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DEVELOPMENT AND APPRAISAL OF ECONOMICAL AND SUSTAINABLE APPROACH FOR WEED MANAGEMENT IN DRILL SEEDED
AEROBIC RICE (Oryza sativa L.)
BY
MUHAMMAD SAQIB M.Sc. (Hons.) Agriculture
A thesis submitted in partial fulfillment of the requirements for the degree of
DOCTOR OF PHILOSOPHY
IN
AGRONOMY
DEPARTMENT OF AGRONOMY
FACULTY OF AGRICULTURE
UNIVERSITY OF AGRICULTURE, FAISALABAD
PAKISTAN
2013
To, The Controller of Examinations,
University of Agriculture,
Faisalabad.
“We the supervisory committee, certify that the contents and form of thesis
submitted by Mr. Muhammad Saqib, Regd. No. 2001-ag-2563, have been found
satisfactory and recommend that it be processed for evaluation by External Examiner(s)
for the award of degree.”
SUPERVISORY COMMITTEE
1. Chairman: ___________________
Dr. Nadeem Akbar
2. Member: ___________________
Prof. Dr. Ehsanullah
3. Member: ___________________
Prof. Dr. Abdul Ghafoor
Declaration
I hereby declare that contents of the thesis, “Development and appraisal
of economical and sustainable approach for weed management in drill
seeded aerobic rice (Oryza sativa L.)” are product of my own research
and no part has been copied from any published source (except the
references, standard mathematical or genetic models/equations/formulae
/protocols etc.). I further declare that this work has not been submitted
for award of any other diploma/degree. The university may take action if
the information provided is found inaccurate at any stage. (In case of any
default, the scholar will be proceeded against as per HEC plagiarism
policy).
MUHAMMAD SAQIB
DEDICATED
TO
HOLY PROPHET MUHAMMAD
(Peace Be Upon Him)
The personality
for whom
world was created
i
ACKNOWLEDGEMENTS
All acclamation, appreciation and praise for the ALMIGHTY ALLAH, the most
gracious and compassionate, whose blessing and exaltation flourished my thought and
thrived my ambition to have the cherished fruit of my modest efforts in the form of this
manuscript from the blooming spring of blossoming knowledge.
My special praises for the HOLY PROPHET MUHAMMAD (Peace Be Upon Him) who is
forever a torch of guidance for the entire humanity.
With a proud sense of gratitude, I acknowledge that this manuscript has found this shape
under the kind supervision, inspiring guidance and sympathetic attitude of Dr. Nadeem Akbar,
Asstt. Professor, Department of Agronomy, University of Agriculture, Faisalabad, who benevolently
extended all possible help for the smooth execution of this humble presentation.
I offer my great sense of gratitude to Dr. Ehsanullah, Professor, Department of Agronomy,
University of Agriculture, Faisalabad, for providing valuable suggestions, competent guidance and
boosting up my morale during the conduct of this study.
I am highly indebted to Dr. Abdul Ghafoor, Professor, Institute of Soil and Environmental
Sciences, University of Agriculture, Faisalabad, for his constructive criticism and valuable
suggestions to improve this manuscript.
I shall be missing something if I don’t extend my admiration and appreciation to Mr. Tariq
Masood Qurashi who helped me to increase my abilities of learning.
I am also thankful to my sincere friends Dr. Abdul Ghafar, Dr. Shahzad Ali Shahid
Chatta, Dr. Attiq-ur-Rehman, Adnan Shakir, Imran Ahmad Chishti, Sajja Anjum Sandhu and
Zafar Iqbal who supported me morally with sentiment throughout my research.
Last but not the least gratitude is to be expressed to my affectionate father Muhammad
Iqbal Goraya, my loving mother, my brother Muhammad Asif Iqbal Goraya, my beloved sister
and my wife for their love, patience, inspiration, good wishes and unceasing prayers for me to achieve
higher goals in life.
MUHAMMAD SAQIB
ii
TABLE OF CONTENTS
Chapter No. TITLE
ACKNOWLEDGEMENTS
TABLE OF CONTENTS
LIST OF TABLES
LIST OF FIGURES
LIST OF ABBREVIATIONS
ABSTRACT
Page No.
i
ii
vi
xi
xiv
xv
1 INTRODUCTION 1
2 REVIEW OF LITERATURE 5
2.1 Rice 5
2.1.1 History of domestication and cultivation 5
2.2 Crop establishment methods 6
2.2.1. Flooded rice 6
2.2.2 Aerobic rice 7
2.2.3 Global distribution of direct seeded rice 8
2.2.4 Crop establishment metods of direct seeded rice 9
2.2.4.1 Wet seedining 9
2.2.4.2 Water seeding 9
2.2.4.3 Zero tillage seeding 9
2.2.4.4 Dry seeding 10
2.3 Weeds 10
2.3.1 Effects of weeds on rice 10
2.3.2 Weeds in direct seeded rice 12
2.3.3 Weed control 12
2.3.4 Planning effective control 12
2.4 Weed control methods 13
2.4.1 Cultural control methods 13
2.4.2 Biological weed control methods 13
iii
2.4.3 Problems of biological weed control methods 13
2.4.4 Mannual weed control 14
2.4.5 Problems of Mannual weed control 14
2.4.6 Chemical weed control methods 15
2.4.7 Problems of chemical weed control methods 15
2.4.8 Mechanical weed control 16
3 MATERIALS AND METHODS
3.1 Location of studies 17
3.2 Characterization of Soil 17
3.2.1 Particle size analysis 17
3.2.2 pH of saturated soil paste 17
3.2.3 Soil saturated extract 18
3.2.4 Soil organic matter 18
3.2.5 Electrical conductivity of saturated soil extract 18
3.2.6 Total soluble salts 18
3.2.7 Available phosphorous 19
3.2.8 Extractable potassium 19
3.3 Treatment and layout of experiments 21
3.4 Planting of crop 25
3.4.1 Land preparation 25
3.4.2 Seeding method, planting geometry and fertilizer application 25
3.4.3 Water management 25
3.4.4 Harvesting and threshing 25
3.5 Weed management in experiment I 25
3.6 Weed management in experiment II 25
3.7 Observations 26
3.7.1 Yield related traits 26
3.7.2 Quality parameters 26
3.7.3 Growth parameters 26
3.7.4 Weed parameters 27
iv
3.8 Statistical analysis 31
4 REULTS AND DISCUSSION
STUDY-I : COMPARISON OF DIFFERENT WEED
MANAGEMENT STRATEGIES IN DRILL SEEDED
AEROBIC RICE
4.1 Weed biomass of sedges (15 DAS) 32
4.2 Weed biomass of broad leaf weeds (15 DAS) 32
4.3 Weed biomass of sedges (30 DAS) 34
4.3 Weed biomass of broad leaf weeds (30 DAS) 34
4.5 Weed biomass of sedges (45 DAS) 34
4.6 Weed biomass of broad leaf weeds (45 DAS) 37
4.7 Total weed biomass (15 DAS) 37
4.8 Total weed biomass (30 DAS) 37
4.9 Total weed biomass (45 DAS) 40
4.10 Plant Height 40
4.11 Total number of tillers 43
4.12 Number of fertile tillers 43
4.13 Number of unfertile tillers 47
4.14 Panicle length 47
4.15 Kernels per panicle 47
4.16 1000-kernel weight 51
4.17 Biological yield 51
4.18 Paddy yield 51
4.19 Straw yield 57
4.20 Harvest Index 57
4.21 Opaque kernels 57
4.22 Abortive kernels 60
4.23 Normal kernels 60
4.24 Sterile spikelets 60
4.25 Leaf area index 62
v
4.26 Crop growth rate 62
4.27 Leaf area duration 62
4.28 Total dry matter 66
4.29 Net assimilation rate 66
4.30 Economic analysis 69
Discussion 73
STUDY-II: EVALUATION OF DIFFERENT INTER
CULTURAL IMPLEMENTS FOR WEED
MANAGEMENT IN DRILL SEEDED AEROBIC RICE
4.31 Weed biomass of broad leaf weeds (15 DAS) 80
4.32 Weed biomass of sedges (15 DAS) 80
4.33 Weed biomass of broad leaf weeds (30 DAS) 83
4.34 Weed biomass of sedges (30 DAS) 83
4.35 Weed biomass of broad leaf weeds (45 DAS) 86
4.36 Weed biomass of sedges (45 DAS) 88
4.37 Total weed biomass (15 DAS) 88
4.38 Total weed biomass (30 DAS) 91
4.39 Total weed biomass (45 DAS) 91
4.40 Plant height 94
4.41 Total number of tillers 97
4.42 Number of fertile tillers 99
4.43 Number of unfertile tillers 101
4.44 Panicle length 103
4.45 Kernels per panicle 103
4.46 1000-kernel weight 106
4.47 Biological yield 108
4.48 Paddy yield 110
4.49 Straw yield 112
4.50 Harvest Index 112
4.51 Opaque kernels 115
vi
4.52 Abortive kernels 117
4.53 Normal kernels 117
4.54 Sterile spikelets 120
4.55 Leaf area index 122
4.56 Crop growth rate 124
4.57 Leaf area duration 124
4.58 Total dry matter 127
4.59 Net assimilation rate 127
4.60 Economic analysis 130
Discussion 135
5 Summary 139
References 144
vii
LIST OF TABLES
Table Title Page
3.1 Physio-chemical analysis of experimental site 19
3.2 Meteorological data during the year 2008 at experimental site 20 3.3 Meteorological data during the year 2009 at experimental site 20 3.4 Weed flora 27 4.1 Effect of different weed management strategies on weed
biomass (45 DAS) 41
4.2 Effect of different weed management strategies on plant height
41
4.3 Effect of different weed management strategies on total number of tillers
44
4.4 Effect of different weed management strategies on number of fertile tillers
44
4.5 Effect of different weed management strategies on number of unfertile tillers
45
4.6 Effect of different weed management strategies on panicle Length
45
4.7 Effect of different weed management strategies on kernels per panicle
48
4.8 Effect of different weed management strategies on 1000 kernel weight
48
4.9 Effect of different weed management strategies on biological yield
52
4.10 Effect of different weed management strategies on paddy yield
52
4.11 Effect of different weed management strategies on straw yield
58
4.12 Effect of different weed management strategies on harvest index
58
4.13 Effect of different weed management strategies on opaque kernels
59
4.14 Effect of different Weed management strategies on abortive kernels
59
4.15 Effect of different weed management strategies on normal kernels
61
4.16 Effect of different weed management strategies on sterile spikelets
61
4.17 Economic analysis 2008 69 4.18 Economic analysis 2009 70 4.19 Dominance analysis 2008 71 4.20 Marginal analysis 2008 71
viii
4.21 Dominance analysis 2009 72 4.22 Marginal analysis 2009 72 4.23 Effect of different weed control implements on weed
biomass at 45 DAS 95
4.24 Effect of different weed control implements on plant height 96 4.25 Effect of different weed control implements on total number
of tillers 98
4.26 Effect of different weed control implements on number of fertile tillers
100
4.27 Effect of different weed control implements on number of unfertile tillers
102
4.28 Effect of different weed control implements on panicle length 104 4.29 Effect of different weed control implements on kernels per
panicle 105
4.30 Effect of different weed control implements on 1000 kernel weight
107
4.31 Effect of different weed control implements on biological yield
109
432 Effect of different weed control implements on paddy yield 111 4.33 Effect of different weed control implements on straw yield 113 4.34 Effect of different weed control implements on harvest index 114 4.35 Effect of different weed control implements opaque kernels 116 4.36 Effect of different weed control implements abortive kernels 118 4.37 Effect of different weed control implements normal kernels 119 4.38 Effect of different weed control implements on sterile
spikelets 121
4.39 Economic analysis 2008 131 4.40 Economic analysis 2009 132 4.41 Marginal analysis 2008 133 4.42 Marginal analysis 2009 134
ix
LIST OF FIGURES Figure Title Page
3.1 Layout plan for experiment-I 22
3.2 Layout plan for experiment-II 24
4.1 Effect of different weed management strategies on weed biomass of sedges and broad leaf weeds at 15 DAS
33
4.2 Effect of different weed management strategies on weed biomass of sedges and broad leaf weeds at 30 DAS
35
4.3 Effect of different weed management strategies on weed biomass of sedges and broad leaf weeds at 45 DAS
36
4.4 Effect of different weed management strategies on total weed biomass at 15 DAS
38
4.5 Effect of different weed management strategies on total weed biomass at 30 DAS
38
4.6 Effect of different weed management strategies on total weed biomass at 45 DAS
39
4.7 Relationship between total weed biomass (45 DAS) and plant height
42
4.8 Relationship between total weed biomass (45 DAS) and fertile tillers
46
4.9 Relationship between total weed biomass (45 DAS) and kernels per panicle
49
4.10 Relationship between total weed biomass (45 DAS) and 1000 kernels weight
50
4.11 Relationship between total weed biomass (45 DAS) and paddy yield
53
4.12 Relationship between number of fertile tillers and paddy yield 54 4.13 Relationship between kernels per panicle and paddy yield 55 4.14 Relationship between 1000 kernel weight and paddy yield 56 4.15 Effect of different weed management strategies on periodic
changes in leaf area index 63
4.16 Effect of different weed management strategies on periodic changes in crop growth rate
64
4.17 Effect of different weed management strategies on cumulative leaf area duration at 105 DAS
65
4.18 Effect of different weed management strategies on total dry matter at 105 DAS
67
4.19 Effect of different weed management strategies on NAR at 105 DAS
68
4.20 Effect of different weed control implements on weed biomass of broad Leaf weeds (15 DAS)
81
4.21 Effect of different weed control implements on weed biomass of sedges (15 DAS)
82
x
4.22 Effect of different weed control implements on weed biomass of broad Leaf weeds (30 DAS)
84
4.23 Effect of different weed control implements on weed biomass of sedges (30 DAS)
85
4.24 Effect of different weed control implements on weed biomass of broad Leaf weeds (45 DAS)
87
4.25 Effect of different weed control implements on weed biomass of sedges (45 DAS)
89
4.26 Effect of different weed control implements on total weed biomass (15 DAS)
90
4.27 Effect of different weed control implements on total weed biomass (30 DAS)
92
4.28 Effect of different weed control implements on total weed biomass (45 DAS)
93
4.29 Effect of different weed control implements on periodic changes in leaf area index
123
4.30 Effect of different weed control implements on periodic changes in crop growth rate
125
4.31 Effect of different weed control implements on cumulative leaf area duration at 105 DAS
126
4.32 Effect of different weed control implements on total dry matter at 105 DAS
128
4.33 Effect of different weed control implements on NAR at 105 DAS
129
xi
LIST OF ABBEREVATIONS
ABBEREVATION DESCRIPTION DAS Days after sowing DSR Direct seeded rice LAI Leaf area index LAD Leaf area duration CGR Crop growth rate TDM Total dry matter NAR Net assimilation rate HI Harvest index LSD Least significant difference Zn Zinc t Ton ha-1 Per hectare cm Centimeter g Gram Fig Figure m-2 Per square meter % percentage @ At the rate of kg Kilogram ppm Parts per million N Nitrogen P Phosphorus K Potassium
xii
ABSTRACT
Traditional rice cultivation by puddling and manual transplanting is a labor intensive activity and
require significant quantities of water and power. The increasing scarcity of water threatens the
sustainability of transplanted rice. In many areas of Asia, transplanting of rice is being replaced
by direct seeding as farmers respond to increased labor cost and decreased water availability but
weed control is one of the major constraints to direct seeding. So, to control weeds in direct
seeded rice studies were designed. Experiments were conducted for two years to develop
sustainable and economical methods for managing weeds in aerobic rice grown by direct-seeding
at Student’s Farm, Department of Agronomy, University of Agriculture, Faisalabad during the
years 2008 and 2009. The first experiment was laid out in RCBD having five weed control
approaches; hand weeding, hoeing (with kasula), inter row cultivation with tine cultivator, inter
row cultivation with spike hoe and chemical control with Nominee 100 SC along with control
(no weeding). Weed dry weight was 300 g m-2, 257 g m-2, 225 g m-2 and 157 g m-2 less in hand
weeding, hoeing tine cultivator and Nominee 100 SC respectively than no weeding. Maximum
fertile tillers were recorded in hand weeding (369.73 m-2) and were followed by hoeing (356.94
m-2) and tine cultivator (346.78 m-2). Hand pulling, hoeing, tine cultivator, Nominee and spike
hoe gave 28, 25, 22, 12 and 6% more number of kernels per panicle respectively. Paddy yield
was 221, 203, 181 and 105% more in hand weeding, hoeing tine cultivator and Nominee 100 SC
respectively than no weeding. Highest net returns (Rs. 56905) were obtained by hand weeding
while highest BCR (1.75) was obtained in tine cultivator. A second experiment was laid out in
split plot design randomizing inter row cultivation implements in main plots and inter row
cultivation frequencies in sub plots. Weed dry weight was 199.16 g m-2 less when tine cultivator
was used at 15, 25, 35 and 45 DAS as compared to weed dry weight in inter row cultivation at15
days after seeding (DAS). More fertile tillers in tine cultivator and spike when used at 15, 25, 35
and 45 DAS were observed. Paddy yield was 159% more when tine cultivator was used at 15,
25, 35 and 45 as compared to paddy yield in inter row cultivation at15 DAS. Tine cultivator gave
maximum net return and BCR when used at 15, 25, 35 and 45 DAS. Tine cultivator gave
maximum net return and BCR when used at 15, 25, 35 and 45 DAS. Both experiments were
replicated thrice. Net plot size was 3.0 m x 6.0 m in both experiments. Weed control by tine
cultivator displayed excellent rice yields when repeated cultivation was done, and with the
reduced labor inputs compared to hand weeding and hoeing, is a viable and economical method.
1
CHAPTER 1
INTRODUCTION
Rice (Oryza sativa L.) is a principal source of food for more than half of the world
population, especially in South and Southeast Asia and Latin America. Rice is the third largest
crop after wheat and cotton and second major grain crop of Pakistan. It accounts for 6.4 % of
value added in agriculture and 0.9 % in GDP of Pakistan. In Pakistan, it is grown on an area of
about 2.88 m ha and its total production is 4.82 million tons with an average paddy yield of 2.03
t ha-1. Rice is food as well as contributing 20 % in foreign exchange earnings of Pakistan (GOP,
2011).
Rice is cultivated in many parts of the world in different ways; transplanted flooded rice,
alternate wetting and drying, rice on raised beds and aerobic rice. The main cropping differences
are between aerobic and puddled rice. Seventy five percent of Asian rice is produced in irrigated
puddled fields with generally high irrigation requirements to sustain sub merged conditions for
most of the growing season (Bouman and Toung, 2003). In Pakistan rice is grown under diverse
climatic and edaphic conditions. Basmati cultivars (Super Basmati, Basmati Pak and Basmati
385) dominate in the traditional rice tract of the Punjab known as “Kalar” tract. At high altitudes
like Swat where temperature is low, temperate Japonica rice (IR-6, Shadab and Shua-92) is
grown. In the South of Khyber Pakhtoon Kha, Sindh and Baluchistan coarse rice (IR-6, IR-9,
DR-82 and DR-83) is grown. Rice is also grown on, ‘salt affected and water logged soils’ such
soils are generally unfit for the production of other agricultural crops (Funakawa et al., 2000).
Water is the major factor limiting crop production in many parts of the world. However,
concerns are increasing about its future availability even in areas where water for irrigation is
currently plentiful (Rijsberman, 2006). Bouman and Tuong (2003) reported that in 2025, 15 out
of 75 m ha of Asian transplanted rice crop will experience water shortage. Water shortage is a
great threat to Pakistan agriculture due to huge exploitation of available water. Agriculture
consumes 70 % of the world water use (IWMI, 2007). Owing to some irrigation water rice is
mostly grown by transplanting method. In this method field is puddled before rice transplantation
then water is impounded. This continuous flooding requires lot of water and high labor (Bhushan
et al., 2007). Pakistan is confronting severe canal water shortage due to lack of interest for the
construction of new surface reservoirs. Surface water availability in 2005-2006 was 100.9 MAF
2
which decreased to 93.3 MAF in 2009-2010 (GOP, 2010). Due to constant decrease in
availability of irrigation water, it seems very difficult to grow rice with transplanting method in
future.
Paddy yield of Pakistan is either stagnant or declining, mostly due to late sowing, low
availability of water, nutrient imbalances, sub-optimum plant population, high infestation of
weeds and labor shortage in transplanted rice (Mann and Ashraf, 2001). Rapid economic
development has great influence on agricultural labor availability as it promotes rural labor
migration to urban areas for higher income from secondary industries resulting labor shortage in
rural area (Habito and Briones, 2005). Technological innovation is needed to allocate less labor
input for production of rice. Agricultural mechanization would be one possibility for labor-
saving. The alternative way is to partly shift from traditional transplanting to more labor and
water saving methods for rice production like alternate wetting and drying, rice on raised beds
and aerobic rice.
Aerobic rice is a good alternative of transplanting. Aerobic rice refers to the process of
establishing rice crop from seeds sown in the field rather than by transplanting seedlings (Rao et
al., 2007). This type of rice is also known as direct seeded rice (DSR). Direct seeded rice avoids
puddling and continuous wet soil conditions and thus decreases considerably the overall water
demand for rice culture while yield potential is equivalent to the transplanted rice (Awan et al.,
1989). In South Asia, direct seeded rice is being practiced in terraces on sloppy lands of
Bangladesh and along Western Himalayan region of India (Gupta et al., 2007). Direct seeding
with subsequent saturated conditions on flat land decreases the amount of water required during
land preparation (Bouman and Toung, 2003). Water saving of 35–57 % has been reported for
aerobic rice sown into non-puddled soil as compared with continuously flooded (up to 5 cm)
transplanted rice (Bhushan et al., 2007). Bouman and Toung (2003) also found that aerobic rice
can reduce water use by as much 50 % while maintaining a moderately high yield as compared
with transplanted rice. The DSR required 66 % less labor than transplanted rice (Ho and Romli,
2000).
Weeds pose a serious threat to the direct seeded rice crop by competing for nutrients,
light, space and moisture throughout the growing season. Ramzan (2004) reported up to 74 %
3
yield reduction in direct seeded rice due to weeds. Aerobic soil conditions, dry-tillage practices
and alternate wetting and drying are favorable for germination and growth of highly competitive
weeds, causing grain yield losses up to 91 % (Elliot et al., 1984). Choubey et al. (2001) and
Bahar and Singh (2004) also reported that weeds can decrease yield of DSR from 75 to 100 %. If
weeding is delayed beyond 20 days after emergence, permanent damage may be done to rice
crop. During early establishment, weeds make 20-30 % of their growth while the crop makes 2-3
% of its growth (Moody, 1990). The optimum time at which crop must be free of the adverse
effect of weeds is referred to as the critical period of weed competition. Almost all the annual
crops are susceptible to weed competition during the early stage of development, particularly
within the first one-third to one-half of the crop life cycle (Mercado, 1979). Chauhan and
Johnson (2011) concluded that the critical period for controlling of rice weeds in DSR was
between 14 - 41 days after sowing (DAS). So, in critical period rice must be free from weeds for
better production.
In the light of above discussion it is concluded that an effective, economical and timely
weed control strategy must be developed for direct seeded rice. With the availability of proper
weed management technology, it is possible to raise the productivity of direct seeded rice. Weeds
in direct sown rice can be controlled by various methods. Weeds could be controlled by hand
weeding, chemical and mechanical methods. Weeds are manually removed or uprooted in hand
weeding. Hand weeding is commonly practiced against weeds on small-scale rice farms
(Adesina et al., 1994). Farmers in Africa use family labor to control weeds in DSR, weeding
starts at 15-30 DAS and continue for many days depending on the type of weeds (Oteng and
Sant’Anna, 1999). Similarly, in Cambodia hand weeding is done at least twice and commonly
thrice (Makara et al., 2001). Manual weeding can control weeds but it is laborious and time
consuming (Stessens, 2002). Herbicides provide effective weed management in DSR (Azmi et
al., 2005) using as pre emergence or post emergence (Rao et al., 2007). Although chemical
control provides a considerable weed control on DSR but availability of required weedicides is
limited in Pakistan. Secondly weed resistance to various chemicals is also a problem which may
be crested in future. Another way to control weeds in direct seeded rice is use of manually or
mechanically operated implements. In line sown DSR weeds can be controlled effectively by the
use of different inter row implements as reported by Islam et al. (2004). Remington and Posner
(2000) demonstrated effective weed control in DSR using a mechanical weeder. Fazlolallh et al.
4
(2001) an Iranian scientist compared two mechanical weeders in rice; mechanical weeder with
engine power and mechanical weeder without engine power both provided effective and
economical weed control resulting in good yield.
The purpose of this study is to develop and test economical weed control methods that
would allow earlier and more rapid weeding in direct seeded rice. These options included: row
seeding rice with automatic drill to maintain row to row distance, mechanical inter row
cultivation with manual drawn equipment and weed control with herbicides. Keeping in view the
above facts this study was planned with the following objectives.
1. To assess the production potential of direct seeded rice.
2. Assessing the performance of different implements for inter-row weed control.
3. To identify the most suitable and economical weed control method.
4. Comparison of different weed management strategies and their impact on growth and
yield parameters of direct seeded rice.
5
CHAPTER 2
REVIEW OF LITERATURE
On the basis of studies conducted on various aspects of aerobic rice cultivation system, this
chapter is divided into following sections.
1. Rice
2. Crop establishment methods of rice
3. Weeds
4. Methods of weed control
2.1 Rice
Rice is monocot plant Oryza sativa (Asian rice) or Oryza glaberrima (African rice). Rice
belongs to the genus Oryza of the sub tribe oryzineae in the family Gramineae. Its genus Oryza
comprises 25 accepted species, of which 23 are wild and two (Oryza sativa and Oryza
glaberrima) are cultivated. All the rice varieties of Europe, America and Asia belong to the
specie Oryza sativa, while many of the cultivated varieties of West Africa belong to the specie
Oryza glaberrima. Rice is used as staple food for more than half of the world (Chauhan and
Johnson, 2011). About 90 % of rice is produced in the South Asian countries on an area of
153.25 m ha with an average annual production of about 610 million tons, extending from the
Indo-Pak subcontinent to Japan (IRRI, 2004).
2.1.1 History of domestication and cultivation
The commonly accepted view is that rice was first domesticated in the region of the
Yangtze River valley in China (Vaughan et al., 2008). Morphological studies of rice phytoliths
from the Diaotonghuan archaeological site show the transition from cultivation of wild rice to
domesticated rice. Changes in the morphology of Diaotonghuan phytoliths dating from 10,000-
8,000 years before present show that rice had by this time been domesticated (Mac Neish and
Libby, 1995). Soon afterwards, the two major varieties of Indica and Japonica/Sinica rice were
grown in Central China (Harris, 1996). In the late 3rd millennium before Christ (BC), there was
a rapid expansion of rice cultivation into mainland of Southeast Asia and westwards across India
and Nepal (Harris, 1996).
6
Korean archaeologists claimed to have discovered the world's oldest domesticated rice
(David, 2003). Although combined effort by the Stanford University, New York University,
Washington University in St. Louis, and Purdue University has provided the strongest evidence
yet there is only one origin of domesticated rice, in the Yangtze Valley of China (Molina et al.,
2011).
Thirty seven archeological sites have shown traces of rice cultivation in ancient time in
Indo-Pak subcontinent. These sites include Mohenjodaro in Pakistan, Gujrat, Lohal, Rangpur and
Uttar Pradesh in India (Chang, 1967). The earliest remains of rice in the Indian subcontinent
have been found in the Indo-Gangetic Plain and date from 7000-6000 BC though the earliest
widely accepted date for cultivated rice is placed at around 3000–2500 BC with findings in
regions belonging to the Indus Valley Civilization (Pokharia et al., 2009). It then spread to all the
fertile alluvial plains watered by rivers. Cultivation and cooking methods are thought to have
spread to the west rapidly and by medieval times, southern Europe saw the introduction of rice as
a hearty grain. According to Zohary and Maria (2000), Oryza sativa was recovered from a grave
at Susa in Iran (dated to the 1st century AD) at one end of the ancient world, another
domestication of rice in the South Asia.
2.2 Crop establishment methods
The production system of Asia is undergoing adjustments in response to rising scarcity of
land, water and labor. A major adjustment can be expected in the method of crop establishment.
Major determinants of farmer’s choice of crop establishment method are rainfall pattern,
sufficient water supply, weed incidence and field elevation. Availability of labor and power for
land preparation and wage rates were found to be among the major economic factors for the
choice of crop establishment method (Pandey and Velasco, 1999). Rice is grown by two methods
as discussed in the following sections.
2.2.1 Flooded rice
Commonly flooded rice is grown throughout the world. In this method first nursery plants
are grown which are then transplanted in the leveled and puddled field for establishment of crop.
This transplantation of rice seedling may be manual or by machines. Traditional method of
transplantation requires lot of labor and continuous water inputs. Shortage of labor at this critical
7
stage often results in low production and increases labor costs. Rice has lower water productivity
than some other cereal crops (Bhuiyan, 1992). Puddling helps to decrease percolation losses and
control of weeds. However, the puddling process results in subsurface compaction (Kukal and
Aggarwal, 2003), which impedes the growth and yield of succeeding wheat crop. Puddling also
affects soil health due to mechanical dispersion of soil particles and making tillage operations
difficult, requiring more energy in succeeding crops such as wheat (Singh et al., 2003). Water in
conventional rice crop represents a major and necessary production cost for the growers.
Farmers in many rice growing areas have restricted availability of irrigation water and in
future it is predicted that in Asia, 17 m ha of irrigated rice areas may experience ‘‘physical water
scarcity’’ and 22 million ha may have ‘‘economic water scarcity’’ (Toung and Bouman, 2003).
Thus water scarcity threatens the sustainability of irrigated rice ecosystems since it may no
longer be feasible for farmers to undertake wet cultivation and flood fields to ensure good crop
growth and control weeds (Johnson and Mortimer, 2005). The increasing cost of labor threatens
the sustainability of transplanted rice within the rice wheat system of the Indo Gagnetic Plains.
2.2.2 Aerobic rice
One of the strategies to combat water shortage is the system of aerobic or direct seeded
rice. Aerobic rice production consists of sowing seeds in dry soils. Decreased water input, low
labor requirement and timely sowing can be listed as possible benefits of the DSR (Bhushan et
al., 2007). In aerobic or DSR, generally cultivars could have high yield, input-responsive and
could adapt to mild to medium water stress (Bouman et al., 2005). Especially on coarse textured
soils with high seepage and percolation rates, aerobic rice has an advantage over systems that
include saturated or partially flooded soil conditions (Wang et al., 2002). Aerobic rice systems
have been developed since the mid-eighties and aerobic rice is now grown commercially in many
countries of the world especially in Brazil and Northern China (Wang et al., 2002).
According to Guimaraes and Stone (2000), aerobic rice yields similar to that from
transplanted. Direct seeding is cost effective, can save water through earlier rice crop
establishment and allows early sowing of wheat (Ladha et al., 2003; Singh et al., 2003). Aerobic
rice cultivation is a new technology that suits to high yielding varieties with a much lower water
input than that required for traditional rice cultivation. A commercial cropping system based on
8
aerobic rice has spread widely in Brazil as a result of the development of improved high yielding
varieties (Pinhiero et al., 2006).
Paddy yield of 5-6 t ha-1 can be achieved in the aerobic rice system (George et al., 2002).
By limiting water losses through seepage, percolation and evaporation, aerobic rice limit total
water use by 27-51 % as compared with transplanted rice crop (Bouman et al., 2005). Similar
observations were reported by Wang et al. (2002) that aerobic rice system can decrease water
application by 44 % compared to the conventionally transplanted rice system, mostly through
decreasing percolation, seepage and evaporation losses, while maintaining yield at a satisfactory
level.
The water requirement of aerobic rice is potentially much less than that of flooded rice
because of: (1) the absence of water use for wet land preparation (puddling), (2) the absence of
continuous seepage and (3) the absence of evaporation losses from the pounded water layer
(Bouman et al., 2005). The potential water saving at field level when rice can be grown as an
upland crop are large, especially on soils with high seepage and percolation rates (Bouman
Toung, 2001). Properly managed aerobic rice results in reasonable yields as Tabbal et al. (2002)
reported that DSR yielded the same as transplanted rice while water inputs were decreased by 35
% and water productivity increased by 45 % compared with that under flooded conditions.
2.2.3 Global distribution of direct seeded rice
Growing of direct seeded rice varies broadly across countries and regions. DSR is planted
either in a dry seeded or wet seeded system in the United States (Gianessi et. al., 2002), Australia
and Europe (Ferrero and Nguyen, 2004). Pandey and Velasco (2002) reported that direct seeding
was rising rapidly in Asia, with 21-22 % of the total rice area being dry seeded or wet seeded.
Dry direct seeding in India is extensively practiced in rain fed lowlands, uplands and
flood prone areas, while wet seeding is a common practice in irrigated areas (Mishra et. al.,
2005). In Vietnam, especially in the Mekong Delta, rice is sown wet seeded and broadcasting is
common practice (Luat, 2000). Kim et al. (2001) reported that 11 % of the total rice was direct
seeded in Korea, dry seeding was 6.4% and wet seeding 4.7 %.
9
In the northern provinces of Cambodia, in wet season, 80-90 % of rice fields are dry
direct seeded (CIAP, 1998). In Philippine both dry and wet seeding is commonly practiced (De
Dios et. al., 2005). In Thailand, the rain fed rice area is dry direct seeded, whereas irrigated area
in the Central Plain is mostly wet seeded (Azmi et al., 2005). In Malaysia, wet seeding is favored
but is encouraged only where efficient water management is possible (Azmi et al., 2005). In
Northern and North Eastern regions of China rice seeded directly into aerobic non puddled soils,
area under direct seeding in these regions is 80,000 and 60,000 ha, respectively (Xie et al.,
2005).
2.2.4 Crop establishment methods of direct seeded rice
Farmers usually adopt four types of crop establishment methods in relation to
hydrological conditions for direct seeding of rice.
2.2.4.1 Wet seeding
Rice seeds are soaked for 24 hrs and incubated from 24 to 36 hrs. Pre-germinated seeds
are then sown on to the soil surface. Typically water is reintroduced into fields for weed
suppression 7-10 days after sowing (DAS). Weed infestation is usually lower in wet seeded rice
than in dry-seeded and zero-tilled rice (Balasubramanian and Hill, 2002).
2.2.4.2 Water seeding
Pre-germinated seeds are sown directly into water. Water seeding enables farmers to
proceed without waiting for the flood water recession necessary for wet seeding and to minimize
the risk of late season drought in the crop. Fields are drained gradually as the crop develops. This
practice substantially reduces weed infestations during early crop establishment (Yamauchi
1996).
2.2.4.3 Zero-tillage seeding
In South Vietnam after the harvest of the wet seeded rice crop in January or February,
rice straw is scattered over the fields, dried for a few days and burned. Fields are then irrigated to
an average depth of 5 cm before sowing pre germinated seeds onto the wet ash layer. In this way,
zero tillage shortens the turnover time when cultivating three rice crops per year. However, zero
tillage results in high weed infestations, particularly of perennial weeds such as Paspalum
distichum (Hach et al., 1997) and insect pest predators have been recorded significantly lower in
fields where stubble is burned (Loc et al., 1998).
10
2.2.4.4 Dry seeding
This method is practiced when water is in short supply. Land preparation involves
ploughing or rotovation. Non-germinated seeds are broadcasted or drill seeded in dry or moist
soil. Broadcasted seeds are covered by harrowing; more seeds are required for broadcast than for
drill seeding and stand establishment is poor with broadcasting. Dry seeded rice culture is
practiced in Africa, Australia, Europe, and USA (Kwesi and De Datta, 1991).
2.3 Weeds
Weeds are plants growing in an improper place in competition with a crop. Thus, a plant
species cannot be classified as a weed under all conditions. The major weeds associated with rice
include grasses Dactyloctenium aegyptium (L.) Echinochloa crus-galli (L.), Echinochloa colona
(L.) and Leptochloa chinensis (L.), Cyperus rotendusand and broadleaf weeds Commelina
benghalensis (L.), Caesulia axillaris, Eclipta prostrate (L.) Euphorbia hirta (L.), Portulaca
oleracea (L.), Trianthema portulacastrum (L.) and Lindernia spp. (Singh et al., 2006).
2.3.1 Effects of weeds on rice
Weeds have different effects on rice. Weed infestations primarily constrain rice
production by reducing grain yield. Yield reductions caused by uncontrolled weeds throughout a
crop season have been estimated to be from 44 to 96 %, depending on the rice culture.
Worldwide some 9 % loss of rice yield can be attributed just to weeds (Oerke and Dehne, 2004).
There is considerable variation in yield loss to weeds among countries.
The cost of rice weed control including herbicides, cultural and mechanical practices and
hand weeding, is estimated to be about 5 % of world rice production and amount to US $ 3.5
billion annually. When the 9 % loss of rough rice paddy yield is added to this cost, the world’s
total estimated cost for rice weeds and their control amounts to 15 % of total annual production
of rice valued at US $ 10.5 billion (Kwesi De Datta, 1991). Similar findings were observed by
Norton et al. 2010 who reported that herbicide sales for rice crops globally grew at an average
rate of more than 2 percent year-1 from US $ 741 million in 1980 to US $ 1.34 billion in 2007
and global sale of herbicides for application in rice farming systems could reach US $ 3 billion
per year by 2025 (Zhang et al., 2004).
11
Weeds indirectly limit production by serving as hosts for organisms that adversely affect
rice. Weeds provide food, shelter and reproduction sites for insects, nematodes, pathogens and
rodents. Some weeds that serve as alternate hosts to rice pests are Cynodon dactylon, Cyperus
iria, Echinochloa colona, Echinochloa crus-galli and Cyperus rotundus. This indicates the
importance of recognizing weeds as secondary hosts for pests and of removing weeds from the
margins of rice fields to prevent continued infection of the rice crop.
Weeds hamper rice harvesting and increase harvest costs through direct interference with
the harvesting operation and by causing lodging. Weed seeds contaminate rough rice, thus
reducing grain quality and market value. For example, the weed red rice has a pigmented layer
that shatters easily and readily contaminates rough rice. Removing all traces of the pigmented
layer requires intense milling resulting in decreased grain quality and increased cost. The
drudgery of weeding and labor shortages has made rice farming unattractive. In most tropical
countries, farmers spend more time on weeding by hand or with simple tools, than on any other
farming task.
Nutrient availability and favorable temperatures throughout the year, especially in the
tropics, allow luxuriant aquatic weed growth in rice fields and weeds compete for nutrients with
rice (Phoung et al., 2005). Heavy aquatic weed infestation causes excessive water loss through
evaporation and impedes water flow in irrigation canals. In some cases, aquatic weeds may be a
health hazard to persons living near impounded water. Weeds interfere with rice growth by
competing for one or more growth limiting resources such as light, nutrients and water.
Allelopathy (chemical production by living or decaying weed plant tissues) may also adversely
affect the growth of a neighboring rice plant. Rice and rice weeds have similar requirements for
growth and development. Competition occurs when one of the limiting resources falls short of
the combined requirements of both. The degree of rice-weed competition depends on rainfall,
rice variety, soil factors, weed density, duration of rice and weed growth. Cultural practices
greatly alter the competitive relationship between rice and weeds. Thus, different cultures
(irrigated, rain fed lowland, upland, and deepwater) will be subjected to different kinds and
degrees of weed competition. To understand this competition, it is essential to know the growth
requirements of rice and helpful to know the growth requirements of weeds.
12
2.3.2 Weeds in direct seeded rice
Weeds are the main problem in direct seeded rice which may cause 100 % failure of the
crop. Aerobic soil, dry tillage and alternate wetting and drying conditions are favorable to the
germination and growth of weeds causing grain yield losses up to 90 % (Rao et al., 2007). Crop
loss due to weed competition varies with the duration of weed infestation of the crop. The crop is
likely to experience yield reduction, unless weeds are kept free during a part of its growing
period (Azmi et al., 2007). Johnson et al. (2004) noted that in direct seeded rice weeds can
emerge at the same time or before the rice plants, causing serious competition.
The labor requirement for weeding is a major impediment to the adoption of water saving
aerobic rice and increasing the productivity of aerobic rice based cropping systems (Singh et al.,
2006). After broadcast or drill seeding rice into dry soil field is irrigated just enough to provide
the soil moisture that allows the seeds to germinate. Thus, aerobic conditions remain ideal for the
germination of upland and aquatic weeds and weed problems are much worse in dry seeded
irrigated than in wet seeded rice (Kwesi and De Detta, 1991). Weeds are a major hurdle to broad
adoption of aerobic rice and greatest yield limiting constraint to aerobic rice, contributing about
50 % to yield losses (WARDA, 1996). Aerobic rice is subjected to more severe weed infestation
than transplanted lowland rice, because direct seeded aerobic rice germinates simultaneously
with weeds, in this way weeds dominates the crop due to the lack of water layer that suppress the
weed germination (Moody, 1983).
2.3.3 Weed control
Weeds have always reduced rice yields. As a result different weed control methods have
evolved. Farmers consider financial resources and availability of labor in deciding what weed
control method to use. Problems of input availability, availability of new technologies, specific
weed problems, farm size and availability of family labor are basic management factors in
making weed control decisions.
2.3.4 Planning effective control
Planning is important in making appropriate decisions on weed control. Unfortunately,
weed control usually is not planned. The decision to control is not made until the problem has
become serious, when control may be uneconomical, ineffective. Advance knowledge of weed
13
problems can be obtained by surveying and recording the weed species in a rice field after rice
emergence. This record is useful in planning weed control and crop rotation programs.
2.4 Weed control methods
Weed control can be grouped into cultural, manual, mechanical, chemical, and biological
control methods. Each control method has advantages and disadvantages.
2.4.1 Cultural control methods
A basic principle of cultural control is to increase the competitive ability of rice and
enable it to suppress weed growth. Vigorous rice crop competes more effectively with weeds.
Cultural control methods includes; use of weed free and certified seed land preparation, cultivar
selection, time of seeding, planting method, crop rotation, plant population, fertilization and
water management (Rao et al., 2007).
2.4.2 Biological weed control methods
Several biological agents such as insects, mites, and fungi have been used successfully to
control rice weeds. The possibility of using tadpole shrimp (Triopus longicaudatus, T. granaris,
and T. cancriformis) for weed control in transplanted rice has been demonstrated in Japan. The
small crustaceans feed on weed seedlings and disturb their roots by mechanical agitation of the
soil. Labor for hand weeding in farmers’ fields was reduced 70-80 % in initial field trials with
tadpole shrimp (Matsunaka, 1975).
2.4.3 Problems of biological weed control methods
Tadpole shrimp (Triopus longicaudatus, T. granaris, and T. cancriformis) decreases
weed infestation but later on becomes a pest cause serious problems to rice crop (Matsunaka,
1975). Biological agents are selective in their control action and their activity may be restricted
to a single weed. Biological control programs may be applicable to an introduced perennial weed
growing in areas that are seldom disturbed, such as pastures, forests and water bodies. In a rice
cropping situation with a mixed weed flora, the selective control of a single weed will not solve
the weed problem and biological agents also work slowly.
14
2.4.4 Manual weed control
Manual weed control includes removing and hand pulling. This method is the oldest and
in many cases, the farmer’s only way of controlling weeds in rice. Manual weeding is very
effective weed control method (Singh, 2005). Ekleme et al. (2009) conducted two field
experiments in Nigeria, results showed that lowest weed biomass (48 g m-2) was produced by
weeding twice with highest plant height (116 cm), tillers (203 m-2) and grain yield (3.62 t h-1) in
direct seeded rice. Singh et al. (2008) noted that hand weeding twice resulted in significantly
lower weed biomass (61.6 g) as compared to no weeding (329.5 g). Singh et al. (2007)
concluded that hand weeding six times in dry seeded rice resulted in lowest weed biomass (0 g
m-2) with highest panicle length (24.5 cm), panicles (160 m-2), grains panicle-1 (96), 1000 grain
weight (25.9 g), grain yield (5.58 t h-1) and straw yield (7.28 t h-1). Mann et al. (2007) conducted
an experiment and results revealed that in hand weeding with lowest weed biomass highest plant
height (92 cm), panicles (215 m-2), panicle length (27.3 cm), grains panicle-1 (120) and grain
yield (3.70 t h-1) were produced as compared to no weeding
Manual weeding is practiced in Cambodia at least twice and commonly three times
(Makara et al., 2001). In Vietnam, Chin et al. (2000a) found that hand weeding twice was the
most effective treatment in terms of both controlling weeds and crop safety in DSR. African
farmers normally rely on family labor for weeding in direct seeded rice, which usually starts at
15-30 DAS (Oteng and Sant’Anna, 1999). Chandra et al. (1998) concluded that hand weeding
resulted in lowest weed density, weed dry weight and highest grain yield over control. Bhaghat
et al. (1991) conducted a field experiment and results showed that hand weeding (15, 30 and 45
days after seedling emergence) produced highest paddy yield of 4.14 t ha-1.
2.4.5 Problems of manual weed control
Hand weeding is a very effective way to control weeds in DSR but it requires high labor
due to which cost of production also increases. Manual weeding can be done only when weeds
have adequate size to be pulled (Singh et al., 2005). Hand weeding in wet-seeded rice requires
more time (Moody, 1983). This labor demanding method requires 250–780 man h ha-1 (Stessens,
2002). Hand weeding is the most widely practiced intervention against weeds on small-scale rice
farms in Africa (Adesina et al., 1994) but shortage of labor, high labor cost, poor weather
15
conditions and the presence of perennial weeds that fragment on pulling are problems of hand
weeding
2.4.6 Chemical weed control methods
Herbicide use is one of the most labor-saving innovations that have been introduced in
rice farming. Hussain et al. (2008) used Nominee 100 SC to control weeds in direct seeded rice
resulting in grain yield (3.61 t ha-1) and giving 90 % weed control as compared to no weeding.
Effective weed management practices are an important requirement in DSR culture, with
herbicide application it is very easy to control weeds (Azmi et al., 2007). Islam et al. (2004)
compared hand weeding with different herbicides and found Pretilachlor as a most successful
weedicide with higher yield and cost benefit ratio. In America, Australia, Europe and East
Asia, over 90 % of DSR areas are treated with herbicides for weed control (Baltazar and De
Datta, 1992). Herbicide options for weed control in DSR differ according to method of crop
establishment because the performance of herbicides varies in relation to water regimes.
Extensive research has been conducted over the years by many researchers to find out the
optimum rate, time, type and method of herbicide application. Labor unavailability, high labor
costs and the urgent need to raise yields and maintain profit on a progressively limited land have
been major drivers for farmers to seek alternatives to manual weeding and herbicides are one
such alternative.
2.4.7 Problems of chemical weed control methods
Labor shortages have led to an increase in direct seeding and a rapid increase in the use
of herbicides. This reliance has resulted in an unwanted shift in weed species and concerns about
environmental pollution. Phenoxy and sulfonylurea compounds are extensively used herbicides
in Malaysia, Vietnam and Thailand to control broadleaf weeds and sedges in DSR. Weeds
resistant to these herbicides have developed and there is confirmation that weed species such as
S. zeylanica, Marsilea minuta, and F. miliacea have developed resistance to phenoxy herbicides
(Watanabe et al., 1997). Chemical weed control through the commonly used pre-emergence
herbicides (such as butachlor and thiobencarb) has been broadly investigated but their efficiency
depends on the water regime, soil tilth, composition of the weed and environmental conditions
(Baltazar and De Datta, 1992). Weedicide used for weed control in transplanted rice in Pakistan
16
are not suitable for weeds in DSR and required weedicide for effective weed control in DSR are
not available.
2.4.8 Mechanical weed control
Mechanical weed control with the use of implements is a practical and economic method
for farmers. Mechanical weeding is almost generally practiced on row seeded rice since inter row
cultivation with either animal or tractor drawn implements reduces time in weed control and
minimizes crop damages. A weeder (with a straight line peg arrangement) showed outstanding
results on a range of soil types with unstable soil moisture levels and weed intensity and saving
57 % labor as compared with hand weeding (Subudhi, 2004). In Bangladesh a rake type weeder
had shown good results and it works in light and heavy soils with same efficacy (Islam et al.,
2004). Victor and Verma (2003) reported that Power operated rotary weeders have improved
weeding efficiency. In Thailand, a mechanized direct seeded rice management system has been
adopted, which includes weed control with a soil cultivator involving tillage between rows of
rice twice; 15 and 28 DAS (Kabaki et al., 2003). Mechanical weed control is similar to the weed
free control in terms of grain yield due to its bigger weed control efficiency (Morthy, 2003). In
Gambia effective weed management has been established with donkey drawn equipment using
twice (Remington and Posner, 2000). Beushening in which dry seeded rice is compressed by
‘‘planking’’ (drawing of a heavy flat wooden object over the crop) is also practiced to control
weeds. This process kills weed species with single main stems, whereas rice is able to retiller
from basal nodes. Sharma (1997) conducted a series of experiments on weed management in
rice, results showed that beushening is a very good technique to control weeds in rice, producing
paddy yield 3.5 t ha-1.
Comparisons of traditional and mechanically modernized weeding equipment suggest the
value of these technologies in resource limited farming communities. With labor shortage
mechanical weed control methods may be more efficient and suitable in DSR. Hence, efforts to
improve the effectiveness of existing weeding tools and implements are desirable, especially for
developing countries.
5
CHAPTER 2
REVIEW OF LITERATURE
On the basis of studies conducted on various aspects of aerobic rice cultivation system, this
chapter is divided into following sections.
1. Rice
2. Crop establishment methods of rice
3. Weeds
4. Methods of weed control
2.1 Rice
Rice is monocot plant Oryza sativa (Asian rice) or Oryza glaberrima (African rice). Rice
belongs to the genus Oryza of the sub tribe oryzineae in the family Gramineae. Its genus Oryza
comprises 25 accepted species, of which 23 are wild and two (Oryza sativa and Oryza
glaberrima) are cultivated. All the rice varieties of Europe, America and Asia belong to the
specie Oryza sativa, while many of the cultivated varieties of West Africa belong to the specie
Oryza glaberrima. Rice is used as staple food for more than half of the world (Chauhan and
Johnson, 2011). About 90 % of rice is produced in the South Asian countries on an area of
153.25 m ha with an average annual production of about 610 million tons, extending from the
Indo-Pak subcontinent to Japan (IRRI, 2004).
2.1.1 History of domestication and cultivation
The commonly accepted view is that rice was first domesticated in the region of the
Yangtze River valley in China (Vaughan et al., 2008). Morphological studies of rice phytoliths
from the Diaotonghuan archaeological site show the transition from cultivation of wild rice to
domesticated rice. Changes in the morphology of Diaotonghuan phytoliths dating from 10,000-
8,000 years before present show that rice had by this time been domesticated (Mac Neish and
Libby, 1995). Soon afterwards, the two major varieties of Indica and Japonica/Sinica rice were
grown in Central China (Harris, 1996). In the late 3rd millennium before Christ (BC), there was
a rapid expansion of rice cultivation into mainland of Southeast Asia and westwards across India
and Nepal (Harris, 1996).
6
Korean archaeologists claimed to have discovered the world's oldest domesticated rice
(David, 2003). Although combined effort by the Stanford University, New York University,
Washington University in St. Louis, and Purdue University has provided the strongest evidence
yet there is only one origin of domesticated rice, in the Yangtze Valley of China (Molina et al.,
2011).
Thirty seven archeological sites have shown traces of rice cultivation in ancient time in
Indo-Pak subcontinent. These sites include Mohenjodaro in Pakistan, Gujrat, Lohal, Rangpur and
Uttar Pradesh in India (Chang, 1967). The earliest remains of rice in the Indian subcontinent
have been found in the Indo-Gangetic Plain and date from 7000-6000 BC though the earliest
widely accepted date for cultivated rice is placed at around 3000–2500 BC with findings in
regions belonging to the Indus Valley Civilization (Pokharia et al., 2009). It then spread to all the
fertile alluvial plains watered by rivers. Cultivation and cooking methods are thought to have
spread to the west rapidly and by medieval times, southern Europe saw the introduction of rice as
a hearty grain. According to Zohary and Maria (2000), Oryza sativa was recovered from a grave
at Susa in Iran (dated to the 1st century AD) at one end of the ancient world, another
domestication of rice in the South Asia.
2.2 Crop establishment methods
The production system of Asia is undergoing adjustments in response to rising scarcity of
land, water and labor. A major adjustment can be expected in the method of crop establishment.
Major determinants of farmer’s choice of crop establishment method are rainfall pattern,
sufficient water supply, weed incidence and field elevation. Availability of labor and power for
land preparation and wage rates were found to be among the major economic factors for the
choice of crop establishment method (Pandey and Velasco, 1999). Rice is grown by two methods
as discussed in the following sections.
2.2.1 Flooded rice
Commonly flooded rice is grown throughout the world. In this method first nursery plants
are grown which are then transplanted in the leveled and puddled field for establishment of crop.
This transplantation of rice seedling may be manual or by machines. Traditional method of
transplantation requires lot of labor and continuous water inputs. Shortage of labor at this critical
7
stage often results in low production and increases labor costs. Rice has lower water productivity
than some other cereal crops (Bhuiyan, 1992). Puddling helps to decrease percolation losses and
control of weeds. However, the puddling process results in subsurface compaction (Kukal and
Aggarwal, 2003), which impedes the growth and yield of succeeding wheat crop. Puddling also
affects soil health due to mechanical dispersion of soil particles and making tillage operations
difficult, requiring more energy in succeeding crops such as wheat (Singh et al., 2003). Water in
conventional rice crop represents a major and necessary production cost for the growers.
Farmers in many rice growing areas have restricted availability of irrigation water and in
future it is predicted that in Asia, 17 m ha of irrigated rice areas may experience ‘‘physical water
scarcity’’ and 22 million ha may have ‘‘economic water scarcity’’ (Toung and Bouman, 2003).
Thus water scarcity threatens the sustainability of irrigated rice ecosystems since it may no
longer be feasible for farmers to undertake wet cultivation and flood fields to ensure good crop
growth and control weeds (Johnson and Mortimer, 2005). The increasing cost of labor threatens
the sustainability of transplanted rice within the rice wheat system of the Indo Gagnetic Plains.
2.2.2 Aerobic rice
One of the strategies to combat water shortage is the system of aerobic or direct seeded
rice. Aerobic rice production consists of sowing seeds in dry soils. Decreased water input, low
labor requirement and timely sowing can be listed as possible benefits of the DSR (Bhushan et
al., 2007). In aerobic or DSR, generally cultivars could have high yield, input-responsive and
could adapt to mild to medium water stress (Bouman et al., 2005). Especially on coarse textured
soils with high seepage and percolation rates, aerobic rice has an advantage over systems that
include saturated or partially flooded soil conditions (Wang et al., 2002). Aerobic rice systems
have been developed since the mid-eighties and aerobic rice is now grown commercially in many
countries of the world especially in Brazil and Northern China (Wang et al., 2002).
According to Guimaraes and Stone (2000), aerobic rice yields similar to that from
transplanted. Direct seeding is cost effective, can save water through earlier rice crop
establishment and allows early sowing of wheat (Ladha et al., 2003; Singh et al., 2003). Aerobic
rice cultivation is a new technology that suits to high yielding varieties with a much lower water
input than that required for traditional rice cultivation. A commercial cropping system based on
8
aerobic rice has spread widely in Brazil as a result of the development of improved high yielding
varieties (Pinhiero et al., 2006).
Paddy yield of 5-6 t ha-1 can be achieved in the aerobic rice system (George et al., 2002).
By limiting water losses through seepage, percolation and evaporation, aerobic rice limit total
water use by 27-51 % as compared with transplanted rice crop (Bouman et al., 2005). Similar
observations were reported by Wang et al. (2002) that aerobic rice system can decrease water
application by 44 % compared to the conventionally transplanted rice system, mostly through
decreasing percolation, seepage and evaporation losses, while maintaining yield at a satisfactory
level.
The water requirement of aerobic rice is potentially much less than that of flooded rice
because of: (1) the absence of water use for wet land preparation (puddling), (2) the absence of
continuous seepage and (3) the absence of evaporation losses from the pounded water layer
(Bouman et al., 2005). The potential water saving at field level when rice can be grown as an
upland crop are large, especially on soils with high seepage and percolation rates (Bouman
Toung, 2001). Properly managed aerobic rice results in reasonable yields as Tabbal et al. (2002)
reported that DSR yielded the same as transplanted rice while water inputs were decreased by 35
% and water productivity increased by 45 % compared with that under flooded conditions.
2.2.3 Global distribution of direct seeded rice
Growing of direct seeded rice varies broadly across countries and regions. DSR is planted
either in a dry seeded or wet seeded system in the United States (Gianessi et. al., 2002), Australia
and Europe (Ferrero and Nguyen, 2004). Pandey and Velasco (2002) reported that direct seeding
was rising rapidly in Asia, with 21-22 % of the total rice area being dry seeded or wet seeded.
Dry direct seeding in India is extensively practiced in rain fed lowlands, uplands and
flood prone areas, while wet seeding is a common practice in irrigated areas (Mishra et. al.,
2005). In Vietnam, especially in the Mekong Delta, rice is sown wet seeded and broadcasting is
common practice (Luat, 2000). Kim et al. (2001) reported that 11 % of the total rice was direct
seeded in Korea, dry seeding was 6.4% and wet seeding 4.7 %.
9
In the northern provinces of Cambodia, in wet season, 80-90 % of rice fields are dry
direct seeded (CIAP, 1998). In Philippine both dry and wet seeding is commonly practiced (De
Dios et. al., 2005). In Thailand, the rain fed rice area is dry direct seeded, whereas irrigated area
in the Central Plain is mostly wet seeded (Azmi et al., 2005). In Malaysia, wet seeding is favored
but is encouraged only where efficient water management is possible (Azmi et al., 2005). In
Northern and North Eastern regions of China rice seeded directly into aerobic non puddled soils,
area under direct seeding in these regions is 80,000 and 60,000 ha, respectively (Xie et al.,
2005).
2.2.4 Crop establishment methods of direct seeded rice
Farmers usually adopt four types of crop establishment methods in relation to
hydrological conditions for direct seeding of rice.
2.2.4.1 Wet seeding
Rice seeds are soaked for 24 hrs and incubated from 24 to 36 hrs. Pre-germinated seeds
are then sown on to the soil surface. Typically water is reintroduced into fields for weed
suppression 7-10 days after sowing (DAS). Weed infestation is usually lower in wet seeded rice
than in dry-seeded and zero-tilled rice (Balasubramanian and Hill, 2002).
2.2.4.2 Water seeding
Pre-germinated seeds are sown directly into water. Water seeding enables farmers to
proceed without waiting for the flood water recession necessary for wet seeding and to minimize
the risk of late season drought in the crop. Fields are drained gradually as the crop develops. This
practice substantially reduces weed infestations during early crop establishment (Yamauchi
1996).
2.2.4.3 Zero-tillage seeding
In South Vietnam after the harvest of the wet seeded rice crop in January or February,
rice straw is scattered over the fields, dried for a few days and burned. Fields are then irrigated to
an average depth of 5 cm before sowing pre germinated seeds onto the wet ash layer. In this way,
zero tillage shortens the turnover time when cultivating three rice crops per year. However, zero
tillage results in high weed infestations, particularly of perennial weeds such as Paspalum
distichum (Hach et al., 1997) and insect pest predators have been recorded significantly lower in
fields where stubble is burned (Loc et al., 1998).
10
2.2.4.4 Dry seeding
This method is practiced when water is in short supply. Land preparation involves
ploughing or rotovation. Non-germinated seeds are broadcasted or drill seeded in dry or moist
soil. Broadcasted seeds are covered by harrowing; more seeds are required for broadcast than for
drill seeding and stand establishment is poor with broadcasting. Dry seeded rice culture is
practiced in Africa, Australia, Europe, and USA (Kwesi and De Datta, 1991).
2.3 Weeds
Weeds are plants growing in an improper place in competition with a crop. Thus, a plant
species cannot be classified as a weed under all conditions. The major weeds associated with rice
include grasses Dactyloctenium aegyptium (L.) Echinochloa crus-galli (L.), Echinochloa colona
(L.) and Leptochloa chinensis (L.), Cyperus rotendusand and broadleaf weeds Commelina
benghalensis (L.), Caesulia axillaris, Eclipta prostrate (L.) Euphorbia hirta (L.), Portulaca
oleracea (L.), Trianthema portulacastrum (L.) and Lindernia spp. (Singh et al., 2006).
2.3.1 Effects of weeds on rice
Weeds have different effects on rice. Weed infestations primarily constrain rice
production by reducing grain yield. Yield reductions caused by uncontrolled weeds throughout a
crop season have been estimated to be from 44 to 96 %, depending on the rice culture.
Worldwide some 9 % loss of rice yield can be attributed just to weeds (Oerke and Dehne, 2004).
There is considerable variation in yield loss to weeds among countries.
The cost of rice weed control including herbicides, cultural and mechanical practices and
hand weeding, is estimated to be about 5 % of world rice production and amount to US $ 3.5
billion annually. When the 9 % loss of rough rice paddy yield is added to this cost, the world’s
total estimated cost for rice weeds and their control amounts to 15 % of total annual production
of rice valued at US $ 10.5 billion (Kwesi De Datta, 1991). Similar findings were observed by
Norton et al. 2010 who reported that herbicide sales for rice crops globally grew at an average
rate of more than 2 percent year-1 from US $ 741 million in 1980 to US $ 1.34 billion in 2007
and global sale of herbicides for application in rice farming systems could reach US $ 3 billion
per year by 2025 (Zhang et al., 2004).
11
Weeds indirectly limit production by serving as hosts for organisms that adversely affect
rice. Weeds provide food, shelter and reproduction sites for insects, nematodes, pathogens and
rodents. Some weeds that serve as alternate hosts to rice pests are Cynodon dactylon, Cyperus
iria, Echinochloa colona, Echinochloa crus-galli and Cyperus rotundus. This indicates the
importance of recognizing weeds as secondary hosts for pests and of removing weeds from the
margins of rice fields to prevent continued infection of the rice crop.
Weeds hamper rice harvesting and increase harvest costs through direct interference with
the harvesting operation and by causing lodging. Weed seeds contaminate rough rice, thus
reducing grain quality and market value. For example, the weed red rice has a pigmented layer
that shatters easily and readily contaminates rough rice. Removing all traces of the pigmented
layer requires intense milling resulting in decreased grain quality and increased cost. The
drudgery of weeding and labor shortages has made rice farming unattractive. In most tropical
countries, farmers spend more time on weeding by hand or with simple tools, than on any other
farming task.
Nutrient availability and favorable temperatures throughout the year, especially in the
tropics, allow luxuriant aquatic weed growth in rice fields and weeds compete for nutrients with
rice (Phoung et al., 2005). Heavy aquatic weed infestation causes excessive water loss through
evaporation and impedes water flow in irrigation canals. In some cases, aquatic weeds may be a
health hazard to persons living near impounded water. Weeds interfere with rice growth by
competing for one or more growth limiting resources such as light, nutrients and water.
Allelopathy (chemical production by living or decaying weed plant tissues) may also adversely
affect the growth of a neighboring rice plant. Rice and rice weeds have similar requirements for
growth and development. Competition occurs when one of the limiting resources falls short of
the combined requirements of both. The degree of rice-weed competition depends on rainfall,
rice variety, soil factors, weed density, duration of rice and weed growth. Cultural practices
greatly alter the competitive relationship between rice and weeds. Thus, different cultures
(irrigated, rain fed lowland, upland, and deepwater) will be subjected to different kinds and
degrees of weed competition. To understand this competition, it is essential to know the growth
requirements of rice and helpful to know the growth requirements of weeds.
12
2.3.2 Weeds in direct seeded rice
Weeds are the main problem in direct seeded rice which may cause 100 % failure of the
crop. Aerobic soil, dry tillage and alternate wetting and drying conditions are favorable to the
germination and growth of weeds causing grain yield losses up to 90 % (Rao et al., 2007). Crop
loss due to weed competition varies with the duration of weed infestation of the crop. The crop is
likely to experience yield reduction, unless weeds are kept free during a part of its growing
period (Azmi et al., 2007). Johnson et al. (2004) noted that in direct seeded rice weeds can
emerge at the same time or before the rice plants, causing serious competition.
The labor requirement for weeding is a major impediment to the adoption of water saving
aerobic rice and increasing the productivity of aerobic rice based cropping systems (Singh et al.,
2006). After broadcast or drill seeding rice into dry soil field is irrigated just enough to provide
the soil moisture that allows the seeds to germinate. Thus, aerobic conditions remain ideal for the
germination of upland and aquatic weeds and weed problems are much worse in dry seeded
irrigated than in wet seeded rice (Kwesi and De Detta, 1991). Weeds are a major hurdle to broad
adoption of aerobic rice and greatest yield limiting constraint to aerobic rice, contributing about
50 % to yield losses (WARDA, 1996). Aerobic rice is subjected to more severe weed infestation
than transplanted lowland rice, because direct seeded aerobic rice germinates simultaneously
with weeds, in this way weeds dominates the crop due to the lack of water layer that suppress the
weed germination (Moody, 1983).
2.3.3 Weed control
Weeds have always reduced rice yields. As a result different weed control methods have
evolved. Farmers consider financial resources and availability of labor in deciding what weed
control method to use. Problems of input availability, availability of new technologies, specific
weed problems, farm size and availability of family labor are basic management factors in
making weed control decisions.
2.3.4 Planning effective control
Planning is important in making appropriate decisions on weed control. Unfortunately,
weed control usually is not planned. The decision to control is not made until the problem has
become serious, when control may be uneconomical, ineffective. Advance knowledge of weed
13
problems can be obtained by surveying and recording the weed species in a rice field after rice
emergence. This record is useful in planning weed control and crop rotation programs.
2.4 Weed control methods
Weed control can be grouped into cultural, manual, mechanical, chemical, and biological
control methods. Each control method has advantages and disadvantages.
2.4.1 Cultural control methods
A basic principle of cultural control is to increase the competitive ability of rice and
enable it to suppress weed growth. Vigorous rice crop competes more effectively with weeds.
Cultural control methods includes; use of weed free and certified seed land preparation, cultivar
selection, time of seeding, planting method, crop rotation, plant population, fertilization and
water management (Rao et al., 2007).
2.4.2 Biological weed control methods
Several biological agents such as insects, mites, and fungi have been used successfully to
control rice weeds. The possibility of using tadpole shrimp (Triopus longicaudatus, T. granaris,
and T. cancriformis) for weed control in transplanted rice has been demonstrated in Japan. The
small crustaceans feed on weed seedlings and disturb their roots by mechanical agitation of the
soil. Labor for hand weeding in farmers’ fields was reduced 70-80 % in initial field trials with
tadpole shrimp (Matsunaka, 1975).
2.4.3 Problems of biological weed control methods
Tadpole shrimp (Triopus longicaudatus, T. granaris, and T. cancriformis) decreases
weed infestation but later on becomes a pest cause serious problems to rice crop (Matsunaka,
1975). Biological agents are selective in their control action and their activity may be restricted
to a single weed. Biological control programs may be applicable to an introduced perennial weed
growing in areas that are seldom disturbed, such as pastures, forests and water bodies. In a rice
cropping situation with a mixed weed flora, the selective control of a single weed will not solve
the weed problem and biological agents also work slowly.
14
2.4.4 Manual weed control
Manual weed control includes removing and hand pulling. This method is the oldest and
in many cases, the farmer’s only way of controlling weeds in rice. Manual weeding is very
effective weed control method (Singh, 2005). Ekleme et al. (2009) conducted two field
experiments in Nigeria, results showed that lowest weed biomass (48 g m-2) was produced by
weeding twice with highest plant height (116 cm), tillers (203 m-2) and grain yield (3.62 t h-1) in
direct seeded rice. Singh et al. (2008) noted that hand weeding twice resulted in significantly
lower weed biomass (61.6 g) as compared to no weeding (329.5 g). Singh et al. (2007)
concluded that hand weeding six times in dry seeded rice resulted in lowest weed biomass (0 g
m-2) with highest panicle length (24.5 cm), panicles (160 m-2), grains panicle-1 (96), 1000 grain
weight (25.9 g), grain yield (5.58 t h-1) and straw yield (7.28 t h-1). Mann et al. (2007) conducted
an experiment and results revealed that in hand weeding with lowest weed biomass highest plant
height (92 cm), panicles (215 m-2), panicle length (27.3 cm), grains panicle-1 (120) and grain
yield (3.70 t h-1) were produced as compared to no weeding
Manual weeding is practiced in Cambodia at least twice and commonly three times
(Makara et al., 2001). In Vietnam, Chin et al. (2000a) found that hand weeding twice was the
most effective treatment in terms of both controlling weeds and crop safety in DSR. African
farmers normally rely on family labor for weeding in direct seeded rice, which usually starts at
15-30 DAS (Oteng and Sant’Anna, 1999). Chandra et al. (1998) concluded that hand weeding
resulted in lowest weed density, weed dry weight and highest grain yield over control. Bhaghat
et al. (1991) conducted a field experiment and results showed that hand weeding (15, 30 and 45
days after seedling emergence) produced highest paddy yield of 4.14 t ha-1.
2.4.5 Problems of manual weed control
Hand weeding is a very effective way to control weeds in DSR but it requires high labor
due to which cost of production also increases. Manual weeding can be done only when weeds
have adequate size to be pulled (Singh et al., 2005). Hand weeding in wet-seeded rice requires
more time (Moody, 1983). This labor demanding method requires 250–780 man h ha-1 (Stessens,
2002). Hand weeding is the most widely practiced intervention against weeds on small-scale rice
farms in Africa (Adesina et al., 1994) but shortage of labor, high labor cost, poor weather
15
conditions and the presence of perennial weeds that fragment on pulling are problems of hand
weeding
2.4.6 Chemical weed control methods
Herbicide use is one of the most labor-saving innovations that have been introduced in
rice farming. Hussain et al. (2008) used Nominee 100 SC to control weeds in direct seeded rice
resulting in grain yield (3.61 t ha-1) and giving 90 % weed control as compared to no weeding.
Effective weed management practices are an important requirement in DSR culture, with
herbicide application it is very easy to control weeds (Azmi et al., 2007). Islam et al. (2004)
compared hand weeding with different herbicides and found Pretilachlor as a most successful
weedicide with higher yield and cost benefit ratio. In America, Australia, Europe and East
Asia, over 90 % of DSR areas are treated with herbicides for weed control (Baltazar and De
Datta, 1992). Herbicide options for weed control in DSR differ according to method of crop
establishment because the performance of herbicides varies in relation to water regimes.
Extensive research has been conducted over the years by many researchers to find out the
optimum rate, time, type and method of herbicide application. Labor unavailability, high labor
costs and the urgent need to raise yields and maintain profit on a progressively limited land have
been major drivers for farmers to seek alternatives to manual weeding and herbicides are one
such alternative.
2.4.7 Problems of chemical weed control methods
Labor shortages have led to an increase in direct seeding and a rapid increase in the use
of herbicides. This reliance has resulted in an unwanted shift in weed species and concerns about
environmental pollution. Phenoxy and sulfonylurea compounds are extensively used herbicides
in Malaysia, Vietnam and Thailand to control broadleaf weeds and sedges in DSR. Weeds
resistant to these herbicides have developed and there is confirmation that weed species such as
S. zeylanica, Marsilea minuta, and F. miliacea have developed resistance to phenoxy herbicides
(Watanabe et al., 1997). Chemical weed control through the commonly used pre-emergence
herbicides (such as butachlor and thiobencarb) has been broadly investigated but their efficiency
depends on the water regime, soil tilth, composition of the weed and environmental conditions
(Baltazar and De Datta, 1992). Weedicide used for weed control in transplanted rice in Pakistan
16
are not suitable for weeds in DSR and required weedicide for effective weed control in DSR are
not available.
2.4.8 Mechanical weed control
Mechanical weed control with the use of implements is a practical and economic method
for farmers. Mechanical weeding is almost generally practiced on row seeded rice since inter row
cultivation with either animal or tractor drawn implements reduces time in weed control and
minimizes crop damages. A weeder (with a straight line peg arrangement) showed outstanding
results on a range of soil types with unstable soil moisture levels and weed intensity and saving
57 % labor as compared with hand weeding (Subudhi, 2004). In Bangladesh a rake type weeder
had shown good results and it works in light and heavy soils with same efficacy (Islam et al.,
2004). Victor and Verma (2003) reported that Power operated rotary weeders have improved
weeding efficiency. In Thailand, a mechanized direct seeded rice management system has been
adopted, which includes weed control with a soil cultivator involving tillage between rows of
rice twice; 15 and 28 DAS (Kabaki et al., 2003). Mechanical weed control is similar to the weed
free control in terms of grain yield due to its bigger weed control efficiency (Morthy, 2003). In
Gambia effective weed management has been established with donkey drawn equipment using
twice (Remington and Posner, 2000). Beushening in which dry seeded rice is compressed by
‘‘planking’’ (drawing of a heavy flat wooden object over the crop) is also practiced to control
weeds. This process kills weed species with single main stems, whereas rice is able to retiller
from basal nodes. Sharma (1997) conducted a series of experiments on weed management in
rice, results showed that beushening is a very good technique to control weeds in rice, producing
paddy yield 3.5 t ha-1.
Comparisons of traditional and mechanically modernized weeding equipment suggest the
value of these technologies in resource limited farming communities. With labor shortage
mechanical weed control methods may be more efficient and suitable in DSR. Hence, efforts to
improve the effectiveness of existing weeding tools and implements are desirable, especially for
developing countries.
17
CHAPTER 3
MATERIALS AND METHODS
3.1 Location of studies
The proposed field studies were conducted at the Agronomic Research Farm,
Department of Agronomy, University of Agriculture, Faisalabad (31o-25‘N, 73o-09‘E),
Pakistan during rice growing seasons of 2008 and 2009. District Faisalabad is situated in
Punjab province (Pakistan), 150 m above sea level with flat plain developed by River Ravi
and Chenab. Faisalabad has extreme climate and area of this district is highly favorable for
canal irrigation. Summer season begins in April, ends in October while winter season starts
from November and continues till March. May, June and July are the hottest months while
December, January and February are the coldest months.
3.2 Characterization of soil
Composite soil samples were collected with auger up to a depth of 30 cm before rice
sowing and were analyzed for physical and chemical properties during both the cropping
seasons (Table 3.1).
3.2.1 Particle size analysis
Hydrometer method (Bouyoucos, 1962) was used for determining the percentage of
sand, silt and clay. Dispersion solution was made by dissolving 10 g of sodium
hexametaphosphate and 2.5 g of sodium carbonate in 250 ml of distilled water. Soil (50 g)
was taken in a 600 ml beaker and 60 ml of dispersing solution was added. Mixture was
covered with watch glass and kept overnight. Next day, it was transferred to stirring cup and
stirred at high speed for 3 minutes. Then material was transferred into I L graduate cylinder.
Required volume was made by adding deionized water. Contents of cylinder were shaken
manually using a metal plunger. After obtaining homogeneous suspension, plunger was taken
out and silt and clay (%) was determined using Bouyoucos hydrometer. Percent sand was
determined by subtracting percent silt and clay from 100. Texture class was determined by
following the International Society of Soil Science Triangle (Moodie et al., 1959).
3.2.2 pH of saturated soil paste
A pH meter (WTW pH 315 i) was used for determination of soil pH. Instrument first
was calibrated with standard buffer solution of pH 4.00, 7.00 and 9.00. Then it was inserted
in soil saturated paste. Stable meter reading was noted as pH.
18
3.2.3 Soil saturated extract
Extract from the soil saturated paste was obtained by applying positive pressure with
the help of air pump. It was preserved in air tight plastic bottles for further analysis. Sodium
hexametaphposphate (0.1%) solution was added @ one drop per 25 mL extract to prevent
precipitation of salts during storage.
3.2.4 Soil organic matter
Soil organic matter (OM) was determined fallowing Walkly-Black method (Nelson
and Sommers, 1982). For this purpose two gram soil was swirled in 10 mL of 1.0 N K2Cr2O7
solution. After adding 20 mL of concentrated H2SO4, the suspension was well mixed and
allowed to stand for 30 minutes. Then it was diluted to 200 mL with distilled water.
Diphenylamine (30 drops) was used as indicator in the presence of 0.5 g sodium fluoride
(NaF). Finally mixture was titrated against ferrous sulphate 7-hydrate (FeSO4.7H2O) to dull
green end point.
Organic matter was calculated by the formula:
OM (%) =[ (Vblank – Vsample) × M × 0.69] / [ Wt. of soil (g)]
Where
Vblank = Volume (mL) of FeSO4.7H2O used in blank
Vsample = Volume (mL) of FeSO4.7H2O used to titrate the sample
M = Molarity of FeSO4.7H2O solution
0.69 =0.003 × 100 × (100/72) × (100/58)
Where
0.003 = Mean weight of carbon
100 = To convert OM in %
100/58 = Factor to convert organic carbon to OM
100/72 = Recovery factor for carbon
3.2.5 Electrical conductivity of saturated soil extract
Clear extract of saturated soil paste was obtained by a vacuum pump. The EC was
measured by using Jenway electrical conductivity meter, Model-4070 (Method 4b. p.89)
3.2.6 Total soluble salts
EC was converted in TSS (m molc L-1) fallowing the graph given in the handbook 60
(US Salinity Lab. Staff, 1954).
19
3.2.7 Available phosphorous
10 g of soil sample was extracted with AB-DTPA solution while shaking on
mechanical shaker. 1 ml of the aliquot was taken and diluted with the 10 ml each of color
developing reagents (ascorbic acid, ammonium molybdate, antimony potassium titrate and
sulphuric acid). After 30 minutes reading was recorded on ANA-730 spectrophotometer at a
wavelength of 880 nm (method 16, p. 134).
3.2.8 Extractable potassium
Extraction was made with ammonium acetate solution and extractable K was
determined by Corning Flame Photometer-410 after calibrating with standard solutions of
potassium (Method 58, a: p. 132).
Table 3.1: Analysis of the experimental site
characteristics Unit 2008 2009
Sand % 52.0 52.2
Silt % 22.2 21.8
Clay % 26.9 26.8
Texture Class - Sandy clay loam Sandy clay loam
pH - 8.0 7.9
TSS % 0.21 0.22
Organic matter % 0.73 0.74
Total Nitrogen % 0.052 0.053
Available P ppm 6.5 6.2
Available K ppm 186 185
20
Table 3.2 Meteorological data during the rice season of 2008 at experimental site Months Rainfall Relative humidity Temperature (C) (mm) (%) Daily
maximum Daily
minimum Daily
average June 41.7 48.00 38.4 27.4 32.9 July 81.6 52.97 37.5 28.3 32.9 August 204.5 65.00 35.1 26.8 30.9 September 28.8 59.33 34.4 23.7 29.0 October 0 57.65 33.1 20.2 26.6 November 0 58.87 27.3 12.2 19.7
Source: Agricultural Meteorology Cell, Department of Crop Physiology, University of Agriculture, Faisalabad, Pakistan.
Table 3.3 Meteorological data during the rice season of 2009 at experimental site Months Rainfall Relative humidity Temperature (C) (mm) (%) Daily
maximum Daily
minimum Daily
average June 9.6 33.6 40.7 27.0 33.8 July 43.5 59.0 38.0 27.9 32.9 August 116.0 65.8 36.6 27.6 32.1 September 20.6 61.0 36.3 24.4 30.3 October 17.5 57.9 32.7 17.1 24.9 November 0.7 64.7 25.7 10.8 18.2 Source: Agricultural Meteorology Cell, Department of Crop Physiology, University of Agriculture, Faisalabad, Pakistan.
21
3.3 Treatments and lay out of experiments
The experiments were conducted during the years 2008 and 2009.
EXPERIMENT-I: COMPARISON OF DIFFERENT WEED
MANAGEMENT STRATEGIES IN DRILL SEEDED AEROBIC RICE
TREATMENTS: WEED MANAGEMENT STRATEGIE
S1: No weeding (weedy check)
S2: Hand pulling 15, 25, 35 and 45 DAS
S3: Hoeing with kasulla 15, 25, 35 and 45 DAS
S4: Inter row cultivation with Tine cultivator 15, 25, 35 and 45 DAS
S5: Inter row cultivation with Spike hoe 15, 25, 35 and 45 DAS
S6: Chemical control (Nominee 100 SC (bispyribac-sodium100 g a.i per L) @ 250 ml ha-1)
Layout
Experiment was laid out in randomized complete block design with 3 replications. Net plot
size was 2.7 m x 6.0 m.
22
Fig 3.1 Layout plan for experiment-I
Sub water channel M
ain
wat
er c
han
nel
R1
S1
S2
S3
S4
S5
S6
Pat
h
Path
R2
S6
S5
S4
S3
S2
S1
Sub water channel
R3
S4
S3
S6
S2
S1
S5
Non experimental area
23
EXPERIMENT-II: EVALUATION OF DIFFERENT INTER CULTURAL
IMPLEMENTS FOR WEED MANAGEMENT IN DRILL SEEDED
AEROBIC RICE
TREATMENTS
A. INTER ROW CULTIVATION IMPLEMENTS (main plots)
S1: Tine Cultivator
S2: Spike Hoe
S3: Plug Weeder
B. INTER ROW CULTIVATION FREQUENCIES (sub plots)
F1: Inter row cultivation at 15 DAS
F2: Inter c row cultivation at 15 and 25 DAS
F3: Inter row cultivation at 15, 25 and 35 DAS
F4: Inter row cultivation at 15, 25, 35 and 45 DAS
Layout
Experiment was laid out in randomized complete block design in split arrangement with 3
replications. Net plot size was 2.7 m x 6.0 m.
24
Fig 3.2 Layout plan for experiment-II
Sub water channel
Mai
n w
ater
ch
ann
el
R1
S1 S2 S3
Pat
h
F1 F2 F3 F4 F2 F3
F4 F1 F4 F1
F2 F3
Path
R2
S2 S3 S1 F2 F3
F4 F1 F4 F2
F1 F3 F2 F3
F4 F1
Sub water channel
R3
S3 S1 S2 F3 F4
F1 F2 F3 F1
F2 F4 F1 F2
F3
F4
Non experimental area
25
3.4 Planting of crop
3.4.1 Land preparation
After the harvesting of wheat, land was ploughed twice followed by planking with
tractor drawn implements to achieve the required soil tilth for direct seeded rice.
3.4.2 Seeding method, planting geometry and fertilizer application
Rice cultivar Super Basmati was sown on 20th June during 2008 and 28th June during
2009 using seed rate of 75 kg ha-1. Direct seeding was done by automatic Rabi drill
maintaining 22.5 cm line to line distance. The crop was provided with 150 kg N, 85 kg P and
67 kg K in the form of urea, DAP and potassium sulphate respectively. Half of N and whole
of the P and k were applied at sowing, while remaining nitrogen was given in two equal
splits, at tillering and panicle initiation stage of the crop.
3.4.3 Water management
First irrigation was of 4 acre inch for seed bed preparation. Then crop was irrigated to
keep the soil moisture at field capacity level; each irrigation was of 3 acre inch. Total 16
irrigations were applied.
3.4.4 Harvesting and threshing
During the year 2008 crop was harvested on 15th November and during the year 2009
on 21st November. Threshing of each plot was done separately.
3.5 Weed management in experiment I
Weeds were left unchecked in all the plots of no weeding treatment. Weeds in hand
pulling treatment were completely removed manually by uprooting and cutting. Weeds in
hoeing treatment were removed manually with the help of kasula, only inter row weeds were
removed by this implement and within row weeds remain unchecked. Tine cultivator was
operated manually and it removed and uprooted inter row weeds only; crop was laid down
after the implementation of tine cultivator just like beushaning. Spike hoe was also operated
manually and its spikes removed minor amount of weeds; crop was laid down after its
implementation as in tine cultivator. The weedicide Nominee 100 SC was dissolved in water
and sprayed after 20 days of sowing with the help of knap sack sprayer @ 250 ml ha-1.
3.6 Weed management in experiment II
All three implements were operated manually at different frequencies. Tine cultivator
removed and uprooted inter row weeds, within row weeds remained unchecked; crop was laid
26
down after its operation just like beushaning. Spike hoe was also operated at different
frequencies but it removed a minor quantity of weed; within row weeds remain unchecked,
crop was laid down after its implementation. Plug weeder was operated manually at different
frequencies and it removed no weeds.
3.7 Observations
Following observations were recorded during both the years.
3.7.1 Weed parameters
1. Weed biomass (g).
3.7.2 Yield related traits
1. Plant height at maturity (cm).
2. Number of total tillers m-2 at maturity.
3. Number of fertile tillers m-2 at maturity.
4. Number of unfertile tillers m-2 at maturity.
5. Panicle length (cm).
6. Number of kernels per panicle.
7. 1000-kernel weight (g).
8. Paddy yield (kg ha-1).
9. Biological yield (kg ha-1).
10. Harvest index (%).
3.7.4 Quality parameters
1. Sterile spikelets (%).
2. Opaque kernels (%).
3. Abortive kernels (%).
4. Normal kernels (%).
3.7.3 Growth parameters
1. Leaf area index (LAI).
2. Lea area duration (days).
3. Total dry matter production (kg ha-1).
4. Crop growth rate (g m-2 day-1).
5. Net assimilation rate (g m-2 day-1).
27
Weed parameters
i. Weed biomass (g)
Weeds from an area of 100 x 100 cm from each plot were collected manually at 15, 30 and 45
DAS and washed. After sun drying these samples were oven dried at 80 oC and then weighed
to record weed dry weight. Weed flora observed in current studies is given in Table 3.4
Table 3.4 Weed flora observed in studies
English name Local name Botanical name
Nut sedge Deela Cyperus rotundas
Jungle rice Swanki Echonocola colona
Barnyardgrass Dhiden Echonocola crusgalli
Egyptian crowfoot grass Madana Dactyloctenium aegyptium
Desert horse purslane Itsit Trianthum portulacastrum
False Daisy Daryae booti Eclipt alba
Spurge Hazardani Euphorbia granulata
Yield related traits
i. Plant height at maturity (cm)
Plant height at maturity was measured from base to leaf tip with the help of a meter rod. The
height of 15 tillers selected randomly in each plot was measured at maturity and then averaged.
ii. Number of total tillers m-2
Number of tillers was counted from four rows of 100 cm length and then converted into 100 x
100 cm area in each plot at maturity.
iii. Number of fertile tillers m-2 at maturity
The number of tillers bearing panicles was counted from four rows of 100 cm length and then
converted into 100 x 100 cm area in each plot at maturity.
iv. Number of unfertile tillers m-2 at maturity
The number of tillers bearing no panicles was counted from four rows of 100 cm length and
then converted into 100 x 100 cm area in each plot at maturity.
28
v. Panicle length (cm)
Fifteen panicles were selected randomly from each plot. Panicle length was measured and then
averaged.
vi. Number of kernels per panicle
Ten panicles of tillers were randomly collected from each plot at harvest and saved in paper bags.
Grains were separated from each panicle and separated on working board. Afterward number of
grains per panicle was counted separately and then average.d
vii. 1000-kernel weight (g)
Out of each treatment random sample of one thousand kernels was taken and weighed with an
electric balance.
viii. Biological yield (t ha-1)
Biological yield per plot was determined after sun drying for one week and expressed in t ha-1.
ix. Paddy yield (t ha-1)
After harvesting and threshing each plot the clean rough rice was sun dried up to 14 % moisture,
bulked and weighed to record the paddy yield. The paddy weight was expressed in t ha-1.
x. Straw yield (t ha-1)
Straw yield per plot was determined after sun-drying for one week and expressed in t ha-1.
xi. Harvest index (%)
Harvest index was calculated as the ratio of grain yield to total (above ground) biological yield
using the formula:
Growth parameters
A randomly selected area of 30 x 30 cm was harvested at 15 days interval from each plot and was
analyzed for leaf area and dry matter accumulation. First reading was taken at 60 DAS.
i. Leaf area index (LAI)
For determining the leaf area index, plant samples were collected from each plot at intervals
of 15 days throughout the growing season of the crop. The green leaves were separated from
the plants and characterized into three categories according to their size. The area of 5 leaves
100yeild Biological
yeild KernelHI
29
was measured using leaf area meter. LAI was computed by using the area–weight
relationship (Watson, 1947).
ii. Crop growth rate (g m-2 d-1)
Crop growth rate was measured using the formula given by Hunt (1978).
CGR = (W2 – W1) / (t2-t1)
Where
W1 = Total dry matter at the first harvest
W2 = Total dry matter at the second harvest
t1 = Date of observation of first dry matter recorded
t2 = Date of observation of second dry matter recorded
iii. Cumulative leaf area duration (LAD)
Leaf area duration (LAD) for entire growing season was estimated using the formula of Hunt
(1978).
LAD = (LAI1+LAI2) (t2-t1)/2
Where
LAI1 = Leaf area index at the first harvest
LAI2 = Leaf area index at the second harvest
t1 = Data of observation of the first harvest
t2 = Data of observation of the second harvest
iv. Net assimilation rate (g m-2 d-1)
Net assimilation rate was estimated by using formula proposed by Hunt (1978).
NAR = TDM/LAD
Where,
TDM = Final total dry matter at harvest.
LAD = Final leaf area duration at harvest.
Kernel quality
To record the occurrence of sterile, abortive, opaqueness and percentage of normal kernels, 10
panicles were randomly selected from each plot and information was recorded after having the
panicles carefully sketched for differentiation of different categories of kernels. A lamp having a
flexible stand was used as a source of light. A panicle was positioned in front of the lamp so that
30
light may pass through it in order to differentiate between different forms of kernel development.
Abortive and opaque kernels were separated.
i. Sterile kernels
Sterile kernels are unfilled and unfertilized spikelets and these are easily distinguished from other
categories.
ii. Opaque kernels
Opaque kernels are those that attain full size but do not become translucent due to lack of
carbohydrates and also do not allow light to pass through them because of overall dull chalky
structure. However, these were bigger than abortive kernels as their development stopped at later
stage. These did not acquired normal size because of retarded development.
iii. Abortive kernels
Abortive kernels are those in which fertilization does take place but kernels do not attain full size
as these stop growing during early stages of kernel development. These look dull and do not
permit light to pass through them.
iv. Normal kernels
Normal kernels are those that attain full size, translucent, show normal starch filling and allow
light to pass through them. Normal, the clear translucent and without any chalky spots kernels,
were computed by deducting all the abnormal kernels from total number of spikelets.
3.8 Economic analysis
Economic analysis was made by calculating the gross income by summing up the total value of
adjusted paddy and straw yields.
Adjusted yield= 10 % less than actual
Cost of production= Fixed cost + Harvesting and threshing charges+ cost of intervention
Net income was calculated by
Net income = Gross income – Cost of production
Benefit cost ratio (BCR) was calculated by the following formula
BCR = Gross income / Cost of production
31
3.9 Marginal rate of return
To calculate marginal rate of return net field benefits were calculated by following formula
Net field benefits= Gross income – Variable cost
Then dominance analysis was carried out
The marginal net benefit (MNB) divided by the marginal cost (MC), expressed in percentage;
is called marginal rate of return (MRR). MRR was calculated with the formula
MRR (%) = 100 MC
MNB
(CIMMYT 1988)
3.10 Statistical analysis
The collected data were analyzed using the Fisher’s analysis of variance technique. Then
treatment means were compared using Least Significant Difference (LSD) test at 5%
probability level (Steel et al., 1997).
32
CHAPTER 4
RESULTS AND DISCUSSION
Results pertaining to different agro-qualitative traits of direct sown rice as affected by
different weed management strategies and weed control implements along with statistical
interpretation are presented and discussed in this chapter.
STUDY-I: COMPARISON OF DIFFERENT WEED MANAGEMENT
STRATEGIES IN DRILL SEEDED AEROBIC RICE
4.1 Weed biomass of sedges (15 DAS)
The Fig. 4.1 showed that different weed management strategies significantly affected
weed biomass of sedges during both the years at 15 DAS. During the year 2008 minimum
weed dry weight (0.31 g m-2) was recorded in hand pulling followed by hoeing (0.65 g m-2)
and tine cultivator (0.93 g m-2). Maximum weed dry weight of sedges was found in spike hoe
(1.28 g m-2) that was statistically similar to no weeding (1.29 g m-2) and Nominee 100 SC
(1.32 g m-2).
During the year 2009 minimum weed dry weight (0.47 g m-2) of sedges was recorded
in hand pulling followed by hoeing (0.90 g m-2) and tine cultivator (1.03 g m-2). Weed dry
weight in spike hoe (1.38 g m-2), no weeding (1.1.40 g m-2) and Nominee 100 SC (1.41 g m-2)
was statistically similar.
4.2 Weed biomass of broad leaf weeds (15 DAS)
The Fig. 4.1 showed that different weed management strategies significantly affected
weed biomass of broad leaf weeds during both the years at 15 DAS. During the year 2008
minimum weed dry weight (0.44 g m-2) was recorded in hand pulling that was similar to
hoeing (0.56 g m-2) followed by tine cultivator (0.74 g m-2). Maximum weed dry weight was
found in Nominee 100 SC (1.54 g m-2) that was statistically similar to weed dry weight in no
weeding (1.53 g m-2) and spike hoe (1.52 g m-2).
During the year 2009 minimum weed dry weight was recorded in hand pulling (0.47 g
m-2) followed by hoeing (0.66 g m-2), tine cultivator (0.81 g m-2). Maximum weed dry weight
was found in Nominee 100 SC (1.62 g m-2) that was statistically similar to weed dry weight
in no weeding (1.58 g m-2) and spike hoe (1.57 g m-2).
33
Fig. 4.1 Effect of different weed management strategies on weed biomass of sedges
and broad leaf weeds at 15 DAS
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
B.Leaves Sedges
g m
-2
(a) 2008
Hand pulling
Hoeing
Tine cultivator
Nominee
Spike hoe
No weeding
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
B.Leaves Sedges
g m
-2
(b) 2009
Hand pulling
Hoeing
Tine cultivator
Nominee
Spike hoe
No weeding
34
4.3 Weed biomass of sedges (30 DAS)
The Fig. 4.2 showed that different weed management strategies significantly affected
weed biomass of sedges during both the years at 30 DAS. During the year 2008 minimum
weed dry weight (2.98 g m-2) of sedges was recorded in hand pulling followed by hoeing
(10.88 g m-2), tine cultivator (18.01 g m-2), Nominee 100 SC (43.5 g m-2) and spike hoe
(55.43 g m-2). Weed dry weight of sedges in no weeding (61.77 g m-2) was found maximum.
During the year 2009 minimum weed dry weight of sedges was recorded in hand
pulling (3.93 g m-2) followed by hoeing (11.24 g m-2), tine cultivator (18.53 g m-2), Nominee
100 SC (45.56 g m-2) and spike hoe (54.82 g m-2). The maximum weed dry weight of sedges
was recorded in no weeding (64.33 g m-2).
4.4 Weed biomass of broad leaf weeds (30 DAS)
The Fig. 4.2 showed that different weed management strategies significantly affected
weed biomass of broad leaf weeds during both the years at 30 DAS. During the year 2008
minimum weed dry weight (3.27 g m-2) was recorded in hand pulling that was similar to
hoeing (10.69 g m-2). Weed biomass in tine cultivator (23.34 g m-2) was similar to Nominee
100 SC (26.91 g m-2). Maximum weed dry weight was found in no weeding (92.92 g m-2) that
was statistically similar to weed dry weight in spike hoe (88.73 g m-2).
During the year 2009 minimum weed dry weight was recorded in hand pulling (6.31 g
m-2) followed by hoeing (14.93 g m-2). Weed biomass in tine cultivator (27.64 g m-2) was
similar to Nominee 100 SC (32.72 g m-2) followed by spike hoe (80.65 g m-2). Maximum
weed dry weight was found in no weeding (95.02 g m-2).
4.5 Weed biomass of sedges (45 DAS)
The fig 4.3 showed that different weed management strategies significantly affected
weed biomass of sedges during both the years at 45 DAS. During the year 2008 minimum
weed dry weight (7.28 g m-2) of sedges was recorded in hand pulling followed by hoeing
(31.39 g m-2), tine cultivator (45.82 g m-2), Nominee 100 SC (98.84 g m-2) and spike hoe
(117.45 g m-2). Weed dry weight (159.46 g m-2) of sedges in no weeding was found
maximum.
35
Fig. 4.2 Effect of different weed management strategies on weed biomass of sedges
and broad leaf weeds at 30 DAS
0
20
40
60
80
100
120
B.Leaves Sedges
g m
-2
(a) 2008
Hand pulling
Hoeing
Tine cultivator
Nominee
Spike hoe
No weeding
0
20
40
60
80
100
120
B.Leaves Sedges
g m
-2
(b) 2009
Hand pulling
Hoeing
Tine cultivator
Nominee
Spike hoe
No weeding
36
Fig. 4.3 Effect of different weed management strategies on weed biomass of sedges
and broad leaf weeds at 45 DAS
0
20
40
60
80
100
120
140
160
180
B.Leaves Sedges
g m
-2
(a) 2008
Hand pulling
Hoeing
Tine cultivator
Nominee
Spike hoe
No weeding
0
20
40
60
80
100
120
140
160
180
200
B.Leaves Sedges
g m
-2
(b) 2009
Hand pulling
Hoeing
Tine cultivator
Nominee
Spike hoe
No weeding
37
During the year 2009 minimum weed dry weight of sedges was recorded in hand
pulling (8.35 g m-2) followed by hoeing (27.56 g m-2), tine cultivator (46.65 g m-2), Nominee
100 SC (108.53 g m-2) and spike hoe (123.56 g m-2). The maximum weed dry weight of
sedges was recorded in no weeding (167.56 g m-2).
4.6 Weed biomass of broad leaf weeds (45 DAS)
The Fig. 4.3 showed that different weed management strategies significantly affected
weed biomass of broad leaf weeds during both the years at 45 DAS. During the year 2008
minimum weed dry weight was recorded in hand pulling (5.19 g m-2) followed by hoeing
(22.27 g m-2), tine cultivator (39.35 g m-2) and Nominee 100 SC (51.80 g m-2). Maximum
weed dry weight was found in no weeding (148.66 g m-2) that was statistically similar to
weed dry weight in spike hoe (148.13 g m-2).
During the year 2009 minimum weed dry weight was recorded in hand pulling (10.97
g m-2) followed by hoeing (36.54 g m-2), tine cultivator (49.87 g m-2) and Nominee 100 SC
(58.91 g m-2). Maximum weed dry weight was found in no weeding (156.92 g m-2) that was
statistically similar to weed dry weight in spike hoe (153.53 g m-2).
4.7 Total weed biomass (15 DAS)
The fig 4.4 showed that different weed management strategies significantly affected
total weed biomass during both the years at 15 DAS. During the year 2008 minimum weed
dry weight was recorded in hand pulling (0.75 g m-2) followed by hoeing (1.21 g m-2) and
tine cultivator (1.68 g m-2). Maximum weed dry weight was found in no weeding (2.85 g m-2)
that was statistically similar spike hoe (2.68 g m-2) and Nominee 100 SC (2.67 g m-2).
During the year 2009 minimum weed dry weight was recorded in hand pulling (1.13 g
m-2) followed by hoeing (1.36 g m-2) and tine cultivator (1.84 g m-2). Maximum weed dry
weight was found in Nominee 100 SC (3.04 g m-2) that was statistically similar spike hoe
(2.99 g m-2) and no weeding (2.98 g m-2).
4.8 Total weed biomass (30 DAS)
The Fig 4.5 showed that different weed management strategies significantly affected
total weed biomass during both the years at 30 DAS. During the year 2008 minimum weed
dry weight was recorded in hand pulling (6.27 g m-2) followed by hoeing (21.52 g m-2), tine
38
Fig. 4.4 Effect of different weed management strategies on total weed biomass at
15 DAS
Fig. 4.5 Effect of different weed management strategies on total weed biomass at
30 DAS
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
2008 2009
g m
-2
(a) 2008
Hand pulling
Hoeing
Tine cultivator
Nominee
Spike hoe
No weeding
0
20
40
60
80
100
120
140
160
180
2008 2009
g m
-2
(b) 2009
Hand pulling
Hoeing
Tine cultivator
Nominee
Spike hoe
No weeding
39
Fig. 4.6 Effect of different weed management strategies on total weed biomass at
45 DAS
0
50
100
150
200
250
300
350
2008 2009
g m
-2
Hand pulling
Hoeing
Tine cultivator
Nominee
Spike hoe
No weeding
40
cultivator (41.36 g m-2), Nominee 100 SC (70.48 g m-2) and spike hoe (144.17 g m-2).
Maximum weed dry weight was found in no weeding (154.77 g m-2).
During the year 2009 minimum weed dry weight was recorded in hand pulling (10.24
g m-2) followed by hoeing (26.15 g m-2), tine cultivator (45.98 g m-2), Nominee 100 SC
(78.28 g m-2) and spike hoe (135.48 g m-2). Maximum weed dry weight was found in no
weeding (159.40 g m-2).
4.9 Total weed biomass (45 DAS)
There was a non significant year effect on total weed biomass at 45 DAS, however
two years average data are discussed. The data presented in table 4.1 indicated the significant
effect of different weed management strategies on weed biomass. Highest weed biomass
(316.61 g m-2) was recorded in no weeding followed by spike hoe (271.30 g m-2), Nominee
(159.04 g m-2), tine cultivator (90.86 g m-2) and hoeing (58.84 g m-2). The lowest weed
biomass (15.89 g m-2) was observed in hand pulling.
4.10 Plant height
Analysis of the data revealed that both the years affected plant height of rice
significantly. Therefore results of both the years are discussed separately. The data regarding
plant height (Table 4.2) showed that different weed management strategies significantly
effected plant height at maturity.
During the year 2008, maximum plant height (95.97 cm) was recorded for the hand
pulling treatment followed by tine cultivator (90.83 cm), Nominee (89.77 cm) and spike hoe
(83.07 cm). Minimum plant height was observed in no weeding (73.97 cm).
During the year 2009, maximum plant height was recorded in hand pulling (94.44 cm)
followed by tine cultivator (89.32 cm) and spike hoe (81.28 cm). Plant height in Nominee
100 SC (87.97 cm) was similar to tine cultivator. Minimum plant height was found in no
weeding (72.02 cm).
Total weed biomass (45 DAS) and plant height were linearly related and the
regression accounted for 91% of the variation during 2008 and 2009 (Fig. 4.7).
41
Table 4.1 Effect of different weed management strategies on weed Biomass at 45 DAS (g m-2) Treatment
2008
2009
Mean
No weeding
308.79
324.43
316.61 a
Hand pulling
12.46
19.31
15.89 f
Hoeing
53.63
64.05
58.84 e Tine cultivator
85.16
96.51
90.86 d
Spike hoe
265.55
277.06
271.30 b
Nominee 100 SC
150.66
167.43
159.04 c
LSD
9.15
Any two means not sharing a letter in common differ significantly (p ≤ 0.05)
Table 4.2 Effect of different weed management strategies on plant height (cm)
Treatment
2008
2009
Mean
No weeding
73.97 d
72.02 d
72.99
Hand pulling
95.97 a
94.44 a
95.20
Hoeing
92.5 ab
90.57 ab
91.53 Tine cultivator
90.83 b
89.32 b
90.07
Spike hoe
83.07 c
81.28 c
82.17
Nominee 100 SC
89.77 b
87.97 b
88.86
LSD
4.57
4.83
Any two means not sharing a letter in common differ significantly (p ≤ 0.05)
42
Fig. 4.7 Relationship between total weed biomass (45 DAS) and plant height
y = -0.0637x + 96.993R² = 0.9126
50
55
60
65
70
75
80
85
90
95
100
0 50 100 150 200 250 300 350
Pla
nt h
eigh
t (c
m)
Weed biomass (g m-2)
(a) 2008
y = -0.0633x + 95.948R² = 0.9116
50
55
60
65
70
75
80
85
90
95
100
0 50 100 150 200 250 300 350
Pla
nt h
eigh
t (c
m)
Weed biomass (g m-2)
(b) 2009
43
4.11 Total number of tillers
There was a significant year effect on total number of tillers of rice at maturity
therefore results of both the years are discussed separately.
The data (Table 4.3) revealed that different weed management strategies had
significant effect on total number of tillers m-2. During the year 2008, maximum total number
of tillers was recorded in hand pulling (401.44 m-2) followed by tine cultivator (379.10 m-2)
and Nominee 100 SC (333.59 m-2). Total number of tillers in spike hoe was 293.67 m-2
followed by no weeding (260.91 m-2).
During the year 2009, maximum total number of tillers (389.27 m-2) was noted in
hand pulling (389.27 m-2) followed by tine cultivator (373.46 m-2), Nominee 100 SC (316.71
m-2) and spike hoe (282.52 m-2). Minimum total number of tillers was recorded in no weeding
(227.89 m-2).
4.12 Number of fertile tillers
Significant year effect on number of fertile tillers of rice was found at maturity
therefore results of both the years are discussed separately.
The data presented in table 4.4 indicated that different weed management strategies
had significant effect on number of fertile tillers m-2. During the year 2008, maximum
number of fertile tillers was recorded in hand pulling (375.11 m-2) followed by hoeing
(364.63 m-2), tine cultivator (350.44 m-2), Nominee 100 SC (302.92 m-2) and spike hoe
(255.00 m-2). Minimum number of fertile tillers was recorded in no weeding (215.58 m-2).
Similar trend was observed during the year 2009. Significantly maximum number of
fertile tillers was observed in hand pulling (363.60 m-2) followed by tine cultivator (343.12 m-
2), Nominee 100 SC (283.38 m-2) and spike hoe (243.19 m-2). In no weeding (181.89 m-2)
significantly minimum number of fertile tillers was recorded.
Total weed biomass (45 DAS) and fertile tillers were linearly related and the
regression accounted for 98 and 96% of the variation during 2008 and 2009, respectively
(Fig. 4.8).
44
Table 4.3 Effect of different weed management strategies on total number of tillers (m-2)
Treatment
2008
2009
Mean
No weeding
260.91 e
227.89 e
244.40
Hand pulling
401.44 a
389.27 a
395.35
Hoeing (with kasula)
392.96 ab
377.27 b
385.11 Tine cultivator
379.10 b
373.46 b
376.28
Spike hoe
293.67 d
282.52 d
288.09
Nominee 100 SC
333.59 c
316.71 c
325.15
LSD
14.22
8.85
Any two means not sharing a letter in common differ significantly (p ≤ 0.05)
Table 4.4 Effect of different weed management strategies on number of fertile tillers (m-2) Treatment
2008
2009
Mean
No weeding
215.58 e
181.89 e
198.73
Hand pulling
375.11 a
363.60 a
369.35
Hoeing (with kasula)
364.63 a
349.27 b
356.94 Tine cultivator
350.44 b
343.12 b
346.78
Spike hoe
255.00 d
243.19 d
249.09
Nominee 100 SC
302.92 c
283.38 c
293.15
LSD
13.80
5.69
Any two means not sharing a letter in common differ significantly (p ≤ 0.05)
45
Table 4.5 Effect of different weed management strategies on number of unfertile tillers (m-2)
Treatment
2008
2009
Mean
No weeding
45.33
46.00
45.66 a
Hand pulling
26.33
25.67
26.00 d
Hoeing
28.33
28.00
28.16 cd Tine cultivator
28.67
30.33
29.5 cd
Spike hoe
38.67
39.33
39.00 b
Nominee 100 SC
30.67
33.33
32.00 c
LSD
4.12
Any two means not sharing a letter in common differ significantly (p ≤ 0.05)
Table 4.6 Effect of different weed management strategies on panicle length (cm)
Treatment
2008
2009
Mean
No weeding
16.13 c
16.01 d
16.07
Hand pulling
23.90 a
23.8 a
23.85
Hoeing
22.93 a
22.79 b
22.86 Tine cultivator
22.77 a
22.62 b
22.69
Spike hoe
20.13 b
19.93 c
20.03
Nominee 100 SC
22.87 a
22.22 b
22.54
LSD
1.09
0.77
Any two means not sharing a letter in common differ significantly (p ≤ 0.05)
46
Fig. 4.8 Relationship between fertile tillers and total weed biomass (45 DAS)
y = -0.5387x + 389.28R² = 0.9865
100
150
200
250
300
350
400
0 50 100 150 200 250 300 350
Fer
tile
til
lers
(m
-2)
Weed biomass (g m-2)
(a) 2008
y = -0.5782x + 385.51R² = 0.9648
100
150
200
250
300
350
400
0 50 100 150 200 250 300 350
Fer
tile
til
lers
(m
-2)
Weed biomass (g m-2)
(b) 2009
47
4.13 Number of unfertile tillers
Year effect was non significant on number of unfertile tillers, however two years
average data are discussed.
The data presented in table 4.7 revealed that there was a significant effect of different
weed management strategies on number of unfertile tillers. Significantly maximum number
of unfertile tillers was recorded in no weeding (45.66 m-2) followed by spike hoe (39.00 m-2)
and Nominee 100 SC (32.00 m-2). Number of unfertile tillers in tine cultivator (29.50 m-2)
and hoeing (28.16 m-2) was similar. Whereas, minimum number of unfertile tillers was
observed in hand pulling (26.00 m-2).
4.14 Panicle length
A significant year effect was found on panicle length at maturity therefore results of
both the years are discussed separately.
The data regarding panicle length (Table 4.6) showed that different weed management
strategies had significant effect on panicle length. During the year 2008, statistically similar
panicle length was observed in hoeing (22.93 cm), hand pulling (23.90 cm), tine cultivator
(22.77 cm) and Nominee 100 SC (22.87 cm) followed by spike hoe (20.13 cm). Minimum
panicle length was recorded in no weeding (16.13 cm).
During the year 2009 maximum panicle length was recorded in hand pulling (23.80
cm). Panicle length in hoeing (22.79 cm) was similar to tine cultivator (22.62 cm) followed
by Nominee 100 SC (22.22 cm), spike hoe (19.93 cm) and no weeding (16.01 cm).
4.15 Kernels per panicle
There was a non significant year effect on kernels per panicle, however two years
average data are discussed.
The data presented in table 4.7 revealed that different weed management strategies
affected kernels per panicle significantly. Maximum kernels per panicle were recorded in
hand pulling (77.19) and hoeing (75.70) followed by tine cultivator (73.73), Nominee 100 SC
(67.70), spike hoe (64.19 ) and no weeding (60.16).
48
Table 4.7 Effect of different weed management strategies on kernels per Panicle Treatment
2008
2009
Mean
No weeding
60.43
59.89
60.16 e
Hand pulling
78.15
76.25
77.19 a
Hoeing (with kasula)
75.92
75.49
75.70 a Tine cultivator
74.31
73.16
73.73 b
Spike hoe
65.5
62.89
64.19 d
Nominee 100 SC
69.43
65.97
67.70 c
LSD
1.92
Any two means not sharing a letter in common differ significantly (p ≤ 0.05)
Table 4.8 Effect of different weed management strategies on 1000 kernel weight (g)
Treatment
2008
2009
Mean
No weeding
14.5
15.17
14.83 e
Hand pulling
20.87
20.4
20.63 a
Hoeing (with kasula)
20.6
20.17
20.38 a Tine cultivator
19.47
19.2
19.33 b
Spike hoe
17.17
16.52
16.84 d
Nominee 100 SC
18.07
17.6
17.83 c
LSD
0.63
Any two means not sharing a letter in common differ significantly (p ≤ 0.05)
49
Fig. 4.9 Relationship between weed biomass (45 DAS) and kernels per panicle
y = -0.0564x + 78.857R² = 0.9801
40
45
50
55
60
65
70
75
80
85
0 50 100 150 200 250 300 350
Ker
nel
s p
er p
anic
le
Weed biomass (g m-2)
(a) 2008
y = -0.0561x + 77.819R² = 0.9632
40
45
50
55
60
65
70
75
80
0 50 100 150 200 250 300 350
Ker
nel
s pe
r pa
nic
le
Weed biomass (g m-2)
(b) 2009
50
Fig. 4.10 Relationship between 1000 kernels weight and weed biomass (45 DAS)
y = -0.0195x + 21.294R² = 0.9317
5
7
9
11
13
15
17
19
21
23
0 50 100 150 200 250 300 350
1000
ker
nel
wei
ght
(g)
Weed biomass (g m-2)
(a) 2008
y = -0.0171x + 20.88R² = 0.9767
5
7
9
11
13
15
17
19
21
23
0 50 100 150 200 250 300 350
1000
ker
nel
wei
ght
(g)
Weed biomass (g m-2)
(b) 2009
51
Total weed biomass (45 DAS) and kernels per panicle were linearly related and the
regression accounted for 98 and 96% of the variation during 2008 and 2009, respectively
(Fig. 4.9).
4.16 1000 kernel weight
Year effect was non significant however two years average data are discussed.
The data (Table 4.8) indicated that there was a significant effect of different weed
management strategies on 1000 kernel weight. Significantly heavier 1000 kernel weight was
recorded in hand pulling (20.63 g) followed by hoeing (20.38 g), tine cultivator (19.33 g),
Nominee 100 SC (17.83 g), spike hoe (16.84 g) and no weeding (14.83 g).
Total weed biomass (45 DAS) and 1000 kernel weight were linearly related and the
regression accounted for 93 and 97% of the variation during 2008 and 2009, respectively
(Fig. 4.10).
4.17 Biological yield
The year effect was found significant on biological yield at maturity therefore results
of both the years are discussed separately.
The data (Table 4.9) revealed that different weed management strategies had
significant effect on biological yield. During the year 2008, maximum biological yield was
recorded in hand pulling (13.18 t ha-1) followed by tine cultivator (12.09 t ha-1), Nominee 100
SC (9.90 t ha-1) and spike hoe (8.65 t ha-1). Biological yield in no weeding (6.03 t ha-1) was
observed minimum.
During the year 2009, higher biological yield was observed in hand pulling (12.62 t
ha-1) and hoeing (12.45 t ha-1) followed by tine cultivator (11.89 t ha-1), Nominee 100 SC
(9.20 t ha-1) and spike hoe (8.13 t ha-1). Lowest biological yield was observed in no weeding
(5.67 t ha-1).
4.18 Paddy yield
There was a significant year effect on paddy yield at maturity therefore results of both
the years are discussed separately.
52
Table 4.9 Effect of different weed management strategies on biological yield (t ha-1) Treatment
2008
2009
Mean
No weeding
6.03 e
5.67 e
5.85
Hand pulling
13.18 a
12.62 a
12.89
Hoeing (with kasula)
12.95 ab
12.45 a
12.69 Tine cultivator
12.09 b
11.89 b
11.99
Spike hoe
8.65 d
8.13 d
8.39
Nominee 100 SC
9.90 c
9.20 c
9.54
LSD
0.95
0.40
Any two means not sharing a letter in common differ significantly (p ≤ 0.05)
Table 4.10 Effect of different weed management strategies on paddy yield (t ha-1)
Treatment
2008
2009
Mean
No weeding
1.47 e
1.27 e
1.37
Hand pulling
4.45 a 4.35 a
4.44
Hoeing (with kasula)
4.21 a
4.11 a
4.16 Tine cultivator
3.91 b
3.81 b
3.86
Spike hoe
2.44 d
2.05 d
2.25
Nominee 100 SC
3.02 c
2.59 c
2.81
LSD
0.29
0.26
Any two means not sharing a letter in common differ significantly (p ≤ 0.05)
53
Fig. 4.11 Relationship between paddy yield and total weed biomass (45 DAS)
y = -0.0095x + 4.6445R² = 0.9681
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
0 50 100 150 200 250 300 350
Pad
dy y
ield
(t
ha-1
)
Weed biomass (g m-2)
(a) 2008
y = -0.0101x + 4.6289R² = 0.9712
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
0 50 100 150 200 250 300 350
Pad
dy
yiel
d (
t h
a-1)
Weed biomass (g m-2)
(b) 2009
54
Fig. 4.12 Relationship between paddy yield and number of fertile tillers
y = 0.0182x - 2.3506R² = 0.9945
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
160 210 260 310 360 410
Pad
dy
yiel
d(t
ha-1
)
Fertile tillers (m-2)
(a) 2008
y = 0.0172x - 2.0156R² = 0.9929
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
160 210 260 310 360 410
Pad
dy
yiel
d(t
ha-1
)
Fertile tillers (m-2)
(b) 2009
55
Fig. 4.13 Relationship between paddy yield and kernels per panicle
y = 0.1571x - 7.8718R² = 0.9775
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
55 60 65 70 75 80 85
Pad
dy
yiel
d(t
ha-1
)
kernels per panicle
(a) 2008
y = 0.1588x - 8.0522R² = 0.9733
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
55 60 65 70 75 80 85
Pad
dy
yiel
d(t
ha-1
)
kernels per panicle
(b) 2009
56
Fig. 4.14 Relationship between paddy yield and 1000 kernel weight
y = 0.5816x - 7.5592R² = 0.98
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
14 15 16 17 18 19 20 21 22
Pad
dy
yiel
d(t
ha-1
)
1000 kernel weight (g)
(a) 2008
y = 0.6142x - 8.2054R² = 0.9703
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
14 15 16 17 18 19 20 21
Pad
dy
yiel
d(t
ha-1
)
1000 kernel weight (g)
(b) 2009
57
The data pertaining paddy yield presented in table 4.10 indicated that all weed
management strategies had significant effect on paddy yield. During the year 2008,
significantly maximum paddy yield (4.45 t ha-1) was recorded in hand pulling similar to
hoeing (4.21 t ha-1) followed by tine cultivator (3.91 t ha-1), Nominee 100 SC (3.02 t ha-1) and
spike hoe (2.44 t ha-1). Minimum paddy yield was recorded in no weeding (1.47 t ha-1).
Similar trend was observed during the year 2009. Significantly maximum paddy yield
(4.35 t ha-1) was recorded in hand pulling and hoeing (4.11 t ha-1) followed by tine cultivator
(3.81 t ha-1), Nominee 100 SC (2.59 t ha-1) and spike hoe (2.05). Lowest paddy yield was
found in no weeding (1.27 t ha-1).
Total weed biomass (45 DAS) and paddy yield were linearly related and the
regression accounted for 96 and 97% of the variation during 2008 and 2009, respectively
(Fig. 4.11). Relationship of paddy yield with number of fertile tillers, kernels per panicle and
1000 kernel weight was linear during 2008 and 2009 (Fig. 4.12-14).
4.19 Straw yield
The year effect was found to be non significant on straw yield however two years
average data are discussed. The data presented in table 4.11 revealed that there was a
significant effect of different weed management strategies on straw yield. Significantly
maximum straw yield was recorded in hand pulling (8.53 t ha-1) followed by tine cultivator
(8.12 t ha-1), Nominee 100 SC (6.73 t ha-1) and spike hoe (6.14 t ha-1). In no weeding (4.47 t
ha-1) minimum straw yield was recorded.
4.20 Harvest index
The year effect was non significant on harvest index however two years average data
are discussed. The data (Table 4.12) showed that there was a significant effect of different
weed management strategies on harvest index. Significantly maximum harvest index was
recorded in hand pulling (34.17) followed by tine cultivator (32.24), Nominee 100 SC
(29.38), spike hoe (26.77) and no weeding (23.40).
4.21 Opaque kernels
There was a non significant year effect on opaque kernels (%), however two years
average data are discussed.
58
Table 4.11 Effect of different weed management strategies on straw yield (t ha-1) Treatment
2008
2009
Mean
No weeding
4.55
4.39
4.47 e
Hand pulling
8.72
8.26
8.53 a
Hoeing
8.73
8.34
8.49 ab Tine cultivator
8.18
8.07
8.12 b
Spike hoe
6.21
6.07
6.14 d
Nominee 100 SC
6.87
6.60
6.73 c
LSD
0.38
Any two means not sharing a letter in common differ significantly (p ≤ 0.05)
Table 4.12 Effect of different weed management strategies on harvest Index Treatment
2008
2009
Mean
No weeding
24.4
22.48
23.4 e
Hand pulling
33.83
34.51
34.17 a
Hoeing
32.55
32.03
32.79 b Tine cultivator
32.36
32.12
32.24 b
Spike hoe
28.2
25.27
26.77 d
Nominee 100 SC
30.55
28.21
29.38 c
LSD
0.05
Any two means not sharing a letter in common differ significantly (p ≤ 0.05)
59
Table 4.13 Effect of different weed management strategies on opaque kernels
Treatment
2008
2009
Mean
No weeding
8.88 a
8.63
8.75 a
Hand pulling
7.32 e
7.42
7.37 e
Hoeing
7.43 e
7.52
7.47 e Tine cultivator
7.72 d
7.75
7.73 d
Spike hoe
8.42 b
8.50
8.46 b
Nominee 100 SC
8.02 c
8.07
8.04 c
LSD
0.19
Any two means not sharing a letter in common differ significantly (p ≤ 0.05)
Table 4.14 Effect of different Weed management strategies on abortive kernels
Treatment
2008
2009
Mean
No weeding
3.86
3.88
3.87 a
Hand pulling
3.31
3.32
3.31 f
Hoeing
3.41
3.04
3.40 e Tine cultivator
3.50
3.52
3.51 d
Spike hoe
3.72
3.78
3.75 b
Nominee 100 SC
3.58
3.63
3.61 c
LSD
0.04
Any two means not sharing a letter in common differ significantly (p ≤ 0.05)
60
The opaque kernels (%) were significantly affected by different weed management
strategies (Table 4.13). Significantly maximum opaque kernels (%) were recorded in no
weeding (8.75 %) followed by spike hoe (8.46 %), Nominee 100 SC (8.04 %) and tine
cultivator (7.73 %) hoeing (7.47 %) and hand pulling (7.37 %).
4.22 Abortive kernels
A non significant year effect was recorded on abortive kernels (%), however two
years average data are discussed. The data presented in table 4.14 indicated that there was a
significant effect of different weed management strategies on abortive kernels (%).
Significantly maximum abortive kernels (%) were recorded in no weeding (3.87 %) followed
by spike hoe (3.75 %), Nominee 100 SC (3.61 %), tine cultivator (3.51 %), hoeing (3.40 %)
and hand pulling (3.31 %).
4.23 Normal kernels
There was a significant year effect on normal kernels (%) at maturity therefore results
of both years are discussed separately.
The data (Table 4.17) revealed that normal kernels (%) were significantly affected by
different weed management strategies. During the year 2008, significantly maximum normal
kernels (%) was recorded in hand pulling (63.21 %) followed by hoeing (62.15 %), tine
cultivator (60.04 %), Nominee 100 SC (58.15 %), spike hoe (55.93 %) and no weeding
(53.74 %).
During the year 2009, significantly maximum normal kernels (%) were recorded for
hand pulling (62.37 %) followed by hoeing (61.21 %), tine cultivator (59.32 %), Nominee
100 SC (58.03 %), spike hoe (56.06 %) and no weeding (53.63 %).
4.24 Sterile spikelets
A significant year effect on sterile spikeleets (%) was observed therefore results of
both the years are discussed separately.
The data presented in Table 4.18 indicated that there was a significant effect of
different weed management strategies on sterile spikeleets (%). During the year 2008,
significantly maximum sterile spikeleets (%) were recorded in no weeding (11.80 %)
followed by spike hoe (10.15 %), Nominee 100 SC (9.46 %) and hand pulling (8.25 %).
61
Table 4.15 Effect of different weed management strategies on normal kernels
Treatment
2008
2009
Mean
No weeding
53.74 f
53.63 f
53.68
Hand pulling
63.21 a
62.37 a
62.79
Hoeing
62.15 b
61.21 b
61.68 Tine cultivator
60.04 c
59.32 c
59.68
Spike hoe
55.93 e
56.06 e
55.99
Nominee 100 SC
58.15 d
58.03 d
58.09
LSD
0.84
0.71
Any two means not sharing a letter in common differ significantly (p ≤ 0.05)
Table 4.16 Effect of different weed management strategies on sterile spikelets
Treatment
2008
2009
Mean
No weeding
11.8 a
12.49 a
12.14
Hand pulling
8.25 e
8.51 e
8.38
Hoeing
8.65 de
9.00 de
8.82 Tine cultivator
8.94 cd
9.27 d
9.11
Spike hoe
10.15 b
11.15 b
10.65
Nominee 100 SC
9.46 c
10.17 c
9.81
LSD
0.58
0.49
Any two means not sharing a letter in common differ significantly (p ≤ 0.05)
62
During the year 2009, significantly maximum sterile spikeleets (%) were recorded in
no weeding (12.49 %) followed by spike hoe (11.15 %), Nominee 100 SC (10.17 %), tine
cultivator (9.27 %) and hand pulling (8.51 %).
4.25 Leaf area index
The Fig. 4.15 showed that during the year 2008 there was a gradual increase in leaf
area index (LAI) of rice under different weed management strategies. Maximum LAI at 90
DAS was noted in hand pulling (4.07) followed by hoeing (3.96), tine cultivator (3.85),
Nominee 100 SC (3.68) and spike hoe (3.31) at 90 DAS. Minimum LAI at 90 DAS was
recorded in no weeding (3.28).
During the year 2009 maximum LAI at 90 DAS was noted in hand pulling (3.96)
followed by hoeing (3.91), tine cultivator (3.79), Nominee 100 SC (3.59) and spike hoe
(3.26) at 90 DAS. Minimum LAI at 90 DAS was recorded in no weeding (3.21).
4.26 Crop growth rate
The Fig. 4.16 showed that during the year 2008 there was a gradual increase in crop
growth rate (CGR) of different weed management strategies. Maximum CGR was noted in
hand pulling (25.14 g m-2 d-1) followed by hoeing (24.68 g m-2 d-1), tine cultivator (24.71 g m-
2 d-1), Nominee 100 SC (24.38 g m-2 d-1) and spike hoe (20.14 g m-2 d-15 ) at 90 DAS.
Minimum CGR at 90 DAS was recorded in no weeding (17.95 g m-2 d-1).
During the year 2009 maximum CGR was noted in hand pulling (25.30 g m-2 d-1)
followed by tine cultivator (24.80 g m-2 d-1), hoeing (24.60 g m-2 d-1), Nominee 100 SC
(24.35 g m-2 d-1) and spike hoe (19.36 g m-2 d-15 ) at 90 DAS. Minimum CGR at 90 DAS was
recorded in no weeding (17.99 g m-2 d-1).
4.27 Leaf area duration
The Fig. 4.17 showed that different weed management strategies significantly affected
the cumulative leaf area duration (LAD) at 105 DAS during the year 2008. The maximum
LAD was noted in hand pulling (136.65 days) followed by hoeing (133.83 days), tine
cultivator (129.95 days), Nominee 100 SC (124.08 days) and spike hoe (105.53 days). The
minimum LAD was observed in no weeding (104.49 days).
63
Fig. 4.15 Periodic changes in leaf area index of different weed management
strategies
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
60 75 90 105
L A
I
D A S
(a) 2008
No weeding
Hand pulling
Hoeing
Tine cultivator
Spike hoe
Nominee
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
60 75 90 105
L A
I
D A S
(b) 2009
No weeding
Hand pulling
Hoeing
Tine cultivator
Spike hoe
Nominee
64
Fig. 4.16 Periodic changes in crop growth rate of different weed management
strategies
0.0
5.0
10.0
15.0
20.0
25.0
30.0
75 90 105
C G
R (
g m
-2d
-1)
D A S
(a) 2008
No weeding
Hand pulling
Hoeing
Tine cultivator
Spike hoe
Nominee
0.0
5.0
10.0
15.0
20.0
25.0
30.0
75 90 105
C G
R (
g m
-2d-1
)
D A S
(b) 2009
No weeding
Hand pulling
Hoeing
Tine cultivator
Spike hoe
Nominee
65
Fig. 4.17 Cumulative leaf area duration of different weed management strategies at
105 DAS
0
20
40
60
80
100
120
140
160
No weeding Hand pulling Hoeing Tine cultivator Spike hoe Nominee
L A
D (
day
s)
(a) 2008
0
20
40
60
80
100
120
140
160
No weeding Hand pulling Hoeing Tine cultivator Spike hoe Nominee
L A
D (
day
s)
(b) 2009
66
Different weed management strategies also significantly affected the cumulative leaf
area duration (LAD) at 105 DAS during the year 2009. The maximum LAD was noted in
hand pulling (136.93 days) followed by hoeing (134.87 days), tine cultivator (129.88 days),
Nominee 100 SC (124.30 days) and spike hoe (101.33 days) at DAS 105. The minimum LAD
was observed in no weeding (101.06 days).
4.28 Total dry matter
The data (Fig 4.18) revealed that during the year 2008 different weed management
strategies significantly affected total dry matter (TDM) production at 105 DAS. Maximum
TDM was noted in hand pulling (905 g) followed by hoeing (850 g), tine cultivator (806.67
g), Nominee 100 SC (737.67 g) and spike hoe (573.67 g). Minimum TDM was recorded in no
weeding (528.33 g).
Different weed management strategies significantly affected total dry matter (TDM)
production at 105 DAS during the year 2009. The maximum TDM was recorded in hand
pulling (894.83 g) followed by hoeing (842.23 g), tine cultivator (796.69 g), Nominee 100 SC
(724.20 g) and spike hoe (562.945g). The minimum TDM was observed in no weeding
(518.72 g).
4.29 Net assimilation rate
The fig 4.19 showed that different weed management strategies significantly affected
net assimilation rate (NAR) at 105 DAS during the year 2008. Maximum NAR was noted in
hand pulling (6.62 g m-2 d-1) followed by hoeing (6.35 g m-2 d-1), tine cultivator (6.20 g m-2 d-
1), Nominee 100 SC (5.94 g m-2 d-1) and spike hoe (5.45 g m-2 d-1). Minimum NAR was
recorded in no weeding (5.06 g m-2 d-1).
During the year 2009 different weed management strategies also significantly affected
net assimilation rate (NAR) at 105 DAS. The maximum NAR was recorded in hand pulling
(6.53 g m-2 d-1) followed by hoeing (6.24 g m-2 d-1), tine cultivator (6.13 g m-2 d-1), Nominee
100 SC (5.82 g m-2 d-1) and spike hoe (5.55 g m-2 d-1). The minimum NAR was observed in
no weeding (5.13 g m-2 d-1).
67
Fig. 4.18 Total dry matter of different weed management strategies at 105 DAS
300
400
500
600
700
800
900
1000
1100
No weeding Hand pulling Hoeing Tine cultivator Spike hoe Nominee
T D
M (
g)
(a) 2008
0
200
400
600
800
1000
1200
No weeding Hand pulling Hoeing Tine cultivator Spike hoe Nominee
T D
M (
g)
(b) 2009
68
Fig. 4.19 Net assimilation rate of different weed management strategies at 105 DAS
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
No weeding Hand pulling Hoeing Tine cultivator Spike hoe Nominee
N A
R (
g m
-2d-1
)
(a) 2008
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
No weeding Hand pulling Hoeing Tine cultivator Spike hoe Nominee
N A
R (
g m
-2d-1
)
(b) 2009
69
4.30 Economic analysis
During the year 2008 maximum net returns were obtained by hand weeding (Rs.
43188) followed by hoeing (Rs. 41035), tine cultivator (Rs. 39525), Nominee (Rs. 16639)
and spike hoe (Rs. 1326). Maximum benefit cost ratio (BCR) was obtained by tine cultivator
(1.62) followed by hoeing (1.58), hand weeding (1.58), Nominee (1.26) and spike hoe (1.02).
During the year 2009 maximum net returns were obtained by hand weeding (Rs.
56905) followed by hoeing (Rs. 53713), tine cultivator (Rs. 50958), Nominee (Rs. 13942).
Maximum benefit cost ratio (BCR) was obtained by tine cultivator (1.75) followed by hoeing
(1.72), hand weeding (1.72), Nominee (1.20).
4.31 Marginal rate of return
During the year 2008 maximum marginal rate of return was found in tine cultivator
(6731%) which was followed by Nominee (3126%), hand pulling (54%) and hoeing (24%).
During the year 2009 maximum marginal rate of return was found in tine cultivator (10887%)
which was followed by Nominee (3154%), hand pulling (80%) and hoeing (43%).
69
Table 4.17 Effect of different weed management strategies on economic returns during the year 2008
Treatments
Paddy yield (t ha-1)
Adjusted paddy yield (t ha-1)
Value (Rs ha-1)
Straw yield (t ha-1)
Adjusted straw yield (t ha-1)
Value (Rs. ha-1)
Gross income (Rs. ha-1)
*Variable cost (Rs. ha-1)
Total cost (Rs. ha-1)
Net return (Rs. ha-1)
BCR
No weeding
1.47
1.32 36960 4.55
4.1 2878 39838 0 62587 -22749 0.64
Hand pulling
4.45
4.01 112280 8.72
7.85 5495 117775 12000 74587 43188 1.58 Hoeing
4.21
3.79 106120 8.73 7.86 5502 111622 8000 70587 41035 1.58
Tine cultivator
3.91
3.52 98560 8.18 7.36 5152 103712 1600 64187 39525 1.62
Spike hoe
2.44
2.20 61600 6.21 5.59 3913 65513 1600 64187 1326 1.02
Nominee 100 SC
3.02
2.72 76160 6.87 6.18 4326 80486 1260 63847 16639 1.26
Paddy price per kg = Rs. 28
Straw price per ton = Rs. 700
Fixed cost of production = Rs. 62587 (Appendix 1)
*Appendix 2
70
Table 4.18 Effect of different weed management strategies on economic returns during the year 2009
Paddy price per kg = Rs. 33
Straw price per ton = Rs. 850
Fixed cost of production = Rs. 66770 (Appendix 4)
*Appendix 2
Treatments
Paddy yield (t ha-
1)
Adjusted paddy yield (t ha-1)
Value (Rs ha-1)
Straw yield (t ha-1)
Adjusted straw yield (t ha-1)
Value (Rs. ha-1)
Gross income (Rs. ha-1)
*Variable cost (Rs. ha-1)
Total cost (Rs. ha-1)
Net return (Rs. ha-1)
BCR
No weeding
1.27 1.14 37620 4.39 3.95 3357 40977 0 66770 -25793 0.61
Hand pulling
4.35 3.92 129360 8.26 7.43 6315 135675 12000 78770 56905 1.72 Hoeing
4.11 3.69 122100 8.34 7.51 6383 128483 8000 74770 53713 1.72
Tine cultivator
3.81 3.42 113157 8.07 7.26 6171 119328 1600 68370 50958 1.75
Spike hoe
2.05 1.84 60885 6.07 5.46 4641 65526 1600 68370 -2844 0.96
Nominee 100 SC
2.59 2.33 76923 6.60 5.94 5049 81972 1260 68030 13942 1.20
71
Table 4.19 Effect of different weed management strategies on dominance analysis during 2008
Treatments Variable cost (Rs. ha-1) Net field benefits (Rs. ha-1)
No Weeding 0 39838
Chemical control 1260 79226
Spike Hoe 1600 63913 D
Tine Cultivator 1600 102112
Hoeing 8000 103622
Hand pulling 12000 105775
Table 4.20 Effect of different weed management strategies on marginal analysis during 2008
Treatments Cost that
vary (Rs. ha-1)
Marginal cost that
vary (Rs. ha-1)
Net field benefits
(Rs. ha-1)
Marginal net
benefits (Rs. ha-1)
Marginal rate of return
(%)
No Weeding 0 0 39838 - -
Chemical control 1260 1260 79226 39388 3126
Tine Cultivator 1600 340 102112 22886 6731
Hoeing 8000 6400 103622 1510 24
Hand pulling 12000 4000 105775 2153 54
72
Table 4.21 Effect of different weed management strategies on dominance analysis during 2009
Treatments Variable cost (Rs. ha-1) Net field benefits (Rs. ha-1)
No Weeding 0 40977
Chemical control 1260 80712
Spike Hoe 1600 63926 D
Tine Cultivator 1600 117728
Hoeing 8000 120483
Hand pulling 12000 123675
Table 4.22 Effect of different weed management strategies on marginal analysis during 2009
Treatments Cost that
vary (Rs. ha-1)
Marginal cost that
vary (Rs. ha-1)
Net field benefits
(Rs. ha-1)
Marginal net
benefits (Rs. ha-1)
Marginal rate of return
(%)
No Weeding 0 0 40977 - -
Chemical control 1260 1260 80712 39735 3154
Tine Cultivator 1600 340 117728 37016 10887
Hoeing 8000 6400 120483 2755 43
Hand pulling 12000 4000 123675 3192 80
73
Discussion
This experiment was conducted during 2008 and 2009 at Agronomic Research Farm,
University of Agriculture Faisalabad. Seed of variety Super Basmati with good vigor and
germination percentage was dry direct seeded using automatic drill (seed-cum-fertilizer drill)
after land preparation. It allowed line sowing maintaining 22.5 cm row to row distance and
facilitated manual and mechanical weeding. Drill tilled the soil at shallow depth (4-5 cm),
sowed the seeds and covered them with soil for better seed to soil contact, all in a single pass.
Kumar and Ladha (2011) also reported that drill sowing facilitated weed control between
rows, saved seed and placed the fertilizer at proper depth. Placing seeds below soil surface
also reduced seed damage caused by high rainfall. High Quality seed was used because larger
seeds with greater carbohydrate reserves have increased ability to emerge even from greater
burial depths, similar findings were reported by Baskin and Baskin (1998). Within rows
distance was maintained at 22.5 cm because ppin this range growth and seed production of
various weeds in aerobic conditions was less as compared to wider rows as reported by
Chauhan and Johnson (2010a). Similarly Akobundu and Ahissou (1985) reported that row
spacing (15–45-cm) in direct-seeded rice had little effect on the paddy yield of the crop in the
absence of weeds but in competition with weeds the wider spacing resulted in significantly
lower grain yield.
Weeds are one of the major biological constraints to direct seeded rice production and
are notorious yield reducers, in many situations economically more important than insects,
fungi or other pest organisms (Savary et al., 1997). Weed biomass differed significantly in all
weed control treatments. Weeds were removed manually in hand pulling treatment, four
times weeding resulted in the lowest weed dry weight (at 45 DAS) among all treatments.
Sharma (1997) and Johnson et al. (2004) also reported minimum weed dry weight in hand
weeding. Hoeing by kasula produced less weed dry weight than no weeding, spike hoe,
Nominee 100 SC and tine cultivator. Hoeing removed inter row weeds which might be the
reason of low weed dry weight. Similar results were observed by Akbar et al. (2011). More
weed dry weight than hand pulling was possibly due to within the rows weeds that were
remained uncontrolled.
Weed dry weight in tine cultivator was less than no weeding, spike hoe and Nominee
100 SC possibly because it uprooted and removed inter row weeds, controlling early flushes
of weeds. Second reason of less weed biomass might be beushaning which killed the weeds
74
with single stem. This was also reported by Rao, et al. (2007) and Sharma (1997). Tine
cultivator produced more weed dry weight than hoeing and hand pulling possibly because
weeds that were within rows not controlled. Results of Fazlollaalh et al. (2011), Remington
and Posner (2000) and Sharma (1997) were also in line with above findings.
Application of Nominee 100 SC resulted in less weed biomass than spike hoe and no
weeding. Reason might be because Nominee (bispyrabic sodium) is an acetol-actate synthase
inhibitor which controlled weeds. Similar observations were reported by Fischer et al. (2000).
Nominee 100 SC produced more weed dry weight than hand pulling, hoeing and tine
cultivator. Increased weed dry weight in this study might have been due to increased critical
period of weed infestation. Nominee 100 SC was applied at 20 DAS and weeds might have
competed with rice crop for a longer period. This prolonged competition period possibly
provided a considerable opportunity for weeds to emerge subsequently and produce seeds.
Studies by Chauhan and Johnson (2011) revealed that in direct seeded rice weed competition
period was prolonged. Findings of Johnson et al., (2004) were contradictory who expressed
that critical period for weed competition in aerobic rice was 29-32 days.
Weed dry weight recorded in spike hoe treatment was higher than hand pulling,
hoeing, tine cultivator and Nominee 100 SC; reason might be because it neither uprooted nor
pulled weeds. Spike hoe produced lower weed biomass than no weeding, possibly due to
beushaning effect because the whole crop and weeds were laid down with soil surface after
inter row cultivation resulting in death of single stem weeds. Similar findings were reported
by Rao et al. (2007). These results showed some contradictions with Sharma (1997) who
reported beushaning as a very effective technique and had a complete control over most of
the rice weeds.
Highest weed biomass was recorded in no weeding, might have been due to several
reasons. Firstly aerobic soil conditions were conducive to the germination and growth of
weeds as reported by Rao et al. (2007). Secondly emerging direct seeded rice seedlings were
less competitive with concurrently emerging weeds (Kumar et al., 2008a). Third reason of
highest weed biomass might be competitive advantage of C4 weeds which increased their
efficiency to use crop nutrients more than rice. Findings of Holm et al. (1991) were in
support of this reason. Heavy and quicker second flush of weeds might be another reason of
highest weed biomass. Almost similar results were reported by Ekleme et al. (2009), Singh et
al. (2008) and Mann et al. (2007).
75
Although plant height is a genetic character but height of aerobic rice substantially
decreases when dry weed biomass exceeds (Zhao et al., 2006). Taller plants in all weed
management treatments as compared to weedy check were observed during this study. Higher
plant height in hand pulling, hoeing, tine cultivator and Nominee 100 SC might be attributed
to availability of proper nutrients and space resulting in good leaf canopy because of low
weed infestation. Phoung et al. (2005) found that N uptake was maximum with good weed
control practices in direct seeded rice, resulting in taller plants. Correlation between weed
biomass and plant height was negative and also supported by studies of Ekleme et al. (2009).
Shorter plants in no weeding and spike hoe were recorded in current study. This might have
been due to more weeds, less available space and nutrients. Phoung et al. (2005) reported that
when weeds were more than more N uptake by weeds as compared to rice plants. Negative
correlation between plant height and weed biomass is also strengthening our results. Similar
findings were observed by Mann et al. (2007).
Variation in fertile tillers among all treatments was observed in current studies. Less
fertile tillers were observed in no weeding as compared to all other treatments. In general
number of effective tillers decreased as the weed-infested period was extended, reasons might
be; Firstly aerobic soil conditions were conducive to the germination and growth of weeds as
reported by Rao et al. (2007). Secondly emerging direct seeded rice seedlings were less
competitive with concurrently emerging weeds (Kumar et al., 2008a). Third reason of less
fertile tillers might be competitive advantage of C4 weeds which increased their efficiency to
use crop nutrients more than rice. Findings of Holm et al. (1991) are in support of this reason.
Heavy and quicker second flush of weeds might be another reason of lees fertile tillers.
Similar findings were reported by Sharma et al. (1977). Negative co relation between weed
dry weight and fertile tillers is supporting but in spike hoe more number of productive tillers
with high weed infestation this might have been due to beushaning effect which increased
fertile tillers. More fertile tillers in hand pulling, hoeing and tine cultivator were observed.
This increase in fertile tillers might have been due to less early competition of crop with
weeds and more availability of moisture and nutrients. Increase in fertile tillers with
decreased weed biomass was also observed by Phoung et al. (2005). These Results were also
in line with findings of Ekleme et al. (2009), Mann et al. (2007), Singh et al. (2007) and
Fischer et al. (2001).
76
The panicle of rice is an organ of photosynthesis as well as storage place of
photosynthetic products (Hirota et al., 1990) and panicle size is closely related to yield
formation. Rice yield is often constrained by panicle size especially in aerobic rice culture
(Kato et al., 2006a). Panicle length in this study varied significantly in all weed control
treatments. Longer panicle length was observed in hand pulling, hoeing, tine cultivator and
Nominee 100 SC. Reasons might be; good crop stand, taller plants, good canopy and less
weed competition. Singh et al., 2007 also reported longer panicles with good weed control
treatments as compared to weedy check. Shorter panicle length in no weeding and spike hoe
may be due to dwarf plants, low photosynthetic activity and more weed infestation.
Correlation between panicle length and weed biomass is also negative. These Results are in
line with findings of Mann et al. (2007).
Rice grain yield is determined by three yield components, number of panicle, kernel
weight and number of kernels per panicle. Kernels per panicle are a key component of the
grain yield (Zhang et al., 2011). Crop with high number of kernels per panicle was found
more resistant to lodging (Liu et al., 2009). Variation in kernels per panicle was observed
among all weed control treatments in our study. Higher number of kernels per panicle in hand
pulling, hoeing tine cultivator and Nominee 100 SC might have been due to taller plants more
photosynthetic activity and proper availability of nitrogen. Kernel formation is usually
affected by the nitrogen availability at panicle initiation. In current studies more kernels per
panicle might have been due to increased nitrogen availability at panicle initiation. Similar
findings were observed by Yang et al. (2005). Less number of kernels per panicle in no
weeding and spike hoe with higher weed biomass attributed to competition amongst the crop
and weeds. The growth of weeds in no weeding and spike hoe went unchecked that might
have been reduced the availability of moisture and other plant nutrients like nitrogen to the
crop plant and eventually resulted in reduced size of panicles with less number of kernels also
reported by Phoung et al. (2005). The number of kernels per panicle correlated negatively
with weed dry weight. Singh et al. (2007) also observed less number of kernel panicle-1 with
poor weed management strategies. Similar results were also observed by Fazlollah et al.
(2011) and Mann et al. (2007).
Although rice is known for its relatively constant 1000-kernel weight because of a
rigid hull limiting kernel size and variable proportions of spikelet sterility that are regulated
according to available assimilates (Yoshida, 1981) but in this study 1000 kernel weight was
77
significantly affected by various weed control methods. Heavier 1000 kernel weight
recorded in hand pulling, hoeing and tine cultivator might be due to less weed competition,
more availability of nutrients with lesser weed dry weight. Due to lower weed biomass the
florets may have utilized the soil nutrients to their fullest extent to develop heavy 1000 kernel
weight. Similar results were observed by Fazlollah et al. (2011) Singh et al. (2007) and
Phoung et al. (2005). Lighter 1000 kernel weight observed in no weeding and spike hoe
might be due to more weed competition and less availability of nutrients. Findings of Singh et
al. (2007) also revealed that poor weed management strategies resulted in lower 1000 kernel
weight.
Significant variation in paddy yield was observed in various weed management
strategies. Yield reduction was in range of 5 - 68% as compared to hand pulling. Similar high
ranges were reported by various researchers. Fujisaka et al. (1993) reported that aerobic soil
conditions are conducive for germination and growth of highly competitive weeds, which
cause grain yield losses of 50 - 91%, similarly Becker et al. (2003) found that yield losses
due to weed competition ranges from 12 - 100%.
Paddy yield in hand pulling and hoeing was 221 and 203% more than no weeding
respectively. This increase in yield might be due to minimum presence of weeds during
critical competition period. Studies of Haefele et al. (2000) suggested that there was a
considerable scope to increase yield with improved weed control in direct seeded rice. Proper
availability of nutrients, space and moisture resulted in more fertile tillers, more kernels
panicle-1 and heavier 1000 kernel weight providing high returns in the form of paddy yield.
Similar findings were observed by Phoung et al. (2005).
Paddy yield observed in tine cultivator was 141, 71 and 37% more than no weeding,
spike and hoe Nominee respectively while 13 and 7% less than hand pulling and hoeing
respectively. Increase in yield might be due to good inter row weed control and low weed dry
weight as compared to no weeding. Fazlollah et al. (2011) reported similar results. Second
reason of increased paddy yield might be due to an increase in soil ventilation resulting in
better growth of root, stem and claw. Findings of Fernandes and Uphoff (2002) favored our
observations. Third reason probably the beushaning effect that was also reported by Sharma
(1997). Similar findings were reported by Kumar (2003); he compared the rotary hand
weeders with the common methods of weeding in India. In that study the mechanical weed
control significantly increased the grain yield of rice plants and mechanical weeding had
78
advantage of 10.9% yield increase per hectare rather than using hand weeding. Fazlolallh et
al. (2001) an Iranian scientist compared two mechanical weeders in rice; mechanical weeder
with engine power and mechanical weeder without engine power both resulting in good yield
as compared to no weeding. Paddy yield is 12% less than hand pulling probably due to more
weed dry weight than hand pulling because with in row weeds were mostly not controlled.
Yield resulted in weed control by Nominee 100 SC was 105 and 25% more than no
weeding and spike hoe respectively while 58 and 37% less than hand pulling and tine
cultivator respectively. Reduction in yield might be due to more weeds infestation for a
longer period. Nominee was applied at 20 DAS and weeds might have competed for a longer
period. This prolonged competition period possibly provided a considerable opportunity for
weeds to emerge subsequently and produce seeds. Studies by Chauhan and Johnson (2011)
revealed that in direct seeded rice weed competition period was prolonged. Findings of
Johnson et al. (2004) were contradictory who expressed that critical period for weed
competition in aerobic rice was 29-32. Findings of current study are also in line with
Remington and Posner (2000) they done a research about weeds control in the direct
cultivation of rice in Gambia and found that delay in weed control during weed competition
period causes 25 kg ha-1 day-1 decrease in rice yield. Nominee 100 SC resulted in more paddy
yield than spike hoe and no weeding. Reason might be because Nominee (bispyrabic sodium)
is an acetol-actate synthase inhibitor which controlled weeds of rice and rice yield was
increased. Similar observations were reported by Fischer et al. (2000).
Paddy yield recorded in weed control by Spike hoe was 97, 71 and 24% less than
hand pulling, tine cultivator and Nominee respectively, might be due to more weed biomass
resulting in more weed competition. Chauhan and Johnson (2011) also reported that rice yield
was decreased with increase in weed biomass. Paddy yield in our study was 64% more than
no weeding. First reason of increase in yield than no weeding might be low weed dry weight
than no weeding and second reason might be beushaning effect resulting in more tillers and
more yield. Results of Sharma (1997) were similar to our findings.
Paddy yield recorded in no weeding was 221, 203, 141, 105 and 64% less than hand
pulling, hoeing, tine cultivator, Nominee and spike hoe respectively. Low yield in no
weeding might have been due to several reasons. Firstly aerobic soil conditions were
conducive to the germination and growth of weeds, they increased their dry weight and paddy
yield was reduced. Oerke and Dehne, (2004) also found that weeds were major constraint in
79
reduction of paddy yield. Secondly emerging direct seeded rice seedlings were less
competitive with concurrently emerging weeds resulting in low paddy yield (Kumar et al.,
2008a). Third reason of paddy yield might be competitive advantage of C4 weeds which
increased their efficiency to use crop nutrients more than rice. Findings of Holm et al. (1991)
were in support of this reason. Fourth reason of low yield in no weeding treatment might be
less availability of moisture and nutrients to the crop. Phoung et al. (2005) reported that when
weeds are more as compared to crop then they used more nutrients than rice and reduced the
rice yield. Similar results were also observed by Chauhan and Johnson (2011) who reported
that weed competition throughout the crop growing season reduced crop yield by
approximately 95% as compared to the weed-free conditions. Positive correlation between
yield components and yield and negative correlation between weed dry weight and yield also
strengthens these findings. Similar results are reported by Ekleme et al. (2009), Juraimi et al.
(2009), Hussain et al. (2008), Singh et al. (2008), Mann et al. (2007) and Singh et al. (2007).
Occurrence of sterility, abortiveness and opaqueness varied significantly among
various weed management strategies. Sterility, abortiveness and opaqueness was high in no
weeding and spike hoe reasons; might be due to shorter plants, less availability of nutrients,
low photosynthetic activity and water stress because weed infestation was very high and
weed also competed for water. Xu and Zhou, (2006) reported that rice is more sensitive to
drought stress. Boonrat et al. (2006) found that under mild drought stress during the
flowering stage, 30–40% sterility was increased. Ekanayake et al. (1990) also reported that
water deficit during flowering leaded to spikelet sterility by inhibiting the anther dehiscence
and pollen germination, as well as reduced the pollen viability and pistil water potential and
crop yield was reduced. Sterility, abortiveness and opaqueness percentages were low in hand
pulling, hoeing and tine cultivator might have been due to less number of weeds, taller plants,
more available nutrients and high photosynthetic rate. Results of Akbar et al. (2011) were
also in line with these findings.
80
STUDY-II: EVALUATION OF DIFFERENT INTER CULTURAL IMPLEMENTS
FOR WEED MANAGEMENT IN DRILL SEEDED AEROBIC RICE
4.31 Weed biomass of broad leaf weeds (15 DAS)
The fig.4.20 showed that weed control implements and frequency levels significantly
affected weed biomass of broad leaf weeds at 15 DAS during both the years.
Tine cultivator resulted in minimum weed biomass of broad leaf weeds at F1 (0.73 g
m-2) similar to F2 (0.79 g m-2), F3 (0.88 g m-2) and F1 (0.88 g m-2) during the year 2008.
Similar trend was observed during the year 2009. Minimum weed biomass was noted in F1
(0.81 g m-2) similar to F2 (0.87 g m-2), F3 (0.96 g m-2) and F1 (0.96 g m-2).
Spike hoe produced minimum weed biomass of broad leaf weeds when inter
cultivation was carried out at F3 (1.43 g m-2) similar to F4 (1.46 g m-2), F1 (1.48 g m-2) and F2
(1.58 g m-2) during the year 2008. During the year 2009 lowest weed biomass was found in
F3 (1.51 g m-2) similar to F4 (1.54 g m-2), F1 (1.56 g m-2) and F2 (1.66 g m-2).
Plug weeder resulted in minimum weed biomass when operated at F1 (1.55 g m-2)
similar to F2 (1.55 g m-2), F3 (1.62 g m-2) and F4 (1.65 g m-2) during the year 2008. Lowest
weed biomass of broad leaf weeds by plug weeder during the year 2009 was noted at F1 (1.63
g m-2) which was statistically similar to all other inter cultivation frequencies by plug weeder.
4.32 Weed biomass of Sedges (15 DAS)
The fig.4.11 showed that weed control implements and frequency levels significantly
affected weed biomass of sedges at 15 DAS during both the years.
Tine cultivator resulted in minimum weed biomass of sedges at F2 (0.92 g m-2) similar
to F3 (0.96 g m-2), F1 (0.98 g m-2) and F4 (0.98 g m-2) during the year 2008. Similar trend was
observed during the year 2009. Minimum weed biomass was noted in F2 (0.98 g m-2) similar
to F3 (1.02 g m-2), F1 (1.04 g m-2) and F4 (1.04 g m-2).
Spike hoe produced minimum weed biomass of when inter row cultivation was done
at F2 (1.25 g m-2) similar to F1 (1.31 g m-2), F4 (1.32 g m-2) and F3 (1.38 g m-2) during the year
2008. During the year 2009 lowest weed biomass was found in F2 (1.31 g m-2) similar to F1
(1.37 g m-2), F4 (1.38 g m-2) and F3 (1.44 g m-2).
81
Fig. 4.20 Effect of different weed control implements on weed biomass of broad leaf weeds (15 DAS) (F1= Inter row cultivation at 15 DAS F2= Inter row cultivation at 15 and 25 DAS F3= Inter row cultivation at 15, 25 and 35 DAS F4= Inter row cultivation at 15, 25, 35 and 45 DAS)
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
F1 F2 F3 F4 F1 F2 F3 F4 F1 F2 F3 F4
Tine cultivator Spike hoe Plug weeder
g m
-2
(a) 2008
0.0
0.5
1.0
1.5
2.0
2.5
F1 F2 F3 F4 F1 F2 F3 F4 F1 F2 F3 F4
Tine cultivator Spike hoe Plug weeder
g m
-2
(b) 2009
82
Fig. 4.21 Effect of different weed control implements on weed biomass of sedges (15 DAS)
(F1= Inter row cultivation at 15 DAS F2= Inter row cultivation at 15 and 25 DAS F3= Inter row cultivation at 15, 25 and 35 DAS F4= Inter row cultivation at 15, 25, 35 and 45 DAS)
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
F1 F2 F3 F4 F1 F2 F3 F4 F1 F2 F3 F4
Tine cultivator Spike hoe Plug weeder
g m
-2
(a) 2008
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
F1 F2 F3 F4 F1 F2 F3 F4 F1 F2 F3 F4
Tine cultivator Spike hoe Plug weeder
g m
-2
(b) 2009
83
Plug weeder resulted in minimum weed biomass when operated at F2 (1.29 g m-2)
similar to F3 (1.32 g m-2), F4 (1.34 g m-2) and F1 (1.38 g m-2) during the year 2008. Lowest
weed biomass of sedges by plug weeder during the year 2009 was noted at F2 (1.35 g m-2)
which was statistically similar to all other inter cultivation frequencies by plug weeder.
4.33 Weed biomass of broad leaf weeds (30 DAS)
The fig.4.22 showed that weed control implements and frequency levels significantly
affected weed biomass of broad leaf weeds at 30 DAS during both the years.
Tine cultivator resulted in minimum weed biomass of broad leaf weeds at F3 (21.76 g
m-2) similar to F2 (23.65 g m-2) and F4 (24.07 g m-2) followed by F1 (56.63 g m-2) during the
year 2008. Similar trend was observed during the year 2008. During the year minimum weed
biomass was noted in F3 (27.74 g m-2) similar to F2 (29.63 g m-2) and F4 (30.05 g m-2)
followed by F1 (62.65 g m-2).
Spike hoe produced minimum weed biomass of broad leaf weeds when inter
cultivation was carried out at F3 (83.84 g m-2) similar to F4 (88.06 g m-2), F1 (89.01 g m-2) and
F2 (92.12 g m-2) during the year 2008. During the year 2009 lowest weed biomass was found
in F3 (89.82 g m-2) similar to F4 (94.08 g m-2), F1 (95.05 g m-2) and F2 (98415 g m-2).
Plug weeder resulted in minimum weed biomass when operated at F1 (84.65 g m-2)
similar to F2 (88.68 g m-2), F3 (90.36 g m-2) and F4 (87.68 g m-2) during the year 2008.
Lowest weed biomass of broad leaf weeds by plug weeder during the year 2009 was noted at
F1 (90.68 g m-2) which was statistically similar to all other inter cultivation frequencies by
plug weeder.
4.34 Weed biomass of Sedges (30 DAS)
The fig.4.23 showed that weed control implements and frequency levels significantly
affected weed biomass of sedges at 30 DAS during both the years.
Tine cultivator resulted in minimum weed biomass at F3 (17.75 g m-2) similar to F2
(18.32 g m-2) and F4 (19.43 g m-2) followed by F1 (42.76 g m-2) during the year 2008. During
the year 2009 minimum weed biomass was noted in F3 (21.62 g m-2) similar to F2 (22.19 g m-
2) and F4 (23.30 g m-2).
84
Fig. 4.22 Effect of different weed control implements on weed biomass of broad leaf weeds (30 DAS)
(F1= Inter row cultivation at 15 DAS F2= Inter row cultivation at 15 and 25 DAS F3= Inter row cultivation at 15, 25 and 35 DAS F4= Inter row cultivation at 15, 25, 35 and 45 DAS)
0
20
40
60
80
100
120
F1 F2 F3 F4 F1 F2 F3 F4 F1 F2 F3 F4
Tine cultivator Spike hoe Plug weeder
g m
-2
(a) 2008
0
20
40
60
80
100
120
F1 F2 F3 F4 F1 F2 F3 F4 F1 F2 F3 F4
Tine cultivator Spike hoe Plug weeder
g m
-2
(b) 2009
85
Fig. 4.23 Effect of different weed control implements on weed biomass of sedges (30 DAS)
(F1= Inter row cultivation at 15 DAS F2= Inter row cultivation at 15 and 25 DAS F3= Inter row cultivation at 15, 25 and 35 DAS F4= Inter row cultivation at 15, 25, 35 and 45 DAS)
0
10
20
30
40
50
60
70
80
F1 F2 F3 F4 F1 F2 F3 F4 F1 F2 F3 F4
Tine cultivator Spike hoe Plug weeder
g m
-2
(a) 2008
0
10
20
30
40
50
60
70
80
F1 F2 F3 F4 F1 F2 F3 F4 F1 F2 F3 F4
Tine cultivator Spike hoe Plug weeder
g m
-2
(b) 2009
86
Spike hoe produced minimum weed biomass of sedges when inter cultivation was
carried out at F1 (54.25 g m-2) similar to F3 (54.26 g m-2) and F4 (54.77 g m-2) followed by F2
(63.39 g m-2) during the year 2008. During the year 2009 lowest weed biomass was found in
F1 (58.12 g m-2) similar to F3 (58.13 g m-2) and F4 (58.64 g m-2) followed by F2 (67.26 g m-2).
Plug weeder resulted in minimum weed biomass when operated at F4 (60.61 g m-2)
similar to F2 (60.82 g m-2), F1 (61.03 g m-2) and F3 (61.39 g m-2) during the year 2008.
Lowest weed biomass of sedges by plug weeder during the year 2009 was noted at F4 (64.48
g m-2) which was statistically similar to all other inter cultivation frequencies by plug weeder.
4.35 Weed biomass of broad leaf weeds (45 DAS)
The fig.4.24 showed that weed control implements and frequency levels significantly
affected weed biomass of broad leaf weeds at 45 DAS during both the years.
Tine cultivator resulted in minimum weed biomass of broad leaf weeds in F4 (31.22 g
m-2) followed by F3 (41.74 g m-2), F2 (58.04 g m-2) and F1 (121.5 g m-2) during the year 2008.
Similar trend was observed during the year 2008. During the year 2009 minimum weed
biomass was observed in F4 (35.67 g m-2) followed by F3 (46.19 g m-2), F2 (62.49 g m-2) and
F1 (129.60 g m-2).
Spike hoe produced minimum weed biomass of broad leaf weeds when inter
cultivation was carried out at F3 (139.51 g m-2) similar to F4 (139.91 g m-2), F2 (140.77 g m-2)
and F1 (141.79 g m-2) during the year 2008. During the year 2009 lowest weed biomass was
found in F3 (143.96 g m-2) similar to F4 (144.36 g m-2), F2 (145.22 g m-2) and F1 (146.24 g m-
2).
Plug weeder resulted in minimum weed biomass when operated at F3 (134.49 g m-2)
similar to F1 (139.31 g m-2), F2 (141.14 g m-2) and F4 (141.76 g m-2) during the year 2008.
Lowest weed biomass of broad leaf weeds by plug weeder during the year 2009 was noted at
F3 (138.94 g m-2) which was statistically similar to all other inter cultivation frequencies by
plug weeder.
87
Fig. 4.24 Effect of different weed control implements on weed biomass of broad leaf weeds (45 DAS)
(F1= Inter row cultivation at 15 DAS F2= Inter row cultivation at 15 and 25 DAS F3= Inter row cultivation at 15, 25 and 35 DAS F4= Inter row cultivation at 15, 25, 35 and 45 DAS)
0
20
40
60
80
100
120
140
160
180
F1 F2 F3 F4 F1 F2 F3 F4 F1 F2 F3 F4
Tine cultivator Spike hoe Plug weeder
g m
-2
(a) 2008
0
20
40
60
80
100
120
140
160
180
F1 F2 F3 F4 F1 F2 F3 F4 F1 F2 F3 F4
Tine cultivator Spike hoe Plug weeder
g m
-2
(b) 2009
88
4.36 Weed biomass of Sedges (45 DAS)
The fig.4.25 showed that weed control implements and frequency levels significantly
affected weed biomass of sedges at 45 DAS during both the years.
Tine cultivator resulted in minimum weed biomass of sedges at F4 (56.73 g m-2)
followed by F3 (69.19 g m-2), F2 (111.46 g m-2) and F1 (42.76 g m-2) during the year 2008.
Similar trend was observed during the year 2009. Minimum weed biomass was noted in F4
(60.07 g m-2) followed by F3 (72.53 g m-2), F2 (114.80 g m-2) and F1 (131.26 g m-2).
Spike hoe produced minimum weed biomass of sedges when inter cultivation was
carried out at F4 (144.71 g m-2) similar to F3 (147.76 g m-2) followed by F1 (164.35 g m-2) and
F2 (168.29 g m-2) during the year 2008. During the year 2009 lowest weed biomass was found
in F3 (131.26 g m-2) similar to F4 (132.23 g m-2) followed by F1 (167.54 g m-2) and F2 (171.63
g m-2).
Plug weeder resulted in minimum weed biomass when operated at F2 (162.65 g m-2)
similar to F3 (164.35 g m-2), F1 (165.03 g m-2) and F4 (165.15 g m-2) during the year 2008.
Lowest weed biomass of sedges by plug weeder during the year 2009 was noted at F2 (165.99
g m-2) which was statistically similar to all other inter cultivation frequencies by plug weeder.
4.37 Total weed biomass (15 DAS)
The fig.4.26 showed that weed control implements and frequency levels significantly
affected total weed biomass at 15 DAS during both the years.
Tine cultivator resulted in minimum weed biomass at F2 (1.72 g m-2) similar to F4
(1.72 g m-2), F3 (1.84 g m-2) and F1 (1.87 g m-2) during the year 2008. Similar trend was
observed during the year 2009. Minimum weed biomass was noted in F2 (1.86 g m-2) similar
to F4 (1.87 g m-2), F3 (1.98 g m-2) and F1 (2.01 g m-2).
Spike hoe produced minimum weed biomass when inter cultivation was carried out at
F4 (2.78 g m-2) similar to F1 (2.79 g m-2), F3 (2.82 g m-2) and F2 (2.84 g m-2) during the year
2008. During the year 2009 lowest weed biomass was observed in F4 (2.92 g m-2) similar to
F1 (2.93 g m-2), F3 (2.96 g m-2) and F2 (2.98 g m-2).
89
Fig. 4.25 Effect of different weed control implements on weed biomass of sedges (45 DAS)
(F1= Inter row cultivation at 15 DAS F2= Inter row cultivation at 15 and 25 DAS F3= Inter row cultivation at 15, 25 and 35 DAS F4= Inter row cultivation at 15, 25, 35 and 45 DAS)
0
20
40
60
80
100
120
140
160
180
200
F1 F2 F3 F4 F1 F2 F3 F4 F1 F2 F3 F4
Tine cultivator Spike hoe Plug weeder
g m
-2
(a) 2008
0
20
40
60
80
100
120
140
160
180
200
F1 F2 F3 F4 F1 F2 F3 F4 F1 F2 F3 F4
Tine cultivator Spike hoe Plug weeder
g m
-2
(b) 2009
90
Fig. 4.26 Effect of different weed control implements on total weed biomass (15 DAS)
(F1= Inter row cultivation at 15 DAS F2= Inter row cultivation at 15 and 25 DAS F3= Inter row cultivation at 15, 25 and 35 DAS F4= Inter row cultivation at 15, 25, 35 and 45 DAS)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
F1 F2 F3 F4 F1 F2 F3 F4 F1 F2 F3 F4
Tine cultivator Spike hoe Plug weeder
g m
-2
(a) 2008
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
F1 F2 F3 F4 F1 F2 F3 F4 F1 F2 F3 F4
Tine cultivator Spike hoe Plug weeder
g m
-2
(b) 2009
91
Plug weeder resulted in minimum weed biomass when operated at F2 (2.85 g m-2)
similar to F1 (2.93 g m-2), F3 (2.94 g m-2) and F4 (3.00 g m-2) during the year 2008. Lowest
weed biomass of broad leaf weeds by plug weeder during the year 2009 was noted at F2 (2.99
g m-2) which was statistically similar to all other inter cultivation frequencies by plug weeder.
4.38 Total weed biomass (30 DAS)
The fig.4.27 showed that weed control implements and frequency levels significantly
affected weed biomass of sedges at 30 DAS during both the years.
Tine cultivator resulted in minimum weed at F3 (39.52 g m-2) followed by F2 (41.98 g
m-2), F4 (43.44.46 g m-2) and F1 (99.40 g m-2) during the year 2008. Similar trend was
observed during the year 2009. Minimum weed biomass was noted in F3 (49.39 g m-2) similar
to F2 (51.85 g m-2) and F4 (114.80 g m-2).
Spike hoe produced minimum weed biomass when inter cultivation was carried out at
F3 (138.10 g m-2) followed by F4 (142.84 g m-2), F1 (143.26 g m-2) and F2 (155.52 g m-2)
during the year 2008. During the year 2009 lowest weed biomass was found in F3 (147.52 g
m-2) followed by F4 (152.71 g m-2), F1 (153.13 g m-2) and F2 (165.39 g m-2).
Plug weeder resulted in minimum weed biomass when operated at F1 (145.75 g m-2)
similar to F4 (148.29 g m-2), F2 (149.50 g m-2) and F3 (151.76 g m-2) during the year 2008.
Lowest weed biomass of sedges by plug weeder during the year 2009 was noted at F1 (155.62
g m-2) which was statistically similar to all other inter cultivation frequencies by plug weeder.
4.39 Total weed biomass (45 DAS)
The data presented in table 4.23 pertaining weed biomass at 45 DAS during the years
2008 and 2009 on three weed control implements and four frequency levels of inter
cultivations revealed that weed control implements and frequency levels significantly
affected weed biomass during both the years. Similarly weed control implements and
frequency levels significantly interacted with each other for weed biomass.
Tine cultivator resulted in minimum weed biomass (87.95 g m-2) when tine cultivator
was used at 15, 25, 35 and 45 DAS followed by inter cultivation at 15, 25 and 35 DAS
(110.93 g m-2) and inter cultivation at 15 and 25 DAS (169.51 g m-2) during the year 2008.
Maximum weed biomass (290.94 g m-2) was found in inter cultivation at 15 DAS during the
year 2008.
92
Fig. 4.27 Effect of different weed control implements on total weed biomass (30 DAS)
(F1= Inter row cultivation at 15 DAS F2= Inter crow cultivation at 15 and 25 DAS F3= Inter row cultivation at 15, 25 and 35 DAS F4= Inter row cultivation at 15, 25, 35 and 45 DAS)
0
20
40
60
80
100
120
140
160
180
F1 F2 F3 F4 F1 F2 F3 F4 F1 F2 F3 F4
Tine cultivator Spike hoe Plug weeder
g m
-2
(a) 2008
0
20
40
60
80
100
120
140
160
180
200
F1 F2 F3 F4 F1 F2 F3 F4 F1 F2 F3 F4
Tine cultivator Spike hoe Plug weeder
g m
-2
(b) 2009
93
Fig. 4.28 Effect of different weed control implements on total weed biomass (45 DAS)
(F1= Inter row cultivation at 15 DAS F2= Inter row cultivation at 15 and 25 DAS F3= Inter row cultivation at 15, 25 and 35 DAS F4= Inter row cultivation at 15, 25, 35 and 45 DAS)
0
50
100
150
200
250
300
350
F1 F2 F3 F4 F1 F2 F3 F4 F1 F2 F3 F4
Tine cultivator Spike hoe Plug weeder
g m
-2
(a) 2008
0
50
100
150
200
250
300
350
400
F1 F2 F3 F4 F1 F2 F3 F4 F1 F2 F3 F4
Tine cultivator Spike hoe Plug weeder
g m
-2
(b) 2009
94
pSimilar results were recorded during the year 2009; lowest weed biomass (95.74 g m-2) was
found when tine cultivator was operated at 15, 25, 35 and 45 DAS followed by inter row
cultivation at 15, 25 and 35 DAS (118.72 g m-2) and inter cultivation at 15 and 25 DAS
(177.30 g m-2). Maximum weed biomass (292.04 g m-2) was recorded at 15 DAS.
Spike hoe produced minimum weed biomass (284.62 g m-2) when inter cultivation
was carried out at 15, 25, 35 and 45 DAS similar to weed biomass (287.26 g m-2) found in
inter cultivation at 15, 25 and 35 DAS followed by weed biomass (305.99 g m-2) noted in
inter cultivation at 15 DAS and at 15 and 25 (309.06 g m-2) during the year 2008. During the
year 2009 minimum weed biomass (274.60 g m-2) when inter cultivation was carried out at
15, 25, and 35 DAS similar to weed biomass (276.59 g m-2) found in inter cultivation at 15,
25, 35 and 45 DAS followed by weed biomass (313.78 g m-2) noted in inter cultivation at 15
DAS and at 15, (316.85 g m-2).
Plug weeder resulted in minimum weed biomass (298.83 g m-2) when operated at 15,
25 and 35 DAS similar to weed biomass in inter cultivation at 15 and 25 DAS, inter
cultivation at 15 DAS (304.34) and inter cultivation at 15, 25, 35 and 45 DAS (306.92 g m-2)
during the year 2008. Minimum weed biomass (306.62 g m-2) was observed in inter
cultivation at 15, 25 and 35 DAS followed by weed biomass (311.58 g m-2) noted in inter
cultivation at 15 and 25 DAS and in inter row cultivation at 15 DAS during the year 2009.
The highest weed biomass (314.71) by plug weeder during the year 2009 was observed at 15
and 25 DAS.
4.40 Plant height
The data regarding plant height during the years 2008 and 2009 on three weed control
implements and four frequency levels of inter cultivations are given in table 4.24. Data
represents that weed control implements and inter cultivation frequency levels significantly
affected the plant height during both the years. Interactions found significant. So, only
interactions are discussed.
95
Table 4.23 Effect of different weed control implements on weed biomass at 45 DAS (g)
(a) 2008
Implement
Inter row cultivation frequency (DAS) Mean
15
15 & 25
15, 25 & 35
15, 25, 35 & 45 Tine
cultivator
290.94 a
169.51 b
110.93 c
87.95 d
164.83 B
Spike hoe
305.99 a
309.06 a
287.50 b
284.60 b
296.73 A
Plug weeder
304.34 a
303.79 a
298.83 a
306.92 a
303.47 A
Mean
300.42 A
260.78 B
232.34 C
226.50 C
LSD= 8.16 (Frequencies)
LSD= 16.90 (Implements)
LSD= 14.14 (Interactions) Any two means not sharing a common letter with in rows differ significantly (p ≤ 0.05)
(b) 2009
Implements
Inter row cultivation frequency (DAS) Mean
15 15 & 25
15, 25 & 35
15, 25, 35 & 45
Tine cultivator
292.06 a
177.30 b
118.99 c
95.74 d
170.96 B
Spike hoe
313.36 a
316.85 a
275.22 b
276.59 b
295.61 A
Plug weeder
312.13 a
311.51a
306.62 a
314.71 a
311.26 A
Mean
305.99 A
268.57 B
233.52 C
229.01 D
LSD= 8.61 (Frequencies)
LSD= 15.98 (Implements)
LSD= 14.91 (Interactions) Any two means not sharing a common letter with in rows differ significantly (p ≤ 0.05)
96
Table 4.24 Effect of different weed control implements on plant height (cm)
(a) 2008
Implement
Inter row cultivation frequency (DAS) Mean
15
15 & 25
15, 25 & 35
15, 25, 35 & 45 Tine
cultivator
70.33 b
69.00 b
94.67 a
95.66 a 82.41 A
Spike hoe
70.00 b
71.00 b
81.00 a
78.67 a
75.16 B
Plug weeder
72.00 a
72.33 a
72.33 a
72.67a
72.33 C
Mean
70.78 B
70.84 B
82.66 A
82.33 A
LSD= 2.11 (Frequencies)
LSD= 2.31 (Implements)
LSD= 3.92 (Interactions) Any two means not sharing a common letter with in rows differ significantly (p ≤ 0.05)
(b) 2009
Implement
Inter row cultivation frequency (DAS) Mean
15
15 & 25
15, 25 & 35
15, 25, 35 & 45 Tine
cultivator
65.66 b
64.46 b
89.93 a
91.26 a
77.83 A
Spike hoe
65.46 b
66.20 b
76.40 a
74.06 a
70.53 B
Plug weeder
67.26 a
68.06 a
67.53 a
67.86 a
67.68 C
Mean
66.13 B
62.24 C
77.95 A
77.73 A
LSD= 2.06 (Frequencies)
LSD= 2.37 (Implements)
LSD= 3.87 (Interactions) Any two means not sharing a letter in similar with in rows differ significantly (p ≤ 0.05)
97
Tine cultivator at 15, 25, 35 and 45 DAS produced maximum plant height (95.66 cm)
during the year 2008 which was at par with plant height (94.67 cm) recorded for inter
cultivation at 15, 25 and 35 DAS. Minimum plant height (69.00 cm) was recorded with inter
cultivation at 15 and 25 DAS. During the year 2009, maximum plant height (91.26 cm) was
recorded in interaction between tine cultivator and inter cultivation at 15, 25, 35 and 45 DAS
which was statistically similar to plant height (89.93 cm) produced in inter cultivation at 15,
25 and 35 DAS. Minimum plant height (65.46 cm) was produced in inter cultivation at 15
DAS.
Spike hoe produced maximum plant height (81.00 cm) when inter cultivation was
carried out at 15, 25 and 35 DAS during the year 2008 which was at par with plant height
(78.67 cm) produced by inter cultivation at 15, 25, 35 and 45 DAS. Minimum plant height
(70.00 cm) was recorded in inter cultivation at 15 DAS. During the year 2009 interaction
between spike hoe and inter cultivation at 15, 25 and 35 DAS produced maximum plant
height (76.40 cm) and minimum plant height (65.46 cm) was observed in inter cultivation at
15 DAS.
Plug weeder produced maximum plant height (72.67 cm) in inter cultivation at 15, 25,
35 and 45 DAS, which is statistically similar to all other inter cultivation frequencies in 2008.
Similar results were observed during 2009, maximum plant height (68.06 cm) was recorded
in inter cultivation at 15 and 25 DAS, which is statistically similar to all other inter
cultivation frequencies by plug weeder.
4.41 Total number of tillers
Data (Table 4.25) revealed that weed control implements and frequency levels of inter
cultivations significantly affected the total number of tillers m-2 during both the years.
Similarly weed control implements and frequency levels significantly interacted with each
other for total number of tillers.
Tine cultivator resulted in significantly maximum total number of tillers (380.59 m-2)
during the year 2008 in inter cultivation at 15, 25, 35 and 45 DAS. While minimum total
numbers of tillers (246.44 m-2) was produced in inter cultivation at 15 DAS. During the year
2009 significantly maximum total number of tillers (371.95 m-2) with tine cultivator was
recorded in inter cultivation at 15, 25, 35 and 45 DAS. Minimum total number of tillers
(235.73 m-2) was observed in inter cultivation at 15 DAS.
98
Table 4.25 Effect of different weed control implements on total number of tillers (m-2)
(a) 2008
Implement
Inter row cultivation frequency (DAS) Mean
15
15 & 25
15, 25 & 35
15, 25, 35 & 45 Tine
cultivator
246.44 c
274.32 b
373.97 a
380.59 a
318.83 A
Spike hoe
229.62 d
240.22 c
264.00 b
276.00 a
252.46 B
Plug weeder
228.11a
233.41 a
227.73 a
234.17 a
230.85 C
Mean
234.72 D
249.32 C
288.56 B
296.92 A
LSD= 6.99 (Frequencies)
LSD= 4.01 (Implements)
LSD= 11.19 (Interactions) Any two means not sharing a common letter with in rows differ significantly (p ≤ 0.05)
(b) 2009
Implement
Inter row cultivation frequency (DAS) Mean
15
15 & 25
15, 25 & 35
15, 25, 35 & 45 Tine
cultivator
243.26 c
291.62 b
365.59 a
371.96 a
318.11 A
Spike hoe
235.73 b
239.58 b
259.01 a
261.92 a
249.06 B
PlugWeeder
237.60 a
231.80 a
233.97a
235.12 a
234.62 C
Mean
238.87 C
254.34 B
286.19 A
289.67 A
LSD= 4.14 (Frequencies)
LSD= 6.62 (Implements)
LSD= 8.99 (Interactions) Any two means not sharing a letter in similar with in rows differ significantly (p ≤ 0.05)
99
Total number of tillers (276.00 m-2) with spike hoe in inter cultivation at 15, 25, 35
and 45 DAS recorded was maximum during the year 2008 similar to total number of tillers
(264.00 m-2) in inter cultivation at 15, 25 and 35 DAS. The lowest total number of tillers
(229.62 m-2) was observed in inter cultivation at 15 DAS. During the year 2009 maximum
total number of tillers (262.92 m-2) was found in inter cultivation at 15, 25 and 35 DAS,
statistically at par with total number of tillers (259.01 m-2) in inter cultivation at 15, 25 and 35
DAS. Minimum total number of tillers (229.62 m-2) was recorded in inter cultivation at 15
DAS.
During the year 2008 total number of tillers (234.17 m-2) was observed maximum by
spike hoe in inter cultivation at 15, 25, 35 and 45 DAS which is similar to the rest of
treatments with plug weeder. Plug weeder resulted in the highest number of total tillers
(235.12 m-2) when operated at 15, 25, 35 and 45 DAS and was statistically at par with all
other treatments by plug weeder during the year 2009.
4.42 Number of fertile tillers
The data (Table 4.26) regarding number of fertile tillers during the years 2008 and
2009 on three weed control implements and four frequency levels of inter cultivations
showed that weed control implements and frequency levels significantly affected the number
of fertile tillers m-2 during both the years. Similarly weed control implements and frequency
levels significantly interacted with each other for number of fertile tillers m-2.
Results produced by tine cultivator revealed that significantly maximum number of
fertile tillers (349.26 m-2) during the year 2008 were observed in inter cultivation at 15, 25,
35 and 45 DAS, similar to number of fertile tillers (339.97 m-2) in inter cultivation at 15, 25
and 35 DAS, followed by number of fertile tillers (237.65 m-2) observed in inter cultivation at
15 and 25 DAS. Number of fertile tillers (205.77 m-2) recorded in inter cultivation at 15 DAS
were minimum by tine cultivator. During the year 2009 maximum number of fertile tillers
(337.62 m-2) were observed in inter cultivation at 15, 25, 35 and 45 DAS. Significantly
minimum number of fertile tillers (199.59 m-2) was found in inter cultivation at 15 DAS.
Spike hoe produced maximum number of fertile tillers (237.33) in inter cultivation at
15, 25, 35 and 45 DAS followed by inter cultivation at 15, 25 and 35 DAS (224.33 m-2)
during the year 2008. Significantly minimum number of fertile tillers (180.59 m-2) observed
in inter cultivation at 15 DAS. During 2009 highest total tillers (220.26 m-2) recorded in inter
100
Table 4.26 Effect of different weed control implements on number of fertile tillers (m-2)
(a) 2008
Implement
Inter row cultivation frequency (DAS) Mean
15
15 & 25
15, 25 & 35
15, 25, 35 & 45 Tine
cultivator
205.77 c
237.65 b
339.97 a
349.26 a
283.16 A
Spike hoe
185.95 c
194.22 c
224.33 b
237.33 a
210.46 B
Plug weeder
187.11 a
191.08 a
185.73 a
189.50 a
188.35 C
Mean
192.94 D
207.65 C
250.01 B
258.70 A
LSD= 7.62(Frequencies)
LSD= 5.0 (Implements)
LSD= 12.43 (Interactions) Any two means not sharing a common letter with in rows differ significantly (p ≤ 0.05)
(b) 2009
Implement
Inter row cultivation frequency (DAS) Mean
15
15 & 25
15, 25 & 35
15, 25, 35 & 45 Tine
cultivator
199.59 d
251.95 c
328.59 b
337.62 a
279.44 A
Spike hoe
189.07 b
190.58 b
216.34 a
220.26 a
204.06 B
Plug weeder
193.60 a
186.46 a
188.97 a
187.45 a
189.25 C
Mean
194.09 C
209.66 B
244.63 A
248.44 A
LSD= 4.19 (Frequencies)
LSD= 6.57 (Implements)
LSD= 9.01 (Interactions) Any two means not sharing a letter in similar with in rows differ significantly (p ≤ 0.05)
101
cultivation at 15, 25, 35 and 45 DAS. Minimum number of fertile tillers (189.07 m-2) was
resulted in inter cultivation at 15 DAS.
Plug weeder produced more number of fertile tillers (191.08 m-2) in inter cultivation
at 15 and 25 DAS, similar to rest of treatments during the year 2008. Similar results were
observed during the year 2009, highest number of fertile tillers (193.60 m-2) were found in
inter cultivation at 15 DAS, statistically at par with all other treatments by plug weeder.
4.43 Number of unfertile tillers
The data regarding number of unfertile tillers during the years 2008 and 2009 on three
weed control implements and four frequency levels of inter cultivations are given in table
4.27. It is clear from the data that weed control implements and frequency levels significantly
affected number of unfertile tillers m-2 during both the years. Similarly weed control
implements and frequency levels significantly interacted with each other for number of
unfertile tillers m-2.
Tine cultivator during the year 2008 produced significantly minimum number of
unfertile tillers (31.33 m-2) in inter cultivation at 15, 25, 35 and 45 DAS, similar to number of
unfertile tillers (34.00 m-2) in inter cultivation at 15, 25 and 35 DAS. Significantly maximum
number of unfertile tillers (40.66 m-2) were recorded in inter cultivation at 15 DAS. During
the year 2009 lowest number of unfertile tillers (34.33 m-2) was observed in inter cultivation
at 15, 25, 35 and 45 DAS. Highest number of unfertile tillers (43.66 m-2) recorded in inter
cultivation at 15 DAS.
Spike hoe produced minimum number of unfertile tillers (38.67 m-2) when inter
cultivation was carried out at 15, 25, 35 and 45 DAS during the year 2008. Highest number of
unfertile tillers (46.00 m-2) recoded in inter cultivation at 15 and 25 DAS. During the year
2009 lowest number of unfertile tillers (41.66 m-2) was observed where spike hoe was used at
15, 25, 35 and 45 DAS. Highest number of unfertile tillers (49.00 m-2) recoded in inter
cultivation at 15 and 25 DAS.
Plug weeder resulted in lowest number of unfertile tillers (41.00 m-2) recoded in inter
cultivation at 15 DAS during the year 2008, similar to remaining treatments. Lowest number
of unfertile tillers (44.00 m-2) during the year 2009 was observed at 15 DAS which is
statistically similar to all other inter cultivations by plug weeder.
102
Table 4.27 Effect of different weed control implements on number of unfertile tillers (m-2)
(a) 2008
Implement
Inter row cultivation frequency (DAS) Mean
15
15 & 25
15, 25 & 35
15, 25, 35 & 45 Tine
cultivator
40.66 a
36.66 b
34.0 b
31.33 b
35.66 B
Spike hoe
43.66 a
46.00 a
39.66 b
38.66 c
42.0 A
Plug weeder
41.00 a
42.33 ab
42.00 ab
44.66 b
42.50 A
Mean
41.78 A
41.66 A
38.55 bB
38.22 B
LSD= 1.72 (Frequencies)
LSD= 2.21 (Implements)
LSD= 3.37 (Interactions) Any two means not sharing a common letter with in rows differ significantly (p ≤ 0.05)
(b) 2009
Implement
Inter row cultivation frequency (DAS) Mean
15
15 & 25
15, 25 & 35
15, 25, 35 & 45 Tine
cultivator
43.66 a
39.66 b
37.00 bc
34.33 c
38.66 B
Spike hoe
46.66 a
49.00 a
42.66 bc
41.66 c
45.00 A
Plug weeder
44.00 b
45.33 a
45.00 a
47.66 a
45.50 A
Mean
44.77 A
44.66 A
41.55 B
41.22 B
LSD= 1.72 (Frequencies)
LSD= 2.21 (Implements)
LSD= 3.37 (Interactions) Any two means not sharing a letter in similar with in rows differ significantly (p ≤ 0.05)
103
4.44 Panicle length
The data regarding panicle length during the years 2008 and 2009 are given in table
4.28. Data represents that weed control implements and inter cultivation frequency levels
significantly affected the panicle length during both the years. Interactions found significant.
So, only interactions are discussed.
Tine cultivator at 15, 25, 35 and 45 DAS produced maximum panicle length (23.89
cm) during the year 2008 which was at par with panicle length (23.01 cm) produced in inter
cultivation at 15, 25 and 35 DAS. Minimum panicle length (17.63 cm) was found in inter
cultivation at 15 DAS. During the year 2009, maximum panicle length (22.91 cm) was
recorded in interaction between tine cultivator and inter cultivation at 15, 25, 35 and 45 DAS
which was statistically similar to panicle length (22.03 cm) produced in inter cultivation at
15, 25 and 35 DAS. Minimum panicle length (16.65 cm) was produced in inter cultivation at
15 DAS.
Spike hoe produced maximum panicle length (19.97 cm) when inter cultivation was
carried out at 15, 25, 35 and 45 DAS during the year 2008 which is at par with panicle length
(19.21 cm) produced in inter cultivation at 15, 25 and 35 DAS. Minimum panicle length
(16.92 cm) was recorded in inter cultivation at 15 and 25 DAS. During the year 2009
interaction between spike hoe and inter cultivation at 15, 25, 35 and 45 DAS produced
maximum panicle length (18.99 cm) and minimum panicle length (15.94 cm) was observed
in inter cultivation at 15 and 25 DAS.
Plug weeder produced maximum panicle length (18.01 cm) in inter cultivation at 15
DAS, which is statistically similar to all other inter cultivation frequencies during the year
2008. Similar results were observed during the year 2009, maximum panicle length (17.03
cm) was recorded in inter cultivation at 15 DAS, which was statistically similar to all other
inter cultivation frequencies by plug weeder.
4.45 Kernels per panicle
The data (Table 4.29) regarding kernels per panicle during the year 2008 and 2009
showed that weed control implements and frequency levels significantly affected kernels per
panicle during both the years. Similarly weed control implements and frequency levels
significantly interacted with each other for kernels per panicle.
104
Table 4.28 Effect of different weed control implements on panicle length (cm)
(a) 2008
Implement
Inter row cultivation frequency (DAS) Mean
15
15 & 25
15, 25 & 35
15, 25, 35 & 45 Tine
cultivator 17.63 c
19.08 b
23.01 a
23.89 a
20.90 A
Spike hoe
17.01 b
16.92 b
19.21 a
19.97 a
18.28 B
Plug weeder
18.01 a
17.27 a
17.26 a
17.00 a
17.38 B
Mean
17.52 B
17.76 B
19.83 A
20.29 A
LSD= 0.60 (Frequencies)
LSD= 1.16 (Implements)
LSD= 1.45 (Interactions) Any two means not sharing a common letter with in rows differ significantly (p ≤ 0.05)
(b) 2009
Implement
Inter row cultivation frequency (DAS) Mean
15
15 & 25
15, 25 & 35
15, 25, 35 & 45 Tine
cultivator
16.65 c
18.10 b
22.03 a
22.91 a
19.92 A
Spike hoe
16.03 c
15.94 b
18.23 a
18.99 a
17.30 B
Plug weeder
17.03 a
16.29 a
16.28 a
16.02 a
16.40 B
Mean
16.57 B
16.78 B
18.84 A
19.30 A
LSD= 0.60 (Frequencies)
LSD= 1.16 (Implements)
LSD= 1.46 (Interactions) Any two means not sharing a letter in similar with in rows differ significantly (p ≤ 0.05)
105
Table 4.29 Effect of different weed control implements on kernels per panicle
(a) 2008
Implement
Inter row cultivation frequency (DAS) Mean
15
15 & 25
15, 25 & 35
15, 25, 35 & 45 Tine
cultivator
62.40 b
65.14 b
74.46 a
76.33 a
69.58 A
Spike hoe
63.92 a
63.79 a
62.37 a
65.55 a
63.91 B
Plug weeder
65.99 a
62.33 a
63.67 a
62.67 a
63.67 B
Mean
64.10 B
63.75 B
66.83 A
68.18 A
LSD= 2.66 (Frequencies)
LSD= 3.55 (Implements)
LSD= 5.30 (Interactions) Any two means not sharing a common letter with in rows differ significantly (p ≤ 0.05)
(b) 2009
Implement
Inter row cultivation frequency (DAS) Mean
15
15 & 25
15, 25 & 35
15, 25, 35 & 45 Tine
cultivator
61.62 b
63.88 b
70.12 b
73.12 a
67.18 A
Spike hoe
61.01 ab
60.23 b
63.19 a
61.90 ab
61.58 B
Plug weeder
60.93 a
61.47 a
60.90 a
60.82 a
61.03B
Mean
61.18 B
61.86 B
64.74 A
65.28 A
LSD= 1.39 (Frequencies)
LSD= 1.59 (Implements)
LSD= 2.60 (Interactions) Any two means not sharing a letter in similar with in rows differ significantly (p ≤ 0.05)
106
Results produced by tine cultivator revealed that significantly highest kernels per
panicle (76.33) during the year 2008 were recorded in inter cultivation at 15, 25, 35 and 45
DAS, similar to kernel per panicle (74.46) in inter cultivation at 15, 25 and 35 DAS. Kernels
per panicle (62.39) recorded in inter cultivation at 15 DAS were minimum by tine cultivator.
During the year 2009 maximum kernels per panicle (73.12) were observed in inter cultivation
at 15, 25, 35 and 45 DAS. Significantly minimum kernels per panicle (61.62) were found in
inter cultivation at 15 DAS.
Spike hoe produced maximum kernels per panicle (65.55) in inter cultivation at 15,
25, 35 and 45 DAS similar to all other frequencies during the year 2008. During the year
2009 highest kernels per panicle (63.19) recorded in inter cultivation at 15, 25 and 35 DAS.
Minimum kernels per panicle (60.23) were resulted in inter cultivation at 15 and 25 DAS.
Plug weeder produced more kernels per panicle (65.99) in inter cultivation at 15 DAS,
similar to rest of treatments during the year 2008. Similar results were observed during 2009,
highest kernels per panicle (61.47) were found in inter cultivation at 15 and 45 DAS,
statistically at par with all other treatments by plug weeder.
4.46 1000 kernel weight
Data (Table 4.30) revealed that weed control implements and frequency levels of inter
cultivations significantly affected 1000 kernel weight during both the years. Similarly weed
control implements and frequency levels significantly interacted with each other for 1000
kernel weight.
Tine cultivator resulted in significantly maximum 1000 kernel weight (19.56 g)
during the year 2008 in inter cultivation at 15, 25, 35 and 45 DAS similar to 1000 kernel
weight (18.67 g) in inter cultivation at 15, 25 and 35 DAS . While minimum 1000 kernel
weight (15.68 g) was recorded in inter cultivation at 15 DAS. During the year 2009
significantly maximum 1000 kernel weight (19.69 g) by tine cultivator was recorded in inter
cultivation at 15, 25, 35 and 45 DAS. Minimum 1000 kernel weight (15.49 g) was observed
in inter cultivation at 15 DAS.
1000 kernel weight (17.71 g) by spike hoe in inter cultivation at 15, 25, 35 and 45
DAS recorded was maximum during the year 2008. Lowest 1000 kernel weight (15.69 g) was
107
Table 4.30 Effect of different weed control implements on 1000 kernel weight (g)
(a) 2008
Implement
Inter row cultivation frequency (DAS) Mean
15
15 & 25
15, 25 & 35
15, 25, 35 & 45 Tine
cultivator
15.68 b
16.66 b
18.67 a
19.56 a
17.65 A
Spike hoe
15.69 b
15.82 ab
17.10 ab
17.71 a
16.58 B
Plug weeder
15.75 a
16.08 a
16.49 a
16.73 a
16.26 B
Mean
15.70 C
16.20 BC
17.42 AB
18.0 A
LSD= 1.26 (Frequencies)
LSD= 0.66 (Implements)
LSD= 1.99 (Interactions) Any two means not sharing a common letter with in rows differ significantly (p ≤ 0.05)
(b) 2009
Implement
Inter row cultivation frequency (DAS) Mean
15
15 & 25
15, 25 & 35
15, 25, 35 & 45 Tine
cultivator
15.49 b
16.32 b
18.72 a
19.69 a
17.56 A
Spike hoe
15.13 b
15.58 ab
16.09 ab
16.54 a
15.84 B
Plug weeder
15.44 a
15.58 a
15.78 a
15.47 a
15.56 B
Mean
15.35 B
15.83 B
16.86 A
17.23 A
LSD= 0.52 (Frequencies)
LSD= 0.85 (Implements)
LSD= 1.14 (Interactions) Any two means not sharing a letter in similar with in rows differ significantly (p ≤ 0.05)
108
observed in inter cultivation at 15 DAS. During the year 2009 maximum 1000 kernel weight
(16.54 g) was found in inter cultivation at 15, 25, 35 and 45 DAS. Minimum 1000 kernel
weight (15.13 g) was recorded in inter cultivation at 15 DAS.
During the year 2008 1000 kernel weight (16.72 g) was observed maximum by spike
hoe in inter cultivation at 15, 25, 35 and 45 DAS which is similar to the rest of treatments.
Plug weeder resulted in highest 1000 kernel weight (15.78 g) when operated at 15, 25 and 35
DAS, statistically at par with all other treatments by plug weeder during the year 2009.
4.47 Biological yield
The data regarding biological yield during the years 2008 and 2009 are given in table
4.31. Data represents that weed control implements and inter cultivation frequency levels
significantly affected the biological yield during both the years. Interactions found
significant. So, only interactions are discussed.
Tine cultivator at 15, 25, 35 and 45 DAS produced maximum biological yield (12.46 t
ha-1) during the year 2008 which was at par with biological yield (11.75 t ha-1) produced in
inter cultivation at 15, 25 and 35 DAS. Minimum biological yield (7.22 t ha-1) was found in
inter cultivation at 15 DAS. During the year 2009 maximum biological yield (11.34 t h-1) was
recorded in inter cultivation at 15, 25, 35 and 45 DAS which was statistically similar to
biological yield (10.61 t ha-1) produced in inter cultivation at 15, 25 and 35 DAS. Minimum
biological yield (6.19 t ha-1) was produced in inter cultivation at 15 DAS.
Spike hoe produced maximum biological yield (9.31 t ha-1) when inter cultivation was
carried out at 15, 25 and 35 DAS during the year 2008. Minimum biological yield (7.06 t ha-
1) was recorded in inter cultivation at 15 DAS. During the year 2009 interaction between
spike hoe and inter cultivation at 15, 25 and 35 DAS produced maximum biological yield
(11.34 t ha-1) and minimum biological yield (6.05 t ha-1) was observed in inter cultivation at
15 DAS.
Plug weeder produced maximum biological yield (7.49 t ha-1) in inter cultivation at
15, 25, 35 and 45 DAS, which is statistically similar to all other inter cultivation frequencies
during the year 2008. Similar results were observed during the year 2009 maximum
biological yield (6.28 t ha-1) was recorded in inter cultivation at 15, 25, 35 and 45 DAS,
which is statistically similar to all other inter cultivation frequencies by plug weeder.
109
Table 4.31 Effect of different weed control implements on biological yield (t ha-1)
(a) 2008
Implement
Inter row cultivation frequency (DAS) Mean
15
15 & 25
15, 25 & 35
15, 25, 35 & 45 Tine
cultivator
7.22 b
8.13 b
11.75 a
12.46 a
9.89 A
Spike hoe
7.04 b
7.19 b
8.43 a
9.31 a
7.99 B
Plug weeder
7.17 a
6.76 a
6.87 a
7.49 a
7.07 C
Mean
7.15 C
7.36 C
9.02 B
9.75 A
LSD= 0.51 (Frequencies)
LSD= 0.87 (Implements)
LSD= 1.15 (Interactions) Any two means not sharing a common letter with in rows differ significantly (p ≤ 0.05)
(b) 2009
Implement
Inter row cultivation frequency (DAS) Mean
15
15 & 25
15, 25 & 35
15, 25, 35 & 45 Tine
cultivator
6.19 c
7.30 b
10.61 a
11.34 a
8.86 A
Spike hoe
6.05 b
6.29 b
7.23a
7.91 a
6.87 B
Plug weeder
6.10 a
5.78 a
5.77 a
6.28 a
5.98 C
Mean
6.11 C
6.46 C
7.87 B
8.51 A
LSD= 0.45 (Frequencies)
LSD= 0.76 (Implements)
LSD= 1.01 (Interactions) Any two means not sharing a letter in similar with in rows differ significantly (p ≤ 0.05)
110
4.48 Paddy yield
Data (Table 4.32) pertaining paddy yield during the years 2008 and 2009 on three
weed control implements and four frequency levels of inter cultivations indicated that weed
control implements and inter cultivation frequency levels significantly affected the paddy
yield during both the years. Interactions found significant. So, only interactions are discussed.
Tine cultivator at 15, 25, 35 and 45 DAS produced maximum paddy yield (3.93 t ha-1)
during the year 2008 which was at par with paddy yield (3.71 t ha-1) produced by inter
cultivation at 15, 25 and 35 DAS. Minimum paddy yield (1.52 t ha-1) was recorded in inter
cultivation at 15 DAS. During the year 2009, maximum paddy yield (3.79 t ha-1) was
observed in interaction between tine cultivator and inter cultivation at 15, 25, 35 and 45 DAS
which was statistically similar to paddy yield (3.55 t ha-1) produced by inter cultivation at 15,
25 and 35 DAS. Minimum paddy yield (1.47 t ha-1) was found in inter cultivation at 15 DAS.
Spike hoe produced maximum paddy yield (2.25 t ha-1) when inter cultivation was
carried out at 15, 25, 35 and 45 DAS during the year 2008 which was at par with paddy yield
(1.96 t ha-1) produced by inter cultivation at 15, 25 and 35 DAS. Minimum paddy yield (1.47
t ha-1) was recorded in inter cultivation at 15 DAS. During the year 2009 interaction between
spike hoe and inter cultivation at 15, 25, 35 and 45 DAS observed maximum paddy yield
(1.84 t ha-1) and minimum paddy yield (1.46 t ha-1) was produced by inter cultivation at 15
DAS.
Plug weeder produced maximum paddy yield (1.57 t ha-1) when inter cultivation was
carried out at 15, 25, 35 and 45 DAS during the year 2008 which is statistically similar to all
other interactions between spike hoe and inter cultivation frequencies. During the year 2009
maximum paddy yield (1.51 t ha-1) was recorded in inter cultivation at 15 and 25 DAS, which
is statistically similar to all other inter cultivation frequencies by plug weeder.
111
Table 4.32 Effect of different weed control implements on paddy yield (t ha-1)
(a) 2008
Implement
Inter Cultivation Frequencies Mean
15
15 & 25
15, 25 & 35
15, 25, 35 & 45 Tine
cultivator
1.52 c
2.22 b
3.71 a
3.93 a
2.84 A
Spike hoe
1.47 b
1.48 b
1.96 a
2.26 a
1.79 B
Plug weeder
1.53 a
1.51 a
1.54 a
1.57 a
1.54 C
Mean
1.51 D
1.74 C
2.40 B
2.58 A
LSD= 0.15 (Frequencies)
LSD= 0.22 (Implements)
LSD= 0.32 (Interactions) Any two means not sharing a common letter with in rows differ significantly (p ≤ 0.05)
(b) 2009
Implement
Inter row cultivation frequency (DAS) Mean
15
15 & 25
15, 25 & 35
15, 25, 35 & 45 Tine
cultivator
1.47 c
2.36 b
3.55 a
3.79 a
2.79 A
Spike hoe
1.46 b
1.57 b
1.74 ab
1.84 a
1.65 B
Plug weeder
1.44 a
1.51 a
1.41 a
1.34 a
1.42 C
Mean
1.46 C
1.81 B
2.23 A
2.32 A
LSD= 0.16(Frequencies)
LSD= 0.12 (Implements)
LSD= 0.27 (Interactions) Any two means not sharing a letter in similar with in rows differ significantly (p ≤ 0.05)
112
4.49 Straw yield
Data (Table 4.33) revealed that weed control implements and frequency levels of inter
cultivations significantly affected straw yield during both the years. Similarly weed control
implements and frequency levels significantly interacted with each other for straw yield.
Tine cultivator resulted in significantly maximum straw yield (8.53 t ha-1) during
2008 in inter cultivation at 15, 25, 35 and 45 DAS similar to straw yield (8.07 t ha-1) in inter
cultivation at 15, 25 and 35 DAS . While minimum straw yield (5.70 t ha-1) was recorded in
inter cultivation at 15 DAS. During 2009 significantly highest straw yield (7.55 t ha-1) by tine
cultivator was recorded in inter cultivation at 15, 25, 35 and 45 DAS. Minimum straw yield
(4.72 t ha-1) was observed in inter cultivation at 15 DAS.
Straw yield (7.05 t ha-1) by spike hoe in inter cultivation at 15, 25, 35 and 45 DAS
recorded was maximum during 2008 similar to straw yield (6.47 t ha-1) in inter cultivation at
15, 25 and 35 DAS. Lowest straw yield (5.57 t ha-1) was observed in inter cultivation at 15
DAS. During 2009 maximum straw yield (6.07 t ha-1) was found in inter cultivation at 15, 25,
35 and 45 DAS, statistically at par with straw yield (5.49 t ha-1) in inter cultivation at 15, 25
and 35 DAS. Minimum straw yield (4.59 t ha-1) was recorded in inter cultivation at 15 DAS.
During 2008 straw yield (5.92 t ha-1) was observed maximum by spike hoe in inter
cultivation at 15, 25, 35 and 45 DAS which is similar to the rest of treatments. Plug weeder
resulted in highest straw yield (4.94 t ha-1) when operated at 15, 25, 35 and DAS, statistically
at par with all other treatments by plug weeder during 2009.
4.50 Harvest index
Data (Table 4.34) pertaining harvest index during 2008 and 2009 on three weed
control implements and four frequency levels of inter cultivations indicated that weed control
implements and inter cultivation frequency levels significantly affected the harvest index
during both the years. Interactions found significant. So, only interactions are discussed.
Tine cultivator at 15, 25 and 35 DAS produced maximum harvest index (31.66) during 2008
which was at par with harvest index (31.54) produced by inter cultivation at 15, 25, 35 and 45
113
Table 4.33 Effect of different weed control implements on straw yield (t ha-1)
(a) 2008
Implement
Inter row cultivation frequency (DAS) Mean
15
15 & 25
15, 25 & 35
15, 25, 35 & 45 Tine
cultivator
5.70 b
5.91 b
8.03 a
8.53 a
7.04 A
Spike hoe
5.57 b
5.71 b
6.47 ab
7.05 a
6.20 B
Plug weeder
5.63 a
5.25 a
5.33 a
5.92 a
5.53 B
Mean
5.63 C
5.62 C
6.61 B
7.16 A
LSD= 0.49 (Frequencies)
LSD= 0.66 (Implements)
LSD=0.98 (Interactions) Any two means not sharing a common letter with in rows differ significantly (p ≤ 0.05)
(b) 2009
Implement
Inter row cultivation frequency (DAS) Mean
15
15 & 25
15, 25 & 35
15, 25, 35 & 45 Tine
cultivator
4.72 b
4.93 b
7.05 a
7.55 a
6.06 A
Spike hoe
4.59 b
4.72 b
5.49 ab
6.07 a
5.22 B
Plug weeder
4.65 a
4.27 a
4.35 a
4.94 a
4.55 B
Mean
4.65 C
4.64 C
5.63 B
6.18 A
LSD= 0.49 (Frequencies)
LSD= 0.66 (Implements)
LSD=0.99 (Interactions) Any two means not sharing a common letter with in rows differ significantly (p ≤ 0.05)
114
Table 4.34 Effect of different weed control implements on harvest index
(a) 2008
Implement
Inter row cultivation frequency (DAS) Mean
15
15 & 25
15, 25 & 35
15, 25, 35 & 45 Tine
cultivator
20.88 c
27.2 b
31.66 a
31.54 a
27.84 A
Spike hoe
21.09 ab
20.72 b
23.32 ab
24.20 a
22.33 B
Plug weeder
21.53 a
22.43 a
22.41 a
21.02 a
21.14 B
Mean
21.14 C
23.47 B
25.61 AB
25.80 A
LSD= 2.20 (Frequencies)
LSD= 1.18 (Implements)
LSD= 3.50 (Interactions) Any two means not sharing a common letter with in rows differ significantly (p ≤ 0.05)
(b) 2009
Implement
Inter row cultivation frequency (DAS) Mean
15
15 & 25
15, 25 & 35
15, 25, 35 & 45 Tine
cultivator
24.12 b
32.38 a
33.55 a
33.45 a
30.87 A
Spike hoe
24.27 a
25.13 a
24.24 a
23.30 a
24.24 B
Plug weeder
23.78
26.21
24.56
21.37
23.98 B
Mean
24.06 B
26.04 AB
27.45 A
27.91 A
LSD= 3.19 (Frequencies)
LSD= 1.66 (Implements)
LSD= 5.05 (Interactions) Any two means not sharing a letter in similar with in rows differ significantly (p ≤ 0.05)
115
DAS. Minimum harvest index (20.88) was recorded in inter cultivation at 15 DAS. During
2009, maximum harvest index (33.55) was observed in interaction between tine cultivator
and inter cultivation at 15, 25 and 35 DAS which was statistically similar to (33.45) observed
in inter cultivation at 15, 25, 35 and 45 DAS. Minimum harvest index (24.12) was produced
by inter cultivation at 15 DAS.
Spike hoe resulted in maximum harvest index (24.20) when inter cultivation was
carried out at 15, 25, 35 and 45 DAS during 2008 which was at par with harvest index
(23.32) produced by inter cultivation at 15, 25 and 35 DAS. Minimum harvest index (20.72)
was recorded in inter cultivation at 15 and 25 DAS. In 2009 interaction between spike hoe
and inter cultivation at 15, 25, 35 and 45 DAS produced maximum harvest index (25.13),
similar to rest of inter cultivations.
Plug weeder produced maximum harvest index (22.43) by inter cultivation at 15 and
25 DAS, which is statistically similar to all other inter cultivation frequencies in 2008. During
2009 maximum harvest index (25.13) was observed in inter cultivation at 15 and 25 DAS,
which is statistically similar to rest of inter cultivation frequencies by plug weeder.
4.51 Opaque kernels
The data regarding opaque kernels % during 2008 and 2009 on three weed control
implements and four frequency levels of inter cultivations are given in table 4.35. It is clear
from the data that weed control implements and frequency levels significantly affected during
both the years. Similarly weed control implements and frequency levels significantly
interacted with each other for opaque kernels %.
Tine cultivator during 2008 produced significantly minimum opaque kernels (7.79 %)
in inter cultivation at 15, 25, 35 and 45 DAS, similar to opaque kernels (7.89 %) in inter
cultivation at 15, 25 and 35 DAS. Significantly maximum opaque kernels (8.42 %) were
recorded in inter cultivation at 15 DAS. During 2009 minimum opaque kernels (7.66 %) were
observed in inter cultivation at 15, 25, 35 and 45 DAS. Highest opaque kernels (8.43 %) were
recorded in inter cultivation at 15 DAS.
Spike hoe produced minimum opaque kernels (8.35 %) when inter cultivation was
carried out at 15, 25, 35 and 45 DAS in 2008. Highest opaque kernels (8.82 %) were recorded
in inter cultivation at 15 and 25 DAS. During 2009 least opaque kernels (8.51 %) were found
116
Table 4.35 Effect of different weed control implements opaque kernels
(a) 2008
Implement
Inter row cultivation frequency (DAS) Mean
15
15 & 25
15, 25 & 35
15, 25, 35 & 45 Tine
cultivator
8.42 a
8.07 b
7.81 c
7.79 c
8.02 C
Spike hoe
8.82 a
8.78 a
8.43 b
8.35 b
8.60 B
Plug weeder
8.83 a
8.76 a
8.79 a
8.68 a
8.76 A
Mean
8.69 A
8.54 B
8.34 C
8.27 C
LSD= 0.12 (Frequencies)
LSD= 0.08 (Implements)
LSD= 0.18 (Interactions) Any two means not sharing a common letter with in rows differ significantly (p ≤ 0.05)
(b) 2009
Implement
Inter row cultivation frequency (DAS) Mean
15
15 & 25
15, 25 & 35
15, 25, 35 & 45 Tine
cultivator
8.43 a
8.03 bc
7.82 c
7.66 c
7.98 B
Spike hoe
8.83 a
8.84 a
8.53 b
8.51 b
8.68 A
Plug weeder
8.86 a
8.87 a
8.83 a
8.48 b
8.76 A
Mean
8.70 A
8.58 A
8.39 B
8.22 C
LSD= 0.15 (Frequencies)
LSD= 0.08 (Implements)
LSD= 0.25 (Interactions) Any two means not sharing a letter in similar with in rows differ significantly (p ≤ 0.05)
117
where spike hoe was used at 15, 25, 35 and 45 DAS. Highest opaque kernels (8.81 %)
recoded in inter cultivation at 15 and 25 DAS.
Plug weeder resulted in lowest opaque kernels (8.68 %) in inter cultivation at 15, 25,
35 and 45 DAS during 2008, similar to remaining treatments by this implement. Lowest
opaque kernels (8.48% ) in 2009 were observed in inter cultivation at 15, 25, 35 and 45 DAS
and maximum opaque kernels (8.87 %) in inter cultivation at 15 and 25 DAS.
4.52 Abortive kernels
Data (Table 4.36) revealed that weed control implements and frequency levels significantly
affected abortive kernels % during 2008 and 2009. Similarly weed control implements and
frequency levels significantly interacted with each other for abortive kernels %.
Tine cultivator during 2008 produced significantly minimum abortive kernels (3.50
%) in inter cultivation at 15, 25, 35 and 45 DAS, followed by abortive kernels (3.59 %) in
inter cultivation at 15, 25 and 35 DAS. Significantly maximum abortive kernels (3.4 %) were
recorded in inter cultivation at 15 DAS. During 2009 lowest abortive kernels (3.52 %) were
observed in inter cultivation at 15, 25, 35 and 45 DAS. Highest abortive kernels (3.77
abortive kernels %) were recorded in inter cultivation at 15 DAS.
Spike hoe produced minimum abortive kernels (3.72 %) when inter cultivation was
carried out at 15, 25, 35 and 45 DAS in 2008. Highest abortive kernels (3.86 %) recoded in
inter cultivation at 15 and 25 DAS. During 2009 lowest abortive kernels (3.68 %) were
observed where spike hoe was used at 15, 25, 35 and 45 DAS. Highest abortive kernels (3.87
%) recoded in inter cultivation at 15 and 25 DAS.
Plug weeder resulted in lowest abortive kernels (3.77 %) in inter cultivation at 15, 25,
35 and 45 DAS during 2008, maximum abortive kernels (3.88 %) were observed in inter
cultivation at 15, 25 and 35 DAS. Lowest abortive kernels (3.78 %) in 2009 were observed at
15, 25, 35 and 45 DAS. Maximum abortive kernels (3.87 %) were observed in inter
cultivation at 15 and 25 DAS.
4.53 Normal kernels
The data (Table 4.37) regarding normal kernels % during 2008 and 2009 on three weed
control implements and four frequency levels of inter cultivations revealed that weed control
118
Table 4.36 Effect of different weed control implements abortive kernels
(a) 2008
Implement
Inter row cultivation frequency (DAS) Mean
15
15 & 25
15, 25 & 35
15, 25, 35 & 45 Tine
cultivator
3.74 a
3.69 a
3.59 b
3.50 c
3.63 B
Spike hoe
3.86 a
3.86 a
3.77 b
3.72 b
3.80 A
Plug weeder
3.83 a
3.87 a
3.88 a
3.77 b
3.83 A
Mean
3.81 A
3.80 A
3.74 B
3.66 C
LSD= 0.07 (Frequencies)
LSD= 0.03 (Implements)
LSD= 0.09 (Interactions) Any two means not sharing a common letter with in rows differ significantly (p ≤ 0.05)
(b) 2009
Implement
Inter Cultivation Intensities DAS Mean
15
15 & 25
15, 25 & 35
15, 25, 35 & 45 Tine
cultivator
3.77 a
3.67 b
3.56 c
3.52 c
3.63 B
Spike hoe
3.86 a
3.87 a
3.72 b
3.68 c
3.78 A
Plug weeder
3.86 a
3.87 a
3.84 b
3.78 c
3.84 A
Mean
3.83 A
3.80 A
3.70 B
3.66 B
LSD= 0.05 (Intensities)
LSD=0.07 (Implements)
LSD= 0.10 (Interactions) Any two means not sharing a letter in similar with in rows differ significantly (p ≤ 0.05)
119
Table 4.37 Effect of different weed control implements normal kernels
(a) 2008
Implement
Inter Cultivation Intensities DAS Mean
15
15 & 25
15, 25 & 35
15, 25, 35 & 45 Tine
cultivator
54.05 c
55.70 b
59.97 a
60.03 a
57.44 A
Spike hoe
53.60 b
53.68 b
55.62 a
55.87 a
54.69 B
Plug weeder
54.07 a
53.43 a
54.12 a
53.98 a
53.90 C
Mean
53.90 B
54.27 B
56.57 A
56.62 A
LSD= 0.48(Intensities)
LSD= 0.34 (Implements)
LSD= 0.73 (Interactions) Any two means not sharing a common letter with in rows differ significantly (p ≤ 0.05)
(b) 2009
Implement
Inter Cultivation Intensities DAS Mean
15
15 & 25
15, 25 & 35
15, 25, 35 & 45 Tine
cultivator
54.03 c
57.15 b
60.10 b
61.14 a
58.10 A
Spike hoe
53.72 b
54.07 b
57.63 a
57.95 a
55.84 B
Plug weeder
54.05 a
53.89 a
54.20 a
54.17 a
54.08 C
Mean
53.93 D
55.04 C
57.31 B
57.75 A
LSD= 0.32 (Intensities)
LSD= 0.26 (Implements)
LSD= 0.54 (Interactions) Any two means not sharing a letter in similar with in rows differ significantly (p ≤ 0.05)
120
implements and inter cultivation frequency levels significantly affected the normal kernels %
during both the years. Interactions found significant. So, only interactions are discussed.
Tine cultivator at 15, 25, 35 and 45 DAS resulted in maximum normal kernels (60.03
%) during 2008 which was at par with normal kernels (59.97 %) observed in inter cultivation
at 15, 25 and 35 DAS. Minimum normal kernels (54.05 %) were found in inter cultivation at
15 DAS. During 2009, maximum normal kernels (61.14 %) were recorded in interaction
between tine cultivator and inter cultivation at 15, 25, 35 and 45 DAS which was statistically
similar to normal kernels (60.10 %) produced in inter cultivation at 15, 25 and 35 DAS.
Minimum normal kernels (54.03 %) were recorded in inter cultivation at 15 DAS.
Spike hoe produced maximum normal kernels (55.87 %) when inter cultivation was
carried out at 15, 25, 35 and 45 DAS during 2008 which was at par with normal kernels
(55.62 %) produced by inter cultivation at 15, 25, 35 DAS. Minimum normal kernels (53.60
%) was recorded in inter cultivation at 15 DAS. In 2009 interaction between spike hoe and
inter cultivation at 15, 25, 35 and 45 DAS resulted in maximum normal kernels (57.95 v) and
minimum normal kernels (53.72 %) were observed in inter cultivation at 15 DAS.
Plug weeder produced maximum normal kernels (54.12 %) in inter cultivation at 15,
25 and 35 DAS, which is statistically similar to all other inter cultivation frequencies in 2008.
Similar results were observed during 2009, maximum normal kernels (54.20 %) were
recorded in inter cultivation at 15, 25 and 35 DAS, which is statistically similar to all other
inter cultivation frequencies by plug weeder
4.54 Sterile spikelets
Data (Table 4.38) revealed that weed control implements and frequency levels
significantly affected sterile spikelets % during 2008 and 2009. Similarly weed control
implements and frequency levels significantly interacted with each other for sterile spikelets
%.
Tine cultivator during 2008 produced significantly minimum sterile spikelets (8.95 %)
in inter cultivation at 15, 25, 35 and 45 DAS, similar to sterile spikelets (8.97 %) in inter
cultivation at 15, 25 and 35 45 DAS. Significantly maximum sterile spikelets (11.37 %) were
recorded in inter cultivation at 15 DAS. During 2009 lowest sterile spikelets (9.38 %) found
in inter cultivation at 15, 25, 35 and 45 DAS. Highest sterile spikelets (12.00 %) recorded in
121
Table 4.38 Effect of different weed control implements on sterile spikelets
(a) 2008
Implement
Inter Cultivation Intensities DAS Mean
15
15 & 25
15, 25 & 35
15, 25, 35 & 45 Tine
cultivator
11.37 a
10.03 b
8.97 c
8.95 c
9.83 C
Spike hoe
11.52 a
11.45 a
10.26 b
10.19 b
10.85 B
Plug weeder
11.70 a
11.59 a
11.85 a
11.81 a
11.74 A
Mean
11.53 A
11.02 B
10.36 C
10.31 C
LSD= 0.31 (Intensities)
LSD= 0.28 (Implements)
LSD= 0.54 (Interactions) Any two means not sharing a common letter with in rows differ significantly (p ≤ 0.05)
(b) 2009
Implement
Inter Cultivation Intensities DAS Mean
15
15 & 25
15, 25 & 35
15, 25, 35 & 45 Tine
cultivator
12.00 a
11.58 a
9.44 b
9.38 b
10.64 C
Spike hoe
12.41 a
12.46 a
10.36 b
10.20 b
11.36 B
Plug weeder
12.46
12.40
12.34
12.27
12.37 A
Mean
12.29 A
12.15 A
10.71 B
10.62 B
LSD= 0.30 (Intensities)
LSD= 0.47 (Implements)
LSD= 0.64 (Interactions) Any two means not sharing a letter in similar with in rows differ significantly (p ≤ 0.05)
122
inter cultivation at 15 DAS.
Spike hoe produced minimum sterile spikelets (10.19 %) when inter cultivation was
carried out at 15, 25, 35 and 45 DAS in 2008. Highest sterile spikelets (11.52 %) recoded in
inter cultivation at 15 DAS. During 2009 lowest sterile spikelets (10.20 %) were observed
where spike hoe was used at 15, 25, 35 and 45 DAS. Highest sterile spikelets (12.46 %)
recoded in inter cultivation at 15 and 25 DAS.
Plug weeder resulted in lowest sterile spikelets (11.59 %) in inter cultivation at 15 and
25 DAS during 2008, similar to remaining treatments by this implement. Lowest sterile
spikelets (12.27 %) in 2009 were observed at 15, 25, 35 and 45 DAS which is statistically
similar to all other inter cultivations by plug weeder.
4.55 Leaf area index
The fig.4.29 showed that weed control implements and frequency levels affected the
leaf area index (LAI) during the years 2008 and 2009.
Tine cultivator attained maximum LAI in F4 (3.86) at 90 DAS followed by F3 (3.81),
F2 (3.67) and F1 (3.31) during the year 2008. Similar trend was observed during the year
2009, maximum LAI was attained in F4 (3.80) at 90 DAS followed by F3 (3.75), F2 (3.61) and
F1 (3.25).
Spike hoe attained maximum LAI in F3 (3.42) at 90 DAS followed by F4 (3.41), F2
(3.33) and F1 (3.27) during the year 2008. Similar trend was observed during the year 2009,
maximum LAI was attained in F3 (3.36) at 90 DAS followed by F4 (3.35), F2 (3.27) and F1
(3.21).
Plug weeder attained maximum LAI in F4 (3.28) at 90 DAS followed by F1 (3.28), F2
(3.27) and F3 (3.26) during the year 2008. Similar trend was observed during the year 2009,
maximum LAI was attained in F4 (3.22) at 90 DAS followed by F1 (3.22), F2 (3.21) and F3
(3.20).
123
Fig. 4.29 Effect of different weed control implements on periodic changes in leaf
area index
(F1= Inter row cultivation at 15 DAS F2= Inter row cultivation at 15 and 25 DAS F3= Inter row cultivation at 15, 25 and 35 DAS F4= Inter row cultivation at 15, 25, 35 and 45 DAS)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
60 75 90 105 60 75 90 105 60 75 90 105
Tine cultivator Spike hoe Plug weeder
L A
I
(a) 2008
F1
F2
F3
F4
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
60 75 90 105 60 75 90 105 60 75 90 105
Tine cultivator Spike hoe Plug weeder
L A
I
(b) 2009
F1
F2
F3
F4
124
4.56 Crop growth rate
The fig.4.30 showed that weed control implements and frequency levels affected the
crop growth rate (CGR) during the years 2008 and 2009.
Tine cultivator attained maximum CGR in F4 (25.69 g m-2 d-1) at 90 DAS followed by
F3 (25.65 g m-2 d-1), F2 (24.35 g m-2 d-1) and F1 (20.89 g m-2 d-1) during the year 2008. Similar
trend was observed during the year 2009, maximum CGR was attained in F4 (24.84 g m-2 d-1)
at 90 DAS followed by F3 (24.66 g m-2 d-1), F1 (20.75 g m-2 d-1) and F2 (18.66 g m-2 d-1).
Spike hoe attained maximum CGR in F3 (20.24 g m-2 d-1) at 90 DAS followed by F1
(20.15 g m-2 d-1), F4 (20.06 g m-2 d-1) and F2 (19.84 g m-2 d-1) during the year 2008. Similar
trend was observed during the year 2009, maximum CGR was attained in F3 (20.55 g m-2 d-1)
at 90 DAS followed by F2 (20.15), F4 (19.92 g m-2 d-1) and F1 (18.39 g m-2 d-1).
Plug weeder attained maximum CGR in F1 (20.20 g m-2 d-1) at 90 DAS followed by
F2 (19.96 g m-2 d-1), F3 (19.71 g m-2 d-1) and F4 (19.55 g m-2 d-1) during the year 2008. Similar
trend was observed during the year 2009, maximum CGR was attained in F1 (20.06 g m-2 d-1)
at 90 DAS followed by F2 (19.82 g m-2 d-1), F3 (19.57 g m-2 d-1) and F4 (19.41 g m-2 d-1).
4.57 Leaf area duration
The fig.4.31 showed that weed control implements and frequency levels significantly
affected cumulative leaf area duration (LAD) at 105 DAS during the years 2008 and 2009.
Tine cultivator during the year 2008 resulted in significantly maximum LAD at F4
(123.23 days) similar to F3 (121.70 days). Minimum LAD was observed in F1 (99.30 days).
During the year 2009 maximum LAD was noted in F4 (120.30 days) similar to F3 (118.78
days). Minimum LAD was observed in F1 (96.38 days).
Spike hoe during the year 2008 resulted in significantly maximum LAD at F3 (102.75
days) similar to F4 (101.25 days), F2 (96.95 days) and F1 (96.80 days). Similar trend was
observed during 2009 maximum LAD was attained by F3 (99.83 days) similar to F4 (98.33
days) and F2 (94.03).
125
Fig. 4.30 Effect of different weed control implements on periodic changes in crop
growth rate
(F1= Inter row cultivation at 15 DAS F2= Inter row cultivation at 15 and 25 DAS F3= Inter row cultivation at 15, 25 and 35 DAS F4= Inter row cultivation at 15, 25, 35 and 45 DAS)
0
5
10
15
20
25
30
75 90 105 75 90 105 75 90 105
Tine cultivator Spike hoe Plug weeder
C G
R (
g m
-2d-1
)
(a) 2008
F1
F2
F3
F4
0
5
10
15
20
25
30
75 90 105 75 90 105 75 90 105
Tine cultivator Spike hoe Plug weeder
C G
R (
g m
-2d-1
)
(b) 2009
F1
F2
F3
F4
126
Fig. 4.31 Effect of different weed control implements on cumulative leaf area
duration at 105 DAS
(F1= Inter row cultivation at 15 DAS F2= Inter row cultivation at 15 and 25 DAS F3= Inter row cultivation at 15, 25 and 35 DAS F4= Inter row cultivation at 15, 25, 35 and 45 DAS)
0
20
40
60
80
100
120
140
F1 F2 F3 F4 F1 F2 F3 F4 F1 F2 F3 F4
Tine cultivator Spike hoe Plug weeder
L A
D (
day
s)
(a) 2008
0
20
40
60
80
100
120
140
F1 F2 F3 F4 F1 F2 F3 F4 F1 F2 F3 F4
Tine cultivator Spike hoe Plug weeder
L A
D (
days
)
(b) 2009
127
Plug weeder resulted in maximum LAD at F4 (97.57 days) similar to F3 (97.25 days),
F1 (96.22 days) and F2 (96.07 days) during the year 2008. During the year 2009 maximum
LAD was noted in F4 (94.64 days) similar to F3 (94.33 days), F1 (93.30 days) and F2 (93.15
days).
4.58 Total dry matter
The fig.4.32 showed that weed control implements and frequency levels significantly
affected total dry matter (TDM) at 105 DAS during the years 2008 and 2009.
Tine cultivator during 2008 resulted in significantly maximum TDM at F4 (806.29 g)
similar to F3 (797.59 g). Minimum TDM was observed in F1 (526.90 g). During the year 2009
maximum TDM was noted in F4 (800.12 g) similar to F3 (791.42 g). Minimum TDM was
observed in F1 (520.73 g).
Spike hoe during the year 2008 resulted in significantly maximum TDM at F4 (573.01
g) similar to F3 (560.94 g), minimum TDM was observed in F2 (520.75 g). Similar trend was
observed during 2009 maximum TDM was attained by F4 (566.84 g) similar to F3 (554.77 g),
minimum TDM was observed in F2 (514.38 g).
Plug weeder resulted in maximum TDM at F1 (537.49 g) similar to F3 (529.49 g),
during the year 2008. During the year 2009 maximum TDM was noted in F1 (531.32 g)
similar to F4 (524.95 g), F3 (523.32 g) and F2 (514.38 g).
4.59 Net assimilation rate
The fig.4.33 showed that weed control implements and frequency levels significantly
affected net assimilation rate (NAR) at 105 DAS the years 2008 and 2009.
Tine cultivator during 2008 resulted in significantly maximum NAR at F3 (6.55 g m-2
d-1) similar to F4 (6.54 g m-2 d-1). Minimum NAR was observed in F1 (5.31 g m-2 d-1). During
the year 2009 maximum NAR was noted in F3 (6.66 g m-2 d-1) similar to F4 (6.65 g m-2 d-1).
Minimum NAR was observed in F1 (5.40 g m-2 d-1)
Spike hoe during the year 2008 resulted in significantly maximum NAR at F4 (5.65 g m-2 d-1)
similar to F1 (5.50 g m-2 d-1), minimum NAR was observed in F2 (5.36 g m-2 d-1). Similar
128
Fig. 4.32 Effect of different weed control implements on total dry matter at 105
DAS
(F1= Inter row cultivation at 15 DAS F2= Inter row cultivation at 15 and 25 DAS F3= Inter row cultivation at 15, 25 and 35 DAS F4= Inter row cultivation at 15, 25, 35 and 45 DAS)
0
100
200
300
400
500
600
700
800
900
F1 F2 F3 F4 F1 F2 F3 F4 F1 F2 F3 F4
Tine cultivator Spike hoe Plug weeder
T D
M (
g)
(a) 2008
0
100
200
300
400
500
600
700
800
900
F1 F2 F3 F4 F1 F2 F3 F4 F1 F2 F3 F4
Tine cultivator Spike hoe Plug weeder
T D
M (g
)
(b) 2009
129
Fig. 4.33 Effect of different weed control implements net assimilation rate at 105
DAS
(F1= Inter row cultivation at 15 DAS F2= Inter row cultivation at 15 and 25 DAS F3= Inter row cultivation at 15, 25 and 35 DAS F4= Inter row cultivation at 15, 25, 35 and 45 DAS)
0
1
2
3
4
5
6
7
8
F1 F2 F3 F4 F1 F2 F3 F4 F1 F2 F3 F4
Tine cultivator Spike hoe Plug weeder
N A
R (
g m
-2d-1
)
(a) 2008
0
1
2
3
4
5
6
7
8
F1 F2 F3 F4 F1 F2 F3 F4 F1 F2 F3 F4
Tine cultivator Spike hoe Plug weeder
N A
R (
g m
-2d
-1)
(b) 2009
130
trend was observed during 2009 maximum NAR was attained by F4 (5.76 g m-2 d-1 g) similar
to F1 (5.60 g m-2 d-1), minimum NAR was observed in F2 (5.46 g m-2 d-1).
Plug weeder resulted in maximum NAR at F1 (537.49 g) similar to F3 (529.49 g),
during the year 2008. During the year 2009 maximum NAR was noted in F1 (5.70 g m-2 d-1)
similar to F2 (5.61), F4 (5.56 g m-2 d-1) and F3 (5.55 g m-2 d-1).
4.60 Economic analysis
Tine cultivator (Table 4.39 & 4.40) gave maximum net returns in F4 (Rs. 40225)
followed by F3 (Rs.34766), during the year 2008. Similar trend was observed during the year
2009, maximum net returns were obtained by F4 (Rs. 50039) followed by F3 (Rs. 36988), F2
(Rs. 6363). Spike hoe and plug weeder gave negative net returns on all frequencies during
both the years
4.61 Marginal rate of return
During the year 2008 maximum marginal rate of return was found in D1F3 (9621%)
followed by D1F2 (4291%) and D1F4 (1365%). During the year 2008 maximum marginal rate
of return was found in D1F3 (7656%) followed by D1F2 (6548%) and D1F4 (3263%).
133
Table 4.39 Effect of different weed control implements on economic returns during the year 2008
Implements
Frequencies
Paddy yield (t ha-1)
Adjusted paddy yield (t ha-1)
Value (Rs ha-1)
Straw yield (t ha-1)
Adjusted straw yield (t ha-1)
Value (Rs. ha-1)
Gross income (Rs. ha-1)
Variable cost ( Rs. ha-1)
Total cost (Rs. ha-1)
Net return (Rs. ha-1)
BCR
Tine
Cultivator
F1 1.52 1.36 38304 5.70 5.13 3591 41895 400 62987 -21092 0.67
F2 2.22 1.99 55944 5.91 5.32 3724 59668 800 63387 -3719 0.94
F3 3.71 3.30 93492 8.03 7.23 5061 98553 1200 63787 34766 1.55
F4 3.93 3.53 99036 8.53 7.68 5376 104412 1600 64187 40225 1.63
Spike Hoe
F1 1.47 1.32 37044 5.57 5.01 3507 40551 400 62987 -22436 0.64
F2 1.48 1.33 37296 5.70 5.13 3591 40887 800 63387 -22500 0.65
F3 1.96 1.76 49392 6.47 5.82 4074 53466 1200 63787 -10321 0.84
F4 2.25 2.02 56700 7.05 6.35 4445 61145 1600 64187 -3042 0.95
Plug
Weeder
F1 1.53 1.37 38556 5.63 5.07 3549 42105 400 62987 -20882 0.67
F2 1.51 1.35 38052 5.25 4.73 3311 41363 800 63387 -22024 0.65
F3 1.54 1.38 38808 5.33 4.80 3360 42168 1200 63787 -21619 0.66
F4 1.57 1.41 39564 5.92 5.33 3731 43295 1600 64187 -20892 0.67
Paddy price per kg = Rs. 28
Straw price per ton = Rs. 700
Permanent cost = Rs. 62587
134
Table 4.40 Effect of different weed control implements on economic returns during the year 2009
Implements
Frequencies
Paddy yield
(t ha-1)
Adjusted paddy yield
(t ha-1)
Value (Rs ha-1)
Straw yield
(t ha-1)
Adjusted straw
yield (t ha-1)
Value (Rs. ha-1)
Gross income
(Rs. ha-1)
Variable cost ( Rs. ha-1)
Total cost (Rs. ha-1)
Net return
(Rs. ha-1)
BCR
Tine
Cultivator
F1 1.47 1.32 43659 4.72 4.25 3611 47270 400 67100 -19830 0.70
F2 2.36 2.12 70092 4.93 4.44 3771 73863 800 67500 6363 1.09
F3 3.35 3.02 99495 7.05 6.35 5393 104888 1200 67900 36988 1.54
F4 3.79 3.41 112563 7.55 6.80 5776 118339 1600 68300 50039 1.73
Spike Hoe
F1 1.46 1.31 43362 4.59 4.13 3511 46873 400 67100 -20227 0.70
F2 1.57 1.41 46629 4.72 4.25 3611 50240 800 67500 -17260 0.74
F3 1.74 1.57 51678 5.49 4.94 4200 55878 1200 67900 -12022 0.82
F4 1.84 1.66 54648 6.07 5.46 4644 59292 1600 68300 -9008 0.87
Plug
Weeder
F1 1.44 1.30 42768 4.65 4.19 3557 46325 400 67100 -20775 0.69
F2 1.51 1.36 44847 4.27 3.84 3267 48114 800 67500 -19386 0.71
F3 1.41 1.27 41877 4.35 3.92 3328 45205 1200 67900 -22695 0.67
F4 1.34 1.21 39798 4.94 4.45 3779 43577 1600 68300 -24723 0.64
Paddy price per kg = Rs. 33
Straw price per ton = Rs. 850
Permanent cost = Rs. 66770
135
Table 4.41 Effect of different weed management strategies on dominance analysis during 2008
Treatments Cost that
vary (Rs. ha-1)
Net field benefits
(Rs. ha-1)
D3F1 400 41705
D1F1 400 41495 D
D2F1 400 40151 D
D1F2 800 58868
D3F2 800 40563 D
D2F2 800 40087 D
D1F3 1200 97353
D2F3 1200 52266 D
D3F3 1200 40968 D
D1F4 1600 102812
D2F4 1600 59545 D
D3F4 1600 41695 D
(D1= Tine cultivator D2= Spike hoe D3= Plug weeder F1= Inter row cultivation at 15 DAS F2= Inter row cultivation at 15 and 25 DAS F3= Inter row cultivation at 15, 25 and 35 DAS F4= Inter row cultivation at 15, 25, 35 and 45 DAS)
136
Table 4.42 Effect of different weed management strategies on dominance analysis during 2009
Treatments Cost that
vary (Rs. ha-1)
Net field benefits
(Rs. ha-1)
D1F1 400 46870
D2F1 400 46473 D
D3F1 400 45925 D
D1F2 800 73063
D2F2 800 49440 D
D3F2 800 47314 D
D1F3 1200 103688
D2F3 1200 54678 D
D3F3 1200 44005 D
D1F4 1600 116739
D2F4 1600 57692 D
D3F4 1600 41977 D
(D1= Tine cultivator D2= Spike hoe D3= Plug weeder F1= Inter row cultivation at 15 DAS F2= Inter row cultivation at 15 and 25 DAS F3= Inter row cultivation at 15, 25 and 35 DAS F4= Inter row cultivation at 15, 25, 35 and 45 DAS)
137
Table 4.43 Effect of different weed management strategies on marginal analysis during 2008
Treatments Cost that
vary (Rs. ha-1)
Marginal cost
(Rs. ha-1)
Net field benefits
(Rs. ha-1)
Marginal net
benefits (Rs. ha-1)
Marginal rate of return
(%)
D3F1 400 0 41705 -
D1F2 800 400 58868 17163 4291
D1F3 1200 400 97353 38485 9621
D1F4 1600 400 102812 5459 1365
Table 4.44 Effect of different weed management strategies on marginal analysis during 2009
Treatments Cost that
vary (Rs. ha-1)
Marginal cost
(Rs. ha-1)
Net field benefits
(Rs. ha-1)
Marginal net
benefits (Rs. ha-1)
Marginal rate of return
(%)
D1F1 400 - 46870
D1F2 800 400 73063 26193 6548
D1F3 1200 400 103688 30625 7656
D1F4 1600 400 116739 13051 3263
138
DISCUSSION
This experiment was conducted during 2008 and 2009 at Agronomic Research Farm,
University of Agriculture Faisalabad. Seed of variety Super Basmati with good vigor and
germination percentage was dry direct seeded using automatic rabbi drill (seed-cum-fertilizer
drill) after land preparation. It allowed line sowing maintaining 22.5 cm row to row distance
and facilitated manual and mechanical weeding. Drill tilled the soil at shallow depth (4-5
cm), sowed the seeds and covered them with soil for better seed to soil contact, all in a single
pass. Kumar and Ladha (2011) also reported that drill sowing facilitated weed control
between rows, saved seed and placed the fertilizer at proper depth. Placing seeds below soil
surface also reduced seed damage caused by high rainfall. High Quality seed was used
because larger seeds with greater carbohydrate reserves have increased ability to emerge even
from greater burial depths also found by Baskin and Baskin (1998). Within rows distance was
maintained at 22.5 cm because studies of Chauhan and Johnson (2010a) revealed that in this
range growth and seed production of various weeds in aerobic conditions was less as
compared to wider (30-cm) rows. Similarly Akobundu and Ahissou (1985) reported that row
spacing (15–45-cm) in direct-seeded rice had little effect on the paddy yield of the crop in the
absence of weeds but in competition with weeds the wider spacing resulted in significantly
lower grain yield.
Weeds are one of the major biological constraints to direct seeded rice production and
are notorious yield reducers, in many situations economically more important than insects,
fungi or other pest organisms (Savary et al., 1997). Weed biomass differed significantly in all
weed control implements at various frequencies. Lower weed biomass was observed when
tine cultivator was used at 15, 25, 35, 45 and 15, 25, 35 DAS. Possibly because it uprooted
and removed inter row weeds and controlling early flushes of weeds. Similar findings were
reported by Fazlollaalh et al. (2011). Second reason of less weed biomass might be
beushaning which killed the weeds with single stem. This was also reported by Rao, et al.
(2007) and Sharma (1997). Inter row cultivation with tine cultivator at 15, 25 and 15 DAS
resulted in more weed biomass than the two earlier ones. Increased weed dry weight might
have been due to increased critical period of weed infestation. Weeds might have competed
for more than 25 DAS. This prolonged competition period possibly provided a considerable
opportunity for weeds to emerge subsequently and produce seeds. Studies by Chauhan and
Johnson (2011) revealed that in direct seeded rice weed competition period was beyond 40
DAS. Lower weed biomass was recorded when spike hoe was operated at 15, 25, 35, 45 and
139
15, 25, 35 DAS. Reason of less weed biomass might be beushaning which killed the weeds
with single stem. This was also reported by Rao, et al. (2007) and Sharma (1997). Plug
weeder produced similar weed biomass at all frequencies of inter row cultivation. It
controlled no weeds resulting in high weed biomass. Reasons of increased weed biomass
might be firstly aerobic soil conditions were conducive to the germination and growth of
weeds as reported by Rao et al. (2007). Secondly emerging direct seeded rice seedlings were
less competitive with concurrently emerging weeds (Kumar et al., 2008a). Third reason of
highest weed biomass might be competitive advantage of C4 weeds which increased their
efficiency to use crop nutrients more than rice. Findings of Holm et al. (1991) were in
support of this reason.
Although plant height is a genetic character but height of aerobic rice substantially
decreases when dry weed biomass exceeds (Zhao et al., 2006). Taller plants were recorded
when tine cultivator was operated at 15, 25, 35, 45 and 15, 25, 35 DAS. Higher plant might
be attributed to availability of proper nutrients and space resulting in good leaf canopy
because of low weed infestation. Phoung et al. (2005) found that N uptake was maximum
with good weed control practices in direct seeded rice, resulting in taller plants. Inter row
cultivation with tine cultivator at 15, 25 and 15 DAS resulted in shorter plants than the two
earlier ones. This might have been due to more weeds, less available space and nutrients.
Phoung et al. (2005) reported that when weeds were more than more N uptake by weeds as
compared to rice plants. Similar findings were observed by Mann et al. (2007).
Taller plants were recorded when spike hoe was operated at 15, 25, 35, 45 and 15, 25,
35 DAS. Reason of taller plants might be less weed biomass due to beushaning which killed
the weeds with single stem and availability of nutrients to rice crop (Phoung et al., 2005).
This was also found by Sharma (1997). Inter row cultivation with spike hoe at 15, 25 and 15
DAS resulted in shorter plants than the two earlier ones. This might have been due to more
weeds, less available space and nutrients. Similar findings were reported by Phoung et al.
(2005).
Plug weeder produced similar plant height at all frequencies of inter row cultivation.
Reason of shorter plants might be less availability of space and nutrients due to increased
weed biomass. Competitive advantage of C4 weeds increased their efficiency to use crop
nutrients more than rice (Holm et al., 1991).
140
Variation in fertile tillers among different treatments was observed in current studies.
Less fertile tillers were observed when tine cultivator was operated at 15, 25 and 15 DAS. In
general number of effective tillers decreased as the weed-infested period was extended,
reasons might be; Firstly aerobic soil conditions were conducive to the germination and
growth of weeds as reported by Rao et al. (2007). Secondly emerging direct seeded rice
seedlings were less competitive with concurrently emerging weeds (Kumar et al., 2008a).
Third reason of less fertile tillers might be competitive advantage of C4 weeds which
increased their efficiency to use crop nutrients more than rice. Findings of Holm et al. (1991)
are in support of this reason. Heavy and quicker second flush of weeds might be another
reason of lees fertile tillers. Similar findings were reported by Sharma et al. (1977). More
fertile tillers in tine cultivator when used at 15, 25, 35, 45 and 15, 25, 35 DAS were observed.
This increase in fertile tillers might have been due to less early competition of crop with
weeds and more availability of moisture and nutrients. Increase in fertile tillers with
decreased weed biomass was also observed by Juraimi et al. (2009) and Phoung et al. (2005).
Spike hoe produced more productive tillers at 15, 25, 35, 45 and 15, 25, 35 DAS with high
weed infestation this might have been due to beushaning effect which increased fertile tillers.
Different inter row cultivations by plug weeder had no effect on productive tillers. These
Results are also in line with findings of Ekleme et al. (2009), Mann et al. (2007), Singh et al.
(2007) and Fischer et al. (2001).
Paddy yield in tine cultivator when used at 15, 25, 35, 45 and 15, 25, 35 DAS was
observed more than two other frequencies. This increase in yield might be due to minimum
presence of weeds during critical competition period. Studies of Haefele et al. (2000)
suggested that there was a considerable scope to increase yield with improved weed control
in direct seeded rice. Proper availability of nutrients, space and moisture resulted in more
fertile tillers, more kernels panicle-1 and heavier 1000 kernel weight providing high returns in
the form of paddy yield. Similar findings were observed by Phoung et al. (2005).
Paddy yield recorded in weed control by Spike hoe at 15, 25, 35, 45 and 15, 25, 35
DAS was observed more than two other frequencies, First reason of increase in yield might
be low weed dry weight and second reason might be beushaning effect resulting in more
tillers and more yield. Results of Sharma (1997) were similar to our findings. Paddy yield
was not effected by any inter row cultivation of plug weeder. Low yield might have been due
to several reasons. Firstly aerobic soil conditions were conducive to the germination and
growth of weeds, they increased their dry weight and paddy yield was reduced. Oerke and
Dehne, (2004) also found that weeds were major constraint in reduction of paddy yield.
141
Secondly emerging direct seeded rice seedlings were less competitive with concurrently
emerging weeds resulting in low paddy yield (Kumar et al., 2008a). Third reason of low
paddy yield might be competitive advantage of C4 weeds which increased their efficiency to
use crop nutrients more than rice. Findings of Holm et al. (1991) were in support of this
reason. Similar results are reported by Ekleme et al. (2009), Juraimi et al. (2009), Hussain et
al. (2008), Singh et al. (2008), Mann et al. (2007) and Singh et al. (2007).
142
CHAPTER 5
SUMMARY
Effect of different weed management strategies on agro qualitative traits of direct seeded
rice was studied on a sandy loam soil during the years 2008 and 2009 at Agronomic Research
Farm, University of Agriculture Faisalabad. The study comprised of two sets of field
experiments. Seed of variety Super Basmati with good vigor and germination percentage was dry
direct seeded in a well prepared seed bed using automatic rabi drill (seed-cum-fertilizer drill).
Sowing was done in 22.5 cm apart rows which facilitated manual and mechanical weeding.
Experiment I was laid out in RCBD having five weed control treatments; hand weeding, hoeing
(with kasula), inter row cultivation with tine cultivator, inter row cultivation with spike hoe and
chemical control with Nominee 100 SC along with control (no weeding). Experiment II was laid
out in split plot design randomizing inter row cultivation implements in main plots and inter row
cultivation frequencies in sub plots. Both experiments were replicated thrice. Net plot size was
2.7 m x 6.0 m in both experiments.
Data on various parameters were analyzed using a standard procedure and Fisher’s
analysis of variance technique for statistical analysis. The treatment means were compared using
LSD test at P 0.05. Within the range of the data obtained following inferences and conclusions
are drawn. Salient findings of both the studies are summarized as following
STUDY-I: COMPARISON OF DIFFERENT WEED MANAGEMENT STRATEGIES IN DRILL SEEDED AEROBIC RICE
Different weed management strategies had significant effects on productive tillers, panicle
length, kernels per panicle, 1000 kernel weight, weed biomass, growth parameters, quality
parameters and yield of direct seeded rice.
Weed dry weight in no weeding was 316.61 g m-2 while in hand weeding, hoeing, tine
cultivator and Nominee -300 g m-2, 257 g m-2, 225 g m-2 and 157 g m-2 respectively less
weed dry weight was produces.
Maximum fertile tillers were recorded in hand weeding (369.73 m-2) and were followed
by those recorded for hoeing (356.94 m-2) and tine cultivator (346.78 m-2).
143
Hand pulling, hoeing, tine cultivator, Nominee and spike hoe recorded 28, 25, 22, 12 and
6% more number of kernels per panicle respectively as compared with those recorded
under no weeding treatment.
Hand pulling, hoeing, tine cultivator, Nominee and spike hoe recorded 39, 37, 30, 20 and
13% heavier 1000 kernel weight respectively as compared with those recorded under no
weeding treatment.
Paddy yield was 221, 203, 181, 105 and 64% more in hand pulling, hoeing, tine cultivator
and Nominee and spike hoe respectively as compared no weeding.
Different weed management strategies enhanced the growth of DSR. Improved LAI,
LAD, CGR, TDM and NAR were observed in hand pulling, hoeing, tine cultivator and
Nominee as compared to no weeding.
Different weed management strategies not only enhanced the growth of DSR but quality
of harvested paddy was also improved. Employing hand pulling, hoeing, tine cultivator
and Nominee resulted in lower sterile spikelets, opaque kernels and abortive kernels as
compared no weeding.
Highest net returns (Rs. 56905) were obtained by hand weeding while highest BCR
(1.75) was obtained in tine cultivator.
STUDY-II: EVALUATION OF DIFFERENT INTER CULTURAL IMPLEMENTS FOR WEED MANAGEMENT IN DRILL SEEDED AEROBIC RICE
Different weed control implements at various frequencies had significant effect on
productive tillers, panicle length, kernels per panicle, 1000 kernel weight, weed biomass, growth
parameters, quality parameters and yield of direct seeded rice.
Lower weed biomass was observed when tine cultivator and spike hoe were used at 15,
25, 35, 45 and 15, 25, 35 DAS.
Different weed management implements had significant effect on fertile tillers. More
fertile tillers in tine cultivator and spike when used at 15, 25, 35 and 45 DAS were
observed.
Longer panicle length was recorded when tine cultivator and spike hoe were operated at
15, 25, 35, 45 and 15, 25, 35 DAS.
144
Heavier 1000 kernel was recorded in tine cultivator and spike when used at 15, 25, 35
and 45.
Different weed management implements enhanced the growth of DSR. Improved LAI,
LAD, CGR, TDM and NAR were observed when tine cultivator was operated at 15, 25,
35 and 45 DAS.
Different weed management implements not only enhanced the growth of DSR but
quality of harvested paddy was also improved. Employing tine cultivator and spike hoe at
15, 25, 35 and 45 DAS resulted in lower sterile spikelets, opaque kernels and abortive
kernels.
Different weed management implements significantly effected paddy yield. More paddy
yield was when tine cultivator and spike hoe were operated at 15, 25, 35, 45 and 15, 25,
35 DAS.
145
CONCLUSIONS
1. Maximum yield of rice obtained was 4.5 t ha-1 that is satisfactory.
2. Tine cultivator performed well for inter row weed control.
3. Weed control by tine cultivator is the most suitable and economical method.
4. Hand weeding produced maximum yield (4.5 t ha-1) while the befit cost ratio was more
tine cultivator (1.75).
FUTURE RESEARCH NEEDS
1. It is suggested that similar studies should be carried out under different agro climatic
conditions to have site specific and conclusive information.
2. There is need to develop a tractor drawn automatic drill for rice sowing.
3. Tine cultivator should be developed in farmer friendly size.
146
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Appendix 1 Fixed cost of production 2008
Operation/ Input No./Amount/ Quantity
Rate/Unit (Rs.)
Cost/ ha (Rs.)
1) Land Preparation Ploughing Levelling Planking
2 1 2
750 500 1000
1500 500 2000
2) Sowing Charges Seed rate Drilling
75 1
75
1250
5625 1250
3) Fertilizer DAP Urea SOP Transportation charges Application charges
3.5bags 5.5 bag 4.73 bags 13 bags 13 bags
1/2 man day
3100 700 1400
10 100
10850 3850 6622 130 1300
4) Irrigation Water rates Water course cleaning Application charges
-
2 man days 16
½ man day
200 200 100
200 400 1600
5) Plant protection Insecticide Application charges
2.5 packs
1/4 man days
400 200
1000 50
Total charges from 1-5 - - 36877 Mark up on investment from 1-5 @ 9 % per annum
6 months 276/month 1660
Management charges 6 months 175 1050 Land rent 6 months 3750 22500 Artisan charges - 500 500 Total permanent cost 62587
161
Appendix 2 Variable cost for experiment 1 during 2008 and 2009
Treatment Weed control charges (Rs. ha -1)
No Weeding 0 Hand pulling
12000
Hoeing
8000
Tine Cultivator
1600
Spike Hoe
1600
Nominee
1260
Appendix 3 Variable cost for experiment 2 during 2008 and 2009
Implements
Frequencies
Weed control charges (Rs.) ha -1
Tine Cultivator
F1 400
F2 800
F3 1200
F4 1600
Spike Hoe
F1 400
F2 800
F3 1200
F4 1600
Plug Weeder
F1 400
F2 800
F3 1200
F4 1600
162
Appendix 4 Fixed cost of production 2009
Operation/ Input No./Amount/ Quantity
Rate/Unit (Rs.)
Cost/ ha (Rs.)
1) Land Preparation Ploughing Levelling Planking
2 1 2
750 500 1000
1500 500 2000
2) Sowing Charges Seed rate Drilling
75 1
90
1250
6750 1250
3) Fertilizer DAP Urea SOP Transportation charges Application charges
3.5bags 5.5 bag 4.73 bags 13 bags 13 bags
1/2 man day
2400 800 1500 10 100
8400 4400 7095 130 1300
4) Irrigation Water rates Water course cleaning Application charges
-
2 man days 16
½ man day
200 200 100
200 400 1600
5) Plant protection Insecticide Application charges
2.5 packs
1/4 man days
400 200
1000 50
Total charges from 1-5 - - 36575 Mark up on investment from 1-5 @ 9 % per annum
6 months 276/month 1645
Management charges 6 months 175 1050 Land rent 6 months 3750 27500 Artisan charges - 500 500 Total permanent cost 66770