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Soil Organic Carbon Assessment for Carbon Farming: A Review

Author

Listed:
  • Theodoros Petropoulos

    (farmB Digital Agriculture S.A., Dekatis Evdomis (17th) Noemvriou 79, 55534 Thessaloniki, Greece
    Institute for Bio-Economy and Agri-Technology (IBO), Centre of Research and Technology-Hellas (CERTH), 57001 Thessaloniki, Greece
    Department of Supply Chain Management, International Hellenic University, 57001 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
    Institute for Bio-Economy and Agri-Technology (IBO), Centre of Research and Technology-Hellas (CERTH), 57001 Thessaloniki, Greece)

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.

Suggested Citation

  • Theodoros Petropoulos & Lefteris Benos & Patrizia Busato & George Kyriakarakos & Dimitrios Kateris & Dimitrios Aidonis & Dionysis Bochtis, 2025. "Soil Organic Carbon Assessment for Carbon Farming: A Review," Agriculture, MDPI, vol. 15(5), pages 1-33, March.
  • Handle: RePEc:gam:jagris:v:15:y:2025:i:5:p:567-:d:1607099
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    References listed on IDEAS

    as
    1. Sam Van Hoof, 2023. "Climate Change Mitigation in Agriculture: Barriers to the Adoption of Carbon Farming Policies in the EU," Sustainability, MDPI, vol. 15(13), pages 1-17, July.
    2. George Kyriakarakos & Theodoros Petropoulos & Vasso Marinoudi & Remigio Berruto & Dionysis Bochtis, 2024. "Carbon Farming: Bridging Technology Development with Policy Goals," Sustainability, MDPI, vol. 16(5), pages 1-18, February.
    3. Daniela De Benedetto & Emanuele Barca & Mirko Castellini & Stefano Popolizio & Giovanni Lacolla & Anna Maria Stellacci, 2022. "Prediction of Soil Organic Carbon at Field Scale by Regression Kriging and Multivariate Adaptive Regression Splines Using Geophysical Covariates," Land, MDPI, vol. 11(3), pages 1-18, March.
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    Cited by:

    1. Dimitrios Triantakonstantis & Andreas Karakostas, 2025. "Soil Organic Carbon Monitoring and Modelling via Machine Learning Methods Using Soil and Remote Sensing Data," Agriculture, MDPI, vol. 15(9), pages 1-17, April.

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