Spatial Variability in Soil Properties of Mango Orchards in Eastern Plateau and Hill Region of India

Abstract

Analysis and interpretation of spatial variability of soil chemical properties is important in framing site-specific fertilizer management practices for mango orchards. This paper aims to study the spatial structure of soil variables in the mango orchards in the eastern plateau region of India. Soil samples were collected from 90 points across the mango orchards from three soil horizons (0-30, 30-60 and 60-90cm). Eleven soil properties were analysed by classical statistical and geo-statistical methods. Soil pH exhibited the lowest statistical variation (CV<15%) while it was highest (CV 68.4 to 97.8%) for the phosphorous (P) content in all the soil layers. Spherical, Gaussian and Exponential semivariogram models were developed after accounting for necessary transformations. The findings of geostatistical analysis showed that spatial structures exist in the soil variables. In surface layers, soil pH, phosphorous (P), iron (Fe), calcium (Ca) and copper, (Cu) have the strong spatial dependence with nugget-sill ratios of less than 25%. Analysis suggested that both the internal and external factors are responsible for the spatial dependence of the soil properties. The magnitude and the pattern of spatial variability in soil chemical properties have implications for variable rate fertilizer application strategies in the mango orchards of the eastern region.

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Mali et al., Vegetos 2016, 29:3
DOI: 10.4172/2229-4473.1000141 Vegetos- An International
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All articles published in Vegetos: International Journal of Plant Research are the property of SciTechnol, and is protected by
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Spatial Variability in Soil
Properties of Mango Orchards in
Eastern Plateau and Hill Region
of India
Mali SS, Naik SK and Bhatt BP*
Abstract
Analysis and interpretation of spatial variability of soil chemical
properties is important in framing site-specic fertilizer management
practices for mango orchards. This paper aims to study the spatial
structure of soil variables in the mango orchards in the eastern
plateau region of India. Soil samples were collected from 90 points
across the mango orchards from three soil horizons (0-30, 30-60
and 60-90cm). Eleven soil properties were analysed by classical
statistical and geo-statistical methods. Soil pH exhibited the lowest
statistical variation (CV<15%) while it was highest (CV 68.4 to 97.8%)
for the phosphorous (P) content in all the soil layers. Spherical,
Gaussian and Exponential semivariogram models were developed
after accounting for necessary transformations. The ndings of
geostatistical analysis showed that spatial structures exist in the
soil variables. In surface layers, soil pH, phosphorous (P), iron (Fe),
calcium (Ca) and copper, (Cu) have the strong spatial dependence
with nugget-sill ratios of less than 25%. Analysis suggested that
both the internal and external factors are responsible for the spatial
dependence of the soil properties. The magnitude and the pattern
of spatial variability in soil chemical properties have implications for
variable rate fertilizer application strategies in the mango orchards
of the eastern region.
Keywords
Spatial variability; Geostatistics; Semivariogram; Soil fertility;
Mango orchard
*Corresponding author: Mali SS, ICAR-Research Complex for eastern Region,
Research Centre Ranchi-834010, Jharkhand, India, Tel: +91-8092-55-6024; E-mail:
santosh.icar@gmail.com
Received: June 18, 2016 Accepted: July 22, 2016 Published: July 29, 2016
Introduction
Mango (Mangifera Indica L.) is one of the most important
commercially grown fruit crop of India. It is cultivated over 2.5 Mha
area producing about 18 Mt mangoes per year [1]. Many factors
play a crucial role on the yield and quality of mango crop, the most
important being the fertility of the soil. Soil physical and chemical
properties vary in space and time due to the combined eect of
physical, chemical and biological processes, which act simultaneously
with dierent intensities at dierent spatiotemporal scales. Prior
knowledge about the spatial variability of the soil fertility indicators
over a eld can be very useful in maintaining optimum nutrient
status in soil and managing other important agronomical measures.
Substantial spatial variability of soil nutrient levels at the macro-scale
and micro-scale oen results in over or under application of fertilizers
[2]. Signicant relation between leaf nutrient concentrations of the
mango plants and the nutrient contents in the soil highlights the need
for mapping spatial variability of soil nutrients in the mango orchards
[3]. Growing body of literature suggests that spatial variability in soil
properties should be considered for making recommendations on
variable-rate fertilizer application in mango.
e variable rate fertilizers application and site specic nutrient
management can be achieved on the basis of the precisely dened spatial
variability of soil nutrients. Geostatistics is one of the most popular set of
statistical tools for analysing spatial variability of geocoded parameters.
Geostatistics is concerned with detecting, estimating and mapping the
spatial variation trends of regional variables. It provides a set of statistical
tools such as tting of a semivariogram model for the description of
spatial patterns of continuous and categorical soil properties [4] and it
has become an important tool in characterizing the spatial variability of
soil properties [5]. is method distinguishes variation in measurement
separated by known distance. Semivariogram models provide the
necessary information for Kriging, which is a method for interpolating
data at unsampled points [6].
Geostatistical methods have been eectively used to assess the spatial
variability in soil parameters and it has become an important tool in
characterizing the spatial variability of soil properties [7,8]. Houlong
et al., [9] used the geostatistical approach and kriging interpolation to
map the spatial variability of soil properties in the Pengshui tobacco
experiment station for better management of experimental treatments to
achieve reliable experimental results. Liu [10] used geostatistical method
to investigate the spatial variability of soil organic matter and nutrients
in paddy elds in southeast China. Behera et al. [11] analysed the spatial
variability in the soil properties of the oil palm plantations in the southern
India and concluded that the soil properties were inuenced by intrinsic,
extrinsic and both intrinsic and extrinsic factors. Conventional and
geostatistical methods can be used to understand the heterogeneity of
soil chemical properties and to identify factors responsible for the spatial
variation of soil properties [5].
Spatial variability of soil physical and chemical properties under
dierent crops and management practices have been analysed
by dierent researchers across the world. Although such studies
provide information on the soil variability at the experimental sites,
the variability at larger spatial scale, such as district, is not well
characterised. Information on variation of soil properties under
mango orchards is seriously lacking. Assessment of spatial variability
of soil properties is important in fertility management of mango
orchards in East India Plateau. e objective of this study was to
evaluate the spatial variability of soil chemical attributes and principal
soil fertility traits in the mango plantations using traditional statistics
and geostatistics at district scale to provide information for better soil
fertility management in mango orchards of the eastern plateau and
hill region of India.
Materials and Methods
Study area
e study was carried out during 2012 and 2013 in the Gumla and
Simdega districts located in the south-western part of the Jharkhand

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Volume 29 • Issue 3 • 1000141
Citation: Mali SS, Naik SK, Bhatt BP (2016) Spatial Variability in Soil Properties of Mango Orchards in Eastern Plateau and Hill Region of India. Vegetos 29:3.
doi:10.4172/2229-4473.1000141
state to assess the fertility status of the soils in the mango orchards.
e study districts lies between 220 19’ 40” N to 230 36’ 43” N latitude
and 840 00’ 21” E to 850 05’ 09” E longitude with average mean sea
level of 300 and 424 m. e climate of the Gumla and Simdega
districts is tropical monsoon type and receives an average annual
rainfall of about 1100 and 1397 mm respectively. Soils of the region
are characterised with red laterite, red and yellow with texture ranging
from loamy to sandy loam having slightly acidic reaction.
Soil sampling and laboratory analysis
A eld survey was undertaken to collect the soil samples from
dierent mango orchards located in the study area. e age of the
mango orchards varied from 6 to 18 years. A total of 90 soil samples
were collected from the study orchards at various soil prole depths.
Mango trees have very well spread, deep, and extensive root system
and the widespreading feeder roots also extend many anchor roots
to deeper depths. To represent the entire root zone more precisely,
the soil samples were collected from0-30, 30-60 and 60-90 cm depth
proles. Each sample was formed from the four samples collected
within 1.5 to 2 m radius of the four dierent trees and mixed well
to form one representative sample of an orchard. e geographic
coordinates (latitude, longitude and elevation) of every sampling
point were recorded with a handheld global positioning system (GPS)
(Oregon 550, Garmin Ltd, Kansas, USA).e soil samples were placed
into plastic bags then air-dried, ground to pass through a 2-mm sieve
and analysed for soil physicochemical properties.
e samples were analyzed for soil acidity (pH), organic carbon
(OC), available nitrogen (N), phosphorus (P), available potassium
(K), exchangeable calcium (Ca) and magnesium (Mg), DTPA-
extractable iron (Fe), DTPA-extractable manganese (Mn), DTPA-
extractable copper, DTPA-extractable zinc. Determination of soil
pH was done based on 1:2.5 soil water ratio (w/v) suspension using
pH meter following half an hour equilibrium [12]. e soil organic
carbon content was determined by Walkley and Black method [13].
e following methods were used to determine available nutrient
contents: the method of Subbiah and Asija [14] for N, that of Bray
and Curtz [15] for P, and the ame photometric method [12] for K.
the exchangeable Ca and Mg was determined by Versenate method
[16]. DTPA-extractable Fe, Mn, Cu and Zn were measured with an
atomic absorption spectrophotometer by following the method of
Lindsay and Norvel [17].
Descriptive statistics
Data were subjected to descriptive analysis. e minimum,
maximum, mean, standard deviation (SD), coecient of variation
(CV), skewness and kurtosis for soil properties were computed.
Skewness is the most common statistic parameter to identify a normal
distribution that is conrmed with skewness values varying form − 1
to + 1. Criterion established by Warrick [18] was used to classify the
parameter variability on the basis of variation coecient values as
low: <15%, moderate: from 15% to 50%, and high: >50%.
Geostatistical analysis
Exploratory Spatial Data Analysis (ESDA) was carried out to
assess and correct the trend, periodicity and extreme values present
in the datasets pertaining to soil properties. e ArcGIS10.0 was used
for performing the ESDA.
Variance of the dierence between two values is assumed to
depend only on the distance h between the two points, and not on
the location x. Spatial patterns were usually described using the
experimental semivariogram γ(h), which measures the average
dissimilarity between data separated by distance h. e semivariance
as a function of both the magnitude of the lag distance was computed
using [6,19];
()
2
1
1
( , ) [ ( ) ( )]
2 (, )
Nh
ii
i
h Zx h Zx
Nh
γα α
=
= +−
∑
Where, γ(h,α)= semivariance as a function of both the magnitude
of the lag distance or separation vector (h) and its direction (α); N(h,α)
= number of observation pairs separated bydistance h and direction
α used in each summation; Z(xi)= random variable at location xi.A
semivariogram consists of three basic parameters that describe the
spatial structure as: γ(h) = C0 + C. C0+ C is the sill (total variance),
which is the lag distance between measurements at which one value
for a variable does not inuence neighbouring values. C0 is the
combination of random errors and sources of variation at distances
smaller than the shortest sampling interval [20]. C is the structural
variance, which is the constant semivariance value where the curve
was stabilized. e range is the distance over which soil property
is spatially related. e nugget ratio (C0/(C0 + C); nugget-sill)
represents the parameters that characterize the spatial structure of a
property [21]. Several semivariogram functions (Spherical, Gaussian,
Exponential, Linear, Linear to sill etc.) were evaluated to choose the
best t with the data. e best t semivariogram model was selected on
the basis of coecient of determination (R2) and the residual sum of
squares (RSS). Semivariance calculation and semivariogram function
model tting were performed using the geostatistical soware GS+
for Windows. Semivariogram, dierences in nugget/sill ratio and
range were examined for various soil properties.
Results and Discussion
Descriptive statistics
e data were analysed using classical statistical methods to
understand the characteristics of the general soil properties prior to
the investigation on the spatial structure (Tab l e 1). e concentrations
of soil properties (pH, OC, N, P, K, Ca, Mg, Fe, Mn, Zn, Cu) were
described by minimum, maximum, mean, median, standard deviation
(SD), coecient of variation (CV), skewness and kurtosis of data
distribution in the study area (Tabl e 1). e summary statistics of soil
properties suggested that all the soil properties exhibited considerable
variability across the study region. e soil pH varied from 4.08 to
7.78 depending on the soil layer. e surface prole (0-30 cm)
showed dominantly acidic reaction with pH varying from 4.08 to 6.61
with mean value of 5.15 ± 0.61. e pH also varied with depth with
mean pH increasing from 6.61 in surface layer to 7.65 at subsurface
layers. e values of CV for soil pH in all the soil layers revealed
their moderate variability and these values were less compared to CV
values of other measured soil properties. e CV values in the range
of 10 to 100 are considered in the class of ‘moderate variability’ [22].
Low CV values for soil pH was due to transformed measurement of
hydrogen ion concentration. Behera et al. [11] also reported the lower
CV values of 17.1 and 19.5 for the pH of surface and subsurface soils
of oil palm plantations in the southern plateau of India. Houlong et
al. [9] observed lowest CV in case of soil pH as compared to other soil
properties recorded in tobacco plantations of southern china.
e organic carbon content in the surface soil layers varied from
0.21 to 0.91% across the study region. e mean organic carbon
content decreased with increasing soil depth. e mean value of

• Page 3 of 6 •
Volume 29 • Issue 3 • 1000141
Citation: Mali SS, Naik SK, Bhatt BP (2016) Spatial Variability in Soil Properties of Mango Orchards in Eastern Plateau and Hill Region of India. Vegetos 29:3.
doi:10.4172/2229-4473.1000141
e CV of these parameters varied in a narrow range of 14.2 to 49.6
%. Among the secondary macronutrient and micronutrient, the
Mg showed highest variation in the surface soil layer (CV=41.9%).
Concentration of Ca was higher (>725 g kg-1) in the soil layers. With
the increasing depth, the concentration of Ca and Mg increased while
that of Fe, Mn, Zn and Cu decreased.
e descriptive statistics of soil properties suggested that all
variable distributions were only slightly skewed, and their medians
values were close to their mean values, identifying a normal
distribution of soil variables. e values for skewness and kurtosis
between -2 and +2 are considered acceptable in order to prove normal
univariate distribution [23]. Highly skewed properties indicated
that these properties had a local distribution, the high values were
recorded for these properties at some points, but most of the values
of these properties were low [24]. e principal reason for some soil
properties having non-normally distributions may be related with
soil management practices [25]. e kurtosis values ranged from
-1.22 to 8.44. e skewness and kurtosis values for available P data
series was very high and also there was signicant dierence in the
mean and medium values observed at all soil layers. e probability
distributions of P concentration data at all the soil layers are positively
the total N content is classed as ‘low’ and it further decreased with
increasing depth. Coecient of variation (CV) of total N at sub-
surface layer is higher (25.7%) than at top-layer (15.8 %), meanwhile
all the CV of total N in the soil could be classied as moderate
variability. Total N content in the top layer was about 24.7% higher
than the sub-surface layer (60-90 cm). is is probably due to the
higher organic residues deposited on the soil surface than the sub
surface soil. Highest spatial variation was observed in case of available
P content of the soil. e variability of available P in deeper layer (CV
= 97.8%) was higher than surface layer (CV=68.5 %). is condition
is probably due to dierence of soil pH and total organic carbon
between the layers. Mean K content in the top layers was about 270.5
kg ha-1 which decreased to 225.0 kg ha-1at the sub soil layer. e
spatial variation in K content in all the studied soil prole was classed
as ‘moderate’ (CV=24.4 to 26.5%). is variability is the result of the
irregular cropping system and non-uniform management practices.
Reported wide variability in values of available N, P and K in sapota
orchards of Karnataka, India.
e secondary macronutrient (Ca and Mg) and micronutrient
(Mn, Fe, Zn, Cu) content in all the soil layers was situated in the
‘moderate variability’ class except for the Mn content in the top layer.
Soil Properties Soil Layer (cm) Min Max Mean Med SD CV (%) Skew. Kurt.
pH 0-30 4.08 6.61 5.15 5.17 0.61 11.84 0.54 0.62
30-60 4.37 7.78 5.46 5.47 0.73 13.47 1.49 1.34
60-90 4.29 7.65 5.56 5.56 0.77 13.90 0.76 1.55
OC, % 0-30 0.26 0.91 0.51 0.49 0.20 38.87 0.47 -0.87
30-60 0.13 0.84 0.40 0.36 0.19 46.00 0.49 -0.47
60-90 0.02 0.72 0.31 0.28 0.14 45.47 0.73 1.81
N, kg/ha 0-30 87.8 175.6 126.4 125.4 19.96 15.80 0.30 0.75
30-60 75.2 138.0 111.3 112.9 17.71 15.91 -0.65 -0.15
60-90 37.6 138.0 101.3 100.4 26.02 25.69 -0.67 0.28
P, kg/ha 0-30 0.16 14.57 4.80 4.29 3.29 68.47 1.44 2.91
30-60 0.16 16.66 3.83 2.92 3.29 85.99 2.52 8.44
60-90 0.16 14.10 3.02 2.53 2.96 97.83 2.40 7.36
K, kg/ha 0-30 173.6 380.8 270.5 266.6 65.90 24.37 0.23 -1.22
30-60 159.0 369.6 241.8 225.1 64.07 26.49 0.40 -1.10
60-90 144.5 336.0 225.0 208.3 59.05 26.24 0.40 -1.14
Ca, g/kg 0-30 414.0 1320.0 725.8 620.0 272.1 37.49 0.56 -0.93
30-60 600.0 1566.0 953.1 928.0 238.4 25.02 0.71 0.18
60-90 488.0 1448.0 975.4 936.0 243.9 25.01 0.22 -0.60
Mg, g/kg 0-30 164.4 648.0 331.6 296.4 138.9 41.92 0.83 -0.31
30-60 159.6 796.8 382.9 362.4 136.8 35.71 0.99 2.00
60-90 220.8 654.0 395.1 387.6 102.5 25.95 0.61 0.52
Fe, g/kg 0-30 8.04 29.40 16.26 15.24 5.54 34.10 0.95 0.29
30-60 5.21 22.76 10.59 9.30 5.25 49.61 1.37 0.95
60-90 3.84 19.56 9.76 8.07 4.62 47.38 0.93 -0.05
Mn, g/kg 0-30 12.98 24.18 19.15 18.99 2.72 14.20 -0.10 0.84
30-60 2.31 23.84 16.50 17.72 5.09 30.83 -1.30 1.72
60-90 3.44 23.56 15.16 15.51 4.51 29.74 -0.78 1.17
Zn, g/kg 0-30 0.22 0.71 0.39 0.37 0.12 31.57 0.79 0.02
30-60 0.22 0.43 0.31 0.29 0.05 17.59 0.35 -0.58
60-90 0.15 0.57 0.30 0.30 0.10 31.32 0.81 0.91
Cu, g/kg 0-30 0.41 1.55 0.96 0.93 0.32 33.53 -0.03 -0.89
30-60 0.36 1.51 0.82 0.75 0.31 38.19 0.64 -0.35
60-90 0.05 1.38 0.77 0.71 0.30 38.45 0.31 0.71
Table 1: Descriptive statistics* for selected soil properties of surface layers (n=90).
*Min-minimum, Max-maximum, mean, SD-standard deviation, CV-coefcient of variation, Skew-skewness, Kurt- kurtosis.

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Volume 29 • Issue 3 • 1000141
Citation: Mali SS, Naik SK, Bhatt BP (2016) Spatial Variability in Soil Properties of Mango Orchards in Eastern Plateau and Hill Region of India. Vegetos 29:3.
doi:10.4172/2229-4473.1000141
skewed and have sharp peaks. e calculation of variation function
should generally be in accordance with the normal distribution.
erefore logarithmically transformed P concentration data were
used in the geostatistical analysis of variation function.
Geostatistical Analysis
Knowledge about the spatial variability of soil properties is
very useful in optimizing and determining the fertilizer application
recommendations in mango orchards. Appropriate use of nutrients
can contribute to enhance crop quantity and quality, while being
environmentally sustainable [26]. e inherent limitation of
predicting the soil properties at the un-sampled sites precludes
the use of classical statistics in variability assessment of soil
properties. Geostatistical analysis however permits examination and
understanding of spatial dependency of a soil property [27].
e results of geostatistical analysis (Tabl e 2) indicated dierent
spatial distribution models for the soil properties. e geostatistical
analysis indicated dierent spatial distribution models and spatial
dependence levels for the soil properties. In most of the parameters,
spherical variogram model was found to be ideal tting model.
Apart from spherical model, the Gaussian and Exponential models
were also tted well in case of some soil properties. In particular,
the Zn had exponential best t model at all soil layers. Among the
major nutrient contents (N, P K) only N had the Gaussian best t
model for the surface layer (0-30) and K had an Exponential best t
model for the deeper soil layer. In selecting the best t model, the
prediction accuracy of the semivariogram model was also taken into
consideration. e plots between observed and predicted values at
the sampled location and the values of coecient of determination
(R2) were considered in selecting the best t model. e sample plot
between observed and predicted values for Cu is shown in Figure 1.
Several researchers [9,11,28] have reported spherical model as the
best t for soil parameters like N, P, K, OC, Fe, Cu, Mn and Zn.
e nugget to sill ratio is used to dene spatial dependence of soil
properties. If the ratio is <0.25, there is strong spatial dependence; if the
ratio is 0.25 to 0.75, there is moderate spatially dependence; and if the
ratio is >0.75, spatial dependence is weak. Strong spatial dependence
of soil properties can be attributed to intrinsic factors such as soil
properties and mineralogy, whereas, weak spatial dependence is due
to extrinsic factors such as anthropogenic activities. Moderate spatial
dependence is owing to both intrinsic and extrinsic factors [11]. As
shown in Tabl e 2, the ratio values indicated the presence of strong
Soil Property Soil Layer (cm) Parameters of Semi Variogram
Model Nugget Sill Range (m) Nugget/sill ratio Spatial Class
pH 0-30 Spherical 0.04 0.40 1430 0.10 Strong
30-60 Spherical 0.00 0.59 2530 0.00 Strong
60-90 Spherical 0.01 0.66 2090 0.02 Strong
OC, g/kg 0-30 Spherical 0.03 0.06 21890 0.50 Moderate
30-60 Spherical 0.03 0.06 21890 0.50 Moderate
60-90 Spherical 0.02 0.03 21890 0.67 Moderate
N, kg/ha 0-30 Gaussian 302.4 604.9 6160 0.50 Moderate
30-60 Spherical 285.8 295.8 6490 0.97 weak
60-90 Spherical 61.00 689.0 1650 0.09 Strong
P, kg/ha 0-30 Spherical 1.84 7.65 4730 0.24 Strong
30-60 Spherical 0.01 11.69 1210 0.00 Strong
60-90 Spherical 0.01 11.44 1210 0.00 Strong
K, kg/ha 0-30 Spherical 3190 8063 16170 0.40 Moderate
30-60 Spherical 2900 8095 19800 0.36 Moderate
60-90 Exponential 2960 7292 14190 0.41 Moderate
Ca 0-30 Spherical 7200 85700 1210 0.08 Strong
30-60 Spherical 18800 54000 1100 0.35 Moderate
60-90 Exponential 47900 95810 19360 0.50 Moderate
Mg 0-30 Spherical 17827 15325 6490 1.16 weak
30-60 Spherical 7950 19300 2750 0.41 Moderate
60-90 Spherical 100.00 9440 660 0.01 Strong
Fe 0-30 Exponential 15.00 78.50 12870 0.19 Strong
30-60 Spherical 19.96 39.90 11000 0.50 Moderate
60-90 Exponential 0.01 24.41 660 0.00 Strong
Mn 0-30 Spherical 2.03 7.86 1430 0.26 Moderate
30-60 Spherical 2.27 29.39 2750 0.08 Strong
60-90 Spherical 0.01 23.87 2750 0.00 Strong
Zn 0-30 Exponential 0.01 0.03 21890 0.33 Moderate
30-60 Exponential 0.00 0.01 14630 0.00 Strong
60-90 Exponential 0.01 0.02 21890 0.50 Moderate
Cu 0-30 Spherical 0.00 0.12 1210 0.00 Strong
30-60 Spherical 0.00 0.10 1210 0.00 Strong
60-90 Spherical 0.00 0.10 1210 0.00 Strong
Table 2: Semivariogram parameters of soil properties at different layers.

• Page 5 of 6 •
Volume 29 • Issue 3 • 1000141
Citation: Mali SS, Naik SK, Bhatt BP (2016) Spatial Variability in Soil Properties of Mango Orchards in Eastern Plateau and Hill Region of India. Vegetos 29:3.
doi:10.4172/2229-4473.1000141
to weak spatial dependence for all soil parameters (values between
0 and 1.16). Stronger spatial dependence observed in case of pH, P
and Cu (low nugget to sill ratio) and indicated that the soil properties
might be aected by the internal factors. Total N content in the sub-
surface layer and concentration of Mg in the surface layer showed
weak spatial dependence. e rest of variables were in moderate
spatial dependence with the nugget-sill, are between 0.25 and 0.75,
illustrating that the soil variables might be aected by internal and
external factors, such as cultivation and fertilization. Wang et al. [14]
found that most of the soil properties in the studied area were classed
as moderately spatially dependent. Liu et al. [10] reported the nugget-
sill ratios of Zn and Cu were less than 0.50.
Range is the distance at which the semivariogram levels o
and beyond which the semivariance is constant [29]. Knowledge
of the range of inuence for various soil properties allows for the
construction of independent data sets that can be used for classical
statistical analysis. A smaller range indicates that observed values of
the soil variable are inuenced by other values of this variable over
lesser distances than soil variables which have larger ranges [30]. In
the present mango orchards, the range for soil properties varied from
660 (deep layer Fe) to 21890 m (Surface layer Zn). In surface layer,
the range for pH, P, Ca, Mn, Cu was about 1430, 4730, 1210, 1430 and
1210 m. Low range for these variables indicated that these values are
inuenced by the neighbouring values at lesser distance than other
variables. e smaller range suggested that smaller sampling intervals
are needed pH, P, Ca, Mn, Cu. In particular, OC, K and Zn showed
consistently higher range values at all the sampling depths.
In present study, N exhibited moderate spatial dependence in
surface soils (0-30 cm), weak spatial dependence in sub-surface layer
(30-60 cm) and strong spatial dependence at deeper layers. Spatial
dependence of K was classed as ‘moderate’ at all depths implying
the impact of both the intrinsic and extrinsic factors on its spatial
variability. is phenomenon might be explained by high mobility
of K in sandy loam soils with low cation exchange capacity, which
can accentuate leaching eects of strong rains characterising the
study districts. Exchangeable K exhibited three spatial patterns:
strong dependence at topsoil (0-0.05 m depth), moderate from
0.05 to 0.2 m depth, and no spatial correlation in the lower layer
(0.2 –0.3 m). e Mg concentration in the surface horizon showed
weak spatial dependence as the nugget-sill ratio was more than 0.75
indicating comparatively lesser inuence of the Mg concentration at
neighbouring points.
e presented results suggested that there is considerable spatial
variability in the soil properties across the mango orchards of the
eastern plateau region. While planning the eld experiments in the
farmers’ eld, it is necessary to obtain coincident soil conditions and
avoid the test errors from the inconsistent soil properties [31]. Results
obtained under this study can be used to facilitate the procedure of
the preparation for eld experiment in the mango plantation areas of
the eastern plateau region.
Conclusion
e classic statistical analysis revealed a considerable statistical
and spatial variability of pH, OC, N, P, K, Ca, Mg, Fe, Mn, Zn and Cu
among soil horizons and across the mango orchards of the eastern
plateau region. en mean values of pH, Ca and Mg increased while
that for OC, N, P, K, Fe, Mn, Zn and Cu decreased with increasing
soil depth. e ndings of geostatistical analysis showed that spatial
structures exist in the soil variables. Apart from surface horizon (0-
30 cm), a strong to moderate spatial dependence was observed for
subsoil and deeper soil horizons. Higher range values for some of
the soil parameters implied that the soil chemical properties had
spatial dependence over larger distances. is study demonstrated
that the variability of soil chemical properties was associated to the
management practices (fertilizer, residue management etc.) and
local conditions (topography, climate etc.). It is concluded that the
orchard specic fertility management recommendations needs to be
considered over the general fertilizer recommendations for entire
region.
Acknowledgements
The authors gratefully acknowledge the help rendered by Mr. Ganga Ram,
Technical ofcer, ICAR Research Complex for Eastern Region, Research Centre,
Ranchi, India for collection of samples. The authors sincerely acknowledge
the help rendered by PRADAN, Jharkhand, India for identication of mango
orchards for the investigation. The authors wish to acknowledge Indian Council
of Agricultural Research, New Delhi, India for providing facilities for carrying out
this research.
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• Page 6 of 6 •
Volume 29 • Issue 3 • 1000141
Citation: Mali SS, Naik SK, Bhatt BP (2016) Spatial Variability in Soil Properties of Mango Orchards in Eastern Plateau and Hill Region of India. Vegetos 29:3.
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Author Afliations Top
ICAR-Research Complex for eastern Region, Research Centre Ranchi,
Jharkhand, India