Assessment of Efficiency of Artificial Insemination Service in Cattle Production in the Western Zone of Tigray Region, Ethiopia

Abstract

The study was conducted before the ignition (before October 2020) of the war in Tigray Region, Ethiopia. Artificial insemination (AI) is the first generation biotechnology and is one of the assisted reproductive techniques (ART) that greatly plays for a faster genetic improvement of farm animals. Though AI was implemented in cattle crossbreeding for many decades in Ethiopia, there is quite insignificant national output of crossbred cattle population (1.54 %). The aim of the survey was to assess the efficiency of AI service in cattle production in the Western Zone of Tigray Region, Ethiopia. Twenty (20) Kebeles and 353 small-scale farmers were purposively involved in the household face-to-face survey interview. The data were analyzed using Statistical Package for Social Sciences (SPSS), and summarized by mean, frequency and percentages. The survey indicated that the existing reproductive efficiency of cattle AI service in the Western Zone of Tigray Region, Ethiopia was extremely poor. The mean number of services per conception (NSC) was very high that could be due to mainly inappropriate heat detections. The recommended, acceptable and excellent grade of NSC fall in the range of 1 − 1.7. The overall conception rate at first insemination (CRFI) was also very poor (20.4 %) and thus calving rate (CR) was poor (20.5 %). About one-third (33.4 %) of the respondents indicated that cows and heifers exhibited heat in 0:01 − 6:00 am of the day. About 30.9 % and 28.6 % of the respondents reported that their cows and heifers were inseminated in 9 − 12 hours and 4 − 8 hours after the onset of heat, respectively. The majority (70.8 %) of the respondents did not practice controlled mating and breeding, and the first preference of 50.1 % of the respondents was natural mating. The most common source of bulls for about 34.3 % of the respondents was from neighbors and followed by own bred bulls (30.0 %). Cattle AI feasibility study and identification of AI strategic interventions should be the first focus to improve the existing poor AI efficiency of the study area.