Understanding the soil and water conservation (SWC) impact of steep‐slope agricultural practices (e.g. terraces) has arguably never been more relevant than today, in the face of widespread intensifying rainfall conditions. In Italy, a diverse mosaic of terraced and non‐terraced cultivation systems have historically developed from local traditions and more recently from the introduction of machinery. Previous studies suggested that each type of vineyard configuration is characterised by a specific set of soil degradation patterns. However, an extensive analysis of SWC impacts by different vineyard configurations is missing, while this is crucial for providing robust guidelines for future‐proof viticulture. Here, we provide a unique extensive comparison of SWC in 50 vineyards, consisting of 10 sites of 5 distinct practices: slope‐wise cultivation (SC), contour cultivation (CC), contour terracing (CT), broad‐base terracing (BT) and oblique terracing (OT). A big‐data analysis approach of physical erosion modelling based on high‐resolution LiDAR data is performed, while four predefined SWC indicators are systematically analysed and statistically quantified. Regular contour terracing (CT) ranked best across all indicators, reflecting a good combination of flow interception and homogeneous distribution of runoff and sediment under intense rainfall conditions. The least SWC‐effective practices (SC, CC, and OT) were related to vineyards optimised for trafficability by access roads or uninterrupted inter‐row paths, which created high upstream‐downstream connectivity and are thus prone to flow accumulation. The novel large‐scale approach of this study offers a robust comparison of SWC impacts under intense rainstorms, which is becoming increasingly relevant for the sustainable future management of such landscapes.

Coastal agriculture faces escalating threats from seawater intrusion (SWI), jeopardizing global food security through freshwater scarcity, soil salinization and crop damage. However, research on SWI often fails to consider its impact on coastal agriculture. Linking georeferenced SWI data with cropland presence, this review examines SWI’s global distribution and primary drivers. Major attested hotspots include the Mediterranean, South and South-East Asia, and the Bohai Sea region in China. Approximately 87 Mha of cropland globally are vulnerable due to low elevation and coastal proximity, including regions where little to no literature has documented SWI. Main drivers include sea-level rise (SLR), drought, groundwater depletion, river modifications, tidal flooding and subsidence. Projections of SLR indicate cropland of North America, the Indian Subcontinent, and South-East Asia as high-risk for SWI. Additionally, regions like South-East Asia and the Indian Subcontinent are expected to experience significant demographic growth in coastal areas. Understanding present and future SWI dynamics is crucial for designing effective mitigation and adaptation strategies in coastal agriculture to support food supply.

Among the environmental problems that could affect agriculture, one of the most critical is ponding. This may be defined as water storage on the surface in concavities and depressions due to soil saturation. Stagnant water can seriously affect crops and the management of agricultural landscapes. It is mainly caused by prolonged rainfall events, soil type, or wrong mechanization practices, which cause soil compaction. To better understand this problem and thus provide adequate solutions to reduce the related risk, high‐resolution topographic information could be strategically important because it offers an accurate representation of the surface morphology. In the last decades, new remote sensing techniques provide interesting opportunities to understand the processes on the Earth's surface based on geomorphic signatures. Among these, Uncrewed Aerial Vehicles (UAVs), combined with the structure‐from‐motion (SfM) photogrammetry technique, represent a solid, low‐cost, rapid, and flexible solution for geomorphological analysis.This study aims to present a new approach to detect the potential areas exposed to water stagnation at the farm scale. The high‐resolution digital elevation model (DEM) from UAV‐SfM data is used to do this. The potential water depth was calculated in the DEM using the relative elevation attribute algorithm. The detection of more pronounced concavities and convexities allowed an estimation and mapping of the potential ponding conditions. The results were assessed by observations and field measurements and are promising, showing a Cohen's k(X) accuracy of 0.683 for the planimetric extent of the ponding phenomena and a Pearson's r_xy coefficient of 0.971 for the estimation of pond water depth. The proposed workflow provides a useful indication to stakeholders for better agricultural management in lowland landscapes.