ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2019
High-throughput phenotyping platforms enhance genomic selection for wheat grain yield across populations and cycles in early stage.
Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed.
- The potential of considering photosynthesis parameters in crop yield breeding by genomic prediction.Journal of experimental botany · 2026Article
- Optimising realised genetic gain with low forward predictive ability across cycles for cooking time and correlated traits in common bean based on multivariate genomic selection.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 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
- Pod Dehiscence in Soybean: Genome Wide Association Study and Genomic Prediction.Plants (Basel, Switzerland) · 2025Article
- Understanding plant phenotypes in crop breeding through explainable AI.Plant biotechnology journal · 2025Review
- 110 years of rice breeding at LSU: realized genetic gains and future optimization.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 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
- Field-based high-throughput phenotyping enhances phenomic and genomic predictions for grain yield and plant height across years in maize.G3 (Bethesda, Md.) · 2024Article
- Enhancing the potential of phenomic and genomic prediction in winter wheat breeding using high-throughput phenotyping and deep learning.Frontiers in plant science · 2024Article
- Univariate and multivariate genomic prediction for agronomic traits in durum wheat under two field conditions.PloS one · 2024Article
- Genomic selection of soybean (Glycine max) for genetic improvement of yield and seed composition in a breeding context.The plant genome · 2023Article
- Review
- Temporal phenomic predictions from unoccupied aerial systems can outperform genomic predictions.G3 (Bethesda, Md.) · 2023Article
- Digitalizing breeding in plants: A new trend of next-generation breeding based on genomic prediction.Frontiers in plant science · 2023Review
- Genomic prediction of optimal cross combinations to accelerate genetic improvement of soybean (Frontiers in plant science · 2023Article
- Genomic prediction and selection response for grain yield in safflower.Frontiers in genetics · 2023Article
- Time-series multispectral imaging in soybean for improving biomass and genomic prediction accuracy.The plant genome · 2022Article
- Advances in integrated genomic selection for rapid genetic gain in crop improvement: a review.Planta · 2022Review
- Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat.BMC genomics · 2022Article
- A new strategy for using historical imbalanced yield data to conduct genome-wide association studies and develop genomic prediction models for wheat breeding.Molecular breeding : new strategies in plant improvement · 2022Article
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Authors and funding
12 authors.
Funding
Abstract
Genomic selection (GS) models have been validated for many quantitative traits in wheat (Triticum aestivum L.) breeding. However, those models are mostly constrained within the same growing cycle and the extension of GS to the case of across cycles has been a challenge, mainly due to the low predictive accuracy resulting from two factors: reduced genetic relationships between different families and augmented environmental variances between cycles. Using the data collected from diverse field conditions at the International Wheat and Maize Improvement Center, we evaluated GS for grain yield in three elite yield trials across three wheat growing cycles. The objective of this project was to employ the secondary traits, canopy temperature, and green normalized difference vegetation index, which are closely associated with grain yield from high-throughput phenotyping platforms, to improve prediction accuracy for grain yield. The ability to predict grain yield was evaluated reciprocally across three cycles with or without secondary traits. Our results indicate that prediction accuracy increased by an average of 146% for grain yield across cycles with secondary traits. In addition, our results suggest that secondary traits phenotyped during wheat heading and early grain filling stages were optimal for enhancing the prediction accuracy for grain yield.
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