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Applied Geography 40 (2013) 40e50

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Applied Geography

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Landscape change in Guatemala: Driving forces of forest and coffeeagroforest expansion and contraction from 1990 to 2010

Mikaela Schmitt-Harsh*

School of Public and Environmental Affairs, Indiana University, 408 N. Indiana Avenue, Bloomington, IN 47408, USA

Keywords:Coffee agroforestsDeforestationLand transitionsLand-use/cover changeDrivers of change

* Present address: Carleton College, EnvironmentaSt., Northfield, MN 55057, USA. Tel.: þ1 507 222 7822

E-mail address: mschmitt@carleton.edu.

0143-6228/$ e see front matter � 2013 Elsevier Ltd.http://dx.doi.org/10.1016/j.apgeog.2013.01.007

a b s t r a c t

This study examines the land-use/cover change (LUCC) dynamics and drivers for two prominent land-use/cover systems in Guatemala: natural forests (FOR) and coffee agroforests (CAF). To-date, very littleresearch has examined the LUCC dynamics of CAF, in large part due to the high degree of spectralsimilarity that exists between agroforests and other forest-cover types. Given the ecosystem and live-lihood services provided by shade-grown coffee production, it is increasingly necessary to map andidentify the dynamics and drivers of CAF changes over space and time. This research uses remote sensinganalysis, land transition matrices, and multinomial regression models to examine LUCC dynamics overtwo ten-year intervals (1990e2000; 2000e2010) in Guatemala. Spatially explicit biophysical (e.g. slope,elevation) and accessibility (e.g. distance to roads) factors are used to model and compare drivers ofchange for CAF and FOR. Results demonstrate LUCC dynamics and drivers for the two land-use/coversystems to be complex over space and time. For example, FOR losses are evident for both time in-tervals, largely associated with conversion to CAF and croplands (CPL) in low slope, low altitude areas,and in areas close to existing croplands, respectively. CAF losses are also evident in the 1990s, but areoutpaced by expansion in the 2000s. Losses are associated with conversion to CPL, particularly near roadsand existing croplands, while expansion and/or persistence of CAF occurs near cities. These resultssuggest that conservation programs aimed at tree cover preservation and expansion should considernatural forests and managed agroforests separately. Further, such programs should be tailored to specificlocations and institutional settings given the influence of topography and accessibility factors in deter-mining localized patterns of landscape transformations over space and time.

� 2013 Elsevier Ltd. All rights reserved.

Introduction

Given rapid rates of deforestation in Central America, on theorder of 300,000 ha per year (FAO, 2011), conservation policy isincreasingly broadening to include human-dominated landscapessuch as managed forests and agroforestry systems. In CentralAmerica, one of the most prominent and economically importantagroforestry systems is shade-grown coffee (“coffee agroforests”).Grown on over 9.8 million ha of land worldwide (FAO, 2009) andtraditionally cultivated under a canopy of shade trees, coffeeagroforests have functional similarities to natural forest systems,and provide important ecological services such as biodiversity,carbon sequestration, reduced soil erosion, flood control, andmicroclimatic buffering (Beer, Muschler, Kass, & Somarriba, 1998;

l Studies, One North College.

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Dossa, Fernandes, & Reid, 2008; Lin, 2007; Lin, Perfecto, &Vandermeer, 2008; Perfecto, Rice, Greenberg, & van der Voort,1996; Perfecto, Armbrecht, Philpott, Soto-Pinto, & Dietsch, 2007;Schmitt-Harsh, Evans, Castellanos, & Randolph, 2012; Soto-Pinto,Anzueto, Mendoza, Ferrer, & de Jong, 2010; Soto-Pinto, Romero-Alvarado, Caballero-Nieto, & Warnholtz, 2001).

Despite their contribution to ecological functioning and servic-ing, and their importance to conservation efforts, many coffeeagroforestry systems have undergone extensive changes since the1980s, with loss of shade trees and/or conversion to maize, beans,or other agricultural land use noted in many Latin Americancountries (Ávalos-Sartorio & Blackman, 2010; Eakin, Tucker, &Castellanos, 2005, 2006; Ellis, Baerenklau, Marcos-Martínez, &Chávez, 2010). Agronomic and commercial forces, in particular,have played integral roles in altering coffee landscapes. For exam-ple, the arrival of the coffee leaf rust, Hemileia vastatrix Berk., inCentral America and parts of South America led to modernizationefforts that promoted new high-yielding varieties, the removal ofshade, and increased density of coffee bushes (Jha et al., 2011).

Fig. 1. The study region is the Department of Sololá, Guatemala, and its 19 municipalities, between the UTM northings of 1,606,500 m and 1,648,000 m and eastings of 661,000 mand 708,000 m (UTM Zone 15N).

1 Land-use/cover change denotes the alteration of land in form (land cover) orfunction (land use). Broadly speaking, land cover refers to the physical and bioticcharacter of Earth’s surface and immediate subsurface, while land use refers to thehuman employment of the land (Meyer & Turner, 1992). Natural forests are com-monly defined as a land cover (though specific uses vary widely), whereas agro-forests are commonly (though not exclusively) defined as a type of land use underthe broad land-cover category of cultivation (Meyer & Turner, 1992; Nair, 1989).

M. Schmitt-Harsh / Applied Geography 40 (2013) 40e50 41

Backed by monetary assistance from the United States Agency forInternational Development (USAID), and the establishment ofPromecafe, the “open-to-sun” modernization movement aimed todiminish the spread of the rust and improve production effi-ciencies. However, even where coffee leaf rust was not expected topose significant problems (e.g. higher elevation areas due to coolertemperatures), landscape-level transformations were widespread.Approximately 40% of Latin American shaded coffee farms were“technified” or converted to sun coffee, a conversion of which hasbeen likened towidespread deforestation of agricultural lands (Rice& Ward, 1996).

More recently, record low international coffee prices between1999 and 2003, in combination with repeated droughts, havecontributed to dramatic declines in employment in the CentralAmerican coffee sector in a period known as the “coffee crisis”(Bacon, 2005; Tucker, Eakin, & Castellanos, 2010). Coffee growershistorically and currently face a number of uncertainties in theproduction of coffee, from the overproduction of lower-qualitycoffee which threatens market stability (Ponte, 2002; Rice, 2003),to climatic changes in Central America trending toward increasedtemperature (Magrin et al., 2007), reduced precipitation (Magrinet al., 2007; Neelin, Münnich, Su, Meyerson, & Holloway, 2006),and increased frequency of extreme storm events (Emanuel, 2005;Webster, Holland, Curry, & Chang, 2005). Those most vulnerable tothese stressors are smallholder coffee producers given their eco-nomic disposition and lack of access to resources (Eakin et al.,2006), and in Central America, an estimated 85% of coffee pro-ducers are smallholders who farm less than 10 ha each (Flores,Bratescu, Martínez, Oviedo, & Acosta, 2002).

A vast amount of literature exists on smallholders’ responses tothe coffee crisis, particularly in Mexico (e.g. Ávalos-Sartorio &

Blackman, 2010; Eakin et al., 2006; Hausermann & Eakin, 2008;Lewis, 2005; Martínez-Torres, 2004, 2008). Much of this researchhas highlighted the extent to which land-use decisions have beenmediated by complex social-ecological factors, such as culturalidentity, remittances, educational opportunities, institutions, landtenure, and access to niche markets. Land-use outcomes have cor-respondingly ranged from abandonment of coffee plantations,conversion of coffee to other agricultural uses, renting or selling ofland, migration, and increased dependence on off-farm labor forincome (Ávalos-Sartorio & Blackman, 2010; Blackman, Ávalos-Sartorio, & Chow, 2007; Ellis et al., 2010; Gordon, Manson,Sundberg, & Cruz-Angón, 2007). To-date, data on such land-useoutcomes have often been gathered from survey or field data ratherthan remotely sensed imagery, likely due to difficulties in linkingpeople with pixels (Rindfuss, Walsh, Turner, Fox, & Mishra, 2004),and difficulties in accurately mapping and identifying coffee giventhe high degree of spectral similarity that exists between coffee andother woody cover types (Bolanos, 2007; Cordero-Sancho & Sader,2007; Langford & Bell, 1997). To that end, very little empiricalwork has examined the land-use/cover change (LUCC)1 dynamics ofcoffee agroforests using remote sensing methodologies.

Among the fewexisting studies that use remotely sensed imagery,case analysis has demonstrated the importance of environmental

2 Corrections for differential illumination were also employed using the non-Lambertian Minnaert model, as described by Colby and Keating (1998). However,the topographic normalization procedure resulted in over-corrected pixels inmultiple sections of each image; therefore the uncorrected images were used tominimize biases.

M. Schmitt-Harsh / Applied Geography 40 (2013) 40e5042

variables and access to roads and markets in mediating LUCC. Forexample, econometric modeling in Oaxaca, Mexico shows shadecoffee and tree cover preservation to be associatedwith proximity toroads and large citieswith coffeemarkets (Blackman, Albers, Ávalos-Sartorio, & Murphy, 2008). Contrastingly, conversion of shade coffeeplots occurred in lower elevations and areas close to smaller townswithout coffee markets (Blackman et al., 2008). Environmental var-iables, such as slope and elevation, were also found to be importantdeterminants of LUCC in shade coffee-growing regions of Veracruz,Mexico (Ellis et al., 2010) andEl Salvador (Blackman, Ávalos-Sartorio,& Chow, 2012),with areas lower in elevation and slopemore prone tocoffee and tree cover loss for the purpose of expanding other agri-cultural or pastoral uses.

The combination of research efforts above suggests a complexset of factors influencing coffee production and LUCC over spaceand time. For example, while biophysical characteristics (e.g. ele-vation, slope, soil type) may initially signal the suitability (or lack ofsuitability) for coffee production in a given area, the persistence ortransformation of coffee agroforests in that area likely involvescomplex assemblages of demographic, economic, institutional,cultural, and technological factors. Such patterns may converge ordiverge from patterns and processes associated with natural forest-cover changes (e.g. Geist & Lambin, 2001, 2002), a finding withconsequences for the design and establishment of conservation-based programs aimed at forest recovery and maintenance (ofboth natural and managed forests). Thus, the development ofeffective conservation programs requires knowledge of the spatialextent of LUCC for natural forests and coffee agroforests, as well asthe prominent forces contributing to LUCC over space and time.

The researchpresentedhere examines the rates andmagnitudes ofLUCC, as well as drivers of change, within a coffee-forest landscape ofGuatemala. The central research questions are as follows: (1) Howhave forests and coffee agroforests changed spatially over time? (2)What biophysical and accessibility factors contribute to forest andcoffee agroforest LUCC over time? Focus is given to biophysical (e.g.slope, elevation, aspect) andaccessibility (e.g. distance to city, distanceto road) factors given research documenting the importance of thesevariables in determining LUCC in Mexico and El Salvador (e.g.Blackman et al., 2008, 2012; Ellis et al., 2010). This research usesremote sensing analysis and multinomial regression models toexamine LUCC over two ten-year periods (1990e2000; 2000e2010).The two time intervals are highly divergent in terms of internationalcoffee markets and prices paid to Guatemalan coffee growers. Forexample, in the1990s, international coffeepricespaid togrowerswerehighly volatile and declined precipitously from themid-1990s to early2000s (i.e. the “coffee crisis”). Contrastingly, in the 2000s, coffee pricespaid to coffee growers increased dramatically (ICO, 2012) alongsideheightened demand for specialty coffee brands (Bacon, 2005).

Coffee has consistently played an integral role in Guatemala’snational economy, representing between 6.6% and 13% of Guate-mala’s GDP, and generating between 30% and 35% of foreign ex-change over the last 20 years (Heidkamp, Hanink, & Cromley, 2008).There are eight major coffee-growing regions in Guatemala, cover-ing approximately 2.3% of the country’s total surface area, and anestimated 98% of the country’s coffee grows beneath a canopy ofshade (though the extent and diversity of shade cover varies widely(ANACAFÉ, 2008; FAO, 2009)). Given the economic and ecologicalimportance of shade coffee, understanding past drivers of LUCCduring periods of unstable and stable market conditions is integralto understanding potential future trajectories of change.

Study area

The study area is the Department of Sololá (Fig. 1), located in theSierra Madre Volcanoes region of the western highlands of

Guatemala. The Department covers an area of 1170 km2, and ismarked by heterogeneous topography with elevations rangingfrom 628 m to 3524 m ASL, and slopes ranging from 0� to 75�.Annual rainfall and temperature averages 2504 mm and 18e24 �Cthough there is high variability associated with altitudinal gradi-ents. Soils in the region are primarily andisols, entisols, and ultisolsformed from volcanic ash (Dix, Fortín, & Medinilla, 2003; MAGA,2002; Simmons, Tarano, & Pinto, 1959). Three volcanoes are loca-ted in the Department of Sololá including San Pedro, Tolimán, andAtitlán, and the natural vegetation in the region is a mix ofbroadleaf, coniferous, and mixed broadleaf and coniferous forests.Most of the forests in the western highlands of Guatemala areunder a mix of indigenous communal and municipal land tenures(Wittman & Geisler, 2005). Privately owned lands are largelydeforested, though in Guatemala more broadly, approximately 40%of existing forests are under private ownership (FAO, 2004). Currentpolicy initiatives, particularly those stemming from the 1996 PeaceAccords, have attempted to increase forest production and promotesustainable natural resource management for communal and pri-vate forests. Further, increased local autonomy has been empha-sized, giving municipalities greater control over monitoring andmanaging forest resources (Gibson & Lehoucq, 2003).

The Department of Sololá contains 19 municipalities (Fig. 1). Thepopulation of the Department, according to 2011 projections,equaled w437,000 marking a 2.9% annual rate of increase from2000 to 2011 (INE, 2011). The majority of the population is indig-enous Maya (96.2%) and the region, situated within the “povertybelt”, is one of the poorest in the country (World Bank, 2004).Coffee is themost important crop economically, thoughmany othercash and subsistence crops are grown including maize, beans, po-tatoes, cardamom, banana, and rubber. Most of the coffee growersin the region employ shade cover in which the canopy is composedof timber, fruit, medicinal, and leguminous species (personalobservation). Coffee farms are generally privately owned, thoughland tenure has historically been insecure, particularly for theindigenous population (Elías & Wittman, 2005). Most of the coffeegrowers in the region are smallholders who farm less than two haof land. In the 2005/06 growing season, there were approximately6400 coffee farms in the Department of Sololá, producingapproximately 638,200 quintales (where 1 quintal ¼ w100 kg) ofcoffee cherries (INE, 2005).

Methodology

Remote sensing analysis

Landsat 5 TM satellite images were obtained for January 1990,February 2000, and February 2010. Images were selected based onminimal cloud cover and month, as JanuaryeMarch represents thedry season in Guatemala when agricultural fields are most easilydistinguished from forests. The images were georeferenced toa 1:50,000 topographic map (5 m resolution). An overlay functionverified that the images overlapped exactly across the three imagedates. Following rectification, all images were corrected for varia-tions in solar angle and atmospheric condition.2 The remotelysensed DN values for TM bands 1e5 and 7 were first converted toat-satellite radiance to correct for sensor gains and offsets (Chander& Markham, 2003; Chander, Markham, & Barsi, 2007). At-satellite

M. Schmitt-Harsh / Applied Geography 40 (2013) 40e50 43

radiance was converted to at-surface reflectance by correcting forsolar angle and the atmosphere using a dark-object subtractionmodel (Chavez,1989,1996; Teillet & Fedosejevs, 1995). Finally, band6 (thermal band), resampled to 30 m � 30 m, was converted to at-satellite radiance as described above, and then converted to effec-tive at-satellite temperature (Chander & Markham, 2003; Chanderet al., 2007). The thermal waveband was re-stacked with the cali-brated multispectral data for further processing.

Supervised classifications using the maximum likelihood algo-rithm in ERDAS Imagine were used to generate five land-use/coverclasses for all images: (a) mature and successional forests (FOR),coffee agroforests (CAF), crops and pasture lands (CPL), banana andrubber plantations (BRP), and urban/rural settlements (URB).Waterand clouds were excluded and masked out equally in all classifiedimages. Lake Atitlán has a surface area of w127 km2, and togetherwith cloud cover, the masked area amounted to 14.5% of the totalpixels in the image.

Because the classification of coffee agroforests using Landsatimagery is complicated by the high degree of spectral similaritybetween coffee and other woody cover types (Bolanos, 2007;Cordero-Sancho & Sader, 2007; Langford & Bell, 1997), this researchused high-resolution (0.5 m � 0.5 m) aerial photographs to collecta large number of well-distributed training datasets. Coffee agro-forests were identifiable from the aerial photographs enabling theselection of high quality training data. In total, 618 training siteswere selectedwithin the image footprint, with spectra extracted forFOR (n ¼ 199), CAF (n ¼ 104), CPL (n ¼ 215), BRP (n ¼ 40), and URB(n ¼ 60). Given the temporal gap between the acquisition date(2006) of aerial photographs and the image acquisition dates(1990; 2000; 2010), the set of spectral signatures associated witheach land-cover class was evaluated for potential change in landcover at the training site. Outlier signatures were removed, theremainder used to train a maximum likelihood classifier.

The accuracy of each classified image was based on GPS pointscollected in the field in 2009 and 2010. GPS point locations weretaken in homogeneous areas of FOR (n ¼ 39), CAF (n ¼ 70), CPL(n ¼ 29), and URB (n ¼ 15). Coordinates were collected usinga Garmin Oregon 400t GPS receiver and integrated into ArcGIS foruse in the accuracy assessment. Because some areas within theimage footprint were not travel-accessible, additional validationpoints were selected using the aerial photographs (n ¼ 218), cre-ating a total of 371 points for use in the accuracy assessment.

Accuracy assessments using the Kappa-Cohen method wereconducted on each classified image (Congalton, 1991; Congalton &Green, 2009). Error matrices were developed, and producer’s accu-racy (PA) anduser’s accuracy (UA) calculated (see Table 1 for the 2010error matrix; the 1990 and 2000 error matrices are available assupplementary material). Given the temporal gap between the datethe ground control points were collected (2009/2010) and theLandsat image acquisition dates (e.g. 1990, 2000), a higher classi-fication accuracy was obtained for the 2010 classification (89.5%;

Table 1Error matrix for the 2010 classified image.

Classified data Reference data

FOR CAF CPL BRP URB Row total UA%a

FOR 97 10 0 1 0 108 89.81CAF 8 98 0 0 0 106 92.45CPL 0 1 77 2 3 83 92.77BRP 0 10 1 17 0 28 60.71URB 0 0 2 0 34 36 94.44Column total 105 119 80 20 37 361PA% a 92.38 82.35 96.25 85.00 91.89

a PA and UA represent producer’s accuracy and user’s accuracy, respectively, foreach land-cover class.

Kappa ¼ 0.8598) than the 2000 classification (85.3%;Kappa¼ 0.8049) and1990 classification (75.9%;Kappa¼ 0.6788). Forall image classifications, the most prominent error stemmed frommisclassification of BRPasCAF.However, this errorwasminimizedasBRP comprised a small percentage of the total land cover (w1%)within the administrative departmental boundary (Department ofSololá), and all classified images were subset to the departmentalboundary for the purpose of documenting LUCC.

Net change and patterns of LUCC

Land transition matrices for the three dominant land-coverclasses (FOR, CAF, CPL) were developed for each time interval(1990e2000; 2000e2010) (see Tables 4 and 5). Transition matriceswere used to document the area or proportion of the landscape thattransitioned from class i to class j between two consecutive images(Pontius, Shusas, & McEachern, 2004). For example, in Table 4, theoff-diagonal bolded values (Cij) represent the proportion of the landclass that changed from 1990 to 2000. The main diagonal elements(Cjj) indicate the proportion of the landscape that persisted overtime. The total column (Ciþ) denotes the proportion of the land-scape occupied by class i in 1990 (or 2000 in Table 5), calculatedusing Equation (1). Similarly, the total row (Cþj) denotes the pro-portion of the landscape occupied by class j in 2000 (or 2010 inTable 5), calculated using Equation (2).

Ciþ ¼Xni¼1

Cij; where isj (1)

Cþj ¼Xnj¼1

Cij; where isj (2)

From this matrix, the gross gains, gross losses, net change, andswap change in each LUCC category were examined (Braimoh, 2006;Pontius et al., 2004) (see Table 3). The gross gain and loss for eachland category were derived by subtracting the diagonal entries (Cjj)from the column and row totals, respectively. Net change, synony-mous with a change in quantity, was calculated as the differencebetween gross gains and losses. Swap change, synonymous witha change in location, was calculated as the total change (grossgains þ gross losses) minus the net change for each category(Braimoh, 2006; Romero-Ruiz, Flantua, Tansey, & Berrio, 2012).

An in-depth analysis of the classic transition matrix was used toseparate random and systematic transitions in each time interval(1990e2000; 2000e2010) (see Tables 4 and 5). Landscape transi-tions were assumed to be random if land categories gained (or lost)from other categories in proportion to the availability of otherlosing (or gaining) categories (Romero-Ruiz et al., 2012). Any largedeviation from such proportions was deemed a systematic transi-tion. Therefore, values close to zero indicate random landscapetransitions, whereas values farther from zero indicate more sys-tematic transitions (Nakakaawa, Vedeld, & Aune, 2011; Pontiuset al., 2004). The expected gain (Gij) of each transition undera random process of gainwas estimated using Equation (3), and theexpected loss (Lij) of each transition under a random process of losswas estimated using Equation (4) (Manandhar, Odeh, & Pontius,2010; Pontius et al., 2004).

Gij ¼ Pþj � PjjPiþ

100� Pjþ

!; where isj (3)

where Gij is the expected transition from category i to j underrandom processes of gain, (Pþj � Pjj) is the observed gain for

M. Schmitt-Harsh / Applied Geography 40 (2013) 40e5044

category i, Piþ is the row total for category i, Pjþ is the row total forcategory j, and (100 � Pþi) represents the sum of row totals acrossall categories except the category j.

Lij ¼ Piþ � PiiPþj

100� Pþi

� �; where isj (4)

where Lij is the expected transition from category i to j underrandom processes of loss, (Piþ � Pii) is the observed loss for categoryi, Pþj is the column total for category j, Pþi is the column total forcategory i, and (100 � Piþ) represents the sum of column totalsacross all categories except the category i.

The off-diagonal unbolded values in Tables 4 and 5 represent theexpected gains (Gij) and losses (Lij) of each land-use/cover transi-tion. Numbers in parentheses represent the difference betweenobserved (bolded off-diagonal) and expected (unbolded off-diagonal) proportions of land cover under random processes ofgain and loss, calculated as Pij � Gij and Pij � Lij respectively. Wherethe gain difference is positive, the category in that row lost more tothe category in the column than would be expected under randomprocesses of gain for that column category. Where negative, thecategory in that row lost less than expected. Similarly, where theloss difference is positive, the category in that column gained morefrom the category in the row than would be expected under ran-dom loss processes. Where negative, the category in that columngained less than expected (Manandhar et al., 2010).

Analysis of the drivers of change

The drivers of LUCC were analyzed based on multinomial lo-gistic regression models derived from overlaying and relating theobserved LUCC transitions between 1990 and 2000, and 2000 and2010, to spatially explicit ancillary data. In determining the influ-ence of independent variables on forest transitions, the reference orbaseline outcome e unchanged FORewas compared to FOR to CAFtransitions and FOR to CPL transitions (Model 1). Similarly, un-changed CAF was compared to CAF to FOR transitions and CAF toCPL transitions (Model 2). Both model runs were therefore three-outcome category models, whereby the unchanged land-coverclass served as the reference (Y ¼ 0), and the transitions served asalternative outcomes, coded as Y ¼ 1 and Y ¼ 2.

Table 2Covariate and dependent variables in the multinomial logistic regression models

Variables

Dependent variableConversion classes (1990e2000; 2000e2010)Model 1: unchanged forest/forest to coffee/forest to croplandModel 2: unchanged coffee/coffee to forest/coffee to croplandModel 3: unchanged cropland/cropland to forest/cropland to coffee

Independent variablesBiophysicalSLOPE SlopeELEV ElevationN_FACE Aspect (S-facing/N-facing)SOILT_1 Soil type: andisolSOILT_2 Soil type: entisolSOILT_3 Soil type: ultisolSOIL_DEP Soil depth

AccessibilityDIST_ROAD Distance to nearest roadDIST_CTY Distance to nearest city with pop >

DIST_TWN Distance to nearest townDIST_CPL Distance to nearest crop or pastureDIST_CAF Distance to nearest coffee

The independent or explanatory variables used in the logisticregression models were grouped into two categories: biophysicaland accessibility (Table 2). Among the biophysical variables utilizedwere slope (SLOPE), elevation (ELEV), aspect (N_FACE), soil depth(SOIL_DEP), and soil type (SOILT_1 through SOILT_3). Slope, ele-vation, and aspect information were derived from ASTER GDEMdata at a resolution of 30 m � 30 m. Both SLOPE and ELEV wereused as continuous variables while N_FACE was treated catego-rically comparing north-facing (coded 1) to south-facing (coded 0)slopes. Soil type and depth were obtained from a digitized1:250,000 scale soil map (MAGA, 2002; Simmons et al., 1959).

The relative influenceof accessibilitywas examinedusing distanceto roads (DIST_ROAD), distance to nearest city with a populationgreater than 2000 (DIST_CTY), distance to nearest town center(DIST_TWN), distance to nearest crop or pasture land pixel(DIST_CPL), anddistance tonearest coffee agroforestpixel (DIST_CAF).A roads vector file was obtained from the Ministerio de Agricultura,Ganadería, y Alimentación (Ministry of Agriculture, MAGA), anda vector file denoting town and city center locations was obtainedfrom 2002 census data. The Department of Sololá has 31 cities withpopulations exceeding 2000, the largest of which are Santiago Atitlánand Sololá, the capital of the municipality. Nearby cities from sur-rounding Departments (e.g. Chimaltenango, Quetzaltenango, Quiché,Suchitepéquez, and Totonicapán, see Fig. 1) were also included tocomputeDIST_CTY. To computeDIST_CPL andDIST_CAF, the 1990 and2000 supervised image classifications were used for the 1990e2000transition, and the 2000e2010 transition, respectively. All GIS layerswere produced using Euclidean distance analyses.

A stratified random sampling approach was used in ERDASImagine to extract points from each LUCC class-of-interest to gen-erate the regression models. The extraction of points was an iter-ative process with the goal being to maximize the number ofsample points while controlling for spatial autocorrelation of theresponse variables (determined from the Moran’s Index). The finalselection of 525 points for the 1990e2000 transition period, and425 points for the 2000e2010 transition, demonstrated the pointsto be independent (Moran’s Index ¼ 0.063; Z-score ¼ 1.23 orbetter).

Modest correlationswere found between some variables such asDIST_ROAD and DIST_TWN (p ¼ 0.433), and DIST_CPL and DIS-T_CTY (p ¼ 0.327), but no covariates were strongly correlated.

.

Units Scale

(0/1/2) 30 m � 30 m(0/1/2) 30 m � 30 m(0/1/2) 30 m � 30 m

% 30 m � 30 mm 30 m � 30 m(0/1) 1:250,000(0/1/2) 1:250,000(0/1/2) 1:250,000(0/1/2) 1:250,000cm 1:250,000

m2000 m

mland m

m

M. Schmitt-Harsh / Applied Geography 40 (2013) 40e50 45

Variable properties were investigated in SPSS using both residualand normal plots; the residual plots were used alongside otherdiagnostic techniques (e.g. Cook’s distance, studentized residuals)to detect and remove extreme outlier points (Cook & Weisberg,1982). In total, five to seven points were removed per regressionmodel to minimize errors associated with extreme influentialpoints.

Results

Persistence, gains, and losses of land-cover classes

Approximately 41,400 ha of forests (FOR) were converted toother land-use/cover categories over the 20-year study period, themajority of which occurred in the 1990e2000 time interval(38,784 ha, or 0.8% yr�1) (Table 3). While net losses of FOR con-tinued in the 2000e2010 time interval, the rate and total extent ofdeforestation slowed considerably (2629 ha of forest lost, or0.07% yr�1) (Table 3). Over both time intervals, the swap changes farexceeded the net changes observed on the landscape suggestingthere were additional areas of FOR recovery and loss that were notcaptured using solely the net change results. This observation isalso evident by examining the gains and losses of FOR, which wereon the order of 67,819 ha and 106,603 ha, respectively, between1990 and 2000 (Table 3).

Contrasting to FOR, coffee agroforests (CAF) increased in areaover the 20-year study period (13,778 ha); however, the dominantLUCC trends of CAF differed considerably within the two time in-tervals. Between 1990 and 2000, the area under CAF decreased(33,803 ha or 1.5% yr�1) while between 2000 and 2010, the areaincreased (47,581 ha, or 2.6% yr�1) (Table 3). Similar to FOR, theswap changes far exceeded the net changes observed on thelandscape suggesting there were additional areas of CAF recoveryand loss that were not captured using the net change results.

While not central to the primary research questions, it should benoted that the areal coverage of crops and pasture lands (CPL)followed a divergent path from CAFs. Between 1990 and 2000, CPLincreased in area, on the order of 7.57%, while between 2000 and2010, CPL decreased in area, on the order of 5.43% (Table 3). The netresult over the 20-year study period was a net gain of CPL(22,460 ha) because the gains obtained in the first 10-year timeinterval exceeded the losses from 2000 to 2010.

Detection of systematic and random transitions

Over both time intervals, FOR losses were largely associatedwith conversion to CAF and CPL (Tables 4 and 5; bolded values). Inlooking at the difference between observed and expected lossesfrom 1990 to 2000, the FOR to CAF transitionwas positive and large(2.48%) (Table 4), suggesting that when FOR loses, it tends to do so

Table 3Landscape persistence, gains, losses, total change, swap change, and net change from 199hectares; values in parentheses represent areal quantities in percentage (relative to the

Total year 1 Total year 2 Gains Losse

(a) 1990e2000FOR 477,945 (45.49) 439,161 (41.80) 67,819 (6.46) 106,6CAF 218,475 (20.79) 184,672 (17.58) 75,616 (7.20) 109,4CPL 308,496 (29.36) 388,033 (36.93) 125,676 (11.96) 46,1

(b) 2000e2010FOR 439,161 (41.80) 436,532 (41.55) 79,139 (7.53) 81,7CAF 184,672 (17.58) 232,253 (22.11) 105,565 (10.05) 57,9CPL 388,033 (36.93) 330,956 (31.50) 56,704 (5.40) 113,7

systematically to CAF. In contrast, the FOR to CPL transition for thesame time interval was negative and large (�2.35%) suggestingsystematic avoidance of loss to CPL. Similar trends were evident inthe 2000e2010 transition period (positive loss difference for FOR toCAF transition; negative for FOR to CPL), though the magnitude ofdifference was reduced (Table 5). Despite net losses of FOR overboth time intervals, there were indications of forest recovery aswell (“gains”) (see Section 4.1). However, analysis of transitionmatrices suggests FOR gains were largely random (given smalldifferences between observed and expected gains) (Tables 4 and 5).

CAF losses during the 1990e2000 transition period were pre-dominantly to CPL (6.13%, Table 4). The difference between observedand expected losses was positive and large (1.46%) for transitions toCPL suggesting thatwhen CAF loses, it tends to do so systematically toCPL (Table 4). This trendwas not evident in the 2000e2010 transitionperiod as the difference between observed and expected losses wasnegative and small (Table 5). Despite net losses of CAF between 1990and2000, therewere indications of CAFrecoveryaswell (“gains”) (seeSection4.1). In linewith the systematic lossof FOR toCAF, as describedabove, the difference between observed and expected gains demon-strated CAF gains from FOR to be positive and large (1.41%, Table 4);this suggests thatwhen CAFgains, it tends to gain systematically fromFOR. Contrastingly, CAF gains from CPL were negative (�1.60%,Table 4) suggesting systematic avoidanceof gains fromCPL.WhileCAFgains between 2000 and 2010 were associated with FOR conversion(4.64%) and CPL conversion (4.49%) (Table 5; bolded values), the dif-ference between observed and expected gains for transitions to CAFwas less than 1 for both FOR and CPL, indicating that the processes ofchange were largely random (Table 5; values in parentheses).

Combined, results from the transition matrices demonstratethat between 1990 and 2000, CAF gained most prominently fromFOR; however, gains were outpaced by losses associated withconversion to CPL. The transition from CAF to CPL in the 1990s waslargely reversed in the 2000s, with gains in CAF from CPL outpacinglosses to CPL (Table 5). Such gains were largely random (as opposedto systematic). These trends coincide with the author’s expecta-tions given volatile international coffee markets in the 1990s, andmore stable and higher prices paid to Guatemala coffee growers inthe 2000s (ICO, 2012).

Drivers of LUCC

Analytical results for the multinomial regression models arepresented in Table 6. Part (a) shows the coefficients and level ofsignificance for nine covariates on the probability of LUCC from1990 to 2000 among FOR, CAF, and CPL land classes. Part (b) showsthe coefficients and level of significance from 2000 to 2010. Forboth transition periods, soil type was removed as initial regressionanalyses demonstrated that soil type was not a significant predictorof LUCC, likely because therewas little to nowithin-class variability.

0 to 2000 (a), and from 2000 to 2010 (b). Bolded values represent areal quantities intotal area under investigation).

s Change

Total Swap Net

03 (10.15) 174,422 (16.60) 135,638 (12.91) L38,784 (�3.69)19 (10.41) 185,035 (17.61) 151,232 (14.39) L33,803 (�3.22)39 (4.39) 171,815 (16.35) 92,278 (8.78) 79,537 (7.57)

68 (7.78) 160,907 (15.32) 158,278 (15.07) L2629 (�0.25)84 (5.52) 163,549 (15.57) 115,968 (11.04) 47,581 (4.53)81 (10.83) 170,485 (16.23) 113,408 (10.79) L57,077 (�5.43)

Table 4Extended transition matrix for the period 1990e2000. Each cell has three rows of numbers. The first row contains bolded numbers that represent the actual percentage of thelandscape observed (persistence and transitions). The second row represents the expected percentage of land under random processes of gain, where numbers within pa-rentheses represent the actual minus expected (in %). The third row represents the expected percentage of land under random processes of loss, where numbers withinparentheses represent the actual minus expected (in %). Numbers highlighted in light gray represent systematic gain transitions; numbers highlighted in dark gray representsystematic loss transitions.

1990e2000 2000

1990 FOR CAF CPL Total 1990a Gross lossa

FOR 35.35 5.54 4.09 45.49 10.15e 4.13 (1.41) 7.70 (�3.62) 48.20 (�2.71) 12.85 (�2.71)e 3.06 (2.48) 6.44 (�2.35) 45.49 (0.00) 10.15 (0.00)

CAF 3.29 10.38 6.13 20.79 10.412.46 (0.83) e 3.52 (2.61) 16.83 (3.97) 6.45 (3.97)5.28 (�1.99) e 4.67 (1.46) 20.79 (0.00) 10.41 (0.00)

CPL 2.66 1.07 24.97 29.36 4.393.48 (�0.82) 2.67 (�1.60) e 31.77 (�2.41) 6.80 (�2.41)2.91 (�0.26) 1.22 (�0.15) e 29.36 (0.00) 4.39 (0.00)

Total 2000a 41.80 17.58 36.93 100.00 27.8041.80 (0.00) 17.58 (0.00) 36.93 (0.00) e 27.80 (0.00)44.75 (�2.95) 15.18 (2.40) 37.15 (�0.22) e 27.80 (0.00)

Gross gaina 6.46 7.20 11.96 27.806.46 (0.00) 7.20 (0.00) 11.96 (0.00) 27.80 (0.00)9.41 (�2.95) 4.80 (2.40) 12.18 (�0.22) 27.80 (0.00)

a Column and row values for “Total 1990”, “Total 2000”, “Gross loss”, and “Gross gain” do not sum because BRP and URB are not included in the table.

M. Schmitt-Harsh / Applied Geography 40 (2013) 40e5046

Drivers of FOR clearingSignificant predictors of FOR clearing over the 20-year study

period included SLOPE, ELEV, N_FACE, DIST_ROAD, DIST_CPL, andDIST_CAF (Table 6; Model 1). For both time intervals, SLOPE wasa significant predictor of FOR to CAF conversions, with flatter areasmore likely to experience FOR clearing than steep areas, all elsebeing equal. Similarly, low altitude areas and South-facing slopeswere more likely to experience FOR clearing to CAF. Because Gua-temala is north of the equator, South-facing slopes receive moredirect sunlight and have lower humidity than northern-facing

Table 5Extended transition matrix for the period 2000e2010. Each cell has three rows of numberlandscape observed (persistence and transitions). The second row represents the expectrentheses represent the actual minus expected (in %). The third row represents the exparentheses represent the actual minus expected (in %). Numbers highlighted in light grasystematic loss transitions.

2000e2010 2010

2000 FOR CAF

FOR 34.01 4.64e 5.10 (�0.46)e 2.94 (1.69)

CAF 3.41 12.052.28 (1.13) e

2.95 (0.46) e

CPL 3.82 4.494.78 (�0.97) 4.50 (�0.02)6.57 (�2.76) 3.50 (0.99)

Total 2010a 41.55 22.1041.55 (0.00) 22.10 (0.00)44.40 (�2.85) 18.95 (3.15)

Gross gaina 7.54 10.057.54 (0.00) 10.05 (0.00)10.38 (�2.85) 6.90 (3.15)

a Column and row values for “Total 1990”, “Total 2000”, “Gross loss”, and “Gross gain

slopes, conditions which the model predicts to favor coffee pro-duction. Finally, the probability of FOR conversion to CAF wasnegatively correlated with distance to CAF, an intuitive result thatsuggests forested areas proximal to existing CAF were more likelyto be cleared than areas farther away.

For both study periods, DIST_CPL was a significant predictor ofFOR to CPL conversion, with areas closer in proximity to existingCPL more likely to be cleared, all else equal (Table 6; Model 1).Distance to road was also a significant predictor of FOR clearing toCPL for the 2000e2010 transition period, but not in the direction

s. The first row contains bolded numbers that represent the actual percentage of theed percentage of land under random processes of gain, where numbers within pa-pected percentage of land under random processes of loss, where numbers withiny represent systematic gain transitions; numbers highlighted in dark gray represent

CPL Total 2000a Gross lossa

2.95 41.80 7.793.58 (�0.63) 44.05 (�2.25) 10.04 (�2.25)4.20 (�1.25) 41.80 (0.00) 7.79 (0.00)

1.66 17.57 5.521.51 (0.15) 16.40 (1.17) 4.35 (1.17)2.23 (�0.57) 17.57 (0.00) 5.52 (0.00)

26.11 36.94 10.83e 36.60 (0.34) 10.49 (0.34)e 36.94 (0.00) 10.83 (0.00)

31.51 100.00 26.1731.51 (0.00) e 26.17 (0.00)33.19 (�1.69) e 26.17 (0.00)

5.40 26.175.40 (0.00) 26.17 (0.00)7.09 (�1.69) 26.17 (0.00)

” do not sum because BRP and URB are not included in the table.

Table 6Multinomial logistic regression results. Values presented in the table correspond tothe parameter coefficient (b).

LULC transitions Model 1 Model 2

FOR to CAF FOR to CPL CAF to FOR CAF to CPL

(a) 1990e2000(Intercept) 4.730 3.650 �8.557 �5.155SLOPE �0.016* �0.016* 0.014 0.020ELEV �0.001* 0.001 0.003** 0.002**N_FACE �0.834* �0.075 2.214** 1.081SOIL_DEP �0.003 �0.006 0.021** 0.020*DIST_ROAD 0.000 0.000 �0.001 �0.001**DIST_CTY 0.000 0.000 0.000 0.001*DIST_TWN 0.000 0.000 0.000 �0.001DIST_CPL 0.000 �0.003** �0.007* �0.017**DIST_CAF �0.009** �0.004* �0.014 �0.026Adjusted R2 0.360 0.488

(b) 2000e2010(Intercept) 3.253 1.571 �3.440 �2.643SLOPE �0.051* �0.017 0.007 �0.004ELEV 0.000 0.000 0.009** 0.003N_FACE 0.714 0.318 0.962 �2.224*SOIL_DEP �0.002 �0.013 0.039 �0.009DIST_ROAD 0.001 0.001* 0.000 �0.002*DIST_CTY 0.000 0.000 0.001* 0.001*DIST_TWN �0.001 0.000 0.002 0.000DIST_CPL �0.007 �0.019** 0.036* �0.003DIST_CAF �0.001 0.001 0.000 0.000Adjusted R2 0.532 0.820

*Significant at 0.05; ** at 0.01; adjusted R2 is the Nagelkerke test statistic.

M. Schmitt-Harsh / Applied Geography 40 (2013) 40e50 47

that was expected. The positive coefficient suggests that FOR closerto roads were less likely to be cleared for CPL than FOR furtheraway, a counterintuitive result that could be the result of municipalreforestation efforts, or monitoring and enforcement efforts nearroads. Road development in topographically complex areas in-creases risks of erosion, a conservation concern that has attractedthe attention of municipal governments and local non-profits whomay have engaged in reforestation efforts within the transitionperiod. Additionally, monitoring and enforcement of illegal clearingis more easily facilitated near roads, thus an institutional approachwould predict this result.

These results demonstrate FOR clearing to be a function ofmultiple biophysical and accessibility factors. The conversion ofFOR to CAF was largely driven by biophysical factors, includingslope, elevation, and aspect (though DIST_CAF was also a significantpredictor), while the conversion of FOR to CPL was largely driven byaccessibility factors, with DIST_CPL prominent among them(Table 6).

Drivers of CAF clearingAs described in Section 4.2, coffee agroforest losses from 1990 to

2000 were largely associated with conversion to CPL, and analyticalresults from regression models suggest the significant predictors ofconversion to include ELEV, SOIL_DEP, DIST_ROAD, DIST_CTY, andDIST_CPL (Table 6a; Model 2). The positive coefficient for ELEVsuggests that high altitude areas were more likely to be convertedto CPL than remain in CAF, a counterintuitive result given that thebest grades of coffee grow at higher altitudes. These results mayreflect local-scale variations in topography and associated climaticconditions that are not captured by the DEM.

The estimated coefficients for DIST_ROAD and DIST_CPL werenegative, indicating that CAF areas close to roads and existing CPLwere more likely to be cleared for agricultural use than areas far-ther away, all else equal. Distance to road was also an importantpredictor of CAF clearing to CPL during the 2000e2010 transitionperiod, along with DIST_CTY (Table 6b; Model 2). Across both study

periods, DIST_CTY was positively correlated with CAF clearing toCPL, suggesting that coffee plots closer in proximity to cities(population exceeding 2000) were less likely to be cleared to CPLthan plots farther away. This relationship was expected given thepresence of coffee markets in larger cities. Finally, the negativeaspect (N_FACE) coefficient in the 2000e2010 period suggests thatCAF to CPL transitions were more probable on South-facing slopes,where direct sun exposure is higher and humidity is lower.

These results demonstrate that CAF losses to CPL were largelyassociated with accessibility factors, such as DIST_ROAD andDIST_CPL (though elevation was also a significant predictor). Dis-tance to city exerted a positive relationship with coffee persistence,indicating the importance of accessibility to markets in preventingland conversion to CPL.

Discussion

Despite complexities in understanding LUCC processes, it isincreasingly necessary to recognize both natural and managedforest-based systems, such as coffee agroforests, in conservationpolicy. Undisturbed primary forests are becoming increasinglyscarce with widespread processes of deforestation continuing inmany tropical locations. In this research, forest-cover losses wereprevalent in both 10-year intervals, findings which correspond tonational-level trends of deforestation documented for the 1950sthrough 1990s (Kaimowitz, 1996), and 1990se2000s (Taylor,Moran-Taylor, Castellanos, & Elías, 2011; UVG, INAB, & CONAP,2006). Though the net balance of forest-cover changes was one ofloss, the rates andmagnitude of forest-cover loss were substantiallylower in the 2000e2010 transition period. Such trends may berelated to the enactment of forestry laws in 1996 which devolvedsignificant authority and financial incentives to municipalities(Gibson & Lehoucq, 2003).

Until the mid-1990s, Guatemalan forest resources were largelycontrolled by the central government. However, following large-scale deforestation from the 1950s to 1990s (Kaimowitz, 1996),new forestry laws were enacted which gave municipalitiesincreased control over forest resources, and provided municipal-ities greater opportunities to gain financial assistance for projectsaimed at reforestation and forest maintenance (Article 71) (Gibson& Lehoucq, 2003). As a result, the last decade has seen increasedinstitutional efforts by municipal governments and independentagencies focused on forest conservation and reforestation efforts,particularly to help control for erosion and mudslides associatedwith large-scale climatic stressors (e.g. Hurricane Mitch in 1998;Hurricane Stan in 2005). Concurrently, increased emphasis hasbeen placed on coffee agroforestry, and the diversification of coffeesystems. An estimated 98% of the country’s coffee grows beneatha canopy of shade (ANACAFÉ, 2008) and the emergence or main-tenance of forest cover has been to-some-degree propped up byshade-grown coffee production (Bray, 2010).

Despite such promise, coffee agroforests are not without threatsof deforestation. In the 1990s, in particular, this research demon-strated widespread loss of coffee agroforests, largely associatedwith cropland expansion. Such findings are intuitive given that theproduction of maize, beans, or other annual crops provides animmediate value to farmers in terms of direct household con-sumption or sale on the local market. The spatial patterns of coffeelosses and gains were influenced by a number of biophysical andaccessibility factors (Table 6), prominent among them were dis-tance to nearest road and distance to nearest cropland. As expected,the conversion of coffee agroforests to croplands was more likely tooccur close to existing croplands and roads. This latter relationshipcorresponds to the literature which commonly links deforestationand forest fragmentation to road development (e.g. Chomitz &

M. Schmitt-Harsh / Applied Geography 40 (2013) 40e5048

Gray, 1996); however, it is an unexpected finding given that roaddevelopment in Guatemala has largely been driven by the export-oriented coffee sector. Thus, road development for the purpose(primarily) of coffee transport has had the unintended consequenceof increasing the probability of coffee clearing for croplands in thisstudy region.

While coffee agroforests competed for land near roads and nearexisting croplands, proximity to cities strengthened the persistenceof coffee agroforests over time, a finding in support of research byBlackman et al. (2008, 2012) inMexico and El Salvador. Proximity tocities, here, is a proxy measure for distance to markets, and theseresults are likely associated with the lower input and output costsassociated with transporting agroforestry products to nearbymarkets. In the Department of Sololá, for example, coffee cherriesare generally picked by hand, carried to a point of sale manually,and sold to intermediaries (“coyotes”) who then sell to local coffeeprocessors (“beneficios”) (unless growers are able to de-pulp anddry the beans themselves, or unless growers participate in a coop-erative that can process the beans) (Eakin et al., 2006). For small-holder coffee growers, selling the cherries to intermediaries (ratherthan beneficios) generally results in a lower price for the coffee;however, the ability to sell directly to beneficios is limited by thecost of transport. In contrast to beneficios, intermediaries oftenhave collection points near plantations so smallholders havea shorter distance to travel to transport their small volume ofproduction. Proximity to markets is thus critical, particularly forsmallholder farmers given their lack of access to resources andsmall volume of production.

While this research suggests the importance of spatially explicitbiophysical and accessibility factors in determining localized pat-terns of forest and coffee agroforest expansion and contraction, theimportance of market, climatic, and institutional factors in media-ting large-scale LUCC changes cannot be ignored. For example,coffee agroforest conversion to croplands between 1990 and 2000was likely exacerbated by international coffee markets and regionalclimatic conditions. In the 1990s, market prices paid to Guatemalacoffee growers were extremely volatile, particularly in the early-and late-1990s (ICO, 2012). Further complicating matters weredrought conditions that swept across Central America in the late1990s, particularly in 1997e1998 and 1999e2002. While neitherdrought nor price volatility is unfamiliar to coffee growers, syn-ergies between the two stressors increases the vulnerability ofcoffee growers, and as result, the vulnerability of land to change. Onthe flip side, market conditions may also have contributed to coffeeagroforest expansion from 2000 to 2010. Prices paid to Guatemalacoffee growers have been rising since the early 2000s (ICO, 2012),likely a function of increased world consumption and increaseddemand for specialty coffees such as organic, fair-trade, and bird-friendly. Cooperatives that engage in producing such specialtycoffees are present in the Department of Sololá (e.g. Lyon, 2007);however, a comprehensive description of these cooperatives,including their spatial extent and location, is not well-documented.

As demonstrated in this research, future coffee expansion willlikely depend on a number of social-ecological factors, among themthe biophysical characteristics of the land itself. Climate, top-ography, and soil characteristics (e.g. type, texture, pH, depth) allinfluence the productivity of coffee and the quality of the coffeebean. For example, coffee (specifically Coffea arabica) is best grownon elevations greater than 1000 m (ASL), with slopes < 15%, dailytemperatures in the range of 18e25 �C, and annual rainfall between1400mmand 2000mm (Wintgens, 2004).While there is, of course,variability in each of these parameters (and additional parametersexert control over coffee production), the modeling of future tra-jectories of coffee agroforest expansion and contractionwill requirefiner resolution (temporally and spatially) climate data to become

available for the tropics. Without such data, accurately identifyingand mapping the suitability of coffee agroforest production ishighly constrained. Further, greater research is needed that linksproducer’s land-use decisions with land-use outcomes. Livelihooddecisions are often mediated by regional histories, cultural identity,pre-existing social and economic conditions, and institutions(among others), and the use of interdisciplinary methods that in-cludes social surveys, ecological inventories, and remote sensing,would improve our understanding of the complex LUCC dynamicsof coffee agroforests at the household and landscape scale.

Conclusions

The development of effective land management programs re-quires an understanding of how land-use/cover systems arechanging over space and time, including the extent and location ofchange, and the drivers of change. To-date, the mapping of coffeeagroforests and associated quantification of LUCC has been limited,in large part due to the high degree of spectral similarity that existsbetween coffee agroforests and other woody cover types. Con-sequently, research regarding the drivers of change in coffeeagroforest landscapes falls far behind research examining theproximate and underlying drivers of tropical forest-cover changes.The research presented here therefore aimed to fill an importantgap by examining LUCC and drivers of change among natural for-ests and coffee agroforests.

This research found the drivers of LUCC for forests and coffeeagroforests to be complex and highly divergent, suggesting thatpolicy prescriptions aimed at preserving tree cover should considernatural and managed forest ecosystems separately. For example,tree cover near cities could bemaintained through targeted policiesaimed at promoting shade-grown coffee production. Contrastingly,tree cover in high altitudes and high slope areas would best beachieved through targeted policies aimed at promoting naturalforests. Such policy prescriptions should include rules in use andmanagement to monitor and enforce the protection of forest- andagroforest-resources, and should of course be considerate ofbroader underlying factors, such as markets, property rights, andpopulation growth, which are influential in driving LUCC trajec-tories over space and time.

Acknowledgments

This research was funded by the National Science Foundation’sGeography and Spatial Sciences Program (DDRI #0927491). Theauthor gratefully acknowledges Tom Evans at Indiana University forhis feedback and review of this manuscript.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.apgeog.2013.01.007.

References

ANACAFÉ (Asociación Nacional del Café de Guatemala). (2008). Green book: Gua-temalan coffees. Guatemala City: ANACAFÉ. http://www.guatemalancoffees.com/index.php/greenbook Accessed 12.12.11.

Ávalos-Sartorio, B., & Blackman, A. (2010). Agroforestry price supports as a conser-vation tool: Mexican shade coffee. Agroforestry Systems, 78, 169e183.

Bacon, C. (2005). Confronting the coffee crisis: can fair trade, organic, and specialtycoffees reduce small-scale farmer vulnerability in Northern Nicaragua? WorldDevelopment, 33(3), 497e511.

Beer, J., Muschler, R., Kass, D., & Somarriba, E. (1998). Shade management in coffeeand cacao plantations. Agroforestry Systems, 38, 139e164.

Blackman, A., Albers, H. J., Ávalos-Sartorio, B., & Murphy, L. C. (2008). Land cover ina managed forest ecosystem: Mexican shade coffee. American Journal of Agri-cultural Economics, 90(1), 216e231.

M. Schmitt-Harsh / Applied Geography 40 (2013) 40e50 49

Blackman, A., Ávalos-Sartorio, B., & Chow, J. (2007). Tree cover loss in El Salvador’sshade coffee areas. RFF discussion paper, DP 07-32. Washington, D.C.: Resourcesfor the Future.

Blackman, A., Ávalos-Sartorio, B., & Chow, J. (2012). Land cover change in agro-forestry: shade coffee in El Salvador. Land Economics, 88(1), 75e101.

Bolanos, S. (2007). Using image analysis and GIS for coffee mapping. Thesis, McGillUniversity, Montreal.

Braimoh, A. K. (2006). Random and systematic land-cover transitions in northernGhana. Agriculture, Ecosystems and Environment, 113, 254e263.

Bray, D. B. (2010). Forest cover dynamics and forest transitions in Mexico andCentral America: towards a “great restoration”. In N. Harini, & J. Southworth(Eds.), Reforesting landscapes: Linking pattern and process (pp. 85e120). Dor-drecht: Springer.

Chander, G., & Markham, B. L. (2003). Revised Landsat-5 TM radiometric calibrationprocedures and postcalibration dynamic ranges. IEEE Transactions on Geoscienceand Remote Sensing, 41(11), 2674e2677.

Chander, G., Markham, B. L., & Barsi, J. A. (2007). Revised Landsat-5 ThematicMapper radiometric calibration. IEEE Transactions on Geoscience and RemoteSensing, 4(3), 490e494.

Chavez, P. S., Jr. (1989). Radiometric calibration of Landsat Thematic Mapper multi-spectral images. Photogrammetric Engineering and Remote Sensing, 55, 1285e1294.

Chavez, P. S., Jr. (1996). Image-based atmospheric corrections e revisited andimproved. Photogrammetric Engineering and Remote Sensing, 62(9), 1025e1036.

Chomitz, K. M., & Gray, D. A. (1996). Roads, land use, and deforestation: a spatialmodel applied to Belize. The World Bank Economic Review, 10(3), 487e512.

Colby, J. D., & Keating, P. L. (1998). Land-cover classification using Landsat TM im-agery in the tropical highlands: the influence of anisotropic reflectance. Inter-national Journal of Remote Sensing, 19(8), 1479e1500.

Congalton, R. G. (1991). A review of assessing the accuracy of classifications ofremotely sensed data. Remote Sensing of Environment, 37, 35e46.

Congalton, R. G., & Green, K. (2009). Assessing the accuracy of remotely sensed data:Principles and practices (2nd ed.). Boca Raton: CRC Press.

Cook, R. D., & Weisberg, S. (1982). Residuals and influence in regression. London:Chapman and Hall.

Cordero-Sancho, S., & Sader, S. A. (2007). Spectral analysis and classification accu-racy of coffee crops using Landsat and a topographic-environmental model.International Journal of Remote Sensing, 28(7), 1577e1593.

Dix, M., Fortín, I., & Medinilla, O. (2003). Diagnóstico Ecológico-Social y Plan Pre-liminar Conservación del Área de Atitlán. Guatemala City: Universidad del Vallede Guatemala, The Nature Conservancy. http://pdf.usaid.gov/pdf_docs/PNACX392.pdf Accessed 25.10.12.

Dossa, E. L., Fernandes, E. C. M., & Reid, W. S. (2008). Above- and below-groundbiomass, nutrient and carbon stocks contrasting an open-grown and a shadedcoffee plantation. Agroforestry Systems, 72, 103e115.

Eakin, H., Tucker, C., & Castellanos, E. (2005). Market shocks and climate variability:the coffee crisis in Mexico, Guatemala, and Honduras. Mountain Research andDevelopment, 25(4), 304e309.

Eakin, H., Tucker, C., & Castellanos, E. (2006). Responding to the coffee crisis: a pilotstudy of farmers’ adaptations in Mexico, Guatemala and Honduras. The Geo-graphical Journal, 172(2), 156e171.

Elías, S., & Wittman, H. (2005). State, forest and community: decentralization offorest administration in Guatemala. In C. J. P. Colfer, & D. Capistrano (Eds.), Thepolitics of decentralization: Forests, power and people (pp. 282e295). London, UK:Earthscan.

Ellis, E. A., Baerenklau, K. A., Marcos-Martínez, R., & Chávez, E. (2010). Land use/landcover change dynamics and drivers in a low-grade marginal coffee growingregion of Veracruz, Mexico. Agroforestry Systems, 80, 61e84.

Emanuel, K. (2005). Increasing destructiveness of tropical cyclones over the past 30years. Nature, 436(4), 686e688.

FAO (FoodandAgricultureOrganization). (2011). State of theworld’s forests. Rome: FAO.FAO (Food and Agriculture Organization of the United Nations). (2004). Inventario

Forestal Nacional 2002e03: Evaluación de los Recursos Forestales. Guatemala:Organización de la Naciones Unidas para la Agricultura y la Alimentación,Departamento de Montes.

FAO (Food and Agriculture Organization of the United Nations). (2009). FAOSTAT:production\crops. http://faostat.fao.org/site/567/default.aspx#ancor Accessed25.11.11.

Flores, M., Bratescu, A., Martínez, J. O., Oviedo, J. A., & Acosta, A. (2002). Cen-troamérica: El impacto de la caída de los precios del café. Mexico: CEPAL.

Geist, H. J., & Lambin, E. F. (2001). What drives tropical deforestation? A meta-analysisof proximate and underlying causes of deforestation based on subnational casestudy evidence. LUCC report series no. 4. Louvain-la-Neuve, Belgium: LUCC In-ternational Project Office.

Geist, H. J., & Lambin, E. F. (2002). Proximate causes and underlying driving forces oftropical deforestation. BioScience, 52(2), 143e150.

Gibson, C. C., & Lehoucq, F. E. (2003). The local politics of decentralized environmentalpolicy in Guatemala. Journal of Environment and Development, 12(1), 28e49.

Gordon, C. R., Manson, R., Sundberg, J., & Cruz-Angón, A. (2007). Biodiversity,profitability, and vegetation structure in a Mexican coffee agroecosystem.Agriculture, Ecosystems and Environment, 118, 256e266.

Hausermann, H., & Eakin, H. (2008). Producing “viable” landscapes and livelihoodsin Central Veracruz, Mexico: institutional and producer responses to the coffeecommodity crisis. Journal of Latin American Geography, 7(1), 109e131.

Heidkamp, P., Hanink, D. M., & Cromley, R. G. (2008). A land use model of the effectsof eco-labeling in coffee markets. Annals of Regional Science, 42, 725e746.

ICO (International Coffee Organization). (2012). ICO: statistics. http://www.ico.org/Accessed 06.01.12.

INE (Instituto Nacional de Estudísticas). (2005). Encuesta nacional agropecuaria:Características generales de las fincas y de los productores(as) agropecuarios,pronósticos de producción, producción obtenida agrícola, tecnología, destino de laproducción, precios recibidos por el productor(a), existencia animal y producciónpecuaria. Guatemala City, Guatemala.

INE (Instituto Nacional de Estudísticas). (2011). Censo Población. Guatemala City,Guatemala.

Jha, S., Bacon, C. M., Philpott, S. M., Rice, R. A., Méndez, V. E., & Läderach, P. (2011).A review of ecosystem services, farmer livelihoods, and value chains in shadecoffee agroecosystems. In W. B. Campbell, & S. López Ortíz (Eds.), Integratingagriculture, conservation, and ecotourism: Examples from the fields (pp. 141e208).Dordrecht: Science þ Business Media.

Kaimowitz, D. (1996). Livestock and deforestation. Central America in the 1980s and1990s: A policy perspective. Jakarta, Indonesia: Center for International ForestryResearch.

Langford, M., & Bell, W. (1997). Land cover mapping in a tropical hillsides envi-ronment: a case study in the Cauca region of Colombia. International Journal ofRemote Sensing, 18(6), 1289e1306.

Lewis, J. M. (2005). Strategies for survival: Migration and fair trade-organic coffeeproduction in Oaxaca, Mexico. Working paper 118. Center for ComparativeImmigration Studies, UC San Diego.

Lin, B. B. (2007). Agroforestry management as an adaptive strategy against potentialmicroclimate extremes in coffee agriculture. Agricultural and Forest Meteorology,144, 85e94.

Lin, B. B., Perfecto, I., & Vandermeer, J. (2008). Synergies between agriculturalintensification and climate change could create surprising vulnerabilities forcrops. BioScience, 58(9), 847e854.

Lyon, S. (2007). Fair trade coffee and human rights in Guatemala. Journal of Con-sumer Policy, 30, 241e261.

MAGA (Ministerio de Agricultura, Ganadería y Alimentación). (2002). Atlas Temáticode la República de Guatemala. Guatemala: Instituto Agropecuario Nacional,Ministerio de Agricultura de Guatemala.

Magrin, G., Gay García, C., Cruz Choque, D., Giménez, J. C., Moreno, A. R., Nagy, G. J.,et al. (2007). Latin America. In M. L. Parry, O. F. Canziani, J. P. Palutikof, P. J. vander Linden, & C. E. Hanson (Eds.), Climate change 2007: Impacts, adaptation andvulnerability. Contribution of Working Group II to the fourth assessment report ofthe Intergovernmental Panel on Climate Change (pp. 581e615). Cambridge, UK:Cambridge University Press.

Manandhar, R., Odeh, I. O. A., & Pontius, R. G., Jr. (2010). Analysis of twenty years ofcategorical land transitions in the Lower Hunter of New South Wales, Australia.Agriculture, Ecosystems and Environment, 135, 336e346.

Martínez-Torres, M. E. (2004). Survival strategies in neoliberal markets: peasant or-ganizations and organic coffee in Chiapas. In G. Otero (Ed.), Mexico in transition:Neoliberal globalism, the state and civil society (pp. 169e185). London: Zed Books.

Martínez-Torres, M. E. (2008). The benefits and sustainability of organic farming bypeasant coffee farmers in Chiapas, Mexico. In C. Bacon, V. E. Mendez,S. Gleissman, D. Goodman, & J. Fox (Eds.), Confronting the coffee crisis: Fair trade,sustainable livelihoods, and ecosystems in Mexico and Central America (pp. 99e126). Boston: MIT Press.

Meyer, W. B., & Turner, B. L., II (1992). Human population growth and global land-use/cover change. Annual Review of Ecology and Systematics, 277(504), 39e61.

Nair, P. K. R. (1989). Agroforestry systems in the tropics. Dordrecht: Kluwer AcademicPublishers.

Nakakaawa, C. A., Vedeld, P. A., & Aune, J. B. (2011). Spatial and temporal land useand carbon stock changes in Uganda: implications for a future REDD strategy.Mitigation and Adaptation Strategies for Global Change, 16, 25e62.

Neelin, J. D., Münnich, M., Su, H., Meyerson, J. E., & Holloway, C. E. (2006). Tropicaldrying trends in global warming models and observations. Proceedings of theNational Academy of Sciences of the United States of America, 103, 6110e6115.

Perfecto, I., Armbrecht, I., Philpott, S. M., Soto-Pinto, L., & Dietsch, T. V. (2007). Shadedcoffee and the stability of rainforest margins in Latin America. In T. Tscharntke,C. Leuschner, M. Zeller, E. Guhadja, & A. Bidin (Eds.), The stability of tropicalrainforest margins, linking ecological, economic, and social constraints of land useand conservation (pp. 225e261). New York: Springer-Verlag Berlin Heidelberg.

Perfecto, I., Rice, R. A., Greenberg, R., & van der Voort, M. E. (1996). Shade coffee:a disappearing refuge for biodiversity. BioScience, 46(8), 598e608.

Ponte, S. (2002). The ‘latte revolution’? Regulation, markets and consumption in theglobal coffee chain. World Development, 30(7), 1099e1122.

Pontius, R. G., Jr., Shusas, E., & McEachern, M. (2004). Detecting important cate-gorical land changes while accounting for persistence. Agriculture, Ecosystemsand Environment, 101, 251e268.

Rice, R. A. (2003). Coffee production in a time of crisis: social and environmentalconnections. SAIS Review, 21(1), 221e245.

Rice, R. A., & Ward, J. (1996). Coffee, conservation, and commerce in the westernhemisphere. Washington, D.C.: The Smithsonian Migratory Bird Center and theNatural Resources Defense Council.

Rindfuss, R. R., Walsh, S. J., Turner, B. L., II, Fox, J., & Mishra, V. (2004). Developinga science of land change: challenges andmethodological issues. Proceedings of theNational Academyof Sciences of theUnited States of America,101(39),13976e13981.

Romero-Ruiz, M. H., Flantua, S. G. A., Tansey, K., & Berrio, J. C. (2012). Landscapetransformations in savannas of northern South America: land use/coverchanges since 1987 in the Llanos Orientales of Colombia. Applied Geography, 32,766e776.

M. Schmitt-Harsh / Applied Geography 40 (2013) 40e5050

Schmitt-Harsh, M., Evans, T. P., Castellanos, E., & Randolph, J. C. (2012). Carbonstocks in coffee agroforests and mixed dry tropical forests in the westernhighlands of Guatemala. Agroforestry Systems, 86, 141e157.

Simmons, C. S., Tarano, J. M., & Pinto, J. H. (1959). Clasificación de Reconocimiento delos Suelos de la República de Guatemala. Guatemala: Instituto AgropecuarioNacional, Ministerio de Agricultura de Guatemala.

Soto-Pinto, L., Anzueto, M., Mendoza, J., Ferrer, G. J., & de Jong, B. (2010). Carbonsequestration through agroforestry in indigenous communities of Chiapas,Mexico. Agroforestry Systems, 78, 39e51.

Soto-Pinto, L., Romero-Alvarado, Y., Caballero-Nieto, J., & Warnholtz, G. S. (2001).Woody plant diversity and structure of shade-grown-coffee plantations innorthern Chiapas, Mexico. Revista de Biología Tropical, 49(3e4), 977e987.

Taylor, M. J., Moran-Taylor, M. J., Castellanos, E. J., & Elías, S. (2011). Burning forsustainability: biomass energy, international migration, and the move tocleaner fuels and cookstoves in Guatemala. Annals of the Association of AmericanGeographers, 101(4), 918e928.

Teillet, P. M., & Fedosejevs, G. (1995). On the dark target approach to atmosphericcorrection of remotely sensed data. Canadian Journal of Remote Sensing, 21(4),374e387.

Tucker, C., Eakin, H., & Castellanos, E. (2010). Perceptions of risk and adaptation:coffee producers, market shocks, and extreme weather in Central America andMexico. Global Environmental Change, 20, 23e32.

UVG (Universidad del Valle Guatemala), INAB (Instituto Nacional de Bosques),& CONAP (Consejo Nacional de Áreas Protegidas). (2006). Dinámica de laCobertura Forestal de Guatemala Durante los Años 1991, 1996 y 2001 y Mapade Cobertura Forestal 2001. Guatemala City, Guatemala: EdicionesSuperiores.

Webster, P. J., Holland, G. J., Curry, J. A., & Chang, H. R. (2005). Changes in tropicalcyclone number, duration, and intensity in a warming environment. Science,309, 1844e1846.

Wintgens, J. N. (2004). Coffee: Growing, processing, sustainable production: Aguidebook for growers, processors, traders, and researchers. Weinheim: Wiley-VCH.

Wittman, H., & Geisler, C. (2005). Negotiating locality: decentralization and com-munal forest management in the Guatemalan highlands. Human Organization,64(1), 62e74.

World Bank. (2004). Poverty in Guatemala. Washington, D.C.: World Bank CountryStudy.