Prediction of Herd Life in Dairy Cows Using Multi-Head Attention Transformers
This work addresses the need for objective, data-driven culling decisions in dairy farming, though it is incremental as it applies an existing AI method to a specific agricultural domain.
The study tackled the problem of predicting dairy cow longevity to aid culling decisions by developing a Multi-Head Attention Transformer model, which achieved an 83% determination coefficient in predicting herd life using historical time-series data from 19,000 cows.
Dairy farmers should decide to keep or cull a cow based on an objective assessment of her likely performance in the herd. For this purpose, farmers need to identify more resilient cows, which can cope better with farm conditions and complete more lactations. This decision-making process is inherently complex, with significant environmental and economic implications. In this study, we develop an AI-driven model to predict cow longevity using historical multivariate time-series data recorded from birth. Leveraging advanced AI techniques, specifically Multi-Head Attention Transformers, we analysed approximately 780,000 records from 19,000 unique cows across 7 farms in Australia. The results demonstrate that our model achieves an overall determination coefficient of 83% in predicting herd life across the studied farms, highlighting its potential for practical application in dairy herd management.