Sugarcane model intercomparison: Structural differences and uncertainties under current and potential future climates |
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| Affiliation: | 1. University of São Paulo (USP), “Luiz de Queiroz” College of Agriculture (ESALQ), PO Box 09, Piracicaba, SP 13418-900, Brazil;2. CSIRO, Agriculture Flagship, GPO Box 2583, Brisbane, QLD 4001, Australia;1. Federal University of Paraíba, João Pessoa, Brazil;2. Department of Renewable Energy Engineering, Federal University of Paraíba, Cidade Universitária, Campus I, 58051-970, João Pessoa, PB, Brazil;1. University of São Paulo, “Luiz de Queiroz” College of Agriculture, Piracicaba, SP 13418-900, Brazil;2. James Cook University, College of Science, Technology and Engineering, Townsville, QLD 4811, Australia;3. Alvean, São Paulo, SP 01311-000, Brazil;4. Gordian Energy and Terracal Alimentos e Bioenergia, Barra da Tijuca, RJ 22640-100, Brazil;1. College of Science, Technology and Engineering, James Cook University, Townsville, Australia;2. Centre for Tropical Environmental & Sustainability Science, Australia |
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| 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. |
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| Keywords: | Sugarcane Simulation Uncertainty Calibration Model intercomparison Process-based model Climate change |
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