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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\u2019s 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 0.923\u00b10.009, an average AUCROC of 0.964\u00b10.004, and an average AUCPRC of 0.963\u00b10.006. 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.<\/jats:p>","DOI":"10.3390\/make6030075","type":"journal-article","created":{"date-parts":[[2024,7,16]],"date-time":"2024-07-16T11:31:57Z","timestamp":1721129517000},"page":"1564-1578","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Climate Change and Soil Health: Explainable Artificial Intelligence Reveals Microbiome Response to Warming"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8773-0636","authenticated-orcid":false,"given":"Pierfrancesco","family":"Novielli","sequence":"first","affiliation":[{"name":"Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Universit\u00e0 degli Studi di Bari Aldo Moro, 70125 Bari, Italy"},{"name":"Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125 Bari, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9119-5638","authenticated-orcid":false,"given":"Michele","family":"Magarelli","sequence":"additional","affiliation":[{"name":"Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Universit\u00e0 degli Studi di Bari Aldo Moro, 70125 Bari, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3828-0469","authenticated-orcid":false,"given":"Donato","family":"Romano","sequence":"additional","affiliation":[{"name":"Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Universit\u00e0 degli Studi di Bari Aldo Moro, 70125 Bari, Italy"},{"name":"Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125 Bari, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2425-6983","authenticated-orcid":false,"given":"Lorenzo","family":"de Trizio","sequence":"additional","affiliation":[{"name":"Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Universit\u00e0 degli Studi di Bari Aldo Moro, 70125 Bari, Italy"}]},{"given":"Pierpaolo","family":"Di Bitonto","sequence":"additional","affiliation":[{"name":"Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Universit\u00e0 degli Studi di Bari Aldo Moro, 70125 Bari, Italy"}]},{"given":"Alfonso","family":"Monaco","sequence":"additional","affiliation":[{"name":"Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125 Bari, Italy"},{"name":"Dipartimento Interateneo di Fisica \u201cM. Merlin\u201d, Universit\u00e0 degli Studi di Bari Aldo Moro, 70125 Bari, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0211-0783","authenticated-orcid":false,"given":"Nicola","family":"Amoroso","sequence":"additional","affiliation":[{"name":"Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125 Bari, Italy"},{"name":"Dipartimento di Farmacia-Scienze del Farmaco, Universit\u00e0 degli Studi di Bari Aldo Moro, 70125 Bari, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9689-7649","authenticated-orcid":false,"given":"Anna Maria","family":"Stellacci","sequence":"additional","affiliation":[{"name":"Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Universit\u00e0 degli Studi di Bari Aldo Moro, 70125 Bari, Italy"}]},{"given":"Claudia","family":"Zoani","sequence":"additional","affiliation":[{"name":"Dipartimento Sostenibilit\u00e0, Circolarit\u00e0 e Adattamento al Cambiamento Climatico dei Sistemi Produttivi e Territoriali, Divisione Biotecnologie e Agroindustria\u2014ENEA, C.R. Casaccia, 00123 Rome, Italy"}]},{"given":"Roberto","family":"Bellotti","sequence":"additional","affiliation":[{"name":"Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125 Bari, Italy"},{"name":"Dipartimento Interateneo di Fisica \u201cM. Merlin\u201d, Universit\u00e0 degli Studi di Bari Aldo Moro, 70125 Bari, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1372-3916","authenticated-orcid":false,"given":"Sabina","family":"Tangaro","sequence":"additional","affiliation":[{"name":"Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Universit\u00e0 degli Studi di Bari Aldo Moro, 70125 Bari, Italy"},{"name":"Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125 Bari, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Allen, D.E., Singh, B.P., and Dalal, R.C. (2011). Soil health indicators under climate change: A review of current knowledge. Soil Health and Climate Change, Springer.","DOI":"10.1007\/978-3-642-20256-8_2"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lal, R. (2011). Soil health and climate change: An overview. Soil Health and Climate Change, Springer.","DOI":"10.1007\/978-3-642-20256-8_1"},{"key":"ref_3","first-page":"2399","article-title":"Impact of climate change on soil health: A review","volume":"6","author":"Patil","year":"2018","journal-title":"Int. J. Chem. Stud"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1038\/s41558-021-01068-9","article-title":"Global patterns of geo-ecological controls on the response of soil respiration to warming","volume":"11","author":"Haaf","year":"2021","journal-title":"Nat. Clim. Change"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/bs.agron.2015.12.003","article-title":"Organic farming, soil health, and food quality: Considering possible links","volume":"137","author":"Reeve","year":"2016","journal-title":"Adv. Agron."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"eaav0550","DOI":"10.1126\/science.aav0550","article-title":"The global soil community and its influence on biogeochemistry","volume":"365","author":"Crowther","year":"2019","journal-title":"Science"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"838","DOI":"10.1126\/science.1189587","article-title":"Global convergence in the temperature sensitivity of respiration at ecosystem level","volume":"329","author":"Mahecha","year":"2010","journal-title":"Science"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1002\/2017GB005644","article-title":"The temperature sensitivity (Q10) of soil respiration: Controlling factors and spatial prediction at regional scale based on environmental soil classes","volume":"32","author":"Meyer","year":"2018","journal-title":"Glob. Biogeochem. Cycles"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3485128","article-title":"Tackling climate change with machine learning","volume":"55","author":"Rolnick","year":"2022","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"124007","DOI":"10.1088\/1748-9326\/ab4e55","article-title":"Machine learning and artificial intelligence to aid climate change research and preparedness","volume":"14","author":"Huntingford","year":"2019","journal-title":"Environ. Res. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wilhelm, R.C., van Es, H.M., and Buckley, D.H.. (2022). Predicting measures of soil health using the microbiome and supervised machine learning. Soil Biol. Biochem., 164.","DOI":"10.1016\/j.soilbio.2021.108472"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Papoutsoglou, G., Tarazona, S., Lopes, M.B., Klammsteiner, T., Ibrahimi, E., Eckenberger, J., Novielli, P., Tonda, A., Simeon, A., and Shigdel, R. (2023). Machine learning approaches in microbiome research: Challenges and best practices. Front. Microbiol., 14.","DOI":"10.3389\/fmicb.2023.1261889"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Di Gilio, A., Catino, A., Lombardi, A., Palmisani, J., Facchini, L., Mongelli, T., Varesano, N., Bellotti, R., Galetta, D., and de Gennaro, G. (2020). Breath analysis for early detection of malignant pleural mesothelioma: Volatile organic compounds (VOCs) determination and possible biochemical pathways. Cancers, 12.","DOI":"10.3390\/cancers12051262"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.patrec.2016.03.024","article-title":"A multi-process system for HEp-2 cells classification based on SVM","volume":"82","author":"Cascio","year":"2016","journal-title":"Pattern Recognit. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Biecek, P., and Burzykowski, T. (2021). Explanatory Model Analysis: Explore, Explain, and Examine Predictive Models, Chapman and Hall\/CRC.","DOI":"10.1201\/9780429027192"},{"key":"ref_16","first-page":"18395","article-title":"On locality of local explanation models","volume":"34","author":"Ghalebikesabi","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"101845","DOI":"10.1016\/j.compenvurbsys.2022.101845","article-title":"Extracting spatial effects from machine learning model using local interpretation method: An example of SHAP and XGBoost","volume":"96","author":"Li","year":"2022","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1382","DOI":"10.1038\/s41558-023-01868-1","article-title":"The soil microbiome governs the response of microbial respiration to warming across the globe","volume":"13","author":"Maestre","year":"2023","journal-title":"Nat. Clim. Change"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"906","DOI":"10.1093\/biosci\/biv109","article-title":"Climate warming and soil carbon in tropical forests: Insights from an elevation gradient in the Peruvian Andes","volume":"65","author":"Nottingham","year":"2015","journal-title":"Bioscience"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.1016\/0038-0717(96)00076-4","article-title":"The Q10 relationship of microbial respiration in a temperate forest soil","volume":"28","author":"Winkler","year":"1996","journal-title":"Soil Biol. Biochem."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Steyerberg, E.W., and Steyerberg, E.W. (2019). Coding of categorical and continuous predictors. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating, Springer.","DOI":"10.1007\/978-3-030-16399-0_9"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ibrahimi, E., Lopes, M.B., Dhamo, X., Simeon, A., Shigdel, R., Hron, K., Stres, B., D\u2019Elia, D., Berland, M., and Marcos-Zambrano, L.J. (2023). Overview of data preprocessing for machine learning applications in human microbiome research. Front. Microbiol., 14.","DOI":"10.3389\/fmicb.2023.1250909"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ahsan, M.M., Mahmud, M.P., Saha, P.K., Gupta, K.D., and Siddique, Z. (2021). Effect of data scaling methods on machine learning algorithms and model performance. Technologies, 9.","DOI":"10.3390\/technologies9030052"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_25","first-page":"986","article-title":"KNN model-based approach in classification","volume":"Volume 2888","author":"Meersman","year":"2003","journal-title":"On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE. OTM"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2395","DOI":"10.1161\/CIRCULATIONAHA.106.682658","article-title":"Logistic regression","volume":"117","author":"LaValley","year":"2008","journal-title":"Circulation"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1109\/TGE.1977.6498972","article-title":"The decision tree classifier: Design and potential","volume":"15","author":"Swain","year":"1977","journal-title":"IEEE Trans. Geosci. Electron."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","article-title":"Extremely randomized trees","volume":"63","author":"Geurts","year":"2006","journal-title":"Mach. Learn."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM sigkdd International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ayd\u0131n, Y., I\u015f\u0131kda\u011f, \u00dc., Bekda\u015f, G., Nigdeli, S.M., and Geem, Z.W. (2023). Use of machine learning techniques in soil classification. Sustainability, 15.","DOI":"10.3390\/su15032374"},{"key":"ref_32","unstructured":"Ferrer, L. (2022). Analysis and comparison of classification metrics. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1016\/j.jclinepi.2015.02.010","article-title":"The precision\u2013recall curve overcame the optimism of the receiver operating characteristic curve in rare diseases","volume":"68","author":"Ozenne","year":"2015","journal-title":"J. Clin. Epidemiol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5062","DOI":"10.1109\/TPAMI.2024.3361861","article-title":"Algorithm-Dependent Generalization of AUPRC Optimization: Theory and Algorithm","volume":"46","author":"Wen","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Novielli, P., Romano, D., Magarelli, M., Bitonto, P.D., Diacono, D., Chiatante, A., Lopalco, G., Sabella, D., Venerito, V., and Filannino, P. (2024). Explainable Artificial Intelligence for Microbiome Data Analysis in Colorectal Cancer Biomarker Identification. Front. Microbiol., 15.","DOI":"10.3389\/fmicb.2024.1348974"},{"key":"ref_36","unstructured":"Lundberg, S.M., and Lee, S.I. (2017). A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst., 30."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.soilbio.2018.09.010","article-title":"Dynamics of soil respiration and microbial communities: Interactive controls of temperature and substrate quality","volume":"127","author":"Ali","year":"2018","journal-title":"Soil Biol. Biochem."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1038\/s41579-019-0265-7","article-title":"Soil microbiomes and climate change","volume":"18","author":"Jansson","year":"2020","journal-title":"Nat. Rev. Microbiol."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Yang, S., Wu, H., Wang, Z., Semenov, M.V., Ye, J., Yin, L., Wang, X., Kravchenko, I., Semenov, V., and Kuzyakov, Y. (2022). Linkages between the temperature sensitivity of soil respiration and microbial life strategy are dependent on sampling season. Soil Biol. Biochem., 172.","DOI":"10.1016\/j.soilbio.2022.108758"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3048","DOI":"10.1111\/1462-2920.15520","article-title":"How do soil microbes exert impact on soil respiration and its temperature sensitivity?","volume":"23","author":"Tong","year":"2021","journal-title":"Environ. Microbiol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.geoderma.2009.06.009","article-title":"Use of indicators and pore volume-function characteristics to quantify soil physical quality","volume":"152","author":"Reynolds","year":"2009","journal-title":"Geoderma"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Popolizio, S., Stellacci, A.M., Giglio, L., Barca, E., Spagnuolo, M., and Castellini, M. (2022). Seasonal and soil use dependent variability of physical and hydraulic properties: An assessment under minimum tillage and no-tillage in a long-term experiment in southern Italy. Agronomy, 12.","DOI":"10.3390\/agronomy12123142"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/S0016-7061(02)00228-8","article-title":"Indicators of good soil physical quality: Density and storage parameters","volume":"110","author":"Reynolds","year":"2002","journal-title":"Geoderma"}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/6\/3\/75\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:12:54Z","timestamp":1760109174000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/6\/3\/75"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,10]]},"references-count":43,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["make6030075"],"URL":"https:\/\/doi.org\/10.3390\/make6030075","relation":{},"ISSN":["2504-4990"],"issn-type":[{"value":"2504-4990","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,10]]}}}