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Multi-metric evaluation of the models WARM, CropSyst, and WOFOST for rice

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  • Confalonieri, Roberto
  • Acutis, Marco
  • Bellocchi, Gianni
  • Donatelli, Marcello

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, 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). On an average, the three models performed similarly for individual validation metrics such as modelling efficiency (EF>0.90) and correlation coefficient (R>0.98). WARM performed best in a weighed measure of the Akaike Information Criterion: (worst) 0

Suggested Citation

  • Confalonieri, Roberto & Acutis, Marco & Bellocchi, Gianni & Donatelli, Marcello, 2009. "Multi-metric evaluation of the models WARM, CropSyst, and WOFOST for rice," Ecological Modelling, Elsevier, vol. 220(11), pages 1395-1410.
    • Handle: RePEc:eee:ecomod:v:220:y:2009:i:11:p:1395-1410
    DOI: 10.1016/j.ecolmodel.2009.02.017
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    1. Nana, E. & Corbari, C. & Bocchiola, D., 2014. "A model for crop yield and water footprint assessment: Study of maize in the Po valley," Agricultural Systems, Elsevier, vol. 127(C), pages 139-149.
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    15. Pagani, Valentina & Guarneri, Tommaso & Busetto, Lorenzo & Ranghetti, Luigi & Boschetti, Mirco & Movedi, Ermes & Campos-Taberner, Manuel & Garcia-Haro, Francisco Javier & Katsantonis, Dimitrios & Stav, 2019. "A high-resolution, integrated system for rice yield forecasting at district level," Agricultural Systems, Elsevier, vol. 168(C), pages 181-190.
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