Xylem vessel blockages in grape pedicel growing in tropical climate observed by microtomography

Agriculture worldwide is threatened by climate change. In particular, declining water resource availability combined with increasing water demand is a key challenge in many rainfed areas, where irrigation appears to be a straightforward adaptation option. In this context, assessing the impacts of irrigation adoption on farm yields and incomes is a necessary step to reflect on the impact of both ex-post and ex-ante policies.

Phenolic acids are phytochemicals that are expansively distributed in daily food intake. Phenolic acids are involved in various physiological activities, such as nutrient uptake, enzyme activity, protein synthesis, photosynthesis, and cytoskeleton structure in seeds, leaves, roots, and stems. Also exhibit antibacterial, antiviral, anticarcinogenic, anti-inflammatory, and vasodilatory activities due to their antioxidant property.

Grapevine cultivars can be unequivocally typed by both physical differences (ampelography) and genetic tests. However due to their very similar characteristics, the identification of clones within a cultivar relies on the accurate tracing of supply records to the point of origin. Such records are not always available or reliable, particularly for older accessions. Whole genome sequencing (WGS) provides the most highly detailed methodology for defining grapevine cultivars and more importantly, this can be extended to differentiating clones within those cultivars.

The aroma of wine grapes is influenced by a complex metabolic network, with terroir factors, especially high temperatures, playing a critical role during berry development.

It is fundamental for wineries to know the potential yield of their vineyards as soon as possible for future planning of winery logistics. As such, non-invasive image-based methods are being investigated for early yield prediction. Many of these techniques have limitations that make it difficult to implement for practical use commercially. The aim of this study was to assess whether yield can be estimated using images taken in-field with a smartphone at different phenological stages. The accuracy of the method for predicting bunch weight at different phenological stages was assessed for seven different varieties.