A calibration protocol for soil-crop models

• DOI: 10.1016/j.envsoft.2024.106147
• Publication Date: 2024-07-17T23:58:14Z
• Authors: Daniel Wallach; Samuel Buis; Diana-Maria Seserman; Taru Palosuo; Peter J. Thorburn; Henrike Mielenz; Eric Justes; Kurt-Christian Ke...; Benjamin Dumont; Marie Launay; Sabine Julia Seidel
• Keywords: Akaike information criterion; http://aims.fao.org/aos/agrovoc/c_24242; http://aims.fao.org/aos/agrovoc/c_37938; F08 - Systèmes et modes de culture; télédétection; adaptation aux changements climatiques; modèle de simulation; modélisation des cultures; crop models; Weighted least squares; http://aims.fao.org/aos/agrovoc/c_9000024; modèle mathématique; http://aims.fao.org/aos/agrovoc/c_1374567058134; [SDV.EE]Life Sciences [q-bio]/Ecology; parameter selection; http://aims.fao.org/aos/agrovoc/c_3081; http://aims.fao.org/aos/agrovoc/c_1666; essai de variété; http://aims.fao.org/aos/agrovoc/c_6498; [SDV.EE]Life Sciences [q-bio]/Ecology, environment; http://aims.fao.org/aos/agrovoc/c_24199; changement climatique; Parameter selection; U10 - Informatique, mathématiques et statistiques; 04 agricultural and veterinary sciences; http://aims.fao.org/aos/agrovoc/c_26833; weighted least squares; 0401 agriculture, forestry, and fisheries; Crop models; environment; évaluation de l'impact

AbstractProcess-based soil-crop models are widely used in agronomic research. They are major tools for evaluating climate change impact on crop production. Multi-model simulation studies show a wide diversity of results among models, implying that simulation results are very uncertain. A major path to improving simulation results is to propose improved calibration practices that are widely applicable. This study proposes an innovative generic calibration protocol. The two major innovations concern the treatment of multiple output variables and the choice of parameters to estimate, both of which are based on standard statistical procedure adapted to the particularities of soil-crop models. The protocol performed well in a challenging artificial-data test. The protocol is formulated so as to be applicable to a wide range of models and data sets. If widely adopted, it could substantially reduce model error and inter-model variability, and thus increase confidence in soil-crop model simulations.