Explore the vast range of titles available.

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 spatial distribution of plants is constrained by demographic and ecogeographic factors that determine the range and abundance of the species. Wild grapevine (Vitis vinifera ssp. sylvestris) is distributed from Switzerland in the north to Israel in the south. However, little is known about the ecogeographic constraints of this species and its genetic and phenotypic characteristics, especially at the southern edge of its distribution range in the Levant region. In this study, we explore the population structure of southern Levantine wild grapevines and the correlation between demographic and ecogeographic characteristics. Based on our genetic analysis, the wild grapevine populations in this region can be divided into two major subgroups in accordance with a multivariate spatial and ecogeographical clustering model. The identified subpopulations also differ in morphological traits, mainly leaf hairiness which may imply adaptation to environmental stress. The findings suggest that the Upper Jordan River population was spread to the Sea of Galilee area and that a third smaller subpopulation at the south of the Golan Heights may represent a distinguished gene pool or a recent establishment of a new population. A spatial distribution model indicated that distance to water sources, Normalized difference vegetation index, and precipitation are the main environmental factors constraining V. v. sylvestris distribution at its southern distribution range. These factors in addition to limited gene flow between populations prevent further spread of wild grapevines southwards to semi‐arid regions. In this paper, genetic analysis, spatial, and eco‐geographical clustering models show there are two major subpopulations of wild grapevine in the south Levant, differing morphologically mainly in leaf hairiness. We also showed that closeness to water sources, medium‐high NDVI levels, and sufficient precipitation rates are the main effectors possibly preventing further distribution of wild grapevines southwards.

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.

Background and Aims Dormancy in grapevines (Vitis vinifera) is a critical adaptive strategy that enables vines to survive harsh winter conditions and is influenced by physiological and environmental factors. This study aimed to investigate the progression of endodormancy across different pruning dates and cultivars and to identify the key meteorological and agrotechnical factors influencing dormancy depth. Methods and Results Seven cultivars (Cabernet Sauvignon, Carignan, Chardonnay, Merlot, Petit Verdot, Shiraz and Tempranillo) were subjected to four pruning dates (e.g., sampling treatments: December, January, early February and late February) over 4 years (2021–2024). Single‐node cuttings from each treatment were placed under controlled growth conditions (14‐h light, 23°C) for dormancy assessment. Dormancy levels reduced with time and subsequent pruning treatments, with cultivar‐specific variations. Tempranillo and Petit Verdot displayed deeper dormancy, while Chardonnay and Carignan exhibited an earlier dormancy release. Shiraz showed the least variation across dates, and Merlot had minimal seasonal fluctuations. A random forest model incorporating temporal and meteorological features identified temperatures > 25°C, vapour pressure deficit (VPD) and chill units (CU) as the most influential meteorological factors for dormancy fluctuations, with relative contributions of 10.7%, 9.7% and 7.9%, respectively. Warm winter conditions in 2024 were associated with shallower dormancy (< 25%), particularly in Tempranillo and Petit Verdot during December, followed by an unusual deeper dormancy state (> 95%) during January in several cultivars. Conclusions Variable dormancy depth was observed across different cultivars and seasons. Meteorological factors, particularly temperature, VPD and CU, influence the dormancy depth, with clear cultivar‐specific differences. Significance of the Study This study provides insights into cultivar‐specific dormancy depth with the relative influence of meteorological factors.

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.

Water and energy are recognized as the most influential climatic vegetation growth-limiting factors. These factors are usually measured from ground meteorological stations. However, since both vary in space, time, and scale, they can be assessed by satellite-derived biophysical indicators. Energy, represented by land surface temperature (LST), is assumed to resemble air temperature; and water availability, related to precipitation, is represented by the normalized difference vegetation index (NDVI). It is hypothesized that positive correlations between LST and NDVI indicate energy-limited conditions, while negative correlations indicate water-limited conditions. The current project aimed to quantify the spatial and seasonal (spring and summer) distributions of LST–NDVI relations over Europe, using long-term (2000–2017) MODIS images. Overlaying the LST–NDVI relations on the European biome map revealed that relations between LST and NDVI were highly diverse among the various biomes and throughout the entire study period (March–August). During the spring season (March–May), 80% of the European domain, across all biomes, showed the dominance of significant positive relations. However, during the summer season (June–August), most of the biomes—except the northern ones—turned to negative correlation. This study demonstrates that the drought/vegetation/stress spectral indices, based on the prevalent hypothesis of an inverse LST–NDVI correlation, are spatially and temporally dependent. These negative correlations are not valid in regions where energy is the limiting factor (e.g., in the drier regions in the southern and eastern extents of the domain) or during specific periods of the year (e.g., the spring season). Consequently, it is essential to re-examine this assumption and restrict applications of such an approach only to areas and periods in which negative correlations are observed. Predicted climate change will lead to an increase in temperature in the coming decades (i.e., increased LST), as well as a complex pattern of precipitation changes (i.e., changes of NDVI). Thus shifts in plant species locations are expected to cause a redistribution of biomes.

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.

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.

‘Malbec’ grapevines commonly have high yield, thus intermittently negatively affecting wine quality parameters. Here, we describe the use of late shoot pruning (LSP) practice applied for wine quality improvement. We examined the effect of timing of LSP on ‘Malbec’ vines grown in Mediterranean conditions during three consecutive seasons (2016–2018) in Israel. The timing of LSP treatment applications (applied one, two and three weeks after bud break) were compared with cluster-thinned, winter-pruned vines (WP + T) and standard winter pruning (WP). The LSP practice postponed bud break of target buds but did not have a temporal effect on the onset of veraison. Midday stem water potential was less negative and stomatal conductance and net CO2 assimilation rate were higher in the LSP vines. This practice led to a substantial reduction in the number of clusters and crop yield. Finally, wine quality was positively affected by applying LSP treatment. Performing the inexpensive LSP treatment at the precise timing after bud burst was found to save labor, decrease crop yield and improve grape and wine parameters. LSP application should be considered in adequate varieties as a significant tool for the enhanced effectiveness of vine growing in warm regions.

Temporal changes and spatial patterns are often studied by analyzing land-cover changes (LCCs) using spaceborne images. LCC is an important factor, affecting runoff within watersheds. The objective was to estimate the effects of 20 years of LCCs on rainfall-runoff relations in an extreme rainfall event. A 1989 Landsat TM-derived classification map was used as input for a Kinematic Runoff and Erosion (KINEROS) hydrological model along with the precipitation data of an extreme rainfall event. Model calibration was performed using measured runoff volume data. Validation of the model performance was conducted by comparing the model results to measured data. A similar procedure was used with a 2009 land-cover classification map as an input to the KINEROS model, along with similar precipitation data and calibration parameters, in order to understand the possible outcomes of a rainfall event of such a magnitude and duration after 20 years of LCCs. The results show an increase in runoff volume and peak discharge between the time periods as a result of LCCs. A strong relationship was detected between vegetation cover and the runoff volume. The LCCs with most pronounced effects on runoff volumes were related to urbanization and vegetation removal.