## 4 Discussion
These results not only corroborate findings from prior studies [46] but also demonstrate the added value of incorporating new indices from IRT (H\({}_{\text{IQR}}\) difference, T\({}_{\text{IQR}}\) difference, T\({}_{\text{IQR}}\) canopy, H\({}_{\text{IQR}}\) Side A, T\({}_{\text{IQR}}\) Side A) and including CWSI, which significantly bolstered the explanatory power of our analyses, allowing a quite accurate estimate of olive tree productivity. We find this particularly significant, as we believe that the connection between our metrics, which has not been established elsewhere, provides an effective means of monitoring centennial olive trees. This is especially noteworthy considering that parameters that could potentially enhance our models, such as soil nutrient content and levels of tree competition--key elements in other predictive models for olive tree productivity [146]--were not included in our study. Regarding water stress in centennial olive trees, as in any other crop, it represents a pivotal ecological parameter significantly influencing both their overall health and productivity [147]. Despite the olive tree's impressive drought resilience [148], it expends considerable energy resources on protective mechanisms, which can, in turn, hinder plant growth and productivity [149; 150]. This abiotic stress factor primarily results from a complex interplay of environmental variables, including temperature, humidity, and soil moisture levels, collectively shaping the water status of these trees [151]. When a tree experiences water stress, it tends to have higher temperatures due to reduced transpiration (the process of water evaporation from leaves), as water is crucial for cooling the tree through transpiration. Elevated temperatures, especially in the canopy, are indicative of water stress and can result in a higher CWSI [104]. Similarly, trees under water stress might exhibit lower humidity values because they are not transpiring as effectively, and the humidity in the surrounding environment remains relatively low [152]. Our findings align with this logic, as reduced transpiration efficiency and lower environmental humidity levels contribute to an elevated CWSI. In fact, among the numerous variables examined, three demonstrated particular significance, collectively explaining 47.5% of the CWSI variance. One of these variables is related to productivity (P\({}_{\text{range}}\)), while the other two are associated with IRT metrics (H\({}_{\text{OPR}}\) difference and T\({}_{\text{OPR}}\) difference), and they all exhibited a significant influence on CWSI (Table 3). These findings not only advance our understanding of CWSI determinants but also have practical implications for the holistic management of centennial olive groves. However, it is essential to recognize that beyond the factors explaining this crucial abiotic stressor, there are several biotic ones that need to be addressed. One such factor is the OLS, which poses a threat to olive trees regardless of their age. Olive trees are susceptible to OLS when environmental conditions, such as high humidity and moderate temperatures, favor fungal growth and infection. This fungal pathogen primarily targets the leaves of olive trees but can also infect other above-ground plant structures [153], such as stems and fruit [53]. The three logistic models exhibited a high level of discriminatory performance and provided valuable insights into the predictors of OLS. In each of these models, the explanatory power was substantial, ranging from 50.1% to 80.5% (Table 4). These results underscore the ability to accurately predict the probability of OLS infection using a combination of variables, which include productivity, two phenotypic traits (CR and HC), and one thermal index (T\({}_{\text{IQR}}\) side A). Furthermore, the OLS-infected trees exhibited distinctions across a wide spectrum of examined variables, encompassing phenotypic traits (Table 5), IRT (Figure 11), and CWSI.