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Simon Elieser
National Research and Innovation Agency (BRIN) Cibinong

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Artificial Insemination Performance Across Different Altitudinal Ecosystems in Serdang Bedagai District, North Sumatera Widiya Mahfuza; Usman Budi; Simon Elieser
Jurnal Agripet Volume 26, No. 1, April 2026
Publisher : Animal Husbandry Department, The Faculty of Agriculture, Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17969/agripet.v26i1.810

Abstract

Artificial insemination (AI) is one of the key reproductive technologies in beef cattle development, as its success is influenced by multiple factors, including breeder characteristics, technical management, and environmental conditions. Therefore, this study aimed to evaluate the performance of AI based on non-return rate (NRR), conception rate (CR), and services per conception (S/C) across highland, midland, and lowland areas of the Serdang Bedagai District, and to determine the effects of the number of female cattle, body condition score (BCS), timing of insemination, breeder’s ability to detect estrus, location distance, air temperature, and humidity on AI success. The study was conducted from July to August 2024 in three different topographical areas of Serdang Bedagai Regency: the highland area in the Kotarih District, the midland area in the Bintang Bayu District, and the lowland area in the Tanjung Beringin District. A total of 86 respondents participated in this study. Data analysis was performed using multiple linear regression analysis. Based on the analysis of AI success indicators, the highlands showed the highest conception rate (CR = 67), followed by the midlands (CR = 63%) and lowlands (CR = 48%). The NRR in the highlands, midlands, and lowlands were 59 %, 52%, and 63 %, respectively. The Service per conception (S/C) values were 1.5 in the highlands and midlands and 2.1 in the lowlands, indicating better reproductive efficiency at higher altitudes. Based on the analysis of the NRR, CR, and S/C of the Artificial Insemination program, it was shown that highland and midland areas performed better than lowland areas. Based on the results of the multiple regression analysis, the observed variables simultaneously influenced the number of pregnant cows by 64.3% (R² = 0.643), while the remaining 35.7% was attributed to other factors not examined in this study.