ArticleFrontiers in genetics2023
Genomics combined with UAS data enhances prediction of grain yield in winter wheat.
Article in Frontiers in genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Integrating genomic and phenomic data improves prediction of key traits in potato breeding.Horticulture research · 2026Article
- The Role of Prior-Induced Regularization in Accuracy and Stability of Genomic Prediction Across Unimodal and Multimodal Models.Plants (Basel, Switzerland) · 2026Article
- Variability in crop responses as a function of environment affects the NDVI relationship with grain yield in wheat.Plant phenomics (Washington, D.C.) · 2026Article
- Next-Generation Precision Breeding in Peanut (Insects · 2026Review
- Leveraging unmanned aerial vehicle derived multispectral data for improved genomic prediction in potato (Solanum tuberosum).The plant genome · 2025Article
- Maui: modular analytics of UAS imagery for specialty crop research.Plant methods · 2025Article
- Enhancing genomic-based forward prediction accuracy in wheat by integrating UAV-derived hyperspectral and environmental data with machine learning under heat-stressed environments.The plant genome · 2025Article
- Enhancing winter wheat prediction with genomics, phenomics and environmental data.BMC genomics · 2024Article
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4 authors.
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Abstract
With the human population continuing to increase worldwide, there is pressure to employ novel technologies to increase genetic gain in plant breeding programs that contribute to nutrition and food security. Genomic selection (GS) has the potential to increase genetic gain because it can accelerate the breeding cycle, increase the accuracy of estimated breeding values, and improve selection accuracy. However, with recent advances in high throughput phenotyping in plant breeding programs, the opportunity to integrate genomic and phenotypic data to increase prediction accuracy is present. In this paper, we applied GS to winter wheat data integrating two types of inputs: genomic and phenotypic. We observed the best accuracy of grain yield when combining both genomic and phenotypic inputs, while only using genomic information fared poorly. In general, the predictions with only phenotypic information were very competitive to using both sources of information, and in many cases using only phenotypic information provided the best accuracy. Our results are encouraging because it is clear we can enhance the prediction accuracy of GS by integrating high quality phenotypic inputs in the models.
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