Impacts of Biochar Application on Inorganic Phosphorus Fractions in Agricultural Soils

文献类型： 外文期刊

第一作者： Lin, Liwen

作者： Lin, Liwen;Peng, Yutao;Chen, Hao;Peng, Yutao;Zhou, Lin;Chen, Qing;Zhang, Baige

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关键词： soil phosphorus form; biochar properties; quantative analysis; soil texture; phosphorus availability

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.8 ）

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年卷期： 2025 年 15 卷 1 期

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收录情况： SCI

摘要： Inorganic phosphorus (P) is a key component of soil P pools, influencing their availability and mobility. Although studies on biochar's effect on inorganic P fractions in various soils are growing, a critical review of these findings is lacking. Herein, we conducted a quantitative meta-analysis of 74 peer-reviewed datasets, drawing general conclusions and confirming the absence of publication bias through funnel plot statistics. The results showed that biochars can influence soil inorganic P fractions, with their effects depending on biochar (i.e., feedstock, pyrolysis temperature and time, C:N ratio, pH, ash and P content) and soil-related properties (i.e., pH, texture, P content). Specifically, the addition of biochar significantly enhanced the diverse soil inorganic P fractions and P availability (as indicated by Olsen-P). Only biochars produced from wood residues and having high C/N ratios (>200) did not significantly increase the labile P fractions (water extracted soil phosphorus (H2O-P), Olsen-P, and soil calcium compounds bound phosphorus (Ca-2-P)). The application of biochars derived from crop residues significantly increased the soil P associated with iron and aluminum oxides, while there was no significant effect on manure- and wood residue-derived biochars. In addition, applications of low temperature biochars and manure residue-derived biochars could increase the proportions of soil highly stable P. We identified knowledge gaps in biochar production and its potential for soil phosphorus regulation. Due to the complex processes by which biochar affects soils, more systematic evaluations and predictive methods (e.g., modeling, machine learning) are needed to support sustainable agriculture and environmental practices.

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