African Journal of
Agricultural Research

  • Abbreviation: Afr. J. Agric. Res.
  • Language: English
  • ISSN: 1991-637X
  • DOI: 10.5897/AJAR
  • Start Year: 2006
  • Published Articles: 6576

Full Length Research Paper

Two approach comparison to define crop management zones (MZs)

Kelyn Schenatto
  • Kelyn Schenatto
  • Department of Computer Science, Technological University of Paraná, Santa Helena, Paraná, Brazil.
  • Google Scholar
Eduardo Godoy de Souza
  • Eduardo Godoy de Souza
  • Technological and Exact Sciences Center, State University of West Paraná, Cascavel, Paraná – Brazil.
  • Google Scholar
Claudio Leones Bazzi
  • Claudio Leones Bazzi
  • Department of Computer Science, Technological University of Paraná, Medianeira, Paraná, Brasil.
  • Google Scholar
Nelson Miguel Betzek
  • Nelson Miguel Betzek
  • Department of Computer Science, Technological University of Paraná, Medianeira, Paraná, Brasil.
  • Google Scholar
Alan Gavioli
  • Alan Gavioli
  • Department of Computer Science, Technological University of Paraná, Medianeira, Paraná, Brasil.
  • Google Scholar


  •  Received: 18 July 2016
  •  Accepted: 01 September 2016
  •  Published: 22 September 2016

Abstract

The use of yield-level management zones (MZs) has demonstrated high potential for site-specific management of crop inputs in traditional row crops. Two approaches were use: all variables approach (all_Var) and stable variables approach (sta_Var). In each approach, variables selected had significant correlation with yield, while all redundant and non-autocorrelated variables were discarded. Two fields were use in this study: Field 1 (17.0 ha soybean field located in Cascavel, Paraná, Brazil); and Field 2 (35.0 ha corn field located in Wiggins, Colorado, US.). Two, three, four, and five MZs were created using fuzzy c-means clustering technique. The proposed methodology for define MZs is simple and allowed create good-quality MZs. It also founded that not-stable-over-time variables are not useful to define MZs.

 

Key words: Precision agriculture, spatial variability, fuzzy clustering, autocorrelation, cross-correlation.