RESEARCH ARTICLE

Impact of Integrated Management on Yield Sustainabilityin Relation to Soil Quality Under a Rice–Wheat Cropping System

Debarati Bhaduri • T. J. Purakayastha •

L. M. Bhar • A. K. Patra • Binoy Sarkar

Received: 21 February 2013 / Revised: 12 August 2013 / Accepted: 22 October 2013 / Published online: 15 February 2014

� The National Academy of Sciences, India 2014

Abstract This study concentrates on developing a soil

quality index (SQI), linking productivity to soil quality indi-

cators, and SQI using grain yield of rice and wheat grown in a

sequence for 8 years in an integrated tillage-water-nutrient

management system. Rice yield was significantly better under

puddling, 3 days of drainage, and both 150 % NPK and

100 % NPK ? FYM treatments, and the yields were posi-

tively correlated with bulk density (BD), available Fe and soil

respiration. The wheat yield was significantly higher under

conventional tillage, five lots of irrigation, and 150 % NPK,

and was positively correlated with BD, water stable aggre-

gates (WSA) and available N. However, it was negatively

correlated with mean weight diameter, soil organic carbon and

hydraulic conductivity. Stepwise regression identified avail-

able Fe, WSA and microbial biomass carbon as the most

important indicators that explained 42 % variability in rice

yield, which further correlated significantly with the PCA-

based SQI (r = ?0.44). Thus, crop yield emerged as an

important indicator for maintaining soil quality to sustain high

productivity under integrated management systems.

Keywords SQI � Indicators � Tillage � Nutrient �Water management � PCA

Introduction

Rice (Oryza sativa L.) and wheat (Triticum aestivum L.) are

two most important food crops that contribute digestible

energy (45 %) and protein (30 %) to the human diet [1]. These

crops are grown sequentially over an area of 24 Mha

throughout South-East Asia, of which the Indo-Gangetic

plains (IGP) grows 32 and 42 % of the rice and wheat,

respectively; representing the region’s share of one quarter to

one-third of total production [2]. However, in recent years, the

productivity of this cropping system in the IGP region has

either stagnated or gone into decline [2, 3]. The causes for yield

stagnation or decline may include changes in soil quality

parameters, perhaps due to repeated transitions from anaero-

bic-rice to aerobic-wheat growing conditions affecting the soil

structure and nutrient transformation [4]. A possible link

between soil quality and climate change [5] may cumulatively

alter crop productivity in future scenario. Soils have several

biological, chemical and physical indicators that interact in a

complex way to give a soil its quality [6]. Traditionally soil

quality has been equated with the agricultural system pro-

ductivity. As an important indicator of system productivity,

crop yield partially depends on soil quality and its associated

indicators, and thus serves as a plant bioassay of the interacting

soil characteristics [7]. The biological variables of soils such as

microbial biomass, enzymatic activities and soil organic

matter [8] and nutrients like N, P [9] are significantly linked to

D. Bhaduri � T. J. Purakayastha � A. K. Patra

Division of Soil Science and Agricultural Chemistry, Indian

Agricultural Research Institute, New Delhi 110012, India

L. M. Bhar

Indian Agricultural Statistical Research Institute,

New Delhi 110012, India

B. Sarkar

Centre for Environmental Risk Assessment and Remediation

(CERAR), Cooperative Research Centre for Contamination

Assessment and Remediation of the Environment (CRC CARE),

University of South Australia, Mawson Lakes Campus,

Mawson Lakes, SA 5095, Australia

Present Address:

D. Bhaduri (&)

Directorate of Groundnut Research, Junagadh 362001,

Gujarat, India

e-mail: debarati.ssiari@gmail.com

123

Natl. Acad. Sci. Lett. (January–February 2014) 37(1):25–31

DOI 10.1007/s40009-013-0202-7

crop yield in various crop rotations. In contrast, limited

information is available on the correlation of crop yield with

the SQI [10], and this warrants further investigation. Few

previous works attempted an effort to correlate the SQI with

system productivity other than rice–wheat system [11, 12].

There are conflicting reports [7, 10] on the impact of dif-

ferent tillage (and puddling) management and nutrient prac-

tices on various soil properties as well as quality in the rice–

wheat system of the Indian subcontinent revealing the role of

non-puddled direct seeding of rice, zero-tillage of wheat and

use of organic manures especially the crop residues. Most of

the previous studies were confined to the development of SQI

from one-time soil sampling and its correlation to combined

yield of the cropping system. However, we attempted to

develop SQIs after the harvest of each crop and correlate both

the soil quality indicators and the quality indices with the

individual yield of each crop. We carried out the present study

by correlating seventeen physical, chemical and biological

indicators and the quality indices developed with the indi-

vidual yield of rice and wheat. Specifically, we examined

integrated water-tillage-nutrient management practices in a

Typic Haplustept of New Delhi, India.

Materials and Methods

Details of Experimental Site

To analyse 17 different quality indicators, soil samples were

collected after the harvest of rice and wheat from an 8-year old

ongoing experiment on a farm operated by the Indian Agri-

cultural Research Institute, New Delhi, India. Rice (cv. Pusa

Sugandh 3) was transplanted (for puddled rice) and directly

sown for non-puddled rice during the wet season (last week of

July to mid of October 2008) and wheat (cv. HD 2687) was

sown during the winter season (mid of November 2008 to end

of March 2009). This area is part of the semi-arid with mean

annual precipitation 650 mm, mean annual temperatures

range 18 �C (min) to 35 �C (max). The soil belongs to the

hyperthermic family of Typic Haplustepts, non-calcareous

and sandy clay loam in texture (48 % sand, 29 % silt, 23 %

clay). The soil had CEC 14.6 cmol (?) kg-1 soil, pH 7.8, EC

0.45 ds m-1, organic carbon 4.8 g kg-1, low available N

(102 mg kg-1), medium available P (9.91 mg kg-1), high

available K (160 mg kg-1) and Fe 9.06 mg kg-1, Mn

3.07 mg kg-1, Zn 1.69 mg kg-1, Cu 2.10 mg kg-1 as

DTPA-extractable micronutrients.

Treatment Details

The experiment was laid out in a split plot design with

three replications. Two tillage treatments, i.e. puddled

(transplanted) and non-puddled (wet seeded rice) for rice,

and the same puddling history for wheat were allocated to

the main plots. Three water treatments both for rice viz.

continuous submergence (W1), irrigation after 1 day of

drainage (W2), and irrigation after 3 days of drainage (W3)

and wheat viz. five irrigations i.e. W1 [crown root initiation

(CRI), tillering, jointing, flowering and dough stages], three

irrigations i.e. W2 (CRI, jointing and flowering stages) and

two irrigations i.e. W3 (CRI and flowering stages) were

allocated to the sub plots. Nine nutrient treatments were

allocated to the sub–sub plots for rice as follows: Control

(T1), 100 % NPK (T2), 150 % NPK (T3), 100 % N [25 %

N substituted by farmyard manure (FYM) (T4), green

manure (T5), biofertilizer (T6), sewage sludge (T7), crop

residues (T8)] ? PK and, 100 % N organic sources (50 %

FYM ? 25 % biofertilizer ? 25 % crop residues/green

manure) (T9). For wheat, splitting was done in each

nutrient treatment plot to impose two tillage treatments, i.e.

conventional tillage (CT) and no-tillage (NT). The rec-

ommended dose of N through urea [CO(NH2)2], P through

single superphosphate (Ca(H2OP4)2�H2O) and K through

muriate of potash (KCl) were applied @ 120, 26.2 and

50 kg ha-1, respectively. N in three splits and uniform

dose of P and K before the transplanting/sowing of the

crops was applied.

Well-decomposed FYM (0.5 % N, 0.25 % P and 0.4 %

K on DW basis), sewage sludge (3.6 % N, 1.2 % P, 0.45 %

K on DW basis), crop residues (rice- 0.58 % N, 0.10 P,

1.38 % K; wheat- 0.49 % N, 0.11 % P, 1.06 % K on DW

basis) and green manure (Sesbania aculeata L.) (2.25 % N,

0.37 % P, 1.25 % on DW basis) at the rate of 2 Mg ha-1

were incorporated 15 days before transplanting or sowing

of the crops. The biofertilizer, Azospirilum brasilense CD

JA was coated on the wheat seed surface and by dipping

the rice seedling roots in an aqueous suspension culture to

maintain a population of about 109 cells g-1 of seed.

Sampling and Methods of Analysis

Composite surface (0–15 cm) soil samples were collected

after the harvest of each crop. The soil samples were

analysed for: bulk density (BD) [13], maximum water-

holding capacity (MWHC) [14], saturated hydraulic con-

ductivity (HC) [15], mean weight diameter (MWD) and

water stable aggregates (WSA) [16, 17] soil organic carbon

(SOC) [18], available N [19], available P [20], available

Zn, Fe, Cu, Mn [21], microbial biomass carbon (MBC)

[22], dehydrogenase activity (DHA) [23], potentially

mineralizable N (PMN) [24] and soil respiration [25].

Metabolic or respiratory quotient (qCO2) was derived as

mg CO2–C per h per mg of MBC. The crop was harvested,

26 D. Bhaduri et al.

123

sun dried for 4–5 days and the grain yield per plot was

recorded as Mg ha-1 after threshing.

Development of SQI

The soil management assessment framework (SMAF) [26]

consisting of three steps -indicator selection, indicator

interpretation and integration was followed (Fig. 1). For

creating the minimum data set (MDS) of the indicators, the

ensuing steps were followed: (1) conceptual framework

(unique combination of the goals, functions and additional

criteria); and (2) principal component analysis (PCA) (data

set reduction). The indicator interpretation means con-

verting the raw data of indicators into unit-less numerical

scores (after finalizing the thresholds or limits) by a non-

linear scoring function (NLSF) [27].

Non-linear score Yð Þ ¼ 1

1þ e�bðX�AÞ½ � ð1Þ

where X refers soil property value, A refers baseline value

of soil property given the score is equal to 0.5 and b is the

slope. Conceptual framework (CF) model described by

Karlen et al. [28] is as follows:

SQIð ÞP ¼ qnc wtð Þ þ qpss wtð Þ þ qwr wtð Þþ qrr wtð Þ for productivity goal½ � ð2Þ

SQIð ÞEP ¼ qnc wtð Þ þ qpss wtð Þ þ qwr wtð Þ þ qrr wtð Þþ qfb wtð Þ þ qbdh wtð Þ� for environmental protection goal½ � ð3Þ

where qnc, qpss, qwr qrr, qfb, qbdh are the ratings for nutrient

cycling, physical stability, water relations and support,

resistance and resilience, filtering and buffering, and bio-

diversity and habitat; (wt) is a numerical weighting for

each soil function.

In PCA the PCs based on high eigenvalues (C1) were

examined and non-correlated variables with high factor

loadings were retained for indexing the soil quality.

The final soil quality equation was developed as:

Soil quality index SQIð Þ ¼Xn

i¼1

Wi � Si ð4Þ

where S is the score for the subscripted variable and W is

the weighing factor derived from the PCA. It was assumed

that the higher the index value (ranging from 0 to 1.0) the

better the soil quality or superior performance of the soil

function.

Statistical Analyses

PCA, correlation, regression were done employing SPSS

16.0 (SPSS Inc., USA). The yields were tested at P = 0.05

by using Windows-based PROC GLM (SAS Institute Inc.,

1985).

Results and Discussion

Effect of Water, Tillage and Nutrient Management

on Rice Grain Yield

The puddling treatment exhibited significant influence on the

grain yield of rice as the puddled plots showed nine times

higher yield (0.96 t ha-1) than the non-puddled plots

(0.11 t ha-1) (Table 1) as it can create optimal soil physical

conditions, conducive for more efficient growth of roots

while non-puddled conditions could make the soil harder for

root penetration [29], resulted in variations in rice produc-

tivity [10]. Of the water management treatments, irrigation

after 3 days of drainage (W3) exhibited the highest grain

yield (0.64 t ha-1) in contrast to the well-known benefits of

continuous submergence to rice crops for bringing the soil

pH to optimum level of nutrients availability [30] also sup-

ported by related studies [31] based on the manipulation of

irrigation intervals to save water without any yield loss. The

grain yield was also greatly influenced by nutrient manage-

ment strategies. The 150 % NPK treatment (T3) showed the

highest grain yield (0.68 t ha-1) which was equivalent to the

100 % NPK ? FYM treatment (T4). Among nutrient treat-

ments, control showed the lowest grain yield (0.40 t ha-1).

Higher demand that crops had for N and P, were limiting in

our experimental soil and also N losses from the soil–plant

system [32] thus plants often respond well to supra-optimal

doses of NPK. In the puddled scenario, increase in the yield

followed: W1 \ W2 \ W3. On the contrary, the yields did

not differ significantly under different water and nutrient

management conditions.

Fig. 1 Schematic diagram of the framework used to develop SQI

Impact of Integrated Management on Yield Sustainability 27

123

Effect of Water, Tillage and Nutrient Management

on Wheat Grain Yield

The grain yield of wheat was not affected by the puddling

history of rice. Overall, the impact of the frequent irrigation

showed an inclining trend of grain yield as W1 [W2 [ W3 (Table 2). In general, the NT plots showed

significantly lower yields than the CT plots. However,

some of the nutrient treatments did not show significant

differences in the yields obtained under CT and NT. The

previously puddled blocks showed higher yields of wheat

under all the nutrient treatments. The five irrigations

(W1) indicated a larger grain yield than both the three

(W3) and two (W2) irrigation treatments under all the

nutrient management. The T3, 150 % NPK treatment

demonstrated the highest grain yield (3.36 t ha-1) followed

by T2 and T4. Partial substitution of the fertilizer-N by

various organic sources (except the FYM) produced a

smaller yield than the chemical fertilizers alone (T2 and

T3) might be attributed to the enhanced nutrient avail-

ability and improved soil physical properties [33]. A

decline in the yield under zero tillage but increase in yield

under frequent irrigations was previously observed in

wheat [34].

Table 1 Effects of puddling, water and nutrient treatments on grain yield (t ha-1) after harvest of rice crop

Nutrient management (N) Puddling condition (P) Water regime (W) Mean

Puddled Non-puddled

Water regime (W) Water regime (W)

W1 W2 W3 Mean W1 W2 W3 Mean W1 W2 W3

T1 0.56 0.68 0.92 0.72 0.09 0.08 0.06 0.08 0.33 0.38 0.49 0.40

T2 0.77 1.10 1.17 1.02 0.15 0.15 0.10 0.13 0.46 0.63 0.64 0.57

T3 0.94 1.41 1.31 1.22 0.17 0.16 0.10 0.15 0.55 0.79 0.71 0.68

T4 0.89 1.12 1.32 1.11 0.16 0.16 0.10 0.14 0.53 0.64 0.71 0.63

T5 0.72 0.88 1.21 0.94 0.13 0.12 0.09 0.11 0.43 0.50 0.65 0.53

T6 0.62 0.75 1.34 0.90 0.11 0.12 0.09 0.11 0.37 0.43 0.72 0.51

T7 0.69 0.74 1.34 0.92 0.12 0.11 0.09 0.11 0.41 0.42 0.72 0.51

T8 0.68 0.74 1.06 0.83 0.12 0.12 0.09 0.11 0.40 0.43 0.57 0.47

T9 0.65 1.24 1.06 0.98 0.10 0.10 0.07 0.09 0.38 0.67 0.56 0.54

Mean 0.72 0.96 1.19 0.96 0.13 0.12 0.09 0.11 0.43 0.54 0.64 0.54

LSD (P = 0.05) P W N P 9 W P 9 N W 9 N P 9 W 9 N

0.10 0.03 0.10 0.10 0.15 0.16 0.24

Table 2 Effects of puddling history, water, tillage and nutrient management on grain yield (t ha-1) after harvest of wheat crop

Nutrient management (N) Puddling history (P) Water regime (W) Tillage management (T)

Puddled Non-puddled Mean W1 W2 W3 Mean CT NT Mean

T1 1.57 1.50 1.54 1.89 1.53 1.19 1.54 1.57 1.50 1.54

T2 3.21 2.71 2.96 3.35 2.93 2.60 2.96 2.97 2.95 2.96

T3 3.60 3.13 3.37 3.69 3.38 3.02 3.36 3.44 3.29 3.36

T4 3.05 2.58 2.82 3.10 2.80 2.55 2.82 2.91 2.72 2.82

T5 2.80 2.37 2.59 2.86 2.44 2.45 2.58 2.79 2.38 2.58

T6 2.64 2.32 2.48 2.83 2.50 2.12 2.48 2.67 2.29 2.48

T7 2.73 2.26 2.50 2.93 2.35 2.21 2.50 2.68 2.31 2.50

T8 2.64 2.24 2.44 2.75 2.32 2.25 2.44 2.61 2.27 2.44

T9 1.71 1.70 1.71 2.16 1.63 1.34 1.71 1.73 1.69 1.71

Mean 2.66 2.31 2.49 2.84 2.43 2.19 2.49 2.60 2.38

LSD (P = 0.05) P W T N P 9 N W 9 N T 9 N

0.40 0.20 0.11 0.20 0.45 0.39 0.29

28 D. Bhaduri et al.

123

Correlation Between Soil Quality Indicators and Crop

Yields

The correlations between the seventeen indicators for soil

quality and the grain yields revealed three indicators, viz,

BD (r = 0.73), available Fe (r = 0.61) and soil respiration

(r = 0.47) were significantly (P \ 0.01) correlated with

the grain yield of rice (Table 3), supported by other studies

[10, 35]. It is evident that soil basal respiration emerged as

a potent indicator for evaluating soil productivity [36].

After the harvest of wheat, MWD (r = -0.29), SOC

(r = -0.26) and MWHC (r = -0.22) showed a negative

correlation with the grain yield of wheat, while BD

(r = 0.22), WSA (r = 0.20) and available N (r = 0.20)

exhibited a positive correlation and this is in conformity

with the results reported [9].

Regression Analysis

Regression equations were developed by encompassing the

indicators emerging from the PCA-based MDS under

productivity goal with the grain yields of rice and wheat

(Table 4). The data were further subjected to stepwise

regression to find evidence for the most influential factors.

It revealed that the available Fe, WSA and MBC were the

three most important indicators explaining a variability of

42 % in the rice grain yield, while SOC along with the

available Mn could explain a small variability of 7 % in the

wheat grain yield. WSA constitute an important indicator

of soil quality as it plays a significant role in improving the

pore size distribution, water infiltration, aeration, SOC

storage and plant root growth, leads to better economic

yield.

Correlation Between SQI and Crop Yields

SQI under conditions of enhanced productivity and envi-

ronmental protection as the management goals, a correlation

study was conducted to judge whether the SQI had an impact

on the crop yield with the assumption that they would be

closely interlinked (Table 5). It demonstrated a clear rela-

tionship between PCASQI-P and the grain yield of rice

(r = 0.44, P \ 0.01) which meant that the SQI was associ-

ated with the crop yield, similar to the previous reported

observation [11]. For wheat, both PCASQI-P (r = -0.28,

P \ 0.01) and CFSQI-EP (r = -0.30, P \ 0.01) were sig-

nificantly but negatively correlated with the grain yield

indicated yield might be sacrificed to maintain good soil and

Table 3 Correlation between soil quality indicators and crop yields

S. No. Indicators Correlation coefficient (r)

Rice grain

yield

Wheat grain

yield

1 BD 0.73** 0.22*

2 MWHC 0.11 -0.07

3 MWD -0.22 -0.29**

4 WSA 0.04 0.20*

5 HC -0.14 -0.22*

6 SOC 0.15 -0.26**

7 Available N 0.18 0.20*

8 Available P 0.05 -0.05

9 Available Zn -0.07 -0.03

10 Available Cu 0.09 -0.06

11 Available Fe 0.61** 0.15

12 Available Mn 0.12 -0.16

13 DHA 0.03 0.04

14 MBC 0.08 -0.05

15 PMN -0.05 -0.11

16 Soil respiration (RESP) 0.47** 0.12

17 Metabolic quotient (qCO2) 0.21 0.10

* Correlation is significant at 0.05 level (two-tailed); ** correlation is

significant at 0.01 level (two-tailed)

Table 4 Regression equations for PCA-based MDS indicators under productivity goal with grain yields

Crop Regression equations R2

Rice Multiple 0.43**

Y = -1.206 ? 0.673(Fe)** - 0.073(HC) - 0.089(Zn) - 0.016(MWHC) ? 0.226(WSA)**

? 0.035(PMN) ? 0.189(MBC)

Stepwise 0.42**

Y = -1.319 ? 0.674(Fe)** ? 0.223(WSA)** ? 0.156 (MBC)

Wheat Multiple 0.10**

Y = 1.892 - 0.063(Cu) ? 0.132(WSA) ? 0.016(P) - 0.112(PMN) ? 0.090(RESP) - 0.102(Mn)

? 0.013(DHA) - 0.184(SOC)**

Stepwise 0.07**

Y = 3.441 - 0.215(SOC)** - 0.131(Mn)

Grain yield (Y) as dependent variable; * coefficient is significant at 0.05 level (two-tailed); ** coefficient is significant at 0.01 level (two-tailed)

Impact of Integrated Management on Yield Sustainability 29

123

environmental quality [37] especially in the NT and organic

nutrients receiving plots. However, previous work reported

that the SQI related well to the wheat grain yield in a maize-

wheat (aerobic) cropping system which differs from our

results probably due to inclusion of both the aerobic and

anaerobic soil environments.

In conclusion, puddling with irrigation after 3 days of

drainage and 150 % NPK or 100 % N (25 % N substituted

by FYM) of rice could maintain the soil quality while CT

with five times irrigation and 150 % NPK in wheat

enhanced the productivity with no adverse effects on soil

quality. The improvement in soil quality via NT might not

always be reflected in higher crop productivity. Fe, WSA

and MBC in soil under rice, and SOC with Mn in wheat,

contributed the most to higher productivity, should be

periodically monitored and properly managed. PCASQI-P

proved to be a powerful tool for monitoring the rice yield.

Therefore, in future, efforts should be directed to balance

high yields and soil quality because both of them are

equally essential for long-term agricultural sustainability

on a global basis.

Acknowledgments Authors are thankful to Dr. A. K. Singh,

Dr. Man Singh, Dr. Manoj Khanna and Mr. P. B. Agarwal of the

IARI-Mega Project. Debarati Bhaduri is grateful to IARI, New Delhi

for awarding her a Senior Research Fellowship.

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Table 5 Correlation between soil quality indices and crop yields

Techniques

used

Developed SQI for

management goals

Correlation coefficient (r)

Rice grain

yield

Wheat grain

yield

CF SQI-P 0.13 -0.18

SQI-EP -0.10 -0.30**

PCA SQI-P 0.44** -0.28**

SQI-EP 0.04 -0.16

SQI-P soil quality index under productivity goal, SQI-EP soil quality

index under environmental protection goal

* Correlation is significant at 0.05 level (two-tailed); ** correlation is

significant at 0.01 level (two-tailed)

30 D. Bhaduri et al.

123

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