Introduction
Soil is the key part of the earth system which controls hydrological,
biological, and geochemical cycles and it offers goods, resources and
services to mankind (Keesstra et al., 2012; Smith et al., 2015; Decock et
al., 2015; Brevik et al., 2015; Berendse et al., 2015). Un-sustainable soil
management practices lead to soil degradation, which is a worldwide topic,
mainly because of loss of soil organic matter (SOM), soil erosion, changes
in soil structure, degradation of the biota in the soils and soil chemical
degradation (Cerda et al., 2009; Mupenzi et al., 2011; Novara et al., 2013;
Mukherjee et al., 2014; Lieskovský and Kenderessy, 2014; Stanchi et al.,
2015; Seutloali and Beckedahl, 2015; Novara et al., 2015). Soil degradation
along with natural processes results in degradation of coastal areas, which
covers more than 10 % of the earth surface area with 35, 6000 and 7517 km
coast line in world and India, respectively (Misdorp, 1990; Sanil Kumar et
al., 2006).
Geographical distribution maps of soil properties, obtained from soil
surveys, help in correct management of soil nutrients (Brevik et al., 2016).
These maps are required to understand the patterns and processes of soil
spatial variability, which is the combined effect of soil physical, chemical
and biological processes operating at different spatiotemporal scales
combined with anthropogenic activities (Goovaerts, 1998). Geostatistical
tools are useful in preparation of the maps based on limited number of
samples collected from agricultural landscapes. Kriging simulation technique
predicts the values at un-sampled locations by spatial correlation and
reduces variance of estimation error and investigation costs (Saito et
al., 2005; Pereira et al., 2015). Spatial variability of soil properties is
assessed effectively by geostatistical methods (Mueller et al., 2003; Pereira
et al., 2013; Ochoa-Cueva et al., 2015) for site-specific management of
nutrients through variable rate fertilizer application to avoid over and
under application of nutrients (Fu et al., 2010). Information regarding
variability of soil properties in soil profile is helpful to assess the
contribution of subsurface soil layers to crop nutrition and potential
capacity of the soil to supply nutrients during crop growth. It also helps
in understanding the effect of different management practices, under a given
cropping system, on the downward movement as well as recycling of nutrients
to the surface layers (Behera and Shukla, 2013; Parras-Alcantara et al.,
2015).
Oil palm (Elaeis guineensis Jacq.) is a high-oil-yielding crop compared to annual oil crops
(Johnston et al., 2009; Murphy, 2009). Oil palm uses about 162, 30, 217, 38
and 36 kg of N, P, K, Mg and Ca ha-1 year-1, respectively, to
produce 2.5 Mg of oil ha-1 year-1 (Mengel and Kirkby, 1987).
Considering oil to bunch ratio of 1:4, 2.5 Mg oil ha-1 is equivalent to
10 Mg FFB ha-1 year-1, but average FFB yield in well-managed
plantations is much higher (Narsimha Rao et al., 2014). Nutrient content in
1 Mg of FFB obtained from Dura palms is 2.94, 0.44, 3.71, 0.77, 0.81 kg of
N, P, K, Mg and Ca, respectively, whereas Mn, Fe, B, Cu and Zn content per
1 Mg of FFB is 1.51, 2.47, 2.15, 4.76 and 4.93 g, respectively (Ng and Thamboo, 1967). Calibrated soil and leaf analysis helps in
effective fertilizer recommendations in most of the crops (Smith and
Loneragan, 1997; McLaughlin et al., 1999). In oil palm, leaf nutrient
analysis is commonly used for estimating fertilizer requirement (Fairhurst
and Mutert, 1999; Corley and Tinker, 2003). The relationship between leaf
analysis and palm productivity is generally evident, and an assessment of
fertilizer needs can be based on such an analysis. However for a
cost-effective approach, leaf analysis has to be integrated with soil
analysis (Goh et al., 2003). It is therefore pertinent to assess soil
nutrient status for effective and sustainable fertilizer management
programme in oil palm.
Spatial distribution of sampling points in south Goa district of
Goa state (western India).
Prasad et al. (2013) reported wide range in quantity of fertilizer applied
indicating that oil palms were either under-fertilized or over-fertilized.
Also, low cost and high availability of some fertilizers have encouraged
farmers to make excessive applications with the belief that high yields
would be ensured. However, this management adversely affects soil fertility,
productivity, fruit quality and ground water quality. Different amount of
fertilizer application to different soil types may alter soil properties. It is therefore pertinent for the farmers to economize on
fertilizer adopting a strategy for site-specific and/or area-specific
management based on spatial variability of soil properties to make oil palm
production environmentally sustainable and economically viable. Spatial
variability of soil properties in oil palm plantations have to be carefully
evaluated to implement sustainable soil management practices. Thus, the
present study was carried out in soils of oil palm plantations south Goa
district of India with the following objectives, (i) to estimate the spatial
variability of some soil properties through semivariogram analysis, (ii) to
assess the relationship among the estimated soil properties and (iii) to
develop spatial maps for soil properties using the parameters of the best
fitted semivariogram model and interpolation using ordinary kriging
technique.
Material and methods
Study site
A survey was carried out in south Goa district of Goa state of India during
2012–2013 to find out soil and plant nutritional status in randomly selected
64 tenera oil palm plantations (with 5 to 21 years of age) (Fig. 1). Oil
palm is cultivated in an area of approximately 1000 ha which is 1 % of
agricultural land in the state. The state lies between 15∘6.8′96 to
15∘41.7′26 N latitudes and 74∘76′60′′ to 73∘56′78′′ E longitudes
with altitude ranging from 4 to 90 m a.s.l. The climate of the
area is tropical monsoon type. Hot and humid climate prevails for most of
the year. Annual mean rainfall (average of 30 years) is 2926 mm,
concentrated from early June to late September. On average, May is the
warmest month, with temperature peaks over 35 ∘C and relative
humidity of 70 %. Goa experiences short winter seasons between
mid-December and February and these months are marked by mean night
temperature of approximately 21 ∘C and a mean day temperature of
around 28 ∘C with relative humidity of 65 %. According to
Bhattacharyya et al. (2013), the main soils in the study area are
Inceptisols, Ultisols, Entisols and Alfisols (classified as in Soil Survey
Staff, 2014), sandy loam to silty loam texture, developed from granite,
granite-gneiss, quartzite/schistose and basalt.
Soil sampling, processing and analysis
A total of 128 soil samples i.e., 64 from 0 to 20 cm (surface) and 64 from 20 to 40 cm
(subsurface) depths were collected at random points inside 3 m radius
from the palm during the survey to assess soil properties of oil palm
plantations at an approximate interval of 1 to 2 km. Five soil samples were
collected at random from each sampling location within a radius of
approximately 60 cm using a hand auger. The five samples were then mixed to
obtain the representative soil sample of the sampling point. The latitude,
longitude, and elevation at each sampling point were recorded using a handheld global positioning system (GPS). The soil samples were dried at room
temperature (25±3 ∘C). Stone and debris from samples were
removed and then ground to pass a 2 mm sieve. The processed soil samples
were tested for acidity (pH), salinity (EC), organic carbon (OC) content,
available K (NH4OAc-K), available P (Bray's P-1) (Bray's-P),
exchangeable Ca2+ (Exch. Ca2+), exchangeable Mg2+ (Exch. Mg2+), available S (CaCl2-S)
and hot water extractable B (HWB). Determination of soil pH and EC (1:2 soil
water ratio (w/v) suspension) were carried out using pH-meter and
conductivity meter (Jackson, 1973). Walkley–Black method (Walkley and Black,
1934) was followed for assessing soil OC content. NH4OAc-K was
estimated after extracting soil samples with neutral 1 N ammonium acetate
solution (Hanway and Heidel, 1952) followed by flame photometry estimation.
Available P was extracted using Bray's P-1 reagent (Bray and Kurtz, 1945)
and estimated through spectrophotometry. Exchangeable Ca2+ and Mg2+ were
extracted using neutral normal ammonium acetate solution (Jones, 1998) and
estimated through atomic absorption spectrometry. Available S was estimated
by the turbidity method (Williams and Steinbergs, 1969). HWB content was
estimated through Azomethine-H reagent (Gupta, 1967) using
spectrophotometry.
Statistical and geostatistical analysis
The descriptive statistics like minimum, maximum, mean, standard deviation
(SD), coefficient of variation (CV), and skewness for soil properties were
computed using the SAS 9.2 software pack (SAS, 2011). Relationship among the
studied soil properties were established using Pearson's correlation
coefficient analysis. Significant differences were observed at P<0.05.
Soil properties of surface (0–20 cm) and subsurface (20–40 cm)
layers (n=64 at each case).
Variable
Soil layer
Mean ± SD
CV (%)
Minimum
Maximum
Skewness
Distribution
pH
Surface
5.35 ± 0.45
8.64
4.25
6.77
0.18
Normal
Subsurface
5.28 ± 0.46
8.63
4.53
6.52
0.65
Normal
EC
Surface
0.13 ± 0.17
125
0.05
1.06
4.06
Transformed
Subsurface
0.08 ± 0.06
75.3
0.03
0.41
3.02
Transformed
OC
Surface
19.8 ± 8.77
44.4
5.07
48.4
0.83
Normal
Subsurface
13.2 ± 7.33
55.5
1.95
31.2
0.75
Normal
NH4OAc-K
Surface
270 ± 29.9
88.7
58.1
1167
1.80
Transformed
Subsurface
199 ± 165
82.8
16.1
856
2.16
Transformed
Bray's-P
Surface
24.7 ± 3.39
127
0.86
141
2.14
Transformed
Subsurface
9.78 ± 13.2
135
0.90
42.3
2.52
Transformed
Exch. Ca2+
Surface
914 ± 588
64.3
200
2997
1.56
Transformed
Subsurface
795 ± 724
91.1
194
5177
3.89
Transformed
Exch. Mg2+
Surface
203 ± 141
69.3
36.0
744
1.75
Transformed
Subsurface
225 ± 156
69.4
24.0
720
1.27
Transformed
CaCl2-S
Surface
23.2 ± 16.4
70.7
3.00
87.7
1.60
Transformed
Subsurface
16.3 ± 10.1
62.0
1.50
43.5
0.93
Normal
HWB
Surface
0.70 ± 0.38
54.7
0.09
2.10
1.43
Transformed
Subsurface
0.64 ± 0.44
68.6
0.04
2.56
1.70
Transformed
SD – standard deviation; CV – coefficient of variation; EC – electrical
conductivity, dS m-1; OC – organic carbon, g kg-1; NH4OAc-K, mg kg-1;
Bray's-P, mg kg-1; exch. Ca2+, mg kg-1; exch. Mg2+, mg kg-1;
CaCl2-S, mg kg-1; HWB, mg kg-1.
ArcMap 10.1 (ESRI, 2012) was used to analyze the spatial structure of soil
properties. Before using geostatistics, normality of data distribution was
checked by Shapiro-Wilk test at 5 % (Shapiro and Wilk, 1965). Soil
properties like pH and OC content in both the soil layers and CaCl2-S
content in subsurface soil layers exhibited normal distribution (Table 1).
While, data transformation to normal distribution was carried out for rest
of the soil properties. Prior to geostatistical analyses, the data were
examined for the presence of trend (by “Geostatistical analyst” of ArcGIS
10.1) and removed (by fitting to second order polynomial). According to
McCormick et al. (2009), trend in the variation signals a departure from
the intrinsic hypothesis in which the process is assumed to be random and it
violates the assumptions on which geostatistics is based on. By removing the
trend, it will be possible to more accurately model the variation because
the trend will not be influencing the spatial analysis (Kerry and Oliver,
2007). The semivariogram was used to measure spatial variability of soil
properties and to obtain input parameters for the kriging method of spatial
interpolation (Goovaerts, 1997; Tesfahunegn et al., 2011). It is half of the
expected squared difference between paired data values to the lag distance
by which locations are separated. The experimental semivariograms of soil
properties were derived as described below.
γ(h)=12m(h)∑i=1m(h)[Z(Xi+h)-Z(Xi)]2
Where γ(h) is the experimental semivariogram, h is the lag, m(h) is number of
sample value pairs separated by h, Z(Xi), Z(Xi+h) are sample values at two
points at Xi and (Xi+h) locations, respectively. The distance
between the sample pairs is rarely equal to h in irregular sampling and h is
often represented by a distance interval.
Semivariogram parameters like nugget/sill ratio and range were obtained for
soil properties. The nugget/sill ratio was used to classify the spatial
dependence of variables (Oliver and Webster, 2014). Ratio values less than
or equal to 0.25, between 0.25 and 0.75, more than 0.75 were considered
strongly, moderately and weakly spatially dependent, respectively (Behera et
al., 2011). Best-fit semivariograms models were selected by cross-validation
technique. Mean square error (MSE) was estimated to predict the accuracy of
models (Utset et al., 2000).
MSE=∑i=1n[z(xi,yi)-z⋅(xi,yi)]2n
Goodness-of-prediction criterium G is one of the methods used for accuracies
of interpolated maps (Agterberg, 1984; Tesfahunegn et al., 2011). Accuracies
of interpolated maps of studied soil properties were checked by G values.
According to Parfitt et al. (2009), positive G values indicate that the map
obtained by interpolating data from the samples is more accurate than a
catchment average. Negative and close to zero G values indicate that the
catchment-scale average predicts the values at unsampled locations as
accurately as or even better than the sampling estimates. Ordinary kriging
interpolation was carried out to develop spatial distribution maps for soil
properties.
Results and discussion
Descriptive statistics of soil properties
The descriptive statistics revealed considerable variability of soil
properties in both surface and subsurface soil layers of oil palm
plantations (Table 1). The mean values of soil properties were 5.35, 0.13 dS m-1,
19.8 g kg-1, 270 mg kg-1, 24.7 mg kg-1, 914 mg kg-1,
203 mg kg-1, 23.2 mg kg-1 and 0.70 mg kg-1 for pH,
EC, OC, NH4OAc-K, Bray's-P, exchangeable Ca2+, exchangeable
Mg2+, CaCl2-S and HWB, respectively, in surface soil layers.
Whereas the mean values were 5.28, 0.08 dS m-1, 13.2 g kg-1, 199 mg kg-1,
9.78 mg kg-1, 795 mg kg-1, 225 mg kg-1, 16.3 mg kg-1
and 0.64 mg kg-1 for pH, EC, OC, NH4OAc-K, Bray's-P,
exchangeable Ca2+, exchangeable Mg2+, CaCl2-S and HWB,
respectively, in subsurface soil layers. The values of CV for soil properties
ranged from 8.63 to 135 %. The values of CV for soil pH in both the soil
layers revealed their low variability (CV < 25 %). The rest of the
soil properties exhibited moderate (CV 25–75 %) variability except
salinity, NH4OAc-K and Bray's-P in both the soil layers and
exchangeable Ca2+ in subsurface soil layers, which had high (CV > 75 %)
variability. Low CV values for soil pH was due to
transformed measurement of hydrogen ion concentration. Skewness values of
0.18 to 3.89 for different soil proprieties revealed that some soil
properties were not normally distributed. This variation and non-normal
distribution of soil properties in the studied areas may be due to adoption
of different soil management practices including variation in fertilizer
application and other crop management practices (Tesfahunegn et al., 2011;
Srinivasarao et al., 2014; Ferreira et al., 2015).
Pearson's correlation coefficients between soil properties at the
surface (0–20 cm) and subsurface (20–40 cm) layers. Only significant
coefficients are shown (*, p<0.05; **, p<0.01) (n=64).
Layer
pH
EC
OC
Bray's-P
Exch. Ca2+
Surface
NH4OAc-K
0.45**
Bray's-P
Exch. Ca2+
0.67**
0.26*
Exch. Mg2+
0.37**
CaCl2-S
0.31*
0.44**
HWB
0.30*
Subsurface
NH4OAc-K
0.48**
Bray's-P
0.32*
Exch. Ca2+
0.42**
Exch. Mg2+
0.33**
CaCl2-S
0.36**
EC – electrical conductivity, dS m-1; OC – organic carbon, g kg-1;
NH4OAc-K, mg kg-1; Bray's-P, mg kg-1; exch. Ca2+, mg kg-1;
exch. Mg2+, mg kg-1; CaCl2-S, mg kg-1; HWB, mg kg-1.
The mean values of soil pH were acidic in both surface (5.35) and subsurface
(5.28) soil layers (Table 1). The acidic nature of soil in the studied area
may be due to acidic parent material and prevailing rainfall pattern. The
values of soil EC indicate the non-saline nature of soils. Soil OC contents
varied widely in both surface and subsurface soil layers. Principal reason
for variation in soil OC content may be due to adoption of different
cultural practices including addition of crop biomass to the soils. Surface
soil layers had slightly higher OC content (mean value 19.8 g kg-1)
than OC content in subsurface soil layers (mean value 13.2 g kg-1).
Surface soil layers had higher NH4OAc-K, Bray's-P, CaCl2-S and HWB
content compared to that in subsurface soil layers (Table 1). The content of
these nutrients varied greatly among the soils because of heterogeneity in
fertilizer application in the area. The mean values of exchangeable
Ca2+ were 914 and 795 mg kg-1 for surface and subsurface soil
layers, respectively, whereas surface soil layers were having 203 and 225 mg kg-1
of mean exchangeable Mg2+ content, respectively. Other
studies reported similar results highlighting different distribution pattern
of soil properties, primary, secondary and micronutrients under different
soil-crop management situations (Franzlubbers and Hons, 1996; Sharma et al.,
2005; Behera and Shukla, 2013).
Relationship among soil properties
The exchangeable Ca2+ content increased significantly with soil pH
(Table 2). Behera and Shukla (2015) also recorded a positive and significant
relationship of soil pH and soil OC with K, exchangeable Ca2+ and
exchangeable Mg2+ content in some cropped acid soils of India. Soil OC
content in surface layers was positively and significantly correlated with
exchangeable Ca2+ and HWB (P<0.05). Most of the soil
properties which influence nutrient storage and availability to plants are
influenced by SOM type and content (Foth and Turk,
1972). Increased soil EC content led to higher NH4OAc-K in both soil
layers (P<0.01), and higher CaCl2-S in surface layer and
Bray's-P in subsurface layer (P<0.05). Soil EC does not directly
affect plant growth but has been used as an indirect indicator of the amount
of nutrients available for plant uptake and salinity levels (Corwin and
Lesch, 2005). EC has been used as a surrogate measure of salt concentration,
organic matter, cation-exchange capacity, soil texture, soil thickness,
nutrients, water-holding capacity, and drainage conditions. In site-specific
management and high-intensity soil surveys, EC is used to partition units of
management, differentiate soil types, and predict soil fertility and crop
yields (Corwin and Lesch, 2005).
Semivariograms of soil properties in surface (0–20 cm) and
subsurface (20–40 cm) soil layers.
Spatial structure and distribution of soil properties
The best-fitted semivariograms for studied soil properties are depicted in
Fig. 2, whereas their parameters are given in Table 3. The best fit models
were exponential, Gausian, stable, exponential, K-Bessel and circular for
different soil layers. The value of nugget varied widely for soil
properties. It was highest for exchangeable Ca2+ and the lowest for
soil pH. A higher nugget value indicates that the selected sampling distance
could not capture well the spatial dependence, whereas lower nugget value
reveals low spatial variability within small distances. Our findings are in
line with the observations made by Tesfahunegn et al. (2011).
Semivariogram parameters of soil properties of studied areas.
Variable
Soil layer
Model
Nugget
Sill
Nugget:
Spatial
Range
Obs. vs.
MSE
G
sill ratio
class
(m)
est.
(%)
pH
Surface
Exponential
0.00
10
0.00
Strong
2367
0.915
0.61
54
Subsurface
Gaussian
0.06
0.18
0.33
Moderate
1892
0.888
0.52
53
EC
Surface
Stable*
0.02
0.06
0.33
Moderate
1656
0.892
0.00
48
Subsurface
Exponential*
0.00
0.01
0.00
Strong
2519
0.953
0.00
43
OC
Surface
K-Bessel
44.1
93.61
0.47
Moderate
1579
0.961
1.56
35
Subsurface
Stable
14.36
69.4
0.21
Strong
1579
0.943
1.89
51
NH4OAc-K
Surface
Exponential*
17 546
32 272
0.54
Moderate
1697
0.912
31.3
55
Subsurface
Exponential*
17 568
35 786
0.49
Moderate
1697
0.855
22.6
47
Bray's-P
Surface
Gaussian*
1193
1708
0.70
Moderate
2401
0.981
22.9
48
Subsurface
K-Bessel*
159.43
323
0.49
Moderate
878
0.915
22.6
39
Exch. Ca2+
Surface
Exponential*
91 642
260 984
0.35
Strong
2767
0.935
123.4
23
Subsurface
Exponential*
65 328
120 128
0.54
Strong
1589
0.971
165.2
24
Exch. Mg2+
Surface
Exponential*
1574
41 995
0.04
Moderate
1656
0.852
54.3
38
Subsurface
Exponential*
26 151
43 836
0.60
Moderate
2905
0.984
42.1
51
CaCl2-S
Surface
Spherical*
234
410
0.57
Moderate
4244
0.912
0.04
38
Subsurface
Spherical
92.2
133.4
0.69
Moderate
3141
0.955
0.03
40
HWB
Surface
Gaussian*
0.06
0.09
0.67
Moderate
1888
0.963
0.02
60
Subsurface
Exponential*
0.13
0.24
0.54
Moderate
1807
0.961
0.02
58
* Transformation for normal distribution.EC – electrical conductivity, dS m-1; OC – organic carbon, g kg-1; NH4OAc-K,
mg kg-1; Bray's-P, mg kg-1; exch. Ca2+, mg kg-1; exch. Mg2+,
mg kg-1; CaCl2-S, mg kg-1; HWB, mg kg-1; MSE-mean square error;
G – goodness-of-prediction criterium.
The nugget/sill ratio values ranged from 0.00 to 0.70 with strong (for
surface pH, subsurface EC, and both surface and subsurface exchangeable
Ca2+) to moderate (for rest soil properties) spatial dependency for the
soil properties. Moderate to strong spatial dependence of soil properties
are ascribed to intrinsic factors (such as mineralogy) as well as extrinsic
factors including fertilization and other crop management practices
(Cambardella et al., 1994). The range of the semivariogram is the maximum
distance over which the soil properties of two samples are related. This can
be an effective criterion for the evaluation of sampling design and the
mapping of soil properties (Utset et al., 2000; Zhang et al., 2015). The
range values of soil properties ranged from 878 to 4244 m (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. The soil sampling distance in the range of 1 to 2 km
in this study was close with models range value. Level of similarity or
disturbance of soil condition can be assessed by spatial dependency. A large
range indicates that the value of measured soil property is influenced more by
natural and anthropogenic factors over great distances than properties
having smaller ranges (Lopez-Granados et al., 2002). Thus, a range value of
about 4244 for CaCl2-S in the study region indicates that the measured
values can be influenced over great distances comparison with other soil
properties having smaller ranges. This is in agreement with the findings of
Foroughifar et al. (2013) who reported range values of 1600 to 7364 m for
different soil properties of northwest Iran. Several studies also reported
different range values of 2.5 to 9.1 km for DTPA extractable Zn (Behera et
al., 2011), 3.30 to 28 km for DTPA extractable Cu (Behera et al., 2012), and
0.7 to 66 km for DTPA extractable Mn and 2.7 to 5.2 km for DTPA extractable
Fe (Behera and Shukla, 2014) in some acid soils of India. According to
Kerry and Oliver (2004), the sampling interval should be less than half the
semivariogram range. It is therefore recommended that for ensuing studies
aimed at characterizing spatial dependency of soil properties in similar
areas, soil sampling should be done at distances shorter than the range
found in this study.
Cross-validation technique was used to identify the most accurate
predictions for soil properties with the lowest MSE values (Table 3). Lowest
MSE values indicate that kriging predictions of soil properties are closer
to measured values. The accuracy of kriged interpolation maps of soil
properties was also measured by the G values (Table 3) which varied from 23
(for exchangeable Ca2+ in surface layer) to 60 % (for HWB in surface
layer). This is in consistent with the observations made by Mueller et al. (2003)
and Tesfahunegn et al. (2011). The G values for the soil properties
reveal the prediction capacity of the data sets using kriging from the sample
points as compared to average values of the area. Greater than zero G values
indicate that kriging is more accurate than the average value of the area.
For example, the G value of 54 % for soil surface pH indicates that the
kriged pH map is 54 % more accurate than those achieved using average
value of the area. Thus, the use of kriging interpolation technique was
appropriate for developing maps of soil properties.
Kriged interpolation maps of soil properties in surface (0–20 cm)
and subsurface (20–40 cm) soil layers.
Spatial distribution maps (Fig. 3) of different soil properties revealed
that oil palm plantations of the area could be divided into homogenous small
zones depending upon the different nutrient ranges. Distribution map of pH
in surface soil layers revealed almost all the area having pH of 5.00 to
6.00. Low pH values occurred in southern and south-eastern parts. In
subsurface soil layers, low pH of < 5.00 occurred in south-eastern
part whereas relatively higher pH prevailed in north-western part. According
to Dessai (2011), areas having low pH values compared to other areas may be
due to acidic parent material from which the soil developed and different soil
management practices. Soil EC had irregular distribution pattern whereas
relatively low values of EC were recorded in north-western parts of both the
soil layers. This may be due to sandy loam soil texture and the presence of low
OC in north-western part (Bhattacharyya et al., 2013). Higher EC values in
other parts of surveyed area probably due to silt loam soil texture with
high water table (Pal et al., 2014). Higher amount of soil OC was found to
be distributed in the southern and south-eastern parts in surface as well as
subsurface soil layers. This may be ascribed to prevalence of higher slope
and low rate of SOM mineralization in south-eastern parts compared to other
areas. Lower amounts of NH4OAc-K were recorded in western parts in
both the soil layers. Higher amount of Bray's-P was found to be distributed
in most parts in surface soil layers whereas low amount of Bray's-P occurred
in south-western part. Bray's-P distribution was irregular in subsurface
soil layers. Build up of P in surface layers may be due to continuous P
addition and their fixation in soil which is acidic in nature. Exchangeable
Ca2+ exhibited irregular distribution pattern in both the soil
layers. In surface as well as subsurface soil layers, lower amount of
exchangeable Mg2+ was found to be distributed in southern parts as
compared to that in northern parts. Irregular distribution pattern of
CaCl2-S was recorded in surface soil layers whereas low values of
CaCl2-S were observed in southern part of the study area. Higher amount
of HWB was found to be distributed in central part in contrast to low values
in north-western and south-eastern part in surface soil layers. Distribution
pattern of HWB was irregular in subsurface soil layers. The different
distribution variability of the soil properties in oil palm plantations of
this area is predominantly due to climate and landscape along with farm
practices including application of different quantities of nutrients through
fertilizers (Behera et al., 2016). The kriged distribution maps for
different soil properties providing quantitative information about soil
properties in both the soil layers is of great use for plantation staff,
farm managers, extension officers and farmers. This will help in visualizing
soil fertility status for planning appropriate strategies for efficient site
specific soil nutrient management and variable-rate fertilizer application
technology. It leads for obtaining optimum output and oil palm yield which
can provide environmentally sustainable maximum return to farmers with
optimum input utilization combined with best management practices (Fu et
al., 2010; Behera et al., 2012). The areas with low and medium nutrient
status require more amount of fertilizer application as compared to areas
having high nutrient status. For example, exchangeable Mg2+ status is
low in southern part of the area compared to northern part.