Rhizosphere Enzyme Activities and Microorganisms Drive the Transformation of Organic and Inorganic Carbon in Saline - Alkali Soil Region

doi:10.21203/rs.3.rs-644355/v1

Download PDF

Research Article

Rhizosphere Enzyme Activities and Microorganisms Drive the Transformation of Organic and Inorganic Carbon in Saline - Alkali Soil Region

https://doi.org/10.21203/rs.3.rs-644355/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Soil inorganic carbon (SIC) is the main existing form of soil carbon pool in arid saline-alkali land, and its quantity distribution affects the pattern of soil carbon accumulation and storage. Previous studies on soil carbon mostly focus on organic carbon (SOC), and pay little attention to SIC. The purpose of this study was to investigate the effect of microorganisms and soil enzymes on the transformation of SOC and SIC in the rhizosphere soil of three maize fields (M1, M2 and M3) and three paddy fields (R1, R2 and R3) under saline-alkali stress, which is a new research topic of soil carbon cycle in saline-alkali soil region. The results showed that the root - soil - microorganism interaction was changed by saline-alkali stress. The results of RDA analysis showed that Proteobacteria, Gemmatimonadetes, Planctomycetes, Nitrospirae, catalase, invertase, amylase and β-glucosidase had significant effects on the transformation of SOC and SIC (P<0.05). SOC and SIC in maize and rice rhizosphere soil were mainly driven by soil enzymes and microorganisms under saline-alkali stress, exchangeable sodium percentage (ESP) and pH were the main factors affecting the conversion process of SOC and SIC.

Environmental Policy

Soil organic carbon

soil inorganic carbon

enzyme activities

microbial community structure

high-throughput sequencing

saline-alkali soil

As the source and sink of atmospheric CO₂, terrestrial ecosystems are an important part of the global carbon cycle. Soil carbon pool is the largest carbon pool in terrestrial ecosystems, so soil carbon cycle is an important part of global carbon cycle research, and one of the core issues of global change research programs such as the Global Carbon Project (GCP) and World Climate Research Program (WCRP). Soil carbon pool mainly includes soil organic carbon (SOC) pool and inorganic carbon (SIC) pool. SOC plays a dominant role in soil carbon pool in humid areas, while SIC is the main form of carbon in arid and semi-arid areas with annual rainfall less than 400mm ^1,2. It is estimated that the global SOC pool is as high as 1.4–1.5 ×10¹⁸ g C, which is about 2.4 times of the total terrestrial biological carbon and 3 times of the total atmospheric carbon ³. A change of 0.1% in soil carbon pool will increase atmospheric CO₂ concentration by 1mg m^− 3 and have a profound impact on global climate change.

Soil enzymes reflect the transformation capacity of soil nutrients and the level of soil microbial activity, and participate in all complex biochemical processes in soil, including the decomposition of litter, the decomposition and synthesis of humus and various organic compounds, the fixation and release of soil nutrients and other redox reactions ^4,5. Soil enzymes can be divided into oxidoreductase, transferase, hydrolase and lyase, which are the important indicators of soil fertility and environmental quality ^6,7. Researchers continue to focus on the relationship between soil enzymes and soil carbon pool. Burns et al. summarized the research progress and future exploration direction of soil enzymes ⁸. Shao et al. analyzed the seasonal dynamics of soil active organic carbon and enzyme activities, and the results showed that vegetation type was an important factor affecting the spatiotemporal changes of soil enzyme activities and active organic carbon. Ruan et al. showed that sucrase activity is most closely related to soil organic matter (SOM), which can promote the decomposition of SOM and provide nutrients for microorganisms to promote microbial reproduction ⁹. Soil enzyme activity can reflect soil biochemical activity stably and sensitively ¹⁰. The study on the activity changes of various enzymes is of great significance for revealing the process, direction and law of SOM transformation.

Soil microorganisms play a key role in regulating matter circulation of ecosystem and are an important index to measure soil properties and functions ¹¹. The exchange of matter and energy between soil, rhizosphere microorganisms, and plants forms a close and special relationship, which makes the abundance and species of rhizosphere microorganisms differ to some extent from those of non-rhizosphere soils¹². Soil physico-chemical properties, nutrient cycling and microbial activity are affected by the type of soil tillage ¹³. The changes of soil temperature, moisture and carbon input will also have a great effect on soil microbial activity, which in turn will affect the nutrient availability due to the turnover of soil organic matter¹⁴. Lenton et al. showed that the increase of temperature led to the change of microbial community structure, which accelerated the decomposition rate of soil organic carbon and released the carbon stored in the soil into the atmosphere ¹⁵. Rey et al. found that moisture changes the oxygen condition and microbial activity of soil environment, thus affecting soil organic carbon mineralization¹⁶.

In recent years, the northeast of China has been increasingly affected by global climate change and human disturbance, which has exacerbated the process of soil salinization and desertification in Songnen Plain¹⁷. Land salinization has become one of the world's resource and environmental problems, which is a huge environmental pressure restricting the development of agriculture and causes billions of agricultural economic losses every year. Soil salinization is a kind of land degradation caused by excessive accumulation of soil salinization under the combined action of natural and human factors¹⁸. It can damage the normal growth of plants, change the structure and function of cell membrane, and have toxic effects on cells. Western Jilin Province, located in the Northeast China, is a typical vulnerable area for global carbon cycle research, and also one of the three major concentrated distribution areas of soda saline-alkali soil in the world ^19,20. The saline–alkali land in Western Jilin Province is mainly made up of saline–alkali soil ¹². Previous studies on soil carbon pool mainly focused on forest, wetland and grassland soils. However, the research on soil carbon pool in saline-alkali land is relatively weak, especially the SIC and the effects of soil enzymes and microorganisms is not clear ^21–23.

After decades of cultivation and development, a special saline alkali agroecosystem has been formed in the study area., with maize and rice as the main crops. We selected the rhizosphere soil of maize and rice in saline-alkali farmland as the research object. The aim of this study is to: (i) analyze the variation of physico-chemical properties, soil enzyme activities and microbial community structure of maize and rice rhizosphere soil in soda saline–alkali field; (ii) assess the response of SOC and SIC in the rhizosphere of maize and rice in soda saline–alkali field; (iii) to explore the effects of soil enzymes and microorganisms on the transformation of SOC and SIC under saline-alkali stress. It provides a basic example for the study of global carbon cycle in saline-alkali farmland, which is of great significance to the protection of saline-alkali land resources and the sustainable development of agriculture.

2.1. Study Area Description

The study area is located in Songyuan City, which belongs to the west of Jilin Province (Fig. 1). The area has experienced multiple desertification and saline-alkali desertification evolution process, forming a large area of saline-alkali soil deposition ²⁴. The region is a semi-arid and semi humid continental monsoon climate zone, with obvious difference in four seasons. The average annual precipitation is 558.3 mm, and the highest average temperature in July is 23.5°C.

2.2. Soil Sampling and Experimental Design

In order to make the sample plots more representative and the test results more universal, we took samples according to the soil type map and land use type map, combined with field investigation. Three typical saline-alkali maize fields and three typical saline-alkali rice fields were chosen for the research and at the same time, three parallel sampling points were randomly selected each site (Table 1). Maize and rice are both monocropped, sown in May and harvested in October. The fertilization and management measures of all the fields were consistent, and the fertilizers (urea, potassium and phosphorus) were applied once on May 8.

Table 1

Background information on sampling sites.
Samplingsites	Soil classifification	pH	Clay (%)	Silt (%)	Sand (%)	Fertilization treatment	Experimentalarea	Type of crop
M1	Loam	8.43	14.03	40.73	45.24	N, P and K	95 m×100 m	Maize
M2	Silty loam	9.23	8.52	73.48	18.00	N, P and K	99 m×103 m	Maize
M3	Sandy loam	9.98	7.94	24.98	47.08	N, P and K	90 m×100 m	Maize
R1	Loam	8.23	14.07	40.18	45.75	N, P and K	98 m×102 m	Rice
R2	Silty loam	9.37	10.75	67.33	21.92	N, P and K	97 m×101 m	Rice
R3	Sandy loam	9.94	4.58	31.30	64.12	N, P and K	95 m×99 m	Rice

Rhizosphere soil samples were collected on August 16, 2019 (vigorous root growth stage). We set up five sampling blocks (15 m × 15 m) in each sampling field, and each sampling block contains three parallel sampling sites (Fig. 1). When sampling, all the roots of plants were dug out. Due to the difference between paddy field and upland, we assume that the soil attached to root system is regarded as rhizosphere soil. Subsamples from the same block were mixed to generate a composite sample. A total of 30 composite samples of maize and rice were placed in sterile sealed bags, stored in incubators filled with ice, and quickly transported to the laboratory. The composite samples were divided into three parts: one part was ground after natural air drying in the laboratory, screened by 2mm and 0.2mm respectively, and bagged for standby; some parts were refrigerated at 4℃ for microbial enzyme activity test; the other part was stored at -80℃ for microbial community structure measurement.

2.3. Physico-Chemical Analysis of Soil

Soil pH, electrical conductivity (EC), Alkali-N (AN), were determined on soil passed through the 2mm sieve. SOM was determined on soil passed through the 0.2mm sieve. Soil physico-chemical properties were determined following the methods described by Zheng ²⁵. Soil pH and EC were tested by pH meter at a ratio of 5 to 1 (water to soil). SOM was determined by oil bath heating dichromate oxidation method. The concentration of exchangeable sodium (c mol (Na⁺) kg^− 1) was measured by using flame photometry (Shimadzu optical doublebeam atomic absorption spectrophotometer, Shanghai). Cation exchange capacity (CEC) (c mol kg^− 1) was determined by the EDTA-ammonium acetate salt exchange method. The exchangeable sodium percentage (ESP) was calculated by the following formula.

ESP = Na⁺/CEC × 100%

2.4. Soil Enzyme Activities

Catalase activity (EC 1.11.1.6) was determined by potassium permanganate titration and was calculated after the blank subtraction according to the volume of consuming of KMnO₄ standard solution and described as mL 0.1 N KMnO₄ g^− 1. Soil invertase (EC 3.2.1.26) and amylase (EC 3.2.1.2) activities were determined by 3,5-Dinitrosalicylic acid colorimetry. Both of them were analyzed by spectrophotometry at 508nm, and the activity of invertase and amylase could be expressed as µg g^− 1 24h^− 1 according to the amount of reducing sugar generated after the reaction. The soil β-glucosidase activity (EC3.2.1.21) was measured by using p-nitrophenol-β-D-glucoside as substrate, p-nitrophenol was hydrolyzed to produce p-nitrophenol under the action of β-glucosidase, and the activity of β-glucosidase was colorimetrically determined at 400 nm.

2.5. Soil Microbial Community Analysis

Soil genomic DNA was extracted from the sample using the E.Z.N.A. soil DNA kit (Omega Biotek, GA 30071, USA). The extracted DNA was purified and quantified by spectrophotometer. (Thermo, MA 02451, USA). The forward and reverse primers were connected with the universal primer of Illumina Miseq high-throughput sequencing platform, and the PCR products with universal primer sequence at both ends were obtained by the first round PCR reaction using genomic DNA as template, and the PCR products obtained in the first round were purified. The PCR product of the sequence was obtained by connecting the two ends of the sequencing tag sequence with the primer sequence matched with the general primer sequence of the first round of PCR. The purified PCR product was used as the template for the second round of PCR reaction. The amplicon extracted from 2% agarose gel was purified by PCR purification kit (Beckman, Indiana 46268, USA) and quantified using a Qubit® 2.0 fluorimeter (Invitrogen, CA 92008, USA). The Illumina MiSeq high-throughput sequencing platform was used for sequencing (Shanghai Sangon Biotechnology Co., LTD., Shanghai, China).

2.6. SOC and SIC Aanalysis

Shimadzu TOC-V (Japan) was used to determine the SOC and SIC. The TOC (total organic carbon) instrument uses combustion oxidation-non-dispersive infrared absorption method for total organic carbon. Using high purity air (N₂ + O₂) as carrier gas, quantitative soil samples were added to the total carbon (TC) and IC (inorganic carbon) reaction chamber, respectively. The TC and IC were measured under their respective working conditions, and the TOC was calculated (TOC = TC - IC). Test conditions: carrier gas (high purity oxygen) pressure: 300kPa; flow rate: 500ml / min. TC condition: temperature 900℃, cobalt oxide platinum catalyst. IC condition: temperature 200℃, 25% phosphoric acid (superior purity) is reactive acid. Glucose (superior purity) and anhydrous sodium carbonate (Reference Reagent) are used as standard samples of TC and IC respectively.

2.7. Statistical Analysis

The experimental data were expressed as mean ± standard deviation (SD). SPSS (SPSS Inc., Chicago, IL, USA; Norusis, 2008) was used for one-way analysis of variance (ANOVA), and the significant differences of soil physico-chemical properties and enzyme activities were obtained. Duncan’s test was used to evaluate significance when p < 0.05. The graph was drawn by using the software package origin 8.5. Redundancy analysis (RDA) was carried out by Canoco5 software (Microcomputer Power, Inc., Ithaca, NY, USA).

3.1. Soil Physico-Chemical Properties

The soil physico-chemical properties of the six farmlands were summarized in Table 2. Mean values of rhizosphere soil pH in maize fields were lowest at M1 (8.50), medium at M2 (8.71), and highest at M3 (9.36). In rice fields, the mean pH values were lowest at R1 (8.40), medium at R2 (8.96) and highest at R3 (9.32). These trends (in both maize and rice fields) were positively related with ESP in each field, M1 had the lowest ESP value of 5.22%, and M3 had the highest value of 13.51%. The EC in maize fields was 0.14–0.51 ds cm^− 1, and the largest value appeared in M3. While it ranged from 0.28 to 0.50 ds cm^− 1 in rice fields. The content of SOM and AN decreased with the increase of soil pH and ESP.

Table 2

Physico-chemical properties of rhizosphere soil.
Sampling Sites	pH	ESP(%)	EC(ds cm^− 1)	SOM(g kg^− 1)	AN(g kg^− 1)
M1	8.50 ± 0.25	5.22 ± 0.28	0.14 ± 0.02	23.14 ± 0.31	0.14 ± 0.04
M2	8.71 ± 0.21	5.75 ± 0.19	0.26 ± 0.05	16.89 ± 0.25	0.12 ± 0.02
M3	9.36 ± 0.19	13.51 ± 0.24	0.51 ± 0.03	15.53 ± 0.18	0.08 ± 0.02
R1	8.40 ± 0.20	5.70 ± 0.17	0.40 ± 0.04	24.23 ± 0.29	0.17 ± 0.05
R2	8.96 ± 0.18	6.25 ± 0.11	0.28 ± 0.03	16.52 ± 0.24	0.14 ± 0.03
R3	9.32 ± 0.21	12.57 ± 0.16	0.50 ± 0.03	15.19 ± 0.11	0.13 ± 0.03

3.2. Soil Carbon Distribution

SOC in maize fields and paddy fields decreased with the increase of soil salinity (Fig. 2). SOC in maize field was 15.68–22.36 g kg^− 1, and that in paddy field was 13.67–17.94 g kg^− 1. SIC in maize field and rice field increased with the increase of soil salinity. SIC in maize and rice were 5.82–8.17 g kg^− 1 and 2.41–3.43 g kg^− 1, respectively. There were significant differences in SOC and SIC between maize and rice (P < 0.05).

3.3. Soil Enzyme Activities

The soil enzyme activities of maize and rice decreased with the increase of salinity (Table 3). The activities of catalase, invertase, amylase and β-glucosidase were 4.38–5.60 ml g^− 1 h^− 1, 189–256µg g^− 1 h^− 1, 39.88–58.05µg g^− 1 h^− 1 and 21.81–23.70µg g^− 1 h^− 1, respectively. The results of soil enzyme in paddy field were 6.29–7.58 ml g^− 1 h^− 1, 142–234µg g^− 1 h^− 1, 60.25–78.92µg g^− 1 h^− 1 and 18.17–20.35 µg g^− 1 h^− 1, respectively. ANOVA revealed a significant difference (P < 0.05) in enzyme activities in the same sampling field and the soil invertase and amylase in different sampling fields.

Table 3

Soil enzyme activities in the rhizosphere soil of maize (M) and rice (R) in different saline-alkali field. Capital letters indicate the significance of the same crop in different fields, and lowercase letters indicate the difference in soil enzyme activities of the same crop in the same field (P < 0.05).
Sampling Sites	Catalase (ml g^− 1 h-¹)	Invertase (µg g^− 1 h^− 1)	Amylase (µg g^− 1 h^− 1)	β-glucosidase (µg g^− 1 h^− 1)
M1	5.60 ± 0.14Aa	256 ± 2.51Ab	58.05 ± 3.76Ac	23.70 ± 1.03Ad
M2	4.97 ± 0.21Ba	231 ± 2.18Bb	41.08 ± 2.15Bc	22.54 ± 2.17Bd
M3	4.38 ± 0.11Ca	189 ± 1.84Cb	39.88 ± 1.73Cc	21.81 ± 0.99Cd
P1	7.58 ± 0.09Aa	234 ± 1.41Ab	78.92 ± 0.98Ac	20.35 ± 1.46Ad
P2	6.54 ± 0.11Ba	221 ± 1.08Bb	75.43 ± 1.24Bc	20.26 ± 0.93Ad
P3	6.29 ± 0.16Ba	142 ± 1.63Cb	60.25 ± 0.87Cc	18.17 ± 0.81Bd

3.4. Diversity Index of Soil Microbial Community

The Shannon index and Simpson index of soil microorganisms can accurately reflect the characteristic function of microbial community diversity of this component. There were differences in the diversity index of soil microbial community structure between saline-alkali maize and rice field. Coverage index actually reflects whether sequencing results represent the real situation of the samples. The coverage of all samples was above 93%, which indicated that the results of sequencing were relatively reliable and basically reflected the situation of soil bacteria. The Shannon, ACE and Chao1 indexes of the six sampling sites revealed a similar trend, which were M1 > M2 > M3, P1 > P2 > P3 (Table 4).

Table 4

Characteristics of soil bacterial richness and diversity indices in the rhizosphere soil of maize (M) and rice (R) in different saline-alkali maize fields.
Sampling Sites	Shannon	ACE	Chao1	Coverage	Simpson
M1	7.54	34852	23036	0.94	0.01
M2	7.31	17410	12868	0.95	0.03
M3	7.02	15948	10885	0.93	0.05
P1	8.00	32016	21388	0.94	0.01
P2	7.70	29489	20566	0.93	0.02
P3	7.08	19368	11617	0.95	0.05

3.5. Bacterial Community Structure

The bacterial 16S rRNA genes collected from sampling sites were sequenced by high-throughput sequencing and cluster analysis. According to the relative abundance of bacteria at the phylum level (Fig. 3), saline-alkali stress significantly affected the rhizosphere soil bacterial community composition. At the phylum level, the main groups of soil bacterial communities in saline-alkali maize field were Proteobacteria, Bacteroidetes, Actinobacteria, Verrucomicrobia, and the relative average abundance ratios were 40.08%, 15.68%, 12.80%, 6.69%, and 5.50%, respectively (Fig. 3). In the rhizosphere soil of maize (M), the sum of the relative abundance of the five bacterial phyla accounted for 81.54% of the total number of soil bacteria. Proteobacteria and Chloroflexi were the main groups of bacterial communities in saline-alkali paddy soil at the phylum level, accounting for 61.33% of the total bacterial sequences recovered (47.33 and 14.00%, respectively) (Fig. 3). The other major phyla (average relative abundance > 1%) were Firmicutes (6.67%), Actinobacteria (6.33 %), Bacteroidetes (4.80 %), Acidobacteria (4.33%), Nitrospirae (4.00%), Verrucomicrobia (2.63%) and Planctomycetes (1.87%). The sum of relative abundance of five phyla accounted for 91.96% of the total number of soil bacteria in the rhizosphere soil of rice (R). The relative average abundance ratio of Bacteroidetes and Actinobacteria in maize rhizosphere soil was 10.8% and 6.4% higher than that in rice rhizosphere soil, respectively, while the relative average abundance ratio of Chloroflexi in rice rhizosphere soil was 12.3% higher than that in maize rhizosphere soil.

The heat map of microbial communities at the phylum level showed that the abundance of bacteria in rhizosphere soil samples of maize and rice was different in different saline-alkali fields. The results of cluster analysis showed that the discrepancy between the relative and absolute abundances of bacteria in maize and rice rhizosphere samples in different saline-alkali fields (Fig. 4). Based on the results of cluster analysis the samples of M1 and M2, R1 and R2 were clustered into two close groups, respectively, while the samples of M3 and R3 were relatively far away from those groups (Fig. 4).

4.1. Variations in SOC and SIC

SOC is the most important and active component of the global terrestrial carbon pool. It is not only an important source of various nutrient in soil, but also can stimulate the growth of plants and improve the physico-chemical properties of soil ²⁶. SIC is dominated by soil carbonate, and free calcium carbonate in the soil affects soil aggregate properties, soil pH and microbial activity, thus affecting SOC pool ²⁷. Both are important markers to judge soil formation and fertility characteristics. The results of this study showed that there were significant differences between SOC and SIC. The variation of SOC content depends on the input and output processes. The input mainly refers to the process of generating humus from plant litter under the action of microorganisms, and the output mainly refers to the mineralization process of SOM ²⁸. In addition, plants fix atmospheric carbon into the soil through photosynthesis, which converts carbon dioxide from the air and releases it into the soil through well-developed plant roots. Soil fertilization is beneficial to crop growth and increases soil carbon accumulation ²⁹. The easily decomposed part of SOC will be decomposed by microorganisms and become carbon sources to release CO₂, thus reducing the content of SOC. The fixation and release of SOC will affect the formation of secondary carbonate, which will undoubtedly have a significant impact on the stability of SIC pool ³⁰.

4.2. Effects of physico-chemical properties on the variations of SOC and SIC

Soil physico-chemical properties are affected not only by SOC, but also by soil mineral composition, especially SIC. The role of SOM is to promote the formation of large aggregates and more voids, so as to maintain good soil physico-chemical properties. Soil pH is the most sensitive indicator to regulate the cycle and sequestration of SOC, and to affect crop growth, soil microbial and enzyme activities ^31–33. The optimal pH of bacteria was 6.5-8, and that of fungi was 5–6 ³⁴. Due to the high content of alkaline carbonate in the soil in this area, strong alkalinity is one of the characteristics of the saline-alkali soil in this area. The soil pH of M3 and R3, is 9.36 and 9.32, respectively, which inhibits soil microbial activity, thus affecting the humification process of roots and litter, and hindering the input and accumulation of SOC ³⁵. The reason for the high pH is that the sodium ions adsorbed on the soil colloid hydrolyze and produce OH⁻ ions, which increase the alkalinity of the soil. In the case of saturated soil colloids, the exchange of sodium hydrolysis may cause soil alkalinity reaction. There was a significant correlation between ESP and pH, and a negative correlation between ESP and organic carbon (Fig. 5) indicating that higher ESP was not conducive to SOC sequestration. In the process of soil root development, SOM will be secreted to increase SOC content. Because there are more carbonate and bicarbonate in the soil, the higher soil pH also promotes the accumulation of SIC. SOC had a negative correlation with SIC, that is, the content of inorganic carbon in rhizosphere soil decreased with the increase of organic carbon content, which was consistent with the research results of Huang et al.³⁶.

4.3. Effects of enzyme activities on the variations of SOC and SIC

Determination of enzyme activities, including catalase, invertase, amylase and β-glucosidase, in the rhizosphere soil of maize and rice in different saline-alkali fields is helpful to understand soil biochemical reactions ³⁷. Catalase is widely distributed in soil by decomposing hydrogen peroxide to prevent its toxicity to organisms ³⁸. Invertase can increase soluble nutrients in soil and play an important role in soil carbon cycle. Amylase is a group of enzymes that can break down starch glycosidic bonds and is an important energy source for microorganisms ³⁹。β-glucosidase is one of the important catalysts for soil carbon cycle ⁴⁰. The results showed that the activities of catalase, invertase, amylase and β-glucosidase decreased with the increase of salinity (Table 3). Saline-alkali stress changes the structure and function of cell membrane and has toxic effects on cells. At the same time, it increases the soil osmotic pressure, hinders the absorptive capacity of plants, limits the growth of plants, makes the root system cannot fully develop, directly affects the secretion of enzymes, leading to great differences in enzyme activities in different soil regions ⁴¹. The increase of salt content will reduce the activities of soil enzymes. Compared with CaCl₂ and Na₂SO₄, NaCl has a stronger inhibitory effect on soil enzymes and microbial activities⁴². However, under high saline-alkali stress conditions, enzyme can still promote the conversion of soil carbon. The results showed that the activity of amylase and catalase in rice field was higher than that in maize field, which may be due to the fact that rice was in the irrigation stage, and the hydrothermal conditions promoted the growth and reproduction of microorganisms. Under suitable growth conditions, plant roots will secrete more organic acids and carbohydrates, stimulating the activities of catalase and amylase. In the process of catalyzing the decomposition of hydrogen peroxide, soil catalase will release oxygen, which is conducive to the life activities of aerobic microorganisms in the soil, thus increasing the soil humification intensity and SOC content. Amylase catalyzes organic matter to increase SOC content in soil⁴³. Compared with maize, rice rhizosphere soil is in the state of water for a long time, which is easy to lead to a large amount of hydrogen peroxide accumulation, so the soil catalase is more active.

4.4. Effects of microorganisms on the variations of SOC and SIC

In the mature stage of maize and rice, the water and heat conditions are suitable, the rhizosphere exudates provide nutrients for microorganisms, and the plant growth and soil quality tend to be stable. Bacteria changed the most under different saline-alkali degree and were always the dominant microorganisms in saline-alkali soil. It was found that at the phylum level, a variety of bacteria decreased with the increase of salinity. It may be that Firmicutes, Proteobacteria and Nitrospirae will increase sharply in adversity ^44,45. And they were significantly affected by pH and ESP, and were positively correlated with SIC in saline-alkali rice rhizosphere soils (P < 0.05) (Fig. 5). The main reasons for community diversity difference were that the higher salinity inhibited the number and activity of microorganisms and the inhomogeneity of microbial species. With the increase of salinization degree, the activity and population number of microorganisms decreased under the adverse growth conditions of saline-alkali soil, which reduced the amount of plant derived organic carbon fixed into soil by microorganisms^46–48. In the long run, the carbon release rate of slow circulation pool has an important impact on ecosystem carbon storage during salinization ⁴⁹. Microorganisms play a key role in the formation of carbonate. Microorganisms can accelerate karstification and promote CO₂ deposition. It can also produce acid through metabolic activities, resulting in dissolution of carbonate and release of CO₂. Microorganisms can also form carbonate from CO₂ produced by respiration. Microbial community plays a key role in the decomposition and transformation of soil carbon. Proteobacteria, Actinobacteria, Bacteroidetes and Acidobacteria are relatively high in maize and rice rhizosphere soils, which may contribute to nutrient uptake by root system and maintain the balance of microenvironment, so as to improve the soil environment and enhance the ability to resist saline-alkali stress. Therefore, they are valuable resources for biological improvement of saline-alkali soil regions.

(1) Saline-alkali stress changed rhizosphere soil physico-chemical properties and affected soil enzyme activities. The activities of catalase, invertase, amylase and β-glucosidase decreased with the increase of salinity, i.e., M1 > M2 > M3, R1 > R2 > R3, with significant differences (P > 0.05), and the activities of amylase and catalase in rice rhizosphere soil was higher than that in maize rhizosphere soil.

(2) Saline-alkali stress changes the community structure of rhizosphere soil. At the phylum level, most of bacteria decreased with the increase of salinity. On the contrary, compared with M1, the relative abundance of Proteobacteria and Firmicutes in M2 and M3 increased by 16.86% and 48.43%, 34.50% and 46.19%, respectively. Compared with R1, the relative abundance of Firmicutes, Proteobacteria and Nitrospirae in R2 and R3 increased by 47.50% and 142.50%, 9.52% and 28.57% and 36.67% and 76.67%, respectively.

(3) There was a significant correlation between ESP and pH, and a negative correlation with SOC (P < 0.05) of maize and rice rhizosphere soil in soda saline–alkali field. Proteobacteria, Gemmatimonadetes, Planctomycetes and Nitrospirae were significantly affected by pH and ESP, and were positively correlated with SIC in saline-alkali maize and rice rhizosphere soils (P < 0.05).

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

We gratefully acknowledge National Natural Science Foundation of China (No.51179073, 41471152) and Specialized Research Fund for Doctoral Program of Higher Education of China (20130061110065).

Author Contributions

Y.Q. and J.T. designed the research; B.L., H.L and S.W. tested the samples; Y.D. and Y.Y. analyzed the data; Y.Q. drafted the manuscript which was revised by J.T. and Z.L. All authors have read and approved the final manuscript.

Data availability statement

All data generated or analysed during this study are included in this published article (and its Supplementary Information files).

1. Bradford, M. A., Fierer, N. & Reynolds, J. F. Soil carbon stocks in experimental mesocosms are dependent on the rate of labile carbon, nitrogen and phosphorus inputs to soils. Funct. Ecol.22, 964-974, doi:10.1111/j.1365-2435.2008.01404.x (2008).

2. He, S., Liang, Z., Han, R., Wang, Y. & Liu, G. Soil carbon dynamics during grass restoration on abandoned sloping cropland in the hilly area of the Loess Plateau, China. Catena137, 679-685, doi:https://doi.org/10.1016/j.catena.2015.01.027 (2016).

3. Schuman, G. E., Janzen, H. H. & Herrick, J. E. Soil carbon dynamics and potential carbon sequestration by rangelands. Environmental Pollution116, 391-396, doi:https://doi.org/10.1016/S0269-7491(01)00215-9 (2002).

4. Duan, C. et al. Reveal the response of enzyme activities to heavy metals through in situ zymography. Ecotoxicol. Environ. Saf.156, 106-115, doi:10.1016/j.ecoenv.2018.03.015 (2018).

5. Bowles, T. M., Acosta-Martínez, V., Calderón, F. & Jackson, L. E. Soil enzyme activities, microbial communities, and carbon and nitrogen availability in organic agroecosystems across an intensively-managed agricultural landscape. Soil Biol. Biochem.68, 252-262, doi:10.1016/j.soilbio.2013.10.004 (2014).

6. Luo, L., Meng, H. & Gu, J. D. Microbial extracellular enzymes in biogeochemical cycling of ecosystems. J. Environ. Manage.197, 539-549, doi:10.1016/j.jenvman.2017.04.023 (2017).

7. Zhao, Q., Tang, J., Li, Z. Y., Yang, W. & Duan, Y. C. The influence of soil physico-chemical properties and enzyme activities on soil quality of saline-alkali agroecosystems in western Jilin province, China. Sustainability10, 1529, doi:10.3390/su10051529 (2018).

8. Burns, R. G. et al. Soil enzymes in a changing environment: Current knowledge and future directions. Soil Biol. Biochem.58, 216-234, doi:10.1016/j.soilbio.2012.11.009 (2013).

9. Ruan, M., Zhang, Y. & Chai, T. Rhizosphere soil microbial properties on tetraena mongolica in the arid and semi-arid regions, China. Int. J. Environ. Heal. R.17, 5142 (2020).

10. Askin, T. & Kizilkaya, R. Assessing spatial variability of soil enzyme activities in pasture topsoils using geostatistics. Eur. J. Soil Sci.42, 230-237, doi:10.1016/j.ejsobi.2006.02.002 (2006).

11. Bais et al. The role of root exudates in rhizosphere interations with plants and other organisms. Annu. Rev. Plant Biol.2006,57, 233-266 (2006).

12. Qu, Y. et al. Soil enzyme activity and microbial metabolic function diversity in soda saline-alkali rice paddy fields of northeast China. Sustainability12, 15, doi:10.3390/su122310095 (2020).

13. Salinas-Garcia, J. R. et al. Tillage effects on microbial biomass and nutrient distribution in soils under rain-fed corn production in central-western Mexico. Soil Tillage Res.66, 143-152, doi:10.1016/s0167-1987(02)00022-3 (2002).

14. Roldán, A., Salinas-García, J. R., Alguacil, M. M. & Caravaca, F. Changes in soil enzyme activity, fertility, aggregation and C sequestration mediated by conservation tillage practices and water regime in a maize field. Appl. Soil Ecol.30, 11-20, doi:https://doi.org/10.1016/j.apsoil.2005.01.004 (2005).

15. Lenton, T. M. & Huntingford, C. Global terrestrial carbon storage and uncertainties in its temperature sensitivity examined with a simple model. Glob. Change Biol.9, 1333-1352, doi:10.1046/j.1365-2486.2003.00674.x (2003).

16. Rey, A., Petsikos, C., Jarvis, P. G. & Grace, J. Effect of temperature and moisture on rates of carbon mineralization in a Mediterranean oak forest soil under controlled and field conditions. Eur. J. Soil Sci. (2005).

17. Wang, X., Zhag, Y., Lv, J. & Fan, X. Effect of long term different fertilization on properties of soil organic matter and humic acids. Scientia Agric. Sinica33, 78-84 (2000).

18. Wei, Y. et al. Updated information on soil salinity in a typical oasis agroecosystem and desert-oasis ecotone: Case study conducted along the Tarim River, China. Sci. Total Environ., 135387, doi:https://doi.org/10.1016/j.scitotenv.2019.135387 (2019).

19. Huang, L. H. et al. Impact of cultivation year, nitrogen fertilization rate and irrigation water quality on soil salinity and soil nitrogen in saline-sodic paddy fields in Northeast China. J. Agric. Sci.154, 632-646, doi:10.1017/s002185961500057x (2015).

20. Liu, Q., Cui, B. & Yang, Z. Dynamics of the soil water and solute in the sodic saline soil in the Songnen Plain, China. Environ. Earth Sci.59, 837-845, doi:10.1007/s12665-009-0079-4 (2009).

21. Lu, Y. & Xu, H. Distribution characteristic of soil organic carbon fraction in different types of wetland in Hongze Lake of China. Sci. World J.2014, 487961, doi:10.1155/2014/487961 (2014).

22. Nitsch, P., Kaupenjohann, M. & Wulf, M. Forest continuity, soil depth and tree species are important parameters for SOC stocks in an old forest (Templiner Buchheide, northeast Germany). Geoderma310, 65-76, doi:10.1016/j.geoderma.2017.08.041 (2018).

23. Zhang, Z., Lu, X., Song, X., Guo, Y. & Xue, Z. Soil C, N and P stoichiometry of deyeuxia angustifolia and carex lasiocarpa wetlands in Sanjiang Plain, northeast China. J Soil Sediment.12, 1309-1315, doi:10.1007/s11368-012-0551-8 (2012).

24. Bian, J., Tang, J., Zhang, L., Ma, H. & Zhao, J. Arsenic distribution and geological factors in the western Jilin province, China. J. Geochem. Explor.112, 347-356, doi:10.1016/j.gexplo.2011.10.003 (2012).

25. Zheng, B. Technical guide for soil analysis. (China agriculture press, 2013).

26. Tang, H., Qiu, J., Wang, L., Hu, L. & Ranst, E. V. Modeling soil organic carbon storage and its dynamics in croplands of China. Agr. Sci. China9, 704-712 (2010).

27. Wang, Y., Li, Y., Ye, X., Chu, Y. & Wang, X. Profile storage of organic/inorganic carbon in soil: From forest to desert. Sci. Total Environ.408, 1925-1931, doi:https://doi.org/10.1016/j.scitotenv.2010.01.015 (2010).

28. Wang, S. et al. Carbon Mineralization under Different Saline-Alkali Stress Conditions in Paddy Fields of Northeast China. Sustainability12, 17, doi:10.3390/su12072921 (2020).

29. Junejo et al. Neoformation of pedogenic carbonates by irrigation and fertilization and their contribution to carbon sequestration in soil. Geoderma262, 12-19 (2016).

30. Schlesinger, W. H. & Andrews, J. A. Soil respiration and the global carbon cycle. Biogeochemistry48, 7-20 (2000).

31. Paul, E. A. The nature and dynamics of soil organic matter: Plant inputs, microbial transformations, and organic matter stabilization. Soil Biol. Biochem.98, 109-126, doi:https://doi.org/10.1016/j.soilbio.2016.04.001 (2016).

32. Solly, E. F., Schoning, I., Herold, N., Trumbore, S. E. & Schrumpf, M. No depth-dependence of fine root litter decomposition in temperate beech forest soils. Plant Soil393, 273-282, doi:10.1007/s11104-015-2492-7 (2015).

33. Steinweg, J. M., Kostka, J. E., Hanson, P. J. & Schadt, C. W. Temperature sensitivity of extracellular enzymes differs with peat depth but not with season in an ombrotrophic bog. Soil Biol. Biochem.125, 244-250, doi:https://doi.org/10.1016/j.soilbio.2018.07.001 (2018).

34. Hartman, W. H., Richardson, C. J., Vilgalys, R. & Bruland, G. L. Environmental and anthropogenic controls over bacterial communities in wetland soils. P. Natl. Acad. Sci. USA.105, 17842-17847, doi:10.1073/pnas.0808254105 (2008).

35. Andersen, R., Chapman, S. J. & Artz, R. R. E. Microbial communities in natural and disturbed peatlands: A review. Soil Biol. Biochem.57, 979-994, doi:https://doi.org/10.1016/j.soilbio.2012.10.003 (2013).

36. Huang, B., Wang, J., Jin, H. & Xu, S. Effects of long- term application fertilizer on carbon storage in calcareous meadow soil. J. Agro-Environ. Sci.25, 161-164 (2006).

37. Bacmaga, M., Wyszkowska, J. & Kucharski, J. Bioaugmentation of soil contaminated with azoxystrobin. Water Air Soil Poll.228, 9, doi:10.1007/s11270-016-3200-9 (2017).

38. Guo, P. P. et al. Enzymatic activities and microbial biomass in black soil as affected by azoxystrobin. Environ. Earth Sci.74, 1353-1361, doi:10.1007/s12665-015-4126-z (2015).

39. Gianfreda, L., Rao, M. A., Piotrowska, A., Palumbo, G. & Colombo, C. Soil enzyme activities as affected by anthropogenic alterations: intensive agricultural practices and organic pollution. Sci. Total Environ.341, 265-279, doi:10.1016/j.scitotenv.2004.10.005 (2005).

40. Pathan, S. I. et al. Seasonal variation and distribution of total and active microbial community of β-glucosidase encoding genes in coniferous forest soil. Soil Biol. Biochem.105, 71-80 (2017).

41. Miller, G., Suzuki, N., Ciftci-Yilmaz, S. & Mittler, R. Reactive oxygen species homeostasis and signalling during drought and salinity stresses. Plant Cell Environ.33, 453-467 (2010).

42. Pathak, H. & Rao, D. L. N. Carbon and nitrogen mineralization from added organic matter in saline and alkali soils. Soil Biology and Biochemistry30, 695-702, doi:https://doi.org/10.1016/S0038-0717(97)00208-3 (1998).

43. Xiao, Y. et al. Response of soil labile organic carbon fractions to forest conversions in subtropical China. Trop. Ecol.57, 691-699 (2016).

44. Broszat, M. et al. Wastewater irrigation increases the abundance of potentially harmful gammaproteobacteria in soils in Mezquital Valley, Mexico. Appl. Environ. Microbiol.80, 5282-5291 (2014).

45. Liu, Y. et al. Alphaproteobacteria dominate active 2-methyl-4-chlorophenoxyacetic acid herbicide degraders in agricultural soil and drilosphere. Environ. Microbiol.13, 991-1009 (2011).

46. Baumann, K. et al. Soil microbial diversity affects soil organic matter decomposition in a silty grassland soil. Biogeochemistry114, 201-212 (2013).

47. Philippot, L. et al. Loss in microbial diversity affects nitrogen cycling in soil. ISME J.7, 1609-1619 (2013).

48. Chambers, L. G., Guevara, R., Boyer, J. N., Troxler, T. G. & Davis, S. E. Effects of salinity and inundation on microbial community structure and fFunction in a mangrove peat soil. Wetlands36, 361-371 (2016).

49. Wong, V. N. L., Greene, R. S. B., Dalal, R. C. & Murphy, B. W. Soil carbon dynamics in saline and sodic soils: a review. Soil Use Manage.26, 2-11 (2010).

The Supplementary Information files are not available with this version.

No competing interests reported.

Download PDF

Editorial decision: Major revision
05 Oct, 2021
Reviewers agreed at journal
17 Sep, 2021
Reviews received at journal
21 Jul, 2021
Reviewers agreed at journal
29 Jun, 2021
Reviewers invited by journal
28 Jun, 2021
Editor assigned by journal
28 Jun, 2021
Editor invited by journal
22 Jun, 2021
Submission checks completed at journal
21 Jun, 2021
First submitted to journal
21 Jun, 2021

You are reading this latest preprint version

Rhizosphere Enzyme Activities and Microorganisms Drive the Transformation of Organic and Inorganic Carbon in Saline - Alkali Soil Region

Status:

Version 1

Abstract

Figures

1. Introduction

2. Materials And Methods

2.1. Study Area Description

2.2. Soil Sampling and Experimental Design

2.3. Physico-Chemical Analysis of Soil

2.4. Soil Enzyme Activities

2.5. Soil Microbial Community Analysis

2.6. SOC and SIC Aanalysis

2.7. Statistical Analysis

3. Results

3.1. Soil Physico-Chemical Properties

3.2. Soil Carbon Distribution

3.3. Soil Enzyme Activities

3.4. Diversity Index of Soil Microbial Community

3.5. Bacterial Community Structure

4. Discussion

4.1. Variations in SOC and SIC

4.2. Effects of physico-chemical properties on the variations of SOC and SIC

4.3. Effects of enzyme activities on the variations of SOC and SIC

4.4. Effects of microorganisms on the variations of SOC and SIC

5. Conclusions

Declarations

References

Supplementary Information

Additional Declarations

Status:

Version 1