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A computational environment to support research in sugarcane agriculture

2016; Elsevier BV; Volume: 130; Linguagem: Inglês

10.1016/j.compag.2016.10.002

1872-7107

Carlos Driemeier, Ling Liu, Guilherme Martineli Sanches, Angélica O. Pontes, Paulo Sérgio Graziano Magalhães, João Eduardo Ferreira,

Rice Cultivation and Yield Improvement

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.