Toward a Better Understanding of Genotype × Environment × Management Interactions—A Global Wheat Initiative Agronomic Research Strategy

Beres, Brian L.; Hatfield, Jerry L.; Kirkegaard, John A.; Eigenbrode, Sanford D.; Pan, William L.; Lollato, Romulo P.; Strydhorst, Sheri; Porker, Kenton; Lyon, Drew J.; Ransom, Joel K.; Wiersma, J. V.

doi:10.3389/fpls.2020.00828

Cited by 42 publications

(39 citation statements)

References 40 publications

(42 reference statements)

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“…Moreover, management practices are often borrowed from research conducted on common wheat. While we have sought to present synergies possible with a G × E × M paradigm, it is clear a coordinated effort and cross-disciplinary approach is yet to be fully realized, which underscores the need for transformational research that exemplifies the G × E × M paradigm (Beres et al, 2020). In many areas of the world, climate change will drive future research and innovation, which requires a re-think of how we link together all the genetic and management components when designing a resilient cropping system (Fletcher et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

A Systematic Review of Durum Wheat: Enhancing Production Systems by Exploring Genotype, Environment, and Management (G × E × M) Synergies

et al. 2020

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According to the UN-FAO, agricultural production must increase by 50% by 2050 to meet global demand for food. This goal can be accomplished, in part, by the development of improved cultivars coupled with modern best management practices. Overall, wheat production on farms will have to increase significantly to meet future demand, and in the face of a changing climate that poses risk to even current rates of production. Durum wheat [Triticum turgidum L. ssp. durum (Desf.)] is used largely for pasta, couscous and bulgur production. Durum producers face a range of factors spanning abiotic (frost damage, drought, and sprouting) and biotic (weed, disease, and insect pests) stresses that impact yields and quality specifications desired by export market end-users. Serious biotic threats include Fusarium head blight (FHB) and weed pest pressures, which have increased as a result of herbicide resistance. While genetic progress for yield and quality is on pace with common wheat (Triticum aestivum L.), development of resistant durum cultivars to FHB is still lagging. Thus, successful biotic and abiotic threat mitigation are ideal case studies in Genotype (G) × Environment (E) × Management (M) interactions where superior cultivars (G) are grown in at-risk regions (E) and require unique approaches to management (M) for sustainable durum production. Transformational approaches to research are needed in order for agronomists, breeders and durum producers to overcome production constraints. Designing robust agronomic systems for durum demands scientific creativity and foresight based on a deep understanding of constitutive components and their innumerable interactions with each other and the environment. This encompasses development of durum production systems that suit specific agroecozones and close the yield gap between genetic potential and on-farm achieved yield. Advances in individual technologies (e.g., genetic improvements, new pesticides,

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Section: Discussionmentioning

confidence: 99%

A Systematic Review of Durum Wheat: Enhancing Production Systems by Exploring Genotype, Environment, and Management (G × E × M) Synergies

et al. 2020

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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 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 ). Proposals to accelerate improvement of crop productivity to account for the effects of climate change have largely ignored the influences of G × E × M interactions, or have assumed that the traditional research sequence of breeding for improved adaptation followed by agronomic optimisation will be effective for our future agricultural systems (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…A number of other studies have described the importance of G × E × M interactions and their contributions to crop yield productivity (Cooper et al 2001 ; Duvick et al 2004 ; Messina et al 2009 ; Hatfield and Walthall 2015 ; Beres et al 2020 ; Peake et al 2020 ; Rotili et al 2020 ; Snowdon et al 2020 ). Beyond the hybrid-by-density maize example above, a few studies have demonstrated effective improvement and utilisation of positive G × M interactions: improvement of sorghum yield for drought-prone environments in Australia (Hammer et al 2014 , 2020 ; Rodriguez et al 2018 ); improvement of wheat for drought-prone and irrigated environments in Australia (Hunt et al 2019 ); and improvement of maize hybrids for drought-prone environments of the US corn-belt by targeting selection for traits contributing to effective water use (Messina et al 2009 , 2018 ; Cooper et al 2014a , Gaffney et al 2015 ).…”

Section: Introductionmentioning

confidence: 99%

“…First, within the current dominant crop improvement paradigm of first breeding to develop new cultivars followed by management optimisation. To date, most considerations of G × E × M interactions for crop improvement have operated from assumptions based on this serial, breeding followed by agronomy, perspective (Beres et al 2020 ; Snowdon et al 2020 ). For example, with advanced understanding of G × E × M interactions, breeders can adjust the design of the METs for one or more stages of the breeding program to enhance testing under different crop management regimes at any stage of the breeding program to create, identify and select new genotypes that demonstrate broad or specific adaptation to Environment–Management combinations that are important within the context of the TPE (Braun et al 2010 ; Chapman et al 2012 ; Snowdon et al 2020 ).…”

Section: Introductionmentioning

confidence: 99%

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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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“…Sustainable improvement of on-farm crop yield productivity, through improving yield potential and yield stability, is a complex long-term objective for both breeders and agronomists (Duvick et al, 2004;Hall and Richards, 2013;Fischer et al, 2014;Hatfield and Walthall, 2015;Beres et al, 2020;Cooper et al, 2021;Hunt et al, 2021). Heterogeneity of current environmental conditions that impact crop yield and the influences of climate change continually challenge the definition of the Target Population of Environments (TPE) for both breeders and agronomists (Chapman et al, 2012;Harrison et al, 2014;Lobell et al, 2015;Voss-Fels et al, 2019;Hammer et al, 2020;Cooper et al, 2021;Smith et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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

Cooper¹,

Messina²

2021

Front. Plant Sci.

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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.

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Toward a Better Understanding of Genotype × Environment × Management Interactions—A Global Wheat Initiative Agronomic Research Strategy

Cited by 42 publications

References 40 publications

A Systematic Review of Durum Wheat: Enhancing Production Systems by Exploring Genotype, Environment, and Management (G × E × M) Synergies

A Systematic Review of Durum Wheat: Enhancing Production Systems by Exploring Genotype, Environment, and Management (G × E × M) Synergies

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

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