2.4 Soil analysis
2.4.1 biochemical analysis
We assessed the soil microbial biomass carbon (MBC) and nitrogen (MBN) by the chloroform fumigation extraction method (Cleveland & Liptzin, 2007). Fresh soil samples transported in an ice-cooled box were separated into two aliquots (15 g on a dry weight basis). One set of soil subsamples was extracted using 0.5 M K2SO4 to measure the MBC and MBN. Organic C in the K2SO4extracted solution was analyzed using a TOC analyzer (Vario TOC, Elementar, Germany). Both MBC and MBN concentrations were corrected for unrecovered biomass using a k factor of 0.45 (Jenkinson et al. , 2004).
Microplate-scale fluorometric procedures were employed to assay the activity of the following hydrolases (Sinsabaugh et al. , 1997): β-1, 4-glucosidase (BG), β-1, 4-N-acetyl-glucosaminidase (NAG), and leucine aminopeptidase (LAP). We prepared substrates and buffer solutions in sterile deionized water. In this study, 1 g of fresh soil sample was homogenized in 125 mL 50 mM sodium acetate buffer. The 50 μl of 50 mM buffer was dispensed into 16 replicate sample wells (sample solution + substrate), eight blank wells (sample solution + buffer), eight reference standard wells (buffer + standard), and eight negative control wells (buffer + substrate). The prepared microplates were then placed in a dark microcosm for 4 h at 20 °C. Finally, the reaction was stopped by adding 1 μl of 1 M NaOH to each well. The fluorescence was measured using an automated fluorometer (BioTek Synergy H1 microplate reader, Winooski, VT, USA) with an excitation wavelength of 365 nm and an emission wavelength of 450 nm. After correction of the assay wells’ fluorescence measurements for the negative controls, blanks, and quench standard wells, the enzymatic activities were expressed as nanomoles of substrate released per hour per gram of dry soil (Saiya-Cork et al. , 2002).
2.4.2 Ecoenzymatic stoichiometry and CUE estimation
We used the activities of the enzymes, the C and N contents of the soil microbial biomass, and labile organic matter to calculate the CUE according to the previous studies (Geyer et al. , 2019; Sinsabaugh et al. , 2016; Sinsabaugh & Shah, 2012). The labile nutrient content was also replaced with soil organic matter (Sinsabaugh et al. , 2016)(Zhou et al. , 2020). Previous study also found that the CUE calculated from stoichiometric models was similar to it according to direct measurements of bacterial and fungal growth and respiration (Sinsabaugh et al. , 2016).
The microbial carbon use efficiency was calculated using the following equation:
CUEC:N =CUEMAX [SC:N /(SC:N+kN )] (1)
where SC:N = (1/EEAC:N )(BC:N /LC:N ), SC:N is a scalar that reflects the ability of the microorganisms to adjust the imbalance between the elemental composition of the available resources and the composition of the microbial biomass through the allocation of enzymatic activities. KN is the half-saturation constant with a value of 0.5. Based on the thermodynamic constraints, CUEmax is assumed to be 0.6 for the highest microbial growth efficiency. EEAC:N is the ratio of the C-acquiring activity to the N-acquiring activity,EEAC:N = BG /(NAG + LAP ). BC:N represents the molar ratio of C to N of the soil microbial biomass. LC:Nrepresents the molar ratio of SOC to TN for the soil substrate that is consumed.
The threshold element ratios (TER) for C:N were estimated by the following function:
TERC:N = LC:N ×EEAC:N (2)
where LC:N and EEAC:N have the same meanings as in Eq. (1).
2.4.3 PLFA analysis
Total microbial biomass and microbial community structure were assessed using phospholipid fatty acid (PLFA) analysis. We used a modified Bligh and Dyer method to extract PLFAs (Börjesson et al. , 1998). Total lipids were extracted overnight from 5 g freeze-dried soil in a solvent phase of 3.0 ml 50 mM phosphate buffer (pH = 7.0), 3.8 ml chloroform, 7.6 ml methanol, and 4 ml Bligh and Dyer reagent (chloroform/methanol/phosphate buffer (1:2:0.8, v/v/v)). The extracted lipids were subsequently added to Discovery® DSC-Si SPE Tubes (Sigma-Aldrich), then separated into neutral lipids, glycolipid, and phospholipid by sequential addition of chloroform, acetone, and methanol solutions. We added PLFA 19:0 (Larodan Malmö, Sweden) to the phospholipid fraction as an internal standard. PLFAs were transesterified to fatty acid methyl esters using 1 ml 0.2 M methanolic-KOH (Chowdhury & Dick, 2012). We analyzed the extracts using a gas chromatograph equipped with a flame-ionization detector (Agilent 6890, Agilent Technologies, Palo Alto, CA, United States). Fungal biomass was the sum of PLFAs 18:2ω6c and 18:1ω9c (Frostegård & Bååth, 1996; White et al. , 1996). PLFAs a15:0, a17:0, i14:0, i15:0, i16:0, i17:0 were used as markers for Gram-positive bacteria, whereas PLFAs 16:1ω9c, 16:1ω11c, 18:1ω5c, 18:1ω7c, cy17:0, and cy19:0 were used as markers for Gram-negative bacteria (Brockett et al. , 2012; Frostegård & Bååth, 1996). Actinomycetes biomass was calculated based on the fatty acid: 10Me16:0 and 10Me18:0 (Willers et al. , 2015). Total bacterial biomass was the sum of G+, G-, and Actinomycetes biomass. We further calculated the ratio of fungal to bacterial biomass (F: B ratio) in soil samples using PLFAs data.
2.4.4 DNA extraction
Five grams aliquots of soil samples were mixed with 25 mL 0.1 mol/L Tris-HCl (pH 8.0), shaken and filtered through three layers of sterile gauze. The filtrate was then centrifuged at 10000 × g for 20 min at 4 °C. DNA was subsequently extracted from the pellets using a GMO food DNA Extraction Kit (Illumina MiSeq 250 PE, Auwigene Company, Beijing, China) according to the manufacturer’s protocols. The total DNA concentration and quality were checked using a spectrophotometer (NanoDrop, ND2000, ThermoScientific, United States) and agarose gel electrophoresis.
2.4.5 16S rRNA gene amplicon sequencing and ITS amplicon sequencing
Variable regions V3-V4 on microbial 16S rRNA gene of bacteria and the ITS2 region of fungi were amplified using PCR (95 °C for 3 min, followed by 30 cycles at 98 °C for 20 s, 58 °C for 15 s, 72 °C for 20 s and a final extension at 72 °C for 5 min). The microbial 16S rRNA gene was amplified by forwarding primer 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and reverse primer 806R (5′- GGACTACVVGGGTATCTAATC -3′) (Lee et al. , 1993). The ITS was amplified with the following forward/reverse primer set: ITS1F/ITS2R (CTTGGTCATTTAG AGGAAGTAA/GCTG-CGTTCTTCATCGATGC) (Luanet al. , 2015). PCR reactions were performed in 30 μL mixture containing 15 μL of 2 × KAPA Library Amplification ReadyMix, 1 μL of each primer (10 μmol/L), 10 ng of template DNA, and ddH2O. The PCR products were detected using 1% agarose gel electrophoresis, then purified with an AxyPrep DNA gel Extraction Kit (Axygen Biosciences, Union City, CA, United States). Amplicon libraries were quantified using a Fluorometer (Applied Biosystems 7500, Thermo Fisher Scientific, United States), after which amplicons were sequenced (Illumina MiSeq PE250, Allwegene Technologies, China).
2.4.6 Soil fractions separation
We used the soil wet-sieving method to separate different soil fractions (Curtin et al. , 2019; Fang et al. , 2019). To separate soil organic matter into labile and stable C fractions, we conducted a combined density and particle size fractionation (Herath et al. , 2014; Six et al. , 1998). The physical fractionation to separate two soil C fractions: light fraction, defined as f-POM, and the heavy fraction that contained aggregate protected organic matter (o-POM, > 53 μm fraction) and mineral protected organic matter (MAOM< 53 μm fraction) (Fanget al. , 2019). Density fractionation of the soils was then performed to isolate light fraction and heavy fraction using sodium polytungstate (SPT, IMBROS, Australia) (Herath et al. , 2014; Sixet al. , 1998).
All fractions were dried (60 °C) and weighed to obtain the mass proportion of each fraction relative to the bulk soil. The soil fractions were ground to < 53 μm for C% analyses. Samples were then acidified with 1.0 M HCl to decompose the carbonate, after which they were dried for 8 hours at 60℃. After drying, the samples were ground (< 0.149 mm) with a mortar and pestle and the SOC was measured by dry combustion method using an elemental analyzer (Vario Macro C/N, Elementar, Germany).
Statistics
The data were analyzed by three-way ANOVA to compare the effects of soil depth, tillage management, nitrogen application rates, and their interaction on enzyme activities, microbial CUE, PLFAs, and microbial diversity. We compared the means by using the least significant difference with a significance level of P < 0.05. Statistical analyses were performed using the SPSS 18.0 software (SPSS Inc., Chicago, United States). Sequences were processed using Quantitative Insights Into Microbial Ecology (QIIME) version 1.9.1 (Caporaso, 2010). Operational taxonomic units clustering at 97% of identity were collected using UCLUST in QIIME software. Changes in the microbial community structures of the soil samples were evaluated by principal coordinate analysis (PCoA) in R (v. 3.4.1). The relationships among agricultural practices, soil microbial diversity and community structure, microbial biomass, soil microbial CUE, and soil POC were explored by using partial least squares path modeling (PLS-PM). Estimates of path coefficients and coefficients of determination (R2) in our path model were validated by R (v.3.4.1) with the ‘plspm’ package (Ai et al. , 2018). The model was assessed using the Goodness of Fit (GoF) statistic, where the GoF value was set to 0.69.