SPATIAL VARIABILITY ASSESSMENT OF SOIL...

SPATIAL VARIABILITY ASSESSMENT OF SOIL ORGANIC CARBON 87

Oriental Geographer

Vol. 60, No. 1&2, 2018

Printed in March 2019

SPATIAL VARIABILITY ASSESSMENT OF SOIL ORGANIC

CARBON: AN APPROACH OF PRECISION FARMING

MUHAMMAD SHAMIMUR RAHMAN1

KHAN TOWHID OSMAN2

MD. JASHIM UDDIN3

Abstract: A study was undertaken to quantify and predict the variability of soil organic

carbon (SOC) in the Meghna alluvial soils of Raipur Upazila of Bangladesh. Thirty nine

soil samples were collected on one minute latitude and longitude interval using GPS at

grid level. The soil samples were analyzed in the laboratory for their SOC contents.

Classical statistics and geostatistics were used to assess the spatial variation

characteristics of the SOC. The classical statistics results indicated that the variability of

the SOC was moderate (CV= 0.25). The semivariogram models indicated that there was a

weak spatial autocorrelation (R2<0.5) of SOC in the study site. The intrinsic factors are

governed by the soil forming factors such as parent materials and are related to strong

spatial dependency of SOC. On the other hand, extrinsic factors are governed by the soil

management practices such as tillage, fertilization and thus related to weak spatial

dependency of SOC. The present study site exhibits with the weak spatial dependency of

SOC which infers an overuse of soil resources by fertilization and crop production with

higher cropping intensity. GPS, GIS and Geostatistics acts as a valuable tools for

assessing SOC loss or sequestration as well as land management and precision farming

options.

Keywords: Spatial variability, GPS-GIS-Geostatistics tools, Soil organic carbon,

Precision farming

INTRODUCTION

Soils are inherently variable over time and space. Thus, an understanding of the

distribution of soil properties at the field scale is important in refining agricultural

management practices and assessing the effects of agricultural land use on soil quality

(Cambardella et al., 1994). Natural variability of soil results from complex interactions

between geology, topography, climate as well as soil use (Quine and Zhang, 2002). As a

consequence, soils can exhibit marked spatial variability (Brejda et al., 2000; Goovaerts,

1998; Vieira and Paz-Gonzalez, 2003). Assessment of the spatial patterns of SOC is

essential for understanding the potential of soils to sequester C, for quantifying the SOC

1 Muhammad Shamimur Rahman is Research Student, Department of Soil Science, University of Chittagong, Bangladesh 2 Dr. Khan Towhid Osman is Professor, Department of Soil Science, University of Chittagong, Bangladesh 3 Dr. Jasim Uddin is Professor, Department of Soil, Water and Environment, University of Dhaka, Bangladesh

88 ORIENTAL GEOGRAPHER

sink or source capacity of soils in changing environments, and for developing strategies

to mitigate the effects of global warming (Venteris et al., 2004 and Hoffmann et al.,

2012). A better understanding of the spatial variability of SOC is also important for

refining agricultural management practices and for improving sustainable landuse. It

provides a valuable base against which subsequent and future measurements can be

evaluated (Liu et al., 2006). Soil organic carbon and its relation to site characteristics is

important in evaluating regional, continental, and global soil C stores and projecting

future changes (Feng et al., 2002). However, due to high soil heterogeneity it is difficult

to obtain an accurate assessment of SOC stock (Don et al., 2007). As a result, there is a

considerable interest in understanding the spatial variability of SOC in different terrestrial

ecosystems (Arrouays et al., 2001; Liu et al., 2006). Geostatistics has been widely used to

assess the spatial characteristics of SOC (Evrendilek et al., 2004). However, the relative

importance of the edaphic factors as drivers or constraints of spatial heterogeneity of

SOC content in the alluvial soils of Bangladesh is not well understood.

Floodplain soils of Bangladesh have shown a high degree of variability where diversified

crops are grown in these soils, and have varying nature of crop residues returned to the

soil (Brammer, 1996). Among the Ganges, Brahmaputra and Meghna (GBM) floodplains,

Meghna floodplain soils are one of the most recently developed soils where new

deposition and erosion are continuously taking place on the land margins. These soils are

regarded as young alluvial sediments and have a long history of land use since deposition

(Brammer, 2002). Low soil organic carbon (SOC) is a general problem in most

agricultural soils of Bangladesh. Almost 50% of the land areas in Bangladesh have <1%

SOC (Karim and Iqbal, 2001). In this case, Meghna floodplain is not an exceptional. This

area is experiencing rapid changes in land uses due to some reasons, including favorable

climate and the growing demand of ever increasing population for growing more food,

fiber and fuel. Subsequently, land use changes and natural processes like floods can cause

changes in SOC (Houghton and Skole, 1990; Houghton, 1995; Tian et al., 2000).

Crops and soils were not uniform within a given field (Cassman and Plant, 1992) where

diversified and complex land types were also reported in Bangladesh (Brammer, 1996).

The farmers are always responded to such variability to take actions, but such actions are

inappropriate and less frequent. Over the last decades, modern methods have been

developed to study land and soil variability which is known as spatially variable crop

production model, global positioning systems (GPS)-based agriculture, site-specific and

precision farming. It is a paradigm shift from conventional management practices of soil

and crops in consequence with spatial variability (Mandal and Ghosh, 2000).

Conventional agriculture is practiced for uniform application of fertilizer, herbicide,

insecticides, fungicides and irrigation, without considering spatial variability. To alleviate

the ill-effects of over and under usage of inputs, the new concept of precision farming has

emerged. Site specific management to spatial variability of farm is developed to

maximize crop production and to minimize environmental pollution and degradation,

leading to sustainable development.

Thus, the concept of precision farming and spatial variability becomes vital with the

inventory of modern technologies viz. global positioning systems (GPS) and


geographical information systems (GIS). GIS is useful to produce interpolated maps for

visualization, and for raster GIS maps; and visualizing the spatial differences between the

maps (Wang et al., 2008). For studies on the spatial distribution patterns of SOC,

geostatistics have been widely applied (Saldana et al., 1998; McGrath and Zhang, 2003;

Liu et al., 2006) and based on the theory of regionalized variables (Webster and Oliver,

2007). Geostatistics also provides tools to quantify the spatial features of soil parameters

and allows for spatial interpolation. This study makes use of GIS in combination with

classical statistics and geostatistics to assess the spatial variation characteristics of SOC

in the Raipur Upazila under the Meghna floodplain of Bangladesh.

The specific objectives of this research were (i) to estimate the SOC contents in the study

site; (ii) to assess the spatial variability of SOC through semivariogram models.

ENVIRONMENTAL CONDITIONS OF THE STUDY SITE

The development of the Ganges–Brahmaputra–Meghna Delta began some 125 million

years ago (Ma) after the fragmentation of the Gondwanaland. Since the early Cretaceous,

it is still continuing to develop (Lindsay et al.,1991; Goodbred and Kuehl, 1999). This is

the largest delta and occupies the lower part of the Bengal basin of the South Asian

region of Bengal (Islam and Gnauck, 2008. Historically, The Meghna River originated

from the Lushai hills in India and flows western by the name Barak. Entering to

Bangladesh it bifurcates, then meets again and renamed as Meghna. This river on its way

down to the sea receives the combined flow of the Ganges and the Brahmaputra and

forms the biggest estuary in the Bay of Bengal. During its early stage, Meghna River

delta was developed the same as other rivers in the Bengal in the quaternary age (Anwar,

1988). The geographical coordinates of the early stage Meghna River delta is 22˚ 15/ 00

//

N latitude to 91˚ 10/ 00

//E longitude. The area of early Meghna delta was about 18,299.55

km2. Meghna delta is now actively pushing out the eastern side of the Bay of Bengal,

whereas the early Meghna delta was formed by south to southwest flow of Meghna River

(Anwar, 1988; Islam and Gnauck, 2008).

Topographic Nature: Meghna floodplain has two landscape units: the middle Meghna

river floodplain and the lower Meghna river floodplain. The middle Meghna river

floodplain is mainly low lying basin with surrounding low ridges along the river-banks.

The lower Meghna river floodplain is a transitional area between the middle Meghna

river floodplain and the young Meghna estuarine floodplain. The landscape is very gently

irregular with little difference in elevation between ridges and depressions (Rahman,

2005).

Landuse: The Meghna estuarine floodplain has the highest cropping intensity in

Bangladesh. Most of this floodplain used for two rice crops followed by dry land rabi

crops. With irrigation, HYV boro followed by HYV transplanted aman is the main

pattern. Increasing, mustard is being grown during end- November to February as a cash

crop. On medium lowlands, mixed aus and aman or jute is grown followed by rabi crops.

It is important to note that the Dakatia River (Fig. 1) has passed through the study site

where farmers are very much fortunate to use the water resources for their irrigation

purposes.


Figure 1: The Study Area


Inundation Land Types and Water Resources: According to FAO-UNDP (1988),

there are five inundation land types were recognized in Bangladesh. These were highland

(HL), medium high land (MHL), medium lowland (MLL), low land (LL) and very low

land (VLL). The study site belongs to the medium high land conditions mainly and its

area is about 10,330 ha. MHL is defined, where it is normally flooded up to about 90 cm

deep during the monsoon season. The river channels e.g. the Meghna and the Dakatia

(Fig. 2) in the study site covers about 3,065 ha of land. These rivers are the only major

water sources to meet the local demand.

Figure. 2: Location of Raipur Upazila under the Meghna Floodplain

http://localhost:1030/Maps/MR_0053.GIF


Soil Resources: The major soils identified in the study site were Ramgati soil series,

Hatiya soil series and the Homna soil series.Ramgatisoil series (TypicHalaquepts) are

developed in tidal deposits of lower Meghna floodplain. They occupy almost level to

very gently undulating ridges. They are poorly drained silt loams. They have an olive-

grey prismatic B horizon with thin to medium nearly continuous pedcutans. Hatiya soil

series (Typic Halaquepts) are tidally flooded, poorly drained; nearly level soils developed

in moderately fine textured very young tidal deposits occurring extensively on the lower

Meghna tidal floodplain. Homna soil series (Typic Endoaquepts) includes seasonally

very deeply flooded, poorly drained soils developed in moderately coarse to medium

textured Meghna floodplain (Rahman, 2005). The soils are enriched with mica, chlorite

and vermiculite minerals (Saheed, 2005).

Flooding and Sedimentation: Flooding is a natural annual phenomenon of a river

system which occupies a unique position in the culture, society and economy of

Bangladesh (Islam, 2016).Seasonal flooding, with variable depths and duration, is the

characteristic property of the floodplain soils of Bangladesh and of course for the study

site. Seasonal flooding possesses new siltation which causes a regular replenishment of

their soil fertility. The three rivers- the Ganges, the Brahmaputra and the Meghna

(GBM), carry nearly 6 million cusecs of water and 13 million tons of suspended sediment

loads per day during the flood season to the Bay of Bengal, nearly 3 times the quantity

borne by the Missisippi River (Anwar, 1988). At present time, the lower Meghna estuary

near by the study site (Fig. 2) is the most active part of the coastal zone of Bangladesh.

This active part is the outlet for discharge of combined flows of sediments into the Bay of

Bengal. This is the most dynamic region for the erosion and accretion for the entire

coastal zone of Bangladesh.

MATERIALS AND METHODS

Free sampling involves randomly taking samples throughout the field; it does not provide

any indication of field variability. The grid sampling uses a systematic method of soil

sampling. The need for intensive grid sampling for evaluating the spatial variability or the

diversity of different soil and agronomic properties has been frequently emphasized

(Cattle et al., 1994; Paz-Gonzalez et al., 2000).

Soil Sampling and Processing: Soil samples were collected on one minute latitude and

longitude interval on grid basis. It extracts information at farm level resources, and helps

in making spatial analysis. GPS Magellan (Model 320) was used to identify the exact

sampling locations. Land and soil resource utilization guide (SRDI, 1989) was used as a

base material during field visit and soil sampling. Hence, a total of 39 soil samples were

collected on a regular grid of the Raipur Upazila of Luxmipur district at the 0-30 cm

depths. It may be noted that SOC is mainly concentrated in the upper 30 cm of the

mineral soil horizon which may be readily depleted by soil erosion and other

anthropogenic activities. Soil samples from each of the grid sites were collected in

polythene bags. The bags were sealed properly precluding moisture loss from the samples

and transferred to the laboratory for SOC analysis. Prior to analysis, the representative


soil samples were spread on a polythene sheet and big lumps were broken and air dried

under shade. The soil samples were then gently ground with rolling wooden rod and also

with a wooden hammer and passed through 2 mm (10 mesh) sieve and mixed thoroughly.

The samples were then preserved in plastic bags for SOC analysis.

SOC Analysis: Organic carbon in soil was determined by the wet oxidation method of

Walkley and Black (1934) as described by Nelson and Sommers (1996).

Spatial Analysis: In the spatial analysis, geostatistical methods such as semivariogram

construction, kriging and mapping have been widely used. So, a flow chart has been

shown below to depict the SOC variability and distribution in the study site (Fig. 3). The

semivariogram analysis was done using the software Gamma Design (Robertson, 2008).

Data interpolation was done with the ARCGIS 9.3 (ESRI, 2000).

Figure 3: Flow Chart Showing the Geostatistics and Data Interpolation of SOC in the Study Site


Geostatistics: Geostatistics(GS+) is largely based on the concept of random function and

soil properties as regarded as a set of spatially dependent random variables. It consists of

semivariogram construction, kriging, and mappings have been widely applied in the

study of SOC distributions (Loescher et al., 2014). In recent years, spatial dependence

models of geostatistics have gained popularity as they allow the quantification of

landscape spatial structure from point-sampled data. One such model that has received

much attention and is used here is the semivariogram (Cressie, 1993). These then provide

powerful capabilities which can be used to analyze realistically the complex spatial

relationship in any ecological systems. Thus, the understanding of the spatial variability

of SOC levels between and within farms is very important for refining the farm

management practices and implementing precision farming. The spatial dependence of

SOC was determined by the semivariogram analysis. In the current study, the tested SOC

was modeled with spherical semivariograms with a nugget effect. The semivariographic

model can be described through the parameters: the sill, the nugget variance, the scale

and the range (Fig. 4). If the ‘nugget-to-sill’ ratio is less than 25%, then the variable can

be considered to have a strong spatial dependence. If the ratio is between 25% and 75%,

the spatial dependence will be considered as moderate and if the ratio equals or exceeds

75% then the spatial dependence will be considered as weak (Cambardella et al., 1994).

GS+

takes into account the distance between the group of pairs and the fitted model called

the residual sum of squares (RSS). The RSS is very useful as it allows the comparison of

the different semivariograms tested. Lowest RSS is also an indicator of spatial

relationships. To summarize, the coefficient of regression (R2) should be greater than 0.8

and the scale to sill ratio should be close to 1, meaning that the nugget variance has to be

as close as possible to the origin (Cambardella et al., 1994; Duffera et al., 2007). Thus,

the semivariogram with the best RS Sreduction has to be selected to represent the

autocorrelation between the data.

Figure 4: An Ideal Semivariogram Model and its Parameters


Data Interpolation: Kriging is based on the assumption where the respective parameters

are being interpolated and known as localized variables (Matheron, 1963). It is assumed

that, given an adequate population, variables will exhibit a degree of continuity within a

finite region. It is also a key assumption that the regionalized variables are subject to a

statistically normal distribution. Krig interpolation provides an optimal interpolation

estimate from observed values and their spatial relationships (Wackernagel, 1995).

Kriging uses nearby points weighted by distance from the interpolate location and the

degree of autocorrelation or spatial structure for those distances, and calculates optimum

weights at each sampling distances (Isaaks and Srivastava, 1989).

Statistical Analysis: SOC variability was tested within the study site where a descriptive

statistical analysis was used (Table 1). This illustrates the trends and the overall variation

of the variables. This test includes descriptions of the minimum, maximum, mean,

skewness, kutosis, standard deviation (SD), and coefficient of variations (CVs). All the

above analyses were done using the statistical package SPSS version 20.0 (SPSS Inc.,

Chicago, USA). The geostatistical analysis was performed with GS+version 10.0 (Gamma

Design Software, Plainwell, Michigan, USA). Data interpolation through kriging was

done with the GIS software ARCGIS version 9.3 (ESRI, 2000).

RESULTS AND DISCUSSION

The spatial variability of SOC has been analyzed for the grid based soil samples using

classical and geostatistical techniques.

Classical Statistics: The calculation of the variation function should generally accord

with the normal distribution; otherwise, it may cause a proportional effect (Li et al.,

1998; Wang, 2000). It also passed the one-sample Kolomogorov-Smirnov (KS) test at

significance level of P<0.01. It can directly be used in the analysis of the variation

function. The variability of a soil property can be described by the minimum, the

maximum, the difference between the median and mean, the standard deviation (SD), and

the coefficient of variation (CV). Among them, CV is the most discriminating factor.

Using CV, Aweto (1982) classified soils as of little variation (where CV is 20%),

moderate variation (where CV is 20-50%) and high variation (CV is >50%). The mean

and median were used as the primary estimate of central tendency, and the standard

deviation and CV were used as estimates of variability. Despite the skewness of the

distributions, the mean and the median values were similar, with median having smaller

values than the means. This indicates that the measures of central tendency are not

dominated by the outliers in the distributions. The statistical datasets of SOC values were

shown in Table 1. The CV value indicates that SOC was moderately variable in the

studied soils.

Table 1: Results of Descriptive Statistical Analysis of SOC Across the Study Site

Parameter Min. Max. Mean Median SD CV Skewness Kurtosis K-S

SOC 0.14 0.82 0.47 0.50 0.16 0.25 -0.22 -0.44 0.53


The values of the different semivariogram parameters e.g. nugget (C0), sill (C+Co),

nugget/sill ratio, spatial class were given in Table 2.

Table 2: Parameters of the Semivariogram Models Estimated for the SOC Contents

Study site Model Nugget (C0) Sill

(C+C0) Co/C+Co RSS* R2

Spatial

class

Raipur Spherical 0.0050 0.0487 0.104 0.001 0.239 Weak

*residual sum of squares

The semivariogram model for SOC across the study site of the Raipur Upazila is given in

Fig. 5. The semivariogram of this site appears to have weak structure with a residual sum

of squares equal to 0.001. This semivariogram appears to exhibit a pure nugget effect,

possibly because of too sparse a sampling to adequately capture autocorrelation.

Regardless of the nugget effect, sill, range and RSS, the coefficient of determination (R2)

clearly show that SOC datasets in the study site exhibits a weak spatial correlation. The

lowest RSS value is one of the criteria of selecting the best fitted models (Robinson and

Metternicht, 2006). Thus, this site shows a weak spatial dependency as they have R2<0.5.

Figure 5: The Semivariogram Model of SOC in the Study Site

Cambardella et al. (1994) noted that the spatial variability of soil properties may be

affected by both intrinsic e.g. soil forming factors such as parent materials and extrinsic

factors e.g. soil management practices such as fertilization. They also added that strong

spatial dependency of SOC can be attributed to intrinsic factors whereas weak spatial

dependency can be attributed to extrinsic factors. The spatial variation in SOC may be

partly attributed to the complex topography in the landscape (Liu et al., 2006).

Agricultural activities such as tillage, cropping system management, irrigation practices,

and land use intensification by high inputs like use of fertilizers as well as higher

cropping intensity, are the random factors which prevail in this study area. Thus, it would


appear that the lack of spatial dependence of SOC is possibly attributed to extrinsic

factors of soil fertilization such as tillage, ploughing and other soil management practices

which weakened their spatial correlation after a long history of cultivation. The weak

spatial dependence of SOC in the study site is likely attributed by the extrinsic variations

e.g. human activities such as tillage, cropping system, management measures, irrigation

practices, land use cover, manure and fertilizer, crop residue management and cropping

intensity etc. (Kilic et al., 2004).

Spatial Interpolation of SOC: In order to apply agricultural practices precisely and

appropriately, it is important to investigate the spatial distribution of SOC in the study

site. The parameters derived from the geostatistical models were used for kriging and

inverse distance weighted (IDW) e.g. spatial interpolation by which spatial distribution

map was produced where SOC ranged from 0.14 to 1.06%. The spatial interpolation of

SOC also showed weak spatial dependence in the study site. It may be noted that IDW

interpolation was used where datasets have weak spatial dependence or no spatial

dependence. IDW is based on values at nearby locations weighted only by distance from

the interpolation location. It helps to compensate for the effects of data clustering,

assigning individual points within a cluster less weight than isolated data points or

treating clusters more like single points. IDW interpolated maps for this site indicated

that the spatial structure is dispersed due to the continuous agricultural use of the soil

resources with diverse cropping patterns.

CONCLUSION

Understanding the spatial variability of soils is very much important to manage and target

precision agricultural practices. Geostatistical analysis coupled with GIS is effective tools

in assessing the spatial variability and SOC distribution. The study site showed weak

spatial dependence where agricultural activities like tillage, cropping system

management, land use intensification by inputs etc. Thus, it is possible to make an

accurate estimation of SOC loss or sequestration in any site using semivariogram models.

Clearly, the study site where SOC loss in intensive, a pragmatic policy may be adopted to

maximize SOC sequestration. Therefore, the spatial variability of SOC can help better

manage agricultural land by targeting management practices appropriate to the SOC

levels as well as precision farming.

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