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

IF 3.4 2区 经济学 Q1 AGRICULTURAL ECONOMICS & POLICY Journal of Agricultural Economics Pub Date : 2023-05-06 DOI:10.1111/1477-9552.12543
Linmei Shang, Jifeng Wang, David Schäfer, Thomas Heckelei, Juergen Gall, Franziska Appel, Hugo Storm
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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.

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使用深度学习对详细的农场级模型进行代理建模
技术变革共同决定着农业环境绩效和农场结构转型。对相关政策进行有意义的影响评估,可以从农场层面的模型中得出,这些模型包含丰富的技术细节和环境指标,并与捕捉农场动态互动的基于代理的模型相结合。然而,这种整合面临着相当大的挑战,影响着模型开发、调试和应用中的计算需求。利用深度学习技术进行代用建模可以促进这种集成,从而实现广泛区域覆盖的模拟。我们利用不同的神经网络架构开发了农场模型 FarmDyn 的代用模型。我们专门设计的评估指标可让从业人员评估模型拟合度、推理时间和数据要求之间的权衡。所有测试的神经网络都达到了较高的拟合度,但在推理时间上却有很大差异。多层感知器在所有标准上都表现出了几乎最高的性能,但与双向长短期记忆相比,它在推理时间上节省了很多。
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来源期刊
Journal of Agricultural Economics
Journal of Agricultural Economics 管理科学-农业经济与政策
CiteScore
7.90
自引率
2.90%
发文量
48
审稿时长
>24 weeks
期刊介绍: Published on behalf of the Agricultural Economics Society, the Journal of Agricultural Economics is a leading international professional journal, providing a forum for research into agricultural economics and related disciplines such as statistics, marketing, business management, politics, history and sociology, and their application to issues in the agricultural, food, and related industries; rural communities, and the environment. Each issue of the JAE contains articles, notes and book reviews as well as information relating to the Agricultural Economics Society. Published 3 times a year, it is received by members and institutional subscribers in 69 countries. With contributions from leading international scholars, the JAE is a leading citation for agricultural economics and policy. Published articles either deal with new developments in research and methods of analysis, or apply existing methods and techniques to new problems and situations which are of general interest to the Journal’s international readership.
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