Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/282906 
Year of Publication: 
2024
Citation: 
[Journal:] Journal of Agricultural Economics [ISSN:] 1477-9552 [Volume:] 75 [Issue:] 1 [Publisher:] Wiley [Place:] Hoboken, NJ [Year:] 2024 [Pages:] 235-260
Publisher: 
Wiley, Hoboken, NJ
Abstract: 
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.
Subjects: 
agent-based model
deep learning
farm modelling
neural networks
surrogate model
upscaling
JEL: 
C45
C63
Q12
Q18
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article
Document Version: 
Published Version

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