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1. Can the cropping systems of the Nile basin be adapted to climate change?

Author

Nkwasa, A. Waha, K. Griensven, A. and Nkwasa, A. Waha, K. Griensven, A.

Abstract

Climate change poses a fundamental threat to agriculture within the Nile basin due to the magnitude of projected impacts and low adaptive capacity. So far, climate change impacts on agriculture for the basin have mostly been assessed for single-cropping systems, which may bias the results considering that the basin is dominated by different cropping systems, with about one-third of the crop area under double cropping. In this study, we simulate single- and double-cropping systems in the Nile basin and assess the climate change impacts on different cropping systems under two scenarios, i.e. “no adaptation” and “adaptation to a late-maturing cultivar”. We find that the mean crop yields of maize, soybean and wheat decrease with future warming without cultivar adaptation. We attribute this to the shortening of the growing season due to increased temperature. The decrease is stronger in all single-cropping systems (12.6–45.5%) than in double-cropping systems (5.9–26.6%). The relative magnitude of yield reduction varies spatially with the greatest reduction in the northern part of the basin experiencing the strongest warming. In a scenario with cultivar adaptation, mean crop yields show a stronger increase in double-cropping systems (14.4–35.2%) than single-cropping systems (8.3–13.7%). In this scenario, farmers could possibly benefit from increasing cropping intensities while adapting to late-maturing cultivars. This study underscores the importance of accounting for multiple-cropping systems in agricultural assessments under climate change within the Nile basin.

Published
2023
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2. A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration

Author

Makowski, D., Asseng, S., Ewert, F., Bassu, S., Durand, J.L., Li, T., Martre, P., Adam, M., Aggarwal, P.K., Angulo, C., Baron, C., Basso, B., Bertuzzi, P., Biernath, C., Boogaard, H., Boote, K.J., Bouman, B., Bregaglio, S., Brisson, N., Buis, S., Cammarano, D., Challinor, A.J., Confalonieri, R., Conijn, J.G., Corbeels, M., Deryng, D., De Sanctis, G., Doltra, J., Fumoto, T., Gaydon, D., Gayler, S., Goldberg, R., Grant, R.F., Grassini, P., Hatfield, J.L., Hasegawa, T., Heng, L., Hoek, S., Hooker, J., Hunt, L.A., Ingwersen, J., Izaurralde, R.C., Jongschaap, R.E.E., Jones, J.W., Kemanian, R.A., Kersebaum, K.C., Kim, S.-H., Lizaso, J., Marcaida, M., III, Müller, C., Nakagawa, H., Naresh Kumar, S., Nendel, C., O’Leary, G.J., Olesen, J.E., Oriol, P., Osborne, T.M., Palosuo, T., Pravia, M.V., Priesack, E., Ripoche, D., Rosenzweig, C., Ruane, A.C., Ruget, F., Sau, F., Semenov, M.A., Shcherbak, I., Singh, B., Singh, U., Soo, H.K., Steduto, P., Stöckle, C., Stratonovitch, P., Streck, T., Supit, I., Tang, L., Tao, F., Teixeira, E.I., Thorburn, P., Timlin, D., Travasso, M., Rötter, R.P., Waha, K., Wallach, D., White, J.W., Wilkens, P., Williams, J.R., Wolf, J., Yin, X., Yoshida, H., Zhang, Z., and Zhu, Y.

Published
2015
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3. Temperature and precipitation effects on wheat yield across a European transect : a crop model ensemble analysis using impact response surfaces

Author

Pirttioja, N., Carter, T. R., Fronzek, S., Bindi, M., Hoffmann, H., Palosuo, T., Ruiz-Ramos, M., Tao, F., Trnka, M., Acutis, M., Asseng, S., Baranowski, P., Basso, B., Bodin, P., Buis, S., Cammarano, D., Deligios, P., Destain, M.-F., Dumont, B., Ewert, F., Ferrise, R., François, L., Gaiser, T., Hlavinka, P., Jacquemin, I., Kersebaum, K. C., Kollas, C., Krzyszczak, J., Lorite, I. J., Minet, J., Minguez, M. I., Montesino, M., Moriondo, M., Müller, C., Nendel, C., Öztürk, I., Perego, A., Rodríguez, A., Ruane, A. C., Ruget, F., Sanna, M., Semenov, M. A., Slawinski, C., Stratonovitch, P., Supit, I., Waha, K., Wang, E., Wu, L., Zhao, Z., and Rötter, R. P.

Published
2015

8. Rising Temperatures Reduce Global Wheat Production

Author

Asseng, S, Ewert, F, Martre, P, Rötter, R. P, Lobell, D. B, Cammarano, D, Kimball, B. A, Ottman, M. J, Wall, G. W, White, J. W, Reynolds, M. P, Alderman, P. D, Prasad, P. V. V, Aggarwal, P. K, Anothai, J, Basso, B, Biernath, C, Challinor, A. J, De Sanctis, G, Doltra, J, Fereres, E, Garcia-Vila, M, Gayler, S, Hoogenboom, G, Hunt, L. A, Izaurralde, R. C, Jabloun, M, C. D. Jones, Kersebaum, K. C, Koehler, A-K, Müller, C, Naresh Kumar, S, Nendel, C, O’Leary, G, Olesen, J. E, Palosuo, T, Priesack, E, Eyshi Rezaei, E, Ruane, A. C, Semenov, M. A, Shcherbak, I, Stöckle, C, Stratonovitch, P, Streck, T, Supit, I, Tao, F, Thorburn, P. J, Waha, K, Wang, E, Wallach, D, Wolf, J, Zhao, Z, and Zhu, Y

Subjects
Meteorology And Climatology
Abstract

Crop models are essential tools for assessing the threat of climate change to local and global food production. Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature. Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 degrees C to 32◦ degrees C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each degree C of further temperature increase and become more variable over space and time.

Published
2015
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10. Climate change impact and adaptation for wheat protein

Author

Asseng, S, Martre, P, Maiorano, A, Roetter, RP, O'Leary, GJ, Fitzgerald, GJ, Girousse, C, Motzo, R, Giunta, F, Babar, MA, Reynolds, MP, Kheir, AMS, Thorburn, PJ, Waha, K, Ruane, AC, Aggarwal, PK, Ahmed, M, Balkovic, J, Basso, B, Biernath, C, Bindi, M, Cammarano, D, Challinor, AJ, De Sanctis, G, Dumont, B, Rezaei, EE, Fereres, E, Ferrise, R, Garcia-Vila, M, Gayler, S, Gao, Y, Horan, H, Hoogenboom, G, Izaurralde, RC, Jabloun, M, Jones, CD, Kassie, BT, Kersebaum, K-C, Klein, C, Koehler, A-K, Liu, B, Minoli, S, San Martin, MM, Mueller, C, Kumar, SN, Nendel, C, Olesen, JE, Palosuo, T, Porter, JR, Priesack, E, Ripoche, D, Semenov, MA, Stockle, C, Stratonovitch, P, Streck, T, Supit, I, Tao, F, Van der Velde, M, Wallach, D, Wang, E, Webber, H, Wolf, J, Xiao, L, Zhang, Z, Zhao, Z, Zhu, Y, Ewert, F, Asseng, S, Martre, P, Maiorano, A, Roetter, RP, O'Leary, GJ, Fitzgerald, GJ, Girousse, C, Motzo, R, Giunta, F, Babar, MA, Reynolds, MP, Kheir, AMS, Thorburn, PJ, Waha, K, Ruane, AC, Aggarwal, PK, Ahmed, M, Balkovic, J, Basso, B, Biernath, C, Bindi, M, Cammarano, D, Challinor, AJ, De Sanctis, G, Dumont, B, Rezaei, EE, Fereres, E, Ferrise, R, Garcia-Vila, M, Gayler, S, Gao, Y, Horan, H, Hoogenboom, G, Izaurralde, RC, Jabloun, M, Jones, CD, Kassie, BT, Kersebaum, K-C, Klein, C, Koehler, A-K, Liu, B, Minoli, S, San Martin, MM, Mueller, C, Kumar, SN, Nendel, C, Olesen, JE, Palosuo, T, Porter, JR, Priesack, E, Ripoche, D, Semenov, MA, Stockle, C, Stratonovitch, P, Streck, T, Supit, I, Tao, F, Van der Velde, M, Wallach, D, Wang, E, Webber, H, Wolf, J, Xiao, L, Zhang, Z, Zhao, Z, Zhu, Y, and Ewert, F

Abstract

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 CO2 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 CO2 . Introducing genotypes adapted to warmer temperatures (and also considering changes in CO2 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.

Published
2019

11. Global wheat production with 1.5 and 2.0°C above pre‐industrial warming

Author

Liu, B., Martre, P., Ewert, F., Porter, J.R., Challinor, A.J., Müller, C., Ruane, A.C., Waha, K., Thorburn, P.J., Aggarwal, P.K., Ahmed, M., Balkovic, J., Basso, B., Biernath, C., Bindi, M., Cammarano, D., De Sanctis, G., Dumont, B., Espadafor, M., Eyshi Rezaei, E., Ferrise, R., Garcia‐Vila, M., Gayler, S., Gao, Y., Horan, H., Hoogenboom, G., Izaurralde, R.C., Jones, C.D., Kassie, B.T., Kersebaum, K.C., Klein, C., Koehler, A.‐K., Maiorano, A., Minoli, S., Montesino San Martin, M., Kumar, S.N., Nendel, C., O'Leary, G.J., Palosuo, T., Priesack, E., Ripoche, D., Rötter, R.P., Semenov, M.A., Stöckle, C., Streck, T., Supit, I., Tao, F., Van der Velde, M., Wallach, D., Wang, E., Webber, H., Wolf, J., Xiao, L., Zhang, Z., Zhao, Z., Zhu, Y., Asseng, S., Liu, B., Martre, P., Ewert, F., Porter, J.R., Challinor, A.J., Müller, C., Ruane, A.C., Waha, K., Thorburn, P.J., Aggarwal, P.K., Ahmed, M., Balkovic, J., Basso, B., Biernath, C., Bindi, M., Cammarano, D., De Sanctis, G., Dumont, B., Espadafor, M., Eyshi Rezaei, E., Ferrise, R., Garcia‐Vila, M., Gayler, S., Gao, Y., Horan, H., Hoogenboom, G., Izaurralde, R.C., Jones, C.D., Kassie, B.T., Kersebaum, K.C., Klein, C., Koehler, A.‐K., Maiorano, A., Minoli, S., Montesino San Martin, M., Kumar, S.N., Nendel, C., O'Leary, G.J., Palosuo, T., Priesack, E., Ripoche, D., Rötter, R.P., Semenov, M.A., Stöckle, C., Streck, T., Supit, I., Tao, F., Van der Velde, M., Wallach, D., Wang, E., Webber, H., Wolf, J., Xiao, L., Zhang, Z., Zhao, Z., Zhu, Y., and Asseng, S.

Abstract

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°C 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 CO2 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 <2°C on wheat production are therefor

Published
2019

12. Climate change impact and adaptation for wheat protein

Author

Asseng, S., Martre, P., Maiorano, A., Rötter, R., O'Leary, G., Fitzgerald, G., Girousse, C., Motzo, R., Giunta, F., Babar, M., Reynolds, M., Kheir, A., Thorbum, P., Waha, K., Ruane, A., Aggarwal, P., Ahmed, M., Balkovic, J., Basso, B., Biernath, C., Bindi, M., Cammarano, D., Challinor, A., De Sanctis, G., Dumont, B., Rezaei, E., Fereres, E., Ferrise, R., Garcia-Vila, M., Gayler, S., Gao, Y., Horan, H., Hoogenboom, G., Izaurralde, R., Jabloun, M., Jones, C., Kassie, B., Kersebaum, K.-C., Klein, C., Koehler, A.-K., Liu, B., Minoli, S., Martin, M.M., Müller, C., Kumar, S., Nendel, C., Oleson, J., Palosuo, T., Porter, J., Priesack, E., Ripoche, D., Semenov, M., Stöckle, C., Stratonovitch, P., Streck, T., Supit, I., Tao, F., Van der Velde, M., Wallach, D., Wang, E., Webber, H., Wolf, J., Xiao, L., Zhang, Z., Zhu, Y., Ewert, F., Asseng, S., Martre, P., Maiorano, A., Rötter, R., O'Leary, G., Fitzgerald, G., Girousse, C., Motzo, R., Giunta, F., Babar, M., Reynolds, M., Kheir, A., Thorbum, P., Waha, K., Ruane, A., Aggarwal, P., Ahmed, M., Balkovic, J., Basso, B., Biernath, C., Bindi, M., Cammarano, D., Challinor, A., De Sanctis, G., Dumont, B., Rezaei, E., Fereres, E., Ferrise, R., Garcia-Vila, M., Gayler, S., Gao, Y., Horan, H., Hoogenboom, G., Izaurralde, R., Jabloun, M., Jones, C., Kassie, B., Kersebaum, K.-C., Klein, C., Koehler, A.-K., Liu, B., Minoli, S., Martin, M.M., Müller, C., Kumar, S., Nendel, C., Oleson, J., Palosuo, T., Porter, J., Priesack, E., Ripoche, D., Semenov, M., Stöckle, C., Stratonovitch, P., Streck, T., Supit, I., Tao, F., Van der Velde, M., Wallach, D., Wang, E., Webber, H., Wolf, J., Xiao, L., Zhang, Z., Zhu, Y., and Ewert, F.

Abstract

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 CO2 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 CO2. Introducing genotypes adapted to warmer temperatures (and also considering changes in CO2 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.

Published
2019

13. Agricultural diversification as an important strategy for achieving food security in Africa

Author

Waha, K., van Wijk, M.T., Fritz, S., See, L., Thornton, P.K., Wichern, J., Herrero, M., Waha, K., van Wijk, M.T., Fritz, S., See, L., Thornton, P.K., Wichern, J., and Herrero, M.

Abstract

Farmers in Africa have long adapted to climatic and other risks by diversifying their farming activities. Using a multi-scale approach we explore the relationship between farming diversity and food security and the diversification potential of African agriculture and its limits on the household and continental scale. On the household scale we use agricultural surveys from more than 28,000 households located in 18 African countries. In a next step we use the relationship between rainfall, rainfall variability and farming diversity to determine the available diversification options for farmers on the continental scale. On the household scale, we show that households with greater farming diversity are more successful in meeting their consumption needs, but only up to a certain level of diversity per ha cropland and more often if food can be purchased from off-farm income or income from farm sales. More diverse farming systems can contribute to household food security, however the relationship is influenced by other factors e.g. the market-orientation of a household, livestock ownership, non-agricultural employment opportunities and available land resources. On the continental scale, the greatest opportunities for diversification of food crops, cash crops and livestock are located in areas with 500-1000mm annual rainfall and 17-22% rainfall variability. Forty three percent of the African cropland lacks these opportunities at present which may hamper the ability of agricultural systems to respond to climate change. While sustainable intensification practices that increase yields have received most attention to date, our study suggests that a shift in the research and policy paradigm towards agricultural diversification options may be necessary.

Published
2018

14. Uncertainty of wheat water use: Simulated patterns and sensitivity to temperature and CO₂

Author

Cammarano, D, Rötter, RP, Asseng, S, Ewert, F, Wallach, D, Martre, P, Hatfield, JL, Jones, JW, Rosenzweig, C, Ruane, AC, Boote, KJ, Thorburn, PJ, Kersebaum, KC, Aggarwal, PK, Angulo, C, Basso, B, Bertuzzi, P, Biernath, C, Brisson, N, Challinor, AJ, Doltra, J, Gayler, S, Goldberg, R, Heng, L, Hooker, JE, Hunt, LA, Ingwersen, J, Izaurralde, RC, Müller, C, Kumar, SN, Nendel, C, O'Leary, G, Olesen, JE, Osborne, TM, Palosuo, T, Priesack, E, Ripoche, D, Semenov, MA, Shcherbak, I, Steduto, P, Stöckle, CO, Stratonovitch, P, Streck, T, Supit, I, Tao, F, Travasso, M, Waha, K, White, JW, and Wolf, J

Abstract

Projected global warming and population growth will reduce future water availability for agriculture. Thus, it is essential to increase the efficiency in using water to ensure crop productivity. Quantifying crop water use (WU; i.e. actual evapotranspiration) is a critical step towards this goal. Here, sixteen wheat simulation models were used to quantify sources of model uncertainty and to estimate the relative changes and variability between models for simulated WU, water use efficiency (WUE, WU per unit of grain dry mass produced), transpiration efficiency (Teff, transpiration per kg of unit of grain yield dry mass produced), grain yield, crop transpiration and soil evaporation at increased temperatures and elevated atmospheric carbon dioxide concentrations ([CO2]). The greatest uncertainty in simulating water use, potential evapotranspiration, crop transpiration and soil evaporation was due to differences in how crop transpiration was modelled and accounted for 50% of the total variability among models. The simulation results for the sensitivity to temperature indicated that crop WU will decline with increasing temperature due to reduced growing seasons. The uncertainties in simulated crop WU, and in particularly due to uncertainties in simulating crop transpiration, were greater under conditions of increased temperatures and with high temperatures in combination with elevated atmospheric [CO2] concentrations. Hence the simulation of crop WU, and in particularly crop transpiration under higher temperature, needs to be improved and evaluated with field measurements before models can be used to simulate climate change impacts on future crop water demand.

Published
2016

15. Classifying simulated wheat yield responses to changes in temperature and precipitation across a european transect

Author

Fronzek, Stefan, Pirttioja, N., Carter, Timothy R., Bindi, M., Hoffmann, H., Palosuo, T., Ruiz-Ramos, Margarita, Tao, F., Trnka, M., Acutis, M., Asseng, S., Baranowski, P., Basso, Benjamin, Bodin, P., Buis, Samuel, Cammarano, D., Deligios, P., Destain, Marie-France, Dumont, Bertrand, Ewert, Franck, Ferrise, R., FRANCOIS, Léa, Gaiser, T., Hlavinka, P., Jacquemin, Ingrid, Kersebaum, K. C., Kollas, C., Krzyszczak, J., Lorite, Ignacio J., Minet, Julien, Minguez, M. Ines, Montesino, M., Moriondo, Marco, MULLER, C, Nendel, C., Öztürk, Isik, Peregon, Anna, Rodríguez, A., Ruane, A. C., Ruget, Francoise, Sanna, Mattia, Semenov, M. A., Slawinski, Cezary, Stratonovitch, P., Supit, I., Waha, K., Wang, E., Wu, L., Zhao, Z., Rotter, R. P., Finnish Environment Institute (SYKE), Abeilles et Environnement (AE), Institut National de la Recherche Agronomique (INRA)-Avignon Université (AU), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Unité Mixte de Recherches sur les Herbivores - UMR 1213 (UMRH), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de la Recherche Agronomique (INRA), Reproduction et développement des plantes (RDP), École normale supérieure - Lyon (ENS Lyon)-Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), FACCE-JPI Knowledge Hub MACSUR, Finnish Environment Institute, Abeilles et Environnement ( AE ), Institut National de la Recherche Agronomique ( INRA ) -Université d'Avignon et des Pays de Vaucluse ( UAPV ), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes ( EMMAH ), Unité Mixte de Recherches sur les Herbivores ( UMR 1213 Herbivores ), VetAgro Sup ( VAS ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de la Recherche Agronomique ( INRA ), Reproduction et développement des plantes ( RDP ), Centre National de la Recherche Scientifique ( CNRS ) -Université Claude Bernard Lyon 1 ( UCBL ), and Université de Lyon-Université de Lyon-Institut National de la Recherche Agronomique ( INRA ) -École normale supérieure - Lyon ( ENS Lyon )

Subjects
[ SDE.MCG ] Environmental Sciences/Global Changes, changement de température, sensitivity analysis, crop model, pluviomètre, [SDE.MCG]Environmental Sciences/Global Changes, région européenne, Milieux et Changements globaux, rendement du blé, transect, snow gauges
Abstract

Classifying simulated wheat yield responses to changes in temperature and precipitation across a european transect. International Crop Modelling Symposium

Published
2016

16. Model improvements reduce the uncertainty of wheat crop model ensembles under heat stress

Author

Maiorano, Andrea, Martre, Pierre, Asseng, S., Ewert, F., Müller, C., Rötter, R. P., Ruane, A. C., Semenov, M. A., Wallach, Daniel, Wang, E., Alderman, P. D., Kassie, B. T., Biernath, C., Basso, B., Cammarano, D., Challinor, A. J., Doltra, J., Dumont, B., Gayler, S., Kersebaum, Kimball, B. A., Koehler, A. K., Liu, L., O'Leary, G., Olesen, J. E., Ottman, Michael J., Priesack, E., Reynolds, M. P., Eyshi Rezaei, E., Stratonovitch, P., Streck, T., Thorburn, P., Waha, K., Wall, G. W., White, J. W., Zhao, Z., Zhu, Y., Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), University of Florida [Gainesville] (UF), INRES, Rheinische Friedrich-Wilhelms-Universität Bonn, Potsdam Institute for Climate Impact Research (PIK), Natural Resources Institute Finland (LUKE), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Computational and Systems Biology Department, Rothamsted Research, AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Agriculture, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), German Research Center for Environmental Health - Helmholtz Center München (GmbH), Michigan State University [East Lansing], Michigan State University System, University of Leeds, International Center for Tropical Agriculture, Catabrian Agricultural Research and Training Center (CIFA), Department of Geological Sciences and W. K. Kellogg Biological Station, Michigan State University System-Michigan State University System, Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), ARS/ALARC, United States Department of Agriculture, Nanjing Agricultural University, Landscape & Water Sciences, Department of Environment of Victoria, Department of Agroecology, Aarhus University [Aarhus], The School of Plant Sciences, University of Arizona, Institute of Soil Science and Land Evaluation, University of Hohenheim, and Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research, Leibniz Association (ZALF). DEU.

Subjects
Vegetal Biology, comparaison de modèles, Modélisation et simulation, modèle de simulation, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, incertitude, blé, Modeling and Simulation, température, modèle phénologique, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Biologie végétale, modèle de production, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2016

17. Effect of different levels of calibration in rotation schemes simulated in five European sites in a multi-model approach

Author

Lana, M., Kersebaum, K. C., Kollas, C., Xiaogang Yin, Nendel, C., Kiril Manevski, Müller, C., Palosuo, T., Armas-Herrera, Ceclia M., Nicolas Beaudoin, Bindi, M., Monia Charfeddine, Tobias Conradt, Julie Constantin, Eitzinger, J., Ewert, F., Ferrise, R., Thomas Gaiser, Iñaki Garcia de Cortazar-Atauri, Luisa Giglio, Hlavinka, P., Hoffmann, H., Hoffmann, Munir P., Marie Launay, Remy Manderscheid, Bruno Mary, Mirschel, W., Moriondo, M., Olesen, Jørgen E., Isik Öztürk, Pacholski, A., Dominique Ripoche-Wachter, Pier Paolo Roggero, Svenja Roncossek, Rötter, R. P., Ruget, F., Behzad Sharif, Mirek Trnka, Domenico Ventrella, Waha, K., Martin Wegehenkel, Hans-Joachim Weigel, Wu, L., Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Wageningen University and Research Center (WUR), Department of Agroecology, Aarhus University [Aarhus], Molecular Ecology Lab, Department of Biological Sciences, Macquarie University, Natural Resources Institute Finland, Unité d'Agronomie de Laon-Reims-Mons (AGRO-LRM), Institut National de la Recherche Agronomique (INRA), University of Florence (UNIFI), Unità di ricerca per i sistemi colturali degli ambienti caldo-aridi, Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria (CREA), Potsdam Institute for Climate Impact Research (PIK), UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Institute of Meteorology, Universität Leipzig [Leipzig], Rheinische Friedrich-Wilhelms-Universität Bonn, UE Agroclim (UE AGROCLIM), Institute of Agrosystems and Bioclimatology, Mendel University in Brno, Global Change Research Institute CAS, Georg-August-Universität Göttingen, Thünen Institute of Biodiversity, Istituto di Biometeorologia [Firenze] (IBIMET), Consiglio Nazionale delle Ricerche (CNR), Leuphana University of Lüneburg, Nucleo di Ricerca sulla Desertificazione e Dipartimento di Agraria, University of Sassari, TROPAGS, Department of Crop Sciences, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institute of Landscape Systems Analysis, Rothamsted Research, Wageningen University and Research [Wageningen] (WUR), Natural resources institute Finland, Agroressources et Impacts environnementaux (AgroImpact), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Consiglio per la Ricerca in Agricoltura e l’analisi dell’economia agraria (CREA), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Agroclim (AGROCLIM), Mendel University in Brno (MENDELU), and Georg-August-University [Göttingen]

Subjects
[SDV]Life Sciences [q-bio]
Abstract

Effect of different levels of calibration in rotation schemes simulated in five European sites in a multi-model approach. iCROPM 2016 International Crop Modelling Symposium "Crop Modelling for Agriculture and Food Security under Global Change"

Published
2016

18. Inter-comparison of wheat models to identify knowledge gaps and improve process modeling

Author

Wang, E., Martre, Pierre, Asseng, S., Ewert, F., Zhao, Z., Maiorano, Andrea, Rotter, R. P., Kimball, B. A., Ottman, Michael J., Wall, G. W., White, J. W., Aggarwal, P. K., Alderman, P. D., Anothai, J., Basso, B., Biernath, C., Cammarano, D., Challinor, A. J., De Sanctis, Giacomo, Doltra, J., Fereres, E., Garcia-Vila, M., Gayler, S., Hoogenboom, G., Hunt, L. A., Izaurralde, R. C., Jabloun, M., Jones, C. D., Kersebaum, K.C., Koehler, A. K., Müller, C., Liu, L., Kumar Naresh, S., Nendel, C., O'Leary, G., Olesen, J. E., Palosuo, T., Priesack, E., Reynolds, M. P., Eyshi Rezaei, E., Ripoche, Dominique, Ruane, A. C., Semenov, M. A., Shcherbak, I., Stöckle, C., Stratonovitch, P., Streck, T., Supit, I., Tao, F., Thorburn, P., Waha, K., Wallach, Daniel, Wolf, J., Zhu, Y., Agriculture, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), University of Florida [Gainesville] (UF), INRES, Rheinische Friedrich-Wilhelms-Universität Bonn, Natural Resources Institute Finland (LUKE), ARS/ALARC, United States Department of Agriculture, The School of Plant Sciences, University of Arizona, CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), AgWeatherNet Program, Washington State University (WSU), Michigan State University [East Lansing], Michigan State University System, German Research Center for Environmental Health - Helmholtz Center München (GmbH), University of Leeds, International Center for Tropical Agriculture, Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), Catabrian Agricultural Research and Training Center (CIFA), Universidad de Córdoba [Cordoba], IAS, Princeton University, Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Department of Plant Agriculture, University of Guelph, Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, Texas A and M AgriLife Research, Texas A&M University System, Department of Agroecology, Aarhus University [Aarhus], Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Potsdam Institute for Climate Impact Research (PIK), Nanjing Agricultural University, Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Landscape & Water Sciences, Department of Environment of Victoria, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Computational and Systems Biology Department, Rothamsted Research, Institute of Soil Science and Land Evaluation, University of Hohenheim, Wageningen University and Research Centre (WUR), Institute of geographical sciences and natural resources research, Chinese Academy of Sciences [Changchun Branch] (CAS), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, and Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research, Leibniz Association (ZALF). DEU.

Subjects
blé, Modeling and Simulation, comparaison de modèles, température, modèle phénologique, Modélisation et simulation, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, ComputingMilieux_MISCELLANEOUS, modèle de production, incertitude
Abstract

International audience

Published
2016

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

Author

Asseng, S, Ewert, F., Martre, P, Rosenzweig, C, Jones, J.W., Hatfield, J L, Ruane, A C, Boote, K J, Thorburn, P, Rötter, RP, Cammarano, D, Brisson, N, Basso, B, Aggarwal, PK, Angulo, C, Bertuzzi, P, Biernath, C, Challinor, AJ, Doltra, J, Gayler, S, Goldberg, R, Grant, R, Heng, L, Hooker, J, Hunt, L A, Ingwersen, J, Izaurralde, RC, Kersebaum, KC, Müller, C, Naresh Kumar, S, Nendel, C, O'Leary, G, Olesen, Jørgen Eivind, Osborne, T M, Palosuo, T, Priesack, E, Ripoche, D, Semenov, MA, Shcherbak, I, Steduto, P, Stöckle, C, Stratonovitch, P, Streck, T, Supit, I, Tao, F, Travasso, M, Waha, K, Wallach, D, White, JW, Williams, J R, Wolf, J., Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), National laboratory for agriculture and the environment, Department of agronomy, University of Florida [Gainesville] (UF), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Plant Production Research, Agrifood Research Finland, Department of Geological Sciences and W. K. Kellogg Biological Station, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, International Water Management Institute, Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), German Research Center for Environmental Health, University of Leeds, Catabrian Agricultural Research and Training Center (CIFA), Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Department of Renewable Resources, University of Alberta, International Atomic Energy Agency [Vienna] (IAEA), Agriculture Department, University of Reading (UOR), Department of Plant Agriculture, University of Guelph, Institute of Soil Science and Land Evaluation, University of Hohenheim, Joint Global Change Research Institute, Institute of landscape systems analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Potsdam Institute for Climate Impact Research (PIK), Division of Environmental Sciences, University of Hertfordshire [Hatfield] (UH), Department of Primary Industries, Department of Agroecology, Aarhus University [Aarhus], NCAS-Climate, Walker Institute, Computational and Systems Biology Department, Rothamsted Research, Food and Agriculture Organization, Biological Systems Engineering, Washington State University (WSU), Wageningen University and Research Centre (WUR), Institute of geographical sciences and natural resources research, Chinese Academy of Sciences [Changchun Branch] (CAS), Institute for Climate and Water, Instituto Nacional de Tecnología Agropecuaria (INTA), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Arid-Land Agricultural Research Center, Texas A&M University System, Consultative Group on International Agricultural Research (CGIAR), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Helmholtz Zentrum München = German Research Center for Environmental Health, Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC), Institute of geographical sciences and natural resources research [CAS] (IGSNRR), Chinese Academy of Sciences [Beijing] (CAS), and Université de Toulouse (UT)-Université de Toulouse (UT)

Subjects
010504 meteorology & atmospheric sciences, [SDV]Life Sciences [q-bio], Climate change, Atmospheric sciences, 01 natural sciences, Earth System Science, [SHS]Humanities and Social Sciences, Anthesis, sensitivity analysis, wheat, Life Science, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Relative humidity, Precipitation, field experimental data, 0105 earth and related environmental sciences, 2. Zero hunger, WIMEK, Humidity, 04 agricultural and veterinary sciences, 15. Life on land, Climate Resilience, Dew point, 13. Climate action, Klimaatbestendigheid, climate change impact, Soil water, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Leerstoelgroep Aardsysteemkunde, Climate model, simulations, Sensitivity analysis
Abstract

International audience; The data set includes a current representative management treatment from detailed, quality-tested sentinel field experiments with wheat from four contrasting environments including Australia, The Netherlands, India and Argentina. Measurements include local daily climate data (solar radiation, maximum and minimum temperature, precipitation, surface wind, dew point temperature, relative humidity, and vapor pressure), soil characteristics, frequent growth, nitrogen in crop and soil, crop and soil water and yield components. Simulations include results from 27 wheat models and a sensitivity analysis with 26 models and 30 years (1981-2010) for each location, for elevated atmospheric CO2 and temperature changes, a heat stress sensitivity analysis at anthesis, and a sensitivity analysis with soil and crop management variations and a Global Climate Model end-century scenario.

Published
2016
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20. Do maize crop models catch the impact of future [CO2] on maize yield and water use?

Author

Delusca, Kenel, Durand, Jean-Louis, Boote, K., Lizaso, J.I., Manderscheid, R., Weigel, H.J., Ruane, A., Rosenzweig, C., Jones, J., Ahuja, L., Anapalli, S., Basso, B., Baron, C., Bertuzzi, Patrick, Biernath, C., Derynge, D., Ewert, F., Gaiser, T., Gayler, S., Heinlein, F., Kersebaum, Kurt-Christian, Kim, S.H., Müller, C., Nendel, C., Priesack, E., Ramirez, J., Ripoche, Dominique, Rötter, R., Seidel, S., Srivastava, A., Tao, F., Timlin, D., Twine, T., Waha, K., Wang, E., Webber, H., Zhao, Z., ProdInra, Archive Ouverte, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de la Recherche Agronomique (INRA), University of Florida [Gainesville] (UF), Technical University of Madrid, Johann Heinrich von Thünen Institute, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), ASRU, USDA-ARS : Agricultural Research Service, CPSRU, Department of Geological Science, University of North Carolina [Chapel Hill] (UNC), University of North Carolina System (UNC)-University of North Carolina System (UNC), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Agroclim (AGROCLIM), Institute of Biochemical Plant Pathology, German Research Center for Environmental Health - Helmholtz Center München (GmbH), Tyndall Centre for Climate Change Research, University of East Anglia [Norwich] (UEA), School of Environmental Sciences [Norwich], Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Institute of Soil Science and Land Evaluation, University of Hohenheim, Institute of Landscape Systems Analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), School of Environmental and Forest Sciences, University of Washington [Seattle], Potsdam Institute for Climate Impact Research (PIK), International Center for Tropical Agriculture, School of Earth and Environment (UWA), The University of Western Australia (UWA), CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), Natural resources institute Finland, Technische Universität Dresden = Dresden University of Technology (TU Dresden), Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences [Beijing] (CAS), Crop Systems and Global Change Laboratory, Department of Soil, Water and Climate, University of Minnesota System, CSIRO, China Agricultural University (CAU), Métaprogramme ACCAF, University of Florida [Gainesville], UE Agroclim (UE AGROCLIM), Institute of Crop Science and Resource Conservation, University of Bonn-Division of Plant Nutrition, Natural Resources Institute Finland, Technische Universität Dresden (TUD), China Agricultural University, and Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, [SDV.SA] Life Sciences [q-bio]/Agricultural sciences, consommation en eau, U10 - Informatique, mathématiques et statistiques, P40 - Météorologie et climatologie, maïs, F62 - Physiologie végétale - Croissance et développement, rendement, maize, Agricultural sciences, modèle de culture, yields catches, dioxyde de carbone, F01 - Culture des plantes, Sciences agricoles, carbonic anhydride
Abstract

Do maize crop models catch the impact of future [CO2] on maize yield and water use?. iCROPM2016 International Crop Modelling Symposium

Published
2016

21. Similar negative impacts of temperature on global wheat yield estimated by three independent methods

Author

Liu, B., Asseng, S., Müller, C., Ewert, F., Elliott, J., Lobell, D.B., Martre, P., Ruane, A.C., Wallach, D., Jones, J.W., Rosenzweig, C., Aggarwal, P., Alderman, P.D., Anothai, J., Basso, B., Biernath, C.J., Cammarano, D., Challinor, A.J., Deryng, D., de Sanctis, G., Doltra, J., Fereres, E., Folberth, C., Garcia-Vila, M., Gayler, S., Hoogenboom, G., Hunt, L.A., Izaurralde, R.C., Jabloun, M., Jones, C.D., Kersebaum, K.C., Kimball, B.A., Koehler, A.-K., Kumar, S.N., Nendel, C., O´Leary, G., Olesen, J.E., Ottmann, M.J., Palosuo, T., Prasad, P.V.V., Priesack, E., Pugh, T.A., Reynolds, M., Rezaei, E.E., Rötter, R.P., Schmid, E., Semenov, M.A., Shcherbak, I., Stehfest, E., Stöckle, C.O., Stratonovitch, P., Streck,T., Supit, I., Tao, F., Thorburn, P.J., Waha, K., Wall, G.W., Wang, E., White, J.W., Wolf, J., Zhao, Z., and Zhu, Y.

Abstract

The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO2 fertilization effects, produce similar estimates of temperature impact on wheat yields at global and national scales. With a 1 °C global temperature increase, global wheat yield is projected to decline between 4.1% and 6.4%. Projected relative temperature impacts from different methods were similar for major wheat-producing countries China, India, USA and France, but less so for Russia. Point-based and grid-based simulations, and to some extent the statistical regressions, were consistent in projecting that warmer regions are likely to suffer more yield loss with increasing temperature than cooler regions. By forming a multi-method ensemble, it was possible to quantify ‘method uncertainty’ in addition to model uncertainty. This significantly improves confidence in estimates of climate impacts on global food security.

Published
2016

22. How accurately do crop models simulate the impact of CO2 atmospheric concentration on maize yield and water use?

Author

Durand, Jean-Louis, Delusca, Kénel, Boote, K., Lizaso, J., Manderscheid, R., Rosenzweig, C., Jones, J., Weigel, H.J., Ruane, A., Anapalli, S., Ahuja, L., Basso, B., Baron, C., Bertuzzi, Patrick, Ripoche, Dominique, Biernath, C., Priesak, E., Derynge, D., Ewert, F., Gaiser, T., Gayler, S., Heilein, F., Kersebaum, K.C., Kim, S.H., Müller, C., Nendel, C., Ramirez, J., Tao, F., Timlin, D., Waha, K., Twine, T., Wang, E., Webber, H., Zhao, Z., Rötter, R., Srivastava, A., Seidel, S., Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de la Recherche Agronomique (INRA), University of Florida [Gainesville] (UF), ETSIA, Johann Heinrich von Thünen Institut, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Colorado State University [Fort Collins] (CSU), Department of geological sciences, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Michigan State University System, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF), Agroclim (AGROCLIM), German Research Center for Environmental Health - Helmholtz Center München (GmbH), Tyndall Centre for Climate Change Research, University of East Anglia [Norwich] (UEA), Water and earth system science [Tübingen] (WESS), Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), School of Environmental and Forest Sciences, University of Washington [Seattle], Potsdam Institute for Climate Impact Research (PIK), School of Earth and Environment, University of Leeds, Institute of geographical sciences and natural resources research, Chinese Academy of Sciences [Changchun Branch] (CAS), United States Department of Agriculture (USDA), Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Department of Soil, Water and Climate, University of Minnesota System, Land and Water, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Rheinische Friedrich-Wilhelms-Universität Bonn, China Agricultural University (CAU), Natural Resources Institute Finland (LUKE), Institute of Crop Science and Resource Conservation [Bonn], Technische Universität Dresden = Dresden University of Technology (TU Dresden), and ProdInra, Migration

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, [SDV.SA] Life Sciences [q-bio]/Agricultural sciences, ComputingMilieux_MISCELLANEOUS
Abstract

National audience

Published
2015

23. Farming and the geography of nutrient production for human use: a transdisciplinary analysis

Author

Herrero, M., Thornton, P.K., Power, B., Bogard, J.R., Remans, R., Fritz, S., Gerber, J.S., Nelson, G., See, L., Waha, K., Watson, R.A., West, P.C., Samberg, L.H., van de Steeg, J., Stephenson, E., van Wijk, M., Havlik, P., Herrero, M., Thornton, P.K., Power, B., Bogard, J.R., Remans, R., Fritz, S., Gerber, J.S., Nelson, G., See, L., Waha, K., Watson, R.A., West, P.C., Samberg, L.H., van de Steeg, J., Stephenson, E., van Wijk, M., and Havlik, P.

Abstract

Background: Information about the global structure of agriculture and nutrient production and its diversity is essential to improve present understanding of national food production patterns, agricultural livelihoods, and food chains, and their linkages to land use and their associated ecosystems services. Here we provide a plausible breakdown of global agricultural and nutrient production by farm size, and also study the associations between farm size, agricultural diversity, and nutrient production. This analysis is crucial to design interventions that might be appropriately targeted to promote healthy diets and ecosystems in the face of population growth, urbanisation, and climate change. Methods: We used existing spatially-explicit global datasets to estimate the production levels of 41 major crops, seven livestock, and 14 aquaculture and fish products. From overall production estimates, we estimated the production of vitamin A, vitamin B₁₂, folate, iron, zinc, calcium, calories, and protein. We also estimated the relative contribution of farms of different sizes to the production of different agricultural commodities and associated nutrients, as well as how the diversity of food production based on the number of different products grown per geographic pixel and distribution of products within this pixel (Shannon diversity index [H]) changes with different farm sizes. Findings: Globally, small and medium farms (≤50 ha) produce 51–77% of nearly all commodities and nutrients examined here. However, important regional differences exist. Large farms (>50 ha) dominate production in North America, South America, and Australia and New Zealand. In these regions, large farms contribute between 75% and 100% of all cereal, livestock, and fruit production, and the pattern is similar for other commodity groups. By contrast, small farms (≤20 ha) produce more than 75% of most food commodities in sub-Saharan Africa, southeast Asia, south Asia, and China. In Europe, west Asia an

Published
2017

24. Letter : Rising temperatures reduce global wheat production

Author

Asseng, S., Ewert, F., Martre, P., Rötter, R.P., Cammarano, D., Kimball, B.A., Ottman, M.J., Wall, G.W., White, J.W., Reynolds, M.P., Alderman, P.D., Prasad, P.V.V., Lobell, D.B., Aggarwal, P.K., Anothai, J., Basso, B., Biernath, C., Challinor, A.J., De Sanctis, G., Doltra, J., Fereres, E., Garcia-Vila, M., Gayler, S., Hoogenboom, G., Hunt, L.A., Izaurralde, C., Jabloun, M., Jones, C.D., Kersebaum, K.C., Koehler, A.K., Müller, C., Naresh Kumar, S., Nendel, C., O’Leary, G., Olesen, J.E., Palosuo, T., Priesack, E., Eyshi Rezae, E., Ruane, A.C., Semenov, M.A., Shcherbak, I., Stöckle, C.O., Stratonovitch, P., Streck, T., Supit, I., Tao, T., Thorburn, P., Waha, K., Wang, E., Wallach, D., Wolf, J., Zhao, Z., and Zhu, Y.

Subjects
dryland wheat, WIMEK, growth, Soil Science Centre, adaptation, drought, PE&RC, yield, Climate Resilience, spring wheat, Plant Production Systems, Klimaatbestendigheid, Plantaardige Productiesystemen, climate-change, co2, Alterra - Centrum Bodem, heat, agriculture
Abstract

Crop models are essential tools for assessing the threat of climate change to local and global food production(1). Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature(2). Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 degrees C to 32 degrees C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each degrees C of further temperature increase and become more variable over space and time.

Published
2015

26. Simulating crop rotations and management across climatic zones in Europe – an intercomparison study using fifteen models

Author

Müller, Christoph, Constantin, J., Trnka, Miroslav, Nendel, Claas, Weigel, H.-J., Pacholski, A., Roggero, Pier Paolo, Ripoche-Wachter, D., Ferrise, Roberto, Wegehenkel, M., Charfeddine, M., Hoffmann, Holger, Mirschel, Wilfried, Bindi, Marco, Gaiser, Thomas, Eitzinger, Josef, Conradt, Tobias, Rötter, Reimund, Ewert, Frank, Beaudoin, N., Giglio, L., Mary, B., Garcia De Cortazar-Atauri, I., Kersebaum, Kurt Christian, Manderscheid, Remy, Armas-Herrera, C.M., Palosuo, Taru, Roncossek, S., Wu, L., Sharif, B., Öztürk, I., Manevski, K., Olesen, Jørgen E., RUGET, Françoise, Takáč, J., Hoffmann, M.P., Ventrella, Domenico, Waha, K., Hlavinka, P., Launay, Marie, and Moriondo, Marco

Abstract

Process based crop simulation models are widely used to assess crop production under current or future climate conditions. Most studies on climate impacts on crop growth are so far focussed on single crops and single-year simulations. However, it is known that the position of crops within a rotation can influence crop growth significantly due to carry-over effects between seasons. We compared crop models on crop rotation effects from five sites across Central Europe providing in total data of 301 cropping seasons and treatments. Treatments comprised irrigation, nitrogen (N) fertilisation, atmospheric [CO2], tillage, residue management, cover crops and soils. Crop rotations were simulated with 15 crop models as single-year simulations and/or continuous simulations over whole crop rotations in “restricted calibration” runs. Lower RMSE between observed and simulated crop yields were obtained for continuous runs as compared to single-year runs. Relatively low carry-over effects were observed due to equilibration of soil water over winter and high N fertilisation levels. Consistently, a sub-set of models applied to an additional rainfed Mediterranean site reproduced larger carry-over effects of soil water. Irrigation, N supply, cover crops and atmospheric [CO2] showed clearer effects than tillage and crop residue management. Model performance varied distinctly between crops showing the necessity to provide experimental data for model calibration also for less prominent crops.

Published
2015

27. Similar estimates of temperature impacts on global wheat yield by three independent methods

Author

Liu, B., Asseng, S., Müller, C., Ewert, F., Elliott, J., Lobell, D.B., Martre, P., Ruane, A.C., Wallach, D., Jones, J.W., Rosenzweig, C., Aggarwal, P.K., Alderman, P.D., Anothai, J., Basso, B., Biernath, C., Cammarano, D., Challinor, A., Deryng, D., Sanctis, G., Doltra, J., Fereres, E., Folberth, C., Garcia-Vila, M., Gayler, S., Hoogenboom, G., Hunt, L.A., Izaurralde, R.C., Jabloun, M., Jones, C.D., Kersebaum, K.C., Kimball, B.A., Koehler, A.-K., Kumar, S.N., Nendel, C., O’Leary, G.J., Olesen, J.E., Ottman, M.J., Palosuo, T., Prasad, P.V.V., Priesack, E., Pugh, T.A.M., Reynolds, M., Rezaei, E.E., Rötter, R.P., Schmid, E., Semenov, M.A., Shcherbak, I., Stehfest, E., Stöckle, C.O., Stratonovitch, P., Streck, T., Supit, I., Tao, F., Thorburn, P., Waha, K., Wall, G.W., Wang, E., White, J.W., Wolf, J., Zhao, Z., Zhu, Y., Liu, B., Asseng, S., Müller, C., Ewert, F., Elliott, J., Lobell, D.B., Martre, P., Ruane, A.C., Wallach, D., Jones, J.W., Rosenzweig, C., Aggarwal, P.K., Alderman, P.D., Anothai, J., Basso, B., Biernath, C., Cammarano, D., Challinor, A., Deryng, D., Sanctis, G., Doltra, J., Fereres, E., Folberth, C., Garcia-Vila, M., Gayler, S., Hoogenboom, G., Hunt, L.A., Izaurralde, R.C., Jabloun, M., Jones, C.D., Kersebaum, K.C., Kimball, B.A., Koehler, A.-K., Kumar, S.N., Nendel, C., O’Leary, G.J., Olesen, J.E., Ottman, M.J., Palosuo, T., Prasad, P.V.V., Priesack, E., Pugh, T.A.M., Reynolds, M., Rezaei, E.E., Rötter, R.P., Schmid, E., Semenov, M.A., Shcherbak, I., Stehfest, E., Stöckle, C.O., Stratonovitch, P., Streck, T., Supit, I., Tao, F., Thorburn, P., Waha, K., Wall, G.W., Wang, E., White, J.W., Wolf, J., Zhao, Z., and Zhu, Y.

Published
2016
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28. Soil nitrogen mineralisation simulated by crop models across different environments and the consequences for model improvement

Author

Nendel, Claas, Thorburn, Peter J., Melzer, Michael, Cerri, Carlos Eduardo P., Claessens, L., Aggarwal, P.K., Adam, Myriam, Angulo, C., Asseng, Senthold, Baron, Christian, Basso, Bruno, Bassu, Simona, Bertuzzi, Patrick, Biernath, Christian, Boogaard, Hendrik, Boote, Kenneth J., Brisson, Nadine, Cammarano, Davide, Conjin, Sjaak, Corbeels, Marc, Deryng, Delphine, De Sanctis, Giacomo, Doltra, J., Durand, J.L., Ewert, Franck, Gayler, Sebastian, Goldberg, Richard, Grant, R.F., Grassini, Patricio, Heng, L., Hoek, Steven B., Hooker, J., Hunt, L.A., Ingwersen, J., Izaurralde, Cesar, Jongschaap, Raymond, Kemanian, Armen, Kersebaum, Kurt Christian, Lizaso, Jon, Makowski, David, Martre, Pierre, Müller, Christoph, Kim, Soo-Hyung, Naresh Kumar, Sora, O'Leary, G.J., Olesen, Jørgen Eivind, Osborne, T.M., Palosuo, Taru, Pravia, Maria Virginia, Priesack, Eckart, Ripoche, Dominique, Rötter, Reimund P., Sau, Federico, Semenov, M.A., Shcherbak, Iurii, Steduto, P., Stöckle, Claudio, Stratonovitch, P., Streck, T., Supit, Iwan, Tao, F., Teixeira, Edmar, Timlin, Dennis, Travasso, M., Waha, K., Wallach, Daniel, White, Jeffrey W., Wolf, Joost, Nendel, Claas, Thorburn, Peter J., Melzer, Michael, Cerri, Carlos Eduardo P., Claessens, L., Aggarwal, P.K., Adam, Myriam, Angulo, C., Asseng, Senthold, Baron, Christian, Basso, Bruno, Bassu, Simona, Bertuzzi, Patrick, Biernath, Christian, Boogaard, Hendrik, Boote, Kenneth J., Brisson, Nadine, Cammarano, Davide, Conjin, Sjaak, Corbeels, Marc, Deryng, Delphine, De Sanctis, Giacomo, Doltra, J., Durand, J.L., Ewert, Franck, Gayler, Sebastian, Goldberg, Richard, Grant, R.F., Grassini, Patricio, Heng, L., Hoek, Steven B., Hooker, J., Hunt, L.A., Ingwersen, J., Izaurralde, Cesar, Jongschaap, Raymond, Kemanian, Armen, Kersebaum, Kurt Christian, Lizaso, Jon, Makowski, David, Martre, Pierre, Müller, Christoph, Kim, Soo-Hyung, Naresh Kumar, Sora, O'Leary, G.J., Olesen, Jørgen Eivind, Osborne, T.M., Palosuo, Taru, Pravia, Maria Virginia, Priesack, Eckart, Ripoche, Dominique, Rötter, Reimund P., Sau, Federico, Semenov, M.A., Shcherbak, Iurii, Steduto, P., Stöckle, Claudio, Stratonovitch, P., Streck, T., Supit, Iwan, Tao, F., Teixeira, Edmar, Timlin, Dennis, Travasso, M., Waha, K., Wallach, Daniel, White, Jeffrey W., and Wolf, Joost

Published
2016

29. Turn down the heat: confronting the new climate normal

Author

Adams, S., Aich, V., Albrecht, T., Baarsch, F., Boit, A., Canales Trujillo, N., Cartsburg, M., Coumou, D., Eden, A., Fader, M., Hare, B., Hoff, H., Jobbins, G., Jones, L., Kit, O., Krummenauer, L., Langerwisch, F., Le Masson, V., Ludi, E., Marcus, R., Mengel, M., Otto, I.M., Perette, M., Pereznieto, P., Rammig, A., Reckien, D., Reinhardt, J., Reyer, C., Robinson, A., Rocha, M., Sakschewski, B., Schaeffer, M., Schaphof, S., Schewe, C.F., Serdeczny, O., Stagl, J., Thonicke, K., Waha, K., Department of Urban and Regional Planning and Geo-Information Management, Faculty of Geo-Information Science and Earth Observation, and UT-I-ITC-PLUS

Subjects
METIS-316088
Abstract

The data show that dramatic climate changes, heat and weather extremes are already impacting people, damaging crops and coastlines and putting food, water, and energy security at risk. Across the three regions studied in this report, record-breaking temperatures are occurring more frequently, rainfall has increased in intensity in some places, while drought-prone regions are getting dryer. In an overview of social vulnerability, the poor and underprivileged, as well as the elderly and children, are found to be often hit the hardest. There is growing evidence, that even with very ambitious mitigation action, warming close to 1.5°C above pre-industrial levels by mid-century is already locked-in to the Earth’s atmospheric system and climate change impacts such as extreme heat events may now be unavoidable.1 If the planet continues warming to 4°C, climatic conditions, heat and other weather extremes considered highly unusual or unprecedented today would become the new climate normal—a world of increased risks and instability. The consequences for development would be severe as crop yields decline, water resources change, diseases move into new ranges, and sea levels rise. The task of promoting human development, of ending poverty, increasing global prosperity, and reducing global inequality will be very challenging in a 2°C world, but in a 4°C world there is serious doubt whether this can be achieved at all. Immediate steps are needed to help countries adapt to the climate impacts being felt today and the unavoidable consequences of a rapidly warming world. The benefits of strong, early action on climate change, action that follows clean, low carbon pathways and avoids locking in unsustainable growth strategies, far outweigh the costs. Many of the worst projected climate impacts could still be avoided by holding warming to below 2°C. But, the time to act is now. This report focuses on the risks of climate change to development in Latin America and the Caribbean, the Middle East and North Africa, and parts of Europe and Central Asia. Building on earlier Turn Down the Heat reports this new scientific analysis examines the likely impacts of present day (0.8°C), 2°C and 4°C warming above pre-industrial temperatures on agricultural production, water resources, ecosystem services and coastal vulnerability for affected populations.

Published
2014

30. Turn Down the Heat. Confronting the New Climate Normal

Author

Schellnhuber, H. J., Reyer, C., Hare, W., Waha, K., Otto, I. M., Serdeczny, O., Schaeffer, M., Schleußner, C.-F., Reckien, D., Marcus, R., Kit, O., Eden, A., Adams, S., Aich, V., Albrecht, T., Baarsch, F., Boit, A., Trujillo, N. C., Cartsburg, M., Coumou, D., Fader, M., Hoff, H., Jobbins, G., Jones, L., Krummenauer, L., Langerwisch, F., Le Masson, V., Ludi, E., Mengel, M., Möhring, J., Mosello, B., Norton, A., Perette, M., Pereznieto, P., Rammig, A., Reinhardt, J., Robinson, A., Rocha, M., Sakschewski, B., Schaphoff, S., Schewe, J., Stagl, J., and Thonicke, K.

Subjects
ddc:550
Published
2014

32. Climate-driven simulation of global crop sowing dates

Author

van Bussel, L.G.J., Waha, K., Müller, C., Bondeau, A., and Potsdam Institute for Climate Impact Research (PIK)

Subjects
model, sown sugar-beet, corn growth, base temperature, soil-temperature, high-resolution, PE&RC, spring wheat, Plant Production Systems, seedling emergence, Plantaardige Productiesystemen, [SDE]Environmental Sciences, ddc:550, tassel initiation, Life Science, s-and-h, ComputingMilieux_MISCELLANEOUS
Abstract

Aim To simulate the sowing dates of 11 major annual crops at the global scale at high spatial resolution, based on climatic conditions and crop-specific temperature requirements. Location Global. Methods Sowing dates under rainfed conditions are simulated deterministically based on a set of rules depending on crop-and climate-specific characteristics. We assume that farmers base their timing of sowing on experiences with past precipitation and temperature conditions, with the intra-annual variability being especially important. The start of the growing period is assumed to be dependent either on the onset of the wet season or on the exceeding of a crop-specific temperature threshold for emergence. To validate our methodology, a global data set of observed monthly growing periods (MIRCA2000) is used. Results We show simulated sowing dates for 11 major field crops world-wide and give rules for determining their sowing dates in a specific climatic region. For all simulated crops, except for rapeseed and cassava, in at least 50% of the grid cells and on at least 60% of the cultivated area, the difference between simulated and observed sowing dates is less than 1 month. Deviations of more than 5 months occur in regions characterized by multiple-cropping systems, in tropical regions which, despite seasonality, have favourable conditions throughout the year, and in countries with large climatic gradients. Main conclusions Sowing dates under rainfed conditions for various annual crops can be satisfactorily estimated from climatic conditions for large parts of the earth. Our methodology is globally applicable, and therefore suitable for simulating sowing dates as input for crop growth models applied at the global scale and taking climate change into account.

Published
2012
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33. How accurately do crop models simulate the impact of CO2 atmospheric concentration on maize yield and water use ? [O-2223-02]

Author

Durand, J.L., Delusca, Kenel, Boote, Kenneth J., Lizaso, Jon, Manderscheid, Remy, Rosenzweig, Cynthia, Jones, Jim, Weigel, Hans Johachim, Ruane, Alex C., Anapalli, Saseendran S., Ahuja, L., Basso, Bruno, Baron, Christian, Bertuzzi, Patrick, Ripoche, Dominique, Biernath, Christian, Priesak, E., Deryng, Delphine, Ewert, F., Gaiser, T., Gayler, S., Heilein, F., Kersebaum, Kurt Christian, Kim, S.H., Müller, C., Nendel, Claas, Ramirez, J., Tao, F., Timlin, D., Waha, K., Twine, Tracy E., Wang, E., Webber, Heidi, Zhao, Zhigan, Rötter, Reimund P., Srivastava, Amit, Seidel, S., Durand, J.L., Delusca, Kenel, Boote, Kenneth J., Lizaso, Jon, Manderscheid, Remy, Rosenzweig, Cynthia, Jones, Jim, Weigel, Hans Johachim, Ruane, Alex C., Anapalli, Saseendran S., Ahuja, L., Basso, Bruno, Baron, Christian, Bertuzzi, Patrick, Ripoche, Dominique, Biernath, Christian, Priesak, E., Deryng, Delphine, Ewert, F., Gaiser, T., Gayler, S., Heilein, F., Kersebaum, Kurt Christian, Kim, S.H., Müller, C., Nendel, Claas, Ramirez, J., Tao, F., Timlin, D., Waha, K., Twine, Tracy E., Wang, E., Webber, Heidi, Zhao, Zhigan, Rötter, Reimund P., Srivastava, Amit, and Seidel, S.

Abstract

Methods and Results Given the incertitudes on the climate change impacts on C4 crops, projections of regional maize production remain speculative. Assessment of the impacts of atmospheric CO2 concentration ([CO2]) on crop yield and resources uses using mechanistic models becomes increasingly important. Free Air [CO2] Enrichment (FACE) studies offer data to test and improve model quality. The objective of this work by the AgMIP Maize group was (i) to test multiple maize models for [CO2] responses against data gathered from a FACE study under two water regimes carried out in Germany during 2007 and 2008, and (ii) to pave the way to potential model modifications so as to improve their simulations of crop responses to [CO2]. The Experiment combined two [CO2] levels with two watering regimes. Yield, leaf area, soil water content and [CO2] levels were recorded both years, 2008 only exhibiting significant water deficit. After a preliminary calibration based on non limiting water conditions and under ambient [CO2] treatments of both years, a blind simulation was undertaken for the other treatments: High [CO2] (550 ppm) 2007 and 2008, both watering regimes, and DRY AMBIENT 2007 and 2008. Secondly, with full growth and yield data along with soil moisture data of all treatments, improvements of simulation results were attempted. Changes made to the models have been documented and submitted for further analysis. The results revealed: minimal [CO2] impacts with low variations among " uncalibrated " models except for the dry season of 2008 where the observed drought impact was simulated by the majority of models; most models caught but underestimated the CO2 impact on crop water status, leaf area, grain number and yield; the CO2 effect on transpiration was generally properly simulated, transpiration per leaf area decreasing but green leaf area duration increasing at 550 ppm [CO2]. As more data from FACE experiments become available, it will be highly desirable to replicate this e

Published
2015

34. Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects

Author

Makowski, D., Asseng, S., Ewert, F., Bassu, S., Durand, J.L., Martre, P., Adam, M., Aggarwal, P.K., Angulo, C., Baron, C., Basso, B., Bertuzzi, P., Biernath, C., Boogaard, H., Boote, K.J., Brisson, N., Cammarano, D., Challinor, A.J., Conijn, J.G., Corbeels, M., Deryng, D., De Sanctis, G., Doltra, J., Gayler, S., Goldberg, R., Grassini, P., Hatfield, J.L., Heng, L., Hoek, S.B., Hooker, J., Hunt, L.A., Ingwersen, J., Izaurralde, C., Jongschaap, R.E.E., Jones, J.W., Kemanian, R.A., Kersebaum, K.C., Kim, S.H., Lizaso, J., Müller, C., Naresh Kumar, S., Nendel, C., O'Leary, G.J., Olesen, J.E., Osborne, T.M., Palosuo, T., Pravia, M.V., Priesack, E., Ripoche, D., Rosenzweig, C., Ruane, A.C., Sau, F., Semenov, M.A., Shcherbak, I., Steduto, P., Stöckle, C.O., Stratonovitch, P., Streck, T., Supit, I., Tao, F., Teixeira, E., Thorburn, P., Timlin, D., Travasso, M., Roetter, R.P., Waha, K., Wallach, D., White, J.W., Williams, J.R., Wolf, J., Makowski, D., Asseng, S., Ewert, F., Bassu, S., Durand, J.L., Martre, P., Adam, M., Aggarwal, P.K., Angulo, C., Baron, C., Basso, B., Bertuzzi, P., Biernath, C., Boogaard, H., Boote, K.J., Brisson, N., Cammarano, D., Challinor, A.J., Conijn, J.G., Corbeels, M., Deryng, D., De Sanctis, G., Doltra, J., Gayler, S., Goldberg, R., Grassini, P., Hatfield, J.L., Heng, L., Hoek, S.B., Hooker, J., Hunt, L.A., Ingwersen, J., Izaurralde, C., Jongschaap, R.E.E., Jones, J.W., Kemanian, R.A., Kersebaum, K.C., Kim, S.H., Lizaso, J., Müller, C., Naresh Kumar, S., Nendel, C., O'Leary, G.J., Olesen, J.E., Osborne, T.M., Palosuo, T., Pravia, M.V., Priesack, E., Ripoche, D., Rosenzweig, C., Ruane, A.C., Sau, F., Semenov, M.A., Shcherbak, I., Steduto, P., Stöckle, C.O., Stratonovitch, P., Streck, T., Supit, I., Tao, F., Teixeira, E., Thorburn, P., Timlin, D., Travasso, M., Roetter, R.P., Waha, K., Wallach, D., White, J.W., Williams, J.R., and Wolf, J.

Abstract

Many simulation studies have been carried out to predict the effect of climate change on crop yield. Typically, in such study, one or several crop models are used to simulate series of crop yield values for different climate scenarios corresponding to different hypotheses of temperature, CO2 concentration, and rainfall changes. These studies usually generate large datasets including thousands of simulated yield data. The structure of these datasets is complex because they include series of yield values obtained with different mechanistic crop models for different climate scenarios defined from several climatic variables (temperature, CO2 etc.). Statistical methods can play a big part for analyzing large simulated crop yield datasets, especially when yields are simulated using an ensemble of crop models. A formal statistical analysis is then needed in order to estimate the effects of different climatic variables on yield, and to describe the variability of these effects across crop models. Statistical methods are also useful to develop meta-models i.e., statistical models summarizing complex mechanistic models. The objective of this paper is to present a random-coefficient statistical model (mixed-effects model) for analyzing large simulated crop yield datasets produced by the international project AgMip for several major crops. The proposed statistical model shows several interesting features; i) it can be used to estimate the effects of several climate variables on yield using crop model simulations, ii) it quantities the variability of the estimated climate change effects across crop models, ii) it quantifies the between-year yield variability, iv) it can be used as a meta-model in order to estimate effects of new climate change scenarios without running again the mechanistic crop models. The statistical model is first presented in details, and its value is then illustrated in a case study where the effects of climate change scenarios on different crops are compar

Published
2015

35. Rising temperatures reduce global wheat production

Author

Asseng, S., primary, Ewert, F., additional, Martre, P., additional, Rötter, R. P., additional, Lobell, D. B., additional, Cammarano, D., additional, Kimball, B. A., additional, Ottman, M. J., additional, Wall, G. W., additional, White, J. W., additional, Reynolds, M. P., additional, Alderman, P. D., additional, Prasad, P. V. V., additional, Aggarwal, P. K., additional, Anothai, J., additional, Basso, B., additional, Biernath, C., additional, Challinor, A. J., additional, De Sanctis, G., additional, Doltra, J., additional, Fereres, E., additional, Garcia-Vila, M., additional, Gayler, S., additional, Hoogenboom, G., additional, Hunt, L. A., additional, Izaurralde, R. C., additional, Jabloun, M., additional, Jones, C. D., additional, Kersebaum, K. C., additional, Koehler, A-K., additional, Müller, C., additional, Naresh Kumar, S., additional, Nendel, C., additional, O’Leary, G., additional, Olesen, J. E., additional, Palosuo, T., additional, Priesack, E., additional, Eyshi Rezaei, E., additional, Ruane, A. C., additional, Semenov, M. A., additional, Shcherbak, I., additional, Stöckle, C., additional, Stratonovitch, P., additional, Streck, T., additional, Supit, I., additional, Tao, F., additional, Thorburn, P. J., additional, Waha, K., additional, Wang, E., additional, Wallach, D., additional, Wolf, J., additional, Zhao, Z., additional, and Zhu, Y., additional

Published
2014
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38. Uncertainty in simulating wheat yields under climate change

Author

Asseng, S., primary, Ewert, F., additional, Rosenzweig, C., additional, Jones, J. W., additional, Hatfield, J. L., additional, Ruane, A. C., additional, Boote, K. J., additional, Thorburn, P. J., additional, Rötter, R. P., additional, Cammarano, D., additional, Brisson, N., additional, Basso, B., additional, Martre, P., additional, Aggarwal, P. K., additional, Angulo, C., additional, Bertuzzi, P., additional, Biernath, C., additional, Challinor, A. J., additional, Doltra, J., additional, Gayler, S., additional, Goldberg, R., additional, Grant, R., additional, Heng, L., additional, Hooker, J., additional, Hunt, L. A., additional, Ingwersen, J., additional, Izaurralde, R. C., additional, Kersebaum, K. C., additional, Müller, C., additional, Naresh Kumar, S., additional, Nendel, C., additional, O’Leary, G., additional, Olesen, J. E., additional, Osborne, T. M., additional, Palosuo, T., additional, Priesack, E., additional, Ripoche, D., additional, Semenov, M. A., additional, Shcherbak, I., additional, Steduto, P., additional, Stöckle, C., additional, Stratonovitch, P., additional, Streck, T., additional, Supit, I., additional, Tao, F., additional, Travasso, M., additional, Waha, K., additional, Wallach, D., additional, White, J. W., additional, Williams, J. R., additional, and Wolf, J., additional

Published
2013
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43. Multiple cropping systems of the world and the potential for increasing cropping intensity.

Author

Waha K, Dietrich JP, Portmann FT, Siebert S, Thornton PK, Bondeau A, and Herrero M

Abstract

Multiple cropping, defined as harvesting more than once a year, is a widespread land management strategy in tropical and subtropical agriculture. It is a way of intensifying agricultural production and diversifying the crop mix for economic and environmental benefits. Here we present the first global gridded data set of multiple cropping systems and quantify the physical area of more than 200 systems, the global multiple cropping area and the potential for increasing cropping intensity. We use national and sub-national data on monthly crop-specific growing areas around the year 2000 (1998-2002) for 26 crop groups, global cropland extent and crop harvested areas to identify sequential cropping systems of two or three crops with non-overlapping growing seasons. We find multiple cropping systems on 135 million hectares (12% of global cropland) with 85 million hectares in irrigated agriculture. 34%, 13% and 10% of the rice, wheat and maize area, respectively are under multiple cropping, demonstrating the importance of such cropping systems for cereal production. Harvesting currently single cropped areas a second time could increase global harvested areas by 87-395 million hectares, which is about 45% lower than previous estimates. Some scenarios of intensification indicate that it could be enough land to avoid expanding physical cropland into other land uses but attainable intensification will depend on the local context and the crop yields attainable in the second cycle and its related environmental costs., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2020 The Author(s).)

Published
2020
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44. Global wheat production with 1.5 and 2.0°C above pre-industrial warming.

Author

Liu B, Martre P, Ewert F, Porter JR, Challinor AJ, Müller C, Ruane AC, Waha K, Thorburn PJ, Aggarwal PK, Ahmed M, Balkovič J, Basso B, Biernath C, Bindi M, Cammarano D, De Sanctis G, Dumont B, Espadafor M, Eyshi Rezaei E, Ferrise R, Garcia-Vila M, Gayler S, Gao Y, Horan H, Hoogenboom G, Izaurralde RC, Jones CD, Kassie BT, Kersebaum KC, Klein C, Koehler AK, Maiorano A, Minoli S, Montesino San Martin M, Naresh Kumar S, Nendel C, O'Leary GJ, Palosuo T, Priesack E, Ripoche D, Rötter RP, Semenov MA, Stöckle C, Streck T, Supit I, Tao F, Van der Velde M, Wallach D, Wang E, Webber H, Wolf J, Xiao L, Zhang Z, Zhao Z, Zhu Y, and Asseng S

Abstract

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 <2°C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade., (© 2018 John Wiley & Sons Ltd.)

Published
2019
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45. Climate change impact and adaptation for wheat protein.

Author

Asseng S, Martre P, Maiorano A, Rötter RP, O'Leary GJ, Fitzgerald GJ, Girousse C, Motzo R, Giunta F, Babar MA, Reynolds MP, Kheir AMS, Thorburn PJ, Waha K, Ruane AC, Aggarwal PK, Ahmed M, Balkovič J, Basso B, Biernath C, Bindi M, Cammarano D, Challinor AJ, De Sanctis G, Dumont B, Eyshi Rezaei E, Fereres E, Ferrise R, Garcia-Vila M, Gayler S, Gao Y, Horan H, Hoogenboom G, Izaurralde RC, Jabloun M, Jones CD, Kassie BT, Kersebaum KC, Klein C, Koehler AK, Liu B, Minoli S, Montesino San Martin M, Müller C, Naresh Kumar S, Nendel C, Olesen JE, Palosuo T, Porter JR, Priesack E, Ripoche D, Semenov MA, Stöckle C, Stratonovitch P, Streck T, Supit I, Tao F, Van der Velde M, Wallach D, Wang E, Webber H, Wolf J, Xiao L, Zhang Z, Zhao Z, Zhu Y, and Ewert F

Subjects
Carbon Dioxide metabolism, Droughts, Food Quality, Models, Theoretical, Nitrogen metabolism, Temperature, Adaptation, Physiological, Climate Change, Grain Proteins analysis, Triticum chemistry, Triticum physiology
Abstract

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., (© 2018 John Wiley & Sons Ltd.)

Published
2019
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46. Agricultural diversification as an important strategy for achieving food security in Africa.

Author

Waha K, van Wijk MT, Fritz S, See L, Thornton PK, Wichern J, and Herrero M

Subjects
Africa, Agriculture statistics & numerical data, Humans, Agriculture methods, Climate, Food Supply methods
Abstract

Farmers in Africa have long adapted to climatic and other risks by diversifying their farming activities. Using a multi-scale approach, we explore the relationship between farming diversity and food security and the diversification potential of African agriculture and its limits on the household and continental scale. On the household scale, we use agricultural surveys from more than 28,000 households located in 18 African countries. In a next step, we use the relationship between rainfall, rainfall variability, and farming diversity to determine the available diversification options for farmers on the continental scale. On the household scale, we show that households with greater farming diversity are more successful in meeting their consumption needs, but only up to a certain level of diversity per ha cropland and more often if food can be purchased from off-farm income or income from farm sales. More diverse farming systems can contribute to household food security; however, the relationship is influenced by other factors, for example, the market orientation of a household, livestock ownership, nonagricultural employment opportunities, and available land resources. On the continental scale, the greatest opportunities for diversification of food crops, cash crops, and livestock are located in areas with 500-1,000 mm annual rainfall and 17%-22% rainfall variability. Forty-three percent of the African cropland lacks these opportunities at present which may hamper the ability of agricultural systems to respond to climate change. While sustainable intensification practices that increase yields have received most attention to date, our study suggests that a shift in the research and policy paradigm toward agricultural diversification options may be necessary., (© 2018 The Authors Global Change Biology Published by John Wiley & Sons Ltd.)

Published
2018
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47. Trends in Global Agricultural Land Use: Implications for Environmental Health and Food Security.

Author

Ramankutty N, Mehrabi Z, Waha K, Jarvis L, Kremen C, Herrero M, and Rieseberg LH

Subjects
Biodiversity, History, 18th Century, History, 19th Century, History, 20th Century, Agriculture history, Conservation of Natural Resources, Environmental Health, Food Supply
Abstract

The eighteenth-century Malthusian prediction of population growth outstripping food production has not yet come to bear. Unprecedented agricultural land expansions since 1700, and technological innovations that began in the 1930s, have enabled more calorie production per capita than was ever available before in history. This remarkable success, however, has come at a great cost. Agriculture is a major cause of global environmental degradation. Malnutrition persists among large sections of the population, and a new epidemic of obesity is on the rise. We review both the successes and failures of the global food system, addressing ongoing debates on pathways to environmental health and food security. To deal with these challenges, a new coordinated research program blending modern breeding with agro-ecological methods is needed. We call on plant biologists to lead this effort and help steer humanity toward a safe operating space for agriculture.

Published
2018
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48. Author Correction: The uncertainty of crop yield projections is reduced by improved temperature response functions.

Author

Wang E, Martre P, Zhao Z, Ewert F, Maiorano A, Rötter RP, Kimball BA, Ottman MJ, Wall GW, White JW, Reynolds MP, Alderman PD, Aggarwal PK, Anothai J, Basso B, Biernath C, Cammarano D, Challinor AJ, De Sanctis G, Doltra J, Dumont B, Fereres E, Garcia-Vila M, Gayler S, Hoogenboom G, Hunt LA, Izaurralde RC, Jabloun M, Jones CD, Kersebaum KC, Koehler AK, Liu L, Müller C, Kumar SN, Nendel C, O'Leary G, Olesen JE, Palosuo T, Priesack E, Rezaei EE, Ripoche D, Ruane AC, Semenov MA, Shcherbak I, Stöckle C, Stratonovitch P, Streck T, Supit I, Tao F, Thorburn P, Waha K, Wallach D, Wang Z, Wolf J, Zhu Y, and Asseng S

Abstract

Nature Plants 3, 17102 (2017); published online 17 July 2017; corrected online 27 September 2017.

Published
2017
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49. Erratum: The uncertainty of crop yield projections is reduced by improved temperature response functions.

Author

Wang E, Martre P, Zhao Z, Ewert F, Maiorano A, Rötter RP, Kimball BA, Ottman MJ, Wall GW, White JW, Reynolds MP, Alderman PD, Aggarwal PK, Anothai J, Basso B, Biernath C, Cammarano D, Challinor AJ, De Sanctis G, Doltra J, Fereres E, Garcia-Vila M, Gayler S, Hoogenboom G, Hunt LA, Izaurralde RC, Jabloun M, Jones CD, Kersebaum KC, Koehler AK, Liu L, Müller C, Kumar SN, Nendel C, O'Leary G, Olesen JE, Palosuo T, Priesack E, Rezaei EE, Ripoche D, Ruane AC, Semenov MA, Shcherbak I, Stöckle C, Stratonovitch P, Streck T, Supit I, Tao F, Thorburn P, Waha K, Wallach D, Wang Z, Wolf J, Zhu Y, and Asseng S

Abstract

This corrects the article DOI: 10.1038/nplants.2017.102.

Published
2017
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50. The uncertainty of crop yield projections is reduced by improved temperature response functions.

Author

Wang E, Martre P, Zhao Z, Ewert F, Maiorano A, Rötter RP, Kimball BA, Ottman MJ, Wall GW, White JW, Reynolds MP, Alderman PD, Aggarwal PK, Anothai J, Basso B, Biernath C, Cammarano D, Challinor AJ, De Sanctis G, Doltra J, Dumont, Fereres E, Garcia-Vila M, Gayler S, Hoogenboom G, Hunt LA, Izaurralde RC, Jabloun M, Jones CD, Kersebaum KC, Koehler AK, Liu L, Müller C, Naresh Kumar S, Nendel C, O'Leary G, Olesen JE, Palosuo T, Priesack E, Eyshi Rezaei E, Ripoche D, Ruane AC, Semenov MA, Shcherbak I, Stöckle C, Stratonovitch P, Streck T, Supit I, Tao F, Thorburn P, Waha K, Wallach D, Wang Z, Wolf J, Zhu Y, and Asseng S

Subjects
Computer Simulation, Models, Biological, Agriculture, Crops, Agricultural growth & development, Temperature
Abstract

Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections.

Published
2017
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