ArticleJournal of experimental botany2013
Quantification of the effects of VRN1 and Ppd-D1 to predict spring wheat (Triticum aestivum) heading time across diverse environments.
Article in Journal of experimental botany, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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36 citing papers in PubMed, 138 citations in OpenAlex.
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- Duplicate Genes Contribute to Variability in Abiotic Stress Resistance in Allopolyploid Wheat.Plants (Basel, Switzerland) · 2023Review
- Novel exotic alleles of EARLY FLOWERING 3 determine plant development in barley.Journal of experimental botany · 2023Article
- The genetic basis of transpiration sensitivity to vapor pressure deficit in wheat.Physiologia plantarum · 2022Article
- Phenological optimization of late reproductive phase for raising wheat yield potential in irrigated mega-environments.Journal of experimental botany · 2022Article
- Emmer Wheat Eco-Geographic and Genomic Congruence Shapes Phenotypic Performance under Mediterranean Climate.Plants (Basel, Switzerland) · 2022Article
- Identification and validation of a QTL for spikelet number on chromosome arm 6BL of common wheat (Molecular breeding : new strategies in plant improvement · 2022Article
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- Long-Amplicon Single-Molecule Sequencing Reveals Novel, Trait-Associated Variants ofFrontiers in plant science · 2022Article
- Advancing understanding of oat phenology for crop adaptation.Frontiers in plant science · 2022Review
- Harnessing translational research in wheat for climate resilience.Journal of experimental botany · 2021Review
- Assessing adaptive requirements and breeding potential of spelt under Mediterranean environment.Scientific reports · 2021Article
- Genetic and Management Effects on Barley Yield and Phenology in the Mediterranean Basin.Frontiers in plant science · 2021Article
- Combining Crop Growth Modeling With Trait-Assisted Prediction Improved the Prediction of Genotype by Environment Interactions.Frontiers in plant science · 2020Article
- High temporal resolution of leaf area data improves empirical estimation of grain yield.Scientific reports · 2019Article
- Accounting for soil moisture improves prediction of flowering time in chickpea and wheat.Scientific reports · 2019Article
- An explanatory model of temperature influence on flowering through whole-plant accumulation ofIn silico plants · 2019Article
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Heading time is a major determinant of the adaptation of wheat to different environments, and is critical in minimizing risks of frost, heat, and drought on reproductive development. Given that major developmental genes are known in wheat, a process-based model, APSIM, was modified to incorporate gene effects into estimation of heading time, while minimizing degradation in the predictive capability of the model. Model parameters describing environment responses were replaced with functions of the number of winter and photoperiod (PPD)-sensitive alleles at the three VRN1 loci and the Ppd-D1 locus, respectively. Two years of vernalization and PPD trials of 210 lines (spring wheats) at a single location were used to estimate the effects of the VRN1 and Ppd-D1 alleles, with validation against 190 trials (~4400 observations) across the Australian wheatbelt. Compared with spring genotypes, winter genotypes for Vrn-A1 (i.e. with two winter alleles) had a delay of 76.8 degree days (°Cd) in time to heading, which was double the effect of the Vrn-B1 or Vrn-D1 winter genotypes. Of the three VRN1 loci, winter alleles at Vrn-B1 had the strongest interaction with PPD, delaying heading time by 99.0 °Cd under long days. The gene-based model had root mean square error of 3.2 and 4.3 d for calibration and validation datasets, respectively. Virtual genotypes were created to examine heading time in comparison with frost and heat events and showed that new longer-season varieties could be heading later (with potential increased yield) when sown early in season. This gene-based model allows breeders to consider how to target gene combinations to current and future production environments using parameters determined from a small set of phenotyping treatments.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.