Improving wheat grain yield genomic prediction accuracy using historical data
Genomic selection is an essential tool to improve genetic gain in wheat breeding. This study aimed to enhance prediction accuracy for grain yield across various selection environments using CIMMYT's (International Maize and Wheat Improvement Center) historical dataset. Ten years of grain yield data...
| Autores principales: | , , , , , , , , , , , , , |
|---|---|
| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Oxford University Press
2025
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/179135 |
Ejemplares similares: Improving wheat grain yield genomic prediction accuracy using historical data
- Enhancing wheat genomic prediction by a hybrid kernel approach
- Deep learning methods improve genomic prediction of wheat breeding
- Genotype performance estimation in targeted production environments by using sparse genomic prediction
- Machine learning algorithms translate big data into predictive breeding accuracy
- Boosting genomic prediction transferability with sparse testing
- A marker weighting approach for enhancing within-family accuracy in genomic prediction