PRODUCTION SYSTEMS TO PROMOTE YIELD AND QUALITY OF GRAPES IN THE PACIFIC NORTHWEST
Location: Horticultural Crops Research
Title: Modeling Seasonal Dynamics of Canopy and Fruit Growth in Grapevine for Application in Trellis Tension Monitoring
| Blom, Paul |
| Shafii, Bahman - UNIVERSITY OF IDAHO |
| Price, William - UNIVERSITY OF IDAHO |
| Olmstead, Mercy - WASHINGTON STATE UNIV |
Submitted to: HortScience
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: December 14, 2008
Publication Date: April 16, 2009
Citation: Tarara, J.M., Blom, P.E., Shafii, B., Price, W.J., Olmstead, M.A. 2009. Modeling seasonal dynamics of canopy and fruit growth in grapevine for application in trellis tension monitoring. HortScience. 44(2):334-340.
Interpretive Summary: The Trellis Tension Monitor (TTM) technology, which was described in an earlier report, offers grape growers a way to monitor the growth of their crop during the season with an automated sensor. This was not possible previously. The TTM also facilitates dynamic predictions of grape yields, which are highly sought by juice processors and wineries and historically were not available on demand. As the vine and the grapes grow, the TTM detects the change in weight that is being supported by the trellis. To best apply the TTM, it is important to distinguish between the weight caused by shoots and leaves (the vine) and the weight caused by fruit (the grapes), which is the variable needed for a yield estimate. Over five years we recorded several measures of canopy and fruit growth in the vineyard then used that data to build models of canopy weight and fruit weight. These variables change during the growing season at varying rates, so the models developed were curves as opposed to the more commonly computed straight-line relationships. Between years, fruit weight (yield) typically varies more than shoot and leaf weight because adverse weather at critical times tends to impact fruit formation or growth more negatively than it does vine growth. Canopy weight was well represented over the course of the season by the curvilinear model. Fruit weight also was adequately represented by the curve that was modeled from the data. However, because of the year-to-year variability in fruit weight, we did not find a suitable model to predict the ratio between shoots and fruit. Models of vine and grape growth will reduce the need for growers, processors, and wineries to measure these variables by hand, and application of such models to the TTM technology will improve yield estimates for grape growers.
Estimates of canopy and fruit fresh mass are necessary for more accurate interpretation of data from the Trellis Tension Monitor, a tool for real-time yield prediction and monitoring of plant growth in trellised crops. In trellised grapevines (Vitis Labruscana Bailey), measurements of shoot and fruit fresh mass were collected at frequent intervals (14 to 21 days) over five years, and these data were correlated with variables that could be obtained non-destructively: shoot length, number of leaves per shoot, number of clusters per shoot. Shoot length provided a good estimator of shoot fresh mass in all years. Nonlinear logistic regression models described the dynamics of canopy growth from bloom to the early stages of ripening, which often is poorly represented by the simple linear regression approach to seasonal data. A generalized function indicated that from about 600 DD onward, any increase in shoot mass could be considered to contribute negligibly to increases in trellis wire tension, thereby facilitating more accurate dynamic estimation of yield. The dynamics of fruit mass were captured adequately by the nonlinear approach, but not as well as vegetative mass because of larger variances in fruit mass. The number of clusters per shoot was associated with fruit mass only after the accumulation of about 550 degree-days or equivalently, the time at which fruit mass exceeded about 25 g per shoot. The seasonal dynamics of the ratio of fruit:vegetative mass per shoot were not sufficiently discernable by the logistic models because of the dominance of the ratio by fruit mass and its large inter-annual variation.