## 5 Conclusions  
Based on the results from Rzecin peatland in 2021, the usefulness of a multi-model approach is proven to be more accurate in GPP estimation than single-model approaches with a better statistical performance. Additionally, the main conclusions can be listed as follows:  
* The use of a multi-model approach increases the statistical performance during the year, increasing the significance, reducing errors and information lost (in CL--RMSE: \(-12.3\%\), AIC: \(-11.4\%\), \(p\)-value: \(-52.2\%\); in CR--RMSE: \(-13.6\%\), AIC: \(-13.8\%\), \(p\)-value: \(-48.3\%\)). Further improvement can be accomplished with a higher frequency of chamber campaigns or a greater number of replicates per campaign. * The rectangular hyperbolas plus temperature factors (TRH and TRH_exp) increase the reliability of winter GPP at the start and the exponential models at the end of the year in the site with a non-floating mat (CL). While, for the CR site--floating-mat area with higher presence of mosses--the reliability of the estimated GPP increased using the exponential model at the start of the year and the TRH model at the end of the year. * Using different models to estimate the annual distribution of GPP can change the distribution and the annual cumulative values, with higher oscillations in the case of the _Sphagnum_-dominated floating mat area (CR: \(\pm 5.23\%\); CL: \(\pm 4.56\%\)). These oscillations can change our interpretation of peatlands' sink or source potential, making the correct selection of modelling strategy necessary. * The growing season determined from GPP is displaced for each model approach. Using the classical RH instead of a multi-model approach displaces the start and end of the season by 2 and 3 days, respectively, and shortens the growing season length up to a week. Hence, selecting the best statistical performance is crucial for phenological analysis. Nonetheless, it must be stated that there is no possible way to prove which model retrieved the most accurate annual GPP value or defined the proper growing season, and the conclusions are based on the assumption that the best statistical performance implies the most reliable results. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/su17020393/s1](https://www.mdpi.com/article/10.3390/su17020393/s1)]([https://www.mdpi.com/article/10.3390/su17020393/s1](https://www.mdpi.com/article/10.3390/su17020393/s1)), Table S1: _p_-value results from the Lilliefors test; Table S2: _p_-value results from the Bartlett test; Table S3: Correlation results of models per campaign and plot; Table S4: RMSE results of models per campaign and plot; Table S5: BIAS results of models per campaign and plot; Table S6: _p_-value results of models per campaign and plot; Table S7: AIC results of models per campaign and plot; Table S8: Average statistic results of each model per plot; Table S9: Multimodel statistical results per campaign and plot; Table S10: Weighted statistics for the multi-model result during each campaign per plot. M.A.-S.: conceptualization, investigation, data curation, formal analysis, methodology, visualization, and writing--original draft. M.S. : data curation, investigation, supervision, and writing--review and editing. A.R. : funding acquisition, project administration, supervision, and writing--review and editing. R.J.: conceptualization, formal analysis, investigation, methodology, visualization, resources, funding acquisition, project administration, supervision, and writing--review and editing. All authors have read and agreed to the published version of the manuscript. This research was funded by National Science Centre of Poland (NCN) under projects No.