10th EAAE PhD Workshop
EAAE PhD WorkshopAbstract
High productivity in the agri-food sector is pivotal for a sustainable future under rising food demand, a transition towards circular economies, and limited natural resources. To effectively support high productivity levels, accurate sector, land, and farm productivity measures are essential. With climate change, however, agro-climatic conditions change and alter production conditions. For instance, more frequent climate extremes lower the potential output and increase production risks. This poses a challenge in maintaining high productivity levels but also in the adequate measurement of productivity and related drivers. To quantify to which extent farms’ productivity can be attributed to climate extremes, e.g., losses due to waterlogging or droughts, farms’ management, or policy, we explicitly account for agro-climatic conditions in productivity measurement. Therefore, we propose a four-component SF to disentangle weather-related, managerial, and policy-induced and overall farm inefficiency but distinguish between transient and persistent components. Using an unbalanced panel of 3,401 specialized crop farms in Germany for 2004-2020 from the EU Farm Accountancy Data Network, we account for farm and regional heterogeneity and technological progress. We select relevant weather variables using machine learning and assign weather realizations based on probabilistic farm locations. Our research will contribute to the productivity analysis and climate change economics literature.
The EAAE PhD workshops are an opportunity for doctoral students across Europe to meet their peers and to get constructive feedback from leading agricultural economists on their work. The workshops are open to any students, regardless of their stage in their PhD program. They include both academic sessions, professional development opportunities and social events designed to facilitate meetings and networking opportunities. I will present our work on the efficiency analysis and climate extremes of FADN data.
