Factors associated with artificial insemination success and perceived economic sustainability among AI-participating smallholder beef cattle farmers in two sub-districts of Sinjai District, Indonesia - MUH HAWIS HAKIM; AHMAD RAMADHAN SIREGAR; SYAHDAR BABA; DARMAWAN RISAL; MUH HAIDIR HAKIM; AHMAD SYAKUR (Smujo International, 2026)
Abstract. Hakim MH, Siregar AR, Baba S, Risal D, Hakim MH, Syakur A. 2026. Factors associated with artificial insemination success and perceived economic sustainability among AI-participating smallholder beef cattle farmers in two sub-districts of Sinjai District, Indonesia. Asian J Agric 10 (2): g100203. https://doi.org/10.13057/asianjagric/g100203. Artificial Insemination (AI) is an important reproductive technology for improving genetic quality and production efficiency in beef cattle (Bos spp.), but its success among smallholder farmers remains variable. This study aimed to examine farmer-, farm-management-, and service-level factors associated with AI success and its relationship with perceived economic sustainability among AI-participating smallholder beef cattle farmers in Sinjai Tengah and Tellulimpoe Sub-districts, Sinjai District, Indonesia. A cross-sectional survey was conducted from October to December 2025 involving 94 beef cattle farmers who had used AI services in Sinjai Tengah and Tellulimpoe Sub-districts. Data were collected through interviewer-administered, structured face-to-face interviews using a standardized questionnaire and analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM) with SmartPLS 4. AI success was operationalized as a standardized farm-level reproductive-performance construct based on services per conception, conception rate, and calving rate. Services per conception was reverse-coded so that higher standardized indicator values consistently represented more favorable reproductive performance. The results showed that inseminator performance had the strongest association with AI success (β = 0.487; p < 0.001), followed by farmer knowledge (β = 0.337; p = 0.002), facilities and infrastructure (β = 0.167; p < 0.001), financial capability (β = 0.134; p = 0.033), and husbandry system (β = 0.073; p = 0.030). Institutional support and access to AI services showed positive but non-significant associations. AI success was positively associated with perceived economic sustainability (β = 0.779; p < 0.001). The model explained 92.8% of the variance in AI success (R² = 0.928) and 60.7% of the variance in perceived economic sustainability (R² = 0.607). These findings indicate that AI success was associated with several farmer-, farm-management-, and service-level factors within the sampled population. Because the study was cross-sectional and limited to AI-participating farmers in two purposively selected sub-districts, the findings should not be interpreted as causal or generalized to all beef cattle farmers in Sinjai District.