Abstract
With the rapid development of big data technology, agricultural research is increasingly adopting advanced data analysis methods to optimize farming practices and improve soil health. This article explores how big data technology can deepen our understanding of the impact of crop rotation on soil microbial communities by introducing the systematic collection and integration process of soil samples, environmental data, and crop data. High-throughput sequencing technology and functional genomics are utilized to analyze microbial communities' structure and functional potential, while soil enzyme activity tests assess their performance. In the data analysis and modeling section, the focus is on using statistical analysis and machine learning techniques to mine patterns and trends within the data, establish predictive models, and perform spatiotemporal analysis to evaluate the specific effects of crop rotation on soil microbial communities. Finally, the importance of interdisciplinary collaboration is emphasized, highlighting that cooperation with bioinformaticians and agricultural scientists can effectively translate research findings into practical applications, promoting sustainable agricultural development. Through these studies, this article aims to provide a scientific reference for agrarian management, enhance soil health and crop productivity, and achieve long-term sustainability in agriculture.
