Uncertainty in crop model predictions: What is the role of users?. (July 2016)

• Record Type: Journal Article
• Authors: Confalonieri, Roberto; Orlando, Francesca; Paleari, Livia; Stella, Tommaso; Gilardelli, Carlo; Movedi, Ermes; Pagani, Valentina; Cappelli, Giovanni; Vertemara, Andrea; Alberti, Luigi; Alberti, Paolo; Atanassiu, Samuel; Bonaiti, Matteo; Cappelletti, Giovanni; Ceruti, Matteo; Confalonieri, Andrea; Corgatelli, Gabriele; Corti, Paolo; Dell'Oro, Michele; Ghidoni, Alessandro; Lamarta, Angelo; Maghini, Alberto; Mambretti, Martino; Manchia, Agnese; Massoni, Gianluca; Mutti, Pierangelo; Pariani, Stefano; Pasini, Davide; Pesenti, Andrea; Pizzamiglio, Giovanni; Ravasio, Adriano; Rea, Alessandro; Santorsola, David; Serafini, Giulia; Slavazza, Marco; Acutis, Marco;
• Is Part Of: Environmental modelling & software. Volume 81(2016:Jul.)
• Page Start: 165
• Page End: 173
• Publication Date: 2016-07
• Subjects: Calibration -- Maize -- Model ensemble -- Parameter uncertainty -- Rapeseed -- Uncertainty in predictions; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2016.04.009
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Crop models are used to estimate crop productivity under future climate projections, and modellers manage uncertainty by considering different scenarios and GCMs, using a range of crop simulators. Five crop models and 20 users were arranged in a randomized block design with four replicates. Parameters for maize (well studied by modellers) and rapeseed (almost ignored) were calibrated. While all models were accurate for maize (RRMSE from 16.5% to 25.9%), they were, to some extent, unsuitable for rapeseed. Although differences between biomass simulated by the models were generally significant for rapeseed, they were significant only in 30% of the cases for maize. This could suggest that in case of models well suited to a crop, user subjectivity (which explained 14% of total variance in maize outputs) can hide differences in model algorithms and, consequently, the uncertainty due to parameterization should be better investigated. Highlights: Five crop models and 20 users were arranged in four randomized blocks. The significance of model factor for maize and rapeseed was evaluated. All models achieved good performance for maize and poor for rapeseed. Differences between models were significant only in 30% of the cases for maize. Parameterization uncertainty should be explicitly managed also in model ensembles.