POVERTY, COMMODITY PRICES AND AGRICULTURALDEFORESTATION: lESSONS FROM THE ECUADOREAN

COAST

by

Patrick Andrew WylieB.Sc.F.,University of New Brunswick 2003

PRO,-IECT SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OF

MASTER OF ARTS

by Special Arrangements

in theFaculty of Arts and Social Sciences

in theSchool for International Studies

© Patrick Andrew Wylie 2008

SIMON FRASER UNIVERSITY

Summer 2008

All rights reserved. This work may not bereproduced in whole or in part, by photocopy

or other means, without permission of the author.

APPROVAL

Name:

Degree:

Title ofResearch Project:

Supervisory Committee:

Patrick Andrew Wylie

Master of Arts in International Studies

Poverty, Commodity Prices and AgriculturalDeforestation: Lessons from the Ecuadorean Coast

Date Approved:

Chair: Dr. John HarrissProfessor of International Studies

Dr. Stephen EastonSenior SupervisorProfessor of Economics

Dr. Paul WarwickSupervisorProfessor of International Studies and Political Science

August 11 th, 2008

ii

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Revised: Fall 2007

ABSTRACT

Deforestation literature commonly associates rising commodity prices with

forest loss. Informal interviews in Manabf, Ecuador suggested that corn crops

from forest conversion are often abandoned before harvest. Medium-resolution

satellite imagery was used to formally measure the conversion of forest to

cornfields. Between 2000 and 2005, despite a 185% increase in real domestic

corn prices, coastal Ecuador experienced a 2.2% increase in total forested area.

This observed forest gain contradicts the widely cited FAO Global Forest

Resources Assessment deforestation rates. A multi-disciplinary approach brings

into question the utility of national level patterns in sub-regional decision-making.

When conducting future research, scholars must consider the original scale of

measurement before applying past deforestation estimates.

Keywords: tropical deforestation; agricultural expansion; commodity prices; landuse; remote sensing; Ecuador

Subject Terms: Deforestation - Ecuador; Deforestation -- Economic aspects ­Ecuador; Deforestation - Tropics; Agriculture - Tropics; Agricultural prices -­Developing countries; Agriculture -- Economic aspects -- Ecuador

iii

DEDICATION

Manabitas: sin ustedes este trabajo no habrfa sido posible. Desde el fondo de

mi coraz6n, gracias por sus direcci6n y colaboraci6n. Espero que podamos

continuar con este espfritu y trabajar unidos para nuestro futuro comtJn.

Residents of Manabf: without you, this work would not have been possible.

Deepest thanks for your immeasurable guidance and collaboration. I hope this

document will help us to continue "working for our common future".

iv

ACKNOWLEDGEMENTS

I would like to thank my committee for their guidance and assistance. As

my senior supervisor, Dr. Stephen Easton provided me boundless opportunities

to explore new ideas. His continual reframing of concepts and approaches

enabled me to complete this project. I am very thankful to Dr. Paul Warwick for

agreeing to be my second reader. The support and input provided by my fellow

graduate students is cherished.

I would also like to thank Dr. Andy Hira and Dr. Eric Hershberg who

inspired me to continue pursuing forestry issues in Latin America.

Finally, I would like to thank my friends and loved ones who provided the

advice and encouragement which allowed for this project to be completed ­

Christine Wenman, Santiago Anria, Scott Murphy, Jon Large, Jeff Colvin,

Catherine Craven and Brooke Tuveson. I am also grateful to friends and

colleagues from my time in Ecuador - Peter Berg, Ricardo Lopez, Jaime Andrade

Ferrin, Ramon Cedeno Loor, Marcelo Luque, Eduardo Cheo and Nils Rodriguez.

My colleagues in Ecuador provided a level of support rivalled only by my mother,

father and sister - Joyce, Murray and Jessica Wylie. Sincerest thanks to all

those I have forgotten.

v

TABLE OF CONTENTS

Approval ii

Abstract iii

Dedication iv

Acknowledgements v

Table of Contents vi

List of Figures viii

List of Tables x

List of Acronyms xi

1: INTRODUC"rION 11.1 Background 11.2 Attempt to Explain the Overlooked 2

2: LITERATURE REViEW 52.1 Deforestation Processes 5

2.1.1 Primary Causes 52.1.2 Deforestation Rates 8

2.2 Agricultural Deforestation 122.2.1 Agricultural Expansion / Frontier Development.. 132.2.2 Agricultural Commodity Prices 14

3: METHODS 183.1 Conceptual Framework 183.2 Study Area 193.3 Approach 21

3.3.1 Deforestation 213.3.2 Commodity Prices 243.3.3 Interaction between Commodity Prices and Deforestation 26

4: RESULTS 284.1 Agriculture Commodity Prices 284.2 Deforestation Rates 294.3 Influence of Agricultural Prices on Deforestation Rate 33

5: DISCUSSION 355.1 Agricultural Prices 35

5.1.2 Domestic Distortions 38

vi

5.1.3 Direct Government Price Interventions 395.2 Trees as Savings - Forestry and the Poor 415.3 Deforestation Rates 42

5.3.1 Measurement Difficulties 425.3.2 Agriculture as a Deforestation Proxy 435.3.3 Challenges of Differing Scales 455.3.4 Research Value of Deforestation Trends? 46

6: CONCLUSiONS 48

Bibliography 50

vii

LIST OF FIGURES

Figure 1:

Figure 2:

Figure 3:

Figure 4:

Figure 5:

Figure 6:

Figure 7:

Figure 8:

Figure 9:

Figure 10:

Figure 11 :

Figure 12:

Determinants of deforestation from the literature with directionof their relationship noted in subtext.. 6

Total forested area in hectares as measured by the Food andAgriculture Organization (FAO) of the United Nations' GlobalForest Resources Assessments (FRA's) 11

Illustration of cobweb cycle dynamics 15

Index of real hard corn market prices (1970=100) andhistorical change in prices as indicated by natural logarithm.(Data Source: Oxford Latin America Economic Database,2008) 17

Map of corn production chain in Ecuador, representingpossible agents of deforestation.(Data Source: SICA, 2003) 19

Location of study area: Bahia de Caraquez, Ecuador. Source;Arriaga, Montano & Vasconez, 1999 by permission 20

Nominal hard corn prices from January 1996 to March 2006(Data Source: MAG, 2008) 25

Real hard corn prices from January 1996 to March 2006,expressed in year 2000 dollars. (Data Source: MAG, 2008) .......... 25

Scatterplot demonstrating association between Ecuador's realdomestic wholesale and producer corn prices 28

Real domestic wholesale hard corn prices used for agriculturaldeforestation assessment in Ecuador (Market: Portoviejo,Manabi; Data Source: MAG, 2008) 29

Manabf, Ecuador depicted as percent forest coverclassification of MODIS imagery for 2000-2005 (DataSources: Hansen et al. 2006; Global Land Cover Facility(GLCF, 2008) 31

Annual and net change in forest cover for Manabf Province,Ecuador from 2000 to 2005, as interpreted from MODISmosaic imagery (purple/pink - newly degraded, green ­improving/newly regenerating, grey - no change) (DataSources: Hansen et al. 2006; Global Land Cover Facility(GLCF, 2008) 32

viii

Figure 13: Interaction between domestic corn prices and Ecuador'sdeforestation rate 34

Figure 14: Scatterplot demonstrating association between realinternational corn prices and Ecuador's real domesticwholesale prices 36

Figure 15: Percent annual change in international and domesticwholesale corn prices (Data Source: MAG 2008) 37

Figure 16: Domestic wholesale price premium over international prices(Data Source: MAG 2008) 37

ix

LIST OF TABLES

Table 1: Estimations of Ecuador's annual deforestation rate from theliterature 9

Table 2: Temporal assessment of land-use in Manabf, Ecuador from2000-2005 as measured from MODIS-VCF imagery 33

Table 3: Land-use change over time (% annual) in Manabf, Ecuador asmeasured from MODIS-VCF imagery (2000-2005) 33

x

LIST OF ACRONYMS

AVHRR Advanced Very High Resolution Radiometer

CAN Comunidad Andina de Naciones(Andean Community)

CIF Cost, Insurance and Freight price

FAO Food and Agriculture Organization of the United Nations

FOB Free on Board price

GOP Gross Domestic Product (per capita)

GIS Geographic Information Systems

INEC Instituto Nacional de Estadfstica y Censos(Office of National Statistics and the Census, Ecuador)

LANDSAT-TM USGS Landsat Thematic Mapper Satellite and Images

MAG Ministerio de Agricultura, Ganaderfa, Acuacultura y Pesca(Ministry of Agriculture, Livestock, Aquaculture and Fisheries,Ecuador)

NOAA National Oceanic and Atmospheric Administration, USA

SAFP Sistema Andino de Franjas de Precios(Andean Price-Band System)

SICA Servicio de Informacion y Censo Agropecuario de Ecuador(Ecuador Agricultural Census & Information System TechnicalAssistance Project)

USDA United States Department of Agriculture

USGS United States Geological Survey

xi

1: INTRODUCTION

1.1 Background

Agriculture and forests have long been the main sources of land use

conflict. The deforestation literature has considered this conflict with agriculture

from a variety of perspectives. Studies of agricultural deforestation have

primarily considered shifting agricultural terms of trade, opportunity land costs

and shortened investment horizons for agricultural returns. As the majority of the

literature conceptually focused exclusively in either a forestry or agricultural

perspective, very few definitive conclusions have been offered.

With the exception of general equilibrium models (Stenberg &

Siriwardana, 2005) there have been few multi-disciplinary approaches in

describing links between agriculture and forestry. Most studies remain sectoral

in nature. Although academic focus has recently shifted to include the impact of

institutional and individual decision-making models, there remains limited

utilization of emerging remote-sensing technologies. Despite recent

improvements in satellite resolution and reductions in imagery costs, the

literature continues using deforestation rates determined by analyst opinion,

forestry export data and broad-resolution satellite imagery.

This author's previous project experience was scoping afforestation

projects on abandoned agricultural lands in Ecuador. By developing agricultural

development plans for individual properties, the goal was to enable land-use

planning for the larger watershed. The planning process was a combination of

field observations in coastal Ecuador and informal interviews with both

landowners and tenant farmers. These activities were originally conceived to

determine socioeconomic and institutional influences on local forest conditions.

Casual interviews quickly uncovered a perception that corn commodity prices

were driving the conversion of forest to agricultural uses. The aim of this study is

to quantify this land-use dynamic.

1.2 Attempt to Explain the Overlooked

Although the observation that forest is cleared when agriculture is more

profitable may seem rudimentary, it raises important questions. What are the

deeper links between poverty, commodity prices and deforestation?

Deforestation of an individual's private land represents a liquidation of assets

(Angelsen & Wunder, 2003; Chambers & Leach, 1989; Food and Agriculture

Organization of the United Nations [FAO], 2003; Sunderlin et aI., 2005).

Conversion of forest to agricultural lands is a potential survival or livelihood

decision.

Focused rurally, Ecuadorean corn production represents 4% of total GOP,

provides direct employment for 140,000 workers and is the sole source of income

for 98,951 households (SICA, 2003). While these numbers tend to portray corn

as the product of the poor, the sector represents hope for the poor. The total

2

area planted nationally has sustained 15-46% annual growth for the past two

decades (Borja & Williams, 2004; SICA, 2003).

The casual observation that mid-slope forests were cleared for crop

development became more intriguing when field visits identified that these areas

were also often out of production. With an average per capita holding of 2.6

hectares, plots in coastal Ecuador are for the majority, forested land on the upper

hillsides. Comprising a further third of the holding, valley bottomland is typically

devoted to a small residence with outbuildings. Remaining valley bottomland is

used for water intensive crops (e.g. tomatoes, melons, and squash) with

uncultivated land creeping up the slope. Farmers and landowners have offered

that these buffering lands between the forest and farmstead were deforested

when corn prices were high.

At present, only informal interviews and casual observations confirm the

observed dynamic. Based on corn commodity prices, the increased clearing of

land for production quickly saturates the local futures market. Corn prices

subsequently plummet with the oversupply from recently cleared land for

production. For resource economists, this cycle is reminiscent of A.A. Harlow's

1960 publication, "The Hog Cycle and Cobweb Theorem". An increased total

area of corn in production, and hence increased supply, results from the

deforestation decision. With increased supply, a reduction in corn prices occurs.

Low wholesale purchase prices lead producers to leave corn crops to spoil. This

price instability results in mid-slope, deforested land above residences and water

sources. Sedimentation, lowered water tables and slope erosion are

3

commonplace in the tropical clay soils (Harden, 1996). These abandoned crops

and 'fields threaten homes, lives and livelihoods. Whereas the hydrological and

forestry impacts of this dynamic are well studied, the economics are not.

The question I intend to research for this project follows. To what extent

does the variation in corn commodity prices explain the rate of deforestation in

Ecuador? Section 2 of this paper provides a review of the both the relevant

deforestation and agricultural economics literature. Following an outline of the

methodology used to evaluate the link between agricultural prices and

deforestation in Section 3, results are offered in Section 4. A discussion of the

quality of the outcome and the methodology/data can be found in Section 5. The

paper concludes with parting lessons from the Ecuadorean case and the

applicability of these results throughout the broader region.

4

2: LITERATURE REVIEW

2.1 Deforestation Processes

2.1.1 Primary Causes

As with many landscape-level problems, the proximate causes of

deforestation in Ecuador, or any country, are highly contested in the literature.

Perhaps the only point of agreement amongst practitioners is that the harvesting

of forest products rarely drives deforestation (Geist & Lambin, 2002; Lambin et

aI., 2001).

A central theme and common conclusion of most publications on

deforestation is that causality is complex and rooted in a combination of factors.

Although the forestry industry has the most logical impact on forests, there is a

multitude of forest users with large impacts. Beyond the consensus, the drivers

of deforestation have been considered from the perspective of: population growth

(Bilsborrow, 1987; Inman, 1993; Rudel, 1989; Southgate, 1994); international

market prices (Capistrano & Kiker, 1995; Perrings, 1989); firewood use (Jokisch,

2002); land tenure policies (Deacon, 1995; Fearnside, 2001; Southgate, Sierra &

Brown, 1991); oil booms (Sunderlin & Wunder, 2000; Wunder, 2001 b; Wunder,

2003); macroeconomic policies (Capistrano & Kiker, 1995; Cruz & Repetto, 1992;

Rich, 1994); and even economic development itself (Inman, 1993; Rudel, 1989).

(summarized in Figure 1).

5

Figure 1: Determinants of deforestation from the literature with direction of theirrelationship noted in subtext

Ehrhardt-Martinez (1998) attempted to categorize the deforestation

literature by differences in the theoretical approaches by geographers,

economists and demographers - (i) l\Jeo-Malthusian, (ii) modernization or (iii)

dependency theory based. Despite the typical, exclusionary portrayal of these

three independent paradigms, Ehrhardt-Martinez found that neo-Malthusian and

modernization theories were both compatible and explained parallel processes

significantly. In this light, the abovementioned agricultural causes of

deforestation could alternatively be categorized as (i) population growth, (ii)

modernization or (iii) dependent development

6

Growing evidence exists for a slowing of Latin American deforestation

rates as economies shift to petroleum-based fuels (Bhattarai & Hammig, 2001 ;

Cropper & Griffiths, 1994; Koop & Tole, 1999; Shafik, 1994). Ehrhardt-Martinez

(1998) put forward the theory that any relationship between the level economic

development and deforestation (modernization theory) would have to be a

curvilinear relationship. This curvilinear relationship has since been expanded

upon, wherein low to intermediate levels of development have high rates of

deforestation, and more highly developed countries use of wood alternatives

offers a lower rate of deforestation. Despite the emerging articles in support of

this dynamic, Barbier & Burgess' 2001 statistical synthesis of the deforestation

literature found inconsistent results for the presence of this "Environmental

Kuznet's Curve".

Ecuador has been the subject of a series of national and region-specific

studies of deforestation dynamics. As with most Ecuadorean works, Sven

Wunder's 1996, 2000 and 2001 studies all examine causes of deforestation with

a focus on the Andean and Oriente regions of the country. These works

conclude that income from wood is a temporary resource that provides income

for reinvestment in agriculture. (Wunder, 1996) In a 2002 sub-regional study of

North-Western Ecuador, Rodrigo Sierra found that during the 1980's there was a

close link between forest degradation and cornmerciallogging. It was noted that

this dynamic was limited to that single region.

7

Nationally, a policy focus on road-building, frontier expansion,

transnational transportation strategies and oil development in the Oriente! have

also been seen as major, possible drivers of deforestation in Ecuador (Wunder,

2001 b). Through statistical analysis, Southgate et al. 1991 found increased

population, proximity to urban centres/roads and tenure insecurity lead to

increased rates of deforestation in eastern Oriente of Ecuador.

The work most relevant to the dynamic offered in my hypothesis is the

paper by Capistrano & Kiker (1995) that concluded there was a significant,

positive relationship between agricultural prices and deforestation. Capistrano &

Kiker cautioned that this relationship was only present in certain periods, and

disappeared in others. Both Barbier & Burgess (1996) and Panayotou &

Sungswuwan (1994) found a positive correlation between agricultural commodity

prices and deforestation rates. Wunder (200-1 b) postulated that agricultural

protection policies had a role in masking the decline in competitiveness of

Ecuadorean agriculture. As agriculture is the end use for most of the nation's

forests, any distortions in the competiveness of agriculture would create an

incentive to deforest where other market opportunities or uses existed. This

study aims to evaluate to applicability of these findings to the Ecuadorean case.

2.1.2 Deforestation Rates

The deforestation literature for Latin America has commonly focused on

the competition of agriculture for land-use or attempted to measure the rate at

1 The Amazonian portion of eastern Ecuador

8

which deforestation processes were occurring. Methodologies for the literature

have varied from micro-level surveys of individuals' land-use patterns to multiple

country regression analyses at the global level. Given the various approaches

taken to forest degradation, it comes as no surprise that estimates of the

deforestation rate are highly varied (Table 1).

Table 1: Estimations of Ecuador's annual deforestation rate from the literature

AuthorEstimated Annual Deforestation

(ha/vear)

FAO 1981 (1980 FRA) 315,020

FAO 1995 (1990 FRA) 215,316

FAO 1997 (1997 FRA) 178,192

FAO 2001 (2000 FRA) 126,684

FAO 2006 (2005 FRA) 184,501

Cabarle et al. 1989 341,000

SUFOREN 1991 120,000

WRI 1992 340,000

Amelung & Diehl 1992 306,000

FAO 1993 (FRA 1990) 238,000

WRI1994 238,0002

INEFAN 1995 106,000

Sierra 1996 15,223

The 1960's and 70's saw extensive anthropological and sociological

studies of human impacts on the forest. These disciplines continued through

the literature of the 1980's and 90's but were ancillary to more frequent

publications on global patterns of forest change. Many reasons have been

postulated for the spatial-consciousness of the 1980's, mostly drawing from a

globalization, neoliberal or Marxist perspective. In the land-use and development

2 Actual quoted figure is a range: 136,000-340,000 ha/yr depending on methodology/definition

9

communities, the global study of forest dynamics is best attributed to the

inaugural Global Forest Resources Assessment (FRA) in 1981 by the Food and

Agriculture Organization of the United Nations (FAO).

Past iterations of the FAO's Forest Resources Assessments, beginning in

1948, had primarily focused on topics for professional foresters and policy

makers. Of the conservation indicators introduced in the 1980 FRA a multi­

temporal assessment of country-specific deforestation rates had the greatest

impact. Prior works had focused on regional rates of deforestation as estimated

by 'professional opinion' of experts. This approach was however both a

snapshot of a dynamic relationship and methodologically impossible to replicate.

Based on national forest inventories in two time-periods, the 1980 rate of

deforestation began measurement in both absolute and proportional terms. The

FAO published further iterations o'f Forest Resources Assessments in 1990, 2000

and 2005, with improvements in methodologies as appropriate.

As retroactive measurements of past FAO assessments have not been

conducted, it remains unclear if the methodological changes over time are fruitful.

The total amount of forest in Ecuador for 1990 as measured in the FRAs for 1990

and 2005 was 11,962,000 and 13,817,000 hectares respectively (Figure 2). On

methodological differences alone, there is nearly a 2 million hectare discrepancy

based. Both Grainger (1996; 2008) and Ehrhardt-Martinez (1998) have strong

summaries on the reliability and measurement of this FAO data. Simply put, a

continuous spectrum of deforestation data from 1990 to present is simply not

available.

10

-----------,

201020052000199519901985

f----..---- -----.--.-.-.-..- - ------.----------.------

~.,., ---- .-s:

_ 18,000,000~ 17,000,000 r---.-----------------­S 16,000,000 f--------------------------------­CJQ) 15,000,000J::-; 14,000,000~ 13,000,000~ 12,000,000l/)

~ 11,000,000af 10,000,000S 9,000,000{:. 8,000,000

1975 1980

Year

-'-FRA1980 ••• FRA 1990 ~FRA2000 -e-FRA2005

Figure 2: Total forested area in hectares as measured by the Food and AgricultureOrganization (FAO) of the United Nations' Global Forest ResourcesAssessments (FRA's)

The integration of satellite imagery and land-use studies has without a

doubt allowed for more detailed and methodologically consistent analyses of

deforestation. Increasingly more nuanced detection of changes in land use and

forest cover is now possible on an annual or even monthly basis. Despite the

increased availability of alternatives and the methodological inconsistencies with

the FAG, deforestation assessments continue to rely upon the FAG datasets.

While simultaneously citing the FAG as the best available data source,

discussion sections of these works commonly question the quality of data,

contain endless caveats for conclusions and deliver scathing smears of the

FAG's assessment methodologies (Allen & Barnes, 1985). Many are conducting

incorrect time-series interpolations of the static FAG data.

11

The goal of this study is to move beyond the FAa datasets. This will not

however be the first attempt. Earlier studies by Barbieri, Bilsborrow & Pan

(2005), Marquette (1998); Mena, Bilsborrow & Mcclain (2006); Perz (2001 ;

2005), Pichon (1997; 2002), Rudel & Horowitz (2001); Sierra (2002) and Sierra &

Stallings (1998) were conducted at the household-level, observing the influence

of annuals as a minor component of a larger forest-use matrix. This study will be

the first to revisit the role of annual crops in Ecuadorean deforestation using

remote sensing.

Outside of Ecuador, there have been Latin American studies linking the

role of annual crops to the rate of deforestation in Brazil (Chomitz &Thomas,

2003; Moran, Siqueira & Brondizio, 2006), Mexico (Abizaid & Coomes,

2004;Deininger & Minten, 1999; Turner, Geoghegan & Foster, 2004) and Bolivia

(Pendleton & Howe, 2002). Prior studies have used finer resolution remote

sensing products to measure deforestation processes in Ecuador, but without

consideration of the proximate or underlying causes. Deforestation rates and

patterns were evaluated using remote sensing by Keating (1997) and Vina &

Rundquist (2004), however those study areas did not include Manabf province.

For Ecuador, this will be the first attempt to measure explicitly the role of

agricultural prices in deforestation processes.

2.2 Agricultural Deforestation

In the same manner that foresters consider the impact of agriculture on

their resource, agronomists examine the influence of forestry on agriculture.

12

Discussions of deforestation are inextricable from those of agricultural expansion

and frontiers. Deforestation-commodity models can be classified by their

sectoral focus: timber models and agricultural commodity models. Barbier et al.

(1995) provides a prime example of a timber model, while commonly used

agricultural models are found in Cannock & Cuadra (1990), Elnagheeb &

Bromley (1994) and Gockowski (1997).

Given their roots in traditional agricultural and timber supply& trade

models, all three find higher agricultural and timber prices lead to greater

deforestation. Most deforestation models only differ 'from other timber and

agricultural supply and trade models in that the authors explicitly note the link

between greater production and deforestation. Under the above conditions,

practically any supply or trade model for a commodity implicated in deforestation

could be considered a deforestation model (Kaimowitz & Angelsen, 1998).

2.2.1 Agricultural Expansion / Frontier Development

There are two dynamics commonly associated with agricultural expansion

and deforestation. The first dynamic occurs under conditions of government

policies actively promoting colonization of forested areas (Browder, 1988; Hecht,

1985; Mahar, 1989). Colonization programs that prioritize land use for small

farmers and new settlement creation best exemplify this (Walker, 1993). Road

infrastructure created to linking these new settlements creates opportunities for

deforestation and land-use conversion (Nepstad et aI., 2002). Infiltration and

development of previously forested areas cause the rate of deforestation to grow

13

exponentially in surrounding regions. Intentional agricultural and farm

development are goal of this first dynamic.

In the second dynamic, agricultural clearing through frontier development

is a secondary outcome of commercial logging interests. Commercial logging

takes place in remote rural areas, and requires road infrastructure between forest

and market. Logging creates incentive for small farmers to clear new settlements

adjacent to the remaining logging road or through creation of secondary branch

roads (Rudel & Roper, 1997). Newly cleared areas are ultimately preferred to

second or third cycle fields, as the intensive rotations of cash and export crops

rapidly deplete nutrients. These new agricultural frontier settlements provide

settlers both market access infrastructure and primary forest to convert to first

generation fields.

It is important to distinguish between frontier environments commonly

portrayed in the literature, and the dynamics at work in coastal Ecuador. Frontier

expansion is indeed underway in the Oriente. In contrast, the majority of areas

with above average and moderately productive soils have already been cleared

in coastal Ecuador. Little forest remains to be infiltrated and few contiguous

corridor tracts exist. Remaining parcels are typically patches at the back of

private land holdings, as previously described.

2.2.2 Agricultural Commodity Prices

According to economic theory, price is established by equation of demand

and supply. However, where a considerable time lag exists between the price

14

change for a commodity and the resulting supply response, Ezekiel's 1938

cobweb relationship may arise (Dean & Heady, 1958). When considered specific

to agricultural markets, where a time lag exists between planting and harvesting,

the amount planted must be determined before future prices are known. The

decision to plant is informed by either past prices (adaptive expectations) or an

estimate of the future price based on all available information (rational

expectations - John Muth,1961)

(Price)

P1

P3

P2

Q1 Q3 Q2

Supply

(Quantity)

Figure 3: Illustration of cobweb cycle dynamics

Under either form of expectations, there is an error between the expected

and realized price. As demonstrated in Figure 3, the equilibrium price is found at

the intersection of the supply and demand curves. Just as was discovered in the

informal interviews, at some point a poor harvest occurs (period one: t=1). As

the corn supplies fail (01), a price increase occurs (P1). Given the time lag

15

between planting and harvesting, the producer assumes today's price (P1) when

making his planting decisions. As the price of corn is high, many producers sow

corn in an attempt to gain from the potential windfall. The rapid rise in the area

planted (02) causes an unexpected reduction in price at harvest time (P2).

Equilibrium price is eventually reached after many iterations and cycles towards

either the equilibrium price or another poor harvest.

There have been many past studies analyzing cobweb applicability. The

cobweb theorem is quite popular in the agricultural economics literature and has

been applied to lemons (French & Bressler, 1962), hogs (Dean & Heady, 1958),

beef (Ehrich, 1969), apples (Carman & Kenyon, 1969), cherries (Carlton, 1956)

and pears (Ricks & Edwards, 1966). Broader analyses of farmer supply

dynamics have also been performed to varying degrees of success (Heady et aI.,

1961; Nerlove, 1958, 1956; Nerlove & Buchman, 1960).

Both Nicholas Kaldor's 1934 and Ezekiel's 1938 cobweb theorems require

three conditions to explain the functioning of a commodity market. Firstly,

producers plan in period t for output in period t + 10n the basis of prices in period

t; (b) production plans, once made, cannot be changed until the following time

period; and (c) price must be determined by the quantity sold. All three of these

conditions are met in the case of coastal Ecuadorean corn markets.

This study of land use interactions between corn commodity prices and

deforestation makes no attempt to model or analyze over the long-term. Despite

a focus on the modern period, some historical context and placement of the

modern period is of use. The long-term dynamics of hard corn prices are

16

demonstrated in Figure 4. Real price indices were created through deflation by

the United Nations Manufacturing Unit Value (MUV) index (Ocampo & Parra,

2003).

300 6

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- Real Index Price (1970~100) Natural Logarithm of Real Index Price

Figure 4: Index of real hard corn market prices (1970=100) and historical change inprices as indicated by natural logarithm. (Data Source: Oxford latin AmericaEconomic Database, 2008)

17

3: METHODS

3.1 Conceptual Framework

Following the work of Angelsen & Kaimowitz (1999), the interaction

between deforestation and agricultural commodity prices can best analyzed

through a conceptual framework with five components: (i) magnitude and location

of deforestation; (ii) agents of deforestation; (iii) choice variables; (iv) agents'

decision parameters; and (v) macroeconomic and policy instruments.

As a basic analysis of the proximate causes of deforestation in coastal

Ecuador, the study hypothesizes that agricultural price is the key choice variable.

The probable influence of other agent choice variables is recognized, and is

addressed in the discussion section. For this analysis, the magnitude and

location of deforestation is the variable of interest. Agricultural supply chains, or

individual farmers as hypothesized here, are considered the agents of

deforestation (Figure 5).

Finally, although the influence of policy instruments is indirect in this

analytical framework, policy has an integral role in any discussion of agriculture.

After an initial evaluation of the interaction between Ecuadorean corn prices and

deforestation rates, the influence of macroeconomic and policy instruments on

both prices and deforestation activities is considered.

18

Inputs

Labor

Silage

Figure 5: Map of corn production chain in Ecuador, representing possible agents ofdeforestation.(Data Source: SICA, 2003)

3.2 Study Area

The Estuary of Rio Chane, Manabf, was been chosen for the study area.

Nearly 40% of Ecuador's corn acreage is located in Manabf province (INEC-

MAG-SICA, 2002)3, The study zone of Bahfa de Caraquez and the estuary of

the Chane River are located on the western coast of the Republic of Ecuador,

approximately to 1'35" of south latitude and 80'25" of longitude west.

As part of the province of Manabf, the study area encompasses three

cantons: Sucre, Rocafuerte and Chane (Figure 6).The Rfo Chane extends from

the town of Salinas west-northwest 17 km to the river's discharge in the ocean at

3 In terms of production it is much less - yields are quite low compared to national average

19

Bahia de Caraquez. Surrounded by coastal hills, the drainage and 25km long

estuary is formed by the confluence of the rivers Chone and Carrizal (Figure 6).

R()CAFUEKIE

CHONE

Figure 6: Location of study area: Bahia de Caraquez, Ecuador. Source: Arriaga,Montano & Vasconez, 1999 by permission.

Bounded by estuary and beaches, the region consists of deciduous dry

tropical forest, mangrove and La Segua swamp. Primary forest is prone to strong

pressures from agriculture and the wood exploitation. Cultivated area is mainly

comprised of forage grass (Paja sp, Panicum sp.) and short cycle crops.

Cultivation of peanut and soybean has grown in the last years, propelled by

edible oil producing companies that guarantee product purchase. (Almeida-

Guerra, 2002). Permanent cultivations occupy a large portion of the land surface

and consist of coffee, citric fruits and banana.

20

3.3 Approach

Traditional trade and commodity models are limited in their examination

the near-term impact of output and input prices. Modelling forest clearing in a

partial equilibrium framework is possible, to the extent that timber production or

cropped areas prove to be good proxies for deforestation. These variables are

typically measured through government reported production area or

extrapolations of harvest/yields.

Past measurements of self-reported agricultural production acreage are

not good proxies for deforestation. Forests can be cleared for different land-uses.

As previously discussed, logging frequently does not lead to complete removal of

tree cover. Agriculture can expand either at the expense of forest or of fallow and

other existing agricultural uses. Given these oversights, I present an alternate

and more direct measure of the area of corn in production. Measurement of corn

acreage in Manabf through annual satellite imagery will facilitate a more accurate

examination of the interaction between corn prices and deforestation.

3.3.1 Deforestation

Studies related to changes in landscape and land-use have been taking

place due to Manabf's growing economic importance. Recent studies were based

on GIS techniques to identify different land-use activities in the region. The

distribution of these activities was then compared over time by repeated analysis

of satellite images (Almeida-Guerra, 2002).

21

Despite advances in remote-sensing technology, past studies and the

often cited FAO deforestation rate for Ecuador all possess a major, critical flaw.

imagery costs often prohibit national level studies from making use of the most

contemporary products. Most national level analyses are conducted with sub­

optimal data of a broader and cheaper resolution. Generally, there is little need

for farm-level data in a national deforestation survey. The differences provided

over time between photos at a national scale are sufficient to make estimates of

the national rate of change in forest cover.

In national level surveys, I\JOAA-AVHRR satellite imagery with a resolution

of 1 kilometre by 1 kilometre (100 hectares) is typically used. Resolution refers

to the 'graininess' of remote sensing and satellite imagery. Each pixel on the

photo represents a small city. l\Jewer LANDSAT-TM and MODIS technology

imagery both have an extensive back-catalogue and resolutions of 30m and

500m respectively. These increased resolutions represent 0.09 hectare pixels

for LANDSAT images and 25 ha pixels for MODIS. The common use of reduced

resolution I\JOAA-AVHRR imagery can be primarily attributed to the further

reduction in the per photo cost from $2500 to $750 US.

For global and national level surveys, coarse resolution imagery use is

scale appropriate. A conceptual problem arises within the literature, however,

when sub-national and regional studies begin applying FAO and NOAA-AVHRR

derived deforestation rates for household and micro-level studies. The purpose

of this study is to link individual farmers' actions to deforestation. A simple

correlation of the FAO's annual deforestation rate with average corn prices is not

22

sufficient. National scale data provides neither the information nor resolution

required for this task. Farm-level information on the temporal trend of

deforestation is required,

To move beyond the FAa deforestation data, changes in agricultural

acreage were used as a proxy for deforestation. The change in acreage was

calculated using 500-metre resolution Moderate Resolution Imaging

Spectroradiometer (MODIS) 32-day mosaics were used to measure annual

deforestation from 2000-2005 at a meso-scale. This spectrum of images is

offered as an alternative methodology for the FAa that is more financially

feasible than global analysis using 30m-resolution LANDSAT images.

The increased resolution of MODIS imagery makes it possible to track

farm-level changes in forest cover. Using Multispec™ Image Classification

software, the area of forest, grassland, rowed crops, urban and bare soil on each

photo were calculated. By comparing data across photos and between years, it

was then possible to track the change in forest area to either cropland or bare

soil.

For the period 2000-2005, MODIS monthly mosaics of surface reflectance

were combined into annual imagery. The annual MOD44B imagery was then

processed into a Vegetation Continuous Fields (VCF) product. Each pixel for

each annual image was assigned a percent vegetative cover value. Using the

temporal spectrum of images, the change in vegetative cover (pixel value) could

then be tracked over time. Increases in pixel values represent forest

improvement, while decreases in pixel values represent forest degradation. As

23

outlined earlier, maintaining consistency with the FAO's terminology,

deforestation was defined as a degradation of forest cover below 10%. This

process simultaneously establishes a deforestation rate that is specific to the

region of corn production and accounts for individual farm decisions.

3.3.2 Commodity Prices

At the farm level, we must also consider the price of corn - tile commodity

price portion of the discussion. Domestic data is available through the joint

World Bank and Ecuadorean Agricultural Census (Servico de Informacion y

Censo Agropecuario - SICA) project. For the period January 1996 to December

2006, the World Bank funded the collection and dissemination of domestic

prices4. The agricultural census facilitated the collection of sub-regional market

price data for both domestic producer and wholesale prices. International prices

are available from the FAO Commodities and Trade Division.

Using the Ecuadorean Census data (SICA), the trend of corn producer

and wholesale prices was established for the region. While domestic

consumption accounts for 98% of corn production in Ecuador (SICA, 2003),

replicability to other regions is essential. As such, an essential step was to

determine whether Ecuadorean domestic prices and price increases are similar

to that of international price fluctuations. With both commodity price and

deforestation trends established, interactions between the two were examined.

4 World Bank Project 10# P007135

24

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3.3.3 Interaction between Commodity Prices and Deforestation

With only three FAO data points directly measured in the 1996-2006

period for which domestic corn prices are available, there were clear data

limitations. In the best-case scenario, nearly 926 weeks of price data (18 years X

52 weeks) of data would be available. If earlier price data had been published,

then all ten (10) FAO Forest Resources Assessment datasets could be

processed. The five-year lapses in FAO deforestation measurement

necessitated the development of a methodology, which adopts past suggestions

of improvements and address ongoing data availability issues.

Medium-resolution MODIS imagery for the inclusive years of 2000-2005

were used to calculate uninterrupted measurements of annual deforested area.

It is important to note that the absolute level of annual deforestation, not the rate

of change, was measured. With these data, there was sufficient data to evaluate

both visual and basic correlations with the more frequent price-series data.

The volume and frequency of data extracted from remote sensing

products often proves insufficient for some statistical tests. An alternative to the

single observation, annual imagery approach adopted here is the development of

a stratified sampling approach for each photo. In spite of this alternative

methodology, FAO estimates of the rate of deforestation show that even a

massive reduction in the area of one million hectares of forest still shows only a

+/- 0.2%variation in the annual rate of deforestation (FAO, 2007).

For an analysis of the influence of commodity prices on deforestation

processes, both the limited annual variation and data points realistically require

26

image stratification to obtain a greater volume of data. As the aim of this study

was not to generate regression coefficients, but to examine the broader

interaction between commodity prices and deforestation, a stratified sampling

approach was not adopted. The core analysis presented is a graphical analysis.

Utilization of the varied measurements of deforestation from the literature (Table

1) is assessed in the discussion section.

27

4: RESULTS

4.1 Agriculture Commodity Prices

As demonstrated in Figure 9, domestic wholesale prices were a strong

predictor of domestic producer prices (R2 = 0.972). Optimally, domestic

producer prices would be assessed. Producer prices are the most likely

information to influence an individual's expectation decision to clear forest for

corn production. Given that domestic data was available for only a period of six

years, and given the strong association between domestic wholesale and

producer prices, wholesale prices were deemed to act as a reasonable measure

of commodity prices in Ecuador (Figure 10).

----------------------

17~ DO

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oo

r- _..- -_..----.,------T-.---~J'-.000 100.00

Re.1 Domestic Whole••le Price (USS/ton)

Figure 9: Scatterplot demonstrating association between Ecuador's real domesticwholesale and producer corn prices

28

700

6" 600o"iioooN

~ 500"0§."2Bo 400't:a;::!:6ii(J)

2. 3004lo

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aill ill ill "- "- "- co co co m m m a a a N N N C'1 C'1 C'1 .... .... .... L{1 L{1 L{1 illa;> m m a;> m m a;> m m a;> m m 9 a a 9 a 9 9 a 9 9 a a 9 a a 9 a a 9<: >- cl c >- cl <: >- cl c >- cl <: >- cl c >- (l- c >- (l- <: >- cl c >- cl c >- cl c

"' "' Q) "' "' Q) "' "' Q) "' "' Q) "' "' Q) "' "' Q) "' "' 4l "' "' Q) "' "' Q) "' "' 4l "'--, ::e en --, ::e en --, ::e en --, ::e en --, :::E en --, :::E en --, ::e en --, ::e en --, ::e en --, :::E en --,

Figure 10: Real domestic wholesale hard corn prices used for agricultural deforestationassessment in Ecuador (Market: Portoviejo, Manabi; Data Source: MAG, 2008)

4.2 Deforestation Rates

For the period 2000-2005, annual MODIS imagery, as seen in Figure 11

could have been evaluated with the naked eye, without any further processing.

Temporal patterns and differences are however, difficult to ascertain in raw

MODIS form. By calculating the vegetation percent information for each pixel

over time, it was then possible to construct an annual deforestation rate based on

actual observations. Annual change in forested area was measured by the net

difference in percent vegetation from MODIS imagery (Figure 12). When

examining the panel figure, degraded and/or newly deforested areas are

29

identified in purple/pink. Regenerating areas or those with increasing forest

cover are seen in green hues. Areas with unchanged forest composition over

time appear clearly in grey.

Spectral histograms were created for each image that summarize

individual images by the number of pixels per hue. On a basic level, the

difference between the number of green and pink pixels for two photos would be

the net deforested rate (Table 2). Year over year change statistics as calculated

per pixel are found in Table 3. There appears to be almost no change in the

proportion of forested and agricultural land between 2000 and 2005. Forested

area increased by 2.2% over the period (green), while agricultural lands

decreased by1.2% (purple).

30

Fig

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31

Figure 12: Annual and net change in forest cover for Manabi Province, Ecuador from2000 to 2005, as interpreted from MODIS mosaic imagery (purple/pink - newlydegraded, green - improving/newly regenerating, grey - no change) (DataSources: Hansen et al. 2006; Global Land Cover Facility (GLCF, 2008)

32

Table 2: Temporal assessment of land-use in Manabi, Ecuador from 2000-2005 asmeasured from MODIS-VCF imagery

Pixel Hue 2000 2001 2002 2003 2004 2005

Unclassified 0,1 40.3% 40.4% 40.7% 41.1% 41.6% 41.4%

Urban 2 2.3% 1.8% 1.5% 1.9% 1.7% 1.8%Non-Vegetated 3-10 7.9% 7.9% 7.9% 7.2% 7.2% 7.4%

Cleared/Agricultural11-40 8.6% 8.2% 8.0% 8.0% 7.2% 7.4%

Modified41-60 4.7% 3.8% 4.2% 3.9% 4.0% 3.6%

61-100 30.5% 32.1% 31.9% 32.1% 32.4% 32.7%Forested

Unclassified 254 5.8% 5.8% 5.8% 5.8% 5.8% 5.8%100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

Table 3: Land-use change over time (% annual) in Manabi, Ecuador as measured fromMODIS-VCF imagery (2000-2005)

Pixel2000/01 2001/02 2002/03 2003/04 2004/05

2000-Hue 2005

Unclassified 0,1 0.2% 0.2% 0.4% 0.5% -0.2% 1.1%Urban 2 -0.5% -0.2% 0.3% -0.2% 0.1% -0.5%Non-Vegetated 3-10 0.0% 0.0% -0.7% 0.0% 0.1% -0.6%

Cleared/Agricultural 11-40 -0.4% -0.2% 0.0% -0.8% 0.2% -1.2%

Modified 41-60 -0.9% 0.4% -0.3% 0.1% -0.5% -1.1%

Forested 61-100 1.6% -0.2% 0.2% 0.3% 0.3% 2.2%

Unclassified 254 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

4.3 In'f1uence of Agricultural Prices on Deforestation Rate

Interaction between real international corn prices and deforestation is

demonstrated in Figure 13. Despite a continual rise in corn prices over the ten-

year period, the total deforested area in Manabf, Ecuador actually decreased

annually. This initial result appears to discredit the informal information from

farmers that as real international prices have increased over time, the

33

deforestation rate has increased (more forested area). When deforestation rates

are high there appears to be no direct influence on the amount of forested area.

ellu"i:a. 300<:o

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Deforestation -- Real Domestic Wholesale Prices (US$/Metric Ton) Linear (Deforestation)

Figure 13: Interaction between domestic corn prices and Ecuador's deforestation rate

34

5: DISCUSSION

The use of medium-resolution satellite imagery has revealed that despite

higher agricultural prices, deforestation has actually decreased in coastal

Ecuador. This directly contradicts the often cited FAO Forest Resources

Assessments which have observed more broadly that the national deforestation

rate in Ecuador has been rapidly increasing over the same period. Could the

findings of this study be applied to the broader region or internationally?

5.1 Agricultural Prices

5.1.1 Temporal and Seasonal Variation

Applicability of this study to the broader region can be explored by an

examination of the correlation between real domestic wholesale prices and the

international price (Figure 14). A revisit to Figure 8 leads to the conclusion that

international and Ecuadorean wholesale prices are not linked (Pearson

Correlation Coefficient = -0.015). In real terms, the international hard corn price

was nearly stagnant over the ten year study period, while the domestic price

continuously climbs. A weak coefficient of determination (r2= -0.103) and Durbin-

Watson measure of nearly zero (0.135) may indicate strong positive

autocorrelation in international and domestic price residuals5.

5 Durbin-Watson measures range from a to 4, where 2 represents no autoregressive behaviour,and the extremes of a and 4 present positive and negative autocorrelation

35

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Real International Hard Corn Price (US$IMetric Ton)

Figure 14: Scatterplot demonstrating association between real international corn pricesand Ecuador's real domestic wholesale prices

Given the Durbin-Watson result and a very weak association between

domestic wholesale and international corn prices (Figure 14), an AR1

transformation was performed. Figure 15 reveals the strong seasonal and

monthly fluctuations in the domestic price data. This is consistent with the

double harvest seasons in Ecuador, wherein the winter plant represents 80% of

the total annual harvest. Rainfall during the November to January season is an

essential determinate of both winter yields and annual crop success. Beyond

the seasonal and annual fluctuations, domestic corn prices began to rise above

international prices in September 1998. Given an average domestic corn

premium of nearly $98 per metric ton over the ten year period (Figure 16), a

discussion of local distortions and environmental factors is warranted.

36

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5.1.2 Domestic Distortions

Higher farm prices, trade liberalization, producer subsidies, and currency

devaluations have all been attributed to increased deforestation (Barbier, 2001).

Specific to agricultural price and deforestation assessments, Ehrhardt-Martinez

(1998) used sectoral inequality as a measure of policy and preference for

agriculture against other sectors. Heath & Binswanger (1996) found that the

value of farmland far exceeds the capitalized value of farm profits. It is salso the

case that throughout Latin America, land represents a hedge against inflationary

pressures endemic in the region. Soaring inflation and the fixed exchange rate

regime up until dollarization in January 2000 reduced Ecuadorean agriculture

competitiveness (Wunder, 2001 b). It is possible that urbanization drew labor

away from land-clearing agriculture during this period.

In studies of Colombian deforestation, John Heath & Hans Binswanger

note that "distortions in the land market in Colombia create a price structure that

limit famers' access to land ownership" (1996, p.65). For the Ecuadorean case,

not all deforestation decisions can be attributed to agricultural investment, as

land tenure laws require modification of forest cover as proof of productive use.

Ecuador remains one of the few remaining countries in the world to recognize

squatters' rights. Forested land that is not in production is frequently the target of

squatters and as a result is often deforested in stages to provide proof of

residency.

38

5.1.3 Direct Government Price Interventions

As a member country of the Comunidad Andina (CAN) trading bloc,

Ecuador coordinates a basket of commodity prices with fellow member countries

Colombia, Bolivia and Peru. The Andean Price Band System (SAFP)6 has been

in place in Ecuador since January 19957, as an annually calculated tariff quota,

with price floors and ceilings for 13 agricultural staples - including corn. SAFP

was initiated with the explicit intention of both stabilizing the volatile cost of

agricultural imports and buffer domestic producer/consumer prices from

international price distortions.

The base tariff rate for hard corn imports in Ecuador is 15%, which is then

subject to a SAPF tariff quota if no bilateral trade agreement exists. Current

international corn prices are then evaluated against a calculated floor and ceiling

price. Based on the average daily close of FOB international prices over the

previous 60 months, price ceilings and floors are calculated each fiscal year by

adding and subtracting the standard deviation from the historical average, If the

current international price falls within the "band" created by the ceiling and floor,

then no additional trade tariff is applied. When international prices are lower than

the floor, tariffs are applied to bring the price of foreign corn within the domestic

band. Similarly, should the international price be higher than the band's ceiling,

the initial 15% tariff would reduced.

6 Sistema Andino de Franjas de Precios - December 7th 1994, Decision 371 of the CartagenaAgreement Commission

7 Ecuadorean Executive Decree 2485A - January 30th 1995

39

It is hypothesized that these price distortions in Ecuador have potentially

shifted the preference for investment to agriculture. This would be consistent

with the CAN's stated aim of domestic protection against international price

volatility. In the process of protecting domestic agricultural/consumer interests, it

is conceivable that this shift in policy preference has externalized the costs to the

forest. Forest resources are subsequently undervalued by this domestic

distortion, increasing investment in agriculture. Increased domestic area

cultivated would require an increased conversion of forest to field.

Although the intention of the Andean Price Band is to protect domestic

interests, this conflicts with the observed prices in tl-Iis study. Despite moderation

of international prices for consumers and producers, domestic prices still tended

to fluctuate more dramatically than internationally (Figure 8). It is still plausible

that that speculation, oversupply and saturation result from investment decisions

under I-Iigh domestic prices.

Further investigation into the abandonment of crops prior to harvest due to

a cobweb related price drop, would require this study to focus solely on a single

MODIS pixel color. Processing capabilities would have to be focused on only

those pixels that were forested in period t-1 (green), became deforested in period

t (purple), and within a silviculturally appropriate period had regenerated to forest

(green). Any corn-related increase in deforestation represents a short-term

investment strategy whereas forest resources in rural Ecuador may actually offer

a long-term, emergency form of capital for the poor.

40

5.2 Trees as Savings - Forestry and the Poor

Earlier in the introduction of this study, it was stated that, "conversion of

forest to agricultural lands is therefore potentially survival and livelihood

decision". The relationship between poverty and deforestation is fairly well

established (Harrison, 1992). This dynamic is best summarized in that, "the poor

are not ignorant of the processes of deforestation or blind to its effect - they cut

because they must." (Eckholm, Foley, Barnard & Timberlake, 1984, p.6) To say

that this statement finds ubiquitous acceptance in the literature is untrue.

Literature on asset-based poverty approaches characterize deforestation as

either 'pull' motives which are opportunity based, or 'push' scenarios of survival­

driven action. Sven Wunder's 2001 study of six Sierra villages confirmed the

observed phenomenon of local deforestation and inheritance related

deforestation. As families grew and resource decisions created intra-family strife,

lands were divided and any forested land was cleared on each new parcel to

provide for the inhabitant family unit.

Alternatively, Sierra 2001 contests forest-based poverty alleviation as a

push motive, but more as a pull. Agricultural deforestation is often viewed as an

investment in future land-use and is most often undertaken by capital-abundant,

middle-class entrepreneurs. For the poor, the upfront costs of labour and inputs

are prohibitive for the landless and/or credit-lacking poor.

41

5.3 Deforestation Rates

5.3.1 Measurement Difficulties

According to the USGS, inter-annual comparison of the MODIS percent

tree cover product (VCF) should be conducted with extreme caution, as there is a

plethora of reasons for temporal fluctuations in forested percentage from year to

year. It is expected that an official multi-year change product derived from VCF

will soon be completed. At present, the only professional product available for

change data is the MODIS Vegetative Cover Conversion product. This product

however has no coverage for dry-tropical forest regions, which dominate the

study area. It was this data limitation that precipitated the author's chosen

methodology.

Although the author accepts the note for caution when comparing images

temporally, attempts were made to control for the variability found in MODIS-VCF

data. The temporal nature of even a single MODIS mosaic accounts for frequent

cloud cover endemic to the Andean foothills. Admittedly, clouds cover influence

could have been further reduced by utilization of the moisture layer from MODIS.

Again, neither time nor funding allowed for the acquisition and process sing of

this dataset.

To assess the quality of pixel classification, for sample pixels ground­

truthing against LANDSAT images and the 1:1,000,000 scale landscape map of

Ecuador was performed. Optimally, classification of imagery requires a stratified

ground-sampling exercise where a selection of classified pixels is visited in

person to verify a valid land use interpretation. Unfortunately, time and funding

42

did not permit for a formal evaluation in this manner. The fieldwork previously

completed by the author in Ecuador, for a previous project, also allowed for a

personal recognition of many land use changes in the imagery.

5.3.2 Agriculture as a Deforestation Proxy

Modelling forest clearing in a partial equilibrium framework is possible, to

the extent that timber production or cropped areas prove to be good proxies for

deforestation. Using changes in agricultural acreage as a proxy for deforestation

also carries certain pitfalls. Although commercial logging is considered a

separate deforestation process from agriculture, it can be difficult to differentiate

between the two using satellite imagery. A second strong factor against the use

of agricultural area as a proxy for deforestation is that acreage expansion may be

into non-forested areas such as savannahs and grasslands (Jokisch & Lair,

2002). Once deforestation data has been collected, it is methodologically

complicated to differentiate between new and pre-existing deforestation

Measurements of deforestation by the FAO and in the broader literature fail to

draw a clear distinction whether deforested area in year t, also includes newly

deforested area tallied in year t-1 that has not yet regenerated.

Beyond the difficulties of measuring agricultural deforestation, it is

plausible that commodity prices are not the sole variable driving deforestation

rates. As mentioned earlier there are traditional supply models and partial

equilibrium trade models for specific agricultural and forest commodities.

Although the use of these models is perhaps beyond the project scope, model

43

makers claim production of these commodities is a direct source of forest

clearing. Hence, any factor that stimulates production indirectly induces

deforestation. As with other supply and trade models, the principal explanatory

variables in these models, as previously described are prices, income and

population.

Cannock & Cuadra (1990) model the production area of corn, rice, beans

and cassava in the Peruvian Amazon. Total production area was modelled as a

function of previous area, output prices, an index of production costs, credit

availability and public investment in agriculture. Output prices are functions of

international prices, exchange rates, the price of substitutes and government

subsidies to grain markets. The anticipated result was a positive effect on total

production area.

Elnagheeb & Bromley (1994) present what they call an "acreage response

model" for sorghum production in Sudan. Despite a different crop and region of

study, their main assumption was that sorghum production is a proxy for

deforestation. Expected sorghum prices, rainfall, yield, charcoal prices,

production costs and risk determine sorghum area. The first four variables are

correlated with higher acreageS (and therefore deforestation) and the second two

with lower acreage. An additional element to be considered at the household

decision level is charcoal prices. High charcoal prices, or other non-timber

forest products (NTFP's) encourage greater land clearing because farmers can

partially recoup their land clearing costs by selling the charcoal produced.

8 Area under cultivation (planted)

44

Gockowski (1997) emphasizes how changes in cocoa prices affect forest

clearing by changing the relative profitability of the various crops planted by

farmers. He proposes that a decline in cocoa prices will lead farmers to shift their

attention towards plantains and cocoa yams, both of which require more forest

clearing. Although the terms of trade for agriculture tend to encourage

deforestation, this does not necessarily hold for relative price changes between

agricultural products. In fact, lower cocoa prices can actually stimulate higher

deforestation, as they induce farmers to shift from perennial crops into more

land-intensive annual crops.

In this study these dynamics were not examined, as land use for annuals

and perennial crops appear similarly without extensive ground-truthing. The

focus of this assessment was to examine the conversion of forest to annual corn

production. Future studies may wish to allocate additional resources to ground­

truthing, specifically to differentiate between annual and perennial crops.

5.3.3 Challenges of Differing Scales

Basing agricultural and forest-based policy decisions on national level

measurements is misguided. Although a direct correlation between corn

commodity prices and deforestation rates was not found for coastal Ecuador, the

FAO's increasing rate of deforestation for Ecuador was found to be inaccurate.

As the largest producing region of corn in Ecuador, Manabf has actually

witnessed a decline in the rate of deforestation despite both increasing corn

45

prices and decreasing corn yields. Efforts to further interpret this dynamic will

rely upon household-level studies to link individual decision-makers with "pixels".

Agricultural policy decisions, such as Ecuador's pending land reform and

agricultural input pricing policies, should take note of the varying spatial rates of

deforestation and clearing. Manabf and neighbouring Esmeraldas province are

both commonly cited as the regions with the most rapidly declining forest

landbase in the country. Wrlile the absolute rate of deforestation remains high

and nearly unchanged, this study certainly draws into question the FAO's claims

regarding the rate and direction of second-order change.

5.3.4 Research Value of Deforestation Trends?

To date only one Shafik (1994) has worked extensively with a true rate of

deforestation, with most authors preparing absolute deforestation levels.

Ehrhardt-Martinez 's scathing 1998 review of the literature noted that most

studies to date have found relationships and constructed regression analyses

that were "largely atheoretical and methodologically lax". These studies

consisted of correlations between deforestation and variables which were either

readily available or already a researcher's field of expertise. Limited original

research has been conducted to capture new data or trends.

Although some may view this study as methodologically lax in its multi­

disciplinary approach, an attempt was made to process new data and trends.

Beyond the limitations of the research environment, the methodology used here

has built upon the past literature. By actually testing the suggested approaches

46

from prior discussions and conclusions, this meso-level study from coastal

Ecuador has attempted to transcend the environment of conflicting relationships

for policy makers.

47

6: CONCLUSIONS

Agriculture and forestry are traditionally portrayed as conflicting land-uses.

This study examined the influence, at the farm-level, that corn commodity prices

had on deforestation rates in coastal Ecuador. The deforestation literature has

previously assumed a positive relationship between corn commodity prices and

deforestation rates in Ecuador. These assumptions have to date been based on

national-level forest inventories by the FAa, involved limited remote-sensing data

and have not been widely examined for Ecuador. It was initially hypothesized

that a one-year time lag should exist between the deforestation rates and corn

commodity prices.

The results of this study found that no relationship existed between

deforestation rates and corn prices in coastal Ecuador. In fact, while the often

cited FAa Global Forest Resources Assessments have reported increasing

deforestation rates in Ecuador, this study found that deforestation rates have

been steadily decreasing in coastal Ecuador (2000-2005). Contrary to the initial

hypothesis, the observed decrease in deforestation occurred during a persistent

rise in the real domestic price for corn.

A comparison to previous publications shows that these new findings are

best attributed to the finer-resolution MODIS satellite imagery and increased

(annual) frequency of measurement employed in this study. The intriguing

results and innovative, multi-disciplinary approach highlight three important

48

considerations for future deforestation assessments. First, while the late

twentieth century saw the reintroduction of individual decision-makers into the

land-use literature, we continue to measure deforestation issues with national­

level tools. The increasing availability and decreasing cost of satellite imagery

warrants the use of finer-resolution satellite data.

Secondly, the academic community must overcome the widespread

reliance on FAG deforestation rates. Each author must consider whether use of

these pre-packaged, readily accessible, national-level data is both temporally

and scale-appropriate. When FAG data is not appropriate, authors must be

willing to adopt alternative methodologies, perhaps borrowing from other

disciplines.

The final lesson from this paper is that further study must be conducted to

discover the true reason for abandonment of recently cleared, mid-slope fields in

coastal Ecuador. Although local farmers have conveyed that domestic corn

prices are precipitating cycles of deforestation and crop abandonment, the

results of this study are inconsistent with that position. Regardless of the findings

of this study, the dynamic of crop abandonment and erosion in rural Ecuador

continues. Given the hydrological, economic and climatic vulnerabilities of

coastal Ecuador, uncovering the cause(s) of this dynamic is essential to

continued growth and prosperity.

49

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