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Geoderma 144 (20

Evaluating impact of irrigation water quality on a calcareous clay soilusing principal component analysis

Uttam Kumar Mandal a,⁎, D.N. Warrington b, A.K. Bhardwaj c, A. Bar-Tal b,L. Kautsky b, D. Minz b, G.J. Levy b

a Central Research Institute for Dryland Agriculture, Santoshnagar, P.O.-Saidabad, Hyderabad-500059, Indiab Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization, The Volcani Center, Bet Dagan, 50250, Israel

c Department of Soil Science, NCSU, 100 Derieux Street, Box 7619, Raleigh, NC-27695, USA

Received 17 February 2007; received in revised form 28 October 2007; accepted 2 November 2007Available online 26 December 2007

Abstract

Declining yields from farmland in the Bet She'anValley, Israel, irrigated with JordanRiver water, raised concerns about resourcemanagement andlong term sustainability. An experiment was conducted in a commercial cotton field of the Bet She'an region, to assess the impact of irrigation waterquality on soil quality. A randomized block design compared four irrigation sources: Jordan River Water, Spring Water, Treated Waste Water, andSalty Spring Water, applied via drip irrigation to a calcareous silty clay soil for 3 years. Soil samples were collected and analyzed for 20 differentphysical, chemical and biological attributes. Samples from an adjacent uncultivated area were used as a reference. Principal component analysis(PCA) identified electrical conductivity (EC), fluorescein diacetate enzymatic activity (FDA), exchangeable Na (Ex-Na), apparent steady statesaturated hydraulic conductivity (Ksat), and available P as the most important indicators for inclusion in a minimum data set (MDS) used to evaluatesoil quality. Multiple regression tested these indicators against each of 4 specified management goals, reflecting soil quality attributes, i.e., sodiumadsorption ratio (SAR) of soil solution, infiltration rate, soil loss, and cotton yield. There was a significant relationship between the indicators and 3 ofthe goals but not with cotton yield. Soil quality indices (SQI) were calculated using a PCAweighting factor and each MDS indicator scored usinglinear transformation. The SQI was highest for uncultivated soil (2.945), followed by Treated Waste Water (1.471), Spring Water (0.923), SaltySpringWater (0.823) and Jordan River Water (0.582) irrigated soils. The order of relative contribution of the indicators to the SQI was FDA (35.2%),Ksat (25.0%), Ex-Na (19.2%), EC (13.4%), and available P (7.2%). The method successfully identified Jordan River Water as the most deleterious,and Treated Waste Water as the best, irrigation treatment when considering soil quality.© 2007 Elsevier B.V. All rights reserved.

Keywords: Soil quality index; Salinity; Jordan River; Water quality

1. Introduction

The Bet She'an Valley is a productive, irrigated agriculturalregion in Israel of wide-ranging agro-ecological significance. The

Abbreviations: EC, electrical conductivity; Ex-Na, exchangeable sodium;FDA, fluorescein diacetate activity; Ksat, apparent steady state saturatedhydraulic conductivity; MDS, minimum data set; PC, principal component;PCA, principal component analysis; SAR, sodium adsorption ratio; SQI, soilquality index.⁎ Corresponding author. Tel.: +91 40 24530161; fax: +91 40 24531802.E-mail addresses: uttamkm@crida.ernet.in, uttam_icar@yahoo.com

(U.K. Mandal).

0016-7061/$ - see front matter © 2007 Elsevier B.V. All rights reserved.doi:10.1016/j.geoderma.2007.11.014

main source for irrigation water is saline–sodic water from theJordan River, supplemented by moderately sodic–saline SpringWater; both waters having electrical conductivity (EC)N3.5 dSm−1. Over the years, a substantial decrease in the yield of irrigatedcrops has been noted in this region which has been ascribed, atleast in part, to deterioration in soil structure and its hydraulicproperties caused by the high sodicity and salinity of the irrigationwater. The need to reduce soil degradation in the Bet She'anValley, or to maintain or improve soil quality over time, promptedthe idea of replacing the local irrigation water with TreatedWasteWater. Treated Waste Water differs from its fresh source waterin, among others, its higher electrolyte concentration and the

Table 1Chemical characteristics of the irrigation waters used in the experiment during2003 to 2005 (mean±one standard deviation)

Parameter Irrigation water quality a

TWW SPW SSPW JRW

pH 8.20±0.64 7.35±0.78 7.25±0.93 7.80±1.27EC (dS m−1) 1.53±0.12 3.55±0.49. 5.4±0.45 5.00±0.85Cl− (mmolc L

−1) 4.30±0.61 27.10±6.65 41.5±0.85 40.75±10.96Na (mmolc L

−1) 4.40±0.74 19.75±3.75 32.4±6.82 32.50±4.10Ca+Mg (mmolc L

−1) 10.70±0.98 21.75±7.57 23.2±6.11 23.35±6.29SAR ((mmol L−1)0.5) 1.91±0.20 6.05±0.07 9.95±0.87 9.55±0.07K (mmolc L

−1) 3.10±0.43 0.15±0.07 0.2±0.03 0.46±0.05N (total, mg L−1) 68.80±5.41 – – –P (total, mg L−1) 11.30±0.92 – – –B (mg L−1) 0.17±0.01 0.13±0.01 0.13±0.01 0.7±0.03HCO3

− (mmolc L−1) 13.40±1.20 3.85±1.63 3.75±0.48 5.00±0.57

HSO4− (mmolc L

−1) 1.20±0.16 1.90±0.28 2.35±0.17 5.85±0.64TSSb (mg L−1) 47±6 – – –BODc (mg L−1) 78±11 – – –CODd (mg L−1) 368±56 – – –a TWW = Treated Waste Water; SPW = Spring Water; SSPW = Salty Spring

Water; JRW = Jordan River Water.b TSS = total suspended solids.c BOD = biological oxygen demand.d COD = chemical oxygen demand.

190 U.K. Mandal et al. / Geoderma 144 (2008) 189–197

presence of dissolved organic matter and suspended solids. InIsrael, the total salt concentration in effluents is 17–20 mmolcL−1, which is about twice that of fresh water (8–9 mmolc L

−1);there is also an increase in the sodium adsorption ratio, from 2.5 inFW to 5–8 in TreatedWasteWater (Feigin et al., 1991). However,these salinity and sodicity levels are considerably lower than thosein Jordan River Water and Spring Water. The levels of dissolvedorganic matter and suspended solids in Treated Waste Waterdepend on the quality of the raw sewage water and the degree ofits treatment. It has been suggested that replacing the saline–sodicirrigation water currently used in the Bet She'an valley with theconsiderably less saline–sodic TreatedWasteWater, may help thesoil regain its structure and hydraulic conductivity and, conse-quently, its agricultural productivity.

One way in which the possible impact of managementpractices (e.g., irrigation water quality) on soil degradation can beidentified in quantitative terms is to perform a thorough as-sessment of the quality of the soil resources of this irrigated agro-ecosystem. To assess soil quality, indicators (soil properties) areusually linked to a specific soil function (Howard, 1993; Larsonand Pierce, 1994; Karlen et al., 1996; Doran et al., 1996) e.g., as amedium for plant growth, and reflect changes over various spatialand temporal scales. To perform as good indicators, the selectedsoil properties should be sensitive, easy to measure, verifiable,and well-related to land management and the effect of environ-mental transformation (Carter et al., 1997; Seybold el al., 2001;Erkossa et al., 2007). Choosing the appropriate soil attributes toinclude in such an index must include a consideration of soilfunction and management goals that are site specific and user-oriented, and which focus on sustainability rather than on cropyields alone. Indicator values can be combined into a quantifiablesoil quality index (SQI) through a flexible model. A valid SQIwould thus help integrate and interpret data from various soilmeasurements, and indicate whether management and land usewas having a desired effect on productivity, environmental pro-tection, and health (Granatstein and Bezdicek, 1992). Moreover,a valid SQI could also (i) provide an early indication of soildegradation and the need for taking remedial measures, and(ii) characterize changes in soil properties that would reflect theextent of rehabilitation or regeneration of degraded soils.

Thus, the objective of this study was to identify several bio-logical, chemical and physical indicators suitable for assessingsoil quality in the Bet She'an region where TreatedWaste Water,Spring Water, and Jordan River Water are used as sources forirrigation. A consideration of how these identified indicatorscould be used to develop an overall SQI that is meaningful to thisagricultural system is then discussed.

2. Materials and methods

2.1. Field site, experiment layout, and treatments

The data used in this paper were collected during the initial2 year period (2003–2005) of an ongoing field experimentbeing carried out to evaluate the effects of different irrigationwater qualities on soil properties and crop production. Theexperiment was located in a 1.25 ha section of a commercial

farmer's field in the eastern Bet She'an Valley (Latitude (DMS):32° 30′ 0 N, Longitude (DMS): 35° 30′ 0 E; elevation of 230 mbelow mean sea level). The climate of the region is a sub-tropical, semi-arid type with winter rain and a dry, hot summer.The mean annual rainfall is 220 mm. The soil of theexperimental field is a calcareous silty clay (Typic Calciorthid(USDA Soil Taxonomy), Calcic Xerosol (FAO Soil Taxon-omy)). The soil is alkaline in nature with an average pH of 8.03for 0–40 cm soil depth. The dominant clay minerals in the soilare smectite (31%), calcite (27%) and palygorskite (22%)(Neaman et al., 1999). Soil particle size distribution is 172, 400and 428 g kg−1 sand, silt and clay, respectively. The soil con-tains 546 g kg−1 CaCO3 and 20 g kg

−1 organic matter and has acation exchange capacity of 23.2 cmolc kg

−1.The experiment was laid out in 20 plots, each measuring

45 m×20 m, in a randomized block design with four treatmentsand five replications. The four treatments consisted of the sameamount of irrigation by water with different qualities: (i) JordanRiver Water; (ii) Spring Water; (iii) Treated Waste Water; and(iv) Salty Spring Water. The Treated Waste Water came from asecondary treatment plant situated in the eastern Bet She'anValley. The Treated Waste Water was subjected to chlorinationand passed through a screen filter with nominal pore size of125 μm prior to being used for irrigation in the experimental sitebecause the local sewage plant provided only a basic secondarytreatment (oxidation ponds). The inclusion of Salty SpringWater was decided upon as a substitute for Jordan River Waterwhich was unavailable during the summer of the first year dueto drought. Therefore, 23 mmolc L

−1of NaCl and 3mmolc L−1

of CaCl2 were added to Spring Water to obtain irrigation waterof approximately the same sodium adsorption ratio (SAR) asJordan River Water. Irrigation water properties are presented inTable 1.

191U.K. Mandal et al. / Geoderma 144 (2008) 189–197

Two crops were grown during the study period with a rotationof corn (Zea mays) in the summer and winter of 2003–4 andcotton (Gossypium hirsutum) during the summers of 2004 and2005. For the summer corn (2003), 428mm of water was appliedas drip irrigation with a total fertilizer dose of 320 kg N ha−1 and140 kg P2O5 ha

−1 applied as fertigation. For the winter corn of2003–4, 339 mm of water was applied as irrigation and the totalfertilizer dose was 250 kg N ha−1 and 100 kg P2O5 ha

−1. Duringthe cotton growing periods, the annual rainfall was 175 and250 mm in 2004 and 2005, respectively. In both years, 800 mmof water was used for irrigation and 210 kg N ha−1 as fertilizerwas applied. No phosphate fertilizer was applied for the cottoncrop. The fertilizer dose was applied evenly across all plots.

2.2. Soil sampling, processing, and analysis

Soil samples, both disturbed and undisturbed, depending uponthe analysis for which theywere intended, were collected from thecenter of each plot from the 0 to 15 cm soil depth after the harvestof cotton in November, 2005. Soil samples were also taken froman uncultivated area adjoining the experimental plots which hadreceived no irrigation for many years. Soil samples from thisuncultivated area served as a control and were used for com-parisonwith those from the irrigated plots to determinewhether ornot irrigation practices were causing deterioration of the soil. Thesoil samples were analyzed for a number of physical, chemicaland biological properties.

2.2.1. Physical propertiesThe soil samples from the various treatments were subjected

to the following physical measurements: bulk density by thecore method (Blake and Hartge, 1986); soil particle size dis-tribution by the bouyoucos hydrometer method (Gee andBauder, 1986); aggregate stability by the High Energy MoistureCharacteristics method (Pierson and Mulla, 1989; Levy andMamedov, 2002).

Apparent steady state saturated hydraulic conductivity wasdetermined by leaching disturbed samples, packed in columns,with deionized water. Final infiltration rate, runoff, and soil losswere measured using a laboratory rainfall simulator using a rainintensity of 37 mm h−1. Data for these measurements were takenfrom Mandal et al. (submitted for publication), where a detaileddescription of the experimental setup is provided.

2.2.2. Chemical propertiesThe following chemical analyses were carried out on the soil

samples from the various treatments: soil organic matter by themodified Walkley–Black method (Nelson and Sommers, 1982);total carbon and total nitrogen by dry combustion of dried, groundsamples using a CN analyzer (Flash EA 1112 series, ThermoFinnigan, NC Soil Analyzer); soil nitrate (NO3

−–N) andammonium (NH4

+–N) by analyzing KCl extracts with an autoanalyzer (Quik Chem 8000, LACHAT Instrument) (Keeney andNelson, 1982); available phosphorus was determined by extract-ing samples with 0.5N NaHCO3 solution and determiningP concentrations with a spectrophotometer (Olsen et al., 1954);exchangeable potassium and exchangeable sodium (Ex-Na) by

emission spectrometry of 1N ammonium acetate extracts. Inaddition, extracts from saturated pastes of the samples tested wereanalyzed for electrical conductivity (EC), pH, sodium (Na+) byemission spectrometry, calcium (Ca+2) and magnesium (Mg+2)by titration, boron by inductively coupled plasma atomic emissionspectrometry (ICP-AES), and chloride using a chloridometer. Thesodium adsorption ratio (SAR) was calculated from the con-centrations of Na+, Ca2+, and Mg2+.

2.2.3. Microbial analysesThree indicators of total microbial activity within field moist

samples were determined: (i) FDAwas estimated by determiningthe conversion of 3,6-diacetylfluorescein to fluorescein (Schurerand Rosswall, 1982), (ii) dehydrogenase activity was deter-mined from the reduction of triphenyl tetrazolium chloride totriphenyl formazan (Casida et al., 1964), and (iii) soil carbonmineralization was determined from CO2 emission from soilsplaced in incubation chambers (Zibilske, 1994).

2.3. Soil quality index

To determine the SQI, four main steps were followed: (i) todefine the management goal(s); (ii) to select a minimum data set(MDS) of indicators that best represent soil function; (iii) toscore the MDS indicators based on their performance of soilfunction; and (iv) to integrate the indicator score into an indexof soil quality.

Crop yield can be an important indicator of soil quality becauseit serves as a plant bioassay of the interacting soil characteristics.However, productivity alone may not always be the main criteriaby which sustainability of an agricultural system should bejudged. Erosion control and water penetration, have also beenconsidered as vital to the sustainability of irrigated agriculture(Letey, 1994). Soil infiltration rate and hydraulic conductivity areknown to be affected by the SAR and total electrolyte con-centration of the irrigation water. Therefore, in the present study,the average cotton yield of 2004–2005, the SAR of the saturationextract from soil–water pastes, final infiltration rate, and interrillerosion were defined as the quantifiable management goals,reflecting soil quality attributes in this agro-ecosystem; ideally,high cotton yields, low SAR values, high final infiltration rates,and small soil losses would be the objectives of a well-managedsystem.

The data collected from the various soil analyses were reducedto an MDS through a series of three uni- and multi-variatestatistical methods using SPSS software (SPSS, 1998):

(i) the non-parametric statistic, Kruskal–Wallis χ2, was usedto identify indicators with significant treatment differ-ences (Pb0.05). This method was preferable because,unlike ANOVA for example, it did not require any as-sumptions of normality or homoscedasticity within thedata sets, thereby avoiding the need to transform the data(Andrews and Carroll, 2001).

(ii) Standardized principal component analysis (PCA)was thenperformed on each of these identified statistically sig-nificant variables (Andrews and Carroll, 2001; Andrews

192 U.K. Mandal et al. / Geoderma 144 (2008) 189–197

et al. 2002). Results of a PCA of a given data set results in anumber of principal components (PC) which are linearcombinations of variables that account for the maximumvariance within the set by describing vectors of closest fit tothe n observations in p-dimensional space, subject to beingorthogonal to one another (Dunteman, 1989). The PCsreceiving high eigenvalues and comprising variables withhigh factor loading were assumed to be the variables thatbest represent the system attributes. Therefore, we selectedonly PCs with eigenvalues N1.0 (Brejda et al., 2000).Within each PC, only variables with highly weighted factorloading, (i.e., those with absolute values for factor loadingwithin 10% of the highest value), were retained for theMDS.

(iii) To reduce redundancy and to rule out spurious groupingsamong the highly weighted variables within a particularPCA, Pearson's correlation coefficients were used todetermine the strength of the relationships amongvariables (Andrews and Carroll, 2001). The final MDSwill then be compiled to include from each PC: (a) thevariable with the highest factor loading as the mostimportant contributor to the PC; (b) the variable with thehighest correlation sum since it is most generally related tothe other variables and is considered to best represent thegroup; (c) the variable(s) with the lowest correlation sumsdue to their implied relative independence from the group.Variables which are highly correlated with anotherindividual variable also selected for the MDS wouldnecessitate a choice of one of the two based primarily onfactor loading. As a check of how well the MDSrepresented the management system goals, multipleregressions were run using the final MDS indicators asindependent variables and measurements representing themanagement goal as dependent variables.

The SQI was calculated for each treatment from:

SQI ¼ ∑n

i¼1Wi � Si ð1Þ

where W is the PC weighting factor, S is the indicator score foreach variable i, and n is the number of variables in the MDS. Inorder to apply this equation, the values of S andW needed to bedetermined.

The values of S for all observations of each MDS indicatorwere obtained by following a linear scoring technique becauseof the simplicity of its design and its dependence on observedfamiliarity with the system (Liebig et al., 2001). Indicators wereranked in either ascending or descending order depending onwhether a higher value was considered ‘good’ or ‘bad’ in termsof soil function. For ‘more is better’ indicators (e.g., FDA), eachobservation was divided by the highest observed value; thus thehighest observed value received a score of 1 while all othersreceived a score of b1. For ‘less is better’ indicators (e.g., Ex-Na), the lowest observed value was divided by each observa-tion; thus the lowest observed value received a score of 1 whileall others received a score of b1. For many indicators, such aspH or P, observations were scored as ‘more is better up to a

threshold value and were then scored as ‘less is better’ abovethat threshold (Liebig et al., 2001).

After calculating the S values for all the MDS variables, eachproperty was weighted using the PCA results. Each PCexplained a certain amount (%) of the variation in the totaldata set. This percentage, when divided by the total percentageof variation explained by all the PCs with eigenvalues N1.0,provided the weighting factor (W) for the variables chosen undera given PC. Having determined the values of S and W, the SQIfor each treatment was then calculated using Eq. (1). Higherindex scores mean better soil quality or superior performance ofsoil functions.

3. Results and discussion

3.1. Effects of irrigation water quality on soil properties

Results for the chemical properties used for the MDSselection process of the soil samples taken from the field afterthe cotton harvest in November 2005 are presented in Table 2.As expected, use of irrigation water increased soil EC, Cl−, andexchangeable sodium percentage. Due to the adverse effect ofcultivation on the decomposition rate of soil organic matter,organic carbon and total nitrogen content of irrigated soils werereduced, albeit to a lesser degree in Treated Waste Water soils(Table 2). Irrigation with Treated Waste Water results in theaddition of organic C and N to the soil. The amount of N addedthrough irrigation with Treated Waste Water in Israel can oftenbe N200 kg ha−1 year−1 (Feigin et al., 1991). Irrigation withTreated Waste Water resulted in the highest available P levels(Table 2) that were well above the optimum available P level inthe soil, i.e., 50 mg kg−1 (Wander et al., 2002; Lee et al.,2006).

Results for the soil physical properties and microbiologicalindicators used for the MDS selection process are presented inTable 3. Generally, irrigation resulted in deterioration of soilphysical properties as reflected by significantly lower aggregatestability ratios (an index, valued between 0 and 1, directly relatedto increasing aggregate stability) and saturated hydraulicconductivities (Ksat). The higher stability ratio and Ksat valuesin the uncultivated soil were associated with the higher content oforganic matter in that soil (Table 2) and the adverse impact of soilcultivation which weakens soil aggregates. The ranking of theirrigation treatments for stability ratio and Ksat were inverselylinked to their exchangeable sodiumpercentage ranking (Table 2).Similar associations between exchangeable sodium percentageand (a) aggregate stability and (b) soil hydraulic conductivitywere reported by Levy and Mamedov (2002) and Levy et al.(2003), respectively.

There were significant differences among the treatments forFDA and dehydrogenase activity (Table 3) which were ranked ashighest in uncultivated soil followed by the TreatedWasteWater-,Salty Spring Water-, Spring Water- and the Jordan River Water-irrigated soils. This ranking was similar to that for organic carboncontent (Table 2) and was considered to be due to the closeassociation between soil organic matter content and enzymeactivities (Gracia et al., 1994).

Table 2Soil chemical properties that were used for the minimum data set (MDS) selection process for the different treatments

Treatment a pH EC TC TN OC N–NH4+ N–NO3

− Av. P Ca+Mg Cl− B Ex-K Ex-Na ESP

Control 7.66 1.34 8.541 0.207 2.204 6.400 38.85 30.25 11.79 13.20 0.344 2.000 0.243 0.718TWW 7.55 5.29 8.200 0.189 1.769 7.550 50.13 70.80 32.12 72.07 0.183 1.748 0.670 2.268SPW 7.57 8.49 8.123 0.170 1.718 6.375 46.30 31.55 51.82 99.60 0.180 1.275 1.138 3.728SSPW 7.60 12.64 8.134 0.169 1.659 4.925 41.83 28.55 41.95 134.30 0.148 1.218 1.358 4.155JRW 7.32 19.91 8.074 0.171 1.716 6.825 40.87 36.80 108.82 177.27 0.393 1.220 1.393 5.468Pbᇠ0.023 0.004 0.208 0.024 0.031 0.190 0.424 0.049 0.014 0.036 0.006 0.035 0.004 0.006

EC, electrical conductivity (dS m−1); TC, total carbon (%); TN, total nitrogen (%); OC, organic carbon (%); N–NH4+ (mg kg−1); N–NO3

− (mg kg−1); Av. P (available P,mg kg−1); Ca+Mg, calcium and magnesium concentration in the saturated paste (mmolc L

−1); Cl−, chloride concentration in the saturated paste (mmolc L−1); B, boron

concentration in the saturated paste (mg L−1); Ex-K, exchangeable potassium (cmolc kg−1); Ex-Na, exchangeable sodium (cmolc kg

−1); ESP, exchangeable sodiumpercentage (%).‡α Level of significance of Kruskal–Wallis test between treatment.a Control = uncultivated field; TWW = Treated Waste Water; SPW = Spring Water; SSPW = Salty Spring Water; JRW = Jordan River Water.

193U.K. Mandal et al. / Geoderma 144 (2008) 189–197

3.2. Results for management goals

Results for the 4management goals decided upon are presentedin Table 4. There were significant differences in the SAR of thesaturated paste among the various treatments studied. However,final infiltration rate of the soil after 70 mm of rain was notsignificantly affected by the treatments tested (Table 4) norwas theamount of cumulative runoff (i.e., runoff obtained from the entire70 mm rain storm) which reflects the integrated value of thechanges in the infiltration rate during the entire rainfall event (datanot presented). However, a trend was noted whereby ranking ofthe treatments for the final infiltration rate (Table 4) was similar totheir ranking for Ksat (Table 3). It was postulated that fast wetting(50 mm h−1), prior to exposing the soil to simulated rain, causedsevere slaking of the surface aggregates which, together with thebeating action of the rain drops, overshadowed the effect soilexchangeable sodium percentage and organic matter contentmight have had on the permeability of the soil surface during theformation of surface seals. Despite the similarity in runoff

Table 3Soil physical and microbiological properties that were used for the minimumdata set (MDS) selection process for the different treatments

Treatment a Bulkdensity

SRb Ksat c FDAd DHAe C-mineralization

Mgm−3

cmh−1

mgfluoresceinh−1 kg−1

dry soil

mgformazanday−1

kg−1

dry soil

mgC–CO2

day−1

kg−1dry soil

Control 1.26 0.882 4.42 122.44 356.77 78.54TWW 1.25 0.817 2.09 78.04 258.51 64.35SPW 1.30 0.812 1.38 61.74 225.94 58.01SSPW 1.26 0.791 1.21 64.70 227.63 75.69JRW 1.30 0.768 0.54 34.20 189.88 60.33Pbα f 0.209 0.025 0.006 0.003 0.012 0.15a Control = uncultivated soil; TWW = Treated Waste Water; SPW = Spring

Water; SSPW = Salty Spring Water; JRW = Jordan River Water.b SR = stability ratio, the indicator for aggregate stability.c Ksat = steady state saturated hydraulic conductivity; data obtained from

Mandal et al. (submitted for publication).d FDA = fluorescein diacetate enzymatic activity.e DHA = dehydrogenase activity.f α Level of significance of Kruskal–Wallis test between treatments.

volumes for the different treatments, soil losses from the un-cultivated soil samples were significantly lower than those fromthe irrigated treatments (Table 4). Among the irrigated soils, theTreated Waste Water-irrigated samples produced the lowest soilloss whereas those from the Jordan River Water-irrigated plotsproduced the highest soil loss. These observations suggested thatsoil loss was inversely related to aggregate stability rather thanbeing directly linked to runoff volume.

Despite differences in many of the abovementioned chemical,physical and biological parameters tested,which can be ascribed tothe quality of the irrigationwater applied, no significant differencesin the yield of the cotton crop were detected among the treatmentsin either 2004 or 2005 (Table 4). This may be because cotton isconsidered to be a crop with low sensitivity to soil salinity.Apparently, greater reductions in soil quality, possibly followinglonger term irrigation with poor-quality irrigation water, would beneeded before changes in cotton yield might be observed.

3.3. Minimum data set (MDS) compilation for soil qualityindicators

An MDS was selected following PCA of soil propertieswhich demonstrated significant differences among treatments,and which were then found to be non-correlated. The non-

Table 4Results of the 4 chosen management goals for the different treatments

Treatment a Final infiltrationrateb

Soil loss b SAR Cotton yieldkg ha−1

mm h−1 g m−2 (mmolcL−1)0.5

Year

2004 2005

Control 3.62 379 1.029TWW 3.46 479 6.218 7220 6994SPW 3.19 488 8.885 6440 5425SSPW 3.09 584 11.150 7300 6400JRW 2.99 595 18.230 7500 6305LSDc

(P=0.05)NS 98 3.915 NS NS

a Control = uncultivated soil; TWW = Treated Waste Water; SPW = SpringWater; SSPW = Salty Spring Water; JRW = Jordan River Water.b Final infiltration rate and soil loss data were obtained from Mandal et al.

(submitted for publication).c LSD = least significant difference, NS = not significant at P=0.05.

Table 5Results of the principal component analysis of the statistically significant soilquality indicators

Statistic PC #1 PC #2 PC #3

Eigenvalue 8.777 2.095 1.316% of variance 58.513 13.966 8.773Cumulative % 58.513 72.479 81.252

Factor loading/eigenvectorVariablespH 0.669 −0.554 −0.209EC −0.868 0.373 −0.111Ca+Mg −0.780 0.497 −0.035Organic carbon (OC) 0.768 0.413 −0.237Boron (B) −0.032 0.908 −0.247Stability ratio (SR) 0.769 0.026 −0.240Available P 0.058 0.088 0.967Exchangeable sodium percentage (ESP) −0.861 −0.141 −0.113Exchangeable K (Ex-K) 0.749 0.285 0.249Total N (TN) 0.790 0.314 0.057Fluorescein diacetate activity (FDA) 0.906 0.045 −0.087Dehydrogenase activity (DHA) 0.891 0.133 0.073Exchangeable Na (Ex-Na) −0.905 −0.096 −0.131Hydraulic conductivity (Ksat) 0.871 0.243 0.148Cl− −0.813 0.334 0.139

PC = principal component.Underlined factor loadings are considered highly weighted when within 10% ofvariation of the absolute values of the highest factor loading in each PC.

Table 6Pearson's correlation coefficient and correlation sums for highly weightedvariables with high factor loading under principal component #1

PC1 variables EC ESP FDA DHA Ex-Na Ksat

Electrical conductivity(EC)

1.000 0.696 −0.724 −0.743 0.785 −0.658

Exchangeable sodiumpercentage (ESP)

0.696 1.000 −0.704 −0.801 0.944 −0.719

Fluorescein diacetateactivity (FDA)

−0.724 −0.704 1.000 0.928 −0.732 0.792

Dehydrogenase activity(DHA)

−0.743 −0.801 0.928 1.000 −0.826 0.778

Exchangeable Na(Ex-Na)

0.785 0.944 −0.732 −0.826 1.000 −0.763

Hydraulic conductivity(Ksat)

−0.658 −0.719 0.792 0.778 −0.763 1.000

Correlation sum 4.606 4.864 4.88 5.076 5.05 4.71

194 U.K. Mandal et al. / Geoderma 144 (2008) 189–197

parametric Kruskal–Wallis test of treatment means revealed that15 of the 20 soil properties considered had significant differencesbetween treatments. Total C, NH4

+–N, and NO3−–N; soil bulk

density; and mineralizable C showed no significant differencebetween treatments at Pb0.05 level (Tables 2 and 3); therefore,these 5 variables were excluded from the PCA. The PCA of theremaining 15 variables resulted in three principal PCs which hadeigenvalues N1 and accounted for 81% of the variance in the data(Table 5). In PC1, FDA is the greatest contributor to the PC asgiven by the factor loading. A further 5 variables had highlyweighted factor loadings, i.e., those having absolute values within10% of that of the FDA factor loading, namely Ex-Na,dehydrogenase activity, Ksat, EC, and exchangeable sodiumpercentage.Under PC2 and PC3only one variable fromeach had ahigh factor loading; boron and available P, respectively.

Pearson's correlation coefficients and correlation sums werederived for each of the highly weighted variables from PC1individually (Table 6). FDA had the highest factor loading andwas therefore retained for the MDS. Although dehydrogenaseactivity, having the highest correlation sum, was considered to bethe best representative of the group, it was excluded from theMDS because it was highly correlated with FDA (r=0.93). SinceEx-Na had the second highest correlation sum, it was included inthe MDS instead of dehydrogenase activity. Exchangeablesodium percentage was excluded from the MDS as it was highlycorrelated with Ex-Na (r=0.94). Both EC and Ksat, having thelowest correlation sums, were selected for the MDS (Table 6).

Boron was the best representative of PC2 having the highestfactor loading (Table 5). Boron is commonly considered as a soilquality indicator due to B toxicity which is frequently observed inarid and semi-arid region soils (Nable et al., 1997). However, in

this study, B concentrations in the soil extracts were not toxic(b0.5 mg L−1) for any treatment and, apart from in the JordanRiver Water treatment, were less than in the uncultivated soil(Table 2). Therefore, in the absence of any instance of B toxicityand thus an effect on soil quality, we excluded B from the MDSwhile bearing inmind that the situation might change in the futureshould B levels increase. Available P had the highest weightedvalue in PC3 (Table 5) and was therefore included in the MDS.

Thus, the final MDS comprised FDA, Ex-Na, Ksat, EC, andavailable P. Most of these properties have been identified asimportant soil quality indicators in previous studies of differentsystems. Both EC and available P were considered by Andrewsand Carroll (2001), Andrews et al. (2002), andWang et al. (2003)to be important variables which should be included in the MDSfor the computation of an SQI for an area of TreatedWasteWater-irrigated cropland in California. Available P was identified by Leeet al. (2006) as an important soil quality indicator for a croprotation system in Taiwan. Enzyme activity has been selected asan effective indicator of soil quality changes resulting fromenvironmental stress or management practices (Quilchano andMaranon 2002; Chaudhury et al., 2005). Ksat has been consideredto be one of themost important soil physical properties affected bysalinity and sodicity (e.g., Shainberg and Letey, 1984), and wasused by Sharma et al. (2005) as a key soil quality indicator indryland Alfisols prone to crusting and hardsetting.

Four management goals, which reflect soil quality attributes inthis agro-ecosystem, had been identified above for the presentstudy, i.e., SAR of the soil solution, final infiltration rate, soil loss,and the average cotton yield. We used data for these managementgoals in a validation check of the MDS representation of thesystem. A multiple regression equation was computed for eachmanagement goal separately using the five MDS indicators (i.e.,FDA, Ksat, Ex-Na, available P, and EC) as independent variables.The coefficients of determinant (R2) for SAR, final infiltrationrate, soil loss, and average cotton yield were 0.856 (Pb0.001),0.712 (P=0.002), 0.616 (P=0.012) and 0.306 (P=0.526),respectively. These MDS regression results suggest significantrelationships between the MDS indicators and three of themanagement goals, cotton yield being the exception.

Fig. 1. Radar graphs for soil quality index (SQI) for each treatments. Each axis depicts an individual indicator and scale represents the indicator scoremultiplied by principalcomponentweighing factor.Measures, away from the centre of the graph are assumed to represent better quality i.e., bigger areameans higher soil quality. FDA= fluoresceindiacetate activity, Av. P = available phosphorus, EC = electrical conductivity, Ex-Na = exchangeable sodium, Ksat = apparent steady state saturated hydraulic conductivity.

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3.4. Soil quality index calculation and interpretation

The SQI for each treatment was calculated using Eq. (1). TheS value in Eq. (1) was obtained by transforming each MDSvariable's value using linear scoring functions so that eachvariable now had a score of between 0 and 1. For both EC andEx-Na, indicators where less is considered to be better, theuncultivated soil, having the lowest values for these properties,received a score of 1. The scores of both indicators for each ofthe irrigation treatments were then derived by dividing thevalue of either the EC or the Ex-Na for the uncultivated soil bytheir respective observed value for the treatment. For FDA andKsat, indicators where more is considered to be better, theuncultivated soil, having the highest values for these properties,received a score of 1. The scores of both indicators for eachof the irrigation treatments were then derived by dividing thevalue of either the FDA or the Ksat for the treatment by theirrespective observed value for the uncultivated soil. For avail-able P values, where higher is better up to an optimum value,i.e., 50 mg kg−1 (Wander et al., 2002; Lee et al., 2006), andthereafter less is better, scores were derived by dividingobserved values less than 50 mg kg−1 by 50 and for observedvalues greater than 50 mg kg−1, scores were determined bydividing 50 by the observed values (Liebig et al., 2001).

The coefficient of weighting factors (W in Eq. (1)) derivedfrom the PCA were determined by dividing the percent ofvariation in the data set explained by each PC that contributedthe indicated variable(s) by the total percentage of variationexplained by all PCs with eigenvaluesN1 (Table 5). For the

variables in PC1, the weighing coefficient was 0.72 and foravailable P (in PC3) it was 0.108.

Finally, calculated SQIs resulted in the order Jordan RiverWater (0.582)bSalty Spring Water (0.823)bSpring Water(0.923)bTreated Waste Water (1.471)buncultivated soil(2.945). Fig. 1 illustrates the relative contribution of each indicatorto the SQI. FDAwas the largest contributor (35.2%) followed byKsat (25.0%), Ex-Na (19.2%), EC (13.4%) and available P (7.2%).

Uncultivated soil had the highest SQI because intensivecultivation under an irrigated agricultural system deteriorates soilquality. Among the irrigation treatments, the highest SQIwas foundin the case of plots irrigated with Treated Waste Water. The waterquality of Treated Waste Water is less deleterious than the otherirrigation waters because of its lower salinity and lower SAR value.Increasing salinity and SAR contents led to progressivelyworsening soil quality and lower SQIs. In the worse case, irrigationwith Jordan RiverWater resulted in an 80% deterioration in the soilquality when compared to the uncultivated soil and a 60%deterioration when compared to the Treated Waste Water-irrigatedsoil. This study showed that an indexing framework can effectivelybe applied to assess the impact of the quality of irrigation water onsoil quality. A major drawback to this framework is the need for alarge existing data set for MDS selection. Additionally, the entireprocess would likely need to be repeated periodically.

4. Conclusions

Intensive agricultural cultivation under irrigation with variouswater qualities resulted in a deterioration of soil quality when

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compared to soil which remained uncultivated. The deteriorationin soil quality was clearly indicated by statistically derived soilquality indices. Thus, the SQI of the uncultivated soil had thehighest value while the SQI of the soil irrigated with saline–sodicJordan River Water had the lowest value and represented thegreatest deterioration in soil quality. Among the differentirrigation treatments, Treated Waste Water-irrigated soil had thehighest SQI.

The SQIs were calculated from the MDS. The multivariatestatistical method, PCA, was very effective in selecting theMDS from a larger existing data set. In our study the MDScomprised FDA, Ex-Na, Ksat, EC, and available P. Prior tousing it in the SQI calculations, the MDS was validated when asignificant relationship was proved between it and 3 definedmanagement goals which reflected soil quality attributes in thisagro-ecosystem; SAR, final infiltration rate, and soil loss.

The use of PCA in order to derive SQIs has the potentialto integrate soil biological, chemical, and physical data forecological management application where such integration hasoften been lacking. This indexing approach can be useful as atool for monitoring the effects of management practices on soilfunctions that best meet defined management goals. Soil qualityindexing may have even broader applications for soil reclama-tion than for agricultural production alone. It is true that MDSindicators and the scoring function may need to be changed fordifferent management systems, climates, soil types, or even overtime. Nevertheless, our case study emphasizes that soil qualityassessment is a tool that can be used to quantify and evaluate theeffects of land management practices on soil function.

Acknowledgments

This research was supported by grant number 301-0379-02from the Chief Scientist, Ministry of Agriculture and RuralDevelopment, Israel. The support of the Chief Scientist isgratefully acknowledged. U.K. Mandal is grateful to Ministry ofForeign Affairs, Israel, The Agricultural Research Organization,Bet Dagan, Israel, Ministry of Human Resource Development,Government of India (GOI) and Indian Council of AgriculturalResearch and Department of Science and Technology, GOI forproviding all facilities including scholarship that enabled him tocontribute to this research work. The authors wish to thank Ms.D. Goldstein, Ms. R. Rosenberg, Ms. M. Kainan, and Mr. H.Tenaw, for their help in the data collection.

References

Andrews, S.S., Carroll, C.R., 2001. Designing a soil quality assessment toolfor sustainable agroecosystem management. Ecol. Appl. 11, 1573–1585.

Andrews, S.S., Mitchell, J.P., Mancinelli, R., Larlen, D.L., Hartz, T.K., Horwarth,W.R., Pettygrove, G.S., Scow, K.M.,Munk, D.S., 2002.On farm assessment ofsoil quality in California's Central Valley. Agron. J. 94, 12–23.

Blake, G.R., Hartge, K.H., 1986. Bulk density. In Klute, A. (Ed.), Methods ofSoil Analysis. Part 1. 2nd ed. Physical and Mineralogical Methods. Agron.Monogr. 9. ASA and SSSA, Madison, WI, pp 364–367.

Brejda, J.J., Moorman, T.B., Karlen, D.L., Dao, T.H., 2000. Identification ofregional soil quality factors and indicators. I. Central and Southern HighPlains. Soil Sci. Soc. Am. J. 64, 2115–2124.

Carter, M.R., Gregorich, E.G., Anderson, D.W., Doran, J.W., Janzen, H.H.,Pierce, F.J., 1997. Concepts of soil quality and their significance. In:Gregorich, R.G., Carter, M.R. (Eds.), Soil Quality for Crop Production andEcosystem Health. Developments in Soil Science 25. Elsevier, Amsterdam,pp. 1–9.

Casida, L.E., Klein Jr., D.A., Santoro, T., 1964. Soil dehydrogenase activity. SoilSci. 98, 371–376.

Chaudhury, J., Mandal, U.K., Sharma, K.L., Ghosh, H., Mandal, B., 2005.Assessing soil quality under long-term rice-based cropping system.Communication Soil Sci. Plant Analysis, 36, 1141–1161.

Doran, J.W., Sarrantonio, M., Lieberg, M.A., 1996. Soil health and sustain-ability. Adv. Agron. 56, 1–54.

Dunteman, G.H., 1989. Principle Components Analysis. Sage Publications,London. UK.

Erkossa, T., Itanna, F., Stahr, K., 2007. Indexing soil quality: a new paradigm insoil science research. Aust. J. Soil Res. 45, 129–137.

Feigin, A., Ravina, I., Shalhevet, J., 1991. Irrigation with treated sewageeffluent, management for environmental protection. Advanced Series inAgricultural Sciences 17. Springer-Verlag, Berlin.

Gee, G.W., Bauder, J.W., 1986. Particle size analysis. In: Klute, A. (Ed.),Methods of Soil Analysis. Part 1. 2nd ed. Physical and MinerologicalMethods. Agron. Monogr. 9. ASA and SSSA, Madison, WI, pp. 404–408.

Gracia, C., Hernandez, T., Costa, F., 1994. Microbial activity in soils underMediterranean environmental conditions. Soil Biol. Biochem. 26, 1185–1191.

Granatstein, D., Bezdicek, D.F., 1992. The need for a soil quality index: localand regional perspectives. Am. J. Alt. Agric. 7, 12–16.

Howard, P.J.A., 1993. Soil protection and soil quality assessment in the EC. Sci.Total Environ. 129, 219–239.

Karlen, D.L., Parkin, T.P., Eash, N.S., 1996. Use of soil quality indicators toevaluate conservation reserve program sites in Iowa. In: Doran, J.W., Jones,A.J. (Eds.),Methods for assessing soil quality. SSSA,Madison, pp. 345–355.

Keeney, D.R., Nelson, D.W., 1982. Nitrogen-inorganic forms. In: Page, A.L.,Miller, R.H., Keeney, D.R. (Eds.), Methods of Soil Analysis, Part 2, 2nd ed.Agronomy Monograph 9. ASA and SSSA, Madison, WI, pp. 643–698.

Larson, W.E., Pierce, F.J., 1994. The Dynamics of Soil Quality as a Measure ofSustainable Management. In Defining Soil Quality for a SustainableEnvironment. Doran J.W., Coleman, D.C., Bezdicek, D.F., Stewart, B.A.,Eds.; SSSA, Spec. Pub. No. 35. ASA, CSSA and SSSA, Madison, WI,pp. 37–51.

Lee, C.H., Wu, M.Y., Asio, V.B., Chen, Z.S., 2006. Using a soil quality index toassess the effects of applying swine manure compost on soil quality under acrop rotation system in Taiwan. Soil Sci. 171, 210–222.

Letey, J., 1994. Is irrigated agriculture sustainable? Soil and Water Science: Keyto Understanding Our Global Environment, SSSA Special Publication 41,SSSA, Madison, WI. pp. 23–37.

Levy, G.J., Mamedov, A.I., 2002. Aggregate stability and seal formation. SoilSci. Soc. Am. J. 66, 1603–1609.

Levy, G.J., Mamedov, A.I., Goldstein, D., 2003. Sodicity and water qualityeffects on slaking of aggregates from semi-arid soils. Soil Sci. 168,552–562.

Liebig, M.A., Varvel, G., Doran, J.W., 2001. A simple performance-based indexfor assessing multiple agroecosystem functions. Agron. J. 93, 313–318.

Mandal, U.K., Bhardwaj, A.K., Warrington, D.N., Goldstein, D., Bar Tal, A.,Levy, G.J. Changes in soil hydraulic conductivity, runoff and soil loss due toirrigation with different types of saline-sodic water. Geoderma (submittedfor publication).

Nable, R.O., Banuelos, G.S., Paull, J.G., 1997. Boron toxicity. Plant Soil. 198,181–198.

Neaman, A., Singer, A., Stahr, K., 1999. Clay mineralogy as affectingdisaggregation in some palygorskite containing soils of the Jordan and Bet-She'an Valleys. Aust. J. Soil Res. 37, 913–928.

Nelson, D.W., Sommers, L.E., 1982. Total carbon, organic carbon and organicmatter, In: Page, A.L., Miller, R.H., Keeney, D.R. (Eds.), Methods of SoilAnalysis, Part 2, 2nd Edition, Agronomy Monograph 9. ASA and SSSA,Madison, WI, pp. 534–580.

Olsen, S.R., Cole, C.V., Watanabe, F.S., Dean, L.A., 1954. Estimation ofavailable phosphorous in soils by extraction with sodium bicarbonate.USDA Circular 9398, 1–19.

197U.K. Mandal et al. / Geoderma 144 (2008) 189–197

Pierson, F.B., Mulla, D.J., 1989. An improved method for measuring aggregatestability of a weakly aggregated loessial soil. Soil Sci. Soc. Am. J. 53,1825–1831.

Quilchano, C., Maranon, T., 2002. Dehydrogenase activity in Mediterraneanforest soils. Biol. Fertil. Soils, 35, 102–107.

Schurer, J., Rosswall, T., 1982. Fluorescein diacetate hydrolysis as a measure oftotal microbial activity in soil and litter. Appl. Environ. Microbiol. 43,1256–1261.

Seybold, C.A., Dick, R.P., Pierce, F.J., 2001. USDA soil quality test kit:approaches for comparative assessments. Soil Survey Horiz. 42, 43–52.

Shainberg, I., Letey, J., 1984. Response of soils to sodic and saline conditions.Hilgardia 52, 1–57.

Sharma, K.L., Mandal, U.K., Srinivas, K., Vittal, K.P.R., Mandal, B., KusumaGrace, J., Ramesh, V., 2005. Long term soil management effects on cropyields and soil quality in a dryland Alfisol. Soil Till. Res. 83, 246–259.

SPSS, 1998. SigmaPlot 5.0 Programming Guide. Chicago, IL.Wander, M.M., Walter, G.L., Nissen, T.M., Bollero, G.A., Andrews, S.S.,

Cavanaugh-Grant, D.A., 2002. Soil quality: science and process. Agron. J.94, 23–32.

Wang, Z., Chang, A.C., Crowley, W.D., 2003. Assessing the soil quality of long-term reclaimed wastewater-irrigated cropland. Geoderma 114, 261–278.

Zibilske, L.M., 1994. Carbon mineralization. In: Weaver, R.W., Angle, J.S.,Bottomley, P.S. (Eds.), Methods of Soil Analysis, Part 2, Microbiologicaland biochemical properties, 5. SSSA, Madison, WI, pp. 835–863.