Deforestation scenarios for the Bolivian lowlands

Environmental Research 144 (2016) 49–63

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Deforestation scenarios for the Bolivian lowlands Graciela Tejada a,n, Eloi Dalla-Nora a, Diana Cordoba b, Raffaele Lafortezza c, Alex Ovando a, Talita Assis a, Ana Paula Aguiar a a

Earth System Science Center (CCST), National Institute for Space Research (INPE), Av. dos Astronautas 1758, 12227-010 São José dos Campos, SP, Brazil Royal Roads University, 2005 Sooke Road, Victoria, BC, Canada c Department of Agriculture and Environmental Science, University of Bari, Via Amendola 165/A, 70126 Bari, Italy b

art ic l e i nf o

a b s t r a c t

Article history: Received 5 May 2015 Received in revised form 13 October 2015 Accepted 13 October 2015 Available online 23 October 2015

Tropical forests in South America play a key role in the provision of ecosystem services such as carbon sinks, biodiversity conservation, and global climate regulation. In previous decades, Bolivian forests have mainly been deforested by the expansion of agricultural frontier development, driven by the growing demands for beef and other productions. In the mid-2000s the Movimiento al Socialismo (MAS) party rose to power in Bolivia with the promise of promoting an alternative development model that would respect the environment. The party passed the world’s first laws granting rights to the environment, which they termed Mother Earth (Law No. 300 of 2012), and proposed an innovative framework that was expected to develop radical new conservation policies. The MAS conservationist discourse, policies, and productive practices, however, have since been in permanent tension. The government continues to guarantee food production through neo-extractivist methods by promoting the notion to expand agriculture from 3 to 13 million ha, risking the tropical forests and their ecosystem services. These actions raise major environmental and social concerns, as the potential impacts of such interventions are still unknown. The objective of this study is to explore an innovative land use modeling approach to simulate how the growing demand for land could affect future deforestation trends in Bolivia. We use the LuccME framework to create a spatially-explicit land cover change model and run it under three different deforestation scenarios, spanning from the present–2050. In the Sustainability scenario, deforestation reaches 17,703,786 ha, notably in previously deforested or degraded areas, while leaving forest extensions intact. In the Middle of the road scenario, deforestation and degradation move toward new or paved roads spreading across 25,698,327 ha in 2050, while intact forests are located in Protected Areas (PAs). In the Fragmentation scenario, deforestation expands to almost all Bolivian lowlands reaching 37,944,434 ha and leaves small forest patches in a few PAs. These deforestation scenarios are not meant to predict the future but to show how current and future decisions carried out by the neo-extractivist practices of MAS government could affect deforestation and carbon emission trends. In this perspective, recognizing land use systems as open and dynamic systems is a central challenge in designing efficient land use policies and managing a transition towards sustainable land use. & 2015 Elsevier Inc. All rights reserved.

Keywords: Deforestation scenarios Amazon forest Land cover change (LCC) model LuccME

1. Introduction Tropical forests in South America play a key role in the provision of ecosystem services (ES) such as carbon sinks, biodiversity conservation, and climate regulation at local, regional, and global scales (Nobre, 2014). However, these unique forests and their services have been threatened by complex, interconnected driving forces such as agricultural expansion, climate variability, and forest degradation (Davidson et al., 2012; Malhi et al., 2008). Bolivia, for example, is listed among the countries with the highest net forest n

Corresponding author. E-mail address: [email protected] (G. Tejada).

http://dx.doi.org/10.1016/j.envres.2015.10.010 0013-9351/& 2015 Elsevier Inc. All rights reserved.

loss during 2000–2010 (FAO, 2010), with 50% of its territory now covered by lowland forests (Killeen et al., 2007). Bolivian lowlands experienced intense colonization from the 1950s to the 1970s due to the migration of peasants from the Andean region (Pacheco, 2006). In the mid-1980s, the agro-industrial corporations engaged in large-scale deforestations mainly in the southwestern portion of the Bolivian Amazon, in Santa Cruz, where current deforestation converted 75% of the land for agricultural purposes (Killeen et al., 2008). More recently, international driving forces, such as the growing demand for agricultural products (mainly soybeans and beef), have been the major cause of deforestation in Bolivia as well as in other Amazonian countries (Dalla-Nora et al., 2014; Müller et al., 2012; Pacheco et al., 2010). In the mid-2000s the Movimiento al Socialismo (MAS) party

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rose to power in Bolivia with the promise of promoting an alternative development model that is more respectful of the environment. The MAS party enacted the Mother Earth Law, (No. 300, of 2012), which recognizes Mother Earth's rights and the State's obligations to ensure these rights. This law also introduced a new non-market based mechanism for forest conservation, the “Joint Mitigation and Adaptation Mechanism for the Integrated and Sustainable Management of Forests and Mother Earth” (Decree 1696 of 2013). This mechanism seeks to ensure sustainable forest management through the knowledge and rights of indigenous people and to become an alternative to dominant market-based mechanisms like Reducing emissions from deforestation and forest degradation and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks in developing countries (REDD þ) (Müller et al., 2014b). MAS conservationist and production policies in Bolivia, however, have been experiencing continuous tension. The lack of governance, land tenure conflicts, and conceptual gaps limit the application of laws and regulations regarding deforestation. Thus, despite the innovative environmental and legal framework, little progress has been made in this direction (Müller et al., 2014b, 2013; Pacheco et al., 2010). Moreover, the application of some environmental laws and regulations contradicts the government’s aim to guarantee food production and exportation, which is increasing the expansion of the agricultural lands from 3 to 13 million ha in the next 10 years with the “13 Pillars of the Patriotic Agenda 2025” (Bolivia, 2013; Chumacero, 2012; Hoiby and Zenteno-Hopp, 2014; IBCE, 2013). A further contradiction is the construction of roads and oil exploration in protected areas (PA) and indigenous territories (IT) (Chumacero et al., 2010; Jiménez, 2013). Understanding the deforestation processes occurring in Bolivian lowland forests, as in the rest of the world, is a challenging task. It deserves a multidisciplinary approach, as seen in the work by Aguiar (2006), Aguiar et al. (2014) and Folhes et al. (2015), that takes into account the multi-dimensional nature of this topic. Few studies have addressed the issue of deforestation in Bolivia through time and space (i.e., Mertens et al., 2004; Müller et al., 2014a, 2012; Sangermano et al., 2012). The lack of pertinent information such as multi-temporal land cover change (LCC) data limits the efforts in this issue because many LCC datasets have only been available since 2012. The aim of this study is to explore an innovative modeling approach for Bolivian lowlands to simulate how the growing demand for agricultural land and different land use policies could affect future deforestation trends. This study also discusses the social and environmental implications related to different land use change scenarios based on deforestation rates and spatial pattern analyses. Ultimately, we seek to assist in the discussion of broader land use policies regarding the sustainable development of agriculture in the Bolivian forests.

2. Materials and methods 2.1. Study area In Bolivia, the physiography and altitude determine significant gradients in temperature, precipitation ( Fig. 2), and consequently, the rich biodiversity (Ibisch and Merida, 2004). Three main physiographic regions can be distinguished: Andean, Sub-Andean, and Lowlands. The Andean region is located at 3000 m.a.s.l. between the Western Range (Cordillera Occidental) and Central Range (Cordillera Central) and is characterized by the Altiplano (high plateau) and high peaks (Navarro and Maldonado, 2002). The Sub-Andean region, located in the transition zone between the highlands and

Fig. 1. Study area in the eastern lowlands of Bolivia below the natural montane tree line (  3000 m). Land cover change (LCC) data from Killeen et al. (2012).

lowlands, is comprised of valleys and piedmont regions. The study area is the Bolivian lowlands, a territory located approximately 3000 m below the natural montane tree line (Killeen et al., 2008, 2007) (Fig. 1). This area covers almost 70% of the national territory, including the whole Amazon Basin and portions of La Plata Basin. Although the lowlands are the most extensive physiographic area in Bolivia, they account for only 31% of the country's population, especially in the department of Santa Cruz (26%), followed by Beni (4%) and Pando (1%) (Fig. 2a) (INE, 2014). The increasing process of deforestation in the Bolivian lowlands occurs in several stages and is promoted by national development policies. First, after the National Revolution of 1952, both the State and international development agencies channelled capital to encourage the development of large-scale cash crop agriculture in the lowlands. Agriculture for domestic consumption was the main driver of deforestation during this period. In parallel, the central government promoted a program to colonize the lowlands called “March to the East” (Sivila, 1977). This program sought to stimulate the migration of spontaneous colonists from the Andean highlands to the lower lands, with the purpose of extending agricultural and supplementing the need for cheap labor in the rising agro-industry (Zeballos, 2006). These migrants settled mainly in the northwestern area of Santa Cruz (Yapacani), north Cochabamba (Chapare) and to the north of La Paz (Yungas) (Pacheco, 2006). Second, in the middle of the 1980s, the agro-industrial corporations, Santa Cruz farmers, and foreign colonies (Menonites and Japanese) engaged in largescale deforestation to enable agricultural production (75% of current Bolivian deforestation) in Santa Cruz (Killeen et al., 2008). Third, this process continued in the 1990s with the support of the government and the World Bank through the Eastern Lowlands Project (WB, 1997); the support included investments in silos, processing facilities, highways, and infrastructure. Finally, during the 2000s, mechanized agriculture (mainly soybean, which was influenced by local and international markets), cattle ranching, and small-scale agriculture were the main drivers of deforestation (Müller et al., 2011; Pacheco, 2006). Periodic soy booms fuelled by international markets have played an important role in the expansion of agroindustrial corporations, which were further triggered by leading foreign producers and their transnational capital (Medeiros, 2008).

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Fig. 2. (a) Population distribution by department, data from INE (2014); (b) Shuttle Radar Topography Mission (SRTM) digital elevation model, data from Jarvis et al. (2008); (c) mean annual precipitation, source Ibisch and Merida (2004); (d) mean annual temperature, source Ibisch and Merida (2004).

In the 1990s, a series of policies were enacted to promote sustainable land and forest management (e.g. Land Law No. 1715 and Forest Law No. 1700 of 1996) and to guarantee better environmental assessments (e.g. Environmental Law No. 1333 of 1997). These policies have resulted in poor outcomes to prevent deforestation due to lack of governance and serious conflicts on land tenure (Müller et al., 2014b, 2013; Pacheco et al., 2010). Deforestation steadily increased from 2000 to 2010. Cattle ranches were the main driver of deforestation, affecting mostly the Chiquitano region and the Northern Amazon (Cobija, Riberalta), followed by mechanized agriculture that decreased from 50% (1990– 2000) to 30% (2000–2010) in Santa Cruz (Müller et al., 2014a, 2012). Small-scale agriculture remained the third driver of deforestation in both periods, mainly in the Chapare region, primarily due to coca cultivation (Chumacero et al., 2010; Müller et al., 2012). 2.2. Modeling approach In this study we adopted a top-down modeling approach (Verburg et al., 2006) where land demand is allocated by space

that is based on cell suitability. The modeling protocol is presented in five steps: (1) spatial database; (2) model description; (3) model parameterization; (4) model validation; and (5) scenario assumptions. The impacts of deforestation in terms of carbon emissions and biodiversity were also considered. All tools and datasets cited in this section are under open access. 2.2.1. Spatial database The construction of the spatial database is a relevant part of our model because it considers land cover and biophysical maps as potential driving factors of the deforestation process in the Bolivian lowlands. The dependent variable is deforestation or LCC, while the independent variables are those socioeconomic, environmental, and connectivity factors that influence deforestation across time and space. The LCC data were drawn from the Noel Kempff Mercado Museum of Natural History (NKMMNH) (Killeen et al., 2012). The NKMMNH has been monitoring the LCC in Bolivia using satellite images since 2001 using aerial photogrammetry data from previous decades. A detailed description of the methods to obtain the LCC data can be found in Killeen et al. (2008, 2007). The LCC data

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were organized into three classes of interest (Fig. 1): (1) Natural vegetation, which includes forests, chaco, cerrado, savannas/wetlands, and puna/andean scrublands; (2) Others, including water, no natural vegetation, and clouds; and (3) Deforestation or LCC (natural vegetation converted to other land cover classes). The 30 m resolution LCC data (Killeen et al., 2012) were plugged in our model cell space of 25 km  25 km, and became a continuous proportion value (percentage of LCC within the cell). The periods of LCC data analyzed were from 1976 to 2001 for statistical analysis, while 2005 was used for calibration and 2008 for validation. For the independent variables, we constructed a database with 420 independent variables divided into three main groups: (1) distances and connections (accessibility and economic attractiveness); (2) public policies (zoning); and (3) environmental factors (Supplement 1). These variables were selected based on the literature regarding deforestation processes in the Amazon (Aguiar, 2006; Geist and Lambin, 2002; Müller et al., 2012; Sangermano et al., 2012). All the variables were integrated in a spatial database of 25 km  25 km created in TerraView GIS based on different spatial operators (Supplement 1). 2.2.2. Model description We used an open-source modeling framework, LuccME (http:// www.terrame.org/doku.php?id ¼luccme), to build a new spatiallyexplicit LCC model for Bolivia called LuccME/Bolivia. LuccME is a multi-scale model that integrates several inputs, which vary in resolution, into a cellular space. LuccME allows the modeler to create different land use and cover change (LUCC) models (e.g. deforestation, agricultural expansion, desertification, forest degradation, urban sprawl) at different scales and to combine different components, such as demand (calculation of the magnitude or quantity of change), potential (calculation of the suitability or propensity of change for each cell), and allocation (spatial distribution of changes based on land demand and each cell’s potential to change) (Fig. 3) (Aguiar et al., 2012a). Several well-known LUCC models follow this structure, including The Conversion of Land Use and its Effects (CLUE) family (Veldkamp and Fresco, 1996; Verburg et al., 1999; Verburg and Veldkamp, 2001), Dinamica- Environment for Geoprocessing Objects (EGO) (Soares-Filho et al., 2002), and Geomod (Pontius et al., 2001), which use a range of different approaches and techniques for their three components. However, these models are implemented in different computing platforms and their code is generally not open access; therefore, they cannot be easily modified or combined. In this sense, LuccME allows the construction of

new models, combining elements of demand, potential, and allocation components, which are designed according to concepts of the main models found in the literature. For the demand component, the amount of deforestation was defined as an input to the model for 2001–2008 through using the observed deforestation rates derived from the LCC dataset (Killeen et al., 2012). For the scenarios, land demand estimates (future deforestation) were based on different assumptions as described in Section 2.2.5 (scenario assumptions). For potential, the LuccME/Bolivia model uses an alternative component based on Spatial Lag Regression (Aguiar et al., 2007; Anselin and Smirnov, 1996). This component (SpatialLagRegression) accounts for spatial auto-correlation and dependence to estimate the cell’s potential to change. In addition, such a mechanism is able to dynamically update the potential of change at each step in time, considering not only the temporal changes in the spatial drivers (according to the scenario premises) but also the distance to previously opened areas. Finally, for allocation we used the LuccME AllocationClueLike component derived from the CLUE model for continuous land use variables (Verburg et al., 1999) to generate annual deforestation maps. In the case of deforestation, cells with a positive change potential received a percentage of annual change proportionate to their potential to be allocated to the whole area. 2.2.3. Model parameterization After compiling the database, we conducted a statistical analysis for 2001 to select a set of variables to be considered in the model. Variables that were highly correlated to each other were excluded, while stepwise selection (Aguiar et al., 2007) was applied to choose the significant variables. We used the results of Spatial Lag Regression coefficients to parameterize the model on GeoDa (Anselin et al., 2006) according to the correlation coefficient R2 and the significance of each variable (Table 1). The resulting determinant variables were: distance to roads, PAs and ITs, connectivity index to regional markets, and flat slope (up to 5%); all of which were highly significant (Table 1). Similar variables (e.g., PAs, distance to roads, market connection) were also determinants in other studies in the Amazon (Aguiar, 2006; Geist and Lambin, 2002; Müller et al., 2012). For distance to roads, we used the Euclidean distance logarithm while taking into account the closest paved and unpaved roads. The spatial datasets were obtained from the Bolivian Road Network Administrator (ABC, 2008, 2010). For the connectivity index to regional markets variable, the Generalized Proximity Matrix (GPM)

Fig. 3. Generic structure of the main spatially-explicit land use/cover change models (adapted from Verburg et al. (2006)).

Table 1 Description of model components, temporal and spatial resolution, explanatory variables and scenario assumptions regarding deforestation projections. General parameters

Spatial scale Temporal scale

Land use/cover classes

Potential: SpatialLagRegression parameters

Regression coefficient

Std B

Significance

W_log_def constant GPM to regional markets

0.91273280  0.04853574 720.66800000

0.011 0.016 114.881

0.000 0.002 0.000

Y

 0.01356610

0.003

0.000

Y

6.64  10  7 0.02201879

0.004 0.004

0.044 0.005

Y

Deforestation allocation parameters – submodel A20

Scenario A

Scenario B

Scenario C

maxError minValue

500 km2 0%

500 km2 0%

500 km2 0%

100%

100%

100%

40%

40%

40%

6%

6%

6%

3%

3%

3%

Scenario A

Scenario B

Scenario C

Flat slope Protected Areas and Indigenous Territories

maxValue changeThresholdValue

maxChange maxChangeAbovethreshold

Demand: PreComputedValues

Deforestation

Spatial autoregressive coefficient Regression constant Connectivity index via the road network to regional markets (cities with 470,000 people) Euclidean distance to the closest paved or unpaved roads (log10 transformed) Percentage of cell area covered by a slope flat (up to 5%) Percentage of cell area covered by protected areas and indigenous territories

Maximum allocation error allowed for each land use Minimum value (percentage) allowed for that land use as a result of new changes Maximum value (percentage) allowed for that land use as a result of new changes Threshold applied to the level of saturation in each cell. The saturation level is dynamically computed, according to the available forest in the neighborhood, deconsidering the protected areas. According to the threshold the speed of change of a given land use in the cell is modified. Maximum change in a given land use allowed in a cell in a time step until (saturation) threshold Maximum change in a given land use allowed in a cell in a time step after (saturation) threshold

Trend of 2005–2008 un- Trend of til 2013, then decrease of 2005–2008 50%

Scenario dependent

G. Tejada et al. / Environmental Research 144 (2016) 49–63

Selected deforestation spatial determinants

Distance to roads

Allocation: AllocationClueLike parameters

Extent Eastern Bolivian lowlands below the natural montane tree line (  3000 m) (based on Killeen et al. (2012)) Resolution Regular cells of 25 km  25 km (625 km2) Extent 2008–2050 Resolution Yearly Data for statistical 2001 (Killeen et al., 2012) analysis Data for calibration 2005 (Killeen et al., 2012) Data for validation 2008 (Killeen et al., 2012) Percentage of natural Vegetation, deforestation and other in the cell

In 2008 starts to increase annually until it reaches 13 million ha in 2025, then replicates the 2005– 2008 trend

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Table 2 Description of dependent variables used in the LuccME/Bolivia model. Category

Name

Description

Distances and connections (accessibility and economic attractiveness)

Distance to roads

Euclidean distance to the logarithm of 2001 2005 closest paved or unpaved road. 2008 Connectivity index via the road net- 2001 work to regional markets (cities 2005 with 470,000 people)

GPM to regional markets

Actualization date

2008

Public Policies (territorial planning and zoning)

Percentage of Protected Areas (PAs) and Indigenous Territories (ITs)

Percentage of cell area covered by protected areas and indigenous territories

2001 2004 2007

Environmental

a

Percentage of flat slope

Percentage of cell area covered by a given slope where X ¼ (1) Steep ( 420%); (2) Moderate (between 11% and 20%); (3) Smooth (between 5% and 10%); (4) Flat (up to 5%)

Not dynamic

Source

Operator to compute Dynamic Unit variable in each cell

ABC (2008), ABC (2010)

Distance to paved and Yes unpaved roads (vector dataset, lines) Yes Connectivity index (vector dataset, lines, points)

km

SERNAP (2005) CI (2008) for PA and FANa, 2012 for IT

Percentage (polygon)

Yes

%

SRTM (Jarvis et al., 2008) 90 m

Percentage of each preprocessed class X (raster)

No

%

INE (2002), ABC (2008) INE (2011), ABC (2008) INE (2011), ABC (2008), ABC (2010)

n/a

FAN: Friends of Nature Foundation (Fundación Amigos de la Naturaleza) provided the indigenous territories spatial data (not published).

(Aguiar et al., 2007) was applied. The GPM relates neighborhoods based on Euclidian distance, proximity, and road network relation (paved or unpaved roads have different weights) and was run for many assumptions (local, national, and export markets; see Supplements 1 and 4). The GPM to regional markets refers to the road connections (Table 2, Supplement 4) from cities with more than 70,000 habitants. The population data were taken from the 2001 Census (INE, 2002), and the INE (2011) population projections were utilized for the years 2005 and 2008. For the GPM, we used the same road maps of 2001, 2005, and 2008 as in the distance to roads variable. Protected areas in Bolivia are divided into two categories: those with integral protection, where the exploitation of natural resources is prohibited (i.e., National, Departmental and Municipal Parks; Wildlife Sanctuary and Refuge), and those categories where the sustainable use of natural resources is allowed (i.e., Integrated Management Natural Area; Wildlife Reserve; Biosphere Reserve) (RAISIG, 2013). After the first statistical analysis, we combined sustainable use and integral protection into one PA category. As PAs change over time, they are treated as a dynamic variable in the model. The updated years are 2001 (all PAs created until 2001), 2004 (PAs created from 2001 to 2004), and 2007 (PAs created from 2004 to 2007). Indigenous Territories in Bolivia are those territories recognized by the State where indigenous people have land and natural resource management rights according to their customs, culture, and organization (Bolivia, 2007; Chumacero et al., 2010). The majority of ITs are in the lowlands and have an important role in future forest conservation, since most are comprised of primary forests in areas with low road connectivity (Müller et al., 2013, 2012). They have had recognition (collective land titling) from the State since 1990 and many are already consolidated (through a title) or are in the process of consolidation. We considered all ITs as a single variable, and after the preliminary statistical analysis we combined the ITs with the PAs into the Protected areas and indigenous territories variable. The environmental variable considered in the model is flat slope (up to 5%). For this variable we used the Shuttle Radar Topography

Mission (SRTM) of 90 m (Jarvis et al., 2008). It was divided into four slope classes: steep (420%), moderate (11–20%), smooth (5– 10%), and flat (o5%). Only the last class (flat) was included in the spatial statistical analysis as an indicator of soil fertility and other environmental variables (see Table 1 and Supplement 1). Flat slope is the only non-dynamic variable in the deforestation model that remains stable in the future. 2.2.4. Model validation For model validation, the multi-resolution analysis (Costanza, 1989; Pontius, 2002) was used to compare model results and observed deforestation during the 2001–2008 period. This method compares observed data with simulated data at different levels of coincidence on a scale from 1 to 20. 2.2.5. Scenario assumptions Scenario assumptions are based on AMAZALERT scenarios for the Brazilian Amazon (Aguiar et al., 2014) that have been adapted for Bolivia. This approach combined exploratory (e.g. “Where, plausibly, are we heading to?”) and normative/anticipatory (e.g. “What do we want and how do we get there?”) scenario approaches. The scenarios vary from Low to High Social Development and High to Low Environmental Development (Fig. 4). We define High Environmental Development as the responsible management of natural resources (e.g., environmental stewardship), which includes high quality and equal access to services, opportunities, and resources supported by strong institutions. These two axes match the IPCC AR5 (2014) global SSPs (Socioeconomic pathways) (Arnell et al., 2011). This study constructed a storyline for each scenario (Table 3). The spatial distribution of dynamic variables (roads, connections, PAs, and ITs) are described in Supplements 2, 3, and 4 and in the Scenario assumptions in Table 4. Scenario A, Sustainability, assumes that all the existing environmental laws are in force, and that policies to reduce deforestation and to preserve and create new PAs and ITs are in place. In this scenario, the country develops an alternative development model, which respects the environment to a greater degree based

G. Tejada et al. / Environmental Research 144 (2016) 49–63

Fig. 4. Representation of AMAZALERT land use scenarios in the context of the Environmental and Social Development axes (also aligned to the IPCC AR5 (2014) Socioeconomic pathways). During the project, qualitative participative scenarios were developed for Brazil and adapted for Bolivia (adapted from Aguiar et al. (2014)).

on the policy framework initiated in the mid-2000s by the MAS party. The Mother Earth Law No. 300, enacted by the MAS party in 2012, recognizes Mother Earth's rights and the State's obligations to ensure these rights. The non-market based mitigation mechanism for forest conservation called “Joint Mitigation and Adaptation Mechanism for the Integrated and Sustainable Management of Forests and Mother Earth” (Decree 1696 of 2013), introduced by the Mother Earth Law, ensures sustainable forest management through the knowledge and rights of indigenous people. This mechanism becomes an alternative to, and/or aligns, with dominant market-based mechanisms like REDD þ (Müller et al., 2014b). Moreover, in this scenario, Law No. 3545 on Community Redirection of the Agrarian Reform Modifies Law 1715 (INRA Law) accelerates collective land titling to indigenous peoples and peasants at the expense of agribusiness and the Supreme Decree No. 29643 of 2012 empowers community forest organizations and enables a more effective multi-level forest governance. Consequently, there are no new roads. Only roads under construction become paved while road maintenance ensures connectivity and development, decreasing deforestation by 50% in 2014. In Scenario B, Middle of the road, we assume that conservationist, agricultural, and extractive policies and initiatives continue to be a motive for tension and contradiction. While the Mother Earth Law claims harmony between nature and development, Bolivia’s main source of income stems from the exploitation of

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hydrocarbons (especially gas). Forest governance continues to be centralized with the national government playing a strong role in decision-making. However, in this scenario, land demand remains at the 2005– 2008 rate (Fig. 6), since government economic and exportation restrictions are imposed on Santa Cruz farmers (Cordoba and Jansen, 2014). The planned road construction and hydrocarbon exploitation in PAs and ITs are delayed until 2045 due to the strong resistance of Amazonian indigenous organizations towards infrastructure projects and agriculture expansion in their territories (Chumacero et al., 2010). In addition, PAs and ITs continue to be protected by Environmental Law No. 1333, the United Nations Declarations on the Rights of Indigenous Peoples (UNDRIP), and Agreement 169 of the International Labor Organization (ILO), among others (Chumacero et al., 2010). Scenario C, Fragmentation, reflects the alliance between the government and the agribusiness sector stated in the “13 Pillars of the Patriotic Agenda 2025” of 2013. This alliance aims to expand the agricultural frontier from 3 to 13 million ha (Bolivia, 2013; Chumacero, 2012; Condori, 2013; IBCE, 2013; Hoiby and ZentenoHopp, 2014). Demand for agricultural land and cattle ranching are increasing considerably. Based on Law No. 337 on the Support of Food Production and Forest Restoration, financial incentives and initiatives sustain the commercialization of agricultural products to benefit and legalize clear-cutting for extensive agriculture. In addition, extractive industries expand over intact forests as well as along roads, including PAs and ITs, violating current environmental laws and international conventions (i.e. Agreement 169 of the ILO). Thus, in line with the international interest to construct roads under IIRSA (COSIPLAN, 2011), all planned and unpaved roads will be paved in 2025 (ABC, 2008, 2010). By 2025, PAs and ITs in oil exploration zones will no longer exist, reflecting the intention to explore oil in these areas (Corz and Lezcano, 2013; Jiménez, 2013). This is the most catastrophic and immediate scenario in terms of deforestation.

2.3. Impacts of deforestation in the key priority areas for biodiversity conservation As an indicator of biodiversity, we used a map of the Key Priority Areas for Biodiversity Conservation of Bolivia (Araujo et al., 2010), here on referred to as Priority Biodiversity Conservation Zones. This map combines priority areas for ecological functions, biological viability, and representation of biodiversity. We estimated the extension of the Priority Biodiversity Conservation Zones that could be affected by deforestation in 2008 and in each scenario (Sustainability, Middle of the road, and Fragmentation) in 2050.

Table 3 Brief storylines of each scenario describing the socioeconomic and institutional contexts. Scenario A: Sustainability

Scenario B: Middle of the road

Scenario C: Fragmentation

“Relying on strong governance; the Environmental and Mother Earth laws are enforced. The Amazon rural landscapes are preserved as protected areas and indigenous territories. Existing roads are improved. The society and diversified economy are well organized and based on the industrial, forest and agricultural sectors. The strong agricultural sector uses intensive and environmentally safe methods”.

“There is the same economic growth trend as in the period from 2005 to 2008. Santa Cruz farmers are opponents of the government, slowing down the growth of the agricultural frontier. The indigenous of the Amazon defend their territories and delay road construction and oil exploration in protected areas and indigenous territories. Deforestation follows the same trend as in 2005–2008; mechanized agriculture and cattle ranching are the main causes of deforestation”.

“The Government and the Santa Cruz farmers reach an agreement to expand the agricultural frontier to 13 million ha by 2025 to ensure food sovereignty. All roads, both planned and unpaved, will be paved in 2025. The economy is based on mechanized agriculture, oil exploration and cattle ranching. Road construction and market commodities for soy are influenced by international interest”.

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Table 4 Assumptions for deforestation scenarios in the LuccME/Bolivia model (Bolivian lowlands).

Scenario A: Sustainability

2015

2025

Roads GPM to regional marketsa PA and IT Deforestation rate

No new roads No new roads

Roads under construction are unpaved Roads under construction are unpaved

PAs and ITs maintained Trend of 2005–2008 until 2013, then decrease by 50%

New PAs are created

Unpaved roads are paved. Roads under construction are unpaved

Planned roads are unpaved Unpaved roads are paved Planned roads are unpaved. Unpaved roads are paved

Scenario B: Middle of the road Roads

Scenario C: Fragmentation

GPM to regional marketsa PA and IT

No new PA or IT

Deforestation rate

Trend of 2005–2008

Roads

Unpaved roads are paved. Roads under construction are unpaved

GPM to regional marketsa PA and IT Deforestation rate

Unpaved roads are paved. Roads under construction are unpaved

Trend of 2005–2008

Roads under construction are paved. Planned roads are paved Unpaved roads are paved. Roads under construction are unpaved Roads under construction are paved. Planned roads are paved No new PA or IT PAs and ITs no longer exist in oil exploration zones Annual increase beginning in 2008 until 13 million ha are removed by 2025 (for in- Reaches 13 million ha, then replicates tensive agriculture); cattle ranching and small-scale agriculture replicate the 2005– the 2005–2008 trend 2008 trend.

GPM: generalized proximity matrix; PA: protected areas; IT: indigenous territories (Terriotorios Indígenas Originarios Campesinos [TIOC] according to Bolivian Law). a

Connectivity index via the road network to regional markets (cities with 470,000 people).

2045

Planned roads are paved

PAs and ITs no longer exist in oil exploration zones Trend of 2005–2008

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Variables

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2.4. Carbon emissions from deforestation For carbon storage, we used the mean above ground biomass data of Saatchi et al. (2011) (1 km  1 km resolution) for each of our cells (25 km  25 km) to calculate the biomass in Pg (carbon is 50% of biomass) for 2008 and for each scenario in 2050. For the deforested areas, we calculated the deforestation gross CO2 (Pg CO2 ¼44/12*Mg C) potential emissions for 2008 and for each scenario in 2050, assuming that 100% of CO2 emissions take place when deforestation occurs (not considering regrowth or other parameters).

3. Results and discussion 3.1. Model performance The performance of the LuccME/Bolivia model was satisfactory, with a spatial adjustment index between the observed and simulated patterns of deforestation resulting in 69% at the first level, 75% at the fifth, and 81% at the tenth level of the validation process (Fig. 5). The model effectively captures the spatial distribution of the deforestation process, especially near the previously cleared areas due to the LuccME allocation component that uses spatial lag regression. Only in the southern region does the model slightly amplify the deforestation distribution; this could be due to the strong influence of the connection to regional markets that gives more weight to paved roads than unpaved roads (Supplement 4). 3.2. Deforestation scenarios by 2050 The deforestation scenarios for the Bolivian Amazon by 2050 represent future deforestation or forest degradation (Fig. 7). The different assumptions for each scenario make the deforestation trends vary in space and time. Deforestation demand has a linear trend (Fig. 6) compared to other deforestation projections (i.e. Müller et al., 2014a; Sangermano et al., 2012). Our total deforested area in each scenario is very large and well represents our attempt to show the trend (deforestation rate of 2005–2008) under the

Fig. 6. Projection of the demand for each scenario by 2050. Scenario A, Sustainability; Scenario B, Middle of the road; and Scenario C, Fragmentation.

MAS party, which has governed since 2006. Until 2025, the differences among the scenarios have not been relevant, whereas in 2050 they become considerable. In a sustainable scenario (Scenario A), deforestation reaches 17,703,786 ha in 2050, which is more than double that of the cleared areas in 2008. By maintaining the same trend as from 2005 to 2008 (Scenario B) deforestation reaches 25,698,327 ha, 7.9 million ha, or 30% more than for Scenario A in the same period. With weak governance, new roads, few PAs and ITs, and a large increase in agricultural land, deforestation is more than 50% (20 million ha) than that in Scenario A, reaching 37,944,434 ha by 2050 (Fig. 6). The spatial distribution of deforestation is strongly influenced by road connectivity (consequently from regional markets) and by previous deforested areas in the three scenarios (Figs. 7 and 8). In 2020, the influence of the recently paved roads appears to not yet be significant in Scenarios A and B and only slightly in Scenario C. In 2025 the increment of land demand, reduction of PAs and ITs, and pavement of planned and unpaved roads in Scenario C make deforestation and degradation more evident, expanding towards the north and, more conspicuously, towards the south in 2030. Scenario B shows the expansion of degradation through new

Fig. 5. Observed (Killeen et al., 2012) versus simulated deforestation in 2008. The red cells indicate completely deforested areas, while the yellow or light green cells (40– 70%) denote progressive degradation (anthropic influence) zones. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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Fig. 7. Deforestation patterns for the Bolivian Amazon in different scenarios. Scenario A, Sustainability; Scenario B, Middle of the road; Scenario C, Fragmentation. Deforested areas are shown in red (80–100%), while progressive degradation (anthropic influence) zones (40–70%) are shown in yellow or light green. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

roads, as it is intensified in previously deforested areas. By 2040 and 2050, only Scenario A remains with a deforestation patterns similar as those of 2008, with large areas of natural vegetation. In Scenario B and more so in Scenario C, the transition from degradation to deforestation is intensified leaving only few areas of intact forest (Fig. 7). To better understand the impacts of deforestation and degradation, we divided Bolivia into regions with similar causes of deforestation, which are discussed in the next section. The Priority Biodiversity Conservation Zones were well preserved until 2008, with only 5% being affected by deforestation (Table 5). In 2050, the situation becomes dramatically different. In Scenario A, more than 1 million ha will undergo deforestation, affecting 14% of the total Priority Biodiversity Conservation Zones. Scenario B shows almost 25% of these zones as being deforested. Lastly, in Scenario C, deforestation reaches more than 4 million ha (38%) (Table 5 and Fig. 8). It is worthy to note the impact of deforestation in terms of

carbon emissions. Before 2008 (considering accumulated deforestation), CO2 emissions reached 1.53 Pg (1 Pg C ¼1015 C g); in 2050, Scenario A reached 3.75 Pg, Scenario B reached 5.33 Pg, and Scenario C 7.65 Pg, more than 5 times total emissions before 2008 (Table 6). 3.3. Discussion: the future of the Bolivian forest Bolivia’s deforestation dynamics differ according to sub-regional characteristics such as population density, road network, and biophysical features (slope, precipitation, temperature or soil fertility). Here, we discuss the scenarios over the forest mask in five regions: (1) Santa Cruz city, the most populated and deforested area of Bolivia characterized mainly by mechanized agriculture (soybean, sugarcane, rice and sunflower production) (Killeen et al., 2008; Müller et al., 2014a); (2) Chiquitanía, a unique ecosystem in the eastern Santa Cruz Department (Fig. 8) with large extensions of chiquitano and cerrado forests, and cattle raising as the main

G. Tejada et al. / Environmental Research 144 (2016) 49–63

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Fig. 8. Deforestation scenarios for 2050 over the forest mask, Priority Biodiversity Conservation Zones from Araujo et al. (2010). (a) Observed deforestation in 2008, five regions with similar deforestation dynamics are shown in light blue; (b) Scenario A, Sustainability; (c) Scenario B, Middle of the road; (d) Scenario C, Fragmentation. The red cells indicate deforested areas (80–100%), while the yellow or light green cells (40–70%) represent progressive degradation (anthropic influence) zones. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

cause of deforestation (less small-scale agriculture) (Müller et al., 2014a); (3) the Northern Amazon (near the Brazilian border) forests with very low population density, where cattle raising and smallscale agriculture are the main causes of deforestation (Pacheco, 2006); (4) Corridor Amboró-Madidi (CAM) national conservation corridor, which comprises piedmont and montane forests (Supplement 5), with local and global relevance for biodiversity conservation (Araujo et al., 2010; Ibisch et al., 2007; Ibisch and Merida,

2004) and where small-scale agriculture causes most deforestation (mainly coca cultivation) (Killeen et al., 2008; Müller et al., 2012); and (5) Chaco, where there are great extensions of boliviano-tucumano and chaco dry forests and few paved roads. All the scenario assumptions for each of the mentioned regions such as PAs, Priority Biodiversity Conservation Zones, paved and unpaved roads, and deforestation trend over the forest mask for 2050 are shown in Fig. 8.

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Table 5 Deforestation in the Key Priority Areas for Biodiversity Conservation of Bolivia (Araujo et al., 2010) in different deforestation scenarios for 2050. Deforestation Deforestation in the Key Priority Scenarios for 2050 Areas for Biodiversity Conservation (ha)

Scenario A: Sustainability Scenario B: Middle of the road Scenario C: Fragmentation Total area of Priority Conservation Zones

(%)

Increased deforestation compared to 2008 Deforestation in (ha) 2008 (ha)

1,717,898

14

548,160 (5%)

1,169,739

2,843,401

24

2,295,242

4,565,493

38

4,017,333

12,000,100

100

3.3.1. Scenario A “Sustainability” In Santa Cruz city, the degraded areas become deforested by 2050; Degradation extends to the east and partly to the west of the Chiquitanía, while the remaining area is still preserved. In the Northern Amazon, degradation is observed only in the main cities where large extensions of forests are preserved. Despite that 14% of the Priority Biodiversity Conservation Zones are deforested, the CAM conservation corridor still plays a fundamental role in biodiversity conservation at national and international scales (Ibisch et al., 2007; Ibisch and Merida, 2004) since only Carrasco and Amboró National Parks are degraded and there are newly created PAs. Despite some degradation in Chaco close to the main cities, considerable forest extensions are still preserved (Fig. 8). As described in the Sustainability Scenario, Bolivia remains a country with considerable development and environmental governance (Fig. 8). Total deforestation (17.7 million ha) is not as low as in other sustainable scenarios, but with low connectivity (improving the current road network), ITs, and new PAs, deforestation is pronounced in previously deforested or degraded areas and advances slightly towards intact forest areas. For this scenario, the State has to play an active role in the environmental and agricultural sectors. First, the State needs to foster the protection of environmental functions of livelihoods. This implies securing food without increasing pressures on forests and establishing a development model that is less sensitive to the global demands for grains and beef. Second, agricultural policy recommendations enhanced by the Productive Revolution Law of 2011 need to be implemented to strengthen “sustainable production systems”. This includes establishing efficient practices based on innovation and technological development in current mechanized agriculture and cattle raising zones and emphasizing agro-ecological practices in small-scale agriculture followed by key actors. This scenario, however, is very unlikely to be implemented due to strong contradictions between the current legal framework’s focus on the government’s rhetoric of Mother Earth rights and the dominance of neo-extractivist practices that

support unsustainable natural resource exploitation (Cordoba and Jansen, 2015; Gudynas, 2011). 3.3.2. Scenario B “Middle of the road” In the surroundings of Santa Cruz city, deforestation expands in areas that are already degraded. In Trinidad, Chiquitanía, and in the Northern Amazon, degradation starts along the roads and main cities (Fig. 8) due to better connections to regional markets as perhaps the increase of cattle ranching (the main deforestation cause from 2000 through 2010 [Müller et al., 2014a]); Only slight degradation in the CAM is observed, south of TIPNIS, Apolobamba, and Madidi, which, in this scenario are no longer PAs. The reason could be that paved roads surrounding the CAM are foreseen only in 2045. Deforestation affected the Priority Biodiversity Conservation Zones by 24%. Chaco has a great deforestation impact on the southern cities and along the roads (Fig. 8). In the long term, this scenario could be the same as the Fragmentation scenario, with the same drivers but in different years. Although the country retains a law to ensure the rights of Mother Earth, the government’s efforts seem insufficient at applying its environmental agenda and instead continue their current deforestation trends. First, revenues from hydrocarbons are crucial for funding the government’s political aims towards redistribution and poverty alleviation. Second, global demand on commodities reverses important initiatives to create more sustainable agricultural production systems. Third, the country's economy is based on the export of raw materials facilitating the expansion of extractive industries. Finally, the government tends to follow the plan of the basic road network integrated with IIRSA that increases the pressure on forests. However, a sign of the probable realization of this scenario is the case TIPNIS, where lowland indigenous communities have contested the construction of a highway through the territory because of lack of technical and legal procedures (e.g., an environmental impact study or popular consultation in breach of current laws). Indigenous organizations place pressure on the government to revoke plans to build this road and move forward with the project (Cordoba and Jansen, 2015). Current and future social pressures from indigenous and peasant populations could oppose increasing deforestation in favor of a less dramatic scenario. 3.3.3. Scenario C “Fragmentation” In Santa Cruz city, deforestation expands in all directions, reaching east Chiquitanía where areas of mechanized agriculture seem to have expanded. Because of the impact of road construction in 2025 and the great demand for land, deforestation expands considerably followed by degradation in all populated areas, principally in the main cities (Fig. 8). CAM, Carrasco and, Amboró become deforested extensions of Santa Cruz city. Without legal protection, current threats such as small-scale agriculture (mainly coca crops) and cattle ranching activities may dominate, leaving no intact forests but only degraded areas (except to the east of what was Madidi PA). The Priority Biodiversity Conservation Zones are significantly affected by deforestation (38%). Lastly, Chaco deforestation intensifies in the degraded areas.

Table 6 Carbon emissions from deforestation for 2008 and 2050 scenarios. Biomass (Pg) Deforestation until 2008 Deforestation Scenarios for 2050: Scenario A: Sustainability Scenario B: Middle of the road Scenario C: Fragmentation

Deforestation (ha)

Carbon loss (Pg)

CO2 emissions (Pg)

10.65

7,548,559

0.42

1.53

8.61 7.75 6.26

17,703,786 25,698,327 37,994,434

1.02 1.45 2.20

3.75 5.33 8.06

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Few forested areas are left intact as the Kaa Iya, Noel Kempff, and Rios Blanco y Negro PAs (Fig. 8, Supplement 5). This scenario attempts to show the worst situation in terms of deforestation. Recent news regarding policies and alliances to extend agricultural land provide some evidence in support of the likelihood of this scenario (Condori, 2013; IBCE, 2013). The concomitant rise in food prices (as the one for 2007–2008) and current fall in oil prices on the global market strengthens the government’s alliance with the agribusiness to bring down domestic food prices and to increase the country's revenues. The strong power of agribusiness elites in the lowlands, who benefit from large tracts of land and unsustainable agricultural practices, is likely to influence and weakened current environmental governance. Also, the road network of IIRSA will be implemented. Contradictions between these environmental governances and government practices could speed the process of deforestation. Revenues from hydrocarbons are crucial for funding the MAS party’s political aims towards redistribution and poverty reduction, which work against the conservation of PAs and ITs. 3.4. Implications for decision-making Harmonized food, fiber, and beef supply, along with natural resource conservation in Bolivia, will depend on broader land use policies and intensification. Also, the recovery of degraded, abandoned, or underused lands may lead an important strategy to increase land use efficiency and reduce deforestation (Lambin et al., 2013). In other words, Bolivian land use policies should treat land use systems as open systems linked with remote land use drivers inside and outside the country. Otherwise, region-focused measures run the risk of simply displacing deforestation pressure across the country, as previously observed in other tropical countries (Dalla-Nora et al., 2014; Lambin and Meyfroidt, 2011). Moreover, institutional challenges still exist in Bolivia, such as the development of alternative markets or robust incentives for biodiversity conservation. Law enforcement by itself cannot ensure sustainable land use control over the agricultural frontiers. As suggested in the reviewed literature, farmers are likely to reduce their managed acreage only if land becomes a scarce resource (Barretto et al., 2013). In this sense, providing new incentives for ecosystem service conservation, beyond carbon sequestration, along with national coverage can become an important mechanism for limiting Bolivian lowland deforestation. However, political measures following this line of reasoning in Bolivia are strongly opposed.

4. Conclusions We assessed deforestation in the Bolivian lowlands by generating a spatially-explicit land cover change model, which considers deforestation driving factors, different land demands, land policies and governance arrangements, and ran it under three scenarios until 2050: Sustainability (optimistic), Middle of the road (similar to business as usual) and Fragmentation (worst). In the Sustainability scenario, the conservation corridor Carrasco-Amboró and large intact forest areas are preserved, even though deforestation is pronounced in previously deforested or degraded areas. Only by applying strict environmental laws against illegal deforestation and infrastructure projects in PAs, ITs, and Priority Biodiversity Conservation Zones is this scenario likely to happen. In the Middle of the road scenario, deforestation and degradation occur in territories where PAs and ITs no longer exist along the roads. In the long term, this scenario could be the same as Scenario C; the difference being that roads will only be paved in 2045, indicating they are a determining factor in the deforestation

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process. In the Fragmentation scenario, Bolivian lowlands are almost completely fragmented by deforestation and degradation. Intact forest areas in the CAM no longer exist, 38% of Priority Biodiversity Conservations Zones are deforested, and only small forests in the Noel Kempff and Kaa Iya PAs remain. Nonetheless, Scenario C is based on the intentions to establish policies supporting agricultural frontier expansion, oil exploration, and road construction in PAs (Chumacero, 2012; Chumacero et al., 2010; Condori, 2013; Corz and Lezcano, 2013; Jiménez, 2013). Preventing the expansion of the agricultural frontier in the Bolivian lowlands cannot ensure biodiversity conservation or carbon savings in the absence of complementary measures committed with land use efficiency, controlled land use expansion, and new economic alternatives. In this perspective, recognizing land use systems as open and human-driven systems is a first and central challenge in designing more efficient land use polices. Otherwise, managing a transition towards more sustainable land use would become utopian. Land cover change scenarios are useful in showing how present and future decisions could affect deforestation trends in the Bolivian lowlands. A real-life scenario could be a combination of the three scenarios presented herein. Observing the potential impacts of deforestation in a spatially-explicit way, as a valuable discussion on the existing laws presented in this study, can help to prevent (or reduce) and influence policy makers’ actions to improve forest governance. Our data were also used to estimate deforestation gross carbon emissions of Bolivian lowlands as an initial approach for 2050 in different scenarios. Using the carbon emissions model of Aguiar et al. (2012b), we could improve this approximation by including more parameters to calculate net carbon emissions. Participatory scenario construction, such as the experience in Aguiar et al. (2014) that included all the Brazilian deforestation stakeholders, might enrich our study and help decision makers to understand the relationship of current policies and future deforestation. Moreover, local or regional deforestation analyses could be accomplished by using a smaller cell size (e.g. 5 km  5 km).

Acknowledgements This study is part of the Land Use and Cover Change Scenarios for the Madeira River Basin of the AMAZALERT Project 282664 founded by the European Union's Seventh Framework Programme. We thank the São Paulo Research Foundation (FAPESP) for Grant no. 2013/20616-6, Liliana Soria from the Noel Kempff Mercado Museum of Natural History for providing the Land Use and Cover Change Data. We also thank Daniel Larrea from the Friends of Nature Foundation (FAN) and Celso vonRandow from the Earth System Science Center (CCST-INPE).

Appendix A. Supplementary material Supplementary data associated with this article can be found in the online version at 10.1016/j.envres.2015.10.010.

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