Gaussian Process Modeling with Genotype x Environment Kernels for Wheat Performance Prediction

TL;DR

This paper introduces Gaussian Process models with specialized kernels to predict wheat performance across diverse genotypes and environments, enhancing accuracy even with limited data and offering broad applicability in agriculture.

Contribution

It presents a novel GP modeling framework with custom kernels for genotype x environment prediction in wheat, improving prediction accuracy over traditional linear models.

Findings

GP models outperform linear mixed-effect models in prediction accuracy

Custom kernels for strings and time series improve modeling of complex data

Effective predictions for new environments and varieties with limited data

Abstract

Optimizing wheat variety selection for high performance in different environmental conditions is critical for reliable food production and stable incomes for growers. We employ a statistical machine learning framework utilizing Gaussian Process (GP) models to capture the effects of genetic and environmental factors on wheat yield and protein content. In doing so, selecting suitable covariance kernels to account for the distinct characteristics of the information is essential. The GP approach is closely related to linear mixed-effect models for genotype x environment predictions, where random additive and interaction effects are modeled with covariance structures. However, while commonly used linear mixed effect models in plant breeding rely on Euclidean-based kernels, we also test kernels specifically designed for strings and time series. The resulting GP models are capable of competitively predicting outcomes for (1) new environmental conditions, and (2) new varieties, even in scenarios with little to no previous data for the new conditions or variety. While we focus on a wheat test case using a novel dataset collected in Switzerland, the GP approach presented here can be applied and extended to a wide range of agricultural applications and beyond, paving the way for improved decision-making and data acquisition strategies.