ArticleAnimals : an open access journal from MDPI2026
Machine Learning Prediction of Litter Moisture and Litter pH in a Commercial Broiler House Using Nested Leave-One Production-Period-Out Cross-Validation.
Article in Animals : an open access journal from MDPI, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Litter moisture content (LMC) and litter pH (LPH) are important indicators of litter quality in broiler production. This study developed machine-learning models to predict LMC and LPH using environmental, flock-related, spatial, and litter-related variables collected from eight temporally distinct production periods in the same commercial broiler house at days 7, 21, and 42 (1600 observations). Four ML algorithms, namely Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGB), and Multilayer Perceptron (MLP), were evaluated under six predictor scenarios (S1-S6) using nested Leave-One-Production-Period-Out cross-validation. For LMC, XGB under S3 achieved the highest mean outer-fold R
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