Bridging Pedology and Data Science: Machine Learning Applications for Soil Organic Matter and Carbon Analysis

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:This review consolidates the state-of-the-art in ML for soil carbon analysis, offering a roadmap for future hybrid method development.

🏢実務担当者:Provides an overview of practical ML techniques for soil carbon measurement, useful for designing cost-effective monitoring in carbon farming projects.

🏛政策担当者:Emphasizes the importance of accurate soil carbon quantification for carbon credit integrity and national greenhouse gas inventories.

Accurate quantification of soil organic matter (SOM) and carbon content is critical for understanding climate change, evaluating soil health, supporting agricultural sustainability, and implementing carbon sequestration policies. For decades, classical analytical and statistical approaches have underpinned soil carbon assessment, but the emergence of machine learning (ML) techniques offers new opportunities to improve prediction accuracy, scalability, and efficiency. This review summarises the current knowledge on classical and ML-based approaches for analysing SOM and carbon content. We examine the strengths, limitations, and practical applications of conventional methods, including wet chemistry, dry combustion analysis, and geostatistical techniques, alongside modern ML approaches such as random forests (RFs), gradient boosting machines, neural networks, deep learning, and hybrid ML-geostatistical frameworks. Special emphasis is placed on comparative analysis across dimensions, including prediction accuracy, computational requirements, data availability needs, interpretability, uncertainty quantification, and scalability. Soil carbon stocks and dynamics are tightly regulated by indigenous soil microbial communities and their management-driven alterations, creating substantial biologically driven variation that remains difficult to capture with current modelling approaches. We therefore explore hybrid approaches that integrate classical pedological knowledge with ML capabilities. Finally, we discuss emerging challenges, future research directions, and the complementary role these approaches play in advancing soil carbon science. This review concludes that neither classical nor ML approaches alone are sufficient for accurate carbon assessment across diverse scales and environments. Instead, their strategic integration, combining classical mechanistic grounding alongside machine learning’s scalability, represents the most promising path toward realistic soil carbon evaluation for climate change mitigation and agricultural sustainability.

• openalex https://doi.org/10.3390/app16115412first seen 2026-06-22 04:46:17