UNIVERSITY OF CAPE COAST

SCHOOL OF AGRICULTURE

GEOSTATISTICAL PREDICTION OF SOIL PROPERTIES ACROSS A

LAND SCAPE (GRAZING FIELD)

BY:

MINTAH SAMUEL

A DESSERTATION PRESENTED TO THE SCHOOL OF AGRICULTURE

UNIVERSITY OF CAPE COAST, IN PARTIAL FULFILMENT OF

THE REQUIREMENTS FOR THE AWARD OF BACHELOR

OF SCIENCE (HONS) DEGREE IN AGRICULTURE

MAY, 2010

DECLARATION

I, Mintah Samuel, hereby declare that the work herein presented is the result of my own

investigations and that no part of it has been presented for another degree in this University or

elsewhere.

Work of other authors which served as source of information have been duly acknowledged by

reference to the authors.

MR. David Oscar Yawson Mintah Samuel

(SUPERVISOR) (STUDENT)

Date Date

ACKNOWLEDGEMENT

It is a pleasure to acknowledge my indebtedness to all those who in diverse ways contributed

directly and indirectly in the conduct of this research.

I am most grateful to the Almighty God for taking me through this course of study successfully.

My sincere heartfelt appreciation and gratitude goes to my supervisor, Mr. David Oscar Yawson,

Agricultural Soil Science Department, School of Agriculture, University of Cape Coast for his

patience and guidance offered me.

God richly bless you.

DEDICATION

I dedicate this work to Paapa Paul, my mother, Mrs. Grace Asantewwah Offei, to my late father,

Mr. John Grahl (also known as Waterproof) and all my brothers and sisters for their outstanding

support throughout my education up to this level and towards this project.

May God richly bless you all.

ABSTRACT

Digital mapping of soil properties is recently on the increase for proper soil and crop

management, and for environmental modeling and planning. The object of the project was to use

spatial prediction methods to evaluate the spatial variation existing across a land scape (a grass

land), map the variation and to investigate the effect of grazing and / or management practice on

the spatial variation across the field of study. Spatial variation on Soil Organic Carbon was the

only soil property predicted and mapped.

A total of 46 samples were collected, laboratory analysis was performed to determine the various

soil properties which included Soil Organic Carbon. Evaluation of the properties was done using

conventional statistics (SPSS software), geostatistics and Geographical Information Systems

(GIS). Arcview, (GIS software) with IDW and Ordinary Kriging being the interpolation tools

were used in prediction and mapping of spatial variation in SOC across the study field.

A map each was developed from the interpolation tools. Each map developed was classified as

low, medium and high representing the SOC content of the field. The map results showed that,

there exist high spatial variations in SOC across the field of study. The study discussed the

significance of the map generated and its implication in soil and crop (grass) management of the

field. However, the concluded that, Ordinary Kriging map generated was preferred to IDW in

terms of prediction power and hence geostatistics and GIS provides a useful tool for examining

spatial variation on a field.

Key words: Agriculture, Spatial, Interpolation, Mapping, Nutrients, Geostatistics, Geographical

Information Systems.

TABLE OF CONTENT

CONTENT PAGE

Declaration …………………………………………………… i

Abstract ………………………………………………………. ii

Acknowledgement …………………………………………… iii

Dedication ………………………………………………….... iv

Table of Content …………………………………………….. v

CHAPTER ONE

1.0 Introduction ………………………………………………. 1

1.1 General objectives ………………………………………….. 2

1.2 Specific objectives …………………………………………… 2

CHAPTER TWO

2.0 Literature Review …………………………………………… 3

2.1 Kriging interpolation ……………………………………….. 4

2.2 Inverse Distance Weighted interpolation …………………… 5

2.2.1 Raster ………………………………………………………. 6

2.3 Spatial interpolation ………………………………………… 7

2.4 Spatial variation ……………………………………………… 7

CHAPTER THREE

3.0 Methodology ………………………………………………….. 8

3.1 Study Area …………………………………………………….. 8

3.2 Soil Sampling ………………………………………………….. 8

3.3 Analysis ………………………………………………………… 9

3.3.1 Moisture content determination …………………………… 9

3.3.2 Soil Organic Carbon determination …………………………… 9

3.3.2.1 Procedure ……………………………………………………… 9

3.3.3 Effective Cation Exchange Capacity determination …………… 10

3.3.4 Exchangeable Acidity determination …………………………… 12

3.3.4.1 Procedure ………………………………………………………… 12

3.3.5 Effective Cation Exchange Capacity calculation ………………… 12

CHAPTER FOUR

4.0 Results and Discussion ……………………………………………... 13

4.1 Descriptive Statistics of results …………………………………….. 18

4.2 IDW interpolation ………………………………………………….. 19

4.3 IDW prediction (validation) ………………………………………. 21

4.4 Ordinary Kriging interpolation ……………………………………. 23

4.5 Discussion …………………………………………………………. 25

CHAPTER FIVE

5.0 Conclusion and Recommendation ………………………………… 29

5.1 Conclusion ………………………………………………………… 29

5.2 Recommendation ………………………………………………….. 29

5.3 Reference …………………………………………………………… 30

LIST OF TABLES AND FIGURES

TABLES PAGE

4.0 Table 1 Field results of boundaries and their coordinates ………………. 13

4.0 Table 2 Analysis results …………………………………………………… 13

4.0 Table 3 Analysis results (2) ………………………………………………. 15

4.1 Table 4 Descriptive statistics of results …………………………………… 18

FIGURES

4.2 Figure 1 IDW cross-validation (predicted) ……………………………….. 19

4.2 Figure 2 IDW cross-validation (error predicted) ………………………….. 19

4.2 Figure 3 IDW cross-validation (predicted map) …………………………... 20

4.3 Figure 4 IDW validation (predicted) ……………………………………….. 21

4.3 Figure 5 IDW validation (error predicted) …………………………………. 22

4.3 Figure 6 IDW validation (predicted map) ………………………………….. 22

4.4 Figure 7 Kriging (predicted function) ……………………………………… 23

4.4 Figure 8 Kriging (error predicted function) ………………………………… 23

4.4 Figure 9 Kriging (predicted map) …………………………………………… 24

CHEPTER ONE

1.0 Introduction

Soil is the medium in which plants grow and from which they extract water and nutrients.

Soil controls how water and chemicals are transported across the landscape to rivers and lakes.

Certain soil conditions are favourable for some activities, while others may be less favourable.

Variations in geology, landscape, climate, and land use history have led to considerable

variations in soil conditions across the landscape. Soil conditions can vary significantly even

within the space of a farm field or construction site and may change over time. Fortunately, soil

conditions can often be amended, managed, or planned for, if they are properly located and

characterized. Therefore, to make the most of any management strategy, accurate, spatially

referenced, soil information is a requirement (Strahler, 2006).

With the great explosion in computation, information technology has generated enormous

amounts of data and tools in all fields of endeavour. Soil science is no exception, with the

ongoing creation of regional, national, continental and worldwide databases. The challenge of

understanding these large stores of data has led to the development of new tools in the field of

statistics and spawned new areas such as data mining and machine learning (Hastie et al., 2001).

In addition to this, in soil science, the increasing power of tools such as geographic information

systems (GIS), geographic positioning system (GPS), remote and proximal sensors and data

sources such as those provided by Digital Elevation Models (DEMs) are suggesting new ways

forward.

For intensive land management, and the monitoring of its potential effects on

environmental quality, spatial prediction of soil and land properties is needed to generate

adequate information which can be placed in sources such as soil surveys, maps, and land

databases for agriculture purposes (McBratney et al., 2003; McKenzie et al., 2000;).

Variations among soil properties across any field are important in any land management

system. There is variation of soil properties over a wide range of length scales. Soil variability

may be deterministic, that is, where soil properties vary in a systematic way or stochastic, that is,

soil properties vary essentially in a random way (Bernston, 1987). However, in practice there

exist component of both kinds of variability. Soil may exhibit a high degree of spatial variability

in its properties. Spatial prediction of soil properties has become a common topic in soil research

and management and this is enhanced by the advancement of technology that enabled collection

of on-the go proximal sensors and geostatistical models for use in agriculture and digital soil

mapping. For many soil properties, variability across a landscape is related primarily to

difference in a particular soil-forming factor (topography). If one understands the influences of

these soil-forming factors in a landscape, it is often to define set of individual soils that tend to

occur together in sequence across a landscape. Identifying one member of the set often makes it

possible to predict soil properties in the landscape positions occupied by other members of the

set (Brady, 2000). One most important and common means by which variations across a field is

predicted is by the use of Geostatistics. Maps have traditionally been used to explore the Earth

and to exploit its resources. GIS technology, as an expansion of cartographic science, has

enhanced the efficiency and analytic power of traditional mapping. Now, as the scientific

community recognizes the environmental consequences of anthropogenic activities influencing

climate change, GIS technology is becoming an essential tool to understand the impacts of this

change over time. GIS enables the combination of various sources of data with existing maps and

up-to-date information from earth observation satellites along with the outputs of climate change

models. This can help in understanding the effects of climate change on complex natural systems

(Fitzgerald and, Joseph, 2007).

This project made use of Geographical Information System to predict and generate a map

that showed variations that existed on a grazing field for some specific soil properties to enhance

effective land management.

1.1 General objective

To predict soil properties of unsampled points across a land scape (grazing field) using

geostatistical models.

1.2 Specific objectives

To map the spatial variation of selected soil properties across a grazing field.

To investigate the influence of grazing and/ or management practices on the variability of

the selected soil properties.

CHAPTER TWO

2.0 Literature Review

Understandably, soil properties of scientific nature vary continuously in space and time,

and as such it is a very difficult if not impossible process to measure soil variables at every point

in space. Thus, in order to represent the spatial variation of soil properties in nature, sample

points have to be used. However deciding on the sampling design is another challenge because of

complexity, variability and dynamic processes of nature. To minimize errors, sample points need

to be dispersed strategically over the study area to ensure representativity of phenomena to be

measured in the area. In spatial analysis, sampling is often performed on regular grid or irregular

set of points which however might not depict the true variation of the studied phenomena in

space. Nonetheless at the present moment geostatistical model is one of the only few feasible and

economical methods to study soil spatial variability. In general stratified random sampling is

often recommended for spatial analysis (Hengl et al, 2007). Based on the sampled data values,

estimated values are assigned in all other unsampled locations to define spatial variation of the

phenomena.

Geostatistics is largely the application of this theory, and provides a set of stochastic

techniques that account for both random and structured nature of spatial variables, the spatial

distribution of sampling sites and the uniqueness of any spatial observation. The most important

and common tool of geostatistics is the interpolation process which relies on estimation and

prediction. Interpolation process is based on the fact that objects that are nearer to each other are

more related or similar in behaviour than those that are far apart. As such the output of the

interpolation process is influenced by the number and distribution of sampled points,

physiographic setup of the study area, and understanding of spatial variation of the phenomena.

There are a number of interpolation methods available but the most commonly used method in

Geographic Information System (GIS) is Kriging. Different authors have used this technique in

comparing between different spatial predictions methods, as well as between different Kriging

methods since Kriging itself have different methods (e.g. ordinary kriging, universal kriging,

simple, co-kriging, kriging with external drift, etc). Geostatistics are incorporated in tools such

as Geographical Information Systems (GIS) and Digital Elevation Models (DEMs). GIS in

geostatistics is any system that captures, stores, analyses, manages, and presents data that are

linked to location. They are tools that allow users to create interactive queries (user-created

searches), analyse spatial information, edit data, maps and present the results of all these

operations. Digital Elevation Models are digital representation of ground surface topography or

terrain (Wilson, and Gallant, 2007). Geostatistics provides tools to describe and predict spatial

variation, carry out spatial interpolation and has applied in the quantification of the distribution

of spatial patterns and changes in soil variability (Chevallier et al., 2000; Frogbrook and Oliver,

2001). Spatial prediction of a variable implies that estimates of this variable are derived at any

location or Sub-areas. An accurate spatial prediction provides not only an unbiased population

estimate mean but also reliable local estimates. The unbiased and reliable estimates are related to

requirements for prediction precision and detailed spatial information (spatial variability). Some

interpolation tools in Geostatistics for predicting variations in soil properties include Kriking,

Inverse Distance Weighted Interpolation, Digital Elevation Models, and Radial Bases Functions

(RBF). The project however made use of Kriking and Inverse Distance Weighted interpolations.

2.1 Spatial Variation

There is variation of soil properties over a wide range of length scales. Soil variability

may be deterministic (that is where soil properties vary in a systematic way) or stochastic (that is

soil properties vary essentially in a random way). However, in practice we may have component

of both kinds of variability. Soil may exhibit a high degree of spatial variability in it properties.

A very large number of samples are needed to reasonably estimate the mean of properties with

high spatial variability. Present methods for spatial analysis, which provides a means for

characterizing soil variability, are limited. A major problem of methods for computing the spatial

scale of soil variation is the dependence of the correlogram on the length of a transect through its

effect on the variance of sample data. It is preferable to use the variogram to quantify scales of

soil variation (Bernston, 1987).

2.2 Spatial Interpolation

Spatial interpolation is defined as the procedure of predicting the value of attributes at

unsampled sites from measurements made at point locations within the same area or region

(Burrough and McDonnell, 1998). Interpolation is the process by which a surface is created, usually a

raster dataset, through the input of data collected at a number of sample points. There are several forms of

interpolation, each which treats the data differently, depending on the properties of the data set. In

comparing interpolation methods, the first consideration should be whether the source data will change

(exact or approximate). Next is whether the method is subjective, a human interpretation, or objective.

Then there is the nature of transitions between points: are they abrupt or gradual. Finally, there is whether

a method is global (it uses the entire data set to form the model), or local where an algorithm is repeated

for a small section of terrain. Interpolation is a justified measurement because of a spatial autocorrelation

principle that recognizes that data collected at any position will have a great similarity to, or influence of

those locations within its immediate vicinity (Lovison-Golob, and Lucia, 2007). Global interpolators use

all available data to provide predictions for the whole area of interest, while local interpolators operate

within a zone around the point being interpolated to ensure that estimates are only associated with the

related neighbourhood and fitting is as good as possible.

2.2.1Interpolation methods

2.2.1.1 Inverse Distance Weighted (IDW)

Inversed distance weighted interpolation (IDW) is based on the assumption that the value

of an attribute z of unvisited points is a distance–weighted average of data points occurring

within a neighbourhood or window surrounding the unvisited point. IDW can be described as

follows [Burrough and McDonnell 1998]:

Wi = 1/ Dk

Where:

Zx: the predation value of unvisited point x.

i: the used known data point from 1 to n.

n: the number of used know points

Zi: the value of point I

D: the distance to know point I

k: a constant that influences the distance weighting

IDW interpolation is used in GIS to create raster overlays from point data. The

difficulties associated to IDW are to decide how many known data points or/and how long the

distance to known data point s should be used to estimate the unknown points. In addition, there

could be a edge effect in the surface made by IDW interpolations and the estimated value never

can be lower than the lowest known data value and higher that the highest known data value

(Burrough and McDonnell 1998).

2.2.1.2 Kriging Interpolation

Kriging is a group of geostatistical techniques to interpolate the value of a random field

(e.g., the elevation, z, of the landscape as a function of the geographic location) at an unobserved

location from observations of its value at nearby locations (Hanefi Bayraktar, F. Sezer and

Turalioglu, 2005). Based on the regionalized variable theory, kriging assumes that the spatial

variation of any variable such as elevation of the land surface is neither totally random nor

deterministic. Instead, the spatial variation can be expressed as the sum of three major

components: a structural component, having a constant mean or trend; a random, and locally

varying but spatially correlated component, known, as the variation of the regionalized variable;

and a spatially uncorrelated random noise or residual error term. It depends on expressing spatial

variation of the property in terms of the variogram, and it minimizes the prediction errors which

are themselves estimated. Kriging is a form of weighted local averaging and depends on first

computing an accurate semi variogram that measures the nature of spatial dependence for the

properties. Estimates of semi-variance are then used to determine the weight applied to the data

when computing the averages and are presented in the kriging equation.

The kriging method can be described as follows [Burrough and McDonnell 1998]:

Z(x) = m(x)+ε ′(x)+ε ′′(x)

where:

x: a position in 1,2, or 3 dimension

Z(x): the value of a random variable

m(x) : a deterministic function describing the structural component

ε ′(x): the regionalized variables

ε ′′(x): a residual, spatially independent Gaussian noise term having zero mean and

variance σ2.

The first step is to specify a function for m(x). The ordinary kriging assumes that the surface has

no trend of drift. M (x) then equals the constant mean value in the sampling area and the average

of the differences between any two places x and x+h separated by a distance vector h will be

zero. Universal kriging assumes that there is a deterministic trend or drift in the surface that

underlies the statistical variation.

Typically, the drift can be approximated using polynomial with lower order, which means it is

either a plane surface or a quadratic surface (Johnston, and Lucas, 2001).

CHAPTER THREE

3.0 Study Methodology

This section describes the various methods and techniques which were used in the research.

3.1 Study Area

The study area is a coastal savannah. The study was carried out at the University of Cape

Coast Teaching and Research grazing field. The grazing field has been used continuously for

two and a half years but without any manage mental practice on the field. The field is composed

of two main grass types being; the Turf and St. Augustine grass specie. The field is relatively

flat with a sandy-loam soil type. There is an all year round temperature averaging around 23 0C

in the area of study. The annual rainfall ranges from 1100-1400 mm. There are two rainfall

seasons separated by a short period of drought. The major rainfall season is from March to July

with maximum rainfall in June. September to November marks the minor rainfall season of the

coastal savannah zone. The dry season starts in December and ends somewhere in February.

The relative humidity is generally higher at nights and early mornings than in the afternoons.

3.2 Soil Sampling Technique

Soil samples were collected at 15cm depth on a grazing field of area 56m x 108m. The

sampling procedure involved was Transect. A common centre of the field was identified and soil

samples were collected at 8m interval starting from the common centre. At each point of soil

sampling, the co-ordinates and elevation of the sampling position were recorded using a Global

Positioning System (GPS) device. In all, 46 soil samples were collected. The co-ordinates for the

boundaries of the field were also recorded.

3.3 Laboratory Analysis

The soil samples were analyzed for selected soil properties such as moisture content, soil

organic carbon and Effective cat ion Exchange Capacity. The soil samples collected from the

field were air dried at room temperature for 72 hours with the exception of those samples for

moisture content determination.

3.3.1 Moisture Content Determination

The moisture content of the soil was determined immediately the samples were brought

to the laboratory. Fresh weights (mass) of the samples were determined using a scale balance.

The samples were then oven dried in an oven at a temperature of 105o for 24 hours. The weights

(mass) of the samples were taken again after oven drying and values recorded until a constant

weight was obtained. The percentage moisture of the soil samples were determined by means of

a mathematical formula given below;

×100%

3.3.2 Percentage Carbon Content Determination

The carbon and organic matter were determined by the walkley-black method. About 0.5

g of soil sample was weighed and transferred into 500ml conical flask and 10 ml of K2Cr2O7 was

transferred with a pipette into the flask and swirled gently. About 20 ml of concentrated H2SO4

was added and again, swirled gently. After the addition of H2SO4, the flask was left to stand for

about thirty minutes after which 200 ml of distilled water was added to the contents in the flask.

The flask was swirled gently to ensure thorough mixing of the contents in the flask.

About 10 ml of H3PO4, 0.2 g of NaF and 1.0 ml diphenylamine indicator was added.

H3PO4 and NaF (sodiumfloride) formed a complex with the Fe3+

which otherwise interferes with

the end point. The excess Cr2O7 was back titrated with 0.5 M ferrous solution from deep blue to a

dark green end point. A blank titration was done in an identical way using the same reagents but

omitting the soil.

In this, percentage organic carbon was obtained after obtaining concentrations of the soil

samples and calculating using the formulae below:

% organic carbon= (B-S) × (Molarity of Fe2+

× 0.003 ÷ weight of soil sample) × (100

÷77) ×100.

Where;

B is Blank titre value

S is soil sample titre value

0.003 is 12/4000 as mill equivalents weight of carbon

100/77 is the factor for converting the carbon actually oxidized to total carbon

100 is the factor to change from decimal to percentage

3.3.3 Effective Cation Exchange Capacity (ECEC)

Potassium, sodium, magnesium and calcium were the exchangeable bases which were

determined. Aluminum and hydrogen on the other hand were the exchangeable acids which were

determined. For the exchangeable bases, concentrations of each cation were determined. For

potassium and sodium, 5 grammes of the soil samples each were weighed into a 100 mL

extraction bottle and 20 mL of Ammonium acetate solution was added, stirred and left to stand

overnight. The suspension was then transferred into a filter funnel with filter paper and filtered

into a 100 mL volumetric flask. The soil was then leached with successive 20 mL volumes of

Ammonium acetate, allowing the funnel to drain between each addition. This was continued

until nearly 100 mL of the filtrate has been collected and Ammonium acetate was added to make

up to the mark. Working standards of 0, 2, 4, 6, 8 and 10µgP/ mill of both sodium and potassium

in ammonium acetate solution were prepared and were aspirated into a flame photometer and the

emissions were recorded. Aliquots of the filtrate were then aspirated and their emissions

recorded.

A graph of the standard emissions against their concentration was then plotted and the

concentrations for sodium and potassium were extrapolated from the graph.

The exchangeable sodium and potassium in Cmolc /Kg or cmolc /Kg in the soil is

calculated by the formula:

Cmolc Kg-1

/ Kg-1

soil =

Where C is the concentration of the soil samples and 39.1 and 22.99 are atomic masses.

For calcium and magnesium, 0.5g of the soil samples were weighed into a 100mL

extraction bottle and 20mL of Ammonium acetate solution was added, stirred and left to stand

overnight. The suspension was then transferred into a filter funnel with filter paper and filtered

into a 100mL volumetric flask. The soil was then leached with successive 20mL volumes of

Ammonium acetate, allowing the funnel to drain between each addition. This was continued

until nearly 100mL of the filtrate has been collected and Ammonium acetate was added to make

up to the mark. An aliquot of 25mL was extracted into a 250mL conical flask and diluted to

about 150mL with distilled water after which 15mL NaOH was added to each solution. 10 drops

each of KCN, NH2OH.HCL, HCl and TEA was added to each of the solutions. After that, 10

drops of calcon indicator was added and the solutions were titrated from red to blue with 0.005M

EDTA. The titre values were then recorded and the exchangeable Calcium and magnesium was

calculated by the formula:

Cmolc Kg-1

and Mg2+

Kg-1

soil =

where T=titre value

3.3.4 Exchangeable Acidity

10 g of the soil samples each were measured into a 100ml volumetric flask and 25ml of KCl was

added, stirred and left for 30minutes. The mixtures were then filtered through a filter paper

funnel and leached with successive 25mL of 1M KCl up to the mark. 50mL of the extract were

then pipette into a 250mL conical flask. 5 drops of phenolphthalein indicator was added and the

solutions were titrated a pink colour using 0.01M NaOH. The titre value was then recorded. This

measures the exchangeable + . The exchangeable acidity is calculated by the formula:

Cmolc + Kg-1

in the soil=

where T=titre value

3.3.5 Effective Cation Exchange Capacity

This is calculated by the formula given as:

ECEC= exchangeable (Ca + Mg + K + acidity)

3.3.6 Spatial Interpolation

The geographical coordinates of the locations of the samples were recorded with a hand-held

G.P.S. The coordinates were stored in Microsoft excel as dbf (data base file) and imported in

ArcGIS software (ESRITM

) as points. The points of soil organic carbon were interpolated using

ordinary kriging and Inverse Distance Weighted (IDW) approach. The interpolated maps were

exported in JPTG format. 19 points (40%) and 27 points (60%) were used as testing and training

sets respectively in validating the prediction model.

CHAPTER FOUR

4.0 Results and Discussion

Table 1: Descriptive statistics of soil properties

Properties Minimum

Maximum Mean

Std.

Deviation Variance

Skewness Moisture Content

(%) 1.16 7.12 3.0498 1.12978 1.276 1.088

Organic Carbon (%) 0.86 2.69 1.4966 0.40480 0.164 0.761 C E C(Cmolc) 4.15 61.74 16.2424 13.58633 184.588 2.097 Elevation(ft) 33.00 95.00 57.4444 15.21297 231.434 0.802 K+ (CmolcKg-1) 0.04 0.87 0.1851 0.15273 0.023 3.058 Na2+ (CmolcKg-1) 0.09 0.43 0.2421 0.10038 0.010 0.119 Ca2+ (CmolcKg-1) 0.39 46.20 9.6388 10.31302 106.358 2.194 Mg2+ (CmolcKg-1) 0.22 21.49 5.6164 4.56313 20.822 1.384 Exchangeable

acidity (CmolcKg-1) 0.06 2.23 0.6382 0.46085 0.212 1.604

Source: Authors own field work, 2009

Table1 above is a descriptive statistics of soil properties. Taken organic carbon into

consideration, it is seen that, the organic carbon content of the field falls within the range of 0.86

to 2.69 with a mean of 1.4966. These values however signify low organic carbon content with a

high variation existing on the field. More so, considering the elevation of the field, it is seen that,

the field is of a relatively flat terrain causing spatial variations on the field.

4.2 Spatial Prediction of Organic Carbon

4.2.1 Inverse Distance Weighted

4.2.1.1 Prediction validation parameters

Mean = 0.05944

RMSE = 0.4019

Training set = 27 (60%)

Testing set = 19 (40%)

Regression (predicted) = 0.050x + 1.477

Regression (error) = -0.950x + 1.477

LEGEND

Figure 1: Predicted spatial distribution map (validation) for Organic Carbon

Source: Authors own work, 2009

Figure 1 is a validation map showing the spatial distribution of organic carbon across the field

developed from inverse distance weighted interpolation. The map reveals a variation in organic

carbon across the field. The parameters given above, that is, mean error and RMSE (Root Mean

Square Error) represents the arithmetic average of the data or predicted error and the arithmetic

root of the mean error. These provide a measure of the centre of the distribution. For any

prediction, the prediction power depends on the error mean and the root mean square error where

the error mean must be close to zero and the root mean square error must be close to 1.

4.2.1.2 Prediction cross-validation parameters

Neighbours = 8

Regression function = 0.098x + 1.344

Mean = -0.01543

RMSE = 0.3962

N =43

Regression (error) = -1.021x + 1.567

Figure 2 below shows a map of spatial distribution of organic carbon across the field from a

cross-validation using inverse distance weighted interpolation. It is seen from the map that

distribution of organic carbon across the field is uneven and hence a high variation exist. The

values were found between the ranges of 0.900 to 1.076477 are described as low content and

those between 2.348691 to 2.69000 are of high content across the field. In IDW, there is the use

of neighbors of a measured point to predict unsampled points and is useful when predicting the

value at an unmeasured location. In this experiment, eight (8) neighbor locations were generated

to predict the values of the unsampled points as given above.

LEGEND

Figure 2: Cross-validation predicted spatial distribution map of Organic Carbon

Source: Authors own work, 2009

4.2.2 Ordinary Kriging interpolation

4.2.2.1 Prediction validation parameters

Regression function (predicted): 0.053x + 1.397

Mean = -0.01027

RMSE = 0.4101

Average standard error: 0.435

Mean standardized = -0.02027

RMSE (standardized) = 0.95

Regression function (error): -0.947x + 1.397

LEGEND

Figure 3: Predicted spatial distributed map (validation)

Source: Authors own work, 2009

Figure 3 is a validated map showing the variation of organic carbon across the field by the use of

ordinary kriging interpolation. The map clearly shows a high variation of organic carbon across

the field. It is evident from the map that, organic carbon content was high at the north-eastern

part of the field and gradually declines towards the north-western parts of the field with reference

to the legend given. It can be said that, heterogeneity exist across the field considering organic

carbon content of the field.

4.3 Discussion

Descriptive statistics of the soil properties are presented in Table 1. The results show a

wide range of organic carbon content from 0.86 to 2.69 signifies a high spatial variation from

one sample point to another on the field. Amount of organic carbon content in any soil is

influenced by the amount of organic matter content of the soil. It can be deduced that, the low

organic carbon content is due to a less accumulation of organic matter in the soil. More so, low

organic matter content in the soil reduces the rate of decomposition by soil microorganism in the

soil to release organic carbon into the soil and hence leading to the high variation of organic

carbon in the field of study.

Availability of water in soils also affects the availability of soil organic carbon. Adequate

moisture in soils enhances activities of microorganisms in soils which facilitates the breakdown

of soil organic matter to release soil organic carbon in soils. From Table 1, it is seen that there is

a low content with a high spatial variation in moisture content of the field. The spatial variation

in soil moisture therefore causes variations in soil microorganisms thereby reducing organic

carbon content of the field.

Table 1 also gives a standard deviation value of organic carbon closer to the mean value.

This implies that, most values obtained for organic carbon are clustered around the mean values.

This is evident in the variation existing on the field and signifies that, the organic carbon content

of the field is located at specific points of the field. Since the field of study has been used for two

and a half years without any management practice, there is therefore less vegetation (grass) on

most parts of the field. It can then be deduced that, activities of the grazing animals are more

concentrated at some parts of the field as compared to other parts leading to the wide variation

across the field. Soil organic carbon is a dynamic component in grassland soils. Variation of soil

organic carbon in such soils is generally attributed to both natural processes and human

activities, reflecting the balance between decomposition of soil organic matter and input from

roots and their litter. The main loss of soil organic carbon in grass lands is through emission of

carbon dioxide (CO2) into the atmosphere (Turner and Lambert, 2000).

Figures 1 and 2 are maps showing the spatial distribution of organic carbon across the

field using Inverse Distance Weighted interpolation. The maps clearly show the heterogeneity of

organic carbon across the field of study. There is an uneven distribution of organic carbon.

Clearly, it is also seen from the maps that, the north-eastern parts of the field recorded the

highest organic carbon content which however declines towards the north-western parts of the

field. This could be attributed to the fact that, the north-eastern parts of the field were more

concentrated with grazing activities by the grazing animals which might defecate to increase the

organic matter content of such parts thereby increasing the organic carbon content. The maps

therefore signifies the spatial distribution and reveals those parts of the field with a low organic

carbon content and how widely it is spread from one point to another across the field. The maps

also gives information on how varied the organic carbon content of the field is at eight (8m)

meters interval across the field. Information provided by the maps is however useful in proper

planning and decision making on proper management of the field.

Figure 3 also predicts the spatial distribution of soil organic carbon across the field of

study, showing an uneven distribution by the use of Kriging interpolation. The average standard

error, mean standard error and the RMSE standardized parameter values obtained from the

kriging interpolation. It signifies that, the organic carbon content of the field is spread unevenly

across the field. This distribution can be attributed to the elevation of the study field.

Comparing maps generated by the use of IDW to the map of Kriging, both maps

predicted a wide spatial variation of organic carbon across the field with both having the north-

eastern parts of the field recording the highest organic carbon contents. Similarly, both maps are

looking the same in pattern but obviously there is a difference. A careful look at figure 3 depicts

a different pattern in the spatial distribution of organic carbon across the field of study. Also, the

error mean and the Root Mean Square Errors (RMSE) recorded for Kriging interpolation were

relatively less than the error mean and RMSE for IDW. This means that, kriging interpolation

provided a better prediction. Also, the prediction power for kriging in predicting the spatial

variation across the field is said to be higher than that of IDW. Therefore, based on the mean

error and RMSE for the two interpolations, the map generated from kriging is preferred to that of

IDW generated maps.

CHAPTER FIVE

5.0 CONCLUSION AND RECOMMENDATION

5.1 Conclusion

The generated maps all predicted a wide spatial variation of soil organic carbon across the field

of study. Two interpolations; Inverse Distance Weighted and Kriging methods were used with

kriging given the best predicted map of the spatial variation of soil organic carbon level of the

field. This wide spatial variation is attributed to over grazing and no management practice

leading to most of the field being bare. However the map generated can be used as a guide if any

land management practice is to be done on the field of study.

5.2 Recommendation

1. Different interpolation methods should be used to investigate the extent of the prediction

power and spatial variation of the field.

2. A different sampling technique could be used to find out whether there is an influence of

the sampling procedure on the variation.

3. Improved sampling tools should be used to facilitate sampling of the soil.

REFERENCE

1. Burrough, P.A. and McDonnell, R.A. (1998) Principles of geographical information

systems.

2. Chevallier, T., Voltz, M., Blanchart, E., Chotte, J.L., Eschenbrenner, V., Mahieu, M.,

Albrecht, A., 2000. Spatial and temporal changes of soil C after establishment of a

pasture on a long-term cultivated vertisol (Martinique). Geoderma 94, 43– 58.

3. Fitzgerald, Joseph H... "Map Printing Methods".

http://www.broward.org/library/bienes/lii14009.htm. Retrieved 2010-04-03.

4. Frogbrook, Z.L., Oliver, M.A., 2001. Comparing the spatial predictions of soil organic

matter determined by two laboratory methods. Soil Use and Management 17, 235– 244.

5. Geospatial Analysis - a comprehensive guide. 2nd edition © 2006-2008 de Smith,

Goodchild, Longley.

Geostatistical Analyst, ESRI, Redlands.

6. Hanefi Bayraktar and F. Sezer. Turalioglu (2005) A Kriging-based approach for locating

a sampling site—in the assessment of air quality, SERRA, v.19, n.4, DOI

10.1007/s00477-005-0234-8, p. 301-305

7. Hastie, T., Tibshirani, R., Friedman, J., 2001. The elements of statistical learning: data

mining, inference and prediction. Springer Series in Statistics. Springer-Verlag, New

York.

8. Hengl, T., A Practical Guide to Geostatistical Mapping of Environmental Variables. Vol.

2007, Italy: Luxembourg: Office for Official Publications of the European Communities.

9. Hengl, T., Heuvelink, G.B.M., Rossiter, D.G., 2007. About regression-kriging: from

equations to case studies. Computers & Geosciences 33 (10), 1301–1315.

10. Johnston K., Hoef J.M.V., Krivorchko K, and Lucas N. (2001). Using ArcGIS

11. Knotters, M., Brus, D.J., Oude Voshaar, J.H., 1995. A comparison of kriging, co-kriging

and kriging combined with regression for spatial interpolation of horizon depth with

censored observations. Geoderma 67, 227– 246.

12. Kravchenko, A.N., Bollero, G.A., Omonode, R.A., Bullock, D.G., 2002. Quantitative

mapping of soil drainage classes using topo- graphical data and soil electrical

conductivity. Soil Science Society of America Journal 66, 235– 243.

13. Longley, P.A., Goodchild, M.F., Maguire, D.J. and Rhind D.W. (2001). Geographic

Information Systems and Science, John Wiley and Sons, Chichester.

14. McBratney, A.B., Mendonca Santos, M.L., Minasny, B., 2003. On digital soil mapping.

Geoderma 117 (1–2), 3–52. A.B. McBratney et al. / Geoderma 117 (2003) 3–52 47.

15. McKenzie, N.J., Gessler, P.E., Ryan, P.J., O'Connell, D., 2000. The role of terrain

analysis in soil mapping. In: Wilson, J.P., Gallant, J.C. (Eds.), Terrain Analysis:

Principles and Applications. Wiley, New York, 500 pp.

16. Strahler, A. H., and Strahler A., 2006, Introducing Physical Geography, 4th Ed., Wiley.

17. Turner, J., Lambert, M., 2000. Change in organic carbon in forest plantation soils in

eastern Australia. Forest Ecology and Management, 133, 231– 247.

18. Webster, R., Oliver, M.A., 2000. Geostatistics for Environmental Scientists. Wiley,

Chichester. 271 pp.

19. Wilson, J.P., Gallant, J.C., 2000. SRAD: a program for estimating radiation and

temperature in complex terrain. Trans GIS., in press.

APPENDIX

Table 2: Field Results

BOUNDRY (B) CO-ORDINATE

B1 N 050 07.889

/

W 0010 17.451

/

B2

N 050 07.872

/

W 0010 17.392

/

B3 N 050 07.845

/

W 0010 17.418

/

B4 N 050 07.911

/

W 0010 17.424

/

Table 3: Laboratory Analysis Results

TRANSECT/SOIL

SAMPLE

ELEVATION

(feet)

K+

(Cmolc

Kg-1

)

Na+

(Cmolc

Kg-1

)

Ca2+

(Cmolc

Kg-1

)

Mg2+

(Cmolc

Kg-1

)

Al3+

/H+

Exchangeable

acidity (Cmolc

Kg-1

)

T1 S1 P1 65 0.107 0.1739 46.1989 15.1698 0.2596

S2 P2 70 0.0868 0.1737 37.372 13.4849 1.8364

S3 P3 77 0.2279 0.3476 18.250 3.9675 0.4066

T2 S1 P1 64 0.2352 0.3479 5.9904 4.7923 0.7189

S2 P2 80 0.0965 0.2592 4.2752 0.7773 0.6242

S3 P3 85 0.2399 0.1738 7.9051 0.3952 0.5194

S4 P4 90 0.0817 0.1737 35.5005 8.3765 0.1293

S5 P5 82 0.8652 0.2587 20.5252 9.4656 1.5640

S6 P6 95 0.7256 0.1726 6.5891 3.4210 0.2543

S7 P7 60 0.2154 0.2567 12.9865 7.9965 2.2316

T3 S1P1 45 0.0866 0.5198 0.7026 4.5670 0.8137

S2 P2 45

0.1072 0.0868 3.1014 1.9384 0.6851

S3 P3 40 0.0866 0.1733 3.6664 0.2199 0.1788

S4 P4 33 0.1272 0.4154 13.1448 6.9308 0.2680

S5 P5 48 0.2757 0.3473 5.7023 1.6727 0.4660

S6 P6 44 0.3142 0.1736 3.4652 0.3145 0.0561

T4 S1 P1 54 0.1379 0.3474 0.3948 3.5531 0.4682

S2 P2 53 0.2195 0.3473 5.1993 4.5968 0.4078

S3 P3 53 0.0511 0.2604 3.3695 1.7222 0.9159

S4 P4 55 0.2272 0.0868 1.8522 2.2226 0.6096

S5 P5 44 0.2650 0.1745 5.0985 2.3269 0.4396

S6 P6 48 0.2235 0.2577 4.5365 2.2135 0.6865

S7 P7 44 0.3014 0.3385 5.5986 2.5614 0.5486

T5 S1 P1 54 0.2041

0.3472 34.9194 21.4888 0.1699

S2 P2 51 0.0817 0.1736 11.9284 5.8052 0.2511

S3 P3 50 0.0959 0.1718 3.8153 3.4338 0.6946

T6 S1 P1 45 0.0818 0.3477 7.0894 6.4592 0.1991

S2 P2 45 0.0817 0.3474 19.8347 6.6116 0.1596

S3 P3 44 0.1278 0.4174 7.9523 1.5905 0.6294

S4 P4 48 0.1065 0.2589 4.6048 1.5349 0.9466

S5 P5 47 0.1658 0.2579 15.3651 4.3524 0.5667

S6 P6 45 0.2365 0.1756 10.6210 3.2564 0.8660

S7 P7 48 0.2433 0.3556 5.2002 2.5656 0.9709

R7 S1 P1 50 0.1583 0.2605 4.1251 1.0313 0.1983

S2 P2 46 0.1201 0.1738 7.0699 7.0699 0.7892

S3 P3 48 0.2046 0.4349 5.4348 13.9752 0.5568

S4 P4 50 0.0407 0.2595 6.3656 4.3764 1.0399

S5 P5 46 0.0868 0.0869 5.1721 7.1613 0.3971

S6 P6 40 0.0658 0.1739 6.0025 7.2531 0.1286

R8 S1 P1 70 0.1182 0.3421 4.5113 4.8872 0.6444

S2 P2 61 0.0971 0.0869 0.7632 9.5402 0.9476

S3 P3 67 0.0970 0.0868 0.7012 3.3216 0.6142

S4 P4 74 0.1379 0.1737 3.6157 12.3361 0.5253

S5 P5 72 0.2165 0.0979 6.9954 12.3657 0.8824

S6 P6 77 0.2456 0.1725 4.3251 6.5781 0.6112

S7 P7 78 0.0987 0.3431 6.6120 7.6254 1.6539

Table 4: Laboratory Analysis result (2)

RADIAL/SOIL

SAMPLE

POINT

CO-

ORDINATES

MOISTURE

CONTENT

(%)

ORGANIC

MATTER

(%)

ORGANIC

CARBON

(%)

CEC

( Cmolc)

T1 S1 P1 N 050 07.873

/

W001017.427

/

4.38 2.55 1.48 61.74

S2 P2

N 050 07.871

/

W001017.429

/

4.48 2.84 1.65 52.78

S3 P3 N 050 07.868

/

W001017.433

/

3.94 2.87 1.66 22.85

T2 S1P1 N 050 07.873

/

W001017.421

/

3.16 2.01 1.17 11.74

S2 P2 N 050 07.866

/

W001017.421

/

1.16 2.41 1.40 5.77

S3 P3 N 050 07.863

/

W001017.421

/

2.94 1.86 1.08 9.06

S4 P4 N 050 07.859

/

W001017.421

/

2.58 2.68 1.55 44.09

S5 P5 N 050 07.855

/

W001017.419

/

2.83 1.67 0.97 32.42

S6 P6 N 050 07.851

/

W001017.419

/

2.51 2.48 1.44 10.99

S7 P7 N 050 07.846

/

W 001017.418

3.716 2.55 1.48 23.43

T3S1 P1 N 050 07.872

/

W001017.419

/

1.75 2.58 1.49 6.17

S2 P2 N 050 07.868

/

W001017.416

/

2.49 2.92 1.69 5.83

S3 P3 N 050 07.866

/

W001017.415

/

1.71 3.14 1.82 4.15

S4 P4 N 050 07.862

/ 1.67 3.41 1.98 20.47

W001017.412

/

S5 P5 N 050 07.858

/

W001017.410

/

2.57 3.64 2.11 8.17

S6 P6 N 050 07.856

/

W001017.407

/

1.68 1.95 1.13 11.67

R4 S1 P1 N 050 07.875

/

W001017.419

/

3.01 2.32 1.35 4.55

S2 P2 N 050 07.873

/

W001017.413

/

3.23 1.77 1.03 10.42

S3 P3 N 050 07.875

/

W001017.409

/

2.87 1.76 1.02 6.06

S4 P4 N 050 07.879

/

W001017.404

/

1.73 2.34 1.36 4.91

S5 P5 N 050 07.875

/

W001017.400

/

2.04 2.33 1.35 8.13

S6 P6 N 050 07.874

/

W001017.396

/

2.44 2.86 1.66 7.66

S7 P7 N 050 07.878

/

W001017.428

/

2.70 1.48 0.86 9.01

T5 S1 P1 N 050 07.880

/

W001017.419

/

3.45 2.58 1.49 56.78

S2 P2 N 050 07.882

/

W001017.417

/

2.24 3.61 2.09 18.07

S3 P3

N 050 07.885

/

W001017.413

/

2.01

3.27

1.89

8.04

T6

S1 P1

N 050 07.880

/

W001017.422

/

5.00 4.63 2.69 13.83

S2 P2 N 050 07.885

/

W001017.423

/

3.64 1.72 0.99 26.69

S3 P3 N 050 07.892

/

W001017.422

/

1.93 1.80 1.05 10.30

S4 P4 N 050 07.894

/

W001017.421

/

1.98 2.78 1.61 7.19

S5 P5 N 050 07.898

/

W001017.422

/

2.18 1.74 1.01 20.45

S6 P6 N 050 07.902

/ 2.38 2.47 1.43 14.98

W001017.422

/

S7 P7 N 050 07.903

/

W001017.422

/

4.44 2.88 1.67 8.90

T7 S1 P1 N 050 07.877

/

W001017.426

/

2.26 3.41 1.98 5.51

S2 P2 N 050 07.882

/

W001017.428

/

3.29 2.51 1.45 15.05

S3 P3 N 050 07.885

/

W001017.430

/

3.81 2.28 1.32 20.17

S4 P4 N 050 07.888

/

W001017.432

/

3.00 2.34 1.36 11.82

S5 P5 N 050 07.893

/

W001017.435

/

3.51 3.75 2.13 12.81

S6 P6 N 050 07.896

/

W001017.438

/

7.12 4.16 2.41 13.45

T8S1 P1

N 050 07.878

/

4.03

1.96

1.14

10.16

W001017.428

/

S2 P2 N 050 07.880

/

W001017.431

/

1.67 2.24 1.30 11.35

S3 P3 N 050 07.881

/

W001017.435

/

4.82 2.91 1.69 4.73

S4 P4 N 050 07.882

/

W001017.438

/

3.52 2.68 1.55 16.62

S5 P5 N 050 07.884

/

W001017.442

/

3.75 2.48 1.44 20.46

S6 P6 N 050 07.885

/

W001017.446

/

3.25 1.55 0.90 11.76

S7 P7 N 050 07.887

/

W001017.450

/

4.12 2.60 1.51 15.89

T = Transects S= Soil sample

P=Point of sample collection