Soil attributes and leaf nitrogen estimating sugar cane quality parameters: Brix, pol and fibre

Research paper by F. A. Rodrigues, P. S. G. Magalhães, H. C. J. Franco

Indexed on: 04 Dec '12Published on: 04 Dec '12Published in: Precision Agriculture


The area of sugar cane production in Brazil has substantially increased in the last few years due to the high demand for ethanol. It is estimated that the actual area, which is approximately 8 Mha, will increase to as much as 15 Mha in the next 10 years. In addition to enlarging the boundaries and installing new industrial units, sugar cane expansion demands better use of production areas and improvement of both yield and quality, combined with a reduction of production costs. Thus, models that can describe the behaviour of sugar cane quality parameters could be important in understanding the effects of soil and plant attributes on these parameters. The objective of this work was to fit mathematical models to the sugar cane Brix, pol and fibre parameters using physical soil attributes, chemical soil attributes and leaf nitrogen as predictors from the previous year. This work was carried out in an area of 10 ha located in Araras, SP, Brazil, from November 2008 until July 2011 in the first (plant cane), second (first ratoon) and third (second ratoon) cycles of the crop. The chemical soil attributes analysed were the macronutrients and micronutrients, and the soil physical attribute analysed was the soil texture. The variables used in the models were chosen using principal component analysis (PCA), and the fit of the models was made as the mean of multiple regressions. The results were compared using kriging to map the Brix, pol and fibre with the true and estimated values. The Brix, pol and fibre models presented R2 values of 0.17, 0.06 and 0.18, respectively, for the first ratoon of the crop and 0.23, 0.19 and 0.52, respectively, for the second ratoon. These results allowed the estimation of Brix, pol and fibre with estimation errors less than 1 % for the first and second ratoons. The PCA approach identified soil organic matter, phosphorus and potassium as the soil attributes that had the higher variance of the dataset during the years studied.