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Abstract

The aim of this study was to evaluate the possibility to predict outcomes of artificial insemi- nation (AI) in dairy cows based on in-line milk progesterone (P4) concentration. The research was carried out on the herd of loose housing 245 dairy cows of 2-4 lactations, with average milk yielding 11.000 kg per cow. Milk sampling, measuring, and recording of milk P4 concentration was carried out using the Herd Navigator (HN). The grouping was performed according to the following three indices: the first by reproductive condition – pregnant or not pregnant after AI, the second by P4 concentration from day 20 before AI to day 20 after AI, and the third by P4 concentration at AI time. There was a significant difference in P4 concentration in the group of pregnant cows from day 15 to day 9 before AI, and it was by 18.3% higher com- pared to that in the group of non-pregnant cows in the said period (p<0.01). The milk P4 concen- trations began to differ mostly from day 10 after AI. At that time, the average P4 concentration in the group of pregnant dairy cows was by 36.8% higher compared to that in the group of non-pregnant cows (p<0.01). A statistically significant difference between the ratio of the cows with high, medium, and low P4 concentration on days 20-16 before AI (p<0.01) was determined. The highest number of cows with up to 2-3 ng/ml P4 concentration became pregnant at the AI time.
In-line milk P4 records captured on day 10-15 before AI can be used to predict the proper for reproduction period. By P4 concentrations on day10 after AI, the ratio of pregnant cows in herd can be assessed.
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Authors and Affiliations

A. Gavelis
1
A. Juozaitis
2
R. Japertienė
1
G. Palubinskas
1
V. Juozaitienė
1
V. Žilaitis
3

  1. Department of Animal Breeding, Veterinary Academy, Lithuanian University of Health Sciences, Tilžės St. 18, Kaunas, Lithuania
  2. Department of Animal Nutrition, Veterinary Academy,Lithuanian University of Health Sciences, Tilžės St. 18, Kaunas, Lithuania
  3. Large Animal Clinic, Veterinary Academy, Lithuanian University of Health Sciences, Tilžės St. 18, Kaunas, Lithuania
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Abstract

There is an increased interest in using automatic milking systems (AMS) to indirectly assess the welfare of dairy cows, but knowledge on analyzing the association between lameness, milk yield characteristics, and reproductive performance in cows is still insufficient. The main aims of this study were to evaluate the influence of lameness on several AMS variables and reproduc- tive performance indicators during the early stage of lactation and estrus in Lithuanian Black and White dairy cows, as well as to assess the associations between lameness, productivity and repro- ductive efficiency. A total of 418 milking cows (50.3±1.2 d postpartum) without any apparent reproductive disorder were monitored for hoof health status. Cows were assigned to two groups on the basis of visual locomotion scoring: “non-lame“cows (group 1; 74.20%) and cows presen- ting “lameness“ (lame cows) (group 2; 25.80%).

Productive and milking performances of dairy cows were recorded from 50 to 100 days in milk (DIM) and 1 day after the first estrus. The lameness was predominantly localized on the hind feet (79.60%) and less frequently - on the front feet (20.40%; p<0.001). Furthermore, the lameness had a tendency to decrease milk production (4.24%; p<0.05) and increase the diffe- rence in milk yield between rear and front quarters of the udder (1.20%; p<0.05). The frequency of milking (5.19%) was lower in lame cows (p<0.05). The lame cows during estrus showed a more pronounced decrement in milk yield and milking frequency (p<0.05), and also higher milk progesterone concentration values (1.55-1.76 time’s; p<0.001), and an increasing number of inseminations (11.69%; p<0.05) were observed. The results highlighted that analysis of data from AMS programs can be a successful tool for reducing risk factors related to the effective management of reproductive performance and hoof health of dairy cows.

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Authors and Affiliations

G. Urbonavicius
R. Antanaitis
V. Zilaitis
S. Tusas
L. Kajokiene
J. Zymantiene
U. Spancerniene
A. Gavelis
V. Juskiene
V. Juozaitiene

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