A computational environment to support research in sugarcane agriculture

•Computational environment created to support sugarcane agricultural research.•Data acquisition, formatting, verification, storage, and analysis.•Three experiments analyzed to demonstrate the applicability of the environment.•Spatial variations in sugarcane quality and yield showed limited temporal...

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Bibliographic Details
Published in:Computers and electronics in agriculture 2016-11, Vol.130, p.13-19
Main Authors: Driemeier, Carlos, Ling, Liu Yi, Sanches, Guilherme M., Pontes, Angélica O., Magalhães, Paulo S. Graziano, Ferreira, João E.
Format: Article
Language:English
Subjects:
Agricultural practices
Agriculture
Computation
Computer simulation
Crops
Electronics
Environment
Farming
Farms
Knowledge management
Mathematical models
Precision agriculture
Sugarcane
Workflow
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Summary:•Computational environment created to support sugarcane agricultural research.•Data acquisition, formatting, verification, storage, and analysis.•Three experiments analyzed to demonstrate the applicability of the environment.•Spatial variations in sugarcane quality and yield showed limited temporal stability.•Two experiments showed common correlation structure in pH-related soil chemistry. Sugarcane is an important crop for tropical and sub-tropical countries. Like other crops, sugarcane agricultural research and practice is becoming increasingly data intensive, with several modeling frameworks developed to simulate biophysical processes in farming systems, all dependent on databases for accurate predictions of crop production. We developed a computational environment to support experiments in sugarcane agriculture and this article describes data acquisition, formatting, storage, and analysis. The potential to support creation of new agricultural knowledge is demonstrated through joint analysis of three experiments in sugarcane precision agriculture. Analysis of these case studies emphasizes spatial and temporal variations in soil attributes, sugarcane quality, and sugarcane yield. The developed computational framework will aid data-driven advances in sugarcane agricultural research.
ISSN:0168-1699
1872-7107
DOI:10.1016/j.compag.2016.10.002