Measuring prices in agricultural e-commerce markets is complicated by promotional spikes, rapid product turnover, and specification heterogeneity. The aim of this study is to develop and validate a reproducible framework that couples promotion-robust weekly price measurement with calibrated short-term forecasting for agricultural e-commerce input markets. Drawing on 452,489 de-identified raw transaction records (448,308 retained after quality control) of tomato, pepper, and lettuce seeds from major Chinese e-commerce platforms (July 2020–October 2025), we construct a weekly Paasche-robust price index with a Top-10 SKU basket and winsorized prices, benchmarked against Laspeyres, Fisher, Törnqvist, and GEKS-TPD formulations. A time-dummy hedonic regression is applied for quality adjustment. Short-term forecasts are generated by a two-level ensemble combining SARIMAX, Temporal Fusion Transformer, and LightGBM via inverse-MAPE weighting, with adaptive conformal intervals for uncertainty quantification. The Paasche-robust index attenuates promotional noise while co-moving closely with superlative formulas (MA-12 correlations > 0.80). Machine-learning models reduce the one-week-ahead MAPE by 42–58% relative to SARIMAX, and the weighted ensemble achieves the best four-week-ahead accuracy for two of three crops. Conformal intervals deliver near-nominal coverage at short horizons. The framework offers a reproducible, deployable tool for real-time monitoring of agricultural e-commerce input markets and supports evidence-based decisions by farmers and policymakers.

Information asymmetry constrains agricultural credit, but repayment and default labels are often unavailable for empirical analysis. Using administrative records for 2970 agricultural enterprises and cooperatives in Beijing linked to bank-authorized credit, this study examines whether multi-source subsidy profiles are associated with observed lending decisions. Two-stage gradient-boosted-tree models compare eight raw administrative features, an externally supplied composite score, and their combination; Shapley Additive exPlanations (SHAP) provides conditional model attributions. No positive authorized credit was observed for economic or share cooperatives. Among the remaining entity types, the combined model predicted accurate positive authorized credit, with an area under the receiver operating characteristic curve (ROC-AUC) of 0.97. Raw administrative features alone had little out-of-fold value for conditional authorized-credit amounts, whereas including the supplied score improved prediction. The framework supports transparent analysis of observed lending where repayment and application process data are unavailable.