ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2024
GIS-FA: an approach to integrating thematic maps, factor-analytic, and envirotyping for cultivar targeting.
Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 25 citations in OpenAlex.
- Dissecting the genetic basis of yield stability in faba bean by multi-environment analysis.Genome biology · 2026Article
- Evaluation and improvement of two European soybean VCU networks.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Longitudinal data improves selection of drought-tolerant Eucalyptus germplasm.Scientific reports · 2026Article
- Towards a sweetpotato genomic-enabled breeding: optimizing two-stage analysis of multi-environment augmented trials.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 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
- Prediction of Australian wheat genotype by environment interactions and mega-environments.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Genomic prediction of stalk lodging resistance and the associated intermediate phenotypes in maize using whole-genome resequence and multi-environmental data.The plant genome · 2025Article
- Optimizing soybean variety selection for the Pan-African Trial network using factor analytic models and envirotyping.Frontiers in plant science · 2025Article
- GIS-based G × E modeling of maize hybrids through enviromic markers engineering.The New phytologist · 2025Article
- Factor analytic selection tools and environmental feature-integration enable holistic decision-making in Eucalyptus breeding.Scientific reports · 2024Article
- Optimizing multi-environment trials in the Southern US Rice belt via smart-climate-soil prediction-based models and economic importance.Frontiers in plant science · 2024Article
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Authors and funding
13 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
key messageWe propose an "enviromics" prediction model for recommending cultivars based on thematic maps aimed at decision-makers. Parsimonious methods that capture genotype-by-environment interaction (GEI) in multi-environment trials (MET) are important in breeding programs. Understanding the causes and factors of GEI allows the utilization of genotype adaptations in the target population of environments through environmental features and factor-analytic (FA) models. Here, we present a novel predictive breeding approach called GIS-FA, which integrates geographic information systems (GIS) techniques, FA models, partial least squares (PLS) regression, and enviromics to predict phenotypic performance in untested environments. The GIS-FA approach enables: (i) the prediction of the phenotypic performance of tested genotypes in untested environments, (ii) the selection of the best-ranking genotypes based on their overall performance and stability using the FA selection tools, and (iii) the creation of thematic maps showing overall or pairwise performance and stability for decision-making. We exemplify the usage of the GIS-FA approach using two datasets of rice [Oryza sativa (L.)] and soybean [Glycine max (L.) Merr.] in MET spread over tropical areas. In summary, our novel predictive method allows the identification of new breeding scenarios by pinpointing groups of environments where genotypes demonstrate superior predicted performance. It also facilitates and optimizes cultivar recommendations by utilizing thematic maps.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.