The Ontario wine industry produces ≈ 65,000 tonnes of grapes and consists of varieties such as Riesling, Chardonnay, and Cabernet franc, with lesser quantities of Merlot, Cabernet Sauvignon, and Pinot noir, with a farmgate value of >$62 million (www.grapegrowersofontario.com ). Although most grapes are destined for wine production, a small portion (2164 tonnes in 2014) is processed into juice while an additional 1500 tonnes are sold as table grapes (www.ontariotenderfruit.ca). Wine production includes 2.7 million cases of VQA wines including 600,000 L of icewines. Soils are characterized as “variable” as a result of widespread glacial activity over 10,000 years ago, and consequently many vineyards are situated on several soil series that can range widely in terms of texture, depth, and water-holding capacity1. This variability in soil characteristics can impact vine vigor, yield, and perhaps water status. A significant growth in the number of small artisanal wineries has permitted production of wines that are unique to individual vineyard sites and in some cases unique to specific vineyard blocks. In the past 10-15 years this interest has expanded to include identification of unique portions of vineyard blocks, some < 1 ha, that might be capable of producing extremely high-value wines based upon yield, vine size, or water status-based quality levels.
Geomatic technologies in viticulture
Large vineyards are variable with respect to soil texture, moisture and depth and other variables such as organic matter, cation exchange capacity, and both major and minor elements. As a consequence, vineyards vary spatially in vigor, yield, and fruit composition. Research has been undertaken in Ontario for several years (beginning 1998) by our research team that has produced spatial maps and consequently quantified degree of spatial variability in numerous vineyards with respect to soil composition, vine elemental composition, vigor, vine water status, vine winter hardiness, yield, and berry composition2-7. Moreover, these variables have been analyzed to determine relevant spatial correlations among them. Maps delineating clear zones of different vigor (Figure 1), yield, and vine water status have allowed researchers to produce wines from these unique zones that are likewise different chemically and sensorially.
The basic premise of precision agriculture is that inputs to farming practices are in response to information gathered with the intent of affecting outputs through an information feedback-loop system8. When applied to viticulture, there is a focus on understanding the spatial and temporal variability in the production of wine grapes9. Grape growers have traditionally accepted variability within vineyards as inherent to the qualities of the site itself, and is a basis for the terroir effect, i.e. the impact of vineyard location. With many years of experience, vineyard areas have been subdivided into individually rated vineyards of higher or lower quality. The increased availability of geomatics software has allowed grape growers to geographically link information from their vineyards into the precision agriculture feedback loop, and target inputs to specific regions of their vineyards. Precision agriculture has been used successfully in grape production in New World regions including California10, Australia11-15, and New Zealand16,17, as well as Old World regions such as Spain18-21 and France22,23. In Ontario, geomatic technologies were used to identify zones of different water status in Cabernet franc2-4 and Riesling7. Zones of lowest water status were associated with highest monoterpenes in Riesling berries7 (Figure 2), and highest anthocyanins and phenols in Cabernet franc2-4.
Attempts have been made with limited success to identify unique zones using remote sensing and to thereafter associate these remotely-sensed regions with variables such as vine water status, soil moisture, vine vigor, yield, and berry composition. Although less laborious than manual data collection and the subsequent production of a multitude of maps, use of aircraft is costly and remote sensing in agricultural systems is in many ways imprecise23. The data that is collected must be converted to variables such as normalized difference vegetative index (NDVI) through computer software such as ENVI6. Moreover, validation of data acquired by remote sensing is still necessary to determine whether ostensibly-unique zones are relevant from a standpoint of physiology, productivity, and berry composition. One particular challenge involved masking of cover crop NDVI from all images to assess the vine canopy-specific NDVI5,6 (Figure 3).
In viticultural applications, remote sensing has been used in modelling vegetative growth, and to infer grape composition from those measurements. Johnson et al.10 used remotely sensed multispectral data to delineate a vineyard site of Chardonnay into small-lot production zones. They found that vine size was related to the vigor zones, as identified by the airborne images. Vigor zones were also related to vine water status and grape composition variables. Thus, indirectly, remote sensing was used to predict vineyard status and grape composition, with direct implications for wine quality10. Relationships between vegetation indices (VIs) and vegetative growth were further explored by Dobrowski et al.24. There was a strong, positive correlation between the extracted VIs and the pruning weights (vine size) in two years. Additionally, the relationship established in the first season was able to predict the vine size in the second study vintage.
The ability of remote sensing to be used to directly predict grape composition variables was explored by Lamb et al.15. They found that re-sampling the image to a final pixel size approximately the same as the distance between rows, effectively combining vine size and density information into a single pixel, resulted in the strongest correlations to total phenols and colour. Strongest correlations (most negative) between NDVI and total phenols and color occurred around the time of veraison (berry softening and color change)15. In the Languedoc region of France, Acevedo-Opazo et al.22,23 performed a study on remotely sensed VIs, vine water status, and grape composition on a number of wine grape varieties in non-irrigated vineyards. Temporally stable relationships occurred between zones delineated based on the NDVI and vegetative growth, vine water status, and yield. These zones were also consistent with soil type. They concluded that a combination of remotely sensed data with intimate vineyard knowledge, especially of the soil, is needed to predict grape composition and ultimately wine quality.
Overall, remote sensing has been proven as a useful tool for monitoring vineyard vegetative growth, and for making inferences about grape composition from multispectral measurements. In Ontario, NDVI data from remote sensing was associated with numerous variables in Riesling vineyards, including vine water status, yield components, and berry composition6,25. Similar applications were made in Pinot noir vineyards5. Remote sensing proved to be a good tool to determine color and phenolic potential of grapes, in addition to water status, yield and vine size. These studies were unique by employing remote sensing in cover-cropped vineyards and thereafter using protocols for excluding the spectral reflectance contributed by the inter-row vegetation.
The recent introduction of Greenseeker and other proximal sensing technologies might allow growers to identify unique zones within vineyards without use of aircraft26,27. If unique zones can be identified easily from the ground, it is possible that different wine products of varying price points could be created from these zones with minimal cost from the producer. Data validation would be required as with remote sensing to determine relationships between proximally-sensed data and other variables of agricultural relevance, but the proximally-sensed data are relatively easy to access. Ground-based (proximal sensing) technologies are relatively recent introductions and their evaluation in viticulture is uncommon. Their initial use was for continuous compilation of NDVI data from vine canopies26,27. Proximal sensing correlated with vine size and berry color in Merlot vineyards in northern Greece28. NDVI sensors explained variation in biomass; its relationship to vine size was nonlinear and was best described by a quadratic regression A linear correlation to stable isotope content in leaves (13C and 15N) provided evidence that canopy reflectance detected plant stresses as a result of water shortage and limited N fertilizer uptake29. It was also successful in detection of downy mildew disease levels in Italian vineyards27. Despite these advances research into proximal sensing has mainly been limited to agronomic crops such as corn30 and thus far no work in Canada has been carried out on grapes to our knowledge until we began research in 201431.
Unmanned aerial vehicles
Remote sensing has been proven as a useful tool for monitoring vineyard vegetative growth, and for making inferences about grape composition from multispectral measurements32,33. However, employment of UAVs for remote sensing in vineyards is a relatively new area of research, heretofore untested in Canada, and capable of acquiring high resolution spatial data without high cost of aircraft. As with proximal sensing there has been little published, and most have confirmed their ability
to acquire NDVI and related images34-36. Zarco Tejada et al.19 explored relationships between photosynthesis and chlorophyll fluorescence by hyperspectral imagery captured via UAVs. Significant relationships were demonstrated between photosynthesis and chlorophyll fluorescence vs. remote measurements. Other relationships were demonstrated between both chlorophyll a and b and leaf carotenoids vs. several vegetation indices based on multispectral images acquired by UAVs21. UAVs were likewise utilized for assessment of vineyard water status by correlation of stem water potential with normalized difference vegetation index37. Further relationships were elucidated between several vegetation indices including NDVI vs. leaf water potential and stomatal conductance20. Additionally, photochemical reflectance index (PRI), renormalized difference vegetation index (RDVI), and red edge index were correlated to water status variables20. Other stresses such as those nutritional in nature have been detected by UAVs; e.g. NDVI was correlated with levels of iron chlorosis, carotenoid pigments in leaves, and anthocyanins in leaves and grape berries38. We began to test the potential of UAVs in Ontario vineyards in the summer of 2015 and will compare their efficacy with that of proximal sensing technologies.
Ultimately the goal of these new technologies is to use the variability detected in vineyards to produce better wines. In agronomic crops, use of variable-rate sprayers, fertilizer and lime spreaders, and other equipment are used to minimize variability based on previous season’s yields. Soil composition can also be mapped and utilized for similar purposes, and UAVs are already being used to detect foliar symptoms of mineral deficiencies that can ultimately be rectified using variable-rate fertilizer/lime spreaders. In viticulture, our group had originally embraced the ideas adopted by agronomic scientists; however, vineyards are perennial systems and vine size and yield variability are inherent in each vineyard due to variable soils. This provides an opportunity to produce different wines from sub-blocks within large vineyards. There are some examples commercially in Ontario, e.g. Thirty Bench Winery produces four Riesling wines based upon individual vineyard blocks (q.v. Marciniak et al.)6, while Coyotes Run produces distinctly different Pinot noir wines from adjacent vineyard blocks5. In these cases the main difference appeared to be vine water status. We anticipate that in a short timespan that these technologies and ideas will become widely accepted in our industry and elsewhere.
Andrew Reynolds, Cool Climate Oenology & Viticulture Institute,
Brock University, St. Catharines, Ontario1
(*Corresponding author email: firstname.lastname@example.org)
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