# Satellite Monitoring of Italian Vineyards and Spatio-Temporal Variability Assessment
## 4 Discussion
### Research Objectives
#### 4.3.3 Vineyard's Variability Structure  
The structure and the stability of the vineyards' variability were evaluated thanks to the semivariogram-specific indices. All vectors seem to be plotted on the same plane except for sill and range, which showed a valuable relationship only with orientation (Figure 17). A suitable explanation is that the structure of the variability is mainly linked to soil properties and vineyard management (training system and row orientation). All the numerical features are linked with the nugget. Again, the latitude plays an essential role by smoothing the impact of the random variability component moving from the southern to the northern vineyards. Area and space have already shown a weak correlation with latitude, but other reasons could explain their relation with the nugget. In particular, increasing the vineyard area and row spacing can reduce the nugget. This fact could be mainly explained by a casual probability of finding a pattern in crop variability by enlarging the cultivated area compared to small plots, which could be mainly affected by random variability. Large row spacing, as mentioned above, is preferred in highly fertile soil and vigorous varieties, allowing plants to vent their maximum biomass production, producing large canopies in fertile areas and small canopies in perished ones. That mechanism could reveal stability and a well-structured variability pattern compared to closer row spacing. The ANOVA outcomes showed just a few significant factors compared with the results of the previous approaches. Firstly, the nugget, which is the most unpredictable item, is affected by all the factors tested in Table 7 (especially vintage, location, and topography). However, the CI assumes much more relevance because it weights the random with the structured variability. Almost all indices may be considered temporal stable because the vintage effect was not found to be significant in Table 7. Actually, the vintage was found to be significant for the nugget. The nugget aims to describe the amount of random variability (not spatially correlated), the vintage, and the climate conditions that mark each season that affected that index during the six-year monitoring. At the same time, sill and range were preserved year by year. Moreover, because sill and range have been stable through the years, almost all the vineyards could be feasible for spatial zoning once common properties among the vineyards or inside vineyards are delineated. Row orientation had a notable impact on all the indices analysed, though its significance varied. This discussion focuses on the range and CI. The lowest range and CI were observed in vineyards with rows oriented in the SW direction. This suggests that an EW row orientation can promote more fragmented zones in a vineyard, leading to a more significant contribution of random variability than spatially correlated variability. This effect is likely due to the physiological responses of the grapevines, as row orientation influences environmental factors like thermal and drought stress. These stresses can disrupt the natural variability patterns in vineyards with EW-oriented rows, causing different vineyard subplots to exhibit distinct responses to RS monitoring. Consequently, EW-oriented vineyards could be more difficult to manage overall in a precision agricultural context, and they could be assessed as less prone to mechanisation. The training systems retrieved different CI values in which CI is higher in AS than VS. Thus, VS vineyards mirror the soil and the spatial variability, while AS ones are more affected by environmental factors. This fact could be explained by the impact of the different training systems on vegetative growth. Finally, the flat areas boosted the CI compared to the sloped areas.