Soil loss poses a threat to hilly and mountainous areas, particularly where local economies strongly depend on agricultural production. Among agricultural landscapes, vineyards are responsible for the highest erosion rates, particularly in steep-slope landscapes. The impact of vineyard mechanisation on soil loss is only marginally explored in published literature. This study provides an estimation of the annual soil loss rate by application of the Revised Universal Soil Loss Equation (RUSLE) in 24 terraced vineyards located in north-eastern Italy. Field observations showed that 13 vineyards consisted of fully mechanised fields, 5 vineyards had no form of mechanisation, while in 6 vineyards a mixture of practices was found. Soil erodibility (K factor) was derived for these practices (based on soil characteristics and varying degrees of compaction), while slope length and steepness (LS factors) were calculated from a 1-m LiDAR-based DTM, and remaining factors were based on datasets by the European Soil Data Centre. Mechanised fields showed 29% higher erosion rates than non-mechanised fields (respectively 53.9 and 69.5 t ha-1 y-1), although this is not statistically significant. Still, the direct impact of mechanisation is underestimated in this comparison, due to the predominant steep slopes in the manually cultivated fields. Furthermore, estimated soil loss from mechanised fields in addition to mechanised paths and roads is significantly higher by 37% than non-mechanised fields. This study thus offers an indication of how machinery and related soil compaction and transformation of terraces and infrastructure, increases soil loss risk.

The presence of roads is closely linked with the activation of land degradative phenomena such as landslides. Factors such as ineffective road management and design, local rainfall regimes, and specific geomorphological elements actively influence landslide occurrence. In this context, recent developments in digital photogrammetry (e.g., Structure from Motion; SfM) paired with Uncrewed Aerial Vehicles (UAV) systems increase our possibilities to realize low-cost and recurrent topographic surveys. This can lead to the development of multi-temporal (hereafter: 4D) and high-resolution Digital Elevation Models (DEMs), which are fundamental to analyse geomorphological features and quantify processes at the fine spatial and temporal resolutions at which they occur. This research proposes a multi-temporal comparison of the main geomorphometric indicators describing a landslide-prone terraced vineyard to assess the observed high-steep slope failures. The possibility to investigate the evolution of landslide geomorphic features in steep agricultural systems through a high-resolution and 4D comparison of such indicators is still a challenge to be explored. In this article, we considered a case study located in the central Italian Alps, where two landslides were activated below a rural road within a terraced agricultural system. The dynamics of the landslides were monitored by comparing repeated DEMs (DEM of difference), which reported erosion values of above 20 m³ and 10 m³ for the two landslide zones and deposition values of more than 15 m³ and 9 m³, respectively. The road network’s role in the alteration of superficial water flows was proved by the elaboration of the relative path impact index. Altered water flows were expressed by values between 2σ and 4σ close to the collapsed surfaces. The increase in profile curvature and roughness index described the landslides evolution over time. Finally, the multi-temporal comparison of feature extraction underlined the geomorphological changes affecting the study area. The accuracy of features extraction was analysed through the quality index computation, expressed in a range between 0 (low accuracy) and 1 (high accuracy), and proved to be equal to 0.22 m (L1-pre), 0.63 m (L1-post), and 0.69 m (L2). Results confirmed the usefulness of high-resolution and 4D UAV-based SfM surveys to investigate landslides triggering due to the presence of roads at hillslope scale in agricultural systems. This work could be a useful starting point for further studies of landslide- susceptible zones on a wider scale to preserve the quality and the productivity of affected agricultural areas.