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Spatial variability prediction of soil chemical properties through in-situ spectroradiometry focusing on sugarcane variable fertilization rates

Grant number: 14/17667-0
Support Opportunities:Scholarships in Brazil - Post-Doctoral
Effective date (Start): January 01, 2015
Effective date (End): May 31, 2015
Field of knowledge:Agronomical Sciences - Agronomy
Acordo de Cooperação: Coordination of Improvement of Higher Education Personnel (CAPES)
Principal Investigator:Paulo Sergio Graziano Magalhães
Grantee:Lucas Rios do Amaral
Host Institution: Centro Nacional de Pesquisa em Energia e Materiais (CNPEM). Ministério da Ciência, Tecnologia e Inovações (Brasil). Campinas , SP, Brazil


Site-specific crop management, as the basic precision agriculture concept, is essential to increase sugarcane yield. Thus, spatial variability characterization of the soil fertility attributes must be done satisfactorily in order to allow the variable fertilization rates. For this, the conventional grid soil sampling has shown time-consuming, costly and prohibitive. Among the alternatives being investigated, soil spectroradiometry emerges as a promising method due to its low cost and great practicality in predicting various soil properties. Although this technique has been proved efficient to many applications, its practical use in predicting soil chemical properties, with sufficient accuracy to properly guide the soil correction and fertilizer application, still challenging. Thus, this research project has as main objective to assess how in-situ spectroradiometry must be handle in order to predict the spatial variability of soil chemical properties in the Brazilian sugarcane conditions. For that, site-specific prediction model will be developed from samplings guided by the soil variability measured with an electrical soil conductivity sensor. Thus, we aim to establish a practical and efficient method to enable in-situ spectroradiometry, aiming minor needs of soil chemical analyzes and high accuracy as possible to provide the recommendation of fertilizers in variable rate, quantifying the information gain demanded for precision agriculture. (AU)

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