## 3 Results
### Assessing the Occurrence of OLS  
Out of the 44 centennial olive trees sampled for fungal infestation, 26 were found to be affected. The logistic regression analysis concerning the phenotypic traits revealed a significant model [\(\chi^{2}\) (1, N = 44) = 33.29, \(p<0.05\); Table 4], demonstrating an overall classification accuracy of 90.9%. Specifically, the accuracy was 88.9% for non-infected trees and 92.3% for infected trees. The model achieved an area under the curve (AUC) of 0.95 (S.E. = 0.03, 95% CI 0.87-1.00, \(p<0.05\)), indicating excellent predictive performance. Nagelkerke R\({}^{2}\) explained 71.6% of the total variance in the data, while the Hosmer-Lemeshow goodness-of-fit test yielded a satisfactory result (Hosmer-Lemeshow = 21.25, \(p>0.05\)).  
The analysis for the thermal and humidity indices yielded an overall classification accuracy of 84.1%, with 77.8% for non-infected trees and 88.5% for infected ones [\(\chi^{2}\) (1, N = 44) = 20.43; \(p<0.05\); Table 4]. The Nagelkerke R\({}^{2}\) value was 0.501, indicating that this model explained a substantial portion of the total variance in the data. The Hosmer  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variable Set** & **Predictor** & **B** & **S.E.** & **Wald’s x\({}^{2}\)** & **df** & \(p\)**-Value** \\ \hline \multirow{3}{*}{Phenotypic traits} & Productivity & \(-\)0.29 & 0.09 & 9.68 & 1 & 0.002 \\  & Constant & 11.76 & 3.84 & 9.39 & 1 & 0.002 \\ \hline \multirow{3}{*}{Thermal and humidity indices} & T\({}_{\text{IQR}}\) Side A & 7.61 & 2.51 & 9.21 & 1 & 0.002 \\  & Constant & \(-\)4.95 & 1.67 & 8.81 & 1 & 0.003 \\ \hline \multirow{3}{*}{Combined model} & CR & \(-\)128.90 & 77.29 & 2.78 & 1 & 0.045 \\  & Productivity & \(-\)0.21 & 0.09 & 5.02 & 1 & 0.025 \\ \cline{1-1}  & HC & \(-\)0.05 & 0.03 & 3.20 & 1 & 0.033 \\ \cline{1-1}  & T\({}_{\text{IQR}}\) Side A & 8.89 & 5.34 & 2.77 & 1 & 0.026 \\ \cline{1-1}  & Constant & 8.92 & 5.08 & 3.08 & 1 & 0.049 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Logistic regression models for the prediction of the OLS (N = 44). B = logistic coefficient; S.E. = standard error of estimate; Wald = Wald chi-square; df = degree of freedom; \(p\)-value = significance.  
Figure 10: Effect plots of the impact of **(a)** productivity range (P\({}_{\text{range}}\)), **(b)** temperature outer percentile range differences between side A and B (T\({}_{\text{IQR}}\) difference), and **(c)** humidity outer percentile range differences between side A and B (H\({}_{\text{IQR}}\) difference) on crop water stress index (CWSI). The \(x\)-axis displays the values of these indices, while the \(y\)-axis indicates predicted productivity values.  
Lemeshow goodness-of-fit test result was 4.28, with a \(p\)-value greater than 0.05, suggesting a good fit of the model to the data. Additionally, the AUC for this model was 0.86 (S.E. = 0.05, 95% CI 0.74-0.97, \(p<0.05\)), which signifies a reasonably high level of predictive accuracy.  
Finally, in the combined model that incorporated both phenotypic traits and thermal and humidity indices, we achieved a significant model [\(\chi^{2}\) (4, N = 44) = 40.02, \(p<0.05\); Table 4]. Collectively, the variables explained 80.5% of the total variance in the data, as evidenced by the Nagelkerke R\({}^{2}\). The model demonstrated a good fit to the data, as indicated by result of the Hosmer-Lemeshow test (Hosmer-Lemeshow = 6.40; \(p>0.05\)). Furthermore, the AUC (AUC = 0.98, S.E. = 0.01, 95% CI 0.94-0.99, \(p<0.05\)) revealed that the model accurately classified trees with and without OLS infection in 93.2% of cases. The predicted classification accuracy was 94.4% for trees without OLS and 92.3% for trees with OLS.