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

Applied Geography 40 (2013) 40e50

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Landscape change in Guatemala: Driving forces of forest and coffee agroforest 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

a b s t r a c t Keywords: Coffee agroforests Deforestation Land transitions Land-use/cover change Drivers of change

This study examines the land-use/cover change (LUCC) dynamics and drivers for two prominent landuse/cover systems in Guatemala: natural forests (FOR) and coffee agroforests (CAF). To-date, very little research has examined the LUCC dynamics of CAF, in large part due to the high degree of spectral similarity that exists between agroforests and other forest-cover types. Given the ecosystem and livelihood services provided by shade-grown coffee production, it is increasingly necessary to map and identify the dynamics and drivers of CAF changes over space and time. This research uses remote sensing analysis, land transition matrices, and multinomial regression models to examine LUCC dynamics over two 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 of change for CAF and FOR. Results demonstrate LUCC dynamics and drivers for the two land-use/cover systems to be complex over space and time. For example, FOR losses are evident for both time intervals, 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 are outpaced by expansion in the 2000s. Losses are associated with conversion to CPL, particularly near roads and existing croplands, while expansion and/or persistence of CAF occurs near cities. These results suggest that conservation programs aimed at tree cover preservation and expansion should consider natural forests and managed agroforests separately. Further, such programs should be tailored to specific locations and institutional settings given the influence of topography and accessibility factors in determining localized patterns of landscape transformations over space and time. Ó 2013 Elsevier Ltd. All rights reserved.

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

* Present address: Carleton College, Environmental Studies, One North College St., Northfield, MN 55057, USA. Tel.: þ1 507 222 7822. E-mail address: [email protected]. 0143-6228/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.apgeog.2013.01.007

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, RomeroAlvarado, Caballero-Nieto, & Warnholtz, 2001). Despite their contribution to ecological functioning and servicing, and their importance to conservation efforts, many coffee agroforestry systems have undergone extensive changes since the 1980s, with loss of shade trees and/or conversion to maize, beans, or other agricultural land use noted in many Latin American countries (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 example, the arrival of the coffee leaf rust, Hemileia vastatrix Berk., in Central America and parts of South America led to modernization efforts that promoted new high-yielding varieties, the removal of shade, and increased density of coffee bushes (Jha et al., 2011).

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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 m and 708,000 m (UTM Zone 15N).

Backed by monetary assistance from the United States Agency for International Development (USAID), and the establishment of Promecafe, the “open-to-sun” modernization movement aimed to diminish the spread of the rust and improve production efficiencies. However, even where coffee leaf rust was not expected to pose significant problems (e.g. higher elevation areas due to cooler temperatures), landscape-level transformations were widespread. Approximately 40% of Latin American shaded coffee farms were “technified” or converted to sun coffee, a conversion of which has been likened to widespread deforestation of agricultural lands (Rice & Ward, 1996). More recently, record low international coffee prices between 1999 and 2003, in combination with repeated droughts, have contributed to dramatic declines in employment in the Central American coffee sector in a period known as the “coffee crisis” (Bacon, 2005; Tucker, Eakin, & Castellanos, 2010). Coffee growers historically and currently face a number of uncertainties in the production of coffee, from the overproduction of lower-quality coffee which threatens market stability (Ponte, 2002; Rice, 2003), to climatic changes in Central America trending toward increased temperature (Magrin et al., 2007), reduced precipitation (Magrin et 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 to these stressors are smallholder coffee producers given their economic disposition and lack of access to resources (Eakin et al., 2006), and in Central America, an estimated 85% of coffee producers are smallholders who farm less than 10 ha each (Flores, Bratescu, Martínez, Oviedo, & Acosta, 2002). A vast amount of literature exists on smallholders’ responses to the 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 research has highlighted the extent to which land-use decisions have been mediated by complex social-ecological factors, such as cultural identity, remittances, educational opportunities, institutions, land tenure, and access to niche markets. Land-use outcomes have correspondingly ranged from abandonment of coffee plantations, conversion of coffee to other agricultural uses, renting or selling of land, migration, and increased dependence on off-farm labor for income (Ávalos-Sartorio & Blackman, 2010; Blackman, ÁvalosSartorio, & Chow, 2007; Ellis et al., 2010; Gordon, Manson, Sundberg, & Cruz-Angón, 2007). To-date, data on such land-use outcomes have often been gathered from survey or field data rather than remotely sensed imagery, likely due to difficulties in linking people with pixels (Rindfuss, Walsh, Turner, Fox, & Mishra, 2004), and difficulties in accurately mapping and identifying coffee given the high degree of spectral similarity that exists between coffee and other woody cover types (Bolanos, 2007; Cordero-Sancho & Sader, 2007; Langford & Bell, 1997). To that end, very little empirical work has examined the land-use/cover change (LUCC)1 dynamics of coffee agroforests using remote sensing methodologies. Among the few existing studies that use remotely sensed imagery, case analysis has demonstrated the importance of environmental

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

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

variables and access to roads and markets in mediating LUCC. For example, econometric modeling in Oaxaca, Mexico shows shade coffee and tree cover preservation to be associated with proximity to roads and large cities with coffee markets (Blackman, Albers, ÁvalosSartorio, & Murphy, 2008). Contrastingly, conversion of shade coffee plots occurred in lower elevations and areas close to smaller towns without coffee markets (Blackman et al., 2008). Environmental variables, such as slope and elevation, were also found to be important determinants of LUCC in shade coffee-growing regions of Veracruz, Mexico (Ellis et al., 2010) and El Salvador (Blackman, Ávalos-Sartorio, & Chow, 2012), with areas lower in elevation and slope more prone to coffee and tree cover loss for the purpose of expanding other agricultural or pastoral uses. The combination of research efforts above suggests a complex set of factors influencing coffee production and LUCC over space and time. For example, while biophysical characteristics (e.g. elevation, slope, soil type) may initially signal the suitability (or lack of suitability) for coffee production in a given area, the persistence or transformation of coffee agroforests in that area likely involves complex assemblages of demographic, economic, institutional, cultural, and technological factors. Such patterns may converge or diverge from patterns and processes associated with natural forestcover changes (e.g. Geist & Lambin, 2001, 2002), a finding with consequences for the design and establishment of conservationbased programs aimed at forest recovery and maintenance (of both natural and managed forests). Thus, the development of effective conservation programs requires knowledge of the spatial extent of LUCC for natural forests and coffee agroforests, as well as the prominent forces contributing to LUCC over space and time. The research presented here examines the rates and magnitudes of LUCC, as well as drivers of change, within a coffee-forest landscape of Guatemala. The central research questions are as follows: (1) How have forests and coffee agroforests changed spatially over time? (2) What biophysical and accessibility factors contribute to forest and coffee agroforest LUCC over time? Focus is given to biophysical (e.g. slope, elevation, aspect) and accessibility (e.g. distance to city, distance to road) factors given research documenting the importance of these variables in determining LUCC in Mexico and El Salvador (e.g. Blackman et al., 2008, 2012; Ellis et al., 2010). This research uses remote sensing analysis and multinomial regression models to examine LUCC over two ten-year periods (1990e2000; 2000e2010). The two time intervals are highly divergent in terms of international coffee markets and prices paid to Guatemalan coffee growers. For example, in the 1990s, international coffee prices paid to growers were highly volatile and declined precipitously from the mid-1990s to early 2000s (i.e. the “coffee crisis”). Contrastingly, in the 2000s, coffee prices paid to coffee growers increased dramatically (ICO, 2012) alongside heightened demand for specialty coffee brands (Bacon, 2005). Coffee has consistently played an integral role in Guatemala’s national economy, representing between 6.6% and 13% of Guatemala’s GDP, and generating between 30% and 35% of foreign exchange over the last 20 years (Heidkamp, Hanink, & Cromley, 2008). There are eight major coffee-growing regions in Guatemala, covering approximately 2.3% of the country’s total surface area, and an estimated 98% of the country’s coffee grows beneath a canopy of shade (though the extent and diversity of shade cover varies widely (ANACAFÉ, 2008; FAO, 2009)). Given the economic and ecological importance of shade coffee, understanding past drivers of LUCC during periods of unstable and stable market conditions is integral to understanding potential future trajectories of change. Study area The study area is the Department of Sololá (Fig. 1), located in the Sierra Madre Volcanoes region of the western highlands of

Guatemala. The Department covers an area of 1170 km2, and is marked by heterogeneous topography with elevations ranging from 628 m to 3524 m ASL, and slopes ranging from 0 to 75 . Annual rainfall and temperature averages 2504 mm and 18e24  C though there is high variability associated with altitudinal gradients. Soils in the region are primarily andisols, entisols, and ultisols formed from volcanic ash (Dix, Fortín, & Medinilla, 2003; MAGA, 2002; Simmons, Tarano, & Pinto, 1959). Three volcanoes are located in the Department of Sololá including San Pedro, Tolimán, and Atitlán, and the natural vegetation in the region is a mix of broadleaf, coniferous, and mixed broadleaf and coniferous forests. Most of the forests in the western highlands of Guatemala are under a mix of indigenous communal and municipal land tenures (Wittman & Geisler, 2005). Privately owned lands are largely deforested, though in Guatemala more broadly, approximately 40% of existing forests are under private ownership (FAO, 2004). Current policy initiatives, particularly those stemming from the 1996 Peace Accords, have attempted to increase forest production and promote sustainable natural resource management for communal and private forests. Further, increased local autonomy has been emphasized, giving municipalities greater control over monitoring and managing forest resources (Gibson & Lehoucq, 2003). The Department of Sololá contains 19 municipalities (Fig. 1). The population of the Department, according to 2011 projections, equaled w437,000 marking a 2.9% annual rate of increase from 2000 to 2011 (INE, 2011). The majority of the population is indigenous Maya (96.2%) and the region, situated within the “poverty belt”, is one of the poorest in the country (World Bank, 2004). Coffee is the most important crop economically, though many other cash and subsistence crops are grown including maize, beans, potatoes, cardamom, banana, and rubber. Most of the coffee growers in the region employ shade cover in which the canopy is composed of timber, fruit, medicinal, and leguminous species (personal observation). Coffee farms are generally privately owned, though land tenure has historically been insecure, particularly for the indigenous population (Elías & Wittman, 2005). Most of the coffee growers in the region are smallholders who farm less than two ha of land. In the 2005/06 growing season, there were approximately 6400 coffee farms in the Department of Sololá, producing approximately 638,200 quintales (where 1 quintal ¼ w100 kg) of coffee 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 on minimal cloud cover and month, as JanuaryeMarch represents the dry season in Guatemala when agricultural fields are most easily distinguished from forests. The images were georeferenced to a 1:50,000 topographic map (5 m resolution). An overlay function verified that the images overlapped exactly across the three image dates. Following rectification, all images were corrected for variations in solar angle and atmospheric condition.2 The remotely sensed DN values for TM bands 1e5 and 7 were first converted to at-satellite radiance to correct for sensor gains and offsets (Chander & Markham, 2003; Chander, Markham, & Barsi, 2007). At-satellite

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

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

radiance was converted to at-surface reflectance by correcting for solar angle and the atmosphere using a dark-object subtraction model (Chavez, 1989, 1996; Teillet & Fedosejevs, 1995). Finally, band 6 (thermal band), resampled to 30 m  30 m, was converted to atsatellite radiance as described above, and then converted to effective at-satellite temperature (Chander & Markham, 2003; Chander et al., 2007). The thermal waveband was re-stacked with the calibrated multispectral data for further processing. Supervised classifications using the maximum likelihood algorithm in ERDAS Imagine were used to generate five land-use/cover classes for all images: (a) mature and successional forests (FOR), coffee agroforests (CAF), crops and pasture lands (CPL), banana and rubber plantations (BRP), and urban/rural settlements (URB). Water and clouds were excluded and masked out equally in all classified images. Lake Atitlán has a surface area of w127 km2, and together with cloud cover, the masked area amounted to 14.5% of the total pixels in the image. Because the classification of coffee agroforests using Landsat imagery is complicated by the high degree of spectral similarity between coffee and other woody cover types (Bolanos, 2007; Cordero-Sancho & Sader, 2007; Langford & Bell, 1997), this research used high-resolution (0.5 m  0.5 m) aerial photographs to collect a large number of well-distributed training datasets. Coffee agroforests were identifiable from the aerial photographs enabling the selection of high quality training data. In total, 618 training sites were selected within the image footprint, with spectra extracted for FOR (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 with each land-cover class was evaluated for potential change in land cover at the training site. Outlier signatures were removed, the remainder used to train a maximum likelihood classifier. The accuracy of each classified image was based on GPS points collected in the field in 2009 and 2010. GPS point locations were taken in homogeneous areas of FOR (n ¼ 39), CAF (n ¼ 70), CPL (n ¼ 29), and URB (n ¼ 15). Coordinates were collected using a Garmin Oregon 400t GPS receiver and integrated into ArcGIS for use in the accuracy assessment. Because some areas within the image footprint were not travel-accessible, additional validation points were selected using the aerial photographs (n ¼ 218), creating a total of 371 points for use in the accuracy assessment. Accuracy assessments using the Kappa-Cohen method were conducted on each classified image (Congalton, 1991; Congalton & Green, 2009). Error matrices were developed, and producer’s accuracy (PA) and user’s accuracy (UA) calculated (see Table 1 for the 2010 error matrix; the 1990 and 2000 error matrices are available as supplementary material). Given the temporal gap between the date the ground control points were collected (2009/2010) and the Landsat image acquisition dates (e.g. 1990, 2000), a higher classification accuracy was obtained for the 2010 classification (89.5%; Table 1 Error matrix for the 2010 classified image. Classified data

FOR CAF CPL BRP URB Column total PA% a

Reference data FOR

CAF

CPL

BRP

URB

Row total

UA%a

97 8 0 0 0 105 92.38

10 98 1 10 0 119 82.35

0 0 77 1 2 80 96.25

1 0 2 17 0 20 85.00

0 0 3 0 34 37 91.89

108 106 83 28 36 361

89.81 92.45 92.77 60.71 94.44

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

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Kappa ¼ 0.8598) than the 2000 classification (85.3%; Kappa ¼ 0.8049) and 1990 classification (75.9%; Kappa ¼ 0.6788). For all image classifications, the most prominent error stemmed from misclassification of BRP as CAF. However, this error was minimized as BRP comprised a small percentage of the total land cover (w1%) within the administrative departmental boundary (Department of Sololá), and all classified images were subset to the departmental boundary for the purpose of documenting LUCC.

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

Ciþ ¼

n X

Cij ; where isj

(1)

Cij ; where isj

(2)

i¼1

Cþj ¼

n X j¼1

From this matrix, the gross gains, gross losses, net change, and swap change in each LUCC category were examined (Braimoh, 2006; Pontius et al., 2004) (see Table 3). The gross gain and loss for each land category were derived by subtracting the diagonal entries (Cjj) from the column and row totals, respectively. Net change, synonymous with a change in quantity, was calculated as the difference between gross gains and losses. Swap change, synonymous with a change in location, was calculated as the total change (gross gains 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 to separate random and systematic transitions in each time interval (1990e2000; 2000e2010) (see Tables 4 and 5). Landscape transitions were assumed to be random if land categories gained (or lost) from other categories in proportion to the availability of other losing (or gaining) categories (Romero-Ruiz et al., 2012). Any large deviation from such proportions was deemed a systematic transition. Therefore, values close to zero indicate random landscape transitions, whereas values farther from zero indicate more systematic transitions (Nakakaawa, Vedeld, & Aune, 2011; Pontius et al., 2004). The expected gain (Gij) of each transition under a random process of gain was estimated using Equation (3), and the expected loss (Lij) of each transition under a random process of loss was estimated using Equation (4) (Manandhar, Odeh, & Pontius, 2010; Pontius et al., 2004).

Gij ¼ Pþj  Pjj

! Piþ ; where isj 100  Pjþ

(3)

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

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

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

 Lij ¼ Piþ  Pii

 Pþj ; where isj 100  Pþi

(4)

where Lij is the expected transition from category i to j under random processes of loss, (Piþ  Pii) is the observed loss for category i, Pþj is the column total for category j, Pþi is the column total for category i, and (100  Piþ) represents the sum of column totals across all categories except the category i. The off-diagonal unbolded values in Tables 4 and 5 represent the expected gains (Gij) and losses (Lij) of each land-use/cover transition. Numbers in parentheses represent the difference between observed (bolded off-diagonal) and expected (unbolded offdiagonal) proportions of land cover under random processes of gain and loss, calculated as Pij  Gij and Pij  Lij respectively. Where the gain difference is positive, the category in that row lost more to the category in the column than would be expected under random processes of gain for that column category. Where negative, the category in that row lost less than expected. Similarly, where the loss difference is positive, the category in that column gained more from the category in the row than would be expected under random loss processes. Where negative, the category in that column gained less than expected (Manandhar et al., 2010). Analysis of the drivers of change The drivers of LUCC were analyzed based on multinomial logistic regression models derived from overlaying and relating the observed LUCC transitions between 1990 and 2000, and 2000 and 2010, to spatially explicit ancillary data. In determining the influence of independent variables on forest transitions, the reference or baseline outcome e unchanged FOR e was compared to FOR to CAF transitions and FOR to CPL transitions (Model 1). Similarly, unchanged CAF was compared to CAF to FOR transitions and CAF to CPL transitions (Model 2). Both model runs were therefore threeoutcome category models, whereby the unchanged land-cover class served as the reference (Y ¼ 0), and the transitions served as alternative outcomes, coded as Y ¼ 1 and Y ¼ 2.

The independent or explanatory variables used in the logistic regression models were grouped into two categories: biophysical and accessibility (Table 2). Among the biophysical variables utilized were slope (SLOPE), elevation (ELEV), aspect (N_FACE), soil depth (SOIL_DEP), and soil type (SOILT_1 through SOILT_3). Slope, elevation, and aspect information were derived from ASTER GDEM data at a resolution of 30 m  30 m. Both SLOPE and ELEV were used as continuous variables while N_FACE was treated categorically comparing north-facing (coded 1) to south-facing (coded 0) slopes. Soil type and depth were obtained from a digitized 1:250,000 scale soil map (MAGA, 2002; Simmons et al., 1959). The relative influence of accessibility was examined using distance to roads (DIST_ROAD), distance to nearest city with a population greater than 2000 (DIST_CTY), distance to nearest town center (DIST_TWN), distance to nearest crop or pasture land pixel (DIST_CPL), and distance to nearest coffee agroforest pixel (DIST_CAF). A roads vector file was obtained from the Ministerio de Agricultura, Ganadería, y Alimentación (Ministry of Agriculture, MAGA), and a vector file denoting town and city center locations was obtained from 2002 census data. The Department of Sololá has 31 cities with populations exceeding 2000, the largest of which are Santiago Atitlán and Sololá, the capital of the municipality. Nearby cities from surrounding Departments (e.g. Chimaltenango, Quetzaltenango, Quiché, Suchitepéquez, and Totonicapán, see Fig. 1) were also included to compute DIST_CTY. To compute DIST_CPL and DIST_CAF, the 1990 and 2000 supervised image classifications were used for the 1990e2000 transition, and the 2000e2010 transition, respectively. All GIS layers were produced using Euclidean distance analyses. A stratified random sampling approach was used in ERDAS Imagine to extract points from each LUCC class-of-interest to generate the regression models. The extraction of points was an iterative process with the goal being to maximize the number of sample points while controlling for spatial autocorrelation of the response variables (determined from the Moran’s Index). The final selection of 525 points for the 1990e2000 transition period, and 425 points for the 2000e2010 transition, demonstrated the points to be independent (Moran’s Index ¼ 0.063; Z-score ¼ 1.23 or better). Modest correlations were found between some variables such as DIST_ROAD and DIST_TWN (p ¼ 0.433), and DIST_CPL and DIST_CTY (p ¼ 0.327), but no covariates were strongly correlated.

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

Units

Scale

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

(0/1/2) (0/1/2) (0/1/2)

30 m  30 m 30 m  30 m 30 m  30 m

Slope Elevation Aspect (S-facing/N-facing) Soil type: andisol Soil type: entisol Soil type: ultisol Soil depth

% m (0/1) (0/1/2) (0/1/2) (0/1/2) cm

30 m  30 m 30 m  30 m 1:250,000 1:250,000 1:250,000 1:250,000 1:250,000

Distance Distance Distance Distance Distance

m m m m m

Independent variables Biophysical SLOPE ELEV N_FACE SOILT_1 SOILT_2 SOILT_3 SOIL_DEP Accessibility DIST_ROAD DIST_CTY DIST_TWN DIST_CPL DIST_CAF

to to to to to

nearest nearest nearest nearest nearest

road city with pop > 2000 town crop or pasture land coffee

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

Variable properties were investigated in SPSS using both residual and normal plots; the residual plots were used alongside other diagnostic 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 regression model to minimize errors associated with extreme influential points. Results Persistence, gains, and losses of land-cover classes Approximately 41,400 ha of forests (FOR) were converted to other land-use/cover categories over the 20-year study period, the majority of which occurred in the 1990e2000 time interval (38,784 ha, or 0.8% yr1) (Table 3). While net losses of FOR continued in the 2000e2010 time interval, the rate and total extent of deforestation slowed considerably (2629 ha of forest lost, or 0.07% yr1) (Table 3). Over both time intervals, the swap changes far exceeded the net changes observed on the landscape suggesting there were additional areas of FOR recovery and loss that were not captured using solely the net change results. This observation is also evident by examining the gains and losses of FOR, which were on the order of 67,819 ha and 106,603 ha, respectively, between 1990 and 2000 (Table 3). Contrasting to FOR, coffee agroforests (CAF) increased in area over the 20-year study period (13,778 ha); however, the dominant LUCC trends of CAF differed considerably within the two time intervals. Between 1990 and 2000, the area under CAF decreased (33,803 ha or 1.5% yr1) while between 2000 and 2010, the area increased (47,581 ha, or 2.6% yr1) (Table 3). Similar to FOR, the swap changes far exceeded the net changes observed on the landscape suggesting there were additional areas of CAF recovery and loss that were not captured using the net change results. While not central to the primary research questions, it should be noted that the areal coverage of crops and pasture lands (CPL) followed a divergent path from CAFs. Between 1990 and 2000, CPL increased in area, on the order of 7.57%, while between 2000 and 2010, CPL decreased in area, on the order of 5.43% (Table 3). The net result over the 20-year study period was a net gain of CPL (22,460 ha) because the gains obtained in the first 10-year time interval exceeded the losses from 2000 to 2010. Detection of systematic and random transitions Over both time intervals, FOR losses were largely associated with conversion to CAF and CPL (Tables 4 and 5; bolded values). In looking at the difference between observed and expected losses from 1990 to 2000, the FOR to CAF transition was positive and large (2.48%) (Table 4), suggesting that when FOR loses, it tends to do so

45

systematically to CAF. In contrast, the FOR to CPL transition for the same time interval was negative and large (2.35%) suggesting systematic avoidance of loss to CPL. Similar trends were evident in the 2000e2010 transition period (positive loss difference for FOR to CAF transition; negative for FOR to CPL), though the magnitude of difference was reduced (Table 5). Despite net losses of FOR over both time intervals, there were indications of forest recovery as well (“gains”) (see Section 4.1). However, analysis of transition matrices suggests FOR gains were largely random (given small differences between observed and expected gains) (Tables 4 and 5). CAF losses during the 1990e2000 transition period were predominantly to CPL (6.13%, Table 4). The difference between observed and expected losses was positive and large (1.46%) for transitions to CPL suggesting that when CAF loses, it tends to do so systematically to CPL (Table 4). This trend was not evident in the 2000e2010 transition period as the difference between observed and expected losses was negative and small (Table 5). Despite net losses of CAF between 1990 and 2000, there were indications of CAF recovery as well (“gains”) (see Section 4.1). In line with the systematic loss of FOR to CAF, as described above, the difference between observed and expected gains demonstrated CAF gains from FOR to be positive and large (1.41%, Table 4); this suggests that when CAF gains, it tends to gain systematically from FOR. Contrastingly, CAF gains from CPL were negative (1.60%, Table 4) suggesting systematic avoidance of gains from CPL. While CAF gains between 2000 and 2010 were associated with FOR conversion (4.64%) and CPL conversion (4.49%) (Table 5; bolded values), the difference between observed and expected gains for transitions to CAF was less than 1 for both FOR and CPL, indicating that the processes of change were largely random (Table 5; values in parentheses). Combined, results from the transition matrices demonstrate that between 1990 and 2000, CAF gained most prominently from FOR; however, gains were outpaced by losses associated with conversion to CPL. The transition from CAF to CPL in the 1990s was largely reversed in the 2000s, with gains in CAF from CPL outpacing losses to CPL (Table 5). Such gains were largely random (as opposed to systematic). These trends coincide with the author’s expectations given volatile international coffee markets in the 1990s, and more stable and higher prices paid to Guatemala coffee growers in the 2000s (ICO, 2012). Drivers of LUCC Analytical results for the multinomial regression models are presented in Table 6. Part (a) shows the coefficients and level of significance for nine covariates on the probability of LUCC from 1990 to 2000 among FOR, CAF, and CPL land classes. Part (b) shows the coefficients and level of significance from 2000 to 2010. For both transition periods, soil type was removed as initial regression analyses demonstrated that soil type was not a significant predictor of LUCC, likely because there was little to no within-class variability.

Table 3 Landscape persistence, gains, losses, total change, swap change, and net change from 1990 to 2000 (a), and from 2000 to 2010 (b). Bolded values represent areal quantities in hectares; values in parentheses represent areal quantities in percentage (relative to the total area under investigation). Total year 1

Total year 2

Gains

(a) 1990e2000 FOR 477,945 (45.49) CAF 218,475 (20.79) CPL 308,496 (29.36)

439,161 (41.80) 184,672 (17.58) 388,033 (36.93)

67,819 (6.46) 75,616 (7.20) 125,676 (11.96)

(b) 2000e2010 FOR 439,161 (41.80) CAF 184,672 (17.58) CPL 388,033 (36.93)

436,532 (41.55) 232,253 (22.11) 330,956 (31.50)

79,139 (7.53) 105,565 (10.05) 56,704 (5.40)

Losses

Change Total

Swap

Net

106,603 (10.15) 109,419 (10.41) 46,139 (4.39)

174,422 (16.60) 185,035 (17.61) 171,815 (16.35)

135,638 (12.91) 151,232 (14.39) 92,278 (8.78)

L38,784 (3.69) L33,803 (3.22) 79,537 (7.57)

81,768 (7.78) 57,984 (5.52) 113,781 (10.83)

160,907 (15.32) 163,549 (15.57) 170,485 (16.23)

158,278 (15.07) 115,968 (11.04) 113,408 (10.79)

L2629 (0.25) 47,581 (4.53) L57,077 (5.43)

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

Table 4 Extended 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 the landscape observed (persistence and transitions). The second row represents the expected percentage of land under random processes of gain, where numbers within parentheses represent the actual minus expected (in %). The third row represents the expected percentage of land under random processes of loss, where numbers within parentheses represent the actual minus expected (in %). Numbers highlighted in light gray represent systematic gain transitions; numbers highlighted in dark gray represent systematic loss transitions. 1990e2000

2000

1990

FOR

CAF

CPL

Total 1990a

Gross lossa

FOR

35.35 e e

5.54 4.13 (1.41) 3.06 (2.48)

4.09 7.70 (3.62) 6.44 (2.35)

45.49 48.20 (2.71) 45.49 (0.00)

10.15 12.85 (2.71) 10.15 (0.00)

CAF

3.29 2.46 (0.83) 5.28 (1.99)

10.38 e e

6.13 3.52 (2.61) 4.67 (1.46)

20.79 16.83 (3.97) 20.79 (0.00)

10.41 6.45 (3.97) 10.41 (0.00)

CPL

2.66 3.48 (0.82) 2.91 (0.26)

1.07 2.67 (1.60) 1.22 (0.15)

24.97 e e

29.36 31.77 (2.41) 29.36 (0.00)

4.39 6.80 (2.41) 4.39 (0.00)

Total 2000a

41.80 41.80 (0.00) 44.75 (2.95)

17.58 17.58 (0.00) 15.18 (2.40)

36.93 36.93 (0.00) 37.15 (0.22)

100.00 e e

27.80 27.80 (0.00) 27.80 (0.00)

Gross gaina

6.46 6.46 (0.00) 9.41 (2.95)

7.20 7.20 (0.00) 4.80 (2.40)

11.96 11.96 (0.00) 12.18 (0.22)

27.80 27.80 (0.00) 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.

Drivers of FOR clearing Significant predictors of FOR clearing over the 20-year study period included SLOPE, ELEV, N_FACE, DIST_ROAD, DIST_CPL, and DIST_CAF (Table 6; Model 1). For both time intervals, SLOPE was a significant predictor of FOR to CAF conversions, with flatter areas more likely to experience FOR clearing than steep areas, all else being equal. Similarly, low altitude areas and South-facing slopes were more likely to experience FOR clearing to CAF. Because Guatemala is north of the equator, South-facing slopes receive more direct sunlight and have lower humidity than northern-facing

slopes, conditions which the model predicts to favor coffee production. Finally, the probability of FOR conversion to CAF was negatively correlated with distance to CAF, an intuitive result that suggests forested areas proximal to existing CAF were more likely to be cleared than areas farther away. For both study periods, DIST_CPL was a significant predictor of FOR to CPL conversion, with areas closer in proximity to existing CPL more likely to be cleared, all else equal (Table 6; Model 1). Distance to road was also a significant predictor of FOR clearing to CPL for the 2000e2010 transition period, but not in the direction

Table 5 Extended transition matrix for the period 2000e2010. Each cell has three rows of numbers. The first row contains bolded numbers that represent the actual percentage of the landscape observed (persistence and transitions). The second row represents the expected percentage of land under random processes of gain, where numbers within parentheses represent the actual minus expected (in %). The third row represents the expected percentage of land under random processes of loss, where numbers within parentheses represent the actual minus expected (in %). Numbers highlighted in light gray represent systematic gain transitions; numbers highlighted in dark gray represent systematic loss transitions. 2000e2010

2010

2000

FOR

CAF

CPL

Total 2000a

Gross lossa

FOR

34.01 e e

4.64 5.10 (0.46) 2.94 (1.69)

2.95 3.58 (0.63) 4.20 (1.25)

41.80 44.05 (2.25) 41.80 (0.00)

7.79 10.04 (2.25) 7.79 (0.00)

CAF

3.41 2.28 (1.13) 2.95 (0.46)

12.05 e e

1.66 1.51 (0.15) 2.23 (0.57)

17.57 16.40 (1.17) 17.57 (0.00)

5.52 4.35 (1.17) 5.52 (0.00)

CPL

3.82 4.78 (0.97) 6.57 (2.76)

4.49 4.50 (0.02) 3.50 (0.99)

26.11 e e

36.94 36.60 (0.34) 36.94 (0.00)

10.83 10.49 (0.34) 10.83 (0.00)

Total 2010a

41.55 41.55 (0.00) 44.40 (2.85)

22.10 22.10 (0.00) 18.95 (3.15)

31.51 31.51 (0.00) 33.19 (1.69)

100.00 e e

26.17 26.17 (0.00) 26.17 (0.00)

Gross gaina

7.54 7.54 (0.00) 10.38 (2.85)

10.05 10.05 (0.00) 6.90 (3.15)

5.40 5.40 (0.00) 7.09 (1.69)

26.17 26.17 (0.00) 26.17 (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) 40e50 Table 6 Multinomial logistic regression results. Values presented in the table correspond to the parameter coefficient (b). LULC transitions

Model 1

Model 2

FOR to CAF

FOR to CPL

CAF to FOR

CAF to CPL

(a) 1990e2000 (Intercept) SLOPE ELEV N_FACE SOIL_DEP DIST_ROAD DIST_CTY DIST_TWN DIST_CPL DIST_CAF Adjusted R2

4.730 0.016* 0.001* 0.834* 0.003 0.000 0.000 0.000 0.000 0.009** 0.360

3.650 0.016* 0.001 0.075 0.006 0.000 0.000 0.000 0.003** 0.004*

8.557 0.014 0.003** 2.214** 0.021** 0.001 0.000 0.000 0.007* 0.014 0.488

5.155 0.020 0.002** 1.081 0.020* 0.001** 0.001* 0.001 0.017** 0.026

(b) 2000e2010 (Intercept) SLOPE ELEV N_FACE SOIL_DEP DIST_ROAD DIST_CTY DIST_TWN DIST_CPL DIST_CAF Adjusted R2

3.253 0.051* 0.000 0.714 0.002 0.001 0.000 0.001 0.007 0.001 0.532

47

periods, DIST_CTY was positively correlated with CAF clearing to CPL, suggesting that coffee plots closer in proximity to cities (population exceeding 2000) were less likely to be cleared to CPL than plots farther away. This relationship was expected given the presence of coffee markets in larger cities. Finally, the negative aspect (N_FACE) coefficient in the 2000e2010 period suggests that CAF 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 largely associated with accessibility factors, such as DIST_ROAD and DIST_CPL (though elevation was also a significant predictor). Distance to city exerted a positive relationship with coffee persistence, indicating the importance of accessibility to markets in preventing land conversion to CPL. Discussion

1.571 0.017 0.000 0.318 0.013 0.001* 0.000 0.000 0.019** 0.001

3.440 0.007 0.009** 0.962 0.039 0.000 0.001* 0.002 0.036* 0.000 0.820

2.643 0.004 0.003 2.224* 0.009 0.002* 0.001* 0.000 0.003 0.000

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

that was expected. The positive coefficient suggests that FOR closer to roads were less likely to be cleared for CPL than FOR further away, a counterintuitive result that could be the result of municipal reforestation efforts, or monitoring and enforcement efforts near roads. Road development in topographically complex areas increases risks of erosion, a conservation concern that has attracted the attention of municipal governments and local non-profits who may have engaged in reforestation efforts within the transition period. Additionally, monitoring and enforcement of illegal clearing is more easily facilitated near roads, thus an institutional approach would predict this result. These results demonstrate FOR clearing to be a function of multiple biophysical and accessibility factors. The conversion of FOR to CAF was largely driven by biophysical factors, including slope, elevation, and aspect (though DIST_CAF was also a significant predictor), while the conversion of FOR to CPL was largely driven by accessibility factors, with DIST_CPL prominent among them (Table 6). Drivers of CAF clearing As described in Section 4.2, coffee agroforest losses from 1990 to 2000 were largely associated with conversion to CPL, and analytical results from regression models suggest the significant predictors of conversion to include ELEV, SOIL_DEP, DIST_ROAD, DIST_CTY, and DIST_CPL (Table 6a; Model 2). The positive coefficient for ELEV suggests that high altitude areas were more likely to be converted to CPL than remain in CAF, a counterintuitive result given that the best grades of coffee grow at higher altitudes. These results may reflect local-scale variations in topography and associated climatic conditions that are not captured by the DEM. The estimated coefficients for DIST_ROAD and DIST_CPL were negative, indicating that CAF areas close to roads and existing CPL were more likely to be cleared for agricultural use than areas farther away, all else equal. Distance to road was also an important predictor of CAF clearing to CPL during the 2000e2010 transition period, along with DIST_CTY (Table 6b; Model 2). Across both study

Despite complexities in understanding LUCC processes, it is increasingly necessary to recognize both natural and managed forest-based systems, such as coffee agroforests, in conservation policy. Undisturbed primary forests are becoming increasingly scarce with widespread processes of deforestation continuing in many tropical locations. In this research, forest-cover losses were prevalent in both 10-year intervals, findings which correspond to national-level trends of deforestation documented for the 1950s through 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 of loss, the rates and magnitude of forest-cover loss were substantially lower in the 2000e2010 transition period. Such trends may be related to the enactment of forestry laws in 1996 which devolved significant authority and financial incentives to municipalities (Gibson & Lehoucq, 2003). Until the mid-1990s, Guatemalan forest resources were largely controlled by the central government. However, following largescale deforestation from the 1950s to 1990s (Kaimowitz, 1996), new forestry laws were enacted which gave municipalities increased control over forest resources, and provided municipalities greater opportunities to gain financial assistance for projects aimed at reforestation and forest maintenance (Article 71) (Gibson & Lehoucq, 2003). As a result, the last decade has seen increased institutional efforts by municipal governments and independent agencies focused on forest conservation and reforestation efforts, particularly to help control for erosion and mudslides associated with large-scale climatic stressors (e.g. Hurricane Mitch in 1998; Hurricane Stan in 2005). Concurrently, increased emphasis has been placed on coffee agroforestry, and the diversification of coffee systems. An estimated 98% of the country’s coffee grows beneath a canopy of shade (ANACAFÉ, 2008) and the emergence or maintenance of forest cover has been to-some-degree propped up by shade-grown coffee production (Bray, 2010). Despite such promise, coffee agroforests are not without threats of deforestation. In the 1990s, in particular, this research demonstrated widespread loss of coffee agroforests, largely associated with cropland expansion. Such findings are intuitive given that the production of maize, beans, or other annual crops provides an immediate value to farmers in terms of direct household consumption or sale on the local market. The spatial patterns of coffee losses and gains were influenced by a number of biophysical and accessibility factors (Table 6), prominent among them were distance to nearest road and distance to nearest cropland. As expected, the conversion of coffee agroforests to croplands was more likely to occur close to existing croplands and roads. This latter relationship corresponds to the literature which commonly links deforestation and forest fragmentation to road development (e.g. Chomitz &

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

Gray, 1996); however, it is an unexpected finding given that road development in Guatemala has largely been driven by the exportoriented coffee sector. Thus, road development for the purpose (primarily) of coffee transport has had the unintended consequence of increasing the probability of coffee clearing for croplands in this study region. While coffee agroforests competed for land near roads and near existing croplands, proximity to cities strengthened the persistence of coffee agroforests over time, a finding in support of research by Blackman et al. (2008, 2012) in Mexico and El Salvador. Proximity to cities, here, is a proxy measure for distance to markets, and these results are likely associated with the lower input and output costs associated with transporting agroforestry products to nearby markets. In the Department of Sololá, for example, coffee cherries are generally picked by hand, carried to a point of sale manually, and sold to intermediaries (“coyotes”) who then sell to local coffee processors (“beneficios”) (unless growers are able to de-pulp and dry the beans themselves, or unless growers participate in a cooperative that can process the beans) (Eakin et al., 2006). For smallholder coffee growers, selling the cherries to intermediaries (rather than beneficios) generally results in a lower price for the coffee; however, the ability to sell directly to beneficios is limited by the cost of transport. In contrast to beneficios, intermediaries often have collection points near plantations so smallholders have a shorter distance to travel to transport their small volume of production. Proximity to markets is thus critical, particularly for smallholder farmers given their lack of access to resources and small volume of production. While this research suggests the importance of spatially explicit biophysical and accessibility factors in determining localized patterns of forest and coffee agroforest expansion and contraction, the importance of market, climatic, and institutional factors in mediating large-scale LUCC changes cannot be ignored. For example, coffee agroforest conversion to croplands between 1990 and 2000 was likely exacerbated by international coffee markets and regional climatic conditions. In the 1990s, market prices paid to Guatemala coffee growers were extremely volatile, particularly in the earlyand late-1990s (ICO, 2012). Further complicating matters were drought conditions that swept across Central America in the late 1990s, particularly in 1997e1998 and 1999e2002. While neither drought nor price volatility is unfamiliar to coffee growers, synergies between the two stressors increases the vulnerability of coffee growers, and as result, the vulnerability of land to change. On the flip side, market conditions may also have contributed to coffee agroforest expansion from 2000 to 2010. Prices paid to Guatemala coffee growers have been rising since the early 2000s (ICO, 2012), likely a function of increased world consumption and increased demand for specialty coffees such as organic, fair-trade, and birdfriendly. Cooperatives that engage in producing such specialty coffees 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 will likely depend on a number of social-ecological factors, among them the biophysical characteristics of the land itself. Climate, topography, and soil characteristics (e.g. type, texture, pH, depth) all influence the productivity of coffee and the quality of the coffee bean. For example, coffee (specifically Coffea arabica) is best grown on elevations greater than 1000 m (ASL), with slopes < 15%, daily temperatures in the range of 18e25  C, and annual rainfall between 1400 mm and 2000 mm (Wintgens, 2004). While there is, of course, variability in each of these parameters (and additional parameters exert control over coffee production), the modeling of future trajectories of coffee agroforest expansion and contraction will require finer resolution (temporally and spatially) climate data to become

available for the tropics. Without such data, accurately identifying and mapping the suitability of coffee agroforest production is highly constrained. Further, greater research is needed that links producer’s land-use decisions with land-use outcomes. Livelihood decisions 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 includes social surveys, ecological inventories, and remote sensing, would improve our understanding of the complex LUCC dynamics of coffee agroforests at the household and landscape scale. Conclusions The development of effective land management programs requires an understanding of how land-use/cover systems are changing over space and time, including the extent and location of change, and the drivers of change. To-date, the mapping of coffee agroforests and associated quantification of LUCC has been limited, in large part due to the high degree of spectral similarity that exists between coffee agroforests and other woody cover types. Consequently, research regarding the drivers of change in coffee agroforest landscapes falls far behind research examining the proximate and underlying drivers of tropical forest-cover changes. The research presented here therefore aimed to fill an important gap by examining LUCC and drivers of change among natural forests and coffee agroforests. This research found the drivers of LUCC for forests and coffee agroforests to be complex and highly divergent, suggesting that policy prescriptions aimed at preserving tree cover should consider natural and managed forest ecosystems separately. For example, tree cover near cities could be maintained through targeted policies aimed at promoting shade-grown coffee production. Contrastingly, tree cover in high altitudes and high slope areas would best be achieved through targeted policies aimed at promoting natural forests. Such policy prescriptions should include rules in use and management to monitor and enforce the protection of forest- and agroforest-resources, and should of course be considerate of broader underlying factors, such as markets, property rights, and population growth, which are influential in driving LUCC trajectories over space and time. Acknowledgments This research was funded by the National Science Foundation’s Geography and Spatial Sciences Program (DDRI #0927491). The author gratefully acknowledges Tom Evans at Indiana University for his feedback and review of this manuscript. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the 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: Guatemalan 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 conservation tool: Mexican shade coffee. Agroforestry Systems, 78, 169e183. Bacon, C. (2005). Confronting the coffee crisis: can fair trade, organic, and specialty coffees reduce small-scale farmer vulnerability in Northern Nicaragua? World Development, 33(3), 497e511. Beer, J., Muschler, R., Kass, D., & Somarriba, E. (1998). Shade management in coffee and cacao plantations. Agroforestry Systems, 38, 139e164. Blackman, A., Albers, H. J., Ávalos-Sartorio, B., & Murphy, L. C. (2008). Land cover in a managed forest ecosystem: Mexican shade coffee. American Journal of Agricultural Economics, 90(1), 216e231.

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