The climate has warmed over the past century or more bringing about changes in numerous aspects in both earth and human systems

Xylotrechus arvicola (Coleoptera: Cerambycidae) is a pest in vineyards (Vitis vinifera) in the main Spain wine-producing regions with Protected Denomination of Origin (PDO). The action of the larvae, associated to the spreading of wood fungi, causes damage especially in important varieties of V. vinifera. X. arvicola females lay eggs concentrated in cracks or under the rhytidome in the wood vines, which allows the emerging larvae to get into the wood and make galleries inside the plant being then necessary to prune intensively or to pull up the bored plants (1). The objective of the study was to evaluate captures of X. arvicola insects in five varieties of V. vinifera in PDO León.

Organic wine production and consumption is one of the sustainable practices contributing to a number of sustainable development goals (SDGs).

Aims: The aims of this study were: (1) to compute recent-past thermal conditions over European vineyards, using state-of-the art bioclimatic indices: chilling portions and growing degree hours; (2) to compute future changes of these thermal conditions using a large ensemble of high-resolution climate models.

Optimizing the use of water and improving irrigation strategies has become increasingly important in most winegrowing countries due to the consequences of climate change, which are leading to more frequent droughts, heat waves, or alteration of precipitation patterns. Optimized irrigation scheduling can only be based on a reliable knowledge of the vineyard water status.

In this context, this work aims at the development of a novel methodology, using a contactless, miniaturized, low-cost NIR spectral tool to monitor (on-the-go) the vineyard water status variability. On-the-go spectral measurements were acquired in the vineyard using a NIR micro spectrometer, operating in the 900–1900 nm spectral range, from a ground vehicle moving at 3 km/h. Spectral measurements were collected on the northeast side of the canopy across four different dates (July 8th, 14th, 21st and August 12th) during 2021 season in a commercial vineyard (3 ha). Grapevines of Vitis vinifera L. Graciano planted on a VSP trellis were monitored at solar noon using stem water potential (Ψs) as reference indicators of plant water status. In total, 108 measurements of Ψs were taken (27 vines per date).

Calibration and prediction models were performed using Partial Least Squares (PLS) regression. The best prediction models for grapevine water status yielded a determination coefficient of cross-validation (r2cv) of 0.67 and a root mean square error of cross-validation (RMSEcv) of 0.131 MPa. This predictive model was employed to map the spatial variability of the vineyard water status and provided useful, practical information towards the implementation of appropriate irrigation strategies. The outcomes presented in this work show the great potential of this low-cost methodology to assess the vineyard stem water potential and its spatial variability in a commercial vineyard.