Abstract:

The development of data factor has injected new vitality into agricultural green development，promoted agricultural innovation，and facilitated the process of agricultural modernization. Based on panel data of 11 provinces（municipalities）in the Yangtze River Economic Belt from 2013 to 2022，this paper introduces data factor as a new input variable and adopts the input-oriented super-efficiency SBM-GML model to measure agricultural green total factor productivity（GTFP）and analyze its spatiotemporal evolution characteristics. The findings show that the agricultural GTFP in the Yangtze River Economic Belt grew at an average annual rate of 3.7%，with technological progress being the main driving factor，while regional heterogeneity in technical efficiency and scale efficiency is significant. Spatially，it presents a complex pattern characterized by “high technological level in the east and low in the west，scale expansion in the west and stagnation in the east，and efficiency depression in the central region”. Guizhou，Shanghai，and Chongqing ranked the top three，while Jiangxi was the only region with negative growth. The input of data factor has strengthened the role of technological progress in improving agricultural GTFP. This study provides empirical evidence for the coordinated development of agricultural green transformation and data factor in the Yangtze River Economic Belt，and offers policy implications for optimizing regional resource allocation and promoting the deep integration of data factor with agriculture.

Key words: Yangtze River Economic Belt, agricultural green total factor productivity, data factor, super-efficiency SBM model, GML index