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

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  • Nguyen, Trung H.
  • Nong, Duy
  • Paustian, Keith

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.

Suggested Citation

  • Nguyen, Trung H. & Nong, Duy & Paustian, Keith, 2019. "Surrogate-based multi-objective optimization of management options for agricultural landscapes using artificial neural networks," Ecological Modelling, Elsevier, vol. 400(C), pages 1-13.
    • Handle: RePEc:eee:ecomod:v:400:y:2019:i:c:p:1-13
    DOI: 10.1016/j.ecolmodel.2019.02.018
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    References listed on IDEAS

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    1. Wu, Xin & Zheng, Yi & Wu, Bin & Tian, Yong & Han, Feng & Zheng, Chunmiao, 2016. "Optimizing conjunctive use of surface water and groundwater for irrigation to address human-nature water conflicts: A surrogate modeling approach," Agricultural Water Management, Elsevier, vol. 163(C), pages 380-392.
    2. Peter C. Fishburn, 1967. "Letter to the Editor—Additive Utilities with Incomplete Product Sets: Application to Priorities and Assignments," Operations Research, INFORMS, vol. 15(3), pages 537-542, June.
    3. Nguyen, Trung H. & Granger, Julien & Pandya, Deval & Paustian, Keith, 2019. "High-resolution multi-objective optimization of feedstock landscape design for hybrid first and second generation biorefineries," Applied Energy, Elsevier, vol. 238(C), pages 1484-1496.
    4. Iebeling Kaastra & Milton S. Boyd, 1995. "Forecasting futures trading volume using neural networks," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 15(8), pages 953-970, December.
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    Cited by:

    1. Shang, Linmei & Wang, Jifeng & Schäfer, David & Heckelei, Thomas & Gall, Juergen & Appel, Franziska & Storm, Hugo, 2024. "Surrogate modelling of a detailed farm‐level model using deep learning," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 75(1), pages 235-260.
    2. Seidel, Claudia & Shang, Linmei & Britz, Wolfgang, 2023. "A critical assessment of neural networks as meta-model of a farm optimization model," Discussion Papers 338200, University of Bonn, Institute for Food and Resource Economics.
    3. Dinesh Shrestha & Jesslyn F. Brown & Trenton D. Benedict & Daniel M. Howard, 2021. "Exploring the Regional Dynamics of U.S. Irrigated Agriculture from 2002 to 2017," Land, MDPI, vol. 10(4), pages 1-16, April.
    4. Xiaobo Xue Romeiko & Zhijian Guo & Yulei Pang & Eun Kyung Lee & Xuesong Zhang, 2020. "Comparing Machine Learning Approaches for Predicting Spatially Explicit Life Cycle Global Warming and Eutrophication Impacts from Corn Production," Sustainability, MDPI, vol. 12(4), pages 1-19, February.

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