Climate change presents an unprecedented global challenge, demanding collective action to both mitigate its effects and adapt to its consequences. Soil health and function are profoundly impacted by climate change, particularly evident in the sensitivity of soil microbial respiration to warming, known as Q10. Q10 measures the rate of microbial respiration’s increase with a temperature rise of 10 degrees Celsius, playing a pivotal role in understanding soil carbon dynamics in response to climate change. Leveraging machine learning techniques, particularly explainable artificial intelligence (XAI), offers a promising avenue to analyze complex data and identify biomarkers crucial for developing innovative climate change mitigation strategies. This research aims to evaluate the extent to which chemical, physical, and microbiological soil characteristics are associated with high or low Q10 values, utilizing XAI approaches. The Extra Trees Classifier algorithm was employed, yielding an average accuracy of (Formula presented.), an average AUCROC of (Formula presented.), and an average AUCPRC of (Formula presented.). Additionally, through XAI techniques, we elucidate the significant features contributing to the prediction of Q10 classes. The XAI analysis shows that the temperature sensitivity of soil respiration increases with microbiome variables but decreases with non-microbiome variables beyond a threshold. Our findings underscore the critical role of the soil microbiome in predicting soil Q10 dynamics, providing valuable insights for developing targeted climate change mitigation strategies. © 2024 by the authors.

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