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)

Estimating the risk of pre-harvest aflatoxin contamination in Peanut with the CROPGRO-Peanut model - Thiago Berton Ferreira; Kenneth J. Boote; Vakhtang Shelia; Willingthon Pavan; P.V. Vara Prasad; Shannon K.A. McAmis; Gerrit Hoogenboom (Agricultural Systems, 2026)

Identification of Genetic Variation Associated With Heat Tolerance in Cowpea ( Vigna unguiculata L. Walp.) - Roland Akakpo; Elaine J. Lee; Jacob B. Pacheco; Esteban F. Rios; Michael B. Kantar; Ousmane Boukar; Kevin M. Volz; Habib Akinmade; Luis Getino; Kenneth J. Boote; María Muñoz‐Amatriaín; Peter L. Morrell (Molecular Ecology, 2026)
Heat tolerance is an important trait in cowpea, a crop that is the primary protein source for a large portion of the human population in sub‐Saharan Africa. Cultivated, landrace, semi‐wild, and wild cowpea grow across the region in diverse climatic conditions. This study used environmental association (envGWAS) and allele‐frequency outlier approaches across a panel of 580 gene bank accessions to identify genomic regions associated with adaptation to heat and limited precipitation. Allele frequency outliers are detected independent of potential selective factors driving differentiation; we used a ranking‐based approach to identify the climate variables most associated with variants among outliers. Precipitation‐related variables dominated the signals we identified for envGWAS and allele frequency outliers. We identified variants on all 11 chromosomes that are putatively associated with cowpea adaptation to higher‐temperature environments. The overlap between variants associated with low precipitation and high temperature suggests that these traits may be inextricably linked in cowpea. The Sahel region is the source of many accessions with derived variants associated with high temperatures, suggesting that accessions from this region could contribute heat tolerance alleles for cowpea improvement.

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.

Inter-comparison of soybean models for simulation of evapotranspiration under rainfed and irrigated conditions - Kritika Kothari; Andrew Suyker; Rafael Battisti; Kenneth J. Boote; Sotirios Archontoulis; Julie Constantin; Santiago V. Cuadra; Philippe Debaeke; Elvis F. Elli; Babacar Faye; David Fleisher; Brian Grant; et al. (Agricultural and Forest Meteorology, 2026)

Assessing methane and nitrous oxide emissions in lowland rice-soybean cropping systems through crop modeling - Evandro H. Figueiredo Moura da Silva; Gerrit Hoogenboom; Kenneth J. Boote; Walkyria Bueno Scivittaro; Cheryl H. Porter; Santiago Vianna Cuadra; Silvio Steinmetz; Carlos E. Pellegrino Cerri (Agricultural Systems, 2025)

Evaluation of multimodel averaging approaches for ensembling evapotranspiration and yield simulations from maize models - Viveka Nand; Zhiming Qi; Liwang Ma; Matthew J. Helmers; Chandra A. Madramootoo; Ward N. Smith; Tiequan Zhang; Tobias K.D. Weber; Elizabeth Pattey; Ziwei Li; Jiaxin Wang; Virginia L. Jin; et al. (Journal of Hydrology, 2025)

Metrics of plant response to CO2 Enrichment - Leon H. Allen; Bruce A. Kimball; James A. Bunce; Kenneth J. Boote; Jeffrey W. White (Agricultural and Forest Meteorology, 2025)

Inter-comparison of soybean models for the simulation of evapotranspiration in a humid continental climate - Evandro H. Figueiredo Moura da Silva; Kritika Kothari; Elizabeth Pattey; Rafael Battisti; Kenneth J. Boote; Sotirios V. Archontoulis; Santiago Vianna Cuadra; Babacar Faye; Brian Grant; Gerrit Hoogenboom; Qi Jing; Fábio R. Marin; et al. (Agricultural and Forest Meteorology, 2025)

Using the CROPGRO-Perennial Forage Model to simulate the effect of asymbiotic nitrogen fixation on the growth of brachiariagrass - Fagner J. Gomes; Kenneth J. Boote; Carlos G.S. Pedreira; Bruno C. Pedreira; Cristiam Bosi; Maira L.B. Bourscheidt; Gerrit Hoogenboom (Field Crops Research, 2025)

Simulation of soil temperature under maize: An inter-comparison among 33 maize models - Bruce A. Kimball; Kelly R. Thorp; Kenneth J. Boote; Claudio Stockle; Andrew E. Suyker; Steven R. Evett; David K. Brauer; Gwen G. Coyle; Karen S. Copeland; Gary W. Marek; Paul D. Colaizzi; Marco Acutis; et al. (Agricultural and Forest Meteorology, 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)

Coupling the CSM-CROPGRO-Soybean crop model with the ECOSMOS Ecosystem Model – An evaluation with data from an AmeriFlux site - Henrique Boriolo Dias; Santiago Vianna Cuadra; Kenneth J. Boote; Rubens Augusto Camargo Lamparelli; Gleyce Kelly Dantas Araújo Figueiredo; Andrew E. Suyker; Paulo Sérgio Graziano Magalhães; Gerrit Hoogenboom (Agricultural and Forest Meteorology, 2023)

Adapting the CROPGRO Perennial Forage model to predict growth and development of Pensacola bahiagrass - Hunter D. Smith; Chris H. Wilson; Stuart J. Rymph; Erick R.S. Santos; Kenneth J. Boote (Field Crops Research, 2023)

Simulation of evapotranspiration and yield of maize: An inter-comparison among 41 maize models - Bruce A. Kimball; Kelly R. Thorp; Kenneth J. Boote; Claudio Stockle; Andrew E. Suyker; Steven R. Evett; David K. Brauer; Gwen G. Coyle; Karen S. Copeland; Gary W. Marek; Paul D. Colaizzi; Marco Acutis; et al. (Agricultural and Forest Meteorology, 2023)

Highlighting the benefits of biological nitrogen fixation on agronomic, physiological, and nutritive value traits of brachiariagrass - Maira L.B. Bourscheidt; Fagner J. Gomes; Carlos G.S. Pedreira; Kenneth J. Boote; Gerrit Hoogenboom; Dalton H. Pereira; Bruno C. Pedreira (European Journal of Agronomy, 2023)

A trait‐based model ensemble approach to design rice plant types for future climate - Livia Paleari; Tao Li; Yubin Yang; Lloyd T. Wilson; Toshihiro Hasegawa; Kenneth J. Boote; Samuel Buis; Gerrit Hoogenboom; Yujing Gao; Ermes Movedi; Françoise Ruget; Upendra Singh; et al. (Global Change Biology, 2022)
Crop models are powerful tools to support breeding because of their capability to explore genotype × environment×management interactions that can help design promising plant types under climate change. However, relationships between plant traits and model parameters are often model specific and not necessarily direct, depending on how models formulate plant morphological and physiological features. This hinders model application in plant breeding. We developed a novel trait‐based multi‐model ensemble approach to improve the design of rice plant types for future climate projections. We conducted multi‐model simulations targeting enhanced productivity, and aggregated results into model‐ensemble sets of phenotypic traits as defined by breeders rather than by model parameters. This allowed to overcome the limitations due to ambiguities in trait‐parameter mapping from single modelling approaches. Breeders' knowledge and perspective were integrated to provide clear mapping from designed plant types to breeding traits. Nine crop models from the AgMIP‐Rice Project and sensitivity analysis techniques were used to explore trait responses under different climate and management scenarios at four sites. The method demonstrated the potential of yield improvement that ranged from 15.8% to 41.5% compared to the current cultivars under mid‐century climate projections. These results highlight the primary role of phenological traits to improve crop adaptation to climate change, as well as traits involved with canopy development and structure. The variability of plant types derived with different models supported model ensembles to handle related uncertainty. Nevertheless, the models agreed in capturing the effect of the heterogeneity in climate conditions across sites on key traits, highlighting the need for context‐specific breeding programmes to improve crop adaptation to climate change. Although further improvement is needed for crop models to fully support breeding programmes, a trait‐based ensemble approach represents a major step towards the integration of crop modelling and breeding to address climate change challenges and develop adaptation options.

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.

Development of a QTL-environment-based predictive model for node addition rate in common bean - Li Zhang; Salvador A. Gezan; C. Eduardo Vallejos; James W. Jones; Kenneth J. Boote; Jose A. Clavijo-Michelangeli; Mehul Bhakta; Juan M. Osorno; Idupulapati Rao; Stephen Beebe; Elvin Roman-Paoli; Abiezer Gonzalez; et al. (Theoretical and Applied Genetics, 2017)

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

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.

Evaluating the fidelity of downscaled climate data on simulated wheat and maize production in the southeastern US - Davide Cammarano; Lydia Stefanova; Brenda V. Ortiz; Melissa Ramirez-Rodrigues; Senthold Asseng; Vasubandhu Misra; Gail Wilkerson; Bruno Basso; James W. Jones; Kenneth J. Boote; Steven DiNapoli (Regional Environmental Change, 2013)

Effects of season-long high temperature growth conditions on sugar-to-starch metabolism in developing microspores of grain sorghum (Sorghum bicolor L. Moench) - Mukesh Jain; P. V. Vara Prasad; Kenneth J. Boote; Allen L. Hartwell; Prem S. Chourey (Planta, 2007)

Super‐optimal temperatures are detrimental to peanut ( Arachis hypogaea L.) reproductive processes and yield at both ambient and elevated carbon dioxide - P. V. Vara Prasad; Kenneth J. Boote; L. Hartwell Allen; Jean M. G. Thomas (Global Change Biology, 2003)
Continuing increases in atmospheric carbon dioxide concentration (CO 2 ) will likely be accompanied by global warming. Our research objectives were (a) to determine the effects of season‐long exposure to daytime maximum/nighttime minimum temperatures of 32/22, 36/26, 40/30 and 44/34°C at ambient (350 μmol mol −1 ) and elevated (700 μmol mol −1 ) CO 2 on reproductive processes and yield of peanut, and (b) to evaluate whether the higher photosynthetic rates and vegetative growth at elevated CO 2 will negate the detrimental effects of high temperature on reproductive processes and yield. Doubling of CO 2 increased leaf photosynthesis and seed yield by 27% and 30%, respectively, averaged across all temperatures. There were no effects of elevated CO 2 on pollen viability, seed‐set, seed number per pod, seed size, harvest index or shelling percentage. At ambient CO 2 , seed yield decreased progressively by 14%, 59% and 90% as temperature increased from 32/22 to 36/26, 40/30 and 44/34°C, respectively. Similar percentage decreases in seed yield occurred at temperatures above 32/22°C at elevated CO 2 despite greater photosynthesis and vegetative growth. Decreased seed yields at high temperature were a result of lower seed‐set due to poor pollen viability, and smaller seed size due to decreased seed growth rates and decreased shelling percentages. Seed harvest index decreased from 0.41 to 0.05 as temperature increased from 32/22 to 44/34°C under both ambient and elevated CO 2 . We conclude that there are no beneficial interactions between elevated CO 2 and temperature, and that seed yield of peanut will decrease under future warmer climates, particularly in regions where present temperatures are near or above optimum.

Effects of elevated temperature and carbon dioxide on seed‐set and yield of kidney bean ( Phaseolus vulgaris L.) - P. V. Vara Prasad; Kenneth J. Boote; L. Hartwell Allen; Jean M. G. Thomas (Global Change Biology, 2002)
It is important to quantify and understand the consequences of elevated temperature and carbon dioxide (CO 2 ) on reproductive processes and yield to develop suitable agronomic or genetic management for future climates. The objectives of this research work were (a) to quantify the effects of elevated temperature and CO 2 on photosynthesis, pollen production, pollen viability, seed‐set, seed number, seeds per pod, seed size, seed yield and dry matter production of kidney bean and (b) to determine if deleterious effects of high temperature on reproductive processes and yield could be compensated by enhanced photosynthesis at elevated CO 2 levels. Red kidney bean cv. Montcalm was grown in controlled environments at day/night temperatures ranging from 28/18 to 40/30 °C under ambient (350 µmol mol −1 ) or elevated (700 µmol mol −1 ) CO 2 levels. There were strong negative relations between temperature over a range of 28/18–40/30 °C and seed‐set (slope, − 6.5% °C −1 ) and seed number per pod (− 0.34 °C −1 ) under both ambient and elevated CO 2 levels. Exposure to temperature > 28/18 °C also reduced photosynthesis (− 0.3 and − 0.9 µmol m −2 s −1 °C −1 ), seed number (− 2.3 and − 3.3 °C −1 ) and seed yield (− 1.1 and − 1.5 g plant −1 °C −1 ), at both the CO 2 levels (ambient and elevated, respectively). Reduced seed‐set and seed number at high temperatures was primarily owing to decreased pollen production and pollen viability. Elevated CO 2 did not affect seed size but temperature > 31/21 °C linearly reduced seed size by 0.07 g °C −1 . Elevated CO 2 increased photosynthesis and seed yield by approximately 50 and 24%, respectively. There was no beneficial interaction of CO 2 and temperature, and CO 2 enrichment did not offset the negative effects of high temperatures on reproductive processes and yield. In conclusion, even with beneficial effects of CO 2 enrichment, yield losses owing to high temperature (> 34/24 °C) are likely to occur, particularly if high temperatures coincide with sensitive stages of reproductive development.

Carbon dioxide and temperature effects on forage establishment: tissue composition and nutritive value - Felix B. Fritschi; Kenneth J. Boote; LynN. E. Sollenberger; L. Hartwell (Global Change Biology, 1999)
Summary Atmospheric CO 2 concentration ([CO 2 ]) and temperature are likely to increase in the future and may change plant growth and composition characteristics. Rhizoma peanut ( Arachis glabrata Benth.) and bahiagrass ( Paspalum notatum Flügge) were grown on a natural field soil in temperature‐gradient greenhouses to evaluate the effects of elevated [CO 2 ] and temperature on tissue composition and digestibility during the establishment year. Carbon dioxide levels were maintained at 365 (ambient) and 640 μL CO 2 L –1 air. The temperature‐gradient greenhouses were regulated to obtain air temperature sectors of 0.2, 1.5, 2.9, and 4.5 °C above ambient. Samples were taken of previously undefoliated herbage at 57, 86, 121, 148, and 217 days after planting and entire plots were harvested at 218 days after planting. Elevated [CO 2 ] increased total nonstructural carbohydrate concentration in rhizoma peanut leaves by almost 50%. Rhizoma peanut leaf N concentration was 6% lower at elevated than at ambient [CO 2 ]. The N concentration in new rhizomes of rhizoma peanut was increased by high [CO 2 ], while the N concentration in bahiagrass was not affected by temperature or [CO 2 ]. No effects of [CO 2 ] and temperature were found on neutral detergent fibre in rhizoma peanut leaves or stems; however, elevated [CO 2 ] increased neutral detergent fibre in bahiagrass leaves. Only at season end was in vitro organic matter digestion of rhizoma peanut higher at ambient (623 g kg –1 ) than at elevated [CO 2 ] (609 g kg –1 ). Elevated [CO 2 ] had a greater effect on tissue composition of rhizoma peanut than of bahiagrass. These data suggest that elevated temperature and CO 2 ‐induced changes in chemical composition of forage species adapted to humid subtropics will be relatively small, particularly for C4 species.

Nonstructural carbohydrates of soybean plants grown in subambient and superambient levels of CO2 - Leon H. Allen; Evelin C. Bisbal; Kenneth J. Boote (Photosynthesis Research, 1998)

Rice responses to drought under carbon dioxide enrichment. 1. Growth and yield - JEFFREY BAKER; L. HARTWELL ALLEN; KENNETH BOOTE; NIGEL PICKERING (Global Change Biology, 1997)
Projections of future climate change include a strong likelihood of a doubling of current atmospheric carbon dioxide concentration ([ CO 2 ]) and possible shifts in precipitation patterns. Drought stress is a major environmental limitation for crop growth and yield and is common in rainfed rice production systems. This study was conducted to determine the growth and grain yield responses of rice to drought stress under [CO 2 ] enrichment. Rice (cv. IR‐72) was grown to maturity in eight naturally sunlit, plant growth chambers in atmospheric carbon dioxide concentrations [CO 2 ] of 350 and 700 μmol CO 2 mol –1 air. In both [CO 2 ], water management treatments included continuously flooded (CF) controls, flood water removed and drought stress imposed at panicle initiation (PI), anthesis (ANT), and both panicle initiation and anthesis (PI & ANT). The [CO 2 ] enrichment increased growth, panicles plant –1 and grain yield. Drought accelerated leaf senescence, reduced leaf area and above‐ground biomass and delayed crop ontogeny. The [CO 2 ] enrichment allowed 1–2 days more growth during drought stress cycles. Grain yields of the PI and PI & ANT droughts were similar to the CF control treatments while the ANT drought treatment sharply reduced growth, grain yield and individual grain mass. We conclude that in the absence of air temperature increases, future global increases in [CO 2 ] should promote rice growth and yield while providing a modest reduction of near 10% in water use and so increase drought avoidance.

Rice responses to drought under carbon dioxide enrichment. 2. Photosynthesis and evapotranspiration - JEFFREY BAKER; L. HARTWELL ALLEN; KENNETH BOOTE; NIGEL PICKERING (Global Change Biology, 1997)
Future climate change is projected to include a strong likelihood of continued increases in atmospheric carbon dioxide concentration ([CO 2 ]) and possible shifts in precipitation patterns. Due mainly to uncertainties in the timing and amounts of monsoonal rainfall, drought is common in rainfed rice production systems. The objectives of this study were to quantify the effects and possible interactions of [CO 2 ] and drought stress on rice ( Oryza sativa , L.) photosynthesis, evapotranspiration and water‐use efficiency. Rice (cv. IR‐72) was grown to maturity in eight naturally sunlit, plant growth chambers in atmospheric carbon dioxide concentrations [CO 2 ] of 350 and 700 μmol CO 2 mol –1 air. In both [CO 2 ], water management treatments included continuously flooded controls, flood water removed and drought stress imposed at panicle initiation, anthesis, and both panicle initiation and anthesis. Potential acclimation of rice photosynthesis to long‐term [CO 2 ] growth treatments of 350 and 700 μmol mol –1 was tested by comparing canopy photosynthesis rates across short‐term [CO 2 ] ranging from 160 to 1000 μmol mol –1 . These tests showed essentially no acclimation response with photosynthetic rate being a function of current short‐term [CO 2 ] rather than long‐term [CO 2 ] growth treatment. In both long‐term [CO 2 ] treatments, photosynthetic rate saturated with respect to [CO 2 ] near 510 μmol mol –1 . Carbon dioxide enrichment significantly increased both canopy net photosynthetic rate (21–27%) and water‐use efficiency while reducing evapotranspiration by about 10%. This water saving under [CO 2 ] enrichment allowed photosynthesis to continue for about one to two days longer during drought in the enriched compared with the ambient [CO 2 ] control treatments.