Efficiency of Artificial Insemination (AI) Technology in Different dairy Herd Management Systems in the Southern Highland Zone (SHZ) of Tanzania

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  •   Lokoo Cbubby Mwaipopo

  •   Said H. Mbaga

Abstract

The objective of the study was to establish the information on the performance of Artificial Insemination (A.I) in different production systems in the Southern Highlands Zone of Tanzania. Three districts namely, Njombe, Mbeya and Mbozi were purposively selected for data collection in smallholder farms and three large scale farms of ASAS, Sao Hill and Kitulo were selected for data collection. Data from 1486 and 163 dairy cow’s records from large and smallholder farms, respectively were used and analyzed using General Linear Model (GLM) procedure of Statistical Analysis System (SAS). The overall number of services per conception (NSC) were 2.49 and 1.39 in smallholder farms and large farms, respectively. In smallholder farms, NSC was influenced by district, breed, parity, AI technician, age at first service, calving to first service Interval (CFSI) (days) and who detect heat. In large farms NSC was influenced by farm location, source of semen and effect of year. First service to conception (FSC) were 43.56% and 72.48% in smallholder farms and large farms, respectively. It was revealed that the value of NSC and FSC under smallholder farms were lower compared to recommended values, indicating inefficiency of AI performance under smallholder farmer’s conditions in SHZ of Tanzania. Contrary NSC and FSC under larger farms were good indicating that AI is efficiency under large farms in SHZ of Tanzania. Hence there is a need to train smallholder dairy farmers on heat detection and good herd management so as to improve dairy reproductive efficiency in the country.


Keywords: A.I. technician, Age at First Service, First Service to Conception, Number of Service per Conception

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How to Cite
Mwaipopo, L. C., & Mbaga, S. H. (2022). Efficiency of Artificial Insemination (AI) Technology in Different dairy Herd Management Systems in the Southern Highland Zone (SHZ) of Tanzania. European Journal of Agriculture and Food Sciences, 4(2), 11–18. https://doi.org/10.24018/ejfood.2022.4.2.458