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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.17496152 |
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| _version_ | 1866901748423065600 |
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| author | Ravleen Kaur Badwal1*, Pavan Chouksey2, Rohit3 |
| author_facet | Ravleen Kaur Badwal1*, Pavan Chouksey2, Rohit3 |
| contents | <p>Climate change threatens global agriculture through rising temperatures, unpredictable rainfall, and extreme weather, reducing crop yields. Genomic selection (GS), using high-throughput genotyping and predictive modelling, enables breeders to efficiently select traits like drought tolerance, heat resilience, and stable yield. This article reviews GS fundamentals, models, and the integration of machine learning and multi-omics data, with case studies in rice, while discussing challenges and opportunities for implementing GS in diverse and resource-limited environments.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17496152 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Genomic Selection for Climate-Resilient Crops: Accelerating Breeding for Climate Change Ravleen Kaur Badwal1*, Pavan Chouksey2, Rohit3 <p>Climate change threatens global agriculture through rising temperatures, unpredictable rainfall, and extreme weather, reducing crop yields. Genomic selection (GS), using high-throughput genotyping and predictive modelling, enables breeders to efficiently select traits like drought tolerance, heat resilience, and stable yield. This article reviews GS fundamentals, models, and the integration of machine learning and multi-omics data, with case studies in rice, while discussing challenges and opportunities for implementing GS in diverse and resource-limited environments.</p> |
| title | Genomic Selection for Climate-Resilient Crops: Accelerating Breeding for Climate Change |
| url | https://doi.org/10.5281/zenodo.17496152 |