Premium
Defining Yield‐Based Management Zones for Corn‐Soybean Rotations
Author(s) -
Brock A.,
Brouder S. M.,
Blumhoff G.,
Hofmann B. S.
Publication year - 2005
Publication title -
agronomy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 131
eISSN - 1435-0645
pISSN - 0002-1962
DOI - 10.2134/agronj2004.0220
Subject(s) - cluster analysis , yield (engineering) , mathematics , statistics , physics , thermodynamics
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 ≤ K w ≤ 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.