Investigation into the production and conformation traits associated with clinical mastitis using artificial neural networks

A data set comprising milk-recording and conformation data was used to inve stigate the usefulness of artificial neural networks in detecting influenti al variables in the prediction of incidences of clinical mastitis. Specific ally, these data contained test-day records from dairy herd analysis, pheno typic cow scores for conformation and genetic conformation proofs for cows and their sires. The data were analysed using the milk-recording data only, the conformation data only, and a combination of the two. Results from sen sitivity analyses, performed with trained neural nets, indicated that stage of lactation, milk yield on test day, cumulative milk yield and somatic ce ll count were the major production factors influencing the ability to detec t the occurrence of clinical mastitis. Among the conformation traits, such variables as phenotypic scores for rear-teat placement, dairy character and size, cow proof for dairy character, sire reliability for final score and sire proofs for pin-setting (desirability) and loin strength were found to have some influence on the network's predictive ability, although they were all very minor in relation to the production variables mentioned. As a gro up, cow genetic proofs seemed more important than either sire genetic proof s or cow phenotypic scores. Given the neural network's general abilities to determine the major factors related to the presence or absence of mastitis on a given test day, it may be appropriate to investigate the possibility of using this technology for actual prediction purposes.