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ARS Home » Pacific West Area » Corvallis, Oregon » Horticultural Crops Research Unit » Research » Publications at this Location » Publication #236305
Title: Improved Estimation by Trellis Tension Monitoring

Author
item Tarara, Julie
item Blom, Paul

Submitted to: Groupe d'Etude des Systèmes de Conduite de la Vigne
Publication Type: Abstract Only
Publication Acceptance Date: 12/1/2008
Publication Date: 7/15/2009
Citation: Tarara, J.M., Blom, P.E. 2009. Improved Estimation by Trellis Tension Monitoring. Groupe d'Etude des Systèmes de Conduite de la Vigne. p. 177.

Interpretive Summary:

Technical Abstract: Most yield estimation practices for commercial vineyards are based on longstanding but individually variable industry protocols that rely on hand sampling fruit on one or a small number of dates during the growing season. Limitations associated with the static nature of yield estimation may be overcome by deployment of Trellis Tension Monitors (TTMs), systems that provide dynamic measurement of changes in the tension of the main trellis support wire. TTMs were installed in 10 commercial vineyards from which 2 commercial juice processors annually collect data to derive yield estimates. Processor and TTM data were subjected to three permutations of the 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 actual yield than did the established computational protocols of the juice processors. There was high vineyard to vineyard variability in the accuracy of the estimate under all approaches, even from those permutations designed to match the spatial scale of the data collected for yield estimation with the spatial scale of the actual harvested yield. The processor protocols appear to be more sensitive than the trellis tension approach to the selection of the antecedent years used for comparison with the current year's data. Trellis tension monitoring may be useful to supplant traditional, labor-intensive yield estimation practices or to supplement longstanding practices with real-time information that can be applied to dynamic revision of static yield estimates.