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ARS Home » Pacific West Area » Corvallis, Oregon » Horticultural Crops Research » Research » Publications at this Location » Publication #232400

Title: Modeling Seasonal Wine Grape Development Using a Mixture Technique

item Price, William
item Shafii, Bahman
item Blom, Paul
item Tarara, Julie
item Sanchez, Luis
item Dokoozlian, Nick

Submitted to: Applied Statistics In Agriculture Conference Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 1/23/2009
Publication Date: 6/1/2009
Citation: Price, W., Shafii, B., Blom, P.E., Tarara, J.M., Sanchez, L.J., Dokoozlian, N. 2009. Modeling seasonal wine grape development using a mixture technique. 20th Annual Kansas State University Conference on Applied Statistics in Agriculture. April 27-29. Manhattan, KS. p.78-91. CDROM.

Interpretive Summary: Knowledge of grapevine growth is needed by grape growers to adjust vineyard management practices like irrigation. Because measurements of growth are costly and laborious to obtain, scientists develop models to describe and predict the growth pattern. The typical model, described mathematically as a sigmoidal function, describes most grapevine growth fairly well, but sometimes cannot explain patterns that occur in real vineyards. Thus, an advanced statistical technique was used to examine the growth process in detail, using data from a commercial vineyard of Merlot and a commercial vineyard of Chardonnay, so that general models of grapevine growth could be improved. As models become more truly representative of real life, growers can apply them with more confidence, and thus become more efficient with some of their farming practices.

Technical Abstract: Biological growth data typically display an increasing sigmoidal pattern over time. Grape development is no exception and shows a similar general trend. A detailed examination of the growth process in grapes, however, reveals a few systematic deviations from this pattern. Specifically, grape development is often characterized by localized areas of growth plateaus leading to an overall growth pattern referred to as a double sigmoidal curve. Capturing and characterizing these local changes in growth is important as they represent key phases in grape development such as veraison. This paper utilizes a model adapted from the technique of mixture models to estimate the growth curve of grapes. The resulting model provides a more accurate description of the growth process and has parameter estimates directly related to the various phases of grape development. The model is demonstrated using data collected from an experimental trellis tension monitoring system in Chardonnay and Merlot grape varieties.