The chaos in calibrating crop models: Lessons learned from a multi-model calibration exercise

• DOI: 10.1016/j.envsoft.2021.105206
• Publication Date: 2021-09-20T10:21:09Z
• Authors: Daniel Wallach; Taru Palosuo; Peter Thorburn; Zvi Hochman; Emmanuelle Gourdain; Fety Andrianasolo; Senthold Asseng; Bruno Basso; Samuel Buis; Neil Crout; Camilla Dibari; Benjamin Dumont; Roberto Ferrise; Thomas Gaiser; Cecile Garcia; Sebastian Gayler; Afshin Ghahramani; Santosh Hiremath; Steven Hoek; Heidi Horan; Gerrit Hoogenboom; Mingxia Huang; Mohamed Jabloun; Per-Erik Jansson; Qi Jing; Eric Justes; Kurt Christian Ke...; Anne Klosterhalfen; Marie Launay; Elisabet Lewan; Qunying Luo; Bernardo Maestrini; Henrike Mielenz; Marco Moriondo; Hasti Nariman Zadeh; Gloria Padovan; Jørgen Eivind Olesen; Arne Poyda; Eckart Priesack; Johannes Wilhelmu...; Budong Qian; Niels Schütze; Vakhtang Shelia; Amir Souissi; Xenia Specka; Amit Kumar Srivas...; Tommaso Stella; Thilo Streck; Giacomo Trombi; Evelyn Wallor; Jing Wang; Tobias K.D. Weber; Lutz Weihermüller; Allard de Wit; Thomas Wöhling; Liujun Xiao; Chuang Zhao; Yan Zhu; Sabine J. Seidel
• Keywords: ANZSRC::300207 Agricultural systems analysis and modelling; 0208 environmental biotechnology; 610; Process-based models; 02 engineering and technology; phenology; ANZSRC::461207 Software quality; Parameter estimation; ANZSRC::419999 Other environmental sciences not elsewhere classified; ta113; processes and metrics; calibration recommendations; Calibration recommendations; 600; Calibration recommendations; Parameter estimation; Phenology; Process-based models; ta4111; ANZSRC::401102 Environmentally sustainable engineering; process-based models; [STAT]Statistics [stat]; Phenology; Calibration Recommendations ; Process-based Models ; Parameter Estimation ; Phenology; [SDE]Environmental Sciences; parameter estimation; info:eu-repo/classification/ddc/004

Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in many applications of process-based models and has an important impact on simulated values. We propose a novel method of developing guidelines for calibration of process-based models, based on development of recommendations for calibration of the phenology component of crop models. The approach was based on a multi-model study, where all teams were provided with the same data and asked to return simulations for the same conditions. All teams were asked to document in detail their calibration approach, including choices with respect to criteria for best parameters, choice of parameters to estimate and software. Based on an analysis of the advantages and disadvantages of the various choices, we propose calibration recommendations that cover a comprehensive list of decisions and that are based on actual practices.