Screening for Relative Risk of Low Soil Fertility in Mown-Grazed Grasslands of the Qinghai–Tibet Plateau Using Multi-Year Hydrothermal Backgrounds

Soil fertility in mown-grazed grasslands on the Qinghai–Tibet Plateau reflects hydrothermal conditions, terrain, grassland type, and management disturbance. We developed a soil fertility index (SFI) from 1037 topsoil samples collected at 0–30 cm during 2023–2025. The modelling framework combined static ecological background variables, conventional climate indicators, multi-year seasonal hydrothermal statistics, soil hydraulic attributes, and self-supervised temporal embeddings. Spatial-block, year-held-out, and ecological-zone-held-out validation were used with area-of-applicability (AOA) analysis and spatial-block conformal prediction. Multi-year seasonal hydrothermal statistics supplied the strongest predictive information and increased spatial-block R2 by 0.2435 relative to the static-background model. A 10-year seasonal window gave the best empirical balance among explained variance, prediction error and rank consistency. The final deployment model achieved R2 = 0.5632, RMSE = 0.1105, Spearman = 0.4473 and AUC = 0.696 under spatial-block validation. These values support regional screening and sampling prioritisation, not local deterministic diagnosis or site-level management prescriptions. Grid prediction identified Zone 2 as the main concentration of relative low-SFI risk. Full-sample AOA schemes covered 82.9%, 81.9% and 85.9% of the system-evaluation grid cells, whereas deployment-grid AOA coverage was lower under the stricter deployment setting. This contrast separates feature-space support from operational grid support. Split conformal prediction achieved 90% coverage close to the nominal level (PICP = 0.899, MPIW = 0.264). Natural-background residuals separated relative low-SFI risk from local deviations below expected natural conditions. The framework provides a reproducible screening tool for regional prioritisation and follow-up field verification in alpine grasslands.