Crops and climate change: progress, trends, and challenges in simulating impacts and informing adaptation

Challinor, Andrew J.; Ewert, Frank; Arnold, S. R.; Simelton, Elisabeth; Fraser, Evan D. G.

doi:10.1093/jxb/erp062

Cited by 347 publications

(228 citation statements)

References 126 publications

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“…A widely applied approach to estimating climate change impact on crop yield is crop simulation modeling (e.g., Lin et al, 2005;Liu et al, 2010;Tao et al, 2009;Xiong et al, 2012;Xiong et al, 2007;Zhang et al, 2013), where key socio-economic factors other than climate variables in crop production are typically out of consideration (Challinor et al, 2009). …”

Section: Introductionmentioning

confidence: 99%

Climate change impacts on crop yield: Evidence from China

Wei

Cherry

Glomrød

et al. 2014

Science of The Total Environment

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Section: Introductionmentioning

confidence: 99%

Climate change impacts on crop yield: Evidence from China

Wei

Cherry

Glomrød

et al. 2014

Science of The Total Environment

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“…We found that the genomic architecture of flowering time has been shaped by the most recent whole-genome duplication, which suggests that ancient paralogues can remain in the same regulatory networks for dozens of millions of years. This genome represents a cornerstone for future research programs aiming to exploit genetic diversity to improve biotic and abiotic stress resistance and oil production, while also considering agricultural constraints and human nutritional needs 8,9 .…”

mentioning

confidence: 99%

The sunflower genome provides insights into oil metabolism, flowering and Asterid evolution

Badouin¹,

Gouzy²,

Grassa³

et al. 2017

Nature

640

705

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The domesticated sunflower, Helianthus annuus L., is a global oil crop that has promise for climate change adaptation, because it can maintain stable yields across a wide variety of environmental conditions, including drought 1 . Even greater resilience is achievable through the mining of resistance alleles from compatible wild sunflower relatives 2,3 , including numerous extremophile species 4 . Here we report a high-quality reference for the sunflower genome (3.6 gigabases), together with extensive transcriptomic data from vegetative and floral organs. The genome mostly consists of highly similar, related sequences 5 and required single-molecule realtime sequencing technologies for successful assembly. Genome analyses enabled the reconstruction of the evolutionary history of the Asterids, further establishing the existence of a whole-genome triplication at the base of the Asterids II clade 6 and a sunflowerspecific whole-genome duplication around 29 million years ago 7 . An integrative approach combining quantitative genetics, expression and diversity data permitted development of comprehensive gene networks for two major breeding traits, flowering time and oil metabolism, and revealed new candidate genes in these networks. We found that the genomic architecture of flowering time has been shaped by the most recent whole-genome duplication, which suggests that ancient paralogues can remain in the same regulatory networks for dozens of millions of years. This genome represents a cornerstone for future research programs aiming to exploit genetic diversity to improve biotic and abiotic stress resistance and oil production, while also considering agricultural constraints and human nutritional needs 8,9 .As the only major crop domesticated in North America, with its sunlike inflorescence that inspired artists, the sunflower is both a social icon and a major research focus for scientists. In evolutionary biology, the Helianthus genus is a long-time model for hybrid speciation and adaptive introgression 10 . In plant science, the sunflower is a model for understanding solar tracking 11 and inflorescence development 12 .Despite this large interest, assembling its genome has been extremely difficult as it mainly consists of long and highly similar repeats. This complexity has challenged leading-edge assembly protocols for close to a decade 13 .To finally overcome this challenge, we generated a 102× sequencing coverage of the genome of the inbred line XRQ using 407 singlemolecule real-time (SMRT) cells on the PacBio RS II platform. Production of 32 million very long reads allowed us to generate a genome assembly that captures 3 gigabases (Gb) (80% of the estimated genome size) in 13,957 sequence contigs. Four high-density genetic maps were combined with a sequence-based physical map to build the sequences of the 17 pseudo-chromosomes that anchor 97% of the gene content (Fig.

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“…Downscaling is necessary for agricultural impacts assessments because factors that affect crops (soil, surface, and farming practices) vary at finer scales than typical GCM spatial resolution (>100 km) (10,11). Some of the earliest downscaling approaches used observed relationships between mesoscale and local climate variables to relate GCM output to local climate (12).…”

mentioning

confidence: 99%

Evaluating the utility of dynamical downscaling in agricultural impacts projections

Glotter¹,

Elliott

McInerney³

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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Interest in estimating the potential socioeconomic costs of climate change has led to the increasing use of dynamical downscalingnested modeling in which regional climate models (RCMs) are driven with general circulation model (GCM) output-to produce fine-spatial-scale climate projections for impacts assessments. We evaluate here whether this computationally intensive approach significantly alters projections of agricultural yield, one of the greatest concerns under climate change. Our results suggest that it does not. We simulate US maize yields under current and future CO 2 concentrations with the widely used Decision Support System for Agrotechnology Transfer crop model, driven by a variety of climate inputs including two GCMs, each in turn downscaled by two RCMs. We find that no climate model output can reproduce yields driven by observed climate unless a bias correction is first applied. Once a bias correction is applied, GCM-and RCM-driven US maize yields are essentially indistinguishable in all scenarios (<10% discrepancy, equivalent to error from observations). Although RCMs correct some GCM biases related to fine-scale geographic features, errors in yield are dominated by broad-scale (100s of kilometers) GCM systematic errors that RCMs cannot compensate for. These results support previous suggestions that the benefits for impacts assessments of dynamically downscaling raw GCM output may not be sufficient to justify its computational demands. Progress on fidelity of yield projections may benefit more from continuing efforts to understand and minimize systematic error in underlying climate projections.agriculture | food security | NARCCAP | CORDEX

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Crops and climate change: progress, trends, and challenges in simulating impacts and informing adaptation

Cited by 347 publications

References 126 publications

Climate change impacts on crop yield: Evidence from China

Climate change impacts on crop yield: Evidence from China

The sunflower genome provides insights into oil metabolism, flowering and Asterid evolution

Evaluating the utility of dynamical downscaling in agricultural impacts projections

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