Decision support system for selection of dairy buffalo based on peak yield

Efficient animal selection is vital for optimizing dairy farming. Traditionally, human reliance introduces errors. In the present study, a user-friendly Decision Support System (DSS) using Artificial Neural Networks (ANNs) was developed to predict peak milk yield of buffalo (Murrah breed) based on linear traits - Udder Depth (UD), Naval Udder Distance (NUD), Rear Teat Distance (RTD), Fore Rear Teat Distance (FRTD), Teat Length (TL), Rump Width (RW), Rear Udder Width (RUW), Rear Udder Height (RUH), and Lactation Number (LN). A dataset of 259 buffaloes was used for model training and validation, with ANN outperforming Support Vector Machine, Random Forest and Multiple Linear Regression. The performance of ANN was assessed with the Correlation Coefficient (CC), Root Mean Squared Error (RMSE) and R2 metrics, revealing maximum CC of 0.86, minimal RMSE of 2.03 and a maximum R2 of 0.748, highlighting its accuracy. These findings provide valuable insights for dairy farmers, enabling informed decisions in selecting elite buffaloes. Implementation of this tool has the potential to revolutionize breeding, boost milk production, and enhance overall efficiency and profitability in buffalo based dairy farming.