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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

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 

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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