Using Machine Learning to Combine Genetic and Environmental Data for Maize Grain Yield Predictions Across Multi-Environment Trials

Aug 4, 2026

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Incorporating feature-engineered environmental data into machine learning-based genomic prediction models enables efficient indirect modeling of genotype-by-environment interactions in maize breeding. The study combined phenotypic traits and molecular markers with high-dimensional climate and soil information using multi-environment trial data from the Genomes To Fields initiative. Machine learning models using environmental inputs improved mean prediction accuracy by up to 7% versus the Factor Analytic Multiplicative Mixed Model across evaluated cross-validation scenarios. The additive G+E model outperformed the multiplicative GEI model while using less memory and time, and tree-based methods captured GEI without explicit interaction terms.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Abstract

Key message

Modeling approach

Results and implications

University of Arkansas, Fayetteville ScholarWorks@UARK | Crop, Soil and Environmental Sciences Faculty Publications and Presentations | Crop, Soil and Environmental Sciences | | --- | --- | | 8-2024
Using Machine Learning to Combine Genetic and Environmental Data for Maize Grain Yield Predictions Across Multi-Environment Trials
Igor K. Fernandes
University of Arkansas, Fayetteville, [igorf@uark.edu](igorf@uark.edu)
Caio C. Vieira
University of Arkansas, Fayetteville, [caioc@uark.edu](caioc@uark.edu)
Kaio O. G. Dias
Universidade Federal de Vicosa, [kaio.o.dias@ufv.br](kaio.o.dias@ufv.br)
[Samuel B. Fernandes](Samuel B. Fernandes)
University of Arkansas, Fayetteville, [samuelbf@uark.edu](samuelbf@uark.edu)
Follow this and additional works at: [https://scholarworks.uark.edu/csespub](https://scholarworks.uark.edu/csespub)
Part of the Agronomy and Crop Sciences Commons, Genetics and Genomics Commons, and the Horticulture Commons
Click here to let us know how this document benefits you. | | Citation Fernandes, I. K., Vieira, C. C., Dias, K. O., & Fernandes, S. B. (2024) . Using Machine Learning to Combine Genetic and Environmental Data for Maize Grain Yield Predictions Across Multi-Environment Trials. Theoretical and Applied Genetics, 137 (8), 189. [https://doi.org/10.1007/s00122-024-04687-w](https://doi.org/10.1007/s00122-024-04687-w) This Article is brought to you for free and open access by the Crop, Soil and Environmental Sciences at ScholarWorks@UARK. It has been accepted for inclusion in Crop, Soil and Environmental Sciences Faculty Publications and Presentations by an authorized administrator of ScholarWorks@UARK. For more information, please contact [uarepos@uark.edu](uarepos@uark.edu). Theoretical and Applied Genetics (2024) 137:189 [https://doi.org/10.1007/s00122-024-04687-w](https://doi.org/10.1007/s00122-024-04687-w) Using machine learning to combine genetic and environmental data for maize grain yield predictions across multi‑environment trials Igor K. Fernandes1 · Caio C. Vieira2 · Kaio O. G. Dias3 · Samuel B. Fernandes1 Received: 12 February 2024 / Accepted: 29 June 2024 / Published online: 23 July 2024 This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2024 Abstract 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. Abstract 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 non-genetic information in genomic prediction models using machine learning. Using the multi-environment trial data from the Genomes To Fields initiative, different models to predict maize grain yield were adjusted using various inputs: genetic, environmental, ora combination of both, either in an additive (genetic-and-environmental; G+E) or a multiplicative (genotype-by-environment interaction; GEI) manner. When including environmental data, the mean prediction accuracy of machine learning genomic prediction models increased up to 7% over the well-established Factor Analytic Multiplicative Mixed Model among the three cross-validation scenarios evaluated. Moreover, using the G+E model was more advantageous than the GEI model given the superior, or at least comparable, prediction accuracy, the lower usage of computational memory and time, and the flexibility of accounting for interactions by construction. Our results illustrate the flexibility provided by the ML framework, particularly with feature engineering. We show that the feature engineering stage offers a viable option for envirotyping and generates valuable information for machine learning-based genomic prediction models. Furthermore, we verified that the geno

FAQs

What is the key message of the study’s abstract?

Feature-engineered environmental data can be incorporated into machine learning-based genomic prediction models as an efficient way to indirectly model genotype-by-environment interactions.

How were genetic and environmental data used to predict maize grain yield?

Models were adjusted using genetic data, environmental data, or both, either additively (G+E) or multiplicatively to represent genotype-by-environment interaction (GEI).

What performance advantages were observed when environmental data were included?

Including environmental data increased mean prediction accuracy by up to 7% compared with the Factor Analytic Multiplicative Mixed Model, and the G+E model provided better or comparable accuracy with lower computational memory and time.

Aug 4, 2026

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