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Carbon Farming: How to Support Farmers in Choosing the Best Management Strategies for Low-Impact Food Production

Author

Listed:
  • Lea Piscitelli

    (CIHEAM Bari, Via Ceglie 9, Valenzano, 70010 Bari, Italy)

  • Annalisa De Boni

    (Department of Soil, Plant and Food Sciences, University of Bari Aldo Moro, Via G. Amendola 165/a, 70126 Bari, Italy)

  • Rocco Roma

    (Department of Soil, Plant and Food Sciences, University of Bari Aldo Moro, Via G. Amendola 165/a, 70126 Bari, Italy)

  • Giovanni Ottomano Palmisano

    (Department of Soil, Plant and Food Sciences, University of Bari Aldo Moro, Via G. Amendola 165/a, 70126 Bari, Italy)

Abstract

The European Commission is directing efforts into triggering the storage of carbon in agricultural soils by encouraging the adoption of carbon farming practices under the European Green Deal and in other key EU policies. However, farmers that want to enter this production model urgently need to define the sustainable practices required for increasing soil organic carbon without overturning production systems and also need to adapt it for optimizing yields and improving carbon stocks. However, there is still a lack of tools that are easy to use and interpret for guiding farmers and stakeholders to find ways in which to increase soil organic carbon content. Therefore, this research aims to set up a novel bottom–up approach, in terms of the methodology and analysis process, for identifying tailored sustainable farming management strategies for the purpose of increasing soil carbon. We investigated 115 real food production cases that were carried out under homogeneous pedo-climatic conditions over a period of 20 years in the Apulia region (Southern Italy), which made it possible to create a dataset of 12 variables that were analyzed through a decision tree (created with the C4.5 algorithm). The overall results highlight that the treatment duration was the most crucial factor and affected the carbon stock both positively and negatively. This was followed by the use of cover crops alone and then those in combination with a type of irrigation system; hence, specific agricultural management strategies were successfully identified for obtaining effective carbon storage in the considered real food production cases. From a wider perspective, this research can serve as guidance to help EU private actors and public authorities to start carbon farming initiatives, pilot projects, or certification schemes at the local and/or regional levels.

Suggested Citation

  • Lea Piscitelli & Annalisa De Boni & Rocco Roma & Giovanni Ottomano Palmisano, 2023. "Carbon Farming: How to Support Farmers in Choosing the Best Management Strategies for Low-Impact Food Production," Land, MDPI, vol. 13(1), pages 1-16, December.
  • Handle: RePEc:gam:jlands:v:13:y:2023:i:1:p:5-:d:1302971
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    References listed on IDEAS

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    1. X. Liu & S.J. Herbert & A.M. Hashemi & X. Zhang & G. Ding, 2006. "Effects of agricultural management on soil organic matter and carbon transformation - a review," Plant, Soil and Environment, Czech Academy of Agricultural Sciences, vol. 52(12), pages 531-543.
    2. Qinghua Wu & Xiaoliang Guan & Jun Zhang & Yang Xu, 2019. "The Role of Rural Infrastructure in Reducing Production Costs and Promoting Resource-Conserving Agriculture," IJERPH, MDPI, vol. 16(18), pages 1-13, September.
    3. G. V. Kass, 1980. "An Exploratory Technique for Investigating Large Quantities of Categorical Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(2), pages 119-127, June.
    4. Fuat Kaya & Ali Keshavarzi & Rosa Francaviglia & Gordana Kaplan & Levent Başayiğit & Mert Dedeoğlu, 2022. "Assessing Machine Learning-Based Prediction under Different Agricultural Practices for Digital Mapping of Soil Organic Carbon and Available Phosphorus," Agriculture, MDPI, vol. 12(7), pages 1-27, July.
    5. Zhang, Fangfang & Wei, Ya'nan & Bo, Qifei & Tang, An & Song, Qilong & Li, Shiqing & Yue, Shanchao, 2022. "Long-term film mulching with manure amendment increases crop yield and water productivity but decreases the soil carbon and nitrogen sequestration potential in semiarid farmland," Agricultural Water Management, Elsevier, vol. 273(C).
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