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Applied Soil Ecology

journal homepage: www.elsevier.com/locate/apsoil

Soil health characterization in smallholder agricultural catchments in India

Phillip S.D. Frosta, Harold M. van Esa,⁎, David G. Rossitera, Peter R. Hobbsa, Prabhu L. Pingalib

a School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USAb Charles H. Dyson School of Applied Economics and Management, Cornell University, Ithaca, NY 14853, USA

A R T I C L E I N F O

Keywords:Soil healthSoil qualityIndiaCatchmentCultivation

A B S T R A C T

Soil health (SH) of managed lands in India is affected by anthropogenic activities such as nutrient mining,excessive tillage, and monocropping, which reduce the productive capacity of soils. A comprehensive SHcharacterization was conducted in 27 catchments in six districts of Jharkhand, India. Each was stratified intofour landscape positions: (i) uncultivated upland in tree vegetation, (ii) cultivated upland in garden or orcharduse, and (iii) midland and (iv) lowland areas in rice-fallow fields, yielding 113 soil samples from 0 to 15 cm and20 from 30 to 40 cm depths. Soil textural separates as well as 15 dynamic physical, biological, and chemicalproperties were assessed using the Comprehensive Assessment of Soil Health framework. Nutrient analyses in-dicate low to very low P and K values, but high micronutrient levels. A district level ANOVA shows effects ofinherent soil factors on the indicators. The influence of tillage, nutrient extraction as well as landscape hydrologyon soil health indicators was apparent, notably showing uncultivated soils with higher overall SH. Puddle tillageaffected the surface and subsurface soil, the latter showing reduced water holding capacity and less favorablebiological indicators. Multivariate analyses showed directional separation of biological and chemical indicatorsin the first two principal components. A Best Subsets Regression analysis revealed organic matter, soil respirationand active carbon as the most predictive in determining overall SH scores (R2

adj = 0.87). In conclusion, acomprehensive soil health assessment using a spatial framework in Jharkhand, India identified multiple SHconstraints related to farmer management, associated organic matter dynamics, and natural factors.

1. Introduction

India is a land-scarce emerging economy with a current populationof over 1.3B that requires sustainable intensification of existing agri-cultural resources. The successful Green Revolution of the mid-late 20thcentury brought high-yielding and pest-resistant varieties, modernfertilizers, and irrigation, primarily for rice and wheat (Eswaran et al.,2005; Lal, 2009). But the degradation of natural resources, notably soiland water, was an unintended consequence. This warrants an emphasison the rejuvenation of soil, enhancing the productivity and sustain-ability in agricultural food systems.

The current interest in soil health (SH) reflects the growingawareness that soil is an essential component of the biosphere (Doranand Jones, 1996) and its restoration is often the first entry point toincreasing the productivity of food insecure farms (Sanchez andSwaminathan, 2005), which also affects global climate (Lal, 1990). Anew comprehension of soils includes an understanding of the physical,biological and chemical processes that goes beyond historically

assessed soil nutrient quantities, and allows diagnostic tests to quantifyimportant dynamic and inherent soil properties (Doran and Safley,1997).

Crop production on the East India Plateau is characterized as lowyielding and drought prone (Bhattacharyya et al., 2013; Cornish et al.,2015), which could be related to deleterious anthropogenic effects onkey soil functions such as water and nutrient provisioning. For the Stateof Jharkhand, Singh and Singh (2014) attribute low productivity of theupland soils to physical and chemical constraints such as coarse texture,low water and nutrient retention capacity, soil acidity, low fertilizer useand deficiencies of N, P, K, S, and B. But this assessment disregardsbiological processes.

The measurement of soil health through indicators that representsoil processes can be used to assess soil degradation (Karlen et al.,1997). It expands on traditional soil testing, which has largely focusedon the measurement of chemical soil properties (i.e., soil pH and nu-trient contents) to evaluate soil fertility (Karlen et al., 2003; Moebius-Clune et al., 2016). The past narrow chemical focus is considered to

https://doi.org/10.1016/j.apsoil.2019.02.003Received 1 October 2018; Received in revised form 29 January 2019; Accepted 3 February 2019

Abbreviations: Agstab, wet aggregate stability; AWC, available water capacity; OM, Total Organic matter by loss on ignition at 500 °C; ActC, permanganate–oxidizable, biologically active carbon; Prot, Citrate buffer extracted soil protein; Resp, Soil respiration measure of CO2 in rewetted soils

⁎ Corresponding author at: Section of Soil and Crop Sciences, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA.E-mail address: hmv1@cornell.edu (H.M. van Es).

Applied Soil Ecology xxx (xxxx) xxx–xxx

0929-1393/ © 2019 Published by Elsevier B.V.

Please cite this article as: Phillip S.D. Frost, et al., Applied Soil Ecology, https://doi.org/10.1016/j.apsoil.2019.02.003

have been a contributor to physical and biological soil degradation(Tilman et al., 2002) and spurred the development of more compre-hensive assessment of soil health that evaluates multiple physical,biological, and chemical soil properties with an emphasis on those thatare most sensitive to land management practices and correlated toecosystem processes (Karlen et al., 2003; Idowu et al., 2009). India hasrecently implemented a large-scale “soil health” scheme (INM Division,2016; National Informatics Centre, 2018) which concentrates ex-clusively on soil fertility chemical properties and organic matter.

The Comprehensive Assessment of Soil Health approach (CASH;Moebius-Clune et al., 2016) was developed for the identification ofspecific soil constraints in agroecosystems as it relates to land pro-ductivity and potential environmental impacts (Andrews et al., 2004;Idowu et al., 2009). CASH provides standardized, field-specific in-formation on agronomically important constraints (Fine et al., 2017)and is part of a broad soil health management framework. It offersmeasurement of physical indicators (wet aggregate stability, availablewater capacity, and penetration resistance), biological indicators(contents of organic matter, active carbon, and extractable protein, aswell as soil respiration), and chemical indicators (pH and availablenutrients) through properties that are linked to important soil processes(Moebius-Clune et al., 2016). The quantification of soil health throughindicators offers opportunities to gain broader knowledge of a soil'squality, target management interventions, and monitor improvementsover time.

Little information exists on the soil health status of managed and un-managed lands in Jharkhand, India. This study is the first step inquantifying a broader set of biological, physical, and chemical in-dicators and to interpret those values. Therefore, the objectives of thisstudy were to (i) use a spatial framework to assess soil health inJharkhand, India following the CASH framework and protocols, (ii)identify constraints and causes to inform suitable solutions for sus-tainable land management, and (iii) to evaluate the suitability of theCASH approach to the Indian context and to identify possible en-hancements.

2. Materials and methods

2.1. Research sites

The state of Jharkhand (Fig. 1) in eastern India (21°58′-25°8′N;3°19′-8°55′E), has an area of 79,710 km2 (22,000 km2 cultivated) and apopulation of 33M (World Bank, 2014). The soil parent material isprimarily granite and gneissic metamorphic rocks (State AgriculturalManagement and Extension Training Institute of Jharkhand, 2016),containing mostly feldspar and quartz with lesser amounts of mica thatare weathered and locally transported. The soil moisture regime is ustic(Buol et al., 2011) based on a 10 year (1991–2000) mean annualrainfall distribution in the capital Ranchi, including a summer monsoon(Kharif; June to October; 1424mm), a winter dry season (Rabi; Octoberto March; 178mm) and a transitional season (March to June; 361mm;State Agricultural Management and Extension Training Institute ofJharkhand, 2016). Soils are hyperthermic, and generally classified asmoderately developed Alfisols (54%), slightly developed Inceptisols(24%) or near-undeveloped Entisols (20%; Soil Survey Staff, 2003;Agarwal et al., 2010). In lower landscape positions soils are terracedand bunded for water management in a generally rice (Oryza sativa)-fallow rotation. Bullock-drawn plows are generally used to puddle soilsto create an impervious layer to pond water, helping weed control inpaddy rice production. After millennia of human agricultural soilpractices these soils could possibly be classified as Anthroposols(Cornish et al., 2015). Upper landscape positions are generally unsuitedfor rainfed rice production due to coarser soils and divergent hydrology.These areas are typically used for home gardens, orchards or silvi-culture (uncultivated). Despite infertile soils, there is low fertilizer use(mainly manure droppings and sometimes compost) and the state has a

severely negative balance sheet for the major nutrients N, P, and K(Dutta, 2018).

2.2. Sampling

A stratified random sampling design was used to select 27 catch-ments within an area of 34,362 km2 in six (Bokaro, Giridih, Gumla,Hazaribagh, West Singhbum and Ranchi) of the 24 districts ofJharkhand, within a 100 km radius of the capital city Ranchi (Fig. 1). Ineach district, a grid of 16 equal-sized squares was overlain using the Rsoftware (R Core Team, 2015) function ‘psample’ to ensure a full spreadof points. The R function ‘sample’ was then used to randomly selectagricultural areas within the overlain grids. Three to six catchments perdistrict were sampled (Fig. 1). As the research focused on agriculturalsoils, selected positions that were not located in cultivated areas wereshifted to the nearest catchment position with bunded paddy fields.

Soil samples were collected from four landscape positions at eachsite along a catchment transect (Fig. 2). They had different land use andmanagement as follows: (i) un-cultivated land, typically located on theupper boundary or side ridges of the catchment characterized by sil-viculture and evidence (i.e., large trees) that no cultivation had takenplace for some time; (ii) upland non-terraced cultivated fields that aregenerally lower in the catchment to the uncultivated land, typicallymanaged as home gardens or orchards during the seasonally wetmonsoon months, and sometimes irrigated in the dry season; (iii) sea-sonally wet bunded or terraced fields, generally in the middle of thecatchment profile, primarily used for kharif paddy rice cultivation; and(iv) lowland bunded or terraced fields in the bottom areas of thecatchment that are often perennially wet and also used for kharif paddyrice cultivation. The rice fields were generally fallowed during the dry(rabi) season, with incidental animal roaming.

After removal of surface residues, disturbed soil samples (0-to-15 cmdepth) were collected in June and July 2015 using a spade according tothe CASH sampling protocol (Moebius-Clune et al., 2016). Metadataincluded GPS position, altitude, village name, and landscape position(Supplement Table 1). Five samples within a radius of 3m of the se-lected position were composited and thoroughly mixed, of which ap-proximately 1 kg was retained. All samples were air dried and stored at4 °C until analysis. Three samples were damaged and unfit for analysis,while 113 samples were analyzed.

In November 2015, five previously sampled micro-watershed sitesclosest to Ranchi were selected for subsoil sampling (30 to 40 cm depth)following the same protocols at each of the four landscape positions.Concurrently, five penetrometer readings (PR) were made from thesurface at 10 cm depth increments to a maximum of 40 cm, using aDicky John soil compaction tester (Auburn, Illinois), resulting in a totalof 479 data points of soil strength. The PR values were not adjusted forwater content, but readings were taken at near-field capacity condi-tions.

2.3. Laboratory analyses

The CASH protocol assesses biological, physical and standard che-mical analyses (Idowu et al., 2009; Moebius-Clune et al., 2011a).Samples were air dried in the lab and then passed through a 2mm sievebefore the following assessments:

2.3.1. Physical propertiesSoil textural separates (sand, silt, clay) were determined using the

rapid method by Kettler et al. (2001) involving combinations of sievingand settling after dispersion by a 3% sodium hexametaphosphate so-lution. Wet aggregate stability (WAS) was determined using a rainfallsimulator fitted with Teflon capillaries generating 0.6mm water dropsand an adjustable Mariotte-type tube to control hydraulic pressure(Ogden et al., 1997). Samples were air-dried to friable consistency,gently crumbled through an 8-mm sieve and oven-dried at 40 °C. Using

P.S.D. Frost, et al. Applied Soil Ecology xxx (xxxx) xxx–xxx

2

stacked sieves of 2 and 0.25mm soil samples were shaken for 10 s on amechanical shaker. Aggregates of 0.25-to-2 mm size were returned to40 °C to achieve consistent water potential. A single layer of aggregateswas spread on a 0.25mmmesh sieve, which was placed 0.5 m below therainfall simulator to apply 2.5 J of energy over a 300-s period. WAS wasdetermined as the fraction of soil remaining on the sieve, correcting forsolid particles> 0.25mm.

For Available Water Capacity (AWC) the difference between soilwater content at field capacity (θfc) and permanent wilting point (θpwp)was assessed gravimetrically (g water g soil−1). Saturated soil sub-samples were equilibrated to pressures of−10 kPa (θfc) and−1500 kPa

(θpwp) on ceramic plates in air pressure chambers (Soil MoistureEquipment Corp., Goleta, CA; Topp et al., 1993).

2.3.2. Biological propertiesSoil organic matter (OM) content was analyzed by mass loss on

ignition in a muffle furnace at 500 °C for 2 h. Active Carbon (ActC) wasassessed as permanganate oxydizable carbon (Weil et al., 2003), mea-sured in duplicate, by reacting a 2.5 g soil sample with 20mL 0.02Mpotassium permanganate (KMnO4) solution (pH 7.2). Extracts wereshaken (120 rpm, 2min), then allowed to settle for exactly 8min. Analiquot of solution was diluted 100 times before measurement for

Fig. 1. Map of India and Jharkhand with district boundaries and sample sites.

Fig. 2. Typical catchment stratified into four landscape positions.

P.S.D. Frost, et al. Applied Soil Ecology xxx (xxxx) xxx–xxx

3

absorbance at 550 nm using a handheld spectrophotometer (Hach,Loveland, CO). Sample absorbance was calibrated with KMnO4 stan-dard curves and converted to mg ActC per kg soil using the equation byWeil et al. (2003).

Soil heterotrophic respiration (Resp) was measured in duplicateafter four-day incubation using a method modified from Haney andHaney (2010). Soil sieved past 8mm was weighed (20 g) in a perforatedaluminum weighing boat and put inside a glass jar sitting atop twostaggered Whatman qualitative filter papers. A preassembled CO2 trap(10mL glass beaker adhered to a plastic stand) was placed onto theweighing boat and the beaker was filled with 9mL 0.5M KOH. Distilledwater (7.5 mL) was pipetted alongside the jar to facilitate rewetting ofthe sample via capillary rise. The amount of CO2 respired and absorbedby the KOH trap over the course of incubation was determined bymeasuring the change in electrical conductivity of the solution with anOrion™ DuraProbe™ 4-Electrode Conductivity Cell (ThermoFisher Sci-entific, Inc., Waltham, MA). The necessary background correction foratmospheric CO2 was quantified using blank (no soil) incubations.

Autoclaved-Citrate Extractable Protein (Prot) content was measuredby first extracting the soil with 0.02M sodium citrate at pH 7. Theextract was then quantified by bicinchoninic acid assay against a bovineserum albumin standard curve for soil protein concentration after asequence of centrifugation and autoclaving steps (Wright andUpadhyaya, 1996).

2.3.3. Chemical propertiesSoil pH was measured in a 1:1 soil:water slurry. Plant available soil

nutrient concentrations (P, K, Mg, Fe, Mn and Zn) were measured afterextraction with a Mehlich-III solution using inductively-coupled plasmaoptical emission spectrometry (SPECTRO Analytical Instruments Inc.,Mahwah, NJ). All nutrient contents were calculated per mass of soil (mgkg−1).

2.4. Statistical analyses

Pearson correlation analysis was performed to identify and measureassociations between pairs of variables. Parameters of the normal dis-tribution for each indicator were determined overall and by landscapeposition. Values further than two standard deviations from the samplemean were verified against transcription and lab errors. Shapiro-Wilkstests and ‘qqnorm’ plots indicated skewed distributions for all indicatorsother than AWC, OM, and pH. Before transformation, all p values wereallocated a small value of 0.01 as there were many zero results. Datawere transformed using the log10 function for chemical nutrients andsquare root function for the biological indicators and wet aggregatestability.

Biological and physical indicators including Agstab, AWC, OM,ActC, Prot, and Resp were scored on a scale of 1 to 100 based on anestimated cumulative normal distribution of samples in the CASH da-tabase (mostly samples from the northeastern and midwestern USA;Fine et al., 2017). For each indicator a Gaussian (normal) distributionfunction is used:

= − ∞ < < ∞

−

f xσ π

e x( ) 12

,x μ

σ

( )2

2 2(1)

for which the parameters μ (mean) and σ (standard deviation) wereestimated from the measured results in the CASH database. A rank scorewas then derived with the cumulative normal distribution function, theintegral of Eq. (1), which shows the probability of values less than themeasured value. It is then multiplied by 100 to yield a standardizedscoring between 0 and 100. This in effect provided a fuzzy scoringfunction for each indicator. Note that these scores are based on differentagroecological and production environments than Jharkhand and wereused for comparison purposes, because it was not yet possible to es-tablish local scoring functions with the small number of observations in

the target environment.Nutrient content interpretations for P, K, Mg, Fe, Mn and Zn are

based on general sufficiency levels obtained with Mehlich III extractantas found in Havlin et al. (2005). High soil nutrient concentrations in-dicate a 90 to 100% sufficiency of available nutrients and low con-centrations indicate a 50 to 70% sufficiency of available nutrients.

Analysis of variance (ANOVA) was performed to determine varia-tion for each indicator among landscape positions using Tukey's HSD(α=0.05) for multiple pairwise comparisons of means. Due to unequalsample sizes, mean indicator values from 0 to 15 cm (n=113) and 30to 40 cm depth (n=20) data were compared using Welch's t-test.

Principal Component Analysis (PCA) was conducted to identifyprincipal factors that incorporate the maximum variation from theoriginal data. Data were standardized with the R formula“scale= TRUE”, and Kaiser's rule (Zwick and Velicer, 1986) was ap-plied, which recommends retaining factors with eigenvalues> 1.

A Best Subsets Regression (BSR) was performed to determine themost determinant SH indicators. The overall SH score was predictedusing subsets of individual soil health indicators. This approach eval-uates which indicator(s) are highly predictive and therefore most sui-table in an abridged soil health assessment (Fine et al., 2017). It is re-cognized that the predictor variables are also used to generate theoverall SH score and the evaluation was therefore restricted to smallsubsets (4 or less).

Statistical analysis and graphics were carried out in the R environ-ment for statistical computing (R Core Team, 2015). Maps were pre-pared using QGIS version 2.14.3 (QGIS Development Team, 2016)using the WGS 84 coordinate reference system and Bing Satellite ima-gery as a background. Political boundaries were obtained as shapefilesfrom www.gadm.org.

3. Results and discussion

3.1. Correlations

Summary statistics for each SH indicator by landscape position arepresented in Table 1. The 113 surface soils are generally high in sandcontent (mean of 53%) and dominant textural classes are sandy loam(37.2%) and loam (18.6%), in accordance with expectation for soilsderived from felsic rock material.

The majority of Pearson correlations among physical, biological andchemical indicators were significant (> |0.18| at α=0.05; two-tailed;n=113; Table 2). Most indicators were strongly negatively correlated(r < −0.5) with sand and positively (r > 0.5) with silt and clay, si-milar to Fine et al. (2017) for USA soils. Among biological indicators(OM, ActC, Prot, and Resp) correlation coefficients were between 0.47and 0.64, suggesting that biological processes tend to be jointly en-hanced, but individually may still be differently expressed. Zn showedstrong correlations with all biological indicators (OM, ActC, Prot, Resp),possibly due to chelation and effects of outliers. Mg is strongly corre-lated with clay and pH, and respiration with zinc.

3.2. Districts

The six districts selected for sampling comprise an area of34,362 km2 stretching 300 km north to south and 250 km east to westand between 170 and 707m above sea level. The variance in parentmaterial and other soil forming factors such as altitude and climatesuggest SH variations among districts. A one-way ANOVA among dis-tricts (assuming random catchments) shows significance (α=0.05) forthe inherent soil indicators sand, silt, and clay, suggesting regionalvariations in parent material (Table 3). These are primarily reflected indifferences between the southernmost West Singhbhum district (Fig. 1)and the others, where the former has finer-textured soils (Table 4).District differences were also associated (Tukey HSD pairwise com-parisons; α =0.05) with several other indicators, including OM, ActC,

P.S.D. Frost, et al. Applied Soil Ecology xxx (xxxx) xxx–xxx

4

Resp, pH, Mg and Mn (Table 3). The finer textured soils in WestSinghbhum presumably have stronger bonds with organic compoundsthat hold higher levels of OM and ActC (Table 4), and may also ex-perience higher levels of primary productivity and C inputs due togreater water availability. The only other district-level SH differenceswere for Giridih, which had significantly higher ActC than Gumla andhigher pH than Ranchi (Table 4).

3.3. Landscape positions

A one-way ANOVA among landscape positions (assuming randomcatchments) did not show significant effects (α =0.05) for sand, silt,and clay, in part due to high variability among districts (Table 3). Thereis a slight trend, however, for lower sand contents and higher siltcontents from upland to lowland positions, suggesting effects of erosionand deposition (Table 1). Landscape position effects were significant (α=0.05) for Agstab and the biologically aligned indicators OM, ActC,Prot, Resp, and the chemical indicators P, K, and Fe (Table 3). MeanProt, Resp, and K were significantly higher in uncultivated landscapepositions than other locations in the catchment, but high standard de-viations indicate considerable variation among catchments (Tables 1,3). This pattern was also observed for most other SH indicators, al-though generally not significant due to high variability. Marginallysignificant effects (p=0.047) were observed among landscape posi-tions for P (Table 3), which may be related to variable organic matterdeposition, but these did not show significance in a means comparison(Table 1). Twenty-one percent of sample values tested undetectableamounts of P, while overall means and standard deviations averaged4.29 and 8.38 ppm, respectively, implying a high CV of 195% (Table 1).This high variability is likely related to uneven compost or manuredeposits from either penned or free grazing animals, resulting in pat-ches of high and low concentrations within fields that even affectedcomposited (5) soil samples.

Higher K contents in uncultivated than cultivated areas could be dueto K mining, with the removal of straw and crop residues for off-sitefodder. Fe was affected by landscape position in that the middle andlowland areas had higher values than upland and uncultivated areas,presumably associated with variations in the redox regime, i.e., longeranaerobic periods leading to higher contents of soluble Fe (Table 1;Havlin et al., 2005).

Overall, comparisons among soil health indicators primarily showedsignificant landscape position effects as it relates to anthropogenicdisturbance, i.e., uncultivated areas vs. cultivated upland, middle,lowland areas for Prot; uncultivated vs. upland for Resp; and un-cultivated vs. middle for K (Table 1). Also, uncultivated positionsgenerally measured the highest values for Agstab, all biological in-dicators (OM, ActC, Prot, Resp.) and macronutrients (P and K). Thissuggests that management related to tillage, nutrients, and above-ground biomass diversity influences the below-ground biological andchemical processes. Lowland areas generally show highest values forpH, Mg, Fe, and Zn, as well as clay, silt, and AWC, presumably due tosoil deposition and low redox environments associated with landscapehydrology within catchments. Surprisingly, the middle and lower areasof catchments did not show much higher levels of OM and labile OMindicators (ActC and Prot; Table 1) despite wetter soil moisture regimes,presumably due to the counteracting effects of intensive tillage (pud-dling). Soil respiration mean values are a prime indicator of biologicalactivity in soils (Moebius-Clune et al., 2016) and comparisons fromvarious landscape positions from Arunachal Pradesh, Northeast India(Arunachalam et al., 1999) show values 3.6 times higher than ourJharkhand samples.

3.4. Interactions

Significant interactions between districts and landscape positionssuggest that effects of cultivation or position in the catchment areTa

ble1

Mean,

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Mean

Stdde

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Mean

Stdde

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Mean

Stdde

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Sand

(%)

55.7

15.6

57.4

19.5

55.7

20.1

56.2

27.1

52.0

22.0

50.2

31.6

49.2

27.5

50.4

47.8

53.0

21.9

54.5

29.9

Silt(%

)26

.211

.226

.312

.627

.214

.023

.819

.329

.015

.530

.020

.431

.919

.027

.130

.728

.715

.326

.620

.0Clay(%

)18

.17.3

15.6

8.3

17.1

8.9

15.7

10.5

19.0

9.7

17.2

15.3

18.9

10.7

19.0

12.6

18.3

9.2

17.7

11.7

Agstab(%

)21

.716

.518

.522

.115

.710

.412

.313

.215

.29.4

11.8

12.8

15.8

11.5

10.4

12.5

17.0

12.4

13.9

14.2

AWC(m

3/m-3)

0.19

0.05

0.19

0.06

0.19

0.06

0.19

0.08

0.20

0.05

0.21

0.05

0.20

0.05

0.20

0.07

0.20

0.05

0.19

0.07

OM

(g/k

g-1)

2.0

0.7

2.0

1.2

1.6

0.9

1.4

0.8

1.9

1.0

1.9

1.0

1.7

0.7

1.8

1.2

1.8

0.8

1.7

1.1

ActC(m

g/kg

−1)

184

127

163

108

111

9410

211

113

390

126

116

178

118

176

214

152

111

119

144

Prot

(mg/

g)2.9a

1.4

2.5

1.0

2.0b

0.8

1.8

0.9

2.0b

0.8

1.9

1.0

1.9b

1.0

1.8

1.0

2.2

1.1

1.9

1.1

Resp(m

gCO2/

g)0.17

a0.08

0.15

0.09

0.10

b0.07

0.08

0.09

0.12

ab0.06

0.10

0.10

0.16

ab0.10

0.15

0.08

0.14

0.08

0.12

0.08

pH5.9

0.7

5.9

0.4

5.6

0.7

5.5

0.9

5.9

0.8

5.8

0.9

6.0

0.9

6.0

1.5

5.8

0.8

6.0

1.1

P(ppm

)6.8

11.2

1.7

11.6

5.7

9.3

1.0

4.1

1.2

1.6

0.9

2.0

3.6

7.6

1.0

2.6

4.3

8.4

1.1

2.2

K(ppm

)15

8a

261

9387

74ab

3968

3965

b35

6256

80ab

5267

5894

136

6759

Mg(ppm

)19

810

620

214

315

712

013

814

123

718

017

626

329

523

027

330

422

417

417

522

2Fe

(ppm

)17

4b

9515

284

182b

111

149

9923

4ab

175

186

106

367a

429

198

198

243

261

175

107

Mn(ppm

)16

510

015

296

148

9515

015

915

412

013

922

812

192

110

132

146

102

136

154

Zn(ppm

)1.7

1.3

1.2

1.1

1.3

0.8

1.2

0.9

1.4

0.7

1.5

1.1

2.8

6.9

1.3

1.0

1.8

3.7

1.2

1.0

Abb

reviations:Agstab:

Wet

Agg

rega

teStab

ility;AWC:A

vaila

bleWater

Cap

acity;

OM:O

rgan

icMatter;

ActC:ActiveCarbo

n;Prot:A

CEProteinInde

x;Resp:

SoilRespiration

.

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variable among the districts. This appeared primarily the case for thetextural separates (Table 3), presumably influenced by the differencebetween West Singhbhum and other districts. Otherwise, only Prot andMg showed significant district by landscape position interactions, withthe latter possibly influenced by outlier values.

3.5. Soil health interpretations through scores

CND indicator scores for the physical and biological indicators ac-cording to landscape position (0–15 cm soil depth) are based on coarse-textured soils in the CASH approach (Moebius-Clune et al., 2016). Theywere subsequently color coded into quintile ranges following CASHprotocols, with red indicating very low scores (0−20), orange lowscores (20–40), yellow medium scores (40–60), light green high scores(60–80), and dark green very high scores (80–100; Table 5). Overall,scores were most often in the lower quintiles for the broader CASHdatabase, presumably due to a combination of low inherent quality andlong-term intensive management compared to temperate zone soils thatare dominant in the CASH database. In our data, the exception is AWCwhich is strongly influenced by inherent soil properties, especially

texture. Uncultivated positions generally show biological constraints,but have higher scores than cultivated landscape positions, reflectingthe effects of agricultural management such as tillage and monocrop-ping on physical soil structure and consequential biological indicators.In a western Kenyan chronosequence study, Moebius-Clune et al.(2011b) measured exponential soil OM and ActC declines of> 75%from virgin tropical forest after 77 years of cultivation, resulting in ActCcontents commensurate with those in the cultivated fields in our study.However, OM contents in virgin forests showed much higher OM andActC levels, suggesting that the uncultivated sites in our study are notvirgin forests but likely reforested areas, possibly after soil degradation.This confirms that long-term intensively cropped soils, especially inhyperthermic regions, are generally of low quality. Nutrient levels wereassessed from Mehlich III extractions and interpreted based on suffi-ciency levels (Havlin et al., 2005). Since CASH database scores arebased on a different extraction method, values reported in Table 5 re-present measured means, not scores. They indicate very low soil Pcontents for all landscape positions even in uncultivated areas, sug-gesting low natural P availability from the parent materials exacerbatedby crop P extraction without fertilizer replacement, especially in therice production zones in the middle and lower catchment positions. Kcontents similarly suggest nutrient depletion in the cropped areas, butadequate levels in the uncultivated positions, presumably impacted bycrop residue removal for animal feed (Table 5) and insufficient return ofnutrients through manure, which is often used as cooking fuel. Othernutrients indicate high levels, suggesting low concerns with minornutrients. pH values show that the soils are slightly acidic (Table 1),presumably posing limited impacts on nutrient availability and cropyields (Table 5).

These results show that the cultivated soils would greatly benefitfrom fertilizer additions, but Marenya and Barrett (2009) argue thatfertilizers sometimes provide limited marginal productivity gains indegraded soils due to von Liebig-type limitations from physical orbiological constraints. Therefore, soil health enhancements require acomprehensive approach where all constraints are addressed.

3.6. Surface vs. subsurface soil health

Samples from surface (0-to-15 cm) and subsurface (15-to-30 cm) soilon average had similar textures, with only a modest increase in claycontent and decrease in silt content in the subsoil (Table 6), presumablydue to clay translocation (Buol et al., 2011). Biological indicators werehigher for the surface horizon, with significant effects for ActC, Prot,and Resp, but OM contents were relatively similar. Notably, ActC and

Table 2Pearson correlations for soil health indicators (0–15 cm).

Sand Silt Clay Agstab AWC OM ActC Prot Resp pH P K Mg Fe Mn Zn

Sand 1.00 −0.93 0.83 −0.14 −0.76 −0.77 −0.33 −0.38 −0.39 −0.20 0.05 −0.17 −0.52 −0.24 #### ###Silt −0.93 1.00 0.57 −0.02 0.77 0.67 0.36 0.45 0.43 0.10 0.04 0.15 0.35 0.35 0.19 0.42Clay −0.83 0.57 1.00 0.35 0.53 0.72 0.20 0.15 0.21 0.29 −0.19 0.15 0.65 0.00 0.26 0.09Agstab −0.14 0.02 0.35 1.00 −0.15 0.11 −0.20 −0.09 0.15 0.01 −0.14 0.00 0.21 −0.20 #### ###AWC −0.76 0.77 0.53 −0.15 1.00 0.75 0.47 0.49 0.43 0.18 −0.01 0.05 0.47 0.28 0.26 0.48OM −0.77 0.67 0.72 0.11 0.75 1.00 0.47 0.56 0.52 0.21 0.02 0.18 0.53 0.18 0.35 0.49ActC −0.33 0.36 0.20 −0.20 0.47 0.47 1.00 0.59 0.52 0.32 0.20 0.23 0.27 0.35 0.19 0.53Prot −0.38 0.45 0.15 −0.09 0.49 0.56 0.59 1.00 0.64 −0.02 0.29 0.26 0.01 0.31 0.29 0.52Resp −0.39 0.43 0.21 0.15 0.43 0.52 0.52 0.64 1.00 0.23 0.21 0.33 0.19 0.45 0.08 0.60pH −0.20 0.10 0.29 0.01 0.18 0.21 0.32 −0.02 0.23 1.00 −0.03 0.10 0.59 −0.04 0.28 0.15P 0.05 −0.04 0.19 −0.14 −0.01 0.02 0.20 0.29 0.21 −0.03 1.00 0.27 −0.17 0.06 #### 0.46K −0.17 0.15 0.15 0.00 0.05 0.18 0.23 0.26 0.33 0.10 0.27 1.00 0.12 −0.01 0.08 0.17Mg −0.52 0.35 0.65 0.21 0.47 0.53 0.27 0.01 0.19 0.59 −0.17 0.12 1.00 −0.03 0.27 0.19Fe −0.24 0.35 0.00 −0.20 0.28 0.18 0.35 0.31 0.45 −0.04 0.06 −0.01 −0.03 1.00 #### 0.51Mn −0.24 0.19 0.26 −0.03 0.26 0.35 0.19 0.29 0.08 0.28 −0.06 0.08 0.27 −0.12 1.00 0.19Zn −0.33 0.42 0.09 −0.16 0.48 0.49 0.53 0.52 0.60 0.15 0.46 0.17 0.19 0.51 0.19 1.00

Abbreviations: Agstab: Wet Aggregate Stability; AWC: Available Water Capacity; OM: Organic Matter; ActC: Active Carbon; Prot: ACE Protein Index; Resp: SoilRespiration.Values> |0.18| are significant (n=113; α=0.05). Values> | 0.50| are bolded.

Table 3ANOVA for districts and landscape positions (catchments as random variable;0–15 cm depth, n= 113).

Indicator District Landscape Position DxLP

df p-Value df p-Value df p-Value

Sand 5 0.001 3 0.895 15 0.0058Silt 5 0.005 3 0.590 15 0.0180Clay 5 0.000 3 0.965 15 0.0218Agstab 5 0.079 3 0.034 15 0.1500AWC 5 0.232 3 0.711 15 0.0800OM 5 0.003 3 0.026 15 0.1209ActC 5 0.001 3 0.012 15 0.1058Prot 5 0.074 3 0.001 15 0.0023Resp 5 0.014 3 0.000 15 0.4602pH 5 0.002 3 0.200 15 0.4934P 5 0.360 3 0.047 15 0.4194K 5 0.516 3 0.029 15 0.9614Mg 5 0.042 3 0.189 15 0.0050Fe 5 0.106 3 0.006 15 0.5735Mn 5 0.040 3 0.491 15 0.4619Zn 5 0.070 3 0.235 15 0.7668

Abbreviations: Agstab: Wet Aggregate Stability; AWC: Available WaterCapacity; OM: Organic Matter; ActC: Active Carbon; Prot: ACE Protein Index;Resp: Soil Respiration.

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Prot levels in the surface soil averaged 2.2 and 1.6 times higher, re-spectively, than the subsoil, while the OM content was only 1.1 timeshigher. This suggests that organic matter in the surface layer is morelabile and biologically active. In terms of nutrients, extractable P con-tents were 2.6 times higher in the surface horizon that the subsurface,presumably from manure and compost additions. Other nutrient con-tents were also always higher (ns) in the surface soil. Zn averagedhigher values in the 0 to 15 cm layer, but mostly associated with ap-parent outlier values that could be related to localized hot spots. AWCwas slightly higher in the subsurface, presumably associated withsomewhat higher silt contents.

3.7. Soil strength

Soil strength (penetrometer resistance) averaged higher in thesubsoil (1791) than the surface horizon (1470; ns), likely due to lowerbenefits from biological activity near the surface and soil consolidationfrom higher overburden pressures. Landscape differences in penet-rometer readings (average of 0–15 and 30–45 cm) were nonsignificantas they were impacted by a combination of confounded hydrological,management, and SH effects (Fig. 3). They were taken subsequent tothe start of the Kharif summer monsoons when many of the bundedmiddle and lower landscape positions were very wet compared to thedryer upland and uncultivated landscape positions. This is reflected inlower positions having less hard soils compared to higher landscape

Table 4Mean and standard deviation for soil health indicators measured from 6 Jharkhand districts. Mean values followed by different letters within a row are significantlydifferent (α=0.05) according to Tukey HSD means comparisons.

Indicator Bokaro Giridih Gumla Hazaribagh Ranchi West Singhbhum

Mean Std dev Mean Std dev Mean Std dev Mean Std dev Mean Std dev Mean Std dev

Sand (%) 65.9 a 17.5 64.6 a 16.8 52.9 ab 20.2 50.5 ab 18.7 50.7 ab 23.3 34.2 b 23.9Silt (%) 20.3 b 12.2 20.7 b 11.6 26.1 ab 10.8 30.5 ab 14.7 31.5 ab 17.7 37.4 a 13.5Clay (%) 13.8 b 7.0 14.7 b 8.1 21.0 ab 10.6 19.0 b 6.9 17.8 b 8.8 28.5 a 12.1Agstab (%) 13.8 9.7 11.3 5.9 21.2 15.3 15.6 11.1 19.3 12.7 20.6 14.9AWC (m3/m-3) 0.19 0.04 0.19 0.04 0.18 0.04 0.19 0.05 0.19 0.07 0.23 0.05OM (g/kg-1) 1.64 b 0.88 1.44 b 0.87 1.60 b 0.72 1.83 b 0.64 1.71 b 0.75 2.63 a 1.08ActC (mg/kg−1) 123 ab 57 190 a 115 77 b 54 145 ab 101 131 ab 122 228 a 143Prot (mg/g) 1.58 0.53 1.98 1.80 1.77 0.70 2.26 0.95 2.09 0.83 2.64 1.52Resp (mgCO2/g) 0.11 ab 0.06 0.11 ab 0.08 0.10 b 0.06 0.17 a 0.09 0.13 ab 0.08 0.16 ab 0.06pH 6.0 ab 0.6 6.4 a 0.7 5.8 ab 0.7 6.1 ab 0.9 5.6 b 0.6 6.5 a 0.7P (ppm) 2.3 4.4 5.6 12.2 2.6 3.7 3.2 6.5 6.3 10.4 2.7 6.0K (ppm) 54 b 27 91 a 79 86 a 57 117 a 228 69 ab 53 120 a 58Mg (ppm) 229 ab 155 250 ab 151 202 b 192 231 ab 167 184 b 157 387 a 278Fe (ppm) 161 80 183 55 215 243 344 405 227 155 192 63Mn (ppm) 163 ab 105 165 ab 125 121 b 78 138 ab 93 122 b 88 224 a 127Zn (ppm) 1.41 0.7 1.6 1.0 0.9 0.4 1.6 1.1 1.5 1.3 1.7 0.8

Abbreviations: Agstab: Wet Aggregate Stability; AWC: Available Water Capacity; OM: Organic Matter; ActC: Active Carbon; Prot: ACE Protein Index; Resp: SoilRespiration.

Table 5Physical and biological indicators scored with CND, nutrients scored with sufficiency levels. CND numbers are scores (0−100) and sufficiency numbers are meanmeasured values and interpreted based on Havlin et al. (2005).

Scoringapproach

Un-cultivated

Upland Middle Lowland All

IndicatorAgstab (%) CND† 25 19 15 16 19AWC (m3/m-3) CND 74 75 80 77 77OM (g/kg-1) CND 49 37 42 36 41ActC (mg/kg-1) CND 12 6 7 11 9Prot (mg/g) CND 10 6 6 6 7Resp (mgCO2/g) CND 8 5 6 8 7pH CND 44 56 90 67 44

P (ppm) Sufficiency‡ 6.8 5.7 1.2 3.6 4.3K (ppm) Sufficiency 158 74 65 80 94Mg (ppm) Sufficiency 198 157 237 295 224Fe (ppm) Sufficiency 174 182 234 367 243Mn(ppm) Sufficiency 165 148 154 121 146Zn (ppm) Sufficiency 1.7 1.3 1.4 2.8 1.8

†CND = Cumulative Normal Distribution‡Sufficiency levelskey: P (ppm) K (ppm) Mg (ppm) Fe (ppm) Mn (ppm) Zn (ppm)Very low <7 <40 <8Low 8 to 14 41 to 80 8 to 16 0 to 2.5 <1 0 to 0.5Med 15 to 28 81 to 120 17 to 24 2.6 to 4.5 0.6 to 1.0

High 29 to 50121 to160 25 to 32 > 4.5 >1 >1

Very high >50 >160 >32

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positions (Fig. 3). Also, field preparation for paddy production was inprogress when measurements were made in the middle and lowlandsites, resulting in lower surface readings with higher subsurface read-ings indicating the deliberate densification from puddling for paddyrice cultivation. These confounded the possible effects of biologicalconditions on soil hardness in the different landscape positions. Also, ofthe 479 locations, 97 (20.2%) reached ≥3500 kPa, the maximumoutput of the device, which was measured 17 times in the uncultivated,33 in upland, 22 in the middle and 25 in the lowland (not included inFig. 3).

3.8. Principal component analysis

The first five principal components of the 16 transformed standar-dized soil properties explained a combined 82% of the total variance(Table 7). PC1 explained 40%, with strong positive loadings from AWCand the biological indicators OM, Prot, and Res, as well as K, Zn, and toa lesser extend other micronutrients, comparable to Fine et al. (2017).PC2 added 17% variance explanation, with positive loading from thechemical indicators pH, Mg, and Mn, and strong negative loading fromP and Fe, two indicators that had influential outliers. PC3 and PC4added a combined 19% variance with mixed physical and biologicaldirectional loadings, including a large effect of AgStab on PC3(Table 7). Overall, the PCA showed that PC1 is dominated by biological

Table 6Welch's two sample t-test comparisons for SH indicators, 0-to-15 cm vs. 30-to-40 cm depths.

Indicator df Means Variance p-Value

0-to-15 cm 30-to-40 cm

Sand (%) 27 53.0 53.5 1% 0.9205Silt (%) 34 28.7 24.8 −14% 0.1800Clay (%) 24 18.3 21.7 19% 0.1909Soil strength (kpa) 343 1470 1792 22% 0.0004Agstaba (%) 26 17.0 18.2 7% 0.7119AWC (m3/m-3) 25 0.20 0.17 −14% 0.0428OM (g/kg-1) 28 1.79 1.63 −9% 0.3953ActC (mg/kg−1) 68 152 69 −55% 0.0000Prot (mg/g) 44 2.17 1.35 −38% 0.0000Resp (mgCO2/g) 69 0.14 0.11 −24% 0.0035pH 25 5.9 6.3 6% 0.1086P (ppm) 125 4.3 1.7 −62% 0.0016K (ppm) 86 94 71 −24% 0.1736Mg (ppm) 23 224 275 23% 0.3497Fe (ppm) 31 243 215 −12% 0.5897Mn (ppm) 26 146 142 −3% 0.8584Zn (ppm) 127 1.8 0.8 −55% 0.0068

Abbreviations: Agstab: Wet Aggregate Stability; AWC: Available WaterCapacity; OM: Organic Matter; ActC: Active Carbon; Prot: ACE Protein Index;Resp: Soil Respiration.

Fig. 3. Penetrometer resistance (average of 0–15 and 30–45 cm) according to landscape position. Differences were non-significant due to high variability.

Table 7Eigenvector loadings from principal component analysis of standardizedtransformed SH attributes.

Proportion of variance % 40% 17% 12% 7% 6%Cumulative proportion % 40% 57% 68% 76% 82%Principal components PC1 PC2 PC3 PC4 PC5EigenvectorsAgstab (%) −0.03 0.20 −0.67 0.20 −0.39AWC (m3/m-3) 0.35 −0.03 0.10 0.25 0.37OM (g/kg-1) 0.37 0.03 −0.15 0.16 0.20ActC (mg/kg−1) 0.29 −0.06 0.34 −0.07 −0.39Prot (mg/g) 0.33 −0.23 −0.22 −0.19 0.15Resp (mgCO2/g) 0.31 −0.17 −0.23 0.09 −0.48pH 0.17 0.38 0.41 −0.27 −0.35P (ppm) −0.06 −0.51 −0.03 −0.55 −0.07K (ppm) 0.32 0.08 −0.27 −0.37 0.03Mg (ppm) 0.30 0.38 0.13 0.17 −0.05Fe (ppm) 0.21 −0.41 0.15 0.41 0.01Mn (ppm) 0.25 0.32 −0.13 −0.34 0.36Zn (ppm) 0.34 −0.22 0.09 −0.01 −0.10

Abbreviations: Agstab: Wet Aggregate Stability; AWC: Available WaterCapacity; OM: Organic Matter; ActC: Active Carbon; Prot: ACE Protein Index;Resp: Soil Respiration.

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SH indicators and PC2 by chemical ones.Biplots (Fig. S1, Supplement) indicate some expected directional

alignments (e.g., pH with P; OM with Resp and ActC, etc.). OM andAgStab showed orthogonality in the PC1-2 biplot, but alignment in thePC3-4 plot. Agstab and OM would be expected to show high associa-tion, but the low Pearson correlation coefficient (Table 2) and thecomplex principal component alignment suggests a confounded re-lationship, possibly due to textural effects, where West Singhbhumshows higher clay and OM levels but similar Agstab compared to otherdistricts.

3.9. Best Subsets Regression

The Best Subsets Regression analysis allows for the evaluation of therelative predictability of overall soil health by subsets of individualindicators. When considering a single predictor, OM predicts two-thirdsof the variability in overall soil health (R2-adj= 68%; Table 8), in-dicating that the single measurement of OM offers high levels of soilhealth insights. Resp was second with R2-adj= 0.55. Combined, OMand Resp explain 82% of the variability in soil health, similar to OM andActC (80%). OM, Resp, and ActC combined have an R2-adj of 87%. Thissuggests that overall soil health is best predicted by a combination oftotal OM and an indicator of labile organic matter or biological activity,and that a soil health test for this region may be simplified by mea-suring a limited number of indicators at low cost. Also, it indirectlysuggests that soil differences primarily result from landscape positionand hydrology (upper, middle, lower) and management (silviculture,garden/orchard, puddled rice), which strongly impact organic matterdynamics. Since most fields do not receive synthetic fertilizers, nutrientindicators are less impacted. It is notable that, in addition to OM, Resp,and ActC, Mn is indicative of overall soil health, which may be ex-plained by its role in organic matter decomposition (Keiluweit et al.,2015) and variable Mn(III) from redox levels within the catchments.Overall, this implies (i) that OM is an important property that impactsother soil health indicators, and (ii) that a simple measurement of OMwith those of labile OM (Resp and ActC) can be highly informativeregarding overall soil health.

4. Conclusions

Soil health is generally the result of combinations of inherent qua-lities and farmer management decisions such as nutrient management,removal of biomass, excessive tillage, and monocropping. This studypresents results on the overall SH of representative sites in JharkhandIndia, an area characterized by small subsistence farms. CASH resultssuggest effects from intensive management on agronomically importantsoil functions, but these are also influenced by naturally infertile soilsand inherent textural indicators. The finer textured soils in WestSinghbhum district show higher contents of OM, active carbon, Mg, andMn.

The within-catchment evaluation of landscape positions indicatedthat uncultivated lands have quantifiably higher soil health values –especially with biological indicators – than the cultivated garden/orchard upland areas as well as the puddled rice fields in middle andlow areas of catchments. The effects of aggressive tillage and man-agement was evident in differences between surface and subsurface soilhorizons through tillage-induced compaction in paddy middle andlowlands, presumably reducing rooting and biological activity. SHscores suggested generally degraded soils for physical and biologicalfunctions, and concerns with very low P levels.

PCA results indicate that biologically-mediated processes primarilydrive regional and catchment-scale SH differences, followed by soilchemical processes. Best Subsets Regression analysis indicates thatmanagement and landscape effects are mostly expressed through or-ganic matter dynamics, and that lower-cost SH assessment can be rea-sonably implemented through just two (OM with respiration or OMwith active carbon) or three biological indicators (OM, active carbon,and respiration) that explained large fractions of the total variance.

In all, this survey of the soil health status of Jharkhand soils in-dicates that the cultivated areas in the landscape are degraded relativeto uncultivated areas, even in the lower landscape positions wherewater is more available. The rice-fallow system practiced in these areashas multiple constraints resulting from negative nutrient balances, andinadequate consideration of biological and physical soil processes.There is potential for intensification of land use by better employingsoil and water resources during the fallow season (e.g., use of foragecrops in lowland positions) and better recycling of carbon and nutrientsfrom manure and compost (perhaps through micro-dosing) and use ofsynthetic fertilizer. Low soil fertility levels for P and K are apparent, butbenefits from synthetic fertilizer additions are likely to be restraineddue to countermanding physical and biological constraints.

The CASH framework, or possibly simplifications, appears to pro-vide beneficial insights into SH status of Indian soils and associatedneeds for more sustainable land management. This especially applies tothe often-ignored biological and physical soil characteristics. A com-prehensive SH assessment framework for India, that goes beyond nu-trient and pH concerns, would therefore be valuable and should includethe development of regionally-appropriate scoring and interpretationmethodologies.

Acknowledgements

This study was funded by the Tata-Cornell Institute (TCi) at CornellUniversity. We acknowledge the help in sampling and logistics from Mr.Thangkhanlal “Lal” Thangsing, as well as staff at ProfessionalAssistance and Development Action (PRADAN), Ranchi office.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.apsoil.2019.02.003.

References

Agarwal, B.K., R. Kumar, and D.K. Shahi. 2010. Soil resource inventory of Jharkhand,problem and solution. Dep. Soil Sci. Agric. Chem. Birsa Agric. Univ. Ranchi. Availableat http://www.bauranchi.org/wp-content/uploads/2013/11/SOIL-RESOURCE-INVENTORY-OF-JHARJHAND-PROBLEM-AND-SOLUTION.pdf (verified 15September 2015).

Andrews, S.S., Karlen, D.L., Cambardella, C.A., 2004. The soil management assessmentframework: a quantitative soil quality evaluation method. Soil Sci. Soc. Am. J. 68,1945–1962.

Arunachalam, K., Arunachalam, A., Melkania, N.P., 1999. Influence of soil properties onmicrobial populations, activity and biomass in humid subtropical mountainous eco-systems of India. Biol. Fertil. Soils 30 (3), 217–223.

Bhattacharyya, T., D.K. Pal, C. Mandal, P. Chandran, S.K. Ray, D. Sarkar, K.Velmourougane, A. Srivastava, G.S. Sidhu, R.S. Singh, and others. 2013. Soils ofIndia: historical perspective, classification and recent advances. Curr. Sci. 104(10):1308–1323.

Table 8Results of Best Subset Regression identifying the most predictive indicators aspredictors of overall soil health (n=133).

# vars R2-adj Best Subset SH indicators

1 0.68 OM1 0.55 Resp2 0.82 OM Resp2 0.80 OM ActC3 0.87 OM ActC Resp3 0.87 OM Resp Mn4 0.92 OM ActC Resp Mn4 0.90 OM ActC Resp Mg

Abbreviations: OM: Organic Matter; ActC: Active Carbon; Resp: SoilRespiration.

P.S.D. Frost, et al. Applied Soil Ecology xxx (xxxx) xxx–xxx

9

Buol, S.W., Southard, R.J., Graham, R.C., McDaniel, P.A., 2011. Soil Genesis andClassification. John Wiley & Sons.

Cornish, P.S., Choudhury, A., Kumar, A., Das, S., Kumbakhar, K., Norrish, S., Kumar, S.,2015. Improving crop production for food security and improved livelihoods on theEast India Plateau II. Crop options, alternative cropping systems and capacitybuilding. Agric. Syst. 137, 180–190.

Doran, J.W., Jones, A.J., 1996. Methods for Assessing Soil Quality, SSSA SpecialPublication. vol. 49 Soil Science Society of America, Madison, WI.

Doran, J. W. and M. Safley. 1997. Defining and assessing soil health and sustainableproductivity. p. 1–28. In Pankhurst, C., Doube B. M, Gupta, V.V.S.R. (eds.), Biologicalindicators of soil health. CAB International, Wallingford, NY.

Dutta, 2018. East India & Bangladesh: Jharkhand - region profile. Int. Plant Nutr. Instit.(http://sap.ipni.net/article/jharkhand), verified 23AUG2018.

Eswaran, H., Almaraz, R., van den Berg, E., Reich, P., 2005. An Assessment of the SoilResources of Africa in Relation to Productivity. World Soil Resources, Soil SurveyDivision, USDA, Natural Resources Conservation Service, Washington D.C.

Fine, A.K., van Es, H.M., Schindelbeck, R.R., 2017. Statistics, scoring functions and re-gional analysis of a comprehensive soil health database. Soil Science Soc. Am. J. 81,589–601.

Haney, R.L., Haney, E.B., 2010. Simple and rapid laboratory method for rewetting dry soilfor incubations. Commun. Soil Sci. Plant Anal. 41, 1493–1501.

Havlin, J.L., J.D. Beaton, S.L. Tisdale, W.L. Nelson, and others. 2005. Soil fertility andfertilizers: An introduction to nutrient management. Pearson Prentice Hall UpperSaddle River, NJ.

Idowu, O.J., Van Es, H.M., Abawi, G.S., Wolfe, D.W., Schindelbeck, R.R., Moebius-Clune,B.N., Gugino, B.K., 2009. Use of an integrative soil health test for evaluation of soilmanagement impacts. Renew. Agric. Food Syst. 24 (03), 214–224.

INM Division, Department of Agriculture, Cooperation & Farmers Welfare, 2016. NationalMission for Sustainable Agriculture (NMSA), Operational Guidelines, 2016th ed.Ministry of Agriculture & Farmers Welfare, New Delhi.

Karlen, D.L., Mausbach, M.J., Doran, J.W., Cline, R.G., Harris, R.F., Schuman, G.E., 1997.Soil quality: a concept, definition, and framework for evaluation. Soil Sci. Soc. Am. J.61, 4–10.

Karlen, D.L., Ditzler, C.A., Andrews, S.S., 2003. Soil quality: why and how? Geoderma114 (3–4), 145–156.

Keiluweit, M.P. Nicoc, M.E. Harmond, J-D Mao, J. Pett-Ridge, and M. Klebera. 2015.Long-term litter decomposition controlled by manganese redox cycling. PNASE5253–E526.

Kettler, T.A., Doran, J.W., Gilbert, T.L., 2001. Simplified method for soil particle-sizedetermination to accompany soil-quality analyses. Soil Sci. Soc. Am. J. 65, 849–852.

Lal, R., 1990. Soil Erosion and land degradation: the global risks. In: Lal, R., Stewart, B.A.(Eds.), Advances in Soil Science. Advances in Soil Science. Springer, New York, pp.129–172.

Lal, R., 2009. Soils and food sufficiency. A review. Agron. Sustain. Dev. 29, 113–133.Marenya, P.P., Barrett, C.B., 2009. State-conditional fertilizer yield response on western

Kenyan farms. Am. J. Agric. Econ. 91, 991–1006.Moebius-Clune, B.N., O.J. Idowu, R.R. Schindelbeck, H.M. van Es, D.W. Wolfe, G.S.

Abawi, and B.K. Gugino. 2011a. Developing standard protocols for soil qualitymonitoring and assessment. p. 833–842. In Bationo, A., Waswa, B., Okeyo, J.M.,Maina, F., Kihara, J.M. (eds.), Innovations as Key to the Green Revolution in Africa.Springer Netherlands.

Moebius-Clune, B.N., Van Es, H.M., Idowu, O.J., Schindelbeck, R.R., Kimetu, J.M., Ngoze,S., Lehmann, J., Kinyangi, J.M., 2011b. Long-term soil quality degradation along acultivation chronosequence in western Kenya. Agric. Ecosyst. Environ. 141 (1),86–99.

Moebius-Clune, B., D. Moebius-Clune, B.K. Gugino, O.J. Idowu, R.R. Schindelbeck, A.J.Ristow, H.M. Van Es, J.E. Thies, H.A. Shayler, M. McBride, D.W. Wolfe, and G.S.Abawi. 2016. Comprehensive Assessment of Soil Health. Cornell University, Geneva,NY. Available at http://soilhealth.cals.cornell.edu/.

National Informatics Centre, Government of India. (2018). National Portal of India: SoilHealth Card. https://www.india.gov.in/spotlight/soil-health-card#tab=tab-1. ver-ified 20AUG2018.

Ogden, C.B., van Es, H.M., Schindelbeck, R.R., 1997. Miniature rain simulator for fieldmeasurement of soil infiltration. Soil Sci. Soc. Am. J. 61, 1041–1043.

QGIS Development Team, 2016. QGIS User Guide 2.14. http://www.qgis.org.R Core Team, 2015. R: A Language and Environment for Statistical Computing. Vienna,

Austria.Sanchez, P.A., Swaminathan, M.S., 2005. Hunger in Africa: the link between unhealthy

people and unhealthy soils. Lancet 365 (9457), 442–444.Singh, S., R.N. Singh, and others. 2014. Proper timing and placement of boron and lime

impacts legumes on acid upland soils. Better Crops Plant Food 98(3): 18–20.Soil Survey Staff. 2003. Keys to soil taxonomy. Department of Agriculture: Natural

Resources Conservation Service.State Agricultural Management & Extension Training Institute, Jharkhand. Available at

http://www.sameti.org/default1_home.htm (verified 5 May 2016).Tilman, D1., Cassman, K.G., Matson, P.A., Naylor, R., Polasky, S., 2002. Agricultural

sustainability and intensive production practices. Nature. 418 (6898), 671–677.Topp, G.C., Galganov, Y.T., Ball, B.C., Carter, M.R., 1993. Soil water desorption curves.

In: Carter, M.R. (Ed.), Soil Sampling and Methods of Analysis. Canadian Society ofSoil Science. Lewis Publishers, Boca Raton, FL, pp. 569–579.

Weil, R.R., Islam, K.R., Stine, M.A., Gruver, J.B., Samson-Liebig, S.E., 2003. Estimatingactive carbon for soil quality assessment: a simplified method for laboratory and fielduse. Am. J. Altern. Agric. 18, 3–17.

World Bank. 2014. Demographic and Economic overview of the Corridor States, Punjab,Harryana, Uttar Pradesh, Bihar, Jharkhand and West Bengal.

Wright, S.F., Upadhyaya, A., 1996. Extraction of an abundant and unusual protein fromsoil and comparison with hyphal protein of arbuscular mycorrhizal fungi. Soil Sci.161, 575–586.

Zwick, W.R., Velicer, W.F., 1986. Comparison of five rules for determining the number ofcomponents to retain. Psychol. Bull. 99 (3), 432–442.

P.S.D. Frost, et al. Applied Soil Ecology xxx (xxxx) xxx–xxx

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