发展数字经济，完善大数据建设，创新各行业大数据应用已成为国家战略，而区域大数据发展水平及政策倾向直接影响区域农业大数据的建设和应用。课题深入调研分析2020年各区域大数据和农业大数据建设现状基础上，建立比较评价指标体系，创新性横向比较区域大数据发展及区域农业大数据建设水平，再纵向立体综合对比各区域各指标内涵及相互关联关系：受区域经济发展水平、区域基础建设、区域政策、人才建设、资金投入、产业集聚的影响，区域大数据发展及区域农业大数据建设不平衡，基本呈东高西低的态势；其中环境发展指数对大数据及农业大数据发展水平影响重大；数据资源建设进程不一，很多省公共数据开放不到位；区域竞争让人才、组织、示范企业高度集中；科研人才、资金等的投入成为中西部及东北各省创新能力的洼点；各省区域农业大数据建设重视度不同，加上农业数据的复杂性，建设进度落后于区域大数据，出现部分区域农业大数据建设与经济及区域大数据发展水平不匹配，发展战略无法有效实施的矛盾现象，成为区域大数据发展的短板，严重阻碍区域及全国大数据及农业大数据发展。基于研究进一步总结大数据及农业大数据战略实施存在的问题及成因，探讨大数据及农业大数据发展策略。

Developing the digital economy, improving construction of big datasets and innovating applications of big data in various industries have become a national strategy. The development level and policy tendency of regional strategies directly affect the construction and application of big data relating to agriculture. An investigation and analysis of the current situation regarding regional big data and agricultural big data in 2020 enables a comparative evaluation index system to be developed. The development of regional big data and the construction level of regional agricultural big data are compared horizontally, and then the connotation and correlation of various indicators in each region are compared vertically. A three-dimensional analysis focusing on the level of regional economic development, regional infrastructure, regional policies, talent construction, capital investment, and industrial agglomeration is also conducted. It’s unbalanced when it comes to the development level of regional big data and the construction of regional agricultural big data in China, which is higher in the east while lower in the west. The environmental development index has a significant impact on the development level of big data and agricultural big data. The process of constructing data resources varies among regions, and public data are not readily available in many provinces. Regional competition leads to the concentration of talent, organizations, and enterprises, and investment in scientific research has suppressed innovation in central, western, and northeast provinces. Different provinces put different levels of energy into the construction of regional agricultural big data. In addition to that, the complexity of agricultural data means that the construction process lags behind that of regional big data. Therefore, there are contradictions between the construction of agricultural big data in some regions and the development level of the economy and regional big data, meaning that development strategies cannot be effectively implemented. This has become a bottleneck for the development of regional big data and seriously hinders the development of regional/national big data and agricultural big data. This paper further summarizes the problems in the implementation of big data and agricultural big data strategies, and discusses appropriate development strategies for big data and agricultural big data.