Desenvolvimento de um modelo biofísico de crescimento da cana-de-açúcar para estudos globais

• Ano de defesa: 2010
• Autor(a) principal: Cuadra, Santiago Vianna
• Orientador(a): Costa, Marcos Heil
• Banca de defesa: Nunes, Edson Luis , Hamakawa, Paulo José , Costa, José Maria Nogueira da
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Viçosa
• Programa de Pós-Graduação: Doutorado em Meteorologia Agrícola
• Departamento: Agrometeorologia; Climatologia; Micrometeorologia
• País: BR
• Palavras-chave em Português: Modelagem agrometeorológica; Cana de açúcar; Modelos mecanicistas de crescimento de culturas; Interação biosfera-atmosfera
• Palavras-chave em Inglês: Agrometeorological modeling; Sugarcane; Mechanistic models of crop growth; Biosphere-atmosphere interaction
• Área do conhecimento CNPq: CNPQ::CIENCIAS EXATAS E DA TERRA::GEOCIENCIAS::METEOROLOGIA::CLIMATOLOGIA
• Link de acesso: http://locus.ufv.br/handle/123456789/1500

Global agricultural lands are predicted to expand over the next few decades due to a combination of increasing global population, the need for increased food production, and exponential growth in crop-based biofuels production. The resultant changes in land use could, in turn, greatly impact biogeochemical and biogeophysical cycles across the globe. Therefore, representation of biofuel crops such as sugarcane should be integrated into the latest models to ensure that accurate simulations of the bidirectional feedbacks between the land surface and atmosphere take place. In this study, we present a new process-based sugarcane model, included as a module within the Agro- IBIS dynamic agro-ecosystem model, that can be applied at multiple spatial scales (from site to global). The model may be coupled to an atmospheric model, allowing the simulation of the bi-directional interactions between atmosphere and the sugarcane cropping system. It includes a series of equations and crop management parameters that differ from the standard formulations for annual crops. The model is evaluated against micrometeorological and biomass observations, obtained for one crop cycle (391 days) in northern São Paulo state (Brazil), and yield at a range of spatial scales. The results of the micro-meteorological validation suggest that the model produces robust simulations of seasonal and daily albedo fluctuations, dry biomass, and relationships between net ecosystem exchange (NEE) and atmospheric variables (temperature and relative humidity). At the site level, the model accurately simulated the intensity and variability of daily evapotranspiration (ET) for two consecutive crop cycles at an experimental site at Kalamia estate, northeast Australia. The model also accurately simulated the average yield for the four largest mesoregions (clusters of municipalities) in the state of São Paulo (Brazil), over a period of 16 years, with a relative bias of -0.68% to 1.08%. Finally, a simulation of the annual average sugarcane yield over 31 years for the State of Louisiana (U.S.) produced a low relative bias (-2.67%) but exhibited lower yiled interannual variability than the estimated yields. Considering the results of all validations, we conclude that the new model is able to accurately capture the relationship between yield and climate variability (temperature and precipitation), indicating that the module may be successfully used to forecast changes in sugarcane cropping systems and associated climate interactions.