Long-term fertiliser (organic and inorganic) input effects on soil microbiological characteristics in hydromorphic paddy soils in China.
Organic matter transformation represents one of the most important attributes for the management of soil fertility (Lalnunzira and Tripathi 2018), and is regulated by the activity of soil microorganisms and enzymes (Schimel and Bennett 2004). Soil microorganisms are considerably affected by organic and inorganic inputs and, therefore, long-term fertiliser application may critically affect soil fertility in paddy fields. Previous studies have shown that soil microbial ecological indices, such as soil microbial biomass, microbial community structure, and enzyme activity can be affected by fertilisation modes and fertiliser types (Su et al. 2015; Garcia-Orenes et al. 2016). For example, Marschner et al. (2003) showed that soil microbial community structure was changed by long-term manure treatment. Furthermore, another long-term fertilisation experiment (Zhang et al. 2006) indicated that enzyme activity in red paddy soil increased after long-term fertilisation, whereas microbial community richness significantly decreased. Additionally, soil microbial carbon (C) and activities of urease and acid phosphatase in chemical fertiliser-treated paddy soil were increased by manure (Zhang et al. 2010). A previous study indicated that soil microbial diversity was significantly higher in manure plus chemical fertiliser treatments than in pure chemical fertiliser treatments (Zhu and Zhu 2015). The long-term use of organic manure plus inorganic fertiliser, especially with double amounts of manure, could significantly increase soil microbial biomass and urease activity (Juan et al. 2008).
Hydromorphic paddy soil, which has high fertility and good conditions in terms of irrigation and drainage, is an important sub-variety of paddy soils in China, with a total estimated area of 200 000 [km.sup.2]. It is mostly distributed in hilly areas with gentle slopes and plains with higher terrain in the middle and lower reaches of the Yangtze River, China, such as Jiangxi, Anhui, and Hunan Provinces; in these areas it is the most important type of the paddy soil. However, soil response to long-term fertilisation in terms of microbiological characteristics in hydromorphic paddy soil has not been well investigated. Exploring the effects of long-term fertilisation on soil microbiological characteristics in hydromorphic paddy soil is regarded as necessary for improving fertilisation management.
This study aimed to understand the effect of long-term fertilisation of nitrogen (N)-phosphorus (P)-potassium (K) additions alone and in combination with organic manure in relation to a control in a 30-year-old hydromorphic paddy soil of China. We tested the hypothesis that long-term fertilisation could affect microbial biomass, enzyme activity, and microbial community diversity in soil.
Materials and methods
The long-term fertilisation experiment was launched under the rice-rice-fallow system at the farm of Jiangxi Academy of Agricultural Science (115[degrees]56'E, 28[degrees]34'N) in 1984. The experimental site is in the mid-subtropical zone. The annual temperature is 17.5[degrees]C; and the cumulative temperature >10.0[degrees]C is 5400.0[degrees]C. Annual rainfall is 1600.0 mm, and evaporation capacity is 1800.0 mm. The frost-free period is 280 days. The hydromorphic paddy soil used was developed from Quaternary red clay. The original soil properties follow: organic matter (SOM), 25.6 g [kg.sup.-1]; total N, 1.36 g [kg.sup.-1]; total P, 0.49 g [kg.sup.-1]; exchangeable K, 240.0 mg [kg.sup.-1]; available N, 81.6 mg [kg.sup.-1]; Olsen P, 20.8 mg [kg.sup.-1]; available K, 35.0 mg [kg.sup.-1]; cation exchange capacity, 75.4 mmol [kg.sup.-1]; and pH 6.50.
There were five treatments: (1) a control without fertiliser (Cont); (2) chemical fertiliser (NPK); (3) 30% manure plus 70% NPK (named 300M70CF); (4) 50% manure plus 50% NPK (500M50CF); and (5) 70% manure plus 30% NPK (70OM30CF). For treatments (2)-(5) with N, P, and K fertilisation, the percentage of application was calculated on the basis ofN, and the deficiency of P and K was supplemented by inorganic fertiliser. Each treatment had three replicate plots in a random arrangement. The area of each plot was 33.3 [m.sup.2]. The N of 15.0 g [m.sup.-2], P (measured by [P.sub.2][O.sub.5]) of 6.0 g [m.sup.-2], and K (measured by [K.sub.2]O) of 15.0 g [m.sup.-2] were applied in early rice, whereas the N of 18.0 g [m.sup.-2] and the same amounts of P and K were applied for late rice. Urea (CO[(N[H.sub.2]).sub.2]), calcium superphosphate (Ca[([H.sub.2]P[O.sub.4]).sub.2] x [H.sub.2]O), and potassium chloride (KC1) were used as the N, P, and K fertilisers respectively. For organic fertiliser, pig manure containing 3.5-5.7 g [kg.sup.-1] of N, 1.6-2.2 g [kg.sup.-1] of P, and 4.4-7.1 g [kg.sup.-1] of K was used, and its application amount varied according to the actual content of nutrients. The phosphate fertiliser, organic fertiliser, and 50% nitrogen fertiliser were used as base fertilisers. There was 25% of N fertiliser and 50% of K fertiliser used as topdressing at the tillering stage, and the remaining N and K fertilisers were used at the young panicle differentiation stage.
In 2013, soil samples were collected from 0-20 cm plots after the rice harvest. For each plot, soil from at least five sites that were located at the cross-point of two diagonals of the four adjacent plants forming a rectangle was sampled using the S-shaped sampling method. Soil samples from all of the sites in each plot were then pooled into one sample for further analyses.
Determination of soil microbial biomass C (SMBC) and N (SMBN)
Using LiquiTOC II (Elementar, Germany), SMBC and SMBN were determined by the chloroform fumigation method described by Brookes et al. (1985) and Vance et al. (1987). The contents of SMBC and SMBN were calculated according to Eqns (1) and (2).
SMBC = EC/0.38 (1)
where EC represents the difference between the measured values of the samples incubated with and without chloroform, and 0.3 8 is the conversion coefficient of SMBC.
SMBN = EN/0.45 (2)
where EN represents the difference between the measured values of the samples incubated with and without chloroform, and 0.45 is the conversion coefficient of SMBN.
The total soil organic C (SOC) was determined according to the method described by Bhandari and Bam (2014). The soil microbial quotient (SMQ) is the ratio of SMBC to SOC.
Determination of enzyme activity
Enzymes are key soil components catalysing important transformations associated with decomposition and nutrient turnover, and their activity in soil is widely used as an indicator of soil health (Kotroczo et al. 2014). Moreover, soil enzyme activities are also used as indices of microbial growth and activity in soils (Frankenberger and Dick 1983). Sucrase activity is related to SOM, N, P, microorganism quantity, and soil respiration intensity. Higher sucrase activity indicates greater soil fertility (Wang et al. 2013). Sucrase (EC 22.214.171.124) activity was determined by the colourimetric method using 3,5-dinitrosalicylic acid and was expressed by the content of glucose (mg) produced for 24 h per gram of soil (Shao and Li 2016). Proteases are enzymes that break the peptide bonds between amino acids of proteins and produce free amino acids. Protease (EC 126.96.36.199) activity was analysed by Folin-Ciocalteu's phenol method, and illustrated by the amount of tyrosine (jag) produced from 1 g of soil after maintaining it in a water bath at 50[degrees]C for 2 h (Jiang et al. 2011). Urease is the enzyme that catalyses the hydrolysis of urea, whose activity is widely used for evaluating the changes in soil quality for soil management. Urease (EC 188.8.131.52) activity was measured by the indophenol colourimetric method, and illustrated by the amount of N[H.sub.3]-N (mg) produced from 1 g soil after being cultured at 37[degrees]C for 24 h (Huang et al. 2012). Phosphatase activity is ubiquitous in soil and sensitive to environmental perturbations, and can serve as an indicator of soil quality (Amador et al. 1997). Phosphatase (EC 184.108.40.206) activity was determined by the disodium p-nitrophenyl phosphate tetrahydrate colourimetric method, and indicated by the amount of paranitrophenol released from 1 g of soil within 1 h (Hoffmann and Teicher 1961). Additionally, soil catalase is the intracellular oxidoreductase enzyme, which can remain active outside microbial cells. Catalase activity in soil is related to both the number of aerobic microorganisms and soil fertility (Margesin et al. 2000). Catalase (EC 220.127.116.11) activity was determined by potassium permanganate titration (Van Elsas 1995), calculated by the volume of 0.1 mol [L.sup.-1] KMn[O.sub.4] consumed in the culture of 1 g of soil at 25[degrees]C for 20 min, and then expressed as [micro]mol [H.sub.2][O.sub.2] [g.sup.-1] [(20 min).sup.-1]. The geometric mean of enzyme activities (GMea) was calculated according to Eqn (3):
GMea = [5 square root of Inv x Pro x Ure x AcP x Cat] (3)
where Inv, Pro, Ure, AcP, and Cat represent the activity of sucrase, proteinase, urease, acid phosphatase, and catalase respectively (Johnson and Temple 1964).
Denaturing gradient gel electrophoresis (DCCE) analysis for bacteria
First, DNA was extracted from the soil samples using an UltraClean[TM] Soil DNA Isolation Kit (MoBio, Carlsbad, CA, USA). After being tested by 1% agarose gel electrophoresis, the 16S rDNA-V3 region of bacteria was synthesised by PCR. The PCR reaction was conducted in a 25-[micro]L total volume containing 2.5 (XL of 10 x buffer solution, 2 [micro]L of deoxyribonucleoside triphosphate (dNTP, 2.5 mmol [L.sup.-1]), 1 [micro]L of primer 1 (10 mmol [L.sup.-1]) and primer 2 (10 mmol [L.sup.-1]), 2.5 [micro]L of Mg[Cl.sub.2] (25 mmol [L.sup.-1]), 1 [micro]L of suitable dilution of soil DNA, 2.5 U of Taq DNA polymerase (5 U/[micro]L), and 12.5 [micro]L of double-distilled [H.sub.2]O (dd[H.sub.2]O). The thermal profile for the PCR reaction follows: initial denaturation at 94[degrees]C for 7 min; then 35 cycles, which included denaturation at 94[degrees]C for 30 s, annealing at 61[degrees]C for 30 s, and extension at 72[degrees]C for 30 s; and a final extension step at 72[degrees]C for 7 min. Common primers for the bacterial 16S rDNA-V3 segment were designed as follows: primer 1 (PRBA338F(338-358)), 5'-ACTCCTACGGGAGG CAGCAG-3'; and primer 2 (PRUN518R(534-518)), 5'-ATTACCGCGGCTGCTGG-3'. The 'GC' clamp sequence at the 5'-side was 5'-CGC CCG CCG CGC GCG GCG GGC GGG GCG GGG GCA CGG GGG G-3'.
Subsequently, DGGE analysis was conducted by a D-Code Universal Mutation Detection System (Bio-Rad Laboratories, Hercules, CA, USA) using 8% polyacrylamide gel and 40-60% urea-formamide denaturant gradient to separate the PCR products. The conditions were as follows: 80 V, 60[degrees]C, electrophoresis for 16 h in 1 x TAE buffer, and scanning after silver colouring. The similarity analysis of DGGE maps was performed using Quantity One computer 4.6.3 software (Bio-Rad) (Navarro-Noya et al. 2010), and the similarity of the electrophoresis maps was calculated using the Dice Coefficient (Cs), defined as Cs = 2j/(a + b), where j represents the number of common bands in both sites, and a and b are the band counts in sites A and B respectively. The index Cs ranged from 1 (sharing exactly the same bands) to zero (no common bands). The cluster dendrogram was created by the unweighted pair group method using arithmetic averages.
The Shannon-Weiner index (H) was calculated from Eqn (4):
H = - [SIGMA] [p.sub.i] In [p.sub.i] (4)
where [p.sub.i] is the relative richness of the i strap of DGGE.
The evenness index (E) was calculated according to Eqn (5):
E = H/lnR (5)
where the richness index (R) is the total number of straps on the DGGE map (Asakawa and Kimura 2008).
DGGE analysis for fungi
In the PCR of fungal 18S rDNA, the primers EF390 (5'-CGA TAA CGA ACG AGA CCT-3') and FR1 (5'-AIC CAT TCA ATC GGT AIT-3') were used. The 'GC' clamp sequence at 5'-side was 5'-CCC CCG CCG CGC GCG GCG GGC GGG GCG GGG GCA CGG GCC G-3'. The PCR reaction system was as follows: 2.5 [micro]L of 10 x buffer solution, 1.5 pL of Mg[Cl.sub.2] (25 mmol [L.sup.-1]), 2 [micro]L of dNTP (2.5 mmol [L.sup.-1]), 0.5 [micro]L of primer (10 mmol [L.sup.-1]) for each one, 1 (iL of DNA, 0.3 [micro]L of Taq enzyme (5 U/[micro]L), and dd[H.sub.2]O added to 25 [micro]L. The thermal profile for amplification of 30 cycles was conducted as described above with annealing temperatures of 50[degrees]C.
The DGGE analysis of the PCR synthesising production was conducted by the D-Code Universal Mutation Detection System (Bio-Rad) with 7.5% polyacrylamide gel and 45-60% urea-formamide denaturant gradient. The conditions were 50 V, 58[degrees]C, electrophoresis for 18 h in 1 x TAE, and scanning after silver colouring. The results were analysed similarly to the DGGE for bacteria.
The SPSS version 19.0 software (SPSS, Chicago, IL, USA) for Windows was used for statistical analysis of data. One-way ANOVA followed by least-significant difference method test was used for comparison between two groups with P < 0.05 as a threshold.
Effect of fertiliser application on SMBC, SMBN, and SMQ
The levels of SMBC, SMBN, and SMQ in response to organic-inorganic combination fertilisers were significantly higher than that with the Cont treatment (P < 0.05). Moreover, the levels of SMBC, SMBN, and SMQ were significantly higher in the combined than the inorganic fertiliser treatment (P < 0.05). Furthermore, SMBC and SMBN were enhanced with the increased percentage of organic fertilisers, and were remarkably increased after 700M30CF compared to 300M70CF treatment (P <0.05) (Table 1). However, although SMQ was also enhanced with the increased percentage of organic fertilisers, there was no significant difference among the three combined treatments (Table 1).
Effect of fertiliser application on soil enzymes
The soil enzyme activities and GMea in the organic-inorganic fertiliser combined treatments were significantly higher than for inorganic fertiliser and Cont treatments (P < 0.05). For example, GMea was up to 9.38 in the organic-inorganic fertiliser combined treatments. Compared with inorganic fertiliser treatment, the activities of sucrase, urease, proteinase, acid phosphatase, catalase, and GMea in the combined treatments increased by 21.7-36.7, 20.1-31.1, 55.1-67.4, 16.6-28.2, 23.3-42.1, and 28.4-37.9% respectively. With the increase in percentage of organic fertilisers, the activities of urease, proteinase, catalase, and GMea showed an increasing trend. In addition, enzyme activities and GMea were significantly higher in the inorganic fertiliser than the Cont treatment (P < 0.05) (Table 2).
Effect of fertiliser application on soil microbial diversity
There were clear differences in electrophoresis straps, intensity, and migration rate in the DGGE map for soil bacterial diversity among the four treatments. The electrophoresis straps in the fertilisation treatments were less than the Cont treatment; and in the inorganic fertiliser treatment were fewer than in organic-inorganic fertiliser combined treatments (Fig. 1 a and b), indicating that fertilisation reduced the soil bacterial community diversity, and the inorganic fertiliser decreased it the most. Furthermore, the number of electrophoresis straps was higher with the increase in percentage of organic fertiliser, suggesting that organic fertiliser could contribute to maintaining soil bacterial community diversity.
The cluster analysis divided the five treatments into two clusters: NPK and the remaining four treatments (Fig. 2a). This indicated that the soil bacterial community composition was significantly affected by long-term inorganic fertiliser application. The three different organic-inorganic fertiliser combined treatments were clustered into one branch, indicating similar effects of the three treatments on soil bacterial community.
In addition, the H, R, and E (equitability) indexes of bacterial community decreased with long-term fertilisation, especially inorganic. Moreover, inorganic fertiliser treatment had a greater effect on the bacterial community than the organic-inorganic fertiliser combined treatments. Both H and R indexes increased with the increase in percentage of organic fertilisers (Table 3).
Fungal diversity increased after fertilisation
Similar to results for soil bacterial diversity, there were clear differences in electrophoresis straps, intensity, and migration rate in the DGGE map for soil fungal diversity among the four treatments. The electrophoresis straps in the fertilisation treatments were greater than that of the Cont treatment, and that of the inorganic fertiliser treatment was greater than for the organic-inorganic combined treatments (Fig. 3a and b), indicating that fertilisation increased the soil fungal community diversity and that inorganic fertiliser increased it the most. Furthermore, the number of electrophoresis straps was greater with the increased percentage of inorganic fertilisers, suggesting that inorganic fertilisers contributed to raising soil fungal community diversity.
The cluster analysis also divided the five treatments into two clusters: Cont and the four fertilisation treatments (Fig. 2b). This indicated that the community composition of soil fungi was seriously disturbed after the long-term fertilisation.
In addition, both H and R indexes of the soil fungal community increased with long-term fertilisation, especially inorganic. Moreover, the effects of inorganic fertiliser on fungal community were stronger than for organic-inorganic fertiliser combined.
In this study, SMBC and SMBN were significantly higher after the organic and inorganic fertiliser treatments in relation to the Cont treatment, and were higher after treatment with combined organic-inorganic than after inorganic fertiliser. Previously, a more than 30-year-old field experiment revealed that organic manures could increase soil microbial biomass (Witter et al. 1993) and another long-term (15 years) field experiment also showed that SMBC and SMBN significantly increased after treatment with mineral fertilisers plus farmyard manure compared with inorganic fertilisation (Liu et al. 2008). Moreover, Shao and Li (2016) also found that application of organic manure could increase the amounts of SMBC and SMBN. Consistent results were obtained in our study, possibly because the combined organic-inorganic fertilisers provided appropriate C and N sources for growth and proliferation of soil microorganisms, thus increasing SMBC and SMBN.
Previous studies suggested that SMQ could be an efficient index of the dynamics of SOC and soil quality (Liu et al. 2006; Raiesi and Beheshti 2015). Generally, the SMQ is 1-4% (Anderson and Domsch 1989). In this study, SMQ was significantly higher in the fertilisation treatments, especially for organic-inorganic combined fertiliser. Masto et al. (2006) reported a similar result wherein manure plus NPK fertiliser significantly increased the SMQ compared with NPK fertiliser treatment. Furthermore, a similar result as for microbial biomass and SMQ was found in paddy soils (Liu et al. 2006). Therefore, long-term combined application of organic and inorganic fertilisers can contribute to the increase of SMQ and soil fertility, but a higher percentage of organic fertiliser may not result in a higher SMQ.
The activities of soil enzymes--sucrase, urease, proteinase, acid phosphatase, and catalase--after combined organic-inorganic fertiliser treatments significantly increased compared with those of inorganic fertiliser and Cont treatments. A previous 15-year long-term field experiment proved that mineral fertilisers plus incremental swine manure increased urease activity compared with mineral fertiliser and Cont treatments (Juan et al. 2008; Liu et al. 2008). In our study, GMea was also improved by the use of organic-inorganic fertiliser, consistent with the report of Garcia-Ruiz et al. (2008), suggesting that organic management could improve soil quality.
The R and E indexes of soil bacteria decreased during longterm fertilisation, especially inorganic, compared with the Cont treatment. Similarly, Meng et al. (2008) showed that soil bacteria richness and the H index of soybean fields with no fertilisation over the long-term were the highest, followed by long-term application of chemical fertilisers with farmyard manure, and those with application of chemical fertiliser alone were lowest. Zhang et al. (2006) also found that fertiliser application in paddy field under double cropping of rice reduced the microbial community diversity. Moreover, Sun et al. (2004) showed that soil bacterial community structure of manured soils was similar to that of untreated soil rather than that of inorganic fertiliser-treated soil. Long-term fertilisation could reduce the soil bacterial community diversity, which might occur because the fertiliser provided the appropriate C/N ratio and other necessary substances for growth of some bacteria, resulting in the generation of dominant bacterial community and decreasing bacterial community diversity. In contrast, our study showed that both the H and R indexes for soil fungi were higher in the long-term fertilisation treatments, especially inorganic, compared with the Cont treatment. In the study by Bi et al. (2010), long-term fertilisation increased fungal diversity. However, they found that organic manure applications were more effective than mineral fertiliser in increasing soil fungal diversity, which was inconsistent with our results--this may result from differences in soil type and management practices. In brief, compared with previous studies on other paddy soils mentioned above, long-term fertilisation significantly reduce the H, R, and E indexes of bacteria, and improved the fungal H and R indexes in the hydromorphic paddy soil. Furthermore, the effect of long-term use of chemical fertiliser on microbial community diversity was greater than that of combined organic-inorganic fertiliser application.
The findings of this study revealed that long-term fertilisation with high doses of combined organic-inorganic input significantly increased microbial biomass, enzyme activity, and fungal community diversity. However, the same input decreased bacterial community diversity. This study will be useful for improving fertilisation management in hydromorphic paddy soils.
Conflicts of interest
The authors declare that they have no competing interests.
The study was supported by the National Natural Science Foundation of China (No. 31460544); Modem Agricultural Research Collaborative Innovation Special Project in Jiangxi (No. JXXTCX2016003); the National Science and Technology Support Program (No. 2012BAD05B05), and the Special Fund for Agro-scientific Research in the Public Interest of China (No. 201203030).
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Handling Editor: Chenrong Chen
Yiren Liu (iD) (A,B), Hongqian Hou (A), lianhua ji (A), Zhenzhen Lv (A), Xiumei Liu (A), Cuangrong Liu (A), and Zuzhang Li (A)
(A) New-typed Fertilizer Laboratory, Institute of Soil Fertilizer and Resource Environment, Jiangxi Academy of Agricultural Sciences, Nanlian Road No. 602, Nanchang, Qingyunpu District, Jiangxi Province, 330200, China.
(B) Corresponding author. Email: firstname.lastname@example.org
Caption: Fig. 1. DGGE band patterns of 16S r DNA of soil bacteria in the different treatments, (a) DGGE finger-print, (b) DGGE map generated from the Quantity One software based on the finger-print. Cont, control treatment without fertiliser; NPK, treated with nitrogen, phosphorus, and potassium fertilisers; 300M70CF, treated with 30% manure plus 70% NPK; 500M50CF, treated with 50% manure plus 50% NPK; 700M30CF, treated with 70% manure plus 30% NPK.
Caption: Fig. 2. DGGE cluster analysis of bacterial (a) and fungal (b) communities in soil samples from the different treatments. Cont, control treatment without fertiliser; NPK, treated with nitrogen, phosphorus, and potassium fertilisers; 300M70CF, treated with 30% manure plus 70% NPK; 500M50CF, treated with 50% manure plus 50% NPK; 700M30CF, treated with 70% manure plus 30% NPK.
Caption: Fig. 3. DGGE band patterns of 16S r DNA of soil fungi in the different treatments. (a) DGGE finger-print, (b) DGGE map generated from the Quantity One software based on the finger-print. Cont, control treatment without fertiliser; NPK, treated with nitrogen, phosphorus, and potassium fertilisers; 300M70CF, treated with 30% manure plus 70% NPK; 500M50CF, treated with 50% manure plus 50% NPK; 700M30CF, treated with 70% manure plus 30% NPK.
Table 1. The SMBC, SMBN, and SMQ in the soil from different treatments Different letters in the same column indicate significant differences at P < 0.05. SMBC, soil microbial biomass carbon; SMBN, soil microbial biomass nitrogen; SMQ, soil microbial quotient. Cont, control treatment without fertiliser; NPK, treated with nitrogen, phosphorus, and potassium fertilisers; 300M70CF, treated with 30% manure plus 70% NPK; 500M50CF, treated with 50% manure plus 50% NPK; 700M30CF, treated with 70% manure plus 30% NPK. '[+ or -]' indicates standard deviation Treatment SMBC SMBN (mg x [kg.sup.-1]) (mg x [kg.sup.-1]) Cont 190.9 [+ or -] 14.1 d 28.3 [+ or -] 3.4 d NPK 396.0 [+ or -] 29.2 c 63.9 [+ or -] 7.6 c 300M70CF 562.1 [+ or -] 21.1 b 89.6 [+ or -] 6.9 b 500M50CF 589.5 [+ or -] 29.5 b 99.5 [+ or -] 7.1 ab 700M30CF 633.5 [+ or -] 19.9 a 104.6 [+ or -] 5.6 a Treatment SMQ (%) Cont 1.63 [+ or -] 0.12 c NPK 2.61 [+ or -] 0.32 b 300M70CF 3.05 [+ or -] 0.32 ab 500M50CF 3.10 [+ or -] 0.22 a 700M30CF 3.24 [+ or -] 0.24 a Table 2. Enzyme activity in soil from different treatments Different letters in the same column indicate significant differences at P < 0.05. GMea, geometric mean of enzyme activities; Cont, control treatment without fertiliser; NPK, treated with nitrogen, phosphorus, and potassium fertilisers; 300M70CF, treated with 30% manure plus 70% NPK; 500M50CF, treated with 50% manure plus 50% NPK; 70QM30CF, treated with 70% manure plus 30% NPK. '[+ or -]' indicates standard deviation Treatment Sucrase Urease (mg x [g.sup.-1] x (mg x [g.sup.-1] x [(24 h).sup.-1]) [(24 h).sup.-1]) Cont 4.02 [+ or -] 0.36 c 0.47 [+ or -] 0.05 d NPK 10.6 [+ or -] 0.47 b 0.73 [+ or -] 0.05 c 300M70CF 12.9 [+ or -] 1.10 a 0.88 [+ or -] 0.01 b 500M50CF 13.0 [+ or -] 0.79 a 0.88 [+ or -] 0.05 b 700M30CF 14.5 [+ or -] 1.43 a 0.96 [+ or -] 0.04 a Treatment Protease Acid phosphatase (mg x [g.sup.-1] x (mg x [g.sup.-1] x [(2 h).sup.-1]) [h.sup.-1]) Cont 101.7 [+ or -] 8.28 d 0.26 [+ or -] 0.04 c NPK 176.2 [+ or -] 9.20 c 0.38 [+ or -] 0.06 b 300M70CF 205.4 [+ or -] 8.65 b 0.59 [+ or -] 0.06 a 500M50CF 219.8 [+ or -] 6.11 ab 0.62 [+ or -] 0.08 a 700M30CF 225.9 [+ or -] 15.3 a 0.64 [+ or -] 0.04 a Treatment Catalase GMea ([micro]mol x [g.sup.-1] x [(20 h).sup.-1]) Cont 639.6 [+ or -] 0.68.8 c 4.19 [+ or -] 0.19 d NPK 682.5 [+ or -] 49.2 c 6.80 [+ or -] 0.12 c 300M70CF 842.5 [+ or -] 41.2b 8.73 [+ or -]0.31 b 500M50CF 931.3 [+ or -] 601.3 ab 9.05 [+ or -] 0.11 ab 700M30CF 969.4 [+ or -] 605.2 a 9.38 [+ or -] 0.26 a Table 3. Genetic diversity indexes of soil microbe communities from DGGE maps Cont, control treatment without fertiliser; NPK, treated with nitrogen, phosphorus, and potassium fertilisers; 30OM70CF, treated with 30% manure plus 70% NPK; 50OM50CF, treated with 50% manure plus 50% NPK; 70OM30CF, treated with 70% manure plus 30% NPK. Similarity analysis of DGGE maps was performed using Quantity One software (version 4.6.3). '[+ or -]' indicates standard deviation Bacteria Treatment Shannon-Weiner (H) Richness Cont 3.023 [+ or -] 0.141 a 22 [+ or -] 1.72 a NPK 2.577 [+ or -] 0.196 b 15 [+ or -] 1.13 d 300M70CF 2.648 [+ or -] 0.213 b 16 [+ or -] 0.98 cd 500M50CF 2.758 [+ or -] 0.254 ab 18 [+ or -] 1.59 c 700M30CF 2.905 [+ or -] 0.172 ab 21 [+ or -] 1.04 b Treatment Evenness Shannon-Weiner (H) Cont 0.978 [+ or -] 0.081 a 2.293 [+ or -] 0.137 c NPK 0.952 [+ or -] 0.037 a 2.940 [+ or -] 0.205 a 300M70CF 0.955 [+ or -] 0.066 a 2.765 [+ or -] 0.226 ab 500M50CF 0.954 [+ or -] 0.082 a 2.691 [+ or -] 0.118 ab 700M30CF 0.954 [+ or -] 0.027 a 2.670 [+ or -] 0.103 b Fungi Treatment Richness Evenness Cont 10 [+ or -] 1.25 c 0.996 [+ or -] 0.043 a NPK 19 [+ or -] 1.54 a 0.998 [+ or -] 0.065 a 300M70CF 16 [+ or -] 1.17 b 0.997 [+ or -] 0.059 a 500M50CF 15 [+ or -] 1.36 b 0.994 [+ or -] 0.088 a 700M30CF 15 [+ or -] 1.28 b 0.997 [+ or -] 0.071 a
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|Author:||Liu, Yiren; Hou, Hongqian; Ji, Lianhua; Lv, Zhenzhen; Liu, Xiumei; Liu, Cuangrong; Li, Zuzhang|
|Date:||Aug 1, 2019|
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