IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v400y2019icp1-13.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380019300870
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2019.02.018?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. Linmei Shang & Jifeng Wang & David Schäfer & Thomas Heckelei & Juergen Gall & Franziska Appel & Hugo Storm, 2024. "Surrogate modelling of a detailed farm‐level model using deep learning," Journal of Agricultural Economics, Wiley Blackwell, vol. 75(1), pages 235-260, February.
    3. 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.
    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.
    5. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Madan K. Jha & Richard C. Peralta & Sasmita Sahoo, 2020. "Simulation-Optimization for Conjunctive Water Resources Management and Optimal Crop Planning in Kushabhadra-Bhargavi River Delta of Eastern India," IJERPH, MDPI, vol. 17(10), pages 1-20, May.
    3. Syed Imran Zaman & Sharfuddin Ahmed Khan & Sahar Qabool & Himanshu Gupta, 2023. "How digitalization in banking improve service supply chain resilience of e-commerce sector? a technological adoption model approach," Operations Management Research, Springer, vol. 16(2), pages 904-930, June.
    4. Andersen, Steffen & Harrison, Glenn W. & Lau, Morten Igel & Rutström, Elisabet E., 2014. "Dual criteria decisions," Journal of Economic Psychology, Elsevier, vol. 41(C), pages 101-113.
      • Andersen, Steffen & Harrison, Glenn W. & Lau, Morten Igel & Rutström, Elisabet, 2009. "Dual Criteria Decisions," Working Papers 02-2009, Copenhagen Business School, Department of Economics.
    5. Bialkowski, Jedrzej & Darolles, Serge & Le Fol, Gaëlle, 2008. "Improving VWAP strategies: A dynamic volume approach," Journal of Banking & Finance, Elsevier, vol. 32(9), pages 1709-1722, September.
    6. Mark T. Leung & An-Sing Chen, 2005. "Performance evaluation of neural network architectures: the case of predicting foreign exchange correlations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(6), pages 403-420.
    7. Seyedeh Hadis Moghadam & Parisa-Sadat Ashofteh & Hugo A. Loáiciga, 2022. "Optimal Water Allocation of Surface and Ground Water Resources Under Climate Change with WEAP and IWOA Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(9), pages 3181-3205, July.
    8. Shaogao Lv & Yongchao Hou & Hongwei Zhou, 2019. "Financial Market Directional Forecasting With Stacked Denoising Autoencoder," Papers 1912.00712, arXiv.org.
    9. Mattei, F. & Franceschini, S. & Scardi, M., 2018. "A depth-resolved artificial neural network model of marine phytoplankton primary production," Ecological Modelling, Elsevier, vol. 382(C), pages 51-62.
    10. Niu, Geng & Zheng, Yi & Han, Feng & Qin, Huapeng, 2019. "The nexus of water, ecosystems and agriculture in arid areas: A multiobjective optimization study on system efficiencies," Agricultural Water Management, Elsevier, vol. 223(C), pages 1-1.
    11. Mladen Krstić & Giulio Paolo Agnusdei & Snežana Tadić & Milovan Kovač & Pier Paolo Miglietta, 2023. "A Novel Axial-Distance-Based Aggregated Measurement (ADAM) Method for the Evaluation of Agri-Food Circular-Economy-Based Business Models," Mathematics, MDPI, vol. 11(6), pages 1-27, March.
    12. Rafael Marin & Pollyana Notargiacomo, 2021. "Multiagent Intelligent Tutoring System for Financial Literacy," International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), IGI Global, vol. 17(7), pages 1-13, November.
    13. Christian Artigues & Nicolas Jozefowiez & Boadu M. Sarpong, 2018. "Column generation algorithms for bi-objective combinatorial optimization problems with a min–max objective," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 6(2), pages 117-142, June.
    14. Matthew F Dixon, 2017. "Sequence Classification of the Limit Order Book using Recurrent Neural Networks," Papers 1707.05642, arXiv.org.
    15. Antonio Nesticò & Piera Somma, 2019. "Comparative Analysis of Multi-Criteria Methods for the Enhancement of Historical Buildings," Sustainability, MDPI, vol. 11(17), pages 1-19, August.
    16. Farajiamiri, Mina & Meyer, Jörn-Christian & Walther, Grit, 2023. "Multi-objective optimization of renewable fuel supply chains regarding cost, land use, and water use," Applied Energy, Elsevier, vol. 349(C).
    17. Ershad Oftadeh & Mojtaba Shourian & Bahram Saghafian, 2016. "Evaluation of the Bankruptcy Approach for Water Resources Allocation Conflict Resolution at Basin Scale, Iran’s Lake Urmia Experience," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(10), pages 3519-3533, August.
    18. Pingtao Yi & Weiwei Li & Lingyu Li, 2018. "Evaluation and Prediction of City Sustainability Using MCDM and Stochastic Simulation Methods," Sustainability, MDPI, vol. 10(10), pages 1-15, October.
    19. James S. Dyer & James E. Smith, 2021. "Innovations in the Science and Practice of Decision Analysis: The Role of Management Science," Management Science, INFORMS, vol. 67(9), pages 5364-5378, September.
    20. Roberto Gerundo & Antonio Nesticò & Alessandra Marra & Maria Carotenuto, 2020. "Peripheralization Risk Mitigation: A Decision Support Model to Evaluate Urban Regeneration Programs Effectiveness," Sustainability, MDPI, vol. 12(19), pages 1-19, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:400:y:2019:i:c:p:1-13. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.