Benchmark data set for wheat growth models: field experiments and AgMIP multi-model simulations

Asseng, Senthold; Ewert, Frank; Martre, Pierre; Rosenzweig, Cynthia; Jones, James W.; Hatfield, Jerry L.; Ruane, Alex C.; Boote, Kenneth J.; Thorburn, Peter J.; Rötter, Reimund P.

doi:10.18174/odjar.v1i1.14746

Cited by 9 publications

(5 citation statements)

References 5 publications

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“…(iii) Modernize data storage and interoperability. Collaboration across researchers in crop modeling in global or regional projects, including the Agricultural Model Inter-comparison Project (AgMIP), has helped the crop modeling community to identify high-value datasets (Asseng et al, 2015;Raymundo et al, 2018), resulting in improved models with greater applicability for breeding under future climates, for example for heat stress response on wheat (Asseng et al, 2013(Asseng et al, , 2014(Asseng et al, , 2019b, or CO 2 response on maize (Durand et al, 2018).…”

Section: Crop Sciencementioning

confidence: 99%

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

et al. 2020

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Crop improvement efforts aiming at increasing crop production (quantity, quality) and adapting to climate change have been subject of active research over the past years. But, the question remains 'to what extent can breeding gains be achieved under a changing climate, at a pace sufficient to usefully contribute to climate adaptation, mitigation and food security?'. Here, we address this question by critically reviewing how model-based approaches can be used to assist breeding activities, with

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Section: Crop Sciencementioning

confidence: 99%

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

et al. 2020

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“…Phase 2a (Asseng et al 2015a(Asseng et al , 2015b Protocol-based multi-model analysis of temperature response at Hot Serial Cereals artificial heating experiment in Arizona and temperature responses in Mexico.…”

Section: Macsur-crop Rotationmentioning

confidence: 99%

“…Phase 2b (Asseng et al 2015a, 2015b, Liu et al 2016 Intercomparison of temperature responses across 30 sites selected as a representative network of well-watered wheat production regions around the world.…”

Section: Macsur-crop Rotationmentioning

confidence: 99%

An AgMIP framework for improved agricultural representation in integrated assessment models

Ruane

Rosenzweig

Asseng

et al. 2017

Environ. Res. Lett.

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“…The water vapor capacity of air is largest in high temperatures, and accordingly, the impacts of RH have been studied most widely in warm conditions. To mention some examples, RH contributes to the growth of crops [e.g., Asseng et al, 2015], human comfort and heat stress [e.g., Willett and Sherwood, 2012;Knutson and Ploshay, 2016], wildfires [e.g., Lehtonen et al, 2014;Vajda et al, 2014], and surface evapotranspiration and soil moisture [e.g., Seneviratne et al, 2010;Ruosteenoja et al, 2017]. Even so, air humidity has multiple implications in areas with cold climate as well.…”

Section: Introductionmentioning

confidence: 99%

Surface air relative humidities spuriously exceeding 100% in CMIP5 model output and their impact on future projections

et al. 2017

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In 17 out of the 29 Phase 5 of Coupled Model Intercomparison Project (CMIP5) climate models examined in this work, near‐surface air relative humidity (RH) frequently exceeded 100% with respect to ice in polar areas in winter. The degree of supersaturation varied considerably across the models, and the same evidently applies to the causes of the phenomenon. Consultations with the modeling groups revealed three categories of explanations for supersaturation occurrence: specification of RH with respect to ice rather than liquid water; inconsistencies in the determination of specific humidity and air temperature for the near‐surface level; and the nonlinearity of saturated specific humidity as a function of temperature. Modeled global warming tended to reduce the artificial supersaturations, inducing a spurious negative trend in the future RH change. For example, over East Antarctica under Representative Concentration Pathway 8.5, the multimodel mean RH would decrease by about 10% by the end of the ongoing century. Truncation of overly high RHs to a maximum value of 100% cut the RH response close to zero. In Siberia and northern North America, truncation even reversed the sign of the response. The institutes responsible for the CMIP6 model experiments should be aware of the supersaturation issue, and the algorithms used to produce near‐surface RH should be developed to eliminate the problem before publishing the RH output data.

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Benchmark data set for wheat growth models: field experiments and AgMIP multi-model simulations

Cited by 9 publications

References 5 publications

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

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

An AgMIP framework for improved agricultural representation in integrated assessment models

Surface air relative humidities spuriously exceeding 100% in CMIP5 model output and their impact on future projections

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