Herd health management of dairy cows in Lithuania: Where are we?

Abstract

The study explores innovative approaches to herd health management in Lithuanian dairy farms, focusing on the use of real-time monitoring systems to enhance disease prevention, improve reproductive management, and optimize overall farm efficiency. Key advancements include automated milk analyzers, in-line sensors, and real-time biomarker monitoring for early detection of subclinical ketosis (SCK) and subclinical acidosis (SCA). A central hypothesis tested was the relationship between milk fat-to-protein (F/P) ratio and metabolic health status. Data from 320 cows during early lactation revealed that cows with SCK exhibited a 36% higher F/P ratio and elevated NEFA levels compared with healthy cows, while SCA cows had a 23.77% lower F/P ratio. These findings establish the F/P ratio as a robust non-invasive biomarker for early metabolic disorder detection. The study also highlights the potential of milk composition analysis for monitoring the energy balance, calving ease, and susceptibility to mastitis. Automated systems like the BROLIS HerdLine Milk Analyzer demonstrated high accuracy in measuring fat, protein, and lactose concentrations, offering actionable insights into cow health. Incorporating these technologies allows for better energy management, reduced greenhouse gas emissions, and enhanced farm sustainability. The findings emphasize the integration of sensor technology in dairy farming as a vital tool for advancing herd health management and addressing challenges related to metabolic disorders and environmental impacts.