“Terroir” and climate change in Franconia / Germany

The wine industry worldwide faces increasing challenges to achieve sustainable levels of carbon emission mitigation. This project seeks to establish the feasibility of harvesting winter pruned vineyard biomass (PVB) for potential use in carbon footprint reduction, through its use as a renewable biofuel for energy production. In order to make this recommendation, technical issues such as the potential environmental impact, chemical composition and fuel suitability, and logistical challenges of harvesting biomass needs to be understood to compare with the results from similar studies. Of particular interest is the role PVB plays as a carbon source in vineyard soils and what effect annual removal might have on soil carbon sequestration. A preliminary trial was established in the Waite Campus vineyard (University of Adelaide) to test current management strategies. Vines are grown in a Eutrophic, Red Dermosol clay loam soil with well managed midrow swards. A comparison was undertaken of mid-row treatments in two 0.25 Ha blocks (Shiraz and Semillon), including annual cultivation for seed bed preparation, the deliberate exclusion of PVB (25 years) and incorporation of PVB (13 years) at an average of 3.4 and 5.5 Mg/Ha-1 for Shiraz and Semillon respectively. In both 0-10cm and 10-30cm soil core sample depths, combined soil carbon % measures in the desired range of 1.80 to 3.50, were not significantly different between treatments or cultivars and yielded an estimated 42 Mg/ha-1 of sequestered soil carbon. Other key physical and chemical measures were likewise not significantly different between treatments. Preliminary results suggest that in a temperate zone vineyard, managed such as the one used in this study, there is no long term negative impact on soil carbon sequestration through removing PVB. This implies that growers could confidently harvest PVB for use in several end fates including as a bio fuel.

Increasingly warm and dry climate conditions are challenging the viticulture and winemaking sector. Digital technologies and crop modelling bear the promise to provide practical answers to those challenges. As viticultural activities strongly depend on harvest date, its early prediction is particularly important, since the success of winemaking practices largely depends upon this key event, which should be based on an accurate and advanced plan of the annual cycle. Herein, we demonstrate the creation of modelling tools to assess grape ripeness, through sugar concentration monitoring. The study area, the Portuguese Côa valley wine region, represents an important terroir in the “Douro Superior” subregion. Two varieties (cv. Touriga Nacional and Touriga Franca) grown in five locations across the Côa Region were considered. Sugar accumulation in grapes, with concentrations between 170 and 230 g l-1, was used from 2014 to 2020 as an indicator of technological maturity conditioned by meteorological factors. The climatic time series were retrieved from the EU Copernicus Service, while sugar data were collected by a non-profit organization, ADVID, and by Sogrape, a leading wine company. The software for calibrating and validating this model framework was the Phenology Modeling Platform (PMP), version 5.5, using Sigmoid and growing degree-day (GDD) models for predictions. The performance was assessed through two metrics: Roots Mean Square Error (RMSE) and efficiency coefficient (EFF), while validation was undertaken using leave-one-out cross-validation. Our findings demonstrate that sugar content is mainly dependent on temperature and air humidity. The models achieved a performance of 0.65

In the context of LIFE project MIDMACC (LIFE18 CCA/ES/001099), several pilots have been installed in vineyards in mid mountain areas of Catalonia (NE Spain) to test well stablished agronomic practices to increase the adaptation of Mediterranean mid mountain to climate change. Soil water content (SWC) at three different depths (15, 30 and 45cm) was measured in continuum from August 2020. One pilot (WC) included a well-established green cover (GC), a new GC (NC) and a conventional soil management (CM, tilling+herbicides). NC presented an intermediate state between WC and CM, responding similarly to CM in autumn but quickly reaching similar SWC to WC, then following the same evolution till next spring, with CM presenting lower values along autumn and winter. Then vegetation activation decreased SWC in all plots, (much slower in CM, lacking GC). Sensibility to spring rains is again intermediate for NC, which joins SWC evolution of CM by the end of spring till next autumn. It is expected that NC will resemble WC more and more as its GC develops. In the pilot combining vine training (VSP vs Gobelet) and hillside management (slope vs terrace), no clear pattern could be related with these conditions. However, both terraces seem to be more sensitive to spring rains. A third pilot included new vineyards (7 and 1 year old). In the new vineyard (N), higher canopy development, a spontaneous green cover and row straw resulted in a slower SWC dynamic, not so sensitive to rains but conserving more soil water in spring and most of summer, even with presumably a higher water extraction by vines. In the newest vineyard (VN) the deepest sensor is still sensitive to rain events all over the year and SWC is always highest at this depth, revealing small water capture by vines.

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.

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.