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Using machine learning to combine genetic and environmental data for maize grain yield predictions across multi-environment trials.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik - 23 Jul 2024

Fernandes Igor K, Vieira Caio C, Dias Kaio O G, Fernandes Samuel B

Abstract excerpt

KEY MESSAGE: Incorporating feature-engineered environmental data into machine learning-based genomic prediction models is an efficient approach to indirectly model genotype-by-environment interactions. Complementing phenotypic traits and molecular markers with high-dimensional data such as climate and soil information is becoming a common practice in breeding programs. This study explored new ways to combine...

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Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

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Identifiers

PMID: 39044035
DOI: 10.1007/s00122-024-04687-w
PMCID: PMC11266441

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