基于灰色关联模型的浙江省数字农业发展水平影响因素研究

A Study on the Influencing Factors of Digital Agriculture Development in Zhejiang Province Based on the Grey Relational Model

摘要: 文章基于2012~2022年浙江省统计数据，构建包含基础设施、产出效益、人才投入、发展环境与绿色投入5个维度的数字农业发展综合评价指标体系，运用熵值法测度其发展水平，并结合灰色关联分析法探究关键驱动因素。研究表明：浙江省数字农业综合指数从2012年的0.1217增长至2022年的0.8875，呈持续上升态势，且2016年后增速显著提升，反映出政策支持与技术应用的协同效应。灰色关联分析表明，科学技术投资(关联度0.813)、农村宽带接入(0.802)、涉农贷款(0.801)及数字人才规模(0.796)是核心驱动要素，而传统要素如化肥用量(0.56)和农业机械总动力(0.561)关联度较低，揭示数字化更依赖技术创新与软性资源投入。据此提出应重点强化数字基建布局，完善“财政 + 金融”政策激励体系，培育复合型数字农业人才，并推动物联网、大数据与传统农业要素的协同升级，以实现农业生产效率提升与可持续发展。

Abstract: Based on statistical data from Zhejiang Province from 2012 to 2022, this study constructs a comprehensive evaluation index system for digital agriculture development, encompassing five dimensions: infrastructure, output efficiency, talent investment, development environment, and green investment. The entropy weight-TOPSIS model is employed to measure the development level, and the grey relational analysis method is used to explore key driving factors. The results indicate that Zhejiang’s digital agriculture composite index increased from 0.1217 in 2012 to 0.8875 in 2022, demonstrating a continuous upward trend, with a significant acceleration after 2016, reflecting the synergistic effects of policy support and technological applications. The grey relational analysis reveals that investment in science and technology (association degree: 0.813), rural broadband access (0.802), agricultural loans (0.801), and the scale of digital talent (0.796) are the core driving factors, whereas traditional factors such as fertilizer usage (0.560) and total agricultural machinery power (0.561) exhibit lower association degrees, highlighting the greater reliance of digital agriculture on technological innovation and soft resource investment. Accordingly, this study suggests strengthening the digital infrastructure layout, improving the fiscal and financial policy incentive system, cultivating interdisciplinary digital agriculture talent, and promoting the coordinated integration of IoT, big data, and traditional agricultural elements to enhance agricultural productivity and achieve sustainable development.