My Account: Log In | Join | Renew
Search
Author
Title
Vol.
Issue
Year
1st Page

Abstract

 

This article in AJ

  1. Vol. 96 No. 1, p. 100-108
     
    Received: Aug 22, 2002
    Published: Jan, 2004


    * Corresponding author(s): kitchenn@missouri.edu
 View
 Download
 Alerts
 Permissions
 Share

doi:10.2134/agronj2004.1000

Management Zone Analyst (MZA)

  1. Jon J. Fridgena,
  2. Newell R. Kitchen *b,
  3. Kenneth A. Sudduthb,
  4. Scott T. Drummondb,
  5. William J. Wieboldc and
  6. Clyde W. Fraissed
  1. a ITD/Spectral Visions, 20407 South Neil Street Suite 2, Champaign, IL 61820
    b USDA–ARS, Cropping Syst. and Water Quality Res. Unit, Columbia, MO 65211
    c Dep. of Agron., University of Missouri, Columbia, MO 65211
    d Agric. and Biol. Eng. Dep., Univ. of Florida, Gainesville, FL 32611

Abstract

Producers using site-specific crop management (SSCM) have a need for strategies to delineate areas within fields to which management can be tailored. These areas are often referred to as management zones Quick and automated procedures are desirable for creating management zones and for testing the question of the number of zones to create. A software program called Management Zone Analyst (MZA) was developed using a fuzzy c-means unsupervised clustering algorithm that assigns field information into like classes, or potential management zones. An advantage of MZA over many other software programs is that it provides concurrent output for a range of cluster numbers so that the user can evaluate how many management zones should be used. Management Zone Analyst was developed using Microsoft Visual Basic 6.0 and operates on any computer with Microsoft Windows (95 or newer). Concepts and theory behind MZA are presented as are the sequential steps of the program. Management Zone Analyst calculates descriptive statistics, performs the unsupervised fuzzy classification procedure for a range of cluster numbers, and provides the user with two performance indices [fuzziness performance index (FPI) and normalized classification entropy (NCE)] to aid in deciding how many clusters are most appropriate for creating management zones. Example MZA output is provided for two Missouri claypan soil fields using soil electrical conductivity, slope, and elevation as clustering variables. Management Zone Analyst performance indices indicated that one field should be divided into either two (using NCE) or four (using FPI) management zones and the other field should be divided into four (using NCE or FPI) management zones.

  Please view the pdf by using the Full Text (PDF) link under 'View' to the left.

Copyright © 2004. American Society of AgronomyAmerican Society of Agronomy