Effect of leguminous green manures on alleviating continuous cropping obstacles in muskmelon cultivation

Cucumis melon, a global economic fruit crop, is primarily classified into two types ingcluding muskmelon (thick-skin) and pellicle melon (thin-skin). China holds a global leadership position in terms of both cultivation area and production output for cucumis melon. These melons are highly favored across national and international markets for their distinctive sweetness and juicy texture. In southern China, successful cultivation of the muskmelon necessitates greenhouse farming to protect the crops from rain, ensuring abundant yields. Such greenhouses typically conduct biannual muskmelon cultivation. However, this practice of long-term continuous cropping, combined with intensive fertilizer use and absence of natural leaching due to rainfall, leads to several soil-related challenges, including acidification, salinization, nutrient and microbial community imbalance, accumulation of pathogens, collectively known as continuous cropping obstacles that resulting in a decrease in crop yield¹. Some scientists have attempted to use biocontrol agents to solve the problem of continuous cropping obstacles in melons, and found that Bacillus subtilis C3 alleviated the continuous cropping obstacles of melon by eliminating phenolic acids and inhibiting the growth of Fusarium (pathogen) and root-knot nematodes, as well as improving the composition and structure of the rhizosphere microbial community². Trichoderma. viride T23 relieved the continuous cropping limitation in muskmelon by improving soil physicochemical properties, elevating the biomass and diversity of soil microbial communities, and stimulating the production of soil active substances¹. However, the use of biocontrol agents is limited by their singular strain, short storage life, and susceptibility to climatic conditions in the field, leading to inconsistent effectiveness and hindering large-scale application. In addition, grafting is another common method to alleviate the continuous cropping obstacles in cucurbit crops, offering the advantages of high efficiency and environmental friendliness. It not only enhances the disease resistance and stress tolerance of plants but also improves nutrient uptake³. Nevertheless, in southern China, there are no rootstock varieties suitable for muskmelon, making it impossible to employ grafting as a solution to the continuous cropping obstacles in muskmelon cultivation.

Some studies have shown that planting green manure, particularly leguminous green manure, can enhance soil fertility, improve soil physicochemical properties, and effectively alleviate the continuous cropping obstacles of some crops such as wheat, potatoes, and cotton, thereby increasing crop yields^4,5,6,7. However, there are relatively few green manure varieties suitable for cultivation in southern China. Whether planting leguminous green manure can alleviate the continuous cropping obstacles in muskmelon cultivation, as well as the mechanisms involved, remains unclear.

Common vetch (Vicia sativa L.) and smooth vetch (Vicia villosa Roth var. glabresens Koch) are two leguminous green manure varieties that are relatively well-suited for cultivation in southern China. Studies have shown that using common vetch as green manure can improve soil fertility, moisture retention, and microbial abundance, thereby increasing potato yields⁸. It has also been demonstrated to effectively enhance the yields of corn and wheat^4,9. Planting common vetch in vineyards reduces soil erosion and improves the soil micro-ecological environment^10,11. Additionally, cultivating common vetch as green manure in tobacco fields effectively suppresses weed growth and boosts tobacco yields¹². In contrast, research on smooth vetch remains limited.

Currently, there are no research reports on the application of common vetch and smooth vetch in muskmelon cultivation. To clarify the effects and micro-ecological mechanisms of these two leguminous green manures in alleviating continuous cropping obstacles in muskmelon, this study involves planting and incorporating common vetch and smooth vetch during the winter fallow period of muskmelon cultivation. The research aims to analyze the impacts of these green manures on muskmelon yield, soil chemical properties (pH, salinity, nutrient content), and soil microbial diversity. By doing so, the study seeks to elucidate the efficacy and micro-ecological mechanisms of these green manures in mitigating continuous cropping obstacles in muskmelon. The research results can provide a theoretical foundation for soil improvement and the sustainable, efficient development of muskmelon in southern China.

Field site description

The study was conducted from November 16, 2023, to June 12, 2024, at the Wumao Farm, Wuming District, Nanning City, Guangxi, located at 23° 38′ N, 108° 22′ E, and 111 m above sea level. This region experiences a subtropical monsoon climate with yearly average temperatures ranging from 20 °C to 27 °C. The experimental greenhouse, measuring 30 m in length, 6 m in span, with sidewall height of 1.8 m and a central height of 3.0 m, had been consistently used for cultivating muskmelon over nine years, accounting for 18 growing cycles since autumn 2014. The cultivation soil was enriched with various decomposed organic materials, including tree bark, sugarcane bagasse, cassava residue, and poultry manure. Soil analysis conducted before the experiment revealed a pH of 5.96, an electrical conductivity (EC) of 1.75 ms/cm (indicating water-soluble salt concentration), organic matter at 219.2 g/kg, total salts at 3.66 g/kg, total nitrogen at 11.1 g/kg, total phosphorus at 13.0 g/kg, total potassium at 7.89 g/kg, available nitrogen at 1.16 g/kg, available phosphorus at 0.14 g/kg, and available potassium at 2.2 g/kg.

Experimental design

The experiment employed a randomized block design, and the data statistical model utilized analysis of variance (ANOVA). The design included two treatments: common vetch (T1), and smooth vetch (T2), and a control group (CK) with no green manure. The common vetch and smooth vetch used in this experiment are cultivated varieties. The Agricultural Resources and Environmental Research Institute, Guangxi Academy of Agricultural Sciences provided the green manure seeds and approved the experimental protocols. Each treatment was replicated across three blocks, each being a single-span greenhouse measuring 30 m by 6 m, totaling an area of 180 m² per block. Post the autumn muskmelon harvest in 2023, green manures were sown on October 20 at a rate of 7.5 g/m². By December 28, the green manures, having reached about 70 cm, were harvested and chopped into 15–20 cm segments. These were evenly distributed over the soil surface, then plowed under and irrigated to maintain approximately 80% soil moisture. The soil was then covered with a white polyethylene film with a thickness of 0.02 mm for 68 days to aid in the fermentation and decomposition of the green manures. The muskmelon variety ‘Huang Meng Cui’ was sown on February 10, 2024, and transplanted to the experiment greenhouses on March 8, 2024. Each block consisted of 300 plants. Identical irrigation and fertilization regimes were maintained across all treatments, culminating in the harvest of the muskmelon on June 12, 2024.

Measurement of muskmelon yield and soluble solids content

On harvest day, the total yield from each treatment plot was weighed to determine the yield per hectare. Additionally, the soluble solids content at the center of the muskmelon was tested using a digital refractometer. For this analysis, 15 muskmelons were randomly selected from each plot to measure and calculate the average soluble solids content.

Soil sampling

Post-harvest, soil from each treatment area was thoroughly mixed and leveled. Samples were collected from the top 20 cm of soil using a five-point sampling method. Each plot’s sample weighted 1 kg, and was divided into two portions: one (0.95 kg) sealed in plastic bags for chemical property analysis (including pH, salinity, and nutrient content) and the other stored in 50 mL centrifuge tubes, instantly frozen in liquid nitrogen, then preserved at −80 °C for microbial diversity analysis (focusing on fungi and bacteria).

Assessment of soil chemical properties

The chemical properties of the soil used for muskmelon cultivation were evaluated using methods adapted from Ding¹³ and Shen¹⁴. The soil pH was determined potentiometrically. EC, reflecting the concentration of water-soluble salts in the soil, was measured using conductivity testing, and the total salt content was quantified using the gravimetric method. To assess soil fertility, organic matter content was determined by the potassium dichromate volumetric method in an oil bath. Nutrient levels were also thoroughly examined: total nitrogen content and available nitrogen content were analyzed using the Kjeldahl method and alkaline diffusion method, respectively, while total phosphorus and available phosphorus were measured via sodium hydroxide fusion and hydrochloric acid-sulfuric acid extraction methods. Lastly, total potassium and available potassium were quantified through acid dissolution and flame photometry, respectively.

Extraction of total microbial DNA from soil and high-throughput sequencing

Total microbial DNA was extracted from the cultivated soil samples using the HiPure Soil DNA Extraction Kit (Magen, Guangzhou, China). DNA concentration and purity were assessed with a NanoDrop 2000 UV-Vis spectrophotometer (Thermo Scientific, Wilmington, USA), and DNA integrity was verified through 1% agarose gel electrophoresis. For the amplification of bacterial DNA, primers 341 F (5’-CCTACGGGNGGCWGCAG-3’) and 806R (5’-GGACTACHVGGGTWTCTAAT-3’) were used to target the V3-V4 hypervariable regions¹⁵, and the fungal internal transcribed spacer (ITS) ITS2 region was PCR-amplified using primers ITS3F (GATGAAGAACGYAGYRAA) and ITS4R (TCCTCCGCTTATTGATATGC)¹⁶. The PCR protocol included an initial denaturation at 95 °C for 5 min, followed by 30 or 35 cycles (for bacteria and fungi, respectively) of denaturation at 95 °C for 1 min, annealing at 60 °C for 1 min, and extension at 72 °C for 1 min, ending with a final extension at 72 °C for 7 min. The PCR products were purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) and quantified with the ABI StepOnePlus Real-Time PCR System (Life Technologies, Foster City, USA). Sequencing libraries were prepared and subjected to paired-end sequencing on the Illumina HiSeq 2500 PE 250 platform.

Data analysis

Analysis of variance (ANOVA)

Differences between data sets were statistically analyzed using SPSS v.20. A one-way ANOVA was performed at a significance level of P = 0.05 to assess the variability.

High-throughput sequencing data analysis for microbial 16 S rRNA and ITS gene amplicons

The raw sequencing data obtained from the Illumina platform were first processed using FASTP (version 0.18.0) to filter out low-quality reads. The resulting high-quality reads, known as ‘clean reads,’ were then merged into longer sequences or ‘tags’ using the FLASH software (version 1.2.11). These tags were subjected to further quality control by removing low-quality segments, thus obtaining ‘clean tags.’ The clean tags were clustered into Operational Taxonomic Units (OTUs) at a similarity threshold of 97% using the UPARSE algorithm (version 9.2.64). To ensure data accuracy, the UCHIME algorithm was employed to remove any chimeric sequences from these tags, resulting in what are termed ‘effective tags.’ For species identification, the representative sequences of bacterial OTUs were compared with the SILVA database (version 132), and fungal OTUs were compared with the UNITE database (version 8.0). The classification of these sequences was then refined using the RDP classifier (version 2.2) with the Naive Bayesian model, with a confidence threshold set between 0.8 and 1, to provide detailed annotations of the microbial species present in each sample.

In this study, a comprehensive suite of advanced statistical and bio-informatics tools was employed to evaluate soil microbial diversity and community composition, thereby providing an in-depth understanding crucial for sustainable agricultural practices. Microbial diversity indices, including Sobs, Chao1, Simpson, and Shannon, were calculated using QIIME software (version 1.9.1)¹⁷. These indices quantitatively assess the microbial richness and diversity within the soil samples. Principal Component Analysis (PCA) was conducted using the ‘vegan’ package (version 2.5.3; Oksanen et al. 2010) in R language to analyze and visually represent the compositional differences in microbial communities based on Operational Taxonomic Units (OTUs). The ‘VennDiagram’ package in R language¹⁸ was used to construct Venn diagrams, elucidating common or unique OTUs across treatments. For the analysis of microbial species composition, the ‘ggplot2’ package (version 2.2.1)¹⁹ in R was utilized to create abundance stack plots. Biomarker analysis involved using the ‘vegan’ package in R for Tukey’s HSD test and Kruskal-Wallis H test to evaluate species abundance differences between treatments, followed by Linear discriminant analysis Effect Size (LEfSe) software (version 1.0) for analyzing biomarkers (LDA > 2; P < 0.05) and generating phylogenetic trees¹³. Finally, Redundancy Analysis (RDA) was performed using the ‘vegan’ package (version 2.5.3)²⁰in R to determine the impact of environmental factors on the microbial community composition.

Impact of different treatments on yield and soluble solids content in muskmelon

The impact of planting and incorporating different green manures on the yield and soluble solids content in the center of muskmelon is summarized in Table 1. The treatments involving the incorporation of common vetch (T1) and smooth vetch (T2) significantly increased the yield of muskmelon compared with the control group (CK). Specifically, the yields were 43,785 kg/ha for T1 and 43,110 kg/ha for T2, representing increases of 13.71% and 10.68% over the control, respectively. Additionally, the soluble solids content in the center of muskmelon was slightly higher in T1 and T2 treatments compared with that in CK treatment, although these differences were not statistically significant.

Impact of different treatments on soil chemical properties

As indicated in Table 2, the treatments with common vetch (T1) and smooth vetch (T2) significantly enhanced the pH and organic matter content of the cultivated soil. Specifically, T1 and T2 treatments increased soil pH by 7.9% and 6.4%, respectively, and raised organic matter content by 11.3% and 10.6%, respectively. Furthermore, there was a notable reduction in the soil’s EC values and total salt content; EC values decreased by 51.1% in T1 and 48.9% in T2, while total salt content decreased by 34.1% and 35.7%, respectively. The T1 treatment also resulted in increases in total nitrogen, available nitrogen, total phosphorus, and available phosphorus levels in the soil, although these increases did not reach a level of statistical significance. However, there was a significant reduction in the content of total potassium and available potassium, with the available potassium content decreasing by 35.0%. T2 treatment showed similar trends, increasing total nitrogen and phosphorus while decreasing total and available potassium, although these changes were not significantly different from the control group. Between the two green manure treatments, no significant differences were observed in parameters other than available potassium.

Soil microbial genome sequence information

High-throughput sequencing of the 16 S rRNA and ITS gene sequences from nine soil samples across the three treatments, following chimera filtering and quality control, yielded 734,250 high-quality 16 S rRNA gene sequences and 711,827 ITS gene sequences (effective tags), which were used for further community analysis. Clustering at 97% similarity resulted in the identification of 29,207 bacterial and 2,844 fungal OTUs. The average Good’s coverage for bacteria was 98.1% and for fungi 99.9%, indicating adequate sequencing depth that likely covered the majority of species present in the samples. Importantly, both the bacterial and fungal rarefaction curves gradually flattened, suggesting that most microbial species in the samples were captured and the sequencing depth was sufficient.

Analysis of microbial diversity indices under different treatments

Alpha (α) diversity analysis provides insights into the abundance and diversity of microbial communities. The Sobs and Chao1 indices, indicative of community abundance, suggested greater species abundance in T1 and T2 treatments compared to those in CK treatment. Simpson and Shannon indices, reflecting community diversity, indicated increased diversity in these treatments. Table 3 shows that both T1 and T2 treatments significantly enhanced the abundance of bacteria and fungi in the cultivated soil. There was a notable increase in bacterial community diversity and a slight but non-significant increase in fungal diversity under these treatments. The abundance and diversity of both bacteria and fungi in T1 treatment were slightly higher than those in T2 treatment, although these differences were not statistically significant.

PCA of microbial communities under different treatments

PCA was employed to elucidate the compositional similarities and disparities among the microbial communities under different treatments based on the abundance of OTUs. This analysis provides an insightful depiction of the spatial relationships between the samples in a multidimensional space, where proximity indicates compositional similarity. The PCA revealed distinct variations in the bacterial and fungal community structures across the treatments. Specifically, both T1 and T2 treatments exhibited bacterial community compositions that were significantly divergent from CK treatment. However, the difference between T1 and T2 bacterial communities was relatively minor (Fig. 1a). In terms of fungal community compositions, a pronounced difference was observed between T1 and T2 treatments, while the differences between T1 and CK, and T2 and CK, were comparatively marginal (Fig. 1b).

Analysis of shared and unique OTUs across different treatments

The distribution of shared and unique OTUs among the treatments was analyzed using Venn diagrams, offering a visual representation of the overlap and exclusivity of microbial communities. The bacterial community analysis (Fig. 2a) indicated a larger number of unique OTUs in T1 treatment, amounting to 868 OTUs, with 2237 OTUs shared with the control group (CK). Conversely, T2 treatment manifested a lower count of unique bacterial OTUs (499), but a larger number of shared OTUs with CK (2338). This pattern suggests that T1 treatment exerts a more substantial influence on the bacterial community than T2 treatment. Fungal community analysis (Fig. 2b) revealed that the T1 treatment harbored a larger number of unique fungal OTUs (207), with 113 OTUs shared with CK. In contrast, T2 treatment had fewer unique fungal OTUs (109), though it shared more OTUs with CK (136), indicating a more pronounced impact of T1 treatment on the fungal community as well.

Comparative analysis of microbial community composition across treatments

The relative abundance of bacteria across treatments was evaluated at various taxonomic levels—phylum, class, and genus (Fig. 3a–c). At the phylum level, the top ten bacterial groups, in descending order of abundance, were Chloroflexi, Proteobacteria, Planctomycetes, Gemmatimonadetes, Acidobacteria, Actinobacteria, Patescibacteria, Rokubacteria, Bacteroidetes, and Verrucomicrobia, constituting 96.65–97.65% of the total bacterial sequences in each treatment. Notably, T1 and T2 treatments enhanced the relative abundance of Planctomycetes, Acidobacteria, Patescibacteria, and Rokubacteria, and reduced that of Chloroflexi, Proteobacteria, Gemmatimonadetes, and Actinobacteria (Fig. 3a). At the class level, the top ten bacterial taxa, arranged from highest to lowest abundance, were Gemmatimonadetes, Planctomycetacia, Alphaproteobacteria, Subgroup_6, KD4-96, Phycisphaerae, Gammaproteobacteria, Gitt-GS-136, Acidimicrobiia, and Chloroflexia. Compared with CK treatment, T1 and T2 treatments significantly favored the abundance of Planctomycetacia, Subgroup_6, and Phycisphaerae, with T1 additionally promoting Gammaproteobacteria and T2 fostering Acidimicrobiia. Notably, CK showed higher relative abundance of Gemmatimonadetes and Alphaproteobacteria (Fig. 3b). At the genus level, Gaiella, Sphingomonas, Pirellula, Gemmata, and RB41 ranked top five, with each having a relative abundance of over 1%. T1 and T2 treatments notably increased the abundance of Pirellula, Gemmata, and SH-PL14, with T1 also enhancing RB41. CK exhibited higher relative abundance of Gaiella and Sphingomonas (Fig. 3c).

The relative abundance of fungus in each treatment was assessed at the phylum, class, and genus levels (Fig. 3d–f). Fungal communities at the phylum level were dominated by Ascomycota, Mortierellomycota, Basidiomycota, and Chytridiomycota. Ascomycota was the most abundant, constituting 72.84–81.38% of the total fungal sequences. As indicated in Fig. 3d, T1 and T2 treatments significantly increased the abundance of Mortierellomycota and Basidiomycota. Furthermore, T2 treatment also significantly enhanced the relative abundance of Chytridiomycota. At the class level, Sordariomycetes, Eurotiomycetes, Pezizomycetes, Dothideomycetes, Agaricomycetes, and Saccharomycetes showed relative abundance of over 1%. Sordariomycetes had the highest relative abundance, accounting for 46.74–48.80% of the total sequences across treatments. As depicted in Fig. 3e, compared with CK treatment, T1 treatment enhanced the relative abundance of Dothideomycetes Agaricomycetes, and Saccharomycetes, and T2 also favored Agaricomycetes. The relative abundance of Sordariomycetes, Eurotiomycetes, and Pezizomycetes decreased in T1 and T2 treatments. The genus-level analysis revealed Scopulariopsis, Penicillium, Aspergillus, Chaetomium, Myceliophthora, Fusarium, Talaromyces, Mycothermus, and Scedosporium as dominant, each exceeding 1% relative abundance. Compared with CK treatment, T1 treatment enhanced Scopulariopsis, Penicillium, Chaetomium, Myceliophthora, Talaromyces, and Scedosporium, while T2 favored Myceliophthora, Talaromyces, and Scedosporium. Notably, the relative abundance of pathogenic fungi like Aspergillus and Fusarium displayed a significant decrease in both the T1 and T2 treatments (Fig. 3f).

Comparative assessment of microbial biomarkers

LEfSe analysis identified unique biomarker species that were significantly different among treatments. For bacteria, five species, including 67_14, AKYG587, and Sandaracinaceae were predominantly associated with T1 treatment, whereas Defluviicoccus and Rhodopirillaceae were more abundant in CK treatment (Fig. 4a).

In the case of fungi, Mycosphaerella, Mycosphaerella etlingerae, Trichoderma, Exobasidiaceae, Exobasidiales and Exobasidium were significantly more abundant in T1 treatment (Fig. 4b).

Impact of environmental factors on soil microbial community composition

RDA was employed to elucidate the influence of environmental variables on the composition of microbial communities within the soil. This investigation was centered on the top ten most abundant bacterial and fungal genera, in relation to their correlation with specific environmental factors. The analysis depicted in Fig. 5a revealed significant relationships between these bacteria and various soil parameters. The RDA plot for bacterial relative abundance (Fig. 5a) demonstrated that the first and second axes accounted for 53.79% and 28.87% of the total variance, respectively. Soil pH, organic matter content, and muskmelon yield were aligned in the same direction, showing significant positive correlations with Pirellula, Gemmata, and SH-PL14, that primarily found in T1 and T2 treatments. This correlation indicates that an increase in soil pH and organic matter content is associated with a higher relative abundance of these bacterial genera, which, in turn, positively influences muskmelon yield. Conversely, EC, total salt, and available potassium content were positively correlated with Marmoricola, Aeromicrobium, and Gaiella in CK treatment, inversely impacting the yield. In addition, the available nitrogen and phosphorus content showed a significant positive correlation with the genera MND1 and Sphingomonas.

The fungal community composition, as depicted in the RDA plot (Fig. 5b), showed that the first and second axes explained 38.62% and 26.33% of the variation, respectively. Soil pH, organic matter content, and yield were aligned in the same direction, displayed a positive correlation with Myceliophthora and Talaromyces in T1 and T2 treatments. This finding indicates that higher levels of soil pH and organic matter are conducive to the proliferation of these fungal genera, which are beneficial for enhancing muskmelon yield. In contrast, EC, total salt, and available potassium content showed a significant positive correlation with Fusarium, Scedosporium, Mycothermus, and Chaetomium in CK treatment. These fungi, negatively correlated with yield, suggest that increased soil salinity and potassium levels may favor fungal species detrimental to muskmelon yield. Additionally, a significant positive correlation was observed between EC and Aspergillus in CK, negatively impacting muskmelon yield, thereby implicating Aspergillus as a potential yield-reducing factor. Moreover, available nitrogen and phosphorus were found to be significantly positively correlated with Penicillium and Cladosporium.

The increasing severity of continuous cropping obstacles in muskmelon cultivation necessitates sustainable solutions. While leguminous green manures have proven effective in alleviating such obstacles for crops like wheat and potato, their efficacy in muskmelon systems, particularly in southern China, remained unexplored. Our study provides the first mechanistic evidence that leguminous green manures alleviate continuous cropping obstacles in muskmelon by synergistically improving soil chemical properties and restructuring microbial communities. The observed yield increases align with earlier findings in wheat and potato systems^4,5, but extend these benefits to a greenhouse muskmelon context where salinity and pathogen pressure are exacerbated by intensive cultivation. Critically, both vetches elevated soil pH and organic matter—key drivers of microbial recruitment—while suppressing salinity (EC, total salt), contrasting with biocontrol approaches like Trichoderma viride T23, which primarily targets microbial restructuring without addressing soil chemistry¹. This dual action underscores the advantage of green manures in tackling multi-faceted degradation.

Both green manures improved soil pH and organic matter while reducing salinity (EC, total salts), aligning with findings in wheat and potato systems^4,5. Notably, the pH elevation counteracts soil acidification common in southern China, while increased organic matter enhances nutrient retention. Though nitrogen and phosphorus levels showed non-significant gains, repeated green manure applications may amplify these effects, as observed in multi-year studies^5,21. The significant potassium reduction, particularly under common vetch, warrants further investigation but likely reflects crop-specific nutrient competition.

Both green manures can significantly increase the abundance of bacterial and fungal species and the diversity of bacterial communities in the soil. The results of microbial community composition analysis indicate that both green manures can recruit bacteria such as Pirellula, Gemata, SH-PL14, and Subgroup-6. In addition, common vetch can also recruit RB41. Among these bacteria, Pirellula, Gemmata, and SH-PL14 belong to the Planctomycetes phylum, which is a common and important bacterium for both the environment and biotechnology. They are key participants in the global carbon and nitrogen cycle²². Subgroup-6 belongs to the Bathyrachaeota phylum and plays important roles in phototroph, autotrophy, as well as nitrogen and sulfur cycling²³. RB41 belongs to Acidobacteria phylum that can enhance carbon and nitrogen cycling and phosphorus absorption in the rhizosphere environment. It is significantly correlated with soil pH and promoted crop growth²⁴. Furthermore, both green manures can recruit some fungi such as Myceliophthora, Talaromyces, and Scedosporium. Specifically, common vetch additionally recruited fungal genera including Scopulariopsis, Chaetomium, and Penicillium. Among these fungi, four genera—Myceliophthora, Talaromyces, Chaetomium, and Penicillium—are recognized as beneficial fungi. Myceliophthora, a thermophilic fungus, has been extensively studied and applied due to its ability to produce thermostable enzymes such as xylanases, heat-stable proteases, amylases, chitinases, and cellulolytic enzymes^25,26. Talaromyces, another thermophilic fungal genus, synthesizes nematode-antagonistic macrocyclic lactones²⁷ and produces bioactive compounds with efficacy against human pathogens including Plasmodium (malaria parasite), tumor cells, and Staphylococcus aureus^28,29. Chaetomium enhances plant disease resistance through antimicrobial metabolite production and has been widely utilized in phytopathology research for plant protection^30,31. Penicillium plays critical ecological roles in environmental processes such as nutrient cycling and pollutant degradation³². Thus it can be seen that both green manures can recruit beneficial bacteria and fungi that may be increase the yield of muskmelon.

On the contrary, both green manure treatments substantially diminished the relative abundance of fungi Fusarium. This fungi is a genus implicated in significant soil-borne diseases affecting muskmelon, including wilt and root rot^33,34. This reduction suggests a potential mitigation of soil-borne disease risks in muskmelon cultivation.

Our RDA at the genus level assessed the impact of environmental factors on the bacterial and fungal communities ranking in the top ten for abundance. The interplay between soil parameters and microbial taxa highlights a dual mechanism: green manures improve soil chemistry, fostering beneficial microbes while suppressing pathogens. This aligns with Ding¹³, who emphasized green manure-induced microbial restructuring. However, our study uniquely identifies Pirellula, Gemmata, Myceliophthora and Talaromyces as key yield-linked taxa in muskmelon systems, advancing the understanding of crop-specific microbial drivers.

Both leguminous green manures can alleviate continuous cropping obstacles and increase muskmelon yield. The micro-ecological mechanism is to enhance soil pH, organic matter content, and reduce salinity indicators (EC value, total salt content), thereby indirectly increasing the microbial diversity and abundance of beneficial microorganisms, and diminishing the relative abundance of harmful microorganisms.