| Grant number: | 12/50049-3 |
| Support type: | Program for Research on Bioenergy (BIOEN) - Research Partnership for Technological Innovation (PITE) |
| Duration: | April 01, 2013 - March 31, 2015 |
| Field of knowledge: | Agronomical Sciences - Agricultural Engineering |
| Cooperation agreement: | Odebrecht Agroindustrial |
| Principal researcher: | Luiz Henrique Antunes Rodrigues |
| Grantee: | Luiz Henrique Antunes Rodrigues |
| Home Institution: | Faculdade de Engenharia Agrícola (FEAGRI). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil |
| Company: | ETH Energia S/A |
| City: | Campinas |
Abstract
For the agricultural planning of sugarcane crop, it is necessary to understand the relationship between the crop yield and the production environment, management performed, climate, and variety and maturity of the plant. Currently, the most widely used yield estimates are subjective; experts perform this work based on their experience and field evaluations. There are two crop yield predictive models: ecophysiological, based on the physiological processes, and empirical-statistical, where analysis of past climate variables and crop yield are made, by using traditional statistical methods. Ecophysiological models are seldom used in the plant level, due to the complexity of data required and lack of detailed knowledge of physiology; empirical-statistical models have not been proven to be the best prediction tool for dealing with such complex data. Among these models, there is no consensus or reason concerning the variables that should be used or the level of detail necessary to achieve better performance. Other difficulty in this industry is to understand the significance of the influence of the interactions of production factors on crop yield. This knowledge may be incorporated in the extensive data bases of the plants. Hence, the use of appropriate tools for their extraction is suitable. Data Mining techniques were popularized due to their ability to find intelligible knowledge in databases and by their potential to predict, including forecast applications in agriculture. In this paper, we propose to use Data Mining techniques to investigate the variability of sugarcane crop yield and determine the best set of variables to accomplish this task; we also propose to use these techniques to find models that provide better sugarcane yield predictions. (AU)
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