Proposal and extensive test of a calibration protocol for crop phenology models
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%.
Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
---|---|
Format: | article biblioteca |
Language: | eng |
Subjects: | F01 - Culture des plantes, F40 - Écologie végétale, U10 - Informatique, mathématiques et statistiques, modèle de simulation, modélisation des cultures, phénologie, modèle mathématique, méthode statistique, essai de variété, changement climatique, modélisation, http://aims.fao.org/aos/agrovoc/c_24242, http://aims.fao.org/aos/agrovoc/c_9000024, http://aims.fao.org/aos/agrovoc/c_5774, http://aims.fao.org/aos/agrovoc/c_24199, http://aims.fao.org/aos/agrovoc/c_7377, http://aims.fao.org/aos/agrovoc/c_26833, http://aims.fao.org/aos/agrovoc/c_1666, http://aims.fao.org/aos/agrovoc/c_230ab86c, http://aims.fao.org/aos/agrovoc/c_3081, http://aims.fao.org/aos/agrovoc/c_714, |
Online Access: | http://agritrop.cirad.fr/606064/ http://agritrop.cirad.fr/606064/1/Wallach%26al2023_ASD.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
dig-cirad-fr-606064 |
---|---|
record_format |
koha |
institution |
CIRAD FR |
collection |
DSpace |
country |
Francia |
countrycode |
FR |
component |
Bibliográfico |
access |
En linea |
databasecode |
dig-cirad-fr |
tag |
biblioteca |
region |
Europa del Oeste |
libraryname |
Biblioteca del CIRAD Francia |
language |
eng |
topic |
F01 - Culture des plantes F40 - Écologie végétale U10 - Informatique, mathématiques et statistiques modèle de simulation modélisation des cultures phénologie modèle mathématique méthode statistique essai de variété changement climatique modélisation http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_9000024 http://aims.fao.org/aos/agrovoc/c_5774 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_26833 http://aims.fao.org/aos/agrovoc/c_1666 http://aims.fao.org/aos/agrovoc/c_230ab86c http://aims.fao.org/aos/agrovoc/c_3081 http://aims.fao.org/aos/agrovoc/c_714 F01 - Culture des plantes F40 - Écologie végétale U10 - Informatique, mathématiques et statistiques modèle de simulation modélisation des cultures phénologie modèle mathématique méthode statistique essai de variété changement climatique modélisation http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_9000024 http://aims.fao.org/aos/agrovoc/c_5774 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_26833 http://aims.fao.org/aos/agrovoc/c_1666 http://aims.fao.org/aos/agrovoc/c_230ab86c http://aims.fao.org/aos/agrovoc/c_3081 http://aims.fao.org/aos/agrovoc/c_714 |
spellingShingle |
F01 - Culture des plantes F40 - Écologie végétale U10 - Informatique, mathématiques et statistiques modèle de simulation modélisation des cultures phénologie modèle mathématique méthode statistique essai de variété changement climatique modélisation http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_9000024 http://aims.fao.org/aos/agrovoc/c_5774 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_26833 http://aims.fao.org/aos/agrovoc/c_1666 http://aims.fao.org/aos/agrovoc/c_230ab86c http://aims.fao.org/aos/agrovoc/c_3081 http://aims.fao.org/aos/agrovoc/c_714 F01 - Culture des plantes F40 - Écologie végétale U10 - Informatique, mathématiques et statistiques modèle de simulation modélisation des cultures phénologie modèle mathématique méthode statistique essai de variété changement climatique modélisation http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_9000024 http://aims.fao.org/aos/agrovoc/c_5774 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_26833 http://aims.fao.org/aos/agrovoc/c_1666 http://aims.fao.org/aos/agrovoc/c_230ab86c http://aims.fao.org/aos/agrovoc/c_3081 http://aims.fao.org/aos/agrovoc/c_714 Wallach, Daniel Palosuo, Taru Thorburn, Peter J. Mielenz, Henrike Buis, Samuel Hochman, Zvi Gourdain, Emmanuelle Andrianasolo, Fety Dumont, Benjamin Ferrise, Roberto Gaiser, Thomas Garcia, Cécile Gayler, Sebastian Harrison, Matthew Hiremath, Santosh Horan, Heidi Hoogenboom, Gerrit Jansson, Per-Erik Jing, Qi Justes, Eric Kersebaum, Kurt Christian Launay, Marie Lewan, Elisabet Liu, Ke Mequanint, Fasil Moriondo, Marco Nendel, Claas Padovan, Gloria Qian, Budong Schütze, Niels Seserman, Diana‑Maria Shelia, Vakhtang Souissi, Amir Specka, Xenia Srivastava, Amit Kumar Trombi, Giacomo Weber, Tobias K. D. Weihermüller, Lutz Wöhling, Thomas Seidel, Sabine I. Proposal and extensive test of a calibration protocol for crop phenology models |
description |
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%. |
format |
article |
topic_facet |
F01 - Culture des plantes F40 - Écologie végétale U10 - Informatique, mathématiques et statistiques modèle de simulation modélisation des cultures phénologie modèle mathématique méthode statistique essai de variété changement climatique modélisation http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_9000024 http://aims.fao.org/aos/agrovoc/c_5774 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_26833 http://aims.fao.org/aos/agrovoc/c_1666 http://aims.fao.org/aos/agrovoc/c_230ab86c http://aims.fao.org/aos/agrovoc/c_3081 http://aims.fao.org/aos/agrovoc/c_714 |
author |
Wallach, Daniel Palosuo, Taru Thorburn, Peter J. Mielenz, Henrike Buis, Samuel Hochman, Zvi Gourdain, Emmanuelle Andrianasolo, Fety Dumont, Benjamin Ferrise, Roberto Gaiser, Thomas Garcia, Cécile Gayler, Sebastian Harrison, Matthew Hiremath, Santosh Horan, Heidi Hoogenboom, Gerrit Jansson, Per-Erik Jing, Qi Justes, Eric Kersebaum, Kurt Christian Launay, Marie Lewan, Elisabet Liu, Ke Mequanint, Fasil Moriondo, Marco Nendel, Claas Padovan, Gloria Qian, Budong Schütze, Niels Seserman, Diana‑Maria Shelia, Vakhtang Souissi, Amir Specka, Xenia Srivastava, Amit Kumar Trombi, Giacomo Weber, Tobias K. D. Weihermüller, Lutz Wöhling, Thomas Seidel, Sabine I. |
author_facet |
Wallach, Daniel Palosuo, Taru Thorburn, Peter J. Mielenz, Henrike Buis, Samuel Hochman, Zvi Gourdain, Emmanuelle Andrianasolo, Fety Dumont, Benjamin Ferrise, Roberto Gaiser, Thomas Garcia, Cécile Gayler, Sebastian Harrison, Matthew Hiremath, Santosh Horan, Heidi Hoogenboom, Gerrit Jansson, Per-Erik Jing, Qi Justes, Eric Kersebaum, Kurt Christian Launay, Marie Lewan, Elisabet Liu, Ke Mequanint, Fasil Moriondo, Marco Nendel, Claas Padovan, Gloria Qian, Budong Schütze, Niels Seserman, Diana‑Maria Shelia, Vakhtang Souissi, Amir Specka, Xenia Srivastava, Amit Kumar Trombi, Giacomo Weber, Tobias K. D. Weihermüller, Lutz Wöhling, Thomas Seidel, Sabine I. |
author_sort |
Wallach, Daniel |
title |
Proposal and extensive test of a calibration protocol for crop phenology models |
title_short |
Proposal and extensive test of a calibration protocol for crop phenology models |
title_full |
Proposal and extensive test of a calibration protocol for crop phenology models |
title_fullStr |
Proposal and extensive test of a calibration protocol for crop phenology models |
title_full_unstemmed |
Proposal and extensive test of a calibration protocol for crop phenology models |
title_sort |
proposal and extensive test of a calibration protocol for crop phenology models |
url |
http://agritrop.cirad.fr/606064/ http://agritrop.cirad.fr/606064/1/Wallach%26al2023_ASD.pdf |
work_keys_str_mv |
AT wallachdaniel proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT palosuotaru proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT thorburnpeterj proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT mielenzhenrike proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT buissamuel proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT hochmanzvi proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT gourdainemmanuelle proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT andrianasolofety proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT dumontbenjamin proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT ferriseroberto proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT gaiserthomas proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT garciacecile proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT gaylersebastian proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT harrisonmatthew proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT hiremathsantosh proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT horanheidi proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT hoogenboomgerrit proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT janssonpererik proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT jingqi proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT justeseric proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT kersebaumkurtchristian proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT launaymarie proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT lewanelisabet proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT liuke proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT mequanintfasil proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT moriondomarco proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT nendelclaas proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT padovangloria proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT qianbudong proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT schutzeniels proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT sesermandianamaria proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT sheliavakhtang proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT souissiamir proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT speckaxenia proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT srivastavaamitkumar proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT trombigiacomo proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT webertobiaskd proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT weihermullerlutz proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT wohlingthomas proposalandextensivetestofacalibrationprotocolforcropphenologymodels AT seidelsabinei proposalandextensivetestofacalibrationprotocolforcropphenologymodels |
_version_ |
1792500612330422272 |
spelling |
dig-cirad-fr-6060642024-02-16T19:02:24Z http://agritrop.cirad.fr/606064/ http://agritrop.cirad.fr/606064/ Proposal and extensive test of a calibration protocol for crop phenology models. Wallach Daniel, Palosuo Taru, Thorburn Peter J., Mielenz Henrike, Buis Samuel, Hochman Zvi, Gourdain Emmanuelle, Andrianasolo Fety, Dumont Benjamin, Ferrise Roberto, Gaiser Thomas, Garcia Cécile, Gayler Sebastian, Harrison Matthew, Hiremath Santosh, Horan Heidi, Hoogenboom Gerrit, Jansson Per-Erik, Jing Qi, Justes Eric, Kersebaum Kurt Christian, Launay Marie, Lewan Elisabet, Liu Ke, Mequanint Fasil, Moriondo Marco, Nendel Claas, Padovan Gloria, Qian Budong, Schütze Niels, Seserman Diana‑Maria, Shelia Vakhtang, Souissi Amir, Specka Xenia, Srivastava Amit Kumar, Trombi Giacomo, Weber Tobias K. D., Weihermüller Lutz, Wöhling Thomas, Seidel Sabine I.. 2023. Agronomy for Sustainable Development, 43:4, 14 p.https://doi.org/10.1007/s13593-023-00900-0 <https://doi.org/10.1007/s13593-023-00900-0> Proposal and extensive test of a calibration protocol for crop phenology models Wallach, Daniel Palosuo, Taru Thorburn, Peter J. Mielenz, Henrike Buis, Samuel Hochman, Zvi Gourdain, Emmanuelle Andrianasolo, Fety Dumont, Benjamin Ferrise, Roberto Gaiser, Thomas Garcia, Cécile Gayler, Sebastian Harrison, Matthew Hiremath, Santosh Horan, Heidi Hoogenboom, Gerrit Jansson, Per-Erik Jing, Qi Justes, Eric Kersebaum, Kurt Christian Launay, Marie Lewan, Elisabet Liu, Ke Mequanint, Fasil Moriondo, Marco Nendel, Claas Padovan, Gloria Qian, Budong Schütze, Niels Seserman, Diana‑Maria Shelia, Vakhtang Souissi, Amir Specka, Xenia Srivastava, Amit Kumar Trombi, Giacomo Weber, Tobias K. D. Weihermüller, Lutz Wöhling, Thomas Seidel, Sabine I. eng 2023 Agronomy for Sustainable Development F01 - Culture des plantes F40 - Écologie végétale U10 - Informatique, mathématiques et statistiques modèle de simulation modélisation des cultures phénologie modèle mathématique méthode statistique essai de variété changement climatique modélisation http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_9000024 http://aims.fao.org/aos/agrovoc/c_5774 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_26833 http://aims.fao.org/aos/agrovoc/c_1666 http://aims.fao.org/aos/agrovoc/c_230ab86c France Australie http://aims.fao.org/aos/agrovoc/c_3081 http://aims.fao.org/aos/agrovoc/c_714 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%. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/606064/1/Wallach%26al2023_ASD.pdf text cc_by info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1007/s13593-023-00900-0 10.1007/s13593-023-00900-0 info:eu-repo/semantics/altIdentifier/doi/10.1007/s13593-023-00900-0 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1007/s13593-023-00900-0 info:eu-repo/grantAgreement/////(USA) Agricultural Model Intercomparison and Improvement Project/AgMIP info:eu-repo/grantAgreement/////(DEU) Soil as a Sustainable Resource for the Bioeconomy/BonaRes info:eu-repo/grantAgreement/////(CZE) Adaption strategies for sustainable ecosystem services and food security under adverse environmental conditions/SustES |