ArticleAnnals of botany2014
Linking ecophysiological modelling with quantitative genetics to support marker-assisted crop design for improved yields of rice (Oryza sativa) under drought stress.
Article in Annals of botany, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 56 citations in OpenAlex.
- Article
- Uncovering the Genomic Regions Associated with Yield Maintenance in Rice Under Drought Stress Using an Integrated Meta-Analysis Approach.Rice (New York, N.Y.) · 2024Article
- Improving crop yield potential: Underlying biological processes and future prospects.Food and energy security · 2023Review
- A model-guided holistic review of exploiting natural variation of photosynthesis traits in crop improvement.Journal of experimental botany · 2022Review
- Integration of Crop Growth Models and Genomic Prediction.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Progressive Genomic Approaches to Explore Drought- and Salt-Induced Oxidative Stress Responses in Plants under Changing Climate.Plants (Basel, Switzerland) · 2021Review
- Incorporating genome-wide association into eco-physiological simulation to identify markers for improving rice yields.Journal of experimental botany · 2019Article
- Next generation crop models: A modular approach to model early vegetative and reproductive development of the common bean (Agricultural systems · 2017Article
- Can increased leaf photosynthesis be converted into higher crop mass production? A simulation study for rice using the crop model GECROS.Journal of experimental botany · 2017Article
- Bridging the genotype-phenotype gap in 3D.Journal of experimental botany · 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
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5 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
aimsGenetic markers can be used in combination with ecophysiological crop models to predict the performance of genotypes. Crop models can estimate the contribution of individual markers to crop performance in given environments. The objectives of this study were to explore the use of crop models to design markers and virtual ideotypes for improving yields of rice (Oryza sativa) under drought stress.
methodsUsing the model GECROS, crop yield was dissected into seven easily measured parameters. Loci for these parameters were identified for a rice population of 94 introgression lines (ILs) derived from two parents differing in drought tolerance. Marker-based values of ILs for each of these parameters were estimated from additive allele effects of the loci, and were fed to the model in order to simulate yields of the ILs grown under well-watered and drought conditions and in order to design virtual ideotypes for those conditions. KEY
resultsTo account for genotypic yield differences, it was necessary to parameterize the model for differences in an additional trait 'total crop nitrogen uptake' (Nmax) among the ILs. Genetic variation in Nmax had the most significant effect on yield; five other parameters also significantly influenced yield, but seed weight and leaf photosynthesis did not. Using the marker-based parameter values, GECROS also simulated yield variation among 251 recombinant inbred lines of the same parents. The model-based dissection approach detected more markers than the analysis using only yield per se. Model-based sensitivity analysis ranked all markers for their importance in determining yield differences among the ILs. Virtual ideotypes based on markers identified by modelling had 10-36 % more yield than those based on markers for yield per se.
conclusionsThis study outlines a genotype-to-phenotype approach that exploits the potential value of marker-based crop modelling in developing new plant types with high yields. The approach can provide more markers for selection programmes for specific environments whilst also allowing for prioritization. Crop modelling is thus a powerful tool for marker design for improved rice yields and for ideotyping under contrasting conditions.
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