Strategic Management Mapping and Reproduction Optimization for Artificial Insemination Success in Cattle Breeding: A Case Study in Dompu Regency, Indonesia

Abstract

Artificial Insemination (AI) success rates in tropical smallholder cattle farms are highly variable and frequently bottlenecked by localized technical and socio-economic management issues. This study analyzed the internal and external strategic determinants influencing AI operational dynamics, established the systemic positioning of AI services and developed comprehensive reproductive management priorities for breeding farms in Dompu Regency, Indonesia. Using a case study framework from December 2025 to February 2026, data were gathered from 101 respondents (96 smallholder recipient farmers and a 5 expert Pentahelix panel). Strategic positioning was quantified via Internal Factor Evaluation (IFE), External Factor Evaluation (EFE) and Internal-External (IE) matrix modeling, while specific strategic alignments were extracted through SWOT and prioritized using the Quantitative Strategic Planning Matrix (QSPM). The quantitative evaluation revealed a total IFE score of 2.61 and an EFE score of 2.76, strictly locating the region's AI ecosystem within Cell V (hold and maintain) of the IE matrix. Critical functional weaknesses included deficient nutritional flushing regimens and compromised estrus recording protocols, while seasonal crop residual availability (corn straw biomass) represented a substantial external opportunity. QSPM analysis prioritized 12 strategic pathways, designating localized self-reliance in tools and raw infrastructure, specifically stabilizing liquid Nitrogen (N2) cold chain logistics as the paramount intervention (Total Attractive Score: 2.88).