Identification, assessment and management of nitrogen load hotspots from livestock farming: Comparative analysis using regional land-use data and ESA World cover product

Efficient nitrogen (N) management is essential for environmental sustainability, soil fertility, and the long-term viability of the livestock sector by reducing nutrient losses, improving resource efficiency, and mitigating environmental impacts. However, accurately estimating N loads and managing them spatially remains challenging. This study evaluates the impact of using two different data sources, traditional land-use maps (CUAS09) and satellite-derived land cover data (WC20), to estimate N loads at a large scale, focusing on livestock manure in the Campania Region, Southern Italy. Specifically, the study aims to assess how differences in spatial resolution and classification accuracy between these datasets influence N load estimation and the identification of suitable manure spreading areas. Furthermore, we investigate the role of Geographic Information Systems (GIS) in integrating multiple data sources to improve spatial analysis. Spatial analyses compared the data sources under two scenarios: (S1) traditional manure spreading techniques and (S2) advanced methods involving rapid manure incorporation. Results showed notable differences in N load estimation and the area identified as suitable for manure spreading. In Caserta, traditional land-use maps identified 21,899 ha, while satellite-based data estimated 20,571 ha. In Salerno, satellite-based data identified 11,019 ha, compared to 9,706 ha using land-use maps. The total N produced in the two study areas amounted to approximately 4,877 Mg. The overall accuracy (OA) between the two data sources was moderate (51.38 %), with a Kappa Coefficient (KC) of 23 %, indicating discrepancies in spatial agreement. These differences highlight the importance of selecting appropriate data sources for N load estimation and their implications for developing precise N management strategies. The findings emphasize the need for integrating advanced and up-to-date spatial datasets to improve accuracy, identify N hotspot zones, and support sustainable agricultural practices.