Livestock Manure Compost Maturity Prediction: Machine Learning with Feature Availability, Target Definition, and Interpretability Analyses

Reliable assessment of livestock manure compost maturity is important for safe agricultural use, yet biological and laboratory-based indicators may not be consistently available during routine monitoring. This study developed and evaluated machine learning models for predicting a composite compost maturity score under five feature availability scenarios representing complete, reduced, monitoring-oriented, and low-cost input conditions. Separately, target definition considerations were addressed by treating the complete-input setting as an upper-bound reference and excluding germination index (GI) and explicit carbon-to-nitrogen (C/N) ratio variable from the reduced scenarios because of their direct or indirect relationship with score construction. Individual regression models, stacking and weighted ensembles, and a multilayer perceptron were compared using 30 repeated 80/20 train–test splits. The selected reduced-feature stacking model achieved a mean test R2 of 0.920 ± 0.020, while the monitoring-oriented setting achieved an R2 of 0.869 ± 0.035 using fewer routinely measurable inputs. Feature-group ablation and simulated Gaussian noise testing examined model dependence and measurement sensitivity, while permutation importance, Shapley additive explanations (SHAP), and accumulated local effects (ALE) provided complementary interpretation of influential variables and response patterns. Exploratory supplementary interpolation and LSTM/GRU experiments further explored data augmentation and temporal modeling feasibility. Overall, the study demonstrates accurate and interpretable maturity score prediction across reduced and monitoring-oriented feature settings.