Integrating trunk dendrometer indices, weather data and plant traits to assess water status in olive (Olea europaea L.) trees

• TWD was the best dendrometric predictor of stem water potential in olive. • Tree size modulated the reliability of dendrometric indices under field conditions. • Combined models outperformed single-index models in predicting plant water status. Accurate monitoring of plant water status is essential for optimizing irrigation in super-high-density (SHD) olive orchards. Trunk diameter fluctuations, measured by dendrometers, offer a non-destructive tool for assessing plant water dynamics. The study was conducted in two SHD olive orchards (cv. Arbequina) in Italy with contrasting plant size. In Site A, three irrigation regimes were tested; in Site B, zones of high and low vigor were identified by NDVI. The relationships between maximum daily shrinkage (MDS), trunk growth rate (TGR) and tree water deficit (TWD) with stomatal conductance (g s ) and stem water potential (Ψ stem ) were assessed. TWD was the best predictor of Ψ stem (R 2 =0.94 and 0.64 in site A and B, respectively). However, in small trees, TWD remained low despite increasing water stress. MDS showed a curvilinear response to Ψ stem , peaking at moderate stress levels and declining under more severe conditions. Stepwise regression identified canopy volume as the structural plant trait significantly affecting MDS and TGR under well-watered conditions, highlighting the need for a size-dependent interpretation of dendrometric indices. Among the climatic variables, vapor pressure deficit and maximum air temperature were also confirmed as significant predictors for these dendrometric indices. Predictive models for g s and Ψ stem were then developed using dendrometric, climatic, and tree structural variables. Combined models integrating all three indices with climatic and structural variables outperformed individual models for both g s (R 2 = 0.87, RMSE = 23) and Ψ stem (R 2 =0.92, RMSE = 0.22). These results support the potential of dendrometers as decision-support tools for precision irrigation in olive.