Academic and Practical Cases: From TFP Variables in DEA-Malmquist Model to the Conduction of County-Level Digital Agriculture

This paper investigates the construction and practical implementation of digital agriculture at the county level in China by integrating academic theories and real-world cases. Utilizing the DEA-Malmquist index model, key input variables such as labor, land, machinery, chemical fertilizers, and irrigation are analyzed to measure total factor productivity (TFP) growth in digital agriculture. The study highlights the critical role of smart sensors, big data, artificial intelligence, and Internet of Things technologies in enhancing agricultural productivity, resource optimization, and sustainable development. The paper further explores digital agriculture management systems, including production management, product traceability, and integrated management platforms, which collectively facilitate precision farming, traceability, and intelligent decision-making. The construction path emphasizes government-enterprise cooperation, data integration, and technological innovation, driving transformation from traditional experience-based agriculture to data-driven precision agriculture. The practical value lies in ensuring food security, promoting industrial upgrading, increasing farmers' income, and supporting the global green transformation. Finally, the paper provides policy recommendations focusing on technological advancement, infrastructure improvement, and talent cultivation to foster the sustainable development of digital agriculture in China.