CGIAR modeling approaches for resource‐constrained scenarios: I. Accelerating crop breeding for a changing climate

Ramírez-Villegas, Julián; Milan, Anabel Molero; Alexandrov, Nickolai; Asseng, Senthold; Challinor, Andrew J.; Crossa, J.; Eeuwijk, Fred van; Ghanem, Michel Edmond; Grenier, Cécile; Heinemann, A. B.; Wang, Jiankang; Juliana, Philomin; Kehel, Zakaria; Kholová, Jana; Koo, Jawoo; Pequeno, Diego Noleto Luz; Quíroz, Roberto; Rebolledo, María Camila; Sukumaran, Sivakumar; Vadez, Vincent; White, Jeffrey W.; Reynolds, Matthew

doi:10.1002/csc2.20048

Cited by 53 publications

(50 citation statements)

References 197 publications

(239 reference statements)

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“…Finally, to realise the benefits, farmers must adopt the positive G-M technology combinations for their on-farm crop production systems. Here we consider some motivating examples and discuss these foundations for prediction of crop productivity within the context of G × E × M interactions (Messina et al 2009(Messina et al , 2020aKholová et al 2013Kholová et al , 2014Ramirez-Villegas et al 2020;Kruseman et al 2020).…”

Section: Seeking Workable Genotype-management Technology Solutionsmentioning

confidence: 99%

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

et al. 2021

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Key message Climate change and Genotype-by-Environment-by-Management interactions together challenge our strategies for crop improvement. Research to advance prediction methods for breeding and agronomy is opening new opportunities to tackle these challenges and overcome on-farm crop productivity yield-gaps through design of responsive crop improvement strategies. Abstract Genotype-by-Environment-by-Management (G × E × M) interactions underpin many aspects of crop productivity. An important question for crop improvement is “How can breeders and agronomists effectively explore the diverse opportunities within the high dimensionality of the complex G × E × M factorial to achieve sustainable improvements in crop productivity?” Whenever G × E × M interactions make important contributions to attainment of crop productivity, we should consider how to design crop improvement strategies that can explore the potential space of G × E × M possibilities, reveal the interesting Genotype–Management (G–M) technology opportunities for the Target Population of Environments (TPE), and enable the practical exploitation of the associated improved levels of crop productivity under on-farm conditions. Climate change adds additional layers of complexity and uncertainty to this challenge, by introducing directional changes in the environmental dimension of the G × E × M factorial. These directional changes have the potential to create further conditional changes in the contributions of the genetic and management dimensions to future crop productivity. Therefore, in the presence of G × E × M interactions and climate change, the challenge for both breeders and agronomists is to co-design new G–M technologies for a non-stationary TPE. Understanding these conditional changes in crop productivity through the relevant sciences for each dimension, Genotype, Environment, and Management, creates opportunities to predict novel G–M technology combinations suitable to achieve sustainable crop productivity and global food security targets for the likely climate change scenarios. Here we consider critical foundations required for any prediction framework that aims to move us from the current unprepared state of describing G × E × M outcomes to a future responsive state equipped to predict the crop productivity consequences of G–M technology combinations for the range of environmental conditions expected for a complex, non-stationary TPE under the influences of climate change.

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Section: Seeking Workable Genotype-management Technology Solutionsmentioning

confidence: 99%

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

et al. 2021

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“…The combination of molecular technologies and digital prediction methodologies has transformed crop improvement over the last decade (Cooper et al, 2014b; Poland, 2015; Ramirez-Villegas et al, 2020) and increasingly enabled farmers to produce enough food, feed, fuel and fiber for society. However, future agriculture is unlikely to balance supply and demand for food (Ray et al, 2013; Fisher et al, 2014), even in the absence of any considerations to reduce greenhouse gas emissions (NASEM, 2019).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Can we harness digital technologies and physiology to hasten genetic gain in U.S. maize breeding?

Diepenbrock

Tang

Jines

et al. 2021

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Genetic gain in breeding programs depends on the predictive skill of genotype-to-phenotype algorithms and precision of phenotyping, both integrated with well-defined breeding objectives for a target population of environments (TPE). The integration of physiology and genomics could improve predictive skill by capturing additive and non-additive interaction effects of genotype (G), environment (E), and management (M). Precision phenotyping at managed stress environments (MSEs) can elicit physiological expression of processes that differentiate germplasm for performance in target environments, thus enabling algorithm training. Gap analysis methodology enables design of GxM technologies for target environments by assessing the difference between current and attainable yields within physiological limits. Harnessing digital technologies such as crop growth model-whole genome prediction (CGM-WGP) and gap analysis, and MSEs, can hasten genetic gain by improving predictive skill and definition of breeding goals. A half-diallel maize experiment resulting from crossing 9 elite maize inbreds was conducted at 17 locations in the TPE and 6 locations at MSEs between 2017 and 2019. Analyses over 35 families represented by 2367 hybrids demonstrated that CGM-WGP offered a predictive advantage (y) compared to WGP that increased with occurrence of drought as measured by decreasing whole-season evapotranspiration (ET; log(y) = 0.80(±0.6) − 0.006(±0.001) × ET; r2 = 0.59; df=21). Predictions of unobserved physiological traits using the CGM, akin to digital phenotyping, were stable. This understanding of germplasm response to ET enables predictive design of opportunities to close productivity gaps. We conclude that enabling physiology through digital methods can hasten genetic gain by improving predictive skill and defining breeding objectives bounded by physiological realities.

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“…Integrating these models and their associated scientific knowledge with socioeconomic models enables ex-ante and strategic foresight studies to evaluate research investments, technologies, and interventions in agricultural systems (Kruseman et al, 2020). Significant developments in crop sciences have enabled application of models in agriculture within the CGIAR (formerly Consultative Group for International Agricultural Research) system (Kruseman et al, 2020;Ramirez-Villegas © 2020 The Authors. Crop Science © 2020 Crop Science Society of America et al, 2020), other public institutions (Hammer et al, 2020;Jones et al, 2017;Sinclair, Soltani, Marrou, Ghanem, & Vadez, 2020), and industry (Cooper et al, 2014;Cooper et al, 2020).…”

mentioning

confidence: 99%

“…Outcomes from these long-term research efforts have contributed to many aspects of the target agricultural systems (e.g., small holder agriculture) and agricultural research, leading to improvements in the sustainability and productivity of diverse production systems. The complexities associated with encoding biological mechanisms for the simulation of credible genotype responses to management and environmental variation (Hammer et al, 2020;Hammer, Messina, Wu, & Cooper, 2019;Messina et al, 2019), acquiring the right and accurate information to exercise prediction models (Archontoulis et al, 2020;Kruseman et al, 2020;Ramirez-Villegas et al, 2020) and the need for transdisciplinary research to connect biological with socio-economic models (Cooper et al, 2020;Kruseman et al, 2020) remain significant barriers to adoption of prediction methods. While it is being advocated that using simple mechanistic models for prediction (Messina et al, 2018;Sinclair et al, 2020) and modern frameworks for collaboration and data exchange (Ramirez-Villegas et al, 2020) can accelerate realizing societal value facilitated by prediction technologies, achieving the right balance between parsimony and biological reality adequate to enable the intended application remains elusive and a fertile area of future research (Hammer et al, 2019).…”

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confidence: 99%

Crop science: A foundation for advancing predictive agriculture

et al. 2020

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CGIAR modeling approaches for resource‐constrained scenarios: I. Accelerating crop breeding for a changing climate

Cited by 53 publications

References 197 publications

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

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

Can we harness digital technologies and physiology to hasten genetic gain in U.S. maize breeding?

Crop science: A foundation for advancing predictive agriculture

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