The role of accessibility for land use and land cover change in the Brazilian Amazon

Roads and infrastructure are fundamental preconditions for the large-scale conversion of natural forests into agricultural landscapes. Therefore, geographic differences in accessibility are critical for understanding land use and land cover change (LULCC) dynamics. As one of the most dynamic agricultural frontiers globally, the Amazon has much attention in LULCC research. While most studies account for roads and infrastructure, LULCC research often relies on one-dimensional measures of accessibility (e.g., Euclidean distances), which may not optimally represent the underlying latent variable concept. In this study, we demonstrate how alternative concepts and measures of accessibility (specifically travel time maps) can have considerably different explanatory value in the prediction of LULCC. We adopt a panel-data model to explain the geographical distribution of pasture and crop expansion in the Brazilian Amazon using land cover data and travel time maps generated with high-quality representations of existing official and non-official road infrastructure. Our approach's novelty consists of comparing travel time to different markets during the wet- and the dry season and their effect on the allocation of LULCC within a macro scale modeling approach. Our results suggest that (1) pronounced differences between wet- and rainy season accessibility (due to mad quality) increase the likelihood of pasture expansion and reduce the likelihood for crops, and (2) that alternative measures of infrastructure access (e.g., to markets versus towns or processing facilities) can explain different socio-economic aspects of LULCC. Our findings suggest that bad infrastructure quality might severely limit the possibility of establishing a less land-intensive agricultural model in the Amazon and that LULCC research can significantly benefit from improved and context-specific measures of accessibility.