Leveraging explainable AI to predict soil respiration sensitivity and its drivers for climate change mitigation
Abstract Global warming is one of the most pressing and critical problems facing the world today. It is mainly caused by the increase in greenhouse gases in the atmosphere, such as carbon dioxide (CO2). Understanding how soils respond to rising temperatures is critical for predicting carbon release...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-96216-y |
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| author | Pierfrancesco Novielli Michele Magarelli Donato Romano Pierpaolo Di Bitonto Anna Maria Stellacci Alfonso Monaco Nicola Amoroso Roberto Bellotti Sabina Tangaro |
| author_facet | Pierfrancesco Novielli Michele Magarelli Donato Romano Pierpaolo Di Bitonto Anna Maria Stellacci Alfonso Monaco Nicola Amoroso Roberto Bellotti Sabina Tangaro |
| author_sort | Pierfrancesco Novielli |
| collection | DOAJ |
| description | Abstract Global warming is one of the most pressing and critical problems facing the world today. It is mainly caused by the increase in greenhouse gases in the atmosphere, such as carbon dioxide (CO2). Understanding how soils respond to rising temperatures is critical for predicting carbon release and informing climate mitigation strategies. Q10, a measure of soil microbial respiration, quantifies the increase in CO2 release caused by a $$10^\circ$$ Celsius rise in temperature, serving as a key indicator of this sensitivity. However, predicting Q10 across diverse soil types remains a challenge, especially when considering the complex interactions between biochemical, microbiome, and environmental factors. In this study, we applied explainable artificial intelligence (XAI) to machine learning models to predict soil respiration sensitivity (Q10) and uncover the key factors driving this process. Using SHAP (SHapley Additive exPlanations) values, we identified glucose-induced soil respiration and the proportion of bacteria positively associated with Q10 as the most influential predictors. Our machine learning models achieved an accuracy of $$0.813 \pm 0.007$$ , precision of $$0.812 \pm 0.008$$ , an AUC-ROC of $$0.884 \pm 0.003$$ , and an AUC-PRC of $$0.883 \pm 0.007$$ , ensuring robust and reliable predictions. By leveraging t-SNE (t-distributed Stochastic Neighbor Embedding) and clustering techniques, we further segmented low Q10 soils into distinct subgroups, identifying soils with a higher probability of transitioning to high Q10 states. Our findings not only highlight the potential of XAI in making model predictions transparent and interpretable, but also provide actionable insights into managing soil carbon release in response to climate change. This research bridges the gap between AI-driven environmental modeling and practical applications in agriculture, offering new directions for targeted soil management and climate resilience strategies. |
| format | Article |
| id | doaj-art-ff96a043ea6b4d1d86d607819e04d9b5 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-ff96a043ea6b4d1d86d607819e04d9b52025-08-20T03:06:49ZengNature PortfolioScientific Reports2045-23222025-04-0115111310.1038/s41598-025-96216-yLeveraging explainable AI to predict soil respiration sensitivity and its drivers for climate change mitigationPierfrancesco Novielli0Michele Magarelli1Donato Romano2Pierpaolo Di Bitonto3Anna Maria Stellacci4Alfonso Monaco5Nicola Amoroso6Roberto Bellotti7Sabina Tangaro8Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo MoroDipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo MoroDipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo MoroDipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo MoroDipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo MoroIstituto Nazionale di Fisica Nucleare, Sezione di BariIstituto Nazionale di Fisica Nucleare, Sezione di BariIstituto Nazionale di Fisica Nucleare, Sezione di BariDipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo MoroAbstract Global warming is one of the most pressing and critical problems facing the world today. It is mainly caused by the increase in greenhouse gases in the atmosphere, such as carbon dioxide (CO2). Understanding how soils respond to rising temperatures is critical for predicting carbon release and informing climate mitigation strategies. Q10, a measure of soil microbial respiration, quantifies the increase in CO2 release caused by a $$10^\circ$$ Celsius rise in temperature, serving as a key indicator of this sensitivity. However, predicting Q10 across diverse soil types remains a challenge, especially when considering the complex interactions between biochemical, microbiome, and environmental factors. In this study, we applied explainable artificial intelligence (XAI) to machine learning models to predict soil respiration sensitivity (Q10) and uncover the key factors driving this process. Using SHAP (SHapley Additive exPlanations) values, we identified glucose-induced soil respiration and the proportion of bacteria positively associated with Q10 as the most influential predictors. Our machine learning models achieved an accuracy of $$0.813 \pm 0.007$$ , precision of $$0.812 \pm 0.008$$ , an AUC-ROC of $$0.884 \pm 0.003$$ , and an AUC-PRC of $$0.883 \pm 0.007$$ , ensuring robust and reliable predictions. By leveraging t-SNE (t-distributed Stochastic Neighbor Embedding) and clustering techniques, we further segmented low Q10 soils into distinct subgroups, identifying soils with a higher probability of transitioning to high Q10 states. Our findings not only highlight the potential of XAI in making model predictions transparent and interpretable, but also provide actionable insights into managing soil carbon release in response to climate change. This research bridges the gap between AI-driven environmental modeling and practical applications in agriculture, offering new directions for targeted soil management and climate resilience strategies.https://doi.org/10.1038/s41598-025-96216-ySoil Respiration SensitivityMachine LearningExplainable Artificial Intelligence (XAI)Q10Climate Change |
| spellingShingle | Pierfrancesco Novielli Michele Magarelli Donato Romano Pierpaolo Di Bitonto Anna Maria Stellacci Alfonso Monaco Nicola Amoroso Roberto Bellotti Sabina Tangaro Leveraging explainable AI to predict soil respiration sensitivity and its drivers for climate change mitigation Scientific Reports Soil Respiration Sensitivity Machine Learning Explainable Artificial Intelligence (XAI) Q10 Climate Change |
| title | Leveraging explainable AI to predict soil respiration sensitivity and its drivers for climate change mitigation |
| title_full | Leveraging explainable AI to predict soil respiration sensitivity and its drivers for climate change mitigation |
| title_fullStr | Leveraging explainable AI to predict soil respiration sensitivity and its drivers for climate change mitigation |
| title_full_unstemmed | Leveraging explainable AI to predict soil respiration sensitivity and its drivers for climate change mitigation |
| title_short | Leveraging explainable AI to predict soil respiration sensitivity and its drivers for climate change mitigation |
| title_sort | leveraging explainable ai to predict soil respiration sensitivity and its drivers for climate change mitigation |
| topic | Soil Respiration Sensitivity Machine Learning Explainable Artificial Intelligence (XAI) Q10 Climate Change |
| url | https://doi.org/10.1038/s41598-025-96216-y |
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