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United States Department of Agriculture

Agricultural Research Service

Research Project: VINEYARD MANAGEMENT PRACTICES AND THE QUALITY OF GRAPES AND GRAPE PRODUCTS IN THE PACIFIC NORTHWEST

Location: Horticultural Crops Research

Title: Improved Yield Estimation by Trellis Tension Monitoring

Authors
item Tarara, Julie
item Blom, Paul

Submitted to: Group of International Experts of Vitivinicultural Systems for CoOperation(GiESCO)
Publication Type: Proceedings
Publication Acceptance Date: March 15, 2009
Publication Date: July 16, 2009
Citation: Tarara, J.M., Blom, P.E. 2009. Improved yield estimation by trellis tension monitoring. Group of International Experts of Vitivinicultural Systems for CoOperation(GiESCO) Proceedings. p.177-182.

Technical Abstract: Most yield estimation practices for commercial vineyards rely on hand-sampling fruit on one or a small number of dates during the growing season. Limitations associated with the static yield estimates may be overcome with Trellis Tension Monitors (TTMs), systems that measure dynamically changes in the tension of the main trellis support wire. In 10 commercial vineyards from which two commercial juice processors annually derive yield estimates, TTMs were installed. Processor-recorded and TTM data were subjected to three permutations of a basic linear computational approach to estimating yield, and their accuracies evaluated given known harvested yield at various spatial scales. On average, TTM data produced more accurate estimates of yield than did the longstanding estimation protocols of the juice processors. There was high vineyard to vineyard variability in the accuracy of the estimate under all approaches, including those designed to match the spatial scale of the input data for estimation with the spatial scale of the actual harvested yield. Processor protocols appear to be more sensitive than the TTM approach to the selection of the antecedent years used for comparison with the current year's data. Trellis tension monitoring may supplant traditional, labor-intensive yield estimation practices or may supplement longstanding practices with real-time information that can be applied to dynamic revision of static yield estimates.

Last Modified: 4/23/2014
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