Machine learning for selecting cropvarieties as climate adaptation measure (KB-46-005-013)

Quantitative tools are essential in the assessment of how varieties will respond to new climates. We combine statistical and machine learning methods for Quantitative Trait Loci analysis and data smoothening (Pérez-Valencia et al., 2022) to identify critical marker genes and essential genotype-by-environment interactions with numerical modelling for quantifying these genotype-by-environment interactions that occur during the growing season. In particular, we use time series data at higher resolutions for this identification. The models are intended to extrapolate how existing and new varieties will perform under new environmental conditions.

Based on fitting to high-resolution time series trial data involving multiple varieties in different environments in Australia across 31 years (Bustos-Korts, 2019), we propose a set of dynamic models of low-complexity that can capture essential environmental limitations to growth. These include local variation in solar radiance and soil water availability, compared to a standard minimalistic logistic model (Van Voorn et al., to appear). These models are used in a Bayesian framework for the simultaneous fitting of multiple varieties to identify differences between varieties. In turn, this can be used to construct functions that include the interaction effects of multiple marker genes with environmental limitations, to accommodate the extrapolation on how new genotypes will perform in new environmental conditions.