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Grant number: 12/20861-8
Support Opportunities:Scholarships in Brazil - Doctorate
Effective date (Start): February 01, 2013
Effective date (End): February 28, 2014
Field of knowledge:Agronomical Sciences - Agronomy - Soil Science
Principal Investigator:Carlos Eduardo Pellegrino Cerri
Grantee:Rafael Butke Baptista
Host Institution: Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil


The negative impacts of forest-to-farming conversion are directly associated with the emission of greenhouse gases (GHG's). In Brazil, the largest land use corresponds to the livestock activity, occupying nearly 198 millions of hectares, from which 70% are in some stage of degradation. This degradation is mostly marked by a sharp reduction in the stocks of soil organic matter. The intensification in the cultivation of these areas may reflect in a double-benefit operation, increasing the stocks of soil organic matter and the cattle production in the converted area. Along the development of this project we aim to quantify the potential of carbon sequestration of Brazilian degraded pastures and clarify the soil organic matter dynamics in the intensification of these pastures cultivations. The project is divided into five action plans: 1 - Creating a database of changes in the soil carbon stocks during the pasture recovering; 2 - Modeling the obtained data; 3 - Modeling the Brazilian potential of carbon sequestration in pastures through the combination of modeled data and geographical information system; 4 - Predicting scenarios for pasture recovering; and 5 - Studying the soil organic matter dynamics in the priority areas. The parameters for the characterization of the dynamics of soil organic matter (SOM) are: carbon and nitrogen stocks, natural abundance of 13C, physical fractionation of SOM, characterization of humic substances, fluorescence and laser-induced breakdown spectroscopy (LIBS). The data analysis will consist of validation and uncertainty calculation of the modeled and simulated data. In the SOM characterization, principal component analysis will be performed using SAS statistical package.

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