Harnessing wheat germplasm to sustain grain-filling under climate warming using a gene-based modelling framework - Yibo Li; Fulu Tao; Zhonghu He; Yuanfeng Hao; Jingyang Tong; Yonggui Xiao; Lichang Yin; Zhao Zhang; Mohamed Darwish; Rashad Elenein; Alex C. Ruane; Gerrit Hoogenboom; et al. (European Journal of Agronomy, 2026)

LAI assimilation schedules to constrain uncertain cultivars and soils in CERES-Maize - Luke Monhollon; Alex C. Ruane; William D. Batchelor (Computers and Electronics in Agriculture, 2026)

The ongoing journey of modelling intercropping systems: A conceptual resource sharing intercomparison from model developers and expert users - Adam Muhammad Adam; Myriam Adam; Folkard Asch; Kenneth J Boote; Vimbayi Grace Petrova Chimonyo; Mathias Christina; Marc Corbeels; Antoine Couëdel; Mathilde De Freitas; Tomas Della Chiesa; Frank Ewert; Gatien N Falconnier; et al. (Field Crops Research, 2026)

An impact-driven framework for climate model evaluation - Maxwell T. Elling; Alex C. Ruane; Manishka De Mel; Jeffrey Jonas; Sanketa Kadam; Nancy Y. Kiang; Keren Mezuman; Larissa Nazarenko; Nick Pelaccio; Meridel Phillips; Anastasia Romanou; David Rind (Climatic Change, 2026)

Beyond Climate Change: The Role of Integrated Soil Fertility Management for Sustaining Future Maize Yield in Sub‐Saharan Africa - Antoine Couëdel; Gatien N. Falconnier; Myriam Adam; Rémi Cardinael; Johan Six; Moritz Laub; Alex C. Ruane; Kenneth Boote; Eric Justes; Ward N. Smith; Anthony M. Whitbread; François Affholder; et al. (Global Change Biology, 2026)
Climate change is projected to exacerbate food insecurity in sub‐Saharan Africa (SSA) by reducing crop yields and soil fertility. Many climate change impact studies in SSA have overlooked long‐term effects of soil fertility on crop yield. We evaluated maize yields under different scenarios of soil fertility (using soil organic carbon as a proxy) and climate change (considering changes in temperature, rainfall, and CO 2 ) at four sites in SSA. Using an ensemble of 15 calibrated soil‐crop models, we found a strong consensus that, without fertilization, soil fertility declines over time, impacting maize yields more strongly than changes in temperature, rainfall, or CO 2 . The model ensemble indicated that when accounting for soil fertility changes, the yield benefits of combined application of organic and mineral inputs increase over time, even under climate change. These findings highlight the importance of considering long‐term change in soil fertility when assessing impacts of climate change and integrated nutrient management on crop production in SSA.

Straw mulching enhances rainfed maize yield under climate change scenarios - Alex Zizinga; Abel Chemura; Alex C. Ruane; Britta Tietjen (European Journal of Agronomy, 2026)

Winter Dormant Wheat Will Benefit From Mean Temperature Increase of 2°C When Well‐Watered and Fertilized in the Main Producing Regions of China - Ruoshi Wang; Chuang Zhao; Senthold Asseng; Bing Liu; Alex C. Ruane; Jiahui Cong; Xi Wang; Zhijuan Liu; Jin Zhao; Xiaoguang Yang (Global Change Biology, 2025)
Rising temperatures are projected to lead to a decline in global wheat production. However, this global trend belies the regional nuances of this impact, such as observed local yield increases in some field experiments in the winter wheat‐growing region of China. This study combines detailed data from eight field warming experiments and outputs of simulation by an ensemble of three point‐based crop models and an ensemble of 10 global gridded crop models to scrutinize the influence of warming on winter wheat yield in the main producing regions of China (MPC). Observed data were obtained from published reports of field experiments, where winter wheat was grown with sufficient water and nitrogen under free‐air‐temperature increase (FATI) by 2°C. Growth and physiology of winter wheat in the field experiments were simulated by three point‐based crop models to validate the effects of warming on wheat growth and yield as simulated by grid‐based crop models. Results of field observations and grid simulations both indicate notable increases in average grain yield (observed +13%, simulated +8%) and aboveground biomass (observed +15%, simulated +7%) under 2°C warming across the MPC. The winter dormancy and pre‐anthesis duration were shorter with warmer temperature, with the effect that the grain filling period between anthesis to maturity was extended by 6 days. The shorter phenology affected wheat photosynthesis because less solar radiation was available (−6%) over the growth period. However, the leaf area index started to develop earlier and reached a higher maximum than un‐warmed control, so the cumulative solar radiation for photosynthesis intercepted by warmed wheat was higher (+9%), as well as the radiation use efficiency (+1%). These findings suggest that well‐irrigated and well‐fertilized winter dormant wheat is likely to experience yield gains with local warming of up to 2°C, bolstering confidence in future adaptation of wheat production in China.

Past and future wheat yield losses in France’s breadbasket - Rogério de S. Nóia-Júnior; Pierre Martre; Jean-Charles Deswarte; Jean-Pierre Cohan; Marijn Van der Velde; Heidi Webber; Frank Ewert; Alex C. Ruane; Tamara Ben-Ari; Senthold Asseng (Field Crops Research, 2025)

How can Future Climate Change Affect the Corn Production System in Nebraska, USA? - Ivo Z. Gonçalves; Izael M. Fattori; Christopher M. U. Neale; Carlos A. C. Santos; Alex Ruane; Meridel M. Phillips; Fábio R. Marin (International Journal of Plant Production, 2024)

Evaluating differences among crop models in simulating soybean in-season growth - Kritika Kothari; Rafael Battisti; Kenneth J. Boote; Sotirios V. Archontoulis; Adriana Confalone; Julie Constantin; Santiago V. Cuadra; Philippe Debaeke; Babacar Faye; Brian Grant; Gerrit Hoogenboom; Qi Jing; et al. (Field Crops Research, 2024)

Long-term soil organic carbon and crop yield feedbacks differ between 16 soil-crop models in sub-Saharan Africa - Antoine Couëdel; Gatien N. Falconnier; Myriam Adam; Rémi Cardinael; Kenneth Boote; Eric Justes; Ward N. Smith; Anthony M. Whitbread; François Affholder; Juraj Balkovic; Bruno Basso; Arti Bhatia; et al. (European Journal of Agronomy, 2024)

Future climate change impacts on U.S. agricultural yields, production, and market - Chengcheng Fei; Jonas Jägermeyr; Bruce McCarl; Erik Mencos Contreras; Carolyn Mutter; Meridel Phillips; Alex C. Ruane; Marcus C. Sarofim; Peter Schultz; Amanda Vargo (Anthropocene, 2023)

The extreme 2016 wheat yield failure in France - Rogério de S. Nóia Júnior; Jean‐Charles Deswarte; Jean‐Pierre Cohan; Pierre Martre; Marijn van der Velde; Remi Lecerf; Heidi Webber; Frank Ewert; Alex C. Ruane; Gustavo A. Slafer; Senthold Asseng (Global Change Biology, 2023)
France suffered, in 2016, the most extreme wheat yield decline in recent history, with some districts losing 55% yield. To attribute causes, we combined the largest coherent detailed wheat field experimental dataset with statistical and crop model techniques, climate information, and yield physiology. The 2016 yield was composed of up to 40% fewer grains that were up to 30% lighter than expected across eight research stations in France. The flowering stage was affected by prolonged cloud cover and heavy rainfall when 31% of the loss in grain yield was incurred from reduced solar radiation and 19% from floret damage. Grain filling was also affected as 26% of grain yield loss was caused by soil anoxia, 11% by fungal foliar diseases, and 10% by ear blight. Compounding climate effects caused the extreme yield decline. The likelihood of these compound factors recurring under future climate change is estimated to change with a higher frequency of extremely low wheat yields.

Correction to: Climate change impacts and adaptation for dryland farming systems in Zimbabwe: a stakeholder-driven integrated multi-model assessment - Sabine Homann-Kee Tui; Katrien Descheemaeker; Roberto O. Valdivia; Patricia Masikati; Gevious Sisito; Elisha N. Moyo; Olivier Crespo; Alex C. Ruane; Cynthia Rosenzweig (Climatic Change, 2022)

Agricultural breadbaskets shift poleward given adaptive farmer behavior under climate change - James A. Franke; Christoph Müller; Sara Minoli; Joshua Elliott; Christian Folberth; Charles Gardner; Tobias Hank; Roberto Cesar Izaurralde; Jonas Jägermeyr; Curtis D. Jones; Wenfeng Liu; Stefan Olin; et al. (Global Change Biology, 2022)
Modern food production is spatially concentrated in global “breadbaskets.” A major unresolved question is whether these peak production regions will shift poleward as the climate warms, allowing some recovery of potential climate‐related losses. While agricultural impacts studies to date have focused on currently cultivated land, the Global Gridded Crop Model Intercomparison Project (GGCMI) Phase 2 experiment allows us to assess changes in both yields and the location of peak productivity regions under warming. We examine crop responses under projected end of century warming using seven process‐based models simulating five major crops (maize, rice, soybeans, and spring and winter wheat) with a variety of adaptation strategies. We find that in no‐adaptation cases, when planting date and cultivar choices are held fixed, regions of peak production remain stationary and yield losses can be severe, since growing seasons contract strongly with warming. When adaptations in management practices are allowed (cultivars that retain growing season length under warming and modified planting dates), peak productivity zones shift poleward and yield losses are largely recovered. While most growing‐zone shifts are ultimately limited by geography, breadbaskets studied here move poleward over 600 km on average by end of the century under RCP 8.5. These results suggest that agricultural impacts assessments can be strongly biased if restricted in spatial area or in the scope of adaptive behavior considered. Accurate evaluation of food security under climate change requires global modeling and careful treatment of adaptation strategies.

Climate change impacts and adaptation for dryland farming systems in Zimbabwe: a stakeholder-driven integrated multi-model assessment - Sabine Homann-Kee Tui; Katrien Descheemaeker; Roberto O. Valdivia; Patricia Masikati; Gevious Sisito; Elisha N. Moyo; Olivier Crespo; Alex C. Ruane; Cynthia Rosenzweig (Climatic Change, 2021)
Decision makers need accurate information to address climate variability and change and accelerate transformation to sustainability. A stakeholder-driven, science-based multi-model approach has been developed and used by the Agricultural Model Intercomparison and Improvement Project (AgMIP) to generate actionable information for adaptation planning processes. For a range of mid-century climate projections—likely to be hotter, drier, and more variable—contrasting future socio-economic scenarios (Representative Agricultural Pathways, RAPs) were co-developed with stakeholders to portray a sustainable development scenario and a rapid economic growth pathway. The unique characteristic of this application is the integration of a multi-modeling approach with stakeholder engagement to co-develop scenarios and adaptation strategies. Distribution of outcomes were simulated with climate, crop, livestock, and economic impact assessment models for smallholder crop livestock farmers in a typical dryland agro-ecological zone in Zimbabwe, characterized by low and erratic rainfall and nutrient depleted soils. Results showed that in Nkayi District, Western Zimbabwe, climate change would threaten most of the farms, and, in particular, those with large cattle herds due to feed shortages. Adaptation strategies that showed the most promise included diversification using legume production, soil fertility improvement, and investment in conducive market environments. The switch to more legumes in the farming systems reduced the vulnerability of the very poor as well as the more resourced farmers. Overall, the sustainable development scenario consistently addressed institutional failures and motivated productivity-enhancing, environmentally sound technologies and inclusive development approaches. This yielded more favorable outcomes than investment in quick economic wins from commercializing agriculture.

Large potential for crop production adaptation depends on available future varieties - Florian Zabel; Christoph Müller; Joshua Elliott; Sara Minoli; Jonas Jägermeyr; Julia M. Schneider; James A. Franke; Elisabeth Moyer; Marie Dury; Louis Francois; Christian Folberth; Wenfeng Liu; et al. (Global Change Biology, 2021)
Climate change affects global agricultural production and threatens food security. Faster phenological development of crops due to climate warming is one of the main drivers for potential future yield reductions. To counter the effect of faster maturity, adapted varieties would require more heat units to regain the previous growing period length. In this study, we investigate the effects of variety adaptation on global caloric production under four different future climate change scenarios for maize, rice, soybean, and wheat. Thereby, we empirically identify areas that could require new varieties and areas where variety adaptation could be achieved by shifting existing varieties into new regions. The study uses an ensemble of seven global gridded crop models and five CMIP6 climate models. We found that 39% (SSP5‐8.5) of global cropland could require new crop varieties to avoid yield loss from climate change by the end of the century. At low levels of warming (SSP1‐2.6), 85% of currently cultivated land can draw from existing varieties to shift within an agro‐ecological zone for adaptation. The assumptions on available varieties for adaptation have major impacts on the effectiveness of variety adaptation, which could more than half in SSP5‐8.5. The results highlight that region‐specific breeding efforts are required to allow for a successful adaptation to climate change.

Modelling climate change impacts on maize yields under low nitrogen input conditions in sub‐Saharan Africa - Gatien N. Falconnier; Marc Corbeels; Kenneth J. Boote; François Affholder; Myriam Adam; Dilys S. MacCarthy; Alex C. Ruane; Claas Nendel; Anthony M. Whitbread; Éric Justes; Lajpat R. Ahuja; Folorunso M. Akinseye; et al. (Global Change Biology, 2020)
Smallholder farmers in sub‐Saharan Africa (SSA) currently grow rainfed maize with limited inputs including fertilizer. Climate change may exacerbate current production constraints. Crop models can help quantify the potential impact of climate change on maize yields, but a comprehensive multimodel assessment of simulation accuracy and uncertainty in these low‐input systems is currently lacking. We evaluated the impact of varying [CO 2 ], temperature and rainfall conditions on maize yield, for different nitrogen (N) inputs (0, 80, 160 kg N/ha) for five environments in SSA, including cool subhumid Ethiopia, cool semi‐arid Rwanda, hot subhumid Ghana and hot semi‐arid Mali and Benin using an ensemble of 25 maize models. Models were calibrated with measured grain yield, plant biomass, plant N, leaf area index, harvest index and in‐season soil water content from 2‐year experiments in each country to assess their ability to simulate observed yield. Simulated responses to climate change factors were explored and compared between models. Calibrated models reproduced measured grain yield variations well with average relative root mean square error of 26%, although uncertainty in model prediction was substantial (CV = 28%). Model ensembles gave greater accuracy than any model taken at random. Nitrogen fertilization controlled the response to variations in [CO 2 ], temperature and rainfall. Without N fertilizer input, maize (a) benefited less from an increase in atmospheric [CO 2 ]; (b) was less affected by higher temperature or decreasing rainfall; and (c) was more affected by increased rainfall because N leaching was more critical. The model intercomparison revealed that simulation of daily soil N supply and N leaching plays a crucial role in simulating climate change impacts for low‐input systems. Climate change and N input interactions have strong implications for the design of robust adaptation approaches across SSA, because the impact of climate change in low input systems will be modified if farmers intensify maize production with balanced nutrient management.

Integrated assessment of climate change impacts on crop productivity and income of commercial maize farms in northeast South Africa - Davide Cammarano; Roberto O. Valdivia; Yacob G. Beletse; Wiltrud Durand; Olivier Crespo; Weldemichael A. Tesfuhuney; Matthew R. Jones; Sue Walker; Thembeka N. Mpuisang; Charles Nhemachena; Alex C. Ruane; Carolyn Mutter; et al. (Food Security, 2020)

Global wheat production with 1.5 and 2.0°C above pre‐industrial warming - Bing Liu; Pierre Martre; Frank Ewert; John R. Porter; Andy J. Challinor; Christoph Müller; Alex C. Ruane; Katharina Waha; Peter J. Thorburn; Pramod K. Aggarwal; Mukhtar Ahmed; Juraj Balkovič; et al. (Global Change Biology, 2019)
Efforts to limit global warming to below 2°C in relation to the pre‐industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre‐industrial period) on global wheat production and local yield variability. A multi‐crop and multi‐climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by −2.3% to 7.0% under the 1.5°C scenario and −2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980–2010, when considering changes in local temperature, rainfall, and global atmospheric CO 2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter‐annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer—India, which supplies more than 14% of global wheat. The projected global impact of warming

Earth Observations and Integrative Models in Support of Food and Water Security - Stephanie Schollaert Uz; Alex C. Ruane; Bryan N. Duncan; Compton J. Tucker; George J. Huffman; Iliana E. Mladenova; Batuhan Osmanoglu; Thomas R. H. Holmes; Amy McNally; Christa Peters-Lidard; John D. Bolten; Narendra Das; et al. (Remote Sensing in Earth Systems Sciences, 2019)

Climate change impact and adaptation for wheat protein - Senthold Asseng; Pierre Martre; Andrea Maiorano; Reimund P. Rötter; Garry J. O’Leary; Glenn J. Fitzgerald; Christine Girousse; Rosella Motzo; Francesco Giunta; M. Ali Babar; Matthew P. Reynolds; Ahmed M. S. Kheir; et al. (Global Change Biology, 2019)
Wheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32‐multi‐model ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO 2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most low‐rainfall regions, with nitrogen availability limiting growth stimulus from elevated CO 2 . Introducing genotypes adapted to warmer temperatures (and also considering changes in CO 2 and rainfall) could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by −1.1 percentage points, representing a relative change of −8.6%. Climate change adaptations that benefit grain yield are not always positive for grain quality, putting additional pressure on global wheat production.

A potato model intercomparison across varying climates and productivity levels - David H. Fleisher; Bruno Condori; Roberto Quiroz; Ashok Alva; Senthold Asseng; Carolina Barreda; Marco Bindi; Kenneth J. Boote; Roberto Ferrise; Angelinus C. Franke; Panamanna M. Govindakrishnan; Dieudonne Harahagazwe; et al. (Global Change Biology, 2017)
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 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

Lessons from climate modeling on the design and use of ensembles for crop modeling - Daniel Wallach; Linda O. Mearns; Alex C. Ruane; Reimund P. Rötter; Senthold Asseng (Climatic Change, 2016)

Exploring climate change impacts and adaptation options for maize production in the Central Rift Valley of Ethiopia using different climate change scenarios and crop models - Belay T. Kassie; Senthold Asseng; Reimund P. Rotter; Huib Hengsdijk; Alex C. Ruane; Martin K. Van Ittersum (Climatic Change, 2015)

Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions - Tao Li; Toshihiro Hasegawa; Xinyou Yin; Yan Zhu; Kenneth Boote; Myriam Adam; Simone Bregaglio; Samuel Buis; Roberto Confalonieri; Tamon Fumoto; Donald Gaydon; Manuel Marcaida; et al. (Global Change Biology, 2015)
Predicting rice ( Oryza sativa ) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluated 13 rice models against multi‐year experimental yield data at four sites with diverse climatic conditions in Asia and examined whether different modeling approaches on major physiological processes attribute to the uncertainties of prediction to field measured yields and to the uncertainties of sensitivity to changes in temperature and CO 2 concentration [ CO 2 ]. We also examined whether a use of an ensemble of crop models can reduce the uncertainties. Individual models did not consistently reproduce both experimental and regional yields well, and uncertainty was larger at the warmest and coolest sites. The variation in yield projections was larger among crop models than variation resulting from 16 global climate model‐based scenarios. However, the mean of predictions of all crop models reproduced experimental data, with an uncertainty of less than 10% of measured yields. Using an ensemble of eight models calibrated only for phenology or five models calibrated in detail resulted in the uncertainty equivalent to that of the measured yield in well‐controlled agronomic field experiments. Sensitivity analysis indicates the necessity to improve the accuracy in predicting both biomass and harvest index in response to increasing [ CO 2 ] and temperature.

Multimodel ensembles of wheat growth: many models are better than one - Pierre Martre; Daniel Wallach; Senthold Asseng; Frank Ewert; James W. Jones; Reimund P. Rötter; Kenneth J. Boote; Alex C. Ruane; Peter J. Thorburn; Davide Cammarano; Jerry L. Hatfield; Cynthia Rosenzweig; et al. (Global Change Biology, 2015)
Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24–38% for the different end‐of‐season variables including grain yield ( GY ) and grain protein concentration ( GPC ). There was little relation between error of a model for GY or GPC and error for in‐season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e‐mean) or median (e‐median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e‐median ranked first in simulating measured GY and third in GPC . The error of e‐mean and e‐median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.

How do various maize crop models vary in their responses to climate change factors? - Simona Bassu; Nadine Brisson; Jean‐Louis Durand; Kenneth Boote; Jon Lizaso; James W. Jones; Cynthia Rosenzweig; Alex C. Ruane; Myriam Adam; Christian Baron; Bruno Basso; Christian Biernath; et al. (Global Change Biology, 2014)
Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [ CO 2 ], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames ( USA ), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly −0.5 Mg ha −1 per °C. Doubling [ CO 2 ] from 360 to 720 μmol mol −1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [ CO 2 ] among models. Model responses to temperature and [ CO 2 ] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.

Carbon–Temperature–Water change analysis for peanut production under climate change: a prototype for the AgMIP Coordinated Climate‐Crop Modeling Project (C3 MP ) - Alex C. Ruane; Sonali McDermid; Cynthia Rosenzweig; Guillermo A. Baigorria; James W. Jones; Consuelo C. Romero; L. DeWayne Cecil (Global Change Biology, 2014)
Climate change is projected to push the limits of cropping systems and has the potential to disrupt the agricultural sector from local to global scales. This article introduces the Coordinated Climate‐Crop Modeling Project (C3MP), an initiative of the Agricultural Model Intercomparison and Improvement Project (Ag MIP ) to engage a global network of crop modelers to explore the impacts of climate change via an investigation of crop responses to changes in carbon dioxide concentration ([ CO 2 ]), temperature, and water. As a demonstration of the C3MP protocols and enabled analyses, we apply the Decision Support System for Agrotechnology Transfer ( DSSAT ) CROPGRO ‐Peanut crop model for Henry County, Alabama, to evaluate responses to the range of plausible [ CO 2 ], temperature changes, and precipitation changes projected by climate models out to the end of the 21st century. These sensitivity tests are used to derive crop model emulators that estimate changes in mean yield and the coefficient of variation for seasonal yields across a broad range of climate conditions, reproducing mean yields from sensitivity test simulations with deviations of ca. 2% for rain‐fed conditions. We apply these statistical emulators to investigate how peanuts respond to projections from various global climate models, time periods, and emissions scenarios, finding a robust projection of modest ( 20%) losses and larger uncertainty at the end of the century under the more severe representative concentration pathway (RCP8.5). This projection is not substantially altered by the selection of the AgMERRA global gridded climate dataset rather than the local historical observations, differences between the Third and Fifth Coupled Model Intercomparison Project ( CMIP 3 and CMIP 5), or the use of the delta method of climate impacts analysis rather than the C3 MP impacts response surface and emulator approach.

Projected climate impacts to S outh A frican maize and wheat production in 2055: a comparison of empirical and mechanistic modeling approaches - Lyndon D. Estes; Hein Beukes; Bethany A. Bradley; Stephanie R. Debats; Michael Oppenheimer; Alex C. Ruane; Roland Schulze; Mark Tadross (Global Change Biology, 2013)
Crop model‐specific biases are a key uncertainty affecting our understanding of climate change impacts to agriculture. There is increasing research focus on intermodel variation, but comparisons between mechanistic ( MM s) and empirical models ( EM s) are rare despite both being used widely in this field. We combined MM s and EM s to project future (2055) changes in the potential distribution (suitability) and productivity of maize and spring wheat in S outh A frica under 18 downscaled climate scenarios (9 models run under 2 emissions scenarios). EM s projected larger yield losses or smaller gains than MM s. The EM s’ median‐projected maize and wheat yield changes were −3.6% and 6.2%, respectively, compared to 6.5% and 15.2% for the MM . The EM projected a 10% reduction in the potential maize growing area, where the MM projected a 9% gain. Both models showed increases in the potential spring wheat production region ( EM = 48%, MM = 20%), but these results were more equivocal because both models (particularly the EM ) substantially overestimated the extent of current suitability. The substantial water‐use efficiency gains simulated by the MM s under elevated CO 2 accounted for much of the EM − MM difference, but EM s may have more accurately represented crop temperature sensitivities. Our results align with earlier studies showing that EM s may show larger climate change losses than MM s. Crop forecasting efforts should expand to include EM − MM comparisons to provide a fuller picture of crop–climate response uncertainties.