Summary
This comprehensive review synthesises current approaches to predicting soil organic carbon, comparing technology-based methods, artificial intelligence, process-based and hybrid modelling paradigms. The authors evaluate the relative performance and limitations of each approach, with implications for improving soil carbon monitoring and modelling at field to global scales. The work appears to identify opportunities for integrating multiple methodologies to enhance prediction accuracy and applicability across varied soil and farming contexts.
UK applicability
The review's synthesis of prediction technologies and modelling approaches is directly applicable to UK soil carbon monitoring programmes, particularly in support of government commitments to soil health and agricultural emissions reduction. Methods and frameworks discussed could inform development of British soil monitoring networks and carbon accounting protocols.
Key measures
Soil organic carbon prediction accuracy; technology performance (remote sensing, proximal sensors, spectroscopy); AI model effectiveness; process-based model outputs; hybrid model integration
Outcomes reported
The study reviews and synthesises technologies, artificial intelligence methods, process-based models, and hybrid approaches for predicting soil organic carbon across diverse contexts. It evaluates the strengths, limitations and integrative potential of these methodologies.
Topic tags
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