Measuring rumination and physical activity as a tool for fresh cows health monitoring

• Autor(a) principal: Silva, Manuel Agustín
• Data de Publicação: 2017
• Tipo de documento: Tese
• Idioma: eng
• Título da fonte: Biblioteca Digital de Teses e Dissertações da USP
• Texto Completo: http://www.teses.usp.br/teses/disponiveis/10/10131/tde-02062017-110056/
• topic: Activity; Atividade; Dairy cow; Disease; Doença; Ruminação; Rumination; Vaca leiteira

The objectives of the current experiment were to characterize patterns of daily rumination time, activity and milk production around the diagnosis of health disorders, and to determine if the addition of rumination and activity data to a commercial dairy farm fresh cow health monitoring program improves sick cow detection and diagnosis of disease during the first 30 DIM. Holstein animals (primiparous = 282, parous = 328) were enrolled in the experiment approximately 60 d before expected calving date, and were divided into two groups (Collar Monitoring-CM-, n=293 ; Control-C-, n=317). Electronic rumination and activity monitoring tags (SCR Engineers Ltd., Netanya, Israel) were fitted on cows neck at enrollment and were kept until approximately 80 ± 3 DIM. Farm personnel checked the cows and performed the diagnosis of disease following the routine of the dairy. Cows from both of the groups were sent to check based on the parameters used by the farm. Additionally, cows from group CM were checked based on the data provided by the tags. Serum calcium concentration was determined using blood samples collected from 0 to 4 DIM. BHBA concentration was determined twice using blood samples collected from 4 to 12 DIM and 7 to 20 DIM. Subclinical hypocalcemia (SCHC) and subclinical ketosis (SCK) were characterized as Ca <8.55 ng/dL, and BHBA >1,000 µmol/L in any blood sample, respectively. Daily rumination time (DRT), daily activity (ACT), and daily milk production patterns for cows with clinical disease showed differences with healthy cows around diagnosis (P $lt;0.05). Cows with subclinical disorders and calving problems had changes in DRT, ACT, and milk production patterns compared to healthy cows around calving (P <0.05). DRT and ACT patterns of regrouped cows were characterized by differences with non-regrouped cows around regrouping (P <0.05). No differences were found for DRT, ACT, and milk production between groups C and CM. The overall sensitivity (Se) of collars to identify health disorders was 56.4% (n = 402 cases), considering a positive outcome as at least 1 alert based on rumination and activity from -7 to +2 d relative to diagnosis. Se was higher for cows with more than one disorder (75.8%) than for cows with one disease only (45.5%) (P <0.001). No differences between groups were found for overall Se, and Se for cows with one disease. However, for cows with more than one disorder, Se was higher in group CM than C (P = 0.005). Overall specificity, positive predicted values, and negative predicted values were 74.5%, 46.4%, and 57.6%, respectively. The overall incidence of disease was 48%. No differences between groups were found for overall incidence of disease and each disease. Among primiparous, group CM (43.3%) had higher overall incidence of disease than group C (32.1%) (P = 0.05). Although were not differences for parous, incidence of metritis tended to be greater in group C than CM (P = 0.1). Incidence of SCK and SCHC was not different between groups. A higher percentage of animals from group CM than C received treatment (P = 0.04), and these differences were seen in primiparous (P = 0.03), but not in parous. However, a higher percentage of parous not diagnosed as sick from group CM received support treatments (drenching and fluids) compared to C. No differences were shown for culling rate, service rate until 150 DIM, conception rate at first service, and percentage of cows marked as do not breed between groups. DRT and ACT patterns for sick cows showed differences around diagnosis compared to healthy cows. The use of DRT and ACT data was able to identify sick cows in a commercial dairy farm. Results suggest that it may be also useful to identify cows needing attention before clinical signs are visible, improving the prevention of health disorders. Its usefulness may vary according to parity, disease, severity of disease and health compromise, and the intensity of the farm system for checking cows. Future research should evaluate different parameters and parameters thresholds based on rumination and activity data for identifying sick cows, and their efficiency in dairies with different degrees of intensity for checking animals health.