Methanotrophic Communities and Cultivation of Methanotrophs from Rice Paddy Fields Fertilized with Pig-livestock Biogas Digestive Effluent and Synthetic Fertilizer in the Vietnamese Mekong Delta

Abstract

Biogas digestive effluent (BDE) has been applied to rice fields in the Vietnamese Mekong Delta (VMD). However, limited information is available on the community composition and isolation of methanotrophs in these fields. Therefore, the present study aimed (i) to clarify the responses of the methanotrophic community in paddy fields fertilized with BDE or synthetic fertilizer (SF) and (ii) to isolate methanotrophs from these fields. Methanotrophic communities were detected in rhizospheric soil at the rice ripening stage throughout 2 cropping seasons, winter-spring (dry) and summer-autumn (wet). Methanotrophs were isolated from dry-season soil samples. Although the continued application of BDE markedly reduced net methane oxidation potential and the copy number of pmoA genes, a dissimilarity ordination ana­lysis revealed no significant difference in the methanotrophic community between BDE and SF fields (P=0.167). Eleven methanotrophic genera were identified in the methanotrophic community, and Methylosinus and Methylomicrobium were the most abundant, accounting for 32.3–36.7 and 45.7–47.3%, respectively. Type-I methanotrophs (69.4–73.7%) were more abundant than type-II methanotrophs (26.3–30.6%). Six methanotrophic strains belonging to 3 genera were successfully isolated, which included type I (Methylococcus sp. strain BE1 and Methylococcus sp. strain SF3) and type II (Methylocystis sp. strain BE2, Methylosinus sp. strain SF1, Methylosinus sp. strain SF2, and Methylosinus sp. strain SF4). This is the first study to examine the methanotrophic community structure in and isolate several methanotrophic strains from BDE-fertilized fields in VMD.

Biogas digestive effluent (BDE) is a byproduct of the methane fermentation process of livestock waste or biodegradable resources (Tang et al., 2021). The process remains a vital nutrient constraint and increases pH (Bachmann et al., 2014). BDE generally contains adequate essential nutrients and minerals that are suitable for plant growth (Fujiwara, 2012; Möller and Müller, 2012; Kumar et al., 2015; Oritate et al., 2016; Xu et al., 2021; Chang et al., 2022). It is recommended for use as an organic fertilizer alone or in combination with inorganic fertilizers (Zhang et al., 2021). Recent studies demonstrated the feasibility of utilizing BDE for rice paddy fields (Win et al., 2015; Oritate et al., 2016; Minamikawa et al., 2019). In this approach, BDE may not only reduce dependence on chemical fertilizers, but also recirculate unutilized nutrient resources for sustainable agricultural production.

Consistent with the preponderance of available nutrients, BDE anchors a labile carbon source (Minamikawa et al., 2021; Tang et al., 2021) and various complex organic compounds (i.e., volatile fatty acids) (Cao et al., 2016; Wang et al., 2022a). Therefore, the application of BDE may drive the modification of soil properties. Previous studies revealed that the application of BDE increased the soil organic carbon (SOC) content (Win et al., 2010; Xu et al., 2019; Głowacka et al., 2020; Tang et al., 2021), available P and exchangeable Mg (Minamikawa et al., 2021), pH, sorption properties (cation exchange capacity [CEC] and exchangeable cations [EAC]), K and Zn (Głowacka et al., 2020), antibiotic accumulation and resistance genes (Lu et al., 2021), and microbial diversity (Xu et al., 2019; Zhang et al., 2021) and suppressed soilborne diseases (Wang et al., 2022a). However, other studies showed that the application of digestates did not affect the SOC content, whereas the enzyme activity of soil microorganisms was reduced (Bachmann et al., 2014). Similarly, Minamikawa et al. (2021) reported no significant differences in pH, organic C, total N, exchangeable K, exchangeable Ca, or CEC. This discrepancy may be partly attributed to differences in BDE characteristics, the short/long-term application of BDE, and the practicing technology. Therefore, further studies are needed on the responses of rice paddy soil to the application of BDE.

The Vietnamese Mekong Delta (VMD) located in the southwest of Vietnam accounts for 53.5% of national rice productivity (Statistical Yearbook of Vietnam, 2022). The application of BDE to rice fields has been performed in various localities in VMD. The primary benefit of applying BDE is reducing dependence on chemical fertilizers for rice without yield loss (Minamikawa et al., 2019; Huynh et al., 2022). However, higher CH₄ emissions in BDE fields versus SF fields (~19%) were reported in VMD (Minamikawa et al., 2021). Reductions in CH₄ emissions were agreed upon at the 2023 United Nations Climate Change Conference of the Parties of the UNFCCC (COP28), and the Vietnamese government is promoting the Action Plan for Methane Emissions Reduction by 2030. Therefore, solving the problem of CH₄ emissions is critical to popularizing the application of BDE to rice cultivation in VMD.

Higher CH₄ emissions in BDE fields is closely associated with the enrichment of labile organic carbon in BDE, which is a preferred substrate of methanogens (methane-producing archaea). Alternatively, the application of BDE stimulates the growth of methanogens. In contrast, the growth of methanotrophic populations (methane-oxidizing bacteria) may be promoted by increases in soil surface-generated CH₄, which is a source of carbon and energy for methanotrophs, or reduced by oxygen deficiency. This argument has remained unclear in fields fertilized with BDE. Therefore, a more detailed understanding of the responses of the methanotrophic community in CH₄ emission-related BDE fields is important for suggesting practical approaches to alleviate CH₄ emissions in the future.

Methanotrophic microbes belong to the phyla Proteobacteria and Verrucomicrobia and Candidatus phylum NC-10 (Ettwig et al., 2010; Dedysh and Knief, 2018; Rahalkar et al., 2021). Methanotrophs are ubiquitous in nature and may be isolated from abundant environments (Cui et al., 2020; Davamani et al., 2020). The latest modified nitrate mineral salts (NMS) medium with a higher nitrate concentration, as recommended by Pandit et al. (2016), was recently used to successfully isolate seven genera from India’s rice paddy fields (Rahalkar et al., 2021), and, thus, has potential in the identification of methanotrophs from the diverse features of rice paddy fields in tropical regions. Moreover, numerous methanotrophic strains remain uncultured, which has recently attracted considerable research interest (Dedysh and Knief, 2018; Guerrero-Cruz et al., 2021). A more detailed understanding of the methanotrophic community structure and indigenous methanotrophic isolation may be helpful for future strategies to reduce the emission of greenhouse gases (GHG) from rice paddy fields (Pandit et al., 2016; Davamani et al., 2020). Therefore, the isolation of methanotrophs is more important for future strategies to reduce indigenous methanotroph-based CH₄ emissions in agriculture production, particularly in rice fields fertilized with BDE from which CH₄ emissions are higher.

Therefore, we herein aimed (i) to examine the methanotrophic community structure of rice fields fertilized with BDE or SF and (ii) to isolate methanotrophic strains in these fields in VMD. To achieve these goals, rhizospheric rice soil samples in fields fertilized with BDE or SF were collected to examine soil physiochemical properties, the copy number of pmoA genes (gene abundance in methanotrophs), and the methanotrophic community composition and to isolate methanotrophs from these rice fields.

Materials and Methods

Study site

Rice paddies in the Vinh Thanh district, Can Tho city (10°15′2.89″N; 105°18′32.27″E) were selected as a representative area of intensive rice production in VMD (Fig. S1). The study site was a low-lying area in a tropical latitude with a monsoon climate zone. The climate region is divided into two specific seasons. The dry season varies from November to April. The wet season is from May to October, accounting for 80% of annual precipitation (Tran et al., 2019). The monthly average temperature, precipitation, and sunshine in the dry season (2020–2022) were 27.5°C, 70.9‍ ‍mm, and 199.7 h, respectively, while those in the wet season were 26.2°C, 219.9‍ ‍mm, and 139.7 h, respectively, (Dang and Hung, 2023).

Soil sampling sites

Rhizospheric soil samples were collected from rice paddy fields fertilized with BDE or SF. These rice fields were intensive rice farming with 3 crops per year. In fields fertilized with BDE, the farmer diluted BDE with surface water from a nearby river prior to the irrigation of rice fields through a BDE storage pond. The characteristics of diluted BDE applied to the field are shown in Table S1. These fields were sown with the rice variety OM18, a typical rice cultivar used in VMD, at a sowing rate of 192.3‍ ‍kg ha^–1. The maturity of this cultivar varies between 95–100 days. The water level was managed based on alternate wetting and drying (AWD) by the farmer (–10 to 10‍ ‍cm), which is a typical water management method as described by Uno et al. (2021). In SF fields, the total amount of N-P₂O₅-K₂O fertilizers based on urea, NPK, and DAP (diammonium phosphate) was applied as follows: 138.5‍ ‍kg N ha^–1, 88.25‍ ‍kg P₂O₅ ha^–1, and 80‍ ‍kg K₂O ha^–1. The fertilizer was applied to rice fields 4 times: 8, 18, 28, and 38 days after sowing (DAS). In BDE fields, diluted BDE was applied every 10 days from 8 to 68 DAS (7 times per crop). These samples were taken in the ripening stage of the Winter-Spring season (Dry) (Dec 2021–Mar 2022) and Summer-Autumn season (Wet) (Apr–Jul 2022). The water level of the fields varied between –5 to 0‍ ‍cm from the soil surface. Five soil samples in each field were collected from rhizospheric areas by uprooting rice plants with the entire root systems and soil attached. The fresh weight of each sample was taken at approximately 0.5‍ ‍kg. Collected samples were immediately placed in airtight sterile polyethylene (PE) sampling bags and stored in a Styrofoam box at 4°C. After being transported to the laboratory, samples were quickly shaken off to remove soil from the root in a laminar flow hood. A similar portion of soil samples collected in the same field was mixed well to achieve a pooled sample correspondingly for isolating methanotrophs. Twenty samples were used to analyze soil physicochemical properties, and DNA was extracted for quantitative PCR (qPCR) and amplicon sequencing (5 samples per field×2 fields×2 seasons). The soil texture of BDE fields (n=5) consisted of clay 65±5.0%, silt 15±5.7%, and sand 20±3.7%, while that of SF fields was clay 64±4.1%, silt 16±4.1%, and sand, 20±3.10%. The soil of BDE and SF fields was classified as Gleysol based on World Reference Base (WRB) systems (IUSS Working Group WRB, 2015).

Preparation of media

All methanotrophic bacteria were cultivated in NMS medium modified by Pandit et al. (2016) and Rahalkar et al. (2021) from Whittenbury et al. (1970). Medium was prepared as follows: MgSO₄ 7H₂O, 1‍ ‍g‍ ‍L^–1; CaCl₂, 0.2‍ ‍g‍ ‍L^–1; KNO₃, 0.25‍ ‍g‍ ‍L^–1; SL10 solution, 1‍ ‍mL L^–1; Fe₃NH₄ citrate solution, 1‍ ‍mL L^–1; HEPES buffer (2 mol L^–1, pH 7), 1‍ ‍mL L^–1; post-autoclave additions included phosphate buffer pH 6.8, 2‍ ‍mL L^–1 and vitamin solution (1×), 10‍ ‍mL L^–1 (Rahalkar et al., 2021). The solidifying agent was 2% agarose in medium to streak methanotrophs.

Enrichment and isolation

Enrichment was conducted in a series of sterile 50-mL glass bottles. Ten serial dilutions (10^–1–10^–10) were implemented by adding 1.5‍ ‍g of each pooled soil sample to 13.5‍ ‍mL of sterilized NMS medium. The bottle was then closed airtight with a butyl rubber septum assembled with an aluminum crimp seal. Each bottle’s headspace air was flushed with nitrogen gas (99.999% N₂, [v/v]) for approximately 3‍ ‍min (0.1 kPa). The enrichment experiment was performed under 20% CH₄, 25% O₂, and 55% N₂. All bottles were then incubated at 25°C for two months in a dark place under static conditions (Rahalkar et al., 2021). Methane concentrations were checked every 10 days, and the gas phase was replaced simultaneously to avoid oxygen depletion. Positive enrichment bottles were simultaneously identified by a decline in the content of CH₄ and visual turbidity.

Fifty microliters of positive cultures was streaked on a 2% agarose plate of NMS and then incubated in air-tight chambers under a 20% CH₄ and 80% air atmosphere. Isolated colonies that grew on the agarose plate were picked up using sterilized toothpicks/inoculation loops and transferred to 2-mL NMS in microtiter plates. These microtiter plates were then continuously incubated under conditions similar to those described above. After incubating for ~4‍ ‍weeks, positive wells were continuously transferred to 50-mL glass bottles to assess methane consumption. Positive bottles were constantly re-streaked on agarose plates for ~4‍ ‍weeks to obtain single colonies. These cycles were repeated several times until pure methanotrophic strains were obtained. The purity check was performed on several heterotrophic media (Pearlcore E-MC35; Bacto^TM Tryptic Soy Broth) and nutrient agar (Pearlcore E-MC63; Difco^TM R2A agar) (Rahalkar et al., 2021). No growth on media indicated purity. The cells of methanotrophs were also observed using a transmission electron microscopy (TEM) (JEM-1400 Flash; JEOL) by negative staining. The bacterial culture medium was dropped onto a formvar-supported copper 200 mesh, which was held with self-locking fine forceps. The droplet on the grid was immediately dried using filter paper. EM stainer (Nisshin EM, #311) diluted ten times with ultrapure water was then dropped onto the grid. The droplet on the grid was immediately dried using filter paper. After drying, the drop was observed by TEM at 100 kV. The cells of methanotrophs were captured using a differential interference contrast microscope under 1000× magnification.

Measurements

Methane oxidation potential (MOP)

The net soil MOP was measured by adding 5‍ ‍g of fresh soil to a 20-mL glass bottle that contained 10-mL sterile distilled water in triplicate (Yang et al., 2022). The CH₄ concentration was initially set by ~10% of the headspace. Bottles were placed on a shaker set at 120‍ ‍rpm in a dark place at 25°C. CH₄ concentrations were measured after 0, 8, 16, and 36‍ ‍h (Fig. S2).

Soil physiochemical characteristics

Soil texture was exami­ned using the Pipette Robinson method (Carter and Gregorich, 2008). pH and electrical conductivity (EC) were extracted at a 1:5 ratio (1‍ ‍g of soil:5‍ ‍mL of distilled water) and detected using a glass pH electrode (9618S-10D; HORIBA Ltd.) and EC meter (ES-14; HORIBA Ltd.). Total organic carbon (TOC), total carbon (TC), and total nitrogen (TN) were detected using CN Corder (MT-700; Yanaco Technical Science), of which TOC was pretreated with concentrated HCl (37%) to eliminate all carbonates, followed by a 10-h dry step. Potential nitrogen mineralization (PNM) was measured by incubating soil anaerobically at 40°C for 7 days. NH₄⁺ and NO₃^– were then detected (Keeney and Bremner, 1966), extracted using 2 mol L^–1 KCl, and assessed by the Indophenol blue method and Vanadium (III) reduction method, respectively (APHA, 1998).

Methane concentrations were measured with gas chromatography (GC-8A; Shimadzu) equipped with a flame ionization detector using the packed column (Porapak N, 80/100 mesh, 0.5 m). Injector, column, and detector temperature conditions were 180, 80, and 180°C, respectively. He gas was used as the carrier gas. Gas was taken directly from bottles using a 0.25-mL Pressure-Lok^® precision analytical syringe (VICI Precision Sampling).

DNA extraction from soil samples

DNA was extracted from 0.5‍ ‍g of fresh soil samples using the FastDNA^TM Spin Kit for soil (MP Biomedicals). DNA extraction procedures followed the manufacturer’s instruction manual. DNA concentrations were measured using a Qubit 4 Fluorimeter (Thermo Fisher Scientific) and stored at –80°C for further ana­lyses.

Amplicon sequencing ana­lysis of pmoA in soil samples

DNA templates were amplified using the universal pmoA primer (A189f and mb661r) (Holmes et al., 1995; Costello and Lidstrom, 1999). The first PCR product was prepared in a 25-μL mixture as follows: 12.5‍ ‍mL Hot Start Taq 2× Master Mix (M0496L; New England Biolabs); 0.5‍ ‍μL each of 10‍ ‍μmol L^–1 A189f and mb661r primers; 5‍ ‍μL of template DNA (1‍ ‍ng μL^–1); and 6.5‍ ‍μL sterilized Milli-Q water. First PCR conditions were as follows: initial denaturation (94°C, 3‍ ‍mins); 30 cycles of denaturation (95°C, 30‍ ‍s), annealing (55°C, 30‍ ‍s), elongation (72°C, 30‍ ‍s); and final elongation (72°C, 10‍ ‍min) (Yang et al., 2022). These PCR products were then sent to Bioengineering Lab.

First PCR products were purified with VAHTS DNA Clean Beads (Vazyme). Second PCR was performed for library quantification. The quality of the prepared library was confirmed using Analyzer and dsDNA 915 Reagent Kit (Agilent Technologies). Sequences were extracted using the fastx_barcode_splitter tool of FASTX-Toolkit (ver. 0.0.14). Sequences with a quality value less than 20 and a length <40 bases were removed using sickle (ver. 1.33). Reads were combined using the paired-end read merging script FLASH (ver. 1.2.11) with a minimum overlap of 10 bases. After removing chimera and noise sequences using the dada2 plugin of Qiime2 (ver. 2023.7), representative sequences were output with the amplicon sequence variants (ASV) table. Phylogeny was inferred from the representative sequence obtained against NCBI performing BLASTN (ver. 2.13.0). These procedures were conducted according to the standard protocols of Bioengineering Lab.

qPCR

The gene abundance of pmoA was analyzed by qPCR (Thermal cycler Dice Realtime System II systems, Takara Bio Inc.) using the specific primers A189f and mb661f. The qPCR product was prepared in a 25-μL mixture (12.5‍ ‍mL SYBR^® Premix EX Taq 2X (Takara Bio); 0.5‍ ‍μL each of 10‍ ‍μmol L^–1 A189f and mb66r primers; 11.5‍ ‍μL of template DNA and sterilized Milli-Q water). qPCR conditions were as follows: initial denaturation at 95°C for 30‍ ‍s followed by 2-step PCR with 40 cycles of denaturation at 95°C for 5‍ ‍s and annealing at 60°C for 30‍ ‍s. Melting curve data were obtained by maintaining temperature at 95°C for 15‍ ‍s, lowering it to 60°C for 1‍ ‍min, then raising it to 95°C for 15 s. A serial standard curve was constructed from a DNA mixture of methanotrophs (Methylosinus sp. strain SF4 and Methylocystis sp. strain BE2) isolated in the present study. The PCR efficiency of pmoA was calculated to be 91.1%.

DNA extraction, amplification, and sequencing of isolated methanotrophs

DNA from isolated methanotrophs was extracted from pure cultures using ISOPLANT (Nippon Gene Co., Ltd.). DNA concentrations were measured using QUBIT procedures. Amplification was performed using primers comprising 27f–1492r for the 16S rRNA gene and A189f-mb661r for the pmoA gene (Rahalkar et al., 2021) in a total volume of 50‍ ‍μL (25‍ ‍mL Hot Start Taq 2× Master Mix, 1‍ ‍μL each of 10‍ ‍μmol L^–1 (i) 27f–1492r and (ii) A189f–mb661r primers, and 23‍ ‍μL of template DNA and sterilized MiliQ water). PCR conditions for the 16S rRNA gene were as follows: initial denaturation (94°C, 3‍ ‍min); 30 cycles of denaturation (95°C, 30‍ ‍s), annealing (46°C, 30‍ ‍s), and elongation (68°C, 90 s); and final elongation (68°C, 5‍ ‍min). PCR conditions for the pmoA gene were as follows: initial denaturation (94°C, 3‍ ‍min); 30 cycles of denaturation (95°C, 30‍ ‍s), annealing (55°C, 30 s), and elongation (72°C, 30‍ ‍s); and final elongation (72°C, 10‍ ‍min) (Yang et al., 2022). PCR products were cleaned with Isospin PCR Product (Nippon Gene). Cleaned template DNAs were checked on gel electrophoresis with 1.5% agarose (Fig. S3). Amplified products were then outsourced for sequencing to Eurofins Genomics. The sequences acquired were aligned using Molecular Evolutionary Genetics Analysis (MEGA11) software and subjected to a BLAST ana­lysis with the 16S rRNA gene and pmoA gene using the NCBI database. Isolated methanotroph gene sequences (pmoA and 16S rRNA) were submitted to the NCBI database. The accession numbers (pmoA; 16S rRNA) of strains were as follows: Methylococcus sp. strain BE1 (PP182324; PP178148), Methylocystis sp. strain BE2 (PP239038; PP218659), Methylosinus sp. strain SF1 (PP239039; PP218660), Methylosinus sp. strain SF2 (PP239041; PP218670), Methylococcus sp. strain SF3 (PP239042; PP218671), and Methylosinus sp. strain SF4 (PP239040; PP218672).

Statistical ana­lysis

Statistical ana­lyses were performed using R version 4.3.2 (R Core Team [2023]—R Foundation for Statistical Computing, Vienna, Austria) with a confidence level of 95%. Soil physiochemical characteristics, MOP, and the copy number of pmoA genes among rice fields were tested using a principal component ana­lysis (PCA). The dataset was standardized using the “Scale” function to calculate the z-scores for each variable. A principal coordinate ana­lysis (PCoA) was performed to measure the dissimilarity matrices of the methanotrophic community among sampling sites using the Euclidean distance at genus levels. Dissimilarity ordinations between fields were exami­ned using a permutational multivariate ana­lysis of variance (PERMENOVA). A redundancy ana­lysis (RDA) was also performed to analyze the relationships between the community structures of methanotrophs and environmental variables. A step function was applied to eliminate the collinearity of explanatory variables and only select the best variables to simplify the model before performing RDA. Variances were standardized using Hellinger transformation before computing. PCoA and RDA computations were performed using the Vegan package (Version 2.6-4). Pearson’s correlation ana­lysis was used to investigate the relationships among environmental variables, MOP, pmoA, and methanotrophic community genus levels using the visualization of a correlation matrix (Corrplot, Version 0.92). Data were visualized using the ggplot2 package (version 3.4.4).

Results

Rhizospheric soil physiochemical properties, soil MOP, and gene abundance of methanotrophs (pmoA)

The physicochemical characteristics of soil rice rhizospheres fertilized with BDE and SF are shown in Table S2. Variations in rhizospheric soil properties slightly differed between the seasons and fields. Fig. 1. shows variations in soil MOP and the gene abundance of methanotrophs (pmoA). MOP values were in the range of 59–172 and 103–190‍ ‍nmol CH₄ (g dry soil)^–1 h^–1 for BDE and SF fields, respectively (Fig. 1A). The mean MOP value in the BDE field (115‍ ‍nmol CH₄ [g dry soil]^–1 h^–1) was lower than in the SF field (143‍ ‍nmol CH₄ [g dry soil]^–1 h^–1). Furthermore, MOP values were higher in the dry season (165‍ ‍nmol CH₄ [g dry soil]^–1 h^–1) than in the wet season (91.7‍ ‍nmol CH₄ [g dry soil]^–1 h^–1). Similarly, pmoA gene abundance in BDE and SF fields varied between 2.4×10⁷ and 1.36×10⁸ and between 8.5×10⁷ and 2.6×10⁸ copies [g dry soil]^–1, respectively (Fig. 1B). pmoA abundance was lower in the BDE field (7.2×10⁷ copies [g dry soil]^–1) than in the SF field (1.7×10⁸ copies [g dry soil]^–1). However, mean values slightly differed between the dry (1.4×10⁸ copies [g dry soil]^–1) and wet (9.6×10⁷ copies [g dry soil]^–1) seasons.

Fig. 1.

Methane oxidation potential (A) and copy numbers of pmoA genes (B) in BDE and SF fields.

Symbols ( and ) indicate the dry and wet season, respectively. Symbols in each boxplot indicate replications (n=10). Filled diamonds denote mean values in BDE and SF fields.

The distribution of soil physicochemical characteristics was distinct in the present study (Fig. 2). Two PCs were extracted that accounted for 61.2% (37.3% PC1 and 23.9% PC2) of observed variables. The BDE field was characterized by high EC and PNM (PC1), and the SF field by higher NH₄⁺ and pmoA (PC2).

Fig. 2.

A principle component ana­lysis (PCA) of variables of soil physiochemical characteristics, methane oxidation potential, and the pmoA gene.

Two PCs were extracted from dimensions that explained 61.2%. Ellipses indicate the dispersion of data points and principal components for each field. Larger ellipse shows greater variance. The change in arrow colors denotes the contribution of response variables in each dimension (PC).

Methanotroph diversity and community composition of methanotrophs

The methanotrophic community was detected based on the amplicon sequencing ana­lysis of pmoA genes. A total of 1,122 high-quality pmoA gene ASVs were obtained for the BDE and SF fields, which resulted in 72 ASVs being identified for methanotrophs. The alpha diversity of the methanotrophic community is shown in Fig. 3. The richness of ASVs (Chao1) obtained in the BDE and SF fields were 6–13 and 5–17, respectively. The Shannon index was in the range of 0.96–2.29 and 0.74–2.11 for the BDE and SF fields, respectively. Correspondingly, the Chao1 index was in the range of 7–17 and 5–14 ASVs in the dry and wet seasons, respectively. The Shannon index was in the range of 0.63–0.88 and 0.74–1.93 for the dry and wet seasons, respectively.

Fig. 3.

Alpha diversity at the genus level (Chao1 [A] and Shannon [B]) of the methanotroph community in BDE and SF fields.

Symbols ( and ) indicate the dry and wet season, respectively. Symbols in each box indicate replications (n=10). Filled diamonds denote mean values in BDE and SF fields.

Fig. 4 shows the PCoA of the methanotrophic community structure. PCoA results explained the structure with 71.68% (PCoA1, 50.54%; and PCoA2, 21.14%). These results revealed that the methanotrophic community did not significantly differ between the BDE and SF fields (P=0.157) or between the dry and wet seasons (P=0.167). Type-I methanotrophs comprising Methylobacter, Methylocaldum, Methylococcus, Methylogaea, Methylomagnum, Methylomicrobium, Methylomonas, Methyloparacoccus, and Methylosarcina and type-II methanotrophs encompassing Methylosinus and Methylocystis were present in the BDE and SF fields (Fig. 5A). Among genera, Methylosinus and Methylomicrobium were dominant in paddy soil. The mean percentages of Methylosinus and Methylomicrobium were in the range of 22.3–43.7 and 45.5–48.6% in the dry and wet seasons, respectively (Fig. S4). In comparisons of the BDE and SF fields, the average percentages of Methylosinus and Methylomicrobium was in the range of 45.7–47.3 and 32.3–36.7%, respectively (Fig. 5B). Type-I methanotrophs (69.4–73.7%) were more abundant than type-II methanotrophs (26.3–30.6%) (Fig. S5).

Fig. 4.

Principle coordination ana­lysis (PCoA) ordination diagram of the methanotrophic community at the genus level in BDE and SF fields.

The methanotrophic community at the genus level in BDE (open) and SF (closed) fields in the dry (triangle) and wet (circle) seasons.

Fig. 5.

Methanotrophic community structure at the genus level in BDE and SF fields in dry and wet seasons.

A and B indicate all samples and mean values, respectively.

Factors controlling the methanotrophic community in rice paddy fields

RDA was performed to identify factors affecting the methanotroph community structure in soil (Fig. 6). The presented RDA model was significant (P<0.01). The percentages of explained variance of RDA1 and RDA2 were 50.6 and 10.9%, respectively, resulting in 61.5% of explained combined variance. RDA1 was significant for explaining the relationship between environmental and response variables (Table S3). EC (P<0.05), TOC (P<0.05), and NO₃^– (P<0.01) had a significant effect on the community structure of methanotrophs at the genus level (Table S4).

Fig. 6.

RDA ordination plot indicating the relationship between soil environmental variables and genus levels of the methanotrophic community.

A step function was applied to the model in order to select the best variables, thereby simplifying the models.

Pearson’s correlation ana­lysis (Fig. 7) showed that Methylomicrobium negatively correlated with PNM (P<0.05) and TOC (P<0.05), but positively correlated with TN (P<0.05) and NO₃^– (P<0.001). Methylosinus positively correlated with NH₄⁺ (P<0.05), TOC (P<0.05), and TC (P<0.05), but negatively correlated with NO₃^– (P<0.05). Similarly, Methylogaea positively correlated with NH₄⁺ (P<0.05). Methylocystis and Methylococcus positively correlated with PNM and EC (P<0.05), respectively. Methylosarcia, Methyloparacoccus, Methylomonas, Methylomagnum, Methylocaldum, and Methylobacter did not correlate with any variables. Notably, pmoA gene abundance negatively correlated with EC (P<0.05), while MOP, the Chao1 index, and the Shannon index did not correlate with any variables.

Fig. 7.

A heatmap of Pearson’s correlation coefficients between soil environmental variables and alpha diversities (Chao1 and Shannon indices), MOP, pmoA, and methanotrophic genus levels.

Significance was tested using Pearson’s correlation coefficient. ***P<0.001, **P<0.01, *P<0.05, ^†P<0.1.

Isolation of methanotrophs from paddy soils

The present study was conducted as a serial step for methanotroph cultivation. Methanotrophs generally grew under a dilution rate of 10^–1–10^–8, resulting in visual turbidity and the formation of pellicles accompanied by CH₄ consumption. On average, 4–5 streaking steps resulted in a pure culture. The dilution rate to achieve axenic cultures varied between 10^–4 and 10^–6. Based on 16S rRNA (Fig. 8A) and pmoA sequences (Fig. 8B), 6 methanotrophic strains with 3 genera belonging to 3 clades of proteobacterial methanotrophs were identified. The strains of isolated methanotrophs included Methylosinus. sp. (3 strains), Methylococcus sp. (2 strains), and Methylocystis sp. (1 strain) (Table 1). Methylocystis sp. and Methylosinus sp., classified as type-II methanotrophs, were isolated from the BDE and SF fields, respectively, while Methylococcus sp., categorized as type-I methanotrophs, were isolated from the BDE and SF fields. The colors of methanotrophic colonies were white, cream and light yellow (Fig. S6). The cell morphology of isolated methanotrophs was ellipsoid (Fig. S7). Furthermore, the pmoA sequences of the isolated strains were similar to the pmoA gene amplicons identified in VMD rice fields (Fig. S8).

Fig. 8.

Neighbor-joining trees showing the polygenetic tree of isolated methanotrophs with their closest member based on 16S rRNA (A) and pmoA gene (B) sequences.

Isolated strains are highlighted in orange (type-I methanotrophs) and blue (type-II methanotrophs). All ambiguous positions were removed for each sequence pair. Bootstrap (1,000 interactions) values are shown. The scale bar indicates 5% estimated sequence divergence.

Table 1.

Methanotroph strains isolated from paddy soils fertilized with BDE and SF

Fields	Genera	Accession number	16S rRNA gene sequence, identity	Partial pmoA gene sequence, identity
BE1 (10–4)	Methylococcus sp.	PP178148† PP182324‡	Methylococcus capsulatus Texas=ATCC 19069T (NR042183), 98.47%	Methylococcus thermophilus AQ4T (OR004532), 100%
BE2 (10–5)	Methylocystis sp.	PP218659† PP239038‡	Methylocystis parvus OBBPT (NR044946), 97.78%	Methylocystis sp. IMET 10484T (AJ458998), 98.50%
SF1 (10–4)	Methylosinus sp.	PP218660† PP239039‡	Methylosinus sporium NCIMB 11126T (NR026512), 98.93%	Methylosinus sporium KS17T (AJ459031), 98.74%
SF2 (10–5)	Methylosinus sp.	PP218670† PP239041‡	Methylosinus sporium NCIMB 11126T (NR026512), 99.01%	Methylosinus sp. B4ST (AB683146), 99.57%
SF3 (10–6)	Methylococcus sp.	PP218671† PP239042‡	Methylococcus capsulatus TexasT=ATCC 19069 (NR042183), 98.51%	Methylococcus thermophilus AQ4T (OR004532), 100%
SF4 (10–5)	Methylosinus sp.	PP218672† PP239040‡	Methylosinus sporium NCIMB 11126T (NR026512), 98.99%	Methylosinus sporium B4ST (AB683146), 99.58%

The numbers in parentheses indicate the dilution of soil from which the isolates were obtained. ^† and ^‡ indicate the accession numbers of the 16S rRNA and pmoA gene sequences submitted to NCBI GenBank, respectively.

Discussion

The present study collected rhizospheric soil from intensive paddy fields fertilized with BDE and SF, with BDE being applied for three years. Slight differences were observed in pH, TOC, TC, TN, NH₄⁺, and NO₃^– between the BDE and SF fields (Table S2). However, PCA results (PC1) revealed increases in EC and PNM with the application of BDE (Fig. 2). Diluted BDE utilized for rice paddy fields contained high levels of EC (22–26 mS cm^–1), TOC (266–283‍ ‍mg L^–1), and TN (234–278‍ ‍mg N L^–1) (Table S1). The accumulation and mineralization of organic carbon and nitrogen following the application of a large amount of BDE markedly increased EC and PNM, which had not been reported in previous studies. The application of BDE did not affect the SOC content which is consistent with previous findings (Bachmann et al., 2014; Minamikawa et al., 2021), while the augmentation of EC and PNM was observed in the current study. Therefore, further studies need to consider the long-term application of BDE in order to change the content of SOC.

Previous studies reported that MOP and the copy number of pmoA genes markedly varied between rice paddy soils: ~20–396‍ ‍nmol CH₄ [g dry soil]^–1 h^–1 and ~5.3×10⁶–1.5×10⁸ copies [g dry soil]^–1, respectively (Shrestha et al., 2010; Yang et al., 2022). However, the effects of the application of BDE on MOP and pmoA in these fields remain unclear. The present results on MOP and pmoA were consistent with findings described above. Nevertheless, MOP and pmoA were significantly lower in the BDE field than in the SF field (PC2), indicating that the long-term application of BDE regulates the structure of the methanotrophic community. The application of BDE has been shown to suddenly increase the concentration of NH₄⁺, thereby inhibiting methanotrophic metabolism due to the competitiveness of NH₄⁺ versus CH₄, resulting in lower MOP and methanotroph abundance (Yang et al., 2022). Lower MOP and pmoA were not associated with higher CH₄ emissions because of their contradictory impacts on CH₄ production compared to CH₄ oxidation (Shrestha et al., 2010), albeit higher CH₄ emissions were observed in BDE-fertilized fields (Huang et al., 2014; Minamikawa et al., 2021). On the other hand, the CH₄ emission rate results from the interaction between active methanotrophs and methanogens in rice paddy ecosystems (Qian et al., 2023). Consistent with this hypothesis, Win et al. (2016) reported that the application of biogas slurry to paddy fields increased CH₄ emissions; however, the number of pmoA genes also increased over those in conventional rice cultivation. The transcription ratio of pmoA/mcrA (methanotrophs/methanogens) may more accurately predict CH₄ emissions from fields. Universally, a higher ratio of pmoA/mcrA typically results in lower CH₄ emissions in paddy fields (Lee et al., 2014; Sakoda et al., 2022). Although the present study did not examine the relationship between pmoA/mcrA and CH₄ emissions, a positive correlation was observed between pmoA and MOP. Therefore, the relationships between MOP, pmoA/mcrA, and CH₄ emissions need to be clarified in further investigations in VMD.

Recent studies reported that the application of biogas slurry changed the bacterial community structure in rice fields (Xu et al., 2019; Wang et al., 2022a; Wang et al., 2022b), and described the methanotrophic community structure in rice ecosystems (Shrestha et al., 2010; Phung et al., 2021; Rahalkar et al., 2021; Sakoda et al., 2022; Yang et al., 2022). However, the responses of methanotrophs under BDE fertilization has remained an obstacle. The present study revealed similarities in methanotrophic communities in BDE and SF fields (Fig. 4), indicating that the application of BDE did not significantly affect the community structure of methanotrophs. The results obtained also showed that type-I methanotrophs (9 genera) outcompeted type-II methanotrophs (2 genera) (Fig. S5), which was consistent with previous findings on rhizospheric soil (Wu et al., 2009; Lüke et al., 2010, 2014; Shrestha et al., 2010), indicating that type-I methanotrophs were responsible for regulating CH₄ emissions in these fields. Therefore, the application of BDE to rice fields may not significantly affect environmental conditions, particularly CH₄ and O₂, which support the thriving of the type-I methanotrophic community. Nevertheless, the present study only exami­ned the methanotrophic community structure of a single stage of rice ripening in these fields. Therefore, the dynamics of the methanotrophic community during rice growing need to be considered in VMD rice fields in further studies.

In rice paddy ecosystems, type-I methanotrophs change more rapidly under the revolution of fields than type-II methanotrophs (Sakoda et al., 2022). Type-I methanotrophs prefer low CH₄ concentrations and are sensitive to nitrogen fertilization, while higher CH₄ concentration accelerate the growth of type-II methanotrophs (Takeda et al., 2008; Knief, 2015; Yang et al., 2022). Additionally, type-II methanotrophs (i.e., Methylosinus sp. and Methylocystis sp.) may tolerate unfavorable environments, such as rice paddy ecosystems (Shrestha et al., 2010). Among the genera identified in the methanotrophic community in the present study, Methylomicrobium (type I) and Methylosinus (type II) were the most abundant, indicating their positive activity in the methanotrophic community of rice fields as well as their potential role in controlling MOP. Among explanatory variables, EC, TOC, and NO₃^– were primary factors that significantly impacted the methanotroph community (Fig. 6), with TOC and NO₃^– being significant in the RDA1 model, which decisively affected Methylosinus and Methylomicrobium. EC was more closely associated with RDA2, which was not significant in the RDA model (Table S4). Pearson’s correlation ana­lysis revealed that Methylosinus positively correlated with NH₄⁺, TOC, and TC and negatively correlated with TN and NO₃^–, while Methylomicrobium positively correlated with TN and NO₃^– and negatively correlated with PNM and TOC (Fig. 7). These results indicate that the dynamics of the methanotrophic community interchanges according to the Methylosinus population with the enrichment of the carbon source coupled with NH₄⁺ or the abundance of Methylomicrobium with increases in TN and NO₃^–. An increased carbon source is a preferred environment for methanogens, resulting in higher CH₄ production (Qian et al., 2023), which also benefits type-II methanotrophs (Shrestha et al., 2010; Vishwakarma et al., 2010; Sakoda et al., 2022; Yang et al., 2022), thereby explaining the present results.

The isolation of methanotrophs faces numerous challenges that have only been addressed by a few studies globally (Rahalkar et al., 2021). In VMD, the isolation of methanotrophs from rice fields as well as the methanotrophic community structure have yet to attract the attention of researchers. This is the first study to isolate methanotrophs from rice paddy fields in VMD. Three methanotrophic genera were isolated from among the 11 methanotrophic genera identified in the methanotrophic community of VMD paddy fields. Six strains were deduced based on 16S rRNA sequences (Fig. 8A), the pmoA gene (Fig. 8B), and the NCBI database, which were affiliated with type I (Methylococcus) and type II (Methylosinus and Methylocystis) (Table 1). Similarities in pmoA gene sequences between isolated strains and amplicons (Fig. S8) indicated the reliability of the methanotroph-isolated strains identified in VMD rice fields. These methanotrophs were isolated between dilution levels of 10^–4–10^–6, which was similar to the series of cultivable-reported methanotrophs (10^–2–10^–8) (Takeda et al., 2008; Pandit et al., 2016; Rahalkar et al., 2021). Moreover, the diluted level for isolating methanotrophs was consistent with the pmoA genes of soil samples collected in the present study (2.4×10⁷–2.6×10⁸ copies [g dry soil]^–1), indicating that the dilution matched the estimation of methanotroph abundance in samples.

Although the present study showed that Methylomicrobium was the most abundant in the methanotrophic community structure of VMD rice fields, we were unable to isolate it from these fields. In addition, several methanotrophic genera, such as Methylobacter, Methylocaldum, Methylogaea, Methylomonas, Methyloparacoccus, Methylomagnum, and Methylosarcina, have been identified in VMD rice fields, but were not successfully isolated in the present study, suggesting the need for further research. Previous findings reported that Methylomicrobium was prevalent in rice ecosystems (Mohanty et al., 2006; Noll et al., 2008). However, the successful isolation of Methylomicrobium in rice fields has recently been reported by Rahalkar et al. (2021). Moreover, these strains were not isolated in the present study and are affiliated with type-I methanotrophs, which prefer low CH₄ and high O₂ concentrations (Megonigal et al., 2003; Kambara et al., 2022), suggesting the need for the optimization of cultivable conditions (i.e., CH₄ or NO₃^–) in further research.

Methylosinus sporium and Methylocystis parva corrig. were isolated from rice rhizospheric soil (Takeda et al., 2008; Rahalkar et al., 2021). Methylococcus strains have recently been isolated from rice fields (Awala et al., 2023), albeit the genus has been widely identified in the methanotrophic community structure in rice rhizospheric soils (Lee‍ ‍et al., 2014; Rahalkar et al., 2021). To the best of our‍ ‍knowledge, several methanotroph genera have been isolated from rice fields: Methylosinus, Methylocystis, Methylomonas, Methylobacter, Methylocucumis, Methylogaea, Methylococcaceae, Methylomagnum, Methylocaldum, Methyloterricola, Methylomicrobium, Methylotetracoccus, and Methylococcus (Dianou and Adachi, 1999; Takeda et al., 2008; Ferrando and Tarlera, 2009; Geymonat et al., 2011; Dianou et al., 2012; Islam et al., 2015; Khalifa et al., 2015; Pandit et al., 2016; Pandit et al., 2018; Ghashghavi et al., 2019; Pandit and Rahalkar, 2019; Khatri et al., 2020; Rahalkar et al., 2021; Awala et al., 2023; Kaise et al., 2023). The present study isolated Methylococcus, Methylosinus, and Methylocystis from the soil of rhizospheric rice fields, which significantly contributes to the unknown representative strains in Vietnam and VMD. These strains may be vital for the development of strategies to reduce GHG emissions from rice production systems using indigenous methanotrophs identified in recent studies (Davamani et al., 2020; Rani et al., 2021).

Conclusions

The present study exami­ned the methanotrophic community structure of rhizospheric paddy soil fertilized with BDE and SF in VMD and isolated several methanotroph strains from these fields. The results obtained revealed that MOB and pmoA gene abundance were lower in the BDE field than in the SF field. In addition, the long-term application of BDE did not affect the methanotrophic community structure of rice fields. In the methanotrophic community, 11 genera were identified in paddy soil, of which Methylomicrobium (Type-I) and Methylosinus (Type-II) were the most abundant. Type-I methanotrophs were more abundant than type-II methanotrophs. Although the difference in soil characteristics between BDE and SF fields was negligible, TOC and NO₃^– were two primary factors affecting community structures. In methanotrophic isolation, we isolated three genera (Methylococcus, Methylocystis, and Methylosinus) with six methanotrophic strains from rice rhizospheric soil in VMD for the first time. These strains were similar to the amplicon of pmoA gene sequences in VMD rice fields. Overall, this study showed that the methanotroph community structure did not change with the application of BDE for three years. The isolation of methanotrophic strains is significant for the development of strategies to further reduce GHG emissions from rice production systems based on indigenous methanotrophs in VMD.

Citation

Thao, H. V., Tarao, M., Takada, H., Nishizawa, T., Nam, T. S., Cong, N. V., and Xuan, D. T. (2024) Methanotrophic Communities and Cultivation of Methanotrophs from Rice Paddy Fields Fertilized with Pig-livestock Biogas Digestive Effluent and Synthetic Fertilizer in the Vietnamese Mekong Delta. Microbes Environ 39: ME24021.

https://doi.org/10.1264/jsme2.ME24021

Acknowledgements

We would like to thank the Laboratory of Environmental Toxicology, Advanced Environmental Microbiology, and Soil and Water Environment, Can Tho University, for their support. We also thank Mr. Dinh Thai Danh, Ms. Huynh Tuyet Nhu, and Mr. Tran Hoang Kha—Researchers at Can Tho University, for supporting the collection of soil samples. We thank the farmers who cooperated in sharing field information and collecting samples in their fields. We would like to express our special thanks Dr. Satoshi Nakaba (Laboratory of Cell Biology of Woody Biomass, Tokyo University of Agriculture and Technology, Japan) for supporting observations of methanotrophs under a differential interference contrast microscope. The Authors also thank the reviewers for their valuable comments, which significantly improved the paper.

References

• APHA (1998) Standard Methods for the Examination of Water and Wastewater, 20th edn. Washington DC: American Public Health Association, American Water Works Association and Water Environmental Federation.
•  Awala,  S.I.,  Kim,  Y.,  Gwak,  J.-H.,  Seo,  C.,  Lee,  S.,  Kang,  M., and  Rhee,  S.-K. (2023) Methylococcus mesophilus sp. nov., the first non-thermotolerant methanotroph of the genus Methylococcus, from a rice field. Int J Syst Evol Microbiol 73: 37824181.
•  Bachmann,  S.,  Gropp,  M., and  Eichler-Löbermann,  B. (2014) Phosphorus availability and soil microbial activity in a 3 year field experiment amended with digested dairy slurry. Biomass Bioenergy 70: 429–439.
•  Cao,  Y.,  Wang,  J.,  Wu,  H.,  Yan,  S.,  Guo,  D.,  Wang,  G., and  Ma,  Y. (2016) Soil chemical and microbial responses to biogas slurry amendment and its effect on Fusarium wilt suppression. Appl Soil Ecol 107: 116–123.
• Carter, M.R., and Gregorich, E.G. (2008) Soil Sampling and Methods of Analysis. Manitoba: Canadian Society of Soil Science.
•  Chang,  Y.,  Zhao,  H.,  Sun,  L.,  Cui,  J.,  Liu,  J.,  Tang,  Q., et al. (2022) Resource utilization of biogas waste as fertilizer in China needs more inspections due to the risk of heavy metals. Agriculture 12: 1–15.
•  Costello,  A.M., and  Lidstrom,  M.E. (1999) Molecular characterization of functional and phylogenetic genes from natural populations of methanotrophs in lake sediments. Appl Environ Microbiol 65: 5066–5074.
•  Cui,  J.,  Zhao,  J.,  Wang,  Z.,  Cao,  W.,  Zhang,  S.,  Liu,  J., and  Bao,  Z. (2020) Diversity of active root-associated methanotrophs of three emergent plants in a eutrophic wetland in northern China. AMB Express 10: 48.
•  Dang,  L.V., and  Hung,  N.N. (2023) Effects of the age of raised beds on the physicochemical characteristics of fruit orchard soil in the Vietnamese Mekong Delta. PeerJ 11: e16178.
•  Davamani,  V.,  Parameswari,  E., and  Arulmani,  S. (2020) Mitigation of methane gas emissions in flooded paddy soil through the utilization of methanotrophs. Sci Total Environ 726: 1–8.
• Dedysh, S.N., and Knief, C. (2018) Diversity and phylogeny of described aerobic methanotrophs. In Methane Biocatalysis: Paving the Way to Sustainability. Kalyuzhnaya, M.G., and Xing, X.-H. (eds). Cham: Springer International Publishing, pp. 17–42.
•  Dianou,  D., and  Adachi,  K. (1999) Characterization of methanotrophic bacteria isolated from a subtropical paddy field. FEMS Microbiol Lett 173: 163–173.
•  Dianou,  D.,  Ueno,  C.,  Ogiso,  T.,  Kimura,  M., and  Asakawa,  S. (2012) Diversity of cultivable methane-oxidizing bacteria in microsites of a rice paddy field: investigation by cultivation method and fluorescence in situ hybridization (FISH). Microbes Environ 27: 278–287.
•  Ettwig,  K.F.,  Butler,  M.K.,  Le Paslier,  D.,  Pelletier,  E.,  Mangenot,  S., Kuypers., et al. (2010) Nitrite-driven anaerobic methane oxidation by oxygenic bacteria. Nature 464: 543–548.
•  Ferrando,  L., and  Tarlera,  S. (2009) Activity and diversity of methanotrophs in the soil-water interface and rhizospheric soil from a flooded temperate rice field. J Appl Microbiol 106: 306–316.
•  Fujiwara,  T. (2012) Concept of an innovative water management system with decentralized water reclamation and cascading material-cycle for agricultural areas. Water Sci Technol 66: 1171–1177.
•  Geymonat,  E.,  Ferrando,  L., and  Tarlera,  S.E. (2011) Methylogaea oryzae gen. nov., sp. nov., a mesophilic methanotroph isolated from a rice paddy field. Int J Syst Evol Microbiol 61: 2568–2572.
•  Ghashghavi,  M.,  Belova,  S.E.,  Bodelier,  P.L.E.,  Dedysh,  S.N.,  Kox,  M.A.R.,  Speth,  D.R., et al. (2019) Methylotetracoccus oryzae strain C50C1 is a novel type Ib gammaproteobacterial methanotroph adapted to freshwater environments. mSphere 4: 1–15.
•  Głowacka,  A.,  Szostak,  B., and  Klebaniuk,  R. (2020) Effect of biogas digestate and mineral fertilisation on the soil properties and yield and nutritional value of switchgrass forage. Agronomy 10: 490.
•  Guerrero-Cruz,  S.,  Vaksmaa,  A.,  Horn,  M.A.,  Niemann,  H.,  Pijuan,  M., and  Ho,  A. (2021) Methanotrophs: discoveries, environmental relevance, and a perspective on current and future applications. Front Microbiol 12: 678057.
•  Holmes,  A.J.,  Costello,  A.,  Lidstrom,  M.E., and  Murrell,  J.C. (1995) Evidence that participate methane monooxygenase and ammonia monooxygenase may be evolutionarily related. FEMS Microbiol Lett 132: 203–208.
•  Huang,  H.-Y.,  Cao,  J.-L.,  Wu,  H.-S.,  Ye,  X.-M.,  Ma,  Y.,  Yu,  J.-G., et al. (2014) Elevated methane emissions from a paddy field in southeast China occur after applying anaerobic digestion slurry. GCB Bioenergy 6: 465–472.
•  Huynh,  K.C.,  Minamikawa,  K.,  Nguyen,  N.V.C.,  Nguyen,  C.H., and  Nguyen,  C.V. (2022) Effects of cattle biogas effluent application and irrigation regimes on rice growth and yield: a mesocosm experiment. JARQ 56: 341–348.
•  Islam,  T.,  Larsen,  Ø.,  Torsvik,  V.,  Øvreås,  L.,  Panosyan,  H.,  Murrell,  J., et al. (2015) Novel methanotrophs of the family Methylococcaceae from different geographical regions and habitats. Microorganisms 3: 484–499.
• IUSS Working Group WRB. (2015) World Reference Base for Soil Resources 2014, update 2015 International Soil Classification System for Naming Soils and Creating Legends for Soil Maps. World Soil Resources Reports No. 106. Rome: FAO.
•  Kaise,  H.,  Sawadogo,  J.B.,  Alam,  M.S.,  Ueno,  C.,  Dianou,  D.,  Shinjo,  R., and  Asakawa,  S. (2023) Methylocystis iwaonis sp. nov., a type II methane-oxidizing bacterium from surface soil of a rice paddy field in Japan, and emended description of the genus Methylocystis (ex Whittenbury et al. 1970) Bowman et al. 1993. Int J Syst Evol Microbiol 73: 10.1099.
•  Kambara,  H.,  Shinno,  T.,  Matsuura,  N.,  Matsushita,  S.,  Aoi,  Y.,  Kindaichi,  T., et al. (2022) Environmental factors affecting the community of methane-oxidizing bacteria. Microbes Environ 37: me21074.
•  Keeney,  D.R., and  Bremner,  J.M. (1966) Comparison and evaluation of laboratory methods of obtaining an index of soil nitrogen availability. Agron J 58: 498–503.
•  Khalifa,  A.,  Lee,  C.G.,  Ogiso,  T.,  Ueno,  C.,  Dianou,  D.,  Demachi,  T., et al. (2015) Methylomagnum ishizawai gen. nov., sp. nov., a mesophilic type I methanotroph isolated from rice rhizosphere. Int J Syst Evol Microbiol 65: 3527–3534.
•  Khatri,  K.,  Mohite,  J.A.,  Pandit,  P.S.,  Bahulikar,  R., and  Rahalkar,  M.C. (2020) Description of ‘Ca. Methylobacter oryzae’ KRF1, a novel species from the environmentally important Methylobacter clade 2. Antonie van Leeuwenhoek 113: 729–735.
•  Knief,  C. (2015) Diversity and habitat preferences of cultivated and uncultivated aerobic methanotrophic bacteria evaluated based on pmoA as mole­cular marker. Front Microbiol 6: 1–38.
•  Kumar,  S.,  Malav,  L.C.,  Malav,  M.K., and  Khan,  S.A. (2015) Biogas slurry: source of nutrients for eco-friendly agriculture 5. International J Ext Res 2: 42–46.
•  Lee,  H.J.,  Kim,  S.Y.,  Kim,  P.J.,  Madsen,  E.L., and  Jeon,  C.O. (2014) Methane emission and dynamics of methanotrophic and methanogenic communities in a flooded rice field ecosystem. FEMS Microbiol Ecol 88: 195–212.
•  Lu,  Y.,  Li,  J.,  Meng,  J.,  Zhang,  J.,  Zhuang,  H.,  Zheng,  G., et al. (2021) Long-term biogas slurry application increased antibiotics accumulation and antibiotic resistance genes (ARGs) spread in agricultural soils with different properties. Sci Total Environ 759: 1–12.
•  Lüke,  C.,  Krause,  S.,  Cavigiolo,  S.,  Greppi,  D.,  Lupotto,  E., and  Frenzel,  P. (2010) Biogeography of wetland rice methanotrophs. Environ Microbiol 12: 862–872.
•  Lüke,  C.,  Frenzel,  P.,  Ho,  A.,  Fiantis,  D.,  Schad,  P.,  Schneider,  B., et al. (2014) Macroecology of methane‐oxidizing bacteria: the β‐diversity of pmoA genotypes in tropical and subtropical rice paddies. Environ Microbiol 16: 72–83.
• Megonigal, J.P., Hines, M.E., and Visscher, P.T. (2003) Anaerobic metabolism: linkages to trace gases and aerobic processes. In Treatise on Geochemistry. Holland, H.D., and Torekian, K.K. (eds). Amsterdam: Elsevier, pp. 317–424.
•  Minamikawa,  K.,  Khanh,  H.C.,  Hosen,  Y.,  Nam,  T.S., and  Chiem,  N.H. (2019) Variable-timing, fixed-rate application of cattle biogas effluent to rice using a leaf color chart: microcosm experiments in Vietnam. Soil Sci Plant Nutr 61: 225–234.
•  Minamikawa,  K.,  Huynh,  K.C.,  Uno,  K.,  Tran,  N.S., and  Nguyen,  C.H. (2021) Cattle biogas effluent application with multiple drainage mitigates methane and nitrous oxide emissions from a lowland rice paddy in the Mekong Delta, Vietnam. Agric Ecosyst Environ 319: 1–11.
•  Mohanty,  S.R.,  Bodelier,  P.L.E.,  Floris,  V., and  Conrad,  R. (2006) Differential effects of nitrogenous fertilizers on methane-consuming microbes in rice field and forest soils. Appl Environ Microbiol 72: 1346–1354.
•  Möller,  K., and  Müller,  T. (2012) Effects of anaerobic digestion on digestate nutrient availability and crop growth: A review: Digestate nutrient availability. Eng Life Sci 12: 242–257.
•  Noll,  M.,  Frenzel,  P., and  Conrad,  R. (2008) Selective stimulation of type I methanotrophs in a rice paddy soil by urea fertilization revealed by RNA-based stable isotope probing: Stimulation of methanotrophs by nitrogen fertilization. FEMS Microbiol Ecol 65: 125–132.
•  Oritate,  F.,  Nakamura,  M.,  Nguyen,  D.P.,  Dang,  H.V.B.,  Nguyen,  K.D., and  Yuyama,  Y. (2016) Feasibility for use of digested slurry by the pouring method in paddy fields of southern Vietnam. Paddy Water Environ 14: 429–438.
•  Pandit,  P.S.,  Rahalkar,  M.C.,  Dhakephalkar,  P.K.,  Ranade,  D.R.,  Pore,  S.,  Arora,  P., and  Kapse,  N. (2016) Deciphering community structure of methanotrophs dwelling in rice rhizospheres of an indian rice field using cultivation and cultivation-independent approaches. Microb Ecol 71: 634–644.
•  Pandit,  P.S.,  Hoppert,  M., and  Rahalkar,  M.C. (2018) Description of “Candidatus Methylocucumis oryzae”, a novel Type I methanotroph with large cells and pale pink colour, isolated from an Indian rice field. Antonie van Leeuwenhoek 111: 2473–2484.
•  Pandit,  P.S., and  Rahalkar,  M.C. (2019) Renaming of ‘Candidatus Methylocucumis oryzae’ as Methylocucumis oryzae gen. nov., sp. nov., a novel Type I methanotroph isolated from India. Antonie van Leeuwenhoek 112: 955–959.
•  Phung,  L.D.,  Miyazawa,  M.,  Pham,  D.V.,  Nishiyama,  M.,  Masuda,  S.,  Takakai,  F., and  Watanabe,  T. (2021) Methane mitigation is associated with reduced abundance of methanogenic and methanotrophic communities in paddy soils continuously sub-irrigated with treated wastewater. Sci Rep 11: 1–10.
•  Qian,  H.,  Zhu,  X.,  Huang,  S.,  Linquist,  B.,  Kuzyakov,  Y.,  Wassmann,  R., et al. (2023) Greenhouse gas emissions and mitigation in rice agriculture. Nat Rev Earth Environ 4: 716–732.
•  Rahalkar,  M.C.,  Khatri,  K.,  Pandit,  P.,  Bahulikar,  R.A., and  Mohite,  J.A. (2021) Cultivation of important methanotrophs from indian rice fields. Front Microbiol 12: 669244.
•  Rani,  V.,  Bhatia,  A., and  Kaushik,  R. (2021) Inoculation of plant growth promoting-methane utilizing bacteria in different N-fertilizer regime influences methane emission and crop growth of flooded paddy. Sci Total Environ 775: 1–12.
•  Sakoda,  M.,  Tokida,  T.,  Sakai,  Y.,  Senoo,  K., and  Nishizawa,  T. (2022) Mitigation of paddy field soil methane emissions by betaproteobacterium Azoarcus inoculation of rice seeds. Microbes Environ 37: me22052.
•  Shrestha,  M.,  Shrestha,  P.M.,  Frenzel,  P., and  Conrad,  R. (2010) Effect of nitrogen fertilization on methane oxidation, abundance, community structure, and gene expression of methanotrophs in the rice rhizosphere. ISME J 4: 1545–1556.
• Statistical Yearbook of Vietnam. (2022) General Statistics Office, Statistical Publishing House (in Vietnamese). URL https://www.gso.gov.vn/wp-content/uploads/2023/06/Sach-Nien-giam-TK-2022-update-21.7_file-nen-Water.pdf. Accessed on: 28/03/2024.
•  Takeda,  K.,  Tonouchi,  A.,  Takada,  M.,  Suko,  T.,  Suzuki,  S.,  Kimura,  Y., et al. (2008) Characterization of cultivable methanotrophs from paddy soils and rice roots. Soil Sci Plant Nutr 54: 876–885.
•  Tang,  Y.,  Luo,  L.,  Carswell,  A.,  Misselbrook,  T.,  Shen,  J., and  Han,  J. (2021) Changes in soil organic carbon status and microbial community structure following biogas slurry application in a wheat-rice rotation. Sci Total Environ 757: 143786.
•  Tran,  T.V.,  Tran,  D.X.,  Myint,  S.W.,  Latorre-Carmona,  P.,  Ho,  D.D.,  Tran,  P.H., and  Dao,  H.N. (2019) Assessing spatiotemporal drought dynamics and its related environmental issues in the Mekong River Delta. Remote Sens (Basel) 11: 2742.
•  Uno,  K.,  Ishido,  K.,  Loc,  N.X.,  Chiem,  N.H., and  Minamikawa,  K. (2021) Multiple drainage can deliver higher rice yield and lower methane emission in paddy fields in An Giang Province, Vietnam. Paddy Water Environ 19: 623–634.
•  Vishwakarma,  P.,  Singh,  M., and  Dubey,  S.K. (2010) Changes in methanotrophic community composition after rice crop harvest in tropical soils. Biol Fertil Soils 6: 471–479.
•  Wang,  C.,  Liu,  J.,  Wang,  C.,  Zhao,  X.,  Wu,  K.,  Yang,  B., et al. (2022a) Biogas slurry application alters soil properties, reshapes the soil microbial community, and alleviates root rot of Panax notoginseng. PeerJ 10: e13770.
•  Wang,  Q.,  Chen,  Z.,  Zhao,  J.,  Ma,  J.,  Yu,  Q.,  Zou,  P., et al. (2022b) Fate of heavy metals and bacterial community composition following biogas slurry application in a single rice cropping system. J Soils Sediments 22: 968–981.
•  Whittenbury,  R.,  Phillips,  K.C., and  Wilkinson,  J.F. (1970) Enrichment, isolation and some properties of methane-utilizing bacteria. J Gen Microbiol 61: 205–218.
•  Win,  A.T.,  Toyota,  K.,  Ito,  D.,  Chikamatsu,  S.,  Motobayashi,  T.,  Takahashi,  N., et al. (2016) Effect of two whole-crop rice (Oryza sativa L.) cultivars on methane emission and Cu and Zn uptake in a paddy field fertilized with biogas slurry. Soil Sci Plant Nutr 62: 99–105.
•  Win,  K.T.,  Nonaka,  R.,  Toyota,  K.,  Motobayashi,  T., and  Hosomi,  M. (2010) Effects of option mitigating ammonia volatilization on CH₄ and N₂O emissions from a paddy field fertilized with anaerobically digested cattle slurry. Biol Fertil Soils 46: 589–595.
•  Win,  K.T.,  Nonaka,  R.,  Win,  A.T.,  Sasada,  Y.,  Toyota,  K., and  Motobayashi,  T. (2015) Effects of water saving irrigation and rice variety on greenhouse gas emissions and water use efficiency in a paddy field fertilized with anaerobically digested pig slurry. Paddy Water Environ 13: 51–60.
•  Wu,  L.,  Ma,  K., and  Lu,  Y. (2009) Rice roots select for type I methanotrophs in rice field soil. Syst Appl Microbiol 32: 421–428.
•  Xu,  M.,  Xian,  Y.,  Wu,  J.,  Gu,  Y.,  Yang,  G.,  Zhang,  X., et al. (2019) Effect of biogas slurry addition on soil properties, yields, and bacterial composition in the rice-rape rotation ecosystem over 3 years. J Soils Sediments 19: 2534–2542.
•  Xu,  W.,  Zhu,  Y.,  Wang,  X.,  Ji,  L.,  Wang,  H.,  Yao,  L., and  Lin,  C. (2021) The effect of biogas slurry application on biomass production and forage quality of lolium multiflorum. Sustainability 13: 3605.
•  Yang,  Y.,  Shen,  L.,  Zhao,  X.,  Shan,  J.,  Wang,  S.,  Zhou,  W., et al. (2022) Long-term incorporation of wheat straw changes the methane oxidation potential, abundance and community composition of methanotrophs in a paddy ecosystem. Appl Soil Ecol 173: 2–8.
•  Zhang,  H.,  Li,  S.,  Zheng,  X.,  Zhang,  J.,  Bai,  N.,  Zhang,  H., and  Lv,  W. (2021) Effects of biogas slurry combined with chemical fertilizer on soil bacterial and fungal community composition in a paddy field. Front Microbiol 12: 655515.