Big data-driven research directions in rice rotation systems

Abstract

The application of big data technology in rice rotation systems provides a scientific basis and decision support for improving agricultural production efficiency and achieving sustainable development. This paper explores the main research directions of rice rotation systems in the context of big data. First, big data analysis and modeling are used to optimize planting patterns and scheduling to increase yields, improve soil health, control pests, and enhance water resource utilization efficiency. Second, by integrating meteorological, soil, and crop growth data, pest and disease prediction models are established to enable real-time warnings and management, reducing chemical pesticides and enhancing system sustainability. Additionally, by analyzing historical soil data and fertilizer usage, the impact of different rotation patterns on soil fertility is assessed, and scientific soil management strategies are proposed to ensure long-term sustainability. Simultaneously, meteorological, hydrological, and crop data optimize irrigation management, reducing water waste and increasing water use efficiency. Finally, big data technology is applied to assess the carbon emissions and environmental impacts of different rotation systems, and low-carbon, efficient rotation models are developed to address the challenges of climate change.