Analysis and classification of data sets for calibration and validation of agro-ecosystem models. (October 2015)

• Record Type: Journal Article
• Authors: Kersebaum, K.C.; Boote, K.J.; Jorgenson, J.S.; Nendel, C.; Bindi, M.; Frühauf, C.; Gaiser, T.; Hoogenboom, G.; Kollas, C.; Olesen, J.E.; Rötter, R.P.; Ruget, F.; Thorburn, P.J.; Trnka, M.; Wegehenkel, M.
• Is Part Of: Environmental modelling & software. Volume 72(2015:Oct.)
• Page Start: 402
• Page End: 417
• Publication Date: 2015-10
• Subjects: Field experiments -- Data quality -- Crop modelling -- Data requirement -- Minimum data -- Software; 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.2015.05.009
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Experimental field data are used at different levels of complexity to calibrate, validate and improve agro-ecosystem models to enhance their reliability for regional impact assessment. A methodological framework and software are presented to evaluate and classify data sets into four classes regarding their suitability for different modelling purposes. Weighting of inputs and variables for testing was set from the aspect of crop modelling. The software allows users to adjust weights according to their specific requirements. Background information is given for the variables with respect to their relevance for modelling and possible uncertainties. Examples are given for data sets of the different classes. The framework helps to assemble high quality data bases, to select data from data bases according to modellers requirements and gives guidelines to experimentalists for experimental design and decide on the most effective measurements to improve the usefulness of their data for modelling, statistical analysis and data assimilation. Highlights: A software is presented to classify and label data suitability for modelling. Data requirements for modelling are specific and vary with model purpose. Quantitative classification of data sets facilitates their use for modelling. Test of model and data consistency improves data usability. Guidelines for experimentalist to improve data suitability for modelling.