SafeAdapt: Selective Deployment of Local Corrections to Reduce Harmful Updates in Spatial Soil Carbon Prediction

Local corrections can improve average prediction error while degrading some target regions. We study when a few-shot residual correction should replace a shared predictor for modeled lateral soil organic carbon. SafeAdapt combines a frozen source model, source-validation selection of correction amplitude, and a support-only empirical lower-confidence-bound (LCB) gate with source fallback. A harmful update increases held-out regional RMSE relative to the source. Across 33,620 Daily Erosion Project (DEP) episodes, selected-amplitude gating lowers harmful-update incidence from 41.56% to 8.04% at 24.35% update coverage and reduces mean RMSE by 0.89%. Method-aligned European Soil Data Centre (ESDAC) tests show similar harm-coverage trade-offs, although their mean-error gains remain uncertain. A separate ten-seed extension adds selected-candidate controls at matched update counts: LCB lowers conditional harm versus random expectation by 3.90-7.57 percentage points across DEP partitions and 5.34-9.91 points across Global2019 partitions, with exploratory paired site-bootstrap intervals below zero. Advantages over point-gain ranking are smaller and setting-dependent; European equal-coverage harm differences remain uncertain, and fixed shrinkage remains competitive. These results support selective deployment as a practical accuracy-harm-coverage trade-off, rather than a universally optimal correction or a statistical safety guarantee.