Moving beyond book values: Using machine learning to improve the accuracy of manure nutrient concentration...

While animal manure is an excellent nutrient source, its high variability across farms makes static averages unreliable for crop demand. This study utilizes the ManureDB database to analyze regional variations in total nitrogen (N), phosphorus (P₂O₅), and potassium (K₂O) across the United States, categorized by animal source and total solid content. Three machine learning (ML) models, Random Forest, Extreme Gradient Boosting, and a hybrid of the two, were used to predict total NPK concentrations using ammonium-N (NH₄-N), pH, moisture percentage, region, and animal source as predictor variables. Models had higher predictability for the total N concentration (R² = 0.8-0.9), moderate for the total P (R² = 0.6-0.7), and lower for the total K (R² = 0.4-0.7). Models provided better predictions for the total N in liquid manure than for solid manure and the opposite trend was observed for total K. For solid manure, animal source and NH₄-N emerged as the primary factors, while moisture percentage was a secondary influence. For liquid manure, NH₄-N and moisture percentage were primary predictors. Static book values often miss site-specific variations due to the complex, nonlinear interactions among manure properties. Using the ManureDB dataset, ML models use variables like NH₄-N, moisture, and pH to precisely predict total NPK. This approach moves beyond traditional averages, offering a more precise tool for nutrient management when laboratory data are incomplete.