Investigating geostatistical methods to model within-field yield variability of cranberries

R. Kerry; P. Goovaerts; D. Gimenez; P. Oudemans

Investigating geostatistical methods to model within-field yield variability of cranberries

In: Precision agriculture '13

Authors:

R. Kerry

R. Kerry ¹Department of Geography, 690 SWKT, Brigham Young University, Provo, UT, USA; ruth_kerry@byu.edu

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P. Goovaerts

P. Goovaerts ²Biomedware Inc., 516 N. State Street, Ann Arbor, MI, USA

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D. Gimenez

D. Gimenez ³Department of Environmental Sciences, Rutgers University, 14 College Farm Road, New Brunswick, NJ, USA

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, and

P. Oudemans

P. Oudemans ⁴Department of Plant Biology & Pathology, Rutgers University, 59 Dudley Road, New Brunswick, NJ, USA

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Type:: Chapter

Pages:: 305–311

DOI:: https://doi.org/10.3920/9789086867783_038

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The method of harvesting cranberries gives just one yield value per field so characterizing within-field variation is difficult. Geostatistical disaggregation of per field yield totals using the enhanced vegetation index (EVI) from imagery as secondary information was investigated. Results were compared to within-field yield variability projected from time-consuming pre-harvest berry counts. Several scales of variation were present in the data so factorial kriging was used to separate these and determine which are most spatially coherent. The trend component of pre-harvest berry counts and/or geostatistical disaggregation of yields both give a reasonable initial definition of potential management zones likely to be related to topography and soil type differences.

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Precision agriculture '13

E-Book ISBN:: 9789086867783

Publisher:: Wageningen Academic

Print Publication Date:: 01 Nov 2013

Subjects
- Life Sciences
  - General
  - Agriculture & Environment

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Investigating geostatistical methods to model within-field yield variability of cranberries

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Precision agriculture '13

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