Modeling sugarcane ripening as a function of accumulated rainfall in Southern Brazil

• Autor(a) principal: Cardozo, Nilceu P.
• Data de Publicação: 2015
• Outros Autores: Sentelhas, Paulo C., Panosso, Alan R. [UNESP], Palhares, Antonio L., Ide, Bernardo Y.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1007/s00484-015-0998-6; http://hdl.handle.net/11449/172241
• topic: Empirical models; Rainfall; Saccharum spp; Total recoverable sugar

The effect of weather variables on sugarcane ripening is a process still not completely understood, despite its huge impact on the quality of raw material for the sugar energy industry. The aim of the present study was to evaluate the influence of weather variables on sugarcane ripening in southern Brazil, propose empirical models for estimating total recoverable sugar (TRS) content, and evaluate the performance of these models with experimental and commercial independent data from different regions. A field experiment was carried out in Piracicaba, in the state of São Paulo, Brazil, considering eight sugarcane cultivars planted monthly, from March to October 2002. In 2003, at the harvest, 12 months later, samples were collected to evaluate TRS (kg t−1). TRS and weather variables (air temperature, solar radiation, relative humidity, and rainfall) were analyzed using descriptive and multivariate statistical analysis to understand their interactions. From these correlations, variables were selected to generate empirical models for estimating TRS, according to the cultivar groups and their ripening characteristics (early, mid, and late). These models were evaluated by residual analysis and regression analysis with independent experimental data from two other locations in the same years and with independent commercial data from six different locations from 2005 to 2010. The best performances were found with exponential models which considered cumulative rainfall during the 120 days before harvest as an independent variable (R2 adj ranging from 0.92 to 0.95). Independent evaluations revealed that our models were capable of estimating TRS with reasonable to high precision (R2 adj ranging from 0.66 to 0.99) and accuracy (D index ranging from 0.90 to 0.99), and with low mean absolute percentage errors (MAPE ≤ 5 %), even in regions with different climatic conditions.