Measuring Agricultural Modernization and Diagnosing Development Bottlenecks in China's Provinces: Evidence from high-quality development and new quality productivity

Abstract

Key conclusions are as follows: (1) provincial agricultural modernization exhibits pronounced spatial heterogeneity—coastal and major economic provinces generally outperform inland regions in both high-quality development and new quality productivity; (2) model-based indicator scoring reveals clear "knee/fault-line" structures, motivating parsimonious feature-retention thresholds (0.04 for the high-quality development set and 0.05 for the new quality productivity set) while maintaining predictive accuracy; (3) for the lagging cluster (2022 comprehensive score < 1.2), obstacle-degree diagnostics indicate that binding constraints concentrate in green production efficiency, openness-related factor conditions, shared public services, and the digital rural environment rather than a uniform weakness; (4) the stacking ensemble delivers the best overall predictive performance (lower RMSLE and smaller MSE/MAE than any single learner), supporting its use as a monitoring tool for key modernization dimensions. These findings imply that modernization policies should shift from average upgrading to cluster-specific, bottleneck-oriented interventions.

Keywords: high-quality development; new quality productivity; agricultural modernization; stacking ensemble learning; K-means clustering; obstacle-degree analysis