抄録
Farmland soil health forms the foundation for crop productivity and sustainable agriculture, with soil nitrogen dynamics playing a critical role in both aspects. This study develops a comprehensive framework for predicting soil nitrogen dynamics by integrating sensor technologies, remote sensing, and microbial community analysis. Using big data analytics and machine learning algorithms, we identify key factors governing nitrogen transformation, availability, and loss, and establish data-driven prediction models. To support intelligent farmland management, we explore multi-source data fusion for nitrogen optimization and develop decision-support strategies for organic fertilizer application and irrigation management. Case studies across diverse environmental conditions demonstrate model performance and identify implementation challenges. This integrated approach provides a scientific foundation for precision nitrogen management and advances the development of intelligent decision-making systems for sustainable agriculture.
