Growing season average temperature range is the optimal choice for Q10 incubation experiments of SOM decomposition.

• Setting the incubation temperature range (ITR) is essential for experiments on Q 10. • We addressed this using 54 soils from typical ecosystems under nine ITR scenarios. • ITRs have a significant impact on Q 10 in different ecosystem or soil types. • The growing season average temperature range is the optimum ITR across ecosystems. • ITR should be considered as a key parameter for Q 10 estimation in the future. Temperature sensitivity (Q 10) of soil organic matter (SOM) decomposition is an essential parameter that reflects the feedback relationship between climate warming and atmospheric CO 2 concentration and plays a key role in accurately estimating changes in soil carbon pools and their feedback to climate change. Setting the incubation temperature range (ITR) scenario is essential to accurately estimate the temperature sensitivity of SOM decomposition; however, this has been widely ignored. To address this issue, we conducted a systematic incubation experiment using 54 soils covering the most typical ecosystems in China and nine ITR scenarios. The results showed that ITR scenarios had a significant effect on Q 10 in different ecosystems and soil types. Combining the results of fitting mean annual temperature (MAT) to the Q 10 of different ITR and the interpretation rate of factors (including climate, soil, and microbial community composition) to the Q 10 of different ITR, the results indicate that the growing season average temperature range (GSA) scenario had better performance than the other ITR scenarios and should be the optimum ITR scenario. Furthermore, the variation of main influence factors of Q 10 across different ecosystems should be accounted for to accurately predict the feedback between soil C cycle and climate change. Overall, our findings highlight the importance of the ITR for the estimates of Q 10 using widespread experimental data, providing a reference for subsequent experiments accurately measure Q 10 , as well as compare and compile global data to better predict the feedback between the global carbon cycle and climate change. [ABSTRACT FROM AUTHOR]