Comprehensive Climate, Soil, Agronomic, and Management Dataset for Data-Driven Cotton Irrigation in NW China

This unique dataset integrates climate data, real-time soil sensor data, agronomic data, and crop management data, specifically designed to enhance smart irrigation decision-making for cotton in the semi-arid climate of Northwest China. The dataset establishes correlations between cotton yields, soil conditions, and climatic factors, including detailed records of irrigation, fertilization, crop yields, and soil moisture. To demonstrate its utility, the dataset was validated through the application of advanced machine learning techniques, including reinforcement learning (RL) and algorithms such as DNN, XGBoost, MLP, and RF, to optimize irrigation strategies and simulate irrigation-yield relationships. These approaches effectively balanced crop yield maximization with water use efficiency, underscoring the dataset’s potential to advance sustainable agricultural practices. This dataset serves as a valuable resource for data-driven agricultural research, enabling smart decision-making, validating crop models, and improving agricultural sustainability.