ArticleJournal of experimental botany2014
Predictions of heading date in bread wheat (Triticum aestivum L.) using QTL-based parameters of an ecophysiological model.
Article in Journal of experimental botany, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Genomic prediction vs. gene-based crop models: a case study on rice trait prediction.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Integrating crop models, single nucleotide polymorphism, and climatic indices to develop genotype-environment interaction model: A case study on rice flowering time.Plant phenomics (Washington, D.C.) · 2025Article
- The complex relationship between disease resistance and yield in crops.Plant biotechnology journal · 2024Review
- From Neglecting to Including Cultivar-Specific Per Se Temperature Responses: Extending the Concept of Thermal Time in Field Crops.Plant phenomics (Washington, D.C.) · 2024Article
- Improving crop yield potential: Underlying biological processes and future prospects.Food and energy security · 2023Review
- Dynamic QTL-based ecophysiological models to predict phenotype from genotype and environment data.BMC plant biology · 2022Article
- Adapting to the projected epidemics of Fusarium head blight of wheat in Korea under climate change scenarios.Frontiers in plant science · 2022Article
- Integration of Crop Growth Models and Genomic Prediction.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Identification of Main-Effect and Environmental Interaction QTL and Their Candidate Genes for Drought Tolerance in a Wheat RIL Population Between Two Elite Spring Cultivars.Frontiers in genetics · 2021Article
- Predicting Rice Heading Date Using an Integrated Approach Combining a Machine Learning Method and a Crop Growth Model.Frontiers in genetics · 2020Article
- Combining Crop Growth Modeling With Trait-Assisted Prediction Improved the Prediction of Genotype by Environment Interactions.Frontiers in plant science · 2020Article
- From QTLs to Adaptation Landscapes: Using Genotype-To-Phenotype Models to Characterize G×E Over Time.Frontiers in plant science · 2019Article
- Article
- Genome-Based Prediction of Time to Curd Induction in Cauliflower.Frontiers in plant science · 2018Article
- Optimization of multi-environment trials for genomic selection based on crop models.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2017Article
- Association mapping of leaf rust resistance loci in a spring wheat core collection.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2017Article
- Improvement of Predictive Ability by Uniform Coverage of the Target Genetic Space.G3 (Bethesda, Md.) · 2016Article
- Toward integration of genomic selection with crop modelling: the development of an integrated approach to predicting rice heading dates.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2016Article
- Genetic Architecture of Flowering Phenology in Cereals and Opportunities for Crop Improvement.Frontiers in plant science · 2016Review
- Article
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8 authors.
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Abstract
Prediction of wheat phenology facilitates the selection of cultivars with specific adaptations to a particular environment. However, while QTL analysis for heading date can identify major genes controlling phenology, the results are limited to the environments and genotypes tested. Moreover, while ecophysiological models allow accurate predictions in new environments, they may require substantial phenotypic data to parameterize each genotype. Also, the model parameters are rarely related to all underlying genes, and all the possible allelic combinations that could be obtained by breeding cannot be tested with models. In this study, a QTL-based model is proposed to predict heading date in bread wheat (Triticum aestivum L.). Two parameters of an ecophysiological model (V sat and P base , representing genotype vernalization requirements and photoperiod sensitivity, respectively) were optimized for 210 genotypes grown in 10 contrasting location × sowing date combinations. Multiple linear regression models predicting V sat and P base with 11 and 12 associated genetic markers accounted for 71 and 68% of the variance of these parameters, respectively. QTL-based V sat and P base estimates were able to predict heading date of an independent validation data set (88 genotypes in six location × sowing date combinations) with a root mean square error of prediction of 5 to 8.6 days, explaining 48 to 63% of the variation for heading date. The QTL-based model proposed in this study may be used for agronomic purposes and to assist breeders in suggesting locally adapted ideotypes for wheat phenology.
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