Modeling water status of vineyards as a differential management strategy

By Kaan Kurtural, Luca Brillante, Johann Martinez, Runze Yu

University of California Davis

Spatial variability in wine grape vineyards is a major limiting factor in achieving maximum fruit and wine composition. Wine grape growers and winemakers have recognized this for decades and have devised a wide range of management practices to overcome this problem. Despite their best and ongoing efforts, variability still exists in most vineyards and continues to contribute to reduced economic returns to growers and wineries. Many crop production attributes contribute to this variability. Despite our understanding of this, it has been difficult to eliminate the effects of spatial variability on vineyard management. Soil nutrient status, water holding capacity, irrigation management, and climate are some of the major contributors to variations in vegetative growth, yield and fruit composition.

The lack of uniformity in grape yield and composition within vineyards may influence the management of grape delivery to the winery. Particularly, spatial variation in fruit composition can limit the opportunity to maximize wine composition either by unconscious mixing of low and high quality grapes or by losing the opportunity to ferment separately the existing high quality grapes.

The application of precision farming practices to viticulture is relatively recent, but has taken advantage of technologies applied in other crops such as yield monitoring.  The variability in yield revealed by this technology, although not unexpected, is still typically enlightening to growers and winemakers alike. In our recent work variability in both yield and fruit composition were reported in several vineyards over three years. The variability in yield showed some spatial consistency within a vineyard, but the range in yield was significantly different between years. We associated this temporal variability with temperatures during the bloom period. This tendency towards spatial consistency was encouraging with respect to the long term objective to understand the source of this variability and hence to develop more precise management practices to improve vineyard uniformity.

The spatial variability in fruit composition was not as consistent as yield and furthermore was not well correlated with spatial yield variability. This strongly indicated that the factors controlling fruit composition are more complex than yield and hence the latter cannot be used as criteria upon which to determine fruit composition management. The advent of variable fertilizer applications, foliar nutrient programs and drip irrigation all help to minimize variability in vine growth as well as fruit composition. But since there are other factors such as slope, aspect, pests and disease, influence of soil texture, canopy size, and sunlight, composition at harvest is still difficult to predict. To help overcome this problem, it has always been desirable to practice differential (both temporal and spatial) harvests. However, this is generally too expensive for most large scale operations. There is a need for development of on-the-go wine grape composition sensing technology, which would enable quality zone delineation for effective harvesting and management of vineyards.

In our work in 2016, we worked with a producing vineyard in Sonoma County. The vineyard was planted to Cabernet Sauvignon clone 7/110R on a high quadrilateral system. Based on anecdotal evidence the vineyard manager informed us that the fruit composition varied greatly at this site. We laid a spatially dense grid after sensing canopy reflectance and soil electrical resistivity (Figure 1).

Figure 1. The research site in Sonoma County

Figure 1. The research site in Sonoma County

The workflow at this site is presented in Figure 2.

Figure 2. Workflow at the research site.

Figure 2. Workflow at the research site.

Our terrain analysis revealed that the topography of the site was quite varied. The absolute elevation of the site ranged from 64 m to 76 m. More interestingly, the slope of the site ranged from 2o to 6o, hence resulting in a catchment at the southern end of the vineyard. (Figures 3a, b and c).

Figure 3. Terrain analysis of research site

Figure 3. Terrain analysis of research site

When plant water status was modeled by clustering analysis, the vineyard was delineated into two water stress zones: Higher and Lower (Figure 4).

Figure 4. Water stress clustering at the research site

Figure 4. Water stress clustering at the research site

This indicated that between the two water stress clusters there was 70% variability. The water stress was directly related to elevation change, but inversely related to total wetness index of the research site. The difference between the two water stress zones drove quite striking difference in primary metabolism such as net carbon exchange, Brix accumulation during the last 40 days of the ripening period (Figure 5), and titratable acidity of the berry.

Figure 5. Clustering of Brix accumulation at the research site.

Figure 5. Clustering of Brix accumulation at the research site.

As previously mentioned we were unable to determine a relationship between yield and the fruit composition values we monitored. Furthermore, we were unable to find a significant relationship between water stress and yield. However, the differences in primary metabolism drove differences in anthocyanin and proanthocyanidin content of the berry at harvest. We associated the differences in anthocyanin content to degradation due to greater water stress as delineated by our cluster analysis. Furthermore, the differences in proanthocyanidin content of the berry responded in similar manner to anthocyanins as modulated by the water stress model at this research site. (Figure 6).

Figure 6. Total anthocyanin content of the berry was affected by the water stress clustering at the research site.

Figure 6. Total anthocyanin content of the berry was affected by the water stress clustering at the research site.

In the initial year of the study we have shown that vineyard variability affected harvest composition. In cases such as this where variability at the research site is too large to coalesce, selective harvest can be a useful management tool. Water status allowed us to effectively discriminate between harvest zones. We can now easily model  and sense water stress deliniation using canopy reflectance with less of a need to take repeated measurements in vineyards.