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Abstract
The global recognition of modern agricultural practices’ impact on the environment has fuelled policy responses to ameliorate environmental degradation in agricultural landscapes. In the US and the EU, agri-environmental subsidies (AES) promote widespread adoption of sustainable practices by compensating farmers who voluntarily implement them on working farmland. Previous studies, however, have suggested limitations of their spatial targeting, with funds not allocated towards areas of the greatest environmental need. We analysed AES in the US and EU—specifically through the Environmental Quality Incentives Program (EQIP) and selected measures of the European Agricultural Fund for Rural Development (EAFRD)—to identify if AES are going where they are most needed to achieve environmental goals, using a set of environmental need indicators, socio-economic variables moderating allocation patterns, and contextual variables describing agricultural systems. Using linear mixed models and linear models we explored the associations among AES allocation and these predictors at different scales. We found that higher AES spending was associated with areas of low soil organic carbon and high greenhouse gas emissions both in the US and EU, and nitrogen surplus in the EU. More so than successes, however, clear mismatches of funding and environmental need emerged—AES allocation did not successfully target areas of highest water stress, biodiversity loss, soil erosion, and nutrient runoff. Socio-economic and agricultural context variables may explain some of these mismatches; we show that AES were allocated to areas with higher proportions of female producers in the EU but not in the US, where funds were directed towards areas with less tenant farmers. Moreover, we suggest that the potential for AES to remediate environmental issues may be curtailed by limited participation in intensive agricultural landscapes. These findings can help inform refinements to EQIP and EAFRD allocation mechanisms and identify opportunities for improving future targeting of AES spending.
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1 School of Geography, University of Leeds, Leeds LS2 9JT, United Kingdom
2 Department of Geographical Sciences, University of Maryland, LeFrak Hall 7251, College Park, Maryland 20742, United States of America
3 UFZ—Helmholtz Centre for Environmental Research, Department of Computational Landscape Ecology, Permoserstr. 15, 04318 Leipzig, Germany
4 Environmental Science and Technology Department, University of Maryland, College Park, Maryland, United States of America
5 National Socio-Environmental Synthesis Center (SESYNC), 1 Park Place, Annapolis, MD 21401, United States of America
6 Sustainability Research Institute, University of Leeds, Leeds LS2 9JT, United Kingdom
7 UFZ—Helmholtz Centre for Environmental Research, Department of Computational Landscape Ecology, Permoserstr. 15, 04318 Leipzig, Germany; Martin Luther University Halle-Wittenberg, Department of Community Ecology, Halle (Saale), Germany
8 UFZ—Helmholtz Centre for Environmental Research, Department of Computational Landscape Ecology, Permoserstr. 15, 04318 Leipzig, Germany; University of California, Davis, Department of Environmental Science and Policy, One Shields Ave, Davis, CA 95616, United States of America
9 Department of Agricultural and Resource Economics, University of Maryland, College Park, Maryland, United States of America