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
fat-to-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.
