Using open source software in viticultural research

Stomatal traits determine grapevine water use, carbon supply, and water stress, which directly impact yield and berry chemistry. Breeding for stomatal traits has the strong potential to improve grapevine performance under future, drier conditions, but the trait values that breeders should target are unknown. We used a functional-structural plant model developed for grapevine (HydroShoot) to determine how stomatal traits impact canopy gas exchange, water potential, and temperature under historical and future conditions in high-quality and hot-climate California wine regions (Napa and the Central Valley). Historical climate (1990-2010) was collected from weather stations and future climate (2079-99) was projected from 4 representative climate models for California, assuming medium- and high-emissions (RCP 4.5 and 8.5). Five trait parameterizations, representing mean and extreme values for the maximum stomatal conductance (gmax) and leaf water potential threshold for stomatal closure (Ψsc), were defined from meta-analyses. Compared to mean trait values, the water-spending extremes (highest gmax or most negative Ysc) had negligible benefits for carbon gain and canopy cooling, but exacerbated vine water use and stress, for both sites and climate scenarios. These traits increased cumulative transpiration by 8 – 17%, changed cumulative carbon gain by -4 – 3%, and reduced minimum water potentials by 10 – 18%. Conversely, the water-saving extremes (lowest gmax or least negative Ψsc) strongly reduced water use and stress, but potentially compromised the carbon supply for ripening. Under RCP 8.5 conditions, these traits reduced transpiration by 22 – 35% and carbon gain by 9 – 16% and increased minimum water potentials by 20 – 28%, compared to mean values. Overall, selecting for more water-saving stomatal traits could improve water-use efficiency and avoid the detrimental effects of highly negative canopy water potentials on yield and quality, but more work is needed to evaluate whether these benefits outweigh the consequences of minor declines in carbon gain for fruit production.

Protected Designation of Origin “Jumilla” (PDO Jumilla) is located in the Spanish provinces of Albacete and Murcia, in the South-eastern part of the Iberian Peninsula, where most of the models predict a severe impact of climate change in next decades. PDO Jumilla covers an area of 247,054 hectares, of which more than 22,000 hectares

The Earth’s system is undergoing major changes through a wide range of spatial and temporal scales as a response to growing anthropogenic radiative forcing, which is pushing the whole system far beyond its natural variability. Sources of greenhouse gases largely exceed their sinks, thus leading to a strengthened greenhouse effect. More energy is thereby being supplied to the system, with inevitable shifts in climatic patterns and weather regimes. Over the last decades, these modifications have been manifested in the full statistical distributions of the atmospheric variables, with dramatic changes in the frequency and intensity of extremes. Natural hazards, such as severe droughts, floods, forest fires, or heatwaves, are being triggered by extreme atmospheric events worldwide, thus threatening human activities. Viticultculture is not only exposed to changing climates but is also highly vulnerable, as grapevine phenology and physiological development are strongly controlled by atmospheric conditions. Therefore, the assessment of climate change projections for a given region is critical for climate change adaptation and risk reduction in viticulture. By adopting timely and suitable measures, the future sustainability and resiliency of the sector can be fostered. Climate-grapevine chain modelling is an essential tool for better planning and management. However, the accuracy of the resulting projections is limited by many uncertainties that must be duly taken into account when transferring knowledge to stakeholders and decision-makers. Climate-smart viticulture will comprise ensembles of locally tuned strategies, envisioning both adaptation and mitigation, assisted by emerging technologies and decision-support systems.

Barolo DOCG is an economically important wine producing region in Northwest Italy. It is a small region of approximately 70 km2 gross area. The topography is very complex with steep sloped hills ranging in elevation from below 200 m to 550 m. Barolo DOCG wine is made exclusively from the Nebbiolo grape. Bioclimatic indexes are often used in viticulture to gain a better understanding of broader climate trends which can be compared temporally and geographically. These indexes are also used for identifying potential phenological timing, growing region suitability, and potential risks associated with expected climatic changes. Understanding how topography influences bioclimatic indexes can help with understanding of mesoscale climate behaviour leading to improved decision making and risk management strategies. The average monthly maximum and minimum temperatures, the Cool Night Index, the Huglin Index, and the monthly diurnal range (from July to October) were calculated using data from 45 weather stations within a 40 km radius of the Barolo DOCG growing area between the years 1996 and 2019. Linear and multiple regression models were developed using independent variables (elevation, aspect, slope) extracted from a digital elevation model to identify significant relationships. Bioclimatic indexes were then kriged with external drift using independent variables that showed significant relationships with the bioclimatic index using a 100 m resolution grid. The maximum monthly temperatures and the Huglin Index showed consistent significant negative relationships with elevation in all years. The minimum monthly temperatures showed no relationship with elevation but in some months a small but significant relationship was observed with aspect. Due to the lack of a relationship between minimum monthly temperatures and elevation compared to the significant relationship between maximum monthly temperatures and elevation, monthly diurnal range had a negative relationship with elevation.

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.