Bud dormancy pattern, chilling requirement, and cold hardiness in Vitis vinifera L. cv. ‘Chardonnay’ and ‘Riesling’

In recent years, new vineyards have been established in southwestern Ontario. The open water of Lake Erie provides some winter protection for Vitis hybrids and winter-hardy Vitis vinifera L. cultivars in this area. However, winter damage is possible when vines are grown distant from the open water or when lakes are frozen. To better understand the risks to winter survival, the dormancy and chilling phenology were studied over three winters from 2013-2016. Ten dormant canes of two V. vinifera cultivars, ‘Chardonnay’ and ‘Riesling’, were collected weekly from September 1 until March 30 from the mature vines in a commercial vineyard located at St. Williams (Ontario). The canes defoliated in early October, and the endodormancy was completed by the end of December. The cumulative chilling hours (0-7.2 °C) from defoliation until the completion of endodormancy were averaged 606 hours for ‘Chardonnay’ and 665 hours for ‘Riesling’. ‘Chardonnay’ buds were slightly less hardy than ‘Riesling’ to cold temperatures, with a threshold of about -24 °C for ‘Chardonnay’ and -25 °C for ‘Riesling’. Most primary buds of both cultivars died after February 16, 2015, and more than half died after February 12, 2014, due to severe low temperatures of -33.1 and -26 °C, respectively.

Explore the Rare—Molecular Identification and Wine Evaluation of Two Autochthonous Greek Varieties: “Karnachalades” and “Bogialamades”

Wines produced from autochthonous Vitis vinifera varieties have an essential financial impact on the national economy of Greece. However, scientific data regarding characteristics and quality aspects of these wines is extremely limited. The aim of the current study is to define the molecular profile and to describe chemical and sensory characteristics of the wines produced by two autochthonous red grapevine varieties—“Karnachalades” and “Bogialamades”—grown in the wider area of Soufli (Thrace, Greece). We used seven microsatellites to define the molecular profile of the two varieties, and then we compared their profile to similar molecular data from other autochthonous as well as international varieties. Grape berries were harvested at optimum technological maturity from a commercial vineyard for two consecutive vintages (2017–2018) and vilification was performed using a common vinification protocol: the 2017 vintage provided wines, from both varieties, with greater rates of phenolics and anthocyanins than 2018, whereas regarding the sensory analysis, “Bogialamades” wine provided a richer profile than “Karnachalades”. To our knowledge, this is the first study that couples both molecular profiling and exploration of the enological potential of the rare Greek varieties “Karnachalades” and “Bogialamades”; they represent two promising varieties for the production of red wines in the historic region of Thrace.

Proximal sensing of vineyard soil and canopy vegetation for determining vineyard spatial variability in plant physiology and berry chemistry

Proximal sensing is used in vineyards to precisely monitor and manage spatial and temporal variability while reducing laborious and repetitive measurements. Soil electrical conductivity (EC) and canopy vegetation indexes are two frequently assessed variables with off-the-shelf sensors. In this study, the ecophysiological variability of a commercial vineyard comprising three varieties in three blocks, Cabernet-Sauvignon (CS), Cabernet franc (CF), and Petit Verdot (PV), was investigated. Random sampling was used to continuously assess spatial variability in plant physiology and berry composition. Soil EC and NDVI were also continuously monitored throughout the season. There was a noticeable spatial pattern in the normalised differential vegetation index (NDVI) in the vineyard and soil EC. The spatial pattern of NDVI can be partially elucidated by the season-long stem water potential (Ψstem), which was lowest in the CS block. However, leaf photosynthesis did not match this spatial pattern. The spatial distribution of NDVI and soil EC did not satisfactorily explain the spatial variations in yield components and berry chemistry. Principal component analyses (PCA) were performed resulting in a clear discrimination of each of the three cultivars. Soil EC showed a significant relationship with Ψstem integrals, total skin anthocyanins and tri- to di-hydroxylated flavonoids. In each cultivar block, soil EC showed some capability to be related to plant water status, and NDVI showed a relationship with yield. Overall, this study provided evidence of the spatial variability of grapevine physiology in a commercial vineyard with three cultivars. Also, it showed that the cultivar effect and uniform crop level management can diminish the efficiency of proximal sensing, thus weakening the relationship of soil and canopy indexes with plant physiology and berry chemistry. Nonetheless, our study showed that it is possible to apply temporal proximal sensing methods when assessing plant water status, primary metabolism, yield and berry secondary metabolism, which give an indication of the possibility of managing the spatial variability of both plant physiology and berry chemistry.

Impact of Leaf Occlusions on Yield Assessment by Computer Vision in Commercial Vineyards

Yield assessment has been identified as critical topic for grape and wine industry. Computer vision has been applied for assessing yield, but the accuracy was greatly affected by fruit occlusion affected by leaves and other plant organs. The objective of this work was the consistent, continuous evaluation of the impact of leaf occlusions in different commercial vineyard plots at different defoliation stages. RGB (red, green and blue) images from five Tempranillo (Vitis vinifera L.) vineyards were manually acquired using a digital camera under field conditions at three different levels of defoliation: no defoliation, partial defoliation and full defoliation. Computer vision was used for the automatic detection of different canopy features, and for the calibration of regression equations for the prediction of yield computed per vine segment. Leaf occlusion rate (berry occlusion affected by leaves) was computed by machine vision in no defoliated vineyards. As occlusion rate increased, R2 between bunch pixels and yield was gradually reduced, ranging from 0.77 in low occlusion, to 0.63.

Sensing Architecture for Terrestrial Crop Monitoring: Harvesting Data as an Asset

Very often, the root of problems found to produce food sustainably, as well as the origin of many environmental issues, derive from making decisions with unreliable or inexistent data. Data-driven agriculture has emerged as a way to palliate the lack of meaningful information when taking critical steps in the field. However, many decisive parameters still require manual measurements and proximity to the target, which results in the typical undersampling that impedes statistical significance and the application of AI techniques that rely on massive data. To invert this trend, and simultaneously combine crop proximity with massive sampling, a sensing architecture for automating crop scouting from ground vehicles is proposed. At present, there are no clear guidelines of how monitoring vehicles must be configured for optimally tracking crop parameters at high resolution. This paper structures the architecture for such vehicles in four subsystems, examines the most common components for each subsystem, and delves into their interactions for an efficient delivery of high-density field data from initial acquisition to final recommendation. Its main advantages rest on the real time generation of crop maps that blend the global positioning of canopy location, some of their agronomical traits, and the precise monitoring of the ambient conditions surrounding such canopies. As a use case, the envisioned architecture was embodied in an autonomous robot to automatically sort two harvesting zones of a commercial vineyard to produce two wines of dissimilar characteristics. The information contained in the maps delivered by the robot may help growers systematically apply differential harvesting, evidencing the suitability of the proposed architecture for massive monitoring and subsequent data-driven actuation. While many crop parameters still cannot be measured non-invasively, the availability of novel sensors is continually growing; to benefit from them, an efficient and trustable sensing architecture becomes indispensable.

Physicochemical and aromatic composition of 'Sauvignon Blanc' wines obtained from the Y-trellis and VSP training systems

The objective of this work was to compare the effect of the Y-trellis and vertical shoot position trellis (VSP) training systems on the physicochemical composition and aromatic profile of 'Sauvignon Blanc' (Vitis vinifera) wines in a high-altitude region of the state of Santa Catarina, Southern Brazil. The experiment was conducted during the 2015 vintage in a commercial vineyard located in the municipality of São Joaquim. The treatments consisted of the training systems: Y-trellis and VSP pruned in spur cordon. Sixty kilograms of grapes were harvested from each training system to make the wines, which were evaluated as to their chemical and phenolic composition and aromatic profile. There is no effect of the training system on the wine chemical variables pH, total acidity, color, and total polyphenols. The aromatic profile and phenolic composition of the wines are affected by the training systems, being related to the variables ethyl acetate, isoamyl acetate, 3-methyl-1-butanol, propanoic acid, and gallic acid in the Y-trellis, and to 1-hexanol, isovaleric acid, caprylic acid, capric acid, and catechin in VSP. The Y-trellis system can be an alternative for high-altitude regions of Southern Brazil.

Spatial Distribution Patterns of Parthenolecanium corni (Hemiptera, Coccidae) and of the Ampelovirus GLRaV-1 and the Vitivirus GVA in a Commercial Vineyard

Distribution patterns of the European fruit lecanium Parthenolecanium corni (Bouché) and of grapevine leafroll-associated virus-1 (GLRaV-1) and grapevine virus A (GVA) were monitored from 2003 to 2015 in a Riesling vine plot in the northeast of France. Virus spread was compared between two periods: 2003–2008 and 2009–2014. The percentage of infected vines increased from 54 to 78% for GLRaV-1 and from 14 to 26% for GVA. The spatial distribution of viruses and of P. corni was analysed using permutation tests and revealed an aggregative pattern. Virus distribution was not associated with the density of P. corni population on grapevines. However, GLRaV-1 and GVA spread mainly from initially infected vines. New GLRaV-1 and GVA infections were more frequent on vines near primarily infected vines, first anisotropically along the row, then between neighbouring rows. Virus spread was similar to those described in literature with grapevine mealybug species. This slow vine-to-vine progression suggests that P. corni was responsible for the virus spread, in accordance with the low mobility and low transmission capacities of its local population.

Control of postharvest gray mold of ‘BRS Nubia’ table grape under cold storage

The demand for high-quality nutritional products has increased fruit consumption, as grapes, for this reason postharvest techniques are required to prevent losses, to preserve quality, to extend shelf life, and to attend to consumer needs. In this way, the objective of this study was to evaluate strategies to control gray mold caused by Botrytis cinerea in ‘BRS Nubia’ grapes during cold storage and shelf life periods. Grape bunches were harvested from a commercial vineyard in Marialva, Parana, Brazil. Grapes were subjected to the following treatments: cold storage at 2 ºC (control), cold storage at 2 ºC with SO2-generating pads, cold storage at 2 ºC and inoculated with B. cinerea suspension, and cold storage at 2 ºC with SO2-generating pads and inoculated with B. cinerea suspension. The experiment was conducted in a complete randomized design with five replications per treatment using four bunches per experimental unit. A factorial arrangement (absence/presence of SO2 pads × absence/presence of Botrytis inoculation) was applied. At the end of 30 days of cold storage and 7 days of shelf life (22 ºC), gray mold incidence, shattered berries, and physicochemical parameters were evaluated. The gray mold incidence on ‘BRS Nubia’ grapes decreased when SO2-generating pads were used during cold storage. Berry weight loss was greater in the treatments without SO2-generating pads after 30 days of cold storage followed by 7 days of shelf life. Berry firmness, soluble solids content (SS), total acidity (TA), SS/TA ratio, and anthocyanins concentration were not negatively affected by SO2-generating pad treatments. However, a slight increase in the shattered berries percentage was recorded for the SO2-generating pad treatments. No significant quality loss of ‘BRS Nubia’ grape was evident after 30 days of cold storage followed by 7 days of exposure at room temperature. In this context, SO2-generating pads can be used to control the gray mold incidence on ‘BRS Nubia’ table grapes during cold storage.

sUAS Remote Sensing of Vineyard Evapotranspiration Quantifies Spatiotemporal Uncertainty in Satellite-Borne ET Estimates

Small Unmanned Aerial Systems (sUAS) show promise in being able to collect high resolution spatiotemporal data over small extents. Use of such remote sensing platforms also show promise for quantifying uncertainty in more ubiquitous Earth Observation System (EOS) data, such as evapotranspiration and consumptive use of water in agricultural systems. This study compares measurements of evapotranspiration (ET) from a commercial vineyard in California using data collected from sUAS and EOS sources for 10 events over a growing season using multiple ET estimation methods. Results indicate that sUAS ET estimates that include non-canopy pixels are generally lower on average than EOS methods by >0.5 mm day−1. sUAS ET estimates that mask out non-canopy pixels are generally higher than EOS methods by <0.5 mm day−1. Masked sUAS ET estimates are less variable than unmasked sUAS and EOS ET estimates. This study indicates that limited deployment of sUAS can provide important estimates of uncertainty in EOS ET estimations for larger areas and to also improve irrigation management at a local scale.

Using Bayesian growth models to predict grape yield

Background and aims: Seasonal differences in vine yield need to be managed to ensure appropriate fruit composition at harvest. Differences in yield are the result of changes in vine management (e.g., the number of nodes retained after harvest) and weather conditions (in particular, temperature) at key vine development stages. Early yield prediction enables growers to manage vines to achieve target yields and prepare the required infrastructure for the harvest.Methods and results: Bunch mass data was collected during the 2016/17, 2017/18 and 2018/19 seasons from a commercial vineyard on the Wairau Plains, Marlborough, New Zealand (41o2’23”S; 173o51’15”E). A Bayesian growth model, assuming a double sigmoidal curve, was used to predict the yield at the end of each season. The accuracy of the prediction was investigated using the Monte-Carlo simulation for yield prediction at various growth stages assuming different prior information.Conclusion: The results show that the model is sensitive to prior assumption and that having a non-informative prior may be more beneficial than having an informative prior based on one unusual year.