Predicting predawn leaf water potential while accounting for uncertainty using vine shoot growth and weather data in Mediterranean rainfed vineyards

Abstract

Monitoring vine water status is crucial for wine production. However, in Mediterranean regions, a key indicator for evaluating this information, predawn leaf water potential (Ψpd), is challenging to obtain in terms of logistics and costs. To address this, the iG-Apex, a plant growth index based on vine shoot growth observations has been proposed as being both low-cost and easy to collect. It has been found that a strong correlation exists between iG-Apex and Ψpd. Nonetheless, the relationship between iG-Apex and Ψpd becomes increasingly uncertain as the growing season progresses. Therefore, while being operationally attempting, modeling Ψpd from iG-Apex necessitates the consideration of prediction uncertainty. This study presents a modeling approach, named the Recursive-Duo-Model (RDM), which integrates predictive modeling and Bayesian resampling to estimate Ψpd with iG-Apex while reducing prediction uncertainty. Using iG-Apex and readily accessible weather data, the RDM aims to reduce the cost to obtain the key indicator for monitoring vine water status. The study evaluated the RDM's performance across four water deficit scenarios: no deficit (-0.3 ≤ observed Ψpd < 0 MPa), mild to moderate deficit (-0.5 ≤ observed Ψpd < −0.3 MPa), moderate to severe deficit (-0.8 ≤ observed Ψpd < −0.5 MPa), and high deficit (observed Ψpd ≤ −0.8 MPa). Results showed satisfactory prediction accuracy (R²=0.61, RMSE=0.14 MPa), with the method effectively detecting the first three water deficit scenarios. In parallel, the RDM reduced prediction uncertainty (mean width of 80 % confidence interval=0.20 MPa) compared to a conventional approach based solely on vine shoot growth data (mean width=0.36 MPa).

Keywords

• Vine water status
• iG-Apex
• Predictive model
• Resampling
• Auto-correlation variable