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Article

Effects of Organic Fertilizer and Biochar on Carbon Release and Microbial Communities in Saline–Alkaline Soil

by
Pengfei Zhang
1,2,
Ziwei Jiang
1,2,
Xiaodong Wu
1,2,3,
Nannan Zhang
1,2,4,
Jiawei Zhang
1,2,4,
Siyuan Zou
1,2,
Jifu Wang
1,2,* and
Shuying Zang
1,2,*
1
Heilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions, Harbin Normal University, Harbin 150025, China
2
Heilongjiang Province Collaborative Innovation Center of Cold Region Ecological Safety, Harbin 150025, China
3
Cryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
4
Modern Experiment Center, Harbin Normal University, Harbin 150025, China
*
Authors to whom correspondence should be addressed.
Agronomy 2024, 14(9), 1967; https://doi.org/10.3390/agronomy14091967 (registering DOI)
Submission received: 4 July 2024 / Revised: 15 August 2024 / Accepted: 28 August 2024 / Published: 31 August 2024
(This article belongs to the Section Agricultural Biosystem and Biological Engineering)
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Abstract

:
Climate change and aridification have increased the risk of salinization and organic carbon loss in dryland soils. Enrichment using biochar and organic fertilizers has the potential to reduce salt toxicity and soil carbon loss. However, the effects of biochar and organic fertilizers on CO2 and CH4 emissions from saline soils in dryland areas, as well as their microbial mechanisms, remain unelucidated. To clarify these issues, we performed a 5-month incubation experiment on typical soda-type saline soil from the western part of the Songnen Plain using five treatments: control treatment (CK), 5% urea (U), straw + 5% urea (SU), straw + 5% urea + microbial agent (SUH), and straw + 5% urea + biochar (SUB). Compared with the SU treatment, the SUH and SUB treatments reduced cumulative CO2 emissions by 14.85% and 35.19%, respectively. The addition of a microbiological agent to the SU treatment reduced the cumulative CH4 emissions by 19.55%, whereas the addition of biochar to the SU treatment increased the cumulative CH4 emissions by 4.12%. These additions also increased the relative abundances of Proteobacteria, Planctomycetes, and Ascomycota. Overall, the addition of biochar and organic fertilizer promoted CO2 emissions and CH4 uptake. This was mainly attributed to an improved soil gas diffusion rate due to the addition of organic materials and enhanced microbial stress due to soil salinity and alkalinity from the release of alkaline substances under closed-culture conditions. Our findings have positive implications for enhancing carbon storage in saline soils in arid regions.

1. Introduction

Under current global warming conditions, increases in potential evapotranspiration and the uneven distribution of precipitation may expand the global dryland area to 56% of the total land surface area by the end of the 21st century [1]. Dryland soil organic carbon (SOC) storage accounts for approximately 32% of the global SOC pool [2]. However, because climate warming and aridity further exacerbate the risk of soil salinization and SOC loss, the global loss of SOC due to salinization is estimated to exceed 1.4 Pg of carbon [3,4]. Changes in carbon sequestration rates and net carbon fluxes (i.e., fluxes of CO2 and CH4) in soils affect the transformation of carbon sources and sinks in terrestrial ecosystems, significantly affecting the global carbon cycle and climate change [5,6]. Therefore, the implementation of effective measures to reduce soil carbon loss due to salinization in drylands is crucial.
The application of soil restructuring and chemical technologies for saline land reclamation has enhanced the economic and ecological properties of saline land [7,8]. However, these remediation measures are expensive and can cause secondary contamination [9,10]. The application of organic materials such as straw, biochar, and other organic fertilizers may be an effective measure for saline land remediation [11,12]. The application of organic fertilizers such as straw effectively increases the organic carbon content of saline soils [11], improves the stability of saline soil aggregates [13], and reduces soil salinity [14]. The application of biochar increases soil porosity, thereby accelerating the leaching of soil salinity [15] and reducing salt accumulation and toxicity [14]. The increased application of nitrogen fertilizer effectively breaks the nitrogen limitation of saline soils [16], promoting the decomposition and transformation of straw in the soil [14], which increases the soil organic matter content. The unique role of exogenous organic matter application in improving saline–alkaline soil has been confirmed by extensive studies that have focused on improving soil physicochemical properties and enhancing productivity [15,17]. However, there is a lack of accurate data on the overall effects of different organic fertilizers on soil carbon emissions.
The use of straw to return organic matter to soils was reported to significantly increase soil CO2 and CH4 emissions [18,19]. This may be related to the increased availability of carbon and nitrogen substrates [20], as well as the material properties of the input organic matter (e.g., carbon-to-nitrogen ratio, lignin content, and recalcitrant carbon fraction) [21] on the microbial activities of carbon degradation and CH4 metabolism. However, Wang et al. and He et al. reported that the input of various carbon-based fertilizers reduced CH4 emissions [20,22]. This may be due to enhanced soil aeration caused by the input of exogenous organic matter, which alters the anaerobic environment of the methanogenic bacteria. The application of microbial fungicides accelerates the stabilization of natural carbon sources such as straw and is a potential climate-change mitigation strategy [23]. Li et al. specifically reported a significant reduction in carbon release during organic matter decomposition when using a bacterial mixture (BM) consisting of Citrobacter freundii, Arthrobacter woluwensis, and Bacillus licheniformis in combination with straw [24]. Additionally, Zhang et al. noted that the application of commercial microbial agents comprising Bacillus subtilis, Bacillus megaterium, and gelatinous Bacillus mixtures can enhance soil carbon stability and effectively mitigate CO2 emissions during straw incorporation into saline–alkali soil [25]. However, various research studies have demonstrated that the introduction of commercial microbial inoculants, comprising a blend of bacteria and fungi, notably Bacillus subtilis and lignin-degrading, thermophilic, and heat-tolerant bacteria, into soil leads to a substantial increase in soil microbial abundance. These microorganisms not only facilitate straw decomposition but also elicit emissions of CO2 and CH4 from the soil, as reported in [26,27,28]. Nonetheless, research investigating the impact of organic fertilizers such as biochar on CO2 and CH4 emissions during the amelioration of saline–alkali soils remains scarce, and comprehensive evaluations are notably absent.
Microorganisms regulate carbon sequestration, CH4 metabolism, and carbon degradation and play a key role in the global carbon cycle and climate change [29]. Soil microorganisms play a pivotal role in regulating the turnover of soil organic matter pools and contribute significantly to climate feedback mechanisms through various ecological processes, including the sequestration and release of carbon in diverse forms [29,30]. The application of organic fertilizers such as biochar affects microbial diversity and community composition by altering the supply of carbon and nitrogen substrates [31], decreasing salt stress [32], and regulating the soil microenvironment [25,33]. Microbial community composition, diversity, and biomass are important indicators for predicting soil carbon emissions [34]. Zhao et al. found a positive correlation between the relative abundance of bacterial phyla such as Proteobacteria and Firmicutes, as well as the fungal phylum Ascomycota, in long-term fertilized soils and straw-derived carbon dioxide emissions [35]. The microbial species composition regulates the abundance of functional genes, subsequently affecting enzymatic activities associated with carbon degradation and methane oxidation [36,37]. Thus, the enzyme activities of soil microorganisms are important limiting factors that mediate carbon degradation and CH4 metabolism [38,39]. The research conducted by He et al. reveals that incorporating both straw and biochar leads to an increase in the relative abundance of the PMOA gene, concurrently resulting in a significant reduction in CH4 emissions [22]. Yu et al. demonstrated that incorporating straw and biochar into saline–alkali wetlands stimulates the opportunistic bacterial subgroups (e.g., Bacillaceae and Cellvibrionaceae), enhancing bacterial carbon metabolism and concurrently augmenting both carbon storage and mineralization processes [40]. However, the microbial mechanisms underlying CO2 and CH4 emissions during saline soil remediation with different types of organic fertilizers remain unclear, and further investigation is necessary.
There is an urgent need to study the mechanisms and effects of different types of organic fertilizers on soil CO2 and CH4 emissions during saline–alkaline soil remediation to improve management measures to reduce the risk of salinization and increase carbon storage in dryland soils. To this end, we performed a 5-month indoor incubation study on saline–alkaline soils collected from the Songnen Plain to compare the effects of different straw treatments and biochar addition on overall carbon emissions and microbial communities. The study objectives were as follows: (1) investigate the changes in CO2 and CH4 emissions from saline–alkaline soils treated with different types of organic fertilizers, (2) analyze the effects of different organic fertilizers on the microbial communities and carbon degradation functions of saline–alkaline soils, and (3) elucidate the microbial mechanisms and environmental drivers of soil carbon emissions from saline–alkaline soils treated with fertilizer.

2. Materials and Methods

2.1. Soil Sampling and Preparation of Organic Materials

The Songnen Plain is one of the three major areas of the world where soda-type saline soils are concentrated. Dulbert County (46°48′56″ N, 124°29′44″ E) in Heilongjiang Province, China, is located in the western–central part of the Songnen Plain, where salinization has resulted in the abandonment of a large amount of land. The area experiences a mesothermal continental climate, with an average annual temperature of 3.6–4.4°C and an average annual precipitation of 400 mm. One of the main agricultural activities in this area is rice cultivation, which yields a large amount of crop straw. Thus, a common agricultural management measure in this area is to return straw to the field. However, because straw decomposes slowly at low temperatures, it is not conducive for agricultural production. An alternative option for returning straw to the field is burning crop residues to produce biochar. Biochar prepared at higher temperatures has a higher percentage of sequestered recalcitrant carbon, which helps weaken SOC mineralization and reduce soil carbon loss. The application of straw and its organic products to improve saline soils fully utilizes local straw to remediate idle saline land resources.
In November 2019, we collected topsoil samples (0–20 cm) containing rice straw from saline soils and paddy fields in Dulbert County. After removing the grass roots from the soil samples, they were air-dried, mixed, and homogenized before pulverization through a 2 mm sieve. The soil samples were classified as sandy loam (7.98% clay, 16.38% silt, and 75.63% sand) according to the Word Reference Base, an international system of soil texture classification, and contained 8.56 g·kg−1 total carbon, 113.27 mg·kg−1 total nitrogen, and 12.08 g·kg−1 dissolved total salt, with a bulk density of 1.28 g·cm−3 and a pH of 10.07.
The rice straw samples were initially air-dried in preparation for subsequent experimentation. Biochar was generated through the pyrolysis of the rice straw at a temperature of 500 °C. Subsequently, both the air-dried rice straw and the rice biochar were individually crushed, sieved using a 2 mm sieve, and then stored separately for future experimentation. Bird and fish market agricultural fertilizers obtained from Harbin City were used as a source of urea. The microbial agent utilized was a W-18 straw decomposer, made of Bacillus subtilis, Bacillus megaterium, and Bacillus jelly-like, with an effective viable count ≥ 2.0 × 108 mL−1, created by Heilongjiang Huxufeng Ecological Technology Company (Harbin, China) [25]. The basic physicochemical properties of soil organic additives are presented in Table 1. The organic element content of the soil additives was determined using a Flash EA-1112 elemental analyzer (Thermo Scientific, Costa Mesa, CA, USA), and the specific surface area and pore size were determined using a fully automated Autosorb-iQ analyzer (Quantachrome Instruments, Boynton Beach, FL, USA).

2.2. Experimental Design and Carbon Emission Measurement

The incubation experiment was divided into two components: (1) gas collection and analysis and (2) analysis of soil physicochemical properties following different treatments. The collected soil was divided into five treatment groups: control treatment, CK; urea, U; straw + urea, SU; straw + urea + microbial agent, SUH; and straw + urea + biochar, SUB. Rice straw, urea, and biochar were added to the dry soil at a concentration of 3.73 mg·g−1, 0.18 mg·g−1, and 7.67 mg·g−1, respectively, with three replicates for each treatment. Straw was added based on the full local rice straw return, urea was added at a concentration of 5% of the full rice straw return, and biochar was added by referring to the 20 t ha−1 application rate recommended by Song et al. [41]. The addition rate was calculated using the amount of different organic materials that were added and the bulk weight of the saline topsoil. At the beginning of the incubation experiment, the soil was rehydrated according to mass loss. The experimental temperatures were determined based on the monthly average temperatures at a soil depth of 20 cm from May to September, recorded from 2001 to 2010 by meteorological stations in Dulbert County, Heilongjiang Province. This was carried out to simulate the growing season (May–September) at the common 20 cm straw-return depth in the local area for accurate observation of the biochar and organic fertilizer decomposition processes. Specifically, the mean experimental temperatures for each month of the 5-month incubation cycle were 14.95 °C (May), 22.3 °C (June), 25.4 °C (July), 24.45 °C (August), and 18.85 °C (September).
Soil CO2 and CH4 fluxes were determined by gas collection on days 0, 4, and 7 and every 7 days thereafter, for a total of 147 days. Prior to gas collection, each headspace bottle was purged with ambient air for 30 min and sealed with caps fitted with plug valves and butyl plugs. After 1 h, 15–20 mL of gas was sampled from the top of the headspace bottle by using a gas-tight syringe. The CO2 and CH4 concentrations were quantified using an Agilent 7890A gas chromatograph (Agilent Technologies, Santa Clara, CA, USA) equipped with a flame ionization detector. The fluxes and cumulative emissions of CO2 and CH4 were calculated according to previously established methods [42].

2.3. Soil Physicochemical Properties and Microbiological Analysis

At the end of the incubation period, destructive sampling methods were used to determine the physicochemical properties of the soil treatment groups, including SOC, labile organic carbon (LOC), alkaline cations (K+, Ca2+, Na+, and Mg2+), and total soluble salts (TSS) content, as well as pH and electrical conductivity, as described by Zhang et al. [25]. Soil inorganic carbon (SIC) content was calculated as the difference between the soil total carbon and SOC content. At the end of the incubation period, soil samples from each treatment group were sent to Shanghai Payseno Biotechnology Co., Ltd. (Shanghai, China) for DNA extraction, polymerase chain reaction amplification, and sequencing using the methods of Zhang et al. [25].

2.4. Data Analysis

Soil physicochemical properties, cumulative CO2 and CH4 emissions, and bacterial and fungal diversity indices were analyzed using one-way analysis of variance (ANOVA). Significance levels were determined using the Duncan multiple range test with a significance threshold of p < 0.05. Figures were plotted using the ggplot2 package, structural equation modeling (SEM) was performed using the lavaan package, and relationships between carbon emissions, soil physicochemical properties, and relative abundance of bacterial and fungal phyla were analyzed using the corrplot package in R v4.2.3.
Nonmetric multidimensional scaling (NMDS) scores based on the Bray–Curtis distance were used to analyze the effects of different combinations of organic matter in each treatment group on the bacterial and fungal communities and to examine their stress indices. Specifically, the sequencing data were analyzed using PICRUSt2 on the GeneCloud platform (https://www.genescloud.cn, accessed on 20 May 2024). This analysis mapped the data against the Kyoto Encyclopedia of Genes and Genomes (KEGG) database to obtain functional prediction profiles of bacteria and fungi across different treatment groups. Subsequently, the data were normalized to facilitate subsequent analytical procedures.

3. Results

3.1. Soil Characteristics

SIC content in the SUB group was significantly higher than the SIC content in the CK group (Table 2). Furthermore, the application of different types of organic fertilizers significantly changed the SOC content (p < 0.001; Table 2) by 6.63% and 48.10% in the SUH and SUB groups, respectively, compared with the CK group. Significant differences in LOC content were observed between the treatment groups (p < 0.01; Table 2), increasing by 7.31% and 32.03% in the SUH and SUB groups, respectively, and decreasing by 46.24% in the SU group. The pH, TSS content, and electrical conductivity increased to some extent in the different organic fertilizer treatment groups (p < 0.01, Table 2). The Ca2+ content significantly decreased in the SU group compared with the other treatment groups (p < 0.001, Table 2). The Mg2+ content was reduced by 33.62%, 10.22%, and 5.80% in the SU, SUH, and SUB groups, respectively, compared to that in the CK group (p < 0.01, Table 2).

3.2. Carbon Emissions

The addition of biochar and organic fertilizer enhanced the role of saline soil as a source of CO2 emissions during the 5-month incubation cycle (Figure 1a). Compared to the CK group, the cumulative CO2 emissions of the U, SU, SUH, and SUB groups increased by 9.17%, 116.68%, 84.5%, and 40.3%, respectively (Table 3). Under the same straw and urea input conditions (SU group), the addition of microbial agents and biochar effectively reduced cumulative CO2 emissions by 14.85% and 35.19%, respectively. Saline soil CO2 emissions were significantly affected by the addition of different types of organic fertilizers (p < 0.01; Table 3). In all treatment groups, the rate of cumulative CO2 emissions increased significantly when the incubation temperature increased from 14.95 °C to 22.3 °C.
The addition of biochar and organic fertilizer enhanced the role of saline soil as a CH4 sink during the 5-month incubation cycle; however, this was not observed in the group where only urea was added (Figure 1b). Cumulative CH4 emissions were reduced by 54.30%, 84.47%, and 47.95% in the SU, SUH, and SUB groups, respectively, compared with the CK group, whereas the U group showed a 2.05% increase in cumulative CH4 emissions (Table 3). Under the same straw and urea input conditions (SU group), the addition of microbial agents reduced cumulative CH4 emissions by 19.55%, whereas the addition of biochar increased cumulative CH4 emissions by 4.12%. The ANOVA revealed that various organic amendments significantly impacted methane absorption in saline–alkali soil (p < 0.01; Table 3). Specifically, except for the U treatment group, the addition of all other organic materials increased methane absorption in saline–alkali soil.

3.3. Microbial Community and Function Prediction

At the end of the 5-month incubation period, it was evident that the fungal Chao1 and observed species indices in saline soil were significantly affected by the addition of different types of organic fertilizers (p < 0.001; Table 3). Compared with the CK group, the fungal Chao1 index in the SU, SUH, and SUB groups decreased by 41.49%, 64.79%, and 64.94%, respectively, whereas the fungal Chao1 index in the U group increased by 6.42%. Compared with the CK group, the observed fungal species index in the SU, SUH, and SUB groups decreased by 41.37%, 64.81%, and 65.10%, respectively, whereas the fungal Chao1 index in the U group increased by 6.58%.
Proteobacteria and Actinobacteria were the dominant phyla in the bacterial communities of all the saline soil treatment groups, accounting for approximately 62.1% of the total bacterial abundance (Figure 2a). The fungal communities were dominated by Ascomycota, which accounts for approximately 83.8% of the total fungal abundance (Figure 2c). Different types of organic fertilizers increased the relative abundance of Proteobacteria, Planctomycetes, and Ascomycota but decreased the relative abundance of Chloroflexi, Gemmatimonadetes, and Basidiomycota. NMDS showed significant differences between the bacterial communities (stress = 0.0508) and fungal communities (stress = 0.00257) in the saline–alkaline soil among the different treatment groups (Figure 2b,d). As the stress values were <0.2, the results were considered reliable [43].
The PICRUSt2 results indicated that the application of soil additives with different characteristics (SAC) increased the predicted abundance of α-glucosidase, pullulanase, cellulose I, cellulase, xylanase, α-L-arabinosidase, xylan 1, and chitinase (particulate methane monooxygenase, pMMO) in bacterial sequences to different extents, and decreased the predicted abundance of α-amylase, glucan 1, isooxygenase (pMMO), and chitinase (pMMO) in bacterial sequences (Figure 3a). Furthermore, SAC application increased the predicted abundance of α-amylase, glucan 1, arabinosidase (ambiguous), and laccase, but decreased the predicted abundance of α-glucosidase, glycogen phosphorylase, cellulose I, β-glucosidase, cellulase, endo-1, α-glucosidase, xylan 1, chitinase, chitosanase, β-mannosidase, and α-N-acetylglucosaminidase in fungal sequences (Figure 3b).

3.4. Relationships between Carbon Emissions, Environmental Factors, and Microbial Communities

Spearman correlation analysis revealed a significant positive correlation between CO2 emission and pH and the relative abundance of Bacteroidetes and Planctomycetes (p < 0.05; Figure 4a) and a significant negative correlation between CO2 emission and CH4 emission, Ca2+ and Mg2+ content, and fungal Chao1 and observed species indices (p < 0.05). Furthermore, CH4 emissions showed a significant positive correlation with Mg2+ content, fungal Chao1 and observed species indices, and the relative abundance of Acidobacteria (p < 0.05; Figure 4b) and a significantly negative correlation with SAC application, pH, electrical conductivity, and the relative abundance of Actinobacteria, Bacteroidetes, Planctomycetes, and Ascomycota (p < 0.05; Figure 4b).
SEM explained 93% and 75% of the data variability in cumulative CO2 emissions and cumulative CH4 emissions from saline soil, respectively (Figure 5). The application of SAC and soil pH exerted major contributing effects on cumulative CO2 emissions (Figure 5a,b). Both cumulative CH4 emissions and Mg2+ content negatively regulated the cumulative CO2 emissions. The fungal Chao1 index regulated cumulative CH4 emissions by exerting a negative indirect effect, and SAC addition exerted a positive indirect effect on cumulative CO2 emissions by regulating the pH, fungal Chao1 index, and fungal observed species index. The observed fungal species index was the main contributor to the cumulative CH4 emissions (Figure 5c,d). However, SAC addition, CO2 flux, and pH negatively regulated cumulative CH4 emissions. The pH also exerted a negative indirect effect on CH4 emissions through CO2 flux and the observed fungal species index. Ca2+ content exerted an indirect positive effect on CH4 emissions by regulating the pH and CO2 flux. SAC addition exerted a negative indirect effect on cumulative CH4 emissions by regulating the pH and the observed fungal species index.

4. Discussion

4.1. Soil Characteristics

Our investigation did not reveal significant changes in the soil characteristics of any of the treatment groups, except for a significant increase in SIC in the SUB group (Table 2). This may be related to changes in the material properties of the organic matter, soil Ca and Mg availability, and CO2 partial pressure levels in the pore space [44,45]. The effect of SIC accumulation in arid regions differs according to the type of exogenous organic matter inputs [45,46]. Furthermore, the material properties of organic matter additives may be a key influencer of SOC content in saline soils following organic matter inputs [11,47]. We found that the addition of biochar and microbial agents to a combination of straw and nitrogen fertilizer significantly enhanced the SOC content compared with the combination treatment alone (Table 2). This observation is similar to those observed in other organic matter addition experiments [18,28,48,49]. Biochar is considered a carbon-negative tool for the sustainable increase in SOC content owing to its high carbon-to-nitrogen ratio, large specific surface area, and structural stability, making degradation by microorganisms difficult [50]. Microbial agent dosing accelerates straw carbon turnover, increases microbial biomass, and promotes the generation of more stable microbial carbon content [24,48,51]. In our study, we observed that different organic matter inputs increased soil pH, TSS, and electrical conductivity. This may be related to the closed nature of the culture system, leading to poor drainage and the release of alkaline ions from exogenous organic matter. Alkaline cation (K+ and Na+) content has been reported to increase with the addition of organic materials, such as biochar, and with increases in retained organic mass in the soil [52,53].

4.2. Carbon Emissions

Our study showed that the incorporation of biochar and microbial agents effectively reduced CO2 emissions from soils treated with a combination of urea and straw. This may be related to the material properties of the input organic matter, such as the carbon-to-nitrogen ratio and percentage of recalcitrant carbon, as well as the rate of organic matter turnover mediated by microbial agents. In arid and semi-arid regions, soil CO2 emissions are usually associated with microbial respiration and dissolved SIC precipitation [54].
Although organic matter enrichment of saline soil increases CO2 emissions while increasing SOC content, appropriate interventions can effectively reduce CO2 emissions during this process [25,49]. Due to its carbon-rich aromatic structure and high porosity, the utilization of biochar by microorganisms is difficult, which helps weaken soil CO2 emissions [55]. Furthermore, microbial agent application helps stabilize microbial carbon production, thereby decreasing CO2 emissions during organic matter decomposition [23,24]. CO2 emissions from inorganic carbon sources in the surface soils of arid zones account for approximately 20% of total emissions, which are mainly related to carbonate dissolution and precipitation processes [54]. Because direct measurement of SIC content is difficult and the results are uncertain, changes in exchangeable Ca2+ and Mg2+ content, pH, and electrical conductivity are often used as alternatives to assess carbonate dissolution and precipitation processes [56]. Our findings revealed that the combined application of straw and urea significantly reduced the Ca2+ and Mg2+ in saline soils (Table 2). This may be because organic fertilizer inputs, such as straw, accelerate carbonate precipitation in arid zone soils [45].
We showed that biochar and each organic fertilizer treatment promoted the CH4 oxidation potential in saline soils in semi-arid zones (Figure 1; Table 3). This correlates with the findings of Kim et al. and Ali et al., who concluded that biochar and fertilizer inputs reduced CH4 emissions by increasing soil aeration and O2 effectiveness [57,58]. This effect is mainly related to the improved gas diffusion at the atmosphere–soil interface caused by organic material inputs. The main limiting factor of the rate of CH4 oxidation in soil is the extent of diffusion of CH4 from the atmosphere and soil [59]. Changes in the soil CH4 diffusion rate are mainly associated with the input of organic fertilizers such as biochar, which significantly increases the bulk density of the soil, improves its permeability, and increases its O2 content [60,61]. In turn, the increased O2 content in the soil inhibits anaerobic methanogenic bacterial activity and weakens the CH4 emission potential [57], which may be responsible for our observation of a significant reduction in soil CH4 emissions after organic fertilizer input. Our study demonstrated that microbial agent addition significantly enhanced soil CH4 oxidation potential under straw and nitrogen fertilizer input conditions (Figure 1b; Table 3). However, Liu et al. showed that microbial agent use increased CH4 emissions by 7–13% under conditions of straw and nitrogen fertilizer addition in rice fields [27]. This differs from our observations and may be attributed to soil type differences, the inoculation effects of microbial agents, and flooding.

4.3. Microbial Communities and Functions

In our study, the dominant phyla in the bacterial community of all treated saline soils were Proteobacteria and Actinobacteria (Figure 2a), whereas the fungal community was mainly dominated by the Ascomycota phylum (Figure 2c). Our findings were similar to those of Wu et al., who investigated the dominant phyla of soil bacteria and fungi in degraded saline meadows in northeastern China [62]. These similarities may be due to the stress of soil salinity and the high pH of the microorganisms. Salinity stress alters osmotic pressure in the extracellular environment, resulting in water loss from microbial cells and toxic effects [63].
We found that SAC application increased the relative abundance of Proteobacteria, Planctomycetes, and Ascomycota and decreased the relative abundance of Chloroflexi, Gemmatimonadetes, and Basidiomycota (Figure 2a,c). This may be because of salinity stress and changes in the nutrient environment. Alburquerque et al. reported that additives such as biochar increased soil conductivity and soluble salt concentration and inhibited plant and microbial growth [64]. The increase in the relative abundance of Proteobacteria, a dominant phylum in bacterial communities in saline soil, may be associated with the increase in alkaline cations and TSS triggered by biochar and organic fertilizer inputs. Looby et al. and Zhao et al. reported that the Ascomycota phylum is crucial for the decomposition of organic matter and carbon emissions [35,65]. In the SUH and SUB treatment groups, the relative abundance of Ascomycota significantly increased in the soil. This phenomenon may be attributed to the enhanced stress effects of alkaline and saline components in saline–alkali soils on microorganisms, exacerbated by the release of alkaline substances from organic materials under semi-enclosed conditions. The study by Wu et al. revealed that Ascomycota fungi are the dominant group in degraded saline–alkali meadow soils in northeastern China and exhibit strong tolerance to saline–alkali environments [62]. Furthermore, Spearman correlation analysis confirmed a significant positive correlation between the relative abundance of Ascomycota and soil pH, as well as electrical conductivity (EC) values.
In contrast, Han et al. reported that fertilizer application decreased the relative abundance of fungi, including Ascomycota, in maize fields [66]. This may be due to differences in fertilizer application methods in terms of differences in soil properties as well as competition between bacteria and fungi under conutrient conditions.
Our NMDS analysis revealed significant differences in the diversity of microbial communities in saline soils among the different organic matter treatments (Figure 2b,d), which may be related to changes in nutrient supply and salinity stress in saline soils following the addition of organic matter. Our indoor incubation experiment demonstrated that the SUH and SUB treatments significantly reduced the fungal Chao1 and observed species indices while increasing the SOC content in saline–alkaline soil compared with the control group (Table 2 and Table 3). This agrees with the results of previous studies [67,68] and may be because nutrient input increases microbial species competition. Using PICRUSt2, we found that the addition of organic material increased the predicted abundance of functional enzymes related to carbon degradation, such as α-glucosidase and cellulase, to varying extents in the bacterial and fungal sequences obtained from the saline soil samples (Figure 3). This correlates with Yu et al., who suggested that the addition of carbon-rich substrates remodels the structure of saline marsh soil bacterial and fungal communities and enhances the abundance of carbon-degrading enzymes in microorganisms [40]. Additionally, bacterial sequencing results indicated that organic material inputs also increased the predicted abundance of pMMO functional enzymes associated with CH4 oxidation (Figure 3a), which may be related to the enhancement of soil permeability and O2 content following the input of organic materials such as biochar [60,61].

4.4. Relationship of Carbon Emission with Environmental Factors and Microorganisms

We found that the application of organic fertilizer in saline soils increased the relative abundance of Bacteroidetes and Planctomycetes, which were positively correlated with CO2 emissions, and our results were consistent with those of Guo et al. [69]. The application of organic fertilizers such as straw increases the supply of soil carbon and nitrogen substrates, improves soil aeration, promotes the growth of aerobic taxa, and stimulates microbial respiration, which may be an important cause of increased CO2 emissions due to fertilizer application [60,70]. Spearman correlation analysis and SEM results showed a significant negative correlation between CO2 emissions and the content of exchangeable ions (Ca2+ and Mg2+) in the soil (Figure 4a). This may be related to the reduction in Ca2+ and Mg2+ availability in arid zone soils due to exogenous organic matter inputs, contributing to the production of inorganic salts and CO2 [45]. Soil pH is an important factor that influences CO2 emissions [71,72]. Our correlation analysis and SEM results showed that pH was a significantly positive contributor to CO2 emissions (Figure 4a and Figure 5a,b), which may be related to alkali stress on microorganisms in saline soils. Environmental stressors such as alkalinity trigger microorganisms to invest in microbial organic carbon used for growth and biosynthesis to tolerate environmental stress, leading to decreased microbial carbon utilization efficiency (CUE) [73]. CUE refers to the ratio of carbon consumed by microorganisms, which is converted to biomass, to carbon lost through respiration. Thus, CUE plays a critical role in soil carbon emissions [74]. Soil pH has been reported to have a quadratic relationship with CUE, and after the peak inflection point, an increase in pH decreases microbial CUE [75]. We also observed a significant negative correlation between the pH and fungal species richness indices (Figure 4a), further confirming the inhibitory effect of high pH on microbial activity in saline soils.
Our study showed that increases in soil pH and salinity under conditions of organic fertilizer input enhanced the potential for CH4 oxidation (Figure 4b and Figure 5c,d), which may be related to the differential responses of methanogenic and CH4-oxidizing microbial communities to salinity stress. Fagodiya et al. reported that salinity exerted a significantly higher inhibitory effect on methanogenic microorganisms than on CH4-oxidizing microorganisms [76]. Saline stress triggers microorganisms to focus their cellular investment on stress tolerance, leading to decreased growth and biosynthesis rates [73], consequently affecting the activity of methanogenic microorganisms and the level of CH4 emissions via multiple pathways.
Our Spearman correlation analyses showed that the relative abundance of species in the Acidobacteria phylum was positively correlated with CH4 emissions, the fungal Chao1 index, and the fungal species index and was decreased under conditions of increasing pH. In contrast, species in the Bacteroidetes phylum were negatively correlated with CH4 emissions, whereas species in the Planctomycetes and Ascomycota phyla showed an increase in relative abundance (Figure 4b). Our findings agree with those of You et al., who suggested that increased pH due to organic matter application suppressed methanogenic communities, such as Acidobacteria [77], and Luo et al., who demonstrated that increased pH exerted a significantly negative effect on methanogenic genes in soil microorganisms [78]. In CH4-oxidizing bacteria, pmoA codes for methane monooxygenase, which is responsible for CH4 oxidization under aerobic conditions and is the main driver of CH4 oxidation in the drylands [79]. In our study, we observed that both biochar and organic fertilizer addition increased the predicted abundance of pMMO (EC 1.14.18.3) in bacterial sequences (Figure 3a). This may be because the addition of organic materials such as biochar increases the gas diffusion rate in the soil, thereby stimulating the activity of CH4-oxidizing bacteria and promoting CH4 uptake [60,61].

5. Conclusions

We investigated the effects of biochar and organic fertilizer addition on CO2 emissions, CH4 uptake, and microbial community and function in saline soils in arid regions by performing a 5-month incubation experiment. Overall, biochar and organic fertilizer addition stimulated CO2 emissions and CH4 uptake, leading to an increased relative abundance of Proteobacteria, Planctomycetes, and Ascomycota, which may be related to improved soil permeability following organic material addition, enhanced salinity stress, and inorganic carbonate precipitation processes in saline soils caused by organic material inputs under closed-culture conditions. This may be an important reason for the increased CO2 emissions and CH4 uptake in each treatment group compared with the control group. Finally, the treatment of saline soils with straw + urea + biochar significantly enhanced the SIC and SOC content and CH4 uptake potential and reduced CO2 emissions under equal straw and urea input conditions. Therefore, the treatment with straw + urea + biochar showed the best capacity for carbon sequestration and emission reduction. Meanwhile, CO2 and CH4 emissions were also effectively reduced following the addition of microbial agents under the same straw and urea input conditions, which is of positive significance for improving carbon storage in saline and alkaline soils in arid areas.

Author Contributions

Conceptualization, S.Z. (Shuying Zang); Funding acquisition, S.Z. (Shuying Zang); Investigation, P.Z., Z.J., N.Z., J.Z. and S.Z. (Siyuan Zou); Supervision, S.Z. (Shuying Zang); Visualization, P.Z. and Z.J.; Writing—original draft, P.Z.; Writing—review and editing, X.W., J.W. and S.Z. (Shuying Zang). All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Ministry of Science and Technology (MOST) under the Special Project of Investigation of Science and Technology Basic Resources: Background Investigation of Permafrost in the Tahe Area, East Slope of Daxinganling (2022FY100701), and the Key Project of the National Natural Science Foundation of China (NSFC) under the Joint Fund: Research on the Response Mechanism of Xing’an-Type Perennial Permafrost to Global Changes and its Carbon Cycle Processes (U20A2082).

Data Availability Statement

The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding authors.

Acknowledgments

Thanks to Zhiyun for providing the language polishing support for this research.

Conflicts of 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.

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Figure 1. Cumulative CO2 emissions (a) and cumulative CH4 emissions (b) in different treatment groups (n = 3). CK, control treatment; U, urea; SU, straw + urea; SUH, straw + urea + microbial agent; SUB, straw + urea +biochar.
Figure 1. Cumulative CO2 emissions (a) and cumulative CH4 emissions (b) in different treatment groups (n = 3). CK, control treatment; U, urea; SU, straw + urea; SUH, straw + urea + microbial agent; SUB, straw + urea +biochar.
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Figure 2. Relative abundances of bacteria (a) and fungi (c) at the phylum level (>1%) for all treatment groups on day 150. Nonmetric multidimensional scaling (NMDS) analysis of bacteria (b) and fungi (d). Each graph is grouped and connected based on the samples from each treatment group. CK, control treatment; U, urea; SU, straw + urea; SUH, straw + urea + microbial agent; SUB, straw + urea + biochar.
Figure 2. Relative abundances of bacteria (a) and fungi (c) at the phylum level (>1%) for all treatment groups on day 150. Nonmetric multidimensional scaling (NMDS) analysis of bacteria (b) and fungi (d). Each graph is grouped and connected based on the samples from each treatment group. CK, control treatment; U, urea; SU, straw + urea; SUH, straw + urea + microbial agent; SUB, straw + urea + biochar.
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Figure 3. Normalized heatmap analysis of predicted abundances of carbon degradation and CH₄ oxidation functional enzymes derived from soil bacterial (a) and fungal (b) sequencing data following cultivation experiments. CK, control treatment; U, urea; SU, straw + urea; SUH, straw + urea + microbial agent; SUB, straw + urea + biochar. Enzymes include α-amylase (EC 3.2.1.1), glucoamylase (EC 3.2.1.3), α-glucosidase (EC 3.2.1.20), isoamylase (EC 3.2.1.68), glycogen phosphorylase (EC 2.4.1.1), pullulanase (EC 3.2.1.41), cyclodextrin glycosyltransferase (EC 2.4.1.19), exocellobiohydrolase (EC 3.2.1.91), β-glucosidase (BG; EC 3.2.1.21), cellulase (EC 3.2.1.4), xylanase (EC 3.2.1.8), β-mannosidase (EC 3.2.1.25), α-L-arabinosidase (EC 3.2.1.55), β-xylosidase (EC 3.2.1.37), hemicellulase (EC 3.1.1.73), chitinase (EC 3.2.1.14), chitobiase (EC 3.2.1.132), α-N-acetylglucosaminidase (EC 3.2.1.50), particulate methane monooxygenase (pMMO; EC 1.14.18.3), laccase (LA; EC 1.10.3.2), and α-D-glucuronidase (EC 3.2.1.20).
Figure 3. Normalized heatmap analysis of predicted abundances of carbon degradation and CH₄ oxidation functional enzymes derived from soil bacterial (a) and fungal (b) sequencing data following cultivation experiments. CK, control treatment; U, urea; SU, straw + urea; SUH, straw + urea + microbial agent; SUB, straw + urea + biochar. Enzymes include α-amylase (EC 3.2.1.1), glucoamylase (EC 3.2.1.3), α-glucosidase (EC 3.2.1.20), isoamylase (EC 3.2.1.68), glycogen phosphorylase (EC 2.4.1.1), pullulanase (EC 3.2.1.41), cyclodextrin glycosyltransferase (EC 2.4.1.19), exocellobiohydrolase (EC 3.2.1.91), β-glucosidase (BG; EC 3.2.1.21), cellulase (EC 3.2.1.4), xylanase (EC 3.2.1.8), β-mannosidase (EC 3.2.1.25), α-L-arabinosidase (EC 3.2.1.55), β-xylosidase (EC 3.2.1.37), hemicellulase (EC 3.1.1.73), chitinase (EC 3.2.1.14), chitobiase (EC 3.2.1.132), α-N-acetylglucosaminidase (EC 3.2.1.50), particulate methane monooxygenase (pMMO; EC 1.14.18.3), laccase (LA; EC 1.10.3.2), and α-D-glucuronidase (EC 3.2.1.20).
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Figure 4. Heatmap of Spearman’s correlation analysis between CO2 emissions (a) and CH₄ emissions (b) with microbial diversity indices and soil physicochemical properties at the phylum-level SAC, soil additives with different characteristics; CCO2, cumulative CO2 emissions; CCH4, cumulative CH4 emissions; CN2O, cumulative N2O emissions; BOS, bacterial observed species index; FChao1, fungal Chao1 index; FOS, fungal observed species index. * p < 0.05, ** p < 0.01, and *** p < 0.001.
Figure 4. Heatmap of Spearman’s correlation analysis between CO2 emissions (a) and CH₄ emissions (b) with microbial diversity indices and soil physicochemical properties at the phylum-level SAC, soil additives with different characteristics; CCO2, cumulative CO2 emissions; CCH4, cumulative CH4 emissions; CN2O, cumulative N2O emissions; BOS, bacterial observed species index; FChao1, fungal Chao1 index; FOS, fungal observed species index. * p < 0.05, ** p < 0.01, and *** p < 0.001.
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Figure 5. Structural equation modeling (SEM) based on the effects of SAC, soil physicochemical properties, and fungal alpha diversity index on cumulative CO2 emissions (a) and cumulative CH4 emissions (b) in saline–alkaline soil samples. SEM-based standardized total effect on cumulative CO2 emissions (c) and cumulative CH4 emissions (d). Blue and red lines indicate significant positive and negative correlations, respectively (p < 0.05), and dashed lines indicate a potential nonsignificant path. Numbers on the arrows indicate standardized path coefficients (* p < 0.05, ** p < 0.01, and *** p < 0.001). Black double arrows indicate the covariance between the exogenous variables. R2 denotes the total variance of the dependent variables explained by the model. SAC, soil additives with different characteristics; FC, fungal Chao1 index; FOS, observed fungal species index; FP, fungal Pielou evenness index.
Figure 5. Structural equation modeling (SEM) based on the effects of SAC, soil physicochemical properties, and fungal alpha diversity index on cumulative CO2 emissions (a) and cumulative CH4 emissions (b) in saline–alkaline soil samples. SEM-based standardized total effect on cumulative CO2 emissions (c) and cumulative CH4 emissions (d). Blue and red lines indicate significant positive and negative correlations, respectively (p < 0.05), and dashed lines indicate a potential nonsignificant path. Numbers on the arrows indicate standardized path coefficients (* p < 0.05, ** p < 0.01, and *** p < 0.001). Black double arrows indicate the covariance between the exogenous variables. R2 denotes the total variance of the dependent variables explained by the model. SAC, soil additives with different characteristics; FC, fungal Chao1 index; FOS, observed fungal species index; FP, fungal Pielou evenness index.
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Table 1. Basic properties of soil additives (mean ± SD, n = 3).
Table 1. Basic properties of soil additives (mean ± SD, n = 3).
PropertiesTC (%)TN (%)H (%)
Rice Straw54.58 ± 0.781.05 ± 0.15.08 ± 0.06
Rice Straw Biochar47.44 ± 0.222.16 ± 0.012.38 ± 0.01
Urea19.72 ± 0.0744.78 ± 0.126.82 ± 0.05
Table 2. Effects of different organic fertilizer treatments on soil physicochemical properties at the end of the 5-month incubation experiment (mean ± SD, n = 3).
Table 2. Effects of different organic fertilizer treatments on soil physicochemical properties at the end of the 5-month incubation experiment (mean ± SD, n = 3).
SACCKUSUSUHSUBp-Values
SIC (g kg−1)5.22 ± 0.08 b4.79 ± 0.41 b4.78 ± 0.93 b4.58 ± 0.46 b6.65 ± 0.22 a**
SOC (g kg−1)5.53 ± 0.15 c5.28 ± 0.15 c5.44 ± 0.2 c5.9 ± 0.02 b8.19 ± 0.16 a***
LOC (g kg−1)4.18 ± 1.09 ab3.85 ± 0.34 b2.25 ± 0.95 c4.48 ± 0.34 ab5.52 ± 0.85 a**
pH10.05 ± 0.14 b10.09 ± 0.06 b10.33 ± 0.06 a10.3 ± 0.1 a10.18 ± 0.02 ab**
TSS (g kg−1)4.88 ± 0.34 c5.09 ± 0.72 c6.49 ± 0.41 b10.42 ± 0.4 a11.28 ± 0.58 a***
EC (ms m−1)112 ± 3.12 b118.25 ± 8.21 b128.15 ± 9.95 b179.48 ± 4.58 a185.49 ± 12.3 a***
Ca2+ (g kg−1)6.47 ± 0.33 a6.59 ± 0.16 a4.52 ± 0.08 b6.44 ± 0.21 a6.83 ± 0.29 a***
Mg2+ (g kg−1)0.5 ± 0.02 a0.5 ± 0.01 a0.33 ± 0.01 c0.45 ± 0.02 b0.47 ± 0.01 b***
Note: CK, control treatment; U, urea; SU, straw + urea; SUH, straw + urea + microbial agent; SUB, straw + urea + biochar. SOC, soil organic carbon; SIC, soil inorganic carbon; LOC, labile organic carbon; TSS, total soluble salts. Basic cations refer to Ca2+. Different lowercase letters (organic matter added) indicate significant differences (p < 0.05, Duncan’s multiple range test). The p-values for these factors were obtained from one-way ANOVA results for the application of soil additives with different characteristics (SAC). *** p < 0.001 and ** p < 0.01.
Table 3. Effect of different treatments on fungal Chao1 and observed species indices and cumulative emissions of CO2 and CH4 throughout the 5-month incubation cycle in saline–alkali soil (mean ± SD, n = 3).
Table 3. Effect of different treatments on fungal Chao1 and observed species indices and cumulative emissions of CO2 and CH4 throughout the 5-month incubation cycle in saline–alkali soil (mean ± SD, n = 3).
SACCKUSUSUHSUBp-Values
Fungal Chao1462.16 ± 84.97 a491.85 ± 71.69 a270.41 ± 17.78 b162.73 ± 30.85 c162.05 ± 27.62 c***
Fungal observed species459.17 ± 83.91 a489.4 ± 72.07 a269.2 ± 16.6 b161.57 ± 30.78 c160.27 ± 26.8 c***
Cumulative CO2 (mg kg−1)2146.45 ± 250.64 d2343.25 ± 99.72 d4650.9 ± 228.47 a3960.11 ± 517.57 b3014.19 ± 235.65 c***
Cumulative CH4 (mg kg−1)−22.37 ± 0.95 a−21.92 ± 4.4 a−34.52 ± 3.86 b−41.27 ± 5.94 b−33.1 ± 5.79 b**
Note: CK, control treatment; U, urea; SU, straw + urea; SUH, straw + urea + microbial agent; SUB, straw + urea + biochar. Different lowercase letters (organic matter added) indicate significant differences (p < 0.05, Duncan’s multiple range test). The p-values for these factors were obtained from one-way ANOVA results for the application of soil additives with different characteristics (SAC). *** p < 0.001 and ** p < 0.01.
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Zhang, P.; Jiang, Z.; Wu, X.; Zhang, N.; Zhang, J.; Zou, S.; Wang, J.; Zang, S. Effects of Organic Fertilizer and Biochar on Carbon Release and Microbial Communities in Saline–Alkaline Soil. Agronomy 2024, 14, 1967. https://doi.org/10.3390/agronomy14091967

AMA Style

Zhang P, Jiang Z, Wu X, Zhang N, Zhang J, Zou S, Wang J, Zang S. Effects of Organic Fertilizer and Biochar on Carbon Release and Microbial Communities in Saline–Alkaline Soil. Agronomy. 2024; 14(9):1967. https://doi.org/10.3390/agronomy14091967

Chicago/Turabian Style

Zhang, Pengfei, Ziwei Jiang, Xiaodong Wu, Nannan Zhang, Jiawei Zhang, Siyuan Zou, Jifu Wang, and Shuying Zang. 2024. "Effects of Organic Fertilizer and Biochar on Carbon Release and Microbial Communities in Saline–Alkaline Soil" Agronomy 14, no. 9: 1967. https://doi.org/10.3390/agronomy14091967

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