大数据支撑现代农业产业高质量发展的内在逻辑与政策建议

Internal logic and policy recommendations of big data to support high-quality development of modern agricultural industry

        李颖明^1,2(中国科学院科技战略咨询研究院 北京 100190;中国科学院大学 公共政策与管理学院 北京 100049)
        王子彤^1,2*(中国科学院科技战略咨询研究院 北京 100190;中国科学院大学 公共政策与管理学院 北京 100049)
        汪明月¹(中国科学院科技战略咨询研究院 北京 100190)
        刘释疑^1,2(中国科学院科技战略咨询研究院 北京 100190;中国科学院大学 公共政策与管理学院 北京 100049)

         现代农业;数据要素;高质量发展;内在逻辑;政策建议

        modern agriculture;data factor;high-quality development;internal logic;policy recommendation

        厘清大数据支撑现代农业生产的内在逻辑，有助于培育和发展农业新质生产力。从经济学视角出发，本文提出大数据在现代农业生产中的作用主要体现在2个层面：提升农业传统生产函数的全要素生产率；改变农业生产的要素禀赋结构，形成新的农业生产函数。基于此，大数据支撑现代农业生产的实现形式可以概括为提升现代农业技术效率、优化农业生产配置效率，以及推动农业深度转型升级，从而形成新的生产范式。根据大数据支撑现代农业发展的逻辑分析，以及我国农业大数据发展现状，文章提出3点建议，以供决策参考：（1）建立农业数据标准化体系，以确保不同来源的数据能够有效整合与分析；（2）推动数据中台等数据基础设施建设，打通涉农数据流通渠道，构建健全的数据共享机制，促进政府、科研机构、企业与农民之间的数据流通；（3）推动农业智能算法模型的联合攻关，引导社会算力资源向农业领域适度倾斜，提高农业大数据分析能力，建立现代农业决策支持系统。

        Clarifying the internal logic of big data to support modern agricultural production is helpful to cultivate and develop new quality productive forces in agriculture. From the perspective of economics, this study proposes that the role of big data in modern agricultural production is mainly reflected in two levels. Firstly, it improves the total factor productivity of the traditional agricultural production function. Secondly, it changes the factor endowment structure of agricultural production and forms a new agricultural production function. Based on these, the realization form of big data to support modern agricultural production can be summarized as improving the efficiency of modern agricultural technology, optimizing the efficiency of agricultural production allocation, and promoting the deep transformation and upgrading of agriculture to form a new production paradigm. According to the logical analysis of big data supporting the development of modern agriculture and the development status of agricultural big data in China, this study puts forward the following suggestions for decision-making. Firstly, establish an agricultural data standardization system to ensure that data from different sources can be effectively integrated and analyzed. Secondly, promote the construction of data infrastructure such as data platform, open up the circulation channels of agriculture-related data, build a sound data sharing mechanism, and promote the data circulation between the government, scientific research institutions, as well as enterprises and farmers. Finally, promote the joint research of agricultural intelligent algorithm models, guide the social computing resources to be moderately inclined to the agricultural field, improve the ability of agricultural big data analysis, and establish a modern agricultural decision support system.