Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects

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

doi:10.1142/9781783265640_0011

Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects

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

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

Abstract

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

Original language	English
Title of host publication	Handbook of Climate Change and Agroecosystems: The Agricultural Model Intercomparison and Improvement Project (AgMIP)
Editors	D. Hillel, C. Rosenzweig
Number of pages	1100
DOIs	https://doi.org/10.1142/9781783265640_0011
Publication status	Published - 2015

Publication series

Name	ICP Series on Climate Change Impacts, Adaptation, and Mitigation
Number	3

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1142/9781783265640_0011

ESS-CC
Project: Other

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Makowski, D., Asseng, S., Ewert, F., Bassu, S., Durand, J. L., Martre, P., Adam, M., Aggarwal, P. K., Angulo, C., Baron, C., Basso, B., Bertuzzi, P., Biernath, C., Boogaard, H., Boote, K. J., Brisson, N., Cammarano, D., Challinor, A. J., Conijn, J. G., ... Wolf, J. (2015). Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects. In D. Hillel, & C. Rosenzweig (Eds.), Handbook of Climate Change and Agroecosystems: The Agricultural Model Intercomparison and Improvement Project (AgMIP) (ICP Series on Climate Change Impacts, Adaptation, and Mitigation; No. 3). https://doi.org/10.1142/9781783265640_0011

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

author = "D. Makowski and S. Asseng and F. Ewert and S. Bassu and J.L. Durand and P. Martre and M. Adam and P.K. Aggarwal and C. Angulo and C. Baron and B. Basso and P. Bertuzzi and C. Biernath and H. Boogaard and K.J. Boote and N. Brisson and D. Cammarano and A.J. Challinor and J.G. Conijn and M. Corbeels and D. Deryng and {De Sanctis}, G. and J. Doltra and S. Gayler and R. Goldberg and P. Grassini and J.L. Hatfield 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 C. M{\"u}ller and {Naresh Kumar}, S. and C. Nendel and G.J. O'Leary and J.E. Olesen 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. Sau and M.A. Semenov and I. Shcherbak and P. Steduto and C.O. St{\"o}ckle and P. Stratonovitch and T. Streck and I. Supit and F. Tao and E. Teixeira and P. Thorburn and D. Timlin and M. Travasso and R.P. Roetter and K. Waha and D. Wallach and J.W. White and J.R. Williams and J. Wolf",

year = "2015",

doi = "10.1142/9781783265640_0011",

language = "English",

isbn = "9781783265633",

series = "ICP Series on Climate Change Impacts, Adaptation, and Mitigation",

number = "3",

editor = "D. Hillel and C. Rosenzweig",

booktitle = "Handbook of Climate Change and Agroecosystems: The Agricultural Model Intercomparison and Improvement Project (AgMIP)",

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Makowski, D, Asseng, S, Ewert, F, Bassu, S, Durand, JL, Martre, P, Adam, M, Aggarwal, PK, Angulo, C, Baron, C, Basso, B, Bertuzzi, P, Biernath, C, Boogaard, H, Boote, KJ, Brisson, N, Cammarano, D, Challinor, AJ, Conijn, JG, Corbeels, M, Deryng, D, De Sanctis, G, Doltra, J, Gayler, S, Goldberg, R, Grassini, P, Hatfield, JL, Heng, L, Hoek, SB, Hooker, J, Hunt, LA, Ingwersen, J, Izaurralde, C, Jongschaap, REE, Jones, JW, Kemanian, RA, Kersebaum, KC, Kim, SH, Lizaso, J, Müller, C, Naresh Kumar, S, Nendel, C, O'Leary, GJ, Olesen, JE, Osborne, TM, Palosuo, T, Pravia, MV, Priesack, E, Ripoche, D, Rosenzweig, C, Ruane, AC, Sau, F, Semenov, MA, Shcherbak, I, Steduto, P, Stöckle, CO, Stratonovitch, P, Streck, T, Supit, I, Tao, F, Teixeira, E, Thorburn, P, Timlin, D, Travasso, M, Roetter, RP, Waha, K, Wallach, D, White, JW, Williams, JR & Wolf, J 2015, Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects. in D Hillel & C Rosenzweig (eds), Handbook of Climate Change and Agroecosystems: The Agricultural Model Intercomparison and Improvement Project (AgMIP). ICP Series on Climate Change Impacts, Adaptation, and Mitigation, no. 3. https://doi.org/10.1142/9781783265640_0011

Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects. / Makowski, D.; Asseng, S.; Ewert, F. et al.
Handbook of Climate Change and Agroecosystems: The Agricultural Model Intercomparison and Improvement Project (AgMIP). ed. / D. Hillel; C. Rosenzweig. 2015. (ICP Series on Climate Change Impacts, Adaptation, and Mitigation; No. 3).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

TY - CHAP

T1 - Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects

AU - Makowski, D.

AU - Asseng, S.

AU - Ewert, F.

AU - Bassu, S.

AU - Durand, J.L.

AU - Martre, P.

AU - Adam, M.

AU - Aggarwal, P.K.

AU - Angulo, C.

AU - Baron, C.

AU - Basso, B.

AU - Bertuzzi, P.

AU - Biernath, C.

AU - Boogaard, H.

AU - Boote, K.J.

AU - Brisson, N.

AU - Cammarano, D.

AU - Challinor, A.J.

AU - Conijn, J.G.

AU - Corbeels, M.

AU - Deryng, D.

AU - De Sanctis, G.

AU - Doltra, J.

AU - Gayler, S.

AU - Goldberg, R.

AU - Grassini, P.

AU - Hatfield, J.L.

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 - Müller, C.

AU - Naresh Kumar, S.

AU - Nendel, C.

AU - O'Leary, G.J.

AU - Olesen, J.E.

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 - Sau, F.

AU - Semenov, M.A.

AU - Shcherbak, I.

AU - Steduto, P.

AU - Stöckle, C.O.

AU - Stratonovitch, P.

AU - Streck, T.

AU - Supit, I.

AU - Tao, F.

AU - Teixeira, E.

AU - Thorburn, P.

AU - Timlin, D.

AU - Travasso, M.

AU - Roetter, R.P.

AU - Waha, K.

AU - Wallach, D.

AU - White, J.W.

AU - Williams, J.R.

AU - Wolf, J.

PY - 2015

Y1 - 2015

N2 - Many simulation studies have been carried out to predict the effect of climate change on crop yield. Typically, in such study, one or several crop models are used to simulate series of crop yield values for different climate scenarios corresponding to different hypotheses of temperature, CO2 concentration, and rainfall changes. These studies usually generate large datasets including thousands of simulated yield data. The structure of these datasets is complex because they include series of yield values obtained with different mechanistic crop models for different climate scenarios defined from several climatic variables (temperature, CO2 etc.). Statistical methods can play a big part for analyzing large simulated crop yield datasets, especially when yields are simulated using an ensemble of crop models. A formal statistical analysis is then needed in order to estimate the effects of different climatic variables on yield, and to describe the variability of these effects across crop models. Statistical methods are also useful to develop meta-models i.e., statistical models summarizing complex mechanistic models. The objective of this paper is to present a random-coefficient statistical model (mixed-effects model) for analyzing large simulated crop yield datasets produced by the international project AgMip for several major crops. The proposed statistical model shows several interesting features; i) it can be used to estimate the effects of several climate variables on yield using crop model simulations, ii) it quantities the variability of the estimated climate change effects across crop models, ii) it quantifies the between-year yield variability, iv) it can be used as a meta-model in order to estimate effects of new climate change scenarios without running again the mechanistic crop models. The statistical model is first presented in details, and its value is then illustrated in a case study where the effects of climate change scenarios on different crops are compared. See more from this Division: Special Sessions See more from this Session: Symposium--Perspectives on Climate Effects on Agriculture: The International Efforts of AgMIP

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

U2 - 10.1142/9781783265640_0011

DO - 10.1142/9781783265640_0011

M3 - Chapter

SN - 9781783265633

T3 - ICP Series on Climate Change Impacts, Adaptation, and Mitigation

BT - Handbook of Climate Change and Agroecosystems: The Agricultural Model Intercomparison and Improvement Project (AgMIP)

A2 - Hillel, D.

A2 - Rosenzweig, C.

ER -

Makowski D, Asseng S, Ewert F, Bassu S, Durand JL, Martre P et al. Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects. In Hillel D, Rosenzweig C, editors, Handbook of Climate Change and Agroecosystems: The Agricultural Model Intercomparison and Improvement Project (AgMIP). 2015. (ICP Series on Climate Change Impacts, Adaptation, and Mitigation; 3). doi: 10.1142/9781783265640_0011

Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects

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