浙江农业学报 ›› 2026, Vol. 38 ›› Issue (4): 731-744.DOI: 10.3969/j.issn.1004-1524.20250738
生物炭-沼液配施对酸性红壤氮素转化的长期效应
应孟飞1(
), 管毅伦2, 靳泽文2,*(), 赵坤3, 平立凤2, 柴彦君2, 李艳4, 沈月5, 颜军5
- 1
绍兴市粮油作物技术推广中心 浙江 绍兴 312000
2浙江科技大学 环境与资源学院 ,浙江省废弃生物质循环利用与生态处理技术重点实验室 浙江 杭州 310023
3浙江树人学院 信息科技学院 浙江 杭州 310015
4浙江省农业科学院 环境资源与土壤肥料研究所 浙江 杭州 310021
5浙江省耕地质量与肥料管理总站 浙江 杭州 310020
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收稿日期:2025-12-06出版日期:2026-04-25发布日期:2026-05-08 -
作者简介:靳泽文,E-mail:119044@zust.edu.cn
应孟飞,研究方向为生态能源。E-mail:172160949@qq.com -
通讯作者:靳泽文 -
基金资助:国家重点研发计划(2022YFE0196000)
Long-term effects of combined application of biochar and biogas slurry on nitrogen transformation in acidic red soil
YING Mengfei1(), GUAN Yilun2, JIN Zewen2,*(), ZHAO Kun3, PING Lifeng2, CHAI Yanjun2, LI Yan4, SHEN Yue5, YAN Jun5
- 1
Shaoxing Grain and Oil Crops Technology Promotion Center Shaoxing 312000, China
2Key Laboratory of Recycling and Eco-Treatment of Waste Biomass of Zhejiang Province ,School of Environment and Natural Resources, Zhejiang University of Science and Technology Hangzhou 310023, China
3College of Information Science and Technology ,Zhejiang Shuren University Hangzhou 310015, China
4Institute of Environment ,Resource, Soil and Fertilizer, Zhejiang Academy of Agricultural Sciences Hangzhou 310021, China
5Zhejiang Cultivated Land Quality and Fertilizer Administration Station Hangzhou 310020, China
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Received:2025-12-06Published:2026-04-25Online:2026-05-08 -
Contact:JIN Zewen
摘要：
土壤酸化与氮素利用效率低下是制约我国南方农业可持续发展的关键瓶颈。本研究通过为期5年的长期田间定位试验,采用白菜-甘薯轮作制度,设置无肥对照(CK)、单施化肥(C0B0)、沼液替代化肥(C0B1)、生物炭配施化肥(C1B0)和生物炭配施沼液(C1B1)5个处理,系统探究了生物炭-沼液协同施用对酸性红壤氮素转化的影响。结果表明,生物炭-沼液配施对土壤酸度影响显著(p<0.05),经过5年处理后,C1B1处理的土壤pH值从初始的5.44提升至7.13,较C0B0处理提高2.05。与CK处理相比,C1B1处理脲酶活性显著升高30.0%,而硝酸还原酶和亚硝酸还原酶活性分别显著降低35.2%和36.3%,表明氮循环从“硝化-反硝化”主导模式转向“固氮-同化”主导模式。微生物群落分析显示,与C0B0处理相比,C1B1处理的固氮细菌相对丰度显著增加,而硝化细菌丰度显著下降。C1B1处理的白菜和甘薯5年平均产量分别为56.31、46.17 t·hm-2,白菜季和甘薯季的氮肥农学效率分别为234.4、220.4 kg·kg-1,均高于C0B0处理。结构方程模型揭示,pH是调控氮循环的核心驱动因子;随机森林模型结果表明,pH值6.5是氮素利用效率跃升的关键阈值。本研究证实,生物炭-沼液协同施用通过pH介导的多层次调控过程,实现了酸性红壤氮素转化的系统性优化,该技术可在保证产量的前提下大幅减少化肥投入,具有良好的经济与生态效益,为化肥减施增效提供了科学依据。
中图分类号:
引用本文
应孟飞, 管毅伦, 靳泽文, 赵坤, 平立凤, 柴彦君, 李艳, 沈月, 颜军. 生物炭-沼液配施对酸性红壤氮素转化的长期效应[J]. 浙江农业学报, 2026, 38(4): 731-744.
YING Mengfei, GUAN Yilun, JIN Zewen, ZHAO Kun, PING Lifeng, CHAI Yanjun, LI Yan, SHEN Yue, YAN Jun. Long-term effects of combined application of biochar and biogas slurry on nitrogen transformation in acidic red soil[J]. Acta Agriculturae Zhejiangensis, 2026, 38(4): 731-744.
图1 生物炭和沼液处理下土壤的pH值和容重 柱上无相同字母的表示处理间差异显著(p<0.05)。下同。
Fig.1 Soil pH value and bulk density under biochar and biogas slurry treatments Bars marked without the same letters indicate significant difference at p<0.05. The same as below.
图2 生物炭和沼液处理下土壤的全氮、铵态氮、颗粒有机氮(PON)和矿物结合有机氮(MAON)含量
Fig.2 Contents of soil total nitrogen, ammonium nitrogen, particulate organic nitrogen (PON) and mineral-associated organic nitrogen (MAON) under biochar and biogas slurry treatments
图3 生物炭和沼液处理下土壤的脲酶、硝酸还原酶和亚硝酸还原酶活性
Fig.3 Activities of soil urease, nitrate reductase and nitrite reductase under biochar and biogas slurry treatments
图4 生物炭和沼液处理下土壤的功能基因丰度和微生物相对丰度 Other,其他;Rhizobium,根据瘤菌属;Bacillus,芽孢杆菌属;Pirellula,小梨形菌属;Amycolatopsis,拟无枝酸菌属;Nitrospira,硝化螺菌属;Nitrosospira,亚硝化螺菌属;Nitrosomonas,亚硝化单胞菌属;Bradyrhizobium,慢生根瘤菌属;Frankiales_norank,弗兰克氏菌目_未分类;Mycobacterium,分枝杆菌属。
Fig.4 Soil functional genes abundance and microbial relative abundance under biochar and biogas slurry treatments
| 处理 Treatment | 历年产量r/(t·hm-2) Yield in each year/(t·hm-2) | AEN/(kg· kg-1) | |||||
|---|---|---|---|---|---|---|---|
| 2020 | 2021 | 2022 | 2023 | 2024 | 平均值Mean | ||
| CK | 4.38±0.31 c | 5.03±0.44 c | 4.07±0.56 c | 3.61±0.27 c | 2.14±0.44 c | 3.85±0.38 c | 0 |
| C0B0 | 21.67±2.42 b | 22.34±1.34 b | 23.09±2.65 b | 19.28±1.24 b | 15.71±2.39 b | 20.42±2.01 b | 86.3 |
| C0B1 | 25.22±3.20 b | 23.90±2.21 b | 22.28±2.37 b | 21.39±2.26 b | 16.07±1.73 b | 21.77±2.24 b | 93.3 |
| C1B0 | 47.58±3.76 a | 46.92±4.32 a | 47.05±6.64 a | 46.39±4.28 a | 42.09±4.32 a | 46.00±5.35 a | 219.5 |
| C1B1 | 49.76±5.25 a | 46.82±3.36 a | 46.40±4.32 a | 44.83±5.76 a | 43.06±2.28 a | 46.17±3.98 a | 220.4 |
表1 生物炭和沼液处理下甘薯的产量和氮肥农学效率(AEN)
Table 1 Yield and agronomic efficiency of nitrogen (AEN) of sweet potato under biochar and biogas slurry treatments
| 处理 Treatment | 历年产量r/(t·hm-2) Yield in each year/(t·hm-2) | AEN/(kg· kg-1) | |||||
|---|---|---|---|---|---|---|---|
| 2020 | 2021 | 2022 | 2023 | 2024 | 平均值Mean | ||
| CK | 4.38±0.31 c | 5.03±0.44 c | 4.07±0.56 c | 3.61±0.27 c | 2.14±0.44 c | 3.85±0.38 c | 0 |
| C0B0 | 21.67±2.42 b | 22.34±1.34 b | 23.09±2.65 b | 19.28±1.24 b | 15.71±2.39 b | 20.42±2.01 b | 86.3 |
| C0B1 | 25.22±3.20 b | 23.90±2.21 b | 22.28±2.37 b | 21.39±2.26 b | 16.07±1.73 b | 21.77±2.24 b | 93.3 |
| C1B0 | 47.58±3.76 a | 46.92±4.32 a | 47.05±6.64 a | 46.39±4.28 a | 42.09±4.32 a | 46.00±5.35 a | 219.5 |
| C1B1 | 49.76±5.25 a | 46.82±3.36 a | 46.40±4.32 a | 44.83±5.76 a | 43.06±2.28 a | 46.17±3.98 a | 220.4 |
| 处理 Treatment | 历年产量/(t·hm-2) Yield in each year/(t·hm-2) | AEN/(kg· kg-1) | |||||
|---|---|---|---|---|---|---|---|
| 2020 | 2021 | 2022 | 2023 | 2024 | 平均值Mean | ||
| CK | 3.16±0.34 c | 3.51±0.28 c | 2.90±0.25 c | 3.67±0.43 c | 3.39±0.52 c | 3.33±0.46 c | 0 |
| C0B0 | 43.65±3.35 b | 36.49±3.56 b | 32.86±2.28 b | 27.36±2.29 b | 20.31±3.34 b | 32.13±2.89 b | 127.4 |
| C0B1 | 46.55±4.48 b | 41.11±7.21 b | 36.72±3.56 b | 33.78±4.21 b | 25.92±3.25 b | 36.82±4.43 b | 148.1 |
| C1B0 | 64.01±5.27 a | 58.24±6.25 a | 51.31±4.89 a | 52.44±3.56 a | 45.45±5.56 a | 54.29±4.76 a | 225.5 |
| C1B1 | 66.11±6.12 a | 60.56±5.73 a | 55.26±5.56 a | 52.27±6.25 a | 47.37±5.23 a | 56.31±5.48 a | 234.4 |
表2 生物炭和沼液处理下白菜的产量和氮肥农学效率(AEN)
Table 2 Yield and agronomic efficiency of nitrogen (AEN) of Chinese cabbage under biochar and biogas slurry treatments
| 处理 Treatment | 历年产量/(t·hm-2) Yield in each year/(t·hm-2) | AEN/(kg· kg-1) | |||||
|---|---|---|---|---|---|---|---|
| 2020 | 2021 | 2022 | 2023 | 2024 | 平均值Mean | ||
| CK | 3.16±0.34 c | 3.51±0.28 c | 2.90±0.25 c | 3.67±0.43 c | 3.39±0.52 c | 3.33±0.46 c | 0 |
| C0B0 | 43.65±3.35 b | 36.49±3.56 b | 32.86±2.28 b | 27.36±2.29 b | 20.31±3.34 b | 32.13±2.89 b | 127.4 |
| C0B1 | 46.55±4.48 b | 41.11±7.21 b | 36.72±3.56 b | 33.78±4.21 b | 25.92±3.25 b | 36.82±4.43 b | 148.1 |
| C1B0 | 64.01±5.27 a | 58.24±6.25 a | 51.31±4.89 a | 52.44±3.56 a | 45.45±5.56 a | 54.29±4.76 a | 225.5 |
| C1B1 | 66.11±6.12 a | 60.56±5.73 a | 55.26±5.56 a | 52.27±6.25 a | 47.37±5.23 a | 56.31±5.48 a | 234.4 |
图5 生物炭调控下土壤pH值对氮素转化、土壤性质及作物产量的结构方程模型 图中椭圆代表模型中的观测/潜变量,包括外源驱动变量(生物炭)、中介调控变量[土壤pH值、脲酶活性、硝酸还原酶(NR)活性、固氮功能基因nifH的(相对)丰度、颗粒有机氮(PON)含量、矿物结合有机氮(MAON)含量],及目标响应变量[氮肥农学效率(AEN)、作物产量]。箭头表示变量间的因果路径,线上数值为标准化路径系数,绝对值大小反映作用强度,箭头方向代表作用方向[蓝色箭头为正向显著(p<0.05)路径,红色箭头为负向显著路径]。
Fig.5 Structural equation model of soil pH value under biochar regulation on nitrogen transformation, soil properties and crop yield Ellipses represent observed/latent variables: exogenous driver (biochar), mediators [soil pH value, urease activity, nitrate reductase (NR) activity, (relative) abundance of nitrogen-fixing functional gene nifH, particulate organic nitrogen (PON) content, mineral-associated organic nitrogen (MAON) content], and responses [agronomic efficiency of nitrogen (AEN), crop yield]. Arrows indicate the causal paths between variables. The values on the lines are standardized path coefficients, and the absolute value reflects the strength of the effect. The direction of the arrow represents the direction of the effect [blue arrows are significantly (p<0.05) positive paths, red arrow is significantly negative path].
图6 影响作物产量与氮肥农学效率的偏最小二乘回归(PLS)分析 AamoA,amoA的相对丰度;ANR,硝酸还原酶活性;AUr,脲酶活性;cPON,颗粒有机氮含量;cMAON,矿物结合有机氮含量;AnifH,nifH的相对丰度。下同。左图为PLS变量重要性(VIP得分),红色虚线为VIP=1阈值(VIP>1的变量对模型解释更重要);右图为PLS成分得分图。
Fig.6 Partial least squares regression (PLS) analysis of factors influencing crop yield and agronomic efficiency of nitrogen AamoA, Relative abundance of amoA gene; ANR, Nitrate reductase activity; AUr, Urease activity; cPON, Particulate organic nitrogen content; cMAON, Mineral-associated organic nitrogen content; AnifH, Relative abundance of nifH gene. The same as below. Left panel shows PLS variable importance (VIP score), with the red dashed line indicating the VIP=1 threshold (variables with VIP>1 contribute more to model explanation). Right panel shows PLS component score plot.
图7 随机森林模型识别影响氮肥农学效率(AEN)的重要变量及其相对重要性 ${c}_{\mathrm{N}{\mathrm{H}}_{4}^{+}\mathrm{-}\mathrm{N}}$,铵态氮含量;AamoB,amoB的相对丰度;ANiR,亚硝酸还原酶活性;BD,容重;cTN,全氮含量。下同。上图为随机森林预测曲线,展示土壤pH值与氮肥农学效率(AEN)的关系,红色虚线为pH值6.5关键阈值,左侧灰色区域为酸性区(pH值<6.5),右侧黄色区域为中性-碱性区(pH值>6.5);下图左侧为氮肥农学效率的特征重要性排序(决定系数为0.916 1),下图右侧为作物产量的特征重要性排序(决定系数为0.765 5)。
Fig.7 Important variables identified by random forest model affecting agronomic efficiency of nitrogen (AEN) and their relative importance ${c}_{\mathrm{N}{\mathrm{H}}_{4}^{+}\mathrm{-}\mathrm{N}}$, Ammonium nitrogen content; AamoB, Relative abundance of amoB gene; ANiR, Nitrite reductase activity; BD, Bulk density; cTN, Total nitrogen content. The same as below.Top panel shows the random forest prediction curve illustrating the relationship between soil pH value and agronomic efficiency of nitrogen (AEN). The red dashed line in the top panel indicates the critical threshold of pH value of 6.5, with the left gray area representing the acidic region (pH value<6.5) and the right yellow area representing the neutral-alkaline region (pH value >6.5). The left part of the bottom panel shows the feature importance ranking for agronomic efficiency of nitrogen (determination coefficient of 0.916 1). The right part of the bottom panel shows the feature importance ranking for crop yield (determination coefficient of 0.765 5).
图8 土壤pH值介导的氮循环过程、酶活性、氮素形态与作物产量、氮肥农学效率(AEN)的相关性网络图 节点代表土壤氮素转化相关变量,节点大小表示变量重要性,连线表示变量间的相关性,蓝色实线为正相关(r>0.6),红色虚线为负相关(r<-0.6),线上数值为相关系数。
Fig.8 The correlation network diagram of the nitrogen cycling process, enzyme activity, nitrogen forms, and crop yield, agronomic efficiency of nitrogen (AEN) mediated by soil pH value Nodes represent variables related to soil nitrogen transformation. Node size indicates variable importance. Lines represent correlations between variables: blue solid lines for positive correlations (r>0.6), red dashed lines for negative correlations (r<-0.6), with values on lines indicating correlation coefficients.
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