Two-Layer Conditional Prediction of Soil Electrical Conductivity Assisted by Post-Irrigation Soil Moisture Trajectories in Farmland of the Bachu Irrigation District

Abstract

Using multi-depth continuous observations from nine farmland sites in the Bachu irrigation district, Xinjiang (November 2024–April 2026), this study developed a two-layer conditional prediction model to test whether predicted soil-moisture trajectories improve apparent electrical conductivity (EC) prediction beyond a persistence benchmark. Four prediction windows (0–1, 1–3, 3–7, and 7–15 d) were evaluated using time-forward validation under observed ERA5 forcing, paired ablation, SHAP, and post-hoc meteorological-deficit linkage analysis. For EC20, the R2 values were 0.978, 0.923, 0.727, and −1.376, with corresponding Skill values of −0.001, 0.277, 0.540, and 0.635; absolute performance at 7–15 d remained unreliable. EC40 achieved R2 of 0.943–0.987, with Skill of 0.029–0.325, and EC60 achieved R2 of 0.737–0.974, with Skill of 0.065–0.177. Ablation showed the clearest gain for EC20 at 3–7 d (ΔR2 = 0.193; ΔSkill = 0.325), whereas most other combinations showed no consistent improvement. At 0–1 d, SHAP contributions of predicted moisture trajectories to EC20, EC40, and EC60 were 39.2%, 37.6%, and 26.8%, respectively. Meteorological-deficit linkage responses varied by depth and horizon but were generally limited. Overall, trajectory benefits were depth- and horizon-dependent and reflected model dependence rather than causal water–salt mechanisms. The framework is currently applicable mainly to monitored, unfrozen post-irrigation periods without additional wetting recharge.