Food production and biodiversity are not incompatible in temperate heterogeneous agricultural landscapes

We need landscape-scale approaches to design and manage agro-ecosystems that can sustain both agricultural production and biodiversity conservation. In this study, yield figures provided by 299 farmers served to quantify the energy-equivalents of food production across different crops in 49 1-km2 landscapes. Our results show that the relationship between bird diversity and food energy production depends on the proportion of farmland within the landscape, with a negative correlation observed in agriculture dominated landscapes (≥ 64–74% farmland). In contrast, neither typical farmland birds nor butterflies showed any significant relationship with total food energy production. We conclude that in European temperate regions consisting of small-scale, mixed farming systems (arable and livestock production), productivity and biodiversity conservation may not be purely antagonistic, particularly when (semi-)natural habitats make up a large fraction of the landscape (≥ 20%).

Methods
The yield data was collected during farmer interviews and information on crop area per landscape was obtained from cantonal agricultural surveys and crop mapping. The data on bird and butterflies was collected on the field by the Swiss Biodiversity Monitoring. The yield dataset was completed using multiple imputation. After post processing the completed data set was used to calculate the (mean) yield per crop per landscape, which was consecutively converted into food energy (given as metabolizable energy for human consumption), summed up and scaled to landscape level. Generalized linear models (GLM) were used to describe the relationship between biodiversity, total produced food energy and farmland area per landscape. Species richness, abundance and Pielou’s evenness index of birds and butterflies were used as response variables. Food energy production per landscape in gigajoule (GJ) and the amount of farmland in hectare (ha) were included as explanatory variables.