The loss of soil available nutrients may affect soil quality and crop growth. Biochar can form a multi-level fixed network because of its rich pore structure and surface functional groups, which can effectively fix available nutrients in soil and maintain nutrient utilization rate. Because it is difficult to directly prepare biochar materials with good adsorption characteristics through experimental results. This study employed an XGBoost machine learning prediction model to determine the optimal nutrient-rich biochar preparation conditions. The R2 value ranged from 0.97 to 0.99. The results indicated that specific surface area was the primary factor influencing ammonium nitrogen adsorption, with a feature importance of 56.13%. Production conditions (hydrothermal temperature and time) significantly affected the adsorption of nitrate nitrogen and available phosphorus, with feature importances of 75.91% and 81.54%, respectively. Mean pore diameter was negatively correlated with potassium ion adsorption characteristics. Biochar prepared under hydrothermal conditions at 202.50–251.25 °C for 3 h exhibited favorable adsorption characteristics for multiple soil available nutrients. This study provides new insights into biochar’s application in the field of soil nutrient adsorption through data analysis. It is helpful to avoid the waste in the process of energy utilization from biomass to biochar.

Study on fixation mechanism of soil available nutrients and optimization of production process of nutrient-rich biochar based on XGBoost machine learning prediction model
    Abstract
    1 Introduction
    2 Materials and methods
        2.1 Materials studio software settings
        2.2 Data compilation and preprocessing
        2.3 Machine learning models and hyper-parameters
        2.4 Model training and evaluation
        2.5 Feature importance analysis and partial correlation analysis
        2.6 Verification experiment
        2.7 Data processing
    3 Results and discussion
        3.1 Analysis of adsorption mechanism
        3.2 Statistical analysis of data sets
        3.3 Hyperparameter adjustment and model evaluation
        3.4 Feature importance analysis
        3.5 Partial correlation analysis
        3.6 Experimental validation
        3.7 Implications and outlook
    4 Conclusions
    Acknowledgements 
    References

• Jikai Lu(College of Engineering, Ocean University of China, 1299 San‑Sha Road, Qingdao 266100, Shandong, China)
• Yan Li(College of Engineering, Ocean University of China, 1299 San‑Sha Road, Qingdao 266100, Shandong, China) Corresponding author
• Kenji Ogino(Graduate School of Bio‑Applications and Systems Engineering, Tokyo University of Agriculture and Technology, Koganei, Tokyo 184‑8588, Japan)
• Bing Wang(School of Environment and Resources, Taiyuan University of Science and Technology, Taiyuan 030024, Shanxi Province, China, Graduate School of Bio‑Applications and Systems Engineering, Tokyo University of Agriculture and Technology, Koganei, Tokyo 184‑8588, Japan)
• Hongyu Si(Shandong Key Laboratory of Biomass Efficient Conversion and Utilization, Energy Research Institute, Qilu University of Technology (Shandong Academy of Sciences), 19 Ke‑Yuan Road, Jinan 250014, Shandong, China) Corresponding author