Monitoring arthropods diversity in the “Costières de Nîmes” viticulture landscape

In the context of the three global crises of global warming, loss of biodiversity and environmental pollution, current agricultural practices need to be reconsidered [1]. Viticulture in particular can contribute to this by optimising plant protection [2].

The use of toasted vine-shoots as an alternative enological tool to make differentiated wines has generated interest among researchers and wineries. However, the evolution of these wines in bottle and the effect on the sensory profile has not been studied so far.

Measuring the effect of oxygen consumption on the colour of wines as the level of dissolved oxygen decreases over time is very useful to know how much oxygen a wine is able to consume without significantly altering its colour. The changes produced in wine after being exposed to high oxygen concen-trations have been studied by different authors, but in all cases the wine has been analysed once the oxygen consumption process has been completed. This work presents the results obtained with the use of an equipment designed and made to measure simultaneously the level of dissolved oxygen and the spectrum of the wine, during the oxygen consumption process from saturation levels with air to very low levels, which indicate the total consumption of the dosed oxygen.

AIM: Cork, the bark of Quercus suber L. is a natural, renewable, sustainable, and biodegradable raw material, representing an abundant and cheap source of raw material. Portugal is the major cork producer (185,000 tons) processing about three-quarters of the world’s cork, generating up to 25 wt % of cork dust as a by-product.

the phenological evolution is a crucial aspect of grapevine growth and development. Accurate detection of phenological stages can improve vineyard management, leading to better crop yield and quality traits. However, traditional methods of phenological tracking such as on-site observations are time-consuming and labour-intensive. This work proposes a scalable data-driven method to automatically detect key phenological stages of grapevines using satellite data. Our approach applies to vast areas because it solely relies on open and satellite data having global coverage without requiring any in-field data from weather stations or other sensors making the approach extensible to other areas.