Development of soybean virtual water map for the Upper Xingu basin, Mato Grosso - Brazil

• Grant number: 13/20377-1
• Support type: Scholarships in Brazil - Doctorate
• Effective date (Start): December 01, 2014
• Effective date (End): October 31, 2018
• Field of knowledge: Biological Sciences - Ecology - Ecosystems Ecology
• Cooperation agreement: Coordination of Improvement of Higher Education Personnel (CAPES)
• Principal Investigator: Maria Victoria Ramos Ballester
• Grantee: Rodnei Rizzo
• Home Institution: Centro de Energia Nuclear na Agricultura (CENA). Universidade de São Paulo (USP). Piracicaba , SP, Brazil
• Associated research grant: 13/50180-5 - Xingu: integrating land use planning and water governance in Amazonia: towards improved freshwater security, AP.PFPMCG.TEM
• Associated scholarship(s): 17/12567-6 - Evaluation of the water balance in southeastern Amazon over the last two decades: Employing a remote sensing based methodology to describe the regional water resources, BE.EP.DR

Abstract

The Population growth, increasing agricultural production and the current climate change scenarios indicate possible depletion of water resources. Aiming to develop a mechanism to help in water resources management, the concept of virtual water was created. The Virtual water is usually applied on a global or national scale. However, the optimization in water resources management requires projects on watersheds scales. Therefore, it is essential to implement the methodology in smaller scales. Our study area corresponds to the Upper Xingu basin in Mato Grosso state, which is an important soybean producer. The local soybean production is rainfed and the region is an important supplier to international market, ensuring food and water security in countries like China. This project aims to use remote sensing and other techniques such geostatistics, to optimize the calculation of soybean virtual water on regional scale. In this case, the crop water requirement will be estimated based on SEBAL model and MODIS satellite images. Furthermore, a regression trees algorithm will be calibrated to estimate soybean yield based on precipitation, temperature, solar radiation and MODIS satellite images. This algorithm will be employed to create a map of virtual water. This map will indicate sites resiliency and vulnerability to climate changes, as well as water efficiency of farms in this region. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

RIZZO, RODNEI; GARCIA, ANDREA S.; VILELA, VIVIAN M. DE F. N.; BALLESTER, MARIA VICTORIA R.; NEILL, CHRISTOPHER; VICTORIA, DANIEL C.; DA ROCHA, HUMBERTO R.; COE, MICHAEL T. Land use changes in Southeastern Amazon and trends in rainfall and water yield of the Xingu River during 1976-2015. Climatic Change, v. 162, n. 3 JUN 2020. Web of Science Citations: 0.

FONGARO, CAIO T.; DEMATTE, JOSE A. M.; RIZZO, RODNEI; SAFANELLI, JOSE LUCAS; MENDES, WANDERSON DE SOUSA; DOTTO, ANDRE CARNIELETTO; VICENTE, LUIZ EDUARDO; FRANCESCHINI, MARSTON H. D.; USTIN, SUSAN L. Improvement of Clay and Sand Quantification Based on a Novel Approach with a Focus on Multispectral Satellite Images. REMOTE SENSING, v. 10, n. 10 OCT 2018. Web of Science Citations: 1.

MELO DEMATTE, JOSE ALEXANDRE; FONGARO, CAIO TROULA; RIZZO, RODNEI; SAFANELLI, JOSE LUCAS. Geospatial Soil Sensing System (GEOS3): A powerful data mining procedure to retrieve soil spectral reflectance from satellite images. REMOTE SENSING OF ENVIRONMENT, v. 212, p. 161-175, JUN 2018. Web of Science Citations: 17.