## 3 Results
### PCA Analysis  
A principal component analysis (PCA) was performed on the correlation matrix relative to productive and efficiency parameters. The results of PCA allowed two factors to be identified, explaining 50% and 36.7% of the total variance, respectively (Table 5). The first factor (PC1) was highly and positively associated with marketable yield and blue water requirement, and highly and negatively associated with IWUE. Thus, PC1 could be \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Irrigation Scheduling Method ***} \\ \cline{2-4}
**Parameter** & **FarMan** & **IriMan** & **PlaSoMan** \\ \hline Soluble solids content (\({}^{\circ}\)Brix) & 4.0 \(\pm\) 0.06 \({}^{\circ}\) & 4.7 \(\pm\) 0.09 \({}^{\circ}\) & 5.0 \(\pm\) 0.04 \({}^{\circ}\) \\ Color index (-) & 1.1 \(\pm\) 0.08 \({}^{\circ}\) & 1.2 \(\pm\)0.01 \({}^{\circ}\) & 1.3 \(\pm\) 0.04 \({}^{\circ}\) \\ Trixtable acidity & 0.25 \(\pm\) 0.02 \({}^{\circ}\) & 0.24 \(\pm\) 0.01 \({}^{\circ}\) & 0.24 \(\pm\) 0.01 \({}^{\circ}\) \\ (g citric acid 100 mL - \({}^{\circ}\) fresh juice) & 0.456 \(\pm\) 0.1 \({}^{\circ}\) & 4.39 \(\pm\) 0.1 \({}^{\circ}\) & 4.38 \(\pm\) 0.1 \({}^{\circ}\) \\ Lycopene (mg 100 g - \({}^{\circ}\) FW) & 16.5 \(\pm\) 2.1 \({}^{\circ}\) & 17.6 \(\pm\) 3.2 \({}^{\circ}\) & 19.2 \(\pm\) 2.1 \({}^{\circ}\) \\ Vitamin C (mg 100 g - \({}^{\circ}\) FW) & 2.2 \(\pm\) 3.3 \({}^{\circ}\) & 21.8 \(\pm\) 2.2 \({}^{\circ}\) & 2.5 \(\pm\) 2.3 \({}^{\circ}\) \\ \(\beta\)-carotene (mg 100 g - \({}^{\circ}\) FW) & 0.88 \(\pm\) 0.08 \({}^{\circ}\) & 0.99 \(\pm\) 0.03 \({}^{\circ}\) & 1.01 \(\pm\) 0.05 \({}^{\circ}\) \\ \hline \hline \end{tabular}  
* FarMan: irrigation scheduling managed by the farmer. IriMan: irrigation scheduling based on “Irriframe” model. PlaSoMan: irrigation scheduling based on the smart system that monitors both plant water content and soil moisture. Tukey HSD significant differences (\(p<0.05\)) are indicated by different letters. Data are reported as means \(\pm\) standard errors (n = 8).  
\end{table} 
Table 4: Effects of the irrigation scheduling mode on the main qualitative parameters. Figure 3: Effect of the interaction year \(\times\) irrigation scheduling method on yield quality indicator (**A**) and water productivity for yield quality (**B**). Tukey HSD significant differences (\(p<0.05\)) are indicated by different letters. Vertical bars indicate standard errors (n = 4). FarMan: irrigation scheduling managed by the farmer. IriMan: irrigation scheduling based on “Irriframe” model. PlaSoMan: irrigation scheduling based on the smart system that monitors both plant water content and soil moisture. considered a factor linked to a situation of high production with poor resource efficiency. The second factor (PC2) was positively associated with water productivity and yield quality. Therefore, PC2 could be considered a factor related to high-quality and water-friendly production (Table 5). Figure 5 reports the biplot relative to the principal component analysis. The red cross symbols indicate the centroid of the categorical variable \"irrigation scheduling method\". Based on this, IrriMan and PlaSoMan were separated from FarMan along PC1, which is linked to situations of high productivity but poor water resource efficiency. FarMan was positioned on the positive side, while IriMan and PlaSoMan were on the negative side. On the other hand, IrriMan and PlaSoMan were separated along PC2, which is associated with high-quality and water-friendly production. PlaSoMan was placed on the positive side, and IriMan was on the negative. Finally, the two years (2015 and 2016) whose centroids were indicated with black cross symbols were separated along both factors, with 2015 positioned on the positive side, and 2016 on the negative one for both PCs.