Hierarchical Modeling of Seed Variety Yields and Decision Making for Future Planting Plans

TL;DR

This paper presents a hierarchical machine learning approach integrated with weather forecasting to optimize seed variety selection for crops, balancing yield maximization and risk under uncertainty, demonstrated on soybean data.

Contribution

It introduces a novel hierarchical prediction model combined with decision-making strategies for risk-sensitive crop planning, advancing agricultural decision support systems.

Findings

Prediction median absolute error of 3.74 bushels per acre.

Models effectively balance yield and risk based on farmer preferences.

Application to soybean data demonstrates practical utility.

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.