Mycoplasma bovis infections affect dairy cattle worldwide, yet few countries have implemented surveillance, control, or monitoring programs. Critical gaps in understanding the disease dynamics, transmission, and epidemiology persist. Additionally, diagnosing M. bovis is challenging due to suboptimal diagnostic tests, subclinical infections and intermittent shedding, making it difficult to identify infected animals. In Chapter 2, an evidence-based evaluation of carbon dioxide (CO2) requirements in culture protocols revealed that a wider range of CO2 conditions than previously described supports M. bovis growth, and CO2 may not be necessary at all. Chapter 3 explored age-stratified transmission dynamics of M. bovis across 20 dairy farms using a Susceptible – Infected – Removed (SIR) model, discovering significant heterogeneity in basic reproduction numbers (R0) and highlighting the role of youngstock as a potential transmission reservoir. These findings were reinforced in Chapter 4, where Bayesian latent class analysis was coupled with individual level transmission modeling, which accounts for ‘prior knowledge’ on varying diagnostic test performances such as the poor sensitivity (Se) and good specificity (Sp), demonstrating improved data fit, and provided more accurate R0 estimates, though farm-to-farm variability remained. The heterogeneity observed in the transmission dynamics may be explained by strain diversity within M. bovis. In Chapter 5, a method was developed to identify strain differences. Using mock milk-samples with pure and mixed infections, and various enrichment proportions, Themisto and mSWEEP outperformed the widely used Kraken2 tool in accurately identifying M. bovis strains in silico. Enrichment of M. bovis DNA to at least 30% of the sequenced reads is enough to obtain PSV level data. Finally, Chapter 6 presents a literature review of theories and methods used to study farmer behaviour in relation to cattle disease control measures. Whereas many studies focused on personal and interpersonal factors influencing the adoption of measures, none addressed actual farmer behaviour change or broader contextual influences. A good understanding of farmers’ motivators and barriers, and what is needed to drive real change is essential before proposing any animal disease control program.