Simple SummaryMeeting the water requirements of fattening pigs is crucial for animal welfare in successful animal husbandry. At the same time, barn climate has a significant influence on the water requirements of fattening pigs. Equations for the estimation of water consumption are useful in this context. However, it is questionable whether existing equations match modern pig genetics and housing conditions. Therefore, the aim of this study was to evaluate the water consumption of modern fattening pigs and compare the measured values with the predicted values using equations from the literature. It was found that the measured water consumption was, in most cases, higher than the calculated one. As a result, six new equations for the prediction of water consumption were derived and discussed. More data are needed to further specify these new equations.AbstractThe water consumption of fattening pigs was recorded under practical conditions and compared with calculated water consumption. The experiment was carried out in the summer of 2020 with 79 fattening pigs. Data loggers were used to record the climate data, such as temperature and relative humidity. These data were used to calculate the temperature-humidity index (THI). It was found that there were sometimes considerable discrepancies between the measured and the calculated water consumption. One possible reason for this discrepancy could be the age of the existing water requirement equations, as in recent decades there has been a clear breeding development and thus a strong increase in pig performance. Based on these deviations, six new water consumption equations were established, which considered the variables body weight (BW), temperature, THI and feed consumption. It was found that the THI and BW should be included in one equation as predictor variables and the evaluation also showed good results. Its use, in practice, should also be considered. Overall, it became apparent that there is still a need for further research to make water consumption equations more precise. This would require a larger database.