Rini Mastuti, Reza Setiawan, Kiagus M Zain Basriwijaya
{"title":"Factors Supporting the Success of Artificial Insemination in Beef Cattle in East Langsa District Langsa City","authors":"Rini Mastuti, Reza Setiawan, Kiagus M Zain Basriwijaya","doi":"10.37149/jia.v8i3.633","DOIUrl":null,"url":null,"abstract":"Cattle farming plays a crucial role in ensuring food security and meeting the protein requirements of humans. It is not only appealing due to its simplicity in care but also because it can make use of plant-based feed. Artificial insemination (AI) can be employed to boost the cattle population. This study aimed to analyze the factors influencing the success of AI in beef cattle within the East Langsa District of Langsa City. The sampling method utilized was purposive sampling, involving 42 respondents. Statistical analysis involved classical assumption tests, multiple linear regression, and hypothesis testing to identify the factors impacting the success of AI in beef cattle. Data processing was conducted using the SPSS computer software. The results revealed that the AI tool variables (X2), including inseminator origin, inseminator service, AI success rate, and AI equipment, had a significant effect (significance value 0.010) on the success of artificial insemination (Y). Similarly, the inseminator variable (X4), involving inseminator ability without special education, AI special education, inseminator ability to detect cattle in heat, and the inseminator's capability to insert frozen semen into the uterus even when the animal is not in heat, demonstrated a significant impact. Conversely, examining the livestock condition variable (X1), such as no sick cows in the last year, cows in good health before AI, and yearly calving, did not exhibit a significant effect (significance value 0.816). Further research is urgently required, incorporating additional variables to strengthen the factors that can enhance artificial insemination and increase livestock production.","PeriodicalId":14834,"journal":{"name":"JIA (Jurnal Ilmiah Agribisnis) : Jurnal Agribisnis dan Ilmu Sosial Ekonomi Pertanian","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"JIA (Jurnal Ilmiah Agribisnis) : Jurnal Agribisnis dan Ilmu Sosial Ekonomi Pertanian","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37149/jia.v8i3.633","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Cattle farming plays a crucial role in ensuring food security and meeting the protein requirements of humans. It is not only appealing due to its simplicity in care but also because it can make use of plant-based feed. Artificial insemination (AI) can be employed to boost the cattle population. This study aimed to analyze the factors influencing the success of AI in beef cattle within the East Langsa District of Langsa City. The sampling method utilized was purposive sampling, involving 42 respondents. Statistical analysis involved classical assumption tests, multiple linear regression, and hypothesis testing to identify the factors impacting the success of AI in beef cattle. Data processing was conducted using the SPSS computer software. The results revealed that the AI tool variables (X2), including inseminator origin, inseminator service, AI success rate, and AI equipment, had a significant effect (significance value 0.010) on the success of artificial insemination (Y). Similarly, the inseminator variable (X4), involving inseminator ability without special education, AI special education, inseminator ability to detect cattle in heat, and the inseminator's capability to insert frozen semen into the uterus even when the animal is not in heat, demonstrated a significant impact. Conversely, examining the livestock condition variable (X1), such as no sick cows in the last year, cows in good health before AI, and yearly calving, did not exhibit a significant effect (significance value 0.816). Further research is urgently required, incorporating additional variables to strengthen the factors that can enhance artificial insemination and increase livestock production.