Agricultural green total factor productivity (AGTFP) is a key indicator for evaluating the level of green development in agriculture. However, conventional approaches to AGTFP measurement often treat intermediate agricultural production processes as a “black box”, overlooking internal system mechanisms and thus leading to biased identification of efficiency bottlenecks. To address this limitation, this study introduces an analysis using a network slack-based measure (NSBM) model to move beyond the traditional single “input–output” transmission framework. By integrating soil sample data from 2017 to 2024, the agricultural production process is decomposed into three sequential stages—material inputs, nutrient transformation, and crop production—to evaluate AGTFP in Baoding, China. The results reveal that AGTFP in Baoding remains at a relatively low level overall, although a steady upward trend is observed over time. Specifically, overall efficiency increased by 18.18%, while the efficiency of converting agricultural inputs into soil nutrients (the input subsystem) improved by 36.13%. The input subsystem serves as the primary driver of AGTFP improvement, with a marginal contribution coefficient of 0.61% to overall efficiency (p < 0.01). Although the efficiency of transforming soil nutrients into agricultural output (the output subsystem) remains relatively high, its growth potential is constrained by biological limits. It is highly sensitive to external factors such as topography and natural disasters. At present, the key bottleneck to enhancing regional AGTFP is the low efficiency with which external inputs are converted into soil nutrients. These findings suggest that policy priorities should shift from simple input reduction to process-oriented management, with an emphasis on improving the conversion efficiency of external inputs into effective soil nutrients, thereby facilitating agricultural green transformation while mitigating non-point source pollution. Based on existing research frameworks, this study supplements and refines the original analytical framework by incorporating soil data from the agricultural production process, providing new empirical evidence for uncovering the “black box” of agricultural production.
Jiang et al. (Mon,) studied this question.