Asset Details
Do you wish to reserve the book?
Leveraging explainable AI to predict soil respiration sensitivity and its drivers for climate change mitigation
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Leveraging explainable AI to predict soil respiration sensitivity and its drivers for climate change mitigation
Do you wish to request the book?
Leveraging explainable AI to predict soil respiration sensitivity and its drivers for climate change mitigation
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Leveraging explainable AI to predict soil respiration sensitivity and its drivers for climate change mitigation
2025
Overview
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 (CO
2
). Understanding how soils respond to rising temperatures is critical for predicting carbon release and informing climate mitigation strategies. Q
10
, a measure of soil microbial respiration, quantifies the increase in CO
2
release caused by a
Celsius rise in temperature, serving as a key indicator of this sensitivity. However, predicting Q
10
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 (Q
10
) 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 Q
10
as the most influential predictors. Our machine learning models achieved an accuracy of
, precision of
, an AUC-ROC of
, and an AUC-PRC of
, ensuring robust and reliable predictions. By leveraging t-SNE (t-distributed Stochastic Neighbor Embedding) and clustering techniques, we further segmented low Q
10
soils into distinct subgroups, identifying soils with a higher probability of transitioning to high Q
10
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.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
