Evaluation of wood starch content on bench grafting success rate in grapevine

Un 94 % del viñedo español se cultiva con métodos y técnicas propias de los sistemas agrícolas desarrollados en secano en regiones de clima semiárido, donde las precipitaciones anuales raramente exceden los 500 mm

Global warning increases evaporative demand and accelerates grapevine phenology. As a consequence, the ripening phase shifts to warmer and drier periods. This results in lower acidity and higher sugar levels in berries, yielding too alcoholic wines with altered organoleptic properties. Agrivoltaics, which combines crop and renewable energy production on the same land using photovoltaic panels, emerged as a promising innovation to counteract these impacts by partially shading the plants.

Temperature has a decisive influence on the growth and development of plants (Carbonneau et al., 1992). In particular, in the case of the vine, the temperature is an omnipresent variable in the climatic indices (Huglin, 1986). For reasons of convenience, these indices use the temperature of the air measured under shelter in a meteorological station, making the implicit hypothesis of a concordance between this temperature and that of the sites of perception of the thermal stimulus by the plant. However, development may be more dependent on soil temperature than air temperature (Kliewer, 1975). Morlat (1989) thus verified that the variability in the precocity of the vine, positively correlated with the quality of the harvest and of the wine in the Loire Valley, was mainly explained by differences in temperature of the root zones.

Considerable advances have been made in the chemical characterization of wine metabolites through its holistic study using both targeted and untargeted metabolomics approach. The metabolite pool is subject to an intense molecular dialogue which reinforces the wine complexity even after bottling.

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.