Genomic prediction for targeted populations of environments in oat (Avena sativa)
Rattachement africain : us. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Context Long-term multi-environment trials (METs) could improve genomic prediction models for plant breeding programs by better representing the target population of environments (TPE). However, METs are generally highly unbalanced because genotypes are routinely dropped from trials after a few years. Furthermore, in the presence of genotype × environment interaction (GEI), selection of the environments to include in a prediction set becomes critical to represent specific TPEs. Aims The goals of this study were to compare strategies for modelling GEI in genomic prediction, using large METs from oat (Avena sativa L.) breeding programs in the Midwest United States, and to develop a variety decision tool for farmers and plant breeders. Methods The performance of genotypes in TPEs was predicted by using different strategies for handling GEI in genomic prediction models including systematic and/or random GEI components. These strategies were also used to build the variety decision tool for farmers. Key results Genomic prediction for unknown genotypes, locations and years within TPEs had moderate to high predictive ability, accuracy and reliability. Modelling GEI was beneficial in small, but not in large, mega-environments. The latest 3 years were highly predictive of performance in an upcoming year for most years but not for years with unusual weather patterns. High predictive ability, accuracy and reliability were obtained when large datasets were used in TPEs. Conclusions Deployment of historical datasets can be accomplished through meaningful delineation and prediction for TPEs. Implications We have shown the performance of a simple modelling strategy for handling prediction for TPEs when deploying large historical datasets.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Genomic prediction for targeted populations of environments in oat (Avena sativa)
- Date Crossref
- 30/04/2024
- Éditeur
- CSIRO Publishing
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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University of Wisconsin–Madison pays non établi dans la noticeUniversité ou école supérieure
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Practical Farmers of Iowa pays non établi dans la noticeOrganisation à but non lucratif
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Cornell University pays non établi dans la noticeUniversité ou école supérieure
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University of Minnesota EDepartment of Agronomy and Plant Genetics pays non établi dans la noticeUniversité ou école supérieure
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South Dakota State University and Plant Sciences pays non établi dans la noticeUniversité ou école supérieure
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North Dakota State University GPlant Sciences pays non établi dans la noticeUniversité ou école supérieure
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University of Wisconsin – Madison ADepartment of Plant and Agroecosystem Sciences pays non établi dans la noticeUniversité ou école supérieure
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BBayer Crop Science pays non établi dans la noticeInstitution
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DSchool of Integrative Plant Science Plant Breeding and Genetics Section pays non établi dans la noticeUniversité ou école supérieure
University of Wisconsin–Madison, Practical Farmers of Iowa et Cornell University, avec 6 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.