## 4 Yield  
Olive growing's profitability is determined by yield, which in turn depends on the grove category [96]. The correct design of the olive grove in orientation, row height and width, alley width, etc., is important for maximizing light interception and productivity [114]. The availability of new planting systems and the introduction of more efficient management strategies contributed to spreading olive growing outside of traditional cultivation areas [115]. Regarding the production and yield of different planting systems, trials under both rain fed and irrigated conditions carried out in low-density olive groves showed higher production at tree level, while higher yield per hectare was obtained in high-density olive groves [116; 117]. Planting density affects the size of individual trees in both trunk and canopy sizes, as reported in [118]. Tree size increases under low-density conditions, while an opposite effect is obtained in high-density olive groves [96]. Canopy size and shape also affect olive trees' fruiting potential [104; 119]. In addition, productivity depends on other factors, including soil fertility, management practices, climate, and meteorology [120]. The development of reliable methods for yield prediction can be very useful for farmers in managing (spatially and temporally) cultivation techniques such as irrigation and fertilization in particular areas or trees characterized by lower yields [121; 122]. Conventional olive yield assessment methods based on ground surveys are time-consuming, expensive, and inaccurate. RS technologies can support the production forecast [123]. For yield forecasting, predicting phenological phases is a valuable tool, as shown in several studies (Table 3). However, satellite or proximal surveys and ground surveys are unlikely to effectively provide information on the seasonal patterns of phenological events due to the different scales of observation. In this regard, statistical models based on meteorological parameters represent a potential solution to combine ground-based phenological observations and those provided by satellite-derived VIS [124; 125]. Machine learning techniques already applied to crop yield optimization and plant phenology forecasting can improve the accuracy of phenological models [126; 127]. [PERSON] et al. [128] developed a phenology prediction model based on climate and geophysical data. Three datasets were used to calculate predictors for the phenological phases of the olive tree: the weather registry obtained from the open-access ERA5 dataset (Copernicus Climate Change Service, 2017); satellite dataset (ALOS Landform) providing geophysical input data, i.e., latitude and longitude of the olive groves, and the slope; MODIS, providing surface spectral reflectance data. [PERSON] et al. [120] developed and tested a multi-step methodology integrating RS and ancillary data to estimate spatial and temporal variations of olive yield within a decade (2000-2009) in Tuscany (Italy). In particular, IKONOS, ETM+ high-resolution imagery and MODIS NDVI were used, while ancillary data included daily temperatures and precipitation for the decade examined, the geographical distribution of olive groves (CORINE land cover), soil feature data, and a digital terrain model (DTM). The data combined within a modified parametric model (C-Fix) [129] allowed the prediction of daily olive tree gross primary production (GPP). GPP is then combined with respiration estimates