Prediction of near‐term climate change impacts on <scp>UK</scp> wheat quality and the potential for adaptation through plant breeding

Fradgley, Nick; Bacon, James; Bentley, Alison R.; Costa‐Neto, Germano; Cottrell, A.; Crossa, José; Cuevas, Jaime; Kerton, Matthew; Pope, Edward; Swarbreck, Stéphanie M.; Gardner, Keith A.

doi:10.1111/gcb.16552

Cited by 13 publications

(6 citation statements)

References 80 publications

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“…The GSA-GxE modeling framework supports the thesis that integrated genetics, climate, and their interactions contribute to identifying the climate variables responsible for the improvement of the predictability of maize yields in US-CA. We consider that the effects of climate on maize predictability can shed some light on how crops respond and adapt to spatiotemporal fluctuations in climate and our abilities to capture such patterns of variability and crop responses in collected data, biophysical, statistical, and data models [8,11,12,17,80,81]. The selection of rainfall, solar radiation, temperature, and relative humidity to create the covariance matrices for the GSA's conditional phase followed studies that indicate their influence on maize growth and production [24,26,70,82].…”

Section: Resultsmentioning

confidence: 99%

“…The study of maize blends scientific and technological advancements with cultural identities, agronomic practices, and variable climate conditions, making it one of the world's top cereals, a critical resource to meet our future food security, and a pillar of civilizations [1][2][3][4][5]. Efforts to understand crop responses to volatile climate are recorded by plant's organic and production variables [6][7][8]. Predicting those responses requires integrating inherently uncertain genetic, climate, phenotypic, and management data to elucidate the underpinning processes responsible for the plants' abilities to adapt.…”

Section: Introductionmentioning

confidence: 99%

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A Method to Estimate Climate Drivers of Maize Yield Predictability Leveraging Genetic-by-Environment Interactions in the US and Canada

Sarzaeim,

Muñoz-Arriola

2024

Agronomy

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Throughout history, the pursuit of diagnosing and predicting crop yields has evidenced genetics, environment, and management practices intertwined in achieving food security. However, the sensitivity of crop phenotypes and genetic responses to climate still hampers the identification of the underlying abilities of plants to adapt to climate change. We hypothesize that the PiAnosi and WagNer (PAWN) global sensitivity analysis (GSA) coupled with a genetic by environment (GxE) model built of environmental covariance and genetic markers structures, can evidence the contributions of climate on the predictability of maize yields in the U.S. and Ontario, Canada. The GSA-GxE framework estimates the relative contribution of climate variables to improving maize yield predictions. Using an enhanced version of the Genomes to Fields initiative database, the GSA-GxE framework shows that the spatially aggregated sensitivity of maize yield predictability is attributed to solar radiation, followed by temperature, rainfall, and relative humidity. In one-third of the individually assessed locations, rainfall was the primary responsible for maize yield predictability. Also, a consistent pattern of top sensitivities (Relative Humidity, Solar Radiation, and Temperature) as the main or the second most relevant drivers of maize yield predictability shed some light on the drivers of genetic improvement in response to climate change.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Method to Estimate Climate Drivers of Maize Yield Predictability Leveraging Genetic-by-Environment Interactions in the US and Canada

Sarzaeim,

Muñoz-Arriola

2024

Agronomy

View full text Add to dashboard Cite

show abstract

“…The GSA-GxE modeling framework supports the thesis that integrated genetics, climate, and their interactions contribute to identify the climate variables responsible for the improvement of the predictability of maize yields in US-CA. We consider that the effects of climate on maize predictability can shed some light on how crops respond and adapt to spatiotemporal fluctuations in climate and our abilities to capture such patterns of variability and crop responses in collected data, biophysical, statistical, and data models [8,11,12,17,80,81]. The selection of rainfall, solar radiation, temperature, and relative humidity to create the covariance matrices for the GSA's conditional phase followed studies that indicate their influence on maize growth and production [24,26,70,82].…”

Section: Resultsmentioning

confidence: 99%

“…The study of maize blends scientific and technological advancements with cultural identities, agronomic practices, and variable climate conditions, making it one of the world's top cereals, a critical resource to meet our future food security, and a pilar of civilizations [1][2][3][4][5]. Efforts to understand crop responses to volatile climate are recorded by plant's organic and production variables [6][7][8]. Predicting those responses requires integrating inherently uncertain genetic, climate, phenotypic and management data to elucidate the underpinning processes responsible for the plants' abilities to adapt.…”

Section: Introductionmentioning

confidence: 99%

A Method to Estimate Climate Drivers of Maize Yield Predictability Leveraging Genetic-by-Environment Interactions in the US and Canada

Sarzaeim,

Muñoz-Arriola

2024

Preprint

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Throughout history, the pursuit of diagnosing and predicting crop yields has evidenced genetics, environment, and management practices' intertwined roles in achieving food security. However, the sensitivity of crop phenotypes and genetic responses to weather and climate remains unclear, hampering the identification of the underlying abilities of plants to adapt to climate change. We hypothesize that the PAWN global sensitivity analysis (GSA) coupled with a genetic by environment (GxE) model -built of environmental covariance and genetic markers structures- can evidence the contributions of climate on the predictability of maize yields in the U.S. and Ontario, Canada (US-CA). The GSA-GxE modeling framework estimates the relative contribution of climate variables such as solar radiation, temperature, rainfall, and relative humidity on improving maize yield predictions in US-CA. We use an improved version of the Genomes to Fields (G2F) initiative multi-dimensional database to build the environmental covariance matrices for the proposed GSA-GxE framework. The PAWN indices show that the aggregated GxE model’s highest sensitivity levels over US-CA were attributed to solar radiation, temperature, rainfall, and relative humidity. In one-third of the locations, rainfall was the primary climate variable responsible for maize yield predictability. Also, a consistent pattern of top sensitivity indices by location indicates that Relative Humidity, Solar Radiation, and Temperature were distributed as the main or the second most relevant drivers of maize yield predictability.

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“…Wheat ( Triticum aestivum L.) is an important commodity, with ∼22% of wheat produced traded internationally (Shewry & Hey, 2015). Shifts in climatic conditions over time are likely to cause further spatiotemporal variations in abiotic and biotic stresses for wheat producers that will likely lead to significant spatial changes in yield and grain quality (Fradgley et al., 2022). These challenges will need to be addressed from multiple perspectives, including research‐based solutions.…”

Section: Plants Experience Multiple Simultaneous Abiotic and Biotic S...mentioning

confidence: 99%

Climate change impacts on crop breeding: Targeting interacting biotic and abiotic stresses for wheat improvement

et al. 2023

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Wheat (Triticum aestivum L.) as a staple crop is closely interwoven into the development of modern society. Its influence on culture and economic development is global. Recent instability in wheat markets has demonstrated its importance in guaranteeing food security across national borders. Climate change threatens food security as it interacts with a multitude of factors impacting wheat production. The challenge needs to be addressed with a multidisciplinary perspective delivered across research, private, and government sectors. Many experimental studies have identified the major biotic and abiotic stresses impacting wheat production, but fewer have addressed the combinations of stresses that occur simultaneously or sequentially during the wheat growth cycle. Here, we argue that biotic and abiotic stress interactions, and the genetics and genomics underlying them, have been insufficiently addressed by the crop science community. We propose this as a reason for the limited transfer of practical and feasible climate adaptation knowledge from research projects into routine farming practice. To address this gap, we propose that novel methodology integration can align large volumes of data available from crop breeding programs with increasingly cheaper omics tools to predict wheat performance under different climate change scenarios. Underlying this is our proposal that breeders design and deliver future wheat ideotypes based on new or enhanced understanding of the genetic and physiological processes that are triggered when wheat is subjected to combinations of stresses. By defining this to a trait and/or genetic level, new insights can be made for yield improvement under future climate conditions.

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Prediction of near‐term climate change impacts on UK wheat quality and the potential for adaptation through plant breeding

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 13 publications

References 80 publications

A Method to Estimate Climate Drivers of Maize Yield Predictability Leveraging Genetic-by-Environment Interactions in the US and Canada

A Method to Estimate Climate Drivers of Maize Yield Predictability Leveraging Genetic-by-Environment Interactions in the US and Canada

A Method to Estimate Climate Drivers of Maize Yield Predictability Leveraging Genetic-by-Environment Interactions in the US and Canada

Climate change impacts on crop breeding: Targeting interacting biotic and abiotic stresses for wheat improvement

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