Assessing reserve nitrogen at dormancy for predicting spring nitrogen status in Chardonnay grapevines

This study was conducted on soil-climate-plant relations (terroir) and their impact on grape composition and wine quality in the canton of Vaud by Agroscope Changins-Wädenswil ACW

After some decades sunk into oblivion, pruning has recently recovered the focus of grape growers and viticulturists worldwide. Attention is now being paid to the respect the sap flow continuity and to pruning wounds, as they may affect the general performance and longevity of the plant. The longevity and profitability are strongly affected by the increasing incidence of grapevine wood diseases (GWD), intensified by the omission of good pruning practices and leading to an increasingly aggressive pruning. The purpose of this study is to provide an objective evaluation of the short- and mid-term implications of different pruning practices that differ in the degree of observation several of pruning principles.

The ongoing challenge of climate change is driving the need for novel oenological approaches aimed at finding effective environmental solutions.

Precocity for fruit ripening is a genetically determined characteristic that is highly variable from one cultivar to another. In traditional wine-growing regions of Europe, growers have used this property to adapt the vines to local climatic conditions in order to maximize terroir expression

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.