Impact of Macroeconomic Change on Deforestation in South ... · Impact of Macroeconomic Change on Deforestation in South Cameroon: Integration of Household Survey and Remotely-Sensed

Impact of Macroeconomic Change on Deforestation

in South Cameroon: Integration of Household Survey

and Remotely-Sensed Data

BENOIT MERTENSCenter for International Forestry Research, Indonesia

WILLIAM D. SUNDERLINCenter for International Forestry Research, Indonesia

OUSSEYNOU NDOYECenter for International Forestry Research, Cameroon

and

ERIC F. LAMBIN *

Universite Catholique de Louvain, Belgium

Summary. Ð The integration of information from household surveys and data on land-coverchanges derived from remote sensing improves our understanding of the causes and processes ofland-use/land-cover changes. A household survey covering 552 households in 33 villages wascarried out in the East Province of Cameroon. This survey focused on land-use changes since the1970s. Those data were related to time series of remote sensing satellite data. A major interest of the®eld data lies in the longitudinal framework of the survey. It highlighted the evolution of thehousehold and its land-use over three periods related to the key macroeconomic periods, andcorresponding to the dates of acquisition of the remote sensing data. The research resultsdemonstrate that macroeconomic changes a�ecting Cameroon have played a fundamental role inthe way land-use practices in¯uence the forest cover. The results show that the annual rate ofdeforestation increased after the economic crisis as compared to the previous period. Thehousehold survey information enables identi®cation of the causal relationships and the processes ofland-use and land-cover changes. Observations reveal that the beginning of the economic crisis(1986) is associated in time with a strong increase of the deforestation rate related to populationgrowth, increased marketing of food crops, modi®cation of farming systems, and colonization ofnew agricultural areas in remote forest zones. Ó 2000 Elsevier Science Ltd. All rights reserved.

Key words Ð macroeconomic change, deforestation, remote sensing, household survey, Africa,

Cameroon

World Development Vol. 28, No. 6, pp. 983±999, 2000Ó 2000 Elsevier Science Ltd. All rights reserved

Printed in Great Britain0305-750X/00/$ - see front matter

PII: S0305-750X(00)00007-3www.elsevier.com/locate/worlddev

* The integration and analysis of the remote sensing and

socioeconomic data was performed in the research

program on satellite remote sensing for the Belgian State,

Services of the Prime Minister, O�ce for Scienti®c,

Technical and Cultural a�airs (contract T4/DD/001). The

Centre for International Forestry Research (CIFOR)

®eld research was enabled primarily through a grant from

the Department for International Development (United

Kingdom). Additional funding came from the Central

African Regional Program for the Environment

(CARPE), a project managed by the Biodiversity Support

Program (BSP) through support from the Global Bureau

of the United States Agency for International Develop-

ment (USAID). We are grateful to the CIFOR ®eld

research team for collecting and processing the socioeco-

nomic data. Final revision accepted: 3 December 1999.

983

1. INTRODUCTION

Both regional and local scale studiesdemonstrate the great variety in the drivingforces that give rise to land-cover change, suchas tropical deforestation (Kummer & Turner,1994; Skole, Chomentowsky, Salas & Nobre,1994). Turner, Moss and Skole (1993) catego-rized the driving forces of land-use change infour groups: (a) variables that a�ect demand(e.g., population), (b) variables that determinethe intensity of land-use (technology), (c) vari-ables that re¯ect access to resources (politicaleconomy), and (d) variables that create incen-tives (political structure). Kates and Haarmann(1992) also identi®ed four major driving forcesin the so-called poverty-environmental degra-dation spiral and made the distinction betweenexternally-driven forces, i.e., natural hazardsand commercialization, and internally-drivenforces, i.e., population growth and existingpoverty. Identifying the causes of land-coverchange requires an understanding of how thesedi�erent factors interact in speci®c environ-mental, historical and social contexts (Turneret al., 1993). The combination of these drivingforces leads to major sequences or processes ofland-use/land-cover changes.

Driving forces are a complex set of actionsand rationales that give rise to proximatecauses, i.e., the near-®nal or ®nal humanactivities that directly a�ect the environment(Turner et al., 1990). Farming systems are oftencited as one of the major proximate causes ofland-cover changes in tropical Africa, throughthe human interventions that alter the vegeta-tion (Myers, 1994; Turner, Meyer & Skole,1994). According to Walker and Homma(1996), the ongoing development of householdsand their strategic responses to macroeconomicconditions lead to farming systems changes, andthereby to land-use/land-cover change, whichcan itself a�ect the householdsÕ economic status.

Land-use denotes human exploitation of theland, while land-cover denotes the physical andbiotic character of the land surface (Meyer &Turner, 1992). There is an increased interesttoday in collaboration among social scientistsand remote sensing experts to improve ourunderstanding of the biophysical forces andhuman activities that shape land-use/land-coverchanges (Geoghegan et al., 1998; Lambin et al.,1999). Remote sensing data provide measuresof the spatial and temporal context of socialphenomena and its e�ects (e.g., environmentalconsequences and proximate causes of land-use

changes), and a connection across levels ofanalysis (e.g., to address regional-level ques-tions from local-scale studies). Social scienceprovides a better understanding of the drivingforces that give rise to proximate causes ofland-use/land-cover change. This understand-ing of causes and processes provides in-depthinterpretations of the patterns of land-coverchange derived from remotely sensed observa-tions (Rindfuss & Stern, 1998).

The objective of this paper is to betterunderstand the causes and processes of land-use/land-cover changes since the 1970s in theforest zone of the East Province of Cameroon.This is achieved by combining informationfrom an extensive socioeconomic householdsurvey and time series of medium spatial reso-lution remote sensing data. These analyses werecarried out against a backdrop of dramaticmacroeconomic change that has occurred inCameroon over the period under study. Fromthe late 1970s through 1985 Cameroon was abooming economy. Beginning in 1985 it expe-rienced a devastating economic crisis, a struc-tural adjustment program in 1989 and, inJanuary 1994, a drastic devaluation of itscurrency (CFA). These transformations haveprofoundly in¯uenced population movementsand land-use (Sunderlin & Pokam 1998).

2. BACKGROUND

A synthesis of the methods for mergingsocioeconomic household survey data andremote sensing-based information is given inLiverman, Moran, Rindfuss and Stern (1998).The major challenges of this approach are: (a)the de®nition of the appropriate spatial obser-vation units, i.e., the appropriate level ofaggregation of information derived from thedomains of social phenomena and naturalenvironment, and (b) development of theappropriate linkages between household-leveland remote sensing datasets. While conceptu-ally straightforward, these links can be di�cultto implement operationally (Entwisle et al.,1998). In remote sensing, the unit of observa-tion is the pixel (from 10 to 80 m for mediumresolution sensors) which is not directly asso-ciated to any social science unit of observation,e.g., individuals, households or villages.

Most of the studies linking remote sensingobservations and socioeconomic data havebeen performed at the scale of the administra-tive units which were used for the collection of

WORLD DEVELOPMENT984

socioeconomic data (e.g., Green & Sussman,1990; Skole et al., 1994). For instance, Woodand Skole (1998) and Pfa� (1996), in theirstudies of deforestation in the Brazilian Ama-zon, have aggregated the land-cover data toconform to the administrative units (municipioor county level, in these cases). In thisapproach, the dependent variable (deforesta-tion derived from remote sensing) refersprecisely to the independent variables (e.g.,socio-demographic data obtained through thenational census and transport infrastructuredensity), and vice versa. But, the aggregation ofland-cover change data to a coarser resolutionleads to a loss of information as it obscures thevariability within the units (Wood & Skole,1998). Moreover, this aggregation can intro-duce ``ecological fallacies'' to the interpretationof the correlations. When the measures ofstatistical association are calculated acrossthese administrative units, the data do notcorrespond at the level of the decision units(Wood & Skole, 1998).

More recently, research e�orts have attemp-ted to integrate remote-sensing observationsand ®eld surveys at ®ner levels of aggregation,i.e., at the scale of individuals, households orvillages. Behrens, Baksh and Mothes (1994)noted that they gained great insight into theprocesses leading to tropical deforestation andchanges in landscape complexity by com-paring land-cover disturbances, as measuredby remote sensing, and land-use changes inVenezuela. Entwisle, Walsh, Rindfuss andChamratrithirong (1998) attempted to linkpopulation dynamics, derived from ®eldsurveys, to land-use/land-cover change data inThailand. They noted the di�culty of relatingremotely-sensed patterns of land-cover changewith ®eld observations of land-use change sincepeople live in nucleated villages away fromtheir ®elds, and since households cultivatemultiple noncontiguous plots. For this reason,the integration of the two datasets wasperformed at the village level. Aggregated tothe village-level, household data o�er an addi-tional perspective to the remotely sensed land-cover dynamics. The village pro®les provide acrosscheck on the dynamics observed byremote sensing, and can be related to remotelysensed landscape variables (Entwisle et al.,1998).

Individual household data allow for a betterunderstanding of the land-use practices withineach village, as most land-use decisions aremade by individuals and households (Myers,

1980; Vosti & Witcover, 1996; Entwisle et al.,1998). Moran and Brondizio (1998) haveinvestigated the linkages between remotelysensed data and traditional ®eld methods in thesocial and biological sciences. Their resultshave led them to new research issues, includingexamination of the role of the developmentalcycle of the household in shaping their trajec-tory of land-use and deforestation (Moran &Brondizio, 1998). Sussman, Green and Suss-man (1994) explored how satellite imagery andethnographic methods can contribute to anunderstanding of the reasons for deforestationin Madagascar, and highlighted the need for in-depth research on current patterns of land-useand existing resources to develop an integratedlong-term conservation-oriented plan. Guyerand Lambin (1993) combined household andremote sensing data to assess the level ofintensi®cation of farming systems in Nigeriaand the relative role of two major drivers ofland-use changeÐi.e. population pressure andurban market expansion.

Previous studies emphasize the challenge ofassessing processes and relationships acrossthemes and across spatial scales, e.g., de®ning ascale continuum for the population, environ-mental and geographical domains includingrespectively: (a) household to village clusters,(b) ®eld plot to watershed, and (c) pixel towatershed. Walsh, Evans, Welsh, Entwisle andRindfuss (1999) examined the relationshipsbetween demographic and environmental vari-ables across nine scale steps (ranging from 30 to1,050 m). The results suggested that relation-ships are not generalizeable across spatialscales. The current study builds on this body ofknowledge by developing a systematic methodto link household level data with remote sens-ing data to highlight the in¯uence of macro-economic factors on local land-use changeprocesses.

3. STUDY AREA

Evergreen and semi-deciduous forests coverapproximately 44% of the area of Cameroon(FAO, 1995), mostly in the southern part of thecountry. In its latest Forest Resource Assess-ment report, the Food and Agriculture Orga-nization of the United Nations (FAO, 1995)reported that Cameroon loses a net 0.6% of itsforest every year. It is di�cult, however, toproduce accurate deforestation ®gures since, inCameroon, most forest-cover changes take

MACROECONOMIC CHANGE AND DEFORESTATION 985

place as forest degradation due to ®res, agri-cultural encroachment, and selective loggingactivities (Sayer, Harcourt & Collins, 1992). Itis generally assumed that population growthand shifting cultivation are among the maincauses of deforestation in Cameroon (Amelung& Diehl, 1992).

The study area is located around the town ofNd�el�el�e, in the East Province of Cameroon,next to the boundary with the Central AfricanRepublic (CAR). It is located in the north-eastern part of the southern Cameroon plateau(Figure 1). The East Province is dominated bysemi-deciduous forests with the forest-savannatransition zone to the North. The East Provincehas the lowest population density in Cameroon(4.1 inhabitants/km2 in 1987) and the highestproportion of rural population (70.1%). Thislow population density has led to high rates ofimmigration, mostly of workers into the agri-cultural and forest logging sectors. Immigra-tion into the East Province is related toagricultural land availability, the presence oflogging companies, and improvements of roadand railway infrastructures. The road network,which in many cases has been built and main-tained in this region by the logging companiesfor timber transport, facilitates access to forestareas by shifting cultivators in search of newagricultural land (Karsenty & Maõtre, 1993).Most of these landless migrants originate fromthe adjacent provinces or from the CentralAfrican Republic.

The East Province is a sensitive zone in termsof deforestation and biodiversity. It containsthe largest remaining segment of primaryforests in Cameroon. Numerous loggingconcessions, however, have been granted toforeign companies and to national citizens.Moreover, due to the growth of the ruralpopulation in the region, primary forests arethreatened by poaching and rapid encroach-ment of cocoa, co�ee, and subsistence plots,particularly on the northern and westernborders of the province. Several roads in theprovince have been rehabilitated or built bylogging companies, or through funding frominternational agencies. This raises concern thatroad development will lead to further defores-tation at the edge of, or within the area forwood extraction, commercial hunting andagricultural encroachment.

Ndoye and Kaimowitz (1998) identi®ed fourmajor periods of macroeconomic change inCameroon since the time of decolonization(Figure 2):

(a) During the pre-oil boom period (1967±76), agriculture was the base of the economy,and high taxation on cocoa and co�ee lim-ited forest clearing to grow those crops;GDP grew annually at 4.8%;(b) During the oil-boom period (1977±85),per capita GDP increased from US dollars485 in 1978 to US dollars 915 in 1985, dueprimarily to oil exploitation;(c) In the economic crisis and structuraladjustment phase (1986±93), Cameroon'sterms of trade deteriorated by 65%, averageper capita GDP fell 6.3% annually during1985±93, and oil production also fell, whichput the burden back on agriculture and tim-ber to provide most of the country's foreignexchange;(d) In the period following the devaluation(1994±present), the CFA Franc was deva-lued by 50% to restore competitiveness of ex-ports which, coupled with increased worldprices for cocoa and co�ee, led to a mild re-bound of these commodities (Sunderlin &Pokam, 1998). The same authors concludedthat macroeconomic transformations,government policies, market ¯uctuationsand demographic changes have all had astrong ecological impact by increasing pres-sure on forest zones.

The study area covers approximately 700km2 in the East Province of Cameroon. Thisstudy area includes the region of Nd�el�el�e,located 10 km south of the N10 road goingfrom Berb�erati (Central African Republic) toYaound�e and Douala (Cameroon), and theadjacent region of Ngoundi, stretching alongthe road from Gari-Gombo (frontier town) toYokadouma.

4. DATASETS

(a) Remote sensing

Maps of land-cover and land-cover changewere derived from a time series of ®ve mediumspatial resolution satellite images for 1973±96.The acquisition dates were associated with theeconomic transition periods observed by Ndoyeand Kaimowitz (1998) (Figure 2). Two LandsatMultispectral Scanner (MSS) images (at anominal spatial resolution of 56 by 79 m) wereobtained at a 13-year interval (11/14/1973and 12/23/1986). Three SPOT Multispectral(XS) (at a nominal spatial resolution of 20by 20 m) were acquired at a three-year interval


(01/26/1993 and two images in 11/15 and 11/02/1996). Thus, all images have been acquired inthe same season. The Landsat MSS and SPOTdata were geometrically recti®ed and registeredto a common UTM projection based on

1:200,000 scale topographic maps of Cameroon(using second-order polynomial based onmore than 100 ground control points, with aroot mean square error below the MSS pixelsize), and were further resampled to a spatial

Figure 1. Study area.


resolution of 50 by 50 m (close to the LandsatMSS resolution) using the nearest neighbortechnique.

Five land-cover classes were discriminatedwith a high level of accuracy, based on anunsupervised classi®cation of the remote sens-ing data: dense forest, fragmented forest, agri-culture, savanna, and bare soils. Classi®cationaccuracy was estimated using an independentsample of 250 observations from the ®eldcampaigns and the existing aerial photographs.The contingency tables between the referencedata and remote sensing-based classi®cationwere produced. The overall accuracy of theclassi®cations, after merging of the classes intoa binary forest/nonforest map, are 88% for1973, 93% for 1986, 95% for 1991, and 90% for1996. The lower accuracy for the 1973 and 1996classi®cations result from, respectively, theintrinsic quality of the image and the presenceof haze. Changes in land-cover among thesuccessive dates were detected by comparingthe binary forest/nonforest maps for each date.

Digital maps of road networks and townswere produced by manual digitization of the1:200,000 scale topographic maps of Cameroon(Centre G�eographique National, 1978) andupdated by ®eld surveys. These data have beenused to measure accessibility variables.

(b) Household survey

The household survey was conducted in1998. The survey questionnaire was adminis-tered to a 30% sample of the households in eachof the 33 study villages, yielding a total of 552households in the survey. The survey focusedon: (i) household information (composition,

level of education, indigenous/migrant, mainand secondary activities); (ii) labor, inputs andtools; (iii) changes in land-use (area of croptype over time); (iv) production by crop typeand land-cover type; (v) marketing strategy(proportion of the production sold, and where);(vi) clearing and/or increase of size of agricul-tural plots; (viii) land availability, crop prefer-ences and fallow period, and (ix) householdsÕland-use strategies for the near future. A majorinterest of the ®eld data lies in the longitudinalframework of the survey. It highlighted theevolution of the household and its land-useover three periods related to the key economicperiods identi®ed by Ndoye and Kaimowitz(1998), and corresponding to the dates ofacquisition of remote sensing data (Figure 2).

5. METHOD

Statistical analyses between remotely sensedland-cover change data and household infor-mation were performed at the scale of thevillages. The ®rst step of the analysis was thede®nition of the village boundaries, and theaggregation of the household survey andremotely sensed data at the village level.Second, the role of driving forces in explainingland-cover changes was investigated throughregression analyses. Finally, the main processesof land-use/land-cover change were identi®ed.

(a) Spatial units for the integration of remotesensing and household-level data

Two distances were considered in the de®ni-tion of the boundaries of the agricultural area

Figure 2. Periods of macroeconomic change and temporal sampling for the acquisition of remote sensing and householdsurvey data.


of each village: (i) the spatial extent of eachvillage into the forest area, and (ii) the spatialextent of each village along the road networkbetween each pair of villages. Since the villagesare close to each other in the study area, theboundary between two adjacent villages along aroad was set at a distance from each villagesuch that the ratio of the distance from thevillage to that boundary and the population ofthat village was equal for the two villages. Inother words, the spatial extent of the agricul-tural area of a village along a road wasproportional to the population of the village.The boundary of the agricultural area of eachvillage from the road into the forest area wasde®ned at a 4-km distance from each villagecentroid. This distance is assumed to representthe maximum distance traveled from the villageto the agricultural plots, based on a ®eld survey(Sunderlin & Pokam, 1998). The average size ofthe villages is 845 ha, most of the villagesranging between 500 and 900 ha.

Di�erent procedures were considered for theaggregation of the household data to the villagelevel, depending on the type of variable (i.e.,continuous, categorical and binary) and thedesired information (Table 1). The sum and themean functions were applied for continuous

variables. Note that the sum value is divided bythe sampling coe�cient (30%) to re¯ect thetotal village value. The mode, median andfrequency of occurrence functions were appliedfor categorical variables.

Statistics on land-cover and land-coverchanges were extracted for each village forthree periods 1973±86, 1986±91, and 1991±96.The frequency of occurrence of deforestationwas measured in hectares within each village.

(b) Driving forces of land-use/land-coverchanges

Five scienti®c questions concerning thedriving forces of land-cover changes wereinvestigated. The ®rst three questions concernendogenous conditions, while the last twoconcern exogenous conditions.

ÐDid population growth lead to an increasein deforestation, i.e., are the villages with thehighest rates of population growth thosewhich are a�ected by the highest rates ofdeforestation?ÐDid technological evolution have an im-pact on land-cover change, i.e., are changesin the level of application of inputs (labor,fertilizers, tools) or changes in the length of

Table 1. Methods for spatial aggregation at the village-level of the household survey variables

Types of variables Values ranges Method of aggregation

Continuous (type A) 0 to n Mean(e.g., head of household age, fallow period)

Continuous (type B) 0 to n Sum (divided by thesampling coe�cient)(e.g., number of created plots, area of co�ee cultivated,

production of cocoa, number of workers)

Categorical (type A) 1 to n ModeCategories (e.g., level of education, matrimonial status,

main activity, origin of migrants, crops preferences)

Categorical (type B) Binary Mode and frequency ofoccurrenceBinary variables (e.g., creation of plots, abandonment of

plots, increased number of plots, native villager/migrant)

Categorical (type C) Higher (1), equal(2) or lower (3)

Median and frequencyof occurrence of (1)State of a variable compared to the previous period (e.g.

number of workers, area cultivated for plantain, production offood crops)

Categorical (type D) Null (1), less than1/2 (2), 1/2 (3),more than 1/2(4), total (5)

Median and frequencyof occurrence of (4±5)State of a variable compared to the previous period (e.g.,

production in forest plots, production sold in urban markets)


the fallow cycle associated with the rate ofdeforestation?ÐDid levels of well being in¯uence land-useand, therefore, land-cover change, i.e., is theevolution of the level of well being in eachvillage associated with particular changes inland-cover?ÐDid the changes in the marketing of foodand/or cash crops lead to a particular evolu-tion of deforestation patterns, i.e., is there anassociation between changes in the propor-tion of the crop production which is mar-keted and the rate of deforestation; is therate of deforestation associated with aparticular evolution of the distribution net-work for marketing; and did accessibilityplay a major role in the marketing strategiesof the households?ÐDid macroeconomic changes in Camer-oon have an impact on land use at thehouseholds-level and, thereby, on land-coverchanges, i.e., are the deforestation rateschanging among the di�erent periods ofmacroeconomic changes?

To address these questions, the householdsurvey and remote sensing datasets werecombined and the relationships betweendeforestation patterns and the evolution ofhousehold characteristics were analyzed statis-tically. First, as an initial veri®cation of theconsistency between the remote sensing and®eld survey data, the relationship betweenremote sensing measurements of land-coverchanges and estimates of land-use changesobtained through the ®eld survey was tested.The linear correlation between both datasetswas computed.

Second, the role of the main driving forceswas investigated at the village-level throughbivariate regression analyses. Finally, linearmultiple regression was used to identify thecombination of variables driving deforestationat the village level. The existence of collinearitybetween the independent variables was ®rsttested. The multivariate analysis provided abetter understanding of the processes of land-use change associated with deforestation. Thiswas computed ®rst over the entire period ofobservation (1986±96) and, second, during thesubperiods 1986±91 and 1991±96. The depen-dent variables were deforestation in the variousperiods of observation (as measured by remotesensing), and the independent variables were:(i) population, (ii) population growth, (iii)evolution of the average household size, (iv)distance from the village center to the largest

market in the region (Nd�el�el�e), (v) evolution ofthe marketing of plantain and nonplantainfood crops (as measured by the increase/de-crease of the proportion of the householdswhich have sold more than the half of theirproduction), (vi) proportion of forest within thevillage boundaries (as measured by remotesensing), (vii) proportion of migrants in thetotal population of villages, (ix) evolution ofwell-being (expressed as the proportion ofhouseholds that have declared an increase intheir well-being), (x) evolution of the use ofinsecticides, (xi) evolution of the use of chain-saws, and (xii) changes in the duration of thefallow period (in number of years).

These statistical analyses were computed atthe village-level. Since the village of Nd�el�el�e hasa population size nine times higher than theaverage population size of the villages, andsince it concentrates 20% of the total defores-tation of the study area, taking into accountthis village would bias the statistical analysesand would obscure the identi®cation of theland-use/land-cover change processes in theregion. Therefore, the statistical analysesexcluded this village, although the speci®cchanges taking place in this village will bediscussed separately when appropriate.

6. RESULTS

(a) Land-use changes: household survey

More than 95% of the surveyed householdsfarmed continuously in their village during1993±97, while only 60% of the householdsfarmed continuously in their village during1985±93. In 76% of the villages, more than 30%of the households created/increased the area ofagricultural plots in 1997, and in 13% of thevillages, more than 30% of the households haveabandoned agricultural plots.

A comparison of the area devoted to di�erentcrops for the two periods (1985±93 and 1993±97) shows a large increase in the area dedicatedto plantain and nonplantain food crops, and aslight increase or stability in the area of cocoaand co�ee (Table 2). A comparison for thesame periods of the quantity harvested showsan increase in the production of plantain andnonplantain food crops (observed in approxi-mately 70% of the households). Even thoughthe area devoted to cocoa and co�ee was stableor increasing, the quantity of cocoa harvesteddecreased across the two periods, and the


quantity of co�ee harvested is stable during1985±93, but decreased after 1993. Theseresults re¯ect a turn away from the productionof cocoa and co�ee toward food crops. Thecultivators, however, maintained their cocoaand co�ee stands in the hope of eventuallyreviving their former source of livelihood. Thesurvey results show that the quantity of plan-tain harvested from freshly-cleared forest ®eldsincreased during 1985±97. Most of theproduction of plantain is achieved on thesenewly-cleared ®elds and only a small fraction ofthat production is obtained from formernonplantain food crops ®elds or from cocoa/co�ee orchards.

(b) Field-based land-use changes versusremotely sensed land-cover changes

The land-cover change analysis based onremote sensing data revealed an increase in thedeforestation rate, mostly after 1986. Theannual rate of deforestation during the lastperiod (1991±96) is slightly higher than that of

the previous period (1986±91), but much highercompared to the ®rst period (1973±86) (Figure3). The average annual deforestation per villageis more than four times higher for the crisisperiod (1986±96) as compared to the precrisisperiod (1973±86). Note that forest cover chan-ges induced by logging activities are welldetected by the satellite images, as they arecharacterized by a speci®c spatial pattern. Thede®nition of the village boundaries, inconjunction with the map of logging conces-sions, avoided any confusion between defores-tation induced by agricultural and that causedby logging activities. Actually, all logging-re-lated forest-cover changes take place outsidethe village perimeters within which the village-level deforestation data were extracted.

The statistical results show a strong rela-tionship (Figure 4) between the area of defor-estation over 1991±96 estimated by remotesensing, and the area of agricultural plotscreated or increased in 1997 (r� 0.884). It canbe assumed that the land-use changes in 1997re¯ect those for the previous years, since a highcorrelation is observed between the proportionof the households in each village which culti-vated in 1997 and during the period 1993±96(r� 0.997). This result o�ers a crossvalidationof the two datasets and con®rms the consis-tency between land-use changes and land-coverchange data.

Note that the above analysis did not considerthe village of Kpokilita because its agriculturalarea is merged with the agricultural area ofanother village located on the border betweenCameroon and CAR which was not included inthe household survey (Gari Gombo).

Figure 3. Evolution of the rates of deforestation during the periods of observation.

Table 2. Proportion of households that declared anincrease in the area cultivated between the periods 1985±

93 and 1993±97 for di�erent crops

Crops Proportion of householdsthat declared an increase inacreage for the considered

crops (%)

Co�ee 48Cocoa 22Plantain 68Other food crops 72


(c) Driving forces of land-use/land-cover change

(i) Population growth and deforestationFor the entire period 1986±96, deforestation

(as measured by remote sensing) is positivelyrelated to population growth at the village level(r� 0.738) (Figure 5). Thus, deforestationseems to be driven in part by an increase inpopulation at the village level. This populationincrease is due to natural increase and in-mi-gration. As population increases the demandfor food increases. This demand can most easilybe satis®ed by increasing the cultivated areainto previously uncultivated areas or bymigration into unsettled areas. This isconceivable in a very low population densityarea, where large areas are still available foragriculture expansion. For our study area, 97%of the surveyed households declared that thiswas the case in 1997.

The highest values of population growth areobserved in medium-size to small villages (e.g.,Yola and Ngoundi II), rather than in largevillages (e.g., Ngoundi I). The remote sensingbased land-cover change analysis revealed thatthe largest increase in deforestation is observedin villages with a small population. Note that,besides deforested areas located around themain village of Nd�el�el�e, deforestation between

two years is weakly associated with the popu-lation size for the ®rst of these years (r value0.403).

(ii) Technological evolution and deforestationTechnological evolution can be assessed,

®rst, by the change in duration of the fallowperiod, and, secondly, by the change in inputsused by households, i.e., labor force, fertilizers,insecticides, fungicides, and mechanized tools.The mean value of the fallow period of agri-cultural plots is stable through time (3.9 yearsin 1985 and 3.8 years in 1997), but the maxi-mum value observed for each period decreasedfrom 11.5 years in 1985 to ®ve years in 1997.This suggests that there is agricultural intensi-®cation occurring on ®elds that used to becultivated in the most extensive way. Note that,if the change in the maximum a�ects the aver-age so little, then there are probably few suchcases and they might be incidental.

There is a general increase in the householdlabor force. More than 50% of the householdsshowed an increase of their labor force. The useof fertilizers and mechanized tools is notwidespread in the region. Only 2% of house-holds used natural fertilizers, and 1% ofhouseholds used a chain saw in 1997. But ageneral decrease in the use of chemical inputs is

Figure 4. Relationship between deforestation measured by remote sensing (1991±96) and the area of new agricultural®elds in forested areas (1997) estimated through household surveys, at the village level.


observed: 5% of households used chemicalfertilizers in 1985, while only 1% used them in1997; and 25% of households used insecticidesin 1985, while only 5% of households used themin 1997. This can be attributed to the decreasein national subsidies for agricultural inputsduring the economic crisis. The statisticalanalysis shows that, at the village level, there isno relationship between the rates of deforesta-tion and any of the variables that representtechnological evolution. Changes in technology(e.g., fallow period) take place in most villagesin more or less the same way (Figure 6).

(iii) Well-being and deforestationThe survey revealed that 46% of the house-

holds declared an improvement in the level ofwell-being during 1993±97 (the post-devalua-tion period), while only 17% declared animprovement in the level of well-being during1985±93 (the economic crisis period). At thevillage level, the perceived increase in the levelof well-being is not signi®cantly associated withthe rate of deforestation. Note, however, thatthe perceived increase in the level of well-beingover 1985±97 corresponds with a stabilizationof the rate of deforestation at the scale of thestudy area.

Previous authors have indicated that a lowerpoverty level at a national scale is likely todecrease the demand for forest land forconversion to agriculture, i.e., the higher theincome level the lower the encroachment offorest land for agriculture (Panayotou &Sungsuwan, 1994). Actually, a growing popu-lation in rural areas characterized by tradi-tional technology, a stagnant agriculturalproductivity and few o�-farm opportunitiescontinuously subdivides agricultural land,leading the poorest people to move into fragileareas (Leonard, 1989; Kates & Haarmann,1992).

(iv) Marketing of crops and deforestationThe amount of co�ee and cocoa produced in

the study area decreased during 1985±97. Theproportion of the overall cocoa and co�eeproduction which is sold by households alsodeclined over the two periods. Whereas in1985, virtually all (98%) of both cocoa- andco�ee-producing households sold all of theirproduction, this proportion declined to 80%for cocoa-producing households and 87% forco�ee-producing households in 1997. Anopposite trend (i.e., increase in marketing) isobserved in the case of plantain and non-

Figure 5. Relationship between deforestation measured by remote sensing (1986±96) and population growth (1987±97)estimated through household surveys, at the village level.


plantain food crops. The evolution of themarketing of plantain and nonplantain foodcrops was computed for each village as theincrease/decrease of the proportion of thehouseholds (in percentage) which have soldmore than half of their production. In 1985,households in fewer than a quarter of thevillages sold more than half of their plantain ornonplantain food crop production. In 1997,households in 80% of the villages sold morethan half of their production of plantain, andhouseholds in 50% of the villages sold morethan half of their production of nonplantainfood crops. Thus, most of the villages showed asharp increase in the marketing of plantain andnonplantain food crops during 1985±97.

The highest deforestation values are observedin the villages characterized by the highestincrease of the marketing of plantain andnonplantain food crops (r� 0.634) (Figure 7).Thus, deforestation seem to be driven in partby an increase in marketing of plantain andnonplantain food crops for the national orregional market. The increase in marketing ofplantain and nonplantain food crops is associ-ated with the development of a provincial-andnational-level distribution network for thesecrops. The results show an increase in theproportion of overall plantain and nonplantain

food crop production which is sold in urbanand semi-urban markets during 1985±97, ascompared to the local village market or athome. The opposite pattern (i.e., decrease inthe proportion of the production which is soldin urban and semi-urban markets) is observedfor cocoa and co�ee production.

Land-cover changes and the evolution ofmarketing seem to be less associated over timewith proximity to the main market. Besidesdeforestation areas associated with the larg-est village of Nd�el�el�e, the distance from thevillage to the main market, and forest cover(which is larger in villages far from the mainmarket) show positive relationships withdeforestation in the last period. These vari-ables were not signi®cantly related to defor-estation in the ®rst period. This indicates thedecreasing role of the proximity from the mainmarket, as, during 1991±96, new agriculturalplots were mostly created away from the mainmarket, deeper in the forest zone. This can bein part due to the improved accessibility ofremote forest areas, because of the betterquality of roads and the increase in the tra�con the main roads. The recent granting ofnew logging concessions in the south has ledlogging companies to invest in the roadnetwork.

Figure 6. Relationship between deforestation measured by remote sensing (1986±96) and changes in the length of thefallow period (1987±97) estimated through household surveys, at the village level.


(v) Combined e�ect of driving forces overdi�erent time periods

The results of the multivariate regression forthe entire period (1985±96) show that, at thevillage level, there was a strong and positiverelationship between the rates of deforestationand: population growth (increase in the numberof households and in the proportion ofmigrants); increase in marketing of plantainand nonplantain food crops; and the distancefrom the main market (Table 3). Thus, defor-estation seems to be driven mainly by theincrease in demand for food by the local andmigrant population and from the provincialand national markets. Note that a high level ofexplanation is achieved with the multipleregression, for all periods of analysis (AdjustedR2� 0.7). Moreover, low levels of collinearitywere found between the independent variables.

The relative explanatory power of the vari-ables changes through time, indicating modi®-cations in the general causes and processes ofdeforestation in relation to macroeconomicchanges. For the ®rst period of observation(1986±91), deforestation was mostly associatedwith demographic variables (populationgrowth and proportion of migrants), while for

the second period of observation (1991±96),one can observe the increase of the relativeimportance of other variables such as the levelof well-being (which was not signi®cant in theprevious bivariate regressions), changes inmarketing of plantain and nonplantain foodcrops, and distance from the main market.Thus, while in the 1980s deforestation wasmostly driven by the addition of cultivators, inthe 1990s, it is the response of these cultivatorsto market demand for food crops, supported byan improvement of the distribution networkthat seem to drive deforestation. The followingdiscussion examines the in¯uence of macro-economic transformations on these changes indriving forces.

7. DISCUSSION

The macroeconomic transformations thata�ected Cameroon have played a fundamentalrole in the way land-use a�ects forest cover.The land-cover change analyses revealed asharp increase in rates of deforestation duringthe period of economic crisis. The beginning ofthe economic crisis corresponds to a decline in

Figure 7. Relationship between deforestation measured by remote sensing (1986±96) and changes in marketing ofplantain and nonplantain food crops [expressed as the evolution of the proportion of households that marketed more thanhalf of their plantain and nonplantain production during 1993±97 as compared to the previous period (1985±93)]

estimated through household surveys, at the village level.


cocoa and co�ee prices and in the governmentprovision of input subsidies, motivating foodcrop expansion in forested areas. Plantain andnonplantain food crops increased in area andbecame an income source for households,and plantain tended to be established inrecently cleared forest areas. The deforestationrate also increased after the devaluation (1994),but at a much lower rate. These tendencieswere also observed in another part of the EastProvince of Cameroon, the region of Bertoua(Mertens & Lambin, 2000). The January 1994devaluation of the CFA made cocoa and co�eeprices more attractive again. An increase of thearea devoted to co�ee was observed after 1993as compared to the previous period (1985±93).This may have reduced pressure on forestareas by reducing dependence on food cropincome.

These land-use/land-cover change processesare con®rmed by the householdsÕ choices forthe near future concerning their land-usestrategies (Figure 8). In 1997, 44.4% of thehouseholds declared that they will increase inthe near future their agricultural area, 14.9% ofthe households declared that they will diversifytheir production, and 13.9% of the householdsdeclared that they will increase their agricul-tural area and their use of agricultural inputs(agricultural expansion and intensi®cation).

Some of the exuberance of recent land-clearing in the Ndelele region could have to do

with the fact that it is a frontier area that couldhave happened to be cleared at the time of thecrisis. The presence of real crisis e�ects inNd�el�el�e, however, is supported by the fact thatthe rate of deforestation has risen not just inthis frontier region, but also in other areas ofthe humid forest zone in Cameroon, i.e.,greater Yaound�e (Laporte, Sunderlin & Ndoye,1999) and Bertoua (Mertens & Lambin, 2000),although at a lesser rate in these other areas(about two times as compared to four times inNd�el�el�e).

Figure 8. Declared land-use strategies of households forthe near future.

Table 3. Linear multiple regression between deforestation and driving forces for the entire period (1986±96) and thesubperiods of observation (1986±91 and 1991±96)

Independent variables Std. coe�cients

1986±96 1986±91 1991±96

Population 0.225 0.249� 0.182Population growth 0.638�� 0.565�� 0.626��

Distance to the main market 0.355�� 0.063 0.677��

Household average size )0.241� )0.176 )0.285�

Evolution of the marketing of plantainand non-plantain food crops

0.293�� 0.101 0.495��

Proportion of migrants 0.404�� 0.478�� 0.242Proportion of forest cover 0.272 0.078 0.646��

Evolution of the level of well-being )0.175 )0.018 )0.349��

Evolution of the use of insecticides )0.268� )0.189 )0.327�

Evolution of the use of chain saw )0.042 0.101 )0.220Evolution of the fallow period 0.043 )0.026 0.126

Squared multiple R 0.824 0.804 0.800Adjusted squared multiple R 0.733 0.703 0.695Prob. 0.000 0.000 0.000

* Signi®cant at the 0.1 level.** Signi®cant at the 0.05 level.*** Signi®cant at the 0.01 level.


Deforestation is associated with a modi®ca-tion of farming systems in the region, inresponse to population growth, marketingevolution and a decrease in subsidies for agri-cultural inputs. The indications of this processare a decrease in the fallow period on ®elds thatused to be cultivated in the most extensive way,an increase in the household labor force, and adecrease in the use of agricultural inputs(fertilizers and mechanical tools). The ®rst twovariables indicate an intensi®cation of thefarming systems (i.e., more frequent use of theland associated with an increase of the laborforce, so as to gain more production from agiven area). Note that the increase in the laborforce might have been compensated by theincrease in agricultural area, therefore meaningno intensi®cation. As mentioned previously,however, some of the plots devoted to co�eeand cocoa production have not been harvested.Therefore fewer labor inputs were allocated topart of the agricultural area. The decrease inthe use of agricultural inputs (fertilizers andmechanical tools) could be interpreted as anindicator of extensi®cation of the farmingsystems (i.e., ®lling up of the land at the mostextensive technology). This decrease must,however, be interpreted in the context ofdecreasing subsidies provided to cultivators foragricultural inputs during the economic crisis.Other authors have reported concomitantprocesses of agricultural intensi®cation andextensi®cation in African farming systems (e.g.,Guyer & Lambin, 1993).

About 10% of the heads of household areallogene (i.e. not being born in the village inwhich they now reside). Few heads of house-hold migrated to the study villages during1980±85, i.e. during the oil boom, the periodwhen migrants were heading in large numbersto the big cities rather than to the countryside.Migration was high in 1978±79, and increasedagain in 1990. More than 70% of the allogeneheads of household were born in the EastProvince itself. It is interesting to note that therate of growth of allogene families is twice asfast as the rate of growth of autochthon fami-lies. Newly-arrived households tend to growfaster because there is a higher likelihood ofpeople joining the household who are ``follow-ing along'' after the household has already beenestablished.

In addition to being driven by populationgrowth and an increase in the demand for food,deforestation was associated with a modi®ca-tion of marketing patterns. The amount of

cocoa and co�ee production, and the propor-tion sold by the households decreased overtime, while an increase in marketing of plantainand nonplantain food crops was observed.Most of the created or increased plots forplantain and nonplantain food crops werelocated in recently cleared forest areas, ratherthan on former co�ee/cocoa plots. Thedecreasing co�ee and cocoa production did not,for the most part, result in the allocation ofcocoa and co�ee plots to other crops. Thecultivators tended to maintain their cocoa andco�ee stands in the hope that they could revivetheir former source of livelihood. This farmingapproach, which seeks to keep economicoptions open in the face of an uncertainmacroeconomic context, has led to an increasein deforestation. This tendency has beenobserved in the Central and South Provinces aswell (Sunderlin & Pokam, 1998).

This modi®cation of marketing pattern alsoresulted in a modi®cation of the distributionnetwork of the marketed plantain andnonplantain food crop production, as theseproducts were increasingly sold by farmersthrough semi-urban or urban markets. This canbe interpreted with regard to the role ofaccessibility. New agricultural plots tend to becreated away from the main market, deeper inthe forest zone. Plantain and nonplantain foodcrops are sold in urban or semi-urban marketsdue to higher demand there and due to the factthat farmers need to go to those markets topurchase basic necessities such as soap,tobacco, salt, oil, sugar etc. The increase inmarketing of plantain and nonplantain foodcrops is related to the need for households toincrease and diversify their income tocompensate for the loss of cocoa and co�eerevenues. Thus farmers bring and sell plantainand nonplantain food crops in urban/semi-ur-ban markets and in turn purchase these basiccommodities to take back to the village.

8. CONCLUSIONS

The research results demonstrate that themacroeconomic transformations that a�ectedCameroon have played a fundamental role inthe way land-use practices a�ect forest coverthrough agricultural pressures on forest areas.The results show that the annual rate ofdeforestation increased in the period of theeconomic crisis (1986±91) as compared to theprevious period (1973±86). The household


survey information enables identi®cation ofthe causal relationships and the processes ofland-use and land-cover changes. It wasobserved that the beginning of the economiccrisis (1986) is associated in time with a severeincrease of the deforestation rate related topopulation growth, increased marketing ofplantain and nonplantain food crops, modi®-cation of farming systems, and colonizationof new agricultural areas in remote forestzones.

The spatio-temporal analysis frameworkdeveloped for the integration of the householdsurvey and remote sensing data allowed for abetter understanding of the drivers of land-use/land-cover change processes in the region.Moreover, time series of remote sensing datacombined with household data allowed anexamination of structural changes related toeconomic variables that are almost impossibleto examine using cross-sectional analysisÐwhether it be household or spatial. Themethodological inability of prior researchersto look at those issues led them to overem-phasize variables that are easy to include incross-sectional analysis (e.g., population, anddistance to roads and markets) compared withother variables that may be more important(e.g., major price or subsidies changes). Thisstudy has emphasized the complementarity ofremote sensing and socioeconomic survey datafor improving the understanding of causes

and processes of land-use/land-cover changesin the region. It also highlighted the impor-tance of analyzing processes of change by``time slices'' corresponding to periods ofmacroeconomic changes. Future research onthe causes of deforestation will be greatlyenriched by being grounded in the macroeco-nomic context of unfolding developmentprocesses, and through longitudinal analysisof combined socioeconomic and remote sens-ing data.

An important lesson to be drawn from thisstudy is that policies outside of the formalforest sector are a key part of the problem oftropical deforestation, and therefore poten-tially a key part of the solution. This does notmean that forest policy per se is unimportant,but rather that the importance of nonforestpolicies is greatly underappreciated. In theCameroon context this means that prior to thecrisis, and also during the crisis, there wasinsu�cient attention to the unintended, unex-pected, and in some cases undesirable conse-quences of, inter alia, structural adjustmentpolicies, urban and public sector employmentpolicies, infrastructure policies, agriculturalpricing and import policies, and exchange ratepolicies. It follows that, in the future, attentionto the full range of relevant policies is a ®rst-order priority to avert undesirable conse-quences for forest-dwelling people and theremaining natural forests.

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