Soil Organic Carbon Assessment for Carbon Farming: A Review

Theodoros Petropoulos, Lefteris Benos (), Patrizia Busato, George Kyriakarakos, Dimitrios Kateris, Dimitrios Aidonis and Dionysis Bochtis ()
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Theodoros Petropoulos: farmB Digital Agriculture S.A., Dekatis Evdomis (17th) Noemvriou 79, 55534 Thessaloniki, Greece
Lefteris Benos: Institute for Bio-Economy and Agri-Technology (IBO), Centre of Research and Technology-Hellas (CERTH), 57001 Thessaloniki, Greece
Patrizia Busato: Interuniversity Department of Regional and Urban Studies and Planning (DIST), Polytechnic of Turin, Viale Mattioli 39, 10125 Torino, Italy
George Kyriakarakos: farmB Digital Agriculture S.A., Dekatis Evdomis (17th) Noemvriou 79, 55534 Thessaloniki, Greece
Dimitrios Kateris: Institute for Bio-Economy and Agri-Technology (IBO), Centre of Research and Technology-Hellas (CERTH), 57001 Thessaloniki, Greece
Dimitrios Aidonis: Department of Supply Chain Management, International Hellenic University, 57001 Thessaloniki, Greece
Dionysis Bochtis: farmB Digital Agriculture S.A., Dekatis Evdomis (17th) Noemvriou 79, 55534 Thessaloniki, Greece

Agriculture, 2025, vol. 15, issue 5, 1-33

Abstract: This review is motivated by the urgent need to improve soil organic carbon (SOC) assessment methods, which are vital for enhancing soil health, addressing climate change, and promoting carbon farming. By employing a structured approach that involves a systematic literature search, data extraction, and analysis, 86 relevant studies were identified. These studies were evaluated to address the following specific research questions: (a) What are the state-of-the-art approaches in sampling, modeling, and data acquisition? and (b) What are the key challenges, open issues, potential advancements, and future directions needed to enhance the effectiveness of carbon farming practices? The findings indicate that while traditional SOC assessment techniques remain foundational, there is a significant shift towards incorporating model-based methods, machine learning models, proximal spectroscopy, and remote sensing technologies. These emerging approaches primarily serve as complementary to laboratory analyses, enhancing the overall accuracy and reliability of SOC assessments. Despite these advancements, challenges such as soil spatial and temporal variability, high financial costs, and limitations in measurement accuracy continue to hinder progress. This review also highlights the necessity for scalable, cost-effective, and precise SOC measurement tools, alongside supportive policies and incentives that encourage farmer adoption. Finally, the development of a “System-of-Systems” approach that integrates sampling, sensing, and modeling offers a promising pathway to balancing cost and accuracy, ultimately supporting carbon farming practices.

Keywords: soil organic carbon measurement; laboratory analysis; remote sensing; spectroscopy; machine learning; kriging; carbon sequestration; technology cost–accuracy trade-offs; roadmap for advancements (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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