ArticleFrontiers in genetics2020
Utility of Climatic Information via Combining Ability Models to Improve Genomic Prediction for Yield Within the Genomes to Fields Maize Project.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed, 91 citations in OpenAlex.
- Improving grain yield prediction in Southern US oat germplasm using genomics information and environmental covariates.The plant genome · 2026Article
- Impact of environmental covariates summarization on predictive ability in genomic selection.The plant genome · 2026Article
- Multi-trait multi-environment genomic prediction strategies forFrontiers in plant science · 2026Article
- Optimizing resource allocation inFrontiers in plant science · 2026Article
- Genomic prediction of agronomic traits in perennial ryegrass (Lolium perenne L.) and genotype x environment interactions at the limit of the species distribution.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Improving genomic prediction for plant disease using environmental covariates.Plant methods · 2025Article
- Integration of crop modeling and sensing into molecular breeding for nutritional quality and stress tolerance.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Genomic selection of maize test-cross hybrids leveraged by marker sampling.The plant genome · 2025Article
- Prediction of biomass sorghum hybrids using environmental feature-enriched genomic combining ability models in tropical environments.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Article
- Prediction of harvest-related traits in barley using high-throughput phenotyping data and machine learning.Frontiers in plant science · 2025Article
- Optimizing the single-step model for predicting fumonisins resistance in maize hybrids accounting for the genotype-by-environment interaction.Frontiers in genetics · 2025Article
- GIS-based G × E modeling of maize hybrids through enviromic markers engineering.The New phytologist · 2025Article
- Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates.bioRxiv : the preprint server for biology · 2024Article
- Enhancing winter wheat prediction with genomics, phenomics and environmental data.BMC genomics · 2024Article
- Reaction norm for genomic prediction of plant growth: modeling drought stress response in soybean.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Feature engineering of environmental covariates improves plant genomic-enabled prediction.Frontiers in plant science · 2024Article
- Sparse testing designs for optimizing predictive ability in sugarcane populations.Frontiers in plant science · 2024Article
- Genomic selection of soybean (Glycine max) for genetic improvement of yield and seed composition in a breeding context.The plant genome · 2023Article
- Leveraging data from the Genomes-to-Fields Initiative to investigate genotype-by-environment interactions in maize in North America.Nature communications · 2023Article
Corrections and comments
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Authors and funding
33 authors at 17 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genomic prediction provides an efficient alternative to conventional phenotypic selection for developing improved cultivars with desirable characteristics. New and improved methods to genomic prediction are continually being developed that attempt to deal with the integration of data types beyond genomic information. Modern automated weather systems offer the opportunity to capture continuous data on a range of environmental parameters at specific field locations. In principle, this information could characterize training and target environments and enhance predictive ability by incorporating weather characteristics as part of the genotype-by-environment (G×E) interaction component in prediction models. We assessed the usefulness of including weather data variables in genomic prediction models using a naïve environmental kinship model across 30 environments comprising the Genomes to Fields (G2F) initiative in 2014 and 2015. Specifically four different prediction scenarios were evaluated (i) tested genotypes in observed environments; (ii) untested genotypes in observed environments; (iii) tested genotypes in unobserved environments; and (iv) untested genotypes in unobserved environments. A set of 1,481 unique hybrids were evaluated for grain yield. Evaluations were conducted using five different models including main effect of environments; general combining ability (GCA) effects of the maternal and paternal parents modeled using the genomic relationship matrix; specific combining ability (SCA) effects between maternal and paternal parents; interactions between genetic (GCA and SCA) effects and environmental effects; and finally interactions between the genetics effects and environmental covariates. Incorporation of the genotype-by-environment interaction term improved predictive ability across all scenarios. However, predictive ability was not improved through inclusion of naive environmental covariates in G×E models. More research should be conducted to link the observed weather conditions with important physiological aspects in plant development to improve predictive ability through the inclusion of weather data.
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