Surrogate-based multi-objective optimization of management options for agricultural landscapes using artificial neural networks

Trung H. Nguyen, Duy Nong and Keith Paustian

Ecological Modelling, 2019, vol. 400, issue C, 1-13

Abstract: We demonstrate the use of a surrogate-based optimization framework for large-scale and high-resolution landscape management optimization, using irrigated corn production systems in eastern Colorado, USA as a case study. An artificial neural network was employed to create a surrogate of the DayCent biogeochemical simulation model. Our optimization considered trade-offs among seven different objectives at different scales, including farm profits, irrigation water use, corn grain, corn stover, soil organic carbon (SOC), greenhouse gas (GHG) emissions, and nitrogen leaching. The results show that the surrogate captured greater than 99% of the variations in the DayCent’s simulated outputs and was 6.2 million times faster than the DayCent model for our analysis. Farm-level optimization increased farm profits by 83%–150%, SOC by 16%–53%, grain yield by 10.1–11.3%, and reduced GHG emissions by 19%–55% compared to the ‘business-as-usual’ scenario.

Keywords: Surrogate-based optimization; Metamodeling; Artificial neural networks; Ecosystem services; Multi-objective optimization; DayCent model (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:400:y:2019:i:c:p:1-13

DOI: 10.1016/j.ecolmodel.2019.02.018

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