Breeding drought-tolerant maize hybrids for the US corn-belt: discovery to product
Open accessAbstract
Germplasm, genetics, phenotyping, and selection, combined with a clear definition of product targets, are the foundation of successful hybrid maize breeding. Breeding maize hybrids with superior yield for the drought-prone regions of the US corn-belt involves integration of multiple drought-specific technologies together with all of the other technology components that comprise a successful maize hybrid breeding programme. Managed-environment technologies are used to enable scaling of precision phenotyping in appropriate drought environmental conditions to breeding programme level. Genomics and other molecular technologies are used to study trait genetic architecture. Genetic prediction methodology was used to breed for improved yield performance for drought-prone environments. This was enabled by combining precision phenotyping for drought performance with genetic understanding of the traits contributing to successful hybrids in the target drought-prone environments and the availability of molecular markers distributed across the maize genome. Advances in crop growth modelling methodology are being used to evaluate the integrated effects of multiple traits for their combined effects and evaluate drought hybrid product concepts and guide their development and evaluation. Results to date, lessons learned, and future opportunities for further improving the drought tolerance of maize for the US corn-belt are discussed.
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The iterative empirical-modelling framework is grounded and motivated by successful outcomes achieved from applications to coordinate the development of improved maize hybrids and agronomy for the heterogeneous environments of the US corn-belt.
“Importantly, the iterative empirical-modelling framework is grounded and motivated by successful outcomes achieved from applications to coordinate the development of improved maize hybrids and agronomy for the heterogeneous environments of the US corn-belt (McFadden et al. 2019; Boyer et al. 2013; Cooper et al. 2014a, 2020; Gaffney et al. 2015).”
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 Cooper et al. 2014a
The Discussion reports successful drought-breeding outcomes, and Additional information says the framework used to develop improved hybrids could also help agronomists select management practices across the US corn-belt (page unavailable). It presents that agronomic use as a possibility, not as an already successful application coordinating hybrid and agronomy development. (LLM, conf=0.83)
Experimental — accuracy not yet measuredResults from METs across multiple breeding program stages and cycles can be combined to conduct meta-analyses and develop robust training data sets to support genomic prediction.
“Further, with appropriate attention to design, results from METs across multiple breeding program stages and cycles can be combined to conduct meta-analyses and develop robust training data sets to support genomic prediction (Cooper et al. 2014a, b).”
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 Cooper et al. 2014a, b
The “enabling prediction” section (page unavailable) describes a training dataset assembled from multiyear, multipopulation early-stage managed-environment experiments and used for marker-based prediction. The paper also describes testing across breeding stages and overlapping cycles, but our copy does not say that results from those stages and cycles were combined for training, or that they can be combined for meta-analyses. (LLM, conf=0.85)
Experimental — accuracy not yet measured
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Cites
Accounting for natural and extraneous variation in the analysis of field experiments
Gilmour, A. R., Cullis, B. R., Verbyla, A. P., & Verbyla, A. P. (1997) · Journal of Agricultural, Biological, and Environmental Statistics · doi:10.2307/1400446 · Cited by 752 · see citing works
Smith, A., Cullis, B., & Thompson, R. (2001) · Biometrics · doi:10.1111/j.0006-341x.2001.01138.x · Cited by 566 · see citing works
Boer, M. P., Wright, D., Feng, L., Podlich, D. W., Luo, L., Cooper, M., & van Eeuwijk, F. A. (2007) · Genetics · doi:10.1534/genetics.107.071068 · Cited by 264 · see citing works
Self-citationModelling Crop Improvement in a G×E×M Framework via Gene–Trait–Phenotype Relationships
Messina, C., Hammer, G., Dong, Z., Podlich, D., & Cooper, M. (2009) · Crop Physiology · doi:10.1016/b978-0-12-374431-9.00010-4 · Cited by 121 · see citing works
Self-citationCooper, M., Woodruff, D. R., Eisemann, R. L., Brennan, P. S., & DeLacy, I. H. (1995) · Theoretical and Applied Genetics · doi:10.1007/bf00221995 · Cited by 110 · see citing works
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Research works printed but not linked: 8
- Cooper M, van Eeuwijk F, Chapman SC, Podlich DW, Löffler C. 2006. Genotype-by-environment interactions under water-limited conditions. In: Ribaut JM, ed. Drought adaptation in cereals. Binghamton, NY: The Haworth Press, Inc., 51-96.
- Cullis BR, Smith AB, Coombes NE. 2006. On the design of early generation variety trials with correlated data. Journal of Agricultural, Biological, and Environmental Statistics 11, 381-393. Breeding drought-tolerant maize | 6203 Downloaded from academic.oup.com/jxb/article/65/21/6191/609140 by guest on 17 September 2026
- Duvick DN, Smith JSC, Cooper M. 2004. Long-term selection in a commercial hybrid maize breeding program. In: Janick J, ed. Plant breeding reviews: long-term selection: crops, animals, and bacteria, Vol. 24, Part 2. New York: John Wiley & Sons, 109-151.
- Federer WT, Reynolds M, Crossa J. 2001. Combining results from augmented designs over sites. Agronomy Journal 93, 389-395.
- Gholipoor M, Choudhary S, Sinclair TR, Messina CD, Cooper M. 2013a. Transpiration response of maize hybrids to atmospheric vapour pressure deficit. Journal of Agronomy and Crop Science 199, 155-160.
- Gholipoor M, Sinclair TR, Raza MAS, Löffler C, Cooper M, Messina CD. 2013b. Maize hybrid variability for transpiration decrease with progressive soil drying. Journal of Agronomy and Crop Science 199, 23-29.
- Messina CD, Podlich D, Dong Z, Samples M, Cooper M. 2011. Yield- trait performance landscapes: from theory to application in breeding maize for drought tolerance. Journal of Experimental Botany 62, 855-868.
- Trethowan RM, Reynolds MW, Sayre K, Ortiz-Monasterio I. 2005. Adapting wheat cultivars to resource conserving farming practices and human nutritional needs. Annals of Applied Biology 146, 405-413.
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Self-citationLeakey, A. D. B., Ferguson, J. N., Pignon, C. P., Wu, A., Jin, Z., Hammer, G. L., & Lobell, D. B. (2019) · Annual Review of Plant Biology · doi:10.1146/annurev-arplant-042817-040305 · Cited by 372 · see citing works
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“54 between environments is within the range typically observed in plant breeding data sets [67] and crossover interaction between environments is a common phenomenon in plant breeding [34, 68, 69].”
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Sinclair, T. R., Soltani, A., Marrou, H., Ghanem, M., & Vadez, V. (2020) · Crop Science · doi:10.1002/csc2.20106 · Cited by 24 · see citing works
- Authors
- Cooper, M., Gho, C., Leafgren, R., Tang, T., & Messina, C.
- Affiliations
- Pioneer Hi-Bred (US)
- Venue
- Journal of Experimental Botany
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-03-04
- Volume
- 65
- Issue
- 21
- Pages
- 6191-6204
- ISSN
- 0022-0957 (ISSN-L)
- 1460-2431
- OpenAlex
- W2114996790
- Cited by
- 417
- References
- 5
- Citations by year
- 2026: 32 · 2025: 39 · 2024: 30 · 2023: 33 · 2022: 55 · 2021: 48 · 2020: 45 · 2019: 36 · 2018: 20 · 2017: 27 · 2016: 22 · 2015: 19 · 2014: 11
- FWCI
- 2.98
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Experimental Botany (publishedVersion)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Topics
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Genetics and Plant Breeding
- Life Sciences › Agricultural and Biological Sciences › Agronomy and Crop Science › Crop Yield and Soil Fertility
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- drought-tolerant maize, Corn Belt, precision phenotyping, genetic architecture, genetic prediction, molecular markers, crop growth simulation, yield performance, germplasm
- MeSH terms
- Breeding, Zea mays, Hybridization, Genetic, United States, History, 20th Century, History, 21st Century, Droughts, Genetic Association Studies
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