The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Evaluating Plant Breeding Strategies by Simulating Gene Action and Dryland Environment Effects
Open accessAbstract
Functional genomics is the systematic study of genome-wide effects of gene expression on organism growth and development with the ultimate aim of understanding how networks of genes influence traits. Here, we use a dynamic biophysical cropping systems model (APSIM-Sorg) to generate a state space of genotype performance based on 15 genes controlling four adaptive traits and then search this space using a quantitative genetics model of a plant breeding program (QU-GENE) to simulate recurrent selection. Complex epistatic and gene × environment effects were generated for yield even though gene action at the trait level had been defined as simple additive effects. Given alternative breeding strategies that restricted either the cultivar maturity type or the drought environment type, the positive (+) alleles for 15 genes associated with the four adaptive traits were accumulated at different rates over cycles of selection. While early maturing genotypes were favored in the Severe-Terminal drought environment type, late genotypes were favored in the Mild-Terminal and Midseason drought environment types. In the Severe-Terminal environment, there was an interaction of the stay-green (SG) trait with other traits: Selection for + alleles of the SG genes was delayed until + alleles for genes associated with the transpiration efficiency and osmotic adjustment traits had been fixed. Given limitations in our current understanding of trait interaction and genetic control, the results are not conclusive. However, they demonstrate how the per se complexity of gene × gene × environment interactions will challenge the application of genomics and marker-assisted selection in crop improvement for dryland adaptation.
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“In the presence of repeatable G × E interactions, breeding and trait performance prediction can be targeted at genetic differences for important repeatable environmental conditions within the context of the TPE (e.g. Chapman et al. 2003; Messina et al. 2011).”
From Tackling G × E × M interactions to close on-farm yield-gaps: creating novel pathways for crop improvement by predicting contributions of genetics and management to crop productivity (2021) · cites this paper as Chapman et al. 2003
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Self-citationThe Quark and the Jaguar: Adventures in the Simple and the Complex
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Self-citationGene expression informatics —it's all in your mine
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Cited by
APSIM – evolution towards a new generation of agricultural systems simulation
Holzworth, D. P., Huth, N. I., deVoil, P. G., Zurcher, E. J., Herrmann, N. I., McLean, G., Chenu, K., van Oosterom, E. J., Snow, V., Murphy, C., et al. (2014) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2014.07.009 · Cited by 1,756
Self-citationImproving drought tolerance in maize: a view from industry
Campos, H., Cooper, M., Habben, J. E., Edmeades, G. O., & Schussler, J. R. (2004) · Field Crops Research · doi:10.1016/j.fcr.2004.07.003 · Cited by 646
Self-citationQuantitative Trait Loci and Crop Performance under Abiotic Stress: Where Do We Stand?: Table I.
Collins, N. C., Tardieu, F., & Tuberosa, R. (2008) · PLANT PHYSIOLOGY · doi:10.1104/pp.108.118117 · Cited by 559
Predicting the future of plant breeding: complementing empirical evaluation with genetic prediction
Cooper, M., Messina, C. D., Podlich, D., Totir, L. R., Baumgarten, A., Hausmann, N. J., Wright, D., & Graham, G. (2014) · Crop & Pasture Science · doi:10.1071/cp14007 · Cited by 411
Self-citationAccelerating crop genetic gains with genomic selection
Voss-Fels, K. P., Cooper, M., & Hayes, B. J. (2019) · Theoretical and Applied Genetics · doi:10.1007/s00122-018-3270-8 · Cited by 381
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Metadata
- Authors
- Chapman, S., Cooper, M., Podlich, D., & Hammer, G.
- Affiliations
- Commonwealth Scientific and Industrial Research Organisation (AU)
- The University of Queensland (AU)
- Department of Primary Industries (AU)
- Corresponding author
- Chapman, Scott
- Venue
- Agronomy Journal
- Publisher
- Wiley
- Published
- 2003
- Volume
- 95
- Issue
- 1
- Pages
- 99
- ISSN
- 0002-1962 (ISSN-L)
- 1435-0645
- OpenAlex
- W2009072918
- Cited by
- 224
- References
- 44
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 4 · 2023: 5 · 2022: 9 · 2021: 15 · 2020: 8 · 2019: 10 · 2018: 10 · 2017: 8 · 2016: 10 · 2015: 17 · 2014: 19 · 2013: 11 · 2012: 9
- FWCI
- 11.00
- Type
- journal article
- Language
- en
- Open access
- green
- era.daf.qld.gov.au
- Locations
- Agronomy Journal (publishedVersion)
- Queensland Department of Agriculture and Fisheries archive of scientific and research publications (Queensland Department of Agriculture and Fisheries) (submittedVersion)
- Topics
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Genetics and Plant Breeding
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- plant breeding strategies, recurrent selection, gene action, gene-environment interaction, epistasis, dryland adaptation, drought tolerance, maize, stay-green trait, transpiration efficiency, osmotic adjustment, genotype performance, marker-assisted selection, crop yield
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- Chapman, S., Cooper, M., Podlich, D., & Hammer, G. (2003). Evaluating Plant Breeding Strategies by Simulating Gene Action and Dryland Environment Effects. Agronomy Journal. https://doi.org/10.2134/agronj2003.0099
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
