Environment Systems and Decisions

, Volume 38, Issue 4, pp 458–470 | Cite as

Hierarchical modeling of seed variety yields and decision making for future planting plans

  • Huaiyang ZhongEmail author
  • Xiaocheng Li
  • David Lobell
  • Stefano Ermon
  • Margaret L. Brandeau


Eradicating hunger and malnutrition is a key development goal of the twenty first century. This paper addresses the problem of optimally identifying seed varieties to reliably increase crop yield within a risk-sensitive decision making framework. Specifically, a novel hierarchical machine learning mechanism for predicting crop yield (the yield of different seed varieties of the same crop) is introduced. This prediction mechanism is then integrated with a weather forecasting model and three different approaches for decision making under uncertainty to select seed varieties for planting so as to balance yield maximization and risk. The model was applied to the problem of soybean variety selection given in the 2016 Syngenta Crop Challenge. The prediction model achieved a median absolute error of 235 kg/ha and thus provides good estimates for input into the decision models. The decision models identified the selection of soybean varieties that appropriately balance yield and risk as a function of the farmer’s risk aversion level. More generally, the models can support farmers in decision making about which seed varieties to plant.


Crop selection Yield prediction Hierarchical modeling Machine learning Random forest Stochastic decision model 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Huaiyang Zhong
    • 1
    Email author
  • Xiaocheng Li
    • 1
  • David Lobell
    • 2
  • Stefano Ermon
    • 3
  • Margaret L. Brandeau
    • 1
  1. 1.Department of Management Science and EngineeringStanford UniversityStanfordUSA
  2. 2.Department of Earth System ScienceStanford UniversityStanfordUSA
  3. 3.Computer Science DepartmentStanford UniversityStanfordUSA

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