Sugarcane model intercomparison: Structural differences and uncertainties under current and potential future climates. (October 2015)

• Record Type: Journal Article
• Authors: Marin, Fábio R.; Thorburn, Peter J.; Nassif, Daniel S.P.; Costa, Leandro G.
• Is Part Of: Environmental modelling & software. Volume 72(2015:Oct.)
• Page Start: 372
• Page End: 386
• Publication Date: 2015-10
• Subjects: Sugarcane -- Simulation -- Uncertainty -- Calibration -- Model intercomparison -- Process-based model -- Climate change; 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.2015.02.019
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Sugarcane is one of the world's main carbohydrates sources. We analysed the APSIM-Sugar (AS) and DSSAT/CANEGRO (DC) models to determine their structural differences, and how these differences affect their predictions of crop growth and production. The AS model under predicted yield at the hotter sites, because the algorithm for computing the degree-days is based in only one upper cardinal temperature. The models did not accurately predict canopy and stalk development through time using growth parameters values developed from observed data, in combination with previously determined RUE for the cultivars. In response to elevated CO2, both predicted higher yields, although AS showed higher sensitivity to CO2 concentration, rainfall and temperature than DC. The Mean of simulations from both models produced better estimations than predictions from either model individually. Thus, applying the two models (in their current form) is likely to give the more accurate predictions than focusing on one model alone. Highlights: APSIM-Sugar and DSSAT/CANEGRO under predicted biomass when calibrated with crop growth data, but keeping previous RUE values. AS showed higher sensitivity to rainfall, CO2 and temperature reduction than DC. The mean of simulations from both models produced better estimations than predictions from either model individually. Applying two models is likely to give more accurate predictions than focusing on one model alone.