Soil & Tillage Research 144 (2014) 83–95

Long-term tillage, water and nutrient management in rice–wheatcropping system: Assessment and response of soil quality

Debarati Bhaduri 1, T.J. Purakayastha *Division of Soil Science and Agricultural Chemistry, Indian Agricultural Research Institute (ICAR), New Delhi 110012, India

A R T I C L E I N F O

Article history:Received 5 August 2013Received in revised form 5 July 2014Accepted 17 July 2014

Keywords:Organic nutrientIrrigationRice–wheat rotationQuality indices of soilMinimum data setMultivariate statisticsZero tillageNon-linear scoring function

A B S T R A C T

Highly productive rice (Oryza sativa L.) and wheat (Triticum aestivum L.) systems are crucial for millions ofrural and urban poor in the Indo-Gangetic Plains (IGP) of south Asia. Our objectives were to identifyimportant biological, chemical and physical indicators of soil quality and incorporate them into a unifiedsoil quality index (SQI) that could be used to help select best management practices for importantcropping systems. Two tillage, three water management and nine nutrient management treatments wereevaluated. Principal component analysis (PCA) was used to identify critical indicators and their relativeweighting for a soil quality index (SQI) that was developed using the Soil Management AssessmentFramework (SMAF). Two primary goals – productivity (PCASQI-P) and environmental protection (PCSQI-EP) were established. For the productivity goal, seven indicators were evaluated for their contribution tonutrient cycling, two for physical stability and support, and three for water relations. The environmentalquality goal used the same functions and indicators plus three additional indicators affecting filtering andbuffering, and one reflecting biodiversity and habitat. The hypothesis of the study was that the set ofsensitive indicators would vary under contrasting tillage, nutrient and water management which couldbe encompassed to develop unified soil quality indices for assessing management induced changes inrice–wheat cropping system. The results confirmed that management goal strongly influenced indicatorselection and that variations in those indicators can provide early warning against deterioration of soilquality. Puddling and irrigating rice after three days of drainage and using no tillage and two irrigationsfor wheat emerged as promising management for improved soil quality. Applying 25% of therecommended fertilizer N dose using farm-yard manure (FYM) for rice and domestic sewage sludge forwheat also improved soil quality. We conclude that the procedure used for indexing soil quality in thisstudy could not only be extended to neighboring areas of Indo-Gangetic Plain but also validated andexpanded for use in south and south–east Asian countries with similar soils and cropping systems.

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1. Introduction

Annual rice and wheat cropping systems occupy an area of10.5 M ha in the Indo-Gangetic Plains (IGP) of south Asia, but oftenproduce low yields because of inadequate nutrient and inappro-priate water management practices. This occurs in part because theannual rice–wheat system represents contrasting agronomicpractices with recurring transitions from anaerobic–aerobic fieldconditions that affect soil structure along with nutrient relations,

* Corresponding author. Tel.: +91 11 25841494; fax: +91 11 25841529.E-mail addresses: debarati.ssiari@gmail.com (D. Bhaduri),

tpurakayastha@gmail.com, tapanjp@iari.res.in (T.J. Purakayastha).1 Present address: Directorate of Groundnut Research (ICAR), Junagadh 362001,

Gujarat, India.

http://dx.doi.org/10.1016/j.still.2014.07.0070167-1987/ã 2014 Elsevier B.V. All rights reserved.

crop growth, and associated pests and diseases (Timsina andConnor, 2001).

In recent years, yields for high-input rice–wheat systemsthroughout the IGP have stagnated or shown a declining trend(Flinn and De Datta, 1984; Cassman and Pingali, 1995). Thistrend has been noted in many long-term experimental trialsconducted at various locations in India (Yadav, 1998; Duxburyet al., 2000) and has resulted in increasing concerns for long-term sustainability.

The causes for yield stagnation or decline are not well-defined,but may include changes in soil quality parameters (Kang et al.,2005). Soil quality and its direction of change with time is aprimary indicator of whether agriculture is sustainable (Karlenet al., 1997). Soil quality refers to the capacity of the soil to sustainproductivity and maintain environmental quality (Lal, 1993) andhas three distinct components: chemical, physical, and biological.

84 D. Bhaduri, T.J. Purakayastha / Soil & Tillage Research 144 (2014) 83–95

Both long-term productivity and the capacity of a soil forenvironmental regulation depend on interactive effects of thesethree attributes (Bezdick et al., 1996).

As a complex functional state, soil quality cannot be measureddirectly; rather soil quality indicators are measurable attributesthat influence the capacity of soil to perform crop production orenvironmental functions and are sensitive to change in land use,management and conservation practices. Hence the necessity wasfelt to create minimum data sets of indicators for different scaleslike fields (Bastida et al., 2006), landscapes and regions (Cluzeauet al., 2012; Tesfahunegn, 2013). Integrated soil quality indicesbased on a combination of soil properties provide a betterindication of soil quality than individual parameters. Thus, Karlenet al. (1994) developed a soil quality index (SQI) through‘Conceptual Framework’ model to quantify parameters such aschemical, biological and physical properties that interact in acomplex way to assess soil quality. Andrews et al. (2004) improvedsoil quality assessment protocol by designing “Soil ManagementAssessment Framework (SMAF)”, where indicators are selectedbased on management goals, associated soil functions and othersite-specific factors (Paz-Ferreiro and Fu, 2013).

In recent years, soil quality research has increased throughoutthe world, due to importance of assessing productivity of differentmanagement and cropping systems. Use of various models andcomputation techniques for indexing was also noted. Soil, water,and nutrient management, such as reduced tillage and use of raisedbeds, can avoid the deleterious effects of puddling on soil structureand fertility, improve water- and nutrient-use efficiencies, andincrease crop productivity (Timsina and Connor, 2001). Neverthe-less, there are conflicting reports on the effect of different puddlingand tillage practices on various soil properties within the annualrice and wheat systems of India. Consequently, it is essential toidentify the most sensitive soil properties to assess the effects ofvarious managements. However, as previously stated, rice andwheat are grown under contrasting soil environments (aerobic–anaerobic) and with different management. Therefore, the set ofsoil quality indicators identified for assessing the two crops areexpected to be diverse. However, if the most sensitive parameterscould be used to develop unified SQIs, they could be used to assesssoil quality and thus sustainability in a holistic way. Our objectivewas to identify important biological, chemical, and physicalindicators of soil quality that are sensitive to evaluate manage-ments used for rice–wheat systems on Typic Haplustept nearDelhi, India. Our goal was to use these indicators to develop anoverall SQI that could be of further help to identify the best

Table 1A subset of potential indicators for each function under a specific management goal fo

Management goal Supporting soil functions

Productivity Nutrients cycling

Physical stability and supp

Water relations

Resistance and resilience

Environmental protection (All functions of productiviFiltering and buffering

Biodiversity and habitat

practices for this important cropping system. The novelty of thisstudy lies in assessing the soil quality under integrated tillage,water and nutrient management where both productivity andenvironmental protection are critical. Successful development ofthis holistic approach for soil quality assessment may also be usefulfor similar agro-climatic areas throughout the world.

2. Materials and methods

2.1. Study area and experimental details

This soil quality assessment study was imposed on a long-termexperiment (7 years) located at the Indian Agricultural ResearchInstitute farm near New Delhi (28�N latitude, 77�E longitude,250 m above MSL). The original experiment was initiated in 2001and has used the same set of nutrient treatments, rice and wheatvarieties, and management each year. The climate is semi-arid,sub-tropical with mean annual precipitation of 650 mm, most ofwhich is received during the rainy season (July–September). Themean annual minimum and maximum temperatures are 18 �Cand 35 �C, respectively. The soil (Holambi series, hyperthermicfamily, Typic Haplustepts) is an alkaline, non-calcareous, alluvial,sandy clay loam texture (48 mg kg�1 sand, 29 mg kg�1 silt,23 mg kg�1 clay) (USDA classification), with low CEC (14.6 cmol(+) kg�1 soil). Initial pH, EC, organic carbon, available nitrogen,phosphorus and potassium concentration were 7.8, 0.45 dS m�1,4.8 g kg�1, 102 mg kg�1, 9.9 mg kg�1 and 160 mg kg�1, respectively.

The field experiment was laid out in a split plot design withthree replications per treatment in plot size of 7.5 m � 5.75 m(buffer rows between treatments were of 60 cm). The rice cultivar‘Pusa Sugandh 3’ was transplanted during wet season of 2008 intothe main tillage treatments of either puddled (transplanted) orunpuddled (wet seeded rice). The main plots were split for threeirrigation treatments: continuous submergence (W1), irrigationafter one day of drainage (W2), and irrigation after three days ofdrainage (W3). The nutrients through chemical fertilizers,organics and their combinations would definitely influence thecrop growth vis-à-vis sensitive soil physical, chemical and moreimportantly biological quality parameters. Nine nutrient man-agement treatments: 0% NPK (T1), 100% NPK (T2), 150% NPK (T3),100% N (25% N substituted by FYM) + PK (T4), 100% N (25% Nsubstituted by green manure, Sesbania) + PK (T5), 100% N (25% Nsubstituted by biofertilizer) + PK (T6), 100% N (25% N substitutedby sewage sludge) + PK (T7), 100% N (25% N substituted by cropresidues incorporated (from previous crop) + PK (T8), and 100% N

r the study period of 2008–2009.

Indicators

Microbial biomass C (MBC)Potentially mineralisable N (PMN)Dehydrogenase activity (DHA)Soil respirationAvailable NAvailable PAvailable micronutrients

ort Soil aggregate stability (WSA)Bulk density (BD)Water holding capacity (WHC)Hydraulic conductivity (HC)Soil organic C (SOC)Soil organic C (SOC)

ty goals) plusBulk density (BD)Hydraulic conductivity (HC)Soil organic C (SOC)Metaboilc quotient (qCO2)

Fig. 1. Results of principal component analysis of soil quality indicators under (a)Productivity (P) and (b) Environmental Protection (EP) goal after harvest of rice; (c)Productivity and (d) Environmental Protection (EP) goal after harvest of wheat.

D. Bhaduri, T.J. Purakayastha / Soil & Tillage Research 144 (2014) 83–95 85

organic sources (50% FYM + 25% biofertilizer + 25% crop residues/green manure) (T9) were allocated to sub-sub plots.

The wheat cultivar, ‘HD 2687’ was sown during winter season of2008–2009 into main plots where the puddled and nonpuddled

treatments were imposed. Three irrigation treatments wereassigned to subplots: five irrigations (W1) at crown root initiation,tillering, jointing, flowering and dough stages, three irrigations(W2) at crown root initiation, jointing and flowering stages andtwo irrigations (W3) at crown root initiation and flowering stages.The same nine nutrient management treatments used for rice wererepeated for wheat. Within each nutrient subplot, two tillagetreatments i.e., conventional tillage (CT) and no-tillage (NT) wereimposed as sub-sub plots.

For both the rice and wheat crops, N (120 kg ha�1) was appliedin the form of urea. All the treatments including the controlreceived recommended doses of P (26.2 kg ha�1) and K (50 kg ha�1)from single super phosphate (SSP) and muriate of potash (MOP),respectively. Well-decomposed farmyard manure (0.5% N, 0.25% Pand 0.4% K on dry weight basis) was incorporated 15 days beforesowing or transplanting of crops for treatments T4 and T9. Greenmanure (Sesbania aculeate L.) at 2 Mg ha�1 was incorporated in soilbefore sowing for treatments T5 and T9. The sewage sludge (3.6% N,1.2% P, 0.45% K on DW basis) was added at the rate of 0.27 t ha�1

year�1 to soil to substitute 25% N in T7 treatment. After separatingthe grain, crop residues were incorporated in the T8 and T9treatments. For wet-seeded rice and wheat, the biofertilizer,Azospirilum brasilense CD JA was coated on the seed surface. Fortransplanted rice, the roots of rice seedlings were dipped in anaqueous suspension of the Azospirillum culture to provide apopulation of 109 cells g�1 of seed for treatments T6 and T9.

2.2. Sampling and methods of analysis

For soil quality assessment, composite surface (0–15 cm) soilsamples were collected from each plot in 2008 after harvesting therice crop and again in 2009 following wheat. A sub-sample of eachcomposite sample was temporarily refrigerated at 4 �C beforeanalysis of biological parameters. The remaining sample was air-dried in shade, ground to pass through a 2 mm sieve, mixedthoroughly and stored for analysis. Separate intact cores with thedimension of 5 cm depth � 5 cm diameter were used for measuringsoil bulk density.

To assess the physical components of soil quality, wemeasured bulk density (Veihmeyer and Hendrickson, 1948),maximum water holding capacity (MWHC) (Keen and Raczkow-ski, 1921), saturated hydraulic conductivity, Ks (Klute and Dirksen,1986), aggregate size distribution (Yoder, 1936) as mean weightdiameter (MWD) and water stable aggregates (WSA) (van Bavel,1953; Kemper and Rosenau, 1986). The chemical components ofsoil quality were assessed by determining soil available organiccarbon by wet digestion method (Walkley and Black, 1934),available N (Subbiah and Asija, 1956), available P (Olsen et al.,1954), and DTPA-extractable minerals Zn, Fe, Cu, and Mn (Lindsayand Norvell, 1978). Available and extractable minerals wereanalyzed by atomic absorption spectrophotometer (Zeenit 700,Analytikjena, Germany). Finally the biological component of soilquality was assessed by measuring of microbial biomass carbon(MBC) (Jenkinson and Powlson, 1976; Snyder and Trofymow, 1984),dehydrogenase activity (DHA) (Tabatabai, 1982), potentiallymineralizable N (PMN) (Keeney, 1982), and soil respiration(Anderson, 1982). The respiratory quotient (qCO2) was calculatedby dividing soil respiration and MBC.

2.3. Soil quality assessment

The Soil Management Assessment Framework (SMAF;Andrews et al., 2004) was used to select, interpret, and integratethe indicator measurements into an overall soil quality indexvalue for both rice and wheat. Two approaches were used forcreating minimum data set (MDS): (1) the conceptual framework

Table 2Final PCA-based equations for different management goals under rice–wheat cropping system.

Crop Management goal PCA-based normalized equations

Rice Productivity SQI(PR) = 0.212 � S(Fe) + 0.167 � S(HC) + 0.145 � S(Zn) + 0.145 � S(MWHC) + 0.135 � S(WSA) + 0.114 � S(PMN) + 0.083 � S(MBC)Environmentalprotection

SQI(EPR) = 0.228 � S(MQ) + 0.163 � S(Cu) + 0.163 � S(MBC) + 0.137 � S(MWHC) + 0.125 � S(WSA) + 0.106 � S(PMN) + 0.078 � S(N)

Wheat Productivity SQI(PW) = 0.188 � S(Cu) + 0.170 � S(WSA) + 0.136 � S(P) + 0.115 � S(PMN) + 0.104 � S(RESP) + 0.096 � S(Mn) + 0.096 � S(DHA) + 0.096 � S(SOC)

Environmentalprotection

SQI (EPW) = 0.196 � S(MBC) + 0.172 � S(Zn) + 0.151 � S(MWHC) + 0.114 � S(PMN) + 0.101 � S(SOC) + 0.091 � S(DHA) + 0.091 � S(Mn) + 0.084 � S(N)

S is the score derived from NLSF and coefficients are derived from weighing factor obtained from the results of PCA.

86 D. Bhaduri, T.J. Purakayastha / Soil & Tillage Research 144 (2014) 83–95

or expert opinion and (2) the principal component analysis. Forthe conceptual framework, “Productivity” and “EnvironmentalProtection” were chosen as primary management goals. Indica-tors chosen to assess various critical functions following bothcrops are presented in Table 1. A principal component analysis(PCA; SPSS 10.0) and an expert opinion (EO or conceptual)method were both used to create the minimum data set (MDS) foranalysis. For additional information about the process, the readeris referred to Andrews et al., 2001, 2004; Kaiser, 1960; Masto et al.,2008; Bhaduri et al., 2014.

Soil Quality IndexðSQIÞ ¼ Sni¼1Wi � Si

ðfor both productivity and environmental protection goalÞ

where, S is the score for the subscripted variable and W is theweighting factor derived from PCA. Here the assumption is thathigher index scores mean better soil quality or greater

Table 3Scoring function of chosen soil quality indicators.

Indicator Scoringcurve

Lowerthreshold (LT)

Upperthreshold (UT)

baseline(A)

Slba

1.PhysicalMaximum water holdingcapacity (%)

More isbetter

40 80 60

Water stable aggregates(%)

More isbetter

15 70 30

Hydraulic conductivity(cm h�1)

More isbetter

0.25 1.0 0.625 1

2. ChemicalSOC(g kg�1)

More isbetter

0 12.9 6.40

Available N (mg kg�1) More isbetter

0 178.5 89.2

Available P (mg kg�1) More isbetter

11.16 44.64 27.9

DTPA Zn(mg kg�1) More isbetter

Rice: 0 1.5 0.75

Wheat: 0.2 6.9 0.9

DTPA Cu(mg kg�1) More isbetter

0.150 0.533 1.70

DTPA Fe(mg kg�1) More isbetter

3.4 15 9.2

DTPA Mn(mg kg�1) More isbetter

0.2 14 7.1

3. Biological MBC(mg kg�1)

More isbetter

75 700 300

DHA (mg TPF g�1 h�1) More isbetter

0 1.5 0.75

Soil respiration (mg CO2-Cg�1 h�1)

More isbetter

20 250 135

PMN (mg kg�1) More isbetter

15 30 22.5

Metabolic quotient (qCO2) Less isbetter

0.16 1.42 0.79 �

performance of soil function. Soil quality indices were tested fortheir level of significance at p = 0.05 (SAS Institute Inc., 1985).

3. Results and discussion

3.1. PCA-based indicator selection following rice

The PCA method selected a minimum data set from thenumerous biological, chemical, and physical properties (Chaud-hury et al., 2005) that had been measured. Seven principalcomponents (PCs) associated with productivity were identified onthe basis of Eigen value >1 and cumulative variation was 72%(Fig. 1a). From PC1 and PC2, available soil Fe and HC were chosenfor MDS and indexing purpose. From PC3, available Zn (0.577) andMWHC (0.574) were chosen. WSA was selected from PC 4 for MDS.From PC5, PMN was retained for indexing purpose. In PC6, MWD inspite of showing higher factor loading was not considered as WSA

ope atseline

R2 value oflogistic curve

Source of limits

0.350 0.63 Natural ecosystem Masto et al. (2007)

0.133 0.71 Cultivated ecosystem Harris et al. (1996)

0.5 0.97 Cultivated ecosystem in IARI soil

0.75 0.69 Grass pasture Glover et al. (2000)

0.04 0.57 Cultivated ecosystem Masto et al. (2007)

0.425 0.92 Cultivated ecosystem Chaudhury et al. (2005)

4.00 0.51 Cultivated ecosystem Masto et al. (2007)

1.25 0.71 Singh (1999)4.00 0.57 Rattan et al. (1999)

0.85 0.80 Rattan et al. (1999)

0.75 0.90 Rattan et al. (1999)

0.033 0.73 Karlen et al. (1994)

5.5 0.93 Cultivated ecosystem in IARI soil

0.055 0.66 Cultivated ecosystem Rudrappa et al. (2006)

0.5 0.84 Natural ecosystem; Andrews et al.(2002)

7.5 0.82 Grass pasture, no-till and cultivated ecosystem;Purakayastha et al. (2008)

Fig. 2. Non-linear scoring functions for MDS – indicators after harvest of rice.

D. Bhaduri, T.J. Purakayastha / Soil & Tillage Research 144 (2014) 83–95 87

was chosen to represent soil aggregation. From PC7, MBC wasretained for indexing. The soil parameters chosen for theproductivity goal were available Fe, HC, available Zn, MWHC,WSA, PMN, and MBC.

Seven PCs were chosen for the environmental protection goalon the basis of Eigen value >1 and cumulative variation was 71%(Fig. 1b). In PC1, MQ (0.775) and available Fe (0.746) wereconsidered important but finally the former parameter wasretained due to its higher factor loading and correlation with

the latter parameter. PC2 was associated with four highly loadedfactors: available Zn (0.500), available Cu (0.536), MBC (0.500) andMWD (�0.526). Among these, available Zn significantly correlatedwith available Cu (r = 0.55) and the latter was retained due to itshigher factor loading. MWD was discarded because of its factorlower loading and its inverse correlation with available Cu(r = �0.30). MBC did not correlate significantly with any of thefactors considered in PC2, and thus it was retained for MDS andindexing. In PC3, MWHC was the only parameter which emerged

Fig. 3. Non-linear scoring functions for MDS – indicators after harvest of wheat.

88 D. Bhaduri, T.J. Purakayastha / Soil & Tillage Research 144 (2014) 83–95

important due to its higher factor loading and thus it was retained.In PC4, DHA (�0.704) correlated significantly with WSA (0.777)and the latter was retained for indexing due to its higher factorloading. From PC5, PC6 and PC7, highly loaded factors were PMN,available Zn and available N. Available Zn was not retained becauseit was correlated with available Cu in PC2. The indicators chosenwere MQ, available Cu, MBC, MWHC, WSA, PMN, and available N.

3.2. PCA-based indicator selection following wheat

Seven principal components (PCs) associated with productivitywere identified on the basis of Eigen value >1 and cumulativevariation was 69% (Fig. 1c). PC1 was associated with available soilCu (loading factor = 0.757) and was directly chosen for MDS. FromPC2 to PC5, the associated single highest loaded factors were WSA

Table 4Effect of puddling, water and nutrient treatments on PCASQI-P after harvest of rice.

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 0.633 0.623 0.641 0.632 0.425 0.430 0.463 0.440 0.529 0.527 0.552 0.536T2 0.526 0.472 0.587 0.528 0.439 0.363 0.480 0.427 0.483 0.418 0.534 0.478T3 0.739 0.653 0.682 0.691 0.540 0.406 0.517 0.488 0.640 0.530 0.599 0.590T4 0.749 0.757 0.689 0.732 0.515 0.536 0.481 0.511 0.632 0.647 0.585 0.621T5 0.422 0.429 0.544 0.465 0.413 0.375 0.628 0.472 0.417 0.402 0.586 0.468T6 0.470 0.488 0.586 0.515 0.475 0.499 0.501 0.492 0.473 0.494 0.544 0.503T7 0.621 0.666 0.541 0.609 0.537 0.567 0.556 0.553 0.579 0.616 0.549 0.581T8 0.613 0.490 0.705 0.602 0.615 0.288 0.506 0.470 0.614 0.389 0.605 0.536T9 0.804 0.483 0.780 0.689 0.721 0.423 0.757 0.633 0.763 0.453 0.768 0.661Mean 0.620 0.562 0.639 0.607 0.520 0.432 0.543 0.498 0.570 0.497 0.591 0.553

LSD P W N P � W P � N W � N P � W � N(P = 0.05) 0.003 0.003 0.002 0.004 0.004 0.005 0.007

D. Bhaduri, T.J. Purakayastha / Soil & Tillage Research 144 (2014) 83–95 89

(0.630), available P (0.524), PMN (0.512) and Respiration (RESP)(�0.495), respectively and the same were retained for indexing.Under PC6, three highly loaded factors, within 10% variation in thevalues of Eigen vectors, were available Mn, DHA and SOC (loadingfactor of �0.458, 0.455 and 0.421, respectively), and all thesefactors were retained for indexing as they did not correlate witheach other. From PC7, DHA was the only parameter which wasfound important due to its high factor loading (0.644). Therefore,the indicators emerged for SQI under productivity goal afterharvest of wheat were available Cu, WSA, P, PMN, Respiration, Mn,DHA and SOC.

Seven PCs were chosen for the environmental protection goalon the basis of Eigen value >1 with the cumulative variation of 69%(Fig. 1d). In PC1, MBC (0.816) and MQ (�0.804) were foundimportant but the former parameter was chosen for indexing dueto its inverse correlation with the latter parameter (r = �0.86). PC2was associated with two highly loaded factors, available Zn (0.744)and available Cu (0.719), but being correlated with each other(r = 0.58), only available Zn was retained for indexing based on itshigher loading. From PC3, MWHC was considered important due toits higher factor loading (factor = 0.629). Though PC4 had PMN(0.658) and HC (0.625), the latter was discarded due to its lowerloading and significant correlation with PMN. In PC5, SOC (0.483)had the highest factor loading and was retained for indexing. PC6had DHA and available Mn (loading factor of 0.469 and �0.459,respectively) and as there was no correlation existed betweenthem, both were retained for indexing. From PC7, only available N(0.487) had higher factor loading, and therefore it was chosendirectly for indexing. Thus, the indicators emerged for SQI underenvironmental protection goal for wheat were MBC, available Zn,MWHC, PMN, SOC, DHA, available Mn and available N.

Based on the MDS, the PCA-based normalized SQI equationsunder productivity and environmental protection goals weredeveloped (Table 2). Most of the indicators identified by theprocess (MWHC, WSA, PMN, MBC for rice and PMN, available Mn,DHA and SOC for wheat) were similar for both the productivity andenvironmental protection management goals, which might be dueto overlapping of some soil functions (nutrient cycling, physicalstability and support, water relations, resistance and resilience).

With regard to the ‘filtering and buffering’, soils have a naturalcapacity to degrade or reduce toxic or hazardous compounds,whereas the ‘biodiversity and habitat’ refers to the soils’ naturalability to provide the necessary conditions to support a variety ofunstressed plants and animals (Andrews et al., 2004). These are thetwo additional soil functions that were considered underenvironmental protection goal. Microbial quotient was proposed

as one of the important indicators for environmental protection(Rudrappa et al., 2006; Purakayastha et al., 2008). This indicatorappeared to be important for rice but not for wheat. In wheat, MBCwas the most important quality indicator for the environmentalprotection goal. Nitrogen, being one of the key nutrients, emergedsignificant for both rice and wheat for environmental protectiongoal. Water stable aggregates was equally important for both thecrops and goals. Among biological indicators, MBC and DHA,representing physiologically active microbial population involvedin many functions of soil ecosystems like nutrient cycling, carbondynamics etc. respond more quickly to changes in management(García-Orenes et al., 2012; Paz-Ferreiro and Fu, 2013) andemerged important for rice and wheat, respectively under boththe goals.

There are some commonalities in the sensitive indicators thatwere emerged from our study with that reported earlier, from Indiaand abroad. Mohanty et al. (2007) used WSA and organic matter astwo important driving indicators for development of SQI underrice–wheat cropping system in a Vertisol. Chaudhury et al. (2005)found that available P, and DHA were the two most importantindicators for PCA-based SQI developed for rice–wheat–jutecropping sequence in an Inceptisol of India. Even in the NorthernMississippi Valley Loess Hills, the most discriminating soil qualityfactors out of the land uses identified were PMN, MBC and WSA(Brejda et al., 2000). The additional sensitive soil qualityparameters that emerged from our study might be due todifference in soil, climate, cropping system and most importantlythe management component of the experiment under study.

3.3. Indicator interpretation

PCA-screened indicator values were normalized on a scale of 0–1 using the non-linear scoring function (NLSF) following theapproach of Andrews et al. (2002) and Masto et al. (2008). Afterdeciding the shape of the anticipated response (more is better, lessis better or optimum is better), the limits or threshold values wereassigned for each indicator (Table 3). Scoring function curves of allMDS-screened indicators were presented for both after harvest ofrice (Fig. 2) and wheat (Fig. 3).

3.4. Effect of integrated tillage-water-nutrient management onPCASQI-P and PCASQI-EP after rice

After rice, the PCA based SQI-P maintained significantly highervalue under puddled than non-puddled condition (Table 4).Puddling is reported to significantly enhance rice yield (Mohanty

Fig. 4. Effect of nutrient management on (a) PCASQI-P, (b) PCASQI-EP and percentage distribution of indicators to SQI after harvest of rice (different lower case lettersfollowing bars indicate that treatment means are statistically different).

90 D. Bhaduri, T.J. Purakayastha / Soil & Tillage Research 144 (2014) 83–95

et al., 2007). This might be due to creation of optimal conditionswhich was congenial rice root growth and minimization of waterpercolation. Thus these factors might have intensified the rhizo-spheric processes (biological, chemical and physical) that resultedin higher grain yield of rice (data not presented). It is a fact thathigher aboveground yield is associated with higher root biomassbecause of definite root to aboveground biomass ratio (Purakayas-tha et al., 2008). Long-term accumulation of root biomass in soilunder rice–wheat cropping system increased SOC content as wellas enhanced other physical, chemical and biological quality

indicators responsible for higher productivity (Bhaduri et al.,2014).

Based on the SQI-P, three-days of drainage (W3) was the bestirrigation practice for rice followed by continuous submergence(W1). Among nutrient management, T9 (full organics) maintainedthe highest SQI (0.661), followed by T4 (25% N by FYM) and T3 (150%NPK), whereas T5 (25% N by green manure) exhibited the lowest PCAbased SQI-P. The effect of water regimes on the PCA based SQI-Pfollowed a decreasing trend (W3 > W1 > W2) in both puddled andnon-puddledsoils. Forpuddledcondition, T4(25%N byFYM) showed

Table 5Effect of puddling, water and nutrient treatments on PCASQI-EP after harvest of rice.

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 0.470 0.507 0.847 0.608 0.407 0.704 0.708 0.607 0.439 0.606 0.778 0.607T2 0.389 0.699 0.794 0.628 0.353 0.513 0.822 0.562 0.371 0.606 0.808 0.595T3 0.704 0.573 0.825 0.701 0.750 0.540 0.769 0.687 0.727 0.557 0.797 0.694T4 0.829 0.905 0.615 0.783 0.792 0.745 0.777 0.771 0.811 0.825 0.696 0.777T5 0.643 0.522 0.465 0.543 0.655 0.575 0.898 0.709 0.649 0.549 0.682 0.626T6 0.689 0.678 0.813 0.727 0.756 0.609 0.691 0.685 0.723 0.644 0.752 0.706T7 0.835 0.939 0.811 0.862 0.876 0.877 0.796 0.850 0.856 0.908 0.803 0.856T8 0.482 0.397 0.818 0.566 0.864 0.467 0.735 0.688 0.673 0.432 0.776 0.627T9 0.828 0.711 0.855 0.798 0.788 0.603 0.841 0.744 0.808 0.657 0.848 0.771Mean 0.652 0.659 0.760 0.691 0.693 0.626 0.782 0.700 0.673 0.643 0.771 0.695

LSD P W N P � W P � N W � N P � W � NP = 0.05) 0.006 0.005 0.008 0.008 0.012 0.014 0.020

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the highest PCA based SQI-P (0.732) followed by T3 (150% NPK) andT9 (full organics), whereas for non-puddled condition, T9 (fullorganics) had the highest PCASQI-P (0.633) followed by T7 (25% N bysewage sludge) and T4 (25% N by FYM). Under non-puddledcondition the full organics outperformed other treatments becauseof less disturbance that contributed significantly to preservation ofcarbon or indirectly influenced functioning of other soil qualityparameters. FYM, being comparatively a stable product, performedwell in spite of disturbance under puddled condition. In this regard,Chaudhury et al. (2005) reported that balanced fertilization alongwith manures improvedsoil aggregation as well as biological activitythat resulted in higher soil quality and sustainable productivity in

Table 6Effect of puddling history, water, tillage and nutrient treatments on PCASQI-P after har

Treatments Pulled Non-puddled

W1 W2 W3 Mean W1 W2

T1 CT 0.295 0.398 0.412 0.369 0.459 0.406NT 0.363 0.319 0.354 0.345 0.415 0.498

T2 CT 0.420 0.308 0.325 0.351 0.462 0.322NT 0.343 0.320 0.327 0.330 0.461 0.396

T3 CT 0.309 0.351 0.332 0.331 0.372 0.480NT 0.358 0.434 0.313 0.368 0.322 0.435

T4 CT 0.384 0.435 0.360 0.393 0.533 0.432NT 0.324 0.475 0.444 0.414 0.340 0.457

T5 CT 0.346 0.465 0.355 0.389 0.372 0.447NT 0.321 0.432 0.480 0.411 0.502 0.480

T6 CT 0.333 0.442 0.462 0.412 0.303 0.340NT 0.489 0.341 0.470 0.433 0.366 0.431

T7 CT 0.301 0.477 0.621 0.466 0.560 0.442NT 0.541 0.552 0.493 0.529 0.358 0.495

T8 CT 0.314 0.440 0.513 0.422 0.434 0.503NT 0.446 0.418 0.468 0.444 0.391 0.490

T9 CT 0.465 0.488 0.468 0.474 0.481 0.519

NT 0.328 0.492 0.478 0.433 0.529 0.401

Mean 0.371 0.421 0.426 0.406 0.426 0.443CT 0.352 0.423 0.428 0.442 0.432NT 0.390 0.420 0.425 0.409 0.454

LSD P W P � W T P � T W � T P � W � T N

(P = 0.05) 0.002 0.001 0.002 0.001 0.002 0.002 0.003 0.002

rice-based cropping system in Indo-Gangetic alluvial soils of India.Nevertheless,Kangetal. (2005) reportedthat long-termapplicationsof organic manures in rice/corn–wheat cropping system increasedthe sustainability index, combining nutrient index, microbial indexand crop index. Percentage wise distribution of major indicatorsrevealed that the most significant indicators contributing to SQIunder different nutrient management were WSA > Zn > MWHC(Fig. 4a).

The PCA based SQI-EP was significantly higher under non-puddled condition (0.700) than puddled condition (0.691) (Table 5)in rice. The effect of water regimes in this goal followed the sametrend as observed in the PCASQI-P goal: W3 > W1 > W2. Irrigation

vest of wheat.

Water treatments Mean

W3 Mean W1 W2 W3 Mean

0.409 0.424 0.377 0.402 0.410 0.396 0.386 0.305 0.406 0.389 0.409 0.329 0.376

0.399 0.394 0.441 0.315 0.362 0.373 0.371 0.368 0.408 0.402 0.358 0.347 0.369

0.334 0.395 0.341 0.416 0.333 0.363 0.373 0.434 0.397 0.340 0.434 0.374 0.383

0.316 0.427 0.458 0.433 0.338 0.410 0.406 0.377 0.391 0.332 0.466 0.410 0.403

0.492 0.437 0.359 0.456 0.424 0.413 0.432 0.488 0.490 0.412 0.456 0.484 0.450

0.460 0.368 0.318 0.391 0.461 0.390 0.409 0.475 0.424 0.427 0.386 0.472 0.428

0.549 0.517 0.430 0.459 0.585 0.492 0.495 0.551 0.468 0.450 0.524 0.522 0.498

0.477 0.471 0.374 0.471 0.495 0.447 0.448 0.486 0.456 0.419 0.454 0.477 0.450

0.416 0.472 0.473 0.504 0.442 0.473 0.462 0.485 0.472 0.428 0.446 0.481 0.452

0.435 0.434 0.398 0.432 0.430 0.420 0.428 0.397 0.427 0.428 0.441 0.400 0.437 0.433

P � N W � N T � N P � W � N P � T � N W � T � N P � W � T � N0.004 0.004 0.003 0.006 0.005 0.006 0.001

Fig. 5. Effect of nutrient management on (a) PCASQI-P, (b) PCASQI-EP and percentage distribution of indicators to SQI after harvest of wheat (different lower case lettersfollowing bars indicate that treatment means are statistically different).

92 D. Bhaduri, T.J. Purakayastha / Soil & Tillage Research 144 (2014) 83–95

after three-days of drainage (W3) seemed to be promising for allthe PCA-based SQIs in rice. Numerous studies conducted on themanipulation of interval of irrigation to save water without anyyield loss have demonstrated that continuous submergence is notessential for obtaining high rice yields (Singh et al., 1996; Ye et al.,2013), which in-turn can maintain good soil quality. In this regard,Rajaram and Erbach (1999) reported that alternate wetting anddrying cycles increased the soil strength as indicated by conepenetration resistance,soil cohesion, and soil aggregate size.

Application of sewage-sludge (T7) showed the highest value forthe PCA based SQI-EP (0.856) followed by T4 and T9 after harvest ofrice. In non-puddled soil condition, the PCA based SQI-EP followedthe same trend across three water regimes, but in puddledcondition W3 showed the highest PCA based SQI-EP. Among thenine nutrient management treatments, T7 (followed by T9 and T4)showed the highest PCA based SQI-EP under both puddled andnon-puddled soil conditions. The indicators that contributedsignificantly to SQI were MQ > WSA > MWHC > MBC (Fig. 4b).

Table 7Effect of puddling history, water, tillage and nutrient treatments on PCASQI-EP after harvest of wheat.

Treatments Puddled Non-puddled Water treatments Mean

W1 W2 W3 Mean W1 W2 W3 Mean W1 W2 W3 Mean

T1 CT 0.392 0.642 0.661 0.565 0.721 0.534 0.570 0.609 0.557 0.588 0.616 0.587 0.568NT 0.475 0.528 0.623 0.542 0.511 0.590 0.563 0.555 0.493 0.559 0.593 0.548

T2 CT 0.588 0.289 0.621 0.499 0.767 0.349 0.682 0.599 0.677 0.319 0.651 0.549 0.509NT 0.585 0.284 0.452 0.440 0.588 0.436 0.467 0.497 0.586 0.360 0.460 0.469

T3 CT 0.595 0.657 0.468 0.573 0.446 0.493 0.477 0.472 0.520 0.575 0.472 0.523 0.534NT 0.453 0.759 0.463 0.559 0.439 0.584 0.568 0.530 0.446 0.672 0.516 0.545

T4 CT 0.675 0.739 0.478 0.630 0.457 0.483 0.460 0.467 0.566 0.611 0.469 0.549 0.558NT 0.598 0.778 0.569 0.648 0.364 0.531 0.561 0.485 0.481 0.654 0.565 0.567

T5 CT 0.323 0.539 0.430 0.430 0.429 0.417 0.758 0.535 0.376 0.478 0.594 0.482 0.508NT 0.384 0.554 0.693 0.544 0.535 0.458 0.575 0.523 0.459 0.506 0.634 0.533

T6 CT 0.428 0.575 0.565 0.522 0.318 0.452 0.761 0.510 0.373 0.513 0.663 0.516 0.555NT 0.574 0.677 0.648 0.633 0.338 0.588 0.732 0.552 0.456 0.632 0.690 0.593

T7 CT 0.548 0.541 0.792 0.627 0.568 0.541 0.562 0.557 0.558 0.541 0.677 0.592 0.577NT 0.512 0.614 0.697 0.607 0.392 0.602 0.557 0.517 0.452 0.608 0.627 0.562

T8 CT 0.450 0.746 0.591 0.596 0.393 0.486 0.586 0.488 0.422 0.616 0.589 0.542 0.572NT 0.546 0.643 0.595 0.595 0.513 0.697 0.621 0.610 0.529 0.670 0.608 0.602

T9 CT 0.488 0.609 0.581 0.560 0.566 0.579 0.533 0.560 0.527 0.594 0.557 0.560 0.549NT 0.410 0.589 0.587 0.528 0.641 0.395 0.613 0.550 0.525 0.492 0.600 0.539

Mean 0.501 0.598 0.584 0.561 0.499 0.512 0.592 0.534 0.500 0.555 0.588 0.548CT 0.499 0.593 0.576 0.518 0.482 0.599 0.508 0.537 0.588NT 0.504 0.603 0.592 0.480 0.542 0.584 0.492 0.573 0.588

LSD P W P � W T P � T W � T P � W � T N P � N W � N T � N P � W � N P � T � N W � T � N P � W � T � N(P = 0.05) 0.004 0.002 0.004 0.001 0.004 0.002 0.005 0.002 0.004 0.004 0.003 0.006 0.005 0.005 0.002

D. Bhaduri, T.J. Purakayastha / Soil & Tillage Research 144 (2014) 83–95 93

3.5. Effect of integrated tillage-water-nutrient management onPCASQI-P and PCASQI-EP after wheat

After wheat, the PCA based SQI-P declined more than afterrice; previously non-puddled soil had higher SQI than puddledsoil. This trend also remained unchanged under three waterregimes and two tillage treatments (Table 6). After wheat,previously non-puddled soil had the higher PCA based SQI-Pthan previously puddled soil. This was possibly due to lessmechanical disturbance in rice and greater maintenance of goodsoil structure that created better soil physical conditions forsubsequent wheat crop. Three irrigations (W2) emerged as themost efficient water management option in wheat for main-taining higher SQI under PCA approach.

NT soils showed significantly higher SQI only under previouslypuddled soil. As compared to CT, NT showed higher PCA based SQI-P and the same was true for the PCA based SQI-EP. In NT-wheatsystem, the improvement in most of the soil quality indicatorsresulted in higher SQI values under both the management goals.Limited soil disturbance in NT is reported to enhance SOC, soilaggregation, bulk density, particulate organic carbon and MBC(Purakayastha et al., 2008). Our results corroborated the findings ofWander and Bollero (1999) who reported that NT practices greatlyimproved the biological and physical condition of the soil in theupper 15 cm despite increased soil consolidation. Mohanty et al.(2007) also reported a higher SQI under zero tillage in wheat. TheNT soil exhibited higher SQI values in all the three water regimesunder PCA based SQI-P (0.423) than PCA based SQI-P (0.417) in CTsoil. Among the three water regimes, W2 exhibited maximum SQIvalue. Though the PCA based SQI-P in all the nutrient managementtreatments were higher in NT than under previously non-puddledsoil, the water regimes did not show any specific trend. Overall, T7

had the maximum PCASQI-P, followed by T9 and T8. The indicatorsthat contributed significantly towards SQI were WSA > Mn > PMN(Fig. 5a).

PCA based SQI-EP declined after wheat than after rice. It wasfound to be higher under previously puddled block (0.561) thannon-puddled block (0.531). PCA based SQI-EP also maintainedmore value in NT than CT though water regimes did not follow thesimilar trend like PCASQI-P (Table 7). PCA based SQI-EP washighest under W3 water regime and NT soils. However, across thethree water regimes, CT and NT plots did not show any specifictrend. Application of sewage sludge (T7) showed the highest PCAbased SQI-EP (0.577) followed by T8 and T1. Across the majority ofthe nutrient management treatments, previously puddled block,W3 water regime, and NT soils showed higher SQI values than theothers. The indicators that contributed significantly to SQI wereZn > MWHC > Mn > PMN (Fig. 5b).

Increased values of SQI were obtained after harvest of rice.Puddling and submerged (anaerobic) condition during riceseemed to increase the availability of several nutrients (N, P andmicronutrients) and also brought down the soil pH to neutralitythat might have enhanced the availability of nutrients and inturn increased the SQI.

The soil quality indices developed after harvest of wheat grownunder aerobic condition was quite different from that developedafter harvest of rice. The long-term transition from aerobic toanaerobic soil environment might have played a pivotal role indecreasing SQI values after harvest of wheat.

4. Conclusions

The study was aimed at to assess soil quality under twomanagement goals, i.e., crop productivity and environmental

94 D. Bhaduri, T.J. Purakayastha / Soil & Tillage Research 144 (2014) 83–95

protection, by creating minimum data set of indicators, SQIs, andtheir responses under various integrated tillage–water–nutrientmanagement. Principal component analysis was a powerful toolfor creating minimum data set by identifying the most sensitiveparameters along with non-linear scoring function to create widevariations in score for development of soil quality indices underlong-term rice–wheat cropping system. The novel finding of thestudy was the emergence of sensitive soil quality indicators underproductivity as well as environmental protection goals. Thefollowing indicators MWHC, WSA, PMN, MBC after rice andPMN, available Mn, DHA, SOC after wheat exhibited early warningof deterioration soil quality. Puddling and irrigation after three-days of drainage, which economized the water requirement in rice,is therefore recommended for enhancing soil quality. No-tillage inwheat also enhanced soil quality. Partial substitution of N withFYM in rice or domestic sewage sludge in wheat not onlyeconomized chemical fertilizer but also enhanced soil quality.The protocol that developed from this study for indexing soilquality may be applicable for neighboring areas of IGP and valid formany south and south-east Asian countries with similar soil andrice–wheat management systems.

Acknowledgements

We convey our sincere thanks to Dr. A.K. Singh, Dr. Man Singhand the technical staffs involved in IARI-Mega Project, Dr. A.K.Patra, Dr. S.P. Datta, Dr. Debashis Chakraborty from IARI, New Delhiand Dr. L.M. Bhar from IASRI, New Delhi. We acknowledge thethoughtful editing by Dr. Julia W. Gaskin, University of Georgia,USA. The first author is grateful to IARI, New Delhi for SeniorResearch Fellowship during her doctoral program.

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