HEIFER FERTILITY, SIRE SELECTION AND SURVIVAL TRENDS FROM AMR DATA.

Integrative management models are an attractive future option for the South African dairy industry. Based on present and historic data analyses, future herd performance trends and economic feasibility can be anticipated. Predictive modelling is possible from automated herd data and farmers may found the models user-friendly and valuable for managing herds towards an optimal future economic scenario. However, current models require large reference populations to be accurately calibrated. Thus, the objective of the study cited was to explore the functionalities of the Afifarm herd management software from Afimilk, for extracting historical herd performance data with reference to heifer fertility, lifetime performance, and Artificial Insemination (AI) sire records in a pasture-based and total mixed ration (TMR) dairy herd.

Historical herd performance data were analyzed, focusing on heifer fertility, sire rankings, and survivability. Key differences emerged between systems: 36% of heifers calved between 25-26 months in the TMR herd, while 53.5% of pasture-based heifers calved at 22-24 months. Time-trend analysis revealed a declining Age at First Calving (AFC) across both systems, highlighting improvements in heifer rearing and fertility management. Progeny performance analyses strongly correlated sires and offspring survivability (TMR: R² = 0.91, Pasture: R² = 0.97), emphasizing AMS software’s role in sire selection. However, survivability declined with successive lactations, with the highest exits occurring between the heifer phase and third lactation (Pasture: 75%; TMR: 83%).

In conclusion: It was shown that data from the Afifarm herd management software in South African pasture-based and TMR herds can offer valuable insights into herd dynamics. The study suggested that historic time-trend herd data analysis can be a tool among animal scientists, veterinarians, and automated dairy data scientists. Future studies should aim to expand the scope by investigating a broader range of records and including more herds for evaluation. By doing so, one can further validate the effectiveness of automated data in monitoring dairy performance and facilitate research on the South African dairy herds.