Dictyocaulus viviparus bulk tank milk seropositivity is correlated with meteorological variables. Issue 10 (September 2022) - Vanhecke, M.; Charlier, J.; Hamdi, R.; Duchêne, F.; Strube, C.; Claerebout, E.
Graphical abstract: Highlights: There were significant correlations between meteorological variables and D. viviparus bulk tank milk ELISA results (ODR). Fine scale meteo model had a good predictive value on the ODR . Addition of the previous ODR further increased predictive power. Results open new avenue for development of preventive control approaches. Abstract: Control of infections with Dictyocaulus viviparus is difficult due to its volatile epidemiology. In the absence of predictive models, 'vigilance and treatment' is today's mainstay for control. In order to evaluate the potential of predictive model development to support a more preventative approach, this longitudinal study aimed at understanding the influence of weather factors on D. viviparus bulk tank milk antibody ELISA results. Bulk tank milk samples were analysed with a Major Sperm Protein-based ELISA (expressed as an optical density ratio) twice monthly on 717 Flemish dairy farms during the grazing season (April–October) in 2018. Meteorological data of the sampled farms were obtained at 1 km spatial scale using the ALARO-SURFEX climate model. A mixed effects model showed that the bulk tank milk optical density ratio was significantly associated with the month of sampling, evapotranspiration, temperature and its quadratic term, the number of hot days and the number of rainy days in the 7–8 weeks prior to sampling. There were significant farm effects involved. The model's accuracy to predict bulk tank milk optical density ratio infection status was 80%, while optical density ratios were generally overestimated by 38%. Inclusion of the previous (2-week-old) optical density ratio values increased accuracy to 86% and reduced the mean square error. We conclude that meteorological parameters have a predictive value for bulk tank milk optical density ratio results, while further research should evaluate model improvements through the addition of herd management factors as well as confirm the predictive power through external validation in additional farms and years.