A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration

D. Makowski, S. Asseng, F. Ewert, S. Bassu, J.L. Durand, G. Li, P. Martre, M.Y.O. Adam, P.K. Aggarwal, C. Angulo, C. Baron, B. Basso, P. Bertuzzi, C. Biernath, H.L. Boogaard, K.J. Boote, B. Bouman, S. Bregaglio, N. Brisson, S. Buis & 71 others D. Cammarano, A.J. Challinor, R. Confalonieri, J.G. Conijn, M. Corbeels, D. Deryng, G. De Sanctis, J. Doltra, T. Fumoto, S. Gayler, D. Gaydon, R. Goldberg, R.F. Grant, P. Grassini, J.L. Hatfield, T. Hasegawa, L. Heng, S.B. Hoek, J. Hooker, L.A. Hunt, J. Ingwersen, C. Izaurralde, R.E.E. Jongschaap, J.W. Jones, R.A. Kemanian, K.C. Kersebaum, S.H. Kim, J. Lizaso, M. Marcaida III, C. Müller, H. Nakagawa, S. Naresh Kumar, C. Nendel, G.J. O'Leary, J.E. Olesen, P. Oriol, T.M. Osborne, T. Palosuo, M.V. Pravia, E. Priesack, D. Ripoche, C. Rosenzweig, A.C. Ruane, F. Ruget, F. Sau, M.A. Semenov, I. Shcherbak, B. Singh, A.K. Soo, P. Steduto, C.O. Stöckle, P. Stratonovitch, T. Streck, I. Supit, L. Tang, F. Tao, E. Teixeira, P. Thorburn, D. Timlin, M. Travasso, R.P. Rötter, K. Waha, D. Wallach, J.W. White, P. Wilkens, J.R. Williams, J. Wolf, X. Ying, H. Yoshida, Z. Zhang, Y. Zhu

Research output: Contribution to journalArticleAcademicpeer-review

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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 re-running 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°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].
Original languageEnglish
Pages (from-to)483-493
JournalAgricultural and Forest Meteorology
Volume214-215
DOIs
Publication statusPublished - 2015

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crop models
statistical analysis
crop
statistical models
temperature
wheat
crops
rice
corn
maize
crop yield
uncertainty
yield response
climate change
climate

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Makowski, D. ; Asseng, S. ; Ewert, F. ; Bassu, S. ; Durand, J.L. ; Li, G. ; Martre, P. ; Adam, M.Y.O. ; Aggarwal, P.K. ; Angulo, C. ; Baron, C. ; Basso, B. ; Bertuzzi, P. ; Biernath, C. ; Boogaard, H.L. ; 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. ; Gayler, S. ; Gaydon, D. ; Goldberg, R. ; Grant, R.F. ; Grassini, P. ; Hatfield, J.L. ; Hasegawa, T. ; Heng, L. ; Hoek, S.B. ; Hooker, J. ; Hunt, L.A. ; Ingwersen, J. ; Izaurralde, C. ; Jongschaap, R.E.E. ; Jones, J.W. ; Kemanian, R.A. ; Kersebaum, K.C. ; Kim, S.H. ; Lizaso, J. ; Marcaida III, M. ; 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. ; Soo, A.K. ; Steduto, P. ; Stöckle, C.O. ; Stratonovitch, P. ; Streck, T. ; Supit, I. ; Tang, L. ; Tao, F. ; Teixeira, E. ; Thorburn, P. ; Timlin, D. ; Travasso, M. ; Rötter, R.P. ; Waha, K. ; Wallach, D. ; White, J.W. ; Wilkens, P. ; Williams, J.R. ; Wolf, J. ; Ying, X. ; Yoshida, H. ; Zhang, Z. ; Zhu, Y. / A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration. In: Agricultural and Forest Meteorology. 2015 ; Vol. 214-215. pp. 483-493.
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title = "A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration",
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 re-running 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°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].",
author = "D. Makowski and S. Asseng and F. Ewert and S. Bassu and J.L. Durand and G. Li and P. Martre and M.Y.O. Adam and P.K. Aggarwal and C. Angulo and C. Baron and B. Basso and P. Bertuzzi and C. Biernath and H.L. Boogaard and K.J. Boote and B. Bouman and S. Bregaglio and N. Brisson and S. Buis and D. Cammarano and A.J. Challinor and R. Confalonieri and J.G. Conijn and M. Corbeels and D. Deryng and {De Sanctis}, G. and J. Doltra and T. Fumoto and S. Gayler and D. Gaydon and R. Goldberg and R.F. Grant and P. Grassini and J.L. Hatfield and T. Hasegawa and L. Heng and S.B. Hoek and J. Hooker and L.A. Hunt and J. Ingwersen and C. Izaurralde and R.E.E. Jongschaap and J.W. Jones and R.A. Kemanian and K.C. Kersebaum and S.H. Kim and J. Lizaso and {Marcaida III}, M. and C. M{\"u}ller and H. Nakagawa and {Naresh Kumar}, S. and C. Nendel and G.J. O'Leary and J.E. Olesen and P. Oriol and T.M. Osborne and T. Palosuo and M.V. Pravia and E. Priesack and D. Ripoche and C. Rosenzweig and A.C. Ruane and F. Ruget and F. Sau and M.A. Semenov and I. Shcherbak and B. Singh and A.K. Soo and P. Steduto and C.O. St{\"o}ckle and P. Stratonovitch and T. Streck and I. Supit and L. Tang and F. Tao and E. Teixeira and P. Thorburn and D. Timlin and M. Travasso and R.P. R{\"o}tter and K. Waha and D. Wallach and J.W. White and P. Wilkens and J.R. Williams and J. Wolf and X. Ying and H. Yoshida and Z. Zhang and Y. Zhu",
year = "2015",
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journal = "Agricultural and Forest Meteorology",
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Makowski, D, Asseng, S, Ewert, F, Bassu, S, Durand, JL, Li, G, Martre, P, Adam, MYO, Aggarwal, PK, Angulo, C, Baron, C, Basso, B, Bertuzzi, P, Biernath, C, Boogaard, HL, Boote, KJ, Bouman, B, Bregaglio, S, Brisson, N, Buis, S, Cammarano, D, Challinor, AJ, Confalonieri, R, Conijn, JG, Corbeels, M, Deryng, D, De Sanctis, G, Doltra, J, Fumoto, T, Gayler, S, Gaydon, D, Goldberg, R, Grant, RF, Grassini, P, Hatfield, JL, Hasegawa, T, Heng, L, Hoek, SB, Hooker, J, Hunt, LA, Ingwersen, J, Izaurralde, C, Jongschaap, REE, Jones, JW, Kemanian, RA, Kersebaum, KC, Kim, SH, Lizaso, J, Marcaida III, M, Müller, C, Nakagawa, H, Naresh Kumar, S, Nendel, C, O'Leary, GJ, Olesen, JE, Oriol, P, Osborne, TM, Palosuo, T, Pravia, MV, Priesack, E, Ripoche, D, Rosenzweig, C, Ruane, AC, Ruget, F, Sau, F, Semenov, MA, Shcherbak, I, Singh, B, Soo, AK, Steduto, P, Stöckle, CO, Stratonovitch, P, Streck, T, Supit, I, Tang, L, Tao, F, Teixeira, E, Thorburn, P, Timlin, D, Travasso, M, Rötter, RP, Waha, K, Wallach, D, White, JW, Wilkens, P, Williams, JR, Wolf, J, Ying, X, Yoshida, H, Zhang, Z & Zhu, Y 2015, 'A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration', Agricultural and Forest Meteorology, vol. 214-215, pp. 483-493. https://doi.org/10.1016/j.agrformet.2015.09.013

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.L.; Li, G.; Martre, P.; Adam, M.Y.O.; Aggarwal, P.K.; Angulo, C.; Baron, C.; Basso, B.; Bertuzzi, P.; Biernath, C.; Boogaard, H.L.; 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.; Gayler, S.; Gaydon, D.; Goldberg, R.; Grant, R.F.; Grassini, P.; Hatfield, J.L.; Hasegawa, T.; Heng, L.; Hoek, S.B.; Hooker, J.; Hunt, L.A.; Ingwersen, J.; Izaurralde, C.; Jongschaap, R.E.E.; Jones, J.W.; Kemanian, R.A.; Kersebaum, K.C.; Kim, S.H.; Lizaso, J.; Marcaida III, M.; 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.; Soo, A.K.; Steduto, P.; Stöckle, C.O.; Stratonovitch, P.; Streck, T.; Supit, I.; Tang, L.; Tao, F.; Teixeira, E.; Thorburn, P.; Timlin, D.; Travasso, M.; Rötter, R.P.; Waha, K.; Wallach, D.; White, J.W.; Wilkens, P.; Williams, J.R.; Wolf, J.; Ying, X.; Yoshida, H.; Zhang, Z.; Zhu, Y.

In: Agricultural and Forest Meteorology, Vol. 214-215, 2015, p. 483-493.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration

AU - Makowski, D.

AU - Asseng, S.

AU - Ewert, F.

AU - Bassu, S.

AU - Durand, J.L.

AU - Li, G.

AU - Martre, P.

AU - Adam, M.Y.O.

AU - Aggarwal, P.K.

AU - Angulo, C.

AU - Baron, C.

AU - Basso, B.

AU - Bertuzzi, P.

AU - Biernath, C.

AU - Boogaard, H.L.

AU - Boote, K.J.

AU - Bouman, B.

AU - Bregaglio, S.

AU - Brisson, N.

AU - Buis, S.

AU - Cammarano, D.

AU - Challinor, A.J.

AU - Confalonieri, R.

AU - Conijn, J.G.

AU - Corbeels, M.

AU - Deryng, D.

AU - De Sanctis, G.

AU - Doltra, J.

AU - Fumoto, T.

AU - Gayler, S.

AU - Gaydon, D.

AU - Goldberg, R.

AU - Grant, R.F.

AU - Grassini, P.

AU - Hatfield, J.L.

AU - Hasegawa, T.

AU - Heng, L.

AU - Hoek, S.B.

AU - Hooker, J.

AU - Hunt, L.A.

AU - Ingwersen, J.

AU - Izaurralde, C.

AU - Jongschaap, R.E.E.

AU - Jones, J.W.

AU - Kemanian, R.A.

AU - Kersebaum, K.C.

AU - Kim, S.H.

AU - Lizaso, J.

AU - Marcaida III, M.

AU - Müller, C.

AU - Nakagawa, H.

AU - Naresh Kumar, S.

AU - Nendel, C.

AU - O'Leary, G.J.

AU - Olesen, J.E.

AU - Oriol, P.

AU - Osborne, T.M.

AU - Palosuo, T.

AU - Pravia, M.V.

AU - Priesack, E.

AU - Ripoche, D.

AU - Rosenzweig, C.

AU - Ruane, A.C.

AU - Ruget, F.

AU - Sau, F.

AU - Semenov, M.A.

AU - Shcherbak, I.

AU - Singh, B.

AU - Soo, A.K.

AU - Steduto, P.

AU - Stöckle, C.O.

AU - Stratonovitch, P.

AU - Streck, T.

AU - Supit, I.

AU - Tang, L.

AU - Tao, F.

AU - Teixeira, E.

AU - Thorburn, P.

AU - Timlin, D.

AU - Travasso, M.

AU - Rötter, R.P.

AU - Waha, K.

AU - Wallach, D.

AU - White, J.W.

AU - Wilkens, P.

AU - Williams, J.R.

AU - Wolf, J.

AU - Ying, X.

AU - Yoshida, H.

AU - Zhang, Z.

AU - Zhu, Y.

PY - 2015

Y1 - 2015

N2 - 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 re-running 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°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].

AB - 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 re-running 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°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].

U2 - 10.1016/j.agrformet.2015.09.013

DO - 10.1016/j.agrformet.2015.09.013

M3 - Article

VL - 214-215

SP - 483

EP - 493

JO - Agricultural and Forest Meteorology

JF - Agricultural and Forest Meteorology

SN - 0168-1923

ER -