Surrogate modelling of a detailed farm-level model using deep learning

Technological change co-determines agri-environmental performance and farm structural transformation. Meaningful impact assessment of related policies can be derived from farm-level models that are rich in technology details and environmental indicators, integrated with agent-based models capturing dynamic farm interaction. However, such integration faces considerable challenges affecting model development, debugging and computational demands in application. Surrogate modelling using deep learning techniques can facilitate such integration for simulations with broad regional coverage. We develop surrogates of the farm model FarmDyn using different architectures of neural networks. Our specifically designed evaluation metrics allow practitioners to assess trade-offs among model fit, inference time and data requirements. All tested neural networks achieve a high fit but differ substantially in inference time. The Multilayer Perceptron shows almost top performance in all criteria but saves strongly on inference time compared to a Bi-directional Long Short Term Memory.

Automated summary

Technological change has a significant impact on both agri-environmental performance and farm structural transformation. The integration of farm-level models with agent-based models capturing dynamic farm interactions can provide meaningful impact assessments of related policies. However, this integration faces challenges in model development, debugging, and computational demands. Surrogate modeling using deep learning techniques can overcome these challenges and facilitate simulations with broad regional coverage.