A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration

Makowski, Dariusz; Asseng, Senthold; Ewert, Frank; Bassu, Simona; Durand, Jean Louis; Li, Tongjie; Martre, Pierre; Adam, Myriam; Aggarwal, Pramod K.; Angulo, Carlos; Baron, Christian; Basso, Bruno; Bertuzzi, Patrick; Biernath, Christian; Boogaard, H.L.; Boote, Kenneth J.; Bouman, B.A.M.; Bregaglio, Simone; Brisson, Nadine; Buis, Samuel; Cammarano, Davide; Challinor, Andrew J.; Confalonieri, Roberto; Conijn, J.G.; Corbeels, Marc; Deryng, Delphine; Sanctis, Giacomo De; Doltra, Jordi; Fumoto, Tamon; Gaydon, Donald S.; Gayler, Sebastian; Goldberg, Robert K.; Grant, R. F.; Grassini, Patricio; Hatfield, Jerry L.; Hasegawa, Toshihiro; Heng, Lee; Hoek, Steven; Hooker, Josh; Hunt, L. A.; Ingwersen, Joachim; Izaurralde, R. C.; Jongschaap, R.E.E.; Jones, James W.; Kemanian, Armen R.; Kersebaum, Kurt Christian; Kim, Sung Hyun; Lizaso, Jon; Marcaida, Manuel; Müller, Christoph; Nakagawa, H.; Kumar, Soora Naresh; Nendel, Claas; O’Leary, G.; Olesen, Jørgen E.; Oriol, Philippe; Osborne, Thomas M.; Palosuo, Taru; Pravia, Maria Virginia; Priesack, Eckart; Ripoche, Dominique; Rosenzweig, Cynthia; Ruane, Alex C.; Ruget, Françoise; Sau, Federico; Semenov, Mikhail A.; Shcherbak, Iurii; Singh, Balwinder; Singh, Upendra; Soo, Hung; Steduto, Pasquale; Stöckle, Claudio O.; Stratonovitch, Pierre; Streck, Thilo; Supit, Iwan; Tang, Liang; Tao, Fulu; Teixeira, Edmar; Thorburn, Peter J.; Timlin, Dennis; Travasso, Maria; Rötter, Reimund P.; Waha, Katharina; Wallach, Daniel; White, Jeffrey W.; Wilkens, Paul W.; Williams, J. R.; Wolf, J.; Yin, Xinyou; Yoshida, Hiroyuki; Zhang, Z.; Zhu, Yan

doi:10.1016/j.agrformet.2015.09.013

Cited by 36 publications

(19 citation statements)

References 16 publications

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“…In this case, assuming a C of 720 ppm along with +9 °C increase for T and −30% decline in W, percent changes in yield with respect to baseline conditions ranged between −21% and −61% and ET between −3% and −35% across the four sites. As noted from the preceding single‐factor discussion, the largest proportion of these relative changes was due to the influence of the highest T level similar to other model intercomparison studies (Makowski et al ., ), and uncertainty was typically at its highest at the warmest T and highest C levels (Fig. ).…”

Section: Discussionmentioning

confidence: 90%

A potato model intercomparison across varying climates and productivity levels

Fleisher

Condori

Quiroz

et al. 2016

Global Change Biology

105

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A potato crop multimodel assessment was conducted to quantify variation among models and evaluate responses to climate change. Nine modeling groups simulated agronomic and climatic responses at low-input (Chinoli, Bolivia and Gisozi, Burundi)- and high-input (Jyndevad, Denmark and Washington, United States) management sites. Two calibration stages were explored, partial (P1), where experimental dry matter data were not provided, and full (P2). The median model ensemble response outperformed any single model in terms of replicating observed yield across all locations. Uncertainty in simulated yield decreased from 38% to 20% between P1 and P2. Model uncertainty increased with interannual variability, and predictions for all agronomic variables were significantly different from one model to another (P < 0.001). Uncertainty averaged 15% higher for low- vs. high-input sites, with larger differences observed for evapotranspiration (ET), nitrogen uptake, and water use efficiency as compared to dry matter. A minimum of five partial, or three full, calibrated models was required for an ensemble approach to keep variability below that of common field variation. Model variation was not influenced by change in carbon dioxide (C), but increased as much as 41% and 23% for yield and ET, respectively, as temperature (T) or rainfall (W) moved away from historical levels. Increases in T accounted for the highest amount of uncertainty, suggesting that methods and parameters for T sensitivity represent a considerable unknown among models. Using median model ensemble values, yield increased on average 6% per 100-ppm C, declined 4.6% per °C, and declined 2% for every 10% decrease in rainfall (for nonirrigated sites). Differences in predictions due to model representation of light utilization were significant (P < 0.01). These are the first reported results quantifying uncertainty for tuber/root crops and suggest modeling assessments of climate change impact on potato may be improved using an ensemble approach.

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Section: Discussionmentioning

confidence: 90%

A potato model intercomparison across varying climates and productivity levels

Fleisher

Condori

Quiroz

et al. 2016

Global Change Biology

105

View full text Add to dashboard Cite

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“…Under climate change conditions, CO 2 fertilization effects on maize growth should also be taken into account. Despite high uncertainties, most experimental and modeling results point to limited CO 2 effects for the C4 crop maize, compared to the other main C3 crops such as wheat and rice (Bassu et al, 2014;Deryng et al, 2014;Kimball, 2016;Makowski et al, 2015;Schleussner et al, 2018;Semenov & Shewry, 2008). Higher CO 2 concentrations potentially increase maize water use efficiency and may alleviate the effects of drought on maize yields (Crafts-Brandner & Salvucci, 2002).…”

Section: 1029/2018ef000995mentioning

confidence: 99%

When Will Current Climate Extremes Affecting Maize Production Become the Norm?

et al. 2019

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We estimate the effects of climate anomalies (heat stress and drought) on annual maize production, variability, and trend from the country level to the global scale using a statistical model. Moderate climate anomalies and extremes are diagnosed with two indicators of heat stress and drought computed over maize growing regions during the most relevant period of maize growth. The calibrated model linearly combines these two indicators into a single Combined Stress Index. The Combined Stress Index explains 50% of the observed global production variability in the period 1980–2010. We apply the model on an ensemble of high‐resolution global climate model simulations. Global maize losses, due to extreme climate events with 10‐year return times during the period 1980–2010, will become the new normal already at 1.5 °C global warming levels (approximately 2020s). At 2 °C warming (late 2030s), maize areas will be affected by heat stress and drought never experienced before, affecting many major and minor production regions.

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“…More recent studies extended the same approach to a larger spatial scale and added the CO 2 concentration to the set of factors affecting yields (Makowski et al, 2015;Oyebamiji et al, 2015). The most recent SE was proposed by Blanc (2017) for wheat, soybean and rice crops.…”

Section: Overview On Statistical Emulatorsmentioning

confidence: 99%

Land degradation and climate change: Global impact on wheat yields

et al. 2020

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This study aims at estimating the wheat yield response to future challenges (land degradation and climate change) through a statistical emulator. Data from Global Agro-ecological Zones on the overall wheat yields of 254 provinces for nine main wheat-producing countries and corresponding climate and land suitability information were collected to estimate the statistical relationship between regressors and wheat yields. Once the statistical emulator was calibrated, three scenarios were developed to predict, separately and jointly, the impact of climate change and land degradation on wheat yields and productions in 2050 using the global climate model ECHAM4 information under the B2 scenario with the CO 2 fertilization effect. Results showed that under temperature and precipitation projections wheat yield would increase on average by 8% (first scenario); however, the impact of future land suitability (second scenario) on wheat yields would be negative, lowering the average yield (−5%), and overall wheat production (−14%). Combining both effects in the third scenario, future wheat yields would increase by 4.6%, while overall production will decrease by 5.5% since the harvest area will be reduced by 9.7%. Thus, land degradation more than climate change is likely to pose significant challenges for overall wheat production in the future.

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A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration

Cited by 36 publications

References 16 publications

A potato model intercomparison across varying climates and productivity levels

A potato model intercomparison across varying climates and productivity levels

When Will Current Climate Extremes Affecting Maize Production Become the Norm?

Land degradation and climate change: Global impact on wheat yields

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