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.
Highlights
A management zone (MZ) is a sub-region of a field that expresses a relatively homogeneous combination of yield-limiting factors for which a single rate of a specific crop input is appropriate (Doerge, 1996)
The coefficient of variation (CV) was low for the pH and sand; medium for yield; high for Zn, organic matter (OM), cation exchange capacity (CEC), ammonium, and K; and very high for P, nitrate, clay, and silt
The methodology proposed for MZs define has practical value and allowed MZs definition with good quality
Summary
A management zone (MZ) is a sub-region of a field that expresses a relatively homogeneous combination of yield-limiting factors for which a single rate of a specific crop input is appropriate (Doerge, 1996). Delineation of MZs and management of crop inputs have proved economical for application of variable rate inputs (Koch et al, 2004). Several researchers have successfully used one or more of these factors in combination with yield maps and sometimes use only multiple year yield maps in delineating MZs (Blackmore, 2000; Fraisse et al, 2001; Johnson et al, 2003; Schepers et al, 2004). Numerous approaches are presented in literature for purpose of delineating MZs using yield maps, using cluster analysis, such as K-means and Fuzzy C-means (Taylor et al, 2007; Li et al, 2007). The following cluster performance indices can be used: 1. Variance reduction (VR; Ping and Dobermann, 2003; Xiang et al, 2007)
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