The yield: quality nexus. Substantiating similarity in the patterns of variation in grape yield/vine vigour and indices of fruit quality
To determine whether remotely sensed imagery and proximal canopy sensing can be used to correlate patterns of within-vineyard spatial variability for yield/vigour with indices of fruit quality. To develop knowledge relating to relationships between yield and quality data collected at similar spatial resolution, and also how an active canopy sensor might be used to predict fruit quality attributes. Thus, to establish at the within-vineyard scale whether or not yield and quality follow the same patterns of variation.
While zones delineated according to yield/vigour are relatively stable between seasons, the zones based on quality are less certain. A part of the reason for this uncertainty is due to the lower number of manual samples (approx. 26 /ha.) taken across the vineyard for fruit assessment, producing a lower data density than that obtained by remote sensing (about 40,000 pixels/ha). Sampling at a rate comparable with yield maps or remote imagery using a proximal canopy sensor would be possible if indices of fruit quality can be predicted from such a sensor. The sensor could also be calibrated with remotely sensed imagery. This would potentially enable remote imagery to be used quantitatively, instead of qualitatively as it is at present.
The project will determine whether the spatial variability of yield/vigour in vineyards can be correlated in a meaningful way with indices of grape quality. The CropCircle active proximal canopy sensor will be trialled as a predictor of indices of fruit quality, in combination with passive airborne remote canopy sensing and the Multiplex on-the-go fruit quality sensor at harvest. This will be cross-checked with hand sampling and laboratory analysis of fruit. Attempts will be made to calibrate the passive remotely sensed imagery using the CropCircle sensor, to allow remote imagery to be used quantitatively. Relationships between crop canopy indices and fruit quality indices (pH, TA, brix, colour) will be examined and ultimately it is hoped that maps of predicted fruit quality can be produced, at high spatial resolution.
Through the adoption of Precision Viticulture principles to optimise grapegrowing and winemaking, the project will provide information to enable better decision making. Improved understanding of yield:quality interactions will allow more efficient, targeted use of management inputs (including labour). In particular, it will promote better decision making around selective harvesting and product streaming, which have been shown to be potentially highly profitable.