Breed lineage prediction model for small ruminant farm production

Sheep are crucial for Malaysian Muslims, which account for 60% of the population. However, local sheep supply is limited due to high mortality caused by diseases such as Tetanus and Foot and Mouth Disease (FMD). Therefore, this study aims to identify the internal and external factors that influence sheep breed lineage continuity, to propose a breed lineage prediction model for small ruminant farm production, and to evaluate the accuracy and time efficiency of the developed model by utilising Feedforward Artificial Neural Network (FANN) deep learning. Qualitative Research (QR) interviews were performed in goat and sheep farms around Peninsular Malaysia for the first and second phase of the Delphi Forecasting Method (Delphi), while the final phase involved validation by experts in the field. From the study, internal and external factors were identified as breed, fodder, medicine, sanitisation, government collaboration, worker’s knowledge, and climate. A new model and algorithm for sheep breed lineage were created and validated for accuracy and time efficiency. The data and analysis from this study will be integrated into the proposed FANN algorithm. In addition, future studies could adopt this method when studying other farm animals.