Copper reduction strategy for sangiovese in organic viticulture

Developing rootstocks adapted to environmental constraints constitutes a key lever for grapevine adaptation to climate change. In this context, Near Infrared Spectroscopy (NIRS) could be used as a high-throughput phenotyping technique to simplify the study of rootstocks in grafted situations. This study is an exploratory analysis to evaluate the potential of NIRS acquired on grafted tissues to reveal rootstock effects as well as the plasticity of combinations of scion/rootstock to better characterize these interactions. Through the study of 25 combinations (5 scions times 5 rootstocks) in a dedicated experimental vineyard, we showed that NIRS obtained from grafted tissues capture rootstock and scion/rootstock interaction signals, up to 20% of the total variance at specific wavelengths.

Sulphur dioxide (SO2) is a well-established preservative in the wine industry. Its ability to act in different stages of the process as an antioxidant and an antiseptic as main characteristics makes it versatile. However, the need for its reduction or even its replacement has been increasing by the regulatory authorities as well as by the final consumer. To understand the impact of SO2 during ageing on volatile organic compounds (VOCs) and amino acids (AAs) profiles, two white wines (one varietal and one blend) were aged under the same conditions, in the presence of different doses of SO2. After fermentation (t=0), 0, 30, 60, 90 and 120 mg/L of SO2 were applied, wines were kept over lees for 3 months (t=3), then were bottled after 3 (t=6) and 9 (t=12) months.

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.

Aim: Through machine-learning modelling of plant water status from environmental characteristics, this work aims to develop a model able to predict grape phenolic composition in space and time to guide selective harvest decisions at the estate scale.

Une étude a été menée pendant neuf ans sur deux vignes non-irriguées de Sauvignon blanc commercialisés, plantées à différentes localités (A et B) dans le district de Stellenbosch. Deux parcelles expérimentales, situées sur deux formations géologiques différentes, ont été identifiées au sein de chaque vignoble. A chaque localité une des