Site-specific nitrogen management in sugarcane fields by mapping apparent electrical conductivity (EC) of soil

• Grant number: 19/18779-0
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Effective date (Start): January 01, 2020
• Effective date (End): December 31, 2020
• Field of knowledge: Agronomical Sciences - Agronomy - Soil Science
• Principal Investigator: Guilherme Martineli Sanches
• Grantee: Mariana Ayres Rodrigues
• Host Institution: Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil

Abstract

Brazilian Energy Research Company (EPE) indicate that in 2030 Brazil is expected to produce 945 million tons of sugarcane, ~40% more than the country produced in the last crop season. 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 PA technologies, the alternative assessment of nitrogen (N) nutritional status in sugarcane fields by remote and proximal sensing has been highlighted in the literature as a promising option for efficient crop fertilization. On the other hand, the spatial variability mapping of soil can also contribute significantly to the application of inputs, including N, although little used in PA studies. Among the several technologies, the soil apparent electrical conductivity (ECa) shows great potential to map the yield and fertility potential of soils. Within this context, this project aims to investigate the response of sugarcane crop to nitrogen fertilization as a function of soil spatial variability identified by ECa, seeking to identify soil parameters that maximize Nitrogen Use Efficiency (NUE). We expected that the results of this research will help in the development of new products and models that allow the optimization of the N application in sugarcane fields, contributing to increase the profitability of production and reduce environmental impacts.