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.

homogeneous zones by intersecting several partial zonings of major factors that influence vineyard growth. Each of them follows specific process from their corresponding disciplines. Soil zoning specifically refers to a Soil Resource Inventory map that has traditionally been generated by conventional soil mapping methods. These methods have shortcomings in reaching fine cartographic and categorical details and involve significant expenses, which undermines their applicability. A new framework named Digital Soil Mapping has introduced quantitative models by statistical techniques to establish soil-landscape relationships and is able to provide intensive scale cartography.

In the present study, a microzoning at 1:10.000 scale is generated from an initial zoning, where the conventional soil map with polytaxic map units is replaced by a new one from digital techniques that disaggregates them. The comparison between the zonings considers a quantitative evaluation of capability for each Homogeneous Terroir Unit by means of the Viticultural Quality Index and its categorization based on its distribution by map. The spatial intersection of both maps gives rise to a confusion matrix in which the flows of class variations after the substitution are assessed.

The results show a five-fold increase in the number of Homogeneous Terroir Units identified and a larger differentiation among them, evidenced by a wider range in the capability index distribution. Both elements are accompanied by an increase in the detection of areas of higher potential within previously undervalued uniform zones.These features are a direct effect of the improvements brought by Digital Soil Mapping techniques and would verify the advantages of their implementation in the Integrated Terroir zoning. Eventually, such new highly detailed terroir units would benefit precision viticulture and sustainable management practices.

Viticulture is entangled with weather and climate. Therefore, areas currently suitable for grape production can be challenged by climate change. Winegrowers in Italy already experiences the effect of climate change, especially in the form of warmer growing season, more frequent drought periods, and increased frequency of weather extremes.
The aim of this study is to investigate the impact of climate variability and change on grape yield in Italy to provide winegrowers the information needed to make their business more sustainable and resilient to climate change. We computed a specific range of bioclimatic indices, selected by the International Organisation of Vine and Wine (OIV), and correlated them to grape yield data. We have worked in collaboration with some wine consortiums in northern and central Italy, which provided grape yield data for our analysis.
Using climate variables from the E-OBS dataset we investigate how the bioclimatic indices changed in the past, and the impact of this change on grape productivity in the study areas. The climate impact on productivity is also investigated by using high-resolution convection-permitting models (CPMs – 2.2 horizontal resolution), with the purpose of estimating productivity in future emission scenarios. The CPMs are likely the best available option for this kind of impact studies since they allow a better representation of small-scale processes and features, explicitly resolve deep convection, and show an improved representation of extremes. In our study, we also compare CPMs with regional climate models (RCMs – 12 km horizontal resolution) to assess the added value of high-resolution models for impact studies. Further development of our study will lead to assessing the future suitability for vine cultivation and could lead to the construction of a statistical model for future projection of grape yield.

The use of rootstocks tolerant to soil water deficit is an interesting strategy to cope with limited water availability. Currently, several nurseries are breeding new genotypes, but the physiological basis of its responses under water stress are largely unknown. To this end, an ecophysiological assessment of the conventional 110-Richter (110R) and SO4, and the new M1 and M4 rootstocks was carried out in potted ungrafted plants. During one season, these Vitis genotypes were grown under greenhouse conditions and subjected to two water regimes, well-watered and water deficit. Water potentials of plants under water deficit down to < -1.4 MPa, and net photosynthesis (AN) <5 μmol m-2 s-1 did not cause leaf oxidative stress damage compared to well-watered conditions in any of the genotypes. The antioxidant capacity was sufficient to neutralize the mild oxidative stress suffered. Under both treatments, gravimetric differences in daily water use were observed among genotypes, leading to differences in the biomass of root, shoot and leaf. Under well-watered conditions, SO4 and 110R were the most vigorous and M1 and M4 the least. However, under water stress, SO4 exhibited the greatest reduction in biomass while M4 showed the lowest. Remarkably, under these conditions, SO4 reached the least negative stem water potential (Ψstem), while M1 reduced stomatal conductance (gs) and AN the most. In addition, SO4 and M1 genotypes also showed the highest and lowest hydraulic conductance values, respectively. Our results suggest that there are differences in water use regulation among genotypes, not only attributed to differences in stomatal regulation or intrinsic water use efficiency at the leaf level. Therefore, because no differences in canopy-to-root ratio were achieved, it is hypothesized that xylem vessel anatomical differences may be driving the reported differences among rootstocks performance. Results demonstrate that each Vitis rootstock differs in its ecophysiological responses under water stress.

Currently, the main goal of agriculture is to promote the resilience of agricultural systems in a sustainable way through the improvement of use efficiency of farm resources, increasing crop yield and quality under climate change conditions. This last is expected to drastically modify plant growth, with possible negative effects, especially in arid and semi-arid regions of Europe on the viticultural sector. In this context, the monitoring of spatial behavior of grapevine during the growing season represents an opportunity to improve the plant management, winegrowers’ incomes, and to preserve the environmental health, but it has additional costs for the farmer. Nowadays, UAS equipped with a VIS-NIR multispectral camera (blue, green, red, red-edge, and NIR) represents a good and relatively cheap solution to assess plant status spatial information (by means of a limited set of spectral vegetation indices), representing important support in precision agriculture management during the growing season. While differences between UAS-based multispectral imagery and point-based spectroscopy are well discussed in the literature, their impact on plant status estimation by vegetation indices is not completely investigated in depth. The aim of this study was to assess the performance level of UAS-based multispectral (5 bands across 450-800nm spectral region with a spatial resolution of 5cm) imagery, reconstructed high-resolution satellite (Sentinel-2A) multispectral imagery (13 bands across 400-2500 nm with spatial resolution of <2 m) through Convolutional Neural Network (CNN) approach, and point-based field spectroscopy (collecting 600 wavelengths across 400-1000 nm spectral region with a surface footprint of 1-2 cm) in a plant status estimation application, and then, using Bayesian regularization artificial neural network for leaf chlorophyll content (LCC) and plant water status (LWP) prediction. The test site is a Greco vineyard of southern Italy, where detailed and precise records on soil and atmosphere systems, in-vivo plant monitoring of eco-physiological parameters have been conducted.