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

Huaiyang Zhong (), Xiaocheng Li, David Lobell, Stefano Ermon and Margaret L. Brandeau
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Huaiyang Zhong: Stanford University
Xiaocheng Li: Stanford University
David Lobell: Stanford University
Stefano Ermon: Stanford University
Margaret L. Brandeau: Stanford University

Environment Systems and Decisions, 2018, vol. 38, issue 4, 458-470

Abstract: Abstract 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.

Keywords: Crop selection; Yield prediction; Hierarchical modeling; Machine learning; Random forest; Stochastic decision model (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10669-018-9695-4

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