Handbook of the charter of the universal holistic metaethics  sustainability 4.1c” for certification and warranty bio-métaétique 4.1c

Successful powdery mildew (PM) infection in plants relies on Mildew Resistance Locus O (MLO) genes, which encode susceptibility factors essential for fungal penetration. In Arabidopsis, loss-of-function mutations in three clade-V MLOs, AtMLO2, 6, and 12 confer complete resistance to PM infection. Since then, efforts are on to discover MLO genes contributing to PM susceptibility in many species to introduce mlo-based PM-resistance. Earlier studies in tomato and grapevine, using the RNAi approach, attributed PM susceptibility to SlMLO1, 5, and 8 and VvMLO3, 13, and 17, respectively indicating likely functional redundancy among MLOs.

Grapevine leaf removal (LR) in the cluster area is typically done between fruit set and cluster closure to create an unfavorable microclimate for fungal diseases, such as Botrytis cinerea and powdery mildew. Grape growers are now turning their attention to pre-flowering LR, which has additional benefits under certain conditions. When applied before flowering, LR strongly affects fruit set and thus the number of berries per cluster. It is therefore a good yield control tool, replacing time-consuming manual cluster thinning (Poni et al. 2006). It also improves berry structure, that is, skin thickness, skin-to-pulp ratio, and berry composition (total soluble solids, titratable acidity, and polyphenols) (Palliotti et al. 2012; Komm and Moyer 2015). By exacerbating competition for assimilates between reproductive and vegetative organs, pre-flowering LR also poses some risks. Excessive yield loss at the same year’s harvest due to a too low fruit set rate is the main concern: intensive pre-flowering LR (100% of the cluster area) can induce up to 50% yield loss in potted vines (Poni et al. 2005). Other parameters, such as cool climatic conditions during flowering, also affect fruit set rate and make it difficult to predict potential yield at harvest. Repeated and overly intensive preflowering LR can have repercussions over time and induce a decline in bud fruiting and plant vigor (Risco et al. 2014).

The need to combine multimodal data for complex samples is due to the different information captured in each of the techniques (modes).

Artificial intelligence (AI) for winegrowers refers to robotics, smart sensor technology, and machine learning applied to solve climate change problems. Our research group has developed novel technology based on AI in the vineyard to monitor vineyard growth using computer vision analysis (VitiCanopy App) and grape maturity based on berry cell death to predict flavor and aroma profiles of berries and final wines.

Linking wine composition to fruit composition is difficult due to the numerous biochemical pathways and substrate transformations that occur during fermentation