Interdisciplinary strategies to enable data-driven plant breeding in a changing climate

Kusmec, Aaron; Zheng, Zihao; Archontoulis, Sotirios; Ganapathysubramanian, Baskar; Wang, Lizhi; Yu, Jianming; Schnable, Patrick S.

doi:10.1016/j.oneear.2021.02.005

Cited by 26 publications

(22 citation statements)

References 123 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the present study, resources and thus selection intensity and accuracy were kept constant across cycles. This is in contrast to what happened in commercial plant breeding operations, where investments have increased considerably over the last few decades ( Duvick and Cassman 1999 ; Kusmec et al 2021 ). Thus, in addition to de novo creation of genetic variation and changing environments, the compensating effect of increased investments can partially explain why no significant decrease in the rate of genetic gain was observed in commercial plant breeding operations.…”

Section: Discussionmentioning

confidence: 63%

Back to the future: implications of genetic complexity for the structure of hybrid breeding programs

Technow¹,

Podlich

Cooper

2021

G3 Genes|Genomes|Genetics

View full text Add to dashboard Cite

Commercial hybrid breeding operations can be described as decentralized networks of smaller, more or less isolated breeding programs. There is further a tendency for the disproportionate use of successful inbred lines for generating the next generation of recombinants, which has led to a series of significant bottlenecks, particularly in the history of the North American and European maize germplasm. Both the decentralization and the disproportionate contribution of inbred lines reduce effective population size and constrain the accessible genetic space. Under these conditions, long term response to selection is not expected to be optimal under the classical infinitesimal model of quantitative genetics. In this study we therefore aim to propose a rationale for the success of large breeding operations in the context of genetic complexity arising from the structure and properties of interactive genetic networks. For this we use simulations based on the NK model of genetic architecture. We indeed found that constraining genetic space through program decentralization and disproportionate contribution of parental inbred lines, is required to expose additive genetic variation and thus facilitate heritable genetic gains under high levels of genetic complexity. These results introduce new insights into why the historically grown structure of hybrid breeding programs was successful in improving the yield potential of hybrid crops over the last century. We also hope that a renewed appreciation for “why things worked” in the past can guide the adoption of novel technologies and the design of future breeding strategies for navigating biological complexity.

show abstract

Section: Discussionmentioning

confidence: 63%

Back to the future: implications of genetic complexity for the structure of hybrid breeding programs

Technow¹,

Podlich

Cooper

2021

G3 Genes|Genomes|Genetics

View full text Add to dashboard Cite

show abstract

“…Successful applications have been demonstrated for commercial maize breeding in the US corn-belt (Cooper et al, 2014a;Gaffney et al, 2015). Opportunities are emerging for development of integrated breeding-agronomy approaches for other crops and target regions to tackle current GxExM challenges and the anticipated impacts of climate change (Hatfield and Walthall, 2015;Hammer et al, 2019;Beres et al, 2020;de los Campos et al, 2020;Messina et al, 2020;Ramirez-Villegas et al, 2020;Crossa et al, 2021;Hunt et al, 2021;Kusmec et al, 2021;Smith et al, 2021;Udvardi et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…Agronomic management strategies that reduce on-farm yield gaps have been developed (Grassini et al, 2011;Assefa et al, 2018). There is ongoing interest in using improved understanding of the environmental determinants of yield performance, adaptation, and reactionnorms of maize hybrids that provide a focus for testing and further development of enviromic methodologies (Cooper et al, 2014a(Cooper et al, ,b, 2020Gage et al, 2017;Messina et al, 2020;Kusmec et al, 2021;Rogers et al, 2021).…”

Section: Example: Harnessing Enviromics For Maize Yield Improvement In the Us Corn-beltmentioning

confidence: 99%

“…Improvements in proximal and remote sensor technologies to quantify and upscale measurement of important environmental variables determining GxExM interactions, e.g., evapotranspiration (Figure 2; Guan et al, 2017;He et al, 2019;Kimm et al, 2020), open a wide range of opportunities for applications of enviromic technologies to accelerate crop improvement by integrating breeding and agronomy (Cooper et al, 2020;Peng et al, 2020;Kusmec et al, 2021) and enabling environment-specific predictions (Rogers et al, 2021). For maize breeding in the US corn-belt, early applications of these enviromic technologies have been integrated into the operations of crop improvement programs and are in operational use today (Cooper et al, 2014a(Cooper et al, ,b, 2020Gaffney et al, 2015;Messina et al, 2018).…”

Section: Example: Harnessing Enviromics For Maize Yield Improvement In the Us Corn-beltmentioning

confidence: 99%

“…Further, for most crop breeding programs the relationships between the environments sampled in METs and the dominant environmental conditions of the TPE are neither well understood nor adequately quantified (Cooper and DeLacy, 1994;Cooper et al, 2021). Improved sensor technologies and prediction methodologies are urgently required to characterize and study environments within breeding and agronomy METs and to quantify the relationships between the environments sampled in METs for all stages of crop improvement programs and their importance for the TPE (Messina et al, 2020;Crespo-Herrera et al, 2021;Kusmec et al, 2021;Potgieter et al, 2021;Smith et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Can We Harness “Enviromics” to Accelerate Crop Improvement by Integrating Breeding and Agronomy?

Cooper¹,

Messina²

2021

Front. Plant Sci.

View full text Add to dashboard Cite

The diverse consequences of genotype-by-environment (GxE) interactions determine trait phenotypes across levels of biological organization for crops, challenging our ambition to predict trait phenotypes from genomic information alone. GxE interactions have many implications for optimizing both genetic gain through plant breeding and crop productivity through on-farm agronomic management. Advances in genomics technologies have provided many suitable predictors for the genotype dimension of GxE interactions. Emerging advances in high-throughput proximal and remote sensor technologies have stimulated the development of “enviromics” as a community of practice, which has the potential to provide suitable predictors for the environment dimension of GxE interactions. Recently, several bespoke examples have emerged demonstrating the nascent potential for enhancing the prediction of yield and other complex trait phenotypes of crop plants through including effects of GxE interactions within prediction models. These encouraging results motivate the development of new prediction methods to accelerate crop improvement. If we can automate methods to identify and harness suitable sets of coordinated genotypic and environmental predictors, this will open new opportunities to upscale and operationalize prediction of the consequences of GxE interactions. This would provide a foundation for accelerating crop improvement through integrating the contributions of both breeding and agronomy. Here we draw on our experience from improvement of maize productivity for the range of water-driven environments across the US corn-belt. We provide perspectives from the maize case study to prioritize promising opportunities to further develop and automate “enviromics” methodologies to accelerate crop improvement through integrated breeding and agronomic approaches for a wider range of crops and environmental targets.

show abstract

Predicting Genotype × Environment × Management (G × E × M) Interactions for the Design of Crop Improvement Strategies

Cooper

Messina

Tang

et al. 2022

Plant Breeding Reviews

View full text Add to dashboard Cite

Interdisciplinary strategies to enable data-driven plant breeding in a changing climate

Cited by 26 publications

References 123 publications

Back to the future: implications of genetic complexity for the structure of hybrid breeding programs

Back to the future: implications of genetic complexity for the structure of hybrid breeding programs

Can We Harness “Enviromics” to Accelerate Crop Improvement by Integrating Breeding and Agronomy?

Predicting Genotype × Environment × Management (G × E × M) Interactions for the Design of Crop Improvement Strategies

Contact Info

Product

Resources

About