ArticleThe New phytologist2025
GIS-based G × E modeling of maize hybrids through enviromic markers engineering.
Article in The New phytologist, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- From triangle to pyramid: Understanding host-pathogen-microniome-environment interplay for sustainable, enviromics-empowered management of plant diseases.Plant communications · 2026Review
- Predicting enviromically adapted varieties with big data.Genome biology · 2026Article
- High-resolution soybean trial data supporting the expansion of agriculture in Africa.Scientific data · 2025Article
- Enhancing enviromics based predictions in common bean multi-environment trials.Scientific reports · 2025Article
- Improving genomic prediction for plant disease using environmental covariates.Plant methods · 2025Article
- Article
- Optimizing soybean variety selection for the Pan-African Trial network using factor analytic models and envirotyping.Frontiers in plant science · 2025Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Through enviromics, precision breeding leverages innovative geotechnologies to customize crop varieties to specific environments, potentially improving both crop yield and genetic selection gains. In Brazil's four southernmost states, data from 183 distinct geographic field trials (also accounting for 2017-2021) covered information on 164 genotypes: 79 phenotyped maize hybrid genotypes for grain yield and their 85 nonphenotyped parents. Additionally, 1342 envirotypic covariates from weather, soil, sensor-based, and satellite sources were collected to engineer 10 K synthetic enviromic markers via machine learning. Soil, radiation light, and surface temperature variations remarkably affect differential genotype yield, hinting at ecophysiological adjustments including evapotranspiration and photosynthesis. The enviromic ensemble-based random regression model showcases superior predictive performance and efficiency compared to the baseline and kernel models, matching the best genotypes to specific geographic coordinates. Clustering analysis has identified regions that minimize genotype-environment (G × E) interactions. These findings underscore the potential of enviromics in crafting specific parental combinations to breed new, higher-yielding hybrid crops. The adequate use of envirotypic information can enhance the precision and efficiency of maize breeding by providing important inputs about the environmental factors that affect the average crop performance. Generating enviromic markers associated with grain yield can enable a better selection of hybrids for specific environments.
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