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 gathered from our experiments and the existing literature was analyzed. Plant yield and IENU exhibited substantial 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%), oats (4.87%), and lettuce (2.02%). Correlation analysis revealed that decomposition duration positively influenced plant yield and IENU (r = 0.42 and 0.44), while temperature and volatile solids had negative correlations. Machine learning models outperformed linear regression in predicting 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.