# Remote and Proximal Sensing Techniques for Site-Specific Irrigation Management in the Olive Orchard
## 3 Results
### Comparing Proximal and Remote Sensing Indices against Tree Performances  
Significant correlations were evident between NDVI and fruit yield (r = 0.54) and between NDVI and the annual TCSA increment (r = 0.33) when the trees from all irrigation treatments were considered together (Figure 5). Among the soil indices derived from soil sensors, only \(\text{ECa}_{2}\) showed a significant, albeit lower, correlation with fruit yield (r = 0.29). Within each irrigation treatment, the highest yield was measured in the most vigorous trees (highest canopy NDVI values) grown in areas with the highest soil \(\text{ECa}_{1}\) values, even though a different impact of NDVI and soil \(\text{ECa}_{1}\) was observed according to the irrigation treatment (Figure 6). In particular, the relationship between NDVI and fruit yield and between NDVI and the annual TCSA increment in fully-irrigated trees produced r values of 0.71 and 0.77, respectively, whereas \(\text{ECa}_{1}\) showed lower, but still significant correlations (r = 0.62 for both fruit yield and annual TCSA increment) (data not shown). Different results were obtained under rainfed conditions, where the \(\text{ECa}1\) showed a higher level of correlation (r = 0.67) with fruit yield than that obtained for the relationship between NDVI and fruit yield (r = 0.54) (data not shown). Figure 5: Nonparametric correlation coefficients (Spearman’s ranks) between NDVI, tree parameters (fruit yield and annual TCSA increment) and soil indices (\(\text{ECa}_{1}\), \(\text{ECa}_{2}\) and \(\text{TC}_{\text{gamma}}\)) derived by proximal sensing sensors at the experimental site. In bold, the coefficient significant for \(p<0.01\) (n = 75, all irrigation treatments). \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Irrigation**} & \multicolumn{2}{c}{**WUE\({}_{\text{f}}\) (g Dry Weight L\({}^{-1}\) H\({}_{2}\)O)**} & \multicolumn{2}{c}{**WUE\({}_{\text{o}}\) (g Oil L\({}^{-1}\) H\({}_{2}\)O)**} \\ \cline{2-7}  & **C1 + C2** & **C1** & **C2** & **C1 + C2** & **C1** & **C2** \\ \hline FI & 0.91 & 0.95 & 0.88 & 0.36 & 0.38 & 0.35 \\ DI & 0.97 & 1.15 & 0.78 & 0.42 & 0.49 & 0.34 \\ RF & 0.66 & 0.82 & 0.50 & 0.27 & 0.34 & 0.19 \\ \hline \end{tabular}
\end{table} 
Table 3: Water use efficiency of fruit (WUE\({}_{\text{f}}\)) and oil (WUE\({}_{\text{o}}\)) production measured in olive trees grown in different orchard zones (Cluster 1, C1, and Cluster 2, C2) and subjected to full irrigation (FI), deficit irrigation (DI) and rainfed conditions (RF). WUE\({}_{\text{f}}\) and WUE\({}_{\text{o}}\) are the ratio between fruit (dry weight) or oil yield, and annual crop evapotranspiration (\(\text{ET}_{\text{c}}\)). A stepwise regression analysis was used to highlight the impact of NDVI, \(\text{ECa}_{1}\), \(\text{ECa}_{2}\), and TC_gamma on fruit yield and TCSA increment prediction within each irrigation regime (Table 4; Table 5). The regression results show that, under full irrigation, the only variable predictive of yield and TCSA increment was NDVI. Under deficit irrigation, soil spatial variability, determined by \(\text{ECa}\) maps, also played an important role on yield and TCSA increment, although NDVI remained the most important variable for the prediction of these olive tree parameters. Under rainfed conditions, NDVI was not useful to predict yield, whereas soil features, defined by \(\text{ECa}_{1}\) and TC_gamma, on yield, became significant explanatory variables in the multiple regression. On the other hand, for the prediction of TCSA increment, NDVI remained a significant explanatory variable, together with \(\text{ECa}_{1}\) and \(\text{ECa}_{2}\) under rainfed conditions.