## 5 Conclusions  
Correlation analysis indicated that in the case of a naturally ventilated cattle building with regularly scraped solid concrete floor particularly the day-time components, and to a minor degree the squared ambient temperature and the ambient temperature, can explain the variability in the emission of methane. In addition, wind speed, wind direction and the year time components modulated the emission pattern. In contrast to other pollutant emissions, such as ammonia, the linkage to the logarithm of the methane emission was lower than to the methane emission itself. Thus, the methane emission value itself should be considered as output variable in the modeling process. It should be noted that the relevant features might be different in husbandry systems where the manure is not almost immediately removed. Our initial assumption that the additional level of complexity due to the two different indoor sources of methane and the non-monotonic influence of temperature may further decrease the accuracy of empirical modeling of methane emissions compared to those of ammonia when applying standard multilinear models could not be verified. With the above mentioned selection of features and a suitable data normalization strategy, most modeling approaches, even the simple multilinear regression, reached in general acceptable agreement with the aggregated emission value, where in most cases the relative deviation was below 10% (confirming Hypothesis 1). In the case of multilinear regression, the results were even better when only the day-time components were included as input features. The simple neural network without hyperparameter tuning obtained the worst results followed by the Gaussian processes. Multilinear regression and neural networks with hyperparameter tuning performed better for scenarios with six measurement periods, but also exhibited high variability for scenarios with fewer measurement periods when all features were included. The results from multilinear regression with regularization (all features included) and from support vector machines were comparable to the former mentioned in the case of six measurement periods, but showed less variability for scenarios with fewer measurement periods. The ensemble methods gradient boosting and random forest resulted in on average small deviations from the actual value combined with the least variable results and can thus be considered very robust in terms of the selection of individual training data within a temporal sampling scenario. Moreover, they had the added value to be rather insensitive against the normalization procedure and the inclusion of nonlinearly related input features. While a recommendation of six times 7 days of measurements seems to be rather general applicable, in specific situations and setups also smaller training datasets were able to achieve good modeling results for methane emissions. We observed that, when using all input features, the ensemble methods with reduced measurement setups (three to four measurement periods) achieved similar accuracy as the multilinear regression with extended measurement setups with six measurement periods (confirming Hypothesis 2). We found that most of the best performing methods focused on training data from transition periods. Our results further support previous findings that scenarios with 7 and 14 days result in a comparable model performance. In consequence, measurement protocols with 7 days and six measurement periods can be considered sufficient, particularly when periods were distributed over the year with a preference for transition periods and features were normalized according to median and quartile range. Either gradient boosting should be used for modeling or only input features that are strongly linearly related to the methane emission should be used for modeling. Nonetheless, with a coefficient of determination of about 66% even the best models in this study could not explain about one third of the variability in the emission data.