Effects of microbial agent and microbial fertilizer input on soil microbial community structure and diversity in a peanut continuous cropping system
Arachis hypogaea , commonly known as peanut, holds a significant position as the primary source of vegetable oil production in China , . The prevalence of peanut continuous cropping is attributed to factors such as land resources, climatic conditions, planting habits, and economic benefits. However, the challenges associated with continuous cropping are significant , . The challenges associated with continuous cropping are influenced by a multitude of factors, and the underlying causes of these challenges are highly intricate. Past research has indicated that impediments to uninterrupted cultivation primarily stem from the following factors: depletion of soil nutrients, degradation of soil's physical and chemical characteristics, alterations in soil's microbial community structure, and plant autotoxicity , , , . Numerous efforts have been undertaken to mitigate the challenges associated with continuous cultivation. These include the implementation of intercropping and crop rotation techniques involving peanuts and other crops , , as well as the utilization of inorganic, organic, and biological fertilizers. These approaches have been explored in various studies , . Crop rotation, or intercropping, solves continuous cropping problems. This practice's negative influence on economic profitability and the difficulty of changing farmers' planting practices prevent its broad adoption. Fertilizer, mostly chemical, is often used to overcome obstacles to continuous cultivation. It is well known that fertilizing practices affect agroecosystem soil microenvironments. In different agricultural environments, chemical fertilization affects soil microbial diversity , . It is well known that soil microbial abundance and diversity are essential for soil fertility and plant development . Microbial fertilizer contains active microorganisms and substrates for microbial life activities, which improve crop production and restore soil equilibrium by reducing compaction, chemical fertilizer effects, and soil-borne diseases , . Microbial agents have been observed to enhance crop productivity. These microbial agents are living microorganisms that have been cultivated using porous adsorbents such as peat and vermiculite. These adsorbents capture the fermentation broth of industrially produced and propagated target microorganisms. It is usually used to coat seeds or soak roots. Research shows that it may improve soil quality, fertility, and rhizosphere microbial equilibrium . Soil enzyme activities have been proposed as a potential indicator of soil quality, along with soil microorganisms. Soil enzymes play a crucial role in facilitating a diverse range of biochemical reactions and metabolic pathways that are integral to the metabolism of organic matter, the preservation of soil structure, the cycling of nutrients, and the generation of energy for both microorganisms and plants. The enzymatic activity is anticipated to increase the accessibility of the scarcest nutrients to fulfill the metabolic requirements of microorganisms, as reported by , . The impact of microbial fertilizers and agents on the diversity of the rhizosphere microbiome and soil enzyme functionality in the context of continuous peanut cultivation remains unexplored. In this study, we conducted peanut field experiments to assess the impact of four distinct fertilizer treatments on both peanut yield and agronomic characteristics. Additionally, we examined the influence of microbial inoculation on the bacterial and fungal community of the rhizosphere soil. Our investigation of alterations in bacterial and fungal communities resulting from various fertilization methods may help in the development of strategies to manage soil microbiome composition, thus addressing challenges to continuous agricultural cultivation. 

Site description We established the peanut cultivation experiment at the Yubao Peanut Planting Cooperative located in Beizhen, Liaoning Province, China (121.788107°E, 41.433816°N) in 2018, with an annual average precipitation of 604.8 mm, an annual average temperature of 8.2 °C, and a frost-free period of 154–164 days. These plots had a history of over 20 years of continuous peanut planting. The soil was brown sandy loam, which had 12 g kg −1 organic matter, 98 mg kg −1 alkali-hydrolyzable nitrogen, 7.8 mg kg −1 available phosphorus, and 120 mg kg −1 available potassium. Experimental design Four different fertilizer treatments were applied, as follows: CF, soil amended with compound fertilizer (NPK, 150 kg ha −1 , 18 kg ha -1 N, 27 kg ha −1 P 2 O 5 , 22.5 kg ha −1 K 2 O); CF + MA, soil amended with compound fertilizer (application amount same as CF) and mixed the peanut seeds with microbial agent (33.33 mL kg −1 seeds); CF + MF, soil amended with compound fertilizer (application amount same as CF) and microbial fertilizer (75 kg ha −1 ); CF + MM, soil amended with compound fertilizer (application amount same as CF) and microbial fertilizer (75 kg ha −1 ), and mixed the peanut seeds with microbial agents (33.33 mL kg −1 seeds). The specific amount of fertilizer is shown in . Microbial fertilizer and microbial agent were provided by the National Technical Research and Extension Center of Yield-Increasing Bacteria (China Agricultural University. Their ingredients included highly active strains of Bacillus subtilis , Trichoderma viride , Streptomyces pactum, and Gliocladium roseum . The total effective number of viable bacteria is ≥ 8.0 × 10 8 CFU g −1 . Microbial agents were prepared as seed coating agents (Aqueous Solution, AS). The microbial agent products utilized in this study were originally acquired in a dry powder formulation. Before their use, these powdered products underwent a process known as reconstitution to create bacterial suspensions. The reconstitution process involved diluting the dry powder with water at a ratio of 1:125 (weight to water), resulting in the formation of bacterial suspensions. Microbial fertilizer is the commercial product of China Lukang (Beijing) Biotechnology Co., Ltd. The compound fertilizer contained 12 % N, 18 % P 2 O 5 , and 15 % K 2 O, and commercially available in local market. The experimental field planting system was continuous cropping of peanut, and the cultivar was Yubao No. 4. This variety is a continuous-flowering medium-grain peanut with a straight root system, upright plant type, thinning branches, a growth period of 125 days, and strong drought resistance. Four treatments (3 replicates each) were established using a randomized block design in 12 plots (each plot was 0.05 ha). The cultivation method was single-row cultivation. Plant density was 200,000 plants ha −1 . The spacing between the rows was 50 cm, and the plant spacing was 20 cm. The peanut seeds were selected without any pre-treatment, except for the fertilizers, and two seeds were used per hole. The compound fertilizer was evenly added into the soil before sowing. Trenches were dug (10–15 cm) in the ridges, compound fertilizer was added to the trenches and the soil was recovered. Microbial fertilizer was applied together with peanut seeds into the soil layer of 5–15 cm during sowing. Microbial agent was intensively mixed into seeds and dried two days before sowing. The weeds were controlled using glyphosate (41 % active ingredient in 3 L ha −1 ) before planting. Plant measuring During the mature stage (BBCH-scale stage 89), 150 days after sowing, five plants were collected randomly in each plot to determine plant height, length of the first pair of lateral branches, pod weight, and hundred kernel weight, and the average value was calculated. The yield of peanuts was measured after harvest. An equally sized area (1.2 m × 2 m) was taken in each plot to harvest peanuts. We counted the total number of plants, harvested the pods in yarn mesh bags, air dried them to a constant weight, and calculated the yield . Soil sampling During the period of peanut flowering in July 2018, samples of rhizosphere soil were taken. However, for soil enzymes study, rhizosphere soil were taken at blossom stage and as well at pod setting stages. This was done specifically at the BBCH-scale stage 65, which is indicative of the growth and development of the peanut plant. A total of five plant specimens were systematically selected from each replicate in a randomized manner. The entire botanical specimen was carefully extracted from the terrestrial substrate using a manual excavation tool known as a spade, reaching a depth of approximately 20 cm. Subsequently, the subterranean network of roots was delicately agitated to dislodge any loosely adhering clumps of soil. The fine roots were subsequently delicately brushed in order to eliminate any residual rhizosphere soil, which was subsequently utilized for analytical purposes. Each experimental treatment consisted of three replicates. For each replicate, rhizosphere soil samples were collected from five plants within the same plot. These samples were then combined to create a single soil sample replicate. Subsequently, the soil samples were stored in a − 80 ℃ ultra-low temperature refrigerator until they were ready for further analyses. Soil enzymatic activities analysis Catalase (CAT), urease (UE), and acid phosphatase (ACP) activity were determined using a soil CAT activity, UE activity, and ACP activity assay kit (No. BC0100, BC0120, and BC0140) purchased from Solarbio Co., Ltd. (Beijing, China) as per the manufacturer's protocol. Catalytic degradation of 1 μmol H 2 O 2 per gram of dry-weight soil mass per day was defined as a CAT activity unit. UE activity was expressed as products (NH 3 -N) per gram of dry-weight soil per incubation time (24 h). One unit of ACP activity was 1 nmol of phenol released per gram of dry-weight soil per day at 37 ℃. DNA extraction and PCR amplification The extraction of DNA from rhizosphere soil was carried out following the method described by Huang (2017). We extracted soil DNA from 0.5 g of soil (fresh weight) using a FastDNA kit from MP BIO, USA. After extraction, we purified the DNA using a PowerClean DNA kit from MO BIO Lab in Carlsbad, CA, USA. Each composite soil sample was extracted in triplicate, and the extracted DNA solutions were pooled. The extracted DNA was measured using a NanoDrop ND-1000 spectrophotometer. The V3-V4 regions of DNA sequence of 16SrRNA gene were amplified by primers 338F (5′-ACTCCTACGGGAGGCA GCA-3′), and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) (Liu et al., 2016). The ITS1 region of DNA sequence rRNA were amplified by primers ITS5-1737F (5′-GGAAGTAAAAGTCGTAAC AAGG-3′), and ITS2-2043R (5′-GCTGCGTTCTTCATCGATGC-3′) (Chen et al., 2020a). The PCR cycle conditions were initial denaturation at 98 ℃ for 2 min and 25–30 cycles of denaturation at 98 ℃ for 15 s, annealing at 55 ℃ for 30 s, extension at 72 ℃ for 30 s and final extension at 72 ℃ for 5 min. Bioinformatics analysis The investigation of bacterial and fungal communities involved using paired-end amplicon sequencing of the 16S rRNA gene and the ITS region of fungal ribosomal DNA. This sequencing was performed on an Illumina MiSeq platform at Personal Biotechnology Co., Ltd. (Shanghai, China). The raw sequence reads were quality-filtered, and processed using the software system QIIME v1.8.0 . The chimeric sequences have been carefully investigated, and removed by employing USEARCH v5.2.236 . The UCLUST method was used to define the operational taxonomic units (OTUs) at a level of 97 % identity. The sequence that was found most often in each OTU was chosen as the OTU's representative sequence. Subsequently, a matrix file of OTU abundance in each sample was constructed according to the sequence quantity contained in each sample. On the basis of this normalized output data, alpha and beta diversity studies were performed. The taxonomic information corresponding to each OTU was obtained by comparing the OTU representative sequence with the template sequence of the corresponding database (the Greengenes database for bacteria and the UNITE database for fungi). The diversity of the alpha community of bacteria and fungi in each sample was estimated using the Shannon index, Chao1, and Good-coverage. The bacterial, and fungal community richness was estimated by the ACE and Simpson index. Heatmap, and partial least squares discrimination analysis (PLS-DA) analysis were performed with the vegan package in R version 2.4. The Variable Importance of Projection (VIP) was calculated to evaluate the strength of influence and interpretability of the abundance of each species on the classification and discrimination of each treatment sample. This helps in identifying the key species by using a screening standard of VIP value > 1.0 was usually used as the screening standard . GraPhlAn, a visual tool, was used to construct a hierarchical tree for the composition of the sample population at each classification level. Each taxon was distinguished by different colors, and their abundance distribution was reflected by the node size . presents the databases, indexes, and applications utilized in the study. General websites and taxons related to biodiversity informatics are given in . Statistical analysis The statistical analyses were conducted utilizing the IBM SPSS 20.0 software program, developed by (IBM Corporation New York, USA). All statistical tests conducted in this study were classified as statistically significant at a significance level of P < 0.05. A One-way analysis of variance (ANOVA) was conducted to ascertain significant differences. 

We established the peanut cultivation experiment at the Yubao Peanut Planting Cooperative located in Beizhen, Liaoning Province, China (121.788107°E, 41.433816°N) in 2018, with an annual average precipitation of 604.8 mm, an annual average temperature of 8.2 °C, and a frost-free period of 154–164 days. These plots had a history of over 20 years of continuous peanut planting. The soil was brown sandy loam, which had 12 g kg −1 organic matter, 98 mg kg −1 alkali-hydrolyzable nitrogen, 7.8 mg kg −1 available phosphorus, and 120 mg kg −1 available potassium. 

Four different fertilizer treatments were applied, as follows: CF, soil amended with compound fertilizer (NPK, 150 kg ha −1 , 18 kg ha -1 N, 27 kg ha −1 P 2 O 5 , 22.5 kg ha −1 K 2 O); CF + MA, soil amended with compound fertilizer (application amount same as CF) and mixed the peanut seeds with microbial agent (33.33 mL kg −1 seeds); CF + MF, soil amended with compound fertilizer (application amount same as CF) and microbial fertilizer (75 kg ha −1 ); CF + MM, soil amended with compound fertilizer (application amount same as CF) and microbial fertilizer (75 kg ha −1 ), and mixed the peanut seeds with microbial agents (33.33 mL kg −1 seeds). The specific amount of fertilizer is shown in . Microbial fertilizer and microbial agent were provided by the National Technical Research and Extension Center of Yield-Increasing Bacteria (China Agricultural University. Their ingredients included highly active strains of Bacillus subtilis , Trichoderma viride , Streptomyces pactum, and Gliocladium roseum . The total effective number of viable bacteria is ≥ 8.0 × 10 8 CFU g −1 . Microbial agents were prepared as seed coating agents (Aqueous Solution, AS). The microbial agent products utilized in this study were originally acquired in a dry powder formulation. Before their use, these powdered products underwent a process known as reconstitution to create bacterial suspensions. The reconstitution process involved diluting the dry powder with water at a ratio of 1:125 (weight to water), resulting in the formation of bacterial suspensions. Microbial fertilizer is the commercial product of China Lukang (Beijing) Biotechnology Co., Ltd. The compound fertilizer contained 12 % N, 18 % P 2 O 5 , and 15 % K 2 O, and commercially available in local market. The experimental field planting system was continuous cropping of peanut, and the cultivar was Yubao No. 4. This variety is a continuous-flowering medium-grain peanut with a straight root system, upright plant type, thinning branches, a growth period of 125 days, and strong drought resistance. Four treatments (3 replicates each) were established using a randomized block design in 12 plots (each plot was 0.05 ha). The cultivation method was single-row cultivation. Plant density was 200,000 plants ha −1 . The spacing between the rows was 50 cm, and the plant spacing was 20 cm. The peanut seeds were selected without any pre-treatment, except for the fertilizers, and two seeds were used per hole. The compound fertilizer was evenly added into the soil before sowing. Trenches were dug (10–15 cm) in the ridges, compound fertilizer was added to the trenches and the soil was recovered. Microbial fertilizer was applied together with peanut seeds into the soil layer of 5–15 cm during sowing. Microbial agent was intensively mixed into seeds and dried two days before sowing. The weeds were controlled using glyphosate (41 % active ingredient in 3 L ha −1 ) before planting. 

During the mature stage (BBCH-scale stage 89), 150 days after sowing, five plants were collected randomly in each plot to determine plant height, length of the first pair of lateral branches, pod weight, and hundred kernel weight, and the average value was calculated. The yield of peanuts was measured after harvest. An equally sized area (1.2 m × 2 m) was taken in each plot to harvest peanuts. We counted the total number of plants, harvested the pods in yarn mesh bags, air dried them to a constant weight, and calculated the yield . 

During the period of peanut flowering in July 2018, samples of rhizosphere soil were taken. However, for soil enzymes study, rhizosphere soil were taken at blossom stage and as well at pod setting stages. This was done specifically at the BBCH-scale stage 65, which is indicative of the growth and development of the peanut plant. A total of five plant specimens were systematically selected from each replicate in a randomized manner. The entire botanical specimen was carefully extracted from the terrestrial substrate using a manual excavation tool known as a spade, reaching a depth of approximately 20 cm. Subsequently, the subterranean network of roots was delicately agitated to dislodge any loosely adhering clumps of soil. The fine roots were subsequently delicately brushed in order to eliminate any residual rhizosphere soil, which was subsequently utilized for analytical purposes. Each experimental treatment consisted of three replicates. For each replicate, rhizosphere soil samples were collected from five plants within the same plot. These samples were then combined to create a single soil sample replicate. Subsequently, the soil samples were stored in a − 80 ℃ ultra-low temperature refrigerator until they were ready for further analyses. 

Catalase (CAT), urease (UE), and acid phosphatase (ACP) activity were determined using a soil CAT activity, UE activity, and ACP activity assay kit (No. BC0100, BC0120, and BC0140) purchased from Solarbio Co., Ltd. (Beijing, China) as per the manufacturer's protocol. Catalytic degradation of 1 μmol H 2 O 2 per gram of dry-weight soil mass per day was defined as a CAT activity unit. UE activity was expressed as products (NH 3 -N) per gram of dry-weight soil per incubation time (24 h). One unit of ACP activity was 1 nmol of phenol released per gram of dry-weight soil per day at 37 ℃. 

The extraction of DNA from rhizosphere soil was carried out following the method described by Huang (2017). We extracted soil DNA from 0.5 g of soil (fresh weight) using a FastDNA kit from MP BIO, USA. After extraction, we purified the DNA using a PowerClean DNA kit from MO BIO Lab in Carlsbad, CA, USA. Each composite soil sample was extracted in triplicate, and the extracted DNA solutions were pooled. The extracted DNA was measured using a NanoDrop ND-1000 spectrophotometer. The V3-V4 regions of DNA sequence of 16SrRNA gene were amplified by primers 338F (5′-ACTCCTACGGGAGGCA GCA-3′), and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) (Liu et al., 2016). The ITS1 region of DNA sequence rRNA were amplified by primers ITS5-1737F (5′-GGAAGTAAAAGTCGTAAC AAGG-3′), and ITS2-2043R (5′-GCTGCGTTCTTCATCGATGC-3′) (Chen et al., 2020a). The PCR cycle conditions were initial denaturation at 98 ℃ for 2 min and 25–30 cycles of denaturation at 98 ℃ for 15 s, annealing at 55 ℃ for 30 s, extension at 72 ℃ for 30 s and final extension at 72 ℃ for 5 min. 

The investigation of bacterial and fungal communities involved using paired-end amplicon sequencing of the 16S rRNA gene and the ITS region of fungal ribosomal DNA. This sequencing was performed on an Illumina MiSeq platform at Personal Biotechnology Co., Ltd. (Shanghai, China). The raw sequence reads were quality-filtered, and processed using the software system QIIME v1.8.0 . The chimeric sequences have been carefully investigated, and removed by employing USEARCH v5.2.236 . The UCLUST method was used to define the operational taxonomic units (OTUs) at a level of 97 % identity. The sequence that was found most often in each OTU was chosen as the OTU's representative sequence. Subsequently, a matrix file of OTU abundance in each sample was constructed according to the sequence quantity contained in each sample. On the basis of this normalized output data, alpha and beta diversity studies were performed. The taxonomic information corresponding to each OTU was obtained by comparing the OTU representative sequence with the template sequence of the corresponding database (the Greengenes database for bacteria and the UNITE database for fungi). The diversity of the alpha community of bacteria and fungi in each sample was estimated using the Shannon index, Chao1, and Good-coverage. The bacterial, and fungal community richness was estimated by the ACE and Simpson index. Heatmap, and partial least squares discrimination analysis (PLS-DA) analysis were performed with the vegan package in R version 2.4. The Variable Importance of Projection (VIP) was calculated to evaluate the strength of influence and interpretability of the abundance of each species on the classification and discrimination of each treatment sample. This helps in identifying the key species by using a screening standard of VIP value > 1.0 was usually used as the screening standard . GraPhlAn, a visual tool, was used to construct a hierarchical tree for the composition of the sample population at each classification level. Each taxon was distinguished by different colors, and their abundance distribution was reflected by the node size . presents the databases, indexes, and applications utilized in the study. General websites and taxons related to biodiversity informatics are given in . 

The statistical analyses were conducted utilizing the IBM SPSS 20.0 software program, developed by (IBM Corporation New York, USA). All statistical tests conducted in this study were classified as statistically significant at a significance level of P < 0.05. A One-way analysis of variance (ANOVA) was conducted to ascertain significant differences. 

Agronomic characters and yield of peanut Different fertilization treatments had an impact on the agronomic characteristics and yield of peanuts ( ). The plant height, lateral branch length, first pair of lateral branch length, pod weight, and kernel weight were highest in the CF + MM treatment and lowest in the CF treatment. The maximum plant height was 46.24 cm by the treatment of CF + MM. Similarly, lateral branch length was 47.36 cm in comparison to other treatments. CF, CF + MA, and CF + MF. In the same way, the pod weight of 0.263 kg and the kernel weight of 0.116 kg were also higher by the treatment of CF + MM. However, there were no significant differences between the CF + MA, CF + MF, and CF treatments. The differences in yield between treatments followed the same trend observed for the agronomic traits. Peanut yields were increased by the other three fertilization treatments compared with the CF treatment. The peanut yield of the CF + MM was the highest 6480.53 kg ha −1 . Compared with treatment CF + MM, treatment CF + MM increased plant height, lateral branch length, pod weight, and kernel weight, and yield. The CF, CF + MA, and CF + MF were significantly different due to the gradient of CF + MM (p < 0.05). , illustrated the growth condition under above mentioned fertilizers in the field. The results showing healthy growth with the treatment of CF + MM. Soil enzyme activity under different fertilization regimes It was seen that the enzyme activities of urease, and acid phosphatase were higher during the pod-setting stage than during the blossom stage. Conversely, the activity of catalase demonstrated a decline during the pod-setting stage. Furthermore, the utilization of various fertilizers exhibited a notable impact on the enzymatic activities within the soil during both the blossom stage, and the pod-setting stage. The enzymatic activities of catalase (as depicted in a), urease (as depicted in b), and acid phosphatase (as depicted in c) exhibited a noteworthy increase in the soil samples treated with CF + MA and CF + MM, in comparison to those treated solely with CF, during both the blossom stage, and pod-setting stage. The catalase activity was observed to be 4.08-fold higher in the CF + MM treatment than in the CF treatment at pod-setting. The observed urease activity exhibited a notable increase in the CF + MA treatment when compared to the CF treatment. Specifically, the urease activity increased by 42.39 % and 40.63 %, respectively, at the blossom stage and the pod-setting stage. Acid phosphatase's behavior changed as the plant went from the blossom stage to the pod-setting stage, which was different from what was seen with urease and catalase. There were no statistically significant differences observed among the remaining three treatment groups. During the pod-setting stage, the application of three alternative fertilization treatments resulted in a significant increase in acid phosphatase activity, as compared to the control group (CF). Alpha diversity under different fertilization regimes Good's coverage for the observed bacterial and fungal Operational Taxonomic Units (OTUs) was an impressive 99 %, highlighting the commendable effort made in sampling the community. This high level of coverage signifies that we have come close to capturing the entirety of the microbial diversity present in the sample. The bacterial Chao1, and ACE indexes of the CF + MA treatment were the highest and significantly different from CF, and CF + MF ( ). The fungal Simpson and Shannon indexes were significantly lower in CF + MA, CF + MF, and CF + MM compared to the CF treatment. These were positively correlated with the increase of bacteria gradient (p < 0.05). These results showed that the application of microbial agents could effectively increase the bacterial community richness, and the fungal community diversity was decreased by the application of both microbial fertilizer and microbial agents. Bacterial and fungal community structure analysis under different fertilization regimes a displays the 20 prominent taxa of bacteria, which include: Proteobacteria , Actinobacteria (phylum, and class), Thermoleophilia , Acidobacteria (phylum, and class), Acidobacteriaceae , Gemmatimonadetes (phylum and class), Gemmatimonadales , Gemmatimonadaceae , Chloroflexi , Alphaproteobacteria , β-betaproteobacteria , Rhizobiales , Sphingomonadales , Sphingomonadaceae , Sphingomonas , Burkholderiales and Burkholderiaceae . Similarly b, shows the 20 abundant taxa of fungi which include: Zygomycota , Mortierellales , Mortierellaceae , Mortierella , Ascomycota , Sordariomycetes , Hypocreales , Bionectriaceae , Clonostachys , Eurotiomycetes , Eurotiales , Trichocomaceae , Talaromyces , Penicillium , Basidiomycota , Agaricomycetes , Agaricales , Agaricaceae , Leucoagaricus , and Trechisporales . a displays the community structure at various levels of bacterial phyla. All of the treatments had the same most common phyla, but the proportions of these bacterial phyla differed between the four treatments. The use of microbial fertilizer and microbial agents resulted in an increase in the presence of certain bacterial phyla. These include Actinobacteria , Saccharibacteria , Firmicutes , Armatimonadetes , Planctomycetes , Verrucomicrobia , and Bacteroidetes . b illustrates the composition of the fungal community at the phylum level. The use of microbial fertilizer and microbial agents resulted in a decrease in the presence of certain fungal phyla, including Ascomycota and Zygomycota . Among them, the relative abundance of Actinobacteria in CF + MA and CF + MF treatment was higher than in CF treatment (42.22 % and 49.14 %, respectively; a). The relative abundance of Saccharibacteria in CF + MA treatment was higher than in CF treatment (123.99 %; b). The relative abundance of Firmicutes in CF + MA, CF + MF, and CF + MM treatment were higher than in CF treatment (153.52 %, 108.84 %, and 146.16 %, respectively; c). The relative abundance of Armatimonadetes in CF + MM treatment was higher than in CF treatment (158.18 %; d). However, the relative abundance of some other bacterial phyla decreased by the application of microbial fertilizer and microbial agent, such as Acidobacteria , Chloroflexi , Gemmatimonadetes , Nitrospirae, Parcubacteria, and Latescibacteria . Among them, the relative abundance of Acidobacteria in CF + MA treatment was lower than in CF treatment (40.59 %; e). The relative abundance of Chloroflexi in CF + MA, CF + MF, and CF + MM treatment were lower than in CF treatment (60.66 %, 59.90 % and 52.12 %, respectively; f). The relative abundance of Gemmatimonadetes in CF + MA, CF + MF, and CF + MM treatment were lower than in CF treatment (46.88 %, 34.36 % and 30.04 %, respectively; g). It can be seen that the CF + MA treatment had the most noticeable effect on increasing the relative abundance of some bacterial phyla. At the same time, the relative abundance of other phyla decreased under this treatment. The relative abundance of Ascomycota in CF + MF, and CF + MM treatment were lower than in CF treatment (41.24 % and 53.71 %, respectively; a). The relative abundance of Zygomycota in CF + MA, CF + MF, and CF + MM treatment were lower than in CF treatment (83.03 %, 64.46 %, and 73.12 %, respectively; b). On the contrary, the relative abundance of Basidiomycota in CF + MF and CF + MM treatment were higher than in CF treatment (215.25 %, and 143.60 %, respectively; c). Community composition heat map combined with cluster analysis A comparative analysis was conducted to examine the bacterial and fungal compositions of four distinct soil sample types. To perform this analysis, we focused on the top 50 genera found in each replicate of the four treatments. These genera were then subjected to clustering analysis. The resulting heatmap diagram was utilized to visually represent the relative abundances of these genera. The taxonomic classification of the four soil sample types was determined based on the compositional similarities of the bacterial population. The results indicate that CF, and CF + MA were grouped together, while CF + MF, and CF + MM were separated from the former cluster ( a). The results of the heatmap analysis indicate that the fungi genera have been segregated into two distinct groups. Specifically, the samples comprising CF + MA, CF + MF, and CF + MM have been grouped together and are clearly separated from CF, as illustrated in b. The bacterial compositions across the four treatments exhibited significant variation in terms of the dominant genera, and their respective abundances. The application of CF + MF and CF + MM resulted in the enrichment of the rhizosphere soil with a wide spectrum of microorganisms, including significant genera such as Amycolatopsis , Candidatus-Solibacter , Streptomyces, Bradyrhizobium, Rhizobium, Methylobacterium, Rhizomicrobium, Burkholderia, and various other taxonomic groups. The rhizosphere soil of the CF + MA treatment displayed a predominance of Mucilaginibacter , Jatrophihabitans , Geodermatophilus , Actinospica , Acidiphilium , Chitinophaga , Mizugakiibacter , and Acidicaldus , as illustrated in . The results indicate a decrease in the relative abundance of certain fungal species, including Chaetomium , Mortierella , Pseudogymnoascus , Cryptococcus , Malassezia , Phoma , Stachybotrys , Acremonium , Myrothecium , and Penicillium , in response to the CF + MA, CF + MF, and CF + MM treatments. Conversely, the relative abundance of other fungal species, such as Scolecobasidium , Cladophialophora , Trichoderma , Colletotrichum , Thielavia , Didymella , Conlarium , and Knufia , exhibited an increase following exposure to these treatments ( ). PLS-DA analysis under different fertilization regimes The PLS-DA analysis showed that the soil samples from different treatments were clearly separated into three separate groups. The four experimental interventions were spatially segregated into three discrete quadrants, as visually depicted in a. It was interesting to see how similar the CF + MA and CF + MF samples were to each other in space within the first quadrant. This suggests that the bacterial communities in these samples were very similar to those in the CF and CF + MM samples. The CF + MA and CF + MF treated samples exhibited a pronounced clustering pattern, positioned towards the right of the PLS-DA1 plot and occupying the upper region of the PLS-DA2 plot. The CF molecule is situated within the third quadrant of the coordinate system, while the sample treated with CF + MM is positioned within the fourth quadrant. The fungal clustering trend of the four treatments was similar to that of the bacteria. The only difference was that CF was across the second and third quadrants ( b). These findings suggested that different fertilization regimes could exert a strong effect in structuring microbial communities when various fertilizers were applied. Based on the establishment of the discriminant model, the PLS-DA analysis method was used to calculate the importance coefficient (VIP) of each genus. The higher the VIP value, the greater the contribution of the species to the difference among treatments. Genera with VIP coefficients higher than 1 and relative abundances significantly different among treatments were selected. The top ten key bacterial genera are given in . More specifically, the relative abundance of key genera went up in CF + MA, CF + MF, and CF + MM treatment, mainly Leucobacter , Jatrophihabitans , Mucillaginibacter , Motilibacterk, and Pseudoclavibacter . On the other hand, Variibacter , Bauldia , Parafilimonas , Nordella, and Polycyclovorans were more prevalent in CF. The top ten key fungus genera are given in . Cladosporium was the only fungal genus for which the abundance of CF + MA, and CF + MM treatments increased. The relative abundance of another nine genera including Guehomyces , Glomus , Fusarium , Scleroderma , Pseudogymnoascus , Cryptococcus , Mortierella , Camarosporium, and Pseudozyma was decreased in these three treatments. We also detected that the genera Glomus and Scleroderma were present in CF but not in CF + MA, CF + MF, and CF + MM. 

Different fertilization treatments had an impact on the agronomic characteristics and yield of peanuts ( ). The plant height, lateral branch length, first pair of lateral branch length, pod weight, and kernel weight were highest in the CF + MM treatment and lowest in the CF treatment. The maximum plant height was 46.24 cm by the treatment of CF + MM. Similarly, lateral branch length was 47.36 cm in comparison to other treatments. CF, CF + MA, and CF + MF. In the same way, the pod weight of 0.263 kg and the kernel weight of 0.116 kg were also higher by the treatment of CF + MM. However, there were no significant differences between the CF + MA, CF + MF, and CF treatments. The differences in yield between treatments followed the same trend observed for the agronomic traits. Peanut yields were increased by the other three fertilization treatments compared with the CF treatment. The peanut yield of the CF + MM was the highest 6480.53 kg ha −1 . Compared with treatment CF + MM, treatment CF + MM increased plant height, lateral branch length, pod weight, and kernel weight, and yield. The CF, CF + MA, and CF + MF were significantly different due to the gradient of CF + MM (p < 0.05). , illustrated the growth condition under above mentioned fertilizers in the field. The results showing healthy growth with the treatment of CF + MM. 

It was seen that the enzyme activities of urease, and acid phosphatase were higher during the pod-setting stage than during the blossom stage. Conversely, the activity of catalase demonstrated a decline during the pod-setting stage. Furthermore, the utilization of various fertilizers exhibited a notable impact on the enzymatic activities within the soil during both the blossom stage, and the pod-setting stage. The enzymatic activities of catalase (as depicted in a), urease (as depicted in b), and acid phosphatase (as depicted in c) exhibited a noteworthy increase in the soil samples treated with CF + MA and CF + MM, in comparison to those treated solely with CF, during both the blossom stage, and pod-setting stage. The catalase activity was observed to be 4.08-fold higher in the CF + MM treatment than in the CF treatment at pod-setting. The observed urease activity exhibited a notable increase in the CF + MA treatment when compared to the CF treatment. Specifically, the urease activity increased by 42.39 % and 40.63 %, respectively, at the blossom stage and the pod-setting stage. Acid phosphatase's behavior changed as the plant went from the blossom stage to the pod-setting stage, which was different from what was seen with urease and catalase. There were no statistically significant differences observed among the remaining three treatment groups. During the pod-setting stage, the application of three alternative fertilization treatments resulted in a significant increase in acid phosphatase activity, as compared to the control group (CF). 

Good's coverage for the observed bacterial and fungal Operational Taxonomic Units (OTUs) was an impressive 99 %, highlighting the commendable effort made in sampling the community. This high level of coverage signifies that we have come close to capturing the entirety of the microbial diversity present in the sample. The bacterial Chao1, and ACE indexes of the CF + MA treatment were the highest and significantly different from CF, and CF + MF ( ). The fungal Simpson and Shannon indexes were significantly lower in CF + MA, CF + MF, and CF + MM compared to the CF treatment. These were positively correlated with the increase of bacteria gradient (p < 0.05). These results showed that the application of microbial agents could effectively increase the bacterial community richness, and the fungal community diversity was decreased by the application of both microbial fertilizer and microbial agents. 

a displays the 20 prominent taxa of bacteria, which include: Proteobacteria , Actinobacteria (phylum, and class), Thermoleophilia , Acidobacteria (phylum, and class), Acidobacteriaceae , Gemmatimonadetes (phylum and class), Gemmatimonadales , Gemmatimonadaceae , Chloroflexi , Alphaproteobacteria , β-betaproteobacteria , Rhizobiales , Sphingomonadales , Sphingomonadaceae , Sphingomonas , Burkholderiales and Burkholderiaceae . Similarly b, shows the 20 abundant taxa of fungi which include: Zygomycota , Mortierellales , Mortierellaceae , Mortierella , Ascomycota , Sordariomycetes , Hypocreales , Bionectriaceae , Clonostachys , Eurotiomycetes , Eurotiales , Trichocomaceae , Talaromyces , Penicillium , Basidiomycota , Agaricomycetes , Agaricales , Agaricaceae , Leucoagaricus , and Trechisporales . a displays the community structure at various levels of bacterial phyla. All of the treatments had the same most common phyla, but the proportions of these bacterial phyla differed between the four treatments. The use of microbial fertilizer and microbial agents resulted in an increase in the presence of certain bacterial phyla. These include Actinobacteria , Saccharibacteria , Firmicutes , Armatimonadetes , Planctomycetes , Verrucomicrobia , and Bacteroidetes . b illustrates the composition of the fungal community at the phylum level. The use of microbial fertilizer and microbial agents resulted in a decrease in the presence of certain fungal phyla, including Ascomycota and Zygomycota . Among them, the relative abundance of Actinobacteria in CF + MA and CF + MF treatment was higher than in CF treatment (42.22 % and 49.14 %, respectively; a). The relative abundance of Saccharibacteria in CF + MA treatment was higher than in CF treatment (123.99 %; b). The relative abundance of Firmicutes in CF + MA, CF + MF, and CF + MM treatment were higher than in CF treatment (153.52 %, 108.84 %, and 146.16 %, respectively; c). The relative abundance of Armatimonadetes in CF + MM treatment was higher than in CF treatment (158.18 %; d). However, the relative abundance of some other bacterial phyla decreased by the application of microbial fertilizer and microbial agent, such as Acidobacteria , Chloroflexi , Gemmatimonadetes , Nitrospirae, Parcubacteria, and Latescibacteria . Among them, the relative abundance of Acidobacteria in CF + MA treatment was lower than in CF treatment (40.59 %; e). The relative abundance of Chloroflexi in CF + MA, CF + MF, and CF + MM treatment were lower than in CF treatment (60.66 %, 59.90 % and 52.12 %, respectively; f). The relative abundance of Gemmatimonadetes in CF + MA, CF + MF, and CF + MM treatment were lower than in CF treatment (46.88 %, 34.36 % and 30.04 %, respectively; g). It can be seen that the CF + MA treatment had the most noticeable effect on increasing the relative abundance of some bacterial phyla. At the same time, the relative abundance of other phyla decreased under this treatment. The relative abundance of Ascomycota in CF + MF, and CF + MM treatment were lower than in CF treatment (41.24 % and 53.71 %, respectively; a). The relative abundance of Zygomycota in CF + MA, CF + MF, and CF + MM treatment were lower than in CF treatment (83.03 %, 64.46 %, and 73.12 %, respectively; b). On the contrary, the relative abundance of Basidiomycota in CF + MF and CF + MM treatment were higher than in CF treatment (215.25 %, and 143.60 %, respectively; c). 

A comparative analysis was conducted to examine the bacterial and fungal compositions of four distinct soil sample types. To perform this analysis, we focused on the top 50 genera found in each replicate of the four treatments. These genera were then subjected to clustering analysis. The resulting heatmap diagram was utilized to visually represent the relative abundances of these genera. The taxonomic classification of the four soil sample types was determined based on the compositional similarities of the bacterial population. The results indicate that CF, and CF + MA were grouped together, while CF + MF, and CF + MM were separated from the former cluster ( a). The results of the heatmap analysis indicate that the fungi genera have been segregated into two distinct groups. Specifically, the samples comprising CF + MA, CF + MF, and CF + MM have been grouped together and are clearly separated from CF, as illustrated in b. The bacterial compositions across the four treatments exhibited significant variation in terms of the dominant genera, and their respective abundances. The application of CF + MF and CF + MM resulted in the enrichment of the rhizosphere soil with a wide spectrum of microorganisms, including significant genera such as Amycolatopsis , Candidatus-Solibacter , Streptomyces, Bradyrhizobium, Rhizobium, Methylobacterium, Rhizomicrobium, Burkholderia, and various other taxonomic groups. The rhizosphere soil of the CF + MA treatment displayed a predominance of Mucilaginibacter , Jatrophihabitans , Geodermatophilus , Actinospica , Acidiphilium , Chitinophaga , Mizugakiibacter , and Acidicaldus , as illustrated in . The results indicate a decrease in the relative abundance of certain fungal species, including Chaetomium , Mortierella , Pseudogymnoascus , Cryptococcus , Malassezia , Phoma , Stachybotrys , Acremonium , Myrothecium , and Penicillium , in response to the CF + MA, CF + MF, and CF + MM treatments. Conversely, the relative abundance of other fungal species, such as Scolecobasidium , Cladophialophora , Trichoderma , Colletotrichum , Thielavia , Didymella , Conlarium , and Knufia , exhibited an increase following exposure to these treatments ( ). 

The PLS-DA analysis showed that the soil samples from different treatments were clearly separated into three separate groups. The four experimental interventions were spatially segregated into three discrete quadrants, as visually depicted in a. It was interesting to see how similar the CF + MA and CF + MF samples were to each other in space within the first quadrant. This suggests that the bacterial communities in these samples were very similar to those in the CF and CF + MM samples. The CF + MA and CF + MF treated samples exhibited a pronounced clustering pattern, positioned towards the right of the PLS-DA1 plot and occupying the upper region of the PLS-DA2 plot. The CF molecule is situated within the third quadrant of the coordinate system, while the sample treated with CF + MM is positioned within the fourth quadrant. The fungal clustering trend of the four treatments was similar to that of the bacteria. The only difference was that CF was across the second and third quadrants ( b). These findings suggested that different fertilization regimes could exert a strong effect in structuring microbial communities when various fertilizers were applied. Based on the establishment of the discriminant model, the PLS-DA analysis method was used to calculate the importance coefficient (VIP) of each genus. The higher the VIP value, the greater the contribution of the species to the difference among treatments. Genera with VIP coefficients higher than 1 and relative abundances significantly different among treatments were selected. The top ten key bacterial genera are given in . More specifically, the relative abundance of key genera went up in CF + MA, CF + MF, and CF + MM treatment, mainly Leucobacter , Jatrophihabitans , Mucillaginibacter , Motilibacterk, and Pseudoclavibacter . On the other hand, Variibacter , Bauldia , Parafilimonas , Nordella, and Polycyclovorans were more prevalent in CF. The top ten key fungus genera are given in . Cladosporium was the only fungal genus for which the abundance of CF + MA, and CF + MM treatments increased. The relative abundance of another nine genera including Guehomyces , Glomus , Fusarium , Scleroderma , Pseudogymnoascus , Cryptococcus , Mortierella , Camarosporium, and Pseudozyma was decreased in these three treatments. We also detected that the genera Glomus and Scleroderma were present in CF but not in CF + MA, CF + MF, and CF + MM. 

Soil microbes, as integral components of the agricultural ecosystem, are known to play crucial roles , , . The utilization of fertilization with effective microbes offers a multitude of benefits in sustainable agriculture. By incorporating these microbes, we can harness their dual capabilities as both plant growth regulators and soil conditioners. This approach not only promotes plant growth but also enhances the overall health and quality of the soil. Additionally, it has been observed that the introduction of effective microbes can bring about changes in the structure of the microbial community, further contributing to the sustainable management of agricultural systems , , . Based on the findings of this experiment, it is noteworthy to mention that the CF + MA treatment exhibited a significantly higher bacterial community richness, as indicated by the elevated Chao1 and ACE indices. Following closely behind, the CF + MM treatment also demonstrated a notable level of bacterial community richness. The fungal Simpson and Shannon indexes of the CF treatment exhibited the most favorable results in terms of biodiversity and ecological balance. The findings from the PLS-DA analysis have unveiled notable disparities in the microbial community across different fertilization methods. These disparities have led to discernible variations in the microbial community not only between the treatments and control group but also among the different treatment groups. This finding further highlights the fact that various fertilizers have distinct effects on the composition and structure of the soil microbial community, as previously studied by . It is stated that the application of microbial agents may potentially enhance the abundance, community composition, and functional diversity of microorganisms dwelling in the rhizosphere , . Similarly, the utilization of biological fertilizers has the potential to enhance soil fertility and promote a higher density of soil microorganisms . Sudini et al., 2011 found that Proteobacteria was the most important bacterial phyla in peanut cultivation. In this experiment, we also found that Proteobacteria had a high abundance in the peanut rhizosphere. The relative abundance of Actinobacteria was increased by the application of microbial fertilizer and microbial agents. Streptomyces , an important genus of Actinobacteria, was reported to not only be decomposers of organic matter, but they could inhibit soil-borne pathogens , . One of the most prominent characteristics of Streptomyces is that they can produce a large number of antibiotics that act as effective biological control agents , . In addition to Actinobacteria , the relative abundance of Firmicutes also was increased by the application of microbial fertilizer and microbial agent. Bacilli , which is one of the important classes in Firmicutes participates in many processes in soil. One promising strategy for optimizing the rhizosphere soil ecosystem of plants involves harnessing the unique abilities of specific microorganisms. They can use enzymes to break down and decompose a wide range of harmful substances in the soil, such as phenolic acids, phenols, and other toxic compounds , . This phenomenon is of particular significance as it has the potential to significantly enhance the overall quality of the soil ecosystem. Several species belonging to the genus Bacillus have exhibited remarkable effectiveness in the management of plant diseases. One interesting aspect of sustainable agriculture is the potential impact of Bacillus amyloliquefaciens on the growth and virulence traits of Ralstonia. This is achieved through the production of volatile organic compounds . Bacillus species have been widely recognized for their ability to solubilize phosphorus, as mentioned in the study by . The potential enhancement of these bacteria could potentially contribute to the facilitation of the phosphorus cycle within the rhizosphere soil. The microbial community composition heat map analysis of different fertilization regimes showed that using CF + MF, and CF + MM led to a strong positive response from more than ten different genera of bacteria. Bradyrhizobium , Rhizobium , and Burkholderia were observed as the prevailing bacterial genera in the experimental groups subjected to the application of microbial agents and microbial fertilizer. Research findings have indicated that the utilization of microbial agents and bio-organic fertilizers exhibits notable enhancements in the rhizosphere microenvironment, exerting regulatory effects on bacterial communities in terms of composition and abundance, ultimately fostering the proliferation of beneficial bacterial populations , . In contrast to bacteria, it is worth noting that soil fungal diversity exhibits a higher degree of sensitivity to soil fertility. This increased susceptibility can be attributed to their remarkable abilities in acquiring organic nitrogen and phosphorus, as well as their crucial roles in nutrient cycling within the soil ecosystem , . Furthermore, it is important to acknowledge that fungi serve as vital primary agents in the process of organic matter decomposition. The presence and composition of the fungal community can significantly influence the rate of decomposition and carbon cycling processes . Nevertheless, the examination of how fungi react to different fertilization methods as highly perceptive indicators of soil fertility has unfortunately not been given sufficient consideration. In this comprehensive study, we observed noteworthy variations in the response patterns of soil fungal community structure and diversity across various fertilizer regimes. Using microbial fertilization has been shown to have big impacts on the general distribution and prevalence of fungal populations, including a wide range of phyla and genera. One interesting finding that we observed was the prevalence of members from the Ascomycota, and Basidiomycota phyla in the fungal community of the rhizosphere soil microbiome, which aligns with previous studies conducted on soils supporting peanut, pea, and vanilla crops , . It is important to note that the application of microbial fertilizer, and microbial agents led to a decrease in the relative abundance of certain fungal phyla, including Ascomycota , Zygomycota , Chytridiomycota , and Glomeromycota . Among these, the Ascomycota phylum, which constituted a significant portion of the fungal community in the rhizosphere soil microbiome across all treatments, harbors numerous plant pathogens, such as Fusarium . It can cause fusarium wilt that affects plant growth and results in huge economic losses every year . Through the VIP index analysis, we discovered that Fusarium was one of the important genus that contributed to the difference between treatments. The relative abundance of Fusarium was significantly reduced under the treatment of microbial fertilizer and microbial agents, and reduced by 90.74 % under the CF + MM treatment. Our results corresponded to previous reports, which showed that an application of bio-organic fertilizer could reduce the relative abundances of Fusarium , . Meanwhile, some beneficial fungi (e.g., Trichoderma and Cladophialophora ) were induced by the application of microbial fertilizer and microbial agents. Cladophialophora is a biocontrol agent that can promote plant growth in addition to the well-known beneficial Trichoderma. , revealed that Cladophialophora possessed superior growth promotion activities as well as disease suppression by significantly increasing plant growth parameters (shoot and root dry mass, chlorophyll content, flower bud initiation, and number of fruit) in comparison to control plants. The yields of the CF + MA, CF + MF, and CF + MM fertilization regimes were significantly increased in this experiment. Numerous studies have consistently demonstrated comparable outcomes with respect to the use of microbial fertilizer application , . These findings highlight the promising potential of microbial fertilizers in mitigating the reliance on chemical fertilizers and their associated environmental impacts. In this study, it was observed that the microbial fertilizer or microbial agent treatments exhibited higher levels of soil catalase, urease, and acid phosphatase activity. These findings align with previous research conducted in this field , . 

In conclusion, our findings indicate that different fertilization practices can impact the composition of microorganisms in the rhizosphere soil of peanut plants. This analysis is derived from the integration of data pertaining to crop productivity and the activity of enzymes present in the soil. The observed increase in the prevalence of potentially beneficial bacteria and fungi within the rhizosphere soil environment has the potential to stimulate plant growth and developmental processes. The decrease in the prevalence of pathogenic fungi within the rhizosphere soil has the potential to mitigate peanut diseases, thereby creating a conducive milieu for peanut growth, ultimately resulting in enhanced yield and enhanced soil enzyme activity. In order to keep the soil healthy and encourage long-term plant growth in a continuous peanut cropping system, it is important to use a fertilization plan that includes both microbial agents and microbial fertilizers, either on their own or in combination with compound fertilizers. In the realm of agricultural production, it is of utmost importance to prioritize the preservation and stability of the rhizosphere soil microecology. Furthermore, it is imperative to conduct additional research on long-term microbial fertilization in order to gain a comprehensive understanding of the dynamic shifts occurring within microbial communities and functional genes in regions where peanuts are continuously cultivated. 

Not applicable. 

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. 

