Study on Factors Affecting Estrus Synchronization in Smallholder Dairy Farming Systems of Tigray, Northern Ethiopia - Dagmawit Kibre; Gebregiorgis Ashebir; Berihu Gebrekidan; Haben Fesseha (Veterinary Medicine International)
Background. Reproductive biotechnology, such as estrus synchronization, can quickly boost the genetics of local cattle breeds, shorten generational cycles, and spread genetic material within populations of breeding animals. Methods. A cross-sectional study was performed on 154 purposively selected smallholder dairy owners using a semistructured questionnaire to assess factors that influence the success of estrus synchronization in smallholder dairy farms located in Agula, Wukro, and Enderta districts, Northern Ethiopia. Results. The estrus synchronization programme was positively accepted by 39.6% of illiterate participants. However, the education level and marital status of the participants had no significant association (p>0.05) between the different study sites. On the other hand, approximately 10% of interviewees did not have awareness of dairy cattle estrus synchronization technology at the time of implementation, whereas 36 (23.4%) farmers who were aware of estrus synchronization gave negative feedback on the technology. Factors such as breed, management system, feed type, feeding, and watering frequency significantly varied (p<0.05) among the three study sites, whereas the breeding practice had no significant association (p>0.05) within these districts. The cause of failure during AI, awareness about synchronization, satisfaction with the AI, and estrus synchronization service have a significant variation (p<0.05) within the three districts. On the other hand, the cause of AI failure, awareness about AI programs, source of synchronization information, and AI programs had no significant association (p>0.05) with study sites. Anestrous (30.5%) and repeat breeders (38.9%) were among the causes of the low conception rate during the synchronization program. There were no significant variations in terms of AI program constraints in the three districts. Conclusion. AI technicians and farm owners need continuous training to improve their heat detection skills, increase their knowledge, and obtain a successful program.