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Incorporating automated daily milk components into modern dairy farm management
Year:
2015
Authors :
Honig, Hen
;
.
Volume :
Co-Authors:
Schcolnik, T., Applied Research Department, Afimilk, Kibbutz Afikim, Israel
Ishay, E., Applied Research Department, Afimilk, Kibbutz Afikim, Israel
Honig, H., Volkani Agricultural Research Center, ARO, Bet Dagan, Israel
Facilitators :
From page:
730
To page:
735
(
Total pages:
6
)
Abstract:
The extreme metabolic adaptations of dairy cows after calving are associated with a negative energy balance (NEB). NEB increases the likelihood of disease incidence, which in turn adversely affects productivity and reproductive performance. This study was conducted to look for an optimal timing to measure beta-hydroxybutyric acid (BHBA), i.e. ketones, in blood and to compare inline automatic measurements of milk components with a test for blood ketone concentrations. Blood BHBA of the same 18 cows was measured three times throughout one day (06:00, 13:00 & 20:00). Cows with BHBA above 1.4mmol/L were considered ketotic. In the morning 2 cows (11%) were found to be ketotic, at noon 5 cows (28%) and in the evening 6 ketotic cows (33%) were identified. In total 7 cows out of the 18 tested were found to be ketotic on that day by BHBA, in at least one of the three sessions. No single measurement session identified all seven ketotic cows together. Looking at the fat to protein ratio (FPR), we found that 6 of the 7 cows (86%) were ketotic by BHBA. In another study we tested 4 cows, identified by FPR, every two hours. Differences in blood ketone levels were detected in the course of the day. Based on these results we conclude that a single daily, direct ketone body measurement is less effective at preventing ketosis. Continuous automatic monitoring makes farmers aware of invisible (sub clinical) metabolic issues and enables them to react effectively. Automatic monitoring therefore improves animal productivity, health and wellbeing.
Note:
Related Files :
Agriculture
Blood
disease incidence
Fat to protein ratio
Ketones
metabolism
proteins
Show More
Related Content
More details
DOI :
Article number:
0
Affiliations:
Database:
Scopus
Publication Type:
Conference paper
;
.
Language:
English
Editors' remarks:
ID:
31303
Last updated date:
02/03/2022 17:27
Creation date:
17/04/2018 01:01
Scientific Publication
Incorporating automated daily milk components into modern dairy farm management
Schcolnik, T., Applied Research Department, Afimilk, Kibbutz Afikim, Israel
Ishay, E., Applied Research Department, Afimilk, Kibbutz Afikim, Israel
Honig, H., Volkani Agricultural Research Center, ARO, Bet Dagan, Israel
Incorporating automated daily milk components into modern dairy farm management
The extreme metabolic adaptations of dairy cows after calving are associated with a negative energy balance (NEB). NEB increases the likelihood of disease incidence, which in turn adversely affects productivity and reproductive performance. This study was conducted to look for an optimal timing to measure beta-hydroxybutyric acid (BHBA), i.e. ketones, in blood and to compare inline automatic measurements of milk components with a test for blood ketone concentrations. Blood BHBA of the same 18 cows was measured three times throughout one day (06:00, 13:00 & 20:00). Cows with BHBA above 1.4mmol/L were considered ketotic. In the morning 2 cows (11%) were found to be ketotic, at noon 5 cows (28%) and in the evening 6 ketotic cows (33%) were identified. In total 7 cows out of the 18 tested were found to be ketotic on that day by BHBA, in at least one of the three sessions. No single measurement session identified all seven ketotic cows together. Looking at the fat to protein ratio (FPR), we found that 6 of the 7 cows (86%) were ketotic by BHBA. In another study we tested 4 cows, identified by FPR, every two hours. Differences in blood ketone levels were detected in the course of the day. Based on these results we conclude that a single daily, direct ketone body measurement is less effective at preventing ketosis. Continuous automatic monitoring makes farmers aware of invisible (sub clinical) metabolic issues and enables them to react effectively. Automatic monitoring therefore improves animal productivity, health and wellbeing.
Scientific Publication
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