Determining tree water status in the field is essential for implementing precision irrigation strategies. This study evaluated the performance of microtensiometer sensors (MTs) in a commercial olive orchard located in the province of Castellón, Spain, by comparing MTs readings with multiple ground-based and proximal sensing indicators related to soil–tree water status. MTs were installed in the trunks of olive trees subjected to different irrigation regimes (full irrigation, FI, and deficit irrigation, DI) during the 2022–2024 period. Throughout the study, periodic measurements of stem water potential (SWP) were conducted to characterize tree water status, alongside continuous monitoring of agrometeorological variables and soil water content (SWC). In addition, spatially distributed visible/near-infrared and thermal data were acquired at the field scale using proximal sensing techniques. The results showed that trunk water potential (TWP), measured by MTs, and SWP followed similar temporal patterns (with average values ranging from −0.5 MPa to −2.5 MPa and −2.0 MPa for SWP and TWP, respectively), with a positive correlation observed between the two variables, although this relationship tended to weaken over time (with coefficient of determination (R²) ranging from 0.50 to 0.21, from 2022 to 2024). The MTs were able to detect significant differences between irrigation regimes (p-value ' 0.05, resulting in average values of −0.78 ± 0.20 MPa and −1.09 ± 0.27 MPa under FI and DI, respectively), while thermal indicators and SWP measurements occasionally identified these differences. Moderate relationships were also found between TWP and SWC under both FI and DI conditions in the upper soil profile (R² = 0.45). However, robust estimation of absolute SWP values requires site-specific sensor calibration across multiple cultivars and water stress levels and MTs sensors usefulness might be limited to a single season.
Determining the water status of olive trees using microtensiometer sensors, on-ground and proximal sensing observations under Mediterranean climate conditions
Daniela VanellaPrimo
;
2026-01-01
Abstract
Determining tree water status in the field is essential for implementing precision irrigation strategies. This study evaluated the performance of microtensiometer sensors (MTs) in a commercial olive orchard located in the province of Castellón, Spain, by comparing MTs readings with multiple ground-based and proximal sensing indicators related to soil–tree water status. MTs were installed in the trunks of olive trees subjected to different irrigation regimes (full irrigation, FI, and deficit irrigation, DI) during the 2022–2024 period. Throughout the study, periodic measurements of stem water potential (SWP) were conducted to characterize tree water status, alongside continuous monitoring of agrometeorological variables and soil water content (SWC). In addition, spatially distributed visible/near-infrared and thermal data were acquired at the field scale using proximal sensing techniques. The results showed that trunk water potential (TWP), measured by MTs, and SWP followed similar temporal patterns (with average values ranging from −0.5 MPa to −2.5 MPa and −2.0 MPa for SWP and TWP, respectively), with a positive correlation observed between the two variables, although this relationship tended to weaken over time (with coefficient of determination (R²) ranging from 0.50 to 0.21, from 2022 to 2024). The MTs were able to detect significant differences between irrigation regimes (p-value ' 0.05, resulting in average values of −0.78 ± 0.20 MPa and −1.09 ± 0.27 MPa under FI and DI, respectively), while thermal indicators and SWP measurements occasionally identified these differences. Moderate relationships were also found between TWP and SWC under both FI and DI conditions in the upper soil profile (R² = 0.45). However, robust estimation of absolute SWP values requires site-specific sensor calibration across multiple cultivars and water stress levels and MTs sensors usefulness might be limited to a single season.| File | Dimensione | Formato | |
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