Machine learning- based gridded soil mapping for Kapiti research station and wildlife conservancy, Kenya - Paliwal, Ambica; Cherotich, Fredah; Leitner, Sonja; Pearce, F.; Rufino, M.; Quinton, J.; Dhulipala, Ram; Salavati, M.; Gluecks, Ilona V.; Whitbread, Anthony M. (International Livestock Research Institute, 2024)
Digital Soil Mapping (DSM) enhances digital twin models by supplying detailed, spatially explicit soil data crucial for accurate virtual representations. With high-resolution maps of soil properties (e.g., texture, moisture, organic matter), DSM allows twins to simulate soil processes precisely and incorporate real-time updates for applications like precision agriculture. This integration supports predictive modeling, letting digital twins assess potential impacts of various scenarios on soil health and productivity. By informing sustainable land management and resilience planning, DSM-powered digital twins offer actionable insights for forage species selection, and conservation practices, enabling better resource management and environmental adaptation decisions.