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Chinese agriculture has intensified greatly since the early 1980s indicated by the dramatic increase use of inor-ganic fertilizers, pesticides, herbicides, and other farming

inputs to meet the food/fiber demand of its increasing popula-tion (Guo et al., 2010). From 1977 to 2005, total annual grain production increased from 2.83 to 4.84 × 109 Tg and the average grain yield increased from 2348 to 4642 kg ha–1 (Ju et al., 2009). However, this success has come at a cost: overuse of inorganic fertilizer, which not only induced low fertilizer use efficiency, but also led to a series of environmental problems, including increased greenhouse gas emissions, nitrate pollution of ground water, acid rain, and soil acidification, nutrient run-off, and loss of biodiversity (Zhu and Chen, 2002; Ju et al., 2009; Kahrl et al., 2010; Zhang et al., 2013). With these problems, there is a growing concern regard-ing the impact of current fertilization management practices on the sustainability and resilience of agronomic production globally. Particularly, soil health and global warming issues have accented the importance of using organic fertilizers/manures and straw retention/return practices to increase soil C stock and improve soil quality.

Soil microbial biomass Cmic and Nmic are frequently used as indicators for evaluation of soil health under different agricultural practices (Mäder et al., 2002; Badalucco et al., 2010; Vallejo et al., 2010). Particularly, Cmic constitutes only 1 to 3% of the total soil C, while Nmic constitutes up to 5% of total soil N. Despite this small contribution, plant nutrient availability and crop productiv-ity of agro-ecosystems depend primarily on the Cmic and Nmic concentrations as well as soil microbial activities (Friedel et al., 1996). The SMB pool is an important labile fraction of soil organic matter, functioning not only as an agent for transforming and cycling of organic matter but also as a sink/source of plant nutri-ents. Furthermore, SMB may serve as an important and sensitive index for diagnosing early changes in soil C stability and nutrient dynamics following land use changes (Sparling, 1992; Joergensen and Emmerling, 2006; Tripathi et al., 2007).

Effects of Long-Term Fertilization Management Practices on Soil Microbial Biomass in China’s Cropland: A Meta-Analysis

Qingping Zhang, Fuhong Miao, Zhennan Wang, Yuying Shen,* and Guoliang Wang

Published in Agron. J. 109:1183–1195 (2017) doi:10.2134/agronj2016.09.0553 Supplemental material available online

Copyright © 2017 by the American Society of Agronomy5585 Guilford Road, Madison, WI 53711 USAAll rights reserved

ABSTRACTSoil microbial biomass (SMB) plays an important role in enhanc-ing soil aggregation, promoting nutrient cycling, and making a substantial contribution to soil organic matter. Little is known about the underlying mechanism and broad-scale responses of SMB to long-term fertilization practices on a regional scale. The objective of this study is to characterize changes in SMB of pre-dominant cropping systems in China (mainly producing maize [Zea mays L.], wheat [Triticum aestivum L.], rice [Oryza sativa L.], and soybean [Glycine max L.]) under different fertilization regimes using meta-analysis. We integrated data from more than 60 studies conducted in China from 1990 to 2015 and found that application of inorganic fertilizer, straw, straw with inorganic fertilizer, manure, and manure with inorganic fertil-izer increased soil microbial biomass carbon (Cmic) concentra-tion, while nitrogen-only (N) and nitrogen plus manure (NM) decreased soil microbial biomass nitrogen (Nmic) concentration compared with control (no fertilizer application). Our results indicated greater Cmic and Nmic responses to inorganic N, P, and K-based fertilizers plus manure (NPKM) in the mid-lati-tude region compared with those in the low- and high-latitude regions of China. The differences in means (Cmic or Nmic con-centration) among different fertilization treatments decreased with experimental duration, and Nmic concentration decreased with increase in N rate. Our results suggested that long-term (~10 yr) continuous annual fertilization of N provided the greatest SMB, and the optimal rate was about 100 kg N ha–1 yr–1. The application of NPKM demonstrated the greatest potential for increasing SMB of major cropping systems in China.

Q. Zhang and G. Wang, Key Lab. of East China Urban Agriculture, Ministry of Agriculture, Shandong Institute of Agricultural Sustainable Development, Shandong Academy of Agricultural Sciences, Jinan 250100, P.R. China;; F. Miao, Institute of Economic Herb Plants, Qingdao Agricultural Univ., Qingdao 266109, P.R. China; Z. Wang, College of Agriculture and Forestry Science, Linyi Univ., Linyi 276000, P.R. China; Y. Shen, State Key Lab. of Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou 730020, P.R. China. Received 27 Sep. 2016. Accepted 15 Apr. 2017. *Corresponding author (yy.shen@lzu.edu.cn).

Abbreviations: Cmic, soil microbial biomass carbon; MAP, mean annual precipitation; MAT, mean annual temperature; MD, mean difference; Nmic, soil microbial biomass nitrogen; SMB, soil microbial biomass; SOC, soil organic carbon.

Core Ideas• The optimal N rate was about 100 kg ha–1 yr–1.• Best duration for fertilization is less than 10 yr.• The application of NPKM demonstrates the greatest potential

for increasing microbial biomass in China’s cropland.

RevIew & InTeRpReTATIon

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Soil management practices (e.g., tillage, cultivation, fertilization, and irrigation) in agricultural systems are important factors affect-ing SMB (Moore et al., 2000; Ekenler and Tabatabai, 2003; Peng et al., 2006; Jangid et al., 2008). In particular, fertilization is one of the easiest ways of inputting exogenous inorganic/organic matter, which in turn affects soil fertility, C/N ratio, and soil microbial activities (Pan et al., 2009). Fertilization is also the overriding long-term constraint affecting the SMB in agricultural soils (Kaschuk et al., 2010).

Currently, numerous studies have investigated the responses of Cmic and Nmic to changes in fertilization management, but results from these studies vary considerably. Kanchikerimath and Singh (2001) showed that balanced application of inorganic fertilizers and organic amendments (cattle manure, farmyard manure, swine, and plant residue) greatly improved the Cmic and Nmic concentra-tions after 26 yr in a semiarid region of India. Ghosh et al. (2012) and Nath et al. (2012) reported that farmyard manures resulted in the highest Cmic (222.8 mg kg–1 soil) in rice– Isatis tinc-toria (Isatis minima Bunge) and rice–wheat cropping systems. Banger et al. (2010) observed that NPK fertilizer had positive effects on Cmic and SOC in a Typic Rhodalfs in the semiarid tropics. Liu et al. (2009) and Nakhro and Dkhar (2010) found that inorganic fertilization increased Cmic concentration by 4.6 to 6.6%, while lack of fertilizer decreased SOC and Cmic in paddy soil. Kautz et al. (2004) found that farmyard manure exhibited a weaker effect on Cmic and Nmic concentrations in sandy arable soil, while straw and green manure greatly affected Cmic and Nmic concentrations. In China, many researchers have used organic fertilizer to rebuild SMB and enhance soil microbial function in many agricultural systems across the nation (Chen et al., 2005; Liu et al., 2006; Bu et al., 2010; Hao et al., 2010; Wang et al., 2013). However, due to the complexity of different environmental and management condi-tions associated with these studies, the inter-relationships among SMB, fertilization types (such as inorganic fertilizer, manure, and straw return), and environmental factors were not yet fully under-stood. Thus, it became challenging to improve existing manage-ment practices and identify innovative strategies to restore soil function in intensively managed agricultural systems at a broad scale.

Meta-analysis is an effective statistical method for quantitatively summarizing and comparing the results from numerous indi-vidual and independent studies, and it allows general conclusions to be drawn at regional and global scales (Gurevitch and Hedges, 1999; Guo and Gifford, 2002). This approach has been recently applied to analyze the effects of forest management on soil C and N storage (Johnson and Curtis, 2001), the effects of N addition on SMB (Treseder, 2008), and changes in SOC under long-term fertilization and various residue practices in paddy soils in China (Tian et al., 2015). The objectives of this study were to use a meta-analysis approach to (i) examine Cmic and Nmic changes in China’s farmland in response to different fertilization practices and (ii) assess the effects of other environmental or managerial sources of variability on Cmic and Nmic.

MATeRIALS AnD MeTHoDSData Sources

Published journal articles from 1990 to 2015 were searched using the ISI Web of Science (http://apps.webofknowledge.com/) and China Knowledge Resource Integrated Database (http://www.cnki.net/) with search terms of “fertilization or microbial

biomass” and “microbial biomass carbon or microbial biomass nitrogen”. The Cmic and Nmic concentrations in the analysis were obtained from the published tables and text of all selected research articles, and some data were extracted from published figures using Get-Data Graph Digitizer software (ver. 2.24, Russian Federation). Experimental sites are shown in Fig. 1. The details of the selected studies and associated references are presented in supplementary material. To avoid bias in the publication selection process, the following criteria were used:

1. Studies were performed in China’s farmland soils, involving production of wheat, maize, soybean, and rice. Soil Cmic and Nmic concentrations data were based on the 30-cm top soil (tillage layer).

2. Studies were based on field trials and the experimental dura-tion was at least 3 yr (Tian et al., 2015).

3. Experiments had at least one control (no fertilization) and a fertilization treatment. Data were obtained from 14 types of inorganic fertilizer, straw, and manure treatments. Inorganic fertilization treatments included inorganic N only (N); N and P (NP); N and K (NK); P and K (PK); and N, P, and K (NPK). Straw treatments included straw return only (S); N plus straw return (NS); N and P plus straw return (NPS); and N, P, and K plus straw return (NPKS). Manure treatments included manure only (M); N plus manure (NM); N and P plus manure (NPM); N and K plus manure (NKM); and N, P, and K plus manure (NPKM). In total, 70 publications were selected and their publication records are presented in the supplementary material.

Data Categorization

For each site, soil texture, climatic and experimental informa-tion were collected in our study. The climatic properties included mean annual precipitation (MAP) and mean annual temperature (MAT); and the experimental information included the duration of the experiment, spatial distribution, planting method, as well as the type and quantity of fertilization. Studies were further catego-rized based on the experimental duration (<10, 11–20, >20 yr), spatial distribution [Northeast (high latitude), Northwest (mid-latitude), and Southeast (low latitude)], level of N fertilizer application (<100, 100–200, >200 kg N ha–1 yr–1), crop rotation (wheat–maize, wheat–rice, rice–rice, rice–maize, wheat–maize–soybean), MAP (<600, 600–1000, >1000 mm yr–1), MAT [frigid (<8), mesic (8–15), and thermic (>15) temperature regimes (Knorr et al., 2005)], and soil texture (clay, silt-loams, loamy, sandy-loams). Due to insufficient data, we conducted a meta-analysis using the N, NPK, S, NPKS, M, and NPKM treatments for analysis of spa-tial distribution and experimental duration, while meta-analyses of concrete edaphic, climatic, and experimental conditions were per-formed under N, NPK, M, NPKM, and NPKS treatments. For spatial distribution, the Northeast region included Heilongjiang, Jilin, Liaoning provinces, north of Hebei province and Northeast Inner Mongolia province; the Northwest included Shaanxi, Gansu, Ningxia, and Shanxi provinces; and the Southeast included Shanghai city, Fujian, Jiangxi, Zhejiang, Hubei, Hunan, Guangdong, Guangxi, Anhui, Jiangsu provinces and South of Henan province; and the Southwest included Sichuan province, Yunnan province, Chongqing city, and Guizhou province (Tian et al., 2015) (Fig. 1). Manure rate, manure type, straw rate, straw type,

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and wheat–maize–soybean rotation were not available for accurate categorization in our study.

Data Analysis

The mean differences (MD) in Cmic and Nmic between the treatment and the control or main effects of fertilization on Cmic and Nmic were analyzed according to Eq. [1] (Osenberg et al., 1999). The wi in Eq. [3] is the weighting factor, the greater the weight factor, the more important the indicator in the comprehen-sive evaluation process. The inverse of the variance (v) calculated by Eq. [2]. To obtain treatment effects across studies, the weighted mean difference (WMD) between treatment and control means was calculated by Eq. [3].

MD t cX X= − [1]

2 2

2 2t c

t t c c

S Svn X n X

= + [2]

( )=

=1ovall =

=1

MDWMD

i nii

i nii

w

w

×= ∑

∑ [3]

where Xt and Xc are the means of the treatment and the control groups, respectively; and St, Sc, nt and nc represent the standard deviation of treatment groups and control groups, and replicate numbers of treatment and control groups, respectively.

A 95% confidence interval (95% CI) was calculated by Eq. [4]:

( )0.5all95% CI mean 1.96= ± ×v

[4]

When a 95% CI value of the response valuable did not overlap with 0, we considered the effect of fertilization on the variable to be significantly different for the control and treatment groups (Gurevitch and Hedges, 2001; Johnson and Curtis, 2001).

Several studies reported standard errors (SE) instead of SD, so the latter was calculated from the formerly used Eq. [5] for all treatments (Luo et al., 2006; Rusinamhodzi et al., 2011; Pittelkow et al., 2015):

CV%SD SE or SD

100 En X = × = ×

[5]

If SD could not be calculated, we reassigned the SD as 1/10 of the mean (Luo et al., 2006).

A random-effect-model meta-analysis was performed and the data were analyzed with R statistical software using the Meta package (Chen and Peace, 2013) to MD. Mean difference for each study was calculated with bias-corrected 95% CIs generated by a bootstrapping procedure. A chi-squared test was used to deter-mine whether the heterogeneity among the MD of changes to Cmic and Nmic under fertilization (Qtotal) was significantly larger than the expected sampling error. Then, a randomization test was used to determine the significance of the between-group hetero-geneity (Qb) (Adams et al., 1997) and to test whether the MD of the categories differed between the levels of the factors (Table 1).

Fig. 1. The spatial distribution of field experiments in China used in study.

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Chi-squared tests were also used to determine whether the remain-ing within-group heterogeneity (Qw) was significant.

ReSULTSDistribution of Cmic and nmic

The Cmic concentration ranged from 22 to 1, 615 mg C kg–1 for all observations with a mean of 316 ± 9.2 (SE) mg C kg–1 (n = 750), including the control treatments (mean = 266 mg C kg–1) (n = 750). The distribution of Cmic values for all observations was skewed to the left, with the majority of observations falling between 100 and 400 mg C kg–1 (Fig. 2a). The Nmic concentration for all observations ranged from 5 to 194 mg N kg–1 with a mean of 41 ± 1.0 mg N kg–1 (n = 657), including the control treatments (mean = 34 mg N kg–1) (n = 657). The distribution of Nmic values

for all observations was also skewed left, with the majority of observations falling between 30 and 50 mg N kg–1 (Fig. 2b). There was a positive linear relationship between Cmic and Nmic (R2 = 0.22, n = 296, P < 0.0001).

variability in Cmic and nmic Responses to Fertilizer

Many inorganic fertilization treatments (NPK, NP, and PK), NPKS and all manure fertilization treatments (NPKM, NPM, NKM, NM, and M) had significantly positive effect on Cmic, other fertilization treatments had either significantly positive or insignificant effect on Cmic (Fig. 3a, Table 1). In particular, the greatest MD in Cmic occurred under NKM treatment, with an increase of 1.55 mg kg–1, while the lowest significant MD was obtained under NPK treatment, with an increase of only 0.22 mg kg–1 over the control treatment. This clearly demonstrates that inorganic fertilization increases Cmic.

Application of NPKM, M, NPKS, NPS, and NPK had signifi-cantly positive effect on Nmic, while N (n = 90) had significantly negative effect on Nmic, other fertilization treatments had either significantly positive or insignificant effect on Nmic (Fig. 3b, Table 1). The greatest MD in Nmic occurred under NPKS and NPKM treatments, both with increases in Nmic of 1.41 mg kg–1, while the lowest significant MD was obtained under NPK treatment, with an increase of 0.29 mg kg–1 compared with that of the control treatment. The Nmic values for NPKS and NPKM treatments were significantly higher than that of NPK treatment. However, N and NM treatments exhibited a negative effect on Nmic. Our results suggest that an increases in Nmic is due to NPK applications coupled with crop residue retention and incorporation of manure.

Geographic variability in Cmic and nmic Responses to Fertilizer

Application of NPKM and M had significantly positive effects on Cmic concentration in all three regions, while N fertilizer treatment had negative effect on Cmic concentration in Northeast China, NPKS, S, and NPK had significantly positive effects on Cmic concentration in Northwest and Southeast China (Fig. 4a). The application of manure and straw with NPK fertilizer resulted in greater MD in Cmic concentration in the Northwest compared with those in northeastern and southeastern China. Regional het-erogeneity in Cmic change was controlled by multiple factors, and it appears that regional effects exert varying influences on different fertilization treatments.

Application of NPKM had significantly positive effects on Nmic concentration in all three regions, M had significantly positive effects on Nmic concentration in the Northeast and Southeast, NPKS had positive effects on Nmic concentration in the Southeast

Table 1. Between-group heterogeneity (Qb) and probability (P) among n observations in different fertilization groups.Soil microbial biomass Groups of fertilization types n Qb P df

Soil microbial biomass C All data 370 43.29 <0.0001† 13Inorganic fertilizer 169 5.70 <0.0001 4Manure group 144 15.20 <0.0001 4Straw return group 57 50.97 0.0309 3

Soil microbial biomass N All data 321 164.92 <0.0001 13Inorganic fertilizer 147 5.29 <0.0001 4Manure group 128 10.01 <0.0001 4Straw return group 46 43.6 0.0227 3

† Significant (P < 0.05) differences between factor levels are indicated in bold.

Fig. 2. Frequency distribution of microbial biomass (a) C (mg C kg soil–1, n = 750) and (b) N (mg N kg soil–1, n = 657) for all observations.

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Fig. 3. Mean differences in soil microbial biomass (a) carbon (Cmic) and (b) nitrogen (Nmic) for different fertilization treatments. The mean difference is an index used to compare the experimental treatment mean to the control treatment mean and is calculated using Eq. [1] in the text. Bars represent the 95% confidence intervals (CIs); the mean differences are significantly different from the controls when their CIs do not overlap zero. The fertilizers considered were N, mineral nitrogen fertilizer only; NP, mineral nitrogen and phosphorus fertilizers; NK, mineral nitrogen and potassium fertilizers; NPK, mineral nitrogen, phosphorus and potassium fertilizers; S, straw return only; NS, mineral nitrogen plus straw return; NPS, mineral nitrogen and phosphorus plus straw return; NPKS, mineral nitrogen, phosphorus, and potassium plus straw return; M, manure only; NM, mineral nitrogen plus manure; NPM, mineral nitrogen and phosphorus plus manure; NKM, mineral nitrogen and potassium plus manure; NPKM, mineral nitrogen, phosphorus, and potassium plus manure. The sample size of each variable modality was displayed beside the bar.

Fig. 4. Mean differences in soil microbial biomass (a) carbon (Cmic) and (b) nitrogen (Nmic) change for different cropland regions. Bars represent the 95% confidence intervals (CIs); the mean differences are significantly different from the controls when their CIs do not overlap zero. The sample size of each variable modality was displayed beside the bar.

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Fig. 5. Mean differences in soil microbial biomass (a) carbon (Cmic) and (b) nitrogen (Nmic) for different experimental duration. Bars represent the 95% confidence intervals (CIs); the mean differences are significantly different from the controls when their CIs do not overlap zero. The sample size of each variable modality was displayed beside the bar.

Fig. 6. Climatic, edaphic and experimental conditions effects on soil microbial biomass (a) carbon (Cmic) and (b) nitrogen (Nmic) under N fertilization. Bars represent the 95% confidence intervals (CIs); the mean differences are significantly different from the controls when their CIs do not overlap zero. MAT: mean annual temperature; MAP: mean annual precipitation. The sample size of each variable modality was displayed beside each bar.

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Fig. 7. Climatic, edaphic, and experimental conditions effects on soil microbial biomass (a) carbon (Cmic) and (b) nitrogen (Nmic) changes under NPK fertilization. Bars represent the 95% confidence intervals (CIs); the mean differences are significantly different from the controls when their CIs do not overlap zero. MAT: mean annual temperature; MAP: mean annual precipitation. The sample size of each variable modality was displayed beside each bar.

Fig. 8. Climatic, edaphic, and experimental conditions effects on soil microbial biomass (a) carbon (Cmic), and (b) nitrogen (Nmic) changes under manure fertilization. Bars represent the 95% confidence intervals (CIs); the mean differences are significantly different from the controls when their CIs do not overlap zero. MAT: mean annual temperature; MAP: mean annual precipitation. The sample size of each variable modality was displayed beside each bar.

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and NPK had positive effects on Nmic concentration in the Northwest and Southeast (Fig. 4b). The application of NPKM fertilizer resulted in greater MD in Nmic concentration in the Northwest compared with those in northeastern and southeastern China. According to these results, it appears that N-only is not the optimal fertilizer strategy for improving Nmic concentration.

experimental Duration

For Cmic, the MD increased with the duration of the experiment under NPKM, M, and NPK fertilization. The MD decreased with the duration of the experiment under NPKS, S, and N fertilization (Fig. 5a). However, regardless of how long the fertilizer was applied, all fertilization treatments had a positive effect on Cmic. The NPKM fertilization experiments longer than 20 yr led to the greatest increases. For Nmic, the MD decreased with the duration of the experiment under all fertilization trials (Fig. 5b). Nitrogen fertilization experiments longer than 10 yr had a negative effect on Nmic, NPKM fertilization experiments less than 10 yr led to the greatest increase in Nmic.

Fertilization effects on Cmic and nmic under various Additional Conditions

Changes in Cmic in response to N fertilization were significant only for loamy soils (Fig. 6a). Changes in Nmic in response to N fertilization were negative only for MAT less than 8°C. The MD of Nmic tended to decrease with increasing N rate; an N rate less than 100 kg ha–1yr–1 increased Nmic (Fig. 6b).

Changes in Cmic in response to NPK fertilization were sig-nificantly positive for wheat–maize and rice–rice rotations; for clay, loamy, and sandy-loam soils; for acidic (pH < 5) and alkaline (pH > 7.5) soils; and for sites with MAP > 600 mm and MAT > 8°C (Fig. 7a). Changes in Nmic in response to NPK fertilization were significantly positive under all edaphic, climatic, and experi-mental conditions (Fig. 7b).

Changes in Cmic in response to M and NPKM fertilization were significantly positive under all edaphic, climatic, and experi-mental conditions (Fig. 8a, 9a). Changes in Nmic in response to M and NPKM fertilization were also significantly positive under all edaphic, climatic, and experimental conditions, with the excep-tion of wheat–rice rotation, sandy-loam soils, and neutral soil (6.5 < pH < 7.5) under M fertilization (Fig. 8b, 9b).

Changes in Cmic and Nmic in response to NPKS fertilization were significantly positive under all edaphic, climatic, and experi-mental conditions, with the exception of sites with a MAT < 8°C for Cmic (Fig. 10a, 10b).

DISCUSSIonoverall effect of Fertilizer on Cmic and nmic

Intensive agricultural management, particularly fertiliza-tion inputs, can strongly influence soil physical and biochemical processes, which can have a direct impact on the structure and function of the soil microbial community, resulting in substantial reductions in Cmic and Nmic (Bossio et al., 1998; Postma-Blaauw et al., 2010). In the long-term period, SMB and enzyme activity

Fig. 9. Climatic, edaphic, and experimental conditions effects on a soil microbial biomass (a) carbon (Cmic) and (b) nitrogen (Nmic) changes under NPKM fertilization. Bars represent the 95% confidence intervals (CIs); the mean differences are significantly different from the controls when their CIs do not overlap zero. MAT: mean annual temperature; MAP: mean annual precipitation. The sample size of each variable modality was displayed beside each bar.

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correlate with total C inputs, particularly the labile fractions of soil C content (Fauci and Dick, 1994; Alvarez and Alvarez, 2000; Chakraborty et al., 2010).

In our study, N fertilization led to the lowest Cmic compared to other fertilization practices, and N fertilization had negative effects on Nmic concentration, which was in agreement with Tian et al. (2015) in a meta-analysis of paddy topsoil. Numerous previous studies reported that SMB was reduced by applica-tion of N fertilizer (Ladd et al., 1994; Hopkins and Shiel, 1996; Sarathchandra et al., 2001; Bittman et al., 2005; Treseder, 2008; Zhang et al., 2008). Particularly, SMB has been found to decrease with an increasing N rate in 7-yr-old cottonwood (Populus del-toides Marsh.) and loblolly pine (Pinus taeda L.) plantations in northwestern Florida (Lee and Jose, 2003). Our results also found that N rate of more than 100 kg ha–1 yr–1 resulted in negative effects on Nmic, which agrees with Jian et al. (2016), who found that N acquisition enzymes (N-acq) were significantly reduced when the N rate was more than 150 kg ha–1 yr–1. Likewise, Kallenbach and Grandy (2011) reported that the highest N rate (more than 200 kg ha–1 yr–1) led to the greatest Cmic and moder-ate N application (100–200 kg ha–1 yr–1) resulted in the highest increase in Nmic through meta-analysis. Lupwayi et al. (2012) also reported that Nmic decreased when N exceeds the recommended values (50–80 kg N ha–1). Therefore, N fertilizer applied at the agronomically recommended rate probably would not have nega-tive effects on SMB. However, the exact mechanism for the reduc-tion in Cmic and Nmic following N fertilization is not yet known

(Compton et al., 2004; Geisseler et al., 2016). Jian et al. (2016) found positive relationships between Cmic/Nmic and C acquisition enzymes (C-acq)/N-acq under different ecosystems, experimental durations, and MATs. The reason for this may be that N fertilizer has a large effect on bacterial communities, especially for arbuscu-lar mycorrhizal (AM) fungi (Jangid et al., 2008; Geisseler et al., 2016) due to decomposition of cellulose, which mainly depends on bacterial communities (Bayer et al., 2006).

Soil Texture

Soil microbial biomass is often related to the SOC concentra-tion and clay content (Dalal and Mayer, 1987; Zak et al., 1994; Fierer et al., 2009). Soils with higher levels of SOC tend to have higher SMB. Soils with high clay content typically have greater physical–chemical protection of SMB and greater water hold-ing capacity (Kallenbach and Grandy, 2011). Zuber and Villamil (2016) found that soils with high levels of clay may lead to increases in microbial activity as there is more water and nutrients for microbes under no-tillage. Kallenbach and Grandy (2011) found that Cmic concentration was positively correlated with clay content and SOC, while Nmic was positively correlated with the rate of N input in a meta-analysis study. Previous studies also found that the inaccessibility of labile C associated with heavy clay soils may limit the positive SMB response to organic inputs such as manures or organic fertilizers (Franzluebbers et al., 1996; Grandy et al., 2010; Sugihara et al., 2010). The SMB is also associated with soil pH (Jenkinson et al., 1979). Liu et al. (2016) found that soil

Fig. 10. Climatic, edaphic, and experimental conditions effects on soil microbial biomass (a) carbon (Cmic), and (b) nitrogen (Nmic) changes under NPKS fertilization. Bars represent the 95% confidence intervals (CIs); the mean differences are significantly different from the controls when their CIs do not overlap zero. MAT: mean annual temperature; MAP: mean annual precipitation. The sample size of each variable modality was displayed beside each bar.

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with low pH could lead to decreases in Cmic and Nmic concentra-tion, and suppressed soil microbial activity in Pinus massoniana soil in China. Zhou et al. (2017) found that qCO2 (ratio of soil respiration and Cmic) was moderately and greatly decreased in acid (pH < 6.5) and neutral (pH = 6.5–7.5) soils but slightly increased in alkaline soils (pH > 7.5) when amended with biochar. In our study, Cmic and Nmic were higher in northeastern China than in northwestern and southeastern China with no fertilization. The differences in Cmic and Nmic between Northeast and Northwest are probably associated with the percent of fine particles, while dif-ferences in Cmic and Nmic between the Northeast and Southeast are probably related with soil pH. Blank and brown soils, with high clay and organic matter content, are the major soil types in northeastern China. Heilu and Huangmian soils with low clay and organic matter content are the major soil types in the Gansu, Shaanxi, and Shanxi provinces of northwestern China. Paddy soil with high clay and low pH, is the major soil type in south-eastern China.

Spatial Distribution

Previous studies have found that SMB geographic distribu-tions are correlated with plant productivity (Zak et al., 1994; Fierer et al., 2009). Wheat–maize and rice–maize rotations with high plant productivity led to the greatest increase in Cmic in our study, which is similar with previous findings (Fierer et al., 2009; Geisseler et al., 2016). However, Kallenbach and Grandy (2011) suggested that SMB may be affected by the variation in climatic constraints on C decomposition or microbial growth rates, rather than aboveground productivity. Because management practices, such as irrigation and fertilization adopted at different geographical sites, can result in similar levels of plant productivity but different SMB concentrations. Similarly, our results revealed a significantly higher Cmic response to NPKM at mid-latitudes compared with that at low- and high-latitude regions. We also found that Cmic concentration was higher under inorganic fertil-ization and straw return at mid-latitudes compared to low- and high-latitude regions in China. The Cmic at higher latitudes may have been limited by cool temperatures and low precipitation, leading to suppression of overall microbial activity and growth. Alternatively, while climate is not a constraining factor at low latitudes, microbial activity and growth may be constrained by the rapid turnover rates of both new organic inputs and the standing SMB (Zech et al., 1997; Santruckova et al., 2000; Kallenbach and Grandy, 2011).

experimental Duration

Several different experimental factors were evaluated as sources of variable, particularly experimental duration. Zuber and Villamil (2016) found that study duration did not have a significant effect on Cmic and Nmic in long-term experiments of no-till vs. tillage systems; however, these experiments were significant for metabolic quotient through meta-analysis, and the results suggested that after 10 yr of no-till practice, the microbial activity increased to match that under tilled soils. Our results showed that Cmic and Nmic concentrations were highly time-dependent, decreasing with the duration of the experiment. In addition, Treseder (2008) found that longer durations of N fertilization elicited stronger declines in microbial abundance through meta-analysis, espe-cially within the first 5 yr. Some studies have reported that the

increment of the MD in SOC change rates decreased signifi-cantly over time (West and Post, 2002; Marland et al., 2003; Tian et al., 2015) and that there was a weak but significant correlation between the MD of the SOC changing rate and the duration of the experiment (Six et al., 2002; West and Post, 2002; Rui and Zhang, 2010). However, most of these stud-ies have focused on Cmic and SOC under different edaphic, climatic, and experimental conditions. Responses of Nmic to organic inputs have not been reported. This is likely because that the SMB response to organic inputs may be primarily driven by C limitations, not N (Kallenbach and Grandy, 2011).

Study Limitations and Future perspectives

This study and out-coming conclusions may have some limita-tions due to the limited data source. Up-scaling the results of small-plot studies to a regional and/or national level may result in errors and uncertainties (Liao et al., 2008; Luo et al., 2010). In addition, MD in meta-analysis can be sensitive to the addition/deletion of published studies if the data set constructed from selected studies is not large enough (Gurevitch and Hedges, 2001). For example, sample size was limited in the straw return treat-ment. Tian et al. (2015) found that the 95% CI of the MD of SOC were significantly logarithmically correlated with the number of observations of straw-return treatment. Furthermore, in our study, we considered the effect of spatial distribution but ignored the effect of soil type on SMB. They are oftentimes correlated, as Huangmian and Heilu, black and paddy soils were the major soil types in the Northwest, Northeast, and Southeast regions of China, respectively.

An increase in SMB can impact the temporal availability of N for plant uptake (Sabahi et al., 2010) and extend the amount of available inorganic N throughout the growing season (Manzoni and Porporato, 2009; Marinari et al., 2010). Further, SMB can improve soil aggregation by directly contributing to stable soil C pools (Simpson et al., 2007; Liang et al., 2010). Our results showed that NPKM had a significantly positive effect on SMB, especially in northwestern China, with less inhibition of this effect over time compared to other fertilization practices. Despite the fact that the magnitude of the response varies depending on duration and environmental conditions, NPKM should be considered the optimal fertilization management practices for rebuilding SMB and associated soil microbial functions in agriculture-intensive soils. Therefore, land managers should consider NPKM as nutrient supplements, rather than exclusively as chemical fertil-izer replacements.

Land use intensification has a certain protective effect on soil productivity and function in the future. Soil degradation will exac-erbate in the decades ahead as the global population approaches 10 billion (Grandy et al., 2010). To meet the demand for food, fuel, and fiber, production of agricultural inputs, especially fertilizer, have increased by 700% over the past 40 yr globally, leading to high energy consumption, severe water pollution, and soil deg-radation (Foley et al., 2005; Cordell et al., 2009). Use of organic amendments such as manure may play an important role in miti-gating the negative effects of land use intensification and reducing the dependency of modern agronomic production on chemical inputs. Therefore, a balance of NPK and M treatments might have the potential to increase soil C input, maintain and restore soil quality and SMB.

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ConCLUSIonSSoil microbial biomass C (Cmic) and N (Nmic) have not been

explicitly represented in meta-analysis approach under different fertilization regime in China. This study summarized Cmic and Nmic under different fertilization practices based on experimen-tal data collected from China’s farmland in last 30 yr. In general, Cmic is greater under fertilization than no fertilization; however, Nmic was lower under N and N with manure than no fertiliza-tion, Nmic also decreased with increasing N rate, with the opti-mal N rate at less than 100 kg N ha–1 yr–1. The treatment with NPKM was the most promising strategy for maximizing Cmic and Nmic levels, especially in northwestern China. In contrast, SMB decreased with increasing experimental duration. Our results demonstrated that long-term (~10 yr) fertilization sig-nificantly improved Cmic and Nmic concentrations in cropland. Environmental conditions, such as precipitation, temperature, and soil texture can also impact the effect of fertilization on SMB. Overall, this study provided the first comprehensive sum-mary of Cmic and Nmic under different edaphic, climatic, and experimental conditions, and Cmic and Nmic responses to fertil-ization practices in China. Future studies should assess SMB of different ecosystems under global scale.

ACKnowLeDGMenTS

This research was supported by grants from the China Agriculture Research System (no. CARS-35-32), Key Application of Technological Innovation Project of Shandong Province (201516), and Shandong Agricultural Science and Technology Innovation Project (CXGC2016B03), and The Forage Industrial Innovation Team, Shandong Modern Agricultural Industrial and Technical System (SDAIT-23-02).

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