Received: 17 April 2017
Revised: 21 April 2018
Accepted: 22 April 2018
DOI: 10.1002/ldr.2992
RESEARCH ARTICLE
Evaluation of spatial distribution and regional zone delineation for micronutrients in a semiarid Deccan Plateau Region of India Arvind Kumar Shukla1
|
Nishant Kumar Sinha1
1
Babu2 |
| P. Surendra Sanjib Kumar Behera Pooja Singh3 | Brahma Swaroop Dwivedi4 5
| Amaresh Kumar Rahul Tripathi Ashok Kumar Patra1
Nayak5 |
|
|
Pankaj Kumar Tiwari1
|
Chandra Prakash1
Patnaik2 |
| M.C. J. Somasundaram Siba Prasad Datta4 | Mahesh C. Meena4
Anil
Kumar6 |
|
1
Kriti
Shukla7 |
|
Sahab Siddiqui1
|
1
ICAR‐Indian Institute of Soil Science, Nabibagh, Berasia Road, Bhopal 462038, Madhya Pradesh, India
2
Abstract Emerging micronutrient deficiencies in different soils of the world is a threat for
PJTS Agricultural University, Rajendranagar, Hyderabad 500030, Telangana, India
sustainability of agriculture. As distribution of micronutrients in soil varies spatially,
3
site‐specific management of micronutrients by delineating regional zones (RZs) is
RVS Krishi Vishwa Vidyalaya, Gwalior 474002, Madhya Pradesh, India
4
ICAR‐Indian Agricultural Research Institute, New Delhi 110012, India
5
ICAR‐National Rice Research Institute, Cuttack 753006, Odisha, India
6
ICAR‐Indian Agricultural Statistics Research Institute, New Delhi 110012, India
7
Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, India
Correspondence S. K. Behera, ICAR‐Indian Institute of Soil Science, Nabibagh, Berasia Road, Bhopal 462038, Madhya Pradesh, India. Email:
[email protected]
an effective strategy for precision agriculture. The current investigation was performed to delineate RZs in a Deccan Plateau Region (DPR) of India by considering spatial variability of some soil properties and available micronutrients for efficient management of micronutrients. Altogether, 4,939 representative soil samples (with geographical coordinates) from surface (0–0.15 m depth) layers were obtained from Telangana state lying in DPR of India. After processing, soil samples were analysed for pH, electrical conductivity, soil organic carbon and available zinc, copper, iron, and manganese. Soil pH, electrical conductivity, and soil organic carbon content had mean values of 7.48 ± 0.95, 0.42 ± 0.22 dS/m and 0.48 ± 0.17%, respectively. Whereas, the mean values of available zinc, iron, copper, and manganese concentrations were 0.83 ± 0.36, 8.79 ± 4.15, 0.99 ± 0.43, and 8.79 ± 4.06 mg/kg, respectively. Geostatistical analysis divulged different distribution pattern of soil properties and available micronutrients with strong to moderate spatial dependency. The four principal components (with >1 eigenvalue) responsible for 73% of total variance were considered for analysis. Six RZs from the study area were created through geostatistical, principal component, and clustering analysis. The measured soil properties and available micronutrients in the RZs varied significantly highlighting the usefulness of RZ delineation technique for precise micronutrients management in DPR of India. KEY W ORDS
distribution variability, geostatistics, micronutrient management, principal component analysis, soil properties
Land Degrad Dev. 2018;1–11.
wileyonlinelibrary.com/journal/ldr
Copyright © 2018 John Wiley & Sons, Ltd.
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SHUKLA
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I N T RO D U CT I O N
ET AL.
variability of micronutrients in Shiwalik Himalayan Region (SHR) and Trans‐Gangetic Plains (TGP) region of India for region wise micronutri-
According to European Commission (2006), soils contribute to gen-
ent management. However, information regarding regional scale
eral ecosystem services (Dominati, Patterson, & Mackay, 2010). Bio-
micronutrient management in DPR of India is limited. In view of above,
mass production from agriculture and forestry is one of the seven
the present investigation was performed (a) to study spatial distribu-
functions of soil. Presently, soil degradation affects global crop pro-
tion pattern of some soil properties and available Zn, Fe, Cu, Mn con-
duction. Out of many reasons, soil degradation due to soil nutrients
centrations using the geostatistical analysis and (b) to delineate
deficiency affects crop productivity in various parts of the world
potential regional zones (RZs) in the DPR of India.
(Lal, 2015) including Deccan Plateau Region (DPR) of India (Bhattacharyya et al., 2015; Biswas et al., 2015), which covers significant geographical area of the country. Therefore, assessment of
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MATERIAL AND METHODS
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changes in soils to achieve the knowledge for improving soil quality and avoiding soil degradation is a pressing need (Muñoz‐Rojas,
2.1
Erickson, Dixon, & Merritt, 2016).
The investigation was carried out in Telangana state of India. The state
|
Details of study area
Wide‐spread deficiencies of micronutrients have been reported in
(lying in 15.83–19.75 N latitude and 77.42–81.75 E longitude) com-
world agricultural soils (Alloway, 2008). Deficiency of micronutrients
poses of 11.48 million ha area of which nearly 43% area is under cul-
(zinc [Zn], iron [Fe], copper [Cu], and manganese [Mn]) has also been
tivation. The state experiences semiarid hot and dry climate. The north
reported in soils of different regions of India including DPR (Shukla
zone of the state receives mean annual rainfall of 810–1,135 mm rain-
et al., 2015; Shukla et al., 2016; Shukla et al., 2017; Shukla, Tiwari, &
fall whereas, south zone receives mean annual rainfall of 560–970 mm
Prakash, 2014). Enhanced use of Zn, Fe, Cu, and Mn‐free fertilizers,
(Satyavathi & Reddy, 2004). Maximum rainfall (80% of total precipita-
increased crop yield through high yielding varieties and intensive cul-
tion) occurs in the months of June to September. Summer in the state
tivation are the predominant causes of deficiencies of these
starts in March and peaks in May with mean temperature of 42 °C.
micronutrients in soils (Alloway, 2008; Fageria, Baligar, & Clark,
Winter starts in late part of November month and extends up to early
2002; Sillanpaa, 1990). The availability of micronutrients in soils is
part of February month with mean temperatures ranging from 22 to
primarily governed by parent material, soil pH, and content of soil
23 °C. Pedologically, the predominant parent materials of soils of the
organic carbon (SOC) and anthropogenic activities (Lindsay, 1979).
state are igneous and metamorphic rocks (Satyavathi & Reddy,
So the spatial and temporal distribution of soil micronutrients differs
2004). Entisols, Alfisols, Inceptisols, Vertisols, and Mollisols are the
across land management units. Little information pertaining to distri-
major soil orders of the state and soils are of sandy loam to clayey
bution variability of soil micronutrients in DPR of India is available.
in texture (Reddy, Shiva Prasad, & Harindranath, 1996). The main
Spatial variation of these nutrients is presumed to be high in DPR of
crops, grown with tillage, crop residue management and fertilizer
India owing to small farm holdings and adoption of different land man-
application practices, of the state are rice (Oryza sativa), maize (Zea
agement practices.
mays), sorghum (Sorghum bicolor), soybean (Glycine max), castor
Since soils are highly heterogeneous and distribution of soil properties (Behera & Shukla, 2015; Bogunovic, Pereira, & Brevik, 2017) and
(Ricinus communis), groundnut (Arachis hypogaea), green chillies (Capsicum annum), cotton (Gossypium arboreum), and pulses.
Zn, Fe, Cu, and Mn concentrations in soils varies spatially (Fageria et al., 2002; Pereira & Ubeda, 2010; Shukla et al., 2016; Shukla et al., 2017), blanket application of these nutrients leads to imbalanced
2.2
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Sampling of soil and their analysis
addition of Zn, Fe, Cu, and Mn (Ferguson, Lark, & Slater, 2003). Thus,
Altogether, 4,939 soil samples (with geographical coordinates) were
the balanced and site‐specific management through varied nutrient
collected from 0 to 0.15 m soil depth with the help of stainless steel
application rate holds the key for economically sustainable agricultural
auger and following stratified random sampling procedure (Cressie &
production (Behera et al., 2016; Tesfahunegn, Tamene, & Vlek, 2011).
Chan, 1989) during 2014 and 2016 (Figure 1). Soil samples were col-
Hence, knowledge regarding spatial distribution of nutrients in soil is
lected from agricultural land, predominantly cultivated with field crops,
needed to address this problem.
of small (3 ha) land holdings. Two
Worldwide, there are several approaches to describe spatial distri-
to three, five to six, and eight to ten subsamples were collected for
bution of nutrients in soils and to classify into different classes. The
making a composite sample from small, medium, and large holdings,
most popular approach is the delineation of soil management zones
respectively. Composite soil samples were prepared to reduce the
(MZs) classifying an area into several subsets based on homogeneous
local noise/sampling effect and to improve the accuracy of prediction
soil and/or plant attributes, which can be used for adoption of variable
(Kerry, Oliver, & Frogbrook, 2010; Webster & Burgess, 1984) as the
rate technology (Ortega & Santibanez, 2007). The MZ technique uti-
present study region covers a large area. Processing (air‐drying and
lizes geostatistical tools for improved soil management. Out of several
removal of debris and stones) of soil samples was carried out. Grinding
methods and techniques, principal component (PC) analysis along with
of the samples were carried out to pass through a sieve (2 mm size)
fuzzy c‐means algorithm have been used by several researchers
before storage in polyethylene container for analysis. Soil properties,
(Behera, Mathur, Shukla, Suresh, & Prakash, 2018; Ferguson et al.,
pH, and electrical conductivity (EC) were estimated using methods
2003; Xin‐Zhong et al., 2009) for MZ delineation based on soil proper-
outlined by Jackson (1973) and SOC by Walkley and Black (1934).
ties. Shukla et al. (2016) and Shukla et al. (2017) reported distribution
Available Zn, Fe, Cu, and Mn concentrations in soil samples were
SHUKLA
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ET AL.
FIGURE 1
Location and sampling points of the study area [Colour figure can be viewed at wileyonlinelibrary.com]
" # c ∑c ∑n ðμ Þ2 1− i¼1 k¼1 ik ; FPI ¼ 1‐ c‐1 n
extracted by diethylenetriaminepentaaceticacid (C14H23N3O10) solution (Lindsay & Norvell, 1978) and estimated by atomic absorption spectrophotometer (AAS; VARIAN‐Z240, GTA 120).
2.3
|
(2)
where c denotes cluster number, n denotes observation number, μik
Statistical analysis
The parameters of descriptive statistics for soil properties and avail-
denotes fuzzy membership, and loga denotes natural logarithm.
able micronutrients were obtained using SAS software (SAS Institute, 2011). The normality of dataset was verified by Kolmogorov–Smirnov
3
|
RESULTS AND DISCUSSION
test (at p < 0.05). Pearson's correlation matrix was obtained for visualizing relationship among soil parameters. Geostatistical analysis of soil properties and available micronutrients was carried out by ArcGIS
3.1 | Overall variability of measured soil properties and available micronutrients
10.4.1. Semivariogram for each soil property and available micronutrient (Goovaerts, 1997) was calculated from averaged values. Best fitted model for each soil parameter was selected through the technique of cross‐validation. Ordinary kriging was applied for interpolation mapping and kriging biasness and accuracy was tested by cross‐
Soils were acidic (pH 4.41) to alkaline (pH 10.71) in reaction and nonsaline (EC 0.15 to 1.31 dS/m) in character with the mean value of 7.48 for soil pH and 0.42 dS/m for EC (Table 1). The SOC content varied from 0.16% to 1.09% with average value 0.42%. Our results support the findings of Reddy et al. (1996) and Satyavathi and Reddy (2004)
validation (Xin‐Zhong et al., 2009).
who reported wide ranges for soil pH, EC, and SOC in the region. This
2.4
|
may be ascribed to varied soils, prevailing climatic conditions and var-
PC analysis and fuzzy clustering
ious crop husbandry practices followed in the region. Available Zn, Fe,
The values of correlation analysis were used for principal component
Cu, and Mn concentrations varied widely with mean values of 0.83,
analysis (PCA) which produced new set of variables called PCs. PCs with
8.79, 0.99, and 8.79 mg/kg, respectively. It was found that 26%,
>1 eigenvalues were considered for RZ delineation (Davatgar,
10%, 9%, and 9% areas were deficient (comprising acute deficient
Neishabouri, & Sepaskhah, 2012). The datasets were partitioned into
and deficient) in Zn, Fe, Cu, and Mn, respectively, as per the critical
two to eight clusters by fuzzy c‐means clustering technique and FuzME
limits of the study region (Shukla et al., 2016). Our finding is similar
software (Berget, Mevik, & Naes, 2008; Minasny & McBratney, 2006).
to the results reported by Shukla et al. (2016) who recorded the mean
Normalized classification entropy (NCE) and fuzzy performance index
values of 1.66, 1.37, 12.20, and 10.30 mg/kg for available Zn, Cu, Fe,
(FPI) were used to obtain optimum cluster number. NCE and FPI
and Mn concentrations, respectively in soils of Indian TGP region.
accounts for extent of disorganization by specific classes and degree
Shukla et al. (2017) also reported the mean concentration of 2.24,
of fuzziness, respectively. According to Fridgen et al. (2004), the highest
1.49, 19.01, and 36.76 mg/kg soil for plant available Zn, Cu, Mn, and
NCE and the lowest FPI values provided the optimum number of cluster.
Fe, respectively, in the soils of SHR of India. Soil properties exhibited
The variance analysis procedure was adopted to test the differences
low (only soil pH) to moderate (rest of soil properties) variability with
among the RZs.
100% of CV values indicating variability to the n n ∑ ∑c μ loga ðμik Þ NCE ¼ − k¼1 i¼1 ik n‐c n
extent of low, moderate, and high degree, respectively (Nielsen & (1)
Bouma, 1985). Bogunovic et al. (2017) reported low, medium, and high variability for pH, organic matter, and EC, respectively in soils of Rasa
4
SHUKLA
TABLE 1
ET AL.
Descriptive statistics parameters of soil properties and available micronutrients of the study area
Soil Properties
CV (%)
Skewness
Kurtosis
Kolmogorov– Smirnov p
Minimum
Maximum
Mean
SD
pH
4.41
10.71
7.48
0.95
8.96
0.156
0.591
−0.580
EC (dS/m)
0.15
1.31
0.42
0.22
36.70
0.985
1.009
−0.203
SOC (%)
0.16
1.09
0.48
0.17
29.06
0.886
0.475
−0.208
Zn (mg/kg)
0.14
2.35
0.83
0.36
43.01
0.824
0.381
0.143
Fe (mg/kg)
0.90
28.48
8.79
4.15
47.19
0.838
0.751
0.079
Cu (mg/kg)
0.09
2.34
0.99
0.43
43.83
0.274
−0.451
−0.072
Mn (mg/kg)
0.81
24.37
8.79
4.06
46.25
0.191
−0.291
0.062
Note. EC = electrical conductivity; SOC = soil organic carbon; Zn, Fe, Cu, and Mn represent available zinc, iron, copper, and manganese in soil, respectively; SD = standard deviation; CV = coefficient of variation.
river valley of Croatia. Behera and Shukla (2015) reported low (for pH
Mn, and Fe. The correlation of soil EC with SOC and available Zn,
and EC) to moderate (for SOC content) variability in Indian acid soils.
Fe, Cu, and Mn was negative. The correlation of SOC content with
Similarly, variability was low for pH and medium for EC and organic
available Zn, Cu, and Mn was positive, whereas it was negative with
matter in soils of Alequeva reservoir of Portugal (Ferreira,
available Fe. Similarly, Wei, Hao, Shao, and Gal (2006) reported nega-
Panagopoulos, Andrade, Guerrero, & Loures, 2015). In soils of north-
tive correlation between pH and plant available Zn, Mn, and Fe in
ern Ethiopia, low (for pH) and medium variability (for SOC and avail-
China's Loess Plateau region soils. The same authors recorded positive
able Fe) were reported (Tesfahunegn et al., 2011). Moderate
correlation of soil organic matter with plant available Zn, Mn, and Fe.
variability for available Zn, Fe, Cu, and Mn was recorded by Wang,
Shukla et al. (2016) and Shukla et al. (2017) reported negative correla-
Wu, Liu, Huang, and Fang (2009) in China's paddy growing soils and
tion of pH with available Fe, Zn, Mn, and Cu in TGP and SHR soils of
by Shukla et al. (2016), Shukla et al. (2017) in Indian TGP and SHR
India. Positive correlation of SOC was recorded with all the four cat-
soils. However, Foroughifar, Jafarzadeh, Torabi, Pakpour, and
ionic micronutrients in soils of TGP and with only Zn and Fe in SHR
Miransari (2013) recorded high variability for available Fe and moder-
soils of India. The negative correlation among soil pH and available
ate variability for available Cu, Mn, and Zn in Dasht‐e‐Tabriz soils of
Fe, Zn, and Mn in our study is obvious as changes in soil pH influence
Iran. Among the soil properties, available Zn, Fe, Cu, and Mn had
soil micronutrient content (Neilsen, Hoyt, & Mackenzie, 1986).
higher CV values than soil pH, EC, and SOC content. High variability
According to Lindsay (1979), with every unit of soil pH enhancement
in soil micronutrients is ascribed to different micronutrients content
in the range of 4–9, Zn, Cu, and Mn solubility reduces by 100‐fold
of parent material, pedogenic processes, and diversity in weathering
as compared to 1,000‐fold reduction for Fe in soil. Positive correlation
regimes (Bowen, 1979).
of SOC with available micronutrient in soil is because of the fact that SOC is a key component of soil organic matter. Soil organic matter enhances nutrient availability to the crop plants by releasing organic
3.2 Relationship among the soil properties and available micronutrients |
substances which can chelate with micronutrients and thereby improving their availability (Tisdale, Nelson, & Beaton, 1985). Positive
Correlation coefficient values in Table 2 reveals the relationship among the soil properties and available micronutrients. Correlation coefficient values indicated negative correlation of pH with soil Zn,
correlations among the available micronutrients, Zn versus Mn, Zn versus Fe, Fe versus Cu, Fe versus Mn, and Cu versus Mn were recorded. This indicates that similar sets of factors influence distribution of these metallic nutrients in soils in the study region. Behera
TABLE 2 Pearson's correlation matrix for soil properties and available micronutrients of the study area
and Shukla (2013) have reported positive correlation between available Zn with available Cu and Fe and between available Cu with available Fe in some Indian acid soils under cultivation. The observed
pH pH EC
EC
SOC
Zn
Fe
Cu
Mn
1 −0.08
variation of studied soil properties and available micronutrient and their relationship in DPR of India is attributed to soil types, climatic
1
condition, and practices of crop managment. This warrants proper
0.02
−0.22***
1
characterization of soil though accurate sampling, use of geostatistics
Zn
−0.11*
−0.13**
0.17*** 1
and appropriate zoning.
Fe
−0.23*** −0.26*** −0.38*** 0.16*** 1
SOC
Cu
0.01
−0.14**
0.22*** 0.01
Mn
−0.11*
−0.17***
0.25*** 0.18*** 0.28*** 0.21*** 1
0.30*** 1
Note. EC = electrical conductivity; SOC = soil organic carbon; Zn, Fe, Cu, and Mn represent available zinc, iron, copper, and manganese in soil, respectively. *, **, *** denote level of significance of correlation at p < 0.05, p < 0.01 and p < 0.001 respectively.
3.3 | Spatial characters of soil properties and available micronutrients Soil
properties
and
available
micronutrients
had
different
semivariogram characters (Table 3, Figure 2). The best fitted model obtained from semivariogram analysis was stable for soil pH and
SHUKLA
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ET AL.
TABLE 3
Semivariogram components for soil properties and available micronutrients of the study area
Soil properties
Model
Nugget
Partial Sill
pH
Stable
0.51
0.43
EC
Exponential
0.03
0.02
SOC
K‐Bessel
0.00
Zn
Exponential
Fe
Stable
Cu
Exponential
Mn
K‐Bessel
Sill
Nugget/Sill
Range (m)
Spatial dependency
0.94
0.543
62,021
Moderate
0.05
0.600
58,482
Moderate
0.01
0.01
0.000
9,574
Strong
0.04
0.21
0.25
0.160
36,720
Strong
58.51
45.62
104.13
0.560
96,024
Moderate
0.59
0.25
0.84
0.700
92,967
Moderate
19.17
56.03
75.20
0.469
56,062
Moderate
Note. EC = electrical conductivity; SOC = soil organic carbon; Zn, Fe, Cu, and Mn represent available zinc, iron, copper, and manganese in soil, respectively.
available Fe, exponential with respect to EC, available Zn, and Cu and
(Figure 3). These areas require immediate attention of the land man-
K‐Bessel with respect to SOC and available Mn. Foroughifar et al.
agers. The locality had very low pH (pH < 5.5) typical of the high alti-
(2013) reported best fitted spherical model for pH, SOC, available
tude hilly terrain. Low soil pH of the study area is ascribed to the
Zn, Cu, and Fe and linear best fitted model for available Mn. Likewise,
parent materials such as granite, gneiss, schist, and ferruginous from
from a study in rice cultivated soils of eastern India, Tripathi et al.
which these soils are developed. These acidic parent materials weath-
(2015) reported best fitted spherical model for soil pH, EC, SOC, and
ered to form red sandy soils and red loam soils. Though the EC values
available Zn and Cu, pentaspherical model for available Fe and expo-
varied in the study area and the map exhibited varied distribution pat-
nential model for available Mn. The nugget values, which is the mea-
tern, soil was not saline as the values of EC were < 2 dS/m. About 53%
sure of variance due to errors, were low (varied from 0.00 to 0.59)
of the area had low (0.75) (Cambardella et al., 1994). In the current investigation,
processes such as biomass production, deposition, and decomposition
SOC (0.000) and available Zn (0.160) recorded strong spatial depen-
of litter (Mao et al., 2015). Distribution maps exhibited different pat-
dency whereas soil pH (0.543), EC (0.600), available Fe (0.560), Cu
tern of distribution for available micronutrients. Higher quantity of
(0.700), and Mn (0.469) recorded moderate spatial dependency.
Cu and Mn was recorded in south and south‐eastern part of the study
Strong spatial dependency for SOC and available Zn is ascribed to soil
area. About 26%, 19%, 8%, and 4% of the study area were having
types and prevailing climatic conditions in the study region. Whereas
