Speaker
Description
Environmental covariates (ECs) have become increasingly abundant and accessible over the past two decades, driven by advancements in remote sensing, data acquisition technologies, and the declining costs of environmental monitoring. Incorporating ECs into multi-environment trials (METs) has several applications, including improving the understanding of genotype-by-environment interactions, serving as selection criteria, and supporting farmers in variety decisions.
This study explores the practical and methodological challenges of using ECs in METs of field crops. Approaches are illustrated using commercial sugar beet data on over 4000 genotypes across more than 20 locations spread over several countries, provided by Strube D&S GmbH in collaboration with the German Research Foundation and the University of Hohenheim. The main focus is on strategies for integrating ECs into linear mixed models - both directly and via synthetic approaches. We analyze several EC data sources, including public and private weather stations within Germany and across countries, highlighting issues such as data quality, interpolation uncertainty, and consistency. Additionally, we discuss approaches for averaging ECs over biologically meaningful periods and explore feature engineering techniques to transform raw data into informative predictors. This work aims to support the robust and interpretable use of ECs in METs of field crops conducted across heterogeneous environments.
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