What type of study is this?

This is a Bivariate Probit Regression study.

September 10, 2025Open Access

Factors Affecting the Adoption of Estrus Synchronization and Artificial Insemination Service in Selected Areas of Southern Ethiopia

Key Points

Education level and access to AI stations significantly impact adoption of artificial insemination and estrous synchronization services.
Challenges include delays by AI technicians and shortages of supplementary feed, which hinder effective service delivery.
Econometric analysis used bivariate probit regression to identify determinants of technology adoption in the region.
Continuous training and focus on resource provision are essential for improving adoption rates of AI and estrus synchronization.

Abstract

Aims: Ethiopia has the largest livestock population in Africa. To enhance the benefits of cattle, AI technologies have long been introduced into the country to improve the genetic potential of the indigenous cattle population. However, lack of recording scheme, wrong selection procedures, poor management of AI bulls, poor motivation and skills of inseminators are gaps in country and regional level. Therefore, this study was initiated to analyze factors affecting adoption of estrus synchronization and artificial insemination service in selected areas of Southern Ethiopia. Place and Duration of the Study: Southern Ethiopia from 2024 to 2025. Methodology: In total, 141 sample households were selected based on a systematic random sampling technique. The bivariate probit regression model was used to analyze the econometric data. Results: Education level, distance to AI station, heard information on failure of AI, mobile ownership, total family, and extension contact frequency were significant determinants of adoption of artificial insemination and estrous synchronization technology. Delay of AI technicians, shortage of supplementary feed, insufficient on-farm implementation of the services, indiscriminate application of AI, encountered failure, and insufficient equipment are challenges. In addition, inputs for the delivery of service, efficiency and specialization problems, shortage of technicians and limitation of access are constraints for sustainable utilization of AI and ES service. Conclusion: continuous training for AI technicians and farmers, focusing on on-farm implementation, fulfilling inputs and equipment, helping AITs to specialize on the area are required. In addition, working on improved/supplementary feed, provision of manuals for AITs in local language and raising awareness of farmers are necessary. Smoothening communication of farmers with AI technicians, and making AI centers functional at every time are also recommended.

Read Full Paperexternally

Bookmark

View Full Paper

Cite This Study

Alemu et al. (Mon,) studied this question.

synapsesocial.com/papers/68c1ae7054b1d3bfb60e6467 https://doi.org/https://doi.org/10.9734/acri/2025/v25i81414

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

Bookmark

View Full Paper