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178 results on '"Nendel, C."'

1. Expected effects of climate change on the soil organic matter content related to contrasting agricultural management practices based on a crop model ensemble for locations in Czechia

Author

Pohanková, E., Hlavinka, P., Kersebaum, K.C., Nendel, C., Rodríguez, A., Balek, J., Dubrovský, M., Gobin, A., Hoogenboom, G., Moriondo, M., Olesen, E.J., Rötter, R., Ruiz-Ramos, M., Shelia, V., Stella, T., Hoffmann, M.P., Takáč, J., Eitzinger, J., Dibari, C., Ferrise, R., Bohuslav, J., Bláhová, M., and Trnka, M.

Published
2024
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2. Assessing the impact on crop modelling of multi- and uni-variate climate model bias adjustments

Author

Galmarini, S., Solazzo, E., Ferrise, R., Srivastava, A. Kumar, Ahmed, M., Asseng, S., Cannon, A.J., Dentener, F., De Sanctis, G., Gaiser, T., Gao, Y., Gayler, S., Gutierrez, J.M., Hoogenboom, G., Iturbide, M., Jury, M., Lange, S., Loukos, H., Maraun, D., Moriondo, M., McGinnis, S., Nendel, C., Padovan, G., Riccio, A., Ripoche, D., Stockle, C.O., Supit, I., Thao, S., Trombi, G., Vrac, M., Weber, T.K.D., and Zhao, C.

Published
2024
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3. Implications of crop model ensemble size and composition for estimates of adaptation effects and agreement of recommendations

Author

Rodríguez, A., Ruiz-Ramos, M., Palosuo, T., Carter, T.R., Fronzek, S., Lorite, I.J., Ferrise, R., Pirttioja, N., Bindi, M., Baranowski, P., Buis, S., Cammarano, D., Chen, Y., Dumont, B., Ewert, F., Gaiser, T., Hlavinka, P., Hoffmann, H., Höhn, J.G., Jurecka, F., Kersebaum, K.C., Krzyszczak, J., Lana, M., Mechiche-Alami, A., Minet, J., Montesino, M., Nendel, C., Porter, J.R., Ruget, F., Semenov, M.A., Steinmetz, Z., Stratonovitch, P., Supit, I., Tao, F., Trnka, M., de Wit, A., and Rötter, R.P.

Published
2019
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5. 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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9. 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

10. Variability of effects of spatial climate data aggregation on regional yield simulation by crop models

Author

Hoffmann, H., Zhao, G., van Bussel, L. G. J., Enders, A., Specka, X., Sosa, C., Yeluripati, J., Tao, F., Constantin, J., Raynal, H., Teixeira, E., Grosz, B., Doro, L., Zhao, Z., Wang, E., Nendel, C., Kersebaum, K. C., Haas, E., Kiese, R., Klatt, S., Eckersten, H., Vanuytrecht, E., Kuhnert, M., Lewan, E., Rötter, R., Roggero, P. P., Wallach, D., Cammarano, D., Asseng, S., Krauss, G., Siebert, S., Gaiser, T., and Ewert, F.

Published
2015

12. 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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13. Modeling intra- and interannual variability of BVOC emissions from maize, oil-seed rape, and ryegrass

Author

Havermann, F., Ghirardo, A., Schnitzler, J.-P., Nendel, C., Hoffmann, M., Kraus, D., and Grote, R.

Subjects
Biogenic Volatile Organic Compounds, Brassica Napus, Lolium Multiflorum, Plant Ontogenetic Stage, Process-based Modeling, Zea Mays
Abstract

Air chemistry is affected by the emission of biogenic volatile organic compounds (BVOCs), which originate from almost all plants in varying qualities and quantities. They also vary widely among different crops, an aspect that has been largely neglected in emission inventories. In particular, bioenergy-related species can emit mixtures of highly reactive compounds that have received little attention so far. For such species, long-term field observations of BVOC exchange from relevant crops covering different phenological phases are scarcely available. Therefore, we measured and modeled the emission of three prominent European bioenergy crops (maize, ryegrass, and oil-seed rape) for full rotations in north-eastern Germany. Using a proton transfer reaction–mass spectrometer combined with automatically moving large canopy chambers, we were able to quantify the characteristic seasonal BVOC flux dynamics of each crop species. The measured BVOC fluxes were used to parameterize and evaluate the BVOC emission module (JJv) of the physiology-oriented LandscapeDNDC model, which was enhanced to cover de novo emissions as well as those from plant storage pools. Parameters are defined for each compound individually. The model is used for simulating total compound-specific reactivity over several years and also to evaluate the importance of these emissions for air chemistry. We can demonstrate substantial differences between the investigated crops with oil-seed rape having 37-fold higher total annual emissions than maize. However, due to a higher chemical reactivity of the emitted blend in maize, potential impacts on atmospheric OH-chemistry are only 6-fold higher.

Published
2022

16. 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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17. Performance of 13 crop simulation models and their ensemble for simulating four field crops in Central Europe

Author

Kostková, Markéta, Hlavinka, Petr, Pohanková, Eva, Kersebaum, K. C., Nendel, C., Gobin, A., Olesen, J. E., Ferrise, R., Dibari, C., Takáč, J., Topaj, A., Medvedev, S., Hoffmann, M. P., Stella, T., Balek, Jan, Ruiz-Ramos, M., Rodríguez, A., Hoogenboom, G., Shelia, V., Ventrella, D., Giglio, L., Sharif, B., Oztürk, I., Rötter, R. P., Balkovič, J., Skalský, R., Moriondo, M., Thaler, S., Žalud, Zdeněk, Trnka, Miroslav, Kostková, Markéta, Hlavinka, Petr, Pohanková, Eva, Kersebaum, K. C., Nendel, C., Gobin, A., Olesen, J. E., Ferrise, R., Dibari, C., Takáč, J., Topaj, A., Medvedev, S., Hoffmann, M. P., Stella, T., Balek, Jan, Ruiz-Ramos, M., Rodríguez, A., Hoogenboom, G., Shelia, V., Ventrella, D., Giglio, L., Sharif, B., Oztürk, I., Rötter, R. P., Balkovič, J., Skalský, R., Moriondo, M., Thaler, S., Žalud, Zdeněk, and Trnka, Miroslav

Abstract

The main aim of the current study was to present the abilities of widely used crop models to simulate four different field crops (winter wheat, spring barley, silage maize and winter oilseed rape). The 13 models were tested under Central European conditions represented by three locations in the Czech Republic, selected using temperature and precipitation gradients for the target crops in this region. Based on observed crop phenology and yield from 1991 to 2010, performances of individual models and their ensemble were analyzed. Modelling of anthesis and maturity was generally best simulated by the ensemble median (EnsMED) compared to the ensemble mean and individual models. The yield was better simulated by the best models than estimated by an ensemble. Higher accuracy was achieved for spring crops, with the best results for silage maize, while the lowest accuracy was for winter oilseed rape according to the index of agreement (IA). Based on EnsMED, the root mean square errors (RMSEs) for yield was 1365 kg/ha for winter wheat, 1105 kg/ha for spring barley, 1861 kg/ha for silage maize and 969 kg/ha for winter oilseed rape. The AQUACROP and EPIC models performed best in terms of spread around the line of best fit (RMSE, IA). In some cases, the individual models failed. For crop rotation simulations, only models with reasonable accuracy (i.e. without failures) across all included crops within the target environment should be selected. Application crop models ensemble is one way to increase the accuracy of predictions, but lower variability of ensemble outputs was confirmed., info:eu-repo/semantics/openAccess

Published
2021

18. Performance of 13 crop simulation models and their ensemble for simulating four field crops in Central Europe

Author

Kostková, M., Hlavinka, P., Pohanková, E., Kersebaum, K. C., Nendel, C., Gobin, A., Olesen, J. E., Ferrise, R., Dibari, C., Takáč, J., Topaj, A., Medvedev, S., Hoffmann, M. P., Stella, T., Balek, J., Ruiz-Ramos, M., Rodríguez, A., Hoogenboom, G., Shelia, V., Ventrella, D., Giglio, L., Sharif, B., Oztürk, I., Rötter, R. P., Balkovič, J., Skalský, R., Moriondo, M., Thaler, S., Žalud, Z., Trnka, M., Kostková, M., Hlavinka, P., Pohanková, E., Kersebaum, K. C., Nendel, C., Gobin, A., Olesen, J. E., Ferrise, R., Dibari, C., Takáč, J., Topaj, A., Medvedev, S., Hoffmann, M. P., Stella, T., Balek, J., Ruiz-Ramos, M., Rodríguez, A., Hoogenboom, G., Shelia, V., Ventrella, D., Giglio, L., Sharif, B., Oztürk, I., Rötter, R. P., Balkovič, J., Skalský, R., Moriondo, M., Thaler, S., Žalud, Z., and Trnka, M.

Abstract

The main aim of the current study was to present the abilities of widely used crop models to simulate four different field crops (winter wheat, spring barley, silage maize and winter oilseed rape). The 13 models were tested under Central European conditions represented by three locations in the Czech Republic, selected using temperature and precipitation gradients for the target crops in this region. Based on observed crop phenology and yield from 1991 to 2010, performances of individual models and their ensemble were analyzed. Modelling of anthesis and maturity was generally best simulated by the ensemble median (EnsMED) compared to the ensemble mean and individual models. The yield was better simulated by the best models than estimated by an ensemble. Higher accuracy was achieved for spring crops, with the best results for silage maize, while the lowest accuracy was for winter oilseed rape according to the index of agreement (IA). Based on EnsMED, the root mean square errors (RMSEs) for yield was 1365 kg/ha for winter wheat, 1105 kg/ha for spring barley, 1861 kg/ha for silage maize and 969 kg/ha for winter oilseed rape. The AQUACROP and EPIC models performed best in terms of spread around the line of best fit (RMSE, IA). In some cases, the individual models failed. For crop rotation simulations, only models with reasonable accuracy (i.e. without failures) across all included crops within the target environment should be selected. Application crop models ensemble is one way to increase the accuracy of predictions, but lower variability of ensemble outputs was confirmed.

Published
2021

19. Yield Response of an Ensemble of Potato Crop Models to Elevated CO2 in Continental Europe

Author

Fleischer, D., Condori, B., Barreda, C., Berghuijs, H.N.C., Bindi, M., Boote, K., Craigon, J., van Evert, Frits, Fangmeier, A., Ferrise, R., Gayler, S., Hoogenboom, G., Merante, P., Nendel, C., Ninanya, J., Pleijel, H., Raes, D., Ramirez, D.A., Raymundo, R., Reidsma, P., Silva, J.V., Stöckle, C.O., Supit, Iwan, Stella, T., Vandermeiren, K., van Oort, Pepijn, Vanuytrecht, E., Vorne, V., Wolf, Joost, Fleischer, D., Condori, B., Barreda, C., Berghuijs, H.N.C., Bindi, M., Boote, K., Craigon, J., van Evert, Frits, Fangmeier, A., Ferrise, R., Gayler, S., Hoogenboom, G., Merante, P., Nendel, C., Ninanya, J., Pleijel, H., Raes, D., Ramirez, D.A., Raymundo, R., Reidsma, P., Silva, J.V., Stöckle, C.O., Supit, Iwan, Stella, T., Vandermeiren, K., van Oort, Pepijn, Vanuytrecht, E., Vorne, V., and Wolf, Joost

Abstract

A multi-model inter-comparison study was conducted to evaluate the performance of ten potato crop models to accurately predict potato yield in response to elevated CO2 (Ce) when calibrated with ambient CO2 data (Ca). Experimental data from seven open-top chambers (OTC) and free-air−CO2-enrichment (FACE) facilities across continental Europe were used. Model ensemble percent errors averaged over all datasets for simulated yields were 26.5 % for Ca and 27.2 % Ce data. Metrics such as Wilmott’s index of agreement (IA) and root mean square relative error (RMSRE) ranged broadly among individual models and locations, such that four of the ten models outperformed the median or mean of the ensemble for about half of the Ce datasets. These top performing models were representative of three different model structural groups, including radiation use efficiency, transpiration efficiency, or leaf-level based approaches. Relative response to an increase in CO2 was more accurately modeled than absolute yield responses when averaged across all locations, and within 3.3 kg ppm−1 (or 5%) of observed values. Specific targets in the model structure needed for improvement were not identified due to large and inconsistent variation in the accuracy of yield predictions across locations. However, models with the lowest calibration errors tended to be top performers for Ce predictions as well. Such results suggest calibration is at least as important as model structure. Where possible, modelers using potato models to estimate Ce responses should use Ce calibration data to improve confidence in such predictions.

Published
2021

21. Ensemble modelling, uncertainty and robust predictions of organic carbon in long‐term bare‐fallow soils

Author

Farina, R., Sándor, R., Abdalla, M., Álvaro‐Fuentes, J., Bechini, L., Bolinder, M.A., Brilli, L., Chenu, C., Clivot, H., De Antoni Migliorati, M., Di Bene, C., Dorich, C.D., Ehrhardt, F., Ferchaud, F., Fitton, N., Francaviglia, R., Franko, Uwe, Giltrap, D.L., Grant, B.B., Guenet, B., Harrison, M.T., Kirschbaum, M.U.F., Kuka, K., Kulmala, L., Liski, J., McGrath, M.J., Meier, E., Menichetti, L., Moyano, F., Nendel, C., Recous, S., Reibold, N., Shepherd, A., Smith, W.N., Smith, P., Soussana, J.-F., Stella, T., Taghizadeh‐Toosi, A., Tsutskikh, E., Bellocchi, G., Farina, R., Sándor, R., Abdalla, M., Álvaro‐Fuentes, J., Bechini, L., Bolinder, M.A., Brilli, L., Chenu, C., Clivot, H., De Antoni Migliorati, M., Di Bene, C., Dorich, C.D., Ehrhardt, F., Ferchaud, F., Fitton, N., Francaviglia, R., Franko, Uwe, Giltrap, D.L., Grant, B.B., Guenet, B., Harrison, M.T., Kirschbaum, M.U.F., Kuka, K., Kulmala, L., Liski, J., McGrath, M.J., Meier, E., Menichetti, L., Moyano, F., Nendel, C., Recous, S., Reibold, N., Shepherd, A., Smith, W.N., Smith, P., Soussana, J.-F., Stella, T., Taghizadeh‐Toosi, A., Tsutskikh, E., and Bellocchi, G.

Abstract

Simulation models represent soil organic carbon (SOC) dynamics in global carbon (C) cycle scenarios to support climate‐change studies. It is imperative to increase confidence in long‐term predictions of SOC dynamics by reducing the uncertainty in model estimates. We evaluated SOC simulated from an ensemble of 26 process‐based C models by comparing simulations to experimental data from seven long‐term bare‐fallow (vegetation‐free) plots at six sites: Denmark (two sites), France, Russia, Sweden and the United Kingdom. The decay of SOC in these plots has been monitored for decades since the last inputs of plant material, providing the opportunity to test decomposition without the continuous input of new organic material. The models were run independently over multi‐year simulation periods (from 28 to 80 years) in a blind test with no calibration (Bln) and with the following three calibration scenarios, each providing different levels of information and/or allowing different levels of model fitting: (a) calibrating decomposition parameters separately at each experimental site (Spe); (b) using a generic, knowledge‐based, parameterization applicable in the Central European region (Gen); and (c) using a combination of both (a) and (b) strategies (Mix). We addressed uncertainties from different modelling approaches with or without spin‐up initialization of SOC. Changes in the multi‐model median (MMM) of SOC were used as descriptors of the ensemble performance. On average across sites, Gen proved adequate in describing changes in SOC, with MMM equal to average SOC (and standard deviation) of 39.2 (±15.5) Mg C/ha compared to the observed mean of 36.0 (±19.7) Mg C/ha (last observed year), indicating sufficiently reliable SOC estimates. Moving to Mix (37.5 ± 16.7 Mg C/ha) and Spe (36.8 ± 19.8 Mg C/ha) provided only marginal gains in accuracy, but modellers would

Published
2020

22. Performance of 13 crop simulation models and their ensemble for simulating four field crops in Central Europe

Author

Kostková, M., primary, Hlavinka, P., additional, Pohanková, E., additional, Kersebaum, K. C., additional, Nendel, C., additional, Gobin, A., additional, Olesen, J. E., additional, Ferrise, R., additional, Dibari, C., additional, Takáč, J., additional, Topaj, A., additional, Medvedev, S., additional, Hoffmann, M. P., additional, Stella, T., additional, Balek, J., additional, Ruiz-Ramos, M., additional, Rodríguez, A., additional, Hoogenboom, G., additional, Shelia, V., additional, Ventrella, D., additional, Giglio, L., additional, Sharif, B., additional, Oztürk, I., additional, Rötter, R. P., additional, Balkovič, J., additional, Skalský, R., additional, Moriondo, M., additional, Thaler, S., additional, Žalud, Z., additional, and Trnka, M., additional

Published
2021
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23. Multi-metric evaluation of an ensemble of biogeochemical models for the estimation of organic carbon content in long-term bare fallow soils

Author

Bellocchi, Gianni, Farina, Roberta, Ehrhardt, Fiona, Chenu, Claire, Soussana, Jean-François, Abd-Alla, M., Álvaro-Fuentes, Jorge, Brilli, Lorenzo, CLIVOT, HUGUES, De Antoni Migliorati, M., Di Bene, Claudia, Dorich, Chris, Ferchaud, Fabien, Fitton, N., Francaviglia, Rosa, Franko, Uwe, Giltrap, D., Grant, B., GUENET, B., Harrison, Matthew T., Kirschbaum, Miko U F, Kulmala, Liisa, Kuka, Katrin, Liski, L., Meier, E., Menichetti, Lorenzo, Moyano, F., Nendel, C., Smith, A.W., Taghizadeh-Toosi, Arezoo, Tsutskikhr, E., Recous, Sylvie, Unité Mixte de Recherche sur l'Ecosystème Prairial - UMR (UREP), Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS), Collège de Direction (CODIR), Institut National de la Recherche Agronomique (INRA), Laboratoire Agronomie et Environnement - Antenne Colmar (LAE-Colmar ), Laboratoire Agronomie et Environnement (LAE), Institut National de la Recherche Agronomique (INRA)-Université de Lorraine (UL)-Institut National de la Recherche Agronomique (INRA)-Université de Lorraine (UL), Agroressources et Impacts environnementaux (AgroImpact), Fractionnement des AgroRessources et Environnement (FARE), Université de Reims Champagne-Ardenne (URCA)-Institut National de la Recherche Agronomique (INRA), and CLIMSOC

Subjects
carbone organique, jachères nues, jachère, [SDE.MCG]Environmental Sciences/Global Changes, Milieux et Changements globaux, ComputingMilieux_MISCELLANEOUS, carbon organique, modélisation
Abstract

National audience

Published
2019

24. 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

25. 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

26. 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

27. Sensitivity of European wheat to extreme weather

Author

Mäkinen, H, Kaseva, J, Trnka, M, Balek, J, Kersebaum, KC, Nendel, C, Gobin, A, Olesen, Jørgen Eivind, Bindi, M, Ferrise, R, Moriondo, M, Rodriguez, A, Ruiz-Ramos, M, Takác, J, Bezák, P., Ventrella, Domenico, Ruget, F, and Kahiluoto, H

Subjects
climate change, European wheat, weather, extreme, yield response, Cultivar, klim
Published
2018

28. Diverging importance of drought stress for maize and winter wheat in Europe

Author

Webber H., Ewert F., Olesen J.E., Müller C., Fronzek S., Ruane A.C., Bourgault M., Martre P., Ababaei B., Bindi M., Ferrise R., Finger R., Fodor N., Gabaldón-Leal C., Gaiser T., Jabloun M., Kersebaum K.C., Lizaso J.I., Lorite I.J., Manceau L., Moriondo M., Nendel C., Rodríguez A., Ruiz-Ramos M., Semenov M.A., Siebert S., Stella T., Stratonovitch P., Trombi G., and Wallach D.

Subjects
extreme events, climate change, crop modelling
Abstract

Understanding the drivers of yield levels under climate change is required to support adaptation planning and respond to changing production risks. This study uses an ensemble of crop models applied on a spatial grid to quantify the contributions of various climatic drivers to past yield variability in grain maize and winter wheat of European cropping systems (1984-2009) and drivers of climate change impacts to 2050. Results reveal that for the current genotypes and mix of irrigated and rainfed production, climate change would lead to yield losses for grain maize and gains for winter wheat. Across Europe, on average heat stress does not increase for either crop in rainfed systems, while drought stress intensifies for maize only. In low-yielding years, drought stress persists as the main driver of losses for both crops, with elevated CO2 offering no yield benefit in these years.

Published
2018
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29. 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., De Sanctis, G., 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., Rosenzweig, C., 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., De Sanctis, G., 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.

Abstract

Core Ideas: SOC decline, due to increased temperatures, reduces wheat and maize yields globally. CO2 increase to 540 ppm partially compensates yield losses due to increased temperatures. Accounting for soil feedbacks is critical when evaluating climate change impacts on crop yield. 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

30. Multimodel ensembles improve predictions of crop-environment-management interactions

Author

Wallach, D, Martre, P, Liu, B, Asseng, S, Ewert, F, Thorburn, PJ, van Ittersum, M, Aggarwal, PK, Ahmed, M, Basso, B, Biernath, C, Cammarano, D, Challinor, AJ, De Sanctis, G, Dumont, B, Rezaei, EE, Fereres, E, Fitzgerald, GJ, Gao, Y, Garcia-Vila, M, Gayler, S, Girousse, C, Hoogenboom, G, Horan, H, Izaurralde, RC, Jones, CD, Kassie, BT, Kersebaum, KC, Klein, C, Koehler, A-K, Maiorano, A, Minoli, S, Mueller, C, Kumar, SN, Nendel, C, O'Leary, GJ, Palosuo, T, Priesack, E, Ripoche, D, Roetter, RP, Semenov, MA, Stockle, C, Stratonovitch, P, Streck, T, Supit, I, Tao, F, Wolf, J, Zhang, Z, Wallach, D, Martre, P, Liu, B, Asseng, S, Ewert, F, Thorburn, PJ, van Ittersum, M, Aggarwal, PK, Ahmed, M, Basso, B, Biernath, C, Cammarano, D, Challinor, AJ, De Sanctis, G, Dumont, B, Rezaei, EE, Fereres, E, Fitzgerald, GJ, Gao, Y, Garcia-Vila, M, Gayler, S, Girousse, C, Hoogenboom, G, Horan, H, Izaurralde, RC, Jones, CD, Kassie, BT, Kersebaum, KC, Klein, C, Koehler, A-K, Maiorano, A, Minoli, S, Mueller, C, Kumar, SN, Nendel, C, O'Leary, GJ, Palosuo, T, Priesack, E, Ripoche, D, Roetter, RP, Semenov, MA, Stockle, C, Stratonovitch, P, Streck, T, Supit, I, Tao, F, Wolf, J, and Zhang, Z

Abstract

A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e-mean) and median (e-median) often seem to predict quite well. However, few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e-mean and e-median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e-mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2-6 models if best-fit models are added first. Our theoretical results describe the ensemble using four parameters: average bias, model effect variance, environment effect variance, and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e-mean will always be smaller than MSEP averaged over models and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e-mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e-mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations.

Published
2018

31. Adaptation response surfaces for managing wheat under perturbed climate and CO2 in a Mediterranean environment

Author

Ruiz-Ramos, M., Ferrise, Roberto, Rodríguez, A, Lorite, I. J., Bindi, Marco, Carter, T. R., Fronzek, S, Palosuo, T., Pirttioja, N., Baranowski, P., Buis, S., Cammarano, D., Chen, Y., Dumont, Bertrand, Ewert, F., Gaiser, T., Hlavinka, P., Hoffmann, H., Höhn, J. G., Jurecka, F., Kersebaum, K. C., Krzyszczak, J., Lana, M., Mechiche-Alami, A., Minet, J., Montesino Pouzols, Federico, Nendel, C., Porter, John Roy, Ruget, F., Semenov, M. A., Steinmetz, Z., Stratonovitch, P., Supit, Iwan, Tao, F., Trnka, M., de Wit, Cynthia A., Rötter, Reimund P, Ruiz-Ramos, M., Ferrise, Roberto, Rodríguez, A, Lorite, I. J., Bindi, Marco, Carter, T. R., Fronzek, S, Palosuo, T., Pirttioja, N., Baranowski, P., Buis, S., Cammarano, D., Chen, Y., Dumont, Bertrand, Ewert, F., Gaiser, T., Hlavinka, P., Hoffmann, H., Höhn, J. G., Jurecka, F., Kersebaum, K. C., Krzyszczak, J., Lana, M., Mechiche-Alami, A., Minet, J., Montesino Pouzols, Federico, Nendel, C., Porter, John Roy, Ruget, F., Semenov, M. A., Steinmetz, Z., Stratonovitch, P., Supit, Iwan, Tao, F., Trnka, M., de Wit, Cynthia A., and Rötter, Reimund P

Abstract

Adaptation of crops to climate change has to be addressed locally due to the variability of soil, climate and the specific socio-economic settings influencing farm management decisions. Adaptation of rainfed cropping systems in the Mediterranean is especially challenging due to the projected decline in precipitation in the coming decades, which will increase the risk of droughts. Methods that can help explore uncertainties in climate projections and crop modelling, such as impact response surfaces (IRSs) and ensemble modelling, can then be valuable for identifying effective adaptations. Here, an ensemble of 17 crop models was used to simulate a total of 54 adaptation options for rainfed winter wheat (Triticum aestivum) at Lleida (NE Spain). To support the ensemble building, an ex post quality check of model simulations based on several criteria was performed. Those criteria were based on the "According to Our Current Knowledge" (AOCK) concept, which has been formalized here. Adaptations were based on changes in cultivars and management regarding phenology, vernalization, sowing date and irrigation. The effects of adaptation options under changed precipitation (P), temperature (T), [CO2] and soil type were analysed by constructing response surfaces, which we termed, in accordance with their specific purpose, adaptation response surfaces (ARSs). These were created to assess the effect of adaptations through a range of plausible P, T and [CO2] perturbations. The results indicated that impacts of altered climate were predominantly negative. No single adaptation was capable of overcoming the detrimental effect of the complex interactions imposed by the P, T and [CO2] perturbations except for supplementary irrigation (sI), which reduced the potential impacts under most of the perturbations. Yet, a combination of adaptations for dealing with climate change demonstrated that effective adaptation is possible at Lleida. Combinations based o

Published
2018

32. 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

33. Sensitivity of European wheat to extreme weather

Author

Mäkinen, H., primary, Kaseva, J., additional, Trnka, M., additional, Balek, J., additional, Kersebaum, K.C., additional, Nendel, C., additional, Gobin, A., additional, Olesen, J.E., additional, Bindi, M., additional, Ferrise, R., additional, Moriondo, M., additional, Rodríguez, A., additional, Ruiz-Ramos, M., additional, Takáč, J., additional, Bezák, P., additional, Ventrella, D., additional, Ruget, F., additional, Capellades, G., additional, and Kahiluoto, H., additional

Published
2018
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34. Adaptation response surfaces for managing wheat under perturbed climate and CO2 in a Mediterranean environment

Author

Ruiz-Ramos, M., primary, Ferrise, R., additional, Rodríguez, A., additional, Lorite, I.J., additional, Bindi, M., additional, Carter, T.R., additional, Fronzek, S., additional, Palosuo, T., additional, Pirttioja, N., additional, Baranowski, P., additional, Buis, S., additional, Cammarano, D., additional, Chen, Y., additional, Dumont, B., additional, Ewert, F., additional, Gaiser, T., additional, Hlavinka, P., additional, Hoffmann, H., additional, Höhn, J.G., additional, Jurecka, F., additional, Kersebaum, K.C., additional, Krzyszczak, J., additional, Lana, M., additional, Mechiche-Alami, A., additional, Minet, J., additional, Montesino, M., additional, Nendel, C., additional, Porter, J.R., additional, Ruget, F., additional, Semenov, M.A., additional, Steinmetz, Z., additional, Stratonovitch, P., additional, Supit, I., additional, Tao, F., additional, Trnka, M., additional, de Wit, A., additional, and Rötter, R.P., additional

Published
2018
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35. A model-based assessment of the environmental impact of land-use change across scales in Southern Amazonia

Author

Schaldach, R., Meurer, Katharina, Jungkunst, H.F., Nendel, C., Lakes, T., Gollnow, F., Göpel, J., Boy, J., Guggenberger, G., Strey, R., Strey, S., Berger, T., Gerold, G., Schönenberg, R., Böhner, J., Schindewolf, M., Latynskiy, E., Hampf, A., Parker, P.S., Sentelhas, P.C., Schaldach, R., Meurer, Katharina, Jungkunst, H.F., Nendel, C., Lakes, T., Gollnow, F., Göpel, J., Boy, J., Guggenberger, G., Strey, R., Strey, S., Berger, T., Gerold, G., Schönenberg, R., Böhner, J., Schindewolf, M., Latynskiy, E., Hampf, A., Parker, P.S., and Sentelhas, P.C.

Abstract

This article describes the design of a new model-based assessment framework to identify and analyse possible future trajectories of agricultural development and their environmental consequences within the states of Mato Grosso and Pará in Southern Amazonia, Brazil. The objective is to provide a tool for improving the information basis for scientists and policy makers regarding the effects of global change and national environmental policies on land-use change and the resulting impacts on the loss of natural vegetation, greenhouse gas emissions, hydrological processes, and soil erosion within the region. For this purpose, the framework combines the regional land-use models, LandSHIFT and alucR, the farm-level model, MPMAS, and the MONICA crop model, with a set of environmental impact models that are operating at the regional and watershed levels. As a first application of the framework, four scenarios with the time horizon 2030 were specified and analysed. Future land-use change will strongly depend on the interplay between the production of agricultural commodities, the agricultural intensification in terms of increasing crop yields and pasture biomass productivity, and the enforcement of environmental laws and policies. On the regional level, the scenarios with the highest increase in agricultural production in combination with weak law enforcement (Trend and Illegal Intensification) generated the highest losses in natural vegetation due to the expansion of agricultural area as well as the highest greenhouse gas emissions. Also, at the watershed level, these scenarios are characterised by the highest changes in river discharge and soil erosion that might lead to a further decline in soil fertility in the long term. Moreover, the analysis of the Sustainable Development scenario indicates that a shift in agricultural production patterns from livestock to crop cultivation, together with effective law enforcement, can effectively reduce land-use change and its negative

Published
2017

36. Experiences of inter- and transdisciplinary research – a trajectory of knowledge integration within a large research consortium

Author

Schönenberg, R., Boy, J., Hartberger, K., Schumann, C., Guggenberger, G., Siebold, M., Lakes, T., Lamparter, G., Schindewolf, M., Schaldach, R., Nendel, C., Hohnwald, S., Meurer, Katharina, Gerold, G., Klingler, M., Schönenberg, R., Boy, J., Hartberger, K., Schumann, C., Guggenberger, G., Siebold, M., Lakes, T., Lamparter, G., Schindewolf, M., Schaldach, R., Nendel, C., Hohnwald, S., Meurer, Katharina, Gerold, G., and Klingler, M.

Abstract

Although inter- and transdisciplinary research has found its way to the forefront of calls, funding and publications, interdisciplinary projects often start from scratch constructing their research environment. In this article we will point to the enormous potential, the learnings, as well as some of the difficulties and pitfalls frequently encountered in large interdisciplinary project consortia. With this in mind, we aim to transparently document and reflect upon our research process, reminding the readers that the authors are not academic specialists in the field of inter- and transdisciplinarity nor in the sociology of knowledge. To explain our motivation, we want to share valuable experiences and point to some learnings, especially regarding the interdependencies between inter- and transdisciplinarity. After a brief historical retrospective of the expectations towards science, the article describes the trajectory of knowledge production and integration of a rather large research consortium attempting to overcome typical communicative and conceptual hurdles while negotiating the strict preconceptions of the respective disciplines. During the process of knowledge integration, scientific recognition and time budgets remain the crucial challenges. Besides joint field research, the construction of four storylines and the continuous integration of data into the various and increasingly interlinked models that ultimately culminate in our future scenarios led to constant communication and disputes among the subprojects involved. During the course of the project, it became obvious that a new generation of young scientists is developing: scientists working in interdisciplinary and transdisciplinary thought communities with a grasp of both fundamental science and transdisciplinary practice, combined with the soft skills necessary to reconcile both worlds.Zusammenfassung: Obwohl inter- und transdisziplinäre Forschung in aller Munde ist, beginnen Forschungskonsortien in der

Published
2017

37. 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

38. 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

39. 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

40. 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., 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 Recherche sur les Herbivores - UMR 1213 (UMRH), Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS), Reproduction et développement des plantes (RDP), École normale supérieure de Lyon (ENS de 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, Knowledge Hub FACCE MACSUR. INT. Agricultural Model Intercomparison and Improvement Project (AgMIP), USA., Institut National de la Recherche Agronomique (INRA)-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, and École normale supérieure - Lyon (ENS Lyon)-Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL)

Subjects
changement de température, sensitivity analysis, crop model, pluviomètre, [SDE.MCG]Environmental Sciences/Global Changes, région européenne, 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

41. Uncertainty in simulating N uptake and N use efficiency in the crop rotation systems across Europe

Author

Yin, X., Kersebaum, K. C., Kollas, C., Armas-Herrera, C. M., Baby, Sabulal, Beaudoin, Nicolas, Bindi, M., Charfeddine, M., Conradt, Tobias, García de Cortázar-Atauri, Iñaki, Ewert, Franck, Ferrise, R., Hoffmann, H., Lana, M., Launay, M., Manderscheid, R., Manevski, Kiril, Mary, B., Mirschel, W., Moriondo, Marco, Müller, C., Nendel, C., Öztürk, Isik, Palosuod, T., Ripoche-Wachte, D., Rötter, R. P., Ruget, Francoise, Sharif, B., Ventrella, D., Weigel, H.J., Olesen, J. E., Chercheur indépendant, Unité d'Agronomie de Laon-Reims-Mons (AGRO-LRM), Institut National de la Recherche Agronomique (INRA), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), and Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
orge d'hiver, blé d'hiver, triticum, crop model, [SDV]Life Sciences [q-bio], pea, sugarbeet, betterave sucrière, rotation culturale, winter wheat, méthode de simulation, crop rotation, pois, méthode d'etalonnage, production grainière, absorption
Abstract

Uncertainty in simulating N uptake and N use efficiency in the crop rotation systems across Europe . International Crop Modelling Symposium

Published
2016

42. 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

43. Comparing the performance of 11 crop simulation models in predicting yield response to nitrogen fertilization

Author

Salo, T. J., Palosuo, T., Kersebaum, K. C., Nendel, C., Angulo, C., Ewert, F., Bindi, M., Calanca, P., Klein, T., Moriondo, M., Ferrise, R., Olesen, J. E., Patil, R. H., Ruget, F., Taká?, J., Hlavinka, P., Trnka, M., Rötter, R. P., Natural Resources Institute Finland, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Institute of Crop Science and Resource Conservation (IRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Department of Agrifood Production and Environmental Sciences (DISPAA), University of Florence (UNIFI), Agroscope, Istituto di Biometeorologia [Firenze] (IBIMET), Consiglio Nazionale delle Ricerche (CNR), Department of agroecology, Aarhus University [Aarhus], Department of Agroecology, Department of Agronomy, University of Agricultural 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), National Agricultural and Food Centre, Soil Science and Conservation Research Institute, Global Change Research Centre, Czech Academy of Sciences [Prague] (ASCR), Institute of Agrosystems and Bioclimatology, Mendel University in Brno, The authors wish to acknowledge the financial assistance provided under the umbrella of COST action 734 'Impacts of Climate Change and Variability on European Agriculture (CLIVAGRI)' and the work of individual researchers was funded by various bodies: T. Palosuo, T. Salo and R. Rotter, the strategic project MODAGS funded by MTT Agrifood Research Finland, and projects FACCE MACSUR and NORFASYS (decision nos. 268277 and 292944) funded by the Ministry of Agriculture and Forestry and the Academy of Finland, respectively, K. C. Kersebaum performed parts of the current study under the umbrella of FACCE MACSUR funded by the German Federal Office for Agriculture and Food and COST ES1106, C. Nendel was supported by ZALF in-house funds, J.E. Olesen and R.H. Patil, CRES funded by Danish Strategic Research Council, P. Hlavinka was supported by the Ministry of Education, Youth and Sports of CR within the National Sustainability Program I (NPU I), grant number LO1415, M. Trnka, Crop modelling as a tool for increasing the production potential and food security of the Czech Republic funded by National Agency for Agricultural Research (QJ1310123) and project LD 13030 - Water resources in the Czech Agriculture under the Climate Change conditions - CZECH-AGRIWAT, Natural resources institute Finland, Institute of Crop Science and Resource Conservation [Bonn] (INRES), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Czech Academy of Sciences [Prague] (CAS), Mendel University in Brno (MENDELU), and Salo, Tapio J.

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, hordeum vulgare, 010504 meteorology & atmospheric sciences, engineering.material, modèle de simulation, 01 natural sciences, finlande, Genetics, Precipitation, Leaf area index, 3d, 0105 earth and related environmental sciences, 2. Zero hunger, Moisture, Phenology, Simulation modeling, 04 agricultural and veterinary sciences, 15. Life on land, Agricultural sciences, fertilisation azotée, Agronomy, Phenotyping, 13. Climate action, 040103 agronomy & agriculture, engineering, 0401 agriculture, forestry, and fisheries, Environmental science, Animal Science and Zoology, minéralisation, Fertilizer, Hordeum vulgare, Crop simulation model, Agronomy and Crop Science, Sciences agricoles, azote du sol, point cloud
Abstract

SUMMARYEleven widely used crop simulation models (APSIM, CERES, CROPSYST, COUP, DAISY, EPIC, FASSET, HERMES, MONICA, STICS and WOFOST) were tested using spring barley (Hordeum vulgare L.) data set under varying nitrogen (N) fertilizer rates from three experimental years in the boreal climate of Jokioinen, Finland. This is the largest standardized crop model inter-comparison under different levels of N supply to date. The models were calibrated using data from 2002 and 2008, of which 2008 included six N rates ranging from 0 to 150 kg N/ha. Calibration data consisted of weather, soil, phenology, leaf area index (LAI) and yield observations. The models were then tested against new data for 2009 and their performance was assessed and compared with both the two calibration years and the test year. For the calibration period, root mean square error between measurements and simulated grain dry matter yields ranged from 170 to 870 kg/ha. During the test year 2009, most models failed to accurately reproduce the observed low yield without N fertilizer as well as the steep yield response to N applications. The multi-model predictions were closer to observations than most single-model predictions, but multi-model mean could not correct systematic errors in model simulations. Variation in soil N mineralization and LAI development due to differences in weather not captured by the models most likely was the main reason for their unsatisfactory performance. This suggests the need for model improvement in soil N mineralization as a function of soil temperature and moisture. Furthermore, specific weather event impacts such as low temperatures after emergence in 2009, tending to enhance tillering, and a high precipitation event just before harvest in 2008, causing possible yield penalties, were not captured by any of the models compared in the current study.

Published
2016

44. 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

45. Uncertainty in simulating N uptake and N use efficiency in the crop rotation systems across Europe

Author

Xiaogang Yin, Kc, Kersebaum, Kollas, C., Armas-Herrera, Cecilia M., Sanmohan Baby, Nicolas Beaudoin, Bindi, M., Monica Charfeddine, Tobias Conradt, Iñaki Garcia de Cortazar-Atauri, Ewert, F., Roberto Ferrise, Hlavinka, P., Hoffmann, H., Lana, M., Marie Launay, Remy Manderscheid, Kiril Manevski, Bruno Mary, Mirschel, W., Moriondo, M., Müller, C., Nendel, C., Isik Öztürk, Palosuo, T., Dominique Ripoche-Wachter, Rp, Rötter, Ruget, F., Behzad Sharif, Trnka, M., Domenico Ventrella, Hans-Joachim Weigel, Wu, L., and Olesen, Jørgen E.

Published
2016

46. 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

47. 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

48. 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

49. 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

50. Reproducing CO2 exchange rates of a crop rotation at contrasting terrain positions using two different modelling approaches

Author

UCL - SST/ELI/ELIC - Earth & Climate, Van Oost, Kristof, Specka , X., Nendel, C., Hagemann, U., Pohl, M., Hoffmann, M., Barkusky, D., Augustin, J., Sommer, M., UCL - SST/ELI/ELIC - Earth & Climate, Van Oost, Kristof, Specka , X., Nendel, C., Hagemann, U., Pohl, M., Hoffmann, M., Barkusky, D., Augustin, J., and Sommer, M.

Abstract

In undulating landscapes erosion is largely responsible for the spatial distribution of C stocks in agricultural soils. Whether these stocks contribute to global atmospheric CO2 concentrations as source or sink of CO2 is under constant debate. Periodic CO2 measurements were carried out at a hummocky ground moraine site grown with maize, fodder rye and sorghum using dynamic non-steady-state transparent and opaque chambers. Flux calculation for CO2 was conducted using the empirical gap-filling model of Hoffmann et al. (2015b), which uses temperature and radiation to simulate ecosystem respiration (Reco) and gross primary production (GPP) and to calculate net ecosystem CO2 exchange (NEE). This model was compared with a process-based agro-ecosystem simulation model, MONICA, which was tested for its ability to simulate Reco, GPP and NEE, using the empirical model as benchmark. Both models simulated GPP and Reco in the same order of magnitude, with MONICA simulating a considerably higher amount of CO2 produced by photosynthesis for maize and less deviating CO2 produced by photosynthesis for the other crops and CO2 consumed by respiration for all crops as compared to the empirical model. Both models largely agree in CO2 flux patterns, but show considerable differences directly after harvest and during bare soil periods. Strengths and weaknesses of both approaches were discussed and synergies of applying both approaches in conjunction were identified in a way that (i) MONICA may act as an independent method to identify significant deviations from the optimum crop growth pattern and thus point at times during which assumptions of the empirical model for simulating NEE may be violated and that (ii) the empirical model may act as a calibration benchmark for MONICA flux simulations.

Published
2016

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