Prediction of Herd Life in Dairy Cows Using Multi-Head Attention Transformers

TL;DR

This paper presents an AI model using Multi-Head Attention Transformers to predict dairy cow herd life from historical data, aiding farmers in making informed culling decisions with high accuracy.

Contribution

It introduces a novel application of Multi-Head Attention Transformers for predicting cow longevity from multivariate time-series data, improving decision-making in dairy farming.

Findings

Achieved 83% determination coefficient in herd life prediction

Analyzed 780,000 records from 19,000 cows across 7 farms

Demonstrated practical potential for herd management

Abstract

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.