Sensory profile of wines obtained from disease-resistant varieties in La Rioja

Nitrogen (N) is quite important nutrient in grapevine development and must quality, but under Mediterranean climatic conditions, available soil water (ASW) during grapevine development can also influence vigour and must quality. The aim was to determine the influence of soil nitrate (NO3-) availability on N foliar, yield, and must quality in vineyards with similar available water holding capacity (AWC). For this purpose, four cv. Tempranillo (Vitis vinifera L.) vineyards were selected. All of them are placed in Uruñuela municipality (La Rioja, Spain), separated less than 2.5 km and in a slope <1 %, in soils with similar soil chemistry properties and with similar rooting depth (ranging between 105 cm and 110 cm).

Grapevine pruning is one of the most important practices in the vineyards. Winegrowers use it to provide the vines the shape needed, or to maintain it once achieved, and also to balance vegetative growth and fruit production. In the last decades, careless pruning has been blamed, among other factors, as responsible of the vineyard decay that is been observed even in young vines. However, to our knowledge, there is a lack of systematic research trying to elucidate to which extent the pruning method used affects plant development or its susceptibility to grapevine trunk diseases (GTD). Within this context, the aim of this work is to study the influence of different pruning method strategies on the development of field-planted young vines.

In grapevine, phenology from bud break to berry maturation, depends on temperature and water availability. Increases in average temperatures accelerates initiation of bud break, exposing newly formed shoots to detrimental environmental stresses. It is therefore essential to identify genotypes that could delay phenology in order to adapt to the environment. The use of different rootstocks has been applied to change scion’s characteristics, to adapt and resist to abiotic and biotic stresses[1].

The main objective of the work was to develop a simple, robust and selective analytical tool that allows predicting the authenticity of Tempranillo wines from DOCa Rioja. The techniques of voltammetry and absorbance-transmission and fluorescence excitation emission matrix (A-TEEM) spectroscopy have been applied in combination with machine learning (ML) algorithms to classify red wines from DOCa Rioja according to region (Alavesa, Alta or Oriental) and category (young, crianza or reserva).

Grapevine cultivation is threatened by the global warming, which combines high temperatures and reduced rainfall, impacting in wine quality and even plant survival. Breeding for varieties resilient to these challenges must address plant traits such as tolerance to supraoptimal temperatures and optimized water use efficiency while minimizing productivity and quality losses. Stomatal abundance (SA) determines the maximum leaf potential for transpiration and thus water loss and cooling. Since SA results from a developmental process during leaf emergence and growth, knowledge on the genetic control of this process would provide specific targets for modification.