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基于Meta分析评价污泥施用对土壤团聚体的影响

朱会军, 吴嘉希, 熊仁瑞, 王雨滢, 黄珏, 董晓全, 杨昊, 罗贞, 曾曙才, 吴道铭

朱会军, 吴嘉希, 熊仁瑞, 等. 基于Meta分析评价污泥施用对土壤团聚体的影响[J]. 华南农业大学学报, 2025, 46(1): 41-52. DOI: 10.7671/j.issn.1001-411X.202311031
引用本文: 朱会军, 吴嘉希, 熊仁瑞, 等. 基于Meta分析评价污泥施用对土壤团聚体的影响[J]. 华南农业大学学报, 2025, 46(1): 41-52. DOI: 10.7671/j.issn.1001-411X.202311031
ZHU Huijun, WU Jiaxi, XIONG Renrui, et al. Evaluating the effect of sludge application on soil aggregates based on Meta-analysis[J]. Journal of South China Agricultural University, 2025, 46(1): 41-52. DOI: 10.7671/j.issn.1001-411X.202311031
Citation: ZHU Huijun, WU Jiaxi, XIONG Renrui, et al. Evaluating the effect of sludge application on soil aggregates based on Meta-analysis[J]. Journal of South China Agricultural University, 2025, 46(1): 41-52. DOI: 10.7671/j.issn.1001-411X.202311031

基于Meta分析评价污泥施用对土壤团聚体的影响

基金项目: 国家自然科学基金(42177011);广东省基础与应用基础研究基金(2022A1515010909)
详细信息
    作者简介:

    朱会军,硕士研究生,主要从事土壤结构改良研究,E-mail: 1370781500@qq.com

    通讯作者:

    吴道铭,教授,博士,主要从事土壤质量改良、植物重金属修复研究,E-mail: dmwu@scau.edu.cn

  • 中图分类号: S156

Evaluating the effect of sludge application on soil aggregates based on Meta-analysis

  • 摘要:
    目的 

    利用Meta分析探讨污泥施用促进土壤团聚的效果,并挖掘其影响因素。

    方法 

    收集整理1990—2023年间已发表文献,在获取的568篇中、英文文献中精筛出36篇高度匹配文献,运用Meta分析总结污泥施用对土壤团聚体和土壤性质的影响,并利用线性拟合和随机森林模型等方法,分析污泥施用条件下土壤团聚体与土壤性质的关系。

    结果 

    污泥施用显著提高土壤大团聚体(粒径>0.25 mm)相对含量以及团聚体平均质量直径;与表施(0.20)相比,污泥混施大团聚体相对含量的效应值(0.84)更高;污泥施用显著提高表层(0~20 cm)和深层(20<~40 cm)土壤团聚体平均质量直径,但仅显著增加表层土壤大团聚体相对含量;土壤黏粒含量越低,污泥施用对大团聚体相对含量的效应值越高,土壤黏粒含量<15%、15%~25%和>25%的大团聚体相对含量效应值分别为0.26、0.13和0.05;针对不同污泥施用量,施用量为100<~200 t/hm2时大团聚体相对含量效应值最高(0.35)。线性拟合显示,污泥施用条件下土壤有机质、碳水化合物、全氮、碱解氮、总磷、速效磷含量和磷酸酶活性与大团聚体相对含量呈显著正相关,土壤碱化度和土壤电导率与大团聚体相对含量呈显著负相关。随机森林分析进一步确认,污泥施用提高土壤有机质和碳水化合物含量是促进土壤大团聚体形成的关键原因。Meta回归分析表明,有机质含量的增长可以解释93.79%的大团聚体相对含量效应值变异,碳水化合物含量的增长可以解释76.30%的大团聚体相对含量效应值变异。

    结论 

    污泥最佳施用条件是按100<~200 t/hm2混施于黏粒含量<15%的0~20 cm土壤中。污泥施用通过提高土壤有机质和碳水化合物含量促进土壤聚集。

    Abstract:
    Objective 

    To evaluate the effect of sludge application promoting soil aggregation by using Meta-analysis, and excavate the influencing factors.

    Method 

    We fine screened 36 highly matched papers from 568 papers published in international and domestic journals between 1990 and 2023. The effects of sludge application on soil aggregates and soil properties were evaluated by Meta-analysis. The relationships between soil aggregates and soil properties under sludge application were further analyzed by linear fitting analysis and random-forest model method.

    Result 

    Sludge application significantly increased the relative content of soil macroaggregate (particle diameter > 0.25 mm) and the mean weight diameter of aggregates. Compared with surface application (0.20), mixed application of sludge had a higher effect size (0.84) in the relative content of macroaggregate. Sludge application significantly increased the mean weight diameter of aggregates in both surface (0−20 cm) and deep (20<−40 cm) soil layers, but only significantly increased the relative content of macroaggregate in the surface soil layer. The effect size of the relative content of macroaggregate in soils with clay content <15%, 15%−25%, and >25% under sludge application was 0.26, 0.13, and 0.05, respectively, indicating that the higher effect size occurred in soils with lower clay content. For different sludge application rates, the highest effect size (0.35) of macroaggregate relative content was found in rate of 100<−200 t/hm2. Linear fitting analysis showed that the relative content of macroaggregate had significant and positive correlations with the contents of soil organic matter, carbohydrate, total nitrogen, alkaline hydrolyzable nitrogen, total phosphorus, available phosphorus, and phosphatase activity, while having significant and negative correlations with soil exchangeable sodium percentage and electrical conductivity. Random-forest analysis further indicated that the increase of soil organic matter and carbohydrate contents by sludge application was the main reason for the improvement of soil aggregation. Meta-regression analysis showed that the increase of organic matter content could explain 93.79% of the effect size of macroaggregate relative content, and the increase of carbohydrate content could explain 76.30%.

    Conclusion 

    The optimal application condition of sludge is mixed in 0−20 cm depth soil with clay content <15% at the amount of 100<−200 t/hm2. The sludge application increases soil organic matter content and carbohydrate content, and then promotes soil aggregation.

  • 图  1   不同类别下土壤团聚体对污泥施用的响应

    各小图中,括号内数字为样本数量;各个数据块的正负误差线表示95% CI,若95% CI与y=0有交点,则效应在统计学意义上不显著。

    Figure  1.   Response of soil aggregates to sludge application under different conditions

    In each figure, numbers in parentheses are sample sizes; The positive and negative error lines of each data block indicate 95% CI, if 95% CI intersects with y=0, effects are not statistically significant.

    图  2   土壤性质对污泥施用方式的响应

    CC:碳水化合物含量,ANC:碱解氮含量,TPC:总磷含量,ESP:碱化度,EC:电导率,APC:速效磷含量,MCC:微生物碳含量,TNC:总氮含量,OMC:有机质含量,PA:磷酸酶活性;括号内数字为样本数量;各个数据块的正负误差线表示95% CI,若95% CI与y=0有交点,则效应在统计学意义上不显著。

    Figure  2.   Response of soil properties to sludge application method

    CC: Carbohydrate content, ANC: Alkaline hydrolyzable nitrogen content, TPC: Total phosphorus content, ESP: Exchangeable sodium percentage, EC: Electrical conductivity, APC: Available phosphorus content, MCC: Microbial carbon content, TNC: Total nitrogen content, OMC: Organic matter content, PA: Phosphatase activity; Numbers in parentheses are sample sizes; The positive and negative error lines of each data block indicate 95% CI, if 95% CI intersects with y=0, effects are not statistically significant.

    图  3   不同深度土壤性质对污泥施用的响应

    ESP:碱化度,EC:电导率,TPC:总磷含量,MCC:微生物碳含量,APC:速效磷含量,OMC:有机质含量,TNC:总氮含量,PA:磷酸酶活性,CC:碳水化合物含量,ANC:碱解氮含量;括号内数字为样本数量;各个数据块的正负误差线表示95% CI,若95% CI与y=0有交点,则效应在统计学意义上不显著。

    Figure  3.   Response of soil properties to sludge application under different soil layers

    ESP: Exchangeable sodium percentage, EC: Electrical conductivity, TPC: Total phosphorus content, MCC: Microbial carbon content, APC: Available phosphorus content, OMC: Organic matter content, TNC: Total nitrogen content, PA: Phosphatase activity, CC: Carbohydrate content, ANC: Alkaline hydrolyzable nitrogen content; Numbers in parentheses are sample sizes; The positive and negative error lines of each data block indicate 95% CI, if 95% CI intersects with y=0, effects are not statistically significant.

    图  4   不同土壤黏粒含量条件下土壤性质对污泥施用的响应

    ESP:碱化度,EC:电导率,TPC:总磷含量,MCC:微生物碳含量,PA:磷酸酶活性,TNC:总氮含量,OMC:有机质含量,APC:速效磷含量,CC:碳水化合物含量,ANC:碱解氮含量;括号内数字为样本数量;各个数据块的正负误差线表示95% CI,若95% CI与y=0有交点,则效应在统计学意义上不显著。

    Figure  4.   Response of soil properties to sludge application under different soil clay content conditions

    ESP: Exchangeable sodium percentage, EC: Electrical conductivity, TPC: Total phosphorus content, MCC: Microbial carbon content, PA: Phosphatase activity, TNC: Total nitrogen content, OMC: Organic matter content, APC: Available phosphorus content, CC: Carbohydrate content, ANC: Alkaline hydrolyzable nitrogen content; Numbers in parentheses are sample sizes; The positive and negative error lines of each data block indicate 95% CI, if 95% CI intersects with y=0, effects are not statistically significant.

    图  5   土壤性质对不同污泥施用量的响应

    ESP:碱化度,EC:电导率,MCC:微生物碳含量,TPC:总磷含量,APC:速效磷含量,TNC:总氮含量,CC:碳水化合物含量,OMC:有机质含量,PA:磷酸酶活性,ANC:碱解氮含量;括号内数字为样本数量;各个数据块的正负误差线表示95% CI,若95% CI与y=0有交点,则效应在统计学意义上不显著。

    Figure  5.   Response of soil properties to different sludge application rates

    ESP: Exchangeable sodium percentage, EC: Electrical conductivity, MCC: Microbial carbon content, TPC: Total phosphorus content, APC: Available phosphorus content, TNC: Total nitrogen content, CC: Carbohydrate content, OMC: Organic matter content, PA: Phosphatase activity, ANC: Alkaline hydrolyzable nitrogen content; Numbers in parentheses are sample sizes; The positive and negative error lines of each data block indicate 95% CI, if 95% CI intersects with y=0, effects are not statistically significant.

    图  6   土壤大团聚体相对含量与土壤性质的相关关系

    Figure  6.   Correlation between relative content of macroaggregate and soil indicators

    图  7   污泥施用条件下驱动大团聚体形成的潜在土壤因素

    OMC:有机质含量,CC:碳水化合物含量,TPC:总磷含量,APC:速效磷含量,PA:磷酸酶活性,MCC:微生物碳含量,ANC:碱解氮含量,EC:电导率,TNC:总氮含量,ESP:碱化度;“*”表示在P<0.05水平影响显著(A3包)。

    Figure  7.   Potential drivers of soil indicators in macroaggregate formation under sludge application

    OMC: Organic matter content, CC: Carbohydrate content, TPC: Total phosphorus content, APC: Available phosphorus content, PA: Phosphatase activity, MCC: Microbial carbon content, ANC: Alkaline hydrolyzable nitrogen content, EC: Electrical conductivity, TNC: Total nitrogen content, ESP: Exchangeable sodium percentage; “*” indicates significant differences at P<0.05 (A3 package).

    图  8   大团聚体相对含量对有机质与碳水化合物含量增加的响应

    括号内数字为样本数量。

    Figure  8.   Response of macroaggregate relative content to increase of organic matter and carbohydrate contents

    Numbers in parentheses are sample sizes.

    图  9   土壤大团聚体相对含量与有机质、碳水化合物含量的Meta回归分析

    各小图中,圆圈大小指该数据在整组分析数据中所占的权重。

    Figure  9.   Meta-regression analysis of soil macroaggregate relative content with organic matter and carbohydrate contents

    In each figure, the circle size refers to the weight of that individual in the whole set of analyzed data.

    表  1   数据分组

    Table  1   Data grouping

    类别 Category 数据分组 Data grouping
    污泥施用方式1) Sludge application method 表施 Surface application 混施 Mixed application
    土壤深度/cm Soil depth 0~20 20<~40
    土壤黏粒含量/% Soil clay content <15 15~25 >25
    污泥施用量/(t·hm−2) Sludge application amount <50 50~100 100<~200 >200
     1)表施:污泥直接铺撒在土壤表面,没有与土壤进行混合;混施:污泥与土壤充分混合。
     1) Surface application: Sludge is spread directly on the soil surface without mixing with the soil; Mixed application: Sludge is well mixed with the soil.
    下载: 导出CSV

    表  2   正态性检验1)

    Table  2   Normality test

    指标
    Indicator
    n P
    R−1 lnR lgR
    MaC 106 0.00 0.19 0.14
    OMC 109 0.00 0.04 0.06
    MWD 74 0.01 0.11 0.10
    MCC 41 0.01 0.36 0.36
    MiC 38 0.52 0.55 0.51
    TNC 34 0.00 0.17 0.17
    ESP 30 0.15 0.35 0.35
    CC 28 0.77 0.98 0.97
    EC 22 0.48 0.82 0.82
    APC 19 0.22 0.34 0.36
    PA 15 0.03 0.14 0.14
    TPC 12 0.53 0.79 0.79
    ANC 10 0.42 0.95 0.95
    pH 14 0.86 0.83 0.81
     1) MaC:大团聚体相对含量,OMC:有机质含量,MWD:团聚体平均质量直径,MCC:微生物碳含量,MiC:微团聚体相对含量,TNC:总氮含量,ESP:碱化度,CC:碳水化合物含量,EC:电导率,APC:速效磷含量,PA:磷酸酶活性,TPC:总磷含量,ANC:碱解氮含量;n指纳入的研究组数,P>0.05指数据呈正态性。
     1) MaC: Macroaggregate relative content, OMC: Organic matter content, MWD: Mean weight diameter of aggregate, MCC: Microbial carbon content, MiC: Microaggregate relative content, TNC: Total nitrogen content, ESP: Exchangeable sodium percentage, CC: Carbohydrate content, EC: Electrical conductivity, APC: Available phosphorus content, PA: Phosphatase activity, TPC: Total phosphorus content, ANC: Alkaline hydrolyzable nitrogen content; n refers to the number of study groups included, P>0.05 indicates data are normal.
    下载: 导出CSV

    表  3   异质性与稳健性分析1)

    Table  3   Heterogeneity and robustness analysis

    指标
    Indicator
    P I2 P 补充 Supplementation
    剪补前 Before clipping 剪补后 After clipping
    MiC 0.16 19.30 <0.01 <0.01 4(11)
    MWD <0.01 96.60 <0.01 <0.01 3(31)
    MaC <0.01 69.00 <0.01 <0.01 5(39)
     1) MiC:微团聚体相对含量,MWD:团聚体平均质量直径,MaC:大团聚体相对含量;P>0.05表示数据无异质性,I2<50表示数据间无异质性;剪补前后P<0.01表示研究结果无发表偏倚;补充指剪补次数,括号中指剪补数据组数。
     1) MiC: Microaggregate relative content, MWD: Mean weight diameter of aggregate, MaC: Macroaggregate relative content; P>0.05 indicates no heterogeneity in data, I2<50 means no heterogeneity between data; P<0.01 before and after clipping implies no publication bias in the findings; Supplementation refers to the number of clippings, and data in parentheses refer to the number of clipping data groups.
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-11-29
  • 网络出版日期:  2024-11-03
  • 发布日期:  2024-10-29
  • 刊出日期:  2025-01-09

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