Routinely collected herd management data in a variety of formats were collated from 468 dairy herds, and novel objective measures of data recording quality were developed and applied. This revealed that there was a substantial amount of variation in data quality between herds, and the vast majority of herds failed to meet the threshold level for at least one of the data quality measures used. Analysis of trends in reproductive performance across the herds with good quality fertility event recording suggested that their fertility was generally declining through the first half of the 2000s, but there was some evidence that improvements in submission rate were beginning to reverse this decline in the later years studied (up to 2007). Associations between reproduction and two endemic diseases common in dairy cattle (mastitis and lameness) were explored using multilevel discrete time survival modelling, and probabilistic sensitivity analysis (PSA) used to contextualise and illustrate the results. In both cases, statistical modelling revealed significant and sizeable associations between disease events and reproductive outcomes at lactation level. However, simulation and application of PSA showed that a herd’s incidence rate of either disease was highly unlikely to influence its overall reproductive performance to a clinically relevant degree when other inputs to herd fertility were also considered. Factors associated with the proportion of serves leading to a pregnancy (pregnancy rate) were explored using multilevel logistic regression modelling. This revealed that relatively little of the variation in herd pregnancy rate is explainable by routinely recorded milk recording data (including constituent concentration in early lactation as well as daily and lactation yields). A large amount of the unexplained variation was revealed to be at herd level and very little at cow level, suggesting that investigation of herd management practices associated with pregnancy rate would be rewarding.