الفهرس | Only 14 pages are availabe for public view |
Abstract Milk yield optimization is critical for the economic sustainability of dairy farms. This study investigates the potential of some classification models, including linear discriminant analysis (LDA), flexible discriminant analysis (FDA), ordinal logistic regression (OLR), and artificial neural networks (ANNs) in predicting milk yield levels. Using 3793 lactation records from cows calved between 2009 and 2020, researchers investigated some predictors such as age at first calving (AFC), lactation order (LO), days open (DO), days in milk (DIM), dry period (DP), calving season (CFS), 305- milk yield (305-MY), calving interval (CI), and total services per conception (S/C). Model performance was assessed using criteria such as overall accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and area under curve value (AUC). The predictors’ significance demonstrated that all the investigated parameters statistically (P < 0.05) contributed to milk yield prediction, with 305- MY, parities, and calving season being the most important characteristics. Furthermore, the current findings revealed that classification models were effective at predicting milk yield. ANNs achieved the best accuracy (0.94), with an AUC of 0.92, then followed by FDA, LDA, and OLR. In conclusion, classification models are a useful tool for accurate and efficient milk yield prediction in dairy farms. This will enable farmers to understand milk yield determinants, optimize production, enhance farm performance, and contribute to animal welfare through informed decisions. |