Analyzing and Predicting the Agronomic Effectiveness of Fertilizers Derived from Food Waste Using Data-Driven Models

This study evaluates and estimates the agronomic effectiveness of food waste-derived fertilizers by analyzing plant yield and the internal efficiency of nitrogen utilization (IENU) via statistical and machine learning models. A dataset of 448 cases from various food waste treatments from our experiments and literature was analyzed. Plant yield and IENU showed high variability, averaging 2268 ± 3099 kg/ha and 32.3 ± 92.5 kg N/ha, respectively. Ryegrass dominated (73.77%), followed by unspecified grass (10.76%), with oats (4.87%) and lettuce (2.02%). Correlation analysis revealed that decomposition duration positively influenced plant yield and IENU (r = 0.42, 0.44), while temperature and volatile solids had negative correlations. Machine learning outperformed linear regression to predict plant yield and IENU, especially after preprocessing to remove missing values and outliers. Random Forest and Cubist models showed strong generalization with high R² (0.79–0.83) for plant yield, while Cubist predicted IENU well in testing, with RMSE = 3.83 and R² = 0.78. These findings highlight machine learning’s ability to analyze complex datasets, improve agricultural decision-making, and optimize food waste utilization.