Variability in reproductive performance is a key aspect in dairy goat herds: it affects the distribution of physiological stages and therefore the way animals will respond to their environmental conditions (resource availability, thermal environment) both in terms of milk production (MP) and body reserves utilization. As a feedback, next reproductive cycle can be affected. Individual lifetime trajectory is thus dependent on the dynamic interplay between reproduction, MP and body reserves. Understanding this interplay is a crucial issue to optimize feeding and reproductive management strategies, under current farm environmental constraints but also under future constraints imposed by climate change impacts. The objective of this work was to evaluate the effects of MP and body reserves utilization on the success at artificial insemination (AI) in dairy goats. Routine data from an experimental station in South France (Le Pradel, French Livestock Institute) were used. The dataset included 574 Alpine goats (1,096 lactations from parity 1 to 9) over 25 years (1996 to 2021). The AI success of each goat was calculated using the interval between AI and next kidding (an interval of 160 days or less is considered as success at AI). A logistic regression model was used to analyse the relationships between AI success and individual factors (parity, stage of lactation at AI, failure at previous AI), thermal conditions (maximum temperature and THI around AI), MP (maximum MP during lactation, MP around AI, somatic cell count around AI, variation of MP 6 weeks before AI) and body reserves (body weight (BW) and body condition score (BCS) around AI, variation of BW and BCS 12 weeks before AI). Average AI success was 69% (±11%). AI success was significantly affected by MP around AI, lumbar BCS around AI, lumbar BCS dynamic before AI and maximum temperature. In contrast, classical factors of variation in AI success such as lactation rank, previous AI success or THI had no effect. These results will contribute to identify animals that best cope with environmental constraints and better manage animals at risk of reproductive failure.