A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration. Makowski, D., Asseng, S., Ewert, F., Bassu, S., Durand, J., Li, T., Martre, P., Adam, M., Aggarwal, P., Angulo, C., Baron, C., Basso, B., Bertuzzi, P., Biernath, C., Boogaard, H., Boote, K., Bouman, B., Bregaglio, S., Brisson, N., Buis, S., Cammarano, D., Challinor, A., Confalonieri, R., Conijn, J., Corbeels, M., Deryng, D., De Sanctis, G., Doltra, J., Fumoto, T., Gaydon, D., Gayler, S., Goldberg, R., Grant, R., Grassini, P., Hatfield, J., Hasegawa, T., Heng, L., Hoek, S., Hooker, J., Hunt, L., Ingwersen, J., Izaurralde, R., Jongschaap, R., Jones, J., Kemanian, R., Kersebaum, K., Kim, S., Lizaso, J., Marcaida, M., Müller, C., Nakagawa, H., Naresh Kumar, S., Nendel, C., O’Leary, G., Olesen, J., Oriol, P., Osborne, T., Palosuo, T., Pravia, M., Priesack, E., Ripoche, D., Rosenzweig, C., Ruane, A., Ruget, F., Sau, F., Semenov, M., Shcherbak, I., Singh, B., Singh, U., Soo, H., Steduto, P., Stöckle, C., Stratonovitch, P., Streck, T., Supit, I., Tang, L., Tao, F., Teixeira, E., Thorburn, P., Timlin, D., Travasso, M., Rötter, R., Waha, K., Wallach, D., White, J., Wilkens, P., Williams, J., Wolf, J., Yin, X., Yoshida, H., Zhang, Z., & Zhu, Y. Agricultural and Forest Meteorology, 214-215:483–493, 2015. MACSUR or FACCE acknowledged.
doi  abstract   bibtex   
Ensembles of process-based crop models are increasingly used to simulate crop growth for scenarios of temperature and/or precipitation changes corresponding to different projections of atmospheric CO2 concentrations. This approach generates large datasets with thousands of simulated crop yield data. Such datasets potentially provide new information but it is difficult to summarize them in a useful way due to their structural complexities. An associated issue is that it is not straightforward to compare crops and to interpolate the results to alternative climate scenarios not initially included in the simulation protocols. Here we demonstrate that statistical models based on random-coefficient regressions are able to emulate ensembles of process-based crop models. An important advantage of the proposed statistical models is that they can interpolate between temperature levels and between CO2 concentration levels, and can thus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without rerunning the original complex crop models. Our approach is illustrated with three yield datasets simulated by 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to these datasets, and are then used to analyze the variability of the yield response to [CO2] and temperature. Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effect of a temperature increase of +2 degrees C in the considered sites. Compared to wheat, required levels of [CO2] increase are much higher for maize, and intermediate for rice. For all crops, uncertainties in simulating climate change impacts increase more with temperature than with elevated [CO2].
@Article {Makowski2015a,
author = {Makowski, D. and Asseng, S. and Ewert, F. and Bassu, S. and Durand, J.L. and Li, T. and Martre, P. and Adam, M. and Aggarwal, P.K. and Angulo, C. and Baron, C. and Basso, B. and Bertuzzi, P. and Biernath, C. and Boogaard, H. and Boote, K.J. and Bouman, B. and Bregaglio, S. and Brisson, N. and Buis, S. and Cammarano, D. and Challinor, A.J. and Confalonieri, R. and Conijn, J.G. and Corbeels, M. and Deryng, D. and De Sanctis, G. and Doltra, J. and Fumoto, T. and Gaydon, D. and Gayler, S. and Goldberg, R. and Grant, R.F. and Grassini, P. and Hatfield, J.L. and Hasegawa, T. and Heng, L. and Hoek, S. and Hooker, J. and Hunt, L.A. and Ingwersen, J. and Izaurralde, R.C. and Jongschaap, R.E.E. and Jones, J.W. and Kemanian, R.A. and Kersebaum, K.C. and Kim, S.-H. and Lizaso, J. and Marcaida, M. and Müller, C. and Nakagawa, H. and Naresh Kumar, S. and Nendel, C. and O’Leary, G.J. and Olesen, J.E. and Oriol, P. and Osborne, T.M. and Palosuo, T. and Pravia, M.V. and Priesack, E. and Ripoche, D. and Rosenzweig, C. and Ruane, A.C. and Ruget, F. and Sau, F. and Semenov, M.A. and Shcherbak, I. and Singh, B. and Singh, U. and Soo, H.K. and Steduto, P. and Stöckle, C. and Stratonovitch, P. and Streck, T. and Supit, I. and Tang, L. and Tao, F. and Teixeira, E.I. and Thorburn, P. and Timlin, D. and Travasso, M. and Rötter, R.P. and Waha, K. and Wallach, D. and White, J.W. and Wilkens, P. and Williams, J.R. and Wolf, J. and Yin, X. and Yoshida, H. and Zhang, Z. and Zhu, Y.}, 
title = {A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration}, 
journal = {Agricultural and Forest Meteorology}, 
volume = {214-215}, 
pages = {483--493}, 
year = {2015}, 
doi = {10.1016/j.agrformet.2015.09.013}, 
abstract = {Ensembles of process-based crop models are increasingly used to simulate crop growth for scenarios of temperature and/or precipitation changes corresponding to different projections of atmospheric CO2 concentrations. This approach generates large datasets with thousands of simulated crop yield data. Such datasets potentially provide new information but it is difficult to summarize them in a useful way due to their structural complexities. An associated issue is that it is not straightforward to compare crops and to interpolate the results to alternative climate scenarios not initially included in the simulation protocols. Here we demonstrate that statistical models based on random-coefficient regressions are able to emulate ensembles of process-based crop models. An important advantage of the proposed statistical models is that they can interpolate between temperature levels and between CO2 concentration levels, and can thus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without rerunning the original complex crop models. Our approach is illustrated with three yield datasets simulated by 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to these datasets, and are then used to analyze the variability of the yield response to [CO2] and temperature. Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effect of a temperature increase of +2 degrees C in the considered sites. Compared to wheat, required levels of [CO2] increase are much higher for maize, and intermediate for rice. For all crops, uncertainties in simulating climate change impacts increase more with temperature than with elevated [CO2].}, 
note = { MACSUR or FACCE acknowledged.}, 
keywords = {climate change; crop model; emulator; meta-model; statistical model; yield; climate-change; wheat yields; metaanalysis; uncertainty; simulation; impacts}, 
type = {CropM}}

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