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Precision Agriculture

, Volume 19, Issue 1, pp 147–160 | Cite as

Canopy sensor placement for variable-rate nitrogen application in sugarcane fields

  • Lucas R. AmaralEmail author
  • Rodrigo G. Trevisan
  • José P. Molin
Article
First Online:

Abstract

Nitrogen (N) fertilization is challenging for sugarcane, and machine-based canopy sensors appear as an alternative to allow variable-rate N fertilization. Top or sidedressing N is applied in each crop row and crop spatial variability behavior must be understood to allow proper sensor placement and applicator configurations in order to optimize N fertilization. Thus, the goal of this study was to investigate sugarcane crop variability and N prescription error when working with various sensor placements and boom sections. The approaches involved post-processing N prescription maps and real-time application, varying the number of sensors used and calculating the N rate for the applicator boom sections. Sugarcane fields show high crop variability due to their semi-perennial cropping system, which causes unpredictability of sensor readings from adjacent rows, ideally suggesting one sensor for each row in order to obtain more detailed plant-vigor information. Moreover, the machine must be able to apply fertilizer for each individual row to allow the most reliable application of N rate, ensuring optimization of crop response to variable-rate N application.

Keywords

Optical sensor Nitrogen fertilization Proximal sensing 
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Notes

Acknowledgements

The authors acknowledge the Studies and Projects Financing Agency (FINEP) from the Ministry of Science and Technology through the PROSENSAP project for its financial support; São Paulo Research Foundation (FAPESP) for providing a graduate scholarship to the first author (Project Numbers 2009/03372-0 and 2011/08882-7); São Martinho Mill team and Máquinas Agrícolas Jacto SA (Jacto Agricultural Machinery) for the partnership.

References

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Lucas R. Amaral
    • 1
    Email author
  • Rodrigo G. Trevisan
    • 2
  • José P. Molin
    • 2
  1. 1.University of Campinas, School of Agricultural EngineeringCampinasBrazil
  2. 2.University of São Paulo, Luiz de Queiroz College of Agriculture, Department of Biosystems Engineering, Precision Agriculture LaboratoryPiracicabaBrazil

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