The advent of Information Technology in agriculture, surveying and data collection became a simple task, starting the era of "Big Data" in agricultural production. Currently, a large volume of data and information associated with the plant, soil and climate are collected quick and easily. These factors influence productivity, operating costs, investments and environment impacts. However, a major challenge for this area is the transformation of data and information (collected in the field) in applicable knowledge. Within the context of Precision Agriculture (PA), which comprises a set of tools and technologies for georeferenced data collection to understand and manage inherent spatial variability within crop fields, the Brazilian sugarcane industry lacks results to assist farmers. The hypothesis of this work is that with the knowledge of the spatial variability of soil fertility and crop productivity, through the application of data mining techniques, it is possible to assist sugarcane producer in the correct management of the crop. Two areas cultivated with sugarcane, with 10 and 30 ha, were monitored over the years 2012, 2013 and 2014. During this period, soil sampling was taking annually (117 and 107 points, respectively) and yield maps registered using a yield monitor. Using a computational environment created to support sugarcane agricultural research, data acquisition, formatting, verification, storage, and analysis of the principal component analysis (PCA) and decision trees for knowledge extraction were performed. The results show that a major factor for variation of sugarcane crops yield is related to texture, the amount of organic matter available and soil pH. Where there was an increase in the levels of organic matter from one year to another there was an increase in capacity cation exchange (CTC) and greater availability of Potassium and Phosphorus. Based on the knowledge rules by a decision trees analysis, it is possible to created specific management zones in the field that support the grower in a decision making. With the expanded dataset, we expect to recognize relevant patterns that are reproduced consistently across distinct experiments, assisting producers in the correct crop management to improving the profitability of production.