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This article in AJ

  1. Vol. 97 No. 1, p. 49-57
     
    Received: Dec 17, 2003


    * Corresponding author(s): gimenez@envsci.rutgers.edu
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doi:10.2134/agronj2005.0049

Spatial Analysis of Cranberry Yield at Three Scales

  1. Larisa Pozdnyakovaa,
  2. Daniel Giménez *b and
  3. Peter V. Oudemansa
  1. a P.E. Marucci Center for Blue/Cranberry Res. and Ext., Rutgers Univ., 125A Lake Oswego Rd., Chatsworth, NJ 08019-2006
    b Dep. of Environ. Sciences, Rutgers Univ., New Brunswick, NJ

Abstract

Cranberry (Vaccinium macrocarpon Ait.) is an intensively managed perennial crop. Patches of disease, local variation in soil properties, and regional changes in soil type and hydrology cause its yield to vary spatially at several scales. We evaluated the spatial variability of cranberry yield with two support sizes and covering three scales: (i) 500 contiguous 0.09-m2 samples covering a 6 by 7.5 m area (small scale, SS), (ii) an average number of 100 variably spaced 0.09-m2 samples from each of 21 fields (medium scale, MS), and (iii) 534 fields (16830 m2 average area) each characterized with a single value of total yield (large scale, LS). Differences in yield calculated from points separated by incremental distances h were raised to power values q (from 0 to 4 in steps of 0.1). The q = 2 data were fitted to either spherical (SS and LS) or exponential (MS) semivariogram models. The logarithm of average differences plotted vs. log h were characterized by their slope, ζ(q). Structure functions [ζ(q) vs. q] were fitted with the universal multifractal model containing three parameters (C, α, and H). Small scale and LS data had nonlinear structure functions typical of multiscale phenomena. Spatial properties of cranberry yield at MS were: (i) better defined in cranberry fields with more than 12 yr in production (small range and nugget variance), and (ii) influenced by multiscale factors (nonlinear structure functions). Younger fields had greater range and nugget variance and a linear structure function. Precision agriculture in perennial crops should consider temporal changes in the spatial structure of crop yield.

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