UNIVERSITY OF NAIROBI
LAND SUITABILITY ANALYSIS FOR TEA CULTIVATION:
CASE STUDY OF KIRINYAGA WEST DISTRICT.
PRESENTED BY: MURIUKI MARTIN WANJOHI.
F19/2551/2008
A PROJECT SUBMITTED TO THE DEPARTMENT OF GEOSPATIAL AND SPACE
TECHNOLOGY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE
AWARD OF THE DEGREE OF:
BACHELOR OF SCIENCE IN GEOSPATIAL ENGINEERING.
APRIL 2013
DEDICATION
To all residents of Kirinyaga West District.
ACKNOWLEDGEMENT
This project received support from various organizations and individuals. On this note, I would
like first to convey heartfelt gratitude to my supervisor Mr. P. C. Wakoli, Department of
Geospatial and Space Technology, University of Nairobi, guidance, insights and attention that
has enabled the success of this project.
I thank too Mr. Matara and Dr. Siriba for their counsel and availability when I needed their
counsel on the project.
My sincere thanks also to all members of staffin the Department of Geospatial and Space
Technology, University of Nairobi, led ably by the chairman Dr.-Ing. S. M. Musyoka for their
input in my training in this profession.
I am grateful to the Ndima Tea factory agriculture officer, Mr. Muita for his invaluable input
especially weighting of the factors considered in this project.
I appreciate Kenya Tea Development Agency, especially Miss Patricia for her assistance in
determining the major factors to be considered in this project.
I extend my gratitude to Mary for her interest and continuous assistance in my project.
Finally to my family, friends and classmates for their attention, advice and encouragement
through the five years of training.
ABSTRACT
This project explored the application of Geographic Information Systems [GIS] as a tool in
Multi-criteria decision making in the agriculture sector. The main objective of this study was to
produce a suitability map showing the relative suitability for tea cultivation in various parts of
Kirinyaga West district.
To achieve this, relevant data of factors influencing tea cultivation was identified and collected
from respective organizations and fields. This data was prepared, standardized, weighted and
overlayed to produce the suitability map.
The map indicates that there is more suitable land for tea cultivation than current zones and
hence hereby recommend re-marking of tea zones in the district.
TABLE OF CONTENTS
Dedication………………………………………………………………………………………….i
Acknowledgements…………………………………………………………… ………………..ii
Abstract………………………………………………………………………………………….. iii
Table of Contents……………………………………...………………………………………….iv
List of Figures………………………………………………...……………………………..……vi
List of Tables………………………………………………………………..……………………ix
CHAPTER ONE: BACKGROUND
1.1 Introduction…………………………………………………………………………………...1
1.1.1 Favourable Factors for TeaCultivation………………………………...............................1
1.1.1.1 Climate…………………………………………………………………………………….1
1.1.1.2 Soil………………………………………………………………………………………..1
1.1.1.3 Topography………………………………………………………………………………..2
1.1.1.4 Other Factors………………………………………………………………………………2
1.1.2 Land Suitability……………………………………………………………………………2
1.1.2.1 Need for Land Suitability Analysis………………………………………………………..3
1.1.3 Tea Cultivation in Kenya………………………………………………………………….3
1.1.3.1 Tea Growing Regions in Kenya…………………………………………………………..4
1.1.3.2 Tea Industry in Kenya…………………………………………………………………….4
1.1.3.3 Tea Cultivation in Kirinyaga West District……………………………………………….4
1.2 Problem Statement…………………………………………………………………………….5
1.3 Objective of the Study………………………………………………………………………...5
1.3.1 Main Objective…………………………………………………………………………….5
1.3.2 Specific Objectives………………………………………………………………………..5
1.4 Scope and Limitation of the Study……………………………………………………………6
1.5 Organization Report…………………………………………………………………………...6
CHAPTER TWO: LITERATURE REVIEW
2.1 Recent Works in Land Suitability……………………………………………………………..7
2.2Approaches to Land Suitability……………………………………………………………….8
2.2.1 Structure of Suitability Classification……………………………………………………….8
2.2.1.1 Land Suitability Orders…………………………………………………………………...9
2.2.1.2 Land Suitability Classes………………………………………………………………….10
2.3
Land-Use Suitability Analysis……………………………………………………………..11
2.4 Multi-Criteria Evaluation…………………………………………………………………….12
2.4.1 Multi-Objective Methods…………………………………………………………………..13
2.4.2 Multi-Attribute Methods…………………………………………………………………...14
2.4.2.1 Weighted Linear Combination Methods…………………………………………………14
2.5 GIs and Multi-Criteria Decision Making…………………………………………………… 15
2.5.1 Role of GIs…………………………………………………………………………………15
2.5.2 Spatial Decision Making…………………………………………………………………...15
2.5.3 Evaluation Criteria…………………………………………………………………………16
2.5.4 Constraints…………………………………………………………………………………16
2.5.5 Quantification……………………………………………………………………………...16
2.5.6 Area of study……………………………………………………………………………….17
2.5.7 Criteria Weights……………………………………………………………………………17
2.6 Area of Study………………………………………………………………………………...18
2.6.1 Physical Location…………………………………………………………………………..19
2.6.2 Climatic Condition…………………………………………………………………………19
2.6.3 Soil Condition……………………………………………………………………………...19
2.6.4 Topography………………………………………………………………………………...19
2.6.5 Land use……………………………………………………………………………………20
2.6.6 Population………………………………………………………………………………….20
CHAPTER THREE: MATERIALS AND METHODOLOGY.
3.1 Tools…………………………………………………………………………………………21
3.1.1 Hardware…………………………………………………………………………………..21
3.1.2 Software……………………………………………………………………………………21
3.2 Overview of Methodology…………………………………………………………………...21
3.2.1 Data Collection and Sources……………………………………………………………….23
3.2.2 Data Preparation…………………………………………………………………………....24
3.2.2.1 Constraints Processing…………………………………………………………………...24
3.2.2.2 Land use and Population Data Preparation……………………………………………....27
3.2.2.3 Soil Data Preparation…………………………………………………………………….28
3.2.2.4 Topography Data Preparation……………………………………………………………29
3.2.2.5 Climate Data Preparation………………………………………………………………...29
3.2.3 Data Processing…………………………………………………………………………….29
3.2.4 Standardization of Criteria………………………………………………………………....30
3.2.4.1 Rainfall Data Standardization……………………………………………………………30
3.2.4.2 Altitude Data Standardization……………………………………………………………31
3.2.4.3 Temperature Data Standardization………………………………………………………32
3.2.4.4 Land Use Data Standardization………………………………………………………….33
3.2.4.5 Soil pH Data Standardization……………………………………………………………34
3.2.4.6 Soil Clay-content Data Standardization………………………………………………….35
3.2.4.7 Soil Texture Data Standardization……………………………………………………….36
3.2.4.8 Population Data Standardization………………………………………………………....37
3.2. 4.9 Slope/Drainage Data Standardization…………………………………………………...38
3.2.5 Weighting and Overlaying sub factors…………………………………………………….39
3.2.6 Weighting and Overlaying factors…………………………………………………………40
CHAPTER FOUR: RESULTS AND DISCUSSIONS.
4.1 Individual Suitability Maps…………………………………………………………………. 42
4.2 Overlayed Factor Map……………………………………………………………………….45
4.3 Overlay of the Factors Map with Constraints………………………………………………..46
4.4 Ground Truthing……………………………………………………………………………..49
CHAPTER FIVE: CONCLUSIONS AND RECOMMENDATIONS.
5.1 Conclusion…………………………………………………………………………………...51
5.2 Recommendations……………………………………………………………………………51
REFERENCES………………………………………………………………………………….52
LIST OF FIGURES
Figure 2.1 Area of Study…………………………………………………………...………..…...18
Figure 3.1 Flow of the Methodoly………………………………………………………….........22
Figure 3.2 Constraint Processing…………………………………………………………….......24
Figure 3.3 Forest Reserve………..…………………………………………………………..…..25
Figure 3.4 Road Reserve……………………………………………………………………........26
Figure 3.5 Constraint Map……………………………...……………………………….…….....27
Figure 3.6 Data Clipping Process…………………..………………………………….……...…28
Figure 3.7 Rainfall Suitability Map……………..…………………………………….……...….31
Figure 3.8 Altitude Suitability Map ……………………………..………………………………32
Figure 3.9 Temperature Suitability Map…………………..………………………………..........33
Figure 3.10 Land Use Suitability Map……………………………………..………….…………34
Figure 3.11 Soil pH Suitability Map……………..……………………………………………....35
Figure 3.12 Clay Content Suitability Map………………………………………………….........36
Figure 3.13 Soil Texture Suitability Map………….…………………………………………….37
Figure 3.14 Population Suitability Map……...…………………………………………………..38
Figure 3.15 Slope and Drainage Suitability………..………………………………………….…39
Figure 4.1 Climate Suitability Map……..……………………………………………………….42
Figure 4.2 Topography Suitability Map……………..……………………………………….…..43
Figure 4.3 Soil Suitability Map……………..……………………………………………....…....43
Figure 4.4 Land Use and Population Suitability Map………………………………….…….......44
Figure 4.5 Four Factors Suitability Map…………………………………………………….…...45
Figure 4.6 Overlay of Factor Map and Constraints………………………………………….......46
Figure 4.7 Final Suitability Map………….……………………………………………..……….48
Figure 4.8 Tea Delimitation Board……..…………………………………………………......…49
Figure 4.9 Ground Truthing Map……….………………………………………………...……...50
LIST OF TABLES.
Table 2.1 Suitability Classification Structure…………………………………………….……9
Table 2.2 Land Suitability Orders……………………………………………………….……...9
Table 2.3 Land Suitability Classes…………………………………………………………….10
Table 2.4 Kirinyaga West Population Data……………………………………….……….….20
Table 3.1 Data Sources and Uses…………………………….…………………………….…..23
Table 3.2 Rainfall Classes………………….…………………………………………………..30
Table 3.3 Altitude Classes…………………………..………………………………………….31
Table 3.4 Temperature Classes………………….……………………………………………..32
Table 3.5 Land Use Classes………………………………………………………………....….33
Table 3.6 Soil pH Classes……………..………………………………………………………..34
Table 3.7 Clay Content Classes………………………………………………………………..35
Table 3.8 Soil Texture……………………………………………………………………..……36
Table 3.9 Population Classes……………..……………………………………………….……37
Table 3.10 Slope and Drainage Classes……………………………………………..…………38
Table 3.11 Weights of the Four Classes……………………………………….………………41
Table 3.12 Suitability Level Classes……………………………….…………………………..41
Table 4.1 Area of Suitability Classes……………………………..…………………………....47
1 INTRODUCTION.
1.1 Tea Plant.
Tea, Camellia sinensis, is native to East, South and Southeast Asia. Today it is cultivated across
the world in tropical and subtropical regions. It is an evergreen shrubthat is usually trimmed to
below 2 m when cultivated for its leaves. It has a strong taproot, yellow-white flowers, and 2.5–
4 cm in diameter, with 7 to 8 petals. The leaves are 4–15 cm long and 2–5 cm broad. Fresh
leaves contain about 4% caffeine. The young, light green leaves are harvested for tea production.
Different leaf ages produce different tea qualities, since their chemical compositions are
different. Usually, the tip (bud) and the first two to three leaves are harvested for processing.
This hand picking is repeated every one to two weeks.
Generally there are two tea varieties, the small leaf variety, known as Camellia sinensis, thrives
in the cool and high mountain regions while the broad leaf variety, known as Camellia assamica,
grows best in the moist, tropical climates.
1.1.1Favourable Factors for Tea Cultivation.
Factors that influence tea production and qualities are identified as: geology, topography,
climate, hydrology, soil and vegetation. Climatological factors are the most important followed
by soil, topography and others in this order.
1.1.1.1 Climate.
Under climate, two factors should be considered, that is rainfall and temperature. It grows best in
temperatures ranging from 10 -30 degrees Centigrade and rainfall ranging between 800-2000
mm per annum. For highest yields the average rainfall should be 1200-1600 mm per annum.
1.1.1.2 Soil.
Under soil there are sub factors that are considered such as soil pH, soil texture, clay content of
the soil, and soil drainage. Soil texture is of great influence to the performance of tea. The
relative proportion of sand, silt and clay determines the texture of the soil hence the most suitable
texture is the loam soil as it is an average of the other two types. Tea does best in volcanic soils
that are well drained and the preferable pH range of soil for rising of tea is 4.5 to 5.5. Soil
pH is the description of level of acidity or alkalinity of soil measured in pH units.
1.1.1.3 Topography.
This refers to the altitude and slope of a region. Slope majorly influences drainage hence the
steeper the slope the higher the drainage level as it necessities flow of water downhill. Tea yields
are highest in steep areas due to this factor. The tea plant can often be grown in a wide range of
altitudes. Although high grown teas exhibit more desirable taste it gets to certain levels where it
is too cold for the plant to grow the plant. The highest teas are grown at 2400m above sea level.
Travelling farther the equator, into drier climate the elevations to which tea can be grown
becomes lower. Hence tea can be grown at grounds of at least 600- 2400 m above sea level.
However the best yields are achieved at 1500-2400 m.
1.1.1.4 Other Factors.
There are other factors affecting tea cultivation including land use, vegetation, population,
hydrology and geology. It is realized that these factors are a result of the above three discussed
factors hence their weight and consideration is encompassed in the above. For example, tea
should be grown agricultural area of the considered land use which is as a result of climate and
soil. Geology is a sub factor of slope as erosion occurs along the slope meaning more eroded
soils will be downhill while rocky soil will be up the slope.
1.1.2 Land Suitability.
Land suitability is the fitness of a given type of land for a defined use. The land may be
considered in its present condition or after improvements. The process of land suitability
classification is the appraisal and grouping of specific areas of land in terms of their suitability
for defined uses.
1.1.2.1Need for Land suitability analysis.
Appropriate land use decisions are vital to achieve optimum productivity of the land and to
ensure environmental sustainability. This requires an effective management of land information
on which such decisions should be based. Land suitability evaluation is one of the effective tools
for such purposes. (Baja et al. 2001). There are generally two kinds of land suitability assessment
approaches, the qualitative and quantitative approaches. These two are discussed in the next
chapter.
Suitability analysis can answer the question (what is to grow where?). In order to define the land
evaluation procedure
suitability for
given
various
by
(Food and Agriculture Organization) FAO
land utilization
types
has
been
used
for
to assess
soil site
the land
suitability for different crops and for generating cropping pattern.
Land suitability evaluation is a prerequisite
(Sys 1985; Van
and opportunities
utilization
for land-use
Rants and others 1996). It provides
planning
information
for the use of the land and therefore guides
and
development
on the constraints
decisions
on optimal
of land resources (FAO1983). The description of land use, at a given spatial level
and for a given area, usually involves specifying the mix of land use types, the particular pattern
of these land use types, the areal extent and intensity of use associated with each type.
1.1.3 Tea Cultivation in Kenya.
Tea was first introduced in Kenya around 1904. It is currently one of the major tea producing
countries of the world with production exceeding 240,000 tons a year. The early introductions
were brought into the country in form of seed. Being highly self-incompatible and predominantly
out-crossing, tea tends to produce highly heterogeneous progenies. The early introductions were
therefore highly variable forming the initial populations of mixed genotypes. Uniformity and
stability in yield and quality of the mixed genotypes could not be maintained; hence this
necessitated the search for more uniform high yielding tea cultivars.
Organized tea improvement started with the formation of Tea Research Institute of East Africa
(TRIEA) in 1961, and later the Tea Research Foundation of Kenya in 1980 with a mandate for
research on all aspects of tea. The department of Botany was given the mandate of plant
improvement hence the development of elite planting cultivars through breeding and selection.
1.1.3.1Tea Growing Regions in Kenya
The main tea growing areas in Kenya are situated in and around the highland areas on both sides
of the Great Rift Valley; and astride the Equator within altitudes of between 1500 meters and
2700 meters above the sea level. These regions include the areas around Mt. Kenya, the
Aberdares, and the Nyambene hills in the Central Kenya and the Mau escarpment, Kericho
Highlands, Nandi and Kisii Highlands and the Cherangani Hills. Tea is picked every 17 days and
the best Kenyan tea is picked in February and March. The best tea is grown in the Regati region
of eastern Kenya.
1.1.3.2 Tea Industry in Kenya.
The Tea Industry in Kenya is unique in that it is comprised of two distinct sectors; the Plantation
or large scale sector and the small holder sector. The Plantation sector is owned by large scale tea
producers and companies while the small holders sector is by small scale growers. The small
holder sector has registered more than half a million growers who are located across tea growing
areas in the country. The small holder sector factories are managed by Kenya Tea Development
Agency Ltd (KTDA). The large plantations are organized under the Kenya Tea Growers
Association (KTGA) and account for about 40% of the Kenyan tea production.
1.1.3.3Tea Cultivation inKirinyaga West District
The variety cultivated in this area is the Camellia sinensis. With tea improvement shifting
emphasis towards developing clones with combined optimum yields and quality, the introduction
of Cambod (Camellia sinensis var. assamica sub spp. lasiocalyx) variety of tea has also been
evaluated under the local environmental conditions. Two clones, TRFK 301/4 and TRFK 301/5
have shown comparable performance in yield and quality to the high yielding and high quality
clones released by the TRFK. The release of these clones has enhanced the range of choices of
varieties available to the farmers in the district.
The tea zone in Kirinyaga West is located on the slope of Mt. Kenya and it extends from the
forest reserve boundary down to the lower established boundary that was marked in the early
nineteen forties.
1.2 Problem Statement.
The existing tea zones in the country were identified in the early nineteen forties considering few
factors like expertise of farmers, availability of water and cost of production.
In Kirinyaga West district, the tea zone is marked by use of boards positioned at intervals along
the boundary and they indicate that areas beyond the boundary are not suitable for tea
cultivation. However, there is tea growing beyond this boundary and growing very well. This
raises the question on suitability accuracy of the existing tea zone as determined in the early
forties. This situation can be attributed to the inexhaustive approach used then but with advent of
Geographic information system (GIS) tool, more accurate zoning can be achieved. GIS has a
unique capability to perform an integrated analysis of spatial and attribute data and hence
information useful for particular application such as land-use suitability can be obtained.
1.3 Objective of the Study.
1.3.1 Main Objective.
The main objective is to use GIs in multi-criteria evaluation of land suitability for tea cultivation
in Kirinyaga west district. The final output is a suitability map showing relative suitability levels
of various parts of the district.
1.3.2 Specific Objectives
1. Identify the factors that influence tea cultivation and their relative weights.
2. Generate suitability maps based on the various identified factors.
3. Generate a land suitability map based on a weighted integration of all factors.
4. Analyse the suitability map vis-à-vis the existing situation in Kirinyaga West District.
5. Make pertinent conclusions and recommendations.
1.4 Scope and Limitations of the Study.
This study is limited to Kirinyaga west district of central province, Kenya. This district is chosen
due to its location on the slopes of Mt. Kenya, other favorable factors affecting cultivation as a
whole and the discrepancy between zoned areas and the actually cultivated areas.
1.5 Organization of the Report.
 Chapter one deals with introduction, background, problem statement, objectives of
the study, scope and limitations of the study and organization of the report.
 Chapter two is the literature review on tea cultivation and the study area.
 Chapter three deals with the methodology which includes equipment, data sources
and data preparation and implementation of the methodology.
 Chapter four presents results, analysis and discusses the results thus obtained.
 Chapter five gives conclusions and recommendations.
2. LITERATURE REVIEW.
2.1 Recent Works on Land Suitability.
There are notable suitability studies in other areas in Kenya and other parts of the world where
success has been achieved through similar projects. However, it is worth mentioning that in the
year 2002, Kangi M. D, F19/1892/1997 carried out a project on creating a spatial database for tea
growing in Mt. Kenya region.
Recently, in the Department of Geospatial and Space Technology, University of Nairobi, Mr.
Kimari Cedric, F19/2042/2005 carried out a project on suitability analysis on rice cultivation in
Nyando District. He came out with exemplary results of suitable areas in three classes. Some
ideas were borrowed from his project but a major difference from is that he developed weights
of for different factors while in this project the weights used were determined through
consultation with the Agriculture Officer of the study area. The other notable difference is the
software used in the two projects. He used mainly Idrisi while this project has applied ArcGIS
10.1.
In the year 2008, Kahora Patrick, F19/2236/2003 carried out an analysis of infrastructure
distribution in Gatanga constituency. Through his project, the use of CDF, constituency
development fund, allocated to this constituency was rated to be best used in the whole country
through the former area member of parliament. This is a good example of to what lengths
suitability analysis.
A study done in Iran, GIs-based multi-criteria land suitability evaluation using ordered weight
averaging with fuzzy quantifier: case study in Shavur plain, by M. Mokarram, also applied
similar steps in achieving results as it is done in this project except that my project zeros in into a
single crop.
Finally and most important mention is a study carried out in 2003, by Prakash T. N. for his
master thesis in Geo-informatics submitted to the International Institute for Geo-information
Science and Earth Observation, Enschede, The Netherlands. The study was entitled; Land
suitability analysis for agricultural crops, a fuzzy multi-criteria decision making approach. He
produced suitability maps indicating three classes of suitability levels for rice cultivation in
Doiwala, India.
2.2 Approaches to Land Suitability Analysis.
There are generally two kinds of land suitability assessment approaches. First, the qualitative
approach is used to assess land potential at a broad scale or is employed as a preliminary method
for more detailed investigation (Baja et al. 2002; Dent and Young 1981). The results of
qualitative classification are given in qualitative terms, such as highly suitable, moderately
suitable, and least suitable.
Second, the quantitative approach applies parametric techniques that involve more detailed land
attributes which allow various statistical analyses to be performed (Baja et al. 2002; 2001).
Quantitative methods such as modeling in land evaluation are necessary for a land use planning
(Van Diepen et al. 1991).
Recently, most studies combined the qualitative and quantitative approaches in the process of
land suitability assessment. One of the most recently used models in land evaluation is fuzzy
model. Fuzzy modeling appears as an alternative to deal with these continuous or uncertain
environments. While in Boolean logic a value is true of false, with fuzzy logic the value could be
partially false of partially true which allows for a representation more according to the reality.
Hierarchy Process (AHP) as proposed by Thomas Saaty in the early 1980s.AHP can be used as a
consensus building tool in situations involving a committee or group decision makes (Saaty
1980). AHP uses a hierarchy of factors where each general factor is subdivided or composed of
several contributing sub factors.
2.2.1 Structure of the Suitability Classification.
The framework has a structure that recognizes the same categories, in all of the kinds of
interpretative classification (see table 2.1). Each category retains its basic meaning within the
context of the different classifications and as applied to different kinds of land use.
Table 2.1 Suitability Classification Structure.
i. Land Suitability Orders:
Reflecting kinds of suitability.
ii. Land Suitability Classes:
Reflecting degrees of suitability within Orders.
iii. Land Suitability Subclasses:
Reflecting kinds of limitation or main kinds of
improvement measures required, within Classes.
iv. Land Suitability Units:
Reflecting minor differences in required management
within Subclasses.
2.2.1.1 Land Suitability Orders.
Land suitability Orders indicate whether land is assessed as suitable or not suitable for the use
under consideration. These two orders are represented in maps, tables, etc. by the symbols S and
N respectively.
Table 2.2 Land Suitability Orders.
Order S: Suitable:
Land on which sustained use of the kind under consideration is
expected to yield benefits which justify the inputs, without
unacceptable risk of damage to land resources.
Order N :Not Suitable:
Land which has qualities that appear to preclude sustained use of the
kind under consideration.
Land may be classed as Not Suitable for a given use for a number of reasons. It may be that the
proposed use is technically impracticable, such as the irrigation of rocky steep land, or that it
would cause severe environmental degradation, such as the cultivation in road reserves.
2.2.1.2 LandSuitability Classes.
Land suitability Classes reflect different degrees of suitability. They are numbered consecutively
in sequence of decreasing degrees of suitability within the particular Order. Within the Order
Suitable, the number of classes is not specified and the researcher can limit them to suit the study
case. The number of classes recognized should be kept to the minimum necessary to meet
interpretative aims and five should probably be the most ever used.
If there are three Classes recognized within the Order Suitable the following names and
definitions may be used in a qualitative classification:
Table 2.3 Land Suitability Classes.
Land having no significant limitations to sustained application of a
Class S1:
Most Suitable:
given use, or only minor limitations that will not significantly reduce
productivity or benefits and will not raise inputs above an acceptable
level.
Land having limitations which in aggregate are moderately severe for
sustained application of a given use; the limitations will reduce
Class S2:
Moderately Suitable:
productivity or benefits and increase required inputs to the extent that
the overall advantage to be gained from the use, although still
attractive, will be appreciably inferior to that expected on Class S1
land.
Land having limitations which in aggregate are severe for sustained
Class S3:
Least Suitable:
application of a given use and will so reduce productivity or benefits,
or increase required inputs, that this expenditure will be only
marginally justified.
In the case of additional refinement, it is recommended that it should be achieved by adding
classes, for example S4, and not by subdividing classes, since the latter procedure contradicts
the principle that degrees of suitability are represented by only one level of the classification
structure, that of the suitability class. This necessarily changesthe meanings of class numbers.
2.3 Land-Use Suitability Analysis.
Land-use suitability analysis aims at identifying the most appropriate spatial pattern for land uses
according to specify requirements, preferences, or predictors of some activity (Hopkins, 1977;
Collin set al., 2001).
The GIS-based land-use suitability analysis has been applied in a wide variety of situations
including ecological approaches for defining land suitability/habitant for animal and plantspecies
(Pereira and Dickstein, 1993; Store and Kanga, 2001), geological favorability (Bonham-Carter,
1994), suitability of land for agricultural activities (Campbell et al., 1992; Kalogeria, 2002),
landscape evaluation and planning( Miller et al., 1998), environmental impact assessment
(Moreno and Seigel, 1988 ), selecting the best site for the public and private sector facilities
(Eastman et al ., 1993; Church, 2002), and regional planning ( Janssen and Rietveld, 1990 ).
In the context of land suitability analysis it is important to make distinctions between the site
selection problem and the site search problem (Cova and Church, 2000a). The aim of site
selection analysis is to identify the best site for some activity given the set of potential (feasible)
sites. In this type of analysis all the characteristics (such as location, size, relevant attributes, etc.)
of the candidate sites are known. The problem is to rank or rate J. Malczewski / Progress in
Planning 62 (2004) the alternative sites based on their characteristics so that the best site can be
identified. If there is not a pre-determined set of candidate sites, the problem is referred to as site
search analysis. The characteristics of the sites (their boundaries) have to be defined by solving
the problem. The aim of the site search analysis is to explicitly identify the boundary of the best
site. Both the site search problem and land suitability analysis assume that there is a given study
area and the area is subdivided into a set of basic unit of observations such as polygons (areal
units) or rasters. The land suitability analysis problem involves classification of the units of
observations according to their suitability for a particular activity (J. Malczewski 2000).
The analysis defines an area in which a good site might exist. The explicit site search analysis
determines not only the site suitability but also its spatial characteristics such as its shape,
contiguity, and/or compactness by aggregating the basic units of observations according to some
criteria (Diamond and Wright, 1988; Brookes, 1997; Cova and Church, 2000a; Aerts, 2002; Xiao
et al., 2002
2.4Multi-Criteria Evaluation.
Multi-criteria Evaluation (MCE) methods are used to analyze the land suitability evaluation.
Land evaluation is carried out to estimate the suitability of land for a specific use and Multicriteria analysis is a mathematical tool that allows the comparison of different alternatives or
scenarios according to several criteria in order to guide the decision maker towards a judicious
choice.
Spatial multi-criteria decision making is the application of multi-criteria analysis in spatial
context where alternatives, criteria and other elements of the problem have explicit spatial
dimensions.Multi-criteria analysis has been coupled with geographical information systems
(GIS) to enhance spatial multi-criteria decision making since the late1980s.
The integration of multi-criteria decision making (MCDM) techniques with GIS has
considerably advanced the conventional map overlay approaches to the land-use suitability
analysis (Carver, 1991; Banai, 1993; Eastman, 1997; Malczewski, 1999; Thill, 1999). GIS-based
MCDA can be thought of as a process that combines and transforms spatial and a spatial data
(input) into a resultant decision (output).
The MCDM procedures define a relationship between the input maps and the output map. The
procedures involve the utilization of geographical data, the decision maker’s preferences and the
manipulation of the data and preferences according to specified decision rules. Accordingly, two
considerations are of critical importance for spatial MCDA:
(i)
The GIS capabilities of data acquisition, storage, retrieval, manipulation and analysis,
and
(ii)
The MCDM capabilities for combining the geographical data and the decision
maker’s preferences into uni-dimensional values of alternative decisions.
A number of multi-criteria decision rules have been implemented in the GIS environment for
tackling land-use suitability problems. The decision rules can be classified into multi-objective
and multi-attribute decision making methods (Malczewski, 1999).
The multi-objective approaches are mathematical programming model oriented methods, while
multi-attribute decision making methods are data-oriented. Multi-attribute techniques are also
referred to as discrete methods because they assume the number of alternatives is given
explicitly, while in the multi-objective methods the alternatives must be generated
2.4.1 Multi-Objective Methods.
Multi-objective methods define the set of alternatives in terms of a decision model consisting of
two or more objective functions and a set of constraints imposed on the decision variables. The
model implicitly defines the alternatives in terms of decision variables. The multi-objective
models are often tackled by converting them to single objective problems and then by solving the
problem using the standard linear/integer programming methods (Diamond and Wright, 1988;
Aerts, 2002). Cambell et al. (1992),
J. Malczewski / Progress in Planning 62 (2004) 3–65 33 Chuvieco (1993) Cromley and Hanink
(1999) have demonstrated the potential of integrating linear programming, as a tool for GISbased land-use suitability analysis. They show how linear programming can be used to optimize
spatial pattern of land use, generate different planning scenarios, and analyze the relationships
between decision variables and the problem constraints.
An advantage of the model (and the linear programming approaches in general) is the ability to
map the patterns of location rent and opportunity costs in addition to the optimal land suitability
pattern. This added information can be used for evaluating the robustness of land suitability
patterns and identifying areas were modifications could be made without significant impacts
(Cromley, 1994; Cromley and Hanink, 1999).
The multi-attribute approaches are much easier to implement in GIS (especially, for the raster
data model). Consequently, there are a considerable number of GIS-multi-attribute applications
to land-use suitability analysis.
2.4.2 Multi-Attribute Methods.
Over the last decade or so, a number of multi-attribute (or multi-criteria) evaluation methods
have been implemented in the GIS environment including WLC (Weighted Linear Combination)
and its variants ( Carver, 1991; Eastman, 1997), ideal point methods ( Jankowski, 1995; Pereira
and Duckstein, 1996), concordance analysis (Carver, 1991; Joerin et al ., 2001 ), and analytic
hierarchy process ( Banai, 1993 ).
2.4.2.1 WLC Methods.
Among these procedures, the WLC and Boolean overlay operations, such as intersection (AND)
and union (OR), are considered the most straightforward and the most often employed. WLC (or
simple additive weighting) is based on the concept of a weighted average. The decision maker
directly assigns the weights of ‘relative importance’ to each attribute map layer. A total score is
then obtained for each alternative by multiplying the importance weight assigned for each
attribute by the scaled value given to the alternative on that attribute, and summing the products
over all attributes. When the overall scores are calculated for all of the alternatives, the
alternative with the highest overall score is chosen. The method can be operationalized using any
GIS system having overlay capabilities. The overlay techniques allow the evaluation criterion
map layers (input maps) to be combined in order to determine the composite map layer (output
map). The methods can be implemented in both raster and vector GIS environments. Some GIS
systems have built-in routines for the WLC method. There are, however, some fundamental
limitations associated with the use of these procedures in a decision making process. Jiang and
Eastman (2000) give a comprehensive discussion of those limitations and suggest that the
Ordered Weighted Averaging (OWA) approach provides an extension to and generalization of
the conventional map combination methods in GIS.
OWA is a class of multi-criteria operators (Yager, 1988). It involves two sets of weights:
criterion importance weights and order weights. An importance weight is assigned to a given
criterion (attribute) for all locations in a study area to indicate its relative importance (according
to the decision-maker’s preferences) in the set of criteria under consideration. The order weights
are associated with the criterion values on a location-by-location (object-by-object) basis. They
are assigned to a location’s attribute values in decreasing order without considering which
attribute the value comes from. The order weights are central to the OWA combination
procedures. They are associated with the degree of ORness, which indicates the degree to which
an OWA operator is similar to the logical connective OR in terms of its combination behaviour.
The parameter is also associated with a trade-off measure indicating the degree of compensation
between the parameters associated with the OWA operations serves as a mechanism for guiding
the GIS-based land-use suitability analysis. The ORness measure allows for interpreting the
results of OWA in the context of the behavioral theory of decision making.
The OWA operations make it possible to develop a variety of land use strategies ranging from an
extremity pessimistic (the minimum-type strategy based of the logical AND combination)
through all intermediate the neutral-towards-risk strategy (corresponding to the conventional
WLC) to an extremely pessimistic strategy (the maximum-type strategy based on the logical OR
combination). Thus, OWA can be considered as an extension and a generalization of the
conventional combination procedures in GIS (Jiang and Eastman, 2000).
2.5 GIs and Multi-criteria Decision Making.
2.5.1 Role of GIs
The distinguishing feature of GIS is its capability to perform an integrated analysis of spatial and
attributes data. GIS can be used not only for automatically producing maps, but it is unique in its
capacity for integration and spatial analysis of multisource datasets such as data on land use,
population, topography, hydrology, climate, vegetation, transportation network, public
infrastructure, etc. The data are manipulated and analyzed to obtain information useful for a
particular application such as land-use suitability analysis. The aim of a GIS analysis is to help a
user to answer questions concerned with geographical patterns and processes.
2.5.2 Spatial Decision Making Process.
Decision alternatives can be defined as alternative courses of action among which the decision
maker must choose. A spatial decision alternative consists of at least two elements:
 action (what to do?) and
 Location (where to do it?).
The spatial component of a decision alternative can be specified explicitly or implicitly.
2.5.3. Evaluation Criteria.
In the spatial context, evaluation criteria are associated with geographical entities and
relationships between entities, and can be represented in the form of maps. A criterion map
models the preferences of the decision maker concerning a particular concept, while a simple
map layer is a representation of some spatial real data.
A criterion map represents subjective preferential information. Two different persons may assign
different values to the same mapping unit in a criterion map.
2.5.4 Constraints.
A constraint represents natural or artificial restrictions on the potential alternatives. Constraints
are often used in the pre-analysis steps to divide alternatives into two categories: acceptable" or
unacceptable.
2.5.5 Quantification.
The evaluation of alternatives may be quantitative or qualitative. Several methods require
quantitative evaluations. In the literature, there are some totally qualitative methods such as the
median ranking method while such as the electre family of methods involves the two types of
evaluations. When most of criteria are qualitative, quantitative criteria may be converted into
qualitative ones and a qualitative method may be used otherwise, a quantification method is
applied.
The scaling approach is the mostly used one and application of a quantification method requires
the definition of a measurement scale. The most used measurement scale is the Likert-type which
is composed of approximately the same number of favorable and unfavorable levels. An example
with three levels is:
 very unfavorable,
 moderately favorable,
 Very favorable.
Other more detailed measurement scales may also be used. The quantification procedure consists
of constructing a measurement scale like the one with three points mentioned above. Then,
numerical values are associated with each level of the scale. For instance, the numbers 1, 2, or 3
may be associated with the three-point scale from very favorable to very unfavorable.
2.5.6 Standardization.
The evaluation of alternatives may be expressed according to different scales (ordinal, interval,
and ratio). However, a large number of multi-criteria methods require that all of their criteria are
expressed in a similar scale. Standardizing the criteria permits the rescaling of all the evaluation
dimensions between 0 and 1. This allows between and within criteria comparisons.
2.5.7 Criteria Weights.
Generally in multi-criteria problems the decision maker more than often considers one criterion
to be more important than the other. This relative importance is expressed in terms of numbers
which are often referred to as weights, and are assigned to different criteria. These weights
deeply influence the final choice and may lead to a non-applicable decision when the
interpretations of such weights are misunderstood by the decision maker.
Many direct weighting techniques have been proposedin the literature. When a simple
arrangement technique is used, the decision maker sets the criteria in an order of preference.
 The cardinal simple arrangement technique involves each criterion being evaluated
according to a pre-established scale.
 Some other indirect methods are also available such as the interactive estimation method.
 There are also a relatively complex weight assignment techniques such as the
indifference trade-offs technique and the analytic hierarchy process (AHP).
2.6 Area of Study.
Diagrammatic representation of the area of study
Figure 2.1 Area of Study.
2.6.1 Physical Location.
Kirinyaga West is one the four districts that make up Kirinyaga county, others being Kirinyaga
East, Kirinyaga Central and Kirinyaga South districts. It has a geographical coverage of 210.80
square kilometers and is located between latitude-0 30 20 degrees and 0 45 00 degreesand
longitude 37 00 00 and 37 28 00 degrees. It has its headquarters at Baricho. It lies on the lower
slope of mt. Kenya and has one industrial town called Sagana and an administrative town,
Baricho.
2.6.2 Climatic Condition.
The area receives an average annual rainfall of 800-1600 mm per annum. The lower part of the
region receives an average rainfall of 800m while going up the mt. Kenya the value increases till
before the peak at which the values decrease again back to 800 mm. as for the temperatures, the
following is a table indicating the average rainfall of the region for the last ten years degrees
Celsius.
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
11.5
13.9
13.5
15
15
13.7
12.3
12.2
12.5
14.1
13.1
The average temperature for the year hence was 12.7 degrees Celsius.
2.6.3 Soil Condition.
The area has varying soil conditions as one move from one place to the next. Eighty percent is
covered by loam volcanic soils that are well drained, ten percent of clay soil and ten percent of
both sandy soils and rocks especially towards the peak of the mountain in the area. As for clay
content, the area is covered eighty seven percent of kaolinite and the rest is composed of
montorillonite. The range of soil pH in the area is 1-6.
2.6.4 Topography.
Kirinyaga west lies on slopes of mt. Kenya hence have high values of altitude. The altitude range
is 650-4586 m above sea level. Slopes are very steep considering the rise of the mountain.
2.6.5 Land Use.
A land use map divides the area into three regions; woodland, agricultural and forest reserve.
This is the order from the lower parts of the district towards the peak of the mountain. The
agricultural region covers averagely seventy percent of the total area.
2.6.6 Population.
As per the population statistics of 1999 census, the total population of the area of study is 99,515.
The following is the population data from IEBC individual county assembly wards.
Table 2.4 Kirinyaga West Population Data.
No.
Name
Population (2009
Area (Sq.
National Census)
Km)
1 509 Mukure 30,536
59.30
2 510 Kiine
41,036
81.30
3 511 Kariti
27,943
70.20
Description
Mukure, Kanyokora, Kagio-Ini, Kianjang’a and
Gitaku Sub–Locations of Kirinyaga County
Nguguini, Kibingoti, Kiangai, Ruiru, Maitharui and
Kithumbu Sub–Locations of Kirinyaga County
Nyangio, Mukui, Thigirichi, Gacharu and Sagana
Sub–Locations of Kirinyaga County
Source: Independent Electoral and Boundaries Commission (IEBC)
CHAPTER 3: MATERIALS AND METHODOLOGY.
3.1 Tools
3.1.1 Hardware.

Computer with specifications of 500 Gb hard disk memory, 2Gb RAM and 2.13 Intel [R]
Pentium [R] M Processor

Flash disk of capacity 8 Gb

SD card of capacity 8 Gb

Digital camera

Printer

Hand held Gps
3.1.2 Software

ArcView version 3.2a

ArcGIS version 10.1

Global mapper version 10.1

Microsoft office 2010 suite
3.2 Overview of Methodology.
The first step was identification of relevant datasets for the study. In this, the factors are soil
characteristics, climate, altitude, land use and population. There were several more factors but
due to the limited time available and relevance these five were considered sufficient.
These datasets were collected from various organizations as outlined in the table3.1. Following
this, data editing and creation of a database for efficient data management was done. The various
factors affecting land suitability were then processed, standardized, weighted and overlaid to
produce individual suitability maps and a final suitability map.
Identification of relevant data sets
Non spatial data
Data collection
Spatial data
Data preparation
NO
Data
Correct?
YES
Standardization
Weighting
Overlaying
Output of results and analysis
Figure 3.1 Flow of Methodoly
3.2.1 Data Collection and Sources.
Table 3.1 Data Sources and Use.
DATA TYPE
DATA SOURCE
GPS coordinates of delimiting Field
boards of existing tea zones:
USE
collection
using
a For ground truthing.
handheld GPS (Garmin 76)
(In UTM projection).
Soil Map
Temperature and Rainfall Data.
Kenya Agricultural Research For
soil
characteristics
Institution (KARI)
rating and ranking.
Meteorology Department
For
rainfall
temperature
and
rating
and
ranking.
Digital Elevation Model
Shuttle
Radar For
( Resolution 90m)
TopographyMission
developing
altitude
(SRTM) factor map.
website
Land Use Factor Map
Kenya Agricultural Research For land use rating and
Institution (KARI)
Tea Cultivation Handbook.
Kenya
Tea
ranking.
Development Identification of weights.
Agency (KTDA)
Population Data
ILRI GIS website
(2009)
For population rating and
ranking.
Topographical map
Survey of Kenya.
(1:50,000)
(SOK)
Base map
3.2.2 Data Preparation.
The collected data was harmonized. This process involved confirming and ensuring that the
datasets were in the same projection and datum. The mapping scale was also unified before
further analysis. Most of the data was in GSC_WGS_1984 projection and hence it was used for
all data and results. Re-projection and transformation was done to any other data in different
format. The re- projecting process was carried using ArcView 3.2a.
There were two sets of attribute data:

Constraints and

Factors
3.2.2.1 Constraints Processing.
The following is a diagrammatic representation for the above process.
attribute vector
data
cropping the
selected area
converting
attribute vector
data to raster data
reclassifying
convdrsion to
boolean images
• attribute vector data
• cropping the selected area
• converting attribute vector data to raster data
• reclassifying
• conversion to boolean images
Figure 3.2 Constraints Processing.
Two constraints are used in analysis of this study i.e. forest reserve and road reserves.The forest
cover map was readily available at KARI the roads were digitized.
 Forest reserve map.
Figure 3.3 Forest Reserve
 Road reserve
The digitized roads were buffered before conversion to raster. The road buffer was 60m from
centerline.
Figure 3.4 Road Reserves.
In this study, the constraints differentiate areas that are considered suitable for cultivation from
those that cannot be considered suitable under any conditions. After preparing the forest reserve
map and the buffered roads map, a single constraint map was created featuring all the
constraints. The forest map and the road map were in vector format and had to be converted into
raster format before reclassification. For both, reclassification was done by inputting value 1
where to the constraint and 0 to the other areas in the map. The following is the Boolean image
of both constraints.
Figure 3.5 Constraints Map.
3.2.2.2 Land Use and Population Data Preparation.
Cropping and clipping was carried out so as to extract the selected area and attribute feature
restricted to the shape file. The following diagrams indicate the procedure of clipping land use of
the area of study from the whole Kenyan data using ArcGIS 10.1
Figure 3.6 Data clipping process.
3.2.2.3 Soil Data Preparation.
The soil data obtained had all sub factors included in a single map and hence required dissolving
so as the three sub classes, texture, PH and clay content would be processed individually which
would yield more accurate results as compared to using a combined soil map. Dissolving was
carried out in ArcGIS 10.1 through the dissolve tool and the following are the three individual
maps of the three sub factors.
3.2.2.4 Topography Data Preparation.
For this a digital elevation model (DEM) of the study area was clipped from east African DEM
obtained from ILRI. The clipping process was similar to that in land use and the DEM was in
raster format.
Slope directly influences drainage hence soil drainage map was used to analyze the steepness of
the slope. The drainage map was similarly clipped from the Kenyan drainage map obtained from
ILRI to shape of study as DEM and land use.
3.2.2.5 Climate Data Preparation.
The rainfall data was clipped to the area of study while the temperature map was geostatisticaly
generated from point data of three stations. The temperature values were converted to a polygon
feature through ArcGIS 10.1. The following are the maps of climate data.
The other factors, land use and population were clipped to shape of area of study as the two were
in form of maps.
3.2.3 Data Processing.
These selected factors adequately represent the decision-making environment and contribute to
the final suitability map. The process of selecting these criteria was through consultation with the
KTDA and the Agricultural officer in the area of study and my supervisor.
Factors were in vector format except for altitude. For uniformity and overlaying purposes, vector
data was first converted to raster. Following conversion was re-classification since the different
factors were in different classes. Three classes were set as the optimum for constituency
throughout the whole processing as the expected classes were three.
All datasets were in same format and bore same number of classes except the Boolean images
and rainfall data which had two classes only. The only existing discrepancy was the matrix as all
factors except altitude had 250 rows by 822 columns while altitude had 49 by 89. To sort out this
resampling was carried out and hence all data sets were ready for further processing. Resampling
was carried out in ArcGIS 10.1 as well.
3.2.4 Standardization of Criteria.
After data preparation and processing, the different factors were arranged in an order matching
their importance or weight. As indicated earlier, the order was climate, soil, topography and the
other two factors combined. This process is also referred to as rating.
As there were three classes, considering the optimum conditions for tea cultivation the classes
were numbered one to three with three being listed in class that fulfilled the optimum
requirements, two indicating the class that moderately met the requirements and one numbered
the class that least met those optimum requirements. Following this individual suitability maps
were obtained. The following are tables showing rating or each interval of data in each of all sub
factors. Using these ratings, the sub factor maps were re-classified and this was performed in
ArcGIS 10.1. the results of this process is the individual suitability maps for sub factors which
would be weighted and overlaid to produce the individual suitability maps for the four identified
main factors. The maps below each table display the rated images of those factors after
reclassification
3.2.3.1 Rainfall Data Standardization.
Table 3.2 Rainfall Classes.
CLASS
AMOUNT OF RAINFALL
1
800-1200mm
2
1200-1600 mm
Figure 3.7 Rainfall Suitability Map.
3.2.3.2 AltitudeData Standardization.
Table 3.3 Altitude Classes.
CLASS
ALTITUDE
DESCRIPTION
1
3274-4586
Least suitable for tea cultivation
2
650-1962m
Moderately suitable for tea cultivation
3
1962-3271m Most suitable for tea cultivation
Figure 3.8 Altitude Suitability Map.
3.2.3.3 TemperatureData Standardization.
Table 3.4 Temperature Classes.
CLASS
Degree Celsius
1
11-13
2
13-15
3
15 and above
Figure 3.9 Temperature Suitability Map.
3.2.3.4 Land UseData Standardization.
Table 3.5 Land Use Classes.
CLASS
LAND USE
1
Barren [land towards the peak of the mountain]
2
Woodland
3
Agricultural
Figure 3.10 Land Use Suitability Map.
3.2.3.5 Soil PHData Standardization
Table 3.6 Soil PH Classes.
CLASS
SOIL pH
DESCRIPTION
1
1.0-4.5
Least suitable for tea cultivation
2
5.5-6.0
Moderately suitable for tea cultivation
3
4.5-5.5
Optimum pH for tea cultivation
Figure 3.11 Soil pH Suitability.
3.2.3.6 Soil Clay ContentData Standardization.
Table 3.7 Clay Content Classes.
CLASS
CLAY CONTENT
DESCRIPTION
1
Montomorillinite
Least suitable for tea cultivation
2
kaolinite
Most suitable for tea cultivation
Figure 3.12 clay content suitability map.
3.2.3.7 Soil TextureData Standardization.
Table 3.8 Soil Texture Suitability Classes.
CLASS
TEXTURE
DESCRIPTION
1
Sandy and rocky soil
Least suitable for tea cultivation.
2
Clay soil
Moderately suitable for ta cultivation.
3
Loam soil
Most suitable for tea cultivation.
Figure 3.13 Soil Texture Suitability Map.
3.2.3.8 PopulationData Standardization.
Table 3.9 Population Classes.
CLASS
Population
description
1
60,897
Highly populated hence fewer land parcels for cultivation
2
30,618
Less populated hence more cultivatable land
Figure 3.14 Population Suitability Map.
3.2.3.9 Slope and Drainage Data Standardization.
Table 3.10 Slope and Drainage Classes.
CLASS
Drainage
slope
1
Poorly drained soils
gentle
2
Well drained soil
steep
Figure 3.15 Slope and Drainage Suitability Map.
3.2.5 Weighting and Overlaying Individual Sub Factor Maps.
The sub factors under each of the four main factors were determined to bear same weight as to
production of factor suitability maps. The combination of sub factors was carried out through the
weighted sum tool in ArcGIS 10.1 under overlay. Following these the four factors and their
suitability in the same three classes were produced.
Taking the example of climate factor, it is composed of two sub factors which are rainfall and
temperature. To arrive at the single factor map of climate, the two had to be overlaid through the
weighted tool in ArcGIS 10.1. Thetwo sub factors contribute equally at the weights of 0.5 each
since the total weight used by the tool must add up to 1.All the other sub factors were processed
similarly.
3.2.6 Weighting and Overlaying the Four Factors.
To get the final suitability map, overlaying had to be carried out. The individual maps had three
classes and the expected final map was to have three classes of suitability. The following table
shows the class and suitability level and the weights given to each factor.
Through consultation with agricultural expert, the factors were listed in order of importance and
a ratio was formed.
1. Climate
2. Soil
3. Altitude and terrain [topography]
4. Land use and population.
The ratio of 1:2:3:4 was attained.
Summing up the ratio you obtain ten. To get the individual weights in percentage, the highest
value of the ratio is divided by the total of ratio and multiplied by a hundred. Following the
above simple arithmetic, the percentage weight was 10% for land use and population, 20% for
altitude ad terrain, 30% for soil and 40% for climatic factors.
In ArcGIS 10.1, weighting as a sum requires total weight to add up to 1. Hence, logically the
above weights were taken as 0.1:0.2: 0.3: 0.4
Table 3.11 Weights of the Four Classes.
FACTOR
WEIGHT
Climate
0.4
Soil
0.3
Topography
0.2
Land use
0.1
With the weights ready and the following table indicating the suitability classes, the final map
was ready to be processed and with the overlay with the constraint map the objective of the study
was close to being achieved.
Table 3.12 Suitability Level Classes.
CLASS
SUITABILITY
1
Least suitable
2
Moderately suitable
3
Most suitable
CHAPTER 4: RESULTS AND ANALYSIS.
4.1 Individual Suitability Maps.
Following overlay of sub factors the first results to be obtained were the individual factor maps
for the four main factors. The four maps show three different classes of suitability and the each
class has been displayed consistently in same colour. This will assist in quick identification and
comparison of suitability level according to influence each factor.
Figure 4.1 Climate Suitability Map.
Figure 4.2 Topography Suitability Map.
Figure 4.3 Soil Suitability Map
Figure 4.4 Land Use and Population Suitability Map.
4.2 Overlay of the Four Factors Influencing Suitability.
Figure 4.5 Four Factors Suitability Map
4.3 Overlay of the Factors Map with the Constraints.
Figure 4.6 Overlay of Factor Map and Constraints.
This map was as a result of overlaying the single map from the criteria factors with the constraint
map. The constraint map as initially explained consisted of forest reserve and road reserve.
The following is a table showing the area in square kilometers each class of suitability covers.
Table 4.1 Total Area of Each Suitability Class.
CLASS
SUITABILITY
AREA
1
Least suitable
44.87 square kilometres
2
Moderately suitable
72.41 square kilometres
3
Most suitable
54.81 square kilometres
Figure 4.7 Final Suitability Map.
The total area of study is 210.80 square kilometers. From the table above, the area identified as
suitable for tea cultivation regardless of the level of suitability is 172.09 square kilometers. This
leaves an area of 38.71 square kilometers which is occupied by the constraints i.e. road reserve
and forest reserve.
4.4 Ground Truthing.
This was carried out to verify the results. The process was done through field visits to the area of
study, especially, the four KTDA tea zone delimitation boards. The following table shows the
GPS coordinates picked from the fieldwork using a handheld Garmin GPs receiver in UTM
projection.
POINT
EASTINGS NORTHINGS
1.Thunguri
296622
9943781
2. Gitahiki
297944
9943655
3. Njoga
298789
9942886
4. Kiania
299464
9943254
Figure4.8 Tea Zone Delimitation Board.
Figure 4.8 Ground Truthing Map.
CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS.
5.1 Conclusions.
5.1.1 The existing tea zone in Kirinyaga West District falls under the most suitable class.
5.1.2 There is more (most) suitable land for tea cultivation beyond the current marked tea zone.
5.1.3 To define the classes of suitability for factors to be considered requires expert knowledge
in the subject of interest or consult experts in the particular discipline.
5.1.4 Suitability analysis is a wide area of study. There are different ways in which one can carry
out the analysis depending on required results and data available.
5.1.5From the study, it was evident that GIS provides the ability to analyze both feature and
attribute data which is core in multi-criteria decision making.
5.1.6 Maps provide an efficient mean of representing and analyzing spatial phenomena.
5.2 Recommendations.
5.2.1 The study involved one crop and the same process can be applied to other crops.
5.2.2 The study narrowed the major factors in consideration to four. The exercise can be carried
out considering all possible factors affecting the crop.
5.2.3 The study was carried out in Kirinyaga district and it is possible to apply the exercise to the
whole Kirinyaga county and possibly the whole country too.
5.2.4 The results of this project could be applied by KTDA to remark their tea zones as there are
areas that are suitable for tea production beyond the present zones.
REFERENCES.
Bonham-Carter, (1994): Geological favorability.
Campbell, (1992) and Kalogeria, (2002):Suitability of land for agricultural activities.
Carver, (1991) Banai, (1993), Eastman, (1997), Malczewski and Thill, (1999): Approaches to
the land-use suitability analysis.
Carver, (1991)andEastman, (1997),WLC (Weighted Linear Combination) and its variants.
Cova and Church, (2000):Site search problem.
Cromley, (1994) and Hanink, (1999):Evaluating the robustness of land suitability patterns.
Eastman, (1993) and Church, (2002):Selecting the best site for the public and private sector
facilities.
Hopkins, (1977) and Collin, (2001):Land use analysis.
Janssen and Rietveld, (1990):Regional planning.
J. Malczewski, (2004): Progress in Planning.
Malczewski, (1999): Multi-objective and Multi-attribute decision making methods.
Miller (1998):Landscape evaluation and planning.
Moreno and Seigel, (1988): Environmental impact assessment.
M. Mokarram(2003): GIs-based multi-criteria land suitability evaluation using ordered weight
averaging with fuzzy quantifier.
Pereira and Dickstein, (1993) and Store and Kanga, (2001):Ecological approaches for
defining land suitability/habitant for animal and plant species.
Prakash, T. N. (2003):Land suitability analysis for agricultural crops, a fuzzy multi-criteria
decision making approach.
(Yager, 1988):Multi-criteria operators.