Soave beyond the zonation

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.

In viticulture, a warming climate can have a very significant impact on grapevine development and therefore on the quality and characteristics of wines across different spatial scales, ranging from global to local. In order to adapt wine-growing to climate change, global climate models can be used to define future scenarios, but only at the scale of major wine regions. Despite the huge progress made over the last ten years in terms of the spatial resolution of climate models (now downscaled to a few square kilometres), they are not yet sufficiently precise to account for the local climate variability associated with such parameters as local topography, in spite of these parameters being decisive for vine and wine characteristics. This study describes a method to downscale future climate scenarios to vineyard scale. Networks of data loggers have been used to collect air temperature at canopy level in the Waipara winegrowing region (New Zealand) over five growing seasons. These measurements allow the creation of fine-scale geostatistical models and maps of temperature (at 100 m resolution) for the growing season. In order to model climate change at pilot site scale, these geostatistical models have been combined with regional climate change predictions for the periods 2031-2050 and 2081-2100 based on the RCP8.5 climate change scenario. The integration of local climate variability with regionalized climate change simulations allows assessment of the impacts of climate change at the vineyard scale. The improved knowledge gained using this methodology results from the increased horizontal resolution that better addresses the concerns of winegrowers. The results provide the local winegrowers with information necessary to understand current processes, as well as historical and future viticulture trends at the scale of their site, thereby facilitating decisions about future response strategies.

Grapevine yield is a key indicator to assess the impacts of climate change and the relevance of adaptation strategies in a vineyard landscape. At this scale, a yield model should use a number of parameters and input data in relation to the information available and be able to reproduce vineyard management decisions (e.g. soil and canopy management, irrigation). In this study, we used data from six experimental sites in Southern France (cv. Syrah) to calibrate a model of grapevine yield limited by water constraint (GraY). Each yield component (bud fertility, number of berries per bunch, berry weight) was calculated as a function of the soil water availability simulated by the WaLIS water balance model at critical phenological phases. The model was then evaluated in 10 grapegrowers’ plots, covering a diversity of biophysical and technical contexts (soil type, canopy size, irrigation, cover crop). We identified three critical periods for yield formation: after flowering on the previous year for the number of bunches and berries, around pre-veraison and post-veraison of the same year for mean berry weight. Yields were simulated with a model efficiency (EF) of 0.62 (NRMSE = 0.28). Bud fertility and number of berries per bunch were more accurately simulated (EF = 0.90 and 0.77, NRMSE = 0.06 and 0.10, respectively) than berry weight (EF = -0.31, NRMSE = 0.17). Model efficiency on the on-farm plots reached 0.71 (NRMSE = 0.37) simulating yields from 1 to 8 kg/plant. The GraY model is an original model estimating grapevine yield evolution on the basis of water availability under future climatic conditions.  It allows to evaluate the effects of various adaptation levers such as planting density, cover crop management, fruit/leaf ratio, shading and irrigation, in various production contexts.

Farming management practices aiming at conserving soil moisture have been developed in arid and semiarid-areas facing water scarcity problems. Organic mulching is an effective method to manipulate the crop-growing microclimate increasing crop yield by controlling soil temperature, and retaining soil moisture by reducing soil evaporation. In this sense, the effectiveness of different organic mulching materials (straw mulch and grapevine pruning debris) applied within the row of a vineyard was evaluated on the soil and on the vine in a Tempranillo vineyard located in La Rioja (Spain). Organic mulches were compared with a traditional bare soil management technique (based on the use of herbicides to avoid weed incidence). Mulching coverages favourably influenced the soil water retention throughout all the grapevine vegetative cycle. However, the soil-moisture variation was not the same under different mulching materials, being the straw mulch (SM) the one that retained more water in comparison with grapevine pruning debris (GPD) based-cover. The changes of soil moisture in the upper surface layer (0–10 cm) were highly dynamic, probably due to water vapour fluxes across the soil-atmospheric interface. However, both, SM and GPD reduced these fluctuations as compared with bare soils. A similar trend occurred with soil temperature. Both organic mulches altered soil temperature in comparison with bare soil by reducing soil temperature in summer and raising it in winter. Moreover, the same buffering effect for the temperature on the covered soil also remains in the deeper layers. To conclude, we could see that organic mulching had a positive impact on soil-moisture storage and soil temperature and the extent of this effect depends on the type of mulching materials. These changes led to higher rates of photosynthesis and stomatal conductivity compared to bare soils, also favouring crop growth and grape yields.

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.