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Digital mapping of soy productivity with emphasis on soils inserted via remote sensing in simulation models (Southeast and Midwest regions of Brazil)

Grant number: 22/11034-2
Support Opportunities:Scholarships in Brazil - Doctorate (Direct)
Effective date (Start): September 01, 2022
Effective date (End): August 31, 2026
Field of knowledge:Agronomical Sciences - Agronomy - Soil Science
Principal Investigator:José Alexandre Melo Demattê
Grantee:Merilyn Taynara Accorsi Amorim
Host Institution: Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil
Associated research grant:21/05129-8 - The Brazilian soil quality determined by geotechnologies: mapping, interpretation and agricultural/environmental applications: a legacy for society, AP.TEM


Determining Production Potential (PP) targets is a key factor for property management and national public policies. Soil is one of the most important pillars of agricultural production, whose fundamentals are little explored in the models used for this purpose. In addition to soil texture, depth, water, and fertility, organic matter was never considered an agronomic attribute, being marginal in the process. In addition to the subsurface attributes of the soil, are also neglected. It is necessary to combine the fundamental concepts of soil with PP and digital methodologies, with automatic data acquisition systems. Therefore, the present work aims to: 1) map the soybean PP through the insertion of variables by remote sensing (SR) in an agricultural modeling software; 2) to relate the soil attributes in surface and subsurface, emphasizing the role of organic matter, and its impact on PP in the Southeast and Midwest regions of Brazil. For this, data from multitemporal satellites will be used, through exposed soil technology (GEOS3 method), to quantitatively estimate the clay contents, cation exchange capacity, and organic matter, in the surface and subsurface. Climate data will also be obtained by SR. With continuous soil and climate variables, together with soybean crop parameters, the PP will be determined via DSSAT, being expressed in an innovative way in the form of maps. The maps will have a resolution of 250 meters and will be validated with 1000 field observations of real productivity. Finally, spatial simulations of the increase and decrease of soil organic matter will be carried out to verify its impact on the PP obtained. It is expected that the work will contribute to an agile and low-cost methodology for the accurate mapping of the soybean PP, helping in decision-making for environmental, agricultural, economic, and public policy management in Brazil. (AU)

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