Methodological approach to zoning

Aims: The optimization of nitrogen use efficiency (NUE, i.e. uptake, assimilation and partitioning) is a solution towards the sustainable production of premium wines, while reducing fertilization and environmental impact. The influence of crop load on the accumulation of N compounds in fruits is still poorly understood. The present study assesses the impacts of bunch thinning on NUE and the consequences on the free amino N (FAN) profile in fruits.

Wine industry has to develop new strategies to reduce the negative impact of global climate change in wine quality while trying to mitigate its own contribution to this climate change. The term “ecosystem services”, whose use has been recently increasing, refers to the benefits that human beings can obtain from the interactions between the different living beings that coexist in an environment or system. The management of biodiversity in the vineyard has a positive impact on this crop. It has recently been reported that practices such as plant cover can reduce the occurrence of pests, increase pollination of the vine, improve plant performance1 and affect the phenolic content of grapes.2

Wine quality maintenance during the storage is a fundamental aspect for both wine producers and consumers. Nowadays, great attention has been given to the light effect

Aim: The aim of this research is to quantify the impacts of different cordon establishment techniques on vine health and longevity. It is hypothesised that wrapping developing cordon arms tightly around the cordon wire will cause a constriction of the vascular system, becoming worse over time and disrupting the flow of water and nutrients.

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.