An accurate prediction of the average somatic cell count (SCC) of the next month would be a valuable tool to support udder health management decisions. In this study, a Linear Mixed Effect (LME) model was used to predict the average herd SCC (HSCC) of the following month. The LME model included data on SCC, herd characteristics, season and management practices determined in a previous study which quantified the contribution of each factor for the HSCC. The LME model was tested on a new dataset of 101 farms and included data from three consecutive years. The 101 farms were split at random in two groups of 50 and 51 farms. The first group of 50 farms was used to check for systematic errors in predicting monthly HSCC. An initial model was based on older data from a different part of The Netherlands and systematically overestimated HSCC in most months. Therefore, the model was adjusted for the difference in average HSCC between the two sets of farms (previous and current study) using the data from the first group of 50 farms. Subsequently, the data from the second group of 51 farms were used to independently assess this final model. A null model (no explanatory variables included) predicted 48 and 59% of the HSCC within the predetermined range of 20,000 and 30,000/mL, respectively. The final LME model predicted 72 and 81% of the HSCC of the next month correctly within these two ranges. These outcomes indicate that the final LME model is a valid additional tool for farmers which could be useful in their short-term decisions regarding udder health management and could be included in dairy herd health programs.
|Number of pages||8|
|Journal||Journal of Dairy Science|
|Publication status||Published - Jan 2010|