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Metagenome of microbial communities involved in the nitrogen cycle in sugarcane soils in Brazil

Grant number: 13/26993-6
Support Opportunities:Scholarships abroad - Research Internship - Doctorate
Effective date (Start): May 19, 2014
Effective date (End): October 18, 2014
Field of knowledge:Agronomical Sciences - Agronomy - Soil Science
Principal Investigator:Fernando Dini Andreote
Grantee:Júlia Elidia de Lima Perim
Supervisor: Romy Chakraborty
Host Institution: Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil
Research place: Lawrence Berkeley National Laboratory, United States  
Associated to the scholarship:12/03949-9 - Microbial diversity involved in the cycling of nitrogen in soils cultivated with sugarcane in São Paulo state, BP.DR

Abstract

The cultivation of sugarcane occupies about 5 million hectares in the state of São Paulo and is a crop of a great impact in Brazilian agriculture. Considering crop productivity and sustainability, a better understanding of the microbial composition in soils with sugarcane, as well as the role that microorganisms involved in the nitrogen (N) cycle play is of utmost importance, since they are responsible for the availability of this element to plants. However, little is known about the microbial community related to this biogeochemical cycle in soil of sugarcane fields and our sample areas, for the first time, offer a range of different management conditions and characteristics. The shotgun metagenome sequencing approach allows detect organisms that exist in very low abundance within complex populations and, as a result, the ability to detect low-abundance populations can profoundly impact the interpretation of microbiological changes. Finally, here we propose the use of metagenomic data which enables the investigation of these microbial groups, allowing full view of the composition of the N-cycling communities that are extremely important to soil system. The metagenomic data will provide the basis for multivariate statistical analysis, linking them with other data already obtained within the PhD project. (AU)

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