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
SIMON FRASER UNIVERSITYLIBRARY
Declaration ofPartial Copyright LicenceThe author, whose copyright is declared on the title page of this work, has grantedto Simon Fraser University the right to lend this thesis, project or extended essayto users of the Simon Fraser University Library, and to make partial or singlecopies only for such users or in response to a request from the library of any otheruniversity, or other educational institution, on its own behalf or for one of its users.
The author has further granted permission to Simon Fraser University to keep ormake a digital copy for use in its circulating collection (currently available to thepublic at the "Institutional Repository" link of the SFU Library website<www.lib.sfu.ca> at: <http://ir.lib.sfu.ca/handle/1892/112>) and, without changingthe content, to translate the thesis/project or extended essays, if technicallypossible, to any medium or fonnat for the purpose of preservation of the digitalwork.
The author has further agreed that permission for multiple copying of this work forscholarly purposes may be granted by either the author or the Dean of GraduateStudies.
It is understood that copying or publication of this work for financial gain shall notbe allowed without the author's written permission.
Permission for public performance, or limited permission for private scholarly use,of any multimedia materials forming part of this work, may have been granted bythe author. This information may be found on the separately cataloguedmultimedia material and in the signed Partial Copyright Licence.
While licensing SFU to permit the above uses, the author retains copyright in thethesis, project or extended essays, including the right to change the work forsubsequent purposes, including editing and publishing the work in whole or inpart, and licensing other parties, as the author may desire.
The original Partial Copyright Licence attesting to these terms, and signed by thisauthor, may be found in the original bound copy of this work, retained in theSimon Fraser University Archive.
Simon Fraser University LibraryBurnaby, BC, Canada
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
0- 250 5aII
aZ....
'" III~ c:III 200 4 ~0';;: ....0.. 0)( <0OJ III
"C :;.f: 3
'" 150 3 2-OJa: :DC
(1)
0 ~
tJ ~
"E0-(1)
III 100 2 )(
:I: "'"~
(i'0 (1)'C0
~:I: 50
/
0 0OMwmN~oo~~~o~wmN~OO~~~OMwmN~OO~~~OMwma 0 a a .- ~ ..- N C\J N (T) ("') ("') ("') "Q" "G" ..:::t LI"J ll) L.() CD «) c.o to r--- r-- r-- co co co m m m mm m m (J) m m m m m en en m m m m en m m m m m m m m rn rn m m rn m m rn m m.- .- .- .- ..- ..- ..- .- .- .- .- ..- ..- ..- ..- ..- ..- ..- ..- ..- ..- ..- ..- ..- ..- ..- ..- ..- ..- .- ..- ..- ..- ..-
- 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
"T1
!!--
!O'
Re
alH
ard
Co
rnP
rIce
(US
SlM
elT
lcIo
n)
(In
de
x2
00
0=
10
0)
toN
om
ina
lH
ard
Co
rnP
rice
(US
SlM
etr
icIo
n)
c:<5
'"<.
>A
(»
a>"
c:<5
'"<.
>A
'"m
".,
t8
00
00
0.,
00
08
00
(1)
00
00
00
0(1
)0
00
00
00
CO
Jan
-96
}J--
JJa
n-9
6:0
o.ro
Ma
y-9
6M
ay-
96
'!! 0S
ep
-96
.I
Se
p-9
6"'
J:1
0S:
::Z0
(1
)3
Jan
-97
»0
tJa
n-9
7o
III
~.O
-n
Ma
y-9
7,,
3z-z
May
-97
Q.=
r"
-,.":;'
o0
Se
p-9
7o
III
&S
ep
-97
33
-.,
c:Ja
n-9
8I
O!!
!.S
'S
'Ja
n-9
8ii
j"Q
.0
0=
r~
~.,
n~
Ma
y-9
8~Ill
5"0
May
-98
!"0
~.,
~~
.,£-
Se
p-9
8Q
.~
roS
ep-9
8..
.-::
::l
'""'
Jan
-99
ng
g.Ja
n-9
9C
"I
0~
-0!.
:3.
Ma
y-9
9.,
~a
May
-99
::::l
-0"-
III
n:0
~-
c:0
0(1
)ro
Se
p-9
9
"n
0S
ep
-99
oII
I'!!
.,m
~0
Jan-
OO
0'
""
Jan-
DO
c:::;
'0
o~
3M
ay-D
O(1
)CD
illM
ay-D
O.,
0
i·II
I£
3S
ep-D
O
-I
Sep
-OO
.•c...
~.,
Jan
-Ol
0Ja
n-0
1S
:::1
ll0
3z
»::
::l
~M
ay-
Dl
0M
ay-0
1
"C:
'"c..
.3
r-.J
~~
ro-S
ep
-Dl
III
:;S
ep
-Dl
U1
-0::::
l'!!
Jan-
D2
"''<
~Ja
n-0
2c:
00
....
III
I0
0(,0
May
-D2
3M
ay-D
2.,
roC
O(,
0S
ep-D
2'<
g;.S
ep-D
2'-
'01
I....
0
6':0
Jan
-D3
(,0
~Ja
n-0
3::r
roM
ay-D
3(,
00
May
-D3
'"en
ro-s:::
- 5""'
Sep
-D3
6''"
Sep
-D3
III
iDro-
()3
Jan-
D4
s:::-0
Jan-
D4
g::>
=r0
May
-D4
III
Iill
May
-D4
~
"''"
'!!.,
0-0
Sep
-D4
nS
ep-0
40
~.=r
~O1
Jan-
DS
'"Ja
n-D
S
(1)
"M
ay-D
S0
May
-OS
00
><CD
Sep
-DS
01S
ep-D
S
"...
-.,
Jan
-D6
CJa
n-D
6(1
)II
III
III
IDi
(1)
Q.
00
:;'0 c:
'<., n
(1)
(1)
III .,
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
o 0
o
o..u..ij 1000Don.u.,: 7':. 00
Eoo
o 0
000
1SO 00 '::00 (10
~ "'''°1~~ 00
--~-----_.- --000
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
~<:
o(J 200
"E'"~iUfl 100
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
ure
11:
Ma
na
bi,
Ecu
ad
or
de
pic
ted
asp
erc
en
tfo
rest
cove
rcl
ass
ific
ati
on
of
MO
DIS
ima
ge
ryfo
r20
00-2
005
(Dat
aS
ou
rce
s:H
anse
net
al.
2006
;G
lob
al
Land
Co
ver
Fa
cilit
y(G
LCF
,20
08)
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
~ 200 I
400
700
600
c-o 500I-U"i:
Oi~...(/l
2.
100
, ......................... ..............•...•...•.••••..•••...
12
10
0co
8 Qco-.. ,. V>........ iDa.
~6 co
Ol
~--i0
~4 ~
co~
2
W 0.697
0 0CD CD CD "- "- "- co co co m m m 0 0 0 N N N C'1 C'1 C'1 <j- <j- <j- lD lD lD CDep m ep ep m ep ep m m ep m m 9 0 0 9 0 0 ':;' 0 0 ':;' 0 0 ':;' 0 ':;' ':;' 0 ':;' ':;'<: >. a. <: >. a. <: >. 6. <: >. 6. c >. 6. c >. 6. c >. 6. c >. 6. c >. a. c >. a. cttl ttl Q) ttl ttl Q) ttl ttl Q) ttl ttl Q) ttl ttl Q) ttl ttl Q) ttl ttl Q) ttl ttl Q) ttl ttl Q) ttl ttl Q) ttl...., :::< UJ ...., :::< UJ ...., :::< UJ ...., :::< UJ ...., :::< UJ ...., :::< UJ ...., :::< UJ ...., :::< UJ ...., :::< UJ ...., :::< UJ ....,
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
° 0~ 0 0
°
00<Q)Oo
'[ 700t-u'i:
~ 600-
oIII
=. 500-GIu'i:D-C 400- °:; 00°000
~ ° 01~~0 0~ 300- 00 008 00 0 0
-oGl 0eo ~o~'O.. 0
~ 200 l--rP---:8~~:n~:O------------~
t; 0 0
~ 100 ~"~ i~8iiiGI a~
I60
I90
I120
I150
I180
I210
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
Pri
ceP
rem
ium
toD
om
est
icW
ho
lesa
lers
over
Inte
rna
tion
al
Pri
ces
(US
S/M
etrlc
To
n)
%A
nn
ua
lC
ha
ng
ein
Rea
lH
ard
Co
rnP
rice
'"o o if-on o if-
o o if-on o if-
o if-on o if-
"T1
to" I: ...0
('D0
.....I
if-tJ
1if-
Jan
-96
)>M
ay-
96
::>...
..."'0
::>O
('D
c:S
ep
-96
III
...
~
-C')
()
Jan
-97
III
('D::
T I!JM
ay-
97
CJ)
::I
<0
o-
ct>S
ep
-97
I:II
I:0
...
::I
ct>Ja
n-9
8C
')::
I~
('DI:
0M
ay-
98
..III
0 3S
ep
-98
=:-
ct>:t>
C')
~Ja
n-9
90"
G>~
:;;M
ay-
99
1\)
::1
::T
0o
tC<D
Se
p-9
9O
('D
~Ja
n-D
OCD
-'
ct>...
....
::1-u
May
-DO
::>"
::I
"ct>S
ep-O
O-
-('D
Jan
-01
... ::I
III
May
-01
=,if-
Sep
-01
0)>
::I
::>Ja
n-0
2::>
!E-c: ~
Ma
y-0
2II
I()
::I
::TS
ep
-02
c..0> ::> <0
Jan
-03
c..ct>
°:0
Ma
y-0
33
'"~S
ep
-03
('D::>
Ul
10Ja
n-0
4-
3n'
0>M
ay-
04
~g.
Se
p-0
4::>
'::J'
~
°:\'
Jan-
OS
CD0"
May
-OS
Ul
~
III
Sep
-OS
CDJa
n-0
6C
') 0 ... ::I
"'C ::::!,
C')
('D Ul
m o oon o o
"o ow o o
'"o oo o
o-:. o o
N o o
"T1
to'
I: ... ('Dw
.....0
Ol
0
Jan
-96
=:0
Ma
y-9
6
:t>
0S
ep
-96
e>3
Jan
-97
I\)(
'DM
ay-
97
ol!l.
0-'
Se
p-9
7co
C')
.......~
Jan
-98
'::J'
Ma
y-9
80 CD
Se
p-9
8U
lJa
n-9
9II
I('D
Ma
y-9
9
"'CS
ep
-99
... n'Ja
n-D
O('D
May
-DO
"'C ...S
ep-O
O('D 3
Jan-
01
WI:
May
-01
--..J
3S
ep-0
1
0Ja
n-0
2< ('D
May
-02
... ::I
Se
p-0
2
-Ja
n-0
3('D ...
May
-03
::I
III
Se
p-0
3- 0
Jan
-04
::I !E-
May
-04
"'CS
ep
-04
...Ja
n-O
Sn' ('D
May
-OS
Ul ......
Sep
-OS
0Ja
n-0
6II
I -III CJ)
0 I: ... C') ('D
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
BIBLIOGRAPHY
Abizaid, C., & Coomes, O. T. (2004). Land Use And Forest Fallowing DynamicsIn Seasonally Dry Tropical Forests Of The Southern Yucatan Peninsula,Mexico. Land Use Policy, 21(1), 71-84.
Allen, J. C., & Barnes, D. F. (1985). The Causes Of Deforestation In DevelopingCountries. Annals Of The Association Of American Geographers, 75(2), 163.
Almeida-Guerra, P. (2002). Use Of SPOT Images As A Tool For Coastal ZoneManagement And Monitoring Of Environmental Impacts In The CoastalZone. Optical Engineering, 41, 2144.
Amelung, T., & Diehl, M. (1992). Deforestation Of Tropical Rain Forests:Economic Causes And Impact On Development. TObingen, Germany: J. C.B. Mohr.
Angelsen, A., & Wunder, S. (2003). Exploring The Forest-Poverty Link: KeyConcepts, Issues And Research Implications. Bogor, Indonesia: Center ForInternational Forestry Research (CIFOR).
Angelsen, A., & Kaimowitz, D. (1999). Rethinking The Causes Of Deforestation:Lessons From Economic Models. World Bank Research Observer, 14(1), 73.
Arriaga, L., Montano, M., & Vasconez, J. (1999). Integrated ManagementPerspectives Of The Bahra De Caraquez Zone And Chane River Estuary,Ecuador. Ocean And Coastal Management, 42(2-4), 229. Reprinted withpermission from Elsevier.
Barbier, E. B. (2001). Introduction To The Environmental Kuznets Curve SpecialIssue. Environment And Development Economics, 2(04), 369-381.
Barbier, E. B., & Burgess, J. C. (1996). Economic Analysis Of Deforestation InMexico. Environment And Development Economics, 1(2), 203-240.
Barbier, E. B., & Burgess, J. C. (2001). The Economics Of TropicalDeforestation. Journal Of Economic Surveys, 15(3), 413.
Barbieri, A. F., Bilsborrow, R. E., & Pan, W. K. (2005). Farm HouseholdLifecycles And Land Use In The Ecuadorian Amazon. Population &Environment, 27(1), 1-27.
50
Bhattarai, M., & Hammig, M. (2001). Institutions And The Environmental KuznetsCurve For Deforestation: A Crosscountry Analysis For Latin America, AfricaAnd Asia. World Development, 29(6), 995-1010.
Bilsborrow, R. E. (1987). Population Pressures And Agricultural Development InDeveloping Countries: A Conceptual Framework And Recent Evidence.World Development, 15(2),183-203.
Borja, I., &Williams, G. (2004). The Economic Structure Of Ecuador's Rice AndCorn Markets No. TAMRC International Market Research Report IM-04-04)
Browder, J. O. (1988). Public Policy And Deforestation In The Brazilian Amazon.In R. Repetto, & M. Gillis (Eds.), Public Policies And The Misuse Of ForestResources (Pp. 247-297). Cambridge, England: Cambridge University Press.
Brown, K., & Pearce, D. W. (1994). The Causes Of Tropical Deforestation: TheEconomic And Statistical Analysis Of Factors Giving Rise To The Loss OfThe Tropical Forests. London: University College London Press.
Cabarle, B. J., Crespi, M., Dodson, C. H., Luzuriaga, C., Rose, D., & Shores, J.N. (1989). An Assessment Of Biological Diversity And Tropical Forests ForEcuador. Quito, Ecuador: USAID.
Cannock, G., & Cuadra, V. (1990). Politicas De Ajuste Econ6mico Y Producci6nAgricola En La Selva. Debate Agrario: Analisis Y Alternativas, 9,43-67.
Capistrano, A. D., & Kiker, C. F. (1995). Macro-Scale Economic Influences OnTropical Forest Depletion. Ecological Economics, 14(1),21-29.
Carlton, D. (1956). Long-Run Equilibrium In Tart Cherry Production No. MichiganAgricultural Experiment Station Technical Bulletin 291)
Carman, H., & Kenyon, D. (1969). Economic Aspects Of Producing AndMarketing California Apples No. Research Report 301 )University OfCalifornia, Gianninni Foundation.
Chambers, R., & Leach, M. (1989). Trees As Savings And Security For TheRural Poor. World Development, 17(3),329-342.
Chomitz, K. M., & Thomas, T. S. (2003). Determinants Of Land Use InAmazonia: A Fine-Scale Spatial Analysis. American Journal Of AgriculturalEconomics, 85(4), 1016-1028.
Cropper, M., & Griffiths, C. (1994). The Interaction Of Population Growth AndEnvironmental Quality. American Economic Review, 84(2), 250-254.
51
Cruz, W., & Repetto, R. (1992). The Environmental Effects Of Stabilization AndStructural Adjustment Programs: The Philippines Case. Washington, D.C.:World Resources Institute.
De Koning, G. H. J., Veldkamp, A., & Fresco, L. O. (1998). Land Use In Ecuador:A Statistical Analysis At Different Aggregation Levels. Agriculture,Ecosystems And Environment, 70(2-3),231-247.
De Koning, G. H. J., Veldkamp, A., & Fresco, L. O. (1999). Exploring Changes InEcuadorian Land Use For Food Production And Their Effects On NaturalResources. Journal Of Environmental Management, 57(4), 221-237.
De Koning, G. H. J., Verburg, P. H., Veldkamp, A., & Fresco, L. O. (1999). MultiScale Modelling Of Land Use Change Dynamics In Ecuador. AgriculturalSystems, 61(2), 77-93.
Deacon, R. T. (1995). Assessing The Relationship Between Government PolicyAnd Deforestation. Journal Of Environmental Economics And Management,28(1),1-18.
Dean, G. W., & Heady, E. O. (1958). Changes In Supply Response And ElasticityFor Hogs. Journal Of Farm Economics, 40(4),845-860.
Deininger, K. W., & Minten, B. (1999). Poverty, Policies, And Deforestation: TheCase Of Mexico. Economic Development And Cultural Change, 47(2),313344.
Eckholm, E., Foley, G., Barnard, G., & Timberlake, L. (1984). Firewood: TheEnergy Crisis That Won't Go Away. London, UK: Earthscan.
Ehrhardt-Martinez, K. (1998). Social Determinants Of Deforestation InDeveloping Countries: A Cross-National Study. Social Forces, 77(2), 567586.
Ehrich, R. L. (1969). Cash-Futures Price Relationships For Live Beef Cattle.American Journal Of Agricultural Economics, 51(1), 26-40.
Elnagheeb, A. H., & Bromley, D. W. (1994). Extensification Of Agriculture AndDeforestation: Empirical Evidence From Sudan. Agricultural Economics,10(2), 193-200.
Ezekiel, M. (1938). The Cobweb Theorem. Quarterly Journal Of Economics,52(2), 255-278.
FAO (United Nations Food And Agriculture Organization). (1981). Los RecursosForestales De La America Tropical No. FRA 1980). Rome: FAO.
52
FAO (United Nations Food And Agriculture Organization). (1993). ForestResources Assessment 1990 - Tropical Countries No. FAO Forestry Paper112). Rome: FAO.
FAO (United Nations Food And Agriculture Organization). (1995). ForestResources Assessment 1990 - Global Synthesis No. FAO Forestry PaperNo. 124). Rome: FAO.
FAO (United Nations Food And Agriculture Organization). (1997). State Of TheWorld's Forests 19971\Jo. FAO Forestry Paper 140). Rome: FAO.
FAO (United l\Jations Food And Agriculture Organization). (2001). FRA 2000Main Report No. FAO Forestry Paper 140). Rome: FAO.
FAO (United Nations Food And Agriculture Organization). (2003). State Of TheWorld's Forests 2003. Rome: FAO.
FAO (United Nations Food And Agriculture Organization). (2006). Global ForestResources Assessment 2005, Main Report. Progress Towards SustainableForest Management. No. FAO Forestry Paper 147). Rome: FAO.
FAO (United Nations Food And Agriculture Organization). (2007). State Of TheWorld's Forests 2007. Rome: FAO.
Fearnside, P. M. (2001). Land-Tenure Issues As Factors In EnvironmentalDestruction In Brazilian Amazonia: The Case Of Southern Para. WorldDevelopment, 29(8), 1361-1372.
French, B. C., & Bressler, R. G. (1962). The Lemon Cycle. Journal Of FarmEconomics, 44(4),1021-1036.
Geist, H. J., & Lambin, E. F. (2002). Proximate Causes And Underlying DrivingForces Of Tropical Deforestation. Bioscience, 52(2), 143-150.
GLCF (Global Land Cover Facility). (2008). GLCF: MODIS VegetationContinuous Fields. Retrieved March 25, 2008, fromhttp://www.Iandcover.org/data/vcf/
Gockowski, J. (1997). An Analytical Model Of Deforestation Effects In RelatedMarkets: The Case Of Cocoa, Plantain, And Cocoyam Production In TheASB Cameroon Benchmark. Yaounde, Cameroon: International Institute ForTropical Agriculture.
Grainger, A. (1996). An Evaluation Of The FAO Tropical Forest ResourceAssessment, 1990. The Geographical Journal, 162(1)
53
Grainger, A. (2008). Difficulties In Tracking The Long-Term Global Trend InTropical Forest Area. Proceedings Of The National Academy Of Sciences OfThe United States Of America, 105(2),818-823.
Hansen, M., R. DeFries, J.R. Townshend, M. Carroll, C. Dimiceli, and R.Sohlberg (2006), Vegetation Continuous Fields MOD44B, 2000-2005Percent Tree Cover, Collection 3, University of Maryland, College Park,Maryland, 2006.
Harden, C. P. (1996). Interrelationships Between Land Abandonment And LandDegradation: A Case From The Ecuadorian Andes. Mountain Research AndDevelopment, 16(3),274-280.
Harlow, A. A. (1960). The Hog Cycle And The Cobweb Theorem. Journal OfFarm Economics, 42(4), 842-853.
Heady, E. 0., Baker, C., Diesslin, H., Kehrberg, E., & Staniforth, S. (1961).Preface. Agricultural Supply Functions (Heady, E.O.; Baker, C.; Diesslin, H.;Kehrberg, E.; Staniforth, S. Ed.,). Ames: Iowa State University Press.
Heath, J., & Binswanger, H. (1996). Natural Resource Degradation Effects OfPoverty And Population Growth Are Largely Policy-Induced: The Case OfColombia. Environment And Development Economics, 1(1),65-84.
Hecht, S. B. (1985). Environment, Development And Politics: CapitalAccumulation And The Livestock Sector In Eastern Amazonia. WorldDevelopment, 13(6),663-684.
INEC-MAG-SICA. (2002). III Censo Nacional Agropecuario: ResultadosProvinciales y Cantonales; Manabi. Instituto Nacional de Estadfsticas yCensos - Ministerio de Agricultura-SICA, Quito-Ecuador.
Inman, K. (1993). Fueling Expansion In The Third World: Population,Development, Debt, And The Global Decline Of Forests. Society And NaturalResources, 6(1), 17-39.
Jokisch, B. D. (2002). Migration And Agricultural Change: The Case OfSmallholder Agriculture In Highland Ecuador. Human Ecology, 30(4), 523550.
Jokisch, B. D., & Lair, B. M. (2002). One Last Stand? Forests And Change OnEcuador's Eastern Cordillera. The Geographical Review, 92(2), 235-257.
Kaimowitz, D., & Angelsen, A. (1998). Economic Models Of TropicalDeforestation: A Review. Bogor, Indonesia: Center For InternationalForestry.
54
Kaldor, N. (1934). A Classificatory Note On The Determinateness Of Equilibrium.Review Of Economic Studies, 1(2), 122-36.
Keating, P. L. (1997). Mapping Vegetation And Anthropogenic Disturbances InSouthern Ecuador With Remote Sensing Techniques: Implications For ParkManagement. Yearbook, Conference Of Latin Americanist Geographers, 23,77-90.
Koop, G., &Tole, L. (1999). Is There An Environmental Kuznets Curve ForDeforestation. Journal Of Development Economics, 58(1),231-244.
Lambin, E. F., Geist, H. J., & Lepers, E. (2003). Dynamics Of Land-Use AndLand-Cover Change In Tropical Regions. Annual Review Of EnvironmentAnd Resources, 28(1), 205-241.
Lambin, E. F., Turner, B. L., Geist, H. J., Agbola, S. B., Angelsen, A., Bruce, J.W., Et AI. (2001). The Causes Of Land-Use And Land-Cover Change:Moving Beyond The Myths. Global Environmental Change, 11(4),261-269.
Latin American Centre at Oxford University. (2008). Oxford Latin AmericanEconomic History Database. Retrieved March 25, 2008, fromhttp://oxlad.qeh.ox.ac.uk/
Lutz, E. (1998). Agriculture And The Environment: Perspectives On SustainableRural Development World Bank Publications.
MAG (Ministerio de Agricultura, Ganaderfa, Acuacultura y Pesca de Ecuador).(2008). Foro de dialogo de Mafz. Retrieved March 25, 2008, fromhttp://www.sica.gov.ec/cadenas/maiz/index.html
Mahar, D. J. Government Policies And Deforestation In Brazil's Amazon Region.Washington, D.C.: World Bank Publications.
Marquette, C. M. (1998). Land Use Patterns Among Small Farmer Settlers In TheNortheastern Ecuadorian Amazon. Human Ecology, 26(4), 573-598.
Mena, C. F., Bilsborrow, R. E., & Mcclain, M. E. (2006). Socioeconomic DriversOf Deforestation In The Northern Ecuadorian Amazon. EnvironmentalManagement, 37(6), 802-815.
Moran, E. F., Siqueira, A., & Brondizio, E. (2006). Household DemograpbicStructure And Its Relationship To Deforestation In The Amazon Basin. In J.Fox, R. Rindfuss, S. Walsh & V. Mishra (Eds.), Moran Et AI 2006; PeopleAnd The Environment: Approaches For Linking Household And CommunitySurveys To Remote Sensing And GIS (Pp. 61-89). Boston: Kluwer AcademicPublishers.
55
Muth, J. F. (1961). Rational Expectations And The Theory Of Price Movements.Econometrica, 29(3), 315-335.
Nepstad, D., Mcgrath, D., Alencar, A., Barros, A. C., Carvalho, G., Santilli, M., EtAI. (2002). Environment. Frontier Governance In Amazonia. Science,295(5555), 629-631.
Nerlove, M. (1956). Estimates Of The Elasticities Of Supply Of SelectedAgricultural Commodities. Journal Of Farm Economics, 38(2), 496-509.
Nerlove, M. (1958). The Dynamics Of Supply: Estimation Of Farmers' ResponseTo Price. Johns Hopkins Press, Baltimore.
Nerlove, M., & Bachman, K. L. (1960). The Analysis Of Changes In AgriculturalSupply: Problems And Approaches. Journal Of Farm Economics, 42(3), 531552.
Ocampo, J. A., & Parra, M. A. (2003). Los Terminos De Intercambio De LosProductos Basicos En EI Siglo XX. Revista De La CEPAL, 79
Pan, W. K. Y., & Bilsborrow, R. E. (2005). The Use Of A Multilevel StatisticalModel To Analyze Factors Influencing Land Use: A Study Of The EcuadorianAmazon. Global And Planetary Change, 47(2-4), 232-252.
Pan, W. K. Y., Walsh, S. J., Bilsborrow, R. E., Frizzelle, B. G., Erlien, C. M., &Baquero, F. (2004). Farm-Level Models Of Spatial Patterns Of Land UseAnd Land Cover Dynamics In The Ecuadorian Amazon. Agriculture,Ecosystems And Environment, 101(2-3),117-134.
Panayotou, T., & Sungsuwan, S. (1994). An Econometric Analysis Of TheCauses Of Tropical Deforestation: The Case Of Northeast Thailand. In K.Brown, & D. Pear (Eds.), The Causes Of Tropical Deforestation: TheEconomic And Statistical Analysis Of Factors Giving Rise To The Loss OfThe Tropical Forests (Pp. 192-209). London: UCL Press.
Pendleton, L. H., & Howe, E. L. (2002). Market Integration, Development, AndSmallholder Forest Clearance. Land Economics, 78(1), 1.
Perrings, C. (1989). An Optimal Path To Extinction? Poverty And ResourceDegradation In The Open Agrarian Economy. Journal Of DevelopmentEconomics, 30(1), 1-24.
Perz, S. G. (2001). Household Demographic Factors As Life Cycle DeterminantsOf Land Use In The Amazon. Population Research And Policy Review,20(3), 159-186.
56
Perz, S. G. (2004). Are Agricultural Production And Forest ConservationCompatible? Agricultural Diversity, Agricultural Incomes And Primary ForestCover Among Small Farm Colonists In The Amazon. World Development,32(6),957-977.
Perz, S. G. (2005). The Effects Of Household Asset Endowments On AgriculturalDiversity Among Frontier Colonists In The Amazon. Agroforestry Systems,63(3), 263-279.
Perz, S. G., &Walker, R. T. (2002). Household Life Cycles And SecondaryForest Cover Among Small Farm Colonists In The Amazon. WorldDevelopment, 30(6), 1009-1027.
Perz, S. G., Walker, R. T., & Caldas, M. M. (2006). Beyond Population AndEnvironment: Household Life Cycle Demography And Land Use AllocationAmong Small Farm Colonists In The Amazon. Human Ecology, 34(6), 829849.
Pichon, F., Marquette, C., Murphy, L., & Bilsborrow, R. (2002). EndogenousPatterns And Processes Of Settler Land Use And Forest Change In TheEcuadorian Amazon. In C. Wood, & R. Porro (Eds.), Deforestation And LandUse In The Amazon (Pp. 241-282). Gainesville: University Press Of Florida.
Pich6n, F. J. (1997). Settler Households And Land-Use Patterns In The AmazonFrontier: Farm-Level Evidence From Ecuador. World Development, 25(1),67-91.
Rich, B. (1994). Mortgaging The Earth: The World Bank, EnvironmentalImpoverishment, And The Crisis Of Development. Boston, Mass.: BeaconPress.
Ricks, D. J., & Edwards, J. A. (1966). Long-Run Projections Of Bartlett PearPrices And Production. Corvallis: Agricultural Experiment Station, OregonState University.
Rodrfguez-Meza, J., Southgate, D., & Gonzalez-Vega, C. (2004). Rural Poverty,Household Responses To Shocks, And Agricultural Land Use: Panel ResultsFor EI Salvador. Environment And Development Economics, 9(02), 225-239.
Rudel, T. (1989). Population, Development, And Tropical Deforestation: A CrossNational Study. Rural Sociology, 54(3), 327-338.
Rudel, T. (1994). Population, Development And Tropical Deforestation: A CrossNational Study. The Causes Of Tropical Deforestation: The Economic AndStatistical Analysis Of Factors Giving Rise To The Loss Of The TropicalForests,
57
Rudel, 1., & Horowitz, B. (1993). Tropical Deforestation: Small Farmers AndLand Clearing In The Ecuadorian Amazon. !\Jew York: Columbia UniversityPress.
Rudel, 1., & Roper, J. (1997). The Paths To Rain Forest Destruction:Crossnational Patterns Of Tropical Deforestation, 1975-90. WorldDevelopment, 25(1),53-65.
Rudel, 1. K., Bates, D., & Machinguiashi, R. (2002). A Tropical Forest Transition?Agricultural Change, Out-Migration, And Secondary Forests In TheEcuadorian Amazon. Annals Of The Association Of American Geographers,92(1),87-102.
Shafik, N. (1994). Economic Development And Environmental Quality: AnEconometric Analysis. Oxford Economic Papers, New Series, 46, 757-773.
SICA ProjecVMAG (Servicio de Informacion y Censo Agropecuario de Ecuador/Ministerio de Agricultura, Ganaderfa, Acuacultura y Pesca de Ecuador).(2003). Mafz Duro Amarillo - Tercer Censo Nacional Agropecuario. ConsejoConsultivo de Mafz y Avicultura. Agosto 2003.
Sierra, R. (1996). La Deforestaci6n En EI Noroccidente Del Ecuador 1983-1993.Quito, Ecuador: Ecociencia.
Sierra, R. (2000). Dynamics And Patterns Of Deforestation In The WesternAmazon: The Napo Deforestation Front, 1986-1996. Applied Geography,20( 1), 1-16.
Sierra, R. (2002). Traditional Resource-Use Systems And Tropical DeforestationIn A Multi-Ethnic Region In North-West Ecuador. EnvironmentalConservation, 26(02), 136-145.
Sierra, R., & Stallings, J. (1998). The Dynamics And Social Organization OfTropical Deforestation In Northwest Ecuador, 1983-1995. Human Ecology,26(1),135-161.
Southgate, D. (1994). Tropical Deforestation And Agricultural Development InLatin America. In K. Brown, & D. W. Pearce (Eds.), The Causes Of TropicalDeforestation: The Economic And Statistical Analysis Of Factors Giving RiseTo The Loss Of The Tropical Forests (Pp. 134-143). London: UniversityCollege London Press.
Stenberg, L. C., & Siriwardana, M. (2005). The Appropriateness Of CGEModelling In Analysing The Problem Of Deforestation. Management OfEnvironmental Quality, 16(5),407-420.
58
Sunderlin, W. D., Angelsen, A, Belcher, B., Burgers, P., Nasi, R., Santoso, L., EtAI. (2005). Livelihoods, Forests, And Conservation In Developing Countries:An Overview. World Development, 33(9), 1383-1402.
Sunderlin, W. D., & Wunder, S. (2000). The Influence Of Mineral Exports On TheVariability Of Tropical Deforestation. Environment And DevelopmentEconomics, 5(03), 309-332.
Turner, B. L., Geoghegan, J. M., & Foster, D. R. (2004). Integrated Land-ChangeScience And Tropical Deforestation In The Southern Yucatan: Final FrontiersOxford University Press, USA
Vina, A, Echavarria, F. R., & Rundquist, D. C. (2004). Satellite Change DetectionAnalysis Of Deforestation Rates And Patterns Along The Colombia-EcuadorBorder. Ambio, 33(3), 118-125.
Walker, R. (1993). Deforestation And Economic Development. Canadian JournalOf Regional Science, 16(3),481-497.
WRI (World Resources Institute). (1992). World Resources, 1992-93. New York:Oxford University Press.
WRI (World Resources Institute). (1994). World Resources, 1994-95. New York:Oxford University Press.
Wunder, S. (1996). Deforestation And The Uses Of Wood In The EcuadorianAndes. Mountain Research And Development, 16(4),367-382.
Wunder, S. (2000). The Economics Of Deforestation: The Example Of Ecuador.St. Antony's Series, London: Macmillan/St. Martin's.
Wunder, S. (2001). Poverty Alleviation And Tropical Forests-What Scope ForSynergies? World Development, 29(11), 1817-1833.
Wunder, S. (2001 b). Deforestation And Economics In Ecuador: A Synthesis.Forestry,
Wunder, S. (2003). Oil Wealth And The Fate Of The Forest: A ComparativeStudy Of Eight Tropical Countries. London & New York: Routledge.
59