Crop 3 After Crop 4 After Crop 6 After Crop 7

0-5 cm soil depth

ST HR1

1.7

1.5 1.7 1.6 0.5 0.7

LR 1.0 1.6 1.6 0.5 0.8

BP HR 0.7 1.5 1.9 0.5 0.6

LR 0.7 1.7 2.0 0.5 0.6

CT HR 1.9 1.7 1.5 0.6 0.8

LR 1.4 1.8 1.6 0.7 0.9

LSD20.05

Tillage (T) 0.4** ns 0.3* ns 0.1**

Residue (R) 0.3** ns ns ns 0.1**

TXR ns ns ns ns ns

5-10 cm soil depth

ST HR

3.0

2.9 3.6 4.7 0.8 1.5

LR 2.4 3.3 5.0 0.9 1.5

BP HR 1.6 2.0 3.6 0.6 0.8

LR 1.4 2.6 3.8 0.7 0.9

CT HR 5.0 3.6 5.0 1.4 1.8

LR 3.8 4.2 5.6 1.4 1.9

LSD0.05

Tillage (T) 1.3** 0.9** 1.0* 0.3** 0.2**

Residue (R) 0.3** ns ns ns ns

TXR 1.3* ns ns ns ns

10-15 cm soil depth

ST HR

6.1

8.3 7.1 7.1 2.5 2.7

LR 8.4 7.8 7.4 2.7 2.7

BP HR 5.8 5.8 6.9 2.0 1.7

LR 6.0 7.0 6.9 2.2 1.9

CT HR 10.8 8.3 7.7 3.9 3.1

LR 9.2 8.4 8.0 4.2 3.2

LSD0.05

Tillage (T) 1.7** 0.9** 0.5* 0.7** 0.4**

Residue (R) ns ns ns ns ns

TXR ns ns ns ns ns

140




The interaction effects of tillage and residue treatment on soil PR were not significantly

different except at 5-10 cm soil depth (Table 4.2). At 5-10 cm soil depth, the soil PR

was higher in CTHR and lower in BPLR (Table 4.2).

4.3.2.3 Trends of volumetric soil water content and penetration resistance following

planting of wheat


The trends of soil drying and penetration resistance at 0-5 cm, 5-10 cm and 10-15 cm

from 5 to 30 DAS at 5 days interval are shown for tillage treatments (Figure 4.19).

Across all treatments, the SWC continued to decrease and soil PR increase at all depths

of measurements from sowing until 30 DAS (Figure 4.19a1-a3 and 4.19b1-b3). After

sowing, the plots under BP continued to have the higher surface SWC for most days of

measurement than ST and CT, but significant differences were only found at 5, 10 and

15 DAS (Figure 4.19a1). There were no differences of SWC for the depths 5-10 cm and

10-15 cm (Figure 4.19a2-a3).

The soil PR continued to increase with days after sowing regardless of treatments at all

depths studied (Figure 4.19b1-b3). At 0-5 cm soil depth, the soil PR of BP was greater

than that of CT and ST at all dates of measurement except at 25 DAS (Figure 4.19b1).

The differences of soil PR at 5-10 cm and 10-15 cm soil depths due to different tillage

followed the order of CT>ST>BP (Figure 4.19b2-b3). Except at 15 DAS for 10-15 cm soil

depth, the soil PR of BP was lower as compared to ST and CT at all dates of

measurement at 5-10 cm and 10-15 cm (Figure 4.19b2-b3).

141


resistance (b1-b3) at 0-5 cm, 5-10 cm and 10-15 cm soil depths for different tillage

treatments at 5 days after sowing (DAS) to 35 DAS during wheat planting in 2013 in

Digram. Values are means across residue levels. Floating error bars indicate the least

significant difference (LSD) at P≤0.05, for the effects of tillage on that date of

measurement and error bars indicates ± 1 standard error of the mean.


Following sowing of wheat (Crop 7), the trend of soil drying and penetration resistance

at 0-5 cm, 5-10 cm and 10-15 cm from 5 DAS to 30 DAS at 5-day intervals are shown

between residue treatments (Figure 4.20). The SWC continued to decrease with days

after sowing irrespective of residue treatments. However, the SWC of HR was

Vo

lum

etr

ic s

oil

wat

er

con

ten

t (%

)

0

8

16

24

32

40

5 10 15 20 25 30

a1) 0-5 cm ST BP CT

0

8

16

24

32

40

5 10 15 20 25 30

a2) 5-10 cm

0

8

16

24

32

40

5 10 15 20 25 30

Days after sowing

a3) 10-15 cm

Soil

pe

ne

trat

ion

re

sist

ance

(M

Pa

)

0.0

1.5

3.0

4.5

6.0

5 10 15 20 25 30

b1) 0-5 cm ST BP CT

0.0

1.5

3.0

4.5

6.0

5 10 15 20 25 30

b2) 5-10 cm

0.0

1.5

3.0

4.5

6.0

5 10 15 20 25 30

Days after sowing

b3) 10-15 cm

142

significantly higher than that of LR at 0-5 cm and 5-10 cm soil depth only at 5 and 10

DAS (Figure 4.20a1-a2). There was no effect of residue on SWC at 10-15 cm depth,

except at 10 DAS, when the SWC of HR was significantly higher than that of LR

treatment (Figure 4.20a3).


resistance (b1-b3) at 0-5 cm, 5-10 cm and 10-15 cm soil depths for different residue

treatments at 5 days after sowing (DAS) up to 35 DAS during wheat planting in 2013

in Digram. Values are means across tillage treatments. Floating error bars indicate

Vo

lum

etr

ic s

oil

wat

er

con

ten

t (%

)

0

8

16

24

32

40

5 10 15 20 25 30

a1) 0-5 cm HR LR

0

8

16

24

32

40

5 10 15 20 25 30

a2) 5-10 cm

0

8

16

24

32

40

5 10 15 20 25 30

Days after sowing

a3) 10-15 cm

Soil

pe

ne

trat

ion

re

sist

ance

(M

Pa)

0.0

1.5

3.0

4.5

6.0

5 10 15 20 25 30

b1) 0-5 cm HR LR

0.0

1.5

3.0

4.5

6.0

5 10 15 20 25 30

b2) 5-10 cm

0.0

1.5

3.0

4.5

6.0

5 10 15 20 25 30

Days after sowing

b3) 10-15 cm

143

the least significant difference (LSD) at P≤0.05, for the effects of tillage on that dates

of measurement and error bars indicate ± 1 standard error of the mean.

At 0-5 cm soil depth, the soil PR of HR was significantly lower than that of LR at 5, 10

and 15 DAS (Figure 4.20b1). When the depth 5-10 cm was monitored, no differences of

soil PR were seen between residue treatments over time (Figure 4.20b2). At 10-15 cm

soil depth, HR had lower soil PR compared to LR at 5 and 10 DAS (Figure 4.20b3).

4.3.2.4 Relationship between soil physical properties

4.3.2.4.1 Relation between soil bulk density and penetration resistance

The soil PR increased with an increase in soil BD after Crop 1, 3, 4 and 6 in Digram

(Figure 4.21a-d).

Figure 4.21. Relationship between soil penetration resistance (MPa) and bulk density

(g/cc) after different crop determined: a) after Crop 1; b) after Crop 3; c) after Crop 4;

y = 26.11x - 35.32 R² = 0.75

0

2

4

6

8

10

12

1.0 1.2 1.4 1.6 1.8

Pe

ne

tra

tio

n r

esis

tan

ce

(M

Pa

)

Bulk density (g/cc)

a) y = 13.42x - 15.32

R² = 0.42

0

2

4

6

8

10

12

1.0 1.2 1.4 1.6 1.8

Pe

ne

tra

tio

n r

esis

tan

ce

(M

Pa

)

Bulk density (g/cc)

b)

y = 15.02x - 16.80 R² = 0.66

0

2

4

6

8

10

12

1.0 1.2 1.4 1.6 1.8

Pe

ne

tra

tio

n r

esis

tan

ce

(M

Pa

)

Bulk density (g/cc)

c) y = 8.16x - 9.92

R² = 0.68

0

2

4

6

8

10

12

1.0 1.2 1.4 1.6 1.8

Pe

ne

tra

tio

n r

esis

tan

ce

(M

Pa

)

Bulk density (g/cc)

d)

144

d) after Crop 6 during 2010-13 in Digram. Values are for all three depths (0-5 cm, 5-10

cm and 10-15 cm). The line represents the regression equation shown above in the

graph.

4.3.2.5 Depth distribution of soil physical parameter at bed planting and strip tillage

system in Digram

4.3.2.5.1 Distribution of soil bulk density

The soil BD was not significantly different (P≤0.05) due to the sampling position of BT

and BF of the BP except at 5-10 cm soil depth after Crop 6 (Figure 4.22a). The soil BD of

BT was higher than that of BF in the 5-10 cm soil depth (Figure 4.22a). In the event of

ST, the soil BD of IS exhibited higher than that of OS in the depth of 10-15 cm after

Crop 7 (Figure 4.22b).

Figure 4.22. Variation of soil bulk density after Crop 6 in Digram relative to depth

from the bed top (BT) and from the level of the bed top in the furrow (BF) of the bed

planting system (a); and in the strip (IS) and off-the strip (OS) of strip tillage system

(b). For comparison, initial values (before starting the experiment) are also shown.

Floating error bars indicate the least significant difference (LSD) at P≤0.05, for the

effects of sampling positions.

0.0

0.4

0.8

1.2

1.6

2.0

0-5 cm 5-10 cm 10-15 cm

Bu

lk d

en

sit

y (

g/cc)


Initial BT BF

BT vs BF

a)

0.0

0.4

0.8

1.2

1.6

2.0

0-5 cm 5-10 cm 10-15 cm

Bu

lk d

en

sit

y (

g/cc)


Initial OS IS

OS vs IS b)

145

4.3.2.5.2 Distribution of volumetric soil water content

After Crop 7, the SWC of BT was higher (P≤0.05) than that of BF in the depth of 5-10

cm depth (Figure 4.23a). In case of ST, the SWC of OS was higher (P≤0.05) than that of

IS at 5-10 cm soil depth (Figure 4.23b).

Figure 4.23. Variation of volumetric soil water content (%) after Crop 7 in Digram






4.3.2.5.3 Distribution of soil penetration resistance

The soil PR increased with increasing soil depth for both BP and ST after Crop 7 (Figure

4.24). The differences of soil PR due to sampling position of BP system were not

significant in 0-5 cm and 10-15 cm depths (Figure 4.24a). The soil PR of BT was higher

(P≤0.05) than that of BF in the 5-10 cm (Figure 4.24a). In the case of ST, the soil PR was

significantly differed (P≤0.05) due to sampling position in all the depths of study

(Figure 4.24b). The soil PR of OS was significantly higher (P≤0.05) than that of IS at all

depths of study (Figure 4.24b).

0

8

16

24

32

40

0-5 cm 5-10 cm 10-15 cm

Vo

lum

etric

so

il w

ate

r c

on

te

nt (

%)


Initial OS IS

OS vs IS b)

0

8

16

24

32

40

0-5 cm 5-10 cm 10-15 cm

Vo

lum

etric

so

il w

ate

r c

on

te

nt (

%)


Initial BT BF

BT vs BF

a)

146

Figure 4.24. Variation of soil penetration resistance (MPa) after Crop 7 in Digram






4.3.2.6 Soil temperature at Digram

The soil temperatures were tracked during day and night from planting to harvesting

of the wheat crop (from 3-10 to 116-117 DAS) (Figure 4.25a and 4.25b). The soil

temperatures both at day and night remained stable during the first 59 DAS in all

treatments (Figure 4.25a and 4.25b). After 59 DAS, the soil temperature tended to rise

and reached a peak at 116-117 DAS (Figure 4.25a and 4.25b). The day soil temperature

was higher with CT (maximum soil temperature was 20.3 °C at 11-117 DAS and

minimum temperature was 16.1 °C at 53-59 DAS) than those recorded in ST and BP

from beginning to 80 DAS. Afterwards, the warmer soil temperature was tracked in BP

from 81-117 DAS to 116-117 DAS (Figure 4.25a). However, the night soil temperature

was slight higher with ST than those recorded in CT and BP from beginning to 59 DAS;

and afterwards from 60-66 DAS to 116-117 DAS, the night temperatures tracked in

BPLR were higher than other treatments (Figure 4.25b).

0

2

4

5

7

0-5 cm 5-10 cm 10-15 cm

So

il p

en

etra

tio

n r

esis

ta

nce

(M

Pa

)


Initial BT BF

BT vs BF

a)

0

2

4

5

7

0-5 cm 5-10 cm 10-15 cm

So

il p

en

etra

tio

n r

esis

ta

nce

(M

Pa

)


Initial OS IS

OS vs IS OS vs IS OS vs IS

b)

147

Figure 4.25. The variation of mean soil day (a) and night soil temperature (°C) (b) due

to different treatments during wheat growing season at Digram in 2012-13. Values

are means of seven day intervals.

4.4 Discussion

4.4.1 Soil bulk density and penetration resistance

Treatment effects on soil BD were not clearly apparent until after Crop 6 when the ST

had lower soil BD of surface soil than CT. The lower soil BD at ST could be attributed to

the development of better soil structure and increased porosity as a result of

improvement of SOC (see Chapter 5) in the surface soil (0-5 cm). The results of the

10

15

20

25

30

35

3-1

0

11

-17

18

-24

25

-31

32

-38

39

-45

46

-52

53

-59

60

-66

67

-73

74

-80

81

-87

88

-94

95

-10

1

10

2-1

08

10

9-1

15

11

6-1

17

Days after sowing

Day soil temperature (°C )

ST HR BP HR CT HR

ST LR BP LR CT LR

10

15

20

25

30

35

3-1

0

11

-17

18

-24

25

-31

32

-38

39

-45

46

-52

53

-59

60

-66

67

-73

74

-80

81

-87

88

-94

95

-10

1

10

2-1

08

10

9-1

15

11

6-1

17

Days after sowing

Night soil temperature (°C ) b)

a)

Me

an s

oil

tem

pe

ratu

re (

°C)

148

present study are consistent with the findings of Singh et al. (2016), who reported that

the soil BD was higher in transplanted puddled rice followed by conventionally tilled

maize than in the conventionally direct-seeded rice/conventionally tilled maize and

zero-tillage direct-seeded rice/conventionally tilled maize treatments in a rice-maize

system. Puddling of soil for rice cultivation in CT, involves the destruction of the soil

aggregates and reduced soil porosity and thereby increased subsoil compaction and

soil BD in medium-textured soil in India (Gathala et al., 2011b). Bhattacharyya et al.

(2015) demonstrated that the plots under mungbean residue + direct seeded rice

followed by ZT wheat with rice residue retention and zero tilled relay summer

mungbean reduced soil BD compared to transplanted rice-conventionally tilled wheat.

Ball et al. (1997) reported that the soil BD modified as a result of improvement of soil

organic matter by practicing of conservation agriculture.

Although the soil BD was not significantly different between ST and CT below 5 cm soil

depth, the soil PR was consistently lower with ST than CT treatment at 5-10 cm and 10-

15 cm soil depths especially at Digram. The emergence of differences of soil PR earlier

in the experiment than soil BD indicated that the soil PR is a more sensitive indicator

than soil BD to changes in soil physical properties. The soil PR was least at surface 0-5

cm soil depth of BP after Crop 1 due to loose and pulverized soil on top of the raised

bed as reported by other researchers who noted the loosening effect of tillage

decreased the soil BD at the surface layer of young beds relative to CT treatment

(Naresh et al., 2012; Jat et al., 2013). However, subsequently the soil BD of surface soil

under BP tended to increase up to Crop 6, particularly after rice crops. This tendency

may be attributed to slaking, settling, reconsolidating and compacting of pulverized

and loose soil of refreshed beds by ponding of standing water and then drying of the

compacted soil following the end of the monsoon rain.

For the subsurface soil (5-10 cm and 10-15 cm soil depth), the soil PR under BP was

consistently lower than that under CT and ST which might be due to the initial burial of

crop residue when beds are reformed. In addition, increased SWC (Figure 4.5 and 4.19)

and SOC (see Chapter 5) as the repeated burial of residue when re-shaping beds may

contribute to the reduced soil BD in the subsurface soil. Further Govaerts et al. (2006b)

149

reported that BP had a unique natural opportunity to decrease compaction by

confining traffic to the furrow bottoms.

At the end of Crop 6 and 7, deposition of increased crop residue of successive crops

decreased soil BD and PR under HR at 0-5 cm and 5-10 cm soil depth. The

improvement of SOC (see Chapter 5) in surface soil (0-5 cm) after Crop 6 and 7 under

HR likely resulted in better soil structure, increased soil porosity and thereby

decreased in soil BD at 0-5 cm. The findings of the present study are in conformity with

those reported earlier by several other researchers in different regions (Bhattacharyya

et al., 2008; Govaerts et al., 2009; Singh et al., 2016), which demonstrated the positive

effects of residue retention on soil BD at 0-10 cm depth. Retention of residue together

with NT in the Chinese Loess Plateau increased SOM and biotic activity, resulting in

decreased soil BD of the surface soil layer (Chen et al., 2008). The results of the present

study suggested that HR is effective in reducing soil BD and PR, which might be

enhanced root growth and contribute to increased yield of lentil and wheat following

rice (see Chapters 2 and 3).

4.4.2 Volumetric soil water content

The increased SWC in subsurface soil of BP and ST found in the present study may

indicate greater water infiltration induced by conservation tillage and surface residue

retention over the 2-2.5 year’s duration of the study. That ZT and residue retention

enhanced infiltration rate compared to CT and residue removal has been reported by

several researchers (Gathala et al., 2011b; Jat et al., 2013). The higher SOC under ZT

was associated with increased SWC at 0-15 cm soil depth than CT in a rice-wheat

system in the Indian Himalayas (Bhattacharyya et al., 2008). The continuity of water

conducting pores and improvement of soil aggregation enhanced infiltration under ZT

due to minimal soil disturbance compared to CT (dry and wet) (Dwivedi et al., 2012). In

the present study, over time retention of high residue and undisturbed soil in ST

increased SOC (see Chapters 5) which suggests the potential for improved surface soil

structure and enhanced infiltration rate after 2-2.5 years (after Crop 6-7). The lower

soil PR in subsurface soil (5-15 cm soil depth) after Crop 7 suggested that the plough

150

pan had been reduced with time in ST. In the current study, the increased SWC under

OS at 5-10 cm soil depth than IS can also be attributed to enhanced infiltration as a

result of residue retention and undisturbed soil of OS. Similar root growth (see Chapter

3) under different tillage suggests that increased water use is not the reason for lower

SWC in the CT treatment.

After Crop 1, the SWC of BP at 0-5 cm soil depth was lower than in other tillage

treatments at Alipur, a rainfed area. This can be attributed to the more rapid drying

following formation of the bed due to the heaping of pulverized and loose soil to form

the bed. Between Crop 1 and Crop 7, the SWCs of the surface soil were not

significantly different between tillage treatments. After Crop 7 at Alipur, the SWC of

the surface soil was higher with CT as compared to ST and BP. At the Alipur

experimental site for the last decade, monsoon rice was generally grown by using

conventional puddled transplanting system followed by dryland crops grown by

intensive tillage with residue removal. Generally puddling created a soil condition

favourable for wetland rice by retaining water in the root zone through reducing

infiltration as a result of the compacted plough pan (Sharma & De Datta, 1986).

Consequently, poor soil structure in the surface 0-7 cm soil layer and a hard plough

pan below 7-8 cm soil depth existed before the experiment regardless of treatment. By

contrast, the minimal soil disturbance and the increase residue retention under ST

create the possibility of restoration of soil structure and alteration of soil strength and

SWC by increases in soil aggregation and by changes in the size, continuity, geometry

and stability of pores (Shaver et al., 2002).

The implementation of CT decreased SWC faster with days after sowing likely due to

higher evaporation loss as compare to ST and BP plus HR in the present study. The

surface soil under CT had warmer day temperatures and decreased SWC compared to

ST and HR treatments in the present research. These findings are in agreement with

Licht and Al-Kaisi (2005b) who observed that intensive tillage disturbs soil and

increases air pockets which tended to enhance evaporation loss and accelerated soil

drying and heating. By contrast, anchored residue retention on ST and BP reduced

evaporation and runoff leading to increased surface SWC at ST and BP compared to CT.

151

In a study of rainfed Mediterranean condition, Vita et al. (2007) found that the storage

of SWC increased by 20 % under NT due to lower evaporation than CT. The increased

SWC at ST and HR also might be associated with reduced soil temperature in the

present study, as reported by Limon-Ortega et al. (2002).

High residue retention during each of the 7 crops was associated with increased SWC

after Crop 6 and 7. Although residue effects on SWC were significantly different at

surface soil, the effects however disappeared at deeper layers. The drying of soil at 0-5

cm and 5-10 cm soil depth was greater in LR compared to HR suggesting that less

residue retention increased surface evaporation. Rasmussen (1999) reported that high

residue retention on soil surface decreased evapotranspiration and increased SWC at

surface 10 cm soil depth. Rahman et al. (2005) and Chakraborty et al. (2008) reported

that rice straw mulch was effective in conserving SWC compared to bare soil. Higher

SWC under HR favoured higher uptake of water at all depth of measurements which

was associated with an increased yield (see Chapter 2) and root growth of cool dry

season crops, lentil and wheat (See Chapter 3).

The soil BD had a positive relation with soil PR in the present study. Tarkiewicz and

Nosalewicz (2005) found that the changes in soil PR are dependent on SWC, BD and

SOC in a similar texture of soils. In this study, the lower soil PR and higher SWC might

be due to improvement soil structure (soil BD) and SOC in ST and HR compared to CT

and LR especially after Crop 7 (See Chapter 5). The findings are well supported by

Bescansa et al. (2006) those who reported greater SWC in conservation tillage might

be attributed to improvement in organic matter and soil structure. Further, the greater

SOC reduced soil BD in ST and HR compared to CT and LR. In a similar value of SWC

across all treatments, the soil PR was positively correlated with soil BD (Sharma & De

Datta, 1986). In the present study, the soil PR increased with decreasing SWC

regardless of treatments except after the dryland crop. The soil PR decrease with

increasing SWC is in agreement with the results reported in other studies (Mapfumo &

Chanasyk, 1998; Kumar et al., 2012). However, this relationship was reverse after

dryland crop. For growing dryland crop, tillage is used to create a loose and pulverized

soil (all parts of CT and most parts of BP; and seed zone of ST) for better seed bed

152

preparation. This pulverized and loose soil immediately after tillage reduced the soil PR

as well as decreased SWC, due to enhanced evaporation.

4.4.3 System differences

Irrespective of treatment differences, the soil BD was lower in the cereal-dominated

system at Digram than in the legume-dominated system at Alipur. This might be due to

its inherent soil properties with less sand and higher SOC at Digram than Alipur (See

Chapter 5 and 6). The SWC was significantly different due to tillage after Crop 1 and 7

at Alipur while no differences were found at surface soil of Digram. Probably 2-3 times

irrigation application for wheat crop negated the treatment effects of tillage. Further,

residue effects on SWC were more pronounced in the legume-dominated system in

Alipur, than the cereal-dominated system in Digram. This could be because the short

canopy structure of lentil allowed soil to receive more radiation energy during crop

growth. In contrast, the closed canopy of wheat resulted in less solar radiation capture

by the soil of Digram irrespective of all treatments; hence this may result in

insignificant differences between treatments on SWC in the surface soil.

4.5 Conclusion

The results from a 2.5-year study demonstrated that ST and BP for non-rice crops and

HR followed by unpuddled rice cultivation facilitated better soil physical conditions as

compared to CT and LR followed by puddled rice cultivation in two rice-based cropping

systems in Bangladesh. Strip tillage and unpuddled rice coupled with HR decreased soil

BD and PR while increasing SWC below the surface for rice-dryland cropping systems.

In Chapter 3, it was reported that reduced soil disturbance and HR also improved in

SWC and decreased soil PR of the surface soil. By contrast with ST, the BP system with

unpuddled soils for rice had mixed effects on soil physical properties. Although there

was lower soil PR at surface soil after Crop 1 (dryland crop) in BP, due to loose and

pulverized soil of the newly constructed bed, there was an increasing tendency to

increase soil PR by settling soil after the rice crop, indicating that the positive effects of

BP in the dryland crop were lost by ponded rice cultivation. After sowing, the SWC was

greater in surface soil of ST and BP due to retention of surface residue. The above

153

results demonstrated that gradual improvement in soil physical properties such as soil

BD at surface soil (0-5 cm) and the SWC at subsurface soil (5-10 cm and 10-15 cm) layer

in ST as well as by switching from puddling by unpuddled rice is likely to be superior to

CT and BP in the long run. However, the physical soil properties of rice-based system

need to be monitored throughout the year. In addition, the present study needs to be

continued for a longer period to evaluate the performance of unpuddled rice followed

by ST/BP dryland crop with high residue retention in rice-based systems representing

different soil, climatic, and socio-economic conditions in the IGP. After long-term

periods, other physical properties such as soil aggregation, and water loss pathways

such as infiltration, deep drainage, runoff, evaporation need to be quantified to

understand the water balance under CA practices.

154

5 Short-medium term effects of conservation management practices on

soil organic carbon pools in rice-based systems in Bangladesh

5.1 Introduction

Soil organic carbon (SOC) depletion is one of the potential reasons suggested for

decline in crop yield and productivity in high-input intensive rice-based systems of the

Indo-Gangetic Plains (IGP), where 2-3 crops per year are grown on the same piece of

land (Duxbury et al., 1989; Ladha et al., 2003a). The current form of tillage and residue

removal for the crops grown after rice in the IGP is known to decrease SOC which may

in turn decrease crop yield. The soil conditions of rice-based crop rotations are distinct

from other lowland or dryland soils as rice and non-rice crops are generally grown

sequentially under contrasting hydrological environments alternating between wetting

and drying resulting in aerobic and anaerobic conditions, respectively (Zhou et al.,

2014; Dossou-Yovo et al., 2016). The deterioration of SOC is accelerated in rice-based

systems due to the crushing of soil aggregates by intensive multiple cultivations each

year involving puddling for rice and rigorous tillage for non-rice crop cultivation (Six et

al., 2004; Shibu et al., 2010). However, prolonged submergence of lowland soils and

anaerobic conditions decrease the rate of heterotrophic respiration (microbial

decomposition) in the anoxic soil while carbon fixation by algal photosynthesis adds to

SOC (Dossou-Yovo et al., 2016). In contrast, upland aerobic conditions hasten the

oxidation of accumulated SOC and losses of carbon dioxide (CO₂) to the atmosphere

(Nakadai et al., 1996). Al-Kaisi and Yin (2005) reported that the extent of CO₂ emission

is highly related to the frequency and intensity of tillage of the soil. Tillage hastens CO₂

evolution by improving soil aeration, and increasing contact of soil and crop residue

(Angers et al., 1993). Additionally tillage could increase the exposure of SOC in inter

and intra-aggregate zones to microbes for rapid oxidation (Jastrow et al., 1996). Crop

management practices, viz. tillage and residue management, can influence soil CO₂

emission but their impacts on soil CO₂ emission are complex and varied (La Scala Jr et

al., 2006). However, CO₂ emission can be reduced by using conservation agriculture

practices (Mosier et al., 1991).

155

Soil organic carbon has a profound effect on soil structure, which in turns affects soil

aeration and soil pore size distribution (Al-Kaisi & Yin, 2005; Nayak et al., 2012). It also

improves infiltration rates, plant available soil water storage and serves as a buffer

against rapid changes in soil reaction (pH). Apart from maintaining and enriching soil

nutrient supply, it also reduces the loading of CO₂ and methane (CH4) into the

atmosphere (Dahal & Bajracharya, 2010; Srinivasarao et al., 2014). It is also a key

source of microbial energy and nutrients. Hence, the maintenance of SOC and nutrient

cycling are invaluable for improving crop productivity and sustainability (Blair et al.,

1995; Franzluebbers, 2010).

Information on SOC-stocks in farmland soil is important due to their effects on climate

change and crop production (Majumder et al., 2008). The SOC-stocks mirror the long-

term balance between SOC input and losses through different pathways, and it is an

indicator of carbon dynamics under different management practices (Farage et al.,

2007). Unlike SOC concentrations, the stock account for changes in both SOC

concentrations and bulk density (Liu et al., 2014b). However, it is difficult to detect

changes in SOC in the short- and medium-term because of the large pool of

recalcitrant SOC relative to annual inputs (Li et al., 2012) and its high spatial variability

(Blair et al., 1995). In contrast, water soluble carbon (WSC) is an important labile

organic C fraction which can respond more rapidly than total SOC to soil management

factors like different tillage and residue retention (Chan et al., 2002; Haynes, 2005;

Roper et al., 2010). It could be used as a primary energy source and an indicator of the

carbon availability for soil microorganisms (Stevenson, 1994). Furthermore, the WSC is

the entire pool of water extractable organic carbon either sorbed on soil particles or

dissolved in interstitial pore water (Tao & Lin, 2000). It is a small portion (~1-3 %) of

the total SOC (Tao & Lin, 2000; Ohno et al., 2007; Scaglia & Adani, 2009; Li et al., 2012)

but is considered as an important mobile and reactive soil carbon source (Lu et al.,

2011). This fraction acts as a substrate for microbial activity, a primary source of

mineralizable N, S, and P and its leaching greatly influences the nutrient content and

pH of groundwater (Haynes, 2005).

156

The study of SOC sequestration in paddy soils is necessary as paddy soils in the IGP are

degrading. Also paddy soils have a greater SOC storage than dryland soils (Xu et al.,

2013). Sequestration of SOC which involves the storage of carbon as organic matter

also contributes to the mitigation of CO₂ emission from soil (Lal, 2004b; Das et al.,

2013). Thus , SOC sequestration is a potential strategy for restoring the degraded soils,

improving crop productivity and diversity, reducing atmospheric CO₂ emission and

thereby mitigating climate change (Wang et al., 2010).

Residue decomposition, soil respiration and SOC mineralization are the major sources

of CO₂ emission in agricultural land (Luo & Zhou, 2006). Soil respiration involves CO₂

production by roots, soil microbes, and soil fauna within soil and litter layers (Luo &

Zhou, 2006). Figure 5.1 shows the CO₂ production process in soil (Luo & Zhou, 2006).

Figure 5.1. Schematic representation of CO₂ production processes in soil. Those

processes are root respiration, rhizosphere respiration, litter decomposition, and

oxidation of SOM. Adapted from Luo and Zhou (2006).

Although the effect of minimum tillage and residue retention on SOC and its fractions

has been studied in many parts of the world, this information is scarce for intensive

rice-based systems in the Eastern IGP. There is thus a clear need to understand the

changes in SOC and its fractions when intensive rice-based systems, as practiced in

Bangladesh, are converted to conservation agriculture practices. It was hypothesized

157

that minimum tillage and increased residue retention would increase the SOC by

capturing C inputs and decreasing C loss as CO₂ emissions. Therefore, the objective of

this study was to determine the changes of SOC and its fractions, and CO₂ emission to

the atmosphere, over a three year period under different tillage practices in intensive

rice-based systems in two soil environments in Bangladesh. The study contrasts a

cereals-dominated crop rotation with high C input to a legume-dominant rotation with

higher symbiotic N input.

5.2 Materials and Methods

5.2.1 Experimental site and treatment details

Details of the experimental site and treatments are described in Chapter 2. In brief,

tillage treatments consisted of strip tillage (ST), bed planting system (BP) and

conventional tillage (CT) factorially combined with high residue (HR) and low residue

retention (LR). Two cropping systems were established at two locations and continued

for three years covering 7 crops in total. The cropping sequence of lentil-mungbean-

monsoon rice was established at Alipur and that with wheat-mungbean-monsoon rice

at Digram. In the cropping sequence, lentil/wheat were Crop numbers 1, 4 and 7,

mungbean was Crop number 2 and 5, and monsoon rice was Crop number 3 and 6.

5.2.2 Quality assurance and quality control procedures

The entire soil and plant samples were analysed maintaining the procedure of quality

assurance and quality control at Murdoch University for the determination of carbon

and N pools. In the present study, there were always blank samples in every step (field

blank, method blank and analysis of blank) for each batch of analyses to ensure that

results were reliable (Estefan et al., 2013). If blank values were inconsistent or larger

values, the entire batch was re-analysed. In addition, a minimum of two samples were

randomly selected from the previous test materials for analysing in every batch of 20

samples. The variation of repeat values was accepted within ±0-10 % of their mean. If

the variation was larger than 10 %, then the entire batch was re-analyzed. For plant

analysis, two plant samples were randomly selected and used as internal reference

samples, and plant reference samples of Land Management Group, Murdoch

158

University were used as external reference for plant analysis. Analyses of all samples

from the three growing seasons (2010-2013) were done together. Results of the

present study were verified compared to those from an independent laboratory

following quality control and quality assurance protocols. Only 0-10 % variation was

accepted. Apart from these measures, accuracy of analytical results was demonstrated

by analyzing homogenized reference samples of known concentrations for each and

every batch analysis. Occasionally, known amounts of standard solution were added to

the homogeneous test sample, and finally the variation was calculated and only 0-10 %

variation was accepted. A baseline soil sample was used as an internal reference

sample and a reference soil sample of the Marine and Freshwater Research Laboratory

was used as external reference for soil analysis.

5.2.3 Estimation of annual C inputs

Annual C inputs from aboveground crop residue refer to the respective amount of C in

crop residue that was left on the soil surface after harvest of each crop and before

establishment of the next crop. Details of residue amount are described in Chapter 2.

According to Liu et al. (2014a), the C input was estimated by assuming that residue

contained on average 40 % C as given C concentrations of crop residue was not

available.

5.2.4 Soil sampling and analytical methods

Soil samples were collected at the end of each winter crop (last week of March-first

week of April) from depths of 0-15 cm in 2011 and 2012; and 0-7.5 cm and 7.5-15 cm

in 2013. The entire sample was collected from: in between the rows and in the rows of

ST plots; in between the rows on centre of the bed and in the furrow of the bed of BP

plots; and between the plants of CT plots. Procedures followed for the calculation of

SOC in the furrow (0-15 cm, 0-7.5 cm and 7.5-15 cm soil depth) of the BP system after

different crops are illustrated in Appendix 1. Sampling was done by an auger from 6-9

different places within individual plots. The sample cores were mixed together to

prepare a composite sample for each plot. Samples were immediately air dried. Visible

root fragments, stones and inert materials were manually removed by sieving through

a 2 mm mesh. The soil was then mixed and stored in sealed plastic jars for analyses of

159

SOC fractions. In order to compare seed bed or field condition under different

treatments, ST [average value of in the strip (IS) and off the strip (OS)] was compared

with centre of the bed and CT treatment. However, the differences between OS (off-

the strip) and IS (in the strip); and between BT (from the bed top) and BF (from the

level of the bed top in the furrow) of the bed planting (BP) system were also examined.

The details method of sampling locations on BP has been clarified in Chapter 4 (Section

4.2.6 Sampling time and location).

5.2.5 Bulk density

The soil was sampled at 0-5 cm, 5-10 cm and 10-15 cm soil depth for the measurement

of soil bulk density (BD). The detail procedures for measuring BD are described in

Chapter 4. However, the BD at 0-7.5 cm, 7.5-15 cm and 0-15 cm soil depths for the

measurement of SOC-stocks of ST and CT were calculated using the following formula:

BD at 0-7.5 cm: [BD0-5 + (BD5-10)/3]

BD at 7.5-15 cm: [(BD5-10)/3 + BD10-15]

BD at 0-15 cm: [(BD0-5 +BD5-10+ BD10-15)/3]

Where, BD0-5, BD5-10 and BD10-15 are the bulk density at 0-5 cm, 5-10 cm and 10-15 cm

soil depth.

5.2.6 Soil organic carbon, SOC-stocks and stratification ratio

Total SOC concentrations were measured according to the Walkley and Black method

(Rayment & Higginson, 1992). In brief, soil samples (~1.0 g) were treated with 10 mL

sodium dichromate, 20 mL concentrated H₂SO₄ was then added, followed by 170 mL

deionised water. After 30 minutes, the extract was centrifuged for 10 minutes at 4000

rpm. The absorbance of the supernatants and standards were read at 600 nm. The

SOC-stocks for a given layer of soil was calculated using the following equation (Lal et

al., 1998):

SOC − stocks (Mg C ha¯¹) =% SOC x bulk density (Mg m¯³) x d (m) x 10⁴ m²ha¯¹

100

160

Where, d is the thickness of soil layer (m), which in this case was 0-0.075 m, 0.075 -

0.15 m or 0-0.15 m. The SOC-stocks and sequestration rate of BP were not computed

in this study due to uncertainty with bed height and changes in bed height over time

among the different crops.

The stratification ratio (SR) was calculated by dividing the SOC concentrations of soil

surface layer (0-7.5 cm) with the corresponding values in the subsurface soil layer (7.5-

15 cm).

5.2.7 Soil carbon sequestration and C build-up or C losses (%)

In the present study, the annualized soil C gains or losses at 0-15 cm soil depth through

tillage and crop residue treatments were calculated using the following formula:

Cseq= (M2010-M2013)/2.5

Where Cseq is the amount of change in SOC-stocks (Mg C/ha), M2010 and M2013 are the

SOC-stocks in the 0-15 cm soil depth in 2010 and 2013, respectively, and 2.5 is the

study period (years) for this experiment. Positive and negative values indicate annual

SOC gains and losses, respectively, at 0-15 cm soil depth for the cropping system.

The estimated amount of mineralized C was equivalent to the C input from crop

residues minus changes in SOC-stocks from 2010 to 2013. The C build-up or C losses

(%) were calculated using the following formula:

C build-up or C losses (%) = [(SOC-stocks, 2013 — SOC-stocks, 2010)/SOC-stocks,

2010]*100

5.2.8 Water soluble organic carbon

Water soluble extracts of soil solution were determined for organic carbon according

to Walkley and Black (1934), with several modifications. Briefly, a fresh sample of each

treatment was extracted with deionized water using a soil/water ratio of 1:4 (W/V) for

45-60 min under agitation in a flask. After the extraction, samples were centrifuged at

4000 revs/min for 30 min. Supernatants were filtered through Whatman 42 filter

161

paper. Water soluble organic carbon of the filtrate was determined by the Walkley and

Black method (Rayment & Higginson, 1992).

5.2.9 Measurement of soil carbon dioxide emission

A modified inverted chamber method (Kirita, 1971) was employed in this study. Briefly,

100 mL of 0.5 N-NaOH in a plastic pot was placed on the soil surface (inside of the

inverted chamber) between plants in CT and between rows in BP and ST. A metal

cylinder (height 25 cm and diameter 15 cm) made of galvanized iron was inserted into

the soil to a depth of 6 cm in the first year. However, chlorinated polyvinyl chloride

(PVC) chambers (height 20 cm and diameter 14.2 cm) were inserted into the soil to a

depth of 6 cm in the second and third year. A galvanized iron lid was tightly sealed

onto the cylinder with an adhesive tape to prevent CO₂ exchange with the external

atmosphere. The untouched sets were left in the field for 48 h. After 48 h, the alkali

solution was recovered and immediately sealed by adhesive tape to prevent aeration.

The NaOH (alkali) solution was transferred to a conical flask and 2 mL of saturated

BaCl2 was added immediately to precipitate any HCO3- in the solution. Total CO₂ in

NaOH solution was determined by titrating against standardized 0.1 N HCl, using

phenolphthaline as an indicator. To reduce temperature inside of the cylinder, rice

straw was placed at the top of the chamber for shading. Two chambers were inserted

per plot. Carbon dioxide emissions were quantified in different growth stages

throughout the growing season of lentil and wheat (Table 5.1).

Table 5.1. Carbon dioxide measurements at different crop growth stages during

2010-13 of rice-based system.

Crop 2010-11 2011-12 2011-12

Lentil Vegetative: 12-14 Jan 2011

Flowering and podding: 14-16

Feb 2011

Harvesting : 4-6 Mar 2011

Vegetative: 1-3 Jan 2012

Flowering: 27-29 Jan 2012

Harvesting: 1-3 Mar 2012

Seedling: 21-23 Dec 2012

Flowering: 24-26 Jan 2013

Podding: 21-23 Feb 2013


Wheat Booting : 11-13 Feb 2011

Dough : 19-21 Mar 2011


Anthesis: 17-19 Feb 2012

Soft dough: 4-6 Mar 2012

Mature: 27-29 Mar 2012


Early booting: 28-30 Jan 2013

Anthesis: 22-24 Feb 2013


162


The GenStat 15th Edition (VSN International Ltd, United Kingdom) software package

was used for all statistical analyses. A split-plot (main plot: tillage and sub-plot:

residue) and split-split-plot (main plot: tillage, sub-plot: residue and sub-sub-plot:

cropping cycle or crop stages in case of CO₂ measurement) analysis of variance

(ANOVA) was employed to assess treatment effects on the measured variables at each

depth. When the F-test was significant, treatment means were separated by least

significant difference (LSD) at P≤0.05. Correlation analysis was performed based on

Pearson correlation coefficients using Microsoft Excel to determine correlations among

SOC pools and the significant probability levels of the results were given at P≤0.05 (*)

and P≤0.01 (**), respectively.

5.3 Results

5.3.1 Alipur

5.3.1.1 Soil organic carbon concentrations

After Crop 1, the SOC concentrations were not affected by different tillage and residue

treatments at 0-15 cm soil depth (Table 5.2). After Crop 4, the SOC concentrations

were 4.9 % greater with HR than LR (Table 5.2). After Crop 7, the SOC concentrations

at 0-7.5 cm soil depth was greater under STHR and BPHR by 17 % and 6 % than with

current farmer practice (CTLR) (Table 5.2). At 7.5-15 cm soil depth, the SOC

concentrations in ST and BP were higher by 8 % and 23 % than CT (Table 5.2). At 0-15

cm soil depth (average of 0-7.5 cm and 7.5-15 cm), the SOC concentrations were 8-9 %

greater in BP or ST than CT, and HR was 4.8 % higher than LR. Irrespective of the

treatments, the SOC concentrations decreased with increasing depth of soil.

163


stratification ratio of SOC concentrations during 2.5 years of legume-dominated rice-

based system at Alipur.

Year Soil

depth

(cm)

Tillage

treatment1

Residue

treatment1

Mean LSD20.05

HR LR Tillage (T) Residue (R) TxR

Soil organic carbon concentrations

2010-11

(after Crop 1)

0-15 ST 0.58 0.57 0.58

ns ns ns BP 0.59 0.58 0.59

CT 0.58 0.58 0.58

Mean 0.59 0.58

2011-12

(after Crop 4)

0-15 ST 0.60 0.58 0.59

ns 0.011** ns BP 0.62 0.60 0.61

CT 0.60 0.56 0.58

Mean 0.61 0.58

2012-13

(after Crop 7)

0-7.5 ST 0.90 0.82 0.86

0.039** 0.017** 0.04** BP 0.80 0.79 0.80

CT 0.81 0.75 0.78

Mean 0.84 0.79

7.5-15 ST 0.39 0.40 0.40

0.030**

ns ns BP 0.49 0.47 0.48

CT 0.38 0.37 0.37

Mean 0.42 0.41

Average

(0-15)

ST 0.65 0.61 0.63

0.028** 0.014** ns BP 0.65 0.63 0.64

CT 0.60 0.56 0.58

Mean 0.63 0.60

Stratification ratio of SOC (%) (0-7:7-1.5 cm)

2012-13

(after Crop 7)

ST 2.31 2.07 2.19

BP 1.62 1.70 1.66 0.16** ns ns

CT 2.15 2.05 2.10

Mean 2.03 1.94




The stratification ratio (SR) of SOC concentrations after Crop 7 for the surface (0-7.5

cm) to subsurface soil depth (7.5-15 cm) was significantly affected by different tillage

164

practices (Table 5.2). The SR of SOC in BP was significantly decreased by 27-32 %

compared with ST and CT after Crop 7 (Table 5.2).

5.3.1.2 Distribution and stratification of SOC concentrations at strip tillage system in

Alipur

After Crop 7, the highest SOC concentrations was measured in OS, 7.7 % and 6.5 %

higher than that in IS in 0-7.5 cm and 0-15 cm depth, respectively (Figure 5.2).

Figure 5.2. Variation of soil organic carbon concentrations at different cropping

seasons in Alipur relative to depth (at 0-15 cm soil depth before starting of the

experiment — Initial, after Crop 1 and after Crop 4, and at 0-7.5 cm and 7.5-15 cm

soil depth after Crop 7) in the strip (IS) and off-the strip (OS) of strip tillage system

(ST). Floating error bars indicate the least significant difference (LSD) at P≤0.05, for

the effects of sampling location of strip tillage system.


in Alipur

At the end of Crop 1 and 4, the highest SOC concentrations was measured in BT, 46 %

and 48 % higher than that in BF in the depth of 0-15 cm (Figure 5.3). After Crop 7, the

SOC concentrations of BT were greater by 69 % in 0-7.5 cm depth while by 33 % in 0-15

cm depth than that in BF (Figure 5.3). Conversely, the SOC concentrations of BT were

24 % lower than that of BF in 7.5-15 cm depth after Crop 7 (Figure 5.3).

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0-15 cm 0-15 cm 0-7.5 cm 7.5-15 cm 0-15 cm

Initial After Crop 4 After Crop 7

So

il o

rga

nic

ca

rbo

n (

%)

Sampling position and depth of ST under different cropping seasons

OS ISInitial

165




soil depth after Crop 7) from the bed top (BT) and from the level of the bed top in the

furrow (BF) of the bed planting system (BP). Floating error bars indicate the least

significant difference (LSD) at P≤0.05, for the effects of sampling location of bed

planting system.

5.3.1.4 Temporal variation of soil organic carbon concentrations

The SOC concentrations at 0-15 cm depth increased with ST and BP treatments from

initial (before experiment) to after Crop 7 (0.61 % to 0.63 %) (Figure 5.4). The SOC

concentrations were greater with ST and BP treatment than CT after 2.5 years.

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0-15 cm 0-15 cm 0-15 cm 0-7.5 cm 7.5-15 cm 0-15 cm

Initial After Crop 1 After Crop 4 After Crop 7

So

il o

rga

nic

ca

rbo

n (

%)

Sampling position and depth of BP under different cropping seasons

BT BFInitial

166

Figure 5.4. Temporal variation of soil organic carbon concentrations at Alipur. The

floating error bar indicates the average least significant difference (LSD) at P≤0.05 for

the different cropping cycles and tillage. Values are means across residue levels.

5.3.1.5 Soil organic carbon stocks and sequestration

Before onset of the experiment, the initial SOC-stocks was 14.7 Mg C/ha. After Crop 1,

the SOC-stocks showed no significant variation due to different tillage and residue

treatments at 0-15 cm depth (Table 5.3). After Crop 4, the SOC-stocks were 2.9 %

greater with HR than LR (Table 5.3). After Crop 7, the SOC-stocks in ST were 8.7 %

greater than in CT at 0-7.5 cm depth and retention of HR improved SOC-stocks over LR

by 6.6 % (Table 5.3). However, the SOC-stocks were 7 % higher in ST than CT;

compared to LR, the SOC-stocks were 5 % higher with HR at 0-15 cm depth (average of

0-7.5 cm and 7.5-15 cm) (Table 5.3). In all the treatments, the SOC-stocks decreased

with soil depth (Table 5.3).

0.50

0.55

0.60

0.65

0.70

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5

Soil

org

an

ic c

arb

on

(%

)

Cropping cycle

ST BP CT

After crop 1 After crop 4 After crop 7Initial

Time X tillage

167

Table 5.3. Tillage and residue effects on soil organic carbon stocks (Mg C/ha) and

sequestration (Mg C/ha/yr) of legume-dominated rice-based system at Alipur.

Year Soil depth

(cm)

Tillage

treatment1

Residue treatment1 Mean LSD20.05


SOC-stocks (Mg C / ha)

Initial (2010) 14.7 14.7 14.7

2010-11

(after Crop 1)

0-15 ST 13.9 13.5 13.7

ns ns ns CT 13.7 14.1 13.9

Mean 13.8 13.8

2011-12

(after Crop 4)

0-15 ST 13.6 13.3 13.4

ns 0.31* ns CT 13.6 13.1 13.4

Mean 13.6 13.2

2012-13

(after Crop 7)

0-7.5 ST 9.5 8.8 9.2

0.26** 0.23** ns CT 8.6 8.2 8.4

Mean 9.1 8.5

7.5-15 ST 4.7 4.8 4.8

ns

ns ns CT 4.8 4.5 4.6

Mean 4.7 4.6

Average

(0-15)

ST 14.6 13.9 14.3

0.53** 0.69* ns CT 13.6 12.9 13.3

Mean 14.1 13.4

Annual rates of C sequestration (Mg C /ha/ yr) at 0-15 cm soil depth: 2010-13 (2.5 years)

ST -0.04 -0.34 -0.19

0.21** 0.28* ns CT -0.44 -0.74 -0.59

Mean -0.24 -0.54




Over the 2.5 years period from the initial soil sampling, annual C sequestration rates at

0-15 cm depth in 2010-13 were -190 kg C/ha with ST and -590 kg C/ha with CT, and -

240 kg C/ha with HR and -540 kg C/ha with LR (Table 5.3).

5.3.1.6 Water soluble carbon

After Crop 4, WSC was 10.4 % and 7.6 % higher with CT than ST or BP (Table 5.4). After

Crop 7, the WSC in ST was 4.4 % and 13.7 % greater than in CT and BP, and HR had 6.8

% higher WSC than LR at 0-7.5 cm soil depth (Table 5.4). However, WSC was 13-16 %

higher with BP than other tillage treatments at 7.5-15 cm soil depth. At 0-15 cm soil

168

depth (average of 0-7.5 cm and 7.5-15 cm), WSC in HR was 5.8 % higher than LR (Table

5.4). Irrespective of treatments, WSC decreased with soil depth (Table 5.4).

Table 5.4. Tillage and residue effects on water soluble carbon (mg/kg) of legume-

dominated rice-based system at Alipur.

Tillage

treatment1

after Crop 4

(2011-12)

after Crop 7

(2012-13)

0-15 cm 0-7.5 cm 7.5-15 cm 0-15 cm (average)

HR1 LR1 Mean HR LR Mean HR LR Mean HR LR Mean

ST 131 126 129 235 219 227 114 114 114 174 166 170

BP 134 132 133 203 189 196 138 134 136 170 161 166

CT 149 139 144 225 209 217 124 114 119 174 162 168

Mean 138 132 221 206 125 120 173 163

LSD20.05

Tillage (T) 10.5* 18.6** 13.7** ns

Residue (R) ns 7.9** ns 4.5**

TxR ns ns ns ns




The SR of WSC in BP was significantly lower by 38 % and 27 % than ST and CT after

Crop 7 (Table 5.4).

5.3.1.7 Carbon dioxide-carbon (CO₂-C) emission

The rate of CO₂ flux did not differ among treatments at different growth stages of lentil

in 2010-11 with a mean CO₂ emission of 3.3 g/m²/day. In 2011-12 and 2012-13, the

treatment effects on soil CO₂ emission throughout the growing season at almost all

measuring times were significant and the emission varied with different crop stages

regardless of treatment (Figure 5.5 and 5.6 ). Compared with CT, the CO₂ emission was

reduced by 20 % at vegetative and 24 % at harvesting stage of lentil under ST (Figure

5.5). Similarly, BP released 49 % lower CO₂ emissions at vegetative stage and 59 %

lower at harvesting stage than CT (Figure 5.5). In 2012-13, the CO₂ emission was lower

by 33 % at seedling stage, 7 % at flowering stage and 6 % at harvesting stage with ST as

compared to CT. Compared to CT, the CO₂ emission was lower by 38 % at seedling

169

stage, 45 % at flowering stage, 25 % at harvesting stage and 59 % at harvesting stage

with BP.

Figure 5.5. Tillage effects on CO₂ flux (g CO₂ m²/day) at different growth stages of

lentil in Alipur in 2011-12 and 2012-13. The floating error bar on each figure

represents the least significant difference (LSD) at P≤0.05, for the different crop

growth stages where they were significantly different. Values are means across

residue levels.

In 2011-12, HR increased release of CO₂ relative to LR by 21 % at vegetative stage, by

13 % at flowering stage and 26 % at harvesting stage (Figure 5.6). In 2012-13, the CO₂

emission under HR was greater by 36 % at seedling stage, 20 % at flowering stage, 15 %

at podding and 5 % at harvesting as compared to LR (Figure 5.6). Also the significant

interaction between tillage and residue treatments, meant that CO₂ emission was

lower by 63 % in BPHR and BPLR and 19 % in STLR than CTLR at harvesting stage in

2012-13.

0

3

6

9

12

15

Vegetative Flowering Harvesting

CO

₂ f

lux

(g

CO

₂ m

²/d

ay

)

Crop stages

ST BP CT2011-12

0

3

6

9

12

15

Seedling Flowering Podding Harvesting

CO

₂ f

lux

(g

CO

₂ m

²/d

ay

)

Crop stages

ST BP CT2012-13

170

Figure 5.6. Residue effects on CO₂ flux (g CO₂ m²/day) at different growth stages of

lentil in Alipur in 2011-12 and 2012-13. The floating error bar on each figure

represents the least significant difference (LSD) at P≤0.05, for the different crop

growth stages where there were significant treatment differences. Values are means

across tillage treatments.

5.3.1.8 Correlation among different organic carbon pools

At Alipur, increases in SOC-stocks, WSC and CO₂ emission were positively and

significantly related to increase in SOC (P≤0.05, P ≤0.01) (Table 5.5).

Table 5.5. Correlation among soil organic carbon forms of legume-dominated rice-

based system at Alipur in 2012-13 (n = 96).

SOC (%) SOC-stocks WSC CO₂

SOC 1

SOC-stocks 0.95** 1

Water Soluble C 0.61** 0.65** 1

CO₂ 0.46** 0.45** 0.49** 1

* - significant at P≤0.05; ** - significant at P≤0.01; SOC - Soil organic carbon; SOC-stocks - Soil

organic carbon stocks; WSC - Water soluble carbon; CO₂ - Carbon dioxide

0

3

6

9

12

15

Vegetative Flowering Harvesting

CO

₂ f

lux

(g

CO

₂ m

²/d

ay

)

Crop stages

HR LR2011-12

0

3

6

9

12

15

Seedling Flowering Podding HarvestingC

O₂ f

lux

(g

CO

₂ m

²/d

ay

)

Crop stages

HR LR2012-13

171

5.3.1.9 Carbon balances

Cumulative C input to the soil over 2.5 years of the legume-dominated rotation ranged

between 2.33 Mg/ha in CTLR and 9.62 Mg/ha in STHR (Table 5.6). The cumulative C

input to the soil over 2.5 years ranged from 2.48 to 9.61 Mg /ha in ST and 2.33 to 9.56

Mg/ha in CT (Table 5.6).The amount of cumulative C input with STHR was only 0.5 %

greater than CTHR while STLR was 6 % greater than CTLR. Conversely, the estimated C

mineralization with CTHR was 9 % greater than STHR while CTLR had 20 % C higher

than STLR (Table 5.6). Compared to high C input in HR, the mineralization was greater

with less C input in LR (Table 5.6).

172

Table 5.6. Estimated carbon balance for the legume-dominated rice-based rotation at

Alipur considering residue of eight consecutive crops in 2010-2013. STHR = strip

tillage-high residue; STLR = strip tillage-low residue; CTHR = conventional tillage-high

residue; CTLR = conventional tillage-low residue.

Treatments STHR STLR CTHR CTLR

Mg C/ha

SOC-stocks,2010, initial (A) 14.7 14.7 14.7 14.7

SOC-stocks,2013, harvest (B) 14.6 (±0.1) 13.9 (±0.4) 13.6 (±0.3) 12.9 (±0.3)

Change in SOC-stocks, 2010-13 (B-A) -0.1 (±0.1) -0.8 (±0.1) -1.1 (±0.1) -1.8 (±0.1)

C build-up or C losses (%) -0.7 -5.4 -7.5 -12.2

C input

2010 Rice 2.00 0.80 2.00 0.80

2010-11 Lentil 0.84 (±0.14) 0 0.76 (±0.05) 0

2011 Mungbean 0.95 (±0.05) 0 0.97 (±0.06) 0

2011 Rice 1.64 (±0.22) 0.76 (±0.04) 1.58 (±0.29) 0.71 (±0.06)

2011-12 Lentil 0.77 (±0.03) 0 0.82 (±0.03) 0

2012 Mungbean 0.95 (±0.05) 0 0.97 (±0.06) 0

2012 Rice 1.72 (±0.09) 0.92 (±0.05) 1.93 (±0.10) 0.82 (±0.06)

2012-13 Lentil 0.74 (±0.09) 0 0.53 (±0.02) 0

C-input ∑2010-2013 (C) 9.61 (±0.39) 2.48 (±0.07) 9.56 (±0.18) 2.33 (±0.10)

Mineralized C input in 2010-2013

(C-B+A)

9.74 3.31 10.69 4.16

±values in parentheses indicate standard error

Compared to initial SOC-stocks (14.7 Mg C/ha) at 0-15 cm depth, the SOC-stocks

declined in CT but the greatest decline was in CTLR. However, the SOC-stocks were

essentially not stabilized in ST particularly in STHR relative to initial values. Under ST

condition, carbon sequestration rate (Y, Mg C/ha/yr) was linearly related to the

cumulative C input (Y = 0.18x-1.73 R² = 0.36). The C input required to produce a net

increase in SOC-stocks was estimated to be > 3.8 Mg/ha/yr. By contrast, while carbon

sequestration rate (Y, Mg C/ha/yr) of CT was also linearly related to the cumulative C

input (X, Mg C input/ha) during the 2.5 years period (Y = 0.09x-1.36 R² = 0.14), C

input > 6 Mg/ha/yr was required to produce a net increase in SOC-stocks (Figure 5.7).

173

Figure 5.7. Relationship between cumulative C input and SOC sequestration during

2.5 years under ST and CT conditions of legume-dominated rice–based system at

Alipur.

5.3.2 Digram

5.3.2.1 Soil organic carbon concentrations

After Crop 1, the SOC concentrations showed no significant variation due to different

tillage and residue treatments at 0-15 cm depth (Table 5.7). After Crop 4, the SOC

concentrations in ST and BP were greater by 11 % and 10 % relative to CT; and

compared to LR, HR had 4 % higher SOC concentrations (Table 5.7). After Crop 7, the

SOC concentrations in ST and BP was higher by 11 % and 3 % than CT; and HR had 7 %

greater SOC concentrations than LR at 0-7.5 cm depth (Table 5.7). At 7.5-15 cm, the

SOC concentrations were greater by 25 % in ST and 42 % in BP than CT (Table 5.7). At

0-15 cm depth (average of 0-7.5 cm and 7.5-15 cm), the SOC concentrations were

greater by 18 % in ST and 16 % in BP than CT; and HR had 5 % higher SOC

concentrations relative to LR (Table 5.7). Irrespective of the treatments, the SOC

concentrations decreased with increasing depth of soil (Table 5.7).

y = 0.18x - 1.73 R² = 0.36

-4.0

-3.0

-2.0

-1.0

0.0

1.0

2.0

SO

C s

eq

ue

ste

re

d (

Mg

C/h

a/y

r)

Cumulative C input (Mg C/ha) 5 10 0

Under ST

15

3.8 Mg C/ha /yr

y = 0.09x - 1.36 R² = 0.14

-4.0

-3.0

-2.0

-1.0

0.0

1.0

2.0

SO

C s

eq

ue

ste

re

d (

Mg

C/h

a/y

r)


Under CT

15

6.0 Mg C/ha /yr

174

Table 5.7.Tillage and residue effects on soil organic carbon concentrations and

stratification ratio of SOC concentrations during 2.5 years of legume-dominated rice-

based system at Digram.

Year Soil depth

(cm)

Tillage

treatment1



SOC (%)

2010-11

(after Crop 1)

0-15 ST 0.77 0.75 0.76

ns ns ns BP 0.76 0.74 0.75

CT 0.72 0.71 0.72

Mean 0.75 0.74

2011-12

(after Crop 4)

0-15 ST 0.81 0.79 0.80

0.050** 0.022** ns BP 0.81 0.78 0.79

CT 0.73 0.69 0.71

Mean 0.78 0.75

2012-13

(after Crop 7)

0-7.5 ST 1.16 1.06 1.11

0.065** 0.038** ns BP 1.04 0.99 1.02

CT 1.01 0.96 0.99

Mean 1.07 1.00

7.5-15 ST 0.50 0.52 0.51

0.033**

ns ns BP 0.66 0.63 0.65

CT 0.38 0.37 0.38

Mean 0.51 0.51

Average

(0-15)

ST 0.83 0.79 0.81

0.029**

0.018**

ns BP 0.85 0.81 0.83

CT 0.70 0.67 0.68

Mean 0.79 0.75

Stratification ratio of SOC (%) (0-7.5:7-1.5 cm)

2012-13

(after Crop 7)

ST 2.36 2.05 2.20

BP 1.58 1.58 1.58 0.25** ns ns

CT 2.67 2.60 2.64

Mean 2.20 2.08




The SR of SOC in BP was lower by 39 % than ST and 67 % than CT after Crop 7 (Table

5.7).

175

5.3.2.2 Distribution and stratification of SOC concentrations at strip tillage system in

Digram

After Crop 1 and 4, the SOC concentrations were not significantly different at different

positions relative to the strip of ST in 0-15 cm depth (Figure 5.8). After Crop 7, the SOC

concentrations was higher by 6 % in OS than IS in 0-7.5 cm depth while the SOC

concentrations was 7.4 % greater in IS than that in OS in 7.5-15 cm depth (Figure 5.8).


seasons in Digram relative to depth (at 0-15 cm soil depth before starting of the






in Digram

After Crop 1 and 4, the highest SOC concentrations was measured in BT, 39 % and 44 %

higher than that in BF in the depth of 0-15 cm (Figure 5.9). After Crop 7, the SOC

concentrations of BT were higher by 69 % in 0-7.5 cm depth while by 38 % in 0-15 cm

depth than that in BF (Figure 5.3). On the other hand, the SOC concentrations of BT

decreased significantly (10 %) than that of BF in the 7.5-15 cm depth after Crop 7

(Figure 5.9).

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0-15 cm 0-15 cm 0-7.5 cm 7.5-15 cm 0-15 cm


Soil

org

an

ic c

arb

on

(%

)


OS ISInitial

176







planting system.

5.3.2.4 Temporal variation of SOC

As shown in Figure 5.10, the SOC concentrations at 0-15 cm soil depth increased from

0.73 % to 0.79 % with ST and 0.73 % to 0.83 % with BP. The SOC concentrations slowly

decreased with CT and were significantly lower than ST and BP from the end of Crop 1

and onwards (Figure 5.10).

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0-15 cm 0-15 cm 0-15 cm 0-7.5 cm 7.5-15 cm 0-15 cm


So

il o

rga

nic

ca

rbo

n (

%)


BT BFInitial

177

Figure 5.10. Temporal variation of soil organic carbon (SOC) at Digram. The floating

error bar indicates the average least significant difference (LSD) at P≤0.05 for the

different cropping cycles where they were significantly different. Values are means

across residue levels.

5.3.2.5 Soil organic carbon stocks and sequestration

The initial SOC-stocks was 16.0 Mg C/ha before beginning of the experiment at Digram.

After Crop 1, the effects of tillage and residue on SOC-stocks were not significant

(Table 5.8). After Crop 4, the SOC-stocks were significantly greater by 9 % in ST than CT

(Table 5.8). After Crop 7, the SOC-stocks were greater in ST by 25 % at 7.5-15 cm depth

and by 13 % for the 0-15 cm depth (average of 0-7.5 cm and 7.5-15 cm) than CT (Table

5.8). In all the treatments, the SOC-stocks decreased with soil depth (Table 5.8).

0.60

0.65

0.70

0.75

0.80

0.85

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5

Soil

org

an

ic c

arb

on

(%

)

Cropping cycle

ST BP CT

After crop 1 After crop 4 After crop 7Initial

Tillage X Time

178

Table 5.8. Tillage and residue effects on soil SOC-stocks (Mg C/ha) and sequestration

(Mg C/ha/yr) of cereal-dominated rice-based system at Digram.

Year Soil depth

(cm)

Tillage

treatment1



SOC-stocks (Mg C/ha)

Initial (2010) 16.0 16.0 16.0

2010-11

(after Crop 1)

0-15 ST 17.2 17.0 17.1

ns ns ns CT 16.8 16.4 16.6

Mean 17.0 16.7

2011-12

(after Crop 4)

0-15 ST 16.9 16.8 16.9

1.07** ns ns CT 16.0 14.7 15.3

Mean 16.5 15.7

2012-13

(after Crop 7)

0-7.5 ST 11.1 10.2 10.7

ns ns ns CT 10.0 9.7 9.9

Mean 10.6 10.0

7.5-15 ST 5.5 5.8 5.7

0.26**

ns ns CT 4.4 4.2 4.3

Mean 4.9 5.0

Average

(0-15)

ST 17.1 16.5 16.8

1.46**

ns

ns CT 15.0 14.4 14.7

Mean 16.0 15.4

Annual rates of C sequestration (Mg C/ha/ yr) at 0-15 cm soil depth: 2010-13 (2.5 years)

ST 0.44 0.20 0.32

CT -0.41 -0.66 -0.53 0.59** ns ns

Mean 0.01 -0.23




Annual C sequestration rates at 0-15 cm soil depth in 2010-13 were 200 to 440 kg C/ha

with ST and -410 to -660 kg C/ha with CT (Table 5.8). However, annual C sequestration

rates were on average near zero with HR but declined by 230 kg/ha with LR.

5.3.2.6 Water soluble carbon

Until Crop 7, the WSC concentrations were not significantly (P≤0.05) different among

treatments (Table 5.9). After Crop 7, the WSC was greater by 9 % in ST and by 7 % in BP

than CT, and HR had 7 % higher WSC as compared to LR at 0-7.5 cm soil depth (Table

179

5.9). At 7.5-15 cm soil depth, the WSC was higher by 51 % in STHR and by 63 % in BPHR

than CTLR (Table 5.9). At 0-15 cm soil depth (average of 0-7.5 cm and 7.5-15 cm), the

WSC was significantly greater by 21 % in ST and by 25 % in BP than CT; and compared

to LR, WSC was 9 % greater with HR (Table 5.9).

Table 5.9. Tillage and residue effects on water soluble carbon (mg/kg) of cereal-

dominated rice-based system at Digram.

Tillage

treatment1

After Crop 4 (2011-12) After Crop 7 (2012-13)

0-15 cm 0-7.5 cm 7.5-15 cm 0-15 cm (average)

HR1 LR1 Mean HR LR Mean HR LR Mean HR LR Mean

ST 167 159 163 397 369 383 183 171 177 290 270 280

BP 170 191 180 382 362 372 243 190 217 312 276 294

CT 173 176 175 360 334 347 95 90 92 227 212 220

Mean 170 175 380 355 173 150 277 253

LSD20.05

Tillage (T) ns 21.1** 8.2** 9.95**

Residue (R) ns 9.4** 11.9** 7.2**

TxR ns ns 15.6** ns




The SR of WSC in BP was significantly lower by 26 % and 120 % than ST and CT after

Crop 7 (Table 5.9).

5.3.2.7 Carbon dioxide-carbon (CO₂-C) emission

In 2010-11, the CO₂ emission was not different among treatments at booting stage but

7.5 % lower in BP compared with CT at dough stage (Table 5.10). In 2011-12, the

treatment effects on CO₂ emission at seedling and soft dough stages were not

significant, but the CO₂ emission with ST was less by 35 % at anthesis stage and by 50

% at harvesting stage than CT (Figure 5.11). By contrast, BP produced higher CO₂

emission by 6 % at anthesis and 25 % at harvesting stages over CT in 2011-12 (Figure

5.11).

180

Table 5.10. Tillage and residue effects on CO2-emission (g CO2 m-2day-1) at different

growth stages of wheat at Digram in 2010-11.

Tillage

treatment1

2010-11

Different growth stages of wheat

Booting stage Dough stage

Residue treatment1 Residue treatment1

HR1 LR1 Mean HR LR Mean

ST 8.9 8.2 8.6 18.0 16.7 17.3

BP 7.2 8.3 7.8 14.3 12.2 13.3

CT 8.6 6.6 7.6 14.7 14.0 14.3

Mean 8.2 7.7 15.7 14.3

LSD20.05

Tillage (T) ns 3.36*

Residue(R) ns ns

TxR ns ns




In 2012-13, the CO₂ emission with ST was less by 58 % at seedling stage, 11 % at

anthesis stage and 13 % at harvesting stage than with CT (Figure 5.11). The CO₂

emission with BP was less by 80 % at seedling stage, 17 % at early booting stage, 43 %

at anthesis stage and 58 % at harvesting stage than with CT in 2012-13 (Figure 5.11).

Figure 5.11. Tillage effects on CO₂ flux (g CO₂ m²/day) at different growth stages of

wheat in Digram. The floating error bar on each figure represents the least significant

0

3

6

9

12

15

Seedling stage Anthesis stage Soft dough stage Harvesting stage

CO

₂ f

lux

(g

CO

₂ m

²/d

ay

)

Crop stages

ST BP CT2011-12

0

3

6

9

12

15

Seedling stage Early booting stage Anthesis stage Harvesting stage

CO

₂ f

lux

(g

CO

₂ m

²/d

ay

)

Crop stages

ST BP CT2012-13

181

difference (LSD) for significant effects at P≤0.05. Values are means across residue

levels.

In 2012-13, HR released 18 % more CO₂ at anthesis and 14 % more CO₂ at harvesting as

compared to LR (Figure 5.12).

Figure 5.12. Residue effects on CO₂ flux (g CO₂ m²/day) at different growth stages of

wheat in Digram. The floating error bars represent the least significant difference

(LSD) for significant effects at P≤0.05 for each sampling time. Values are means

across treatments.

5.3.2.8 Correlation among different organic carbon pools

Increases in SOC-stocks, WSC and CO₂ emission were positively and significantly

related to increase in SOC (P≤0.05, P ≤0.01) (Table 5.11).

Table 5.11. Correlation among soil organic carbon forms of cereal-dominated rice-

based system at Digram in 2012-13 (n = 96).

SOC (%) SOC-stocks WSC CO₂

SOC (%) 1

SOC-stocks 0.87** 1

Water Soluble C 0.78** 0.63** 1

CO₂ 0.57** 0.41** 0.41** 1

* - significant at P≤0.05; ** - significant at P≤0.01; SOC - Soil organic carbon; SOC-stocks - Soil

organic carbon stocks; WSC - Water soluble carbon; CO₂ - Carbon dioxide

0

3

6

9

12

15

Seedling stage Early booting stage Anthesis stage Harvesting stage

CO

₂ fl

ux

(g C

O₂

m²/

day

)

Crop stages

HR LR2012-13

182

5.3.2.9 Carbon balances

The cumulative C input to the soil over 2.5 years ranged from 4.7 to 12.9 Mg/ha in ST

and 4.9 to 12.4 Mg/ha in CT (Table 5.12). The cumulative C input with STHR was only

3.4 % greater than CTHR while STLR was 4.3 % lower than CTLR. Conversely, the

estimated C mineralization with CTHR was 12.5 % greater than STHR while CTLR had

35.6 % greater C mineralization than STLR (Table 5.12).

Compared to initial SOC-stocks (16 Mg C/ha) at 0-15 cm soil depth, the SOC-stocks

declined in CT and the greatest decline was observed in CTLR. However, the SOC-stocks

had increased in ST particularly in STHR relative to initial values. Under both ST and CT,

carbon sequestration rate (Y, Mg C/ha/yr) was linearly related to the cumulative C

input (Y = 0.05x-0.13 R² = 0.45) and (Y = 0.05x-0.96 R² = 0.55), respectively during

the 2.5 years period.

183

Table 5.12. Estimated carbon balance for the cereal-dominated rice-based rotation at

Digram considering residue of eight consecutive crops in 2010-2013. STHR = strip




Mg C/ha

SOC-stocks,2010, initial (A) 16.0 16.0 16.0 16.0

SOC-stocks,2013, harvest (B) 17.1 (±0.3) 16.5 (±0.5) 15.0 (±0.3) 14.4 (±0.3)

Change in SOC-stocks, 2010-13 (B-A) 1.1 (±0.1) 0.5 (±0.2) -1.0 (±0.1) -1.6 (±0.1)

C build-up or C losses (%) 6.9 3.1 -6.3 -10.0

C input

2010 Rice 1.31 0.69 1.31 0.69

2010-11 Wheat 1.09 (±0.05) 0.58 (±0.06) 0.98 (±0.04) 0.53 (±0.02)

2011 Mungbean 1.54 (±0.14) 0.0 1.53 (±0.06) 0.0

2011 Rice 2.19 (±0.08) 0.99 (±0.04) 2.17 (±0.14) 0.99 (±0.05)

2011-12 Wheat 1.43 (±0.06) 0.72 (±0.03) 1.33 (±0.06) 0.73 (±0.01)

2012 Mungbean 1.54 (±0.14) 0.0 1.53 (±0.06) 0.0

2012 Rice 2.16 (±0.06) 0.93 (±0.15) 2.15 (±0.13) 1.29 (±0.13)

2012-13 Wheat 1.60 (±0.06) 0.79 (±0.03) 1.40 (±0.03) 0.67 (±0.03)

C-input ∑2010-2013 (C) 12.9 (±0.12) 4.7 (±0.20) 12.4 (±0.04) 4.9 (±0.20)

Mineralized C input in 2010-2013

(C-B+A)

11.8 4.2 13.5 6.5

±values in parentheses indicate standard error

A minimum of 1 Mg C/ha/yr under ST condition and 7.7 Mg C/ha/yr for CT are required

to compensate for SOC loss but under the conditions of the experiment, only the ST

treatment showed a net C sequestration ranging between 0.65 and 0.05 Mg C/ha

(Figure 5.13).

184

Figure 5.13. Relationship between cumulative C input and SOC sequestration during

2.5 years under ST and CT conditions of cereal-dominated rice–based system at

Digram.

5.4 Discussion

In the present study, the effects of tillage and residue retention on SOC were largely

independent. That is, the increase in SOC at both sites after Crop 4 occurred due to

both tillage type and residue retention with no significant interaction between the two

effects.

5.4.1 Tillage effects

In the present study, the SOC-stocks increased at 0-15 cm soil depth under ST

treatment after 4-7 Crops (1.5-2.5 years). Similar findings were reported by Chen et al.

(2009a) and Hernanz et al. (2009) from their 11 years of study that SOC-stocks

increased under NT relative to CT in the upper 15 cm. Several previous studies

indicated that the SOC increased during the initial 10 years of NT practice (Hernanz et

al., 2009). In the present study, the ST and HR practice over 2.5 years resulted in

accumulation of SOC pools at 0-15 cm depth over the current form of intensive tillage

and minimal residue retention, CT and LR, in both legume- and cereal-dominated rice-

based rotations. The reason might be due to the minimum soil disturbance, less

aeration and exposure of SOC fractions within soil aggregates that may have slowed

y = 0.05x - 0.13 R² = 0.45

-4.0

-3.0

-2.0

-1.0

0.0

1.0

2.0

SO

C s

eq

ue

ste

re

d (

Mg

C/h

a/y

r)


Under ST

20

1.0 Mg C/ha /yr

15

y = 0.05x - 0.96 R² = 0.55

-4.0

-3.0

-2.0

-1.0

0.0

1.0

2.0

SO

C s

eq

ue

ste

re

d (

Mg

C/h

a/y

r)


Under CT

20

7.7 Mg C/ha /yr

15

185

the mineralization of SOC under ST compared to CT (Eghball et al., 1994; Al-Kaisi et al.,

2005). In contrast, soil disturbance by tillage disrupted physically protected SOM

within aggregates and together with residue incorporation increased direct contact of

microorganisms with organic matter which increased decomposition in CT (Six et al.,

2002; Verachtert et al., 2009). In addition to decomposition, intensive tillage caused

greater dilution through mixing and redistribution of soil with lesser SOM content from

deeper depths in CT (Doran et al., 1998). In the 0-7.5 cm soil layer, ST and HR

significantly improved SOC concentrations and stocks compared with CT after Crop 7. A

study conducted in the rainfed dryland farming areas of northern China by Liu et al.

(2014b) showed that adoption of NT for 17 years significantly increased the SOC

concentrations over CT only in the 0-10 cm layer. The greater storage of SOC pools at

ST in the current study corroborated the findings of several long-term tillage trials in

rice-based systems of South Asia (Ghimire et al., 2011; Li et al., 2012; Singh et al.,

2014b). Similarly Xu et al. (2013) reported that the SOC-stocks were greater with NT as

compared to plough tillage in a rice-ecosystem. The different residue placement and

position (anchored, mulch, lying and incorporation) under different tillage systems

could be influenced SOC pools. The decomposition rate of incorporated crop residues

was 1.5 times faster than surface-placed residues (Kushwaha et al., 2000; Balota et al.,

2004). In the current study, anchored residues were left on about 70-80 % of

undisturbed soil surface while about 20-30 % soil surface was disturbed for seeding in

ST. Hence, the greater SOC accumulation with ST at 0-15 cm depth could be related to

slower decomposition of the standing residue caused by decreased contact with soil

and residue and to greater conversion of residues into stable SOC (Blanco-Canqui &

Lal, 2008). Similar findings were reported by other researchers at different locations

that the increase of SOC with NT (no-tillage) could be attributed to slower

mineralization caused by reduced contact of residue and soil (Salinas-Garcia et al.,

1997; Al-Kaisi et al., 2005). Both standing residue and residue lying on the surface

under ST may have slower decomposition while also protecting the soil surface from

raindrop splash that breaks down peds. Similarly, Bhattacharyya et al. (2009a) and Das

et al. (2013) reported that ZT accumulated greater SOC through the protection of soil

surface by residue left on the undisturbed soil surface and slow decomposition as a

result of minimum contact between residue and soil.

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The BP increased SOC concentrations and SOC-stocks relative to CT particularly at 7.5

to 15 cm depth; this was attributable to subsurface burial of crop residues within the

permanent bed during reshaping and limited soil disturbance in BP that protected

residue from decomposing organisms. In addition, bed formation involved heaping

topsoil from the furrow onto the bed so that the 7.5-15 cm layer under the bed

probably represents former topsoil. Govaerts et al. (2007) also observed significantly

higher SOM concentrations under permanent raised beds with full residue retention in

comparison with conventionally tilled raised beds in Mexico. By contrast, in CT,

repeated tillage before sowing of each crop causes more soil disturbance and mixing

and incorporation of residue and thereby enhanced rapid decomposition which led to

lower SOC concentrations than in BP.

In the present study, WSC in surface layer of soil was higher under ST than under CT at

both sites after Crop 7; this might be due to the cumulative effects of high crop residue

retention and ST; and increased in SOC which released greater WSC. Liu et al. (2014b)

recorded greater WSC at the upper layers under NT compared to CT; this might be due

to the residue placement near the soil surface. Bhattacharyya et al. (2012a)

demonstrated in a four-year study that application of rice straw was effective in

increasing WSC. However, the highest WSC in the subsurface layer (7.5-15 cm) was

measured in BP. The reason for greater WSC in the subsurface layer of BP might be due

to greater residue buried and SOC from the previous top soil. However, the WSC

concentrations were higher in CT than in BP and ST at 0-15 cm depth after 4 Crops in

Alipur, suggesting more rapid decomposition of SOC and incorporated crop residue in

CT. Guo et al. (2014) found that there was no tillage and residue effect on WSC in a

rice-wheat experiment that lasted for less than 2 years in China.

The emission of CO₂ was in the sequence CT>ST>BP. Possible reasons for greater CO₂

emission at CT attributed to an increase an oxidation of SOC due to increase in soil

aeration, soil-crop residues contact, exposing of soil aggregates and disrupting SOM in

both inter- and intra-aggregate zones (Beare et al., 1994a; Roscoe & Buurman, 2003;

Liu et al., 2006). The present results are consistent with earlier findings that frequency

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and intensity of soil disturbance by tillage is highly related to the extent of CO₂

emission from the soil (Al-Kaisi & Yin, 2005). Repeated rigorous soil disturbance and

residue incorporation with CT accelerated the mineralization of SOC compounds,

thereby increasing CO₂ emission (Reicosky et al., 1995). Verachtert et al. (2009) made a

similar observation in Mexico where they recorded higher CO₂ emission under

disturbed soil (conventionally formed new beds) than under less disturbed permanent

beds. The increased CO₂ emission under CT treatment is consistent with lower WSC

and lower carbon stocks after Crop 7. Al-Kaisi and Yin (2005) also found greater

intensity of soil disturbance under CT, resulting in higher CO₂ emissions. Likewise,

minimum soil disturbance through minimum tillage was effective in increasing SOC

sequestration by decreasing CO₂ emission (Lal, 2004a; Freibauer et al., 2004; Baker et

al., 2007), all these studies corroborating the result of the present study.

The present short-medium term tillage results of both legume- and cereal-dominated

rotations confirm the potential benefit of ST over CT for increasing SOC-stocks.

Restricting release of CO₂ to the atmosphere by using ST and BP will result in increased

C storage and sequestered SOC in the soil. Sequestration of SOC in the soil could

reduce the CO₂ to the atmosphere and thereby contribute to alleviation of global

warming and climate change (Chan, 2008). Alam et al. (2016) have observed consistent

findings in the legume-dominated site of the present study that the mitigation of GHG

emission was enhanced by unpuddled rice transplanting following ST and LR over

conventional puddled transplanting with HR and LR.

5.4.2 Residue effects

In the present study, HR increased SOC concentrations over LR starting from after 4

consecutive crops (after 1.5 years). Given the low SOC levels across Bangladesh, this is

a significant finding. The higher concentrations of SOC after 4 Crop under HR reflect

cumulative residue additions which amounted to 15 t/ha in the legume-dominated

rotation and 19 t/ha in the cereal-dominated rotation, which is in accordance with

other studies (Blanco-Canqui & Lal, 2008). In a Nigerian Alfisol, Juo et al. (1996) found a

75 % decrease in SOC concentrations under no-till maize cropping with residue

removal for 15 years while residue retention doubled SOC concentrations. Overall the

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greater SOC concentrations with HR at 0-15 cm soil depth were most likely due to

greater annual nutrient recycling and carbon input in the form of crop residue (Al-Kaisi

& Yin, 2005; Chivenge et al., 2007). In addition, with increasing microbial activity, fresh

residue acts as nucleation centres for aggregation and enhances the binding of residue

and soil particles into macro aggregates (Six et al., 1999; Zhu et al., 2014) which in turn

contributes to SOC accumulation through increasing protected C in aggregates

(Govaerts et al., 2007). Residue effect on SOC sequestration was also influenced by the

residue retention in addition to the amounts. Incorporating the increased residue

levels into soils (CT and BP) tended to increase residue decomposition that lead to SOC

loss by contrast with standing or anchored residue in ST.

In the present study, HR consistently increased WSC in the surface soil layer at both

sites presumably by the leaching of soluble carbon compounds directly from residues.

Other researchers also found higher WSC concentrations in the surface soil layer under

residue retention than no residue retention after 2 years of rice-wheat rotation (Guo

et al., 2014; Zhu et al., 2014). An additional explanation of higher WSC with HR might

be that with greater substrate, soil microorganisms produced more labile C (Li et al.,

2012). In the current research, HR significantly increased CO₂ emission in both cereal-

and legume- dominated rotations probably due to the increase in C substrate quantity.

These findings correspond to the findings of Dendooven et al. (2012a), who reported

that residue retention as a C substrate increased CO₂ flux compared to residue

removed in a rice-wheat rotation in Mexico. Many other researchers (Bhattacharyya et

al., 2012a; Dossou-Yovo et al., 2016) also reported that residue retention increased

greater CO₂ emission compared with no residue. There are many factors involved that

may affect to soil C mineralization such as soil management, amount and quality of

organic C, soil moisture and temperature, soil structure, clay content, mineral of the

clay, soil pH and sodicity (Vanhala et al., 2007; Dendooven et al., 2012a). In the current

research, increased soil water content in HR compared to LR (See Chapter 4) could

increase microbial activity and hence CO₂ emission. Increased soil water and porosity

at surface soil under residue retention has been reported to improve the soil diffusivity

and contribute to CO₂ emission (Jabro et al., 2008). Greater CO₂ emission with HR

might be due to the higher availability of WSC for decomposition by microbial biomass

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(Chen et al., 2009a). Although there was greater CO₂ emission, the SOC sequestration

rate was also higher in the HR treatment in the present research. The results were

supported by Lal (2004c) who found addition of crop residue sequestered SOC. Chen et

al. (2009a) also found similar results in wheat monoculture in the Loess Plateau of

China. High residue retention increased the sequestration of SOC through providing

the greater physical protection of SOC in macro-aggregates (Halvorson et al., 2002; Six

et al., 2004). It can be concluded that high residue retention together with root

residues react with clay particles and organo-mineral complexes to favour more stable

SOC (Blanco-Canqui & Lal, 2008).

5.4.3 Dynamics of soil organic carbon concentrations

Time elapsed since implementation of tillage and residue treatments is important for

assessing the effects of tillage on SOC (Christopher et al., 2009). Several previous

studies suggested that soil at 0-30 cm depth required about 5-6 years to establish a

new balance between C inputs and outputs after converting from CT to NT in double

cropping systems in temperate and tropical soils (Hou et al., 2012; Singh et al., 2016).

In the present study, the changes of SOC due to tillage were found after Crop 4 (1.5

years) in cereal-dominated system while the changes were evident after Crop 7 (2.5

years) in the legume-dominated system. The effects of HR on SOC were evident in both

systems after Crop 4 (1.5 years) and onwards. The changes of SOC due to tillage and

residue retention became measurable in a relatively short-term in the present study.

This might be due to the addition of increased cumulative residue from three crops per

year, especially from greater amount of rice residue in rice-dryland system in tropics as

compared to one crop per year in only dryland system in temperate and cooler

regions. In the present study, the rice field was inundated for about 3-4 months, which

could enhance the accumulation of SOM through decreasing the decomposition rate of

residue. In addition, temperature and rainfall are two most important factors that

strongly affect the mineralization of SOC (Haddix et al., 2011; Manna et al., 2013). The

experimental site was characterized by high rainfall (annual rainfall>1000 mm), high

humid and hot temperature zone (temperature goes up to ~35-40 ⁰C during summer

months) which enhanced decomposition of residue and caused treatment variation

relatively quickly. Soils with a low initial SOC content often exhibit faster initial C

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sequestration rate than those with high SOC (West & Six, 2007). Geisseler and Horwath

(2009) reported that tillage effects on SOC may not be early detectable under soil of

high SOC content. For example, in a rice–wheat system of Nepal which also had high

residue inputs, Ghimire et al. (2011) found that after 3.5 years of residue retention and

ZT, there was greater increase carbon sequestration compared to that in a CT system.

After four years, Das et al. (2013) found an increase in total SOC-stocks under ZT and

partial or full residue retention in a cotton-wheat experiment in India. Majumder et al.

(2008) found that high temperature, a strong oxidative environment and the disrupting

effect of intensive cultivation led to a rapid oxidation of SOC of rice-wheat system in

West Bengal, India. Hence, the reduced soil disturbance with ST, plus the increased

residue retention may lead to relatively rapid changes in SOC. Furthermore, wetland

rice cropping sequences may have a higher potential for carbon sequestration relative

to other tropical ecosystems due to slower decomposition in anaerobic soil for 3-4

months per year, plus the contribution of algal photosynthesis to C accumulation

(Kukal et al., 2009).

The build-up of SOC-stocks with C inputs under different tillage was proportional to the

total C inputs. In the present study, a significant positive linear relationship was found

between the cumulative crop residue C inputs and the SOC sequestration to the soils

over a 2.5 years period. These results are consistent with those reported by other

researchers (Kong et al., 2005; Majumder et al., 2007). The linear relationship between

cumulative C inputs and the SOC sequestration indicated that the addition of C inputs

at a reasonably high rate (6-7.7 Mg C input/ha/yr) is required for CT to maintain the

antecedent level (zero change). The conventional cultivation techniques declined the

SOC-stocks even after continuous addition of C input for 2.5 years. On the other hand,

the lower rates of C inputs (1-3.8 Mg C input/ha/yr) are required for ST to allow a net C

sequestration in the rice-based system.

The experimental soils of the present study may still have a capacity for further C

sequestration. Majumder et al. (2008) added 7.5-10.0 Mg C/ha/yr through farm yard

manure, paddy straw, and green manure and 3.9-4.1 Mg C/ha/yr through crop

residues to the soil in a 19-yr-old rice-wheat experiment of India and showed that the

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soil still had a capacity to sequester further SOC. However, even the application of C

input 9.61 Mg C/ha under STHR and 9.56 Mg C/ha under CTHR through crop residue

could not prevent N loss and resulted in the net decline of 0.1 to 0.18 Mg C/ha during

2.5 years in the legume-dominated rotation at Alipur. The greatest loss of SOC-stocks

in CTLR was 1.8 Mg C/ha in the legume-dominated rotation. However, the extent of

SOC depletion was reduced by ST and retention of HR. Therefore, higher rates of C

inputs are required to compensate the losses of SOC; in the rainfed legume-dominant

rotation of the current study at least 3.8 Mg C/ha/yr of C input under ST and 6 Mg

C/ha/yr of C input under CT were required to maintain SOC-stocks at the antecedent

level. However, in the irrigated cereal-dominant rotation 7.7 Mg C/ha/yr of C input

under CT and 1 Mg C/ha/yr of C input under ST were required to maintain SOC at the

antecedent level. This critical rate of C input compares with 3.3 Mg C/ha/yr reported

by Srinivasarao et al. (2014) for pearl millet-cluster bean-castor rotation in Western

India, 3.3 Mg C/ha/yr reported by Kong et al. (2005) for Davis, California, 3.56 Mg

C/ha/yr by Majumder et al. (2008) for irrigated rice-wheat systems in IGP, and 4.59 Mg

C/ha/yr by Majumder et al. (2007) for long term intensive triple cropping (rice-wheat-

jute) systems in hot humid and subtropics of India. Likewise, Manna et al. (2005)

reported that 4.3 Mg C/ha/yr for soybean-wheat, 5.5 Mg C/ha/yr for rice-wheat-jute

and 6.1 Mg C/ha/yr for sorghum-wheat system was required to maintain SOC in sub-

humid and semi-arid tropical India. Also, Srinivasarao et al. (2012) reported that a

minimum quantity of 2.47 Mg C/ha/yr is required to maintain a stable SOC level for

mineral fertilizer and manuring treatments of a rice-lentil rotation in the IGP. However,

in present study, the amounts of C input requirement under CT were higher, probably

due to higher losses of SOC (Figure 5.7 and 5.13), indicating that intensive tillage for

three crops per year with addition of LR exposes soil and reduces SOC storage.

5.4.4 Distribution and stratification of soil organic carbon concentrations

After Crop 7, tillage had greatly affected the vertical distribution of SOC

concentrations, SOC-stocks and WSC both in the legume-dominated and cereal-

dominated rotations. In the present study, the SOC and WSC at surface (0-7.5 cm)

under ST were greater compared to CT and BP while BP had greater SOC and WSC in

the subsurface soil (7.5-15 cm soil depth). The reasons for lower SOC accumulation in

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the surface soil of CT have been already discussed above. However, the reason for

lower SOC accumulation in subsurface soil (7.5-15 cm soil depth) in CT was linked with

tillage depth. In the present study, maximum tillage depth was confined to 7-8 cm

depth and thus crop residue was not incorporated into the soil at 7.5-15 cm depth. For

the same reason, HR enhanced SOC and WSC compared to LR in the top 7.5 cm of soil,

but the effects of residue disappeared in the subsurface layer.

In the present study, residue effects suggested that retention of HR resulted in a

stratification of SOC and WSC concentrations. Similar observations have been reported

by other researchers (Dolan et al., 2006; Jagadamma & Lal, 2010). It might be

attributed to accumulation and decomposition of high residue at surface soil than

subsurface soil layer. Some other studies showed a similar observation in that high

values of WSC at surface soil might be due to the decomposition of crop residues

(Wright et al., 2007).

In the current study, greater SOC and WSC accumulated off the strip than in the strip.

Since the position of the strip was shifted from crop to crop, the differences between

off and in the strip on soil carbon accumulation depends on the current crop, previous

rice crop and the placement of residue and fertilizer. In the current study, soil sampling

took place every year at the end of the dryland crop. In the strip, tillage could break

down the soil aggregates and expose the protected SOC to microbial decomposition

and limit the accumulation of SOC (Chen et al., 2009a). On the other hand, the

retention of previous rice crop residue on undisturbed zone between the strip may

favour the formation of macro-aggregates and accumulation of SOC in the surface soil.

As a result, active roots of previous rice crop may help formation of aggregation. Kong

et al. (2005) found that active roots can contribute to increased aggregation. By

contrast for the cereal-dominated system, the SOC concentrations at 7.5-15 cm depth

were greater in the strip than off the strip. This could be due to the penetration of

wheat roots to deeper in the soil profile and greater wheat root biomass which might

have contributed to greater SOC accumulation in the strip at 7.5-15 cm.

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5.4.5 Cropping system differences

After changing from the conventional system to a conservation agriculture system for

2.5 years, ST increased SOC sequestration by 0.20-0.44 Mg C/ha/yr at 0-15 cm depth in

the cereal-dominated rotation. In contrast, in the legume-dominated system neither

ST nor CT sequestered the SOC, however STHR declined by 0.04 Mg C/ha/yr and CTLR

declined by 0.74 Mg C/ha/yr at 0-15 cm depth, suggesting that STHR is near a steady-

state condition and reduced SOC depletion than CTLR in this system. Globally, the C

sequestration rates by different management practices range from 0.11 to 3.04 Mg

C/ha/yr with a mean of 0.54 Mg C/ha/yr (Conant et al., 2001). Hence the rate for ST

with HR at 0.44 Mg C/ha/yr in the cereal-dominated system was close to the mean of

the previous studies. Lam et al. (2013) showed from a meta-analysis of Australian

studies that improved management practices increase stores of SOC only at 0-10 cm

depth but over time the rate of accumulation diminishes. In this research, the SOC

increased by 12-14 % under ST and BP with HR while there were no changes in the

legume-dominated system relative to the initial value. In the current study, the SOC-

stocks showed no or slightly negative changes even with ST and HR in legume-

dominated system while positive changes with ST and HR in cereal-dominated system.

The increase in SOC under ST and HR might be due to the increased crop residue inputs

(7 crops) over 2.5 years, leading to a recovery of SOC levels in soils depleted through

previous intensive soil management. As for example, the inputs of C on STHR in the

cereal-dominated rotation were ~12.9 Mg C/ha greater compared with legume-dominated

rotation and this may account for much of the difference between the C sequestration

rates in the two rotations. Paul et al. (1999) reported that the crop with high residue e.g.

cereal crops can increase SOC than crops with low residue e.g. legume crops. In addition,

anchored residue retention of three wheat crops in the cereal-dominated system may

create a cooler soil environment which decreases the decomposition of wheat residue

leading to a greater C:N ratio than in the legume-dominated system. By contrast,

retention of loose lentil residue having lower C:N ratio may increase the contact

between soil and residue and thereby accelerated the decomposition in legume-

dominated system (Singh et al., 2005b). In a study, Fontaine et al. (2004) demonstrated

that fresh C supply under nutrient-limited conditions accelerated the decomposition of

soil C, increased soil C losses through increasing mineralization and induced a negative

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C balance. Consequently, the lower inherent soil fertility and lower available nitrogen

resulted in faster breakdown of residue in the legume-dominated rotation than the

cereal-dominated rotation in present study (See Table 2.2 in Chapter 2). In a

comparison of cropping systems, Witt et al. (2000) reported 11–12 % greater C

sequestration in soils that were continuously cropped with rice for two years than in

maize-rice rotation systems in the Philippines. Sainju et al. (2005) also reported that

non-legume crops are more effective in increasing soil organic matter by supplying C

through increased biomass production compared with legume crops. In the current

study, the SOC sequestration found in the cereal-dominated system with ST was likely

a consequence of higher C input and high C:N ratio which slowed decomposition in the

cereal-dominated system compared to lower amount and rapidly decomposable

legume residue (low C:N ratio) in the legume-dominant system. The C sequestration in

the cereal-dominated rotation would also be favored by the higher N fertilizer

application than legume-dominated rotation. The application of N fertilizer caused to

increase the amount of crop residue inputs to the soil which in turn affects the change

in SOC as discussed above. From a study with dryland crops, Halvorson et al. (1999)

reported that application of high N significantly increased crop residue input to the soil

that resulted in increased SOC sequestration. In addition, greater root biomass and

higher root exudates of wheat may also contribute to a significant quantity of SOC in

the cereal-dominated system as compared to the legume-dominated system.

Moreover, irrigation for wheat crop increases the above and below ground biomass

and thereby increased SOC sequestration in cereal-dominated system. In the cereal-

dominated system, application of irrigation increased the decomposition of residue

and thereby increased CO₂ emission relative to legume-dominated system. Kochsiek et

al. (2009) also found that irrigation could increase the CO₂ emission through the

decomposition of residue. Nevertheless, the SOC sequestration rate was still the

dominant process in cereal-dominated system, consistent with observations by Hazra

et al. (2014), who reported that greater SOC substances and higher fertilizer

application for cereal crops might be contributed to increased SOC. Though all C input

did not go into the soil and some was lost directly to the atmosphere which was

unaccounted in this study. Hence, determination the amount of residue input lost

directly to the atmosphere can be considered for future research.

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Clay content is one of the major factors influencing the capacity of SOC storage (Six et

al., 2002). In the present research, higher clay content in the soil in the cereal-

dominated system (See Chapter 2) might have contributed to higher SOC accumulation

as compared to the legume-dominated system. Soil with higher clay content tends to

restrict the decomposition rates of residue (Franzluebbers, 1999; Balesdent et al.,

2000; Liu et al., 2014a; Xu et al., 2016). In addition, Huang et al. (2002) found a strong

negative relationship between the decomposition of wheat residue and clay content.

Xu et al. (2016) reported that high clay content in soils and higher C:N ratio reduces

the decomposition rates which could account for higher SOC accumulation in the

cereal-dominated system. From a long-term experiment of southern piedmont and

coastal plain in USA, Causarano et al. (2008) measured increased SOC under high clay

content and cooler temperature.

5.5 Conclusions

In the rice-based system in Bangladesh there was greater carbon sequestration after

Crop 7 (2.5 years) from ST and BP with HR compared to CT with LR retention. The

changes of SOC from tillage and residue management started to become evident after

Crop 4 (1.5 years) in the cereal-dominant cropping system. In contrast in the legume-

dominated cropping system, only the HR increased SOC after Crop 4. The results

suggest that ST promoted an accumulation of crop residues in the upper soil layers (0-

7.5 cm) and thereby increased the storage of SOC, WSC (the labile form of carbon) and

SOC-stocks in the medium-term (within 2-3 years). Bed planting treatments also

improved SOC contents, WSC and SOC-stocks through HR accumulation especially at

7.5-15 cm depth. Furthermore, the BP and ST treatments are effective in reducing soil

CO₂ emission and improving soil C sequestration and thus posed the least threat to the

environment through alleviating global warming and climate change relative to CT.

However, low SOC in CT is likely due to greater losses of carbon through SOC

mineralization and residue incorporation under prevailing hot and humid weather that

accelerated the loading of CO₂ to the atmosphere and thereby increasing global

warming potential. In the present study, the relative efficacy of tillage in storing SOC

was in the order of ST>BP>CT. Strip tillage sequestered 0.44-0.20 Mg C/ha/yr while

there were 0.41-0.66 Mg C/ha/yr losses in CT at 0-15 cm depth in the cereal-

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dominated rotation. By contrast in the legume-dominated rotation, neither CT nor ST

sequestered SOC. However, to maintain an equilibrium level of SOC under ST, a

minimum of 3.8 Mg C/ha/yr for legume-dominated system and 1 Mg C/ha/yr for the

cereal-dominated system. Under CT condition, the critical amount of C input to the soil

to maintain the antecedent level (zero change) is 6 Mg C/ha/yr for the legume-

dominated system and 7.7 Mg C/ha/yr for cereal-dominated system during 2.5 years.

Nevertheless, the antecedent rate is minimal and lower than the threshold level. The

results of the present study suggested that ST and HR management are effective in

improving the productivity and sustainability through the improvement of SOC, SOC-

stocks, WSC and SOC sequestration compared to CT. However, ongoing studies are

needed to confirm in the long-term that more soil carbon is stored under minimum

tillage and increased residue retention and at what point in time a new equilibrium of

SOC is reached. The implications of increased SOC for nitrogen availability and

turnover will be examined in Chapter 6.

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6 Effects of tillage and residue on N cycling and dynamics in two paddy

soils in Bangladesh

6.1 Introduction

Crop and soil management affect N mineralization through the frequency and extent

of tillage, nitrogen fertiliser use and organic matter inputs, especially those through

residue management (Angus et al., 2006). Decomposition of crop residues in soil with

accompanying mineralization and immobilization of N are major processes in the soil-

plant N cycle (Watkins & Barraclough, 1996). However, the impact of minimum tillage

and residue retention on N mineralization is still inconclusive (Verhulst et al., 2010).

Intensive tillage increases the mineralization of soil total N (Schomberg & Jones, 1999).

Schoenau and Campbell (1996) reported that initially crop residue enhanced N

immobilization which led to initial higher N fertilizer requirements that decreased over

time because of the build-up a larger pool of readily-mineralizable organic N. Nitrogen

storage increased due to surface residue retention under strip tillage while residue

incorporation in conventional systems increased microbial activity and N

mineralization (Sainju et al., 2013).

Crop residues help to meet the N requirement of crops but their effectiveness as a

source of N depends on the amount and composition of residue added, as well as the

rate and timing of mineralization of N in relation to the crop demand (Kushwaha et al.,

2000). Thus it was hypothesized that minimum tillage and increased level of residue

retention can be used to enhance the magnitude of soil N pools and their availability in

soil, which might offset the potentially negative impact of residue removal and

conventional tillage (CT) on N supply. Due to large pool size and inherent spatial

variability of total N (TN), it takes a long time to measure the changes in TN of soil due

to changes in management practices (Franzluebbers et al., 1995). Therefore,

measurement of TN alone in the short-term may not sufficiently reflect changes in soil

N supply and availability (Franzluebbers et al., 1995). Biologically active fractions such

as potentially mineralizable N (PMN) and active fractions of TN such as total soluble

nitrogen (TSN) change more rapidly with time (e.g. within a growing season) which

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would more rapidly reflect changes in N dynamics (Bremer & Van Kessel, 1992). There

are many factors involved in breaking down of SOM and the subsequent N

transformation process. Briefly, the important pathways of these transformations

process under the condition of conservation agriculture are presented in the following

conceptual diagram (Figure 6.1).

Figure 6.1. The conceptual model of N cycling of conservation agriculture system.

Tillage frequency and residue management greatly influenced the distribution and

cycling of soil organic carbon (SOC) and nutrient availability to plants (House et al.,

1984). Doran et al. (1998) found that no-tillage (NT) increased SOC and N storage, and

biological activity at surface soil (0-7.6 cm) and thus increased the potentiality for

immobilizing of plant available N in organic forms. Tillage and residue management

also greatly affect the active fraction of soil organic N, PMN (Mikha et al., 2006) which

is an approximation of mineralizable soil N supply in the coming season (St. Luce et al.,

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2011). Theoretically, PMN is the amount of N that will mineralize in infinite time at

optimum temperature and moisture (Curtin & Campbell, 2007). Doran (1987) found

that PMN was greater in the surface 7.5 cm soil layer under ZT than in CT. From four

long-term tillage trials in Canada, Sharifi et al. (2008) concluded that there was greater

PMN under NT than under CT at three of the four sites.

Total soluble soil nitrogen (TSN) is a sensitive indicator of soil quality changes in

response to alterations in soil management (Zhang et al., 2011). Total soluble N is an

active and labile pool of organic N (Murphy et al., 2000; Liu et al., 2005) and plays a

vital role in many soil biogeochemical processes (Kalbitz & Geyer, 2002). Consequently,

TSN in soils is increasingly recognized as an important component of N cycling and

biological processes in soil-plant ecosystems (Ghani et al., 2007). It originates from

many compounds which enter the soil from a range of sources including deposition of

crop residue, litter fall, root and microbial exudation, turnover of roots and organisms,

urine and faeces, and organic fertilizer additions to soil (Kalbitz et al., 2000; Haynes,

2005). In most ecosystems, TSN plays an important role in mineralization, N leaching

and plant uptake (Jones et al., 2004). It is often analyzed to predict the effect of these

processes and nutrient management practices on N availability (Ros et al., 2009).

In the previous Chapter, effects of tillage type and residue retention on C pools,

accumulation, availability and balance were examined. However, limited research has

been carried out on the effect of minimum tillage and crop residue retention on the

dynamics of soil N, its storage, availability and N balance in intensive rice-based

systems. Moreover, this study provided a unique opportunity to examine the short-

term (2.5 years) effects of tillage and residue management on soil N concentrations,

storage, and availability indices of soil N in the surface 0-7.5 cm and 7.5-15 cm layer

and their effects on plant N in rice-based systems in Bangladesh. The objectives of the

study were to: 1) evaluate the short-term influence of tillage and residue on N

concentrations and accumulation, N availability, N uptake, labile forms of N, plant N

and N balance, 2) examine the relationship of different N forms, crop yield and SOC

changes under tillage and residue levels.

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6.2 Materials and methods

6.2.1 Site description and field management

Details of the experimental site and establishment were described in Chapter 2.

Briefly, tillage treatments consisted of strip tillage (ST), bed planting (BP) and

conventional tillage (CT) with two residue levels, high (HR) and low residue (LR)

applied. Cropping systems studied over three years were lentil-mungbean-monsoon

rice at Alipur and wheat-mungbean-monsoon rice at Digram. In the cropping

sequence, lentil or wheat was Crop numbers 1, 4 and 7, mungbean was Crop numbers

2 and 5, and monsoon rice was Crop numbers 3 and 6.

6.2.2 Plant measurements

Leaf, grain and straw sampling, and processing

For plant N analysis, 200-250 youngest fully expanded lentil leaves per plot were

collected just before flowering or at initiation of flowering (at 55-60 DAS) and 25-30

flag leaves or youngest emerged leaves of wheat at booting stage (at 60-65 DAS). After

harvesting and drying, 100 g of grain and straw samples of lentil and wheat were

collected randomly from each plot for N analysis. Samples were oven-dried at 72 °C for

three days and ground to <0.1 mm and total N of plant samples was determined using

a Kjeldahl method (O'Neill & Webb, 1970).

6.2.3 Soil measurements

6.2.3.1 Soil sampling procedures

Initial soil samples prior to the establishment of these two experiments and at the end

of each winter crop (last week of March-first week of April) at 0-15 cm depth in 2011

and 2012; and 0-7.5 cm and 7.5-15 cm depths in 2013 were collected. Additionally,

samples were taken at these depths in the middle of the winter crop season (at 30-60

DAS) and at harvest for the determination of mineral N.

201

The entire sample was collected from: in between the rows and in the rows of ST plots;

in between the rows on centre of the bed and in the furrow of the bed of BP plots; and

between the plants of CT plots. Sampling was done by an auger from 6-9 different

places within individual replicated plots. For each sampled depth in an individual plot,

the samples were mixed together to prepare a composite sample. After collection, the

samples were air dried, visible root fragments, stones and inert matter were manually

removed and the soil sieved through a 2 mm mesh, then mixed, placed in plastic bags

and stored in a freezer for analyses of N fractions. In order to compare seed bed or

field condition under different treatments, ST — average value of in the strip (IS) and

off the strip (OS) was compared with centre of the bed and CT treatment. However,

the comparisons between OS (off-the strip) and IS (in the strip); and between BT (from

the bed top) and BF (from the level of the bed top in the furrow) of the bed planting

(BP) system were also examined. The details method of sampling locations on BP has

been clarified in Chapter 4 (Section 4.2.6 Sampling time and location).

6.2.3.2 Bulk density

The soil was sampled at 0-5 cm, 5-10 cm and 10-15 cm soil depth for the measurement

of bulk density (BD). The detail procedures for measuring BD are described in Chapter

4. However, the BD at 0-7.5 cm, 7.5-15 cm and 0-15 cm soil depths for the

measurement of N-stocks of ST and CT were calculated using the following formula:

BD at 0-7.5 cm: [BD0-5 + (BD5-10)/3]

BD at 7.5-15 cm: [(BD5-10)/3 + BD10-15]

BD at 0-15 cm: [(BD0-5 +BD5-10+ BD10-15)/3]

Where, BD0-5, BD5-10 and BD10-15 are the bulk density at 0-5 cm, 5-10 cm and 10-15 cm

soil depth.

6.2.3.3 Total soil N and N-stocks

Total N of soil samples was determined using a Kjeldahl method (O'Neill & Webb,

1970). The N-stocks for a layer of soil was calculated using the equation of Lal et al.

(1998):

202

N − stocks (Mg N ha¯1) =TN (%) X bulk density (Mg m¯³) X d (m)X 104m²ha¯1

100

Where, d is the thickness of soil layer (m), which in this case was 0-0.075 m, 0.075 -

0.15 m or 0-0.15 m. The N-stocks and sequestration rate in BP was not computed in

this study due to uncertainty with bed height and changes over time in bed height

among the different crops.

6.2.3.4 Soil N accumulation

In this study, the annualized soil N gains or losses for 0-15 cm soil depth through tillage

and crop residue treatments were calculated using the following formula:

Nacc = (M2010-M2013)/2.5

Where, Nacc is the amount of change in N-stocks (Mg N/ha), M2010 and M2013 are the

amount of N-stocks at 0-15 cm soil depth in 2010 and 2013, respectively, and 2.5 is the

study period (years) for the experiment. Positive and negative values indicate annual

TN gains and losses respectively at 0-15 cm soil depth for the cropping system.

6.2.3.5 Nitrogen uptake

Crop N uptake (kg/ha) was calculated by multiplying grain and biomass yields by their

N concentrations.

6.2.3.6 Mineral N pools

Air-dried, ground and sealed soil samples for mineral N (NH₄-N plus NO₃-N)

determination were stored at ~2-3 °C in a refrigerator until the analysis. Soil NO₃-N and

NH₄-N in filtered 2 M KCl extracts were determined by the copper-cadmium reduction

method (Johnson, 1983) and the alkaline phenate method (Switala, 1993),

respectively. Analysis was carried out on a Lachat quick-chem 8500 Series II automated

flow injection analyser.

6.2.3.7 Anaerobic potentially mineralizable N

Potentially MN resulting from anaerobic incubation was determined under laboratory

conditions as the difference between NH₄-N measured in two separate soil extracts

using 2 M KCl (Rayment & Lyons, 2011). The first is extracted immediately and the

203

second after a seven-day anaerobic incubation of soil covered by water at 40°C. The

seven-day incubation was carried out in sealed containers that have minimal

headspace. After incubation, 2.5 M KCl extracting solution was added (extractant now

≈2 M KCl) and mixed prior to analysis. The initial (day zero) extraction was the same as

that carried out for mineral-N, based on 2 M KCl extraction (see Section 6.2.4.4).

6.2.3.8 Total soluble N

An air-dried soil sample from each treatment was treated with deionized water using

soil/water ratios 1:4 (W/V) for 60 minutes under agitation in a flask. After extraction,

samples were centrifuged at 4000 rpm for 30 minutes. Supernatants were filtered

through Whatman 42 filter paper. Total soluble N (TSN) of the filtrate was determined

from autoclave digests with potassium persulphate (Valderrama, 1981). Analysis was

carried out on a Lachat quick-chem 8500 series II automated flow injection analyser.

6.2.3.9 Nitrogen balance calculations

The N balance was calculated by accounting for changes in TN between 2010 and

2013, comprising the N inputs from residue, fertilizer, irrigation and rainfall, and net N

removal by grain and straw (i.e., total N in above-ground dry matter minus the N

returned to soil in crop residues). Grain N removed was calculated from total grain

yield multiplied by their N concentrations. Straw N removed was calculated from total

residue minus residue retained multiplied by their N concentrations. The N

concentrations of retained residue and harvested above ground residue of cereal crop

were determined separately. The residue N of rice, wheat and lentil was determined

by a modified Kjeldahl method (O'Neill & Webb, 1970). Nitrogen inputs from rainfall

were computed from the estimated amount of rainfall during the experimental period

(3142 mm rainfall occurred in the experimental period). According to Timsina and

Connor (2001) the annual rainfall of 1000 mm provides ~ 10 kg N/ha, thus 3142 mm

rainfall that occurred in the experimental site contributed 31.4 kg/ha N in the present

experiment. Based on the study by Timsina et al. (2006) at Ishurdi, Bangladesh, a

nearby location of the present experimental site, the amount of N contributed from

irrigation during the total experimental period was about 12.6 kg N/ha in the present

study (annually the average contribution of N was 4.2 kg N/ha through irrigation). In

204

the present research other important sources of N input or output not measured were:

N inputs from biological nitrogen fixation (BNF), gaseous N losses from ammonia

volatilization and de-nitrification, leaching and runoff losses. Thus, the N balance

estimates only net N gains or losses in the current study.


The GenStat 15th Edition software package was used for all statistical analyses. A split-

plot (main plot: tillage and sub-plot: residue) and split-split-plot (main plot: tillage; sub-

plot: residue; sub-sub-plot: cropping cycle) analysis of variance (ANOVA) was employed

to test the difference among treatments. When the F-test was significant, treatment

means were separated by least significant difference (LSD) at P≤0.05. Correlation

analysis were performed based on Pearson correlation coefficients using Microsoft

Excel to determine correlations among soil N fractions and the significant probability

levels of the results were given at P≤0.05 (*) and P≤0.01 (**).

6.3 Results

6.3.1 Alipur

6.3.1.1 Total soil N concentrations

After Crop 4, TN concentrations at 0-15 cm depth showed no significant differences

(P≤0.05) due to different tillage and residue treatments (Table 6.1). After Crop 7, the

TN concentrations in CT was 13 % and 6 % lower than BP and ST, respectively; and HR

was 4.7 % higher than LR at 0-7.5 cm depth (Table 6.1). At 7.5-15 cm depth, the TN

concentrations in CT were lower by 11 % and 18 % than ST and BP (Table 6.1). At 0-15

cm depth (average of 0-7.5 cm and 7.5-15 cm), it was 11 % greater in BP or ST than CT;

and HR was 4 % higher than LR. Irrespective of the treatments, the TN concentrations

decreased with increasing depth of soil (Table 6.1).

205

Table 6.1. Tillage and residue effects on total soil N concentrations and stratification

ratio of TN concentrations during 2.5 years of the legume-dominated rice-based

cropping system at Alipur.

Year Soil depth

(cm)

Tillage

treatment1


HR LR Tillage (T ) Residue (R) TxR

Total soil N concentrations

2010-11

(after Crop 1)

0-15 ST 0.079 0.080 0.080

ns ns ns BP 0.081 0.081 0.081

CT 0.080 0.081 0.080

Mean 0.080 0.081

2011-12

(after Crop 4)

0-15 ST 0.083 0.081 0.082

ns ns ns BP 0.083 0.081 0.082

CT 0.080 0.079 0.079

Mean 0.082 0.080

2012-13

(after Crop 7)

0-7.5 ST 0.114 0.109 0.111

0.005** 0.004** ns BP 0.106 0.103 0.104

CT 0.102 0.094 0.098

Mean 0.107 0.102

7.5-15 ST 0.061 0.061 0.061

0.008**

ns ns BP 0.068 0.066 0.067

CT 0.055 0.054 0.055

Mean 0.061 0.060

Average

(0-15)

ST 0.088 0.085 0.086

0.006**

0.002**

ns

BP 0.087 0.084 0.086

CT 0.079 0.074 0.077

Mean 0.084 0.081

Stratification ratio of TN concentrations (0-7.5:7.5-15 cm)

2012-13

(after Crop 7)

ST 1.86 1.78 1.82

BP 1.56 1.57 1.57 0.17** ns ns

CT 1.88 1.74 1.81

Mean 1.77 1.69




The stratification ratio (SR) of TN concentrations after Crop 7 for the surface (0-7.5 cm)

relative to subsurface soil depth (7.5-15 cm) was significantly affected by different

tillage practices (Table 6.1). The SR for the TN of the BP treatment significantly

decreased by 15-16 % compared with ST and CT (Table 5.2).

206

The treatment effects on C:N ratio were not significantly different in all the sampling

times at Alipur(Appendix 2).


system in Alipur

The soil TN concentrations did not alter significantly (P≤0.05) by location relative to the

tilled strip of ST (Figure 6.2).








system in Alipur

After Crop 1 and 4, the highest soil TN concentrations were measured in BT, 48 % and

45 % higher than that in BF in the depth of 0-15 cm (Figure 6.3). At the end of Crop 7,

the soil TN concentrations of BT increased by 71 % in 0-7.5 cm while 37 % in 0-15 cm

depth than that of BF (Figure 6.3). Conversely in 7.5-15 cm depth after Crop 7, the soil

TN concentrations of BT decreased by 14 % than that of BF (Figure 6.3).

0.00

0.03

0.06

0.09

0.12

0.15

0-15 cm 0-15 cm 0-7.5 cm 7.5-15 cm 0-15 cm


Tota

l so

il n

itro

gen

(%

)


OS ISInitial

ns

ns

ns ns

207







planting system.

6.3.1.4 Temporal variation of soil total nitrogen concentrations

The TN concentrations at 0-15 cm depth increased under ST and BP from initially

(before experiment) to after Crop 7 (0.074 % to 0.086 %) (Figure 6.4). The TN

concentrations were greater under ST and BP than CT after 2.5 years.

0.00

0.03

0.06

0.09

0.12

0.15

0-15 cm 0-15 cm 0-15 cm 0-7.5 cm 7.5-15 cm 0-15 cm


Tota

l so

il n

itro

ge

n (

%)


BT BFInitial

208

Figure 6.4. Temporal variation of total soil nitrogen (%) at Alipur. The floating error

bar indicates the average least significant difference (LSD) at P≤0.05 for the different

cropping cycles and tillage. Values are means across residue levels.

6.3.1.5 N-stocks

The soil N-stocks were not significantly (P≤0.05) influenced by treatments until Crop 7

(Table 6.2). At the end of Crop 7, the N-stocks under ST were significantly higher by 11

% than CT at 0-7.5 cm depth. At 0-15 cm depth (average of 0-7.5 cm and 7.5-15 cm),

the N-stocks under ST after Crop 7 were 11 % higher than CT and 8 % greater than

initial N-stocks (Table 6.2). After Crop 7, the N-stocks under HR were 3 % higher than

LR and 7 % greater than initial value of N-stocks at 0-15 cm soil depth (average of 0-7.5

cm and 7.5-15 cm) (Table 6.2). In all treatments, the N-stocks decreased with soil

depth (Table 6.2).

0.070

0.075

0.080

0.085

0.090

0 1 2 3 4 5

Tota

l so

il n

itro

ge

n (

%)

Cropping cycle

ST BP CT

After crop 4 After crop 7After crop 1Initial

Tillage X Time

209

Table 6.2. Tillage and residue effects on N-stocks (Mg N/ha) and N-accumulation rate

during 2.5 years of the legume-dominated rice-based cropping system at Alipur.

Year Soil

depth

(cm)

Tillage

treatment1



2010-11

(after Crop 1)

0-15 ST 1.90 1.88 1.89 ns ns ns

CT 1.88 1.98 1.93

Mean 1.89 1.93

2011-12

(after Crop 4)

0-15 ST 1.89 1.84 1.86 ns ns ns

CT 1.81 1.84 1.83

Mean 1.85 1.84

2012-13

(after Crop 7)

0-7.5 ST 1.20 1.16 1.18 0.05** ns ns

CT 1.08 1.02 1.05

Mean 1.14 1.09

7.5-15 ST 0.74 0.74 0.74 ns

ns ns

CT 0.66 0.66 0.66

Mean 0.70 0.70

Average

(0-15)

ST 1.98 1.93 1.95 0.14* 0.06* ns

CT 1.77 1.70 1.74

Mean 1.88 1.81

Annual rates of N accumulation (Mg N /ha/ yr) at 0-15 cm soil depth: 2010-13 (2.5 years)

ST 0.076 0.054 0.065

CT -0.008 -0.035 -0.022 0.05** 0.02* ns

Mean 0.034 0.009




6.3.1.6 Nitrogen accumulation

Annual N accumulation rates at 0-15 cm soil depth during the period 2010-13 were 65

kg N/ha with ST and -22 kg N/ha with CT. Compared to LR, the N accumulation rate

was 3.6 times higher with HR (Table 6.2).

6.3.1.7 Nitrogen uptake by lentil plants

The N uptake by grain was greater by 4.8 % in 2010-11 and by 19 % in 2011-12 with HR

than LR (Table 6.3). In 2012-13, the N uptake by grain and straw of lentil were greater

by 28 % with ST and 21 % with BP than CT (Table 6.3). The N uptake by straw was 24.4

210

% greater with HR than LR in 2011-12 (Table 6.3). In 2012-13, the N uptake by straw

was greater by 31 % in ST and 28 % in BP than CT (Table 6.3).

Table 6.3. Tillage and residue effects on N uptake by lentil plants in 2010-11, 2011-12

and 2012-13.

Tillage

treatment1

2010-11 2011-12 2012-13


N uptake by lentil grain (kg/ha)

ST 85.5 86.1 85.8 99.4 70.0 84.7 130.1 107.8 118.9

BP 72.6 63.4 68.0 96.6 81.3 89.0 112.8 105.0 108.9

CT 83.8 80.7 82.3 105.1 92.6 98.8 90.9 81.4 86.2

Mean 80.6 76.7 100.4 81.3 111.2 98.1

LSD20.05


Residue (R) 1.6* 8.1** ns

TxR ns ns ns

N uptake by lentil straw (kg/ha)

ST 19.8 21.9 20.9 17.9 11.4 14.7 17.4 16.4 16.9

BP 13.5 14.5 14.0 16.1 13.0 14.5 17.0 15.1 16.1

CT 17.6 16.6 17.1 18.7 15.6 17.1 12.0 11.3 11.6

Mean 16.9 17.7 17.6 13.3 15.5 14.3

LSD0.05



TxR ns ns ns




6.3.1.8 Mineral N pools (NH₄-N plus NO₃-N)

In 2011-12 at 0-15 cm soil depth, the NH₄-N concentrations were 29 % higher with CT

than BP; the NO₃-N concentrations were 28 % higher with ST than BP at 30-60 DAS

(Table 6.4). At harvest, the NH₄-N concentrations were greater by 10.7 % and the NO₃-

N concentrations were greater by 17.7 % with HR than LR at 0-15 cm soil depth (Table

6.4). At harvest, the NO₃-N concentrations with BP were lower by 37 % than CT or 30 %

than ST at 0-15 cm soil depth (Table 6.4). Irrespective of treatment differences, NH₄-N

concentrations were greater than the NO₃-N concentrations (Table 6.4). Mineral N

211

(NH₄-N plus NO₃-N) concentrations at 30-60 DAS were higher than at harvest (Table

6.4).

Table 6.4.Tillage and residue effects on mineral N (mg N/kg) at 0-15 cm depth at

Alipur in 2011-12.

Tillage1 At 30-60 DAS at harvest

NH₄-N NO₃-N NH₄-N NO₃-N

Residue1 Mean Residue1 Mean Residue1 Mean Residue1 Mean

HR LR HR LR HR LR HR LR

ST 11.8 10.0 10.9 9.1 8.5 8.8 10.4 9.6 10.0 8.3 7.1 7.7

BP 9.8 8.4 9.1 5.8 6.8 6.3 9.8 8.6 9.2 5.7 5.0 5.4

CT 12.9 12.8 12.9 7.8 7.8 7.8 10.8 9.5 10.1 9.8 7.3 8.6

Mean 11.5 10.4 7.6 7.7 10.3 9.2 7.9 6.5

LSD20.05

Tillage (T) 3.02* 1.77* ns 1.09**

Residue (R) ns ns 0.62** 1.14**

TxR ns ns ns ns




In 2012-13, the NH₄-N concentrations at 0-7.5 cm and 7.5-15 cm soil depth were not

affected by different tillage and residue treatments at 30-60 DAS (Table 6.5). In the top

7.5 cm of soil, the NO₃-N concentrations at 30-60 DAS were affected (P≤0.05) by tillage

and residue, and were lower by 52 % and 53 % under BP than under ST and CT; the

NO₃-N concentrations in LR were 27 % greater compared to HR (Table 6.5). At 7.5-15

cm soil depth, the NO₃-N concentrations at harvest under BP were higher by 23 % than

in ST and 15 % than in CT (Table 6.5). However, at 0-7.5 cm soil depth at harvest, the

NH₄-N concentrations with BP were 18 % lower than ST or CT (Table 6.5). The NH₄-N

concentrations were greater than the NO₃-N concentrations in all treatments (Table

6.5). Mineral N (NH₄-N plus NO₃-N) at 30-60 DAS was higher than at harvest (Table

6.5).

212

Table 6.5.Tillage and residue effects on mineral N (mg N/kg) at 0-7.5 and 7.5-15 cm

soil depth at Alipur in 2012-13.

Tillage1 NH₄-N NO₃-N NH₄-N NO₃-N



Soil depth

0-7.5 cm 7.5-15 cm

Mineral N at 30-60 DAS

ST 29.0 30.8 29.9 14.5 16.6 15.6 22.4 24.4 23.4 3.4 3.6 3.5

BP 24.3 29.5 26.9 5.7 9.3 7.5 25.4 25.0 25.2 3.5 3.4 3.4

CT 28.1 25.4 26.8 12.7 19.0 15.8 25.5 22.6 24.0 4.6 3.8 4.2

Mean 27.1 28.6 10.9 15.0 24.4 24.0 3.8 3.6

LSD20.05

Tillage (T) ns 7.41* ns ns

Residue (R) ns 3.42* ns ns

TxR ns ns ns ns

Mineral N at harvest

ST 11.0 13.3 12.2 11.4 10.6 11.0 10.0 9.2 9.6 3.9 4.0 4.0

BP 10.0 10.6 10.3 8.4 12.4 10.4 10.1 10.2 10.1 5.2 5.2 5.2

CT 12.2 12.2 12.2 11.3 11.0 11.1 9.1 9.4 9.3 4.4 4.3 4.4

Mean 11.1 12.0 10.4 11.3 9.7 9.6 4.5 4.5

LSD0.05

Tillage (T) 0.81** ns ns 0.87*

Residue (R) ns ns ns ns

TxR ns ns ns ns





At 30-60 DAS, the PMN concentrations with HR were 15 % greater than LR at 0-7.5 cm

depth (Table 6.6). At 7.5-15 cm depth, the PMN with BP was 38 % higher than ST and

42 % higher than CT. However, tillage treatments did not influence (P≤0.05) PMN

averaged over 0-15 cm depth. At harvest, the PMN concentrations in CT were lower by

23 % and 21 % than ST and BP at 0-7.5 cm depth (Table 6.6). At 7.5-15 cm depth, the

PMN concentrations with BP were greater by 129 % than CT treatment (Table 6.6). The

PMN at 7.5-15 cm depth showed a negative value with ST and CT, with a greater

213

negative value at ST than CT. At 0-15 cm depth, the PMN concentrations were not

influenced (P≤0.05) by tillage and residue treatments (Table 6.6). The PMN

concentrations decreased with increasing soil depth and were consistently greater at

30-60 DAS than at harvest in all treatments (Table 6.6).

Table 6.6.Tillage and residue effects on potentially mineralizable N (PMN) at 0-7.5

and 7.5-15 cm soil depth at Alipur in 2012-13.

Tillage

treatment1

Residue1 Mean Residue1 Mean Residue1 Mean

HR LR HR LR HR LR

0-7.5 cm 7.5-15 cm 0-15 cm

PMN (mg N/kg) at 30-60 DAS

ST 20.5 16.5 18.5 3.2 3.3 3.3 11.8 9.9 10.9

BP 17.1 16.2 16.7 5.7 4.9 5.3 11.4 10.6 11.0

CT 16.0 13.1 14.5 3.2 3.1 3.1 9.5 8.1 8.8

Mean 17.9 15.3 4.0 3.8 10.9 9.51

LSD20.05


Residue (R) 2.6* ns ns

TxR ns ns ns

PMN (mg N/kg) at harvest

ST 9.4 8.6 9.0 -2.8 -2.9 -2.9 3.3 2.8 3.1

BP 9.1 8.5 8.8 0.8 0.5 0.7 5.0 4.5 4.7

CT 8.6 6.0 7.3 -0.4 -1.5 -0.9 4.1 2.3 3.2

Mean 9.0 7.7 -0.8 -1.3 4.1 3.2

LSD0.05

Tillage (T) 1.1** 2.7* ns


TxR ns ns ns





Until Crop 7, TSN was not significantly different due to different tillage and residue

treatments at 0-15 cm depth (Table 6.7). At the end of Crop 7, the TSN was greater by

9 % in ST than CT and compared to LR, HR had 7 % higher TSN at 0-7.5 cm depth (Table

6.7). At 7.5-15 cm depth, TSN was greater by 31 % in ST and by 43 % in BP than CT

214

(Table 6.7). After Crop 7, TSN in CT was 19 % lower than ST and 21 % lower than BP,

and HR had 6 % greater TSN than LR at 0-15 cm depth (Table 6.7).

Table 6.7. Tillage and residue effects on total soluble N in legume-dominated rice-

based cropping system at Alipur in 2011-13.

Year Soil depth

(cm)

Tillage

treatment1



Total soluble N (mg N/kg)

2011-12

(after Crop 4)

0-15 ST 21.7 21.2 21.5

ns ns ns BP 21.5 20.6 21.0

CT 23.3 21.9 22.6

Mean 22.2 21.2

2012-13

(after Crop 7)

0-7.5 ST 57.4 54.8 56.1

3.96* 3.87* ns BP 52.8 50.0 51.4

CT 54.0 47.5 50.8

Mean 54.7 50.8

7.5-15 ST 26.4 26.0 26.2

1.75** ns ns BP 32.6 30.5 31.5

CT 17.8 18.2 18.0

Mean 25.6 24.9

Average

(0-15)

ST 41.9 40.4 41.1

1.98* 1.88** ns BP 42.7 40.2 41.5

CT 35.9 32.9 34.4

Mean 40.2 37.8




6.3.1.11 Plant N concentrations of lentil

The leaf N concentrations of lentil under HR, 52.1 g/kg in 2011-12 and 50.7 g/kg in

2012-13, were higher than the 49.2 g/kg in 2011-12 and 49.8 g/kg in 2012-13 under LR

(Table 6.8). In 2012-13, the leaf N concentrations of lentil under ST (51.3 g/kg) were

higher than CT (49.1 g/kg) and BP (50.3 g/kg). In 2011-12, the grain N concentrations of

lentil were higher under HR (49.6 g/kg) than 47.5 g/kg in LR (Table 6.8). Straw N

concentrations were not significantly (P≤0.05) affected by different treatments in all

the study years (Table 6.8).

215

Table 6.8.Tillage and residue effects on plant N concentrations of lentil in 2010-11,

2011-12 and 2012-13.

Tillage

treatment1

2010-11 2011-12 2012-13

HR1 LR1 Mean HR1 LR1 Mean HR1 LR1 Mean

Leaf N concentrations (g/kg)

ST 49.5 48.2 48.9 52.0 50.7 51.3

BP 52.7 49.6 51.1 50.8 49.8 50.3

CT 53.9 49.8 51.9 49.3 48.8 49.1

Mean 52.1 49.2 50.7 49.8

LSD20.05

Tillage (T) ns 1.56*

Residue (R) 0.14** 0.89*

TxR ns ns

Grain N concentrations (g/kg)

ST 49.3 45.7 47.5 50.4 46.8 48.6 52.2 49.1 50.6

BP 46.3 46.0 46.2 49.3 48.3 48.8 49.7 49.5 49.6

CT 43.5 46.0 44.8 49.1 47.3 48.2 49.3 46.6 48.0

Mean 46.4 45.9 49.6 47.5 50.4 48.4

LSD0.05



TxR ns ns ns

Straw N concentrations (g/kg)

ST 9.2 9.4 9.3 9.3 8.9 9.1 9.3 9.7 9.5

BP 8.6 9.8 9.2 8.9 8.7 8.8 9.4 9.3 9.4

CT 9.3 8.8 9.1 9.1 9.0 9.1 8.9 9.2 9.1

Mean 9.1 9.3 9.1 8.8 9.2 9.4

LSD0.05



TxR ns ns ns





Total N was positively correlated with N-stocks, TSN and PMN at 60 DAS (P≤0.01) but

negatively correlated with NO₃-N concentrations at 60 DAS (Table 6.9). The N-stocks

was strongly correlated (P≤0.01) with TSN and PMN at 60 DAS. The PMN at 60 DAS was

216

positively correlated (P≤0.01) with TSN and lentil grain N. Finally, leaf N concentrations

were strongly correlated with grain N of lentil (P≤0.01) (Table 6.9).

217

Table 6.9. Correlation matrix for the relationships among soil total N (TN), N-stocks, lentil plant N and the indices of N availability at 0-15 cm

at Alipur in 2012-13 (n = 24).

TN N-stocks PMN at

MS

PMN at

harvest

TSN NH₄-N at

MS

NO₃-N at

MS

NH4-at

harvest

NO3-at

harvest

Straw N

conc.

Grain N

conc.

Leaf N

conc.

TN 1

N-stocks 0.95** 1

PMN at MS 0.60** 0.55** 1

PMN at harvest -0.04 -0.10 -0.02 1

TSN 0.69** 0.67** 0.48* 0.25 1

NH₄-N at MS 0.10 0.02 -0.02 0.03 -0.09 1

NO₃-N at MS -0.43* -0.31 -0.20 -0.48* -0.42* -0.03 1

NH4-at harvest -0.33 -0.28 -0.33 -0.13 -0.09 0.38 0.54** 1

NO₃-at harvest 0.03 0.06 0.12 -0.26 0.18 -0.12 0.31 0.29 1

Straw N conc. -0.06 0.00 -0.18 0.11 0.24 -0.23 -0.11 -0.28 -0.02 1

Grain N conc. 0.33 0.28 0.48* 0.17 0.18 0.18 -0.13 -0.18 0.03 -0.05 1

Leaf N conc. 0.34 0.30 0.40 -0.17 0.31 0.08 -0.02 -0.21 -0.13 0.14 0.52** 1

* — significant at P≤0.05; ** — significant at P≤0.01; TN — total soil nitrogen concentrations; N-stocks — nitrogen stocks (Mg N/ha); TSN — total soluble

nitrogen (mg/kg); PMN — potentially mineralizable nitrogen (mg/kg); MS — 30 to 60 DAS; Concentrations — conc.

218

6.3.1.13 Nitrogen balances

The differences between 2010 and 2013 values for total N inputs to crops and relative

to outputs were used to estimate N balance or N loss (Table 6.10). Cumulative residue

N input to the soil during 2.5 years of the legume-dominated rotation ranged between

0.36 Mg/ha in STLR and CTLR to 0.53 Mg/ha in STHR and CTHR (Table 6.10). The N

losses from 0-15 cm soil depth ranged from 0.01-0.32 Mg/ha, with the highest losses

(0.32 Mg/ha) measured in CTLR followed by those in CTHR (0.01 Mg/ha) (Table 6.10).

The N balance between 2010 and 2013 indicated a net increase in N-stocks of 0.19 Mg

N/ha in STHR (+11 %) and 0.14 Mg N/ha in STLR (+8 %) while there was a slight

decrease of 0.02 Mg N/ha in CTHR (-1 %) and 0.09 Mg N/ha in CTLR (-5 %) (Table 6.10).

Compared to the initial N-stocks measured in 2010, there was a greater increase (8-11

%) in ST and decrease in CT, with slight decrease (0.9 %) in CTHR and larger decrease in

CTLR (5 %) (Table 6.10). Nitrogen accumulation occurred in ST while there were losses

in CT (Table 6.10). There was a net negative N partial balance of 0.14-0.26 Mg N/ha in

ST and CTLR but the net positive N partial balance of 0.01 Mg N/ha in CTHR (Table

6.10). The net N losses exceeded N inputs by 0.12 Mg N/ha in STLR, 0.01 Mg N/ha in

CTHR and 0.32 Mg N/ha in CTLR (Table 6.10). Nitrogen balance was positive in STHR

(0.05 Mg N/ha) while a negative balance was measured in CT (0.01-0.32 Mg N/ha)

(Table 6.10).

219

Table 6.10. Estimated nitrogen balance for the legume-dominated rice-based

rotation at Alipur considering residue of eight consecutive crops in 2010-2013. STHR

= strip tillage-high residue; STLR = strip tillage-low residue; CTHR = conventional

tillage-high residue; CTLR = conventional tillage-low residue.


Mg N/ha

N-stocks,2010, initial (A) 1.79 1.79 1.79 1.79

N-stocks,2013, harvest (B) 1.98 (±0.05 a) 1.93 (±0.02) 1.77 (±0.05) 1.70 (±0.08)

Change in N-stocks (B-A) 0.19 (±0.05) 0.14 (±0.02) -0.02 (±0.05) -0.09 (±0.08)

N gain or loss (%) 10.76 (±2.92) 7.76 (±1.21) -0.92 (±2.94) -4.79 (±4.27)

N-inputs from crop residueb, 2010-2013

2010 Rice 0.03 0.01 0.03 0.01

2010-11 Lentil 0.02 (±0.001) 0.00 0.02 (±0.001) 0.00

2011 Mungbean 0.04 (±0.002) 0.00 0.04 (±0.002) 0.00

2011 Rice 0.03 (±0.001) 0.01 (±0.001) 0.03 (±0.001) 0.011 (±0.001)

2011-12 Lentil 0.02 (±0.0003) 0.00 0.02 (±0.001) 0.00

2012 Mungbean 0.04 (±0.002) 0.00 0.04 (±0.002) 0.00

2012 Rice 0.03 (±0.001) 0.01 (±0.001) 0.03 (±0.001) 0.01 (±0.001)

2012-13 Lentil 0.02 (±0.001) 0.00 0.01 (±0.001) 0.00

N-input ∑2010-2013 0.21 (±0.007) 0.04 (±0.002) 0.21 (±0.01) 0.04 (±0.001)

N-inputs from fertilizer, irrigation water and rainfall, 2010-2013

Fertilizer 0.28 0.28 0.28 0.28

Irrigation water 0.01 0.01 0.01 0.01

Rainfall 0.03 0.03 0.03 0.03

N-input ∑2010-2013 0.32 0.32 0.32 0.32

Total N-input ∑2010-2013 (C) 0.53 (±0.007) 0.36 (±0.002) 0.53 (±0.005) 0.36 (±0.001)

N removal, 2010-2013

Grain 0.62 (±0.08) 0.42 (±0.02) 0.46 (±0.03) 0.40 (±0.05)

Straw 0.05 (±0.01) 0.20 (±0.01) 0.06 (±0.01) 0.20 (±0.002)

Total removal

∑2010-2013 (D)

0.67 (±0.09) 0.63 (±0.03) 0.53 (±0.04) 0.59 (±0.06)

N balance, 2010-2013

Partial N budget (C-D) -0.14 (±0.08) -0.26 (±0.03) 0.01 (±0.04) -0.23 (±0.06)

Estimated N balance

(B+C-A-D)

0.05 (±0.11) -0.12 (±0.05) -0.01 (±0.07) -0.32 (±0.06)

a standard error

b Values reported for N inputs from rice, mungbean and lentil residue are based on

measurement of biomass retained at the time of crop harvest and the measured N

concentrations in those residues.

220

6.3.2 Digram

6.3.2.1 Total soil N concentrations

In 2010-11 and 2011-12, TN concentrations showed no significant variation due to

tillage and residue treatments; however, obvious differences (P≤0.05) of TN

concentrations were detected in different tillage systems in 2012-13 (Table 6.12). At

the end of Crop 7, CT had 12 % and 7 % lower TN concentrations than ST and BP at 0-

7.5 cm depth (Table 6.12). However, at 7.5-15 cm depth, TN in CT was lower by 22 %

and 36 % than ST and BP. Furthermore, ST and BP had 13 % and 14 % higher

concentrations than CT at 0-15 cm depth (Table 6.12).

Table 6.11.Tillage and residue effects on total soil N (TN) concentrations and

stratification ratio (SR) of TN concentrations during 2.5 years of a cereal-dominated

rice-based cropping system at Digram.

Year Soil depth

(cm)

Tillage

treatment1

Residue

treatment1

Mean LSD20.05


2010-11

(after Crop 1)

0-15 ST 0.094 0.095 0.094

ns ns ns BP 0.095 0.094 0.095

CT 0.093 0.093 0.093

Mean 0.094 0.094

2011-12

(after Crop 4)

0-15 ST 0.099 0.097 0.098

ns ns ns BP 0.098 0.096 0.097

CT 0.094 0.090 0.092

Mean 0.097 0.094

2012-13

(after Crop 7)

0-7.5 ST 0.128 0.125 0.126

0.010* ns ns BP 0.123 0.118 0.121

CT 0.114 0.112 0.113

Mean 0.122 0.118

7.5-15 ST 0.071 0.072 0.072

0.003*

* ns ns

BP 0.081 0.078 0.080

CT 0.060 0.059 0.059

Mean 0.071 0.070

Average

(0-15)

ST 0.100 0.098 0.099

0.006*

* ns ns

BP 0.102 0.098 0.100

CT 0.087 0.085 0.086

Mean 0.096 0.094

221

Stratification ratio of TN concentrations (0-7.5:7.5-15 cm)

2012-13

(after Crop 7)

ST 1.80 1.72 1.76

BP 1.53 1.51 1.52 0.128** ns ns

CT 1.91 1.90 1.91

Mean 1.75 1.71




The stratification ratio (SR) of TN concentrations after Crop 7 for the surface (0.7.5 cm)

to subsurface soil depth (7.5-15 cm) was significantly affected by different tillage

practices (Table 6.12). The SR of TN under CT treatment was 21 % and 7 % greater

compared with BP and ST for 0-7.5:7.5-15 cm (Table 6.12).


system in Digram

After Crop 4, the soil TN concentrations in the 0-15 cm depth were not significantly

different due to different positions relative to the strip in ST (Figure 6.5). After Crop 7,

the soil TN concentrations in IS was distinctly 18.3 % and 9.2 % higher than that in OS

in 7.5-15 cm and 0-15 cm depth, respectively (Figure 6.5).





0.00

0.03

0.06

0.09

0.12

0.15

0-15 cm 0-15 cm 0-7.5 cm 7.5-15 cm 0-15 cm


Tota

l so

il n

itro

gen

(%

)


OS ISInitial

222




system in Digram

In the depth of 0-15 cm after Crop 1 and 4, the BT contained significantly higher

(p≤0.05) TN concentrations by 43 % and 40 % than that in BF, respectively (Figure 6.6).

At the end of Crop 7, the soil TN concentrations of BT increased by 68 % in 0-7.5 cm

while 40 % in the depth of 0-15 cm than that of BF (Figure 6.6). On the other hand, the

soil TN concentrations of BT decreased by 11 % than that of BF in 7.5-15 cm depth

after Crop 7 (Figure 6.6).







planting system.

0.00

0.03

0.06

0.09

0.12

0.15

0-15 cm 0-15 cm 0-15 cm 0-7.5 cm 7.5-15 cm 0-15 cm


Tota

l so

il n

itro

gen

(%

)


BT BFInitial

223

6.3.2.4 C: N ratio

There were no treatment effects on C:N ratio at all the sampling times except after

Crop 7 at 7.5-15 cm depth (Table 6.13). At the end of Crop 7 at 7.5-15 cm depth, a

significantly greater C:N ratio was found in BP (8.13) and lower in CT (6.37).

The SR of C:N ratio after Crop 7 for the surface (0.7.5 cm) to subsurface depth (7.5-15

cm) was significantly affected due to different tillage treatments (Table 6.13). The SR of

C:N ratios with CT and ST were 24 % and 16 % greater compared with BP (Table 6.13).

Table 6.12. Tillage and residue effects on C-N ratio during 2.5 years of a cereal-

dominated rice-based cropping system at Digram.

Year Soil depth

(cm)

Tillage

treatment1

Residue

treatment1

Mean LSD20.05


2010-11

(after Crop 1)

0-15 ST 8.32 8.01 8.17

ns ns ns BP 8.07 7.96 8.01

CT 7.81 7.66 7.73

Mean 8.07 7.88

2011-12

(after Crop 4)

0-15 ST 8.19 8.10 8.15

ns ns ns BP 8.29 8.25 8.27

CT 7.77 7.72 7.75

Mean 8.09 8.02

2012-13

(after Crop 7)

0-7.5 ST 9.04 8.48 8.76

ns ns ns BP 8.57 8.46 8.52

CT 8.92 8.60 8.76

Mean 8.84 8.52

7.5-15 ST 6.95 7.14 7.05

0.57** ns ns BP 8.18 8.08 8.13

CT 6.44 6.29 6.37

Mean 7.19 7.17

Average

(0-15)

ST 7.99 7.81 7.90

ns ns ns BP 8.37 8.27 8.32

CT 7.68 7.45 7.56

Mean 8.02 7.84

Stratification ratio of C:N ratio (0-7.5:7.5-15 cm)

2012-13

(after Crop 7)

ST 1.31 1.19 1.25

BP 1.05 1.05 1.05 0.16** ns ns

CT 1.40 1.38 1.39

Mean 1.26 1.21

224




6.3.2.5 N-stocks

As shown in Table 6.14, N-stocks were not influenced by different treatments until the

end of Crop 7. Also, N-stocks were not influenced by different treatments at 0-7.5 cm

depth after Crop 7 (Table 6.14). However, the N-stocks were greater in ST by 15 % at

7.5-15 cm depth and by 10 % for the 0-15 cm depth (average of 0-7.5 cm and 7.5-15

cm) than CT after Crop 7 (Table 6.14). In all the treatments, the N-stocks decreased

with depth (Table 6.14).

Table 6.13. Tillage and residue effects on N-stocks (Mg N/ha) and N accumulation

rate of the cereal-dominated rice-based cropping system in 2010-13 at Digram.

Year Soil

depth

(cm)

Tillage

treatment1

Residue

treatment1

Mean LSD20.05


2010-11

(after Crop 1)

0-15 ST 2.10 2.14 2.12 ns ns ns

CT 2.17 2.15 2.16

Mean 2.13 2.14

2011-12

(after Crop 4)

0-15 ST 2.07 2.08 2.07 ns ns ns

CT 2.07 1.90 1.98

Mean 2.07 1.99

2012-13

(after Crop 7)

0-7.5 ST 1.23 1.21 1.22 ns ns ns

CT 1.13 1.13 1.13

Mean 1.18 1.17

7.5-15 ST 0.79 0.82 0.80 0.04** ns ns

CT 0.69 0.68 0.68

Mean 0.74 0.75

0-15 ST 2.07 2.07 2.07 0.13* ns ns

CT 1.86 1.84 1.85

Mean 1.97 1.96

Annual rates of N accumulation (Mg N /ha/ yr) at 0-15 cm soil depth: 2010-13 (2.5 years)

0-15 ST -0.01 -0.01 -0.01

CT -0.09 -0.09 -0.09 0.05* ns ns

Mean -0.05 -0.05

225




Annual N accumulation rates at 0-15 cm depth in 2010-13 were -10 kg N/ha with ST

and -90 kg N/ha with CT (Table 6.14).

6.3.2.6 N uptake by wheat plants

Tillage and residue management had no impact on N uptake by wheat plants in 2010-

11 (Table 6.15). In 2011-12, N uptake by wheat grain in CT was greater by 15 % and 39

% while the uptake by straw in CT was greater by 17 % and 33 % than ST and BP; N

uptake by straw was 20 % greater with HR than LR (Table 6.15). In 2012-13, the N

uptake by wheat grain in CT was lower by 11 % and 8 % than ST and BP; relative to LR,

the N uptake by wheat grain was 4 % greater with HR (Table 6.15). However, the N

uptake by wheat straw in CT was lower by 18 % and 9 % than ST and BP in 2012-13

(Table 6.15). Effects of treatments on straw N uptake closely followed those for grain N

uptake (Table 6.15).

Table 6.14. Tillage and residue effects on N uptake by wheat plants in 2010-11, 2011-

12 and 2012-13.

Tillage

treatment1

2010-11 2011-12 2012-13

HR1 LR1 Mean HR LR Mean HR1 LR1 Mean

N uptake by wheat grain (kg/ha)

ST 75.6 76.7 76.1 98.8 91.3 95.1 113.9 108.1 111.0

BP 67.8 78.2 73.0 69.9 66.6 68.2 110.1 107.3 108.7

CT 68.6 64.0 66.3 121.5 103.5 112.5 102.5 98.4 100.4

Mean 70.7 73.0 96.7 87.1 108.8 104.6

LSD20.05

Tillage (T) ns 15.6** 4.7**

Residue (R) ns 8.1* 3.1**

TxR ns ns ns

N uptake by wheat straw (kg/ha)

ST 28.8 31.4 30.1 35.1 28.2 31.7 46.1 46.7 46.4

BP 25.0 27.3 26.1 29.7 21.7 25.7 46.4 39.7 43.1

CT 32.7 26.5 29.6 41.6 35.3 38.4 35.6 43.2 39.4

226

Mean 28.8 28.4 35.5 28.4 42.7 43.2

LSD0.05

Tillage (T) ns 5.2** 3.4**


TxR ns ns ns




6.3.2.7 Mineral N pools (NH₄-N plus NO₃-N)

In 2011-12, tillage and residue management had no impact on mineral N pools (NH₄-N

plus NO₃-N) at 30-60 DAS. However, the NH₄-N concentrations with ST and BP were 27

% greater than CT at 0-15 cm but there were no effects on NO3-N at harvest (Table

6.16). The NH₄-N concentrations were greater than the NO₃-N concentrations in all

treatments (Table 6.16). Mineral N (NH₄-N plus NO₃-N) was consistently higher at 30-

60 DAS than at harvest (Table 6.16).

Table 6.15. Tillage and residue effects on mineral N (mg N/kg) at 0-15 cm soil depth

in 2011-12 at Digram.

Tillage1 at 30-60 DAS at harvest

NH₄-N NO₃-N NH₄-N NO₃-N

HR1 LR1 Mean HR1 LR1 Mean HR1 LR1 Mean HR1 LR1 Mean

ST 55.5 62.3 58.9 38.9 40.5 39.7 20.6 23.8 22.2 16.5 25.9 21.2

BP 50.8 46.5 48.7 34.6 24.9 29.8 24.6 19.8 22.2 15.4 10.4 12.9

CT 46.5 65.0 55.8 26.1 20.6 23.4 17.6 15.1 16.3 14.1 11.1 12.6

Mean 51.0 58.0 33.2 28.7 20.9 19.6 15.3 15.8

LSD20.05

Tillage (T) ns ns 4.8* ns

Residue (R) ns ns ns ns

TxR ns ns ns ns




In 2012-13, although tillage had no impact on NH₄-N and NO₃-N concentrations at 30-

60 DAS, the NH₄-N concentrations were 16 % greater with LR than HR at 0-7.5 cm soil

227

depth (Table 6.17). At 7.5-15 cm soil depth, the NH₄-N concentrations in ST and BP

were 29 % and 38 % higher than in CT (Table 6.17). At harvest, the NH₄-N

concentrations in ST were greater by 3 % and 15 % than CT and BP; and NO₃-N

concentrations in HR were lower by 25 % compared to LR at 0-7.5 cm soil depth (Table

6.17). However, the NH₄-N concentrations were higher by 16 % in ST and 24 % in BP

than CT at 7.5-15 cm soil depth (Table 6.17). The NH₄-N concentrations were greater

than the NO₃-N concentrations in all treatments (Table 6.17). Mineral N (NH₄-N plus

NO₃-N) was consistently higher at 30-60 DAS than at harvest (Table 6.17).

Table 6.16. Tillage and residue effects on mineral N (mg N/kg) at 0-7.5 and 7.5-15 cm

soil depth in 2012-13 at Digram.

Tillage1 NH₄-N NO₃-N NH₄-N NO₃-N



Soil depth

0-7.5 cm 7.5-15 cm

Mineral N at 30-60 DAS

ST 90.4 106.3 98.4 29.9 31.2 30.5 56.6 51.4 54.0 3.0 3.1 3.0

BP 74.5 84.1 79.3 18.1 16.8 17.4 66.7 57.7 62.2 2.4 1.5 2.0

CT 82.7 105.1 93.9 32.4 47.0 39.7 37.6 39.4 38.5 5.4 6.0 5.7

Mean 82.5 98.5 26.8 31.6 53.7 49.5 3.6 3.5

LSD20.05

Tillage

(T)

ns ns 9.6** 0.9**

Residue

(R) 9.6**

ns ns ns

TxR ns ns ns ns

Mineral N at harvest

ST 24.1 24.2 24.2 8.9 10.0 9.5 14.9 15.5 15.2 2.9 3.5 3.2

BP 19.8 21.2 20.5 8.8 12.2 10.5 16.4 17.4 16.9 4.1 3.9 4.0

CT 21.4 25.5 23.4 6.2 9.9 8.1 13.0 12.6 12.8 2.8 3.5 3.2

Mean 21.8 23.7 8.0 10.7 14.8 15.2 3.3 3.6

LSD0.05

Tillage

(T)

2.8* ns 2.8* ns

Residue

(R) ns

2.6* ns ns

TxR ns ns ns ns

228




Concentrations of NH₄-N and NO₃-N were higher during the middle of the cropping

season and lowest at the end of the season (Table 6.17). The NH₄-N and NO₃-N

concentrations decreased with increasing soil depth across all treatments, (Table 6.17).


At 30-60 DAS in 2012-13, the PMN concentrations under CT were lower by 42 % and 20

% compared to ST and BP; compared to LR, HR had 19 % higher PMN at 0-7.5 cm depth

(Table 6.18). The PMN concentrations at 7.5-15 cm depth were not influenced by

tillage and residue treatments (Table 6.18). However, the PMN concentrations with HR

were greater by 18 % than LR at 0-15 cm depth (Table 6.18). At harvest, the PMN

concentrations under ST were 41 % greater compared to CT at 0-7.5 cm depth (Table

6.18). The PMN concentrations were not significantly (P≤0.05) influenced by different

treatments at 7.5-15 cm depth at harvest. At 0-15 cm depth, PMN concentrations

under ST and BP were greater by 63 % and 50 % than under CT (Table 6.18). The PMN

concentrations decreased with increasing soil depth and were consistently greater at

30-60 DAS than at harvest in all treatments (Table 6.18).

229

Table 6.17. Tillage and residue effects on anaerobic potentially mineralizable N

(PMN) at Digram in 2012-13.

Tillage

treatment1

Residue

treatment1

Mean Residue

treatment1

Mean Residue

treatment1

Mean

HR LR HR LR HR LR

0-7.5 cm 7.5-15 cm 0-15 cm

PMN (mg N/kg) at 30-60 DAS

ST 41.6 31.3 36.5 6.5 6.1 6.3 24.0 18.7 21.4

BP 27.1 25.7 26.4 10.0 7.7 8.8 18.5 16.7 17.6

CT 24.2 18.3 21.2 4.8 4.6 4.7 14.5 11.4 12.9

Mean 31.0 25.1 7.1 6.1 19.0 15.6

LSD20.05

Tillage (T) 11.5* ns ns

Residue (R) 4.2** ns 2.1**

TxR ns ns ns

PMN (mg N/kg) at harvest

ST 8.2 7.5 7.8 -1.7 -2.5 -2.1 3.2 2.5 2.9

BP 5.2 4.3 4.8 0.7 -1.5 -0.4 2.9 1.4 2.2

CT 5.4 3.9 4.6 -2.1 -2.9 -2.5 1.7 0.5 1.1

Mean 6.3 5.2 -1.1 -2.3 2.6 1.5

LSD0.05

Tillage (T) 2.3* ns 1.2*


TxR ns ns ns





At the end of Crop 4, TSN with HR was 17 % higher than with LR (Table 6.19). At the

end of Crop 7, TSN at 0-7.5 cm depth was 11 % greater under ST than under CT; and

compared to LR, HR had 5 % higher TSN (Table 6.19). At 7.5-15 cm depth, TSN with

STHR and BPHR was 31 % and 47 % higher than with CTLR (Table 6.19). In addition, ST

and BP had 16 % and 17 % greater TSN than under CT; and HR had 6 % higher TSN than

LR at 0-15 cm depth, after Crop 7 (Table 6.19).

230

Table 6.18. Tillage and residue effects on total soluble N in legume-dominated rice-

based system at Digram in 2011-13.

Year Soil depth

(cm)

Tillage

treatment1

Residue

treatment1

Mean LSD20.05


2011-12

(after Crop 4)

0-15 ST 39.5 29.0 34.2

ns 4.7* ns BP 28.0 26.8 27.4

CT 27.5 23.2 25.4

Mean 31.7 26.3

2012-13

(after Crop 7)

0-7.5 ST 54.5 52.5 53.5

4.1* 1.6** ns BP 49.3 47.8 48.5

CT 49.5 45.8 47.6

Mean 51.1 48.7

7.5-15 ST 25.0 24.2 24.6

2.1** 0.9** ** BP 32.4 27.8 30.1

CT 18.8 17.2 18.0

Mean 25.4 23.0

Average

(0-15)

ST 39.8 38.3 39.0

3.0** 0.9** ns BP 40.8 37.8 39.3

CT 34.1 31.5 32.8

Mean 38.2 35.9




6.3.2.10 Plant N concentrations in wheat

Tillage and residue had no impact (P≤0.05) on leaf N concentrations in wheat in all the

study years (Table 6.20). In 2010-11, the N concentrations of grain were higher under

ST (23.0 g/kg) than under BP (21.3 g/kg) and CT (19.5 g/kg) (Table 6.20). In 2011-12,

the N concentrations of grain were not affected by different tillage treatments (Table

6.20). However, the grain N concentrations under HR in 2011-12 (24.3 g/kg) and in

2012-13 (23.3 g/kg) were higher than under LR in 2011-12 (22.5 g/kg) and in 2012-13

(23.1 g/kg) (Table 6.20). The N concentrations of straw was also not affected by tillage

in all the study years while in 2011-12, HR had higher straw N concentrations (6.2 g/kg)

than LR (5.6 g/kg) (Table 6.20).

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Table 6.19. Tillage and residue effects on plant N concentrations of wheat in 2010-11,

2011-12 and 2012-13.

Tillage

treatment1

2010-11 2011-12 2012-13


Leaf N concentrations (g/kg)

ST 43.6 44.1 43.9 40.8 38.7 39.7 40.7 40.2 40.5

BP 44.9 44.4 44.6 41.4 39.7 40.6 40.3 39.8 40.0

CT 45.7 46.2 45.9 39.6 40.5 40.0 39.6 39.1 39.4

Mean 44.7 44.9 40.6 39.7 40.2 39.7

LSD20.05



TxR ns ns ns

Grain N concentrations (g/kg)

ST 22.5 23.5 23.0 22.5 22.1 22.3 23.3 23.2 23.3

BP 19.6 23.0 21.3 25.1 22.8 23.9 23.4 23.2 23.3

CT 19.7 19.2 19.5 25.2 22.5 23.9 23.3 23.1 23.2

Mean 20.6 21.9 24.3 22.5 23.3 23.1

LSD0.05

Tillage (T) 1.8** ns ns

Residue (R) ns 1.6* 0.1*

TxR ns ns ns

Straw N concentrations (g/kg)

ST 5.7 7.0 6.4 5.9 5.5 5.7 6.2 6.5 6.3

BP 5.5 6.0 5.7 6.1 5.3 5.7 6.7 6.2 6.4

CT 6.9 5.9 6.4 6.5 6.0 6.2 5.3 6.8 6.0

Mean 6.0 6.3 6.2 5.6 6.1 6.5

LSD0.05



TxR ns ns ns





Correlations among soil N availability indices, TN, N-stocks and plant N are presented

in Table 6.21. The TN was highly and positively correlated with N-stocks, TSN, PMN at

60 DAS and with grain N (P≤0.01) but negatively correlated with NO₃-N at 60 DAS

232

(Table 6.21). The N-stocks were significantly correlated (P≤0.01) with TSN, leaf and

grain N concentrations (Table 6.21). At 60 DAS, the TSN and PMN were significantly

and positively correlated (P≤0.01). The soil NO₃-N concentrations at harvest strongly

correlated (P≤0.01) with straw N concentrations (Table 6.21).

233

Table 6.20. Correlation matrix for the relationships among TN, N-stocks, plant N and the available indices of N at Digram at 0-15 cm in 2012-

13 (n = 24).

TN N-stocks TSN NH₄-N at

MS

NO₃-N at

MS

NH₄-N at

harvest

NO₃-N at

harvest

PMN at

MS

PMN at

harvest

Leaf N

conc.

Grain N

conc.

Straw N

conc.

TN 1

N-stocks 0.93** 1

TSN 0.66** 0.51** 1

NH₄-N at MS 0.26 0.24 0.25 1

NO₃-N at MS -0.43* -0.29 -0.37 0.20 1

NH₄-N at harvest 0.18 0.13 0.07 0.40 0.08 1

NO₃-N at harvest 0.32 0.29 0.14 0.34 0.08 0.11 1

PMN at MS 0.44* 0.37 0.75** 0.38 -0.07 0.13 0.14 1

PMN at harvest 0.33 0.30 0.38 -0.25 -0.08 -0.10 -0.19 0.28 1

Leaf N conc. 0.37 0.43* 0.31 -0.14 -0.08 0.10 -0.16 0.24 0.20 1

Grain N conc. 0.43* 0.44* 0.28 -0.03 -0.23 -0.38 0.23 0.13 0.17 0.14 1

Straw N conc. 0.22 0.21 0.17 0.33 0.29 -0.09 0.70** 0.19 0.09 -0.17 0.25 1

* — significant at P≤0.05; ** — significant at P≤0.01; TN — total soil nitrogen concentrations; N-stocks — nitrogen stocks (Mg N/ha); TSN — total soluble

nitrogen (mg/kg); PMN — potentially mineralizable nitrogen (mg/kg); MS — 30 to 60 DAS; Concentrations — conc.

234

6.3.2.12 Nitrogen balances at Digram

The differences between 2010 and 2013 values for total N inputs to crops and relative

to outputs were used to estimate N balance or N loss (Table 6.22). Cumulative residue

N input to the soil during 2.5 years of the cereal-dominated rotation ranged between

0.70 Mg/ha in STLR and CTLR to 0.90 Mg/ha in STHR and CTHR (Table 6.22). The N

losses from 0-15 cm depth ranged from 0.06-0.29 Mg/ha, with the highest losses (0.29

Mg/ha) measured in CTLR followed by that in STLR (0.06 Mg/ha) (Table 6.22). The N

balance between 2010 and 2013 indicated a net decrease in N-stocks of 0.02 Mg N/ha

in STHR (-0.7 %), 0.01 Mg N/ha in STLR (-0.7 %), 0.22 Mg N/ha in CTHR (-10 %) and 0.24

Mg N/ha in CTLR (-11 %) (Table 6.22). Compared to the initial N-stocks measured in

2010, there was a slight decrease (0.7 %) under ST and larger decrease (10-11 %) under

CT (Table 6.22). Nitrogen accumulation occurred with HR while there were losses with

LR (Table 6.22). There was a net positive N partial balance of 0.30 Mg N/ha in STHR and

0.28 Mg N/ha in CTHR (Table 6.22). The net N losses exceeded N inputs by 0.06 Mg

N/ha in STLR and 0.29 Mg N/ha in CTLR (Table 6.22). Nitrogen balance was positive in

STHR (0.28 Mg N/ha) as well as in CTHR (0.07 Mg N/ha) (Table 6.22).

235

Table 6.21. Estimated nitrogen balance for the cereal-dominated rice-based rotation

at Digram considering residue of eight consecutive crops in 2010-2013. STHR = strip




Mg N/ha

N-stocks,2010, initial (A) 2.08 2.08 2.08 2.08

N-stocks,2013, harvest (B) 2.07 (±0.06a) 2.07 (±0.05) 1.86 (±0.06) 1.84 (±0.03)

Change in N-stocks (B-A) -0.02 (±0.06) -0.01 (±0.05) -0.22 (±0.06) -0.24 (±0.028)

N build-up or N losses (%) -0.70 (±2.68) -0.66 (±2.15) -10.44 (±2.73) -11.40 (±1.34)

N-inputs from crop residueb, 2010-2013

2010 Rice 0.02 0.01 0.02 0.01

2010-11 Wheat 0.02 (±0.002) 0.01 (±0.001) 0.02 (±0.001) 0.01 (±0.0004)

2011 Mungbean 0.06 (±0.005) 0.00 0.06 (±0.002) 0.00

2011 Rice 0.04 (±0.002) 0.02 (±0.001) 0.04 (±0.002) 0.02 (±0.001)

2011-12 Wheat 0.02 (±0.002) 0.01 (±0.001) 0.02 (±0.001) 0.01 (±0.001)

2012 Mungbean 0.06 (±0.005) 0.00 0.06 (±0.002) 0.00

2012 Rice 0.04 (±0.002) 0.02 (±0.003) 0.04 (±0.003) 0.02 (±0.003)

2012-13 Wheat 0.03 (±0.002) 0.01 (±0.003) 0.02 (±0.001) 0.01 (±0.002)

N-input ∑2010-2013 0.27 (±0.008) 0.08 (±0.007) 0.26 (±0.007) 0.08 (±0.006)

N-inputs from fertilizer, irrigation water and rainfall, 2010-2013

Fertilizer 0.58 0.58 0.58 0.58

Irrigation water 0.01 0.01 0.01 0.01

Rainfall 0.03 0.03 0.03 0.03

N-input ∑2010-2013 0.62 0.62 0.62 0.62

Total N-input

∑2010-2013 (C)

0.90 (±0.008) 0.70 (±0.007) 0.89 (±0.007) 0.70 (±0.006)

N removal, 2010-2013

Grain 0.50 (±0.011) 0.51 (±0.014) 0.51 (±0.016) 0.50 (±0.009)

Straw 0.10 (±0.004) 0.25 (±0.006) 0.10 (±0.007) 0.26 (±0.015)

Total N removal

∑2010-2013 (D)

0.60 (±0.011) 0.75 (±0.009) 0.60 (±0.015) 0.76 (±0.019)

N balance, 2010-2013

Partial N budget (C-D) 0.30 (±0.02) -0.05 (±0.01) 0.28 (±0.01) -0.06 (±0.01)

Estimated N balance (B+C-A-D) 0.28 (±0.07) -0.06 (±0.04) 0.07 (±0.06) -0.29 (±0.02)

a standard error

b Values reported for N inputs from rice, mungbean and wheat residue are based on

measurement of biomass retained at the time of crop harvest and the measured N

concentrations in those residues.

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6.4 Discussion

The capacity of N release from soil and residue and uptake by crop are largely

influenced by tillage and residue management practices. In the present study, the

effects of tillage and residue retention on N cycling and process of N transformation

are briefly described.

6.4.1 Soil total N concentrations and N-stocks

In the present study, there were no clear effects of tillage and residue retention on soil

TN at 0-15 cm depth after 4 consecutive crops (~ 1.5 years) at either site. However,

after Crop 7 (after 2.5 years) the soil TN at 0-7.5 cm depth was greater with ST and BP,

compared to CT, in both legume-and cereal-dominated systems. The improvement of

soil TN found under ST and BP, along with HR after 2.5 years, was found in a relatively

short term in these rice-based systems. In temperate regions it generally takes longer

(7-8 years) to observe such treatment effects (Hou et al., 2012; Xue et al., 2015). There

are many factors that could affect the changes of soil TN concentrations such as soil

type, cropping system, climatic differences, cultivation techniques and duration of

treatments (Chatterjee & Lal, 2009; Mishra et al., 2009). In a recent study on rice-

based cropping system, the relatively fast response of TN to tillage and residue could

be attributed to large amounts of residues added annually from three crops including

rice residue compared with residues produced in dryland cropping systems involving

only one crop per year in a temperate and cooler region (Huang et al., 2006; Guo et al.,

2015). About 50 % of cereal residues along with all of the legume residues were

retained directly on the present HR treatment (contributing in total 3.8 t/ha and 5.1

t/ha of C input per year, respectively, in legume-and cereal-dominated systems) which

would likely enhance treatment effects. Majumder et al. (2008) found that high

temperature, a strong oxidative environment and the disrupting effect of intensive

cultivation led to a rapid oxidation of SOC in the rice-wheat system of West Bengal,

India. By contrast, the reduced soil disturbance under ST, plus the increased residue

retention may lead to the accumulation of SOC and soil TN. These contrasting

situations under different treatments would likely enhance treatment effects.

Furthermore, the period of wetland rice each year in the cropping sequences may

enhance C sequestration relative to other tropical cropping systems due to slower

237

decomposition of SOC in anaerobic soil for 3-4 months per year, plus a possible

contribution of algal photosynthesis to C accumulation (Kukal et al., 2009).

Tillage effects on the concentrations of soil TN followed a similar pattern in both

cereal- and legume-dominated systems. In the present study, the soil TN at 0-7.5 cm

depth was greater under ST and BP by 12-13 % and 6-7 % than under CT after Crop 7

(after 2.5 years). The implementation of ST and BP along with HR for 2.5 years is

advantageous for improvement of soil TN concentrations in the 0-15 cm depth as

compared with conventional practice that involves CT and LR in these rice-based

systems. Current farmer cultivation practice (i.e. CT and LR), involves intensive mixing

of surface and subsurface soil (tilled to a depth of about 6 to 9 cm). This hastens

mineralization of SOM by increasing the exposure of soil aggregates and together with

rapid decomposition of crop residue may inhibit the accumulation of TN (Doran et al.,

1998; Al-Kaisi et al., 2005; Xue et al., 2015). In contrast, the greater soil TN

concentrations in the surface soil under ST could be attributed to slower

decomposition of inter-row anchored residue as a result of decreased contact with soil

microorganisms and undisturbed surface soil. In many previous studies also greater TN

contents in the surface soil were reported under NT and attributed to slower

decomposition of crop residue (Schomberg et al., 1994; Dolan et al., 2006; Dikgwatlhe

et al., 2014; Xue et al., 2015). Kushwaha et al. (2000) also reported that increased

residue retention and reduced tillage increased the soil TN concentrations. By contrast,

several studies indicated that TN concentrations increased in the sub-soil layer under

CT due to a greater depth of residue incorporation caused by mechanical mixing of the

soil and residues (Chatterjee & Lal, 2009; Dikgwatlhe et al., 2014). In the current study,

the CT involved tillage only to a depth of 7-8 cm, which likely led to concentrations of

residue in the 0-8 cm soil profile. Hence, less residue input incorporated in the

compacted and untilled subsurface soil could be the major reason for the lowest TN

concentrations in the subsoil under CT among the tillage treatments.

In the current research, the TN concentrations under BP were higher in the subsurface

(7.5-15 cm) soil layer more so than in other tillage treatments; this was attributable to

subsurface burial of crop residues within the permanent bed during initial formation

238

and by reshaping and limited soil disturbance on beds that protected residue in the

beds from decomposing organisms. Bed formation involved heaping topsoil from the

furrow onto the bed so that the 7.5-15 cm layer under the bed probably represents

former topsoil. However, the concentrations of soil TN decreased with an increase in

soil depth for all treatments, this was probably due to higher N input retained on the

soil surface than subsurface soil, as has also been reported in other studies

(Franzluebbers, 2002; Du et al., 2010; Lou et al., 2012; Xue et al., 2015).

Also the C:N ratio exhibited a declining trend with increasing soil depth across all

treatments in both sites. The accumulation of main carbon input from crop residue and

root distribution near the surface soil might be contributed to exhibit higher C:N ratio

at surface soil. In addition, this might be due to the differences in composition of the

microbial population with depth (McGill et al., 1975) and increased ammonia fixation

in clay minerals with increased depth (Baisden et al., 2002). These results were

consistent with those reported by Kirkby et al. (2016), who observed that the C:N ratio

decreased significantly with depth possibly due to differences in fungi to bacteria ratio.

In the present study, minimal soil disturbance and the presence of a layer of crop

residue under ST and shallow tillage depth (7-8 cm) under CT caused a strong gradient

of N concentrations from the surface (0-7.5 cm) to subsurface layer (7.5-15 cm),

relative to BP. Chatterjee and Lal (2009) found a strong N concentrations gradient

under NT systems from the surface to subsurface layers relative to plough tillage in

Ohio. In the present study, the stratification ratio was increased under ST compared to

other tillage treatments, which was consistent with previous studies reporting that

nutrient stratification increased under ZT than under CT (Franzluebbers, 2002; Duiker

& Beegle, 2006; Lou et al., 2012). The limited tillage depth in CT may be the main

reason for the lowest TN concentrations in sub-soil profile among tillage treatments.

Apparently the three strip tillage operations per year in ST did not cause sufficient

mixing of soil to prevent stratification. Lou et al. (2012) also found similar results in

Cambisols of in north-eastern China. In contrast, subsurface burial of crop residues

within the permanent bed during formation and reshaping can incorporate crop

residues into subsoil, thereby causing a more uniform distribution of the residues and

239

soil TN concentrations at 0-15 cm than under CT and ST. The treatment effects on

stratification ratio of soil TN reflected those for SOC concentrations (see Chapter 5).

Implementing minimum tillage (ST and BP) and increased residue retention increased

soil TN concentrations while TN remained stable with current cultivation practices, CT

and LR, by Crop 7 in the legume- and cereal-dominated cropping sequence. The soil TN

concentrations did not differ due to depth distribution in relative to the strip e.g. OS

and IS under ST in legume-dominated system. This might be due to the small amount

of N fertilizer applied in IS for legume production and higher N containing residue

retention at OS negated the location effects in the legume-dominated system. In the

case of BP, the soil TN concentrations were consistently greater on centre of the bed

than in the furrow of the bed. This was due to retention of residue and carry over N

fertilizer at top of the bed. In addition, the top of bed was built up by former top soil

which is N rich than furrow of the bed.

6.4.2 Nitrogen balance

After 2.5 years, nitrogen balance (i.e. including changes in soil N-stocks as well as

inputs and outputs) was positive under HR treatment across all tillage treatments with

a greater positive trend under ST. The results of the study suggested that lesser

retention of residue diminished N balance in the rotation both in legume or cereal-

dominated systems. Compared to net losses with LR, HR maintained a positive N

balance which might be attributed to higher N recycling in crop residues. Hence, the

results presented the importance of residue additions, and their N content in

maintaining the N balance. The negative N balances in rice-wheat-maize and rice-

wheat-mungbean cropping systems after 3-4 years of experiment at Ishurdi, Joydebpur

and Nashipur in Bangladesh with removal of residue also indicated reduced N cycling

(Timsina et al., 2006). The negative N balance could be due to accelerated

mineralization of TN and crop residue as a result of increased tillage frequency and

intensity in CT treatment, which could result in N loss through leaching, runoff,

volatilization and de-nitrification (Halvorson et al., 2000; Sainju et al., 2009). In the

legume-dominated system, the removal of N through grain and straw were higher than

the total N input suggesting that there was no loss of N through different pathways

240

from the experimental field across all treatments. These results were consistent with

those reported by Devkota et al. (2013), who found that removal of N was higher than

N input. However, in cereal-dominated system, the removal was lower with HR than

total N input while there was greater removal with LR than total N input. These

findings suggested that although no loss of N occurred with LR there was a possibility

of N immobilization or loss of N from the experimental field with HR. The processes of

N loss pathways were not measured or directly accounted for in this study. Also the

estimation of N fix by legumes in the rotation was not measured in this study. The lack

of estimation of N fix by the legumes is a gap of this study. Hence, further refinement

of the N balance could be achieved by quantifying these potential loss pathways. The

positive N balance in ST and HR will be helpful to sustain the productivity of rice-based

systems and protect environmental quality through reducing the losses of N.

6.4.3 Nitrogen turnover and cycling

The N transformation processes in present study are influenced by different soil

properties. In the legume-dominated system, both the NH₄-N and NO₃-N were greater

under CT than other tillage treatments. The increasing tillage frequency in CT hastens

N mineralization of crop residues and soil organic N (Halvorson et al., 1999; Sainju &

Singh, 2001). Intensive tillage loosens and inverts SOM, and allows greater oxygen

diffusion resulting in higher N mineralization (Beare et al., 1994b). Also, tillage exposed

the soils to the sun and increased soil temperature, thereby enhanced the

mineralization (Wang et al., 2006). In addition, macropores formed while tilling soil

favour higher aeration and greater N mineralization (Pandey et al., 2007). Schoenau

and Campbell (1996) reported that residue management and placement also

influenced the nutrient availability. Intensive tillage incorporated residue with soil and

accelerated N mineralization (Verhulst et al., 2010; Dendooven et al., 2012b). The

residue incorporation into the soil tends to decompose 1.5 times faster under CT than

residue left on the soil surface under ZT (Verhulst et al., 2010). However, the increased

NH₄-N and NO₃-N release from SOM are prone to loss to the environment under CT

treatments upon intense rainfall or irrigation (George et al., 1993).

241

On the other hand, there are different processes such as immobilization or reduced

mineralization or loss of N through de-nitrification, leaching and volatilization that

could be involved to decrease the concentrations of NH₄-N and NO₃-N under ST and

BP. By contrast, N availability may decline under CA in the short-term period due to tie

up of NH₄-N and NO₃-N (Bhattacharyya et al., 2013). The processes and pathways of N

loss and tie-up, e.g. immobilization, de-nitrification, volatilization, and leaching were

not measured or directly accounted for in this study. The lower concentrations of NH₄-

N and NO₃-N under ST and HR in the current study could be related to a number of

factors that have not been directly measured. The slow breakdown of intact residue on

the middle of rows under ST decreased the mineralization process and resulted in

lower NH₄-N and NO₃-N in soil. Ussiri et al. (2009) found that the N mineralization

under ZT was lower due to retention of residue on the surface soil. In addition, the

retention of high residue under ST made the surface soil environment cooler and

wetter (see Chapter 4) and these factors might be contributed to decrease the

decomposition and inhibited mineralization (Blevins & Frye, 1993; Edwards et al.,

2000). By contrast, greater N uptake in Year 3 (see Table 6.3 and 6.15) could also

account for decreased NH₄-N and NO₃-N concentrations under ST and HR, consistent

with results reported by Devkota et al. (2013). Pekrun et al. (2003) reported that the

immobilization of N as a result of slow turnover of SOM during the transition period

from conventional to CA system might account for lower mineralization under ST and

BP. Indeed, Jahiruddin et al. (2014) found that greater N immobilization increased N

requirement for the first wheat crop after rice under ST and residue retention plots in

a rice-wheat-mungbean sequence compared to CT and low residue retention. In the

current study, ST resulted in greater TSN concentrations in comparison to that under

BP and CT in both legume- and cereal-dominated systems. These findings are closely in

accordance with the findings of other researchers (Wilts et al., 2004; Cui et al., 2014),

who also observed that plots under NT had higher TSN concentrations than under

rotary, plough tillage and CT. Probably less mineralized SOM in the present study

contributed to increased TSN under ST and BP. Also the greater SOC and N

concentrations under HR compared to LR likely contributed to increase in TSN

concentrations which was similar to the results reported in other studies (Xue et al.,

2015).

242

While lower NH₄-N and NO₃-N under CT in the cereal-dominated system could be due

to increased leaching and run-off from irrigation, quite different processes would be

expected in the legume-dominated system under dry land condition. For wheat, 2/3 of

the total amount of N fertilizer was applied during planting. For ST, N fertilizer was

applied in band while it was broadcasted in CT. Broadcasted fertilizer on loose surface

soil under CT may be more prone to losses while banding fertilizer are generally

protected (Beegle, 1996; Ladha et al., 2005). The clay soil at Digram was compacted

after intensive tillage and irrigation events under CT. Hence, the soil under CT after

irrigation was saturated for a couple of days and took a few more days to drain and dry

out compared to that under ST and BP (described in Chapter 4). This saturated soil

condition along with incorporated residue is favourable for denitrifying bacteria. As a

result, the de-nitrification process enhanced the loss of N under CT compared to that

under ST and BP.

In the present study, despite the greater retention of crop residues in 2.5 years from

eight consecutive crops, the HR treatment depleted the soil N for both legume-and

cereal-dominated systems. The lower NO₃-N under HR treatment might be linked to

greater plant N uptake and immobilization and possibly to greater losses through

different pathways such as leaching, runoff, de-nitrification and ammonification

(Tripathi et al., 1997). However, higher PMN and TSN under ST and BP together with

HR indicated decreasing mineralization or increasing immobilization or occurring both

as compared to CT and LR. Similarly, Doran et al. (1998) had similar observations that

cooler and wetter biological environment at surface soil (0-30 cm) under reduced

tillage likely decreased the mineralization or increased immobilization or both of N and

resulted in increased PMN than under conventional tillage. The increased PMN under

HR suggests that the lower concentrations of NH₄-N and NO₃-N were associated with

immobilization or reduced mineralization or for both. The retention of HR created a

cooler and more moist soil environment compared to incorporated residue (see

Chapter 4) which could decrease the decomposition of residue and inhibit the

mineralization. These results were consistent with Kushwaha et al. (2000), who

observed that N-mineralization rates were lower in residue retained treatments than

243

residue removed treatments at seedling stage of both rice and barley crops in dryland

(rainfed) agro-ecosystem of India. On the other hand, the greater N use efficiency

under LR likely owing to rapid mineralization suggested reduced losses through

different pathways before uptake by the crop (Singh et al., 2014a). The effects of

residue on N transformation process are shown in Figure 6.7. More detailed studies on

the forms and turnover of C and N under residue management level would be a fruitful

area of research for these intensive rice-based cropping systems.

Figure 6.7. Influences of crop residue on inorganic nitrogen transformation process.

Modified from Chen et al. (2014).

6.4.4 Responses of plant growth and plant N to N supply

Greater plant N in leaf, straw and grain and subsequent yield increase under ST might

be associated with the N supply and the N fertilizer placement. In the present

experiment, N fertilizer was broadcasted under CT but drilled with seed under ST and

BP. Fertilizer N contributed to the larger proportion of total N input to plant N and crop

yield. For example, fertilizer N had 53 % and 78 % share in the legume-dominated

system while it had 64 % and 83 % of total N input in the cereal-dominated system

both under HR and LR treatments. In this study, the estimation of N fix by legumes in

the rotation did not measure regardless of treatments. Thus the lack of estimation of N

244

fix by the legumes is a gap in this study. Under ST and BP treatments, banding fertilizer

in the strip below the surface soil at root zone resulted in more N availability to plants

and thereby increased plant N concentrations and yield. The results are in agreement

with those of other researchers (Schnier et al., 1993; Devasenapathy & Palaniappan,

1995) who reported that band placement of N fertilizer in to the anaerobic subsoil

layer resulted in higher yield through reducing volatilization and de-nitrification losses

of N. In addition, banding N decreased atmospheric N losses by 1.5 to 3 times relative

to broadcasting (Bielek et al., 1988). By contrast, broadcast N fertilizer under CT

occasionally causes uneven fertilizer distribution, with unequal accessibility between

individual plants. Under dryland condition, surface broadcast fertilizer N under CT

leaches into the rooting zone to become accessible to the roots (Ladha et al., 2005). In

addition, broadcast N fertilizers could suffer losses through ammonia volatilization and

reduced NUE (Mohanty et al., 1998).

6.4.5 Cropping system differences

The amount of N fertilizer and application method, irrigation, soil type and residue

amount were different between cropping systems. In the legume-dominated system, N

accumulated within 2.5 years under ST while there were N losses under CT. The

retention of higher N-containing residue probably accounted for much of the

difference between the N accumulation rates in the two rotations. In the legume-

dominated system, the variation of soil TN concentrations due to different levels of

residue retention appeared after Crop 7 (after 2.5 years) while no treatments effects

were seen in the cereal-dominated system. Higher N containing residue might be a

primary contributor to make the early variation of residue treatments in legume-

dominated system. Regardless of treatment differences, both NH₄-N and NO₃-N

concentrations were usually greater in the cereal-dominated system than the legume-

dominated system, this was probably due to the differences of amount residue

retention, soil water status, N fertilizer, inherent soil N status, the nature of soil

leaching. The greater residue retention, inherent soil N and higher soil moisture

conservation (see Chapter 4) in the cereal-dominated system released greater amount

of NH₄-N and NO₃-N as a result of mineralization and nitrification. In addition, carry-

245

over of greater amount N fertilizer applied for wheat crop compared to legume crop,

lentil, increased the concentrations of NH₄-N and NO₃-N in cereal-dominated system.

6.4.6 Optimum N management under CA system

The shift of cultivation system from conventional to CA will change the management of

N. Practicing ST and HR over a longer period of time improves soil moisture and

nutrient supplying capacity and thereby probably decreases the fertilizer requirement

for growing crops. Generally N immobilization in CA occurs in the first few years which

leads to greater fertilizer requirement. However, better fertilizer placement may

increase NUE, and offset the immobilization of N and ammonia volatilization compared

to conventional system especially in the cereal-dominant rotation. Further, quantifying

the net N mineralization of a soil and residue; and crop demand could overcome the

overuse of N fertilizer and their losses. Also nitrification and urease inhibitor can be

used to reduce N immobilization, ammonia volatilization, denitrification and leaching

loss (Weber & Mengel, 2009).

6.5 Conclusions

The study assessed the short to medium-term (2.5 years) effects of tillage and residue

management on N cycling in soil and plant in two rice-based cropping systems of

Bangladesh. Strip tillage and HR increased the accumulation of soil TN, TSN, N-stocks,

PMN at surface soil (0-7.5 cm), and plant N over conventional practice based on CT and

LR in both cereal and legume-dominated sites. The TN concentrations increased over

time under ST and BP, but remained stable under CT at 0-15 cm depth. The negative N

balance under CT was exacerbated by LR and there was a net soil N loss under CT and

LR. The present results suggest that the increased TN concentrations, N-stocks and N

accumulation, and N gain under ST and HR can improve soil productivity and reduce N

loss in rice-based systems in Bangladesh. If this is so, then it should be possible to

reduce recommended rates of N fertilizer addition in these cropping systems, but the

amount is yet to be quantified. The frequent monitoring of the effects of tillage and

residue retention on NH₄-N and NO₃-N in the season deserves further study, with a

broader emphasis to include other attributes of soil quality that affect yield in rice-

based system. In addition, other active pools of N and routes of loss (leaching,

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volatilization, surface runoff, erosion, de-nitrification) and immobilization were not

examined in these studies. Therefore, the current studies need to be continued over

time focusing on tillage and residue effects on N dynamics, various active pools,

immobilization and N losses pathways of both dryland and wetland conditions in rice-

based systems of Bangladesh.

247

7 General discussion and conclusions

This study investigated effects of soil disturbance and residue retention levels on crop

performance and soil properties in two rice-based cropping systems in Bangladesh

located in the Eastern Indo-Gangetic Plains (which comprises Eastern India and

Bangladesh). One was a cereal-dominated rotation (wheat-mungbean-monsoon rice)

and the other a legume-dominated rotation (lentil-mungbean-monsoon rice) located in

contrasting agro-ecological zones in Bangladesh. In both zones, rice is conventionally

transplanted into puddled fields and followed by aerobic crops after intensive tillage

and residue removal. These cultivation practices have potential to degrade soil physical

properties, decline in soil organic carbon (SOC) and soil total N (TN), and constrained

crop yield that compromise the sustainability of crop production. It was hypothesized

that application of the three principles of conservation agriculture (CA), i.e. minimum

soil disturbance, permanent soil cover and diverse crop rotation, might offset the

existing negative consequences of the conventional system. Conservation agriculture

in intensive rice-based cropping systems has only recently been introduced in

Bangladesh and studies on its effects on soil conditions and crop performance are

limited. This thesis examined how minimum soil disturbance, applied as strip tillage,

and increased residue retention levels affected soil properties and crop performance in

a legume-dominant and a cereal-dominant rotation as compared with the

conventional cropping practices and bed planting. Detailed study was focused on the

lentil and wheat components of the two cropping systems.

7.1 Tillage and residue management effects on crop performance and soil properties

Grain and straw yield of lentil were significantly higher with strip tillage (ST) and bed

planting (BP) than with conventional tillage (CT) by the third crop of lentil (Crop 7 in

the sequence, in the third year). The lower yield with BP than other tillage treatments

in the first crop could be attributed to lower moisture content at the soil surface on

the bed as a result of pulverized and loose soil in the freshly-made bed. High residue

treatment (HR) increased lentil yield in the second year (Crop 4) but the effect was not

significant in the third year which might be due to immobilization of N caused by the

accumulated residue of the preceding crops. Similar to lentil, ST and BP increased

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wheat yield by the third year while poorer crop performance with BP in the second

year was due to poor emergence caused by moisture scarcity in the seed bed at

sowing. High residue improved wheat straw yield by the second year but grain yield

only by the third year (Crop 7). The positive effects of the strip tillage and HR

treatments in the third growing season of both lentil and wheat (Crop 7) suggest scope

for improvement of the respective cropping systems by the inclusion of CA techniques.

It is also encouraging that the CA treatments had no deleterious effects on the initial

crops of lentil or wheat. By contrast, crop performance was somewhat less reliable

with BP since at each site, one out of three crops had depressed yield in BP. The yield

depression with BP appeared as a result of moisture scarcity in the new bed and

difficulties of residue management that resulted in shallow seed and fertilizer

placement which may have caused germination failure and poor nutrient use

efficiency, and thereby yield losses in the BP treatment. The findings that 2-3 years

were required to derive the main benefits of CA practices for crop productivity are in

agreement with the findings for the rice-wheat rotation of the Eastern Gangetic Plains

in South Asia (Jat et al., 2014). From a recent study on rice-based systems in

Bangladesh, Alam et al. (2014) also reported that wheat yield started to increase with

residue retention from the second year. Jat et al. (2014) found that wheat yield

responses to CA practices were greater and immediate in rice-wheat rotations in the

IGP. From 4 years of 69 on-farm trials, Gathala et al. (2015) concluded that the yield of

winter maize was higher in ST and BP compared to CT; while rice yield was similar

across all treatments of a rice-maize system in Northwest Bangladesh. The yield

improvements due to CA practices in the present study occurred within a relatively

short term compared to previous studies in temperate and cooler regions. This is

probably partly due to cumulative retention of substantial residue from three crops

per year (23.5-24.1 t/ha in the legume dominant rotation and 30.1-32.2 t/ha in the

cereal-dominant rotation). In addition, where a soil is heavily disturbed by tillage by 2-

4 operations or even more before each of three crops per year (i.e. 6-12 full

disturbance operations involving extreme disturbance during wet cultivation of rice),

the minimal soil disturbance by ST involves a more substantial decrease in soil

disturbance on an annual basis than in the one-crop per year cropping systems where

most CA is currently practiced globally. Further, the agro-ecosystems of the present

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experimental sites are located in the warm subtropics which are characterized by mild

cooler temperatures in the winter season and extremely hot in early summer, high

rainfall (annual rainfall>1000 mm) and high humidity. All of the above factors induced

faster decomposition of soil organic matter and lead to accelerated appearance of

treatment effects. In a temperate climate, He et al. (2011) reported that after 11 years

in a wheat-maize cropping system in North China Plain, yields of wheat increased by

3.5 % and maize by 1.4 % under NT relative to CT. Pittelkow et al. (2015b) showed from

678 studies based on a global data set, across 50 crops and 63 countries involving 6005

paired observations, that yields of all crops excluding oilseeds and cotton under NT

decreased in first 1-2 years of experimentation. However, after 3-10 years NT yields

matched those with CT except for maize and wheat in humid climates. The diversity of

crop yield responses across studies suggests that the short- and medium-term CA

effects depend on local conditions related to cropping intensity, biomass production,

residue retention and type, soil type and climate.

Congruent with the crop growth data, the soil physical conditions namely soil water

content (SWC), soil penetration resistance (PR) and bulk density (BD) in CA treatments,

compared to CT and LR, improved over time. The gradual improvement of SWC, soil PR

and BD at 0-5 cm, 5-10 cm and 10-15 cm soil depth under ST and HR corresponded

with greater root proliferation, permitting greater access of the root system to water

and nutrients from deeper in the soil profile. Increased SOC levels were measurable

with HR by the third year. Soil organic carbon increased at 0-7.5 cm soil depth under ST

as standing intact residue remained on the surface of ST and decomposed slowly. With

BP, the increase in soil organic carbon was mainly in the subsurface soil (7.5-15 cm

depth) of the bed due to shallow burial of previous surface soil and moderate

incorporation of crop residue. Total N levels reflected SOC in terms of treatment

effects over time. Available nitrogen (NH₄-N and NO₃-N) levels were suppressed with

ST and BP together with HR likely due to immobilization or less mineralization, or both,

while the greater N-stocks and potentially mineralizable nitrogen indicated decreased

losses of N. Jimenez et al. (2002) reported that enrichment of SOC with minimum

tillage and HR contributed to improvement in soil physical properties and increased

biological activity. Chen et al. (2008) showed from an eight-year experiment on the

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Chinese Loess Plateau that zero or minimum tillage and residue cover increased SOC,

microbial biomass and total N, and thus improved soil structure through reducing soil

BD, with an increase in crop yield.

The positive changes in soil properties with minimum tillage and HR in the present

study correlate with growth responses of lentil and wheat by the third year. For

example, the improved water holding capacity resulting from increased SOC and

decreased BD would only stimulate growth and yield if water limited growth. Similarly,

increased available N levels would only be beneficial when N limits growth. Hence

there may be differential benefits between irrigated wheat supplied with N fertiliser

compared to non-irrigated lentil dependent mostly on symbiotic N₂ fixation. Assuming

the improvement of soil conditions continues over time with the CA practices (ST and

HR) there may be reduced input requirements for water and N in wheat, and greater

crop resilience to temporary shortages of soil water in lentil.

Although there were no treatment differences in rice and mungbean yield in the

cereal-dominated system (wheat-mung bean-monsoon rice), in the legume-dominated

system (lentil-mung bean-monsoon rice) yield of mungbean (Crop 5) was greater under

BP or CT than under ST. This might be attributed to lower soil penetration resistance in

the root zone resulting from the renovated bed and rotary tillage in CT for the

mungbean crop. By contrast, higher soil penetration resistance in the root zone in ST

during the hot dry summer condition may inhibit mungbean yield in untilled soil (Crop

5). Continued assessment of mung bean crops over a longer time would be necessary

to ascertain whether the Crop 5 result is repeated in subsequent years or whether the

decrease in BD under ST and HR, as seen in the Rabi season (cool dry season), may

eventually alleviate constraints for mung bean crops.

In the present study, rice yields in puddled and unpuddled soils were not significantly

different in either of the two growing seasons of the experiments (see Chapter 2).

Similarly, in the third year of rice in the two present experiments, rice yields in puddled

and unpuddled soils were not significantly different (Haque et al., 2016). The present

results are similar to those of Gathala et al. (2015) but are different from some other

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studies (Kumar & Ladha, 2011; Jat et al., 2014), that reported lower yield of non-

puddled rice as compared to puddled transplanted rice for the initial years. By

contrast, Haque et al. (2016) reported that with strip tillage unpuddled transplanted

rice, the same system as used in the present study, rice yields were the same as those

in conventional puddled transplanting. In the present study, the benefits of CA over

the conventional system became fully apparent from Crop number 7 (third growing

season), with the winter crop after rice in both legume- and cereal-dominant rotations.

Perhaps eliminating puddling effects over time with ST had a significant yield benefit

on subsequent winter crops, due to better germination and root growth through the

improvement of soil physical conditions (Gathala et al., 2011b). However, Singh et al.

(2016) found from his five years rice-maize study in Northwest India that ZT and direct

seeded rice resulted in similar rice yield in the first three years but declined in the last

two years compared with transplanted puddled rice and conventionally tilled maize

followed by direct seeded rice. Further, Govaerts et al. (2008) showed that ZT without

residue retention over time resulted in poorer crop performance in maize-wheat

systems. Therefore, the present study needs to be continued to understand the overall

performance of all crop over time under ST, BP and HR treatments.

Growing crops of bed planting system in the present study was not as good as that

under ST in terms of total system productivity. There was no yield penalty of the first

rice crop (Crop 3) but yield of the second rice crop (Crop 6) was lower and thereby

decreased total system productivity on BP in the legume-dominated system. Similarly,

several researchers (Yadvinder-Singh et al., 2009; Kukal et al., 2010; Gathala et al.,

2011a) reported that rice yields on fresh or permanent beds were reduced compared

to a conventional system in a rice-wheat system of Northwest IGP. There are several

reasons proposed for yield depression on permanent beds of BP system including:

erosion, consolidation and finally flattening of beds on the light soils due to rainfall and

irrigation; weeds and nematodes infestation; Fe deficiency, and; missing rows on the

centre of the raised beds (Choudhury et al., 2007; Kukal et al., 2010; Gathala et al.,

2011b; Gathala et al., 2011a). However, the yield of the third rice crop on BP was not

significantly different from CT and ST (Haque et al., 2016). Hence, the effects of bed

planting on rice yield performance were inconsistent which may be related to changes

252

in water balance, including greater water losses through percolation in light textured

soil (Hobbs et al., 2002; Connor et al., 2003).

7.2 Non-treatment factors affecting crop growth and yield

Other factors besides the direct effects of soil disturbance and residue retention levels

may have interacted with treatments to affect crop growth and yield. Seasonal rainfall

can have a substantial influence on most of the crops grown in the two rotations, even

those grown with irrigation. Drought appears to accentuate the yield benefits of CA

from the global meta-analysis reported by (Pittelkow et al., 2015b). In the present

study, there was a heavy rainfall at pre-sowing of lentil and wheat in the third growing

season. Hence, improved plant establishment through better seed placement into

moist soil increased plant population under machine sowing and resulted in higher

yield in the third growing season. Since wet soil hampers tillage operations of CT, the

press roller behind the strip till and bed planter facilitated the cover of seed by soil as a

means of increased seed protection and seed-soil contact in wet soil. By contrast press

rollers of the machine are often unable to cover up the seed of the dryland crop

(lentil/wheat and mungbean) in dry soil condition (cool dry season and extreme dry

season) which resulted in poor seed-soil contact and germination failure or poor crop

establishment. In addition, enhanced infiltration of surface soil improved SWC in the

subsurface soil and created a favorable environment, allowing better dryland crop

establishment under ST and HR treatment. On the other hand, greater water losses

through evaporation and infiltration of newly tilled soil resulted in poor dryland crop

establishment under CT.

The successful outcomes from CA depends on specialized equipment and operational

skills not required in conventional agriculture (Hobbs et al., 2008). Minimum tillage

planters were modified over the duration of the two experiments for improved seed

placement into high residues and at optimum soil depth, which might have led to

better plant establishment in the third year. The cases of poor performance of planters

for sowing under the novel experimental conditions that resulted in lower plant

population (See Table 2.9 and 2.13 in Chapter 2) and lower yield under ST and BP

occurred in the first two years. Hence, increased plant population and yield under ST

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and BP over CT in third growing season may be related to the improvement of planter

performance and operator skills for seeding into seed beds.

Weed control is one of the major concerns for farmers in relation to cultivation

systems (Ritchie & Baker, 2007). Under CA, residues left on the soil surface contribute

to weed control through allelopathic inhibitory effects and by acting as a physical

barrier for light penetration (Bellinder et al., 2004). The shading effects of heavy intact

standing residue left between the rows inhibits weed germination and lowers weed

density (Blevins & Frye, 1993). In the current study, lower weed density under ST and

HR treatments (data not given) might have also contributed to higher yield of the

winter crops. Amuri et al. (2010) also reported that most of the weed species were

suppressed under zero tillage and high residue treatments. However, weeds and their

effective control are the biggest concern in CA systems in the world (Buhler et al.,

1994; Bajwa, 2014). Still, weed behavior and their association with the crop under ST

and HR in rice-based systems is not well understood and may result in losses of

efficacy of herbicide and increased herbicide cost (Johansen et al., 2012; Chauhan et

al., 2012b). Greater understanding of the nature of weed dynamics and their

interaction with crops under CA, and effective use of herbicides and non-chemical

control measures for weeds under ST and CA could overcome the problem (Hossain et

al., 2015b; Zahan et al., 2015).

Changing from conventional agronomic practices to CA involves larger amounts of

residue left on the surface soil which could introduce new foliar and root diseases or

exacerbate existing pathogens (Johansen et al., 2012). In the present study, a small

proportion of seedlings of lentil were affected by foot and root rot diseases in every

year but were severely affected in the third growing season under BP. This might be

due to carryover of lentil foot and collar rot pathogens from previous rice crop residue

left in the seeding zone. This may explain the lower plant population found at harvest

than following emergence on beds with high residue treatment (see Table 2.9 in

Chapter 2). However, despite lower plant population the final yield was not depressed

due to compensation of other yield components (branches/plant, plant height, spike

length etc.) under BP. Alternatively, ST and HR treatment may control soil borne

254

diseases by creating a less favorable soil environment for the pathogens or a favorable

environment for antagonists.

Finally, the CA practices may alter nutrient balance through controlling erosion, runoff

and nutrient forms and cycling by minimising soil disturbance and increasing SOC

levels. In the present study, banding N and P fertiliser in the strip close to the seed may

have improved nutrient availability to emerging seedlings and contributed to increased

crop yield. A similar observation was made by Jat et al. (2012b) in the semi-arid

tropics.

7.3 Constraints of different treatments and their potential solution

Despite many advantages of the BP system, a form of reduced tillage relative to

conventional planting systems (Hobbs et al., 2002; Connor et al., 2003), there are some

limitations associated with machine handling of retained crop residue on bed planting

system. Tough, tall, fresh and wet rice residue on top of the bed hindered the

operation of bed planter and seed bed preparation. Openers clogged with soil and

blocked seed and fertilizer delivery resulting in shallow placement depth that reduced

stand establishment. More experienced operators and improved machine capabilities

may overcome these problems. If available machinery for BP cannot manage tall and

heavy rice residues properly, reduced residue height below 50 % (~50 cm height) might

be the best option to minimize this constraint. For larger-scale planters a steel forward

residue mover has been developed to push away the residue in front of the beds to

create a suitable planting zone (Torbert et al., 2007). This device could be considered

for the VMP also to determine if planting reliability on beds can be improved.

However, farmers could choose ST instead since with the VMP retention of 50 % (~50

cm) rice residue or even with 70 % (~70 cm) rice residue (data unpublished) resulted in

satisfactory placement of seed and fertilizer. In ST, the rotary blades cleared standing

residue of preceding crops from in front of the tyne openers and resulted in reduced

blockage to seed and fertilizer placement, but there may still be clumping of loose

residue around the tynes that can impede controlled seed and fertiliser placement

(data unpublished).

255

Often, failure to place seed in the centre of gap between previous crop rows and

seeding at sub-optimal depth under ST lead to poor seed-soil contact. Trained and

skilled operators are important to offset the above said problems. In developed

countries, the real-time kinematic global positioning system based on satellite and

ground-based radio signals are used to achieve the accurate location of planting rows

(Norberg, 2010). A light press roller trailing behind the tyne openers of the strip till unit

is often unable to cover the seed in rows under dry soil condition and this leads to

failure of germination or poor crop establishment. Hence, the further modification of

the strip tillage planter (VMP) by an improved press roller or press wheels could

achieve more reliable cover of seed under a wider range of soil conditions.

Retention of surface residue between rows helps in protecting the nutrient-rich

surface soil (Blanco-Canqui & Lal, 2009) from wind and water erosion, and it also

conserves soil moisture. Although there are many advantages of high residue

retention, its effects on CH₄ and CO₂ emissions may be negative. Alam et al. (2016)

reported that increase total greenhouse gas emissions, mostly due to increased CH₄

loss for unpuddled transplanted rice grown under high residue retention. However, the

effects of increased residue retention and ST on GHG emissions during the other 2

crops per year has not been assessed yet. In addition, mulching effects of high residue

might be another reason for poor plant population as a result of creating impedance of

germination or poor establishment (see Chapter 2). This finding agrees with that of

Rieger et al. (2008) who reported that increased residue is accountable for poor crop

establishment. Several authors reported that increases in residue level reduced the

emergence rate of corn and wheat (Chastain et al., 1995; Swan et al., 1996). Hence, it

is necessary to determine the optimum amount of residue to achieve maximum

benefit without any adverse effect on crop and soil.

In these studies, addition of high residue decreased soil mineral N (NH₄-N + NO₃-N) in

the third growing season probably as a result of N immobilization and lower

mineralization or both. Greater potentially mineralizable nitrogen under HR is a

plausible reason for decreased soil mineral N. Lower soil temperature and improved

soil structure in HR may have reduced mineralization compared to LR. On the other

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hand improved soil organic carbon and soil water content with HR increased microbial

immobilization of N. The N immobilization however could be minimized under ST

through banded placement of N and P in the strip below the surface residue with likely

benefits from improved nutrient use efficiency (Jat et al., 2012b). On the other hand,

there is a risk of toxicity when banded fertilizer is placed close to the seed, especially in

sand textured soils (Kabir et al., 2010). However, placing fertilizer in a separate band

close to the seed can avoid toxicity effects (Johansen et al., 2012).

While there is a risk of yield penalty during initial years which may discourage farmers

to adopt CA (Pittelkow et al., 2015b), other benefits such as timely sowing, reduced

labour and fuel costs, saving of soil water, and thus increased farm profits (Hobbs &

Gupta, 2004; Thomas et al., 2007b; Johansen et al., 2012), serve as incentives towards

CA adoption.

7.4 Prospects and future research directions

Since the development of minimum tillage planters to suit two wheel tractors, and the

accumulation of sufficient evidence that they operate reliably in small rice fields, CA

has started to emerge in Bangladesh (Johansen et al., 2012). Planting directly into the

soil without any prior tillage implies less labor, less fuel and machinery costs under CA

in rice-based systems. Research findings from different studies indicated that CA

techniques resulted in equal or higher productivity (Haque et al., 2016); saved

irrigation water, decreased labour requirements and improved farm profitability

(Ladha et al., 2009; Jat et al., 2012b); reduced greenhouse gas emissions and global

warming potential (Dendooven et al., 2012b; Alam et al., 2016).

Although the present study has shed some light on the complexity of soil physical

properties, C and N pools and crop performance under different tillage and residue

management in rice-based systems, much remains to be done. In CA-based

management, there is no universal form of practice since actual practices employed for

CA require refinement and local adaptation to optimize system performance in

different environments (Kienzler et al., 2012). The following studies are suggested to

fill the gaps identified in this study:

257

• Long-term evaluation of the present experiments and confirmatory studies

over a wider range of soils (less fertile, saline and eroded), environments

(drought and moist soil), socio-economic conditions (small holder farming) are

needed in the Eatern IGP on rice-based cropping systems (to determine the

scope for CA to improve soil and crop production).

• Since residue is used for fodder, as well as household and construction

materials, further study needs to define the optimum amounts of residue

retention that: will not hamper the machine operation; meet other demands

for use in the farming system and; reduce the straw-induced greenhouse gas

emissions for different soils and environments.

• Quantification of loss pathways of N through different soil processes such as

leaching, runoff, volatilization, de-nitrification and immobilization to be

measured under different tillage and residue retention across a wide range of

rice-based cropping systems. Data inputs for these processes would reduce the

levels of uncertainty about N balance in these cropping systems. Inputs of N

under different cropping systems from symbiotic and asymbiotic biological

nitrogen fixation are particularly important to quantify.

• The crop N requirements should be assessed and appropriate N fertilizer

recommendations should be developed for CA systems. Further detailed study

is necessary for evaluating mineral N dynamics during the season, for different

crops and cropping seasons including the period when dry soils transition to

soil flooding in the monsoon season.

• Since weed infestation is a major constraint for the successful adoption of CA in

establishing all crops in the rotation, effective weed management strategies are

required for a range of cropping systems (Chauhan et al., 2012b). At present

poor knowledge of weed dynamics and unavailability of effective herbicides

can lead to over-reliance on a few herbicides that will accelerate the evolution

of herbicide resistant populations of weeds under CA (Lemerle & Hashem,

2014). Therefore, the greater understanding of safe use of herbicide and cost-

effective non-chemical weed control strategy are urgently required to be

determined.

258

• A detailed study is necessary on the main soil physical properties that are

directly linked with soil degradation such as soil BD, distribution of soil

aggregation, aggregate-associated carbon and its stability, porosity, infiltration,

evaporation and water holding capacity under different tillage and residue

management (Six et al., 2000; Rasool et al., 2007; Gathala et al., 2011b; Kumari

et al., 2011). In addition to physical fractions of soil organic carbon, chemical

fractions such as permanganate oxidizable carbon, hot-water extractable

carbon, soil microbial biomass C, pyrophosphate extractable organic carbon

and soil enzymatic activities need to be assessed under long-term CA practice.

• A life cycle assessment of greenhouse gas emission from boro rice (irrigated dry

season rice) of the monsoon rice-mustard-boro rice cropping pattern was

developed by Alam et al. (2016). However, life cycle analyses for other crops in

different seasons and cropping systems under ST and increased residue

retention are also required to identify alternative options to reduce

greenhouse gas emissions over existing cultivation techniques.

• Further study is necessary on the role of CA on the movement of agricultural

chemicals such herbicide, pesticide and N fertilizer to natural water sources.

• The present study examined the tillage and residue effects on only N dynamics,

however other important mineral nutrients that may limit the crop growth

under tillage and residue are important to study.

7.5 Conclusion

Application of CA, particularly ST and high residue retention, were promising for crop

production in intensive rice-based systems in Bangladesh. Strip tillage, a novel soil

management practice in terms of yield performance and soil properties, was more

effective and reliable than a reduced tillage, BP. The results of these experiments

conducted over 2.5 years’ demonstrated that there was no yield penalty even in the

initial years under ST and high residue retention. However, the yield of lentil and

wheat with ST and BP increased by 18-23 % and 7-9 % compared with CT in third

growing season. Increased root growth at deeper soil profile was associated with

improved soil physical properties under ST with HR as compared to CT with LR.

Implementation of ST with HR for 2.5 years’ increased the concentrations and stocks of

259

SOC and soil TN, WSC, total soluble nitrogen, potentially mineralizable nitrogen but

decreased NH₄-N and NO₃-N in surface soil (0-7.5 cm depth) in both legume- and

cereal-dominated rotations. Greater SOC losses through CO₂ emission occurred under

CT rather than ST and BP. High residue caused a positive N balance at both sites. The

gradual improvement of SOC was associated with increased SWC and decreased soil

BD under ST with HR, which might have stimulated crop growth and yield as compared

to CT with LR. The present results suggested that the input and capital-intensive

conventional system can be avoided by adopting ST with HR for growing crops in

intensive rice-based systems in Bangladesh. Regardless of treatment, carry-over of

higher N fertilizer applied to the cereal crop, and greater above- and below-ground

biomass contributed to increased concentrations of SOC and TN, C and N-stocks and

available N in the cereal-dominated cropping system than in the legume-dominated

system. The increased use of nitrogenous residue in the legume-dominated system

resulted in an early treatment response of TN concentrations as compared with the

cereal-dominated system. Greater yields with improved soil C and N, and improved

SWC and root growth at deeper soil profile under ST with HR should encourage rice

farmers to adopt CA practices in intensive rice-based systems. However, further

studies are required over a longer time period to evaluate the performance of CA

based systems on soil properties and crop performance in intensive rice-based systems

under diverse agro-ecological and soil conditions in the Eastern Indo-Gangetic Plains.

260

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Appendix 1: The soil organic carbon concentrations at different depths in furrow of

the bed

Calculation of soil organic carbon concentrations at 0-15 cm of bed furrow after Crop

1 and 4

The distance was about 5 cm from the level of bed top in furrow to bottom of the

furrow and there is no soil in this area. Hence, 0-5 cm depth in furrow of the bed has

been considered as “O” (Please see the explanation of section 4.2.6 Sampling time and

location in Chapter 4). So, in order to calculate the SOC concentrations at 0-15 cm in

furrow of the bed, 0-5 cm has been excluded from the level of the bed top in furrow to

bottom of the furrow. The following calculation has been used to calculate the SOC

concentrations at 0-15 cm depth in furrow of the bed.

Soil organic carbon concentrations at 0 − 15 cm in furrow of the bed

= SOC concentrations at 0 − 15 cm in furrow of the bed

− (SOC concentrations at 0 − 15 cm in furrow of the bed)/3

Calculation of SOC concentrations at 0-7.5 and 7.5-15 cm depth in furrow of the bed

after Crop 7

There was a gap of about 5 cm from the level of the bed top to base of the furrow. For

calculation, bed furrow was vertically divided into three parts with 5 cm depth

increments (Figure 1).

Suppose, the first 5 cm depth from the level of the bed top to base of the furrow = C₁,

the second 5 cm depth = C₂, and the third 5 cm depth = C₃ (Figure 1).

As there was no value of 0-5 cm soil depth,

So, C₁ = 0

However, the soil samples of two different depths were collected from the base of the

furrow, one from base furrow to 7.5 cm soil depth and another from 7.5 cm to 15 cm

depth.

Now, the following calculation was used to estimate the SOC concentrations:

314

At 0-7.5 cm in furrow of the bed = C₁+ C₂/3

At 7.5-15 cm in furrow of the bed = C₂*2/3 + C₃/3

Figure 1. Schematic diagram of furrow of the bed showing the depth of soil sampling

corresponding to actual soil depth

C1 — 0-5 cm (Gap)

C2 — 0-7.5 cm

(Soil sample collected)

C3 — 7.5-15 cm

(Soil sample collected)

Calculated depth

(0-7.5 cm)

Calculated depth

(7.5-15 cm)

315

Appendix 2: Tillage and residue effects on C-N ratio in legume-dominated rice-based

system at Alipur in 2011-13

Year Soil depth

(cm)

Tillage

treatment1



2010-11

(after Crop 1)

0-15 ST 7.42 7.22 7.32

ns ns ns BP 7.28 7.17 7.22

CT 7.32 7.15 7.24

Mean 7.34 7.18

2011-12

(after Crop 4)

0-15 ST 7.23 7.27 7.25

ns ns ns BP 7.49 7.40 7.44

CT 7.57 7.15 7.36

Mean 7.43 7.27

2012-13

(after Crop 7)

0-7.5 ST 7.97 7.60 7.78

ns ns ns BP 7.52 7.74 7.63

CT 7.96 8.05 8.01

Mean 7.82 7.80

7.5-15 ST 6.43 6.57 6.50

ns ns ns BP 7.25 7.15 7.20

CT 7.00 6.85 6.93

Mean 6.89 6.86

Average

(0-15)

ST 7.42 7.22 7.32

ns ns ns BP 7.41 7.51 7.46

CT 7.61 7.62 7.61

Mean 7.48 7.45

1HR - high residue; LR - low residue, ST - strip tillage; BP - bed planting; CT - conventional tillage; 2the

least significant difference (LSD) at the P≤0.05, ns - not significant, * - significant at P≤0.05 and ** -

significant at P≤0.01.

316

Publications from this study

Journal article

Haque, M.E., Bell, R.W., Islam, M.A., Rahman, M.A. 2016. Minimum tillage unpuddled

transplanting: An alternative crop establishment strategy for rice in conservation

agriculture cropping systems. Field Crops Research, 185, 31-39.

Conference paper

Islam, M.A., Bell, R.W., Johansen, C., Jahiruddin, M., Haque, M.E. 2016. Nitrogen

cycling enhanced by conservation agriculture in a rice-based cropping system of

the Eastern Indo-Gangetic Plain. Proceedings of the 2016 International Nitrogen

Initiative Conference, "Solutions to improve nitrogen use efficiency for the

world", 4 – 8 December 2016, Melbourne, Australia. www.ini2016.com.

Islam, M.A., Bell, R.W., Johansen, C., Jahiruddin, M., Haque, M.E. 2015. Minimum

tillage and increased residue retention improves soil physical conditions and

wheat root growth in a rice-based cropping system. in: Proceedings of the

conference on conservation agriculture for smallholders in Asia and Africa. 7-11

December 2014, (Eds.) W. Vance, R.W. Bell, M.E. Haque. Mymensingh,

Bangladesh, pp. 135-136.

Haque, M.E., Bell, R.W., Jahiruddin, M., Vance, W., Islam, M.A., Salahin, N. 2015.

Residue handling capacity of the versatile multi-crop planter for two-wheel

tractors. in: Proceedings of the conference on conservation agriculture for

smallholders in Asia and Africa. 7-11 December 2014, (Eds.) W. Vance, R.W.

Bell, M.E. Haque. Mymensingh, Bangladesh, pp. 13-14.

Islam, M.A., Bell, R.W., Haque, M.E., Johansen, C., Jahiruddin, M., Vance, W. 2014a.

Conservation agriculture in rice-based cropping systems: Its effect on crop

performance. 6th World Congress on Conservation Agriculture, Winnipeg,

Manitoba, Canada. 22-26 June 2014.

Islam, M.A., Bell, R.W., Johansen, C., Jahiruddin, M. 2014b. Three years of minimum

tillage and residue management in intensive rice-based cropping systems in

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Bangladesh: effects on soil nitrogen and organic carbon. in: Crop nutrition

syposium, 9th June, 2014. Murdoch University, WA.

Islam, M.A., Bell, R.W., Jahiruddin, M., Johansen, C. 2013a. Soil organic carbon and

nitrogen associated with wheat yield after 7 consecutive crops in wheat-

mungbean-rice rotation under different residue and tillage practices. MUPSA

Multidisciplinary Conference, 3 October 2013, Murdoch University, WA.

Islam, M.A., Bell, R.W., Jahiruddin, M., Johansen, C., Vance, W., Haque, M.E. 2013b.

Crop residue influences N availability and crop yield under conservation

agriculture in Bangladesh. 17th International Plant Nutrition Colloquium (IPNC),

19-22 August, 2013, Turkey, Istanbul.

Islam, M.A., Bell, R.W., Haque, M.E., Jahiruddin, M., Johansen, C. 2011. Effect of

minimum tillage and residue on lentil (Lens culinaris Medik.) growth and soil

physical properties in an alluvial soil, Bangladesh. Western Australia Soil Science

Conference, 23-24 September 2011, Busselton, Western Australia. Australian

Society of Soil Science Inc.

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