Vine field monitoring using high resolution remote sensing images: segmentation and characterization of rows of vines

The mechanization of vineyard work originally led to a reduction in planting densities due to the lack of machinery adapted to the vineyard. The current availability of specific machinery makes it possible to establish higher planting densities. In this work, three planting densities (1.40×0.80 m, 1.80×1 m and 2.20×1.20 m, corresponding to 8928, 5555 and 3787 plants/ha respectively) were studied with four varieties autochthonous of Galicia (northwestern Spain): Albariño and Treixadura (white), Sousón and Mencía (red). The vines were trained in a vertical shoot positioning system using a single Royat cordon, and pruned to spurs with two buds each. Agronomic data (yield, pruning wood weight, Ravaz index) and oenological data in must were collected. The higher planting density (1.40×0.80 m) had no significant effect on grape yield per vine in white varieties, although production per hectare was much higher due to the greater number of plants. In red varieties, this planting density resulted in a significantly lower production per vine, compensated by the greater number of plants. In addition, it significantly reduced the Brix degree in the must of the Albariño, Treixadura and Sousón varieties, and increased the total acidity in the latter two and Mencía. It also caused an increase in extractable and total anthocyanins and IPT in red grapes. The effects of high planting density on grapes are of great interest for the adaptation of varieties in the context of climate change. In the future, it could be advisable to modify the limits imposed by the appellations of origin on the planting density of these varieties in order to obtain more balanced wines.

A large varietal collection including over 1700 varieties was maintained in Conegliano, ITA, since the 1950s. Phenological data on a subset of 400 grape varieties including wine grapes, table grapes, and raisins were acquired at bud break, flowering, veraison, and ripening since 1964. Despite the efforts in maintaining and acquiring data over such an extensive collection, the data set has varying degrees of missing cases depending on the variety and the year. This is ubiquitous in phenology datasets with significant size and length. In this work, we evaluated four state-of-the-art methods to estimate missing values in this phenological series: k-Nearest Neighbour (kNN), Multivariate Imputation by Chained Equations (mice), MissForest, and Bidirectional Recurrent Imputation for Time Series (BRITS). For each phenological stage, we evaluated the performance of the methods in two ways. 1) On the full dataset, we randomly hold-out 10% of the true values for use as a test set and repeated the process 1000 times (Monte Carlo cross-validation). 2) On a reduced and almost complete subset of varieties, we varied the percentage of missing values from 10% to 70% by random deletion. In all cases, we evaluated the performance on the original values using normalized root mean squared error. For the full dataset we also obtained performance statistics by variety and by year. MissForest provided average errors of 17% (3 days) at budbreak, 14% (4 days) at flowering, 14.5% (7 days) at veraison, and 17% (3 days) at maturity. We completed the imputations of the Conegliano dataset, one of the world’s most extensive and varied phenological time series and a steppingstone for future climate change studies in grapes. The dataset is now ready for further analysis, and a rigorous evaluation of imputation errors is included.

In problematic conditions for production of grapes and wine caused by the COVID-19 pandemic and the resulting occurrence of wine surpluses, producers are increasingly turning to the innovative viticulture and winemaking of products that are more appealing to the market and the consumers. On the other hand, consumption of the food safety or organic products, and therefore of organic grapes and wine, is increasingly common in the world, in particular in Europe. The Regional Rural Development Standing Working Group (SWG RRD), as a regional intergovernmental organization gathers actors in the viticulture and winemaking sector from states and territories of the Western Balkans (South-East Europe) in the Expert Working Group for Wine, with the aim of improving viticulture and winemaking in this region through joint activities. In accordance with the aforementioned, the SWG RRD is working on advancing organic production of grapes and wine, and on recognition of specificities of the terroir of wine-growing areas in Western Balkans. In addition, as part of the project “Facilitation of Exchange and Advice on Wine Regulations in Western Balkan Countries” helmed by the German Federal Ministry of Food and Agriculture, in addition to harmonization of relevant legislation with EU regulations, efforts are being invested towards recognition of organic wines. Within activities and project implemented by this organization, expert analyses and scientific research of the terroir of Western Balkans were carried out, and some of the results are presented in this paper.

Climate influence on grapevine physiology is prevalent and this influence is only expected to increase with climate change. Although governed by a general determinism, climate influence on grapevine physiology may present variations according to the terroir. In addition, these site-specific differences are likely to be enhanced when climate influence is studied using farm data. Indeed, farm data integrate additional sources of variation such as a varying representativity of the conditions actually experienced in the field. Nevertheless, there is a real challenge in valuing farm data to enable grape growers to understand their own terroir and consequently adapt their practices to the local conditions. In such a context, this article proposes a framework to site-specifically study climate influence on grapevine physiology using farm data. It focuses on improving the analysis of time series of weather data. The analytical framework includes the synchronization of time series using site-specific thermal indices computed with an original method called Extended Growing Degree Days (eGDD). Synchronized time series are then analyzed using a Bayesian functional Linear regression with Sparse Steps functions (BLiSS) in order to detect site-specific periods of strong climate influence on yield development. The article focuses on temperature and rain influence on grape yield development as a case study. It uses data from three commercial vineyards respectively situated in the Bordeaux region (France), California (USA) and Israel. For all vineyards, common periods of climate influence on yield development were found. They corresponded to already known periods, for example around veraison of the year before harvest. However, the periods differed in their precise timing (e.g. before, around or after veraison), duration and correlation direction with yield. Other periods were found for only one or two vineyards and/or were not referred to in literature, for example during the winter before harvest.

Water is the main limiting factor for yield in viticulture. Improving drought adaptation in viticulture will be an increasingly important issue under climate change. Genetic variability of water deficit responses in grapevine partly results from the rootstocks, making them an attractive and relevant mean to achieve adaptation without changing the scion genotype. The objective of this work was to characterize the rootstock effect on the diurnal regulation of scion transpiration. A large panel of 55 commercial genotypes were grafted onto Cabernet Sauvignon. Three biological repetitions per genotype were analyzed. Potted plants were phenotyped on a greenhouse balance platform capable of assessing real-time water use and maintaining a targeted water deficit intensity. After a 10 days well-watered baseline period, an increasing water deficit was applied for 10 days, followed by a stable water deficit stress for 7 days. Pruning weight, root and aerial dry weight and transpiration were recorded and the experiment was repeated during two years. Transpiration efficiency (ratio between aerial biomass and transpiration) was calculated and δ13C was measured in leaves for the baseline and stable water deficit periods. A large genetic variability was observed within the panel. The rootstock had a significant impact on nocturnal transpiration which was also strongly and positively correlated with maximum daytime transpiration. The correlations with growth and water use efficiency related traits will be discussed. Transpiration data were also related with VPD and soil water content demonstrating the influence of environmental conditions on transpiration. These results highlighted the role of the rootstock in modulating water deficit responses and give insights for rootstock breeding programs aimed at identifying drought tolerant rootstocks. It was also helpful to better define the mechanisms on which the drought tolerance in grapevine rootstocks is based on.