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This article in AJ

  1. Vol. 97 No. 4, p. 1115-1128
     
    Received: Aug 21, 2004
    Published: July, 2005


    * Corresponding author(s): sbrouder@purdue.edu
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doi:10.2134/agronj2004.0220

Defining Yield-Based Management Zones for Corn–Soybean Rotations

  1. A. Brock,
  2. S. M. Brouder *,
  3. G. Blumhoff and
  4. B. S. Hofmann
  1. Department of Agronomy, 3351 Lilly Hall of Life Sciences, 915 W. State St., Purdue Univ., West Lafayette, IN 47907-2054

Abstract

Unsupervised clustering has been proposed for developing georeferenced agronomic information into management zones (MZs). Our objectives were to use fuzzy c-means clustering to identify yield-based MZs, and to compare spatial association and agreement among corn (Zea mays L.) yield-based (CYB) MZs, soybean [Glycine max (L.) Merr.] yield-based (SYB) MZs, and published soil survey map units. Six years of yield monitor data (three per species) from four fields were used with the clustering software MZ Analyst. Clustering success was evaluated with four performance measures. Two measures of variance reduction and the fuzziness performance index (FPI) indicated clustering optimization with 4 to 6 MZs. In contrast, the normalized classification entropy (NCE) indicated that yield data were optimally organized with only 2 MZs. On average, the 4-MZ delineation reduced the yield variance to 40% of the whole field variance (corn within CYB MZs and soybean within SYB MZs); mean relative yields within MZs were significantly different from each other, ranging from 23% below to 12% above the whole-field mean. With 4 MZs, CYB and SYB MZs were significantly associated in all fields, but weighted agreement between CYB and SYB MZs was only slight (0.06 ≤ Kw ≤ 0.34), indicating crop-specificity in MZ delineation. In general, highest yielding MZs were significantly associated with areas mapped as a poorly drained, level soil series while lower yielding MZs corresponded to map units for eroded or more slopping soils. However, clustering yields by soil series reduced yield variance less than unsupervised, yield-based clustering. Routine application of MZ Analyst likely requires more decision support for identifying clustering success.

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