Title: Hierarchical Modeling of Seed Variety Yields and Decision Making for Future Planting Plans,
Abstract: Eradicating hunger and malnutrition is a key development goal of the 21st
century. We address the problem of optimally identifying seed varieties to
reliably increase crop yield within a risk-sensitive decision-making framework.
Specifically, we introduce a novel hierarchical machine learning mechanism for
predicting crop yield (the yield of different seed varieties of the same crop).
We integrate this prediction mechanism with a weather forecasting model, and
propose three different approaches for decision making under uncertainty to
select seed varieties for planting so as to balance yield maximization and
risk.We apply our model to the problem of soybean variety selection given in
the 2016 Syngenta Crop Challenge. Our prediction model achieves a median
absolute error of 3.74 bushels per acre and thus provides good estimates for
input into the decision models.Our decision models identify the selection of
soybean varieties that appropriately balance yield and risk as a function of
the farmer's risk aversion level. More generally, our models support farmers in
decision making about which seed varieties to plant.