Automating Spatial Data Integration for HOLOS-IE: Irish Case Study with Soil and Land Cover

Irish agriculture faces the challenge of balancing productivity with agri-environmental sustainability, where system models could play an important role in precise land use planning. This study focuses on enhancing the predictability of HOLOS-IE, an agricultural system model, by integrating geographically referenced soil data on detailed Land Parcel Identification System (LPIS) maps of Ireland. Soil data sourced from SoilGrids (https://soilgrids.org/) via Google Earth Engine (GEE) and the National Soil Database of Ireland were processed using ArcGIS tools and the Multiple Imputation by Chained Equations (MICE) method to ensure data completeness and accuracy. This refined soil information was integrated with LPIS maps to develop a robust soil database and thereby soil health indices using observed over referenced typical values. The findings identify the significant regional soil differences, with the western counties characterized by acidic, sandy soils with high organic carbon (pH 4.0-5.5, sand >40%, SOC >0.10 kg kg-1), com-pared to the counties in the east with neutral, clay soils (pH >6.0, clay >20%, bulk density >0.95 g cm-3 ), with clear northwest southeast trends in moisture retention and nutrient. The Soil Health Index (SHI) reveals that regions with extensive grasslands, mostly in central and southern areas, exhibit higher soil health indices compared to other areas. The data generated are overlaid on LPIS maps and integrated into the HOLOS-IE model to initiate and serve as driving variables for predicting growth, changes in soil organic carbon density, greenhouse gas emissions, and soil health, leading to inform precise land-use planning for climate change mitigation and adaptation.