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Soil & Tillage Research 163 (2016) 282–289

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Spatial variability of soil micronutrients in the intensively cultivated Trans-Gangetic Plains of India Arvind K. Shuklaa,* , Sanjib K. Beherab , Narendra K. Lenkaa , Pankaj K. Tiwaria , Chandra Prakasha , R.S. Malikc , Nishant K. Sinhaa , V.K. Singhd, Ashok K. Patraa , S.K. Chaudharye a

ICAR-Indian Institute of Soil Science, Nabibagh, Berasia Road, Bhopal, Madhya Pradesh, 462038, India ICAR-Indian Institute of Oil Palm Research, Pedavegi, West Godavari, Andhra Pradesh, 534450, India c ChaudharyCharan Singh Haryana Agricultural University, Hisar, Haryana, 125004, India d ICAR-Indian Institute of Farming System Research, Modipuram, Meerut, UP, 250110, India e Indian Council of Agricultural Research, New Delhi, 110012, India b

A R T I C L E I N F O

Article history: Received 13 October 2015 Received in revised form 6 July 2016 Accepted 11 July 2016 Available online 18 July 2016 Keywords: Indo-Gangetic Plain Cationic micronutrient Spatial distribution Geostatistics Semivariogram Precision farming

A B S T R A C T

Soil micronutrient deficiency adversely affects crop production in intensive agriculture. However, information on the spatial variability of key micronutrients in intensively cultivated regions of India is limited. Thus, the present study was carried out in the Trans-Gangetic Plains (TGP) region of India with the hypothesis that spatial variability of micronutrient availability is high due to small farms and varied management. The major objectives of the study were (i) to assess the spatial variability of plant available micronutrients, viz. extractable zinc (Zn), copper (Cu), manganese (Mn) and iron (Fe) at a regional scale through geostatistical methods, (ii) to develop distribution maps for soil micronutrients using ordinary kriging and (iii) to assess the relationships of micronutrient availability with several soil properties. A total of 5638 soil samples, representative of the surface (0–15 cm) horizon were collected (covering Inceptisols, Entisols, Alfisols and Aridisols) during April to June between 2011 and 2014 from farms in 21 districts of the TGP. For each micronutrient, semivariograms were calculated and their main parameters (nugget effect, sill and range) were obtained. Moderate spatial dependence for extractable Zn, Cu and Fe and strong spatial dependence for extractable Mn were recorded. The nugget/sill ratio values were 0.60, 0.37, 0.34 and 0.19 for extractable Zn, Fe, Cu and Mn, respectively. Available Fe, Zn, Mn and Cu deficiencies (including acute deficiencies) were observed in 28, 15, 14 and 13% of soil samples, respectively. Soil pH showed significant and negative correlations with the concentrations of extractable Zn, Cu, Mn and Fe; whereas the correlation was significant and positive with soil organic carbon (SOC) concentration. The distribution maps generated could be used as a guide for precise and site-specific micronutrient management in the study region. ã 2016 Elsevier B.V. All rights reserved.

1. Introduction The Indo-Gangetic Plain (IGP) region of India, covering about 15% of the total area of the country, is one of the most intensively cultivated regions of the world (Yadav, 1998; Singh et al., 2015). The Indian IGP consists of four sub-regions, namely (1) Trans-Gangetic Plains (TGP) covering the states of Punjab and Haryana, (2) Upper Gangetic Plains covering the states of Uttarakhand and Uttar Pradesh, (3) Middle and Lower Gangetic Plains covering the states

* Corresponding author.

E-mail address: arvindshukla2k3@yahoo.co.in (A.K. Shukla). http://dx.doi.org/10.1016/j.still.2016.07.004 0167-1987/ã 2016 Elsevier B.V. All rights reserved.

of Bihar and West Bengal (Singh et al., 2007). Rice (Oryzasativa) Wheat (Triticumaestivum) cropping sequence is the dominant system in the Trans- and Upper-Gangetic Plains whereas rice based cropping sequences are common in the middle and lower Gangetic Plains. The role and contribution of the IGP region over the last four decades to the food and nutrition security of India is well documented (Yadav, 1998; Kumar et al., 2002). However, declining groundwater table and soil degradation are the two critical constraints for sustainable food production in the region (Kumar et al., 2002; Singh et al., 2007), particularly in TGP. Unsustainable intensification accompanied by imbalanced soil nutrient management is one of the major causes of declining

A.K. Shukla et al. / Soil & Tillage Research 163 (2016) 282–289

productivity and land degradation in the region (Singh et al., 2007).

Though a balanced soil nutrient management includes appropriate mix of organics, and addition of macro- as well as micro- nutrients through chemical fertilizers, very often the mined nutrients are not optimally replenished. Such distortions in the soil nutrient management are highly probable in intensively cultivated regions such as IGP (Singh et al., 2007, 2015) primarily due to high cropping intensity, low or non-availability of organics and over-dependence on chemical fertilizers leading to deficiency of several micronutrients.

Large scale deficiency of cationic micronutrients like zinc (Zn), copper (Cu), manganese (Mn) and iron (Fe) in different soils has been reported world-wide (Sillanpaa 1990; Shukla et al., 2014).

Recent Indian studies report extensive deficiency of micronutrients in farms due to regular withdrawal of these nutrients through crop uptake (Shukla et al., 2014; Shukla et al., 2015). The distribution of micronutrients may vary in space and time across management units. In Indian soils, spatial variability in micronutrient availability is presumed to be high due to small farms and varied management.

Geostatistical tools are useful to estimate spatial variability of soil properties and soil nutrients at field, catchment as well as regional scales (Tesfahunegn et al., 2011; Tripathi et al., 2015).

Geostatistical estimation helps in predicting values at unsampled locations by taking into account the spatial correlation between sampled points (Webster and Oliver, 1990; Cambardella et al., 1994). At the catchment scale, Tesfahunegn et al. (2011) reported strong (8%) to moderate (63%) degrees of spatial dependence for the soil properties like soil pH, soil organic carbon (SOC), total nitrogen, available phosphorus, cation exchange capacity and available Fe and indicated that soil properties mapped on the basis of kriging interpolation were more accurate than the catchment average values. Information on the spatial variability of micronutrients in Indian soils is limited. Thus, the present study in cultivated soils of the TGP of India (one of the most intensively cultivated regions of the country)was undertaken with the following objectives, (i) to estimate the spatial variability of extractable Zn, Cu, Mn and Fe at a regional scale through

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semivariogram analysis, (ii) to assess the relationship of micronutrient availability with key soil properties, and (iii) to develop spatial maps for soil micronutrients using the parameters of the best-fitted semivariogram model and interpolation by ordinary kriging.

2. Materials and methods 2.1. Study area The study region is one of the two most intensively cultivated states of the country and comprises all of the districts of Haryana state in the TGP of India. For the study, surface (0–15 cm) soil samples were collected from farms in twenty-one districts of Haryana state (27 500 to 30 N latitude, 76 500 to 77 300 E longitude and 200–1200 metres altitude) (Fig. 1) spreading over 44212 km2. Most part of the study area experiences arid to semiarid climate except in the north-east where the climate is relatively humid. The average annual rainfall ranges between 300 mm (south-west) to 1300 mm (north) with a state average of 617 mm.

The weather is hot (highest mean temperature 40 C and relative humidity 35%) in summer and cold (lowest mean temperature 7.5 C and relative humidity 55%) in winter. Soils are alluvial in nature with sandy to sandy loam texture and belong to the Inceptisols, Entisols, Alfisols and Aridisols classes (Bhattacharyya et al., 2013).

2.2. Soil sampling and processing A total of 5638 geo-referenced soil samples, representative of the surface (0–15 cm) horizon were collected during April to June between 2011 and 2014 from farms in 21 districts of the TGP region of India, following a multistage stratified random sampling method (Cochran, 1977; Gelfand and Schliep, 2016) and using stainless steel soil augers. The soil was sampled under the aegis of the All India Coordinated Research Project of Micro- and Secondary Nutrients and Pollutant Elements in Soils and Plants (AICRP-MSN), after harvest of wheat crop. Samples were collected covering

Fig. 1. Location of the sampling sites within the Trans-Gangetic Plains in India.

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different categories of farm sizes, viz. large (>3 ha land holding), medium (1–3 ha) and small (<1 ha) farmers. During soil sampling, farm size was taken into consideration. The number of subsamples for making a composite sample was 8–10 for a large holding, 5–6 for medium and 2–3 for a small holding. Depending upon the size of the district, about 100–600 soil samples were collected from each one. The collected samples were air dried, stone and debris were removed, and then they were ground to pass through 2 mm sieve and stored in polyethylene bottles for analysis.

2.3. Soil analysis Soil properties including soil pH, electrical conductivity (EC), calcium carbonate (CaCO3) content and SOC content were determined following Jackson (1973). Available Zn, Cu, Mn and Fe in soils were extracted in triplicate by diethylene triamine penta acetic acid (DTPA) (soil to solution ratio 1:2, shaking time 2 h) (Lindsay and Norvell, 1978). Estimation of these four micronutrient cations was done on the clear extract with an atomic absorption spectrophotometer (AAS) (AA240FS model, Varian Inc., Palo Alto, USA), with wavelength of measurement being 214, 325, 280 and 248 nm for Zn, Cu, Mn and Fe, respectively.

2.4. Statistical and geostatistical analysis The minimum, maximum, mean, standard deviation (SD), coefficient of variation (CV), skewness and kurtosis values for each analyzed soil property were computed. To find out the relationship between soil properties and available micronutrients, Pearson’s correlation coefficients were computed. The normal frequency distribution of data was verified by the Kolmogorov–Smirnov (K–S) test. The results indicated that the DTPA extractable Zn, Cu, Mn and Fe data passed the K–S normality test at a significance level of 0.05 after logarithmic transformation.

ArcMap 10.1 was used to analyze the spatial structure of DTPA extractable Zn, Cu, Mn and Fe data and to define the semivariograms. The semivariogram analyses were carried out before application of ordinary kriging interpolation as the semivariogram model determines the interpolation function (Goovaerts, 1997) as given below.

g ðhÞ ¼

1 X 2 ½zðX i þ hÞ Z ðX i Þ 2mðhÞ i¼1 mðhÞ

ð1Þ

Where, g (h) is the experimental semivariogram value at a distance interval h; m(h) is the number of sample pair values within the distance interval h; Z(Xi), Z(Xi + h) are sample values at two points separated by the distance h. Different semivariogram functions were evaluated to select the best fit with the data. Exponential

model was fitted to the empirical semivariograms. The exponential model that fitted to experimental semivariograms is defined below (Burgess and Webster, 1980) as:

h g ðhÞ ¼ C o þ C 1 1 exp ð2Þ a Where, C0 is the nugget, C1 is the partial sill, and a is the range of spatial dependence to reach the sill (C0 + C1). The nugget/sill ratio, i.e. C0/(C0 + C1) and the range are the parameters which characterize the spatial structure of a soil property. The range defines the distance over which the soil property values are correlated with each other. A low valueofC0/(C0 + C1) and a high range generally indicates that high precision of the property can be obtained by kriging (Cambardella et al., 1994). The nugget/sill ratio was used as the criterion to classify the spatial dependence of variables. Ratio values lower than or equal to 0.25 were considered to have strong spatial dependence, whereas values between 0.25 and 0.75 indicate moderate dependence and those greater than 0.75 show weak spatial dependence (Cambardella et al., 1994).

The semivariogram models were chosen by using the cross validation technique, i.e. by comparing the actual values with the values estimated by kriging using the semivariogram model.

Prediction accuracy of semivariogram models was evaluated by mean square error (MSE).

Pn ½zðxi ; yi Þ z ðxi ; yi Þ 2 MSE ¼ i¼1 ð3Þ n Where, n is the number of observation for each case (DTPA extractable Zn, Cu, Mn and Fe), z(xi, yi) is the observed soil parameter, z*(xi, yi) is the estimated soil parameter, and (xi, yi) are sampling coordinates. Using the geospatial parameters of the bestfitted exponential semivariogram model, interpolation was made through ordinary kriging (Goovaerts, 1997).

3. Results and discussion 3.1. Soil properties and DTPA extractable Zn, Cu, Mn and Fe in soil The data showed high variability for EC and CaCO3 content, in contrast to low and medium variability for pH and SOC, respectively (Table 1). The CV values of <10%, 10–100% and >100% indicate low, moderate and high variability, respectively (Nielsen and Bouma, 1985). The present dataset involved samples from four soil orders and different size of landholding units and thus variability in soil properties was expected. The mean concentration values followed the order: Fe > Mn > Zn > Cu. According to the classification adopted for India (Shukla et al., 2015), about 15, 13, 14 and28% soil samples were deficient (including acute deficiency) in Zn, Cu, Mn and Fe, respectively (Fig. 2).

Table 1 Statistical summary of selected soil properties and DTPA extractable Zn, Cu, Mn and Fe (n = 5638).

Variables pH EC (dSm 1) SOC (g kg 1) CaCO3 (g kg 1) DTPA-Zn (mg kg 1) DTPA-Cu (mg kg 1) DTPA-Mn (mg kg 1) DTPA-Fe (mg kg 1)

Minimum

Maximum

Mean

SD

CV (%)

Skewness

Kurtosis

4.50 0.04 0.20 0.10 0.10 0.10 0.53 0.12

10.80 8.50 17.10 68.00 8.00 7.97 26.60 48.80

8.03 0.49 4.40 7.90 1.66 1.37 10.30 12.20

0.50 0.64 1.70 1.27 1.21 1.29 5.62 9.08

6.26 127.00 38.40 161.00 72.70 93.90 54.50 74.10

0.83 4.81 1.88 1.93 1.58 2.07 0.33 1.12

3.16 10.50 6.65 3.69 2.92 5.19 0.76 0.98

Abbreviations: EC = electrical conductivity, SOC = soil organic carbon, CaCO3 = calcium carbonate, DTPA-Zn = diethylene triamine penta acetic acid extractable zinc, DTPACu = diethylene triamine penta acetic acid extractable copper, DTPA-Mn = diethylene triamine penta acetic acid extractable manganese, DTPA-Fe = diethylene triamine penta acetic acid extractable iron, SD = standard deviation, CV = coefficient of variation.

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Soils samples 60 50

The variability observed in the available micronutrient concentrations was largely due to variation in soil parent material, rainfall and soil management (Li et al., 2008). Although alluvial plain constitutes the large part of the study region, diversity in the physiography is observed with Shivalik Hills to the northeast, Aravalli Range in the south and semi-desert sandy plain lying to the southwest. Calcium concretions are a common feature in the alluvial soils. Differences in soil managements also resulted from, rainfall variability and cropping sequences. The deficiencies in Zn and Fe are possibly caused by higher CaCO3 concretions in the soil profile. However, Zn deficiency though was prevalent in the study region has declined over time caused by regular use of zinc sulphate fertilizer, particularly in rice based systems (Shukla et al., 2015).

Area

(a)

%

40 30 20 10 0 <0.3

≥0.3 to <0.6

≥0.6 to <0.9

≥ 0.9 to <1.2

≥ 1.2 to <1.5

≥1.5

DTPA-Zn (mg kg-1) 60

(b)

%

50 40 30

3.2. Relationship of DTPA extractable Zn, Cu, Mn and Fe with soil properties

20 10 0 <3.5

≥3.5 to <5.5

≥5.5 to <7.5

≥ 7.5 to <9.5 ≥ 9.5 to <11.5

The DTPA extractable Zn, Cu, Mn and Fe concentrations were significantly and negatively correlated with soil pH (r = 0.222; P < 0.01) whereas significantly positive correlated with SOC (r = 0.286; P < 0.01) (Table 2). Low correlation coefficient was due to huge variability in dataset. The results are in agreement with the observations of Katyal and Sharma (1991) that soil pH, lime content, organic matter, clay content and water content of soil had a strong influence on the micronutrient distribution. In another study, the total Cu content was positively and significantly correlated with soil organic matter and cation exchange capacity of soil but was negatively and significantly correlated with soil pH (Wu et al., 2010). In our study, the concentrations of Zn, Cu, Mn and Fe reduced with the increase in soil pH, which was in agreement with the observations of Lindsay (1979) who reported that by each unit increase of soil pH in the range from 4 to 9, the solubility of Fe in soil decreases by 1000 fold compared with100-fold decrease for Mn, Cu and Zn. The concentrations of Zn, Cu, Mn and Fe in soil increased with SOC as revealed by the significant and positive correlation coefficient. The SOC, improve soil structure and supply soluble chelating agents and reduces oxidation and precipitation of cations, thus resulting in increased concentrations of Zn, Cu, Mn and Fe (White and Zasoski, 1999).

≥11.5

DTPA-Fe (mg kg-1)

60 50

(c)

%

40 30 20 10 0 <2.0

≥2.0 to <4.0

≥4.0 to <6.0

≥ 6.0 to <8.0 ≥ 8.0 to <10.0

≥10.0

DTPA-Mn (mg kg-1)

60 50

%

285

(d)

40 30 20 10 0 <0.2

≥0.2 to <0.4

≥0.4 to <0.6

≥ 0.6 to <0.8

≥ 0.8 to <1.0

≥1.0

DTPA-Cu (mg kg-1) Fig. 2. Frequency distribution showing per cent soil samples and area in the particular range of concentration for (a) DTPA-Zn (b) DTPA-Fe (c) DTPA-Mn (d) DTPA-Cu.

3.3. Spatial structure and spatial distribution of DTPA extractable Zn, Cu, Mn and Fe

Compared with other micronutrients, acute deficiency of Fe was observed in a higher number of samples (15%) spread throughout the study area.

The best-fitted model was exponential for all the four micronutrients (Fig. 3) with low MSE values (Table 3). The nugget (an indication of micro-variability) was highest for Fe, which is ascribed to the fact that the selected sampling distance could not

Table 2 Pearson’s correlation coefficients for DTPA extractable Zn, Cu, Mn and Fe and selected soil properties.

Variables

pH

EC

SOC

CaCO3

DTPA-Zn

DTPA-Cu

DTPA-Mn

DTPA-Fe

pH EC SOC CaCO3 DTPA-Zn DTPA-Cu DTPA-Mn DTPA-Fe

1.000 0.254** 0.213** 0.050 0.222** 0.156** 0.349** 0.153**

1.000 0.060 0.051 0.075 0.088* 0.180** 0.125**

1.000 0.044 0.286** 0.300** 0.258** 0.332**

1.000 0.049 0.016 0.074 0.042

1.000 0.510** 0.351** 0.424**

1.000 0.341** 0.430**

1.000 0.449**

1.000

Abbreviations: EC = electrical conductivity, SOC = soil organic carbon, CaCO3 = calcium carbonate, DTPA-Zn = diethylene triamine penta acetic acid extractable zinc, DTPACu = diethylene triamine penta acetic acid extractable copper, DTPA-Mn = diethylene triamine penta acetic acid extractable manganese, DTPA-Fe = diethylene triamine penta acetic acid extractable iron. *and ** denote significance at 5% and 1% level respectively.

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Fig. 3. Experimental semivariograms and their fitted models for (a) Zn, (b) Cu, (c) Mn and (d) Fe.

Table 3 Theoretical model parameters fitted to experimental semivariograms for the studied micronutrients.

Soil micronutrients

Model

DTPA-Zn DTPA-Cu DTPA-Mn DTPA-Fe

Exponential Exponential Exponential Exponential

Nugget (C0)

Partial Sill (C1)

Sill (C0 + C1)

Range (km)

Nugget/Sill

0.85 0.72 4.25 33.84

0.57 1.42 17.7 57.93

1.42 2.14 21.95 91.77

32.49 61.40 5.37 140.00

0.60 0.34 0.19 0.37

Spatial Dependence

MSE

Moderate Moderate Strong Moderate

1.01 0.98 1.00 0.98

Abbreviations: DTPA-Zn = diethylene triamine penta acetic acid extractable zinc, DTPA-Cu = diethylene triamine penta acetic acid extractable copper, DTPA-Mn = diethylene triamine penta acetic acid extractable manganese, DTPA-Fe = diethylene triamine penta acetic acid extractable iron, MSE = mean square error.

capture the spatial dependence well. The nugget/sill ratio values were 0.60, 0.37, 0.34 and 0.19 for Zn, Fe, Cu, and Mn, respectively indicating moderate spatial dependence for Zn, Cu, Fe and strong spatial dependence for Mn. This is attributed to inherent soil properties (such as soil pH, EC, SOC and soil mineralogy)as well as management factors including fertilization and cropping sequences practiced.

The semivariogram range values of Fe, Cu, Zn and Mn were 140, 61, 32 and 5 km, respectively (Table 3). Samples separated by distances lower than the range are spatially related, whereas those separated by a distance greater than the range are considered not to be spatially related. A large range indicates the value of measured soil property to be influenced by natural and anthropogenic factors over great distances than properties having smaller

ranges (Lopez-Granados et al., 2002). The different range values for Zn, Cu, Mn and Fe in these soils might be due to combined effect of parent material, climate and adoption of different land management. In agreement with the present study, several authors reported range values of 2.5–9.1 km for Zn, 3.30–28 km for Cu (Behera et al., 2012), 0.7–66 km for Mn and 2.7–5.2 km for Fe (Behera and Shukla, 2014) in some acid soils of India. Information on the range in semivariogram of Zn, Cu, Mn and Fe acts as a guide in future soil sampling designs in similar areas. The sampling interval should be less than half the semivariogram range (Kerry and Oliver, 2004). It is therefore recommended that for ensuing studies aimed at characterizing spatial dependency of Zn, Cu, Mn and Fe in similar areas, soil sampling should be done at distances shorter than the range found in this study.

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Fig. 4. Distribution maps of DTPA extractable Zn, Cu, Mn and Fe concentrations in the soil generated by ordinary kriging.

The distribution pattern of the four micronutrients in soils of the studied region was rather similar (Fig. 4), which corroborates our finding of significant and positive correlations among Zn, Cu, Mn and Fe in these soils. Higher values of the micronutrients were recorded in the northern part of the state, whereas lower values in the southern, south-western and eastern parts of the state. Spatial heterogeneity of micronutrients concentration was more in southern and south-western parts of the state. The different spatial distributions in micronutrient concentrations were expected due to physiographic variation viz, northern part of the state in the alluvial plain zone and hilly and desert sand features in the south and south-western part of the study region. Furthermore, anthropogenic activities like cultivation of high yielding varieties of different crops coupled with non-inclusion of micronutrients in fertiliser schedulings also contributed to spatial variability of micronutrients (Shukla et al., 2015).

4. Conclusions The current study showed high spatial variability with moderate spatial dependence for DTPA extractable Zn, Cu, Fe and strong spatial dependence for Mn in the intensively cultivated region of the TGP region of India. Thus, the TGP region may be grouped into different classes based on similar range of micronutrient concentrations for precise and efficient micronutrient management. The concentration of micronutrients varied widely and about 15, 13, 14 and 28% soil samples were deficient (including acute deficiencies) in Zn, Cu, Mn and Fe, respectively.

The distribution maps developed for the four micronutrients could be the primary guide for region specific micronutrient management and designing future soil sampling strategies in the intensively cultivated TGP region of India.

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Fig. 4. (Continued)

Acknowledgements This research was supported by the Indian Council of Agricultural Research (ICAR), New Delhi, India. The authors thank the editor and anonymous reviewers for the useful comments and suggestions for improving the quality of the manuscript.

Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.still.2016.07.004.

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