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The major factors of wine trade have been showing distinct patterns of temporal trends worldwide in past decades. Wine consumption, production, imports, and exports differ according to their location and classification to Old World and New World wine markets. Using datasets from various sources, this work focused on quantifying long-term trends (1995–2021) of these wine industry factors for each country, including long-term means and temporal trends, using the Mann-Kendall trend test, and resulting in Z-scores. The temporal relationships between these global factors were quantified by applying Pearson correlation to the original values, as well as by correlating the Z-scores. Our findings show that Old World wine consumers and producers (e.g., Spain, France, and Italy) have been experiencing gradual decreasing trends of wine consumption and production. In New World countries, some of the largest wine-consuming countries were found to have strong, significant increases in wine consumption and new wine production markets show rapid growth trends. About 80% of the countries demonstrated increasing trends of wine imports, signifying the impact of globalization on the wine market and the growing demand for foreign wine. Globally, consumption per capita was found to have significantly decreased. Wine production showed a strong, significant, and lagged dependence on wine consumption, which was also related to the temporal trends of wine imports and exports. The major forces driving the wine market are possibly economic growth and wider competition, with climate change acting as a disruptive force.

Monitoring and tracking the long-term dynamics of vineyard coverage and fresh grape production can support sustainable agricultural planning under evolving climate, market, and land-use pressures. This study presents a comprehensive, data-driven analysis of global viticulture trends from 1961 to 2023, integrating the official statistical database of the Food and Agriculture Organization of the United Nations (FAOSTAT) for grape-producing countries. We applied statistical trend analysis (Mann–Kendall test), Random Forest regression modeling, cross-correlation functions, and dissimilarity analysis to examine patterns and drivers of change in vineyard area, production volume, yield efficiency, and land-use intensity. Our results reveal a significant global decoupling of production from vineyard areas, driven by increasing yields and technological intensification, particularly in rapidly expanding table grape markets in Asia. While traditional European wine regions are reducing vineyard coverage, emerging producers such as China and India are achieving high production with improved land efficiency. Production volume emerged as the dominant predictor of vineyard-harvested areas, while climatic factors, urbanization, and socio-economic dynamics also exerted significant influence. Our findings point to growing polarization in production amounts, alongside convergence in yield and management efficiency across countries. These findings contribute to the understanding of global viticulture transformation and provide insights into optimizing land-use strategies for sustainable grape production under climate change and market evolution.

The Leaf Area Index (LAI) is a key physiological metric in viticulture, associated with vine health, yield, and responsiveness to environmental and management factors. This study, conducted in a Mediterranean Sauvignon Blanc vineyard (2017–2023), examines how irrigation and environmental variables affect LAI across phenological stages, and their impact on yield (clusters per vine, cluster weight, total yield) and pruning parameters (cane weight, pruning weight). Results show that irrigation is the primary driver of LAI, with increased water availability promoting leaf area expansion. Environmental factors, including temperature, vapor pressure deficits, and solar radiation, influence LAI dynamics, with chilling hours playing a crucial role post-veraison. Excessive LAI (>1.6–1.7) reduces yield due to competition between vegetative and reproductive sinks. Early-season LAI correlates more strongly with yield, while late-season LAI predicts pruning weight and cane growth. Machine learning models reveal that excessive pre-veraison LAI in one season reduces cluster numbers in the next. This study highlights LAI as a critical tool for vineyard management. While irrigation promotes vegetative growth, excessive LAI can hinder fruit set and yield, emphasizing the need for strategic irrigation timing, canopy management, and climate adaptation to sustain long-term vineyard productivity.

Spectral-based vegetation indices (VI) have been shown to be good proxies of grapevine stem water potential (Ψstem), assisting in irrigation decision-making for commercial vineyards. However, VI-Ψstem correlations are mostly reported at the leaf or canopy scales, using proximal canopy-based sensors or very-high-spatial resolution images derived from sensors mounted on small airplanes or drones. Here, for the first time, we take advantage of high-spatial resolution (3-m) near-daily images acquired from Planet’s nano-satellite constellation to derive VI-Ψstem correlations at the vineyard scale. Weekly Ψstem was measured along the growing season of 2017 in six vines each in 81 commercial vineyards and in 60 pairs of grapevines in a 2.4 ha experimental vineyard in Israel. The Clip application programming interface (API), provided by Planet, and the Google Earth Engine platform were used to derive spatially continuous time series of four VIs—GNDVI, NDVI, EVI and SAVI—in the 82 vineyards. Results show that per-week multivariable linear models using variables extracted from VI time series successfully tracked spatial variations in Ψstem across the experimental vineyard (Pearson’s-r = 0.45–0.84; N = 60). A simple linear regression model enabled monitoring seasonal changes in Ψstem along the growing season in the vineyard (r = 0.80–0.82). Planet VIs and seasonal Ψstem data from the 82 vineyards were used to derive a ‘global’ model for in-season monitoring of Ψstem at the vineyard-level (r = 0.78; RMSE = 18.5%; N = 970). The ‘global’ model, which requires only a few VI variables extracted from Planet images, may be used for real-time weekly assessment of Ψstem in Mediterranean vineyards, substantially improving the efficiency of conventional in-field monitoring efforts.

This study investigated the relationship between Subsoil Geological Structure (SSGS) and the yield, berry composition, and wine attributes of Merlot grapevines in a mountainous region. The research found significant differences in vine physiology, yield, and berry chemistry of grapevines between five adjacent rows, which corresponded with the underlying SSGS. The middle row, planted over filling material and a karst layer, had the highest yield (1.96 kg·vine−1), consistent with better water availability, but produced berries and wine with the lowest concentrations of anthocyanins, phenolics, and soluble solids, resulting in the lowest wine quality score (82.33 points). In contrast, the northernmost row planted over bedrock had the lowest yield (0.12 kg·vine−1), consistent with limited water availability, but produced highly concentrated berries, though extreme stress compromised overall wine balance. The southern row, positioned over filling material on bedrock with moderate water stress (stem water potential −1.4 MPa), achieved an optimal balance between yield and quality, producing wine with the highest sensory score (88.78 points) and favorable chemical composition. Geophysical methods, including electric resistivity tomography (ERT) and ground-penetrating radar (GPR), identified the subsurface structure, revealing the karst layer beneath high-yielding rows and consolidated bedrock beneath severely stressed rows. Chemical analyses of berries and wine confirmed the dilution effect of higher water availability on quality-determining compounds, providing mechanistic evidence linking SSGS to wine quality. This study demonstrates the utility of integrating geophysical, physiological, and enological approaches for understanding terroir effects and optimizing vineyard management in complex geological settings.

Daily or weekly irrigation monitoring conducted per sub-field or management zone is an important factor in vine irrigation decision-making. The objective is to determine the crop coefficient (Kc) and the leaf area index (LAI). Since the 1990s, optic satellite imagery has been utilized for this purpose, yet cloud-cover, as well as the desire to increase the temporal resolution, raise the need to integrate more imagery sources. The Sentinel-1 (a C-band synthetic aperture radar—SAR) can solve both issues, but its accuracy for LAI and Kc mapping needs to be determined. The goals of this study were as follows: (1) to test different methods for integrating SAR and optic sensors for increasing temporal resolution and creating seamless time-series of LAI and Kc estimations; and (2) to evaluate the ability of Sentinel-1 to estimate LAI and Kc in comparison to Sentinel-2 and Landsat-8. LAI values were collected at two vineyards, over three (north plot) and four (south plot) growing seasons. These values were converted to Kc, and both parameters were tested against optic and SAR indices. The results present the two Sentinel-1 indices that achieved the best accuracy in estimating the crop parameters and the best method for fusing the optic and the SAR data. Utilizing these achievements, the accuracy of the Kc and LAI estimations from Sentinel-1 were slightly better than the Sentinel-2′s and the Landsat-8′s accuracy. The integration of all three sensors into one seamless time-series not only increases the temporal resolution but also improves the overall accuracy.

Continually increasing global temperature could severely affect grape berry metabolite accumulation and ultimately wine polyphenol concentration and color intensity. To explore the effect of late shoot pruning on grape berry and wine metabolite composition, field trials were carried out on Vitis vinifera cv. Malbec and cv. Syrah grafted on 110 Richter rootstock. Fifty-one metabolites were detected and unequivocally annotated employing UPLC-MS based metabolite profiling. Integrating the data using hierarchical clustering showed a significant effect of late pruning treatments on must and wine metabolites. Syrah metabolite profiles were characterized by a general trend of higher metabolite content in the late shoot pruning treatments, while Malbec profiles did not show a consistent trend. In summary, late shoot pruning exerts a significant effect, though varietal specific, on must and wine quality-related metabolites, possibly related to enhanced photosynthetic efficiency, which should be taken into consideration when planning mitigating strategies in warm climates.

Wine quality is the final outcome of the interactions within a vineyard between meteorological conditions, terrain and soil properties, plant physiology and numerous viticultural decisions, all of which are commonly summarized as the terroir effect. Associations between wine quality and a single soil or topographic factor are usually weak, but little information is available on the effect of terrain (elevation, aspect and slope) as a compound micro-terroir factor. We used the topographic wetness index (TWI) as a steady-state hydrologic and integrative measure to delineate management zones (MZs) within a vineyard and to study the interactions between vine vigor, water status and grape and wine quality. The study was conducted in a commercial 2.5-ha Vitis vinifera ‘Cabernet Sauvignon’ vineyard in Israel. Based on the TWI, the vineyard was divided into three MZs located along an elongate wadi that crosses the vineyard and bears water only in the rainy winter season. MZ1 was the most distant from the wadi and had low TWI values, MZ3 was closest to the wadi and had high TWI values. Remotely sensed crop water stress index (CWSI) was measured simultaneously with canopy cover (as determined by normalized difference vegetation index; NDVI) and with field measurements of midday stem water potential (Ψstem) and leaf area index (LAI) on several days during the growing seasons of 2017 and 2018. Vines in MZ1 had narrow trunk diameter and low LAI and canopy cover on most measurement days compared to the other two MZs. MZ1 vines also exhibited the highest water stress (highest CWSI and lowest Ψstem), lowest yield and highest wine quality. MZ3 vines showed higher LAI on most measurement days, lowest water deficit stress (Ψstem) during phenological stage I, highest yield and lowest wine quality. Yet, in stage III, MZ3 vines exhibited a similar water deficit stress (CWSI and Ψstem) as MZ2, suggesting that the relatively high vigor in MZ3 vines resulted in higher water deficit stress than expected towards the end of the season, possibly because of high water consumption over the course of the season. TWI and its classification into three MZs served as a reliable predictor for most of the attributes in the vineyard and for their dynamics within the season, and, thus, can be used as a key factor in delineation of MZs for irrigation. Yet, in-season remotely sensed monitoring is required to follow the vine dynamics to improve precision irrigation decisions.

In semi-arid areas, vineyards grown for winemaking are usually mildly irrigated by drip irrigation systems in a manner maintaining drought stress. This practice ensures the proper development of vegetative and reproductive organs on the one hand, and on the other, the development of high-quality grapes which can be hampered by overly abundant water application. In previous work, we have developed and demonstrated an irrigation model suitable for high-quality grape production in semi-arid areas. Here, we tackle the question of proper irrigation initiation dates—should one wait for vines to develop drought stress before the initiation of irrigation, or rather commence irrigation earlier? Our results show that vines which undergo initial irrigation late in the growing season tend to develop a lower midday stem water potential even after irrigation initiation. In addition, these vines tend to produce a lower number of bunches per vine and smaller berry size, leading to lower yields. The wine produced from the late-irrigated treatments had a higher phenolic content, primarily due to higher levels of catechin and epicatechin. Their levels increased as irrigation initiation dates were delayed, while caffeic acid levels showed an opposite trend. Late irrigation also led to higher color intensities compared to those of irrigation at earlier stages, due to higher levels of most anthocyanins. Finally, we show that the overall wine sensory score, representing its overall quality, was approximately five points higher for wines made from delayed irrigation treatments compared to wines made from early season irrigation treatments.

A well-planned irrigation management strategy is crucial for successful wine grape production and is highly dependent on accurate assessments of water stress. Precision irrigation practices may benefit from the quantification of within-field spatial variability and temporal patterns of evapotranspiration (ET). A spatiotemporal modeling framework is proposed to delineate the vineyard into homogeneous areas (i.e., management zones) according to their ET patterns. The dataset for this study relied on ET retrievals from multiple satellite platforms, generating estimates at high spatial (30 m) and temporal (daily) resolutions for a Vitis vinifera Pinot noir vineyard in the Central Valley of California during the growing seasons of 2015-2018. Time-series decomposition was used to deconstruct the time series of each pixel into three components: long-term trend, seasonality, and remainder, which indicates daily fluctuations. For each time-series component, a time-series clustering (TSC) algorithm was applied to partition the time series of all pixels into homogeneous groups and generate TSC maps. The TSC maps were compared for spatial similarities using the V-measure statistic. A random forest (RF) classification algorithm was used for each TSC map against six environmental variables (elevation, slope, northness, lithology, topographic wetness index, and soil type) to check for spatial association between ET-TSC maps and the local characteristics in the vineyard. Finally, the TSC maps were used as independent variables against yield (ton ha-1) using analysis of variance (ANOVA) to assess whether the TSC maps explained yield variability. The trend and seasonality TSC maps had a moderate spatial association (V = 0.49), while the remainder showed dissimilar spatial patterns to seasonality and trend. The RF model showed high error matrix-based prediction accuracy levels ranging between 86% and 90%. For the trend and seasonality models, the most important predictor was soil type, followed by elevation, while the remainder TSC was strongly linked with northness spatial variability. The yield levels corresponding to the two clusters in all TSC were significantly different. These findings enabled spatial quantification of ET time series at different temporal scales that may benefit within-season decision-making regarding the amounts, timing, intervals, and location of irrigation. The proposed framework may be applicable to other cases in both agricultural systems and environmental modeling.