Improving nitrogen use efficiency in sugarcane using remote sensing data linked to crop modeling and yield mapping technologies

• Grant number: 19/19246-6
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate (Direct)
• Start date: February 03, 2020
• End date: February 02, 2021
• Field of knowledge: Agronomical Sciences - Agronomy - Soil Science
• Principal Investigator: José Eduardo Corá
• Grantee: Hugo Miranda Faria
• Supervisor: Bruno Basso
• Host Institution: Faculdade de Ciências Agrárias e Veterinárias (FCAV). Universidade Estadual Paulista (UNESP). Campus de Jaboticabal. Jaboticabal , SP, Brazil
• Institution abroad: Michigan State University (MSU), United States
• Associated to the scholarship: 17/18418-2 - Improving nitrogen use efficiency in sugarcane using remote sensing data linked to crop modeling and yield mapping technologies, BP.DD

Abstract

The overarching goal is to develop new knowledge that can lead to improvements in assessment of sugarcane yield variability and sugarcane nitrogen management. To achieve these goals, we will monitor nitrogen treatment field experiments with UAVs to validate vegetation indices used to assess N uptake by sugarcane and to link proximal sensing data with crop modeling. The knowledge and tools gained through this proposed project would improve the methods and tools to quantify nitrogen needs of sugarcane, and help farmers implement efficient and sustainable nitrogen management systems at farm and watershed levels. Although most people can see the benefits of using a more precise approach to manage crops with additional information, the tools provided by precision agriculture and other information technologies have not yet moved into mainstream agricultural management. The increased complexity of the systems makes calculations of financial benefits complicated and uncertain, slowing down their adoption and diffusion. These issues can be resolved by improving the decision making process by integrating advanced biophysical systems with economic decision models and by combining precision agriculture solutions with innovative e-platforms for extension of new solutions. This project will develop technology transfer tools (decision support systems and integrated biophysical and economic simulation models) to disseminate the knowledge and technological solutions generated during the project to students, extension, and stakeholders. In this regard, the project will integrate biophysical, extension, education and socio-economic components to promote the adoption of the technology solutions. (AU)