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UNIVERSITY OF CAPE COAST
DEPARTMENT OF GEOGRAPHY AND TOURISM
LAND USE DYNAMICS IN BIEHA,
SISSILI PROVINCE, SOUTHERN
BURKINA FASO
BY
ISSA OUEDRAOGO
A DISSERTATION SUBMITTED TO THE DEPARTMENT OF GEOGRAPHY
AND TOURISM OF THE FACULTY OF SOCIAL SCIENCES, UNIVERSITY
OF CAPE COAST, IN PARTIAL FULFILMENT OF THE REQUIREMENTS
FOR THE AWARD OF MASTER OF ARTS DEGREE IN GEOGRAPHY.
AUGUST 2007
DECLARATIONS
Candidate’s Declaration
I hereby declare that this dissertation is the result of my own original work and
that no part of it has been presented for another degree in this university or
elsewhere.
Candidate’s Signature: issadeh@yahoo.fr Date: 2007/08/30
Name: Issa Ouedraogo
Supervisors’ Declaration
We hereby declare that the preparation and presentation of the dissertation were
supervised in accordance with the guidelines on supervision of dissertation laid
down by the University of Cape Coast.
Principal Supervisor’s Signature: skendie2001@yahoo.com Date: 2007/08/30
Name: Professor Stephen B. Kendie
Co-Supervisor’s Signature: emmanjeb@yahoo.com Date: 2007/08/30
Name: Professor E. Jeurry Blankson.
i
ABSTRACT
Remote Sensing and Geographical Information Systems tools were used to
detect land use dynamics of Bieha District from 1986 to 2002 on the basis of
Landsat Thematic Mapper imageries processing.
During the 16-year period, important changes occurred on the main
geographical units of land use of the area, namely farming fields, shrubby and
wooded savannahs, and gallery forest. The farming surface increased from 3,438.7
hectares to 33,686.6 hectares and the shrubby savannah decreased from 67,427.5
hectares to 35,818.8 hectares. The wooded savannah and gallery forest remained
unchanged in terms of surface but spatially, each of these four units underwent
profound changes.
The deforestation caused by farming activities most amplified by high in-
migration of population was about 1,798.5 hectares annually. Wood extraction and
bushfires contributed to a loss of 2,105.5 hectares of forest per annum. Policy
initiatives that could lead to environmental conservation are suggested.
ii
ACKNOWLEDGEMENT
This work is the result of the support of consolidated goodwill persons
from Burkina Faso, Sweden and Ghana. I would like to express my gratitude to all
those who helped with the preparation and presentation of this thesis.
I wish to register my profound gratitude and appreciation to my
supervisors: Professor S. B. Kendie and Professor E. Jeurry Blankson for their
immeasurable contribution in the form of suggestions, guidance, constructive
criticisms and pieces of advice from the initiation of the research to its completion.
Without their intellectual dynamism, fruitful ideas and comments, this venture
would not have been possible. May God richly bless you in your endeavours.
I would like to specifically express my sincere gratitude towards Professor
K. Awusabo-Asare, Dean of the Faculty of Social Sciences and Professor Albert
M. Abane, Head of the Department of Geography and Tourism for receiving me at
the Department, taking time out of their busy schedules to go through my drafts,
and providing necessary conditions for the study at the University.
I express my thankfulness to all lecturers of the Department of Geography
and Tourism, particularly, Professor L. A. Dei, Dr. Roy Cole, Dr. Oheneba
Akyeampong, Dr. A. Kumi-Kyeremi, Mr. Tanle, Eshun and Afful for their
understanding and encouragement. To my fellow course mates, namely, Simon,
Foster and Gerard, I say a big thank you.
To all chiefs of Bieha District, the entire population, foresters and
translators who responded to the interview schedules during the study, I express
my gratitude. I am indebted to all the authors whose works I used as references.
iii
My thanks are also due to Dr. Basile Guissou, General Director of the
National Centre of Scientific and Technologic Research (CNRST) of Burkina
Faso; Dr. Jean-Marie Ouadba, Head of the Department of Forestry (DPF) of the
Institute for Environment and the Agronomic Research (INERA/CNRST), and Dr.
Maxim Compaoré, Director of the Scientific Coordination and Cooperation
(DCCS/CNRST) and National Coordinator of SIDA/SAREC Project for providing
the necessary fund for the study.
I am grateful to Professors Ulf Soderberg and Mats Sandewall of the
Department of Forest Resource Management and Geomatics at the Swedish
University of Agricultural Sciences (SLU-Umea) and Mr. Souleymane Paré, PhD
student of the same university for their contributions in terms of suggestions, and
encouragements.
Finally, my sincere thanks go to my family, friends and relatives who
endured my absence due to this work and for their moral and material support. I
am particularly grateful to my wife, Aguira Dera, whose patience and
encouragement enabled me to complete the study and my boy, Abdoul Razack
Ouédraogo, who was born during the early period of the study.
iv
DEDICATION
To my wife Aguira Dera and my son, Abdoul Razack Ouédraogo.
v
TABLE OF CONTENTS
Content Page
DECLARATIONS i
ABSTRACT ii
ACKNOWLEDGEMENT iii
DEDICATION v
TABLE OF CONTENTS vi
LIST OF TABLES xii
LIST OF FIGURES xiv
LIST OF PLATES xv
ACRONYMS xvi
CHAPTER ONE : BACKGROUND OF THE STUDY 1
Introduction 1
Statement of the problem 3
Objectives 4
Hypotheses 5
Rationale 5
Conceptual framework 6
The study area 9
Climate and vegetation 10
Fauna 12
Population and activities 13
Social infrastructure 15
vi
Tenure management 16
Structure of the dissertation 17
CHAPTER TWO : DEFINITION OF CONCEPTS AND TECHNIQUES 18
Introduction 18
Land use and land cover 18
Land use change and consequences 19
Remote sensing 20
Landsat thematic mapper sensor and multispectral imagery 23
Image classification techniques 25
Global positioning systems (GPS) 27
Geographic information systems (GIS) 28
Land use evolution detection 29
Competing models for land use dynamics 30
Summary 40
CHAPTER THREE : REVIEW OF ENVIRONMENTAL ISSUES IN
BURKINA FASO 41
Introduction 41
Agricultural practices and deforestation 43
Migration and environmental degradation 44
Overgrazing 46
Firewood and timber request 47
Bushfire 48
vii
CHAPTER FOUR : METHODS OF DATA COLLECTION AND
ISSUES FROM THE FIELD 49
Introduction 49
Data and sources 49
Images processing 50
Satellite images 50
Geometric correction 52
Classification 52
Detection of land changes 54
Problems encountered during the images processing 58
Population interviews 58
Instruments used 58
Method of sampling 59
Pre-survey activity in the villages 61
The fieldwork 62
Issues from the interview 62
Response rate 62
Problems encountered 63
Diagram of the methodology 64
Limitation of the study 67
viii
CHAPTER FIVE : LAND USE ASSESSMENT 69
Introduction 69
Land use detection 69
State of the land use in 1986 69
State of land use in 2002 71
Land use dynamics from 1986 to 2002 73
Respondents’ perception of the environment and their welfare 82
Dynamics of the environment 82
Dynamics of the vegetation 82
Wildlife dynamics 84
Soil fertility dynamics 85
Water dynamics 87
Dynamics of farming practices 88
Crops productivity 88
Farming techniques 89
Change in acreage per household 89
Dynamics of the population welfare 90
Food security 90
Drinking water 91
Income evolution 92
Population mobility 92
Summary 94
ix
CHAPTER SIX : FACTORS INFLUENCING CHANGES AND
IMPLICATIONS FOR LAND USE 96
Introduction 96
Changes detected 96
Factors affecting land use dynamics in Bieha 97
Leading factors in the farm fields dynamics 98
Population pressure 98
Agri-business 100
Poverty 101
Consequences of increasing farm lands 102
Forests dynamics 103
The shrubby savannah 103
The wooded savannah 103
The gallery forest 108
Consequences of the deforestation 109
CHAPTER SEVEN : SUMMARY, CONCLUSIONS AND
RECOMMENDATIONS 112
Introduction 112
Summary of the methodology 112
Summary of the findings 113
Summary of the discussion 114
Conclusion 115
Recommendations and strategies for further research 116
x
REFERENCES 119
APPENDIX : Questionnaire 132
xi
LIST OF TABLES
Table Page Table 1 Wild animal’s population in Bieha, 2004 13
Table 2 Dynamics of the population of Bieha 14
Table 3 Yields of the main crops in Bieha in 2002 15
Table 4 Livestock in Sissili 1986 and 2003 15
Table 5 Summary of land use dynamics models 34
Table 6 Satellite images used for land use detection of Sissili 51
Table 7 Land use classes considered in image classification 53
Table 8 Structure of the questionnaire 59
Table 9 Selected villages and number of respondents 61
Table 10 Respondents and response rate by selected villages 63
Table 11 Surface area and proportion of land use units in 1986 70
Table 12 Surface area and proportion of land use units in 2002 72
Table 13 Codification of land use units 73
Table 14 Legend of the land use dynamics 74
Table 15 Land use change in Bieha 78
Table 16 Dynamics of land use units 80
Table 17 Dynamics of the vegetation 83
Table 18 Causes of vegetation loss 84
Table 19 Dynamics of wild animals 85
Table 20 Causes of wildlife dynamics 85
Table 21 Soil fertility change 86
xii
Table 22 Causes of fertility change 86
Table 23 Solution to fertility reduction 86
Table 24 Water evolution in the rivers 87
Table 25 Causes of water reduction 87
Table 26 Evolution of crops productivity 88
Table 27 Dynamics of farming practices 89
Table 28 Change in acreage per household 90
Table 29 Food security according to population 91
Table 30 Evolution of the sources of drinking water 91
Table 31 Evolution of incomes according to population 92
Table 32 Permanent in-migration 93
Table 33 Causes of permanent in-migration 93
Table 34 Temporary in-migration 94
xiii
LIST OF FIGURES
Figure Page
Figure 1 Three-dimensional framework for land use change 7
Figure 2 Map of Bieha District (the study area) 9
Figure 3 Rainfall isohyets and floristic zone of Burkina Faso 11
Figure 4 Rainfall evolution of Bieha from 1988 to 2002 11
Figure 5 Monthly rainfall of Bieha district in 2002 12
Figure 6 GPS National Constellation 27
Figure 7 Landsat TM image mosaic of Burkina Faso 50
Figure 8 False colour composites of satellite imageries 56
Figure 9 Supervised classification of landsat image 57
Figure 10 Selected villages for the survey in Bieha 60
Figure 11 Methodological approaches for the land use dynamics 66
Figure 12 Land use units in Bieha in 1986 70
Figure 13 Land use units in Bieha in 2002 72
Figure 14 Land use dynamics in Bieha from 1986 to 2002 76
Figure 15 Comparison between land use 1986 - 2002 77
Figure 16 Observation of land use change in Bieha 77
Figure 17 Tendency curves of land use units in Bieha 78
Figure 18 Dynamics of land use units 81
xiv
LIST OF PLATES
Plate Page
Plate 1 Cashew plantation in Neboun 101
Plate 2 Pile of wood for sale 104
Plate 3 Wood transportation 104
Plate 4 Afzelia africana cut for animals 106
Plate 5 Burnt shrubby savannah 107
Plate 6 Burnt wooded savannah 108
xv
ACRONYMS
BNF Biological Nitrogen Fixation
CILSS Comite Inter-Etats de Lutte contre la Secheresse au Sahel
CLUE Conversion of Land Use and its Effects
CLUE-CR Conversion of Land Use and its Effects–Costa Rica
CNRST Centre National de la Recherche Scientifique et Technologique
(BF)
CONAGESS Comission Nationale de Gestion et de Securite
CUF California Urban Futures
CURBA California Urban an Biodiversity Analysis Model
DPAHRH Direction Provinciale de l’Agriculture de l’hydraulique et des
Ressources Halieutiques
DGEP Direction Générale des Etudes et de la Planification
DGSA Direction Générale des Statistiques Agricoles
DPECV Direction Provinciale de l’Environnement et de Cadre de Vie
ERTS 1 Earth Resources Technology Satellite – 1
ERTS 2 Earth Resources Technology Satellite – 2
ESRI Environmental Systems Research Institute
FAO Food and Agriculture Organization
GDP Gross Domestic Product
GEM General Ecosystem Model
GPS Global Positioning Systems
GIS Geographical Information System
xvi
IBS INYPSA/BDPA-SCETAGRI/SOPEX
INERA Institut pour l’Environment et de la Recherche Agricole
INSD Institut National des Statistiques et de la Demographie
IGB Institut Geographique du Burkina Faso
Landsat TM Landsat Thematic Mapper
MECV Ministère de l’Environnement et de Cadre de Vie
MEE Ministère de l’Environnement et de l’Eau
MEF Ministère de l’Economie et des Finances
MET Ministère de l’Environment et du Tourisme
MRA Ministère des Ressources Animales
NASA National Aeronautic and Space Administration
PLM Patuxent Landscape Model
PNGT Programme National de Gestion des Terroirs
PNK Phosphate – Nitrogen – Potassium
PNLD Programme National de Lutte contre la Désertification
PNLCD Plan National de Lutte Contre la Désertification
PSB Programme Sahel Burkina
PSSA Programme Sectoriel du Secteur Agricole
RAF Réorganisation Agraire et Foncière
RAV Responsable Administratif Villageois
RS/GIS Remote Sensing and Geographical Information Systems
SOM Soil Organic Matter
USD United States Dollars
xvii
CHAPTER ONE
BACKGROUND OF THE STUDY
Introduction
Burkina Faso, like the other Sub-Saharan Africa countries, is confronted
with problems of development in a context of accelerated degradation of her
natural resources caused by repeated droughts and human activities. The
phenomenon of deterioration became more pronounced these last three decades
due to increasing population growth (2.7 % per year) in conjunction with irregular
rainfall pattern (Yameogo, 2005). This situation has caused food deficits with
some corollary effects such as general poverty and the development of internal
migration from the northern and central parts to the southern and south-western
regions. These movements have also created new problems linked to the
concentration of people and their activities in opened up areas, and threats to
protected zones and national reserves (Ministere de l’Environment et du Tourisme,
1991). The nutrient production basis of the country has deteriorated because the
natural habitat has become fragile and incapable of satisfying the food needs of the
population.
Among the natural resources in decline are flora, fauna, soils and surface
waters. They are declining mainly because of the climatic risks and more
especially due to human activities such as over-harvesting of wood and wild
1
animals, unhealthy agricultural practices, overgrazing, and bush fires (M.E.E,
1999; Ganemtore and Aboubacar, 2002; Henry et al, 2002).
Development strategies have been instituted by the government since 1970
in order to increase food production, to improve population welfare and to reduce
natural resources depletion. Among these initiatives are:
• The National Desertification Control Program (PNLD) in 1970,
• The National Programme for Villagers’ Forestry in 1984,
• The Agrarian System Reorganization (RAF) in 1984,
• The adoption of the National Plan for Desertification Control (PNLCD) in
1986,
• The National Plan of Action for the Environment in 1991,
• The National Programme of Soil Management (PNGT) in 1992,
• The Adjustment Programmes of the Agricultural Sector (PASA) from 1991
to 1999,
• The Decentralization Programme since 2000.
Each of the programmes placed emphasis on natural resources
management and socio-economic development of the country in conformity with
the 1992 Rio declaration (Yameogo, 2005).
In spite of these efforts, the southern provinces of the country, namely
Sissili, Ziro and Nahouri where the natural resources were until recently almost
intact are today under the pressure of the agricultural and pastoral activities and of
bush fires. These pressures have the potential to impact adversely on the natural
resources base of the region.
2
Statement of the problem
Burkina Faso is a Sahelian country where agriculture and livestock rearing
constitute the mainstay of the economy. These two spheres of activities account
for 90 % of employment in the country and contribute about 34.5 % to the GDP
(CONAGESS, 1998; Ganemtore and Aboubacar, 2002). These activities are
undertaken in a rudimentary and extensive way, with a low level of intensification.
They are believed to contribute to degradation of the environment (Howorth and
O'Keefe, 1998; M.E.E, 1999). Indeed, the government through national and sub-
regional levels has initiated programs (PNGT, PSSA, PSB, and CILSS) to fight
environmental degradation.
The eastern and south-western parts of the country, where population
density is low, possess the largest forest reserve of the country (Ministere de
l’Environment et de Carde de Vie, 2004). However, during the last two decades,
the natural resources in these areas have been subjected to pressure because of
agricultural and pastoral migrations, domestic energy requirements and periodic
bush fires (M.E.E, 1996; Henry et al, 2002).
The Sissili province in southern Burkina Faso is currently concerned about
population migration. In 1985, 11,945 migrants arrived in this province which
contributed to a rise in the immigration rate to 4.88 % (Henry et al, 2002). This
figure seems to be rising considerably not only due to the current increase in
cotton and yam cultivation, the expansion of agro-businesses and the return of
Burkinabe migrants due to the political crisis in Cote d’Ivoire, but also, and
especially, due to the high birth rate of 5.01 % which is currently the highest in the
country (I.N.S.D, 1996).
3
Agrotechnik (1991) has suggested that Sissili province could only support
30 persons per km2 without irreparable damage and IBS (1994) forecasted that
some 43 % of the Sissili area would be deforested by 2010 due to land-use
activities. These estimations which were based on five-year interval studies seem
to have been over-generalized. In order to understand the real situation of the
natural resources, the questions that need to be addressed are:
1) To what extent are land use activities such as farming, harvesting of
fuelwood and bush burning degrading the environment in the Sissili
province?
2) Is the degradation sufficient enough to ultimately undermine ecosystem
balance, human welfare and its long-term sustainability?
This study therefore sought to provide answers to these questions and to
put the land use activities in the study area into their proper perspective.
Objectives
General aim of the study was to assess land use change in Sissili Province
from 1986 to 2002. In order to achieve the purpose of the study, four specific
objectives were set, namely to:
1. Trace in time series (1986 to 2002) the land-use in Sissili province and in
particular the Bieha District;
2. Assess local activities on natural resources management;
3. Analyse the dynamics of each type of land-use unit in the study area; and,
4. Based on the findings, suggest interventions for a more sustainable land
use for the province.
4
Hypotheses
Two main hypotheses guided the study. These were:
1. Natural resources in Bieha district have experienced significant degradation
since 1986 resulting from population pressure;
2. Agro-pastoral exploitation of the land has by far had significant impacts on
natural resources.
Rationale
Land-use in Sub-Saharan Africa is principally focused on food and cash
crop production. Land-use activities, whether converting landscapes for human use
or changing management practices in areas already under management have
transformed a large proportion of the planet’s land surface (Foley et al, 2005).
Therefore, understanding the changes in land-use has long been a major focus of
research in agronomy and in long-term environment sustainability perspectives.
The rationale of this study can be seen in two perspectives.
1. Contribution to environmental sustainability
The study aims to trace in time series, the land-use in Sissili province and
particularly in the Bieha area. Examination of the state of land-use (whether
retrogressive, stable, or progressive), should be a major contribution towards
generating strategies for the long-tem sustainability of natural resources (Human
Development Report, 2003). It will help build a sustainable society, defined as
one that manages its economy and controls its population size without doing
irreparable environmental harm; satisfying the needs of its people without
5
depleting the environment or jeopardizing the prospects of future generations of
humans or other species (Miller,1994). It will also help to build strategies or rules
on the use of the natural resources to reduce the over-use of the environment by
farmers.
2. Contribution to knowledge and further investigations
The study will be an opportunity to test the Pessimists’ and Optimists’
concepts of the relationship between population and environment. It will bring to
the fore knowledge on the persistent causes and consequences of environmental
degradation and the state of ecosystem dynamics and the development of agro-
pastoral areas.
The study was carried out within the context of a broad project being
undertaken by the National Institute of Scientific and Technology Research of
Burkina Faso (CNRST) in collaboration with the Department of Forest Resource
Management and Geomatics of the Swedish University of Agricultural Sciences in
Umeå (Sweden) titled “Sub-national approach to integrated natural resource
management in southern region of Burkina”. This present study should contribute
to understanding land use change as a fundamental basis of natural resources
management of the region.
Conceptual framework
Land use is determined by the interaction in space and time of biophysical
factors (constraints) such as soils, climate, topography, and human factors like
population, technology and economic conditions (Veldkamp and Fresco, 1996a).
6
To assess land- use dynamics, a framework based on three critical dimensions is
proposed for summarizing models of human-environmental dynamics. Time and
space are the first two dimensions and provide a common setting in which all
biophysical and human processes operate (Agarwal et al, 2002). In other words,
models of biophysical and/or human processes operate either in a temporal context
or a spatial context or both (Figure 1).
SPACE (Y)
TIME (X)
Human Decision-
Making (Z)
Figure 1: Three-dimensional framework for land use change
Source: Agarwal et al (2001).
In land use dynamics, two distinct and important attributes must be
considered; namely model scale and model complexity. Model scale refers to Time
step and duration; Spatial resolution and extent, and Scale of human decision
making. Time step is the smallest temporal unit of analysis for change to occur for
a specific process in a model. In this present study for instance, farm fields’
7
surface area may change annually. Duration on the other hand, refers to the length
of time that the model is applied. In this case, the duration is 16 years (1986 –
2002).
Resolution represents the smallest geographic unit of analysis of the model.
The study uses Landsat Thematic Mapper images with resolution 30m X 30 m
(900 m²). Extent describes the total geographic area to which the model is applied.
Here, the extent is Bieha District (1,754 km²).
To date, social scientists have not yet described human decision making in
terms that are as concise and widely accepted for modeling, as time step/duration
or resolution/extent (Agarwal et al, 2002). Therefore, an analogous approach can
be used to articulate scales of human decision-making in terms of agent and
domain. Agent refers to the human actor or actors in the model who are making-
decision and domain constitutes the broadest social organization incorporated in
the model. In this study, agent is the individual human and domain is the set of
communities in Bieha District.
Model complexity embraces temporal complexity, spatial complexity and
human decision making complexity. These represent, respectively, the extent to
which a model is explicit at temporal, spatial or at the human decision-making
scale. There are possible important interactions between temporal complexity and
human decision-making. For instance, some human decisions are made in very
short time intervals, such as the decision of which tree to cut for grazing a herd is
made daily. Other decisions such as to increase household farm fields are made
over longer term periods.
8
The study area
Bieha is one of the seven districts of Sissili province of Burkina Faso
(Figure 2). It covers 1,754.6 km2 and represents 25 % of the total area of the
province. The District comprises 22 villages.
Figure 2: Map of Bieha District (the study area)
Source: Geographical Institute of Burkina (IGB), 2006
9
Climate and vegetation
The study area is part of the humid sudanian climatic zone (Guinko, 1984,
Fontès and Guinko, 1995) characterized by an alternation of a dry season from
November to April and a rainy season from May to October (Figure 5). The
climate is determined by the swinging of the intertropical fronts. The intertropical
fronts represent the contact zone between the continental dry air mass of the
northeast (harmattan) and of the south-eastern humid air mass (monsoon). The dry
season is subdivided in two periods: a dry and cool period from November to
February during which blows the harmattan and a dry and hot period from March
that precedes the advent of rains in May-June until October ending. The average
annual precipitations are between 800 and 1000mm (Figure 3) and can often go up
beyond 1000mm or lower on this side of 800mm (Figure 4).
The vegetation of Bieha is the Soudano-Guinean type according to the
phytogeographical zoning made by Guinko (1984). Vegetation is dominated by
shrubby and wooded savannas. The woody vegetation is dominated by Vitelarea
paradoxa, Terminalia spp and Combretum spp. The dominant herbaceous
perennials are Andropogon ascinodis and Schizachyrium sanguineum. The woody
species of the valleys are Anageissus leiocarpus, Daniella oliveri and Mitragina
inermis, associated with Andropogon gayanus and Viteveria nigritina as the
dominant herbaceous perennials.
Bieha District is endowed with a classified forest (Safari Ranch Sissili)
with a surface area of 353.3 km² and a local forest (35 km²) at Bori (Figure 2).
Vegetation is especially dense in these forests because they are protected from
harvesting and animal grazing.
10
Figure 3: Rainfall isohyets and floristic zones of Burkina Faso (1971-2000)
Source: Guinko, 1984, 1995.
0
200
400
600
800
1000
1200
1400
1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
Year
Rai
nfal
l (m
m)
Figure 4: Rainfall evolution of Bieha from 1988 to 2002
Source: Meteorological station of Po (2002)
11
0
50
100
150
200
250
Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec
Month
Rai
nfal
l (m
m)
0
5
10
15
20
25
Days
Rainfall Number of day
Figure 5: Monthly rainfall of Bieha district in 2002
Source: Meteorological station of Po (2002)
Fauna
A wild animal inventory taken in 2004 in the Safari Ranch Sissili by
Bouché et al. (2004) showed a rich diversity of wildlife. Twelve hoofed species
shown in Table 1 were enumerated in the ranch.
12
Table 1: Wild animal’s population in Bieha, 2004
Animals population
Scientific English
Loxodonta africana Elephant 17
Cyncerus caffer brachyceros Buffalo 25
Hyppotragus equimus Hippotrague 233
Alcephalus buselaphus Bubale 175
Kobus ellipsyprymmus defassa Waterbuck 24
Reduna reduna and Kabus cob Cobe 2
Traelaphus scriptus Guibs harnaches 3
Ourebia ourebi, Ourebi 2
Phacochoerus aethiopicus Warthog 33
Sylvicarpia grimmia Cephaloph 4
- Baboon 8
Total 523
Source: Bouché et al (2004)
Population and activities
The District of Bieha is composed of 22 villages inhabited by three main
ethnic groups: the native Nuni, the migrant Mossi and the pastoralists Fulani. The
actual population as at January 2006 consists of 25,634 people with a crude
density of 14.6 inhabitants per square kilometre (Table 2).
13
Table 2: Dynamics of the population of Bieha
1985 1996 2002 2006 Year
Pop Density Pop Density Pop Density Pop Density
Bieha 15 043 08.4 17 728 09.93 20 643 11.56 25 634 14.6
Source: INSD, 1985 and 1996; Police headquarters of Bieha
The Nuni are autochthones and have a secular relationship with the area.
The Mossi are migrants, pushed from the northern and central region of the
country by the scarcity of arable lands, pastures and water. The Fulani are agro-
pastoralists and have recently come to Sissili, although some came earlier to herd
the cattle of the Nuni. In total, 7 per cent of the Fulani arrived more than 20 years
ago, the remaining, 93 per cent, have arrived in the last 15 years (Howorth and
O'Keefe, 1998). The main reason behind the immigration was resource
degradation in the north and a consequent lack of pasture and dry season watering
points.
The Fulani have now settled in most parts of Sissili. They tend to
concentrate their animal herding in the zones of low-intensive agriculture in the
periphery/wooded areas of the villages.
Agriculture and breeding constitute the main economic activities of the
district. Crops grown include yam, maize, red and white sorghum, millet,
groundnut, sweet potato, cowpea, black-eyed beans and cotton (Table 3). The
stock farming involves bovine, ovine, goats and donkeys (Table 4).
14
Table 3: Yields of the main crops in Bieha in 2002
Cereals Cotton Tubers and others
Area (ha)
Quant. (tons)
Yield (kg/ha)
Area (ha)
Quant (tons)
Yield (kg/ha)
Area (ha)
Quant. (tons)
Yield (kg/ha)
8,075 6,363 788 2,980 2,166 727 3,312 11,811 3,566
Source: Annual report of DPAHRH (2002)
Table 4: Livestock in Sissili 1986 and 2003 Years Bovine Ovine Goats Donkeys Horses
1986 145,000 45,000 25,000 10,000 60
2003 320,321 239,768 338,982 59,012 100
Source: DEP, (1986, 2003)
Social infrastructure
A monograph of Sissili province prepared in 2004 by the General Office of
Economy and Planning (DGEP) found the following infrastructures as available in
the District. First, in the area of health, Bieha had five dispensaries and five
maternity hospitals distributed among Bieha Centre, Yalle, Neboun, Koumbogoro
and Yelbouga. Four pharmacies exist in the district. A total of 12 medical staff
were working in the district.
For education, Bieha district had 14 primary schools and one college at
Bieha centre. The school enrolment rate was 36.6 % in 2000 of which 43.3 % were
males and 29.6 % were females.
15
Bieha district had a drinking water coverage rate of 126 % in 2003,
comprising 49 fountains, 87 modern wells and five solar fountains.
In terms of energy and communication, only Bieha centre had light
provided by solar energy and land line phones. Some areas of the district are
covered by cell phone networks (Celtel and Telmob). There are motorable roads
linking all the villages of the district but some of them are unusable during rainy
seasons.
Tenure management
Each village in Sissili has its own definite village territory that has its
origins in the local history of the area and in the first settlers. Tenure management
in the villages is controlled, under customary law arrangements by the Nuni land
chief (Howorth and O'Keefe, 1998). The principal roles of the land chief are to
oversee and to supervise everything that has to do with the land, including the
bush, the farms and wildlife. He is seen as the mediator between the human world
and the divine world of the ancestors and spirits. If a person needs new land to
farm, the land chief must first be consulted. He will indicate which piece of land
the person can cultivate and what he must do first, i.e. the sacrifices he must carry
out and how much land is available. Likewise, when the immigrants arrive in the
village territory with the desire to settle, the first person they address is the village
chief, then the land chief. It is the latter who decides whether there is land in the
territory for the immigrants to farm. Depending on the village, there are different
systems that the land chiefs use to allocate land and control the immigrant's effects
on the village environments.
16
Structure of the dissertation
The study is divided into six main chapters. Chapter one deals with the
background to the study. It looks at the introduction, statement of the problem,
objectives, hypotheses and a description of the study area. Following this chapter
is the second chapter which presents the concepts of land use; land use change;
remote sensing, GIS and GPS; classification systems; and techniques and the
review of local environmental issues. Chapter Three introduces the methods and
procedures employed in data collection from the field. Land use assessment in
1986 and 2002, and the analysis of land use evolution within these two periods
constitute the main components of the fourth chapter. Chapter Five interprets the
findings and establishes a link between dynamics of land use and local socio-
economic and cultural context; and a comparison between the specific case of
Bieha and other cases found elsewhere. Chapter Six summarises the study,
concludes the discussion while suggesting steps towards sustainable development
of the study area.
17
CHAPTER TWO
DEFINITION OF CONCEPTS AND TECHNIQUES
FOR LAND USE DETECTION
Introduction
This chapter defines concepts related to land use and the tools used for the
processes it entails by way of a literature review. It also looks at some issues of the
environment in Burkina in general, and in Sissili province in particular.
Land use and land cover
Land use refers to the purposes for which humans exploit the land cover
(Fresco, 1994). Land cover is defined as the layer of soils and biomass, including
natural vegetation, crops and human structures that cover the land surface. Land
cover change is the complete replacement of one cover type by another, while land
use dynamics also include the modification of land cover type, e.g., intensification
of agricultural use, without changing its overall classification (Turner II et al.
1993). According to Meyer (1995) and Bottomley (1998), every parcel of land on
the Earth’s surface is unique in the cover it possesses.
Land use is therefore the manner in which human beings employ the land
and its resources. Examples of land use include agriculture, urban development,
grazing, logging, and mining. In contrast, land cover describes the physical state of
the land surface. Land cover categories include cropland, forests, wetlands,
pasture, roads, and settlements. The term land cover originally referred to the kind
18
and state of vegetation, such as forest or grass cover, but it has broadened in
subsequent usage to include human structures such as buildings or pavement and
other aspects of the natural environment, such as soil type, biodiversity, and
surface and groundwater.
Land use is determined by the interaction in space and time of biophysical
factors (constraints) such as soils, climate, topography, etc., and human factors like
population, technology and economic conditions (Veldkamp and Fresco, 1996b).
Land use change and consequences
Land use affects land cover and changes in land cover affect land use
(Riebsame et al 1994). Land-use activities, whether converting natural landscapes
for human use or changing management practices on human-dominated land, have
transformed a large proportion of the planet’s land surface. Foley et al (2005)
reported that land-use practices vary greatly across the world; their ultimate
outcome is generally the same; namely the acquisition of natural resources for
immediate human needs, often at the expense of degrading environmental
conditions. They also argued that land use has caused decline in biodiversity
through the loss, modification, and fragmentation of the habitats; degradation of
soil and water; and overexploitation of native species. Land use thus presents us
with a dilemma. Foley et al (2005) further claimed that while on one hand, many
land-use practices are absolutely essential for humanity, because they provide
critical natural resources and ecosystem services such as food, fiber, shelter, and
fresh water; on the other hand, some forms of land use are degrading the
ecosystems and services upon which we depend.
19
However, Riebsame et al (1994) stated that changes in land cover as a
result of land use do not necessarily imply a degradation of the land. Land cover
can be altered by forces other than anthropogenic. Natural events such as weather,
flooding, fire, climate fluctuations, and ecosystem dynamics may also initiate
modifications upon land cover (Meyer, 1995).
Turner and Butzer (1992) noted two broad types of global change which
were systemic and cumulative. Systemic change operates directly on the bio-
chemical flows that sustain the biosphere and, depending on its magnitude, it can
lead to global change, just as fossil fuel consumption increases the concentration
of atmospheric carbon dioxide. Cumulative change has been the most common
type of human induced environmental change since antiquity. Cumulative changes
are geographically limited, but if repeated sufficiently, become global in
magnitude. Changes in landscape, cropland, grasslands, wetlands, or human
settlements are examples of cumulative change.
Riebsame, Meyer and Turner (1994) categorised changes in land cover
driven by land use into two types: “modification” and “conversion”.
“Modification” is a change of condition within a cover type; for example,
unmanaged forest modified to a forest managed by selective cutting, “conversion”
is a change from one cover type to another, such as deforestation to create
cropland.
Remote sensing
The acquisition of information about the environment without being in
direct contact with it is traditionally called remote sensing. Examples range from
20
aerial photography to multi-spectral scanning and radar (Lillesand and Kiefer,
1987; IDRISI, 1999). The focus of remote sensing is the measurement of emitted
or reflected electromagnetic radiation, or spectral characteristics, from a target
object by a multispectral satellite sensor. A multispectral sensor acquires multiple
images of the same target object at different wavelengths (bands). Each band
measures unique spectral characteristics about the target. A spectral band is a data
set collected by the sensor with information from discrete portions of the
electromagnetic spectrum. The electromagnetic (EM) spectrum is a range of
electromagnetic radiation ranging from cosmic waves to radio waves (Richards,
1986).
In most contemporary land use studies that employ remote sensing imagery
from multispectral sensors, the foremost task is the observation of spectral
characteristics of measured electromagnetic radiation from a target or landscape.
Analysts develop signatures based upon the detected energy’s measurement and
position in the electromagnetic spectrum. A signature is a set of statistics that
defines the spectral characteristic of a target phenomenon. Image analysts
determine the measurement of signature separability by determining quantitatively
the relation between class signatures. Signatures are refined by improved ground-
truth and accuracy assessment analysis. By utilizing the developed signatures in
multispectral classification and thematic mapping, the analyst generates new data
for analysis (ERDAS, 1999).
Resolution is an important term commonly used to describe remotely
sensed images. However, there are four distinct types of resolution that must be
considered. These four types of resolution are spatial, spectral, radiometric, and
21
temporal. These characteristics help to describe the functionality of both remote
sensing sensors and remotely sensed data.
Spatial resolution is the minimum size of terrain features that can be
distinguished from the background in an image, or the ability to differentiate
between two closely spaced features in an image. Spectral resolution refers to the
number and dimension of specific wavelength intervals in the electromagnetic
spectrum to which a sensor or sensor band is sensitive or can record. Radiometric
resolution refers to the dynamic range, or number of possible data files values in
each band. This is referred to by the number of bits into which the recorded energy
is divided. The total intensity of the energy, from 0 to the maximum amount, the
sensor measures is broken down, for example, into 256 brightness values for 8-bit
data. The data file values range from 0, for no energy return, to 255, for maximum
return, for each pixel.
Temporal resolution is a measure of how often a given sensor system
obtains imagery of a particular area, or how often an area can be revisited. The
temporal resolution of satellites is on a fixed schedule. The fixed schedule of
satellites allows for more repetitive views. This revisit capability makes it possible
to use several passes, covering perhaps two or three seasons or multiple years, for
interpretation.
Remote sensing has become an important tool applicable to developing and
understanding the global, physical processes affecting the earth. As current trends
continue, additional and higher resolution satellites will become available
providing the means to produce more accurate land use and land cover maps
characterized by finer levels of detail (Bottomley, 1998).
22
Landsat thematic mapper sensor and multispectral imagery
Lauer et al (1997, cited in Bottomley 1998) give a brief history of the
Landsat system and its renowned success. The United States has pioneered land
remote sensing from space and has been the leader in the development of earth
observing technology over the past thirty years. The evolution of the Landsat
programme has been a fundamental genesis for the better measurement and
monitoring of the Earth and its precious resources. Despite early
military/intelligence programmes in space during the 1950s and 1960s, the
scientific and industrial communities in the U.S. became aware of the potential of
earth observing vehicles in space. The National Aeronautics and Space
Administration (NASA), in cooperation with other federal agencies, successfully
launched on July 23, 1972, the first Earth Resources Technology Satellite (ERTS-
1), which was later renamed Landsat 1. Landsat 1 was a Nimbus-type platform
which carried sensor package and data-relay equipment. ERTS-2 was launched on
January 22, 1975, and was also renamed Landsat 2. Additional Landsats were
launched in 1978, 1982, and 1984 and renamed Landsats 3, 4, and 5 respectively.
Each successive satellite system has had improved sensor and communication
capabilities.
The Landsat programme has had an enormous impact on numerous
application arenas. In addition to the inauguration of global research, the Landsat
program use has also provided researchers with real-world data and access to
greatly enhanced spatial and analytical tools. The premise of the Landsat program
use is that the Earth’s features and landscapes can be discriminated, identified,
categorized, and mapped on the basis of their spectral reflectances and emissions.
23
The sensors of the Thematic Mapper record electromagnetic radiation in
seven bands. Bands 1, 2, and 3 are in the visible portion of the spectrum. Bands 4,
5, and 7 are in the reflective-infrared portion of the spectrum. Band 6 is in the
thermal portion of the spectrum. The following list describes each of the seven TM
bands:
Band 1 – Visible Blue, 0.45 – 0.52 um: useful for mapping coastal water areas,
differentiating between soil and vegetation, forest type mapping, and
detecting cultural features.
Band 2 – Visible Green, 0.52 – 0.60 um: corresponds to the green reflectance of
healthy vegetation. It is also used for cultural feature identification.
Band 3 – Visible Red, 0.63 – 0.69 um: useful for discriminating between many
plant species. It is also useful for determining soil boundary and geological
boundary delineations as well as cultural features.
Band 4 – Reflective - infrared, 0.76 – 0.90 um: this band is especially responsive
to the amount of vegetation biomass present in a scene. It is useful for crop
identification and emphasizes soil/crop and land/water contacts.
Band 5 – Mid - infrared, 1.55 – 1.74 um: sensitive to the amount of water in
plants. It is useful in crop drought studies and in plant health analyses. This
is also one of the few bands that can be used to discriminate between
clouds, snow, and ice.
Band 6 – Thermal - infrared, 10.40 – 12.50 um: useful for vegetation and crop
stress detection, heat intensity, insecticide applications, and for locating
thermal pollution. It can also be used to locate geothermal activity.
24
Band 7 – Mid - infrared, 2.08 – 2.35 um: important for the discrimination of
geologic rock type and soil boundaries, as well as soil and vegetation
moisture content.
Different combinations of the TM bands can be displayed to create
different composite effects. The following combinations are commonly used to
display images:
Bands 3, 2, and 1 create a true colour composite. True colour means that
objects look as though they would to the naked eye, similar to a photograph.
Bands 4, 3, and 2 create a false colour composite. False colour composites
appear similar to an infrared photograph where objects do have the same colours
or contrasts as they would naturally. For instance, in an infrared image, vegetation
appears red, water appears navy or black.
Bands 5, 4, and 2 create a pseudo colour composite. (A thematic image is
also a pseudo colour image.) In pseudo colour, the colours do not reflect the
features in natural colours. For instance, roads may be red, water yellow, and
vegetation blue.
With adequate knowledge of band properties and the appropriate
combination of Landsat TM bands, the extraction of numerous themes, land use
and land cover classes can be achieved for various mapping applications
(Bottomley, 1998).
Image classification techniques
Within the scope of this study, image classification is defined as the
extraction of distinct land use and land cover categories from satellite imagery.
25
There are two primary methods of image classification utilized by image analysts,
namely unsupervised and supervised classification.
Unsupervised image classification is a method in which the image
interpreting software separates the pixels in an image based upon their reflectance
values into classes or clusters with no direction from the analyst. Once this process
is completed, the image analyst determines the land cover type for each class
based on image interpretation, ground truth information, maps, field reports, etc.
and assigns each class to a specified category by aggregation (Bottomley, 1998;
IDRISI, 1999; ERDAS, 1999).
Supervised image classification is a method in which the analyst defines
small areas, called training sites, on the image which are representative of each
desired land cover category. The delineation of training areas to represent cover
types is most effective when an image analyst has knowledge of the geography of
a region and experience with the spectral properties of the cover classes
(Skidmore, 1989). The image analyst then trains the software to recognize spectral
values or signatures associated with the training sites. After the signatures for each
land cover category have been defined, the software then uses those signatures to
classify the remaining pixels (Bottomley, 1998; IDRISI, 1999; ERDAS, 1999).
When classifying satellite imagery, single supervised or unsupervised
classification techniques are often not enough to effectively classify an image.
Automated classification accuracies can often be unacceptably low, < 80%, at the
required level of categorical detail for many applications (Bolstad and Lillesand,
1992). Modifications of image classification techniques are most often required in
order to assess for classification accuracy. Experimentation with proven or
26
standardized classification techniques can produce accurate land cover classes as
well as lead to the development of new classification procedures. Modifications of
image classification techniques are often required in order to obtain adequate
classification accuracy.
Global positioning systems (GPS)
Global Positioning Systems (GPS) provide the mapping community with
powerful tools for acquiring accurate and current digital data. Combined with high
resolution remote sensing and Geographical Information System (GIS) for land
use studies, GPS can provide high accuracy ground-truth data for training-site
development (Pearson II and Frederick, 1990; Dana, 1995; Bottomley, 1998;
IDRISI, 1999). The constellation of satellites around the world which provide
geographical information to the receptors of GPS on Earth is shown in figure 6.
24 Satellites in 6 Orbital Planes 4 Satellites in each Plane 20,200 km Altitudes, 55 Degree Inclination
Figure 6: GPS National Constellation Source: Djebre (2004)
27
Essentially, the GPS satellites broadcast a continuously available time
signal using an on-board atomic clock. Receivers use these time signals to
calculate the distance to the satellite to establish an accurate position. However,
the accuracy of GPS positions can vary substantially and the user must be aware of
the factors that influence the precision of a GPS signal. The type of GPS service
accessed, the type of GPS equipment and processing techniques utilized, and
satellite geometry are some of the key factors affecting GPS precision (Bobbe,
1992).
Geographic information systems (GIS)
Another recent development in the use of satellite data is to take advantage
of increasing amounts of geographical data available in conjunction with
geographic information systems to assist in interpretation (Bottomley, 1998).
Geographical data describe objects from the real world in terms of (a) their
position with respect to a known coordinate system, (b) their attributes that are
unrelated to position (such as colour, type, cost, pH, incidence of disease, etc.) and
(c) their spatial interrelations with each other (topological relations), which describe
how they are linked together or how one can travel between them (Burrough, 1986).
The concept of geographic information system emerged during the 1960’s and
1970’s as new trends arose in the ways in which maps were being produced and
used for resource assessment, land evaluation, and planning. Essentially, this
concept focuses on the ability to develop a powerful set of tools for collecting,
storing, retrieving at will, transforming, and displaying spatial geographic data
from the real world for specific analysis and inquiry. This set of tools constitutes a
28
geographical or geographic information system. Geographic information systems
comprised three main components: computer hardware, sets of application
software modules, and a proper organization context (Gersmehl, 1991; ESRI,
1994; Burrough, 1986).
In addition, Robinove (1986) defines a geographic information system as a
collection of computer programs in a given hardware environment which operate
on a geographic database to analyze individual database elements or for synthesis
of multiple database elements.
With the increasingly widespread, combined implementation of remote
sensing and GIS technology namely, natural resource professionals have been
provided with efficient and accurate tools for mapping and maintaining
management information on forests and other natural resources in regional areas
(Bottomley, 1998). GIS technology is expanding, and allowing for greater
integration of remote sensing with digital cartography; thus providing the means to
produce more accurate land use and land cover maps.
Land use evolution detection
An increasingly common application of remotely sensed data is for change
detection. Change detection is the process of identifying differences in the state of
an object or phenomenon by observing it at different times (Singh, 1989; Turner
II, Ross, and Skole, 1993; Foley et al. 2005). Change detection is an important
process in monitoring and managing natural resources and urban development
because it provides quantitative analysis of the spatial distribution of the
population of interest. It is also useful in such diverse applications as land use
29
change analysis, monitoring shifting cultivation, assessment of deforestation, study
of changes in vegetation phenology, seasonal changes in pasture production,
damage assessment, crop stress detection, disaster monitoring, day/night analysis
of thermal characteristics as well as other environmental changes (Singh, 1989;
Zhongchao et al. 2002; You et al. 2004).
Macleod (1998, cited in Botomley, 1998) listed four aspects of change detection
which are important when monitoring natural resources: namely detecting the
occurrence of change, identifying the nature of the change, measuring the area
extent of change and assessing the spatial pattern of the change.
Scientific literature has revealed that digital change detection is a difficult
task to perform accurately and unfortunately many of the studies concerned with
comparative evaluation of these applications have not supported their conclusions
by quantitative analysis (Singh, 1989). All digital change detection is affected by
spatial, spectral, temporal, and thematic constraints. The type of method
implemented can profoundly affect the qualitative and quantitative estimates of the
change. Even in the same environment, different approaches may yield different
change maps. The selection of the appropriate method therefore takes on
considerable significance. Not all detectable changes, however, are equally
important to the resource manager. On the other hand, it is also probable that some
changes of interest will not be captured very well, or at all, by any given system.
Competing models for land use dynamics
Several models to be used for land use change exist depending on the
interest of each study. A summary of these models pointing out the variables used
30
31
for each model, including the strengths and weaknesses of each model are shown
in Table 5.
The weaknesses of most of the models concern their inability to take into
account one or two of the critical dimensions of time, space and human decision-
making, hence the choice of the model used by Agarwal et al (2002) to assess
human-environmental dynamics in this study. This model highly involves time
scale and complexity, spatial scale and complexity and human decision-making.
Table 5: Summary of land use dynamics models Model name
Model type Components/modules
Variables Strengths Weaknesses
1. General Ecosystem Model (GEM) (Fitz . et al1996)
Dynamic systems model
14 Sectors (modules), e.g. Hydrology Macrophytes Algae Nutrients Fire Dead organic matter Separate database for each secto
Captures feedback among abiotic and biotic ecosystem components
103 input parameters, in a set of linked databases, representing the modules, e.g., Hydrology Macrophytes Algae Nutrients Fire Dead organic matter
Spatially dependent model, with feedback between units and across time Includes many sectors Modular, can add or drop sectors Can adapt resolution, extent, and time step to match the process being modeled
Limited human decision making
2. Patuxent Landscape Model (PLM) (Voinov et al. 1999)
Dynamic systems model
Based on the GEM model (#1, above), includes the following modules, with some modification: 1) Hydrology 2) Nutrients 3) Macrophytes 4) Economic model
Predicts fundamental ecological processes and land-use patterns at the watershed level
In addition to the GEM variables, it -adds dynamics in carbon-to-nutrient ratios -introduces differences between evergreen and deciduous plant communities -introduces impact of land management through fertilizing, planting, and harvesting of crops and trees
In addition to the strengths of the GEM, the PLM incorporates several other variables that add to its applicability to assess the impacts of land management and best management practices
Limited consideration of institutional factors
3. CLUE Model (Conversion of Land Use and Its Effects) (Veldkamp
Discrete, finite state model
1) Regional biophysical module 2) Regional land-use objectives module 3) Local land-use allocation module
Predicts land cover in the future
Biophysical drivers Land suitability for crops Temperature/Precipitation Effects of past land use (may explain both biophysical degradation and
Covers a wide range of biophysical and human drivers at differing temporal and spatial scales
Limited consideration of institutional and economic variables
34
Model name
Model type Components/modules
Variables Strengths Weaknesses Table 5 continued
and Fresco 1996a)
improvement of land, mainly for crops) Impact of pests, weeds, diseases Human Drivers Population size and density Technology level Level of affluence Political Structures (through command and control, or fiscal mechanisms) Economic conditions Attitudes and value
4. CLUE-CR (Conversion of Land Use and Its Effects – Costa Rica) (Veldkamp and Fresco 1996b)
Discrete finite state model
CLUE-CR an application of CLUE (#3, above) Same modules
Simulates top-down and bottom-up effects of land-use change in Costa Rica
Same as CLUE (#3, above)
Multiple scales - local, regional, and national Uses the outcome of a nested analysis, a set of 6x5 scaledependent land-use/landcover linear regressions as model input, which is reproducible, unlike a specific calibration exercise
Authors acknowledge limited consideration of institutional and economic factors
5. Chomitz et al. (1996)
Econometric (multinomial logit) model
Single module, with multiple equations
Predicts land use, aggregated in three classes: Natural vegetation Semi-subsistence agriculture Commercial farming
Soil nitrogen Available phosphorus Slope Ph Wetness Flood hazard Rainfall National land
Used spatially disaggregated information to calculate an integrated distance measure based on terrain and presence of roads Also, strong theoretical underpinning of Von Thünen’s model
Strong assumptions that can be relaxed by alternate specifications Does not explicitly incorporate prices
35
Model name
Model type Components/modules
Variables Strengths Weaknesses Table 5 continued
Forest reserve Distance to markets, based on impedance levels (relative costs of transport) Soil fertility
6. Wood et al. 1997
Spatial Markov model
Temporal and spatial land-use change Markov models
Land-use change
Models under development
Investigating Markov variations, which relax strict assumptions associated with the Markov approach Explicitly considers both spatial and temporal change
Not strictly a weakness, this is a work in progress and, hence, has not yet included HDM factors
7. CUF (California Urban Futures) (Landis 1995, Landis et al. 1998)
Spatial simulation
Population growth submodel Spatial database, various layers merged to project Developable Land Units (DLUs) Spatial Allocation submodel Annexation-incorporation submodel
Explains land use in a metropolitan setting, in terms of demand (population growth) and supply of land (underdeveloped land available for redevelopment)
Population growth, DLUs, and intermediate map layers with: Housing prices Zoning Slope Wetlands Distance to city center Distance to freeway or BART station Distance to sphere-of-influence boundaries
Underlying theory of parcel allocation by population growth projections and price, and incorporation of incentives for intermediaries - developers, a great strength Large-scale GIS map layers with detailed information for each individual parcel in 14 counties provide high realism and precision
Compresses long period (20 years) in a single model run Has no feedback of mismatch between demand and supply on price of developable land/housing stock Does not incorporate impact of interest rates, economic growth rates, etc.
8. Swallow et al. 1997
Dynamic model
Three components: 1) Timber model 2) Forage production function
Simulates an optimal harvest sequence
Present values of alternative possible states of the forest, using the three model components
The long time horizon, and the annual checking of present values under alternate possible states of the forest makes it a useful forest management tool
Authors note that the optimal management pattern on any individual stand or set of stands
36
37
Model name
Model type Components/modules
Variables Strengths Weaknesses
3) Non-timber benefit function
for maximizing multiple-use values
requires specific analysis rather than dependence on rules of thum
b
9. Clarke et al. 1998, Kirtland et al. 2000
Cellular automata model
Simulation module consists of complex rules Digital dataset of biophysical and human factors
Change in urban areas over time
Extent of urban areas Elevation Slope Roads
Allows each cell to act independently according to rules, analogous to city expansion as a result of hundreds of small decisions Fine-scale data, registered to a 30 m UTM grid
Does not unpackhuman
e
map
not
decisions that lead to spread of built areas Does not yet include biological factors
10. CURBA (California Urban and Biodiversity Analysis Model) (Landis et al. 1998)
Overlay of GIS layers with statistical urban growth projections
1) Statistical model of urban growth 2) Policy simulation and evaluation model 3) Map and data layers of habitat types, biodiversity, and other natural factors
The interaction among the probabilities of urbanization, its interaction with habitat type and extent, and, impacts of policy changes on the two
Slope and elevation Location and types of roads Hydrographic features Jurisdictional boundaries Wetlands and flood zones Jurisdictional spheres of influence Various socioeconomic data Local growth policies Job growth Habitat type and extent maps
Increases understanding of factors behind recent urbanization patterns Allows projection of future urban growth patterns, and of the impact of projected urban growth on habitat integrity and quality
Human decision making not explicitly considered Further, errors arlikely from misclassification of data at grid level or misalignment offeature boundaries Errors also possible from limitations in explaining historical urban growth patterns
11. Gilruth et al. 1995
Spatial dynamic model
Several subroutines for different tasks
Predicts sites used for shifting cultivation in terms of topography and proximity to population centers
Site productivity (# of fallow years) Ease of clearing Erosion hazard Site proximity
Replicable Tries to mimic expansion of cultivation over time
Long gap between data collection; does include impact of landquality
Table 5 continued
38
Model name
Model type Components/modules
Variables Strengths Weaknesses
and socioeconomic variables
Population, as function of village size
Source: Literature review, 2006
Table 5 continued
Summary
The chapter has defined concepts and techniques related to land use
detection. These concepts are land use/land cover, land use change and its
consequences, land use evolution detection and competing models. Remote
sensing, GIS, GPS and Landsat TM imageries are tools to be used for land use
dynamics assessment. The local environmental issues concern the man who
contributes at a local level to the land use change.
The literature suggests that remote sensing and GIS are accurate tools for
land use change detection. Data from remote sensing platforms such as satellite
images provide information for GIS database. These data can be used for resource
monitoring, environmental analysis, forecasting and assessment. It was revealed
that several models for detecting land use change exist, but the one that takes into
account the dimensions Time, Space and Human decision-making ensures a better
understanding of the dynamics. Obviously, solutions must be found to resolve the
debate between the IBS and Howorth and O’Keefe’s findings on the state of the
environment in the province, and to generate results that best represent the reality
on the ground; hence the focus on land-use dynamics at a district scale (Bieha)
from 1986 to 2002 using Landsat TM images.
40
CHAPTER THREE
REVIEW OF ENVIRONMENTAL ISSUES
IN BURKINA FASO
Introduction
The Burkina Faso environment is characterised by great vulnerability; any
stability is only maintained by human management. However, several
unsustainable management practices are contributing to environmental
degradation, thus increasing people’s vulnerability through reduced productivity
and resilience to stress (Simonsson, 2005). According to MEE (1996) and MECV
(2004), from 1980 to 1992 the surface area of the forest of Burkina Faso reduced
from 15.42 million hectares to 14.16 million hectares. The annual loss of forest
was estimated at 105,000 ha (MEE, 1996). According to Kramer (2002), the
World Bank estimated the annual loss of wooded land surface in Burkina Faso at
80,000 to 100,000 ha while the FAO (2000) assumes an annual loss of 15.266 ha
equivalent to 0.2 % of wooded land surface, exclusively for those surfaces cleared
to make way for agriculture. According to Mongabay (2005), between 1990 and
2000, Burkina Faso lost an average of 24,000 ha of forest per year which
amounted to an average annual deforestation rate of 0.34 %. Between 2000 and
2005, the rate of forest change increased to 0.35 % per annum. In total, between
1990 and 2005, Burkina Faso lost 5.0 % of its forest cover, or around 360,000
hectares. Measuring the total rate of habitat conversion (defined as change in
41
forest area plus change in woodland area minus net plantation expansion) for the
1990-2005 intervals, Burkina Faso lost 2.8 % of its forest and woodland habitat.
Two studies related to land-cover using remote sensing and GIS tools have
been reported. The first was undertaken by IBS (1994) and aimed at examining
deforestation rates in Sissili since 1988 using Landsat and Spot images from 1988
and 1993. On the basis of maps produced from the study, extrapolations were
made on future deforestation which stated that the rate will increase from 21.6 %
in 1988 to 43.1 % by the year 2010. The second study was conducted by Howorth
and O’Keefe (1998) to investigate the new resource-use pattern that had developed
as a result of demographic changes. Based on maps of 1955 and 1983 and
interviews conducted in three villages of the province it was concluded that there
was a peaceful coexistence between the three ethnic groups living together in the
villages and there was no destruction of the environment at all in Sissili as IBS
forecasted. The study of Howorth and O’Keefe (1998) concluded that the
environment was improving in vegetation cover in Sissili.
The conclusions of the two studies were however contradictory. The time
interval used by IBS (1988 to 1993) may have been too limited to detect land use
changes, and worse of all, to forecast long-term changes in the environment. The
second study which used aerial photographs from 1955 and 1983 to map land use
changes in three villages of less than 40 km2 each, without detecting
environmental degradation, may be accurate at the village scale but, in terms of the
whole of Sissili province, it may be an exaggeration or overgeneralization.
As already reported in the literature the five broad and inter-related human
factors that lead to environmental degradation in Burkina Faso and specifically in
42
the study area have been identified as agricultural practices, migration,
overgrazing, fuel wood harvesting, timber logging and bushfires. Each of these is
briefly explained.
Agricultural practices and deforestation
The agricultural activities in Burkina Faso are undertaken in a rudimentary
and extensive way, with a low level of intensification (Bandre and Batta, 1998). In
this context, the only possible way to secure food security is to cultivate more land
(Reenberg and Lund, 2001). This is done by decreasing the ratio between fallow
and cultivated land within a village or by including new territory for cultivation,
thus contributing to the degradation of the environment (Howorth and O'Keefe,
1998; MEE, 1999). Under increasing population pressure, marginal lands are used
as farmers cultivate large area to maintain production but they do little to sustain
soil nutrient levels and the productive capacity of the soil. Short fallow periods and
inadequate use of fertilizers; coupled with overgrazing and deforestation through
fuelwood harvesting tend to cause loss of soil and vegetation cover, and water
degradation (MEF, 2000; Simonsson, 2005).
Elshout et al (2001) have argued that in the south and west zone of Burkina
Faso, the land clearance for extensive farming is the key contributor to vegetation
loss. In the study area, farming practices vary from one ethnic group to another.
Howorth and O'Keefe (1998) reported that the indigenous Nuni practice a gentle
form of agriculture which is exclusively manual with little inputs, relatively low
soil usage and use approximately 4.5 ha per family. This allows the retention of a
large number of trees and root systems, without causing great disturbance to the
43
agro-ecological system. The Fulani have settled extensively in Sissili, and tend to
concentrate their animal herding in the zones of low-intensive agriculture usually
in the periphery/wooded areas of villages. They cultivate about 1.5 ha per family
in old pasture zones containing high levels of cattle manure and, consequently,
have comparatively high yields. The Mossi, on the other hand, practise an
extensive form of agriculture with almost total field clearing, mainly for cereal
production. They tend so to exploit the lands in such a way as to degrade the
environment hence, there is an emergence of the relationship between migration
and deforestation.
Migration and environmental degradation
Migration is usually ignored in models of land use change (Veldkamp and
Fresco, 1996a; Shen, 2000; Stéphenne and Lambin, 2001), even though it is often
recognized to be the dominant demographic factor influencing land use (Lambin et
al, 2001). Many authors cite population growth as the single most important cause
of deforestation (Allen and Douglas, 1985; World Rainforest Movement, 1990;
World Bank, 1992). Population growth often leads to migration to the forest by
peasants seeking land to clear for subsistence farming.
One of the features of the population of Burkina Faso is its mobility (Kress,
2006). In the period between 1985 and 1991, 10 % of the Burkina Faso’s
population of 7.5 million inhabitants migrated from one province to another or
abroad (Jeune Afrique Atlases, 1998). Within the country, people tend to migrate
to the agricultural zone and to Ouagadougou and Bobo Dioulasso (Simonsson,
2005; Kress, 2006). SIDA’s Poverty Profile of Burkina Faso concluded that
44
migration seems to be a strategy for households to reduce poverty, more important
in rural than in urban areas; among men more than women; and among the poor
more than the rich (Haberg, 2000). Due to the migration, the population growth
rate in the savannah region was reduced by 1.1 % while, in the forest regions, it
increased by 0.6 % (Zachariah and Conde, 1981). The regions of departure lose
labour force while the social and economic infrastructure in regions of arrival may
have problems coping with the rapidly growing population (Henry et al, 2002).
A study on the inter-provincial migration in Burkina in 2003 (Henry et al,
2002) showed the highest immigration rate of 4.88 % in the Sissili province. With
large number of immigrants, the province faces difficulties related to land conflicts
and access to social infrastructure. As a consequence, the population of some
villages in the Province has more than doubled in 20 years. An example is the
Bieha district whose population increased from 15,043 inhabitants in 1985 to
25,634 inhabitants in 2006. The effect of this migration driven population growth
on land use was quickly visible. The newcomers tended to reproduce the same
extensive farming practices followed in the centre-north and, encouraged by large
private companies, are keen to produce cotton and maize (Gray, 1999). The land
requirement for migrants was thus larger than that for the sedentary populations
and land supply became limited (Mathieu, 1998).
Authors such as Hardin (1968), Ehrlich (1968), Ehrlich and Ehrlich (1990)
and Meadows et al (1972) have a pessimist view of the relationships between
population pressure and environment. According to them, population control must
be a part of any development strategy, otherwise environment will collapse. On the
other hand, some optimists argue that population pressure does not necessarily
45
lead to environment degradation. It stimulates development rather than slowing it
down, and moreover, it leads to innovation in agricultural technology and
techniques which support the increased number of population (Boserup, 1972;
Simon, 1980; Tiffen and Mortimore, 1994; Fairhead and Leach, 1996; Bassett and
Bi Zueli, 2000).
Overgrazing
Chikamai and Kigomo (2003) reported that overgrazing is the most notable
factor in causing de-vegetation and hence degradation. The heaviest impact of
overgrazing takes place in the Sahel countries especially areas falling within arid
and semi-arid zones. Overgrazing is concentrated around settlements and is often
related to recent sedentarisations of nomadic herders.
In Sissili province, the number of animals has been increasing substantially
due to the continued in-migration of the pastoralist Fulani (Howorth and O'Keefe,
1998). In 1986 the total number of bovines, ovine and goats was estimated at
215,000, but these increased to 900,000 in 2003, and thus jeopardizing the
carrying capacity of the province (DPAHRH, 2006). During the 1980s, the
government created a pastoral zone in Yalle (Bieha District) with the purpose of
settling about 75 families of breeders (Fulani), to promote the best quality of the
livestock and to limit the conflicts linked to competition for space between farmers
and breeders. Unfortunately, the breeders of the area refused this offer and
preferred to walk along the forest to graze their animals by cutting palatable
species.
46
Firewood and timber request
Wood fuel is the principal source of domestic energy in developing
countries (Openshaw, 1974; Eckholm, 1975; Arnold and Jongma, 1978). Wood
fuel includes charcoal as well as firewood, brushwood, twigs and cut branches
(Openshaw, 1986). Bandré and Batta (1998) argued that wood is used in Burkina
Faso for two main purposes: source of energy and as building material. Wood
represented 96 % of the domestic energy consumed in 1993 and accounted for 64
% of the national requirement in primary energy. The average per capita
consumption is 300 kg/year in the north while in the southern and western areas it
is over 800 kg/year (Bandré and Batta, 1998). The annual commercial value is
estimated at 35 billion CFA (58,000,000 USD) for the firewood and 6 billion CFA
(10,000,000 USD) for the timber (Kessler and Greeling, 1994). According to
Kramer (2002), the consumption of wood in Burkina Faso is higher than the
production. This means that there is no ecological sustainability. At the same time,
because of the low economic productivity, import of necessary amount of energy
cannot be envisaged neither on the macro-economic level nor on the family budget
level.
The rate of deforestation in the eastern, southern and western zones of the
country to meet the needs for energy and timber consumption became so high that
the Ministry in charge of environment and water took a decision to suspend
charcoal production from July, 15th 2005 (Le Pays, 2005). The Ministry estimated
at 593,092 tons the quantity of charcoal consumed in 2004 in Burkina Faso while
the annual loss of forest to charcoal production was estimated at 370,000 hectares.
47
Bushfire
Fire has always played a major role in most of Sub-Sahara Africa in
clearing the field, hunting, improving visibility, accelerating the re-growth of
perennial grasses and in customary rituals as reported by Bandré and Batta (1998);
who also argued that frequent fire, and especially late fires, not only killed most of
the perennial plants, but also impoverished the soil and reduced its productivity.
Fire also causes loss of certain nutrients (nitrogen and sulphur), which are usually
dispersed in the atmosphere, and loss of organic matter.
Bushfire practices are ancient in Burkina Faso going back to the pre-
colonial period (Aubreville, 1949; Belloward, 1959; Ministère de l’Environment et
du Tourisme, 1991; Kambou and Poussi, 1997; Yameogo, 2005). Three types of
fires exist in Burkina: early fire, intermediate fire and late fire (Yameogo, 2005).
According to Ministère de l’Environment et du Tourisme (1991) and Zida (1993),
the surface annually touched by fires is estimated at 98,568 km², which is about 55
% of the forest surface of the country. In the provinces of the south of the country
(Sissili, Ziro and Nahouri), which possess 12,305 km² of forest, 9,844 km² (80 %)
are fired each year (Ministère de l’Environment et du Tourisme, 1991). Bushfires
cause a loss of 200 million Euros in animal resources, 10.7 million Euros in wood
production and more than one million in wildlife and cotton production (Zida,
1993).
48
CHAPTER FOUR
METHODS OF DATA COLLECTION AND
ISSUES FROM THE FIELD
Introduction
This chapter describes the data and sources as well as the methods and
tools employed in the data collection. It also covers the sampling techniques and
the problems encountered in the field and how they were solved.
Data and sources
The data collected were basically quantitative arising from primary and
secondary sources. The primary data were divided into two broad categories:
a) Data resulting from the satellite image processing that dealt with
quantitative variables and concerned the surface areas of the land use units
in time series;
b) Data from interviews of sample population in the study area which dealt
with quantitative variables such as the perception of density of trees and
the wild animals, the productivity and availability of food, etc.
The secondary data were collected from textbooks in documentation
centres (offices of agriculture and animal resources, etc.) and centres of primary
data storage such as the national meteorology, the national statistics and
demography office. These data concern the agricultural and pastoral practices and
yields, the rainfall and the quality of soils in the study area.
49
Images processing
Satellite images
Remote sensing and Geographical Information System (RS/GIS) tools
were used to carry out the different land-use units and their respective surfaces
based on satellite data. The main data used in the research included Landsat
Thematic Mapper satellite images of 1986 and 2002 (hereafter referred to as TM
images). Sissili province is covered entirely by the images number 195/52 of
Landsat TM (Figure 7). A brief description of the satellite images used is shown in
Table 6. Digital topographic data with contour interval of 10 m produced by the
Geographic Institute of Burkina Faso (IGB) were also used.
Figure 7: Landsat TM image mosaic of Burkina Faso
Source: Database of the Geographical Institute of Burkina (IGB), 2006
50
Table 6: Satellite images used for land use detection of Sissili
Satellite
Type
Sensor Image
number
Number of
bands
Pixel
spacing
Observation
date
Landsat TM 195/52 7 30 x 30 18 Nov. 1986
Landsat TM 195/52 7 30 x 30 21 Oct.2002
Source: Landsat database (2006)
The TM images were provided by the Institute for Environmental and
Agronomic Research (INERA) of Ouagadougou (Burkina Faso). They were
acquired within the same season (end of the rainy season) and are at the same
resolution: 30 meters resolution. The two dates have the same vegetation
conditions according to the climate of the study area. According to the farming
practices, October and November are the harvesting periods during which
precocious bush fires occur. As a consequence, there is a lot of haze in the images.
Those factors give effects on vegetation status causing reflectance values of land
use quite difficult to compare. However, possible similar nomenclatures were set
up based on physical characteristics of land use.
The ground-truth information required for the classification and accuracy
assessment of the images was collected from the field during January, 2006 using
a training sample protocol. In addition, a self-designed format was used to collect
vegetation level information on vegetation types, condition and history of land use
provided by the local people and direct observation in the field.
51
Geometric correction
Subsets of satellite images were rectified first for their inherent geometric
errors using digital topographic maps in Modified Universal Transverse Mercator
coordinate system obtained as the reference material. The image was registered to
the digital topographic maps using distinctive features such as road intersections
and stream confluences that are also clearly visible in the image. A first-degree
Rotation Scaling and Translation transformation function and the Nearest
Neighbour re-sampling method were applied. This re-sampling method uses the
nearest pixel without any interpolation to create the warped image. A total of 20
points were used for registration of TM image subset with the rectification error of
0.1083 pixels.
A very high level of accuracy in the geo-referencing of the images was
possible because of the use of digital source as the reference data that allowed
zooming to the nearest possible point location.
Classification
The supervised Maximum Likelihood Classification method was used for
the classification of all the images. Training areas corresponding to each
classification item (or, land use class), were chosen from among the training
samples collected from the field.
To produce land use maps of 1986 and 2002 and to investigate changes
that occurred between these periods, the following four land use classes were
considered in image classification: gallery forest, wooded savannah, shrubby
savannah and farm fields. The choice of these land use classes was guided by: i)
52
the objective of the research, ii) expected certain degree of accuracy in image
classification, and iii) the easiness of identifying classes on false composite of the
images and the ground. A brief description of each of the land use classes is
presented in Table 7.
Table 7: Land use classes considered in image classification and change detection Land use class General description
Gallery forest Forest areas mostly along the rivers with estimated 75
percent or more of the existing crown covered by
broadleaf trees. The predominant species are: Pterocarpus
erinaceus, Afzelia africana, Kaya senegalensis,
Anageissus leiocarpus, Parkia biglobosa, Cassia
sieberiana,Mitragina innermis, etc.
Wooded
savannah
Wooded areas with estimated 50 percent or more of the
existing crown covered by naturally growing trees. It
includes also old fallows. Common species are Vitelaria
paradoxa, Parkia biglobosa, Lannea microcarpa, Lannea
acida, Sclerocarya birrea, Saba senegalensis, Diospyros
mespiliformis, detarium mocrocarpum, etc.
Shrubby
savannah
Land covered by shrubs, bushes and young broadleaf
regeneration including recent fallows. Degraded forest
areas with estimated <10 % tree crown cover are also
included. The common tree species are: Calotropis
procera, Peliostigma reticulatun, Guiera senegalensis,
53
Combretum micranthum, Vitelaria paradoxa, Lannea
acida, detarium mocrocarpum, etc.
Table 7 continued
Farm fields Agricultural lands with or without barren lands,
settlements, roads, construction sites and other built-up
areas. The main crops grown are: cereals (Sorghum,
millet, maize), oleaginous (groundnut, sesame), cash crops
(cotton), tubers (cassava, yam, potato) and plantation
(cashew, mangoes, orange, etc.).
Source: Landsat database (2006)
Detection of land changes
The Winships programme was used to convert the images from geotif
format to Idrisi raster (tfw). Bands 2, 3 and 4 were used for the image
classification because they are especially responsive to the amount of vegetation
biomass present in the images. Band 4 is put in the channel Red, band 3 in the
channel Green and band 2 in the channel Blue. A 432/RGB false colour
composites were produced (Figure 8). The specificity of each band of Landsat TM
images was described in Chapter Two.
After selectively combining classes, classified images were filtered before
producing the final output (Figure 9). A 3x3 median filter was applied to smooth
the classified images. All activities related to image processing were performed
with IDRISI 32.
54
Classified images were converted into vector format, and then exported to
ArcView-GIS Version 3.2 from IDRISI. In ArcView environment, the vectors
were clipped with the real limit of the study area and intersected each other in
order to detect the land use change within these two dates. All these operations
were done with the module “geo-processing”. The land use polygon themes for
1986 and 2002 were converted into MapInfo format with the module “universal
translator”. Land use units computing and the finishing of the maps were done
with MapInfo. The data base was exported to EXCEL (dbf format) for further
analyses.
55
Figure 8: False colour composites of satellite imageries
Source: Landsat TM image and author’s design, 2005.
56
Figure 9: Supervised classification of Landsat image of Bieha district in 2002
Source: Landsat image processing, 2006.
57
Problems encountered during the images processing
It was difficult to separate fallow from the other units since they appeared
like farm fields, shrubby savannah or wooded savannah in accordance with their
duration. Presence of cloud in parts of the TM image was the second major
problem encountered during image classification. The clouds were classified as
separate classes and later combined with their respective classes with the help of
ground-truth information. The third and last problem concerned the bushfire
detection. It was quite difficult to map the bushfires due to the fact that the early-
fires did not destroy completely the grasses since they were still green.
Immediately after the fire, grasses and leaves re-grew and affected the detection of
the impact of the fire in the ground by the satellite.
Population interviews
Instruments used
For the primary data, a structured questionnaire was developed to collect
information. The questionnaire was mostly close-ended and was categorised into
sixteen (16) sections (appendix 1). Each section represented a specific sub-theme
from the set of information to be collected (Table 8).
58
Table 8: Structure of the questionnaire
Section Sub-theme
1 Vegetation dynamics
2 Wild animals dynamics
3 Crops productivity
4 Food security at household level
5 Type of crops produced
6 Farming practices
7 Soil dynamics
8 Arable land dynamics
9 Household size
10 Income level
11 Drinking water sources
12 Living condition
13 Permanent migration
14 Temporary migration
15 Permanence of water in the rivers after the rainy season
16 Availability of fishes in the rivers
Source: Author’s construct, 2005.
Method of sampling
The target population was the total population (male and female) of 25,
634 in Bieha district who were 40 years old or more and have been living in the
district for at least 20 years. The assumption was that people who satisfied these
two conditions were old and qualified enough to provide accurate information
related to the sixteen sub-themes of the questionnaire in 1986, 1996 and in the
recent time. The target population was multiplied by 0.16 to obtain sampling frame
59
of 4,101 as the population of those old enough to provide the right information.
This was based on the fact that the population aged of at least forty years old
formed about 16 % of the total population in 2006 of Sissili province (I.N.S.D,
1996). Based on the sample frame, a sample fraction of 0.03 was purposively
chosen and used to generate a sample size of 123. From the 22 villages that made-
up Bieha district, 11 villages were randomly chosen for the survey (Figure 10).
The number of respondents selected from each village was based on the population
size of that village (Table 9).
Figure 10: Selected villages for the survey in Bieha district
Source: Author’s construct, 2005.
60
Table 9: Selected villages and number of respondents for survey
Selected villages Population in 2006 Sample size
Bieha 2,193 13
Binou 1,240 10
Boala 700 10
Danfina 1646 11
Prata 1,098 10
Kumbo 1,839 11
Kumbogoro 2,713 12
Livara 972 10
Pissai 1,933 12
Yalle 3,437 14
Yelbouga 1,549 10
Total 19,320 123
Source Author’s construct, 2005
Pre-survey activity in the villages
Official permission was first sought from the Prefect of Bieha district and
the central Chief of Bieha; while, in each of the selected villages, permission was
sought from the chief, elders and representatives (RAV) before interviews
commenced. The intension was to gain the support and cooperation of members of
the communities through these opinion leaders. At least one literate person in the
village was employed to translate the questions and answers from French to the
local language and vice-versa.
The Offices of Environment and Earth (DPECV) of Sissili and Bieha were
also informed of the survey to be conducted and its purpose. As the officers
responsible for the local environment, the foresters were also involved in the
61
administration of the questionnaire. One copy of the questionnaire translated into
French was given to each forester to enable them better understand the work.
The fieldwork
The fieldwork began on March 18, 2006 and ended on 28th of the same
month. In each of the selected villages, the questionnaire was administered using
snowball for two reasons: on one hand it was not evident to know at the first view
those fulfilling the age condition and on the other hand those who have lived in the
village since the last 20 years.
Issues from the interview
Response rate
Out of the sample size of 123 proposed, a total of 113 respondents were
interviewed. This gives a response rate of 91.8 % (Table 10).
62
Table 10: Respondents and response rate by selected village.
Selected villages Proposed
sample size
Total
respondents
Response
rate (%)
Bieha 13 11 85
Binou 11 11 100
Boala 10 10 100
Danfina 11 11 100
Prata 10 9 90
Kumbo 11 10 91
Kumbogoro 11 8 73
Livara 10 7 70
Pissai 11 11 100
Yalle 14 14 100
Yelbouga 11 11 100
Total 123 113 92
Source: Field work, 2006
Problems encountered
In Bieha, Prata, Kumbo, Kumbogoro and Livara, the total number of
respondents expected was not reached. This was due to the high number of recent
migrants in these villages. Most of the adults who were present during the survey
did not fulfil the condition of having lived in the village for 20 years. Some of
them had, due to the dry season, left for distant markets or towns to look for jobs.
Particularly in Kumbogoro, we were forced to leave before we reached the
proposed number for security reasons. In fact, a poacher from the village had been
apprehended carrying bush meat in the Safari Ranch of Bieha and had been
severely injured by the patrolmen of the ranch. According to the villagers, the
63
forester of Bieha district who was doing the survey with me that day was
responsible for the poacher being apprehended. One of the poacher’s parents
threatened the forester with a dagger. We therefore suspended immediately the
interview and drove quickly to Leo (the provincial city).
The second problem encountered was the lack of effective communication
with respondents. This was because our dialect i.e. the forester and researcher,
differs slightly from the Nuni dialect. Fortunately in each village, we could find
someone who could speak both Moore and Nuni or French and Nuni. However,
the translation of the names of plants and animals was a little bit difficult and we
were obliged to write these names in the local language and later find out the
corresponding names in French and English.
Diagram of the methodology
On the basis of the conceptual model previously discussed and on the
methods of data collection (image processing and interview of population), a
scheme in four steps that summarizes all the methodological processes is presented
in Figure 11.
Step 1: Use of Remote Sensing (RS)
This referred to the processing of Landsat TM images of 1986 and 2002 as
a modern method to assess land use change and also to estimate the extent to
which the natural resources have been depleted as a result of human activities.
This step involved the fieldwork, GIS processing and interpretation of data.
64
Step 2: Fieldwork activities
This combined checking and confirming real field situation through check
points and observations and the collection of data related to the various uses of the
natural resources by the local population. This step involved also RS, GIS and
interpretation of data.
Step 3: Use of Geographical Information System (GIS)
This integrated raster and vector information and traced the maps of the
land use dynamics. The GIS provided preliminary maps that facilitated fieldwork
and interpretation of data.
Step 4: Validation of the changes
This step validated the dynamics that have taken place from 1986 to 2002.
The extent of resources degradation in the area and its consequences on the long-
term sustainability were verified.
65
(1) REMOTE SENSING
(2) FIELD WORK
Figure 11: Methodological approaches for the land use dynamics
Source: Author’s construct, 2006
Processing of Landsat TM images: 2, 3, 4 RGB (1986 and 2002). Result: raster and vectors.
- Observation - Ground-truth - Questionnaires to assess resource use
(3) GIS
Use of vectors to map the dynamics of land use and to compute the surface areas of the land use units.
(4) INTERPRETATION
Maps interpretation will show the change in the area from 1986 to 2002 while the resource use assessment points out the role of the population in the degradation of the environment.
66
Limitation of the study
The multispectral mapping of the land associated with digital remote
sensing and GIS techniques is characterized by inherent limitations. No map
produced by digital manipulation of multispectral data is ever 100 % correct when
it is produced by a computer (Robinove, 1981). By nature, the process of
classifying such a broad range of the Earth’s features into specific and often
simplified land use and land cover classes introduces error by drawing boundaries
around geographically located classes that are ‘homogeneous’ or acceptably
heterogeneous (Bottomley, 1998). However, these limitations can often be
overcome by sound statistical analysis to produce acceptably accurate land use and
land cover maps as derived from multispectral satellite data.
Three main difficulties were encountered during the field work which may
constitute the limitations of this study. The first limitation focuses on the
separation between fallows and the other land use units. The geo-processing
module permitted to identify and quantify the fallows in 2002, but the
identification of those of 1986 requested training areas corresponding to fallows
unit for the supervised classification, which unfortunately was not possible
because of their confusion with the other units, namely farm fields, shrubby and
wooded savannas. The identification of the fallow in 1986 would have permitted
to know whether they are diminishing as the population reported.
The second limitation concerned the identification of the bushfires in the
images. Bushfires were not drawn on the land use maps because the dates of image
captures corresponded to the period of early fires which left very few marks in the
ground.
67
The third limitation is the ground truth data acquired for accuracy
assessment. By utilizing the process of obtaining the ground truth data by
extensive GPS field surveys, bias with respect to proximity to roads is
characteristic of the data. It should be noted that this is not critical to the overall
accuracy assessment of the land use map; however, it is important to mention.
The forth limitation dealt with the population interview. It was difficult to
reach the expected number of respondents who satisfied the selectivity conditions,
so that in some villages, the number of respondents interviewed was below the
needed number. The situation may affect the high representativeness of the results.
68
CHAPTER FIVE
LAND USE ASSESSMENT
Introduction
This chapter, in two broad sections, presents the output of images
processing and population interviews. The first section deals with the states of land
use detected in 1986 and 2002 and the changes which occurred during that period.
The second section discusses the population’s perception on the environmental
change since 1986.
Land use detection
State of the land use in 1986
In 1986, the farm fields’ area represented only 2 % of the entire Bieha
district. The area was mostly confined to just around the villages. The most
important unit was the shrubby savannah which occupied 38 % of the district,
followed by the wooded savannah (32 %). The gallery forest was located along the
rivers and represented 27 % of the district (Figure 12 and Table 11).
In all, 3,438 hectares were used for farming activities in Bieha district. The
rest of the district was occupied by natural vegetation: shrubby savannah, wooded
savannah or gallery forest. It was obvious that some portions of the shrubby and
wooded savannah included both recent and old fallows, even though the image
processing did not permit their detection. The relatively low cropping surface is
likely to be due to the fact that at that time the population was less dense in the
69
district and the cotton and maize cultivation as well as the cashew production were
not so much practised.
Figure 12: Land use units in Bieha in 1986
Source: Image processing and field work, 2006
Table 11: Surface area and proportion of land use units in 1986
Land use units Surface area in 1986 (ha) Proportion (%)
Farm fields 3,438.69 2.0
Shrubby savanna 67,427.46 38.5
Wooded savanna 56,967.57 32.5
Gallery forest 47,634.09 27.0
Total 175,467.81 100.0
Source: Image processing, 2006
70
State of land use in 2002
Land use units, surfaces and proportions in Bieha district in the year 2002
are presented in Figure 13 and Table 12. On the whole, the farming area reached
33,686 hectares, about 19 % of the whole district while the shrubby savannah
dropped to 20 % of the total surface area of Bieha. The wooded savannah and
gallery forest did not change in terms of surface area (Figure 13, Table 12). Apart
from the ranch and the forest reserve which are excluded from farming activities
and the extreme south-west of the district dominated by forests and perennial
rivers, the rest of the district was almost used for farming (Figure 13). The
remaining shrubby savannah was located in the centre-north of the district. The
cropping acreage increased at the detriment of the shrubby savannah; an indication
that most agricultural activities were practised in the shrubby savannah.
71
Figure 13: Land use units in Bieha in 2002
Source: Image processing and field work, 2006
Table 12: Area and proportion of land use units in 2002
Land use units Surface area in 2002 (ha) Proportion (%)
Farm fields 33,686.64 19.0
Shrubby savannah 35,818.88 20.5
Wooded savannah 58,714.6 33.5
Gallery forest 47,240.36 27.0
Total 175,460.48 100.0
Source: Image processing, 2006
72
Land use dynamics from 1986 to 2002
Two maps representing the period from 1986 to 2002 were superimposed
to enable significant evolution in land use dynamics to be determined. The four
land use units as previously defined were codified and used to present a better
understanding of the changes that were detected (see Table 13).
Table 13: Codification of land use units
Land use units Codes
Farm fields F
Shrubby savannah Ss
Wooded savannah Ws
Gallery forest Gf
Source: Author’s design, 2006
By using the Geo-processing module (ArcView) and the Cross Tabulation
module (Excel), the following combinations which present the dynamics of land
use in Bieha district was obtained (Table 14)
73
Table 14: Legend of the land use dynamics from 1986 to 2002
Combinations
(1986/2002)
significance
D y n a m i c s o f t h e f a r m f i e l d s
FF Farm fields in 1986, still Field in 2002
FGf Farm fields in 1986 changed into Gallery forest in 2002
FSs Farm fields in 1986 changed into Shrubby savannah in 2002
FWs Farm fields in 1986 changed into Wooded savannah in 2002
D y n a m i c s o f t h e g a l l e r y f o r e s t s
GfF Gallery forest in 1986 changed into Farm fields in 2002
GfGf Gallery forest in 1986, still Gallery forest in 2002
GfSs Gallery forest in 1986 changed into Shrubby savannah in
2002
GfWs Gallery forest changed in 1986 into Wooded savannah in
2002
D y n a m i c s o f t h e s h r u b b y s a v a n n a h
Shrubby savannah in 1986 changed into Farm fields in 2002
SsGf Shrubby savannah in 1986 changed into Gallery forest in
2002
SsSs Shrubby savannah in 1986, still Shrubby savannah in 2002
SsWs Shrubby savannah in 1986 changed into Wooded savannah in
2002
D y n a m i c s o f t h e w o o d e d s a v a n n a h
WsF Wooded savannah in 1986 changed into Farm fields in 2002
WsGf Wooded savannah in 1986 changed into gallery forest in 2002
WsSs Wooded savannah in 1986 changed into Shrubby savannah in
2002
WsWs Wooded savannah in 1986, still Wooded savannah in 2002
Source: author’s construct, 2006
74
75
The results of the combinations are presented in Figures 14, 15, 16, and 17.
Figures 14 and 15 illustrate the comparison between land use types in 1986 and
2002. The size of the total farm fields in 2002 was about nine times its original
size in 1986. The surface area of the shrubby savannah was reduced to half. The
sizes of the wooded savannah and the gallery forest had almost remained
unchanged.
Figure 16 presents the observations of land use change from 1986 to 2002.
The farm field and wooded savannah units increased in surface area (gain) while
the shrubby savannah and the gallery forest experienced losses in surface area.
Figure 17 shows the trend of land use in Bieha. The size of farm fields
increased within the period 1986 to 2002 with an expansion of 880 % in 16 years,
and annual expansion rate of 55 %. The surface area of the shrubby savannah was
reduced drastically from 65,427 hectares in 1986 to 35,818 hectares in 2002. The
reduction rate was 46.8 %; about 3 % annual average rate of reduction. The
tendency curves (Figure 17) of the wooded savannah and the gallery forest
remained horizontal, meaning that the change was not noticeable. The annual
expansion rate of the wooded savannah was 0.19 % and the reduction rate of the
gallery savannah was 0.05 % annually (Table 15).
76
Figure 14: Land use dynamics in Bieha district from 1986 to 2002
Source: Image processing, 2006
0
10000
20000
30000
40000
50000
60000
70000
Surface area (ha)
Farm field Shrubby savannah Wooded savannah Gallery forest
Land use units1986 2002
Figure 15: Comparison between the size of land use types in 1986 and 2002
Source: Image processing, 2006.
-40000 -30000 -20000 -10000 0 10000 20000 30000 40000
Farm Field
Shrubby savannah
Wooded savannah
Gallery forest
Surface area (ha)
GainLoss
Figure 16: Observation of the land use change in Bieha
Source: Image processing, 2006
77
Table 15: Land use change in Bieha
Land use
Units
Area in
1986
Area in
2002
Change Proportion
within
16 years
(%)
Annual
exten-
sion
rate
(%)
Farm fields 3,438.69 33,686.64 30,247.95 +879,6 +54,9
Shrubby
savannah
67,427.46 35,818.88 31,608.58 -46,8 -2,9
Wooded
savannah
56,967.57 58,714.6 1,747.03 +3,0 +0,19
Gallery forest 47,634.09 47,240.36 -393.73 -0,8 -0,05
Source: Image processing, 2006
0
10000
20000
30000
40000
50000
60000
70000
80000
1986 2002
Year
Surface area
Farm Field
Gallery forest
Shrubby savannahWooded savannah
Figure 17: Tendency curves of land use units in Bieha
Source: Image processing, 2006
78
Table 16 and Figure 18 present the dynamics of land use units in Bieha
and also define new types of land use units resulting from the combination of
the various units between 1986 and 2002.
It was observed that 66.8 % of the farm fields unit (i.e. 1.3 % of Bieha
district) mainly old farm fields was more than 16 years old, while about 33.2 %
had been transformed into shrubby savannah, wooded savannah and gallery
forest. These were classified as fallows and represent 0.6 % of the district.
Some areas which were covered by natural vegetation in 1986 were converted
into farm fields within the 16-year period. These new farm fields comprised 8
% of the gallery forest, 27.8 % of the shrubby savannah and 15.4 % of the
wooded savannah; representing 18 % of the district.
Some areas covered by natural vegetation in 1986 had remained intact
by 2002. These consisted of 46.9 % of the gallery forest, 24.3 % of the shrubby
savannah and 38.6 % of the wooded savannah; and represent 34.7 % of the
district.
In the 16-year period, crude deforestation to make way for agricultural
activities represented 17 % of the district. The net deforestation was calculated
by excluding the fallow areas from the crude deforestation. The average annual
deforestation rate due to farming activities was 1.025 %. This means about
1,798.5 hectares were cleared every year to make way for agriculture. The
deforestation caused by other factors in the natural vegetation concerned those
areas which lost vegetation cover and hence were transformed into degraded
units. These included 7 % of wooded savannah which changed into shrubby
savannah and 12.2 % of gallery forest which changed into wooded savannah
and shrubby savannah. That type of deforestation covered 19.2 % of the district
79
during the 16 years. The average annual deforestation rate due to agents other
than from agriculture was 1.2 %, which was about 2,105.5 ha.
Some areas did improve their vegetation cover during the 16-year
period. These included fallow covering 0.6 % of the district, gallery forest
cover of 7.9 % (which changed from wooded savannah), and 12.2 % of gallery
forest also from shrubby savannah. The afforestation rate of 26.9 % of the
district during the 16-year period covered 2,950 ha annually. It is worth noting
that the afforestation occurred exclusively in the protected areas where
vegetation suffered no stress from grazing, fuel wood extraction and bushfires.
Table 16: Dynamics of land use units
Land
use
change
Area (ha) General
rate (%)
in 1986
Change rate per
unit (%) in 2002
Observations
D y n a m i c s o f t h e F a r m f i e l d s
FF 2,287.87 1.3 66.83 Old farm fields
FGf 204.88 0.1 5.98 Fallow/Afforestation
FSs 538.01 0.3 15.72 Fallow/Afforestation
FWs 392.75 0.2 11.47 Fallow/Afforestation
Total 3,423.51 1.9 100.0 -
D y n a m i c s o f t h e G a l l e r y f o r e s t
GfF 3,810.7 2.2 8.03 New field/Deforestat
GfGf 22,275.66 12.8 46.96 Not disturbed
GfSs 6,501.11 3.7 13.70 Deforestation
GfWs 14,843.01 8.5 31.29 Deforestation
Total 47,430.48 27.2 100.0 -
80
Land
use
change
Area (ha) General
rate (%)
in 1986
Change rate per
unit (%) in 2002
Observations
D y n a m i c s o f t h e S h r u b b y s a v a n n a h
SsF 18,694.83 10.7 27.85 New field/Deforestat
SsGf 10,787.09 6.2 16.07 Afforestation
SsSs 16,367.74 9.4 24.38 Not disturbed
SsWs 21,285.01 12.2 31.70 Afforestation
Total 67,134.67 38.5 100.0 -
D y n a m i c s o f t h e W o o d e d s a v a n n a h
WsF 8,746.05 5.0 15.42 New field/Deforestat
WsGf 13,772.86 7.9 24.28 Afforestation
WsSs 12,258.55 7.0 21.61 Deforestation
WsWs 21,944.88 12.5 38.69 Not disturbed
Total 56,722.34 32.3 100.0 -
Source: Image processing, 2006
0
5000
10000
15000
20000
25000
Surface area (ha)
FF FGf
FSs
FWs
GfFGfG
fGfSs
GfWs
SsF
SsGf
SsSs
SsWs
WsFWsG
fWsS
s
WsWs
Dynamics of land use
Figure 18: Dynamics of land use units
Source: Image processing, 2006
81
Respondents’ perception of the environment and their welfare
The sample population was asked to indicate their views on the changes
that had occurred in the study area and the implication on their welfare. The
information collected is presented in the rest of the chapter
Dynamics of the environment
The environment refers to vegetation (trees and grasses), wild animals
(big and small animals, birds and fishes), water and soil. In general, the
perceptions are classified into “very dense, dense, medium and scarce” for
vegetation, “many, few and rare” for animals and “high, medium and low” for
soil fertility and water availability. The perceptions were estimated by the
respondents in three time-series of 20 years ago (1986), 10 years ago (1996)
and the current situation.
Dynamics of the vegetation
Table 17 indicates that the vegetation of the district had reduced from
“very dense” twenty years ago to “medium” at the present time through “dense”
ten years ago. The reduction was in terms of number, height and tree species.
The threatened tree species were Isoberlinia doka, Bombax costatum, Parkia
biglobosa, Acacia albida, Ficus gnafalocarpa, Khaya senegalensis,
Pterocarpus erinaceus, Lanea microcarpa, Burkea africana, Acacia seyal,
Cadaba forinosa, Maerua angolensis, Ceiba pentandra, Vitelaria paradoxa,
Tamarindus indica, Afzelia africana and Daniellia oliveri. Among the grass
species, the most threatened were Andropogon gayanus, Andropogon
pseudrapus, Eragrostis stremula, Cymbopogon citratus. These species were
82
important for the communities since they provide fruits, wood and folk
medicine to the population and pasture for their animals.
The proliferation of some species such as Detarium microcarpum,
Mangifera indica, Azadiratcha indica, Pileostigma thonningii, Tamarindus
indica, Anacardium occidentale, Zizyphus mauritiana, Balanites aegyptiaca,
Acacia siebrian and Dichrostachys cimeira was also observed. The new
grasses were Siderhom bifolia, Penicetum pedicellatum and Boheira diffusa.
This proliferation was caused by the reduction of the rainfall and the long
distance mobility of the herds which brought the grains of these new species
from the arid zones.
Also, the factors (Table 18) that led to the degradation of the vegetation
were overgrazing (83.2 %) and climate change (72.6 %) followed by
population growth (71.7 %), bushfires (58.4 %) and farming activities (53.1
%).
Table 17: Dynamics of the vegetation according to the population
Dynamics of the trees Dynamics of the grasses
1986 1996 2002 1986 1996 2002
Very dense 85.5 0 1.8 90.2 0.9 0.9
Dense 12.4 79.6 4.4 7.1 78.6 8.0
Medium 1.8 20.4 92 2.7 20.5 86.6
Scarce 0 0 1.8 0 0 4.5
Total 100% 100% 100% 100% 100% 100%
Source: Field survey, 2006; Sample size (N) = 113
83
Table 18: Causes of vegetation loss
Causes Yes (%) No (%) Total (%)
Population pressure 71.7 28.3 100
Over-grazing 83.2 16.8 100
Climate 72.6 27.4 100
Bushfires 58.4 41.6 100
Farming activities 53.1 46.9 100
Source: Field survey, 2006; Sample size (N) = 113
Wildlife dynamics
The population’s perception of wildlife changes was almost similar to
that of vegetation change (Table 19). All the classes of animals were reduced
from “many” in 1986 to “scarce” by 2006 as a result of forest reduction,
hunting, population growth, farming activities, bushfires, overgrazing and lack
of water in that order (Table 20). For the birds, they were observed to be
“many” or “few” but not scarce; which was the result of the efforts of the local
foresters to preserve wildlife and also to the creation of a ranch, which
constituted a safety habitat for animals and birds. Furthermore, the birds were
not often the focus of hunters and could easily move away from bushfires.
84
Table 19: Dynamics of wild animals
Big animals Small animals Birds
1986 1996 2002 1986 1996 2002 1986 1996 2002
Many 93.8 5.4 3.6 95.5 2.7 3.7 86.6 4.5 12.6
Few 1.8 90.2 12.5 1.8 92.7 19.3 9.0 91.0 36.9
Scarce 3.5 4.5 83.9 2.7 4.5 77.1 4.5 4.5 50.5
Total 100% 100% 100% 100% 100% 100% 100% 100% 100%
Source: Field survey, 2006; Sample size (N) = 113
Table 20: Causes of wildlife dynamics
Causes Yes (%) No (%) Total (%)
Population pressure 69 31 100
Hunting 71.4 28.6 100
Bushfires 46.4 53.6 100
Farming activities 58.9 41.1 100
Forest reduction 80.5 19.5 100
Source: Field survey, 2006; Sample size (N) = 113
Soil fertility dynamics
The fertility of the soil as shown in Table 21 was observed to have
declined over the 16-year period; from high (98.2 %) through medium (91.1 %)
to low (89.3 %). About 72.3 % of the population attributed the change to the
reduction of the rainfall and 69 % to overuse (Table 22). Some also mentioned
overgrazing, and the use of modern production tools which disturbed the soil
and created room for harmful grasses. To address the problem, fertilizer
application was highly adopted (88.5 %), followed by animal ploughs (61.6 %)
85
and fallow (60.4 %). However, fallow could not be practised due to the non-
availability of land.
Table 21: Soil fertility change
Fertility of the soils
1986 1996 2002
High 98.2 3.6 5.4
Medium 1.0 91.1 5.4
Low 0 5.4 89.3
Total 100% 100% 100%
Source: Field survey, 2006; Sample size (N) = 113
Table 22: Causes of fertility change
Causes Yes (%) No (%) Total (%)
Overuse 69 31 100
Climate 72.3 27.7 100
Source: Field survey, 2006; Sample size (N) = 113
Table 23: Solution to fertility reduction
Solutions Yes (%) No (%) Total (%)
Fertilizers 88.5 11.5 100
Fallow 60.4 39.6 100
Plough 61.6 39.4 100
Migration 11 89 100
Source: Field survey, 2006; Sample size (N) = 113
86
Water dynamics
Table 24 reports that since 1996, availability of water in the rivers after
the rainy season has shortened. Water shortage is attributed, according to Table
25, to the reduction of rainfall (81.3 %), the sedimentation of the rivers (71.4
%) and to overgrazing (70.5 %). As a result, fishes were no more abundantly
available in the rivers.
Table 24: Water evolution in the rivers according to the respondents
Availability of water in the rivers after rainy
season
1986 1996 2002
Long time (≥3
months)
95.5 42.9 3.6
Short time (≤2
months)
4.5 57.1 96.4
Total 100% 100% 100%
Source: Field survey, 2006; Sample size (N) = 113
Table 25: Causes of water reduction in the rivers
Causes Yes (%) No (%) Total (%)
Population pressure 51.4 48.6 100
Animal pressure 70.5 29.5 100
Climate 81.3 18.7 100
Blinding 71.4 28.6 100
Source: Field survey, 2006; Sample size (N) = 113
87
Dynamics of farming practices
Crops produced in 1986 in order of magnitude were sorghum (red and
white), millet, maize, rice, beans, groundnut, yam, potato, cassava, cotton,
sesame and garden peas. All the crops produced were for household
consumption, apart from yam and cassava which were mostly sold. In 1996,
cotton and maize production as cash crops gained importance as peasants
became interested in the production of these two crops in addition to their
subsistence crops. Today, production is concentrated on cotton, maize and at
least sorghum, millet, beans and groundnut; with improved seeds developed to
satisfy the climate change, the poor soils and the growth of the population.
Crops productivity
Crop productivity which, as presented in Table 26, was high in 1986,
had after 20 years decreased substantially (more than 80 %). Factors
responsible for the decrease were reduction of rainfall, poor soils and lack of
labour in that order. Others were lack of education on sustainable use of the
soil, erosion due to surface runoff and overgrazing which tended to destroy the
soil.
Table 26: Evolution of crops productivity according to the population
Dynamics of crops productivity
Year 1986 1996 2002
High 97.3 3.6 4.5
Medium 0.9 93.8 12.5
Low 1.8 2.7 83.0
Total 100% 100% 100%
Source: Field survey, 2006; Sample size (N) = 113
88
Farming techniques
The use of traditional tools in the farming system prevailed in the 1980s
(Table 27) with traditional skills based on the use of human energy in the
process of production. By 1996, the same system was still practised even
though the use of modern tools was fairly noticeable (42 %). The modern
system involves the use of ploughs, machines, and agro-chemicals (fertilizers,
pesticides, etc.) in the production process. Today, 61.6 % of the population
makes use of modern tools which, according to them, not only compensate for
the lack of labour and help produce higher yield but also improve the quality of
the soil.
Table 27: Dynamics of farming practices
Evolution of farming practices
Year 1986 1996 2002
Modern 10.8 42.0 61.6
Traditional 86.5 53.6 31.3
Both 2.7 4.5 7.1
Total 100% 100% 100%
Source: Field survey, 2006; Sample size (N) = 113
Change in acreage per household
In general, the households cultivated less than 5 hectares each even
though the current tendency is to cultivate between 5 and 10ha (Table 28), due
to increase in household sizes and reduction in crop yields per area. However,
89
most of the population is forced to maintain their field sizes intact due to lack
of new arable lands resulting from increased in-migration since 1996.
Table 28: Change in acreage per household
Evolution of acreage per household
Year 1986 1996 2002
≥ 10ha 2.7 3.7 15.2
5-10ha 14.3 35.7 42.0
≤5ha 83.0 60.6 42.9
Total 100% 100% 100%
Source: Field survey, 2006; Sample size (N) = 113
Dynamics of the population welfare
Human welfare is measured here by food security, access to potable
drinking water and income levels at household level.
Food security
Food security was observed to be dropping constantly (Table 29). Food
shortage was caused by low productivity of soils and increase in household
sizes. Large quantities of the cereals produced were sold and the proceeds
spent on education and health care. Others argued that there is now a conflict in
the production process between cotton and the cereals for two reasons: not only
that land suitable for cereals growing is being used to cultivate cotton, but also
cotton needs more maintenance and this is usually at the detriment of the other
crops.
90
Table 29: Food security according to the population
Food security
Year 1986 1996 2002
High 92.0 5.4 7.1
Medium 3.6 87.5 17.0
Low 4.5 7.1 75.9
Total 100% 100% 100%
Source: Field survey, 2006; Sample size (N) = 113
Drinking water
It was observed (Table 30) that sources of drinking water had improved
over the years due to construction of boreholes and solar pump which now
satisfies much of the demand (72 %) as against water from rivers and wells
which were more consumed (66.2 %) in 1986. While change was due mainly to
lack of water in the rivers and wells it has also been enhanced by improved
sanitation in the villages.
Table 30: Evolution of the sources of drinking water
Sources of drinking water
Year 1986 1996 2002
Rivers 62.2 4.5 0.9
Wells 37.8 75.7 27.0
Pipe/borehole 0 19.8 72.1
Total 100% 100% 100%
Source: Field survey, 2006; Sample size (N) = 113
91
Income evolution
Incomes in general have not improved as faster as expected (Table 31).
Furthermore, the bulk of the revenue was based on the cattle trade and
agricultural production which were subject to or affected by fluctuations in
rainfall and external prices. Education and health care were expensive and
these consumed large portions of incomes.
However, the shift from huts to sheet metal house (45 %), from the use
of donkeys to bicycle or motorcycle as means of transportation (75 %), and
from traditional systems of farming to the use of ploughs, tractors and chemical
fertilizers were considered to be signs of improvement of income.
Table 31: Evolution of incomes according to the population
Dynamics of incomes
Year 1986 1996 2002
High 28.8 23.4 46.8
Steady 51.4 65.8 20.7
Low 19.8 10.8 32.4
Total 100% 100% 100%
Source: Field survey, 2006; Sample size (N) = 113
Population mobility
Population mobility refers to permanent and temporary migration in the
district. Table 32 indicates that in 1986, in-migration was popularly observed
(81.2 %) to be low which in 2002 it was observed to be high. The driven factor
92
of the mobility is said to be the abundance of natural resources in the area
(Table 33).
Table 32: Permanent in-migration
Dynamics of in-migration
Year 1986 1996 2002
High 18.8 61.6 76.6
Low 81.2 38.4 22.4
Total 100% 100% 100%
Source: Field survey, 2006; Sample size (N) = 113
Table 33: Causes of the permanent in-migration
Causes Yes (%) No (%) Total (%)
Abundance of resources 94.7 5.3 100
Parenthood relationships 9 91
Return migration 9.9 90.1 100
Source: Field survey, 2006; Sample size (N) = 113
Table 34 also shows that the temporary in-migration remained very low
from as far back as 1986 simply because it consisted of mainly herds men
coming to graze their animals.
93
Table 34: Temporary in-migration
Dynamics of in-migration
Year 1986 1996 2002
High 10.6 6.9 14.9
Low 89.4 93.1 85.1
Total 100% 100% 100%
Source: Field survey, 2006; Sample size (N) = 113
Summary
The results of the images processing show that in the 1986s, Bieha
district was dominated by flourishing natural vegetations composed of 38 % of
shrubby savannah, 32 % of wooded savannah and 27 % of gallery forest. The
land devoted to farming activities represented only 2 % of the district.
However, in the 2002, the farm fields increased in size and occupied 19 % of
the district. The shrubby savannah dropped to nearly 50 %. The wooded
savannah and the gallery forest kept nearly their original size within the 16-
year period.
The comparison of land use maps of the two periods permitted a clear
picture to be realised in terms of vegetation change. The net annual
deforestation caused by farming activities, wood supply, grazing and bushfires
was about 3,904 hectares in the district.
Respondents from the population helped to capture their perception on
the trend of change of the environment. They recognized that the vegetation
(both trees and grasses) was decreasing in size, number and species. This trend
also applied to the wild animals, the availability and the productivity of the
94
arable land, and to the perpetuity of the water in the district. To meet the needs
of the increasing household sizes, the peasants were forced to enlarge their
farm fields and so contributed to the environmental deterioration.
95
CHAPTER SIX
FACTORS INFLUENCING CHANGES AND
IMPLICATIONS FOR LAND USE
Introduction
This chapter establishes links between the changes detected and local
socioeconomic and cultural contexts, and also the national legislation on
environment. It also makes a comparison between the specific cases in Bieha
district and some other related case studies.
Changes detected
The land use pattern in Bieha through several years has had different
characteristics. In 1986, shrubby savannah was the major land use unit and was
uniformly distributed in the district. The second important unit was the wooded
savannah, followed by the gallery forest generally located along the rivers. The
natural vegetation covered 98 % of the total surface of Bieha. The farming area
was minute and represented only 2 % of the district. By 2002, important
changes had occurred in the land use. The shrubby savannah shifted sharply
from its rate of 38.5 % to 20.5 % of the district, and the farming surface (farm
fields) reached 19 %. The other units (wooded savannah and gallery forest)
remained nearly unchanged.
During the 16-year period, the land use dynamics had a noticeable
trend: 53 % of the gallery forest had altered to wooded savannah, shrubby
savannah and field crops; 61 % of the wooded savannah changed to farm
96
fields, shrubby savannah and gallery forest while 76 % of the shrubby
savannah was changed to field, wooded savannah and gallery forest. Among
the units, about 67 % of the 1986 estimated agricultural land, 47 % of the
gallery forest, 24 % of the shrubby savannah and 39 % of the wooded savannah
remained unchanged in 2002.
The change was most profound between the shrubby savannah and the
field crops within the period. The southwest and the northern zones were the
most affected (Figure 13). The land use types had common physical and
geographical interconnections; namely an increase in one type of land use
category was associated with a decrease in another land use category. The
change to farm fields happened in the areas with soil fertile enough to grow
crops and close to former field crops areas.
The annual deforestation rate in Bieha due to farming activities was
estimated at 1.025 % within the period. By implication, about 1,798.5 ha were
cleared annually to make way for agriculture. The deforestation caused by
factors other than agricultural activities was estimated at 1.2 % annually, about
2,105.5 ha. Some level of afforestation was observed and its rate was estimated
at 1.6 % annually, representing 2,807.3 ha. These changes may be associated
with several factors, which are examined next.
Factors affecting land use dynamics in Bieha
Natural and human factors may be responsible for the changes which
occurred in land uses in the district. The natural causes may be associated with
the fluctuations in rainfall, while the human factors may have their origins in
97
agricultural activities, poverty and population pressures, over-harvesting of fuel
wood and bushfires.
Leading factors in the farm fields dynamics
In 1986, the farming area in the district covered some 3,438.69
hectares. By 2002 however, the area had substantially increased to 33,686.64
hectares, an addition of 30,247.95 hectares during the 16-year period (Table
15). The fallow areas represented 33.2 % of the entire cultivated areas in 1986
and 3.3 % of farm fields in 2002 (Table 16). The annual deforestation rate
caused by farming activities was 1.025 % during the period; which is higher
than the national deforestation rate caused by agriculture estimated at 0.2 % by
FAO (2000) and 0.34 % by Mongabay (2005). These changes resulted mainly
from the population pressure, agri-businesses and poverty in the area.
Population pressure
The population of Bieha was 15,043 in 1985, 20,643 in 2002 (INSD,
1985, 1996) and 25,634 in 2006 (District Population Census, 2006). On the
basis of the natural growth rate of the population of Burkina Faso which was
estimated at 2.4 % in the 1996 census (INSD, 2004), the population of Bieha
should be roughly 21,983 inhabitants in 2006. The current high population
shows the importance of in-migration in the district. In fact, from 1996 to 2006,
about 3,651 immigrants arrived in the district. During the survey, a rising trend
in in-migration was observed (Table 32) over a twenty-year period namely 18.8
% in 1986, 61.6 % in 1996 and 76.6 % by 2006. The region had become one of
the main destinations for migrants from the poor and overexploited lands of the
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northern and central parts of the country. The migration flow was amplified by
the interest given to cotton and maize production that constitute the recent
basic cash crops. Following the rapid population growth through migration,
new lands have been brought under cultivation in the region, a move facilitated
by the wide diffusion of draught animals in recent years as reported in Chapter
Five. Yet, a shortening of the fallow period and an increasing use of fertilizers
were now noticeable tendencies that unmistakably reflected the rising pressure
on cultivated land, particularly land suitable for cotton growing.
Under conditions of increased demographic pressure, the most pressing
issue for farmers was to change land use practices or land use patterns, or both,
to ensure food security and income. The population of Bieha is facing this
reality by increasing farming acreage, intensifying the production, reducing
duration of fallow and even suspending it and resorting to the use of fertilizers
and draught animals.
The pressure driven by the migration was encouraged by the land
tenure system in the area. Breusers (2001), in his study on “land and mobility
in Burkina Faso”, reported that mobility was not only made possible by the
prevailing land tenure regime but also underpinned its flexibility and allowed
the merging and shifting of rights. In Burkina Faso, the land tenure system in
force is the Land and Agrarian Reforms (RAF) adopted in 1985 which
stipulated that the management of urban and rural lands, water, forests, fauna,
fisheries and mines belongs to the State. Unfortunately, the application of the
RAF is not yet widely accepted and in most of the villages in the country, it is
the local land right that is applied. In the Sissili province, each village has its
own defined village territory that has its origin in the local history of the area
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and in the first settlers. Tenure management, according to Howorth and
O’keefe (1998) was based on customary law arrangements between the land
chief and those searching for land to farm. In this context the population (or
migrants) forced out of their origins due to deteriorating physical conditions
easily acquired farm land in this region.
The population pressure also increased competition for resources in
Bieha and forced some farmers to abandon sustainable farming methods and
exploit marginal lands in an effort to secure their incomes and feed their
families. Conflict becomes highly likely when this process leads to deepened
poverty, widespread food insecurity, large scale in-migration, sharpened social
cleavage and weakened institutions.
Agri-business
There used to be large-scale farms in Bieha involving individuals and
mostly government ministers, directors of services and traders. These officers
and traders tend to be absentee farmers for most of them are based in the
capital city and employ casual labourers. Such labour was not surveyed since
the target populations in this study were the permanent residents of the district.
However, the information on the large-scale farms was demanded from
key informants such as the land chiefs since these absentee farmers formally
come to the land chiefs for the land on which to farm. According to the local
authorities, each farm covered 40 to 100 hectares of land for cotton and maize
growing and/or for cashew plantation (Plate 1). Once they got the land from the
chiefs, they quickly went back to Ouagadougou (capital city) or Leo (province
city) to register them on long term security. With the use of tractors and other
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machinery, and sometimes irrigation, such farming activities have contributed
to the immense change in land use and the landscape in Bieha district in
particular and in Sissili province in general.
Plate 1: Cashew plantation in Neboun
Source: Field work, 20/01/2006
Poverty
Haberg (2000) reported that migration in Burkina Faso seems to be a
strategy for households to reduce poverty. Poverty usually drives those affected
to rely more on natural resources for survival. The survey showed that the
population of Bieha was all farmers and/or breeders, thus depending deeply on
natural resources for their survival. The focus was more on resources for their
immediate needs rather than on those whose benefits may materialize only in
the long term. Furthermore, there is lack of relevant resources, hence reducing
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options available for proper conservation practices and inappropriate use of
land resulting in degradation.
Consequences of increasing farm lands
The immediate consequence of the increased farm land is degradation.
Land degradation is the aggregate or reduction of the productivity potential of
the land, including its major uses (arable, irrigated, forest, etc), its farming
system and its value as an economic resource (Stocking and Murnaghan,
2001). Land has been degrading in Bieha depending upon the modes of its use.
The degradation was observed from decrease in crop production, decrease in
soil fertility and the increasing food shortage. It was also worsened by non-
availability of water in the rivers immediately after the rains. Farmers have had
to resort to the application of chemical fertilizer and animal dung to replenish
exhausted soils.
Related long term consequences of the degradation may be conflicts
linked to competition for land between indigenes and migrants. Agrotechnik
(1991) suggested that Sissili province could only support 30 persons per km2
without irreparable damage while IBS (1994) forecasted that some 43 % of the
Sissili area would be deforested by 2010 due to land-use activities. The current
crude density of Bieha is 14.6 inhabitants per km2. When we exclude the
protected zones (Safari ranch of Bieha and the villagers’ forest of Bori) which
cover 388.31 km², the net density (population over habitable and exploitable
land) becomes 18.7 inhabitants per km2. Assuming that the rest of the district
could be used for farming, then it appears that 24.6 % of this arable land was
under cultivation in 2002. Considering the current rate of the population
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growth, the district will be in shortage of arable land in the near future. This
situation may cause the indigenes to reclaim their land from the migrants,
hence generating conflicts.
Forests dynamics
The shrubby savannah
The surface area of the shrubby savannah in 1986 reduced to 46.8 % in
2002 with an annual loss of about 3 %. Within the loss, 27.8% was converted
to farm fields while 46.7 % improved in vegetation cover and changed to
wooded savannah or gallery forest. The improvement occurred mostly in the
protected zone. Farming activities and the conservation systems initiated by the
creation of the ranch in 1985 were the factors that affected the dynamics of the
shrubby savannas. In fact, to the farmers, shrubby savannas were easily
accessible and exploitable because of the reduced number of trees and their
short height, as opposed to gallery forests and wooded savannas in which the
density of big trees discouraged the use of draughts.
The wooded savannah
During the 16-year period, the wooded savannah increased by 3 %
which it gained from the improvement in vegetation cover of the shrubby
savannah (12.2 %) and the degradation of the gallery forest (8.5 %). Other
factors like wood extraction, overgrazing and bushfires contributed to the loss
of 21.6 % of the wooded savannah to the benefit of the shrubby savannah.
Fuel wood constitutes the principal source of energy not only in Bieha
district but also in the whole country (Chapter Three). In the district, wood was
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cut for making charcoal for sale or sale directly as fuel wood. Several locations
of charcoal making and places of wood and charcoal selling were identified
during the field work (Plate 2). Fuel wood and charcoal were constantly sent to
Ouagadougou by truck loads daily (Plate 3).
Plate 2: Pile of wood fore sale Source: Field work, 24/03/2006
Plate 3: Wood transportation from Sissili to Ouagadougou Source: Field work, 24/03/06
104
In the district, two offices of foresters were in operation; one based in
Bieha village and the other at Neboun. These two posts were charged to plan
the wood cutting in the district, to give licence for cutting and to collect taxes
from cutting, selling and transporting wood or charcoal. However, some people
operated clandestinely and thus escaping the foresters’ patrols.
Grazing was allowed beyond the protected zones in the district. As
grasses were nearly always abundant and green, the area attracted several
breeders from Sissili and other distant provinces. There is no document which
provides the exact number of domestic animals at the district scale due to their
constant mobility along the countryside, but the 900,000 animals reported for
the whole Sissili province in 2003 was substantial (DEP, 2003). There was no
form of stabling or fodder cultivation for animals and worse of all, the project
on grazing zone management in the district was suspended due to the resistance
of the villagers. During dry seasons, the breeders frequently cut Afzelia
africana, Andansonia digitata, and other palatable species whose leaves
remained green to feed their animals (Plate 4). The effects of animals on
vegetation are numerous (Middleton, 1997; Middleton and Thomas, 1997;
Chikamai and Kigomo, 2003). On one hand, they destroy young trees by
grazing and stamping and on the other hand, their stamping on the land reduces
the infiltration of water.
105
Plate 4: Afzelia africana cut for animals Source: Field work, 24/03/06
Bushfire is one of the monstrous factors that caused deforestation and
loss of species in most of Sub-Sahara Africa (Aubreville, 1949; Kambou and
Poussi, 1997; Yameogo, 2005). Most of the population of Bieha did not ignore
the negative role of bushfires on vegetation and wildlife as 58.6 % of the
respondents recognised the destructive effects of the fires on the vegetation.
They also estimated that the small number of foresters (two for the whole
district) rendered very ineffective the control of the fire. During the field work,
apart from the protected zones and the bushes of Biniou, Livara which were not
yet burnt, it was observed that the entire district had suffered from bushfire at
least once (Plate 5 and 6). Three types of bushfires are practised in the area,
namely early fire, intermediate fire and late fire (Yameogo, 2005). The early
fire takes place a month after the last rains (December). The ominous effects of
this fire on the vegetation are negligible because at that time, grasses and
leaves of the trees are still green and so, only the dry grasses are consumed.
106
The early fires are used by the foresters in the national parks to stimulate the
sprouting of grasses for wild-animals. The intermediate fires come at a time
when half of the grasses are dry (January-February). The latter occurs in
March-April, when grasses are dry. It causes severe effects on the vegetation
killing important number of trees and reducing the productivity of the trees,
because these periods correspond to the flowering period of Vitelaria
paradoxa, Bombax costatum, Sclerocarya birrea, Adansonia digitata, Lanea
microcarpa, Lanea acida, Parkia biglobosa, Saba senegalensis, detarium
microcarpum, etc.
Plate 5: Burnt shrubby savannah Source: Field work, 24/03/06
107
Plate 6: Burnt wooded savannah Source: Field work, 24/03/06
There are punishments for people who cause bushfires (M.E.C.V, 2004)
but up to now no culprit of bushfire has been found. Accusations are levelled
against breeders (Fulani), hunters, cigar smokers but obviously there is lack of
clear political will to fight bushfires in the area.
The gallery forest
The gallery forest lost only 0.8 % of its surface area of 1986 but
internal changes occurred. About 8 % was converted to farm fields and 45 %
was degraded into wooded savannah or shrubby savannah. The pace of
degradation was more perceptible in this unit. The relative balance was due to
the compensation of improved shrubby savannah and wooded savannah which
contributed 43 % to gallery forests in 2002. The causes of degradation were the
same as discussed above. Up to now, gallery forests were not noticeable to the
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farmers in Bieha but rather the rapid decrease of the savannah could change
their attitudes towards this geographical unit.
Consequences of the deforestation
The degradation of vegetation cover in Sissili, if not checked, will lead
to unpleasant consequences which may be physical, demographic and
economic.
Physical consequences refer to soil degradation and desertification and
food insecurity. According to Sanchez et al. (1997), trees have a different
impact on soil properties than annual crops; because they remain in the soil
longer, have longer biomass accumulation, and longer-lasting, more extensive
root systems. There are four ways in which trees can have beneficial effects on
soil properties, crop production, and environmental protection. First, trees can
provide nutrients to crops in agroforestry systems through biological nitrogen
fixation (BNF), and nutrient cycling.
Biomass transferred from one site to another also provides nutrient
inputs. These nutrients become inputs to the soil when the tree biomass is
added to and is decomposed in the soil. Secondly, trees in agro-forestry
systems can increase the availability of nutrients in the soil through the
conversion of nutrients to more labile forms of soil organic matter (SOM).
Plants convert inorganic forms of nitrogen (N) and phosphate (P) in the soil
solution into organic forms in their tissues. Thirdly, trees decrease nutrient
losses from the soil due to agents such as winds and water. Smaling (1993)
reported that losses caused by surface runoff, erosion and leaching account for
109
about half of the Nitrogen (N), Phosphate (P) and Potassium (K) depletion in
Africa.
Agro-forestry systems have been found to decrease nutrient losses by
surface runoff and erosion to minimal amounts (Lal 1989; Young 1989).
Finally, trees improve environmental benefits by protecting the soil surface
with their two layer canopies namely, the litter layer and the leaf canopy,
thereby decreasing runoff and erosion, dampening temperature and moisture
fluctuations and in most cases, maintaining or improving soil physical
properties (Sanchez et al. 1985). In agroforestry systems, the beneficial effects
of protecting the soil surface depend on the spatial and temporal coverage of
the tree component. Also, tree roots can loosen the topsoil by radial growth,
and improve porosity in the subsoil when roots decompose.
When forest is depleted, all these properties are lost, giving room to soil
degradation which leads, in turn, to desertification as defined by Grouzis
(1981), Thiombiano (2000) and Ouédraogo (2002); as “the loss of the
biological productivity of the arid soils which progressively transform to desert
or to skeletal irreparable soil”.
Bieha forest was home to hundreds of plant species and thousands of
animal species. Forest degradation will also cause the loss of its biological
diversity in terms of genetic, species and ecological losses.
Once soil is degraded, food production becomes low; hence food
insecurity and conflicts that in turn may lead to the mobility of the population
to other areas. Most of the population in the district depend on crop, wood and
charcoal production for their incomes. Furthermore, the women of the district
extract their livelihood from non wooded forest products such as shea nut
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harvesting to produce butter widely exported to Europe and America. They
also use the grain of Parkia biglobosa to produce Sumbala (Dawadawa)
widely consumed at the national scale. Fruits of Saba senegalensis, Detarium
microcarpum and zizuphus mauritiana are also commercialized by the women.
Loss of forest thus means loss of economic activities to the women.
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CHAPTER SEVEN
SUMMARY, CONCLUSIONS AND RECOMMENDATIONS
Introduction
This section deals with the summary of the methodology and findings
of the study, conclusions drawn from the study and recommendations for
improvement in land use in the Sisili Province and further studies.
Summary of the methodology
The basis of this research comprised the multitemporal classification of
Landsat TM satellite images to detect, delineate, and map the specific units of
land use in Bieha District from 1986 to 2002. Thus, the aim of this research
was to produce both current and past land use of Bieha from recent and historic
satellite imagery spanning the period of study to detect and map this land
change; in addition, assess the factors that led to the change.
The creation of the 1986 and 2002 land use maps was derived utilizing
standardized digital remote sensing classification techniques. The classification
employed two multitemporal Landsat scenes dated, November 18, 1986 and
October 21, 2002. A hierarchical level of land use classification comprised of
four classes namely; farm fields, shrubby savannah, wooded savannah and
gallery forest. Final classification accuracy was determined to be satisfactory
by means of employing standardized accuracy assessment measures and
comparison with ground data obtained from extensive GPS field surveys.
112
The process of change detection was employed by utilizing a previously
documented, image differencing method and the three-dimensional model of
Agarwal et al (2002). Specifically, bands 2, 3 and 4 were used because of their
especially responsive to vegetation biomass, to make false colour composites
for the classification for each scene. The supervised classification type was
used to detect land use state for each period. The results of the image
processing were supported by an interview of the local population.
Summary of the findings
According to the statistics calculated from the land use change data, the
farming surface increased from 3,438.7 ha (2 %) to 33,686.6 ha (19 %); the
shrubby savannah decreased from 67,427.5 ha (38.4 %) to 35,818.8 ha (20.4
%); the wooded savannah increased from 56,967.5 ha (32.4 %) to 58,714.6 ha
(33.4 %) and the gallery forest declined slightly from 47,634 ha (21.7 %) to
47,240.3 ha (20.9 %) in Bieha District from 1986 to 2002 (see Chapter Five).
These statistics also indicated that farming activities contributed to forest
degradation which annual loss rate was estimated at 1.025 % (1,798.5 ha) and
that the fallow practices within the period was 33.1 % of the field area or 0.6 %
of the district. Other factors contributed to the degradation of the gallery
forests, wooded savannah and shrubby savannah estimated at 1.2 % annually
(2,105.5 ha). A form of afforestation was also indicated by the statistics which
represents about 1.6 % annually (2,950 ha); however, the map of the dynamics
shows the afforestation was taking place especially in the protected areas
namely; Safari Ranch Sissili and the forests. Assuming these statistics to be
113
accurate and precise, the net amount of forest degraded annually was 0.623 %
or 1,098.8 ha in Bieha District over a time period of 16 years.
The statistics from the population interview revealed that the local
population is aware that their natural resources, namely the forests, arable
lands, soil, water, wild animals, etc. have been degraded since the 1980s. The
degradation resulted in lowering productivity of the crops, food insecurity, and
incomes reduction. The factors that led to the depletion of resources according
to the population included farming activities, over-harvesting of wood and
animals, overgrazing, bushfires and climate. They faced their environment
realities with increasing the acreage of the farm fields, using draughts and
fertilizers, and growing improved seeds; abandoning crops that needed much
more care.
Summary of the discussion
Comparison between the findings from the present study and other
related studies showed that the space of the deforestation in Bieha District was
higher than those estimated by MEE (1996), MECV (2004), FAO (2000) and
Mongabay (2005). The evolution of the cultivated lands was rapid and was
mostly focused on the shrubby savanna unit because of its aptitudes in
exploitation. The factors that led to the increase of the farming lands were
discussed and were mainly the population pressure, the emergence of agro-
businesses and poverty in the area. The demographic pressure was driven by
the in-migration of people from the crowded and infertile northern and central
regions of the country. The pressure was also facilitated by the smoothness of
114
the tenure management which was based on customary and orally law
arrangements between new-comers and land chiefs.
Under condition of increasing cultivated land, marginal lands were also
put under cultivation, hence accelerating soil degradations, runoffs, and
bindings. Under such a tendency, sooner or latter, large scale degradation of
forest and lands; conflicts linked to competition for space between farmers and
breeders on one hand and between autochthones and migrants are likely
foreseeable.
The degradation of the vegetation in gallery forests, wooded and
shrubby savannas was caused, beyond the agricultural activities, by the wood
cutting, overgrazing and bushfires. Wood was cut and sent to towns as energy
sources in its natural form or transformed to charcoal; domestic animals were
too many in such a way that compromised the carrying capacity of the area,
and forests were burnt at least once annually, hardening the soils, killing plants
and animals, hence threatening the biological diversity.
Conclusions
The sixteen year time span, 1986 - 2002, considered in this study is a
short increment of time in a long history of land use dynamics. This time
period was chosen based upon the availability of current and compatible
satellite imagery for classification and change detection as well as a means to
provide current land use trends. This period also coincides with a period of
substantial increases in agricultural activity in the area due to the interest given
to maize and cotton cultivation, and cashew and mango plantation.
115
It is important to consider this time period in the grand scheme of land
use and land cover characteristics in Sissili province. Natural resources in the
province are degrading due to unsustainable agro-pastoral activities undertaken
by local stockholders in a context of high in-migration rate. The province was
mostly forested before migrant Fulani and Mossi settlement arrived in the
1980s following the drought of 1970s. From these findings, one may conclude
that the two hypotheses that guided the study are accepted.
Bieha District, which occupies 25 % of Sissili province surface area and
homed 11 % of the population of the province in 2002, is a representative
sample of the province. The findings from this study in Bieha District reflect
the real state of the natural resources in the whole Sissili. Natural resources are
degrading at a considerable pace in the province due mostly to human
activities, namely agriculture, grazing, wood fuel requests, hunting and
bushfires.
Recommendations and strategies for further research
Strategies that aim at sustainable management of natural resources
(conservation and restoration) in Sissili province must take into consideration:
a. The reduction of environmental refugees. Evidences showed that rural
to rural population mobility is driven by lack of farming land and
poverty in their provinces, and so large irrigation programmes around
dams may be foreseeable. Initiatives were taken in this way in Sourou,
Bagré, Kompienga, etc. but further efforts must be done to reinforce
irrigation programmes.
116
b. Intensification of crop productivities without large expansion of farm
lands. Sensitization programmes on water, soil and forest conservation
may be widely initiated in the area.
c. Reduction in overgrazing. The pasture zone creation project in Yallé
village must be resuscitated with large sensitization of the population.
Stalling programmes coupled with fodder cultivation may also be
encouraged.
d. Strategies to find other energy sources. Solar energy, gas and electricity
facilities must be promoted in order to reduce the dependence on wood
as main source of energy especially in towns.
e. Combat against bushfires. It may be possible through sensitizations and
the increase of the number of foresters in the area.
There are potential possibilities to consider which may give additional
strategies for further study upon the conclusion of this research. The detection
and delineation of forest loss and subsequent conversion to farm land in Bieha
district, Sissili province has been determined with satellite imagery. In
addition, an assessment of this land use change has been compiled with GIS
analysis. However, the resulting spatial data yielded from this study offers
prospects for further analysis.
For instance, the mapping of the bushfires may help to understand and
quantify its extension and its impacts on the natural resources. The study can
be possible by using Landsat TM images captured in indicated period during
which the marks of fires are perceptible in the ground. It may also be possible
by using GPS to trace the fires limits in a localised area.
117
The quantification of the wood used for fuel and transformed to charcoal per
year is also an interesting area of investigation. This is possible in collaboration
with the local foresters and the wood and charcoal sellers and transporters. It
may be interesting also to know the species of trees commonly cut to make
charcoal.
The third area of investigation concerns the migration. The exact
number of migrants living in Sissili province is unknown. The exact number
may help to make prediction on the future trend of population growth and
resource allocation and management in the area.
The last area of investigation to be considered in the province for more
sustainable land use is to develop participatory approach to natural resource
management that take into consideration local indigenous knowledge and
interdisciplinary scientific knowledge. The key issue here for resources
management is how to balance conservation values and the need to exploit
these resources to sustain life and economic development.
118
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