A Bayesian inference approach to quantify average pathogen loads in farmyard manure and slurry using open-source Irish datasets

Farm-to-fork quantitative microbial risk assessments (QMRA) typically start with a preliminary estimate of initial concentration (Cinitial) microorganism loading at farm level, consisting an prevalence (P) and the resulting pathogen levels in animal faeces. An average estimation pathogens can be achieved by combining P estimates populations colonised animals' faeces cumulative herd farmyard manure slurry (FYM&S). In present study, 14 years data were collated assessed using Bayesian inference loop to assess likely pathogens. this regard, historical current survey exists on for number pathogens, including Cryptosporidium parvum, Mycobacterium avium subspecies paratuberculosis (MAP), Salmonella spp., Clostridium Campylobacter pathogenic E. coli, Listeria monocytogenes several species (cattle, pigs, sheep) Ireland. The results revealed that spp. has potentially highest mean (Pmean) (25.93%), followed MAP (15.68%) (8.80%) cattle. Pmean coli is (7.42%) while sheep was estimated 7.94%. Cinitial MAP., cattle derived 2.69, 4.38, 4.24, 3.46, 3.84 log10 MPN g ?1, respectively. Average pig as 1.27, 3.12, 3.02, 4.48 It only possible calculate 1.86 ?1. This study creates basis future farm-to-fork assessment models base values enhance efforts.