Evaluation of effective management practices on pregnancy rate in several herds of ‎Holstein dairy cows in Iran

Document Type : Research Paper

Authors

1 Ph.D. Candidate, Department of Animal Science, Faculty of Agriculture, Isfahan University of ‎Technology, Isfahan, Iran‎

2 Associate Professor, Department of Animal Science, Faculty of Agriculture, Isfahan University of Technology, Isfahan, Iran‎

Abstract

Pregnancy rate is an important trait in productivity of the dairy herds. To evaluate the effective management practices on pregnancy rate of Iranian Holstein herds, the reproductive management data of 60 herds (Tehran, Isfahan, Alborz, and Mashhad provinces) were collected using a questionnaire based on herd management on 2017. Statistical analysis was performed using the mixed model procedure (Proc MIXED) of SAS software, considering the management factors and region as fixed effects and herd nested in the region, as a random effect. The average number of time per day cows observed for heat was 5.7±0.5 and 11.2±1.3 minutes per time, and 80 % of farms used synchronization. Infertility problems were the reasons of 46% of the total culling rate. Also, cows with milk yield less than 17.2±0.9 kg were removed from the herd. The mean pregnancy rate of herds was 22±0.3%. Pregnancy rate was influenced by management factors such as productive herd size, production level, age and weight of heifers as the criteria for first insemination, number of active expert people in reproduction management criteria, milk yield for culling cows that do not get pregnant, genetic aspect of fertility of bulls, single observer for heat detections, frequency of pregnancy examination during pregnancy period and method of pregnancy examination. The results obtained in this study can provide useful information to benchmark Iran dairy farms for reproductive performance.

Keywords

References

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Iranian Journal of animal Science
Volume 51, Issue 4
March 2021
Pages 253-262

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History

  • Receive Date: 13 April 2020
  • Revise Date: 06 November 2020
  • Accept Date: 11 November 2020

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