Explaining farming systems spatial patterns: A farm-level choice model based on socioeconomic and biophysical drivers

Abstract

Abstract CONTEXT Efforts to bring together landscape analysis and farming systems have failed to explain the drivers behind their spatial distribution. Since agricultural landscapes are an outcome of farmers decisions, understanding the role of socioeconomic and biophysical drivers of such decisions is essential for policy-making targeting landscape-level provision of public goods and ecosystem services from agriculture. OBJECTIVE Aiming to better understand the role of these drivers, we focused on a region dominated by agricultural use, with extensive variability in biophysical and socioeconomic conditions. A typology of farming systems was derived from spatially explicit farm-level data provided by the Portuguese agency responsible for Common Agricultural Policy payments, for 2017. Farms were thoroughly characterized through relevant biophysical and socioeconomic variables considered as potential drivers of farming systems. METHODS A random forest approach was used to develop a farming system choice-model, dependent on those biophysical and socioeconomic variables. Variable importance measures and partial dependence plots were used to explore the role of these variables in explaining the spatial distribution of farming systems and to predict spatial patterns at the landscape scale. RESULTS AND CONCLUSIONS Results showed that both biophysical and socioeconomic drivers play a significant role in the spatial distribution of most agricultural systems. Its importance, however, varies significantly across farming systems, being crucial for some and almost irrelevant for others. Farm size and climate have proved to be the most relevant drivers for most farming systems. Overall, our approach proved to be quite accurate in predicting patterns of farming systems at the landscape scale. SIGNIFICANCE The proposed framework has shown great potential as a tool to support information-based policy design to improve agricultural landscape planning, by linking farm-level management decisions with the provision of socially valued public goods from agriculture, perceived at the landscape-level.