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Cited by 411Genetic and phenotypic traits in livestockGenetics and Plant BreedingGenetic Mapping and Diversity in Plants and Animals

Predicting the future of plant breeding: complementing empirical evaluation with genetic prediction

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Cooper, Mark, Messina, Carlos D., Podlich, Dean, Totir, L. Radu, Baumgarten, Andrew, Hausmann, Neil J., Wright, Deanne, Graham, Geoffrey·2014·Crop & Pasture Science
DOI 10.1071/cp14007
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Abstract

For the foreseeable future, plant breeding methodology will continue to unfold as a practical application of the scaling of quantitative biology. These efforts to increase the effective scale of breeding programs will focus on the immediate and long-term needs of society. The foundations of the quantitative dimension will be integration of quantitative genetics, statistics, gene-to-phenotype knowledge of traits embedded within crop growth and development models. The integration will be enabled by advances in quantitative genetics methodology and computer simulation. The foundations of the biology dimension will be integrated experimental and functional gene-to-phenotype modelling approaches that advance our understanding of functional germplasm diversity, and gene-to-phenotype trait relationships for the native and transgenic variation utilised in agricultural crops. The trait genetic knowledge created will span scales of biology, extending from molecular genetics to multi-trait phenotypes embedded within evolving genotype–environment systems. The outcomes sought and successes achieved by plant breeding will be measured in terms of sustainable improvements in agricultural production of food, feed, fibre, biofuels and other desirable plant products that meet the needs of society. In this review, examples will be drawn primarily from our experience gained through commercial maize breeding. Implications for other crops, in both the private and public sectors, will be discussed.

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  • Beyond prediction, there must be mechanisms for detection and selection of positive G-M combinations by breeders and/or agronomists during the crop improvement process.

    “Further, beyond prediction, there must be mechanisms for detection and selection of the positive G–M combinations by either or both breeders and agronomists during the crop improvement process (Cooper et al. 2014b; Hammer et al. 2020).”

    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. 2014b

    The Introduction (page unavailable) identifies genotype–management interactions as important and anticipates co-development of germplasm and resource-efficient management strategies. The Discussion’s Fig. 2 description (page unavailable) also describes testing and selecting superior hybrids across environments. The paper does not explicitly call for mechanisms to detect and select positive genotype–management combinations by breeders or agronomists beyond prediction. (LLM, conf=0.82)

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  • At present, breeders and agronomists largely work as a sequential tag team, or independently, conducting empirical research and making predictions to seek answers for pressing questions within their own domains.

    “At present, breeders and agronomists largely work as a sequential tag team, or independently, conducting empirical research and making predictions to seek answers for the pressing questions within their own domains (e.g. Reynolds 2010; Fischer et al. 2014; Cooper et al. 2014b; Assefa et al. 2018; Edreira et al. 2018; Beres et al. 2020; Rotili et al. 2020; Snowdon et al. 2020).”

    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. 2014b

    The Introduction describes plant breeders using field evaluation and genetic prediction to address breeding questions. It does not establish that breeders and agronomists largely work independently or as a sequential team; instead, the Introduction calls cross-disciplinary integration a hallmark of successful plant breeding and describes continuing interplay between genetic improvement and crop management. Page numbers were not available in our copy. (LLM, conf=0.82)

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  • To realise potential of these opportunities, integrated modelling methods are required that can deal with high dimensionality of the G × E × M problem space and combine genomic prediction for breeding with agronomic prediction for management to improve crop productivity and reduce on-farm yield-gaps.

    “To realise the potential of these opportunities, we require integrated modelling methods that can deal with the high dimensionality of the G × E × M problem space and can combine genomic prediction for breeding with agronomic prediction for management to improve crop productivity and reduce on-farm yield-gaps (Cooper et al. 2014b, 2020; Messina et al. 2018, 2020a, b; Hammer et al. 2020).”

    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. 2014b

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  • The use of data derived from environmental typing analysis can be an important source to bridge the gap between phenotypic and genomic correlations across MET.

    “On the other hand, the use of data derived from environmental typing analysis (e.g., environmental covariable, W) can be an important source to bridge the gap between phenotypic and genomic correlations across MET (Cooper et al. 2014).”

    From Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials (2021) · cites this paper as Cooper et al. 2014

    The ‘Improved phenotyping methodology: managed environments’ section (page unavailable) describes envirotyping as characterising environments in multi-environment trials and as a complement to genotyping and phenotyping. The ‘experimental design and analysis’ section discusses genetic correlations between environments, but the copy does not say that envirotyping data bridge a gap between phenotypic and genomic correlations. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • For maize in the US corn-belt, Cooper et al. (2014b) advocated G × E × M modelling throughout the breeding program cycle to augment the empirical testing footprint of METs.

    “Similarly, for maize in the US corn-belt, Cooper et al. (2014b) advocated G × E × M modelling throughout the breeding program cycle to augment the empirical testing footprint of METs.”

    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. 2014b

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Cites

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Research works printed but not linked: 7

  1. [8]8. Borlaug NE, Dowswell CR (2005) Feeding a world of ten billion people: A 21st century challenge. In 'In the wake of the double helix from the Green Revolution to the Gene Revolution. Proceedings of International Congress'. 27-31 May 2003, University of Bologna, Italy. (Eds R Tuberosa, RL Phillips, M Gale) pp. 3-23. (Avenue Media: Bologna, Italy)
  2. [16]16. Comstock RE (1977) Quantitative genetics and the design of breeding programs. In 'Proceedings of the International Conference on Quantitative Genetics'. 16-21 August 1976. (Eds E Pollack, O Kempthorne, TB Bailey Jr) pp. 705-718. (Iowa State University Press: Ames, IA, USA)
  3. [43]43. Fehr WR (Ed.) (1984) 'Genetic contributions to yield gains of five major crop plants.' CSSA Special Publication No. 7. (American Society of Agronomy and Crop Science Society of America: Madison, WI, USA)
  4. [66]66. Kush GS (2005) Green revolution: challenges ahead. In 'In the wake of the bouble helix from the Green Revolution to the Gene Revolution. Proceedings of an International Congress'. 27-31 May 2003, University of Bologna, Italy. (Eds R Tuberosa, RL Phillips, M Gale) pp. 37-51. (Avenue Media: Bologna, Italy)
  5. [67]67. Lamkey KR, Lee M (Eds) (2006) 'Plant breeding: The Arnel R. Hallauer International Symposium.' (Blackwell Publishing Ltd: Oxford, UK)
  6. [103]103. Tuberosa R, Phillips RL, Gale M (Eds) (2005) 'In the wake of the double helix from the Green Revolution to the Gene Revolution. Proceedings of an International Congress.' 27-31 May 2003 University of Bologna, Italy. (Avenue Media: Bologna, Italy)
  7. [113]113. Wright S (1932) The roles of mutation, inbreeding, crossbreeding and selection in evolution. In 'Proceedings of the 6th International Congress of Genetics'. Ithaca, New York. pp. 356-366. (Brooklyn Botanic Garden: Menasha, WI, USA)

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Authors
Cooper, M., Messina, C. D., Podlich, D., Totir, L. R., Baumgarten, A., Hausmann, N. J., Wright, D., & Graham, G.
Affiliations
Pioneer Hi-Bred (US)
Corresponding author
Cooper, Mark
ORCID
Venue
Crop & Pasture Science
Publisher
CSIRO Publishing
Published
2014-04-23
Volume
65
Issue
4
Pages
311-336
ISSN
  • 0004-9409 (ISSN-L)
  • 1836-0947
  • 1836-5795
DOI
10.1071/cp14007
OpenAlex
W2113927720
Cited by
411
References
109
Citations by year
2026: 52 · 2025: 42 · 2024: 33 · 2023: 25 · 2022: 42 · 2021: 48 · 2020: 41 · 2019: 27 · 2018: 26 · 2017: 23 · 2016: 23 · 2015: 26 · 2014: 2
FWCI
16.16
Type
journal article
Language
en
Open access
Licence
doi.org/10.1071/journalslicense (vor, 4046 d)
Locations
  • Australian Journal of Agricultural Research (publishedVersion)
  • BioOne Complete (BioOne) (submittedVersion)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Genetics and Plant Breeding
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
Keywords
plant breeding, quantitative genetics, genetic prediction, germplasm diversity, genetically modified crops, maize breeding, sustainable agricultural production

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