# Remote and Proximal Sensing Techniques for Site-Specific Irrigation Management in the Olive Orchard
## 4 Discussion
### Comparing Proximal and Remote Sensing Indices against Tree Performance  
The ability of proximal and remote sensing in estimating tree growth and yield has been poorly investigated in olive growing so far. Few previous studies used UAV imagery to estimate fruit yield and vegetative growth of olive trees [25; 26; 32; 33]. In the current work, we observed a general (all irrigation treatments) linear relationship between NDVI and fruit \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Irrigation** & **Predictive** & **B** & \begin{tabular}{c} _p-Value_ \\ **(\(\mathbf{t}\)-Test)** \\ \end{tabular} & **R\({}^{2}\).adj** & **F (df)** & \begin{tabular}{c} _p-Value_ \\ **(Regression)** \\ \end{tabular} & **SEP** \\ \hline FI & \begin{tabular}{c} Intercept \\ NDVI \\ \end{tabular} & \begin{tabular}{c} \(-0.12\) \\ 0.85 \\ \end{tabular} & \begin{tabular}{c} 0.250 \\ \(<\)0.001 \\ \end{tabular} & 0.57 & 23.8 (16) & \begin{tabular}{c} \(<\)0.001 \\ \end{tabular} & 0.027 \\ \hline DI & \begin{tabular}{c} Intercept \\ NDVI \\ \end{tabular} & \begin{tabular}{c} \(-0.15\) \\ 0.51 \\ \end{tabular} & \begin{tabular}{c} 0.017 \\ \(<\)0.001 \\ \end{tabular} & 0.49 & 20.1 (37) & \begin{tabular}{c} \(<\)0.001 \\ \end{tabular} & 0.018 \\ \hline RF & \begin{tabular}{c} Intercept \\ NDVI \\ \end{tabular} & \
\begin{tabular}{c} 0.07 \\ 0.43 \\ \end{tabular} &
\begin{tabular}{c} 0.636 \\ 0.033 \\ \end{tabular} & 0.61 & 9.5 (13) & 0.001 & 0.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of forward stepwise regressions calculated for the trunk cross sectional area increment and the three irrigation regimes (FI: Full irrigated; DI: Deficit irrigation; RF: Rainfed, no irrigation). Legend: B, coefficient of the regression; _p_-value, value of the _t_-test of the coefficient; \({}^{2}\).adj, adjusted R\({}^{2}\); F, F-test value of the multiple regression and degrees of freedom; SEP, Standard Error of Pre-diction. yield (r = 0.54), similarly to previous findings in a hedgerow olive orchard [26]. In that same study, there was no relationship between NDVI and fruit yield in the year when abiotic (spring frost) and biotic (olive fruit fly) factors dramatically affected final fruit yield [26]. A specific response to location and growing season in the NDVI-fruit yield relationship also appeared when results obtained in similar experiments were compared [32; 33], highlighting the limit of the fruit yield prediction carried out several months before harvest. A lower level of correlation (r = 0.33) was observed between NDVI and the TCSA increment when all the trees from the three irrigation treatments were considered together. In a previous study carried out using the same cultivar, [PERSON] et al. [25] reported a combined effect of tree water status and canopy NDVI on the final TCSA increment. Soil indices derived by proximal sensing (ECa\({}_{1}\), ECa\({}_{2}\), and TC_gamma) were less able to predict fruit yield (r = 0.18, 0.29 and 0.01, respectively) and TCSA increment (0.02, 0.15 and 0.01) when all irrigation treatments were considered together. A wide range of correlation coefficients between ECa and fruit yield (R\({}^{2}\) comprised between 0.01 and 0.94) was reported in previous studies carried out on different cultivars of apple trees [44; 66]. In a previous study carried out in a commercial olive orchard, significant relationships between the soil characteristics (organic matter, B and Ca) derived by a systematic sampling grid and the fruit yield were measured only in one of the two experimental years [67]. To discriminate the impact of proximally (soil) and remotely (tree) determined indices on tree parameters (yield and TCSA increment) under different irrigation regimes we used a stepwise multiple regression analysis. Under full irrigation conditions, the NDVI was the only variable able to describe the variability in fruit yield and vegetative growth, whereas under rainfed conditions the soil parameters (ECa\({}_{1}\), ECa\({}_{2}\) and TC_gamma) were identified as the only factors affecting yield.