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TOWARD A FRAMEWORK FOR MONITORING LAND DEGRADATION IN
RWANDA

By

Julia Eve Flagg

A THESIS

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

MASTER OF ARTS

Department of Geography

1996

ABSTRACT

TOWARD A FRAMEWORK FOR MONITORING LAND DEGRADATION IN
RWANDA

By

Julia Eve Flag g

The use of indicators for monitoring land degradation has been proposed and
justified over the last 30 years. Although a number of indicators and ways of monitoring
them have been proposed individually, there has been no real suggestion of a systematic
way of integrating these indicators. Often only the physical signs of land degradation are
investigated. In this study both biophysical and societal indicators will be used for
analysis. Regression, I’rincipal Components Analysis. and a Matrix method are evaluated
for efficiency and validity in monitoring land degradation in Rwanda based on the
integrated use of indicators. Data from the Michigan State University Rwanda Society-
Environment Project was used in this study as an example of available real world data.
The results of the three methods was compared to those of the original project to assess
the validity of this study’s findings. The Matrix method was found to be the most useful

method for monitoring land degradation.

Copyright by
JULIA EVE FLAGG
1996

ACKNOWLEDGEMENTS

I would like to express my gratitude to Dr. David J. Campbell for all the
inspiration, advice, and guidance over the past two-and-a-half years. Without his valuable
insight I could never have gotten this far and wouldn’t be so eager to continue in my
studies. I would also like to thank my committee, Dr. Asseffa Mehretu and Dr. Alan
Arbogast for their constructive criticisms. To my family, and especially my mother, thank
you for all the sacrifices you have made for me in order for me to get through school.
There is no way that I could ever let you know how much I appreciate all that you have
done for me. To my biggest cheerleader, Eric Fahrenkrog, for never letting my spirits stay
down for long. The word friend doesn’t say enough to express what you mean to me.
Thanks go to Steve Aichele for making the data analysis possible in this thesis. To Greg
Bierly for the matrix concept, my first publication, and all you did for me while you were
at MSU. Thanks also to Todd Yarch for putting up with me for the last year while I
complained and for looking out for me. Thanks also to Shawn Rydahl and Ross Hock for
housing me and putting up with me in my time of need. To my comrades in Illinois,
thanks to Cesar Tique for staying up late sharing our theses, some soda, and my change.
And finally, to Ellen Shiels, you are always there for me no matter what time of day it is

and I will always appreciate how much you have done for me over the past years.

TABLE OF CONTENTS

LIST OF TABLES .................................................................................. Vii
LIST OF FIGURES ................................................................................ viii
CHAPTER I .................................................................................................................... 1
INTRODUCTION ........................................................................................................ 1
REVIEW OF LITERATURE ....................................................................................... 4
Human-Environment Interaction ............................................................................... 4
Political Economy and Political Ecology ................................................................... 8
Population Pressure and Carrying Capacity ............................................................ I 3
Indicators of Land Degradation .............................................................................. I 8
CHAPTER H ................................................................................................................. 24
REVIEW OF STUDY AREA ..................................................................................... 24
Physical Characteristics .......................................................................................... 24
Historical Outline on Rwanda ................................................................................. 27
Land Use in Rwanda ............................................................................................... 29
Previous Studies on Rwanda .................................................................................... 33
CHAPTER III ................................................................................................................ 39
METHODS ................................................................................................................ 39
Need For Research .................................................................................................. 39
Statement of Problem .............................................................................................. 40
Data Set .................................................................................................................. 41
Analytical Methods ................................................................................................................... 42
CHAPTER IV ................................................................................................................ 48
DATA ANALYSIS AND FINDINGS ........................................................................ 48
Selection of Indicators ............................................................................................. 48
Adjustments in Matrix ............................................................................................. 56
Data Analysis .......................................................................................................... 58
Comparisons ........................................................................................................... 60
Regressions ............................................................................................................. 65
Principal Components Analysis ............................................................................... 66

CHAPTER V ................................................................................................................. 71

SUMMARY AND CONCLUSIONS .......................................................................... 71
Data Gaps ............................................................................................................... 73
Conclusions and Work Still to be Done .................................................................... 75

LIST OF REFERENCES ............................................................................................... 77

vi

LIST OF TABLES

Table 1: Critical Values of CDLI Index ..........................................
Table 2: Slope and Rain Classes ...................................................
Table 3: Soil Capability Classes ...................................................
Table 4: Adjusted Critical Values for CDLI .......................................
Table 5: Regression Results .........................................................

Table 6: Principal Components Loadings ..........................................

vii

46

50

52

58

67

69

Figure 1:
Figure 2:
Figure 3:
Figure 4:

Figure 5:

LIST OF FIGURES

Map of Rwanda ..............................................................
Typical Farm .................................................................
Hypothetical Matrix and Indicators ......................................

Map of Matrix Scores ......................................................

Map of Summary Environmental Stress Scores .........................

viii

25

3O

43

61

63

CHAPTER I

INTRODUCTION

Rwanda is one of the most densely populated countries in Africa. It is also a
country whose people depend mainly on agriculture. This combination of dense
population and dependence on land-based resources would to many be indicative of a
country experiencing increasing land degradation. Experience has shown, though, that
population density alone is not always a good indicator of degradation. Many researchers

feel that increasing density can cause increased innovation and adaptation.

Much of the previous research on land degradation has focused only on the
physical signs of degradation in choosing attributes for monitoring, such as soil crusts and
deforestation, but the environment should not be taken separately from the actions of
people who inhabit the land. Politics, economy, culture, and the environment are all part
of a dynamic system. To only look at one part of that system is to miss important

information, and possibly, early detection of land degradation.

This thesis will explore the utility and validity of using indicators of land
degradation, both human and physical, to assess the level of land degradation present in
Rwanda. Human and physical processes are inherently linked and cannot be monitored

1

2

separately. Thus far, there has been no successful integration of these human and physical
indicators. Indicators here will be defined as, "A phenomenon or statistic so strictly
associated with a particular environmental condition that its presence can be taken as
indicative of the existence of that condition" (Mabutt 1986). A more recent and refined
definition by the UNDP/UNSO describes desertification indicators "as complex attributes
that enable the grouping together of several underlying land characteristics or
phenomena" (in Wangati 1996, p. 4). They also suggest that indicators should be
"quantifiable, measurable and verifiable wherever possible" (in Wangati 1996, p. 4). Most
authors also suggest that land degradation need not necessarily be limited to soil
degradation, but rather to water, fauna and flora, as all of these contribute to the
ecosystem under observation and its capacity to sustain communities that are dependent

on the area (Wangati 1996, Berry 1996).

This thesis will examine ways of monitoring land degradation, using data on socio-
economic and physical conditions in Rwanda. A framework based a systematic integration
of socio-economic and physical indicators will be developed and compared to other
statistical methods for assessing land degradation, specifically regression and principal
components analysis. The framework will also be compared to findings from the original

project from which the data was taken to assess validity.

3

The data to be used for analysis come from the Michigan State University Rwanda
Society-Environment Project‘. This was a large scale collection of data relating to both
human and environmental characteristics of Rwanda. The data set from this project will
be used: 1) because it is a large and fairly complete set of data for an African country,
and 2) it includes both human (income, population, agriculture) and physical (soil
capability, elevation, rainfall) information. The data set itself is indicative of what many
researchers or government organizations would be monitoring for many other purposes
besides land degradation. The lessons learned from this study may be indicative for many

African countries.

Regional political ecology will be used as a framework for investigation in
selecting indicators. The objective of this thesis is to test the utility of using a systematic
approach based on indicators to monitor land degradation and also to evaluate the
available information as to its feasibility with different methods. Recommendations will be
made concerning suitability of data for the integrated analysis and suggestions for an

optimal set of data will also be offered.

 

1The Rwanda Society-Environment Project began in 1989 in the MSU Center for Advanced
Study of International Development (CASID). The project was a collaborative effort including
the Center for Advanced Study of International Development, the MSU Department of
Geography, the Government of Rwanda: Ministries of Environment and Tourism, Agriculture,
and Department of Statistics, as well as, the National University of Rwanda Department of
Geography and the United Nations Environment Programme (UNEP-GRID). Research in
country continued until 1994.

4

Review of Literature

In this chapter a review of recent literature concerning topics relevant to
monitoring of land degradation in tropical nations dependent on agriculture is presented.
The first section introduces literature concerning interactions between humans and the
environment. The review of human-environment interaction literature is broken down into
1) previous research in human geography and, 2) an examination of work concerning
political economy and political ecology as a theoretical basis for research concerning land
degradation. The third section is a review of research on population pressure and its
effects on the environment The fourth section focuses on the use of indicators for

monitoring environmental degradation.

Human-Environment Interaction

The literature on human-environment interaction relevant to this study can be
subdivided into two major areas of research. The first of these divisions is the area of
research concerning human geography. The realm of human geography research initially
evolved from the idea of environmental determinism. Environmental determinism
purposes that human behavior is "determined" by nature and one's actions are thus decided
not by individuals themselves, but by the environmental conditions of the area which they
inhabit This school of thought first appeared in the nineteenth century in geography in

America and continued to be a recuning theme through the middle of the twentieth

5
century (Palm 1990). The most important writers within this paradigm were Ellsworth

Huntington with his work Climate and Civilization (1922) and Ellen Semple's American
Histog and It's Geographic Conditions (1903). Huntington argued that civilizations first
developed in moist and cool climates and had greater technology and innovation.
Huntington based this in historical analysis, but this concept did not hold up when
compared to the ancient civilizations such as Egypt and Mesopotamia. Semple's (1903)
work included a proposal that living in or near mountain passes made people more apt to
be robbers. This is now used as a good example of selective empirical analysis. Semple
largely chose case studies that would further her argument, rather than investigating a
general phenomenon. Within environmental determinism, though, there were actually
degrees to which proponents ascribed nature's power in determining human behavior.
Because of this dissension as to the degree of determination nature had in dictating the

behavior of humans, determinism began to evolve into more plausible theories.

The next branch of ideas to evolve was called "possibilism." This way of thinking
considered the environment as passive and humans as the active agents able to choose
from a range of possibilities that nature offered (Palm 1990). Humans in this case were
the decision-makers, but their options were constrained, as opposed to dictated by, nature.

This branch of thought further evolved into "positivism." This theory "assumed general
principles could account for the ways in'which individuals decide among various
environmental alternatives" (Palm 1990, p. 63). This concept basically ascribed human's
motivation in decisions to maximization of economic benefits. In other words, a rational

person would always choose the alternative that allowed him/her to make the most profit.

6

The influence of culture, space, and time were not considered in the model of decision-
making as significant Positivists do not necessarily propose that individual human
behavior can be predicted, but some norm for a population could be suggested. This
created a reactionary philosophy termed "humanism." In this branch of thought, the
individual, not the generic group, was the focus of inquiry. The idea of economic
maximization as humankind's only impetus in decision-making was rejected, and the focus
changed to exploring the reasons for decision-making as opposed to relying on general

principles.

The most recent philosophy is "structuralism." This branch of thought proposes
that "political and economic structure determines or constrains individual adjustment to
the environment" (Palm 1990, p. 69). In considering less developed regions, such as
Rwanda, many proponents of structuralism argue that economic development and the
accompanying political economic structure enhance the potential for natural hazards.
Within this philosophy, structuralism can be divided into structural determinism and
structural probabalism (Palm 1990). Structural determinism takes the power of free will
out of the hands of the decision-maker, as the underlying political and economic structure
determines the behavior of individuals. In structural probabalism, the behavior of
decision-makers is not completely determined by. but is constrained by, political and
economic structure. Inherent within structuralism, power in decision-making increasingly

shifts from the individual to a hegemonic power structure.

7
Robert Kates distinguishes between the industrial and pre-industrial societies and

their decision-making rationales. He argues that, "for preindustrial adjustments, criteria of
environmental fit and social conformity seem most important, while those of technological
feasibility and economic gainfuhress appear more prominent in considering industrial
adjustment" (Kates 1971, p. 441). Kates’ work in natural hazards, while still
encompassing the idea of a human decision-maker, is among the first to ascribe an active
role for the environment. He argues that the "human use system" and the "natural events
system" interact to create a hazard. This not only gives environment an active role, it also

promotes the idea that human-land interaction is dynamic.

As well as ascribing a dynamic element to human-environment interaction, an
element of complexity was added by Risa Palm in her work Natural Hazards (1990). In
examination, she argues that potentially conflicting goals and objectives of different
individuals, groups, and the political economic structure involved can create complexity in
decision-making concerning natural hazards, including land degradation. In the most
recent works in human geography the political economy, rather than merely structure has
come to be used. One will not only find the term political economy in natural hazards

research. but also in its own category as a philosophy outside on the hazards realm.

8

Politicfl Economy and Political Ecology

Political economy at this point probably needs some clarification as a framework
for investigation. The idea of political economy depends on historical development and its
impacts on society. No society is without a past and investigation of events in a society's
history is necessary to understanding current conditions. Political economy also includes
the political and economic forces that act on all humans everyday. Power is also brought
into the paradigm as it determines the use and distribution of available resources. Power
according to Giddens is "the capacity or likelihood of actors to achieve desired or intended
outcomes" (Giddens 1979, p.88). Structure is yet another element in this paradigm as
was previously discussed. All of these together contribute to a methodology for looking
at problems in a more holistic and realistic way. Much research involving political ecology
looks at local level issues and then works "outward" toward influences coming from the

larger scale, such as national and international policy.

The works of Kates were labeled under the banner of Human Ecology. This
perspective placed societies in a position in which they developed both based on, and in
reaction to, nature (Olson 1994). In response to this position the idea of political
economy evolves. Many of the ideas of human and cultural ecology are included in
political economy, such as that of political-economic structure. But political economy
encompasses more than human ecology in creating a framework for investigation. Power
and spatial scale begin to be included as factors in decision-making, and mono-causal

factors are rejected in favor of chains of causation. This philosophy has many roots, the

9

first of which is Marxism. In an explanation by Peet and Thrift in New Models in
Geography, they explain "economy" as, "social economy, or way of life, founded in
production. In turn, social production is viewed not as a neutral act by neutral agents but
as a political act carried out by members of classes and other social groupings" (Peet and
Thrift 1989, p. 3). The influence of Marx's writings in this element of political economy is

clear, but political economy cannot be merely relegated to a neo-Marxist realm.

Other theories were also influential within political economy. Doreen Massey in
her work Spatial Divisions of Labour (1984), introduces the idea of space. This, of
course, should not be confused with the Ricardian value of space which has to do with the
substance value of land (environment), as opposed to the locational (or non-primary
producer) value of land. According to Massey, geographical space should not be thought
of passive, but rather as an active force. She argues that, "Just as there are no purely
spatial processes, neither are there any non-spatial processes" (Massey cited in Peet and
Thrift 1989, p.14). The idea of space being an integrative part of political economy made

it attractive to geographers.

Another influence in the development of political economy was once again the
structure-agency debate, especially "structuration." The debate over structure (underlying
political-economic structure) versus human agency (individual rationale) as the major
influence in decision-making has been a long-standing problem for geographers. The
structure-agency debate was a product of changing times. and with this, changing

philosophies in all realms of social science. The debate began in the mid-1970's.

10
According to Peet and Thrift (1989), there were five catalysts for the ensuing debate over

structure versus agency. These catalysts included changes in political views, a theoretical
change toward realism and the limits of theory itself, space as a part the realm of society,
changes in human geography, and changes in the nature of society itself (Peet and Thrift
1989). Combined, these catalysts produced a seemingly unsolvable problem for

geographers.

The debate over structure versus agency focused on whether individuals make
decisions of their own free will (human agency) or if the underlying political and economic
structures of society created a defined pattern of behavior that dictated decision-making.
Structuration, "emphasized the importance of hermeneutics at all scale levels" and made
light of the importance of geography, thereby offering geographers an alternative to the
structure-agency debate (Peet and Thrift 1989, p. 19). Agency needs to be situated as a
"continuous flow of conduct" in time and space and should have a theory of an "acting
subject" (Giddens 1979, p.2). Structuration according to Anthony Giddens began because
of a lack of "theory of action in the social sciences" (Giddens 1979, p. 2). Giddens
argues that without action there is no structure and without structure there is no action.

"The same structural characteristics participate in the subject

(the actor) as in the object (society). Structure forms 'personality'

and 'society' simultaneously-but in neither case exhaustively: because

of the significance of unintended consequences of action, and because

of unacknowledged conditions of action" (Giddens 1979, p. 70).

In other words, neither structure nor agency works independently. Political and

economic structures and human free will influence decision-making. So in examining

11

human and environment interaction it is necessary to analyze hegemonic structures and
human character traits that will influence how problems will be dealt with. Both the idea
of structure versus agency and its critique, structuration, were major influences on what
would become political ecology. These are just some of the many philosophies from
which the paradigm of political economy, and later its predecessor, political ecology,

came.

Political ecology entails many of the same concepts as political economy, including
power, structure, and political and economic forces. Political ecology, however, offers the
researcher a more flexible framework. Political ecology argues that the relationship
between humans and the environment is reflexive and dynamic (Blaikie and Brookfield
1987). According to Peet and Watts, political ecology was a response to "the theoretical
need to integrate land-use practice with local- global political economy and as a reaction to
the growing politization of the environment" (Peet and Watts 1993, p. 238). Moreover
they argued that political ecology gained appeal as it attempted to synthesize production in
a global economy with new environmental rules and management practices (Peet and
Watts 1989). As Blaikie and Brookfield explain,

"Three characteristics of the relationship between land

degradation and society have been identified: the importance of

interactive and feedback effects through time, the importance of scale

considerations, and the contradictions between social and

environmental changes through time. These have to be recognized

as placing difficult demands upon the way in which land degradation
and society is studied" (Blaikie and Brookfield 1987, p.15).

12
Although others argue for various scales, Blaikie and Brookfield further expanded

on the paradigm of political ecology by arguing that problems should be studied at a
regional scale. In this way "Regional Political Ecology" could take into account specific
ecological, political, social, economic, and cultural context. Influences from both local,
national, and global actors are examined, but the region itself is the area of focus. A
region here could be defined as an area linked by either culture, environment, etc., that
forms a functional space for research analysis. A watershed is an example of a region
based on purely physical characteristics. The regional political ecological approach tries
to integrate information concerning both humans and the environment to examine complex
relationships without trying to over-simplify, and thereby, losing valuable insight and
irfiormation. Regional political ecology has been criticized by some for being too complex
(Peet and Watts 1993). Without looking into all facets of decision-making, though, the
situation being examined becomes somewhat like that of a puzzle with a missing piece.
Some other criticisms that have been directed at political ecology also include the lack of
actual "politics" in the concept and that political ecology relies on outdated concepts of

ecology. such as systems theory (Peet and Watts 1993).

In this thesis, political ecology will be used to examine the problem of land
degradation in Rwanda. Political ecology recognizes that physical processes are also
human processes. The two are linked and should be studied as such. The problem in
studying land degradation to this point has been the narrow focus on only the physical

processes and indicators. Political ecology has been chosen in this case as a framework to

13

guide this research into ways of monitoring land degradation as a process involving the

interaction of both biophysical and societal processes.

Population Pressure and Cming Capacity

Rwanda has long had one of the densest and fastest growing populations in Africa.
Many have argued that land degradation is a result of these demographic trends while

others see this as a catalyst to agricultural innovation.

The earliest ideas of what a rapidly growing population would eventually face,
were proposed by Thomas Malthus. Malthus argued that there was a critical threshold
when population and its ability to feed itself were in equilibrium. If a population were to
grow larger than its ability to feed itself, then war and/or famine would ensue. This
philosophy assumes that the ability of a population to produce food is a constant. It also
assumes that extensification of agriculture will occur, with good land being brought into
production first and land of lesser quality being continuously brought into production as
good land becomes scarce (Arizpe, et al., 1994). This also assumes that there is a finite
amount of land available. The value of land seems, according to Malthus to be of
constant value and that access to land is open. Malthusian outcomes in contemporary
research have also been expanded to include land degradation, as well as war and famine.
According to many, Malthus was wrong in his assumptions of catastrophic endings in all

cases of rapid population growth (Boserup 1965; Arizpe, et al., 1994). In a study of

14

Rwanda, John May argues that, "demographic pressure induces utilization of marginal
land, shortening of fallow periods, and conversion of pasture and (natural) forest lands
into cropland" (May 1995, p.324). He goes on to explain that this will lead to soil
degradation, which in turn reinforces the demographic pressure on the land. This will lead
to a cycle of continuing environmental degradation, which neo-Malthusians would

consider a Malthusian outcome.

The main disagreements most contemporary researchers on population have with
Malthusian theory is that technological innovation and local action are ignored.
According to many researchers studying the effects of population growth, such as Boserup
(1965), Davis (1963), Bilsborrow and Okoth-Ogendo (1992), Malthusian outcomes can
be averted, or mediated by, factors that make better and more efficient use of land as
population grows. The first and most prominent advocate of sustainable population
growth was Ester Boserup. In The Conditions pf Agricultural Growth (1965), she argued
that a growing population could sustain itself not through extensification, but rather

intensification.

Boserup and others who disagree with Malthus, such as Mary Tiffen (Tiffen, et al.,
1994), believe that population growth facilitates and encourages adoption of new
technology. This is made possible by the addition of increased labor supply. Boserup
saw changes in technology as coming from endogenous, rather than purely exogenous,
causes. Some of the positive effects of population growth according to these authors

include: increased labor supply, increased food needs, "increased interaction of ideas

15

leading to new technologies, and economies of scale in the provision of social and physical
infrastructure" (Tiffen, Mortimore, and Gichuki 1994, p.266). They believe, based on
their research, that in the medium to long-term these positive effects could outweigh the

negative effects of an increasing population.

The availability of additional labor gives farmers the opportunity to make
improvements to farms or to grow crops that require more attention; in this way, addition
of labor can be seen as an intensification strategy. Other systems of intensification often
used are increased inputs, such as manure or fertilizer, and intercropping. Changes can be
made in agricultural practices to increase output, but other changes may also occur.
According to Kingsley-Davis (1992), there may be a "multi-phasic response." He
hypothesized that several responses to population growth could occur at once. These
responses included postponing marriage, out-migration, and reducing fertility. Only
demographic changes were considered in this theory, whereas Boserup only considered
non-demographic changes (Bilsborrow and Okoth Ogendo 1992). It is possible, however,
that both demographic and non-demographic changes could be responses to increasing
population. According to Bilsborrow and Okoth Ogendo, there are four phases of
response that can occur as a result of increasing population: tenurial, extensification,
technological, and demographic (Bilsborrow and Okoth Ogendo 1992). In this way they
combine Malthusian ideas of extensification, intensification or technological change as
argued by Boserup, the demographic changes as advocated by Kingsley-Davis, and the
additional idea of changes in tenure created by increasing land scarcity. The

extensification phase is when people in situations of increasing land pressure cultivate

16

more area to make up for decreasing crop yields or additional food requirements brought
on by a growing family. The tenurial phase is the adjustment in land tenure laws or
customs to accommodate an increasing population, such as the process of fragmenting
farms. The technological phase represents intensification, or using the same amount of
land but with increased inputs to agriculture on that land. And finally, the demographic
phase occurs when people facing increasing population pressure begin to change habits
and customs concerning childbearing, such as limiting the number of children they have.
The responses that occur are directly influenced by the "context" of the area. Quality of
land, the country's history, structural factors, and attitudes of the population all help to

determine which responses will be chosen from the available possibilities.

Thus far, a growing population has been considered as a rise in the number of
people in a region. According to many researchers though, neither the actual number of
people nor, the rate at which a population is increasing is as important as the density and
distribution of that population. A theory that originated from biology is related to the idea
of an upper limit above which degradation or destruction will occur. This is the concept
of "carrying capacity." Fundamentally, carrying capacity refers to, "a given area can only
support a given population of a particular species and at this upper limit-the carrying
capacity-population will have reached its maximum sustainable level" (Pearce, Hamilton,
and Atkinson, 1995). It is argued though, that when applying carrying capacity to human
populations, one must also consider the amount and composition of economic pursuits
(Pearce, Hamilton, and Atkinson, 1995). The idea of carrying capacity, though, is

inflexible in that it does not allow for substitution of assets, creating the potential for a

17

very pessimistic outcome in most cases. Carrying capacity is an intuitively attractive
concept, but in the real world, there are too many variables to be specific in predicting the
exact number of people that will put the population beyond that which the ecosystem can
tolerate. As was mentioned earlier, many believe that a growing population will apply
technology to increase the population's ability to inhabit an area. Density itself can be a
catalyst for innovation through physical proximity. According to Mary Tiffen, et al.
(1994), a higher population density makes communication of ideas easier and also creates
new markets for products outside local areas. This exchange and application of ideas
would not happen as quickly if people are separated by long distances and tend to produce

food only for household or local use.

Contemporary research on population is beginning to adopt a political ecological
stance, as well as, one that is purely demographic. According to Arizpe, et al. (1994),
"the population problem does not just involve absolute numbers of people nor even just
population densities or overall rates of increase, but also, in important ways, social,
political, and institutional factors" (Arizpe, et al., 1994, p. 3). The authors call for
additional comparative data at several scales, such as national, regional, and international.
Research on population does seem to be coming to a consensus that numbers of people
and rates of increase alone are not meaningful without a context in which to interpret
them. Political ecology can help to interpret why the rates of increase are occurring by

looking at causal factors, which most will agree is much more meaningful.

18
Indicators of Land Degradation

The use of indicators for looking at land degradation is a relatively recent
occurrence. The first recognized work on using indicators was written by Berry and Ford
in 1977. In this work, the authors suggest variables that could be used to suggest the
presence of increasing degradation of arid and semi-arid environments. They offered
suggestions as to what should be monitored and ways of measuring them. Both physical
and socio-economic indicators were offered as possible ways of monitoring desertification.
Although the work by Berry and Ford was called "a system for monitoring," it really did
not suggest a way in which to integrate the collection and integration of data. Indicators

and ways to monitor were offered, but each was considered separately.

A work by Patricia Reining the next year also offered suggestions for indicators
that could be measured in the monitoring of desertification, as well as, ways of monitoring
and justifications of why these indicators should be monitored (Reining 1978). This book
came as a product of the United Nations Conference on Desertification, which was held in
1977. The Conference was among the first to recognize the severity of desertification
(Krugmann in Hamby and Angura 1996). This in turn, brought on many studies
suggesting indicators to monitor this problem. But again, monitoring of indicators of
desertification were offered on a single indicator basis with conclusions stemming from a
synthesis of summarized individual indicators. No way of monitoring multiple indicators
as part of a system was offered, although both of these works broke ground in the field of

monitoring based on use of indicators. Both works also attempted, quite efficiently, to

19

justify why both physical and socio-economic indicators should be used, but a more
systematic approach to monitoring using indicators is still missing. According to Bierly
and Flagg, "examining individual indicators within the context of the behavior of related
indicators is more informative than univariate analysis and reduces the possibility of

decision error" (1995, p. 108).

International organizations also began to research the utility of indicator
monitoring. In the late 1980's, the World Resources Institute began looking into the use
of indicators for monitoring environmental health and development, and held a conference
on the subject of environmental and development indicators in 1992 (Hammond, et al.,
1995, p. 5). Also in 1992 the United Nations Conference on Environment and
Development in Rio de Janeiro called for more research into the use of indicators in
Agenda 21. More recently the United Nations has also recognized the utility and validity
of using indicators to monitor desertification and land degradation. The Convention to
Combat Desertification in 1994 set a mandate to address increasing concern over
desertification by monitoring through the use of indicators. The recommendations from
the conference were that programs should be initiated to, "support and further develop
bilateral and multilateral programs and projects aimed at defining, conducting, assessing
and financing the collection, analysis and exchange of data and information, including,
inter alia, integrated sets of physical, biological. social and economic indicators" (cited in

Berry 1996, p. 2).

20

Thus far, indicators have only been specifically recognized for monitoring of
desertification. But in recent years the use of indicators for monitoring on non-dryland
areas is also being put into use. The World Bank, FAO, UNDP, and UNEP have began
supporting a joint project to use indicators in the measurement of land quality. Land
Quality Indicators will be developed to monitor not only land degradation, but also soil
and water conservation efforts and land use pressures (Berry 1996). The use of
indicators in their capacity to monitor not only desertification, but other degradational

processes is becoming more accepted, and in fact suggested.

Indicators themselves are usually grouped into two categories, physical and socio-
economic (human) indicators. Physical indicators, such as gullies and devegetation, are
the most easily recognized and most easily justified group of indicators. Mabutt (1986)
used the terms "direct" and "indirect" to denote physical and human indicators
respectively, but this is not necessarily true. Human indicators can be directly caused by a
degrading land quality. Human actions either in response to, or as causes of, land
degradation can be acute predictors of a degrading situation. Changes in agricultural
practices may come long before some physical signs of degradation appear on the
landscape. According to Wangati,

"It is widely recognized that the main causes of land degradation

can be traced directly or indirectly to human activities. These in turn

are influenced or driven by social and economic conditions to which

the human population has to respond. Socio-economic pressures on

the population, equitable access to available land resources, and the

availability of alternative livelihood systems that reduce direct

dependence on land for subsistence can therefore be good indicators
of the probability (risk) that the available land resources will be

21
exploited beyond their carrying capacity resulting in degradation. It

is therefore accepted that poverty within a community can be an

indicator of risk or existence of land degradation" (1996, p. 11).
Human indicators then can be considered as a valid and useful tool in examining land
degradation. According to Bierly and Flagg, "A more accurate representation treats the

human indicators as an integral part of the system, not merely as phenomena responding to

feedback" (1995, p. 108).

In order to be useful, indicators need to have certain characteristics. According to
Hammond, et al. (1995), these characteristics include being "user-driven, policy-relevant,
and highly aggregated" (Hammond, et al., 1995, p. 2). Hartmut Krugmann suggests that
indicators also need be hierarchical by scale, dynamic, contexturalized. quantitative or
qualitative, and descriptive or performance-oriented (Krugmann in Hamby and Angura
1996). There have been two studies which attempted to use indicators for monitoring of
land degradation very recently (Krugmann in Hamby and Angura 1996). One project was
set in Kenya and focused on two field areas, the other project was undertaken in Western
Mali and focused on two districts. Both of these projects attempted to use indicators to
monitor land degradation, but again, the missing component seems to be a system whereby
indicators are integrated. There were valuable lessons, however, to be learned from these
projects. The first has to do with scale (spatial). Both projects concluded that monitoring
needed to be done at a local or regional scale, as costs at a larger scale were prohibitive.
Secondly, the researchers in the Kenyan case study also assumed that information would

be available to them which they later found was unfortunately not available, because it was

22
either not collected or too highly aggregated. These field studies offer valuable ways in

which to modify monitoring systems and could offer new indicators for specific countries

and regions.

The research on indicators has shown that they are of great value in monitoring
land degradation. It is essential to remember, however, that indicators are based in the
context of a society. According to Batie, "the expectation of a common set of indicators
is naive" (Batie, ed., 1995, p. l). Indicators used in Southern Africa will probably not be
exactly like those used in Asia or South America. Many indicators are based on an
assumed, and observed, pattern of behavior based on the cultural, political, economic, and
societal context of a society. Before indicators are suggested for an area some knowledge
of the land use system, customs, etc., must be examined. Indicators are geographically
specific, and as such based on the behavior of individuals which is constrained by both
societal and environmental context. Within this thesis, both human and physical indicators

will be analyzed.

The literature to this point has shown that interactions between humans and the
environment are complex and dynamic, with a reflexive relationship. The validity of
indicators in addressing environmental problems has also been shown in past research.
This paper attempts to contribute to the literature an assessment of methods for looking at
the problem of land degradation. Guiding the analysis will be the framework of political
ecology. This framework helps to contexturalize the data gathered, and will assist in the

selection of indicators relevant to Rwanda, as well as. interpretation of results. The

23

availability, utility, and validity of information concerning environmental and socio-
economic information, as well as, necessity of future cooperative data collection will be
discussed. Previous research has focused on identifying potential indicators of land
degradation. This study seeks to assess the feasibility of a systematic approach to

monitoring using these proposed indicators.

Data used in this study is an example of what could be available from some less
developed countries. Rwanda is used as the case study for this thesis because of the
availability of this data. If methods are not useful with the available data, then what
information would be necessary to make the monitoring systems feasible? Other statistical
methods are available, but how do the matrix results compare to these? This study will
compare the results of a proposed matrix method, with the results of regression and
principle components analysis, as well as, with the results from the original Rwanda
Society-Environment Project. It is possible that the suggested monitoring scheme is more
useful as a conceptual tool to show interaction of factors influencing land degradation as

opposed to being a tool for monitoring.

CHAPTER H

REVIEW OF STUDY AREA

In this chapter physical, cultural, and historical characteristics of Rwanda will be
examined. In using Regional Political Ecology as a framework for analysis of land
degradation it is necessary to take into consideration the history and agricultural setting of
Rwanda. No study of an area using a political ecology framework could be undertaken
without first familiarizing oneself with important aspects of the area in question. History,
politics, and economics contribute to contemporary situations and some knowledge of
them is important. In this case, though, context will be used to identify indicators that will

be most useful to assess land degradation.

Physical Characteristics

Rwanda is an area of the East African Plateau surrounded by Burundi to the
South, Zaire to the West, Tanzania on the East, and Uganda to the North (Figure 1). The
Congo-Nile Divide runs North to South through the country and separates Rwanda into
two drainage basins. The average altitude in this divide is around 9,000 feet (Nyrop, et
al., 1969). The Western side of the divide is mainly very steep slopes running into the

Great Rift Valley. The Eastern side of the divide is characteristically rolling hills

24

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26

descending into areas of savanna and swamp. Most of the population lives at elevations
of around 5,000 - 7,500 feet, and the average elevation of the country itself is 5,200 feet

(Nyrop, et al., 1969).

Most of the soils in Rwanda are granitic in origin, with the exception of the
Northwest portion of the country where they are volcanic. Nyrop, et al. (1969), suggests
that soils in Rwanda have many characteristics of laterite soils, but that they are more
fertile and generally better for farming than true laterites (N yrop, et al., 1996). It has also
been suggested that erosion and leaching are major problems in Rwanda (Lewis, 1994,
Nyrop, et al., 1969). The steep slopes of many areas, along with variable and often rapid

rainfall account for much of the erosional problem (Lewis, 1994).

There are two wet and two dry seasons in Rwanda. The short wet season occurs
from January to February and the long wet season from June through September. The
longer dry season occurs from March through May with a shorter dry period from
October through December. The average rainfall for the country is about 1200 millimeter
annually, but is highly variable and dependent on elevation. Rainfall is generally greater in
the high areas of the West, with decreasing precipitation in the lower altitudes of the

Eastern Plains.

Temperatures in Rwanda also vary with elevation. The Central Plateau region has
an average temperature of 65 degrees Fahrenheit daily, but the daily range may be as much

as 25 degrees (Nyrop, et al., 1969). The lower savannas of the East can reach daily

27

temperatures of 90 degrees. Some areas of the mountains in the West are too cold for
even trees to grow, and temperature decrease quickly with increasing altitude in these

steep Western mountains.

Historical Outline on Rwanda

Settlement in Rwanda first began about 1000 AD with the Hutu farmers in the
Western Highlands of Rwanda. The Tutsi pastoralists arrived about four to five hundred
years later from the north and settled in the eastern regions of the country. There was an
ensuing patron/client relationship in which the Tutsi kingdom gradually expanded to
include the Hutu regions. The period from 1744-1895 in which increasing area was
incorporated has been called "Le Grande Expansion" (Olson 1990). The concept of
ethnicity at this point was relatively open; it was more generally based on cattle
ownership (Olson 1995). That was the type of relationship in place when the first
Europeans entered Rwanda in 1880. The Western Highlands were still home to the
farmers, with part of the Northwest controlled by the Tutsi king but governed by the Hutu

elite that resided there.

In colonial times, Rwanda was first ruled indirectly by the Germans until 1915, and
then by the Belgians. The Belgian government reinforced the system of rule already in
place. Communal labor for erosion ditch building was enforced after this point. This

would become an important point of contention between the indigenous people and the

28

colonizers later. The population began to grow after the 1940's and, as a result
intensification began. Farming was still limited to the highland areas, as the rest of the
country was reserved for the pastoralists. It is suggested that because of the growth of
population and the area reserved for farmers being limited politically, intensification began

here earlier than in the rest of Africa (Olson 1995. Olson et a1, 1996).

In 1959 a Social Revolution occurred in which the Tutsi monarchy was deposed
and the Belgians switched their support to the new Hutu government (Olson 1995). The
United Nations oversaw the first independent election in Rwanda in 1962, and the capital
was moved to Kigali in the center of the country. In addition, there was also a wave of
migration to the savanna of the east during the 1960's and 1970's in a government initiated
resettlement scheme (paysannat)(Clay and Lewis 1990, Olson 1995). The cast in turn
became an area of large commercial farms considered rather wealthy in comparison to the

rest of the country (Olson 1995. Olson 1990).

The year 1973 saw a new president installed though a coup d'etat, and Rwanda
became an area of relative political and economic stability until the 1980's. During the
1980's the situation declined in most of the country, except the Northwest where
government investment was channeled. The early 1990's were the origin period for
formation of opposition parties to the ruling MRND and a breakdown in the stability of
the country (Olson 1995). Eventually the political situation completely failed and resulted

in the chaos that the region continues to confront.

29

Land Use in Rwanda

Rwanda is a small country of only 26,338 square kilometers with a population of
6.2 million people (Olson 1990). As a result, the country has the highest population
density in Africa (238 people per square kilometer), and a rather high population growth
rate at 3.2% (Olson 1990, Gourou in Olson 1990). Virtually all the arable land in Rwanda
in now is cultivated, and 95% of the population is rural and involved in agriculture (Ford
in Turner, et a1 1993). The country can be divided into three broad ecological regions.
The Western Highlands have the most abundant rainfall and most fertile soils of the three
regions, and are historically the most densely populated. The center of the country is
mostly foothills of the Highlands. This area receives less rainfall and has less fertile soils
relative to the Highlands, and therefore, less agricultural potential. The savannas of the
East have the least agricultural potential due to unreliable rainfall and poor soils. Rwanda
in general has two rainy seasons: a short one from October to January, and a long one

from March through May.

While the Western Highlands and foothills of the central areas were originally
forested, they had been degraded to grass covered hills by the time of European contact
(Olson et al, 1996). A traditional pattern of agricultural has emerged from the original
settlement in the Highland areas, though not found often today in its original pure form
(Clay and Lewis 1990). In this traditional pattern farmers located their settlements near
the tops of hillsides (Figure 2). Within this settlement, near the center, was a home

(urugo). Banana trees usually surrounded the urugo. this area being called the urutoke.

30

 

 

 
  
  
 

Pasmre URWURI

Woodlot - fallow

 

sorghum maize (Wet Season)
beans Ptas ure- fallow

 
 

 

  

sorghum beans
sweet potatoes
fallow

  

Figure 2: Typical Farm
(From: Ford 1993)

 

31

Bananas are an important crop both economically and socially in Rwanda as it is used to
make traditional banana beer. According to Clay and Lewis 40% of Rwandans obtain

income from the production and sale of banana beer (Clay and Lewis 1990).

The most important cr0ps for the household are planted near the bananas, such as
coffee, condiments, and vegetables. This area is called the imirima, and receives most of
the fertilizer available. Traditionally, manure from animals and household waste has been
used as fertilizer. The reduction of pasture and decreasing amount of animals kept by
households has caused manure to become scarce in more recent periods, though. Coffee,
which is usually planted farther from the urugo, is the main export crop of Rwanda and

promoted heavily by the government (Clay and Lewis 1990).

Beyond the coffee is an area of less important crops such as sorghum, maize, and
beans, the indare. This area may also be used for fallow in the wet season in some areas.
Typically the tops of hills, urwuri, are used for woodlots, fallow, or pasture. The base of
slopes contain ridges areas, built up to ensure drainage, that are cultivated during the dry
season. This area, called the imigende, contains crops such as beans, yarns, and maize.
Increasing population in the face of a finite amount of land has caused many changes in
this system (Clay and Lewis 1990). The main effects have been that plots of land are
smaller and more fragmented, plots are often discontiguous, and often less fertile areas
such as hilltops are cultivated. The system of tenure rights has also contributed to this

more recent system of fragmented farm. In this tenure system sons inherit land from their

32

fathers, but an increasing scarcity of land has reduced both the availability of land to give

and a reduction in size of plots given.

Although brief, this overview of Rwandan agriculture and history has been
presented to provide context for analysis of land degradation in the country. A Regional
Political Ecological approach to looking at the problem of land degradation requires that
one look not for a single cause, but rather a for a process catalyzed possibly by many
factors. Regional Political Ecology is a holistic way of assessing dynamic and complicated
processes. Historical, economic, and social context needs to be taken into consideration.
Rwanda is a poor country, with a large subsistence-farming based population. The land
use patterns evolved from former dominant pasture and regional hillside farming to
cultivation of virtually all areas, including very marginal land. Political and demographic
pressures have shaped the landscape so that today it is characterized by small, fragmented
farms often on marginal land. Most farms lack manure to fertilize crops as the amount of
pasture and fodder for animals has become increasingly scarce. Much of the loss of soil
fertility has been attributed by farmers to this lack of manure (May 1995, Olson 1995).
This having been said Rwanda did once enjoy a period where intensification kept pace
with rising population densities. This thesis will attempt in the following section to assess

the level of land degradation at the time of data collection.

33

Previous Studies on Rwanda

This study uses information obtained from the Rwanda Society-Environment
Project undertaken by the Department of Geography and the Center for Advanced Study
of International Development at Michigan State University. This project was a
collaborative effort by the university departments, United Nations Environment
Programme (UNEP-GRID), various government agencies in Rwanda, and the Department
of Geography at the National University of Rwanda. The objectives of the project were to
"assess data needs for environmental management in developing countries in Africa and to
determine how to effectively use physical and socio-economic data sets interactively to
address Natural Resource Management (NRM) policy" (Campbell 1994b). A series of
working papers each related to specific issues addressed in the project was produced.
Within these only those most relevant to this study undertaken in this thesis will be

reviewed.

The data used in the Rwanda Society-Environment Project was a compilation of
both physical and socio-economic data, and the analysis was done in the context of
regional political ecology (RPE). Suggestions included in the conclusions of the papers
included requirements for minimum and optimal data sets. The minimum data set was
more specific in offering basic data that most African countries could and should be
collecting for monitoring of natural resources issues, while the optimal included broad
principles. Included in this data set as minimums were a topographical map of the

country, a decennial population census, and basic human and environmental data to

34

compliment them. Suggestions for scales, both temporal and spatial, are also offered in

association with suggested information assessment.

Some of the most important findings to come out of these works included analysis
on population and land degradation. David Campbell argues, "that there is no direct
relationship between the distribution of population and that of environmental stress"
(Campbell 1994a). This argument is based on analysis of stress variables and population
density as they are correlated across the country. The author concedes, though, that the
environmental stress variables used in this analysis were indirect. An interesting premise
is put forward later in that areas of low environmental stress also had high population
growth rates. If this process of growth in these areas continues there could be resultant
degradation unless other alternatives to the current patterns of land use are made. The
findings from this study then should be examined for not only the state of degradation at
the time of the study, but also keeping in mind the implications of future changes based on

the project data.

One of the main problems found in assessing data available to the study was that
information came from many different sources, but also at many different scales spatially.
As a consequence, results could only be relied on to show broad regional patterns, as that
was the lowest level of aggregation for some of the data. The differences in scale and
source of data collection also brought about some questions as to the ability and validity

of analyzing data from different data sets. These same problems will hold true in my

35
analysis as regards data, although in this study utility of the methods for assessing land

degradation is the central question, rather than the validity of the data.

Results in this study will be compared to results in the Rwanda Society-
Environment Project. Some of the broad conclusions of the project were: the need for
cooperation in collection of data, illustration of broad patterns representing the state of
environmental stress in Rwanda, as well as, future implications of this state, and
recommendations for future data collection within Rwanda and other non-national areas.
The conclusions also stated that land degradation, "is a multi-dimensional process and
encouraged further analysis that integrated societal and environmental processes"
(Campbell 1994a, p.23). The project itself was both an exercise in data collection and
exploration, as well as, an exercise in approaching land degradation analysis from a
regional political-ecology perspective by examining both human and environmental factors

within the context of a specific country.

According to two other sources dealing specifically with Rwanda, the agricultural
situation has been on a continuous decline in recent years (Clay and Lewis 1990, May
1995). Rushahigi attributed the decline in the situation to rising density with much
adoption of technology (in Arizpe, et al., 1994). According to Rushahigi, poor countries
like Rwanda have little access to advanced technology because of their limited economic
capacity (in Arizpe, et al., 1994). Clay and Lewis on the other hand suggest that, "the
traditional settlement pattern and the organization of farming, coupled with more recent

demographic pressure, have impeded the development of a cropping system that

36

effectively contributes to the conservation of precious land resources" (1990, p. 149).
Clay and Lewis go on to present the typical farming patterns traditionally found in
Rwanda and empirical evidence to suggest a declining agricultural situation. The authors
found that most farmers say they attributed declines in production to over-cultivation and

soil erosion (Clay and Lewis 1990).

Both the study by Clay and Lewis and the comments by Rushahigi are limited by
their focus on a single or limited cause for the complex problem of land degradation.
Rushahigi leaves out societal and environmental characteristics present in Rwanda that it.
could also be factors in the role land degradation. And Clay and Lewis (1990) leave out
important political and economic forces that play a large part in decision-making in
Rwandan agriculture. Both studies seem to lack an integrative framework for analysis that
takes into account economics, politics, societal and cultural, and environmental

characteristics that make the study area unique and contribute to the processes they are

describing.

The World Wildlife Fund and USAID conducted a study of the area adjacent to
the Nyungwe Forest Reserve had more positive results, although with some exceptions.
This project was interested in land use changes at the edge of the aforementioned forest
preserve. The study area in question was and still is a high population density area. The
researchers in this case found that a combination of intensification processes and migration
has been relatively successful in areas that have better soils and resources. The

counterside is that poorer households that have less suitable ecological bases are not faring

37

as well and are, in fact, experiencing land degradation. The overall findings suggested,
though, that in some areas productivity had improved through "local strategies to mitigate
land shortage" (Olson, et a1, 1996, p. 7). The specific study findings concluded that,
"total forested area and biodiversity of forest-agriculture landscape have increased"
(Olson, et al, 1996, p. 57). The root cause of the degradation occurring was proposed to

be caused not by population density and rapid growth, but rather poverty.

One of the most interesting issues to be addressed in the World Wildlife Fund
paper is deforestation. It had been widely believed that deforestation was a fairly recent
occurrence, but through use of historical photographs the study determined that Rwanda
is actually less deforested now than before 1960. The authors suggest that pastoralists cut
down trees for over 1500 years in order to clear grazing land for their herds (Olson, et al.
1996). Relatively recent changes in land use from pastoralism to cultivation of crops has
helped to convert a previously severely degraded landscape to one of increased production

(Schyns, cited in Olson, et al, 1996).

The most positive outlook on the situation in Rwanda comes from Robert Ford.
Ford's case study of the Ruhengeri prefecture of Northwest Rwanda (1993). This area, as
in the previously mentioned case study, is also a densely populated area. Ford suggests in
his work that, "all indications show that total production has improved over the last few
years" (Ford 1993, p. 175). Although this is proposed, he also notes that 82 percent of
farmers in the prefecture think that the productivity of the land has declined. Ford argues.

though, that through a govemment strategy of keeping food production just above

38
population growth the population will be able to feed itself until its growth stabilizes in 20-

30 years. He does admit that if stabilization does not occur then land degradation and
economic hardship will ensue. His conclusions were that "the rate of intensification and
political maturation is sufficient for the near future to maintain current living standards and
even improve them slightly without degrading the resource base" (Ford 1993, p. 180).
This is a positive outlook, but there are still other factors to be considered in looking at

environmental health.

There has been widespread disagreement in previous case studies, such as Ford
(1993), May (1995), and Olson (1994) concerning Rwanda as to the amount, rates, and
causes of land degradation. In all of the studies the rapid growth of population is
mentioned, but the importance given to population growth varies considerably. Many of
the studies cited here were also regional studies and as such did not draw conclusions for
the entire country, such as Olson (1994) using a prefecture as a study area, rather than the
country. The previous studies provide valuable insight into the assessment of land
degradation to this point in Rwanda. The present study examines a number of methods
for monitoring land degradation at both regional and national levels, using a combination

of indicator variables of both physical and societal processes.

CHAPTER III

METHODS

Need For Research

Research into the use of indicators has shown that they are a valid way of
monitoring land degradation. The use of indicators has been justified in previous research
and suggestions for monitoring specific indicators have been proposed. The problem lies
in the integration of indicators, both physical and human, in a systematic and efficient way.
Indicators are much more effective when analyzed together, thereby eliminating merely
monitoring a trend in a specific indicator. A monitoring scheme, composed of many
indicators, should give a better picture of the broader situation, as opposed to looking at
single indicators. By using a multiple indicator scheme there is less chance of missing
information that could lead to early detection, and hopefully, alleviation of degrading

situations.

There are agencies at the local, national, and international levels, all of which
collect data on environmental problems. But what data are being collected and how are
data being aggregated? The collection of information is important not only for monitoring
the environment, but also the economy and demographics. To expect one agency to

collect all relevant information for all of these is impossible. Data should be shared by

39

40

those for whom it is useful and should be accessible. Accessibility is not the case in most
nations. Information is often difficult to come by, if it is available at all. Nations need to
organize what information is most necessary to be collected and work to delegate these

information needs to apprOpriate organizations and departments.

Statement of Problem

Political Ecology identifies land degradation as arising from complex interactions
between biophysical and societal processes (Blaikie and Brookfield 1987 ). This suggests a
need for monitoring systems to incorporate indicators that reflect these interactions.
Previous studies have shown that indicators are a valid way of monitoring land
degradation, but they have been dominated by biophysical indicators and have not
integrated the societal ones. In the real world the processes illustrated by the indicators

do not work independently, and as such should be monitored together.

The objective of this thesis is to examine approaches for monitoring land
degradation by comparing regression analysis, principal components analysis, and a matrix
framework based on indicators. Data on societal and biophysical characteristics available
collected by the Rwanda Society-Environment Project will be used to determine which
method is most effective in assessing the extent of land degradation in a simple and clear

manner using data that might be available in many developing nations.

41

The study will attempt to answer three main questions. 1) Based on the
assumption that the data set acquired for use in this study is among the better available on
any African country, which methods are feasible with the available infonnation? 2) If
monitoring is not feasible with the data currently available, what would the optimal set of
data include? 3) The final research question is concerned with how the results of each
method compare to those of the Rwanda Society-Environment Project. The results of the
Michigan State University Rwanda Society-Environment Project will be used as ground-

truthing for this remote analysis.

Data Set

The choice of variables from within the available set of data is based on suitability
as an indicator of land degradation in Rwanda. A range of relevant indicators is found in
the works of Berry and Ford (1977), Reining (1978), Mabutt (1986), and others.
Justification of the use of specific indicators is grounded in previous literature and reflects
an approach based on Political Ecology in which the historical, socio-economic, and
environmental conditions were taken into account in choosing indicators that best fit the

conditions in Rwanda.

42
Analytical Methods

Three approaches to the evaluation of indicator sets are considered. Two,
principal components analyses and regression analysis, examine a set of discreet variables
while a third, a matrix framework developed by Bierly and Flagg (1995), attempts to
provide an approach in which the co-impact of different indicators is explicit. The matrix

approach is compared with PCA and Regression to assess its validity.

The proposed scheme for monitoring land degradation is based on the use of
paired human-physical indicators (Figure 3). The matrix itself is presented in three
dimensional space: physical indicators comprise the x-axis, human indicators comprise the

y-axis, and the z-axis represents time.

Indicators are arranged on the x- and y-axes in sequence according to relative
severity, with indicators near the origin being the least severe and severity of contribution
to land degradation increasing away from the origin of the axes. Less severe are
considered as those whose occurrence might be temporary, easily reversed, or does not
necessarily mean degradation is ensuing. An example would be decreased precipitation,
which is often an anomaly and does not necessarily signal intense degradation without
other signs being apparent. On the other hand, severe indicators are often considered
chronic symptoms (i.e., persistent deforestation or devegetation) usually thought to more

strictly

43

 

PERSISTENCE

o
migralion

 

XXXXY

 

 

nulrilional slalus

 

 

 

cereal prices
liveslack sales

crap decreases

 

HUMAN

mechanized awi rainlall
IN 0 IC A I 0 R S "I M)

soil lerlilily

soil depth
soil crusts
groundvaler
veqelalion
PHYSICA I.
INDICATO RS

 

Figure 3: Hypothetical Matrix and Indicators
(Source: Bierly and Flagg, 1995)

44

accompany degradation. The relative severity of the variables is assessed by reference to

the literature on land degradation discussed previously.

Sampling sites (commune, region, etc.) within the study area are checked for the
occurrence of variables chosen as most suitable indicators. A Pearson r-value is calculated
for each of the human/physical variable pairs. The correlation value is based on tallies
indicating occurrence or lack of occurrence on a binary scale (1 = occurrence, 0: no
occurrence). The Pearson r—values are used to weight the pairs multiplicatively. These
weights for each pair should be indicative of association and degree of coincidence.
Assessment of correlation values of each pair recognizes that the symptomatic and causal
nature of degradation is not governed by human or physical systems independently of one
another (Bierly and Flagg 1995). Additional multiplicative weights may also be applied
to indicate severity of each variable through analysis of the observed normal statistical
distribution of each indicator. Percentile rankings within the distribution are used to

calculate these weights.

Each pair of human/physical indicators is also given an actual value which is the
mathematical product of the ordinal rank of both variables. Indicators at the origin of
both axes, the "weakest," are given a score of 1. The scores increase by one with each
discrete indicator as variables move away from the axis. "Multiplication is appropriate

because variables are expected to be proportional (after empirical weighting) to

45
desertification levels" (Bierly and Flagg 1995, p. 109).2 The sum of the products of all

human/physical indicator pairs after the aforementioned weights are applied are equal to a

Composite Land Degradation Index (CLDI) value.3

A temporal dimension is added through the inclusion of a z-axis. Earliest periods
of monitoring occur near the axis with chronological movement away from the axis. The
most recent monitoring period is furthest from the origin of the z-axis. All z-values for the
entire matrix describe a surface indicating the relationship and severity of "each physical
and human indicator occurring in tandem and the contribution" to the degradation scenario
(Bierly and Flagg 1995, p. 110). The shape of the surface indicates both the level of
degradation occurring and also important areas of interaction of indicators that may need
more intense assessment or remediation. Logically, an intense scenario of land
degradation that has been monitored from an early stage of progression should show

strong correlation of highly ranked indicators.

A single value indicative of a level of degradation in a region is achieved through
the sum of the weighted components of the matrix. The sum is compared to "a scale of

critical levels based upon geography and distribution of population" (Bierly and Flagg

 

2It should be noted here that this matrix was originally intended for assessing the level and
progression of desertification in dryland areas. Desertifrcation is, in essence, degradation
limited to dryland areas, and therefore, the principles behind the matrix are still applicable. The
difference lies in that land degradation may occur anywhere while desertification in limited in
its spatial extent by climate and landscape.

31n the original publication of the matrix concept the sum of the weighted products was called
the Composite Desertification Index or CD1. From this point on the sum of the weighted
products in this study will be referred to as the Composite Land Degradation Index or CDLI for
clarity.

46
1995, p. 111). The CLDI table (T able 1) presented, " is based upon severity and r-values

incremented by 15 percent during each phase, beginning with the weakest indicators and
adding one human and one physical variable with each iteration" (Bierly and Flagg 1995,

p.111)

 

Table 1: Critical Values of CDLI INDEX

 

 

Observed CDLI Description of Conditions
1 - 9 Slight Development/Degradation
10 - 51 Minor Development/Degradation
52 - 175 Intermediate Degradation
176 - 478 Advanced Degradation
479 - 1073 Critical Degradation
1074 or > Extreme Degradation

 

The matrix, in summary, seeks to use indicators of land degradation specific to the
region of study to assess the presence and status of land degradation. This is
accomplished statistically through the computation of a composite score from the matrix,
which is then compared to critical scores of the Composite Land Degradation Index
(CLDI). The breakpoints in the table shown are based on 15 percent increases in r-values
and also by addition of one human and physical variable pair as an occurrence for each
class. A graphical illustration of the matrix itself can also be accomplished by plotting the

scores of the communes on a national degradation surface. This will then show areas in

47

the country where the most critical interaction of indicators is occurring. It also shows
regional patterns well and anomalous areas. The findings within this study will compare
only the composite scores with the results of principal components and multiple

regression, as well as, the results of the Rwanda Society-Environment Project analysis.

Principal components analysis will be used to look for surrogate indicators that
could be used in monitoring land degradation. Two principal components analyses were
performed; one regarding the larger pool of possible, suitable indicators from within the
data set, and the other only on the indicators chosen for the matrix. The regression
analysis was used to determine if there is a relationship between the scores and the
indicators chosen for use within the matrix. A regression was also performed using the
compound stress scores determined in the original Rwanda Society-Environment Project
as the dependent variable. These regressions will be compared to determine if either of
the two equations has a better fit with the project data. The process of data analysis and

findings will be discussed in the following chapter.

CHAPTER IV

DATA ANALYSIS AND FINDINGS

Selection of Indicators

The first step in the analysis of the available data was selection of indicators. As
previously mentioned, the pool of all possible indicators was taken from the previous
literature concerning the use of indicators in monitoring degradation. Indicators were also
selected on the basis of suitability to the specific conditions and socio-political context of
Rwanda. All analysis was done on the spatial scale of the commune. A commune is a
lower political unit in Rwanda, of which there are one hundred forty-three. The commune

is analogous to a county in the United States.

For this study four physical and four human variables were selected to illustrate the
approaches to monitoring land degradation. The physical variables chosen include mean
annual rainfall, propensity to erode, soil capability, and intensity of cultivation. Mean
annual rainfall in millimeters was available in raw data form“. The data is the average for
the years 1929-1988. Rainfall was chosen as an indicator because although Rwanda is not
a dryland area, rainfall is still a limiting factor in areas. Rwanda depends almost totally on

agriculture, and the effect of rainfall on agricultural options is important. Climate must be

48

49
included in some form in any attempt to monitor human-environment interaction, and in

this case, rainfall was chosen as a surrogate for climate.

The "propensity to erode" variable is the weighted sum of two variables taken
from 018 dataS. This indicator was not available as an average per commune within the
data set itself, but rather, was calculated from available data. Rainfall and slope variables
were summed and multiplied by the area in each polygon in the GIS data. These sums
were then added together for the entire commune and divided by the total area of the
commune. Both variables were divided into six sub-classes for each variable before the
data was summed. As a result, the highest possible score in the propensity to erode is

twelve (Table 2).

This indicator was chosen because Rwanda is a country with high relief in which
steep slopes are often farmed. Cultivation in these conditions is limited by erosion.
According to Clay and Lewis (1990), the most serious declines in productivity in Rwanda
are on the slopes of 15 degrees or more. Most soil loss equations are derived in the
temperate latitudes and reflect farming practices analogous to the United States. There is
no intensive soil loss equation for the tropics and all soil loss equations, even the United

States models, are generally useful only where exact conditions to those used in test plots

 

4This variable was calculated by the Michigan State University Rwanda Society-Environment Project from
statistics in MINITRAP, 1992.

5In this case some of the data was in digital form and had to be interpolated from the polygons in
ARCINFO.

50

 

Table 2: Slope and Rain Classes

 

Slope Classes

1) 0-2%
2) 2-6%
3) 6-13%
4) 13-25%
5) 25-55%
6) >55%)

Rain Classes (mm per commune average)

1) 745 - 1041

2) 1042 - 1150
3) 1151 - 1250
4) 1251 - 1298
5) 1299 - 1354
6) 1355 - 1545

 

51
are replicated. The propensity to erode is also a somewhat crude variable, but areas
recognized by high scores are more susceptible to erosion if proper care is not taken.
Conservation measures such as choice of crops with low 0 values6 or erosion ditches and
terracing can help to prevent erosion. These measures can be labor and/or capital
intensive, though, and are not assumed for all areas. This variable then, simply targets

areas with highest potential for degradation due to soil erosion and its associated effects.

The third physical variable is soil capability, which was derived from digital data

27‘ “:1

and associated polygons7. Soil capability is also a variable weighted by area. Potential soil
capability was divided by actual soil capability and this number was then multiplied by the
area of the polygon. The sum of the weighted products was then computed and divided
by the total unweighted area. This variable is essentially the ratio of the actual capability
has to the potential the soil has in an area. Because the classes decreased in potential as
value of the capability rose in each variable the reciprocal ratio of the intended ratio was
used. Thus, the soils with the highest potential and actual capabilities had the lowest class
numbers (Table 3). The given formula provided a measure of the degree to which the soil
was being used compared to its potential capability. For example an area that had an
actual capability of 4 and a potential of 2 was considered as only being maintained at 50
percent of its potential capability. Although averaging this data which is computed from

smaller spatial units (soil unit level) is crude, it is offset by the use of the weighting by

 

6A c-value is the ratio of soil loss from an area with a specified cover (vegetation\ crop mix) to the soil loss
from that same area if it were in continuous fallow (Clay and Lewis, 1990).
7 These values are derived from Carte Pedologique du Rwanda (1992).

52

 

Table 3: Soil Capability Classes

 

--Actual and potential soil capability classes bound by most limiting soil
variable.

--Potential is determined by the class the soil would be if all possible
soil improvements were to be made.

1--Very suitable for most crops but unsuitable for tea. Unsuitable in current
conditions for rice, but with improvements could be suitable for irrigated rice.

2--Very suitable for most crops, but marginally suitable for tea. Unsuitable in
current condition for rice. but with improvements could be suitable for irrigated
rice.

3--Medium suitable for most crops, but marginally suitable for tea. Unsuitable in
current conditions for rice, but with improvements could be suitable for irrigated

l‘lCB.

4--Marginally suitable. Unsuitable for demanding crops, but marginally
suitable for less demanding crops. The deeper soils are very suitable for tea.

5--Suitable for pasture, and for appropriate valley crops during the dry season.
Also suitable for irrigated rice and tea.

6--Suitable for pasture. Unsuitable as arable land in current conditions, but
suitable for less demanding to demanding crops with major improvements.

7--Suitab1e for forest. Unsuitable as arable land in current conditions, but suitable
for less demanding to demanding crops with major improvements.

8--Very intense limitations. This class evaluated according to soil depth
(shallow, medium, deep).

(From: Carte Pedologique du Rwanda (1992) as reported in Rwanda Society-Environment Project, 1994.)

53
area. This variable was chosen to target areas where the soils were not as productive as
they could potentially be. This indicator is basically a surrogate for soil health. The larger
the difference between what could be grown given the full potential of the soil and what

the soil can sustain in reality the lower the resulting percentage.

The last physical variable is intensity, which was computed from two raw data
variables, total available area in a commune and the area being cultivated. The cultivated
area was divided by the total available area to provide a measure of how much land was
still available for farming. Rwanda is a country in which sons inherit land from their
fathers. When the population begins to become too large, the first response is for people
to move to unoccupied, uncultivated areas. When all available land is taken the practice of
inheritance continues farm size begins to shrink and farms become fragmented. This
indicator thenis a measure of the pressure on the available land in a commune. It
measures the ability of the commune to absorb a growing population as well as to

accommodate agriculture.

The four socio-economic variables chosen include population density, agricultural
yield, amount of tubers grown, and migration. The first of these variables, population
density, was taken from raw data for the year 1991 and is the number of persons per
square kilometer. In any situation where subsistence farming is the major activity of the
population, density and distribution of the population can have strong influences on
opportunities and options for farmers. Density can reduce available land for fallow,

growing of fodder, and wood fuel, as well as, for gifts to sons (May 1995). Population

54
density is not directly attributable to environmental stress. Intensification of agriculture
can alleviate problems and according to Boserup (1965) may even be a catalyst for
technological innovation. Often, though, intensification and innovation are limited by
capital, both human and financial. In this case, however, the assumption is that density
may have some effect on alternatives available to farmers. It limits the use of
extensification as a mitigating strategy and, if the rate of technology adaptation is less than
the rate at which density is rising, then intensification may not be enough to prevent
degradation. In conclusion, density is included because of its potential to impact

agricultural societies more heavily than those that are more diversified.

Yield is an indicator taken directly from raw data (MINAGRI l989)8. This
variable is the total agricultural production per commune in kilocalories. The data in this
case was already adjusted in the previous study for size of commune. So in this case yield
is defined as output per hectare for the commune. Yield was chosen as an indicator, once
again, because Rwanda is an agriculturally based society. Higher yields per unit
theoretically indicate that soils are more fertile or that more efficient agricultural
techniques are being practiced (i.e. higher yielding varieties being used, intercropping,

etc.). Yield should give a measure of the overall agricultural health of a commune.

The next indicator chosen was a surrogate for tuber production. This variable is

the sum of manioc (cassava) and sweet potatoes grown in a commune divided by the total

 

8 Data are for the year 1987.

55
cultivated area (MINAGRI 1989). According to Clay and Lewis (1990), tubers are
planted when soil productivity declines and/or land holdings become insufficient for other
more land extensive crops. Tubers have a higher caloric value and grow in even highly
degraded soils. Olson (1994) has suggested from research in Rwanda that manioc is the
choice of farmers when few other cropping options exist Manioc, as well as, sweet
potatoes are often grown in Rwanda because of their hardiness and ability to feed larger
quantities of individuals. This indicator is also related to the propensity to erode. Tubers
have a relatively low C-value, reflecting a minimal amount of protection by the plants to
soil from erosion. According to Lewis (1988), the presence of manioc in certain areas,
especially those of moderate to steep slopes, generally is indicative of soil loss and
degradation. Only manioc and sweet potatoes as undesirable because in reasons for
increasing/decreasing areas under certain crops, white potatoes were mentioned as having
monetary value (Olson 1994). In these same interviews, the main reason for increasing
area under sweet potatoes and cassava were because of their value as an inexpensive food
source adapted to poor soils (Olson 1994). Land under these two crops significantly
increased in areas where the perception was that productivity of the land was declining
(Olson 1994). This indicator points to a change in people's behavior as a response to
degrading conditions when measured along with other accepted indicators of land
degradation. Interviews have shown that the tubers used in this variable are not planted as
a source of monetary income. The taste is not preferred compared to other foods and that
the main reason for growing is to compensate for lower soil productivity (Olson 1994).
These reasons combined suggest that the use of tubers as an indicator of degradation is a

rational decision and reflect the context of Rwandan agriculture.

56

The migration variable was computed from the rates of population growth from
1978-1991. This was the best amount of time for which census information was available,
because it is closest to the time period being monitored for the rest of the indicators. Data
for 1987 was included in the available set, but the 1987 data were estimated. In this study,
the use of actual data as opposed to derived was preferred, because of the loss of accuracy
already incurred in scoring for the matrix. The data for population growth was divided by
considering observations less that one-half standard deviation from the mean as out-
migration and those above one-half standard deviation as in-migration. In this indicator
out-migration is considered a sign of, or reaction to, environmental or social stress. Olson
(1990) argued that migration patterns may reflect degrees of environmental stress. Areas
of perceived low stress attract people from areas of high density or environmental stress
because of the availability of land in the low stress or density areas. Olson (1990) also
suggests that in-migration may have future effects upon the area depending upon the
choice of livelihood and degree of technology available. The out-migration portion of the
data, then, was considered the most indicative of degradation. The communes with a rate
suggesting in-migration were considered to be potentially degraded in the future. And
finally, areas where the rate of growth near the average were considered to be most stable

and perceived as the best case scenario.

WW

Some adjustment was necessary to the matrix because of the characteristics of the

available data. The first of these changes was in the occurrence/ lack of occurrence tally

57
system. Without baseline values (i.e., what is the normal level) for each variable there was
no starting point with which to compare observations. It was observed within this analysis
that without some sort of knowledge concerning the maximum level a variable may reach
before it is considered a problem, there was no way to define occurrence and lack of
occurrence. The problem was exacerbated by the lack of time series data. Observations
could not be compared over time to look for continuous increases in severity of a variable
which could have been interpreted as occurrence. To compensate for this problem the
data was divided into three classes. The classes were derived by separating data into
thirds by using one-half standard deviation from the mean as the break between classes. In
each indicator, the observations were given ranks from one to three suggesting severity
relative to the rest of the communes. The rank of one suggested the least problematic
observations and three indicates a problem area. The assumption here is that the relative
values at one time period will target areas of most degradation concerning that indicator.
This adds a degree of robustness to the matrix by giving weights not only to the specific

indicators, but also to observations within those indicators.

This made changing critical CDLI values necessary. The new critical values
reflect both the relative scores from the communes in the final analysis using the matrix
and the total of hypothetical scores possible (Table 4). The lowest possible score for any
commune is 100 reflecting a commune that was in the "best" categories for every
indicator. The highest! worst possible score theoretically is 900 reflecting assignment to
the three category in every indicator instance. Critical evaluation of scores will be more

useful using the observed values computed for each commune, as it will show the relative

58
level of degradation compared to the rest of the country. Classes were divided into
groups based on one-half standard deviation increments. Each successive class is an

increase in the critical score of one-half standard deviation.

 

Table 4: Adjusted Critical Values for CDLI

 

 

Scores Relative Situation

100 - 200 Least Degradation

251 - 350 Slight Degradation

351 - 450 Minor Degradation

451 - 550 Intermediate Degradation
551 - 650 Significant Degradation
651 - 800 Severe Degradation

801 - 900 Most Degraded

**Classification of scores is relative to scores within Rwanda.

 

Only one period of analysis is possible due to availability of data. This made the
use of the Pearson's r-value unnecessary. The Pearson's r is only of value when data can
be compared over a long monitoring period where observed values can be analyzed over
time. The addition of the weighting within indicators compensates for the removal of the

Pearson's r-value as well.

Data Analysis

The first step in the analysis of the data was to score each observation within each

indicator for relative severity. As previously mentioned, this was accomplished by

59
dividing the data into thirds based on breaks one-half standard deviation from the mean.
One was the score given to those observations falling into the "best" category and three

was the score of areas with the relatively "worst" situation.

The next step was the summation of the matrix for each commune. An equation
was derived where the scores of each observation would be weighted, multiplied by its

human or physical match, and summed for the entire matrix. This equation is as follows:

(h1w1)(p1w1)+(h2w2)(p1wl)+(h3w3)(p1wl)+(h4w4)(plwl)+
(h1w1)(p2w2) + (h2w2)(p2w2) + (h3w3)(p2w2) + (h4w4)(p2w2) +
(h1w1)(p3w3) + (h2w2)(p3w3) + (h3w3)(p3W3) + (h4w4)(p3w3) +
(h1wl)(p4w4) + (h2w2)(p4w4) + (h3w3)(p4w4) + (h4w4)(p4w4) z score

where: hl = observation for intensity
h2 = observation for yield
h3 = observation for tubers
h4 = observation for migration
p1 = observation for rainfall
p2 = observation for propensity to erode
p3 = observation for capability
p4 = observation for intensity
wl = weight of l
w2 = weight of 2
w3 = weight of 3
w4 = weight of 4

The resultant scores computed for the matrices of the 143 communes in Rwanda
ranged from a minimum of 120 to a maximum of 784. The mean was 395 with a standard
deviation of 141. The scores were mapped (Figure 4) and regional patterns were able to

be identified. The highest scores relative to the rest of the country were in the Northwest

60
and the Southwest. The lowest scores, or best situations for the time period monitored,
were in the Eastern part of the country. The Central part of the country had mixed values,
as did the far Southwest near Lake Kivu. The categories for the map were once again
aggregated as to compare relative scores. Each increase in class is a one-half standard

deviation increase in the scores.

Comparisons

The first comparison that should be made is to that of the findings of the Rwanda
Society-Environment Project. Campbell (1994a) used three indicators to calculate a
summary environmental stress score. These indicators were calorie production in 1987 as
a percent of need within the commune, potential additional calorie production, and
uncropped arable land. These three indicators were standardized and the z-scores were
summed to find an environmental stress score for each commune. The comparison of
these scores with the scores from the matrix became the most important element in the
data analysis, because of the element of “ground truth” it added to the matrix scores. The
stress scores for each commune were verified to be true both regionally and on the

commune level by colleagues of the project authors living in Rwanda (Campbell 1994a).

61

 

 

Scores
- 100 250
* 251 - 350

351 - 450

 

 

Figure 4: Map of Matrix Scores

62

The matrix scores, when compared to relative regional patterns of the original
environmental stress scores (Figure 5, Campbell, 1994a), are similar over most of the
country. Both measures have low scores in the east and high scores in the southwest.
The only area of the country that differs is the northwest. The region as a whole is not
significantly different, but some communes do differ from those of the environmental
stress summary scores. The Rwandan colleagues of the original project authors had
cautioned that the maps they were shown were univariate and suggested a more integrated
process of analysis be considered (Campbell 1994a). Comments by those knowledgeable
about the situation in Rwanda reflect concern that the summary scores were not integrated
enough. These comments encouraged my continued belief in the validity of the matrix
scores. The differences between the summary stress scores of the original project and the
matrix scores are only slight. I believe the matrix scores to be reliable because of their
regional patterns reflecting the acknowledged areas of high stress by the Rwandan
colleagues of the original project authors. In general the matrix seemed to produce

effective results in targeting areas of stress present during the period being monitored.

Correlations were run on the variables that were chosen for use in the matrix. The
results seem to suggest that the hypotheses concerning relationships were correct. The
first of these correlations was the negative relationship between out-migration and tuber
production. The original hypotheses for using these variables was that degrading areas
will have higher tuber production and low population growth rate interpreted as out-

migration. The negative correlation in this case results from the use of the real data as

 

 

 

Figure 5: Map of Summary Environmental Stress Scores
(Source: Campbell 1994a)

64
opposed to the scores. High values on population growth (in-migration) correlated with
low values in tuber production. The opposite relationship can also be considered true,
that is, high tuber production is correlated with out—migration. This was found to be an

important point in justifying the use of these two indicators within the matrix.

The next correlations found positive relationships between calories produced in the
commune and level of intensity. This relationship seems intuitive and was not significant
in the justification for use of the variable within the matrix. Three relationships were
found to be statistically significant though regarding physical variables. The first of these
is the relationship between propensity to erode and capability. This relationship is a
negative one, because large erosion values are correlated with the areas where the
disparity between actual and potential erosion is the greatest, or rather the value itself is
small because it is a percentage of its potential. This also supports the hypothesis in the
original selection of the indicators that areas without significant conservation measures
would have a better chance of being degraded. This degradation is reflected in the small

value for the capability.

Rainfall and propensity to erode are also significantly positively correlated, but this
relationship is also an intuitive one. Both of the indicators here are based on rainfall, at
least in part, and should be correlated. Soil capability and rainfall were also found to be
significantly negatively correlated. The hypothesis here is that areas of less degradation
will receive relatively high amounts of rainfall. This hypothesis is confirmed in that high

capability values (actual and potential capability equal) were correlated with areas that

65
actually received lower rainfall totals. Although this is counter to the hypothesis, I still
feel rainfall is important. The low rainfall areas here are those of less population and
intensity so the difference between the condition the soil could be in and the condition the
soil is actually in is a relatively small one. So although the correlation did not confirm my
hypothesis, it does help to confirm matrix findings. It should be kept in mind also that
rainfall was given the lowest weight, or least importance, in the weighting system because
of this lack of significance to the causation of degradation. All of these correlations
usually confirming hypotheses about individual indicators, have furthered the explanation

and validity of matrix scores as shown in spatial patterns on the Rwandan landscape.

Regressions

Two regressions were performed on the indicators used within the matrix. In one
the matrix scores were used as the dependent variable and in the second the environmental
summary stress score from the original project was the dependent In the regression
performed on the stress scores by Campbell, the equation itself was found to be significant
(F-ratio), but problems were encountered concerning the independence of variables and
the heteroscedasticity of residuals. The tolerance values in all except for tuber production
were too low to consider the indicators as independent. This was especially pronounced.
as it should be, with the related variables of rainfall and erosion. The adjusted r-squared

of .657 was high, but not enough to be considered statistically significant. The overall

66
regression using these variables was strong, but some problems such as tolerance make the

findings suspect.

The regression performed using the scores from the matrix had a higher adjusted r-
squared at .794. The overall equation (F-ratio) was still significantly high, but tolerance
problems were the main concern. The tolerance values in this case were higher, but only
two were found to be significantly independent (tubers, population density). The residuals
in this case all fit the necessary criteria (homoscedastic, normal, and probability plot in a
diagonal line). The independent variables here, with the exclusion of rain and erosion,
were all statistically significant to the 95 percent confidence level. Overall the linear
regression in the second case, that of the matrix scores, fit the model better than that of

the summary scores (Table 5).

Principal Components Analysis

Two principal components analyses were performed; one was exploratory and the
other specifically examines the indicators used within the matrix. The exploratory PCA9
analysis was done to look for surrogate indicators. The variables used in this analysis
were greater in number than those used in the matrix. The pool of the variables best

suited to indicator use were selected and put through a principle components analysis.

 

9 PCA denotes Principal Components Analysis.

67

 

Table 5: Regression Results

 

Dependent Variable: Matrix Scores

 

Independent Variables:
Tolerance T-Value Prob Value
Constant 0.000 7.780 0.000
Rain 0.096 -1.475 0. 143
Erosion 0.080 2.097 0.038
Capability 0.438 -5.086 0.000
Migration 0.508 -4.693 0.000
Pop. Density 0.828 2.011 0.046
Intensity 0.456 7.295 0.000
Total Kcal 0.482 -3.840 0.000
Tubers 0.713 7.816 0.000

Adjusted r2 = .794 F-Ratio = 69.472 Probability F = 0.000

Dependent Variable: Summary Stress Scores

Adjusted r2 = .657

F-Ratio = 33.774

Independent Variables:
Tolerance T-Value Prob Value
Constant 0.000 -5.815 0.000
Rain 0.093 -0.279 0.780
Erosion 0.080 - 1.386 0.168
Capability 0.437 2.439 0.016
Migration 0.552 4.867 0.000
Pop. Density 0.390 -7. 125 0.000
Intensity 0.306 5.7 14 0.000
Total Kcal 0.479 1.579 0.117
Tubers 0.723 5.825 0.000

Probability F = 0.000

68
The loadings were saved and analyzed for the most significant in each dimension (Table
6). The high loadings in the first dimension represented the same as those of the principal
components using only the indicators included in the matrix analysis. These high loadings
in the first dimension represented physical variables including rainfall, erosion, and
capability. In the PCA on all possible variables, income and percent of calories needed
produced in the commune also had significantly high loadings. This appearance of the first
dimension to be related to physical characteristics of a commune furthers the justification

for using physical indicators in monitoring for land degradation.

The second dimension loaded highly in the PCA of all possible variables on
variables involving tuber production. Manioc, tubers, and sweet potatoes all had positive
loadings, while white potatoes showed a negative loading. This reinforces my conclusion
that white potatoes are a tuber unlike sweet potatoes and manioc. I had earlier based my
hypothesis concerning tubers and the differences in use on previous work by Olson (1994)
and this dimension of the PCA seems to confirm the hypothesis that white potatoes are not
highly related to tuber production. The second dimension in the PCA, on only the matrix
variables, was mainly one of agricultural production. Tubers, intensity, and total

production in kilocalories were the three highest loading variables.

The third dimension in the overall PCA was concerned with stress in land use. The
high loading variables here were intensity, population density, total production in

kilocalories, and land remaining uncropped. The third dimension in the PCA concerning

69

 

Table 6: Principal Components Loadings

 

Only Matrix Indicators

Dimension 1
Indicator
Rain 0.856
Migration -0.570
Pop. Density -0.099
Total Kcal -0.491
Erosion 0.885
Capability -0.804
Intensity 0.030
Tubers 0.441
Percent of Variance
Explained 36.695

All Possible Indicators

Dimension 1

Indicator

Elevation 0.855
Rain 0.907
Migration -0.37 6
Pop. Density 0.460
Income -0.694
Total Kcal -0.303
Kcal Needed -0.682
Cattle Number -0.401

Sweet Potatoes (pc) 0.132
Irish Potatoes (pc) 0.427

Manioc (pc) 0507
Land Remaining 0.001
Erosion 0.902
Capability -0.688
Intensity 0.082
Tubers 0.032
Percent of Variance

Explained 30.357

0.339
0.042
0.293
0.702
0.254
-0. 107
0.768
-0.515

20.322

-0.278
0.100
-0.642
0.073
-0.322
-0.272
0.417
0.143
0.820
-0.559
0.586
-0.388
0.094
-0.293
0.286
0.834

20.179

-0. 194
-0.680
-0.543
0.198
-0.256
0.168
0.513
0.380

16.696

-0.027
0.043
-0.083
0.622
0.228
0.710
0.399
-0.569
0.014
0.129
-0. 109
-0.688
-0.044
0.072
0.888
-0.361

17.890

70
only the matrix variables was similar in that population density and intensity both loaded

highly, but population growth was also high here.

My conclusions from running both the exploratory and analytical PCA were that
the indicators selected for the matrix were roughly equivalent to the best of the available
pool. In both analyses, physical characteristics included in the matrix, variables related to
agricultural production, and variables related to population density were found to be
significant components. The indicators chosen for the matrix seem to reflect the
surrogate dimensions discovered through the principal components analysis as these three

dimensions were accounted for in the matrix.

CHAPTER V

SUMMARY AND CONCLUSIONS

This thesis has attempted to assess the efficiency of various methods of monitoring
land degradation. This was accomplished through comparison of results from three
different methods (PCA, regression, and matrix) with results from the Rwanda Society-
Environment Project through which the original data was compiled. The use of indicators
has been suggested and justified in previous research. The use of regional political
ecology as a framework for investigation of suitable indicators for monitoring has proven
to be useful. Historical, political, environmental and socio-economic contexts were taken
into consideration when selecting indicators relevant to the specific Rwandan situation at

the time of data collection.

Variables chosen as indicators were ranked according to severity. In the final
matrix analysis, the resulting scores were also ranked relative to the rest of the country.
The matrix also has a weighting system to rank indicators according to severity along both
the x and y-axes. By ranking observations within the indicators chosen, identification of
areas of greatest potential degradation can be achieved. A constraint in using this adjusted
matrix system is that the matrix scores can only be compared with those in the rest of the

country. Because all elements of the adjusted matrix are relative to the observations and

71

72
indicators used, comparisons cannot be made across areas not included in the specific
matrix. For example, matrix scores from Rwanda cannot be compared with those of
Sudan because of differences in climate, agricultural practices, etc. (Batie, ed., 1995).

Not only would scores be different, but so would the indicators chosen for analysis.

The matrix analysis included in this thesis also included scores for only one time
period due to data availability. This analysis did show the utility of using the matrix to
target areas, but whether this process can be continually integrated to continue monitoring
over time has yet to be proven. The results of the Rwanda Society-Environment Project
were used as “ground-truth” information in comparing the matrix scores to the probable
real-world situation in Rwanda at the time of data collection. The scores computed within
the matrix analysis were similar to the patterns found in the original project and as such

should be considered to resemble the situation in Rwanda.

In comparing both the results and the functions of the different methods of
analysis, the matrix was found to be the most useful for both data analysis and visualizing
results. Maps of the matrix scores for the country are efficient in showing areas of least
and most degradation. The mathematical concepts behind the matrix are also easier to
conceptualize than those of regression and PCA. The computations could possibly be
done with only a simple calculator, while the other methods (PCA and regression) require
computers and a statistical background to perform analysis and interpret results.
Regression was found to be the least useful in analyzing indicator interaction. Principal

components was found to have value as an exploratory tool for finding indicators or

73
surrogate, composite indicators from available data. Within this analysis, agriculture,
population density, and physical characteristics of the country were found to be the
dimensions explaining the greater part of the variance of the data set. Within these three
dimension, indicators picked for use in the matrix had high loading suggesting that they

were appropriately picked.

Principal components could be used in available data sets to help to validate
indicators chosen for use within the analysis. Caution should be exercised though, in that
PCA alone can suggest indicators, but suitability for use must also be considered.
Previous literature and theory should guide the initial selection of indicators in order to

incorporate suitability of indicator to the study area.

Data Gaps

The most important problem encountered in this analysis was the use of data from
different spatial scales. Two of the indicators chosen were taken from soils data. This
then had to be weighted and averaged to coincide with the other data. By aggregating to
larger spatial units much of the detail and data validity was lost. Political boundaries are
the most often used level of analysis because of ease of collection for different agencies, as
these units are the most commonly recognized. The problems that can be encountered by
using political units, though, is that they may overlap more than one environmental

category. Depending on the level of political unit, there maybe steep hills, forests, and

74
grass lands all included within one region. This makes analysis of physical variables
difficult to analyze. This problem is also encountered with socio-economic interaction
when groups of people with different sociological characteristics are both included in a
single political region for analysis. These different traits could include farming practices,

land tenure systems, etc.

Another problem encountered in analyzing data is that much of the data is
collected by different agencies. As a result, duplication of collection may be encountered.
different levels of sampling may be practiced, and different levels of access to the public
for integrative analysis may occur. There seems to be no integrative, focused system of
monitoring human-environmental problems. Often socio-economic data is collected, but is
only used for analysis concerning socio-economic phenomena rather than as part of a
larger system. A system, or ministry, needs to be established to organize data collection
and sharing. Targets for monitoring need to be set as well. A larger matrix system might
provide some conceptual insight into data needs for monitoring land degradation, while at
the same time collecting information for other purposes (economic reports. natural

resource reports, etc.).

Within the available data set, the indicator found to be missing that could have
added the most to the analysis generally concerned vegetation. It is possible to measure
vegetation as biomass, or with a leaf area index, through the use of remote sensing.
Other physical variables such as soil moisture and albedo also could be measured through

use of remote sensing platforms. The costs of monitoring are the most prohibitive factor

75
here, as this type of data would be costly for countries that are already economically
stressed. If an optimal data set were to be established, though, a more complete overall

vegetative indicator, either biomass or health, would have to be included.

Conclusions and Work Still to be Done

The matrix framework for monitoring land degradation has been found within this
case study to be a useful statistical and conceptual tool for targeting areas of land
degradation within a country. The mathematical computations and graphic visualizations
of the matrix make it a useful instrument for those that lack a statistical background. The
results are also easy to interpret, as they are not as abstract as those of regression or PCA.
The results can be easily mapped to show areas of high and low stress, and these results
can be communicated to an informed audience. The simplicity of the matrix is its

strongest point.

There are weaknesses in this method. The reliance on relative measures means
that the results can not be compared to other regions. This. along with the subjectivity of
indicator rankings along the axes based on perceived severity, make the matrix method
one that could give misleading results if caution is not exercised. The matrix still needs to
be tested on other areas and with other available data sets to identify key groups of
indicators that might be collected. The temporal element of the matrix also needs to be
tested in the future to evaluate the monitoring potential of the matrix in the long term.

These could not be accomplished with this thesis alone.

76

It is important in using this method to remember that the context, sociological,
economic, environmental, and political is key to selecting indicators. That is why the use
of regional political ecology for exploring the problem at hand is necessary before
indicators can be selected, as well as, for interpretation of results. Indicators were
selected based on characteristics specific to Rwanda. As such, the results can be
interpreted to represent the relative power of these forces in action within each commune.
The national degradation surface represents the relative magnitude of these indicators and
their collective power in interaction. Political ecology helps both to construct the matrix,

as well as, to deconstruct results in order for their component parts to be interpreted.

In conclusion, the matrix seems to have value and utility as a conceptual and
analytical tool for a broad group of users as compared to regression and principal
components analysis. The results of the matrix are easily communicated and mathematical
concepts kept simplified. The matrix framework, in conjunction with regional political
ecology, could be an effective method for targeting areas of highest land degradation
within a country relative to other areas. This may facilitate early remediation of high
stress situation areas. Work is still necessary to find the full capabilities of the matrix
framework, but to this point it seems a valuable tool for general targeting of potential

areas of land degradation.

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