Genomic prediction in multi-environment trials in maize using statistical and machine learning methods - Research findings

Aug 4, 2026

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Genomic prediction is evaluated as a tool for multi-environment trials (MET) to forecast the phenotype of single-cross maize hybrids not tested in field experiments. The study targets grain yield and female flowering time, comparing machine learning approaches with GBLUP using non-additive effects. Results show both methodologies can accurately predict hybrid performance in specific environments, with effectiveness depending on modeling quality and the case at hand. Predicting new, untested hybrids is harder under sparse test designs than when richer testing is available.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Introduction

Challenges in multi-environment trials

Genomic prediction and GBLUP

Machine learning in genomic prediction

[www. nature.com/scientificreports](www. nature.com/scientificreports) OPEN Genomic prediction in multi‑environment trials in maize using statistical and machine learning methods Cynthia Aparecida Valiati Barreto1, Kaio Olimpio das Graças Dias2, Ithalo Coelho de Sousa 3, Camila Ferreira Azevedo1, Ana Carolina Campana Nascimento1, Lauro José Moreira Guimarães4, Claudia Teixeira Guimarães4, Maria Marta Pastina4 & Moysés Nascimento1* In the context of multi‑environment trials (MET), genomic prediction is proposed as a tool that allows the prediction of the phenotype of single cross hybrids that were not tested in field trials. This approach saves time and costs compared to traditional breeding methods. Thus, this study aimed to evaluate the genomic prediction of single cross maize hybrids not tested in MET, grain yield and female flowering time. We also aimed to propose an application of machine learning methodologies in MET in the prediction of hybrids and compare their performance with Genomic best linear unbiased prediction (GBLUP) with non‑additive effects. Our results highlight that both methodologies are efficient and can be used in maize breeding programs to accurately predict the performance of hybrids in specific environments. The best methodology is case‑dependent, specifically, to explore the potential of GBLUP, it is important to perform accurate modeling of the variance components to optimize the prediction of new hybrids. On the other hand, machine learning methodologies can capture non‑additive effects without making any assumptions atthe outset of the model. Overall, predicting the performance of new hybrids that were not evaluated in any field trials was more challenging than predicting hybrids in sparse test designs. Maize (Zea mays) has emerged as an important crop for food, feed production, and various industrial applications, providing livelihoods for millions of people around the world1,2. However, its production is affected by several factors, with drought being one of the most common causes of agricultural shortages in rainfed systems3. This fact, combined with the high demand for this crop and the prospect of a worldwide growth of more than 2 billion people over the next 20 years4, makes it necessary to cultivate increasingly productive crops, as well as more adapted to climate change and also to different planting regions, such tropical conditions. Cultivars can exhibit differentiated phenotypic responses between environments, and it is possible that a genotype may perform well in one environment but not in another5–7. To address this, breeders must submit the developed hybrids to multiple environment trials (MET). In MET, the main objectives are to study the interaction between genotypes and environments and to evaluate genotypic overall performance and stability8. However, MET phenotyping faces challenges such as the limited seeds availability, a high number of genotypes to be tested in the preliminary trials, and the associated costs, resulting in unbalanced experimental designs in different environments9, 10. In sparse designs, where hybrids are not evaluated in all environments, accurately selecting superior hybrids for the next cycle can be difficult, as some hybrids may not be stable in many environments, and other genotypes that are discarded may outperform in untested environments. To address these challenges, genomic prediction (GP) is proposed as a tool to predict the genetic value of individuals that were not evaluated in the field11, 12. Several GP methods have been proposed, with Genomic Best Linear Unbiased Predictor (GBLUP) being one of the most commonly used methods. In the context of MET, predicting the genetic value of individuals not observed in specific environments has led to the development 1Department of Statistics, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil. 2Department of General Biology, Universidade Federal deViçosa, Viçosa, Minas Gerais, Br

FAQs

What is the goal of using genomic prediction in multi-environment trials for maize?

To predict the phenotype of single-cross hybrids that were not evaluated in field trials, helping breeding programs save time and costs.

Which traits are analyzed in the study?

The study evaluates grain yield and female flowering time in single-cross maize hybrids not tested in MET.

How do machine learning methods compare with GBLUP with non-additive effects?

Both methodologies are efficient and can be used for accurate prediction, but the best approach is case-dependent; accurate variance-component modeling supports GBLUP, while machine learning can capture non-additive effects without preset assumptions.

Aug 4, 2026

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