Meta-analysis of Total Organic Carbon in Agricultural Soils Under Biochar Amendment Model

SONGKai, GAOYu, ZUOYutian, LIUYang

Chin Agric Sci Bull ›› 2025, Vol. 41 ›› Issue (9) : 125-131.

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Chin Agric Sci Bull ›› 2025, Vol. 41 ›› Issue (9) : 125-131. DOI: 10.11924/j.issn.1000-6850.casb2024-0644

Meta-analysis of Total Organic Carbon in Agricultural Soils Under Biochar Amendment Model

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Abstract

Biochar, a carbon-rich material renowned for its distinctive physical properties, holds significant promise for improving soil texture. However, the performance of biochar-amended soils is contingent upon a variety of factors, including the inherent characteristics of the biochar, the prevailing soil conditions, and the specifics of the experimental framework employed. In this study, we undertook a comprehensive Meta-analysis to assess the impact of biochar application on soil organic carbon (SOC) content. By synthesizing literature published between 2013 and 2024, we meticulously screened 207 datasets derived from 84 studies. The results indicated that the enhancement of total organic carbon (TOC) content in soils after biochar application was more pronounced under specific conditions: when the biochar pH < 9, the soil type was sandy, and the initial organic carbon content was low. Notably, the maximum increase in soil TOC, an impressive 53.43%, was observed in sandy soils. Furthermore, with the increase of biochar application, a corresponding rise in soil TOC content was also documented. This study elucidated the influence of biochar on soil organic carbon, integrating factors such as biochar characteristics, soil conditions, and experimental parameters. Through this comprehensive analysis, we aimed to underscore the significant potential of biochar as a transformative agent for soil enhancement.

Key words

biochar / total organic carbon / soil organic carbon fraction / Meta-analysis / soil improvement

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SONG Kai , GAO Yu , ZUO Yutian , et al. Meta-analysis of Total Organic Carbon in Agricultural Soils Under Biochar Amendment Model[J]. Chinese Agricultural Science Bulletin. 2025, 41(9): 125-131 https://doi.org/10.11924/j.issn.1000-6850.casb2024-0644

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