The features for assessing the authenticity of wines by region of origin are studied, based on the relationship between the mineral composition of the wine, the grapes, and the soil profile (0 to 160 cm) from the place of growth of Cabernet Sauvignon grapes. Soil, grape, and wine samples were taken from the territories of six vineyards in the Anapa district of Krasnodar Territory, Russia. Using the methods of ICP-OES, thermal, and X-ray phase analysis, the soils were differentiated into three groups, differing in mineralogical and mineral compositions. The soil samples of the first group contained up to 31% quartz, the second group up to 25% quartz and 19% mixed calcite, and the third group up to 32% calcite and 15% quartz. The formation of the elemental image of the grapes was studied, taking into account the total content and mobile forms of metals in the soil. The territorial proximity of the vineyards did not affect the extraction of elements from the soil into the grape berry, and the migration of metals for each territory was selective. According to the values of the biological absorption coefficient, the degree of transition of metals from the soil to a berry was estimated. For K, Ti, Zn, Rb, Cu, and Fe in all berries, the coefficient was higher than 1.00, which means that the berry extracts contained not only mobile-form, but also difficult-to-dissolve metal compounds. The migration of macro-components from the soil to the berry was low, and amounted to 6–7% for Ca, 0.8–3.0% for Na, and 25–70% for Mg of the concentration of their mobile forms. For all territories, the maximum correlation between metal concentrations in grapes and soil was observed for samples from a depth of 0–40 cm. The discriminant model based on concentrations of Rb, Al, K, Sr, Co, Na, Pb, Ca, and Ni showed the formation of clusters in the territories of vineyard cultivation. The developed model allow the problems of identifying wines by region to be solved with high accuracy, using their elemental image.

The aim of this work is to study the correlations of the elemental composition in the “soil–grape–wine” chain to determine the regional origin of Chardonnay grapes and wine. Soil samples (n = 40) from five vineyards in the Anapa region, Russia, taken from eight different depths, grapes from these vineyards (n = 75), and wines obtained from these grapes (n = 5) were analyzed using inductively coupled plasma atomic emission spectrometry and inductively coupled plasma mass spectrometry. The mineralogical composition of the soils was determined using thermal and X-ray phase analysis. The mineralogical composition of vineyard soils mainly consists of calcite, quartz, nontronite, vermiculite, and muscovite. According to spectrometric analysis, the distribution of both the total content and the mobile forms of elements in soil profiles turned out to be similar. The content of Na, Ca, and Sr increased with increasing sampling depth, while the content of Co, Cu, Fe, Ni, Mn, Pb, and Zn decreased. Regardless of the area of cultivation, the predominant elements in grapes are K, Ca, Na, and Mg. It is established that the elemental profiles of grapes and wine are correlated. At the same time, during the winemaking process, a decrease in the concentration of most elements (Al, Ba, Ca, Cu, K, Mg, Mn, Ni, Rb, Sr, Ti, and Zn) is observed. It has been shown that the vine is able to accumulate not only mobile but also less bioavailable forms of metals from the soil (Cu, Fe, K, Rb, Ti, and Zn), while the migration of Ca and Na remains low (<7%). Using discriminant analysis, a model of grape identification based on the concentrations of Al, Li, Mn, Na, Pb, and Rb was developed. This model demonstrated a high accuracy (100% for training and test datasets) in grape classification by region, confirming that the elemental “fingerprint” is a reliable marker of terroir.

Introduction. Soils play an important role in the approximately 30-year period of operation of a grape planting, influencing plant growth, their yield and the quality of the grapes. In this study, the morphometric parameters of the surface soil layer of a grape plantation were studied using spectral channels of satellite images.Methodology. The methodology included the use of a “random forest” algorithm to classify soil cover using spectral channels and normalized satellite image indices and analyze the main physicochemical properties of soils. Accuracy was assessed using RMSD and confidence intervals calculated via bootstrapping.Results. The study revealed significant differences in the spectral reflectivity of different site options, which was due to carbonate content, humidity levels and the amount of humus. Areas with high carbonate and moisture content showed higher standard deviation values in the spectral channels. Studying the spectral characteristics of the soil surface makes it possible to effectively classify different areas based on remote sensing data. Analysis of combinations of spectral channels revealed an optimal set of three channels (B12, B11, B8A) with a minimum standard deviation when classifying an image into six soil variants of areas. For classification, a composition of five normalized indices can also be used, but in this case the calculation time increases significantly with a larger standard deviation and a larger confidence interval range. Using machine learning, six distinct soil surface types were segmented, demonstrating the complexity of the field›s soil mosaic. These results are critical for improving vineyard management and productivity

The aim of the research was to study the composition of soil micromycetes with different systems of soil maintenance on vine plantations. The studies were carried out in the farms of Anapa district of Krasnodar Kray according to generally accepted methods in soil science and mycology. The scientific novelty lies in the insufficient knowledge of soil fungi of ampelocenoses under conditions of increased anthropogenic impact. As a result of research in 2019, it was found that the largest number of colony-forming units of micromycetes in the soil was observed in the variant with sodding. The number of КОЕ in 1 gram of absolutely dry soil in the variant with sodding in May exceeded the variant with black fallow by 5 times, and in August by 2 times. Species diversity was represented by 11 species belonging to 8 genera: Mucor, Trichoderma, Penicillium, Aspergillus, Fusarium, Alternaria, Cladosporium, Vertecillium. In the complex of the isolated micromycetes in the variant with sodding in the spring, the species of r. Trichoderma. Its frequency of occurrence was 80%. On the black fallow in this period, the dominant position was occupied by the species of the r. Penicillium (30.4%). In the summer period, in the variant with turfing, the species of the genera Fusarium, Alternaria, Cladosporium, Vertecillium were isolated with a higher frequency of occurrence, while the dominant position was occupied by species of the r. Penicillium (27.4%). In the variant with the system of soil maintenance, black fallow species dominated Penicillium and Aspergillus. The number of micromycetes in the soils of ampelocenoses depends on the system of soil maintenance and environmental conditions, while a regrouping of the species composition of fungi is observed.

Relevance. Vegetation indices (VI) indices allow one to correlate the estimated signs of growth vigor of a grape plant with productivity values in different periods of phenophases. Grapes respond especially subtly to conditions of weather and climate changes and abnormal weather manifestations. For monitoring vineyards, NDVI is the most effective. The use of Sentinel-2 satellite data in monitoring vineyards has shown high efficiency throughout the entire growing season, and in many countries research is underway on the use of VI to assess the development and productivity of vineyards.The aim of the work is to find the optimal formula for calculating the productivity of a grape plant based on the values of NDVI.Methods. Stationary field experience of agrobiological characteristics of grape plantations, processing of digital images of spectral channels of the Sentinel-2 satellite platform. Digital image processing and calculation of NDVI VI were carried out in the GIS SNAP Desktop.Results. Based on the VI values, the phenological periods of the grape planting were determined to calculate the predicted yield. The presence of a close relationship between vegetation indices, crown density and yield makes it possible to determine the strength of development of grape plants during phenological periods using multispectral satellite images. The developed method for assessing the predicted yield based on the NDVI VI of a grape plant in the phenophases of flowering and growth allows one to calculate the predicted yield with high accuracy in relation to the actual one.