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Machine learning and optimization based decision-support tool for seed variety selection

Author

Listed:
  • Durai Sundaramoorthi

    (Washington University in St. Louis)

  • Lingxiu Dong

    (Washington University in St. Louis)

Abstract

Every year agribusinesses develop and market new seed varieties with traits desirable for different planting environments. When agribusinesses experiment the new varieties at different farms, data is generated about the performance of these new seed varieties. However, farmers do not have a decision support tool to process the vast amount of yield performance data to make an informed seed variety selection decision for their farm. An informed decision requires accurate estimation of yield performances of seed varieties on the targeted farmland and balancing trade-offs between the expected yield and the risk associated with the seed varieties selected to grow. This research uses a real data set provided by Syngenta—an agribusiness—to create a decision-support tool. The data set used in this research contains yield information of different soybean varieties experimented at different farms located in the Midwest of the US, as well as information on location, soil, and weather conditions prevailing in those farms. In addition to this data, we also surveyed soybean farmers to understand their preferences and current practices in choosing seed varieties to grow in their farms. We are the first to capture and document farmers’ preferences and practices in selecting and growing soybean varieties. The data collected from the survey enabled us to compare the results emerging from the proposed methodology with the status quo practices. Using the Syngenta data and survey responses, this paper proposes an analytics framework that integrates machine learning, clustering, simulation, and portfolio optimization to optimally select soybean varieties to grow at the target farm. We choose a machine learning model, which simulates the yield performance of soybean varieties under different plausible weather scenarios derived from the neighborhood of the target farm. The simulated yields are then used to estimate parameters in a portfolio optimization formulation that selects the optimal portfolio of seed varieties to grow at the target farm. The main methodological contribution of this research is in the development of an approach that integrates machine learning, clustering, simulation, and portfolio optimization to help farmers make an important decision. Specifically, we introduce a novel data-driven simulation-based approach to estimate the parameters needed to solve a portfolio optimization problem. Our analysis indicates that an average farmer will gain as much as $177,369 per year in revenue by utilizing the analytics framework introduced in this research. The methodology developed in this research can be applied to variety selection decisions for other crops and influence farming practice positively. By embracing the machine learning and analytics powered framework introduced in this paper, agribusinesses can position themselves as the innovation leader and create business value by unleashing the potential of the scientific discoveries of agronomy to offer tailored farming decision support to individual farmers.

Suggested Citation

  • Durai Sundaramoorthi & Lingxiu Dong, 2024. "Machine learning and optimization based decision-support tool for seed variety selection," Annals of Operations Research, Springer, vol. 341(1), pages 5-39, October.
  • Handle: RePEc:spr:annopr:v:341:y:2024:i:1:d:10.1007_s10479-022-04995-8
    DOI: 10.1007/s10479-022-04995-8
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    1. Onur Boyabatlı & Javad Nasiry & Yangfang (Helen) Zhou, 2019. "Crop Planning in Sustainable Agriculture: Dynamic Farmland Allocation in the Presence of Crop Rotation Benefits," Management Science, INFORMS, vol. 67(5), pages 2060-2076, May.
    2. Saurabh Bansal & Mahesh Nagarajan, 2017. "Product Portfolio Management with Production Flexibility in Agribusiness," Operations Research, INFORMS, vol. 65(4), pages 914-930, August.
    3. Tim Noparumpa & Burak Kazaz & Scott Webster, 2015. "Wine Futures and Advance Selling Under Quality Uncertainty," Manufacturing & Service Operations Management, INFORMS, vol. 17(3), pages 411-426, July.
    4. Barkley, Andrew P. & Peterson, Hikaru Hanawa & Shroyer, James, 2010. "Wheat Variety Selection to Maximize Returns and Minimize Risk: An Application of Portfolio Theory," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 42(01), pages 1-17, February.
    5. Durai Sundaramoorthi & Victoria Chen & Jay Rosenberger & Seoung Kim & Deborah Buckley-Behan, 2009. "A data-integrated simulation model to evaluate nurse–patient assignments," Health Care Management Science, Springer, vol. 12(3), pages 252-268, September.
    6. Nalley, Lawton Lanier & Barkley, Andrew P. & Chumley, Forrest G., 2008. "The Impact of the Kansas Wheat Breeding Program on Wheat Yields, 1911–2006," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 40(3), pages 1-13, December.
    7. Wolfram Schlenker & Michael J. Roberts, 2006. "Nonlinear Effects of Weather on Corn Yields ," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 28(3), pages 391-398.
    8. Oskar Marko & Sanja Brdar & Marko Panić & Isidora Šašić & Danica Despotović & Milivoje Knežević & Vladimir Crnojević, 2017. "Portfolio optimization for seed selection in diverse weather scenarios," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-27, September.
    9. Lindon J. Robison & John R. Brake, 1979. "Application of Portfolio Theory to Farmer and Lender Behavior," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 61(1), pages 158-164.
    10. Dixon, Bruce L. & Hollinger, Steven E. & Garcia, Philip & Tirupattur, Viswanath, 1994. "Estimating Corn Yield Response Models To Predict Impacts Of Climate Change," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 19(01), pages 1-11, July.
    11. William F. Sharpe, 1963. "A Simplified Model for Portfolio Analysis," Management Science, INFORMS, vol. 9(2), pages 277-293, January.
    12. Durai Sundaramoorthi & Andrew Coult & Dung Hai Nguyen, 2012. "A Data-Integrated Tree-Based Simulation to Predict Financial Market Movement," International Journal of Operations Research and Information Systems (IJORIS), IGI Global, vol. 3(3), pages 74-86, July.
    13. Saurabh Bansal & Genaro J. Gutierrez & John R. Keiser, 2017. "Using Experts’ Noisy Quantile Judgments to Quantify Risks: Theory and Application to Agribusiness," Operations Research, INFORMS, vol. 65(5), pages 1115-1130, October.
    14. Ying-Ju Chen & J. George Shanthikumar & Zuo-Jun Max Shen, 2015. "Incentive for Peer-to-Peer Knowledge Sharing among Farmers in Developing Economies," Production and Operations Management, Production and Operations Management Society, vol. 24(9), pages 1430-1440, September.
    15. Durai Sundaramoorthi, 2014. "A data-integrated simulation model to forecast ground-level ozone concentration," Annals of Operations Research, Springer, vol. 216(1), pages 53-69, May.
    16. Daniel Kinn, 2018. "Reducing Estimation Risk in Mean-Variance Portfolios with Machine Learning," Papers 1804.01764, arXiv.org, revised Jul 2018.
    17. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    18. Rose A. Nyikal & Willis O. Kosura, 2005. "Risk preference and optimal enterprise combinations in Kahuro division of Murang'a district, Kenya," Agricultural Economics, International Association of Agricultural Economists, vol. 32(2), pages 131-140, March.
    19. Zehua Yang & Victoria C. P. Chen & Michael E. Chang & Melanie L. Sattler & Aihong Wen, 2009. "A Decision-Making Framework for Ozone Pollution Control," Operations Research, INFORMS, vol. 57(2), pages 484-498, April.
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