## 6 Discussion
In this case, all areas outside the gully landforms are considered as \"no-guilly\" but the current absence of gullies does not necessarily mean that, in these areas, there is no likelihood that gullies might develop in the future. Additionally, this method can manage and use large datasets and can recognise and consider the nonlinear relationship between independent variablesand dependent variables [58; 59]. The MaxEnt method maximises the likelihood of the calibration dataset to calculate the weights of predisposing factors selected for building the prediction model [60; 86]. As previously discussed, the final susceptibility map was produced by averaging the probability estimates computed by the 10 iterations (folds) of the MaxEnt method. Thus, this map shows the probability of gully occurrence for each pixel of the study area. The probability values, ranging from 0 to 1, were classified into five levels (very high, high, moderate, low, and very low). The susceptibility map reveals a very heterogeneous spatial distribution due to the several geo-environmental factors affecting gully erosion processes. The areal distribution of the different susceptibility levels shows a decreasing trend from very low to very high classes of susceptibility (Figure 5); therefore, most portions of the study area fall in zones with low susceptibility values and only a very small area shows a higher susceptibility level. High and very high susceptibility classes are mainly located along the upstream catchment (Figure 4a) and accounted for 8.2% and 4.9% of the total area, respectively. In these zones, the highest susceptibility values were found in cultivated areas or areas covered by sparse scrub and/or herbaceous vegetation, characterised by steep clayey slopes with a concave morphology. The high probability of gully formation in cultivated fields can be related to improper cultivation practices [13; 14; 18; 46]. In contrast, the lowest levels of susceptibility were recorded in the downstream catchment, where alluvial deposits crop out and a flat morphology characterises the landscape. Moreover, low susceptibility classes were detected in forest areas, where water erosion processes are generally minimal because the dense aboveground biomass dissipates the raindrop energy. In agreement with [PERSON] et al. [87], these results show that the susceptibility map is reliable because it classified a significant portion of the study area as low and very low susceptibility (about 80%). In contrast, the highest susceptibility classes include only small zones of the catchment (Figure 4a). Moreover, the goodness of the susceptibility map was also confirmed by the elevated presence of the gully landforms (92.5%) in high and very high susceptibility classes. Thus, this map can correctly identify the spatial distribution of ephemeral and permanent gullies and suggests those areas that are actually not affected by gully erosion phenomena but have a high predisposition to developing gully processes in the future [13; 52]. Furthermore, the outcomes achieved by the R-index calculation testify to the consistency of the gully erosion susceptibility map [72]. Indeed, high R-index values were collected in high and very high susceptibility classes, which cover only a small percentage of the study area. In contrast, the R-index values decrease for the low and very low classes, showing a poor occurrence of gully erosion (Figure 5). Furthermore, we analysed the uncertainty of the susceptibility map by determining the standard deviation of the probability estimates computed using the 10-fold cross-validation. The standard deviation of gully occurrence probability measures the prediction model uncertainty associated with several susceptibility estimates [88]. A higher standard deviation value indicates higher uncertainty (high variability of the susceptibility values between the different estimates), while lower values indicate better prediction results.