
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12157-3
10.1016/j.heliyon.2024.e36126
e36126
Research Article
Effects of tomato straw fermentation on nutrients and bacterial community structure
Xu Xiaodong a
Xu Peng a
Li Yang a
Zhang Guanzhi a
Wu Yongjun b
Yang Zhenchao yangzhenchao@126.com
a⁎
a College of Horticulture, Northwest A&F University, Yangling, Shaanxi, 712100, China
b College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, 712100, China
⁎ Corresponding author. yangzhenchao@126.com
10 8 2024
15 9 2024
10 8 2024
10 17 e361263 12 2023
7 8 2024
9 8 2024
© 2024 Published by Elsevier Ltd.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Unsustainable straw treatment methods detrimentally affect the environment and ecology. Aerobic fermentation (AE) and anaerobic fermentation (AN) are environmentally friendly treatments that better utilise straw resources. In this study, high-throughput sequencing was used to investigate the effects of AE and AN on nutrient content and microbial community structure during tomato straw fermentation. Nitrate nitrogen, available phosphorus, available potassium, and fulvic acid contents following AE were 1250.04 mg/kg, 80.34 %, 161.39 %, and 49.31 %, respectively, which were higher than those following AN. Ammonium nitrogen, humic acid, and humic substance levels following AN were 309.07 %, 31.18 %, and 17.38 %, respectively, which were higher than those following AE. Firmicutes (24.76 %) and Actinobacteria (12.93 %) were more abundant following AE, whereas Proteobacteria (33.82 %) and Bacteroidetes (33.82 %) exhibited higher abundance following AN. AE more effectively eliminated pathogenic bacteria (22.01%–0.26 %) and encouraged stronger interactions between dominant bacterial genera. Redundancy and Mantel test analyses revealed that electrical conductivity and temperature were the most important environmental factors affecting bacterial communities in AE and AN, respectively. AE had a stronger effect on effective nutrient release from tomato straw, implying its greater application potential as a fertiliser. Overall, our study provides a theoretical basis for the optimisation of fermentation methods and processes.

Keywords

Correlation analysis
Green agriculture
Microbial diversity
Nutrient cycling
Resource utilisation
==== Body
pmc1 Introduction

China is one of the largest agricultural producers globally and produced 799.97 million tons of vegetables in 2022 [1]. A large amount of agricultural straw is produced each year; in 2018 alone, the amount of agricultural straw produced in China reached 886 million tons [2], making it one of the richest countries worldwide in terms of agricultural straw resources. In China, agricultural straw is typically burned and buried [3]; however, these methods are harmful to the environment. In particular, the emission of carbon dioxide (CO2) from fires pollutes the atmosphere and accelerates climate warming, while landfill treatments increase soil degradation. In addition, burning effectively destroys the large amount of nutrients contained in straw, resulting in a waste of organic matter [4] while burying transfers pathogenic bacteria carried by plants to the soil, increasing the risk of diseases for crops and humans. The proper disposal of agricultural straw can reduce water and soil pollution, local air pollution, and resource wastage and have a positive impact on climate change.

Green agricultural development is a future direction for global agriculture [5], and the recycling of agricultural straw is an important component of sustainable agricultural development [6]. Numerous studies have been conducted on straw recycling methods, such as aerobic composting and anaerobic digestion [[7], [8], [9]]. The basic principle of these approaches is to degrade organic waste into low-molecular-weight compounds using aerobic and anaerobic microorganisms, kill pathogenic bacteria and insect eggs during the fermentation process, and obtain products rich in available nutrients and stable humus [10]. These products can then be used as fertilisers, soil modifiers, and cultivation substrates that are beneficial to the soil and crops [[11], [12], [13]]. Bacterial communities exert a strong influence on aerobic fermentation (AE) and anaerobic fermentation (AN), particularly on the quality of fermented products, which generally depends on biologically available nutrients. Bacterial community activity is not only crucial to the nitrogen cycle but is also an important factor affecting phosphorus conversion, cellulose degradation, and humic substance formation [[14], [15], [16]]. Tomatoes are among the most commonly consumed vegetables worldwide. In 2019, the global tomato planting area reached approximately 5.03 million hectares, accounting for close to 23 % of the world's total fresh vegetable planting area [17]. Large amounts of waste straw are generated annually, resulting in numerous environmental problems. Tomato waste straw has a higher water content and lower C/N ratio than do corn or wheat straw and is rich in plant essential elements (e.g., N, P, and K). Wei combined tomato straw with chicken manure to determine the effect of biochar addition on microbial community dynamics and variations in key physical and chemical characteristics during the composting process [18]. Sevik investigated the effects of the C/N ratio and free space on composting during the co-composting of tomato straw, sludge, and cow manure [19]. Previous studies on AN have focused on the community structures of methanogens and acidogenic bacteria because of their key roles in methane production [20,21]. However, variations in the available nutrient content, humic substance (HS) content, and bacterial community structure during the AE and AN of tomato straw remain unclear.

2 Materials and methods

2.1 Fermentation material and experimental design

Tomato straw was collected from a vegetable greenhouse in Yangling (34°20′ N, 108°24′ E) in Shaanxi Province, China. Table 1 reports the physical and chemical characteristics of the raw materials. The Teaching and Research Farm of the College of Horticulture, Northwest A&F University, provided the indoor laboratory space and equipment (Yangling, Shaanxi, China). Tomato stalks were crushed into pieces of lengths less than 4 cm using a straw pulveriser (Hawnyce, China) and subjected to either AE or AN. For AE treatment, crushed tomato stalks were mixed with clear water and the moisture content was maintained at 40–60 %. A pile 1.5 m × 1 m × 1 m (length × width × height) was then made with the soaked tomato stalks and left to undergo AE. The pile was turned every five days to ensure an aerobic process. For AN treatment, 200 kg of crushed tomato straw and 640 L of water were mixed in a space of 1.5 m × 1 m × 1 m (length × width × height) to achieve normal-temperature dry fermentation with 20 % total solids. AN was performed in a sealed fermentation tank with a gas port. For both treatments, the experiment lasted for 60 days. Fig. 1 shows a schematic of the experimental site. Solid fermentation samples were collected on the 0th (AE1 and AN1), 2nd (AE2 and AN2), 8th (AE3 and AN3), 18th (AE4 and AN4), 30th (AE5 and AN5), 42nd (AE6 and AN6), and 60th (AE7 and AN7) days. The collected samples were divided into sub-groups that were stored at −80 °C or air-dried.Table 1 Characteristics of raw material fermentation.

Table 1Resource materials	TOC (g/kg)	TN (g/kg)	C/N ratio	pH	Moisture content (%)	
Tomato straw	236.38	15.77	14.99	7.6	20 %	
TOC, total organic carbon; TN, total nitrogen; C/N, carbon-to-nitrogen ratio.

Fig. 1 Schematic of the experimental site.

Fig. 1

2.2 Physical and chemical analyses

pH and electrical conductivity (EC) were measured using a pH meter (ST10, OHAUS, United States) and an EC meter (ST10C–C, OHAUS, United States), respectively. MT-8X temperature sensors (MT-8X, Shenhua, China) were installed on the upper, middle, and lower floors of each treatment, and data were recorded at 9 a.m. and 3 p.m. each day. To determine the moisture content of the raw material, 10 g of the sample were placed in an oven at 105 °C and dried to a constant weight. Total organic carbon (TOC) content was estimated following the method of [22], total nitrogen (TN) content was determined using the Kjeldahl digestion method (A33 continuous flow analyzer, SEAL, Germany), and nitrate nitrogen (NO3−-N) and ammonium nitrogen (NH4+ –N) contents were estimated using the KCL extraction method (A33 continuous flow analyzer, SEAL, Germany). Available phosphorus (AP) and potassium contents were measured according to the method described by Ref. [23]. Humic substances (HS), humic acid (HA), and fulvic acid (FA) were extracted and separated using the methods described by Wu [24]. The seed germination index (GI) was determined as described by Zhang and Sun [25] and expressed as:(1) GI%=Seedgermination×rootlengthofsample(mm)Seedgermination×rootlengthofcontrol(mm)×100%

Three biological replicates were used to determine and analyse all physical and chemical parameters.

2.3 DNA extraction and 16S rDNA sequencing

We used the E. Z.N.A. ®Soil DNA Kit (D4015, Omega, Inc., USA) following the manufacturer's instructions to extract DNA from different samples and analysed its quality using 2 % agarose gel electrophoresis. V3–V4 gene amplification was performed using the 341 F (5′-CCTACGGGNGGCWGCAG-3′) and 805R (5′-GACTACHVGGGGTATCTAATCC-3′) primers. The PCR products were purified using AMPure XT beads (Beckman Coulter Genomics, Danvers, MA, USA) and quantified using a Qubit instrument (Invitrogen, USA). Amplicon pools were prepared for sequencing, and the size and quantity of the amplicon libraries were assessed using an Agilent 2100 Bioanalyzer (Agilent, USA) and a Library Quantification Kit for Illumina (Kapa Biosciences, Woburn, MA, USA), respectively. Samples were sequenced on an Illumina NovaSeq platform following the manufacturer's recommendations.

2.4 Statistical analysis

Microsoft Excel (2010, Microsoft Corporation) and SPSS20 (IBM Corp.) were used to determine the standard deviation and statistical significance of the differences between samples (P < 0.05). Origin 2018 (OriginLab Corporation) was used to display the results, and chimeric sequences were filtered using Vsearch (v2.3.4). Feature tables and sequences were obtained after dereplication using DADA2 (version 2019.7). Principal coordinate analysis (PCoA), non-metric multidimensional scaling analysis (NMDS), redundancy analysis (RDA), Spearman's correlation, and Mantel's test were performed using OmicStudio (https://www.omicstudio.cn/tool). We referred to a database developed by the National Institute for Communicable Disease Control and Prevention at the Chinese Center for Disease Control and Prevention (Pathogen_16sPIP) to identify pathogens that appeared during the fermentation process, calculate the abundance of the bacterial pathogens in the samples from each time point, and record the proliferation period of each bacterial pathogen present in large numbers.

3 Results

3.1 Changes in physical and chemical characteristics

The physicochemical properties of tomato straw differed significantly depending on the type of fermentation. Table 2 lists the temperature, pH, and EC measured during the experiment. The temperature during AE peaked on the second day and subsequently declined to 33.95 °C. Temperature fluctuations in AN were relatively small, with temperatures staying within 22–30 °C throughout the fermentation process. During AE, the pH reached a maximum of 8.33 on day 18 and a minimum of 7.90 on day 30, after which it gradually rose to 8.27 by day 60; ultimately, there was no significant difference in pH between the first and last days of AE. During AN, the pH reached a minimum of 7.47 on day 18 and a maximum of 8.53 on day 60. EC exhibited an upward trend during AE, with values of 3.71 mS/cm and 7.54 mS/cm at the beginning and end of composting, respectively. In contrast, during AN, EC exhibited lower values that fluctuated downward until the end of fermentation, eventually reaching 1.39 mS/cm. Table 2 also lists the changes in the TOC, TN, and C/N ratio during the fermentation cycle. The TOC content exhibited a declining trend during AE, with a 28.61 % reduction from the first to last day. During AN, the TOC content also exhibited a downward trend, with a 21.32 % reduction from day 30–60. During AE, the TN content initially gradually increased to 25.36 g/kg on day 18 and then subsequently decreased, whereas that during AN rose rapidly on day 2 and eventually fell to 21.70 g/kg by day 60. At the end of fermentation, the TN content of the AN samples was 21.79 % higher than that of the AE samples. The C/N ratios in both treatments generally exhibited a downward trend, and no significant differences were observed between the two treatments at the end of fermentation. Both treatments exhibited a GI (see Equation (1)) greater than 80 % at the end of fermentation (Table 2.).Table 2 Physicochemical parameters during the fermentation process.

Table 2Treatment	Time (day)	Temperature (°C)	pH	EC (mS/cm)	TOC (mg/kg)	TN (g/kg)	C/N	GI (%)	
AE	0	34.22±2.20d	8.17±0.06bc	3.71±0.09d	209.28±3.58a	16.17±0.34e	12.95±0.39a	23.80±0.95d	
2	65.27±1.24a	7.93±0.06d	3.92±0.75d	191.58±2.82b	17.02±0.71de	11.27±0.58b	27.07±2.49d	
8	51.98±0.97b	8.10±0.10c	5.14±0.25c	158.27±2.26c	24.34±0.28b	6.50±0.09d	50.25±3.58c	
18	54.75±3.32b	8.33±0.06a	6.55±0.26b	149.35±1.35d	25.36±0.46a	5.89±0.16d	63.90±3.55b	
30	42.58±0.87c	7.90±0.10d	7.32±0.28a	157.54±4.26c	18.21±0.43c	8.65±0.31c	64.28±9.46b	
42	34.22±0.71d	8.13±0.06c	7.23±0.13a	158.87±1.87c	17.81±0.60cd	8.93±0.40c	100.02±4.77a	
60	33.95±0.71d	8.27±0.06 ab	7.54±0.07a	149.40±1.14d	17.19±0.61d	8.70±0.38c	106.10±6.68a	
AN	0	22.02±0.51b	8.13±0.23b	2.25±0.35a	210.11±4.55bc	16.35±0.25c	12.85±0.27a	22.82±4.30e	
2	29.43±1.84a	8.03±0.06b	1.50±0.09cd	233.67±3.15a	24.14±0.76a	9.69±0.43c	21.00±2.67e	
8	27.28±0.84a	7.87±0.35b	2.00±0.07 ab	219.34±0.83b	22.02±0.44b	9.96±0.17c	37.22±4.70d	
18	27.60±0.96a	7.47±0.12c	1.88±0.43abc	244.28±7.75a	21.45±0.15b	11.39±0.37b	53.57±6.73c	
30	29.03±0.28a	8.20±0.10b	2.04±0.12 ab	215.17±3.86bc	17.18±0.41c	12.53±0.46a	42.74±2.62cd	
42	27.42±0.66a	8.03±0.06b	1.71±0.02bcd	204.97±10.79c	16.47±0.38c	12.45±0.66a	78.34±7.98b	
60	27.67±0.36a	8.53±0.06a	1.39±0.11d	192.19±9.07d	21.70±0.77b	8.86±0.22d	104.57±12.31a	
AE, aerobic fermentation; AN, anaerobic fermentation; EC, electrical conductivity; TOC, total organic carbon; TN, total nitrogen; C/N, carbon-to-nitrogen ratio; GI, germination index. Results are the means of three replicates, and values in the same row followed by different lowercase letters are significantly different at P < 0.05.

3.2 Changes in nutrient content

We observed significant differences in nutrient accumulation during tomato straw fermentation between the AE and AN treatments. During AE, NH4+ –N exhibited a sharp increase on day 2, followed by a gradual decrease, stabilising at 140.64 mg/kg. In contrast, following AN, the NH4+ –N content was 575.33 mg/kg on day 60, which was 309.08 % higher than that following AE (Fig. 2a). NO3− –N levels remained low during AN but increased during AE (Fig. 2b). The AP content followed the same trend in both treatments, reaching a maximum on day 2, followed by a gradual decrease (Fig. 2c). At the end of fermentation, the AP content in AE exceeded that of AN by 80.34 %. The change in available potassium (AK) content generally followed an increasing trend throughout AE, while the opposite was true during AN (Fig. 2d). At the end of fermentation, the AK content in AE and AN was 44000.00 mg/kg and 16833.33 mg/kg, respectively (Fig. 2d).Fig. 2 Evolution of different nutrients, humus forms, and bacterial diversity during AE and AN. (a–g) Changes in (a) ammonium nitrogen, (b) nitrate nitrogen, (c) available phosphorus, (d) available potassium, (e) humic acid, (f) fulvic acid, and (g) humic substances. (h) Principal coordinate analysis (PCoA) of bacterial composition at the genus level. (i) Non-metric multidimensional scaling (NMDS) analysis of bacterial composition at the genus level. Results are the mean of three replicates, and error bars represent standard error. AE, aerobic fermentation; AN, anaerobic fermentation.

Fig. 2

The changes in HA, FA, and HS contents during fermentation are shown in Fig. 2e–g. During the AE process, the HA content rose sharply from 3.82 g/kg (AE1) to 11.54 g/kg (AE3), and subsequently stabilised until AE6, at which time it began to increase again, reaching 15.54 g/kg at AE7 (4.06 times that of AE1). For AN, the HA content at AN1 and AN7 were 7.20 g/kg and 20.38 g/kg, respectively, representing a 183.06 % increase. During AE, the FA content decreased from 43.02 g/kg (AE1) to 22.29 g/kg (AE6), and subsequently increased until the end of fermentation. The FA content during AN was observed to decrease from 52.71 g/kg (AN1) to 25.27 g/kg at the end of fermentation, which was 49.31 % lower than that at AE7. The total amount of HS resulting from the fermentation process varied according to the fermentation method. The HS content during AN was higher than that during AE throughout the experiment (Fig. 2g); by the end of the fermentation process, the HS content following AN was 17.38 % higher than that following AE.

3.3 Diversity and composition of the bacterial community

PCoA and NMDS analyses were used to assess the relationship between the samples and species (Fig. 2h and i). PCoA and NMDS roughly divided the bacterial community composition profiles of the two treatments along the diagonal. The positions of AE1 and AN1 in the distance matrix were similar, indicating that the bacterial community compositions of the two treatments were comparable in the initial stage. However, significant differences were observed in the bacterial community compositions of the different treatments across the other stages, which manifested as larger distances (ANOSIM, R = 0.8921, P = 0.001). Differences were also observed between stages of the same treatment.

The bacterial community compositions during AE and AN exhibited different temporal dynamics at the phylum level as fermentation progressed (Fig. 3a). Four dominant groups were detected during AE, namely, Proteobacteria (30.53 %), Firmicutes (24.76 %), Bacteroidetes (15.06 %), and Actinobacteria (12.93 %). During AN, Proteobacteria (33.82 %), Bacteroidetes (33.82 %), and Firmicutes (16.24 %) were the dominant groups. In all the AE and AN samples, genes from these groups accounted for 66.60%–98.96 % and 78.64%–96.86 % of the total 16S rDNA sequences, respectively. The abundance of Proteobacteria was the lowest at AE2 (5.15 %) and was relatively high at AE3, AE4, and AE5, accounting for 36.25 %, 42.64 %, and 38.98 % of total bacterial abundance, respectively. During AN, the opposite was true; at AN2, the abundance of Proteobacteria was the highest, reaching 53.48 %, whereas at AN3, AN4, and AN5 Proteobacterial abundance was relatively low, accounting for 19.60 %, 23.86 %, and 22.48 % of total bacterial abundance, respectively. Bacteroidetes tended to first increase and then decrease during AE, from 2.42 % at AE1 to 32.30 % at AE5 and finally down to 19.36 % at AE7. A similar trend was observed during AN, but with slight differences; as shown in Fig. 3a, the relative abundance of Bacteroidetes during AN reached its highest at AN3, accounting for 66.41 % of the total bacterial abundance, and then decreased to 14.42 % after fermentation. The abundance of Firmicutes and changes in the Firmicutes population were also significantly different between the AE and AN samples. Firmicutes increased sharply to 80.16 % of the total abundance at AE2, and then declined sharply to 3.48 % by the end of fermentation. During AN, the relative abundance of Firmicutes first decreased, reaching a minimum at AN3, and then increased and remained relatively stable throughout AN4–AN7. For Actinobacteria, we observed a population decline at AE1 followed by the maintenance of a relatively stable level ranging from 8.39 % to 12.50 % throughout AE2–AE7. During AN, the relative abundance of Actinobacteria was low throughout the fermentation period, ranging from 0.46 % to 2.69 %.Fig. 3 (a–b) Phylum (a) and genus (b) level composition of the bacterial communities during tomato straw AE or AN. (c) Classification and abundance of pathogenic bacteria at each stage of the AE and AN process at the species level. NA, non-pathogenic bacteria; AE, aerobic fermentation; AN, anaerobic fermentation.

Fig. 3

Fig. 3b depicts the bacterial abundance dynamics during AE and AN at the genus level. The dominant genera during AE were identified as Bacillus (6.48 %), Fodinicurvataceae_unclassified (5.17 %), Luteimonas (4.80 %), and Staphylococcus (4.72 %), whereas those during AN were Bacteroides (15.62 %), MacelliBacteroides (8.71 %), Staphylococcus (5.60 %), and Enterobacter (2.72 %). Staphylococcus exhibited a high abundance during the initial stages of both treatments, followed by a rapid reduction, reaching relative abundances of 1.01 % and 0.05 % in AE and AN, respectively, at the end of fermentation. Bacillus had the highest relative abundance at AE2, which decreased significantly with temperature. Fodinicurvataceae_unclassified was dominant (7.67%–9.15 %) during the middle AE stages and gradually decreased to 5.54 %. The relative abundance of Luteimonas during AE remained low (2.29 %) for the first 8 days and subsequently increased significantly until the end of fermentation (6.34 %). Note that Thermobacillus had the highest relative abundance at AE2, reaching 13.53 %. During AN, Luteimonas accounted for just 0.19%–1.22 % of bacteria. Bacteroides and MacelliBacteroides, both belonging to Bacteroidetes, exhibited the same trends during AN, reaching their highest relative abundances at AN3 before declining as the fermentation process progressed. However, these two genera were not detected in the AE-treated samples. The relative abundance of Enterobacter in the initial stages of AE and AN was relatively high (6.39 % and 5.70 %, respectively), yet as the fermentation progressed, the relative abundance levels dropped rapidly in both treatments, reaching undetectable levels at AE4. In particular, the relative abundance of Enterobacter at AN7 was only 0.30 %.

A total of 42 pathogens were detected in all samples. Fig. 3c shows variations in the abundance of bacterial pathogens at the species level (top 20) during AE and AN. The pathogenic bacteria in AE1 and AN1 both initially exhibited higher abundance, accounting for 22.01 % and 19.66 % of the total abundance of all bacteria, respectively. Enterobacter aerogenes (6.80 %), Staphylococcus aureus (6.22 %), Enterobacter cloacae (3.70 %), and Enterobacter asburiae (2.19 %) were the top four pathogens detected during AE. Citrobacter freundii (3.03 %), Enterobacter aerogenes (2.40 %), Enterobacter cloacae (1.48 %), and Clostridium botulinum (0.89 %) were the top four most abundant pathogens during AN. The total abundance of pathogens decreased rapidly to 0.61 % by AE2 and remained low until AE7 (0.26 %). The total abundance of pathogenic bacteria briefly increased at AN2, followed by a rapid downward trend. The overall variation in pathogenic bacteria ranged from 23.45 % to 4.97 %. The relative abundance of Clostridium botulinum was opposite that of the other pathogens tested, increasing slowly from 0.01 % at AN1 to 2.24 % at AN7, but was not observed in AE.

3.4 Correlation analysis

RDA is typically used to determine the correlation between microbial communities and environmental changes. Fig. 4a and b shows the relationship between physicochemical factors and bacterial communities. Six physical and chemical indicators were selected as the environmental factors: temperature, pH, EC, TOC, TN, and C/N ratio. During AE, axes 1 and 2 accounted for 49.67 % and 12.14 % of the total variance, respectively, whereas during AN, axes 1 and 2 accounted for 38.68 % and 26.91 % of the total variance, respectively. As shown in Fig. 4a, EC was positively correlated with the dominant bacterial genera at AE5, AE6, and AE7. TOC and C/N were positively correlated with the dominant bacterial genera at AE1 and AE2 and negatively correlated with the dominant bacterial genera at other fermentation stages. Temperature, TOC, and TN were strongly positively correlated with the dominant bacterial genera at AN3, AN4, and AN5, whereas pH, C/N, and EC exhibited strong positive correlations with the dominant bacterial genera at AN6 and AN7. Combined with the Mantel test results shown in Table 3, EC was the most important environmental factor affecting bacterial community changes during AE, with a correlation coefficient of 0.83622, whereas temperature was the most important environmental factor during AN, with a correlation coefficient of 0.54257. These results demonstrate that the bacterial communities in the AE and AN-treated samples were greatly affected by environmental factors, which varied among treatments.Table 3 Mantel test of environmental factors and bacterial community.

Table 3Parameter	AE	AN	
RDA1	RDA2	r	P	RDA1	RDA2	r	P	
Temp (°C)	0.45271	0.70959	0.39970	0.001**	0.24877	−0.81849	0.54257	0.001**	
pH	−0.22495	−0.07940	−0.06046	0.699	0.11257	0.44443	0.09007	0.161	
EC (ms/cm)	−0.97181	0.02194	0.83622	0.001**	−0.09118	0.26661	0.26324	0.01*	
C/N	0.63651	−0.44586	0.65860	0.001**	0.07997	0.37386	0.18861	0.016*	
TOC (mg/kg)	0.87801	−0.35574	0.78074	0.001**	−0.34707	−0.65172	0.19939	0.038*	
TN (g/kg)	−0.20455	0.39369	0.15142	0.058	−0.28786	−0.65985	0.07920	0.14	
r, correlation coefficient; *P < 0.05; **P < 0.01.

Fig. 4c and d presents cluster heat maps of the correlation between the top 15 most abundant bacterial genera and physicochemical factors based on Pearson analysis. During AE, Actinomadura, Anseongella, Galbibacter, Membranicola, S0134_terrestrial_group_unclassified, unclassified, B1–7BS_unclassified, Luteimonas, Fodinicurvataceae_unclassified, and Truepera were significantly positively correlated with NO3− –N, AK, EC, and HA (P < 0.05) and significantly negatively correlated with TOC and FA (P < 0.05). Bacillus, Limnochordaceae_unclassified, and Thermobacillus were positively correlated with NH4+ –N and temperature (P < 0.01). During AN, Clostridiales_vadinBB60_group_unclassified and Synergistaceae_unclassified were significantly positively correlated with NH4+ –N (P < 0.01) and significantly negatively correlated with AP, AK, and FA (P < 0.01). Brachybacterium, Staphylococcus, Enterobacter, Siccibacter, Cellvibrio, and Sphingobacterium were negatively correlated with NH4+ –N (P < 0.01) and positively correlated with AP, AK, and FA (P < 0.01). Acinetobacter, Citrobacter, and Comamonas were positively correlated with TN, TOC, and HA (P < 0.01) and negatively correlated with NO3− –N (P < 0.01). Significant differences in nutrient content between the AE- and AN-treated tomato straw samples were caused by the combined action of their dominant bacterial communities.

3.5 Network analysis and function prediction

Correlation network analysis of the top 15 bacterial genera in the two fermentation systems was carried out to explore the interactions between dominant bacterial genera. We discovered that the network connections between bacterial genera were more complex during AE than during AN, indicating that AE bacterial genera had stronger interactions (Fig. 5). Correlation network analysis can be used to filter out the most influential species in a community. In the AE-treated samples, Luteimonas and S0134_terrestrial_group_unclassified, exerted stronger positive effects than did the other bacterial genera, whereas the opposite was observed for Corynebacterium_1 (Fig. 5a). Cellvibrio exhibited the largest positive correlation and was most closely related to the other bacterial genera in AN-treated samples, whereas Stenotrophomonas, MacelliBacteroides, and Bacteroides were not significantly related to the dominant bacterial genera (Fig. 5b).

4 Discussion

4.1 Physicochemical properties and nutrients

The changes of physicochemical properties during AE were more significant than those during AN, reflecting more vigorous activity of the bacterial community in AE and leading to more significant changes during tomato straw fermentation. The AE and AN treatments were also distinct in terms of the accumulation of effective nutrients and humus components (Fig. 2a–g). The decrease in NH4+ –N and increase in NO3− –N during AE were caused by nitrification by nitrifying bacteria (Fig. 2a and b), which was in line with the findings by Yu [26]. The NO3− –N content was consistently low during AN (Fig. 2a), which may be attributed to the mineralisation and weak nitrification of organic nitrogen during AN. Organic nitrogen mineralisation, NH3 volatilisation, and nitrification during fermentation strongly influence NH4+ –N and NO3− –N content [27,28]. Phosphorus is the second most limiting nutrient, after nitrogen, in most soils used for crop production. The AP in the two treatments increased rapidly in the initial stage (Fig. 2c), which may be linked to the release of AP via the degradation of organic matter [14]. The decrease in AP may be related to its consumption by microorganisms or the mineralisation of organic phosphorus (Fig. 2c) and the conversion of unstable to moderately stable phosphorous by the mineralisation of organophosphate [29,30].

Similar to nitrogen and phosphorus, potassium is a key macronutrient in plant biological processes. Potassium is indispensable for the function of enzymes and coenzymes, protein synthesis, and photosynthesis. The differences in the elemental contents of the two fermentation processes may be due to element-specific chemical features that cause different interactions with or uptake by microbes and changes in chemical forms through chelation with released secondary metabolites [31].

HS comprise complex and stable macromolecular organic matter formed by microorganisms during the composting process. HS can adjust the pH, enhance the water retention capacity, and improve fertiliser efficiency. The increased HA levels in both treatments may be attributed to low-molecular-weight compounds produced by the degradation of cellulose and hemicellulose during the later stages of fermentation via the condensation of compounds [10] (Fig. 2e). In addition, owing to the influence of microbial activity and species, some microorganisms can use small molecules and substances with simple structures (e.g., FA) as energy sources to produce structurally stable HA [32] (Fig. 2f). This may be the reason for the reduction in FA.

Overall, our results demonstrate that AE is more favourable for the accumulation of NO3−-N, AP, AK, and FA, whereas AN is more beneficial for the accumulation and formation of NH4+ –N, HA, and HS.

4.2 Bacterial community structure

PCoA and NMDS analyses demonstrated that the different stages of AE and AN had a significant impact on the microbial community. The distinct effects of the two fermentation methods on the physical and chemical characteristics of the fermentation process affected changes in microbial groups to different degrees (Fig. 2h and i); this is because microorganisms are highly sensitive to their environment [33].

The genera-level distribution of the two fermentation methods revealed different compositions of dominant bacteria between the two treatments, with the distribution characteristics of Staphylococcus identified as the only common factor (Fig. 3b). Staphylococcus spp. are often regarded as pathogens [34]; the decrease in Staphylococcus abundance at the end of fermentation demonstrated the effectiveness of the two fermentation methods in the elimination of pathogens. The abundance of Bacillus was the highest in AE during the thermophilic period and subsequently decreased with temperature (Fig. 3b), indicating that Bacillus has high resistance to temperature and a strong ability to decompose complex organic matter. The abundance of Thermobacillus was consistent with that of Bacillus (Fig. 3b). These bacteria can grow at high temperatures and participate in hemicellulose degradation [35,36], and thus, temperature is a key factor influencing their absence in AN. Bacteroides and MacelliBacteroides were highly abundant in AN (Fig. 3b), and some studies have shown that they are beneficial for the degradation of cellulose and hemicellulose [37,38]. Bacteroidetes are obligate anaerobes (Fig. 3b) and can produce volatile fatty acids as acid-producing bacteria during the AN of vegetable products [39]. These results indicate that there are significant differences in the bacterial community structure between the two fermentation methods, and lignocellulose-degrading bacteria are widely present in both.

The application of products containing pathogenic bacteria in soil affects the soil microbial environment. Such products can enter the human body through the food chain and adversely affect human health [40]. As symbiotic bacteria in humans, Enterobacter aerogenes, Enterobacter cloacae, and Staphylococcus aureus commonly cause nosocomial infections and are naturally resistant to certain antibiotics [[41], [42], [43]]. We observed a significant negative correlation between temperature and pathogen abundance, indicating that high temperatures are an important factor for eliminating pathogenic bacteria. Clostridium botulinum has been proven to be harmful to human health and can produce botulinum neurotoxins, which may cause lethal flaccid paralysis. In addition, Clostridium botulinum can grow in anaerobic environments [44]; consistent with this, the anaerobic environment generated during AN enhanced the relative abundance of Clostridium botulinum. In summary, while AN reduced the abundance of most pathogens (with the exception of Clostridium botulinum), AE removed pathogens more effectively.

4.3 The correlation among factors

During AE, the abundance of most bacterial genera was negatively correlated with TOC and positively correlated with available nutrients and HA (Fig. 4c), indicating that these bacterial genera were involved in TOC degradation, organic matter mineralisation, and humification. Membranicola, belonging to the family Saprospiraceae, plays an important role in the breakdown of complex organic polymers under aerobic conditions [45]. Luteimonas, an ammonia-oxidising bacterium that plays an important role in nitrification [46], was the dominant bacterial genus in AE and was important for NO3− –N production. These bacteria may use FA as a substrate for conversion to the more stable HA. Most of the bacteria related to available nutrients found in AN were closely associated with cellulose degradation (Fig. 4b) [[47], [48], [49], [50], [51]]. This indicates that the bacteria simultaneously perform nutrient conversion while decomposing organic matter during tomato straw fermentation.Fig. 4 Relationship between physicochemical factors and bacterial community. (a–b) Redundancy analysis assessing the relationship between environmental factors and bacterial communities during (a) AE or (b) AN. (c–d) Cluster heat maps showing the correlation between bacterial communities and (c) available nutrients during AE or (d) humus substances during AN.

Fig. 4

Fig. 5 Correlation network diagram of the top 15 most abundant bacterial genera in the fermentation samples based on Pearson correlation analysis (positive correlation threshold, rho >0.5; negative correlation threshold, rho < −0.5). (a) Correlation following AE. (b) Correlation following AN. The size and colour of each node is proportional to the number of connections. Solid lines represent positive interactions; dotted lines represent negative interactions. The thickness of the edges indicates the row strength.

Fig. 5

4.4 Bacterial function prediction and correlation network analysis

The interactions between bacteria result from mutually beneficial coexistence, predation, or competition [39]. The edges between the bacterial genera in the AE samples were more complex than those in the AN samples (Fig. 5), indicating stronger interactions among the top 15 most abundant bacterial genera in AE. This indicates that AE has a strong impact on the make-up of bacterial communities and symbiotic trends. This may be due to the richer nutrition resulting from AE compared to AN [28].

5 Conclusion

In this study, we found that different fermentation treatments differentially affected the composition of the bacterial community during tomato straw fermentation, thus altering the physicochemical properties, effective nutrient content, and humus content. AE of tomato straw effectively increased the levels of NO3− –N, AP, AK, and FA, whereas AN led to the accumulation of NH4+ –N, HA, and HS. These changes were closely correlated to the composition and function of bacterial communities in both fermentation treatments. During AE, Anseongella, Galbibacter, Membranicola, S0134_terstrical_group_unclassified, unclassified, Actinomadura, B17BS_unclassified, Luteimonas, and Truepera were the key bacterial genera that affected the levels of NO3− –N, AK, and HA, and Bacillus and Thermobacillus were the key bacterial genera that affect the levels of NH4+ –N and AP. In AN, Clostridiales_vadinBB60_group_unclassified and Synergistaceae_unclassified were the key bacterial genera affecting NH4+ –N content; Brachybacterium, Staphylococcus, Enterobacter, Siccibacter, Klebsiella, Cellvibrio, and Sphingobacterium were the key bacterial genera affecting AP, AK, and FA contents; and Acinetobacter, Citrobacter, and Comamonas were the key bacterial genera affecting TN and HA contents. During AE, the interactions between the dominant bacterial communities were stronger and the elimination of pathogenic bacteria was more effective than those in AN. These results can provide some reference and basis for us to improve the quality of tomato straw compost.

Availability of data and materials

All data generated or analysed during this study are included in this published article. Data will be made available on request.

Ethics declarations

All authors of the manuscript certify that this manuscript fully complies with the ethical standard of this Journal, and there is no conflict of interest among the authors to publish the manuscript. They are in full agreement with this publication.

CRediT authorship contribution statement

Xiaodong Xu: Writing – review & editing, Writing – original draft, Methodology, Formal analysis. Peng Xu: Validation. Yang Li: Project administration. Guanzhi Zhang: Visualization. Yongjun Wu: Supervision, Resources. Zhenchao Yang: Funding acquisition.

Declaration of competing interest

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

1 CNBS https://data.stats.gov.cn/easyquery.htm?cn=C01 2022
2 Song T. Shen Y. Jin Q. Feng W. Cai W. Bacterial community diversity, lignocellulose components, and histological changes in composting using agricultural straws for Agaricus bisporus production PeerJ 9 2021 e10452 10.7717/peerj.10452
3 Wei Y. Wu D. Wei D. Zhao Y. Wu J. Xie X. Zhang R. Wei Z. Improved lignocellulose-degrading performance during straw composting from diverse sources with actinomycetes inoculation by regulating the key enzyme activities Bioresour. Technol. 271 2019 66 74 10.1016/j.biortech.2018.09.081 30265954
4 Jorge Medina Carlos Monreal José Miguel Barea César Arriagada Fernando Crop residue stabilization and application to agricultural and degraded soils: a review Waste Manag. 2015 10.1016/j.wasman.2015.04.002
5 Guo H. Gu J. Wang X. Nasir M. Yu J. Lei L. Wang J. Zhao W. Dai X. Beneficial effects of bacterial agent/bentonite on nitrogen transformation and microbial community dynamics during aerobic composting of pig manure Bioresour. Technol. 298 2020 122384 10.1016/j.biortech.2019.122384
6 Liu Y. Sun D. Wang H. Wang X. Yu G. Zhao X. An evaluation of China's agricultural green production: 1978-2017 J. Clean. Prod. 243 2020 118483.1 118483.12 10.1016/j.jclepro.2019.118483
7 Chang R. Yao Y. Cao W. Wang J. Wang X. Chen Q. Effects of composting and carbon based materials on carbon and nitrogen loss in the arable land utilization of cow manure and corn stalks J. Environ. Manag. 233 2019 283 290 10.1016/j.jenvman.2018.12.021
8 Ghimire A. Valentino S. Frunzo L. Trably E. Escudie R. Pirozzi F. Lens P.N.L. Esposito G. Biohydrogen production from food waste by coupling semi-continuous dark-photofermentation and residue post-treatment to anaerobic digestion: a synergy for energy recovery Int. J. Hydrogen Energy 40 2015 16045 16055 10.1016/j.ijhydene.2015.09.117
9 Bilgen S. Sarıkaya İ. Utilization of forestry and agricultural wastes Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 38 23 2016 3484 3490 10.1080/15567036.2016.1169338
10 Wu Q. Haishi Xinning H. Dan W. Yue Z. Zimin W. Qian L. Ruju Z. Tianjiao T. How does manganese dioxide affect humus formation during bio-composting of chicken manure and corn straw? Bioresour. Technol. 2018 10.1016/j.biortech.2018.08.079 S0960852418311830-
11 Insam H. Gómez-Brandón M. Ascher J. Manure-based biogas fermentation residues – friend or foe of soil fertility? Soil Biol. Biochem. 84 2015 1 14 10.1016/j.soilbio.2015.02.006
12 Zhao Y. Lu Q. Wei Y. Cui H. Zhang X. Wang X. Shan S. Wei Z. Effect of actinobacteria agent inoculation methods on cellulose degradation during composting based on redundancy analysis Bioresour. Technol. 219 2016 196 203 10.1016/j.biortech.2016.07.117 27494100
13 Ribeiro H.M. Romero A.M. Pereira H. Borges P. Cabral F. Vasconcelos E. Evaluation of a compost obtained from forestry wastes and solid phase of pig slurry as a substrate for seedlings production Bioresour. Technol. 98 2007 3294 3297 10.1016/j.biortech.2006.07.002 16919933
14 Wei Y. Zhao Y. Lu Q. Cao Z. Wei Z. Organophosphorus-degrading bacterial community during composting from different sources and their roles in phosphorus transformation Bioresour. Technol.: Biomass, Bioenergy, Biowastes, Conversion Technologies, Biotransformations, Production Technologies 2018 264
15 Wan W. Wan W. Wang Y. Wang Y. Tan J. Tan J. Qin Y. Qin Y. Zuo W. Zuo W. Alkaline phosphatase-harboring bacterial community and multiple enzyme activity contribute to phosphorus transformation during vegetable waste and chicken manure composting Bioresour. Technol. 297 2020 122406 10.1016/j.biortech.2019.122406
16 Lei L. Gu J. Wang X. Song Z. Zhao W. Effects of phosphogypsum and medical stone on nitrogen transformation, nitrogen functional genes, and bacterial community during aerobic composting Sci. Total Environ. 753 2020 141746 10.1016/j.scitotenv.2020.141746
17 FAO https://www.fao.org/faostat/zh/#data/QCL 2021
18 Wei L. Shutao W. Jin Z. Tong X. Biochar influences the microbial community structure during tomato stalk composting with chicken manure Bioresour. Technol. 154 2014 148 154 10.1016/j.biortech.2013.12.022 24384321
19 Sevik F. Tosun I. Ekinci K. The effect of FAS and C/N ratios on co-composting of sewage sludge, dairy manure and tomato stalks Waste Manag. 80 2018 450 456 10.1016/j.wasman.2018.07.051 30082199
20 Yu Y. Lee C. Kim J. Hwang S. Group-specific primer and probe sets to detect methanogenic communities using quantitative real-time polymerase chain reaction Biotechnol. Bioeng. 89 2005 670 679 10.1002/bit.20347 15696537
21 Lee C. Kim J. Shin S.G. Hwang S. Monitoring bacterial and archaeal community shifts in a mesophilic anaerobic batch reactor treating a high-strength organic wastewater: bacterial community shifts in anaerobic digestion FEMS (Fed. Eur. Microbiol. Soc.) Microbiol. Ecol. 65 2008 544 554 10.1111/j.1574-6941.2008.00530.x
22 Wei Huawei Wang Liuhong Hassan Muhammad Xie Bing Succession of the functional microbial communities and the metabolic functions in maize straw composting process, Bioresource Technology: biomass, Bioenergy, Biowastes, Conversion Technologies, Biotransformations Production Technologies 256 2018 333 341
23 Bao S.D. Soil and agricultural chemistry analysis http://www.researchgate.net/publication/301822463_Soil_and_agricultural_chemistry_analysis 2000
24 Wu J. Zhao Y. Wang F. Zhao X. Dang Q. Tong T. Wei Z. Identifying the action ways of function materials in catalyzing organic waste transformation into humus during chicken manure composting Bioresour. Technol. 303 2020 122927 10.1016/j.biortech.2020.122927
25 Zhang L. Sun X. Effects of earthworm casts and zeolite on the two-stage composting of green waste Waste Manage. (Tucson, Ariz.) 39 2015 119 129 10.1016/j.wasman.2015.02.037
26 Yu J. Gu J. Wang X. Guo H. Wang J. Lei L. Dai X. Zhao W. Effects of inoculation with lignocellulose-degrading microorganisms on nitrogen conversion and denitrifying bacterial community during aerobic composting Bioresour. Technol. 313 2020 123664 10.1016/j.biortech.2020.123664
27 Raj D. Antil R.S. Evaluation of maturity and stability parameters of composts prepared from agro-industrial wastes Bioresour. Technol. 102 2011 2868 2873 10.1016/j.biortech.2010.10.077 21075622
28 Yang W. Jing X. Guan Y. Zhai C. Wang T. Shi D. Sun W. Gu S. Response of fungal communities and Co-occurrence network patterns to compost amendment in black soil of northeast China Front. Microbiol. 10 2019 1562 10.3389/fmicb.2019.01562 31354663
29 Wei Y. Zhao Y. Xi B. Wei Z. Li X. Cao Z. Changes in phosphorus fractions during organic wastes composting from different sources Bioresour. Technol. 189 2015 349 356 10.1016/j.biortech.2015.04.031 25912971
30 Meng X. Liu B. Zhang H. Wu J. Yuan X. Cui Z. Co-Composting of the biogas residues and spent mushroom substrate: physicochemical properties and maturity assessment Bioresour. Technol. 276 2018 281 287 10.1016/j.biortech.2018.12.097 30640023
31 Zaffar N. Ferchau E. Feuerstein U. Heilmeier H. Moschner C. Wiche O. Concentration of selected plant nutrients and target elements for phytoremediation and phytomining in biogas digestate, carpath J. Earth Environ. Sci. 15 2020 43 48 10.26471/cjees/2020/015/107
32 Zhou Y. Selvam A. Wong J.W.C. Evaluation of humic substances during co-composting of food waste, sawdust and Chinese medicinal herbal residues Bioresour. Technol. 168 2014 229 234 10.1016/j.biortech.2014.05.070 24951275
33 Malinowski M. Wolny-Koladka K. Vaverkova M.D. Effect of biochar addition on the OFMSW composting process under real conditions Waste Manag. 84 2019 364 372 10.1016/j.wasman.2018.12.011 30691911
34 Qiu X. Zhou G. Wang H. Wu X. The behavior of antibiotic-resistance genes and their relationships with the bacterial community and heavy metals during sewage sludge composting Ecotoxicol. Environ. Saf. 216 2021 112190 10.1016/j.ecoenv.2021.112190
35 Fang Y. Jia X. Chen L. Lin C. Chen J. Effect of thermotolerant bacterial inoculation on the microbial community during sludge composting Can. J. Microbiol. 65 2019 10.1139/cjm-2019-0107
36 Zhang X. Zhu Y. Li J. Zhu P. Liang B. Exploring dynamics and associations of dominant lignocellulose degraders in tomato stalk composting J. Environ. Manag. 294 2021 113162 10.1016/j.jenvman.2021.113162
37 Chen H. Chang S. Impact of temperatures on microbial community structures of sewage sludge biological hydrolysis Bioresour. Technol. 2017 502 510 10.1016/j.biortech.2017.08.143
38 Ma C. Lo P.K. Xu J. Li M. Jiang Z. Li G. Zhu Q. Li X. Leong S.Y. Li Q. Molecular mechanisms underlying lignocellulose degradation and antibiotic resistance genes removal revealed via metagenomics analysis during different agricultural wastes composting Bioresour. Technol. 314 2020 123731 10.1016/j.biortech.2020.123731
39 Zhang Q. Lu Y. Zhou X. Wang X. Zhu J. Effect of different vegetable wastes on the performance of volatile fatty acids production by anaerobic fermentation Sci. Total Environ. 748 2020 142390 10.1016/j.scitotenv.2020.142390
40 Shen W. Yu Y. Zhou R. Song N. Bu Y. Occurrence, distribution, and potential role of bacteria and human pathogens in livestock manure and digestate: insights from the guangxi, China Environ. Eng. Sci. 2021 10.1089/ees.2020.0432
41 Mégarbane B. Shabafrouz K. Raskine L. Delahaye A. Baud F. [Nosocomial pneumonia due to resistant Enterobacter aerogenes] Presse Med. 33 2004 940 941 10.1016/s0755-4982(04)98801-x
42 Wertheim H.F.L. Melles D.C. Vos M.C. Leeuwen W.V. Nouwen J.L. The role of nasal carriage in Staphylococcus aureus infections Lancet Infect. Dis. 5 2005 751 762 10.1016/S1473-3099(05)70295-4 16310147
43 Avner R. [Endocarditis due to Stenotrophomonas maltophilia and Enterobacter cloacae] M Decine Et Maladies Infectieuses 40 2010 664 10.1016/j.medmal.2010.04.005
44 Pernu N. Keto-Timonen R. Lindstrm M. Korkeala H. High prevalence of Clostridium botulinum in vegetarian sausages - ScienceDirect Food Microbiol. 91 2020 10.1016/j.fm.2020.103512
45 Jang Y.-N. Hwang O. Jung M.-W. Ahn B.-K. Kim H. Jo G. Yun Y.-M. Comprehensive analysis of microbial dynamics linked with the reduction of odorous compounds in a full-scale swine manure pit recharge system with recirculation of aerobically treated liquid fertilizer Sci. Total Environ. 777 2021 146122 10.1016/j.scitotenv.2021.146122
46 Sun Y. Men M. Xu B. Meng Q. Bello A. Xu X. Huang X. Assessing key microbial communities determining nitrogen transformation in composting of cow manure using illumina high-throughput sequencing Waste Manag. 92 2019 59 67 10.1016/j.wasman.2019.05.007 31160027
47 Kang J.H. Kim D. Lee T.J. Hydrogen production and microbial diversity in sewage sludge fermentation preceded by heat and alkaline treatment Bioresour. Technol. 109 2012 239 243 10.1016/j.biortech.2012.01.048 22306077
48 Zhang L. Chung J. Jiang Q. Sun R. Zhang J. Zhong Y. Ren N. Characteristics of rumen microorganisms involved in anaerobic degradation of cellulose at various pH values RSC Adv. 7 2017 40303 40310 10.1039/c7ra06588d
49 Ziganshina E.E. Ibragimov E.M. Vankov P.Y. Miluykov V.A. Ziganshin A.M. Comparison of anaerobic digestion strategies of nitrogen-rich substrates: performance of anaerobic reactors and microbial community diversity Waste Manage. (Tucson, Ariz.) 59 2017 160 171 10.1016/j.wasman.2016.10.038
50 Wang M. Zhang S.C. Tang Q. Shi L.D. Tian G.M. Organic degrading bacteria and nitrifying bacteria stimulate the nutrient removal and biomass accumulation in microalgae-based system from piggery digestate Sci. Total Environ. 707 2019 134442 10.1016/j.scitotenv.2019.134442
51 Raut JagroopWright M.P.P. Phillip C. Effective pretreatment of lignocellulosic co-substrates using barley straw-adapted microbial consortia to enhanced biomethanation by anaerobic digestion Bioresour. Technol.: Biomass, Bioenergy, Biowastes, Conversion Technologies, Biotransformations, Production Technologies 321 2021 https://www.zhangqiaokeyan.com/journal-foreign-detail/0704029443196.html
