Title: Multi-Metric Evaluation of the Models WARM, CropSyst, and WOFOST for Rice
Authors: CONFALONIERI ROBERTOACUTIS MarcoBELLOCCHI GIANNIDONATELLI Marcello
Citation: ECOLOGICAL MODELLING vol. 220 no. 11 p. 1395-1410
Publisher: ELSEVIER SCIENCE BV
Publication Year: 2009
JRC N°: JRC41603
ISSN: 0304-3800
URI: http://dx.doi.org/10.1016/j.ecolmodel.2009.02.017
http://publications.jrc.ec.europa.eu/repository/handle/JRC41603
DOI: 10.1016/j.ecolmodel.2009.02.017
Type: Articles in Journals
Abstract: WARM (Water Accounting Rice Model) simulates paddy rice (Oryza sativa L.), based on temperature-driven development and radiation-driven crop growth. It also simulates: biomass partitioning, floodwater effect on temperature, spikelet sterility, effect of diseases, floodwater and chemicals management, and soil hydrology. Biomass estimates from WARM were evaluated and compared with the ones from two generic crop models (CropSyst, WOFOST). The test-area was the Po Valley (Italy). Data collected at six sites from 1989 to 2004 from rice crops grown under flooded and non-limiting conditions were split into a calibration (to estimate some model parameters) and a validation set. For model evaluation, a fuzzy-logic based multiple-metrics indicator (MQI) was used: 0 (best)=MQI=1 (worst). WARM estimates compared well with the actual data (mean MQI=0.037 against 0.167 and 0.173 with CropSyst and WOFOST, respectively). In average, the three models performed similarly for individual validation metrics such as modeling efficiency (EF>0.90) and correlation coefficient (R>0.98). WARM performed best in a weighed measure of the Akaike Information Criterion: (worst) 0<wk<1 (best), considering estimation accuracy and number of parameters required to achieve it (mean wk=0.983 against 0.007 and ~0.000 with CropSyst and WOFOST, respectively). WARM resulted sensitive to 30% of its parameters (ratio being lower with both CropSyst, <10%, and WOFOST, <20%), but appeared the easiest model to use because of the lowest number of crop parameters required (10 against 15 and 34 with CropSyst and WOFOST, respectively). This study demonstrates the importance of using a range of assessment metrics to evaluate model estimates, effectiveness, and complexity.
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