Recently, an approach was described for continuous monitoring of growth and estimating yield in grapevines or other trellised crops [Trellis Tension Monitor (TTM); Tarara et al., 2004, 2005]. Briefly, the technique involves continuous measurement of the tension in the primary horizontal support wire of the trellis. Temporal change in wire tension is used to determine rates of canopy and fruit growth and to facilitate estimates of final yield through various predictive routines. We identified transient and systematic variables like wind gusts and wire temperature that influence the change in tension in the trellis wire, some of which can be removed from the data by choice of averaging interval or by postprocessing with compensatory algorithms (Tarara et al., 2004). What is more difficult to quantify are the relative contributions of fruit and canopy growth to the dynamic change in trellis wire tension. In the absence of an extensive TTM database, dynamic estimates of canopy and fruit mass provide valuable ancillary information so that the most useful real-time interpretation of the tension trace can be derived.
The development process for the TTM technique required some validation, the most direct means of which is point measurement of plant fresh mass recorded through destructive sampling. However, the inherent sensitivity of the TTM to changes in the total mass being supported by the trellis wire (Tarara et al., 2004) highlights the need for nondestructive approaches to model building and verification. In general, there are well-recognized limitations to destructively sampling perennial plants, including its labor-intensive nature for large plants. These constraints reinforce the value of predictive models that can be verified by variables measured rapidly and nondestructively in the field (e.g., Castelan-Estrada et al., 2002; Montero et al., 2000).
Although canopy and fruit growth are nonlinear functions of time, they often are described using linear analyses (e.g., Rufat and DeJong, 2001; Williams et al., 1985) that may suffice for static point estimates (e.g., Castelan-Estrada et al., 2002). For dynamic modeling, such simplification can result in a poor representation of the data early or late in the season. Additionally, the relationship between reproductive and vegetative mass changes dynamically. The functionality of the TTM could be maximized with knowledge of the ratio of fruit to vegetative mass, which could be quantified throughout the growing season by direct measurement or modeling. Ultimately, with an extensive historical database, this information could be derived directly from trellis tension data. In either case, interpretation of the trellis tension trace and the accuracy of subsequent yield predictions could be improved. Real-time knowledge of the balance between fruit and canopy mass, defined as crop load, also can be applied to production decisions like fruit thinning or shoot thinning. Crop load is understood to influence a number of fruit quality components in grapevines (e.g., Edson et al., 1995; reviewed by Kliewer and Dokoozlian, 2005; Naor et al., 2002; Reynolds et al., 2005). The vegetative contribution to crop load commonly is quantified using leaf area (e.g., Edson et al., 1993; Reynolds et al., 2005) and previously we investigated its seasonal dynamics in grapevine (Blom and Tarara, 2007). Another commonly measured static variable, the mass of dormant cane prunings (e.g., Bennett et al., 2005; Naor et al., 2002), does not support real-time application of a technology like the TTM.
The objective of this study was to assess the dynamics of fruit and shoot fresh mass in grapevines trained to a single curtain, within and between seasons of a wide range, to develop functional relationships of expected responses to improve the potential for meaningful interpretation of TTM data in vineyards. Nonlinear regression analyses using logistic model forms were applied to produce average representations of canopy and fruit growth between about bloom and ripening, the period most important to application of the TTM for estimating yield. We also assessed the associations between direct measurements of plant fresh mass and a few select, easily measured variables that could be recorded nondestructively.
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