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ARS Home » Pacific West Area » Corvallis, Oregon » Horticultural Crops Research Unit » Research » Publications at this Location » Publication #239882

Title: Improved Yield Estimation by Trellis Tension Monitoring

Author
item Tarara, Julie
item Blom, Paul

Submitted to: Group of International Experts of Vitivinicultural Systems for CoOperation(GiESCO)
Publication Type: Proceedings
Publication Acceptance Date: 3/15/2009
Publication Date: 7/16/2009
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.

Interpretive Summary:

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.