The economic impact of subclinical ketosis at the farm level: Tackling the challenge of over-estimation due to multiple interactions.

Subclinical ketosis (SCK) is a major metabolic disorder that affects dairy cows, and its lactational prevalence in Europe is estimated to be at 25%. Nonetheless, few data are available on the economics of SCK, although its management clearly must be improved. With this in mind, this study develops a double-step stochastic approach to evaluate the total cost of SCK to dairy farming. First, all the production and reproduction changes and all the health disorders associated with SCK were quantified using the meta-analysis from a previous study. Second, the total cost of SCK was determined with a stochastic model using distribution laws as input parameters. The mean total cost of SCK was estimated to be Є257 per calving cow with SCK (95% prediction interval (PI): Є72-442). The margin over feeding costs slightly influenced the results. When the parameters of the model are not modified to account for the conclusions from the meta-analysis and for the prevalence of health disorders in the population without SCK, the mean cost of SCK was overestimated by 68%, reaching Є434 per calving cow (95%PI: Є192-676). This result indicates that the total cost of complex health disorders is likely to be substantially overestimated when calculations use raw results from the literature or-even worse-punctual data. Excluding labour costs in the estimation reduced the SCK total cost by 12%, whereas excluding contributors with scarce data and imprecise calibrations (for lameness and udder health) reduced costs by another 18-20% (Є210, 95%PI=30-390). The proposed method accounted for uncertainty and variability in inputs by using distributions instead of point estimates. The mean value and associated prediction intervals (PIs) yielded good insight into the economic consequences of this complex disease and can be easily and practically used by decision makers in the field while simultaneously accounting for biological variability. Moreover, PIs can help prevent the blind use of economic results in the field when only the mean value is considered.