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1. 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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2. Soil Organic Carbon and Nitrogen Feedbacks on Crop Yields Under Climate Change

Author

Basso, B, Dumont, B, Maestrini, B, Shcherbak, I, Robertson, G. P, Porter, J. R, Smith, P, Paustian, K, Grace, P. R, Asseng, S, Bassu, S, Biernath, C, Boote, K. J, Cammarano, D, Sanctis, G. De, Durand, J.-L, Ewert, F, Gayler, S, Hyndman, D. W, Kent, J, Martre, P, Nendel, C, Priesack, E, Ripoche, D, Ruane, A. C, Sharp, J, Thorburn, P. J, Hatfield, J. L, Jones, J. W, and Rosenzweig, C

Subjects
Earth Resources And Remote Sensing
Abstract

A critical omission from climate change impact studies on crop yield is the interaction between soil organic carbon (SOC), nitrogen (N) availability, and carbon dioxide (CO2). We used a multimodel ensemble to predict the effects of SOC and N under different scenarios of temperatures and CO2 concentrations on maize (Zea mays L.) and wheat (Triticum aestivum L.) yield in eight sites across the world. We found that including feedbacks from SOC and N losses due to increased temperatures would reduce yields by 13% in wheat and 19% in maize for a 3°C rise temperature with no adaptation practices. These losses correspond to an additional 4.5% (+3°C) when compared to crop yield reductions attributed to temperature increase alone. Future CO2 increase to 540 ppm would partially compensate losses by 80% for both maize and wheat at +3°C, and by 35% for wheat and 20% for maize at +6°C, relative to the baseline CO2 scenario.

Published
2018
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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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14. Comparison of N2O Emissions from Soils at Three Temperate Agricultural Sites

Author

Frolking, S. E, Moiser, A. R, Ojima, D. S, Li, C, Parton, W. J, Potter, C. S, Priesack, E, Stenger, R, Haberbosch, C, Dorsch, P, and Peterson, David L

Subjects
Environment Pollution
Abstract

Nitrous oxide (N2O) flux simulations by four models were compared with year-round field measurements from five temperate agricultural sites in three countries. The field sites included an unfertilized, semi-arid rangeland with low N2O fluxes in eastern Colorado, USA; two fertilizer treatments (urea and nitrate) on a fertilized grass ley cut for silage in Scotland; and two fertilized, cultivated crop fields in Germany where N2O loss during the winter was quite high. The models used were daily trace gas versions of the CENTURY model, DNDC, ExpertN, and the NASA-Ames version of the CASA model. These models included similar components (soil physics, decomposition, plant growth, and nitrogen transformations), but in some cases used very different algorithms for these processes. All models generated similar results for the general cycling of nitrogen through the agro-ecosystems, but simulated nitrogen trace gas fluxes were quite different. In most cases the simulated N20 fluxes were within a factor of about 2 of the observed annual fluxes, but even when models produced similar N2O fluxes they often produced very different estimates of gaseous N loss as nitric oxide (NO), dinitrogen (N2), and ammonia (NH3). Accurate simulation of soil moisture appears to be a key requirement for reliable simulation of N2O emissions. All models simulated the general pattern of low background fluxes with high fluxes following fertilization at the Scottish sites, but they could not (or were not designed to) accurately capture the observed effects of different fertilizer types on N2O flux. None of the models were able to reliably generate large pulses of N2O during brief winter thaws that were observed at the two German sites. All models except DNDC simulated very low N2O fluxes for the dry site in Colorado. The US Trace Gas Network (TRAGNET) has provided a mechanism for this model and site intercomparison. Additional intercomparisons are needed with these and other models and additional data sets; these should include both tropical agro-ecosystems and new agricultural management techniques designed for sustainability.

Published
1997

15. Modelling nitrous oxide emission of high input maize crop systems

Author

Bassu, S., Acutis, M., Amaducci, S., Argenti, G., Baranowski, P., Berti, Antonio, Bertora, C., Bindi, M., Bosco, S., Brilli, L., Cammarano, D., Doro, L., Ferrise, R., Grignani, C., Harrison, M. T., Iocola, I., Krzyszczak, J., Lai, R., Morari, Francesco, Mula, L., Nendel, C., Oygarden, L., Perego, A. ., Priesack, E., Pulina, A., Stella, T., Wu, L., Zubik, M., and Roggero, P. P.

Published
2017

17. 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

18. 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

19. Crop yields, soil organic carbon and soil nitrogen content change under climate change

Author

Dumont, B., Basso, B., Shcherbak, I., Asseng, S., Bassu, Simona, Boote, K., Cammarano, D., De Sanctis, Giovanni, Durand, Jean-Louis, Ewert, F., Gayler, S., Grace, P., Grant, R., Kent, J., Martre, Pierre, Nendel, C., Paustian, K., Priesack, E., Ripoche, Dominique, Ruane, A., Thorburn, P., Hatfield, J., Jones, J., Rosenzweig, C., Department of geological sciences, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, 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), Agronomie, Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Department of agronomy, University of Florida [Gainesville] (UF), The James Hutton Institute, Joint Research center, European Commission, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de la Recherche Agronomique (INRA), Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, WESS-Water and Earth System Science Competence Cluster, Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Institute for Future Environments, Queensland University of Technology, Natural Resource Ecology Laboratory [Fort Collins] (NREL), Colorado State University [Fort Collins] (CSU), 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), Institute of landscape systems analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Institute of Soil Ecology, Helmholtz-Zentrum München (HZM), Agroclim (AGROCLIM), National Aeronautics and Space Administration, Partenaires INRAE, Ecosystem sciences, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), United States Department of Agriculture (USDA), and Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research, Leibniz Association (ZALF). DEU.

Subjects
blé, maïs, comparaison de modèles, [SDE.MCG]Environmental Sciences/Global Changes, température, conduite de la culture, modèle continu, interaction sol plante climat, Milieux et Changements globaux, co2 atmosphérique, modèle de production, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2016

20. 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

21. 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
Full Text
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22. TERENO-SOILCan - A lysimeter-network in Germany observing soil functions influenced by climate change

Author

Pütz, T., Kiese, R., Wollschläger, U., Groh, J., Rupp, H., Zacharias, S., Priesack, E., Gerke, H.H., Gasche, R., Bens, O., Borg, E., Baessler, C., Kaiser, K., Herbrich, M., Munch, J.C., Sommer, M., Vogel, H., Vanderborght, J., and Vereecken, H.

Subjects
Weighable precision lysimeter, Controlled lower boundary, Water balance, Matter flux, Climate change, Soil processes, Plant functional diversity
Abstract

The aim of TERENO (TERrestrial ENvironmental Observatories) is to collect long-term observation data on the hydrosphere, biosphere, pedosphere, lower atmosphere and anthroposphere along multiple spatial and temporal gradients in climate sensitive regions across Germany. The lysimeter-network SOILCan was installed as a part of TERENO between March and December 2010 within the four observatories. It represents a long-term large-scale experiment to study the effects of climate and management changes in terrestrial ecosystems, with particular focus on the impact of these changes on water, energy and matter fluxes into groundwater and atmosphere. SOILCan primarily focuses on soil hydrology, the carbon and nutrient cycle and plant species diversity. Time series measurements of states and fluxes at high spatial and temporal resolution in the soil and biosphere are combined with remote sensing information for the development and calibration of process-based models simulating impacts of climate change in soil processes at field to regional scale. Within the framework of SOILCan, 132 fully automated lysimeter systems were installed at 14 highly equipped experimental field sites across the four TERENO observatories. Relevant state variables of grassland and arable ecosystems are monitored characterizing climate, hydrology and matter fluxes into the atmosphere and within the hydrosphere as well as plant species diversity. Lysimeters are either being operated at or near their original sampling location or were transferred within or between the four TERENO observatories thereby using temperature and rainfall gradients to mimic future climatic conditions (space for time), which allow measuring impacts of climate change on terrestrial ecosystems. The lysimeters are cultivated as grassland (intensive, extensive and non-used) or arable land, the latter with a standardized crop rotation of winter wheat—winter barley—winter rye—oat. This publication describes the general design of the SOILCan experiment including a comprehensive description of the pedological characteristics of the different sites and presents a few exemplary results from the first years of operation.

Published
2016

23. Importance of tree height and social position for drought stress and induced mortality

Author

Grote, R., Gessler, A., Hommel, R., Poschenrieder, W., and Priesack, E.

Subjects
fungi, food and beverages, Drought Stress
Abstract

Drought stress can trigger tree mortality but the impact depends on stress intensity (water demand and availability) and on the vulnerability of the individual. Therefore, most research focusses on the species-specific properties such as water use efficiency or hydraulic conductivity that determine vulnerability. At the ecosystem scale, however, tree properties that have been found important for drought sensitivity or resistance vary with individual size and resource availability within a forest—also within the same species. This is caused by different environmental conditions for each tree and hence different growth histories of individuals generating specific anatomical and physiological features. Individual drought stress sensitivity might thus be considerably different from stand scale sensitivity. Indeed, empirical evidence shows that drought stress impact depends on tree social position which can be defined in degrees of suppression but correlated to resource availability, stress sensitivity and stress exposure. In this review, we collect such evidence and discuss the role of microclimate and soil water distribution as well as anatomical and physiological adjustments, which might serve as foundation for better-adapted management strategies to mitigate drought stress impacts. Finally, we define model requirements aiming to capture stand-scale drought responses or management impacts related to drought stress mitigation.

Published
2016

24. 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

25. 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. Interactions of Generated Weather Raster and Soil Profiles in Simulating Adaptive Crop Management and Consequent Yields for Five Major Crops throughout a Region in Southern Germany

Author

Parker, P., Warrach-Sagi, K., Ingwersen, J., Högy, P., Troost, C., Winzemann, H. D., Priesack, E., Aurbacher, J., Deutsche Meteorologische Gesellschaft, and KlimaCampus Hamburg

Subjects
Ingenieurwissenschaften (620)
Abstract

Klimaanpassung und Mitigation The ability of bioeconomic simulation modelling to realistically predict agricultural adaptation is limited by the degree of detail in crucial model components. Model robustness must be tested before localized calibrations can be applied to regions of heterogenous environmental conditions. The agent-based model FARMACTOR was used to simulate the timing of field management actions (planting, harvest etc.) in response to environmental conditions, and consequent yields of winter wheat, barley and rapeseed, spring barley and silage maize as the predominant crops in a distinct region of Germany, by linking weather data and the crop growth simulation model EXPERT-N. The integrated models were calibrated to observed experimental data and official phenological observations and then run from 1990 to 2009, forced with climate data from ERA-interim Reanalyses data which was downscaled with the Weather and Research Forecast (WRF) model to a 12 X 12 km² grid. Variability in regional soils was replicated with 10 different soil profiles mapped at 1/25,000 scale. The nature of the forcing climate data dictates temporal aggregation for analysis, so that validity is examined by comparing mean simulated planting and harvest dates and yields to official records in the area. The mean predicted planting dates are very close to observations over the period, within a few days of observations, but show less variance. Harvest dates are accurately predicted as well, within one to two weeks, and the variances are closer to observations. Predicted winter wheat yields are well simulated in comparison to observed data, but maize yields are underestimated, while winter and spring barley and winter rapeseed yields are greater than observed district ("Landkreis") yields. The degree of variance in simulated yields is acceptable in wheat, winter barley and maize, but excessive in spring barley and winter rapeseed. Cross-sectional examination of yields shows that the different soil profiles are responsible for more yield variance than simulated weather cells in all crops. While the coupled models appear accurate in predicting crop management dates and physiological development, the inaccuracy in yields in all crops except winter wheat calls into question the reliability of the integrated models when applied, as is, outside of calibration conditions. That soil parameterization is responsible for more variance than generated weather is helpful in seeking to improve performance and encouraging in terms of the method of weather generation. Reliable extension of the coupled models to include all soils in an area together with artificial spatial climatic variability may require regionalized calibration to increase crop model stability.

Published
2015

27. Individual tree branch-level simulation of light attenuation and water flow of three F. sylvatica L. trees

Author

Bittner, S., Legner, N., Beese, F., and Priesack, E.

Abstract

A leaf stomatal conductance model was combined with a hydrological tree and soil water flow model and a spatially explicit three-dimensional canopy light model. The model was applied to single, old-growth Fagus sylvatica L. trees, and the measured daily values of stem sap flux could be reproduced with a normalized root mean square error of 0.10 for an observation period of 32days in the summer of 2009. The high temporal resolution of the model also makes it possible to simulate the diurnal dynamics of transpiration, stem sap flux, and root water uptake. We applied new data-processing algorithms to information from terrestrial laser scans to represent the canopies of the functional-structural model. The high spatial resolution of the root and branch geometry and connectivity makes the detailed modeling of the water usage of single trees possible and allows for the analysis of the interaction between single trees and the influence of the canopy light regime on the water flow inside the xylem. In addition to the laser scans of the observed trees, the model needs tree-species-specific physiological input parameters, which are easy to obtain. The model can be applied at various sites and to different tree species, allowing the up-scaling of the water usage of single trees to the total transpiration of mixed stands.

Published
2012

29. Simulating regional climate-adaptive field cropping with fuzzy logic management rules and genetic advance.

Author

PARKER, P., INGWERSEN, J., HÖGY, P., PRIESACK, E., and AURBACHER, J.

Abstract

Agriculture is a largely technical endeavour involving complicated managerial decision-making that affects crop performance. Farm-level modelling integrates crop models with agent behaviour to account for farmer decision-making and complete the representation of agricultural systems. To replicate an important part of agriculture in Central Europe a crop model was calibrated for a unique region's predominant crops: winter wheat, winter and spring barley, silage maize and winter rapeseed. Their cultivation was then simulated over multiple decades at daily resolution to test validity and stability, while adding the dimension of agent behaviour in relation to environmental and economic conditions. After validation against regional statistics, simulated future weather scenarios were used to forecast crop management and performance under anticipated global change. Farm management and crop genetics were treated as adaptive variables in the milieu of shifting climatic conditions to allow projections of agriculture in the study region into the coming decades. [ABSTRACT FROM PUBLISHER]

Published
2016
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35. Modeling Nitrous Oxide Emissions from Potato-Cropped Soil.

Author

Klier, C., Gayler, S., Haberbosch, C., Ruser, R., Stenger, R., Flessa, H., and Priesack, E.

Subjects
POTATOES, NITROUS oxide, EMISSIONS (Air pollution), MATHEMATICAL models, RUNOFF
Abstract

Intensive agricultural land use is considered to be the major source of the anthropogenic contribution to the increase in atmospheric N2O concentration during the last decades. A reduction of anthropogenic N2O emissions therefore requires a change in agricultural management practices. Mathematical models help to understand interacting processes in the N cycle and state variables affecting N2O emissions. The aim of this study was to test two modeling approaches for their ability to describe and quantify the seasonal variations of N2O fluxes in a potato (Solanum tuberosum L.)-cropped soil. Model 1 assumes a fixed N2O/N2 ratio for N2O production and neglects the transport of N2O in the soil profile; Model 2 explicitly considers N2O transport and assumes a dynamic reduction of N2O to N2 Data for model evaluation came from an experiment where N2O fluxes were monitored during the vegetation period using a closed chamber technique. Generally, both modeling approaches were able to describe the observed seasonal dynamics of N2O emissions and events of high N2O emissions due to increased denitrification activity after heavy precipitation. The inclusion of a gas transport module in the modeling approach resulted in simulated N2O emission dynamics showing a smoother transient behavior. Extremely high emission rates from the interrow soil of the potato field were underestimated by both models. The lower N2O release from the ridge soil was mainly due to better aeration because of a lower soil bulk density and lower water contents caused by lateral runoff and root water uptake. [ABSTRACT FROM AUTHOR]

Published
2011
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37. Comparison of N2O emissions from soils at three temperate agricultural sites: simulations of year-round measurements by four models.

Author

Frolking, S.E., Mosier, A.R., Ojima, D.S., Li, C., Parton, W.J., Potter, C.S., Priesack, E., Stenger, R., Haberbosch, C., Dörsch, P., Flessa, H., and Smith, K.A.

Abstract

Nitrous oxide (N2O) flux simulations by four models were compared with year-round field measurements from five temperate agricultural sites in three countries. The field sites included an unfertilized, semi-arid rangeland with low N2O fluxes in eastern Colorado, USA; two fertilizer treatments (urea and nitrate) on a fertilized grass ley cut for silage in Scotland; and two fertilized, cultivated crop fields in Germany where N2O loss during the winter was quite high. The models used were daily trace gas versions of the CENTURY model, DNDC, ExpertN, and the NASA-Ames version of the CASA model. These models included similar components (soil physics, decomposition, plant growth, and nitrogen transformations), but in some cases used very different algorithms for these processes. All models generated similar results for the general cycling of nitrogen through the agro-ecosystems, but simulated nitrogen trace gas fluxes were quite different. In most cases the simulated N2O fluxes were within a factor of about 2 of the observed annual fluxes, but even when models produced similar N2O fluxes they often produced very different estimates of gaseous N loss as nitric oxide (NO), dinitrogen (N2), and ammonia (NH3). Accurate simulation of soil moisture appears to be a key requirement for reliable simulation of N2O emissions. All models simulated the general pattern of low background fluxes with high fluxes following fertilization at the Scottish sites, but they could not (or were not designed to) accurately capture the observed effects of different fertilizer types on N2O flux. None of the models were able to reliably generate large pulses of N2O during brief winter thaws that were observed at the two German sites. All models except DNDC simulated very low N2O fluxes for the dry site in Colorado. The US Trace Gas Network (TRAGNET) has provided a mechanism for this model and site intercomparison. Additional intercomparisons are needed with these and other models and additional data sets; these should include both tropical agro-ecosystems and new agricultural management techniques designed for sustainability. [ABSTRACT FROM AUTHOR]

Published
1998
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38. Closed-Form Expression for the Multi-Modal Unsaturated Conductivity Function.

Author

Priesack, E. and Durner, W.

Subjects
SOIL permeability, SOILS, POROSITY, HYDRAULICS, ZONE of aeration
Abstract

In this note we derive a closed-form expression representing the hydraulic conductivity function for soils with a multi-modal pore size distribution. By combining the multi-modal representation of the retention function of Durner with the conductivity representation model of Mualem and following van Genuchten, we derive a simple analytical expression for the conductivity of soils with heterogeneous pore systems. Examples for the representation of bi-modal and tri-modal retention and corresponding conductivity curves demonstrate the applicability of the simple analytical expressions. It is concluded that the usefulness of the multi-modal representation of retention functions is increased by providing an analytical expression to directly calculate unsaturated hydraulic conductivities. [ABSTRACT FROM AUTHOR]

Published
2006
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45. The chaos in calibrating crop models: Lessons learned from a multi-model calibration exercise

Author

Wallach, D, Palosuo, T, Thorburn, P, Hochman, Z, Gourdain, E, Andrianasolo, F, Asseng, S, Basso, B, Buis, S, Crout, N, Dibari, C, Dumont, B, Ferrise, R, Gaiser, T, Garcia, C, Gayler, S, Ghahramani, A, Hiremath, S, Hoek, S, Horan, H, Hoogenboom, G, Huang, M, Jabloun, M, Jansson, P-E, Jing, Q, Justes, E, Kersebaum, KC, Klosterhalfen, A, Launay, M, Lewan, E, Luo, Q, Maestrini, B, Mielenz, H, Moriondo, M, Nariman Zadeh, H, Padovan, G, Olesen, JE, Poyda, A, Priesack, E, Pullens, JWM, Qian, B, Schütze, N, Shelia, V, Souissi, A, Specka, X, Srivastava, AK, Stella, T, Streck, T, Trombi, G, Wallor, E, Wang, J, Weber, TKD, Weihermüller, L, de Wit, A, Wöhling, T, Xiao, L, Zhao, C, Zhu, Y, and Seidel, SJ

Published
2021
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46. Multi-model evaluation of phenology prediction for wheat in Australia

Author

Wallach, D, Palosuo, T, Thorburn, P, Hochman, Z, Andrianasolo, F, Asseng, S, Basso, B, Buis, S, Crout, N, Dumont, B, Ferrise, R, Gaiser, T, Gayler, S, Hiremath, S, Hoek, S, Horan, H, Hoogenboom, G, Huang, M, Jabloun, M, Jansson, P-E, Jing, Q, Justes, E, Kersebaum, KC, Launay, M, Lewan, E, Luo, Q, Maestrini, B, Moriondo, M, Olesen, JE, Padovan, G, Poyda, A, Priesack, E, Pullens, JWM, Qian, B, Schütze, N, Shelia, V, Souissi, A, Specka, X, Kumar Srivastava, A, Stella, T, Streck, T, Trombi, G, Wallor, E, Wang, J, Weber, TKD, Weihermüller, L, de Wit, A, Wöhling, T, Xiao, L, Zhao, C, Zhu, Y, and Seidel, SJ

Published
2021
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47. How well do crop modeling groups predict wheat phenology, given calibration data from the target population?

Author

Wallach, D, Palosuo, T, Thorburn, P, Gourdain, E, Asseng, S, Basso, B, Buis, S, Crout, N, Dibari, C, Dumont, B, Ferrise, R, Gaiser, T, Garcia, C, Gayler, S, Ghahramani, A, Hochman, Z, Hoek, S, Hoogenboom, G, Horan, H, Huang, M, Jabloun, M, Jing, Q, Justes, E, Kersebaum, KC, Klosterhalfen, A, Launay, M, Luo, Q, Maestrini, B, Mielenz, H, Moriondo, M, Nariman Zadeh, H, Olesen, JE, Poyda, A, Priesack, E, Pullens, JWM, Qian, B, Schütze, N, Shelia, V, Souissi, A, Specka, X, Srivastava, AK, Stella, T, Streck, T, Trombi, G, Wallor, E, Wang, J, Weber, TKD, Weihermüller, L, de Wit, A, Wöhling, T, Xiao, L, Zhao, C, Zhu, Y, and Seidel, SJ

Published
2021
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48. Simulation of winter wheat response to variable sowing dates and densities in a high-yielding environment.

Author

Dueri S, Brown H, Asseng S, Ewert F, Webber H, George M, Craigie R, Guarin JR, Pequeno DNL, Stella T, Ahmed M, Alderman PD, Basso B, Berger AG, Mujica GB, Cammarano D, Chen Y, Dumont B, Rezaei EE, Fereres E, Ferrise R, Gaiser T, Gao Y, Garcia-Vila M, Gayler S, Hochman Z, Hoogenboom G, Kersebaum KC, Nendel C, Olesen JE, Padovan G, Palosuo T, Priesack E, Pullens JWM, Rodríguez A, Rötter RP, Ramos MR, Semenov MA, Senapati N, Siebert S, Srivastava AK, Stöckle C, Supit I, Tao F, Thorburn P, Wang E, Weber TKD, Xiao L, Zhao C, Zhao J, Zhao Z, Zhu Y, and Martre P

Subjects
Biomass, Seasons, Temperature, Climate Change, Triticum
Abstract

Crop multi-model ensembles (MME) have proven to be effective in increasing the accuracy of simulations in modelling experiments. However, the ability of MME to capture crop responses to changes in sowing dates and densities has not yet been investigated. These management interventions are some of the main levers for adapting cropping systems to climate change. Here, we explore the performance of a MME of 29 wheat crop models to predict the effect of changing sowing dates and rates on yield and yield components, on two sites located in a high-yielding environment in New Zealand. The experiment was conducted for 6 years and provided 50 combinations of sowing date, sowing density and growing season. We show that the MME simulates seasonal growth of wheat well under standard sowing conditions, but fails under early sowing and high sowing rates. The comparison between observed and simulated in-season fraction of intercepted photosynthetically active radiation (FIPAR) for early sown wheat shows that the MME does not capture the decrease of crop above ground biomass during winter months due to senescence. Models need to better account for tiller competition for light, nutrients, and water during vegetative growth, and early tiller senescence and tiller mortality, which are exacerbated by early sowing, high sowing densities, and warmer winter temperatures., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Society for Experimental Biology.)

Published
2022
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49. Modelling climate change impacts on maize yields under low nitrogen input conditions in sub-Saharan Africa.

Author

Falconnier GN, Corbeels M, Boote KJ, Affholder F, Adam M, MacCarthy DS, Ruane AC, Nendel C, Whitbread AM, Justes É, Ahuja LR, Akinseye FM, Alou IN, Amouzou KA, Anapalli SS, Baron C, Basso B, Baudron F, Bertuzzi P, Challinor AJ, Chen Y, Deryng D, Elsayed ML, Faye B, Gaiser T, Galdos M, Gayler S, Gerardeaux E, Giner M, Grant B, Hoogenboom G, Ibrahim ES, Kamali B, Kersebaum KC, Kim SH, van der Laan M, Leroux L, Lizaso JI, Maestrini B, Meier EA, Mequanint F, Ndoli A, Porter CH, Priesack E, Ripoche D, Sida TS, Singh U, Smith WN, Srivastava A, Sinha S, Tao F, Thorburn PJ, Timlin D, Traore B, Twine T, and Webber H

Subjects
Fertilizers, Mali, Nitrogen, Climate Change, Zea mays
Abstract

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

Published
2020
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50. Photosynthesis in a Changing Global Climate: Scaling Up and Scaling Down in Crops.

Author

Baslam M, Mitsui T, Hodges M, Priesack E, Herritt MT, Aranjuelo I, and Sanz-Sáez Á

Abstract

Photosynthesis is the major process leading to primary production in the Biosphere. There is a total of 7000bn tons of CO 2 in the atmosphere and photosynthesis fixes more than 100bn tons annually. The CO 2 assimilated by the photosynthetic apparatus is the basis of crop production and, therefore, of animal and human food. This has led to a renewed interest in photosynthesis as a target to increase plant production and there is now increasing evidence showing that the strategy of improving photosynthetic traits can increase plant yield. However, photosynthesis and the photosynthetic apparatus are both conditioned by environmental variables such as water availability, temperature, [CO 2 ], salinity, and ozone. The "omics" revolution has allowed a better understanding of the genetic mechanisms regulating stress responses including the identification of genes and proteins involved in the regulation, acclimation, and adaptation of processes that impact photosynthesis. The development of novel non-destructive high-throughput phenotyping techniques has been important to monitor crop photosynthetic responses to changing environmental conditions. This wealth of data is being incorporated into new modeling algorithms to predict plant growth and development under specific environmental constraints. This review gives a multi-perspective description of the impact of changing environmental conditions on photosynthetic performance and consequently plant growth by briefly highlighting how major technological advances including omics, high-throughput photosynthetic measurements, metabolic engineering, and whole plant photosynthetic modeling have helped to improve our understanding of how the photosynthetic machinery can be modified by different abiotic stresses and thus impact crop production., (Copyright © 2020 Baslam, Mitsui, Hodges, Priesack, Herritt, Aranjuelo and Sanz-Sáez.)

Published
2020
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