|Support type:||Scholarships in Brazil - Post-Doctorate|
|Effective date (Start):||October 01, 2018|
|Effective date (End):||September 30, 2021|
|Field of knowledge:||Agronomical Sciences - Agronomy - Soil Science|
|Principal Investigator:||Rafael Otto|
|Grantee:||Guilherme Martineli Sanches|
|Home Institution:||Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil|
The Brazilian agreement signed in September 2016 during COP-21 reaffirms the central role of Brazil in the Bioenergy production from sugarcane. The expansion of sugarcane production is associated with increased consumption of nitrogen fertilizers, which contribute to greenhouse gas emissions. If nitrogen fertilizer management is not handled properly, impacts to the environment can be harmful. The approach offered by Precision Agriculture (PA) can help achieve higher yields and sustainability of production. Among the different technologies, the evaluation of nitrogen (N) nutritional status in sugarcane by remote and proximal sensing has been highlighted in the literature as a promising option for efficient crops fertilization. On the other hand, the mapping of soil spatial variability can also contribute significantly to the rationalization of the inputs application, including N, although little used in PA studies. The soil apparent electrical conductivity (ECa) has been shown to be adequate to evaluate the soil fertility variability. This project is innovative in the sense of developing a recommendation model of N application in variable rates using two approaches: the first that evaluates aspects of the canopy reflectance indices of plants, and the second that uses parameters of the soil spatial variability by ECa assessment. This project aims to: i) carry out a literature review on soil factors that affecting the sugarcane response to N, using data mining, multivariate analysis and meta-analysis strategies; ii) to create maps of soil apparent electrical conductivity (ECa) and vegetation indices in sugarcane commercial fields; and iii) to create a recommendation algorithm for nitrogen fertilization at a variable rate, using data from plant canopy variability and soil parameters to create an optimized nitrogen fertilization model for sustainable sugarcane production.