Landscape context and farm characteristics are key to farmers' adoption of agri-environmental schemes

Abstract

Agri-environmental schemes (AES) belong to the main instruments of the European Union’s Common Agricultural Policy (CAP) to foster sustainable farming practices that contribute to the conservation of biodiversity, ecosystem services, climate change mitigation and adaptation. Farmers’ attitudes towards these voluntary measures and the socio-economic factors influencing their decisions have been widely studied through interviews or surveys. However, it remains unclear whether the spatial patterns of AES adoption can be predicted based on farm structural and environmental variables.In this study, we combine biophysical maps with information on farm structure and landscape context to model the influence of these variables on AES implementation at both farm and field level. We fit a set of regression models using farm characteristics (e.g. farm size and specialization, field size) as well as landscape context variables (e.g. elevation, soil fertility, presence of protected areas) as predictors using the Mulde River Basin in Germany as a case study.Our analysis reveals that the spatial distribution of AES can be explained by these factors:AES tend to be implemented by larger farms specialized in permanent grassland cultivation and are typically located in protected areas with lower soil fertility. At the field level, AES are preferably allocated on fields close to water bodies and small woody features. The effect of the different environmental and farm-related variables on AES adoption varies across different AES-schemes indicating the complex set of factors farmers take into consideration when allocating a scheme on a field.As our study shows a quantifiable tendency to place AES in unproductive and/or protected areas, it supports previous evidence criticizing the global tendency to allocate environmental protection measures in regions with low agricultural value, which results in conservation goals not being met. The models presented here can support the development of future AES, e.g. by developing schemes tailored to fit farms and fields that are currently unlikely to adopt AES, thus improving the effectiveness of environmentally friendly agricultural practices.