Policy options to reduce deforestation in the Bolivian lowlands based on spatial modeling of land use change

Tropical deforestation represents one of the most urgent environmental problems of our time; it contributes heavily to climate change, causes immense losses of biodiversity and endangers important environmental services. Bolivia is among the countries with the highest deforestation rates in the world. In light of the current international efforts to reduce deforestation within the framework of REDD, effective and efficient country-specific policy options need to be identified to make progress on the ground. A prerequisite for the prioritization of such policy options is a detailed understanding of the complex processes driving deforestation. Spatial models can contribute valuable information to this end. They can provide quantitative evaluations of hypothesized drivers of deforestation in the past and also generate scenarios that represent probable developments in the future. This study applies spatially explicit regression models as a key instrument for the systematic identification of specific policy options suitable for mitigating the expansion of the main forest-depleting land uses. The entire study is based on Bolivia as a model country. The expansion of mechanized agriculture in the department of Santa Cruz is analyzed as a first case study. Soybean production has converted this area into one of the hotspots of deforestation in the entire Amazon. A logistic regression model covering five time steps (1976, 1986, 1992, 2001 and 2005) identifies the main determinants of the expansion of mechanized agriculture and explores the development of their effects over time. It shows that – while deforestation dynamics have been generally stable over time – there is a tendency of increased penetration into the more humid Amazonian forests in northern Santa Cruz, a development that is also known from Brazil. The model's results are thoroughly validated, including a comparison between projected and observed deforestation patterns and the investigation of hidden correlations between independent variables. The case study shows that logistic regression is a suitable tool for the purposes of the entire study, provided that careful evaluations and plausibility checks of the model outputs are conducted. In a subsequent analysis covering the entire Bolivian lowlands, three main proximate causes of deforestation are identified: mechanized agriculture was responsible for 54% of deforestation between 1992 and 2004, followed by cattle ranching with 27 %, and small-scale agriculture with 19%. A multinomial logit model is applied to analyze the determinants of each of these proximate causes of deforestation. The results suggest that the expansion of mechanized agriculture occurs mainly in response to good access to export markets, fertile soil and intermediate rainfall conditions. Increases in small-scale agriculture are mainly associated with a humid climate, fertile soil and proximity to local markets. Forest conversion into pastures for cattle ranching occurs mostly irrespective of environmental determinants and can mainly be explained by access to local markets. Land use restrictions, such as protected areas, seem to prevent the expansion of mechanized agriculture but have little impact on the expansion of small-scale agriculture and cattle ranching. An analysis of future deforestation trends reveals possible hotspots of future expansion for each proximate cause and specifically highlights the possible opening of new frontiers of deforestation due to mechanized agriculture in the areas of Puerto Suarez and San Buenaventura. The quantitative insights of the model are substantiated with a qualitative analysis of historical processes that have shaped land use patterns in different zones of the Bolivian lowlands to date. Whereas the quantitative analysis effectively elucidates the spatial patterns of recent agricultural expansion, the interpretation of long-term historic drivers reveals that the timing and quantity of forest conversion are often triggered by political interventions and historical legacies. In a third analysis, a systematic approach is developed in order to prioritize policy options for effective and efficient deforestation reduction, making use of the model outputs, among other things. Again, Bolivia is taken as a model country. The derivation of policy options is based on analyses of the spatial and economic potential of agricultural expansion, the expected costs of deforestation reduction, and the current legal and political framework in Bolivia. All analyses focus on the three proximate causes of deforestation; and specific policy options are discussed for these types of land use. It is concluded that, although mechanized agriculture caused more than half of all past deforestation in lowland Bolivia, cattle ranching activities should be targeted as a priority since their expansion threatens forests in many different locations and improvements could be achieved at relatively low costs. Enforcing legislation while strengthening institutions on both national and local levels is of utmost importance for the reduction of the expansion of all three land use categories. Specific measures should aim at giving an advantage to more efficient production on existing farms over the expansion into forested areas. In this context, a higher legal fee for deforestation has potential to mitigate forest conversion due to mechanized agriculture and cattle ranching farms, while a removal of subsidies for agro-diesel may specifically reduce the expansion of mechanized agriculture. Such measures could be complemented by a support for higher production efficiency, such as better access to fertilizer and techniques allowing increased cattle stocking densities. The expansion of small-scale agriculture seems to be difficult to control, due to the large number of agents; measures should focus on mitigating the encroachment into areas with land use restrictions, fostering more sustainable and space-efficient agricultural practices, as well as off-farm employment. Models of deforestation are found to be important analytical tools for a better understanding of the processes leading to deforestation; they can render important information for the development of policy options to combat deforestation. Further investigations may explore the possibilities of building more complex scenarios by adding dynamic elements that are contained in some existing land use modeling frameworks. In the outlook of this study, the mapping of opportunity costs of forest conservation is shortly introduced as a promising possibility of generating scenarios based non-spatial factors such as prices of agricultural goods. It is however concluded that, for practical applications, it seems reasonable to keep the transparency of models as high as possible in order to allow for constant plausibility checks of the model outputs. The study concludes that more research is needed to identify and evaluate suitable policy options to reduce deforestation on the ground. In the discussion on REDD, only little attention seems to be given to the development of mitigation strategies for large forest clearings driven by corporate agents and large cattle farms. This may be due to a certain prevalence of traditional approaches to biodiversity conservation within selected conservation areas and an unjustified focus on smallholders. Also the strong focus on market-based solutions may be questionable; according to this study it would be more appropriate to directly support the governments of tropical countries to implement the most promising measures. It may also be important to target existing markets that drive deforestation, i.e., global markets for beef, soybean, palm oil and tropical timber stemming from clear-cuts.