Crop model; Model ensemble; Prediction error; Protocol; Variability; Environmental Engineering; Agronomy and Crop Science
Abstract :
[en] A major effect of environment on crops is through crop phenology, and therefore, the capacity to predict phenology for new environments is important. Mechanistic crop models are a major tool for such predictions, but calibration of crop phenology models is difficult and there is no consensus on the best approach. We propose an original, detailed approach for calibration of such models, which we refer to as a calibration protocol. The protocol covers all the steps in the calibration workflow, namely choice of default parameter values, choice of objective function, choice of parameters to estimate from the data, calculation of optimal parameter values, and diagnostics. The major innovation is in the choice of which parameters to estimate from the data, which combines expert knowledge and data-based model selection. First, almost additive parameters are identified and estimated. This should make bias (average difference between observed and simulated values) nearly zero. These are “obligatory” parameters, that will definitely be estimated. Then candidate parameters are identified, which are parameters likely to explain the remaining discrepancies between simulated and observed values. A candidate is only added to the list of parameters to estimate if it leads to a reduction in BIC (Bayesian Information Criterion), which is a model selection criterion. A second original aspect of the protocol is the specification of documentation for each stage of the protocol. The protocol was applied by 19 modeling teams to three data sets for wheat phenology. All teams first calibrated their model using their “usual” calibration approach, so it was possible to compare usual and protocol calibration. Evaluation of prediction error was based on data from sites and years not represented in the training data. Compared to usual calibration, calibration following the new protocol reduced the variability between modeling teams by 22% and reduced prediction error by 11%.
Disciplines :
Agriculture & agronomy Computer science
Author, co-author :
Wallach, Daniel ; Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany
Palosuo, Taru; Natural Resources Institute Finland (Luke), Helsinki, Finland
Thorburn, Peter; CSIRO Agriculture and Food, Brisbane, Australia
Mielenz, Henrike; Institute for Crop and Soil Science, Julius Kühn Institute (JKI) – Federal Research Centre for Cultivated Plants, Braunschweig, Germany
Buis, Samuel; INRAE, UMR 1114 EMMAH, Avignon, France
Hochman, Zvi; CSIRO Agriculture and Food, Brisbane, Australia
Gourdain, Emmanuelle; ARVALIS - Institut du végétal Paris, Paris, France
Andrianasolo, Fety; ARVALIS - Institut du végétal Paris, Paris, France
Dumont, Benjamin ; Université de Liège - ULiège > TERRA Research Centre > Plant Sciences
Ferrise, Roberto; Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Florence, Italy
Gaiser, Thomas; Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany
Garcia, Cecile; ARVALIS - Institut du végétal Paris, Paris, France
Gayler, Sebastian; Institute of Soil Science and Land Evaluation, Biogeophysics, University of Hohenheim, Stuttgart, Germany
Harrison, Matthew; Tasmanian Institute of Agriculture, University of Tasmania, Launceston, Australia
Hiremath, Santosh; Aalto University School of Science, Espoo, Finland
Horan, Heidi; CSIRO Agriculture and Food, Brisbane, Australia
Hoogenboom, Gerrit; Agricultural and Biological Engineering Department, University of Florida, Gainesville, United States ; Global Food Systems Institute, University of Florida, Gainesville, United States
Jansson, Per-Erik; Royal Institute of Technology (KTH), Stockholm, Sweden
Jing, Qi; Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, Canada
Justes, Eric; PERSYST Department, CIRAD, Montpellier, France
Kersebaum, Kurt-Christian; Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany ; Global Change Research Institute CAS, Brno, Czech Republic ; Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), University of Göttingen, Göttingen, Germany
Launay, Marie; INRAE, US 1116 AgroClim, Avignon, France
Lewan, Elisabet; Department of Soil and Environment, Swedish University of Agricultural Sciences (SLU), Uppsala, Sweden
Liu, Ke; Tasmanian Institute of Agriculture, University of Tasmania, Launceston, Australia
Mequanint, Fasil; Institute of Soil Science and Land Evaluation, Biogeophysics, University of Hohenheim, Stuttgart, Germany
Moriondo, Marco; CNR-IBE, Firenze, Italy
Nendel, Claas; Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany ; Global Change Research Institute CAS, Brno, Czech Republic ; Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
Padovan, Gloria; Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Florence, Italy
Qian, Budong; Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, Canada
Schütze, Niels; Institute of Hydrology and Meteorology, Chair of Hydrology, Technische Universität Dresden, Dresden, Germany
Seserman, Diana-Maria; Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany
Shelia, Vakhtang; Agricultural and Biological Engineering Department, University of Florida, Gainesville, United States ; Global Food Systems Institute, University of Florida, Gainesville, United States
Souissi, Amir; Swift Current Research and Development Centre, Agriculture and Agri-Food Canada, Swift Current, Canada
Specka, Xenia; Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany
Srivastava, Amit Kumar; Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany
Trombi, Giacomo; Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Florence, Italy
Weber, Tobias K. D.; Institute of Soil Science and Land Evaluation, Biogeophysics, University of Hohenheim, Stuttgart, Germany ; Faculty of Organic Agriculture, Soil Science Section, University of Kassel, Witzenhausen, Germany
Weihermüller, Lutz; Institute of Bio- and Geosciences - IBG-3, Agrosphere, Forschungszentrum Jülich GmbH, Jülich, Germany
Wöhling, Thomas; Institute of Hydrology and Meteorology, Chair of Hydrology, Technische Universität Dresden, Dresden, Germany ; Lincoln Agritech Ltd., Hamilton, New Zealand
Seidel, Sabine J. ; Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany
DFG - Deutsche Forschungsgemeinschaft [DE] Academy of Finland [FI] Rheinische Friedrich-Wilhelms-Universität Bonn [DE]
Funding text :
Open Access funding enabled and organized by Projekt DEAL. This study was implemented as a co-operative project under the umbrella of the Agricultural Model Intercomparison and Improvement Project (AgMIP). This work was supported by the Academy of Finland through projects AI-CropPro (316172 and 315896) and DivCSA (316215) and Natural Resources Institute Finland (Luke) through a strategic project EFFI, the German Federal Ministry of Education and Research (BMBF) in the framework of the funding measure “Soil as a Sustainable Resource for the Bioeconomy - BonaRes”, project “BonaRes (Module B, Phase 3): BonaRes Centre for Soil Research, subproject B” (grant 031B1064B), the BonaRes project “I4S” (031B0513I) of the Federal Ministry of Education and Research (BMBF), Germany, the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC 2070 -390732324 EXC (PhenoRob), the Ministry of Education, Youth and Sports of Czech Republic through SustES - Adaption strategies for sustainable ecosystem services and food security under adverse environmental conditions (project no. CZ.02.1.01/0.0/0.0/16_019/000797), the Agriculture and Agri-Food Canada’s Project J-002303 “Sustainable crop production in Canada under climate change” under the Interdepartmental Research Initiative in Agriculture, the JPI FACCE MACSUR2 project, funded by the Italian Ministry for Agricultural, Food, and Forestry Policies (D.M. 24064/7303/15 of 6/Nov/2015), and the INRAE CLIMAE meta-program and AgroEcoSystem department. The order in which the donors are listed is arbitrary.
Ahuja LR, Ma L (eds) (2011) Methods of introducing system models into agricultural research. American Society of Agronomy and Soil Science Society of America, Madison, WI, USA. ISBN 13: 9780891181804
Asseng S, Ewert F, Rosenzweig C et al (2013) Uncertainty in simulating wheat yields under climate change. Nat Clim Chang 3:827–832. 10.1038/nclimate1916 DOI: 10.1038/nclimate1916
Asseng S, Martre P, Maiorano A et al (2019) Climate change impact and adaptation for wheat protein. Glob Chang Biol 25:155–173. 10.1111/gcb.14481 DOI: 10.1111/gcb.14481
Bassu S, Brisson N, Durand J-L et al (2014) How do various maize crop models vary in their responses to climate change factors? Glob Chang Biol 20:2301–2320. 10.1111/gcb.12520 DOI: 10.1111/gcb.12520
Boogaard H, Wolf J, Supit I et al (2013) A regional implementation of WOFOST for calculating yield gaps of autumn-sown wheat across the European Union. F Crop Res 143:130–142. 10.1016/j.fcr.2012.11.005 DOI: 10.1016/j.fcr.2012.11.005
Brisson N, Gary C, Justes E et al (2003) An overview of the crop model stics. Eur J Agron 18:309–332. 10.1016/S1161-0301(02)00110-7 DOI: 10.1016/S1161-0301(02)00110-7
Brisson N, Beaudoin N, Mary B, Launay M., (2009) Conceptual basis, formalisations and parameterization of the STICS crop model. Quæ. ISBN 2759202909, 9782759202904
Buis S, Lecharpentier P, Vezy R et al. (2021) SticsRPacks/CroptimizR: v0.4.0. 10.5281/ZENODO.5121194
Chakrabarti A, Ghosh JK (2011) AIC, BIC and recent advances in model selection. Philos Stat 583–605. 10.1016/B978-0-444-51862-0.50018-6
Chatelin MH, Aubry C, Poussin JC et al (2005) DéciBlé, a software package for wheat crop management simulation. Agric Syst 83:77–99. 10.1016/j.agsy.2004.03.003 DOI: 10.1016/j.agsy.2004.03.003
Confalonieri R, Orlando F, Paleari L et al (2016) Uncertainty in crop model predictions: what is the role of users? Environ Model Softw 81:165–173. 10.1016/j.envsoft.2016.04.009 DOI: 10.1016/j.envsoft.2016.04.009
Coucheney E, Buis S, Launay M et al (2015) Accuracy, robustness and behavior of the STICS soil–crop model for plant, water and nitrogen outputs: evaluation over a wide range of agro-environmental conditions in France. Environ Model Softw 64:177–190. 10.1016/j.envsoft.2014.11.024 DOI: 10.1016/j.envsoft.2014.11.024
Coucheney E, Eckersten H, Hoffmann H et al (2018) Key functional soil types explain data aggregation effects on simulated yield, soil carbon, drainage and nitrogen leaching at a regional scale. Geoderma 318:167–181. 10.1016/j.geoderma.2017.11.025 DOI: 10.1016/j.geoderma.2017.11.025
Farina R, Sándor R, Abdalla M et al (2021) Ensemble modelling, uncertainty and robust predictions of organic carbon in long-term bare-fallow soils. Glob Chang Biol 27:904–928. 10.1111/gcb.15441 DOI: 10.1111/gcb.15441
Fath B, Jorgensen SE (2011) Fundamentals of ecological modelling: applications in environmental management and research. 4th edition. Elsevier, Amsterdam. ISBN 10: 0444535675 ISBN 13: 9780444535672
Fodor N, Challinor A, Droutsas I et al (2017) Integrating plant science and crop modeling: assessment of the impact of climate change on soybean and maize production. Plant Cell Physiol 58:1833–1847. 10.1093/pcp/pcx141 DOI: 10.1093/pcp/pcx141
Gao Y, Wallach D, Hasegawa T et al (2021) Evaluation of crop model prediction and uncertainty using Bayesian parameter estimation and Bayesian model averaging. Agric For Meteorol 311:108686. 10.1016/j.agrformet.2021.108686 DOI: 10.1016/j.agrformet.2021.108686
Gate, P (1995) Ecophysiologie du blé: de la plante à la culture. Lavoisier Editeur, Paris, France p 424
Herbst M, Hellebrand HJ, Bauer J et al (2008) Multiyear heterotrophic soil respiration: evaluation of a coupled CO2 transport and carbon turnover model. Ecol Modell 214:271–283. 10.1016/j.ecolmodel.2008.02.007 DOI: 10.1016/j.ecolmodel.2008.02.007
Holzworth DP, Huth NI, deVoil PG et al (2014) APSIM – evolution towards a new generation of agricultural systems simulation. Environ Model Softw 62:327–350. 10.1016/j.envsoft.2014.07.009 DOI: 10.1016/j.envsoft.2014.07.009
Hoogenboom G, Porter CH, Boote KJ et al. (2019a) The DSSAT crop modeling ecosystem. In: Boote KJ (ed) Advances in crop modeling for a sustainable agriculture. Burleigh Dodds Science, Cambridge, United Kingdom, pp 173–216 https://doi.org/10.1201/9780429266591
Hoogenboom G, Porter CH, Shelia V et al. (2019b) Decision Support System for Agrotechnology Transfer (DSSAT) Version 4.7. In: DSSAT found. Gainesville, Florida, USA. www.DSSAT.net, Accessed 05/07/2023
Jägermeyr J, Müller C, Ruane AC et al (2021) Climate impacts on global agriculture emerge earlier in new generation of climate and crop models. Nat Food 2:873–885. 10.1038/s43016-021-00400-y DOI: 10.1038/s43016-021-00400-y
Jansson P-E (2012) CoupModel: model use, calibration, and validation. Trans ASABE 55:1337–1346. 10.13031/2013.42245 DOI: 10.13031/2013.42245
Jones JW, Hoogenboom G, Porter CH et al (2003) The DSSAT cropping system model. Eur J Agron 18:235–265. 10.1016/S1161-0301(02)00107-7 DOI: 10.1016/S1161-0301(02)00107-7
Keating B, Carberry P, Hammer G et al (2003) An overview of APSIM, a model designed for farming systems simulation. Eur J Agron 18:267–288. 10.1016/S1161-0301(02)00108-9 DOI: 10.1016/S1161-0301(02)00108-9
Kersebaum KC (2007) Modelling nitrogen dynamics in soil–crop systems with HERMES. Nutr Cycl Agroecosystems 77:39–52. 10.1007/s10705-006-9044-8 DOI: 10.1007/s10705-006-9044-8
Kersebaum KC (2011) Special features of the HERMES model and additional procedures for parameterization, calibration, validation, and applications. In: Ahuja LR, Ma L (eds) Methods of introducing system models into agricultural research. American Society of Agronomy, Madison, pp 65–94. ISBN 13: 9780891181804
Khorashadi Zadeh F, Nossent J, Woldegiorgis BT et al (2022) A fast and effective parameterization of water quality models. Environ Model Softw 149:105331. 10.1016/j.envsoft.2022.105331 DOI: 10.1016/j.envsoft.2022.105331
Klosterhalfen A, Herbst M, Weihermüller L et al (2017) Multi-site calibration and validation of a net ecosystem carbon exchange model for croplands. Ecol Modell 363:137–156. 10.1016/j.ecolmodel.2017.07.028 DOI: 10.1016/j.ecolmodel.2017.07.028
Kobayashi K (2004) Comments on another way of partitioning mean squared deviation proposed by Gauch et al. (2003). With reply. Agron J 96:1206–1207 DOI: 10.2134/agronj2004.1206
Kuha J (2004) AIC and BIC: Comparisons of assumptions and performance. Sociol Methods Res 33:188–229. 10.1177/0049124103262065 DOI: 10.1177/0049124103262065
Lawes RA, Huth ND, Hochman Z (2016) Commercially available wheat cultivars are broadly adapted to location and time of sowing in Australia’s grain zone. Eur J Agron 77:38–46. 10.1016/j.eja.2016.03.009 DOI: 10.1016/j.eja.2016.03.009
Li T, Hasegawa T, Yin X et al (2015) Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions. Glob Chang Biol 21:1328–1341. 10.1111/gcb.12758 DOI: 10.1111/gcb.12758
Martre P, Wallach D, Asseng S et al (2015) Multimodel ensembles of wheat growth: many models are better than one. Glob Chang Biol 21:911–925. 10.1111/gcb.12768 DOI: 10.1111/gcb.12768
McNunn G, Heaton E, Archontoulis S et al (2019) Using a crop modeling framework for precision cost-benefit analysis of variable seeding and nitrogen application rates. Front Sustain Food Syst 3:108. 10.3389/fsufs.2019.00108 DOI: 10.3389/fsufs.2019.00108
Meier, U (Ed.) (1997) BBCH-Monograph. Growth stages of plants. Entwicklungsstadien von Pflanzen. Estadios de las plantas. Stades dedéveloppement des plantes. Blackwell Wissenschafts-Verlag Berlin, Wien p 622
Menzel A, Yuan Y, Matiu M et al (2020) Climate change fingerprints in recent European plant phenology. Glob Chang Biol 26:2599–2612. 10.1111/gcb.15000 DOI: 10.1111/gcb.15000
Nelder JA, Mead R (1965) A simplex method for function minimization. Comput J 7:308–313. 10.1093/comjnl/7.4.308 DOI: 10.1093/comjnl/7.4.308
Nendel C, Berg M, Kersebaum K, Mirschel W (2011) The MONICA model: testing predictability for crop growth, soil moisture and nitrogen dynamics. Ecol Model 222(9):1614–1625. 10.1016/j.ecolmodel.2011.02.018 DOI: 10.1016/j.ecolmodel.2011.02.018
Piao S, Liu Q, Chen A et al. (2019) Plant phenology and global climate change: current progresses and challenges. Glob Chang Biol gcb.14619. https://doi.org/10.1111/gcb.14619
Press WH, Teukolsky SA, Vetterling WT, Flannery BP (2007) Numerical recipes : the art of scientific computing, 3rd ed. Cambridge University Press, Cambridge ISBN 978-0-521-88068-8
Rafiei V, Nejadhashemi AP, Mushtaq S et al (2022) An improved calibration technique to address high dimensionality and non-linearity in integrated groundwater and surface water models. Environ Model Softw 149:105312. 10.1016/j.envsoft.2022.105312 DOI: 10.1016/j.envsoft.2022.105312
Rezaei EE, Siebert S, Hüging H, Ewert F (2018) Climate change effect on wheat phenology depends on cultivar change. Sci Rep 8:4891. 10.1038/s41598-018-23101-2 DOI: 10.1038/s41598-018-23101-2
Senapati N, Jansson P-E, Smith P, Chabbi A (2016) Modelling heat, water and carbon fluxes in mown grassland under multi-objective and multi-criteria constraints. Environ Model Softw 80:201–224. 10.1016/j.envsoft.2016.02.025 DOI: 10.1016/j.envsoft.2016.02.025
Sisheber B, Marshall M, Mengistu D, Nelson A (2022) Tracking crop phenology in a highly dynamic landscape with knowledge-based Landsat–MODIS data fusion. Int J Appl Earth Obs Geoinf 106:102670. 10.1016/j.envsoft.2016.02.025 DOI: 10.1016/j.envsoft.2016.02.025
Soltani A, Maddah V, Sinclair TR (2013) SSM-Wheat: a simulation model for wheat development, growth and yield. Int J Plant Prod 7:711–740. 10.22069/IJPP.2013.1266 DOI: 10.22069/IJPP.2013.1266
Specka X, Nendel C, Wieland R (2015) Analysing the parameter sensitivity of the agro-ecosystem model MONICA for different crops. Eur J Agron 71:73–87. 10.1016/j.eja.2015.08.004 DOI: 10.1016/j.eja.2015.08.004
Specka X, Nendel C, Wieland R (2019) Temporal sensitivity analysis of the MONICA model: application of two global approaches to analyze the dynamics of parameter sensitivity. Agriculture 9:1–29. 10.3390/agriculture9020037 DOI: 10.3390/agriculture9020037
Stockle CO, Donatelli M, Nelson R (2001) CropSyst, a cropping systems simulation model. Eur J Agron 18:289–307. 10.1016/S1161-0301(02)00109-0. DOI: 10.1016/S1161-0301(02)00109-0
Stuble KL, Bennion LD, Kuebbing SE (2021) Plant phenological responses to experimental warming – a synthesis. Glob Chang Biol gcb.15685. https://doi.org/10.1111/gcb.15685
Tao F, Rötter RP, Palosuo T et al (2018) Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments. Glob Chang Biol 24:1291–1307. 10.1111/gcb.14019 DOI: 10.1111/gcb.14019
van Bussel LGJ, Stehfest E, Siebert S et al (2015) Simulation of the phenological development of wheat and maize at the global scale. Glob Ecol Biogeogr 24:1018–1029. 10.1111/geb.12351 DOI: 10.1111/geb.12351
Vanuytrecht E, Raes D, Steduto P et al (2014) AquaCrop: FAO’s crop water productivity and yield response model. Environ Model Softw 62:351–360. 10.1016/j.envsoft.2014.08.005 DOI: 10.1016/j.envsoft.2014.08.005
Wallach D (2011) Crop model calibration: a statistical perspective. Agron J 103:1144–1151. 10.2134/agronj2010.0432 DOI: 10.2134/agronj2010.0432
Wallach D, Martre P, Liu B et al (2018) Multimodel ensembles improve predictions of crop-environment-management interactions. Glob Chang Biol 24:5072–5083. 10.1111/gcb.14411 DOI: 10.1111/gcb.14411
Wallach D, Palosuo T, Thorburn P et al (2021) How well do crop modeling groups predict wheat phenology, given calibration data from the target population? Eur J Agron 124:126195. 10.1016/j.eja.2020.126195 DOI: 10.1016/j.eja.2020.126195
Wallach D, Palosuo T, Thorburn P et al (2021) Multi-model evaluation of phenology prediction for wheat in Australia. Agric For Meteorol 298–299:108289. 10.1016/j.agrformet.2020.108289 DOI: 10.1016/j.agrformet.2020.108289
Wallach D, Palosuo T, Thorburn P et al (2021) The chaos in calibrating crop models: lessons learned from a multi-model calibration exercise. Environ Model Softw 145:105206. 10.1016/j.envsoft.2021.105206 DOI: 10.1016/j.envsoft.2021.105206
Wang E (1997) Development of a generic process-oriented model for simulation of crop growth. Utz, Wissenschaft. ISBN 3896752332, 9783896752338
Webber H, Lischeid G, Sommer M et al. (2020) No perfect storm for crop yield failure in Germany. Environ Res Lett 15: 10.1088/1748-9326/aba2a4
Wolf J (2012) User guide for LINTUL5: Simple generic model for simulation of crop growth under potential, water limited and nitrogen, phosphorus and potassium limited conditions Research report, Wageningen University p 63
Zhang L, Zhang Z, Tao F et al (2022) Adapting to climate change precisely through cultivars renewal for rice production across China: when, where, and what cultivars will be required? Agric For Meteorol 316:108856. 10.1016/j.agrformet.2022.108856 DOI: 10.1016/j.agrformet.2022.108856
