Using remote sensing to quantify the temporal and spatial effects of extreme weather events in vineyards

The main goal of the current study is the development of a cost-effective and easy-to-use method suitable for use in the laboratory of commercial wineries to analyze wine aroma. Additionally, this study attempted to establish a prediction model for wine quality gradings based on their aroma, which could reveal the important aroma compounds that correlate well with different grades of perceived quality METHODS: Parameters of the SHS−GC−IMS instrument were first optimized to acquire the most desirable chromatographic resolution and signal intensities. Method stability was then exhibited by repeatability and reproducibility. Subsequently, compound identification was conducted. After method development, a total of 143 end-ferment wine samples of three different quality gradings from vintage 2020 were analyzed with the SHS−GC−IMS instrument. Six machine learning methods were employed to process the results and construct a quality prediction model. Techniques that aim to explain the model to extract useful insights were also applied.

Context description and research question: an increasing number of farmers are considering the impact of conservation practices on soil health to guide sustainable management of vineyards. Understanding impacts of soil management on soil organic carbon (SOC) is one lever for adoption of agroecological practice with potential to help maintain or improve soil health while building SOC stocks to mitigate climate change (Amelung et al., 2020).

Enzymatic browning of grape must remains a major issue in winemaking, especially when grapes are affected by grey rot.

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

The decision for grape berries to ripen involves a complex interplay of genetic regulation and environmental cues. This review explores the molecular mechanisms underlying the transition from vegetative growth to ripening, focusing on transcriptomic studies and the role of the NAC gene family. Transcriptomic analyses reveal a significant rearrangement of gene expression patterns during this transition, with up-regulation of ripening-related genes and down-regulation of those associated with vegetative growth. A molecular phenology scale providing a high-precision map of berry transcriptomic development, indicates that key molecular changes occur well before the onset of ripening.