Agronomy Journal Journal of Natural Resources and Life Sciences Education
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
 QUICK SEARCH:   [advanced]


     


This Article
Right arrow Figures Only
Right arrow Full Text Free
Right arrow Full Text (PDF) Free
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via ISI Web of Science (2)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Pozdnyakova, L.
Right arrow Articles by Oudemans, P. V.
Right arrow Search for Related Content
PubMed
Right arrow Articles by Pozdnyakova, L.
Right arrow Articles by Oudemans, P. V.
Agricola
Right arrow Articles by Pozdnyakova, L.
Right arrow Articles by Oudemans, P. V.
Related Collections
Right arrow Fractal Approaches
Right arrow Spatial Variability
Right arrow Production Agriculture
Right arrow Spatial Distribution
Published in Agron. J. 97:49-57 (2005).
© American Society of Agronomy
677 S. Segoe Rd., Madison, WI 53711 USA

Agronomic Modeling

Spatial Analysis of Cranberry Yield at Three Scales

Larisa Pozdnyakovaa, Daniel Giménezb,* and Peter V. Oudemansa

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

* Corresponding author (gimenez{at}envsci.rutgers.edu)

Received for publication December 17, 2003. 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, {zeta}(q). Structure functions [{zeta}(q) vs. q] were fitted with the universal multifractal model containing three parameters (C, {alpha}, 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.

Abbreviations: Lng_Rng, long range • PRR, Phytophthora Root Rot • Srt_Rng, short range







HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
The SCI Journals Crop Science Vadose Zone Journal
Journal of Natural Resources
and Life Sciences Education
Soil Science Society of America Journal
Journal of Plant Registrations Journal of
Environmental Quality
The Plant Genome
Copyright © 2005 by the American Society of Agronomy.