Abstract:
The integration of Artificial Intelligence (AI) into dairy production medicine offers transformative opportunities for herd
health management, early disease detection, and nutritional optimization. This study focuses on the use of the milk fatto-
protein ratio as a key indicator of the nutritional status of dairy cows. By analyzing individual milk composition data,
AI models can identify deviations from optimal values, revealing dietary imbalances such as over - or under - consumption
of concentrates, insufficient structural fiber, or excessive fiber intake. In our study, 235 dairy cows were monitored across
two testing periods, generating a total of 470 observations. During the first control, the distribution was dominated by
Optimal animals (n=157), with fewer in the Below 1.1 category (n=83) and very few Above 1.5 (n=6). In the second
control, the Below 1.1 group increased (n=105), while Optimal declined (n=134); the Above 1.5 group remained small
and stable (n=7). Based on these insights, the AI system can provide automated recommendations for adjusting feed
rations or regrouping animals according to their milk production performance. AI technologies enable dairy farmers and
researchers to process large datasets with unprecedented precision, enhancing decision-making in feeding strategies,
disease management, and genetic selection. The adoption of AI not only improves productivity and operational efficiency
but also supports sustainable farming practices by optimizing resource use and reducing environmental impact. hese
applications highlight AI’s capacity to power integrated e-extension solutions that improve animal health, boost
production efficiency, and promote the long-term sustainability of dairy farming systems.
