Experimental design.

The experimental site was located in Liupan Mountain, Ningxia, China (long. 35°41′N, lat. 106°32′E). The region is characterized by an average temperature ranging from 7.4 to 8.5 °C, a frost-free period lasting 140 to 170 d, and annual precipitation of 350 to 550 mm. Pepper and oat seeds were provided by our laboratory. The test site represented pepper soil subjected to 12 years of continuous cropping that exhibited a pH of 8.52 and a total nitrogen content of 4.7 (g/kg), total phosphorus content of 1.8 (g/kg), total potassium content of 14.5 (g/kg), organic matter content 24.7 (g/kg), quick-acting phosphorus content 45 (mg/kg), and bulk density of 1.03 g/cm³. Biochar, which was sourced from corn straw (preparation temperature 450 °C; supplied by Shenyang Longtai Bioengineering Co., Liaoning, China), had a pH of 8.43, conductivity of 1.27 ms/cm, NH₄⁺-N of 8.61 mg/kg, NO₃⁻-N of 38.82 mg/kg, available phosphorus of 106.12 mg/kg, available potassium of 3540.00 mg/kg, and a bulk weight of 0.35 g/cm³.

The experiment was conducted from Oct 2022 to Apr 2023. The experimental soil was placed in 50-cm × 29-cm pots. Pepper seeds were sown in germination trays and transplanted (one per pot) at the four-leaf stage. Based on previous research methods (Gao and Zhang 2023), germination rates, and emergence times, oat seeds were sown in a semi-circular area 5 d after transplanting of the pepper seedlings ∼5 cm away from them. All treatments were randomly assigned and placed in a greenhouse under standard management. No chemical fertilizers were applied during the experimental period; regular watering and manual weeding were performed as needed. During tests with added biochar, 2% biochar (weight/weight) was thoroughly mixed with the soil of continuously cropped peppers in a mass ratio and subsequently divided into 50-cm × 29-cm pots. The experimental design included pepper monoculture and oat intercropping, resulting in the following four treatments: oat + biochar (T1); biochar amendment (T2); oat intercropping (T3); and pepper monoculture (CK). Each treatment group consisted of four biological replicates.

With the CK treatment, two pepper seedlings were planted per pot; however, with the cocultivation treatment, two pepper seedlings were planted per pot ∼15 d after initial pepper growth. Then, 10 oat seedlings were planted around the pepper plants and positioned ∼5 cm apart. Furthermore, all treatments groups were divided into the following three developmental stages: 25 d (S1; seedling stage), 35 d (S2; flowering stage), and 45 d (S3; fruiting stage).

Sample preparation.

The root soil of peppers was collected from the four treatment groups at S1, S2, and S3 using the peeling separation method (Gao and Zhang 2023). Additionally, soils from both inter-root and noninter-root areas were collected from the 10- to 15-cm root system of the pepper plants. Soil samples from the same treatment were mixed and divided into two parts that were then stored at 4 °C and −80 °C until use to determine soil physicochemical properties and soil DNA extraction, respectively. The soil samples were rapidly frozen in liquid nitrogen and transferred to the laboratory, where they were stored at −80 °C for subsequent microbiota analyses.

These soil samples were sieved through a 2-mm mesh and placed in 5-mL centrifuge tubes for storage at 4 °C for the analysis of physicochemical properties. Finally, the fresh weights of various plant parts were determined 30 d after transplanting the pepper seedlings in 2023.

DNA extraction, amplification, and sequencing.

Extraction of microbial DNA from soil microbes in the rhizosphere of pepper plants was performed using a HiPure Soil DNA Extraction Kit (Magen, Guangzhou, China) according to manufacturer’s instructions. To amplify the V3-V4 region of the highly variable region of the 16S rRNA gene, the bacterial universal primers 314F (5′-CCTACGGGGNGGCWGCAG-3′) and 806R (5′-GGACTACHVGGGG-TATCTAAT-3′) were used. For amplification of the ITS2 rDNA region, the specific primers ITS3-KYO2 (5′-GATGAAGAACGYA-GYRAA-3′) and ITS4 (5′-TCCTCCGCTTATTGA-TATGC-3′) with barcodes were applied (Huang et al. 2022a).

The 25-μL polymerase chain reaction (PCR) reaction system included 100 ng of DNA template, 5 μL of PCR buffer (Mg²+plus), 200 μmol/L of dNTPs, 200 nmol/L of primers, and 0.2 μL of KOD polymerase (Toyobo, Osaka, Japan). The PCR cycling procedure was performed using a denaturation step at 95 °C for 5 min, followed by 32 cycles of 95 °C for 5 s, 50 °C for 45 s, 72 °C for 90 s, and, finally, an extension step at 72 °C for 10 min. The PCR-related reagents were purchased from Toyobo (Osaka, Japan). Total DNA was extracted from 2% agarose gels for both sets of treated samples and purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) according to manufacturer’s instructions. The PCR products were quantified using a QuantiFluor Fluorometer (Promega Biotech, Madison, WI, USA). The DNA concentration and purity (A260/A280 1.8–2.0) were determined using a Nanodrop 2000 (Thermo Fisher Scientific, Waltham, MA, USA). Subsequently, purified PCR products were sequenced on an HiSeq 2500 platform (2 × 50 double-ended; Illumina, San Diego, CA, USA) at Guangzhou Gene Denovo Biotechnology Co., Ltd. (Guangzhou, China). Raw data were filtered and quality-controlled using the Illumina platform to validate the reliability of the raw sequencing data and remove contig sequences containing consecutive low-quality bases of 5 < Q < 15 bp, as well as sequencing information (Huang et al. 2022b). We used Flash (version 1.2.11; Adobe, San Jose, CA, USA) for quality-filtering and sequence-splicing to prepare raw FASTQ files for labeling purposes. We used QIIME (version 1.9.1, USA) to filter low-quality tags to obtain high-quality clean tags, which were further filtered using UPARSE software (version 9.2.64; Robert Edgar, Developer, Canada) and clustered into operational taxonomic units (OTUs) with at least 97% similarity. Sequences of the most abundant tags were selected as representative sequences for each OTU, which were then compared with the UNITE database (version 8.0). Additionally, RDP annotation software (version 1.9.1; Ribosomal Database Project, Michigan State University, East Lansing, MI, USA) was used for taxonomic annotation of species with a confidence threshold of 1.0.

Determination of soil physicochemical properties.

Fresh soil samples were used to determine the soil pH, soil ammonium nitrogen (NH₄⁺-N) content, and nitrate nitrogen (NO₃⁻-N) content. Moreover, based on air-dried soils, the levels of soil electrical conductivity (SEC), total organic matter (OM), total phosphorus, available phosphorus, and available kalium were analyzed. Soil pH was determined using the pH meter method, which involved homogenizing soil samples through a 1-mm sieve with a soil–water mass volume ratio of 1:2.5 (Liu et al. 2023). The NO₃⁻-N contents were assessed using ultraviolet spectrophotometry, whereas the indophenol blue method was used to determine the NH₄⁺-N values (Liu et al. 2023). The available phosphorus content was determined using molybdenum–antimony colorimetry through 0.5 M NaHCO₃ leaching. In turn, the soil OM content was determined by using the potassium dichromate external heating method. The SEC was determined by utilizing a standard curve of conductivity, which was employed to compare the concentration of the potassium chloride solution at room temperature (Liu et al. 2021). The total phosphorus content was measured using molybdenum–antimony inverse colorimetry. Finally, the available kalium was extracted using ammonium acetate and subsequently determined by flame photometry (Pang et al. 2021).

Determination of fruit quality and biomass.

The soluble protein contents of peppers at S3 were assessed using Coomassie Brilliant Blue G-250 staining. A digital refractometer (DR201–95; Kruss, Hamburg, Germany) was used to measure and evaluate the soluble solids content in the fruits (Valsikova-Frey et al. 2022). The soluble sugar concentration was determined using the anthrone method, whereas the β-carotene contents in fruits were assessed using a previously described method (Duah et al. 2021). To quantify the ascorbic acid content, a spectrophotometric method was used (Martinez-Ispizua et al. 2021) after slight modifications. The absorption of the solution was measured at 520 nm using a spectrophotometer (Uvikon XS; Bio-Tek, Winooski, VT, USA), and the concentration of ascorbic acid was expressed as milligrams per gram of fresh weight. Finally, 10 plants were randomly selected from each treatment group, and the fresh weights of the roots, shoots, and leaves were measured. Each treatment group included four replicates.

Bioinformatics analysis of the microbial community.

The Shannon and Chao1 indices for biodiversity assessment were calculated using QIIME software (version 1.9.1) (Liu and Howell 2021). Additionally, the R software ggplot2 package (version 2.2.1) was used to visualize rarefaction curves of the diversity indices. The “Venn Diagram” package and “UpSetR” package in R software (version 3.5.0) were used to explore the overlap of OTUs shared by bacterial and fungal communities in the inter-root zone of pepper plants grown under the different treatments. Subsequently, Tukey’s honestly significant difference (P < 0.05) test was conducted using the Vegan package in R software (version 2.5.3) to assess the differences in the alpha diversity index among treatments (Huang et al. 2022a). Additionally, using R software (version 2.5.3), a principal coordinate analysis was performed to estimate the distribution pattern of inter-root microbial communities and structural differences among different pepper cultivation systems (Huang et al. 2022a). To determine whether sample classifications (stage, cultivation type) contained differences in species diversity, ANOSIM and permutational multivariate analysis of variance (ANOVA) with 999 permutations were used to test significant differences between sample groups based on weighted UniFrac and Bray-Curtis distance matrices using the Vegan package (R software version 2.5.3) (Huang et al. 2022a; Liu and Howell et al. 2021).

The Silva (release 115; https://www.arb-silva.de) and Unite (https://unite.ut.ee/) databases were used to compare and analyze annotation information of bacteria and fungi at the phylum and genus levels (Huang et al. 2022a). To explore potential correlations between the core community composition and physicochemical properties of the soil, a redundancy analysis and Spearman correlation analysis (|r| > 0.5) were performed using the Vegan package in R software (version 2.5.3). Additionally, PICRUSt software (version 2.1.4) was used to predict Kyoto Encyclopedia of Genes and Genomes metabolic pathways in the microbial communities (Wang et al. 2023).

Statistical analysis.

An orthogonal partial least squares discriminant analysis was performed using the “ropls” package in R software (version 2.5.3) to screen and predict key soil–physicochemical parameters under the cultivation types tested (Huang et al. 2022b). Soil physicochemical properties, fruit quality, and biomass differences among treatment groups were analyzed using an ANOVA and SPSS software (version 17.0; SPSS, Chicago, IL, USA), with a significance level of P < 0.05. The data reported are means ± SDs, and graphs were created using GraphPad Prism software (version 8.0; GraphPad Inc., Boston, MA, USA).

Effects of oats and biochar on the quality and biomass of peppers.

To evaluate the effects of the oats–pepper intercropping/biochar treatment combination on pepper fruit quality, five relevant fruit quality parameters were assessed at the S3 stage. The results indicated that the T1 and T3 treatments significantly reduced soluble protein in fruits by 27.66% and 36.88% (P < 0.05), respectively, compared with the CK treatment (Table 1). In turn, the soluble solids contents significantly increased by 33.35% and 16.39% in treatments T2 and T3, respectively. Although T1 increased the fruit soluble solids content, the difference with regard to the control treatment was not statistically significant (P < 0.05) (Table 1). Furthermore, compared with CK, both T1 and T2 significantly increased the ascorbic acid content by 13.52% and 13.15%, respectively. Notably, the highest soluble sugar content was observed in plants grown under T3, whereas T1 significantly reduced the soluble sugar content in fruits by 6.54%. Additionally, all three treatments (T1, T2, and T3) significantly increased β-carotene in peppers, which exhibited 8.83-fold, 1.54-fold, and 1.25-fold higher β-carotene contents than those of plants under CK, respectively (Table 1).

Table 1.

Fruit quality of peppers under various treatments.

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The most significant (P < 0.05) gain in root fresh weight (0.31-fold increase compared to that of the CK group) was recorded for plants under T2 (Fig. 1A). The highest shoot fresh weight was observed in plants grown under T1, and it was significantly higher than that of the other treatment groups (Fig. 1B). Regarding the leaf, the lowest leaf fresh weight was recorded for plants grown under T3, and it was ∼12.23% lower than that grown under the CK. Notably, T1 significantly increased pepper leaf fresh weight, which surpassed that of CK plants by ∼4.88% (Fig. 1C).

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Biomass dynamics of plants under various treatments. Fresh weight of (A) roots, (B) shoots, and (C) leaves. Different letters in the same column represent significant differences (P < 0.05).

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Effects of oats and biochar on physicochemical properties of pepper rooting soil.

The soil pH remained comparable across all cultivation systems during the three developmental stages of peppers (range, 8.73–8.90) (Table 2, Fig. 2A). A significant decrease in the soil NO₃⁻-N content was observed in the T1 group with the progression of pepper development. In the T2 group, the soil NO₃⁻-N content peaked during the S3 period and was significantly higher (by approximately 1.42-fold) than that of the S1-T2 group (P < 0.05) (Table 2, Fig. 2B). The NO₃⁻-N levels in the T3 treatment group continued to accumulate as the peppers matured, reaching their highest point at S3 (∼1.86-fold greater than that of the S1-T1 group) (Table 2). The soil NH₄⁺-N concentration was highest in the T3 group during the same period of pepper growth (Table 2, Fig. 2C). The soil NH₄⁺-N level was lower in the T1 and T2 groups than in the CK group, regardless of pepper development (Table 2). As the peppers matured, the available phosphorus concentration in the soil of the T3 group initially decreased but then increased to 19.78 mg/kg in the S3-T3 group; however, it remained lower than that of the S1-T3 group by 1.25%. The available phosphorus levels in soil under the T1 treatment were consistent at all stages (Table 2, Fig. 2D).

Table 2.

Trends in soil physicochemical properties during pepper ripening.

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Dynamics of soil physicochemical characteristics during pepper growth under different treatments: (A) pH; (B) nitrate nitrogen (NO₃⁻-N); (C) ammonium nitrogen (NH₄⁺-N); (D) available phosphorus (AP); (E) total organic matter (OM); (F) total phosphorus (TP); (G) available kalium (AK); and (H) soil EC (SEC). Different letters in the same column represent significant differences (P < 0.05).

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The soil OM content in the S1-T2 group (17.14 g/kg) was significantly higher than that in the other treatment groups (Fig. 2E). Except for the S1-T1 and S3-CK groups, the total phosphorus content in the soil was almost independent of the type of pepper cultivation and period of development (Table 2, Fig. 2F). During the S1 stage of fruit development, treatments T1, T2, and T3 showed significantly higher (50%) contents of soil available kalium compared with that of the CK treatment; however, soil available kalium decreased significantly as fruit development progressed from S1 to S2 (Table 2, Fig. 2G). The soil SEC under T1 was highest during S1 and continued to decrease significantly (P < 0.05) with pepper development (range, 1298.00–818.72 μs/cm (P < 0.05) (Table 2, Fig. 2H). Interestingly, the soil SEC continued to increase significantly with T2 and T3 treatments as peppers matured and peaked during S3 at 1248.25 μs/cm and 1227.00 μs/cm in the S3-T2 and S3-T3 groups, respectively (P < 0.05) (Table 2, Fig. 2H).

An orthogonal partial least squares discriminant analysis of the eight soil physicochemical characteristics measured was performed. The first and second axes explained 9.6% and 35.1% of the cumulative variance, respectively. A distinct separation was evident between CK and T3, whereas a substantial clustering overlap was observed among the other treatment groups (i.e., between CK and T1 and between CK and T2) (Fig. 3A). Additionally, variable importance in projection (VIP) >1 for soil NO₃⁻-N and NH₄⁺-N contents and the SEC indicated that companion oat/biochar treatments differed from those of the CK treatment group most significantly precisely with regard to the measured values of these physicochemical parameters (Fig. 3B).

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Orthogonal partial least squares discriminant analysis (OPLS-DA) S-plot distributions of soil physicochemical properties under the four treatments (A) and screened differential physicochemical parameters (B) with variable importance in projection (VIP) >1.

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Effects of the oats and biochar treatment combination on the microbial community of the pepper rhizosphere soil.

Illumina MiSeq sequencing was used to characterize the microbiota community structure and migration patterns in the inter-root soil during pepper development under the companion oat/biochar treatment groups. The rarefaction curves of the bacterial and fungal communities (Shannon and Chao1 indices) suggested that sequencing provided excellent overall OTU coverage (Supplemental Fig. 1). Throughout pepper development, 28.91% of the bacterial OTUs were shared. In comparison with all other samples, the highest number of bacterial OTUs was observed in S1-CK, with a unique count of 381 OTUs (Supplemental Fig. 2A). The bacterial OTUs in the T1 treatment were 3.85%, 2.73%, and 5.75% lower than those in the CK treatment at S1, S2, and S3, respectively (Supplemental Fig. 2A). All treatment groups shared 186 fungal OTUs, accounting for 22.33% of the total fungal OTUs, with a small percentage of unique fungal taxa observed at each developmental stage (Supplemental Fig. 2B). For example, during S1, distinct counts of 95, 74, and 125 fungal OTUs were noted in T1, T2, and T3, respectively, accounting for 11.40%, 8.88%, and 15.01% of the total fungal OTUs (Supplemental Fig. 2B). Furthermore, the total bacterial OTUs were 50.06% higher than the fungal OTUs (Supplemental Fig. 2A and B).

The inter-root soil microbial community diversity shifted significantly as pepper development advanced. The fluctuations in species diversity indices, such as Shannon and Chao1 indices, are detailed in Supplemental Table 1. A consistent decrease (P < 0.05) in bacterial diversity, as measured by the Shannon index, was observed from S1 to S3 across all treatments (Fig. 4A), indicating a reduction in the diversity of the inter-root soil bacterial community throughout fruit maturation. Furthermore, fungal diversity based on the Shannon index was lowest in the T1, T2, and T3 treatments during the S2 fruit development stage, and then it increased at the S3 stage. Conversely, fungal diversity in the CK group continued to decrease during the pepper growth period (Fig. 4B). A principal coordinate analysis revealed the spatial distribution of soil microbial communities. For bacteria, the first two principal coordinate axes accounted for 32.85% of the variance, with T1 showing a partial overlap with the CK group (Fig. 4C) (permutational multivariate analysis of variance, R² = 0.1349 and P = 0.001). For fungi, the first two principal coordinates explained 23.29% of the total variation, and T3 significantly overlapped with the CK treatment (Fig. 4D) (permutational multivariate analysis of variance, R² = 0.1183 and P = 0.001). This finding confirmed the potential effects of companion oats/biochar on the structure and distribution of the microbial community in the pepper plant roots.

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Dynamics of microbial diversity and structure. Bacterial (A) and fungal (B) diversity trends based on the Shannon index. A principal coordinate analysis (PCoA) (Bray-Curtis distances) of bacterial (C) and fungal (D) community distributions under different treatments: S1 = seedling stage; S2 = flowering and setting fruit stage; and S3 = at the middle of fruiting stage. Different letters in the same column represent significant differences (P < 0.05).

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Further investigation of the association between microbial community patterns and pepper cultivation types revealed taxonomic differences between bacterial and fungal taxa, as detected by an analysis of similarities and permutational multivariate analysis of variance. At S1, the fungal community structure showed considerable variation with treatment, and the effect was significant (R_ANOSIM = 0.230, P < 0.05; R²_ADONIS = 0.260, P < 0.01). Furthermore, significant differences in the bacterial and fungal community structures were detected among treatments at S2 (P < 0.01) (Table 3). Similarly, a noticeable effect of the cultivation type on soil microbial community structures was observed at S3. Overall, the effect of the cultivation type on the bacterial community structure changed from S2 to S3, whereas its effect on the fungal community structure was evident throughout fruit development (P < 0.01) (Table 3). A highly significant modulating effect on the structural distribution of both bacterial and fungal communities under the same cultivation type for all pepper developmental stages was observed (P < 0.01) (Table 3).

Table 3.

Analysis of similarities (ANOSIM) and permutational multivariate analysis of variance (PERMANOVA) of the oat/biochar effects on microbial diversity patterns.

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Dominant bacterial phyla included Proteobacteria, Actinobacteria, and Bacteroidetes, which accounted for averages of 27.02%, 11.37%, and 11.33% of the total bacterial population, respectively (Supplemental Fig. 3A). Compared with CK, the T1 treatment reduced Proteobacteria by 15.13%, whereas treatments T1, T2, and T3 increased the Actinobacteria abundance by 34.03%, 31.79%, and 10.45%, respectively. In contrast, in T3, Bacteroidetes were least abundant and 4.99% less abundant than in CK (Supplemental Fig. 3A). Additionally, Ascomycota, Chlorophyta, Anthophyta, and Chytridiomycota were the dominant fungal phyla, with Ascomycota exceeding 50% in all treatments and peaking at 57.03% in T3 (Supplemental Fig. 3B). Additionally, T1, T2, and T3 reduced the Chytridiomycota abundance by 31.78%, 49.90%, and 9.94%, respectively (Supplemental Fig. 3B). At the genus level, the top 10 bacterial genera were identified by relative abundance. Sphingomonas was the most abundant phylum, comprising at least 2.64% of the community. Escherichia–Shigella significantly increased in the companion oat/biochar treatment, reaching at least 64-times that of the CK (Fig. 5A). In contrast, Pseudomonas proportions were reduced by 52.09%, 9.12%, and 13.3% in the T1, T2, and T3 treatments, respectively, compared with the CK treatment. The average abundance of Nitrospira was 1.64% in all samples. In addition, Bacillus was notably enriched in T1 (1.83%), T2 (1.07%), and T3 (1.58%) treatments compared with the abundance of 0.99% in the CK treatment. Regarding fungal taxa, 12 dominant genera, including Chrysosporium, Mortierella, Pratylenchus, and Pseudogymnoascus, were identified across all samples (Fig. 5B). Of these, the T1 and T3 treatments notably lowered the proportion of Chrysosporium by 15.03% and 36.22%, respectively, whereas T2 increased its proportion by 10.02%. Furthermore, T1, T2, and T3 increased the abundance of Mortierella by 31.61%, 68.76%, and 54.27%, respectively (Fig. 5B).

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Microbiota composition during pepper development under various treatments. Succession of pepper development in dominant bacterial (A) and fungal (B) genera. Random forest classification model based on the mean decreasing accuracy values predicted important bacterial (C) and fungal (D) genus features in pepper development.

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Subsequently, a random forest classification model was constructed for the dominant microbial genera to identify the critical features of the soil microbiota during pepper development under various cultivation types (Fig. 5C, D). The mean decrease in the accuracy index revealed the most important bacterial genera characterized in the pepper inter-root soil, namely, Sphingobium, Lysobacter, and Escherichia–Shigella under T3, and Pontibacter, Nitrospira, and Bacillus under T1 (Fig. 5C). Sphingomonas and Pseudomonas were identified as important bacterial genera in CK (Fig. 5C). Furthermore, CK was predictive of key fungal genera such as Mortierella and Metarhizium. In treatment T1, genera such as Tetracladium, Chrysosporium, Pratylenchus, and Pseudogymnoascus emerged as significant features (Fig. 4D). Fusarium was identified as a pivotal fungal genus in T2 treatment. However, in T3, notable fungal taxa included Brassica, Cirsium, Cladosporium, Chaetomium, and Spizellomyces (Fig. 5D).

Correlations between soil factors and core microbiota in pepper cultivation under combined oats and biochar treatments.

A redundancy analysis was conducted to examine the associations between the inter-root microbial diversity patterns of pepper plants and soil physicochemical properties, with the two axes accounting for 74.48% of the variance in bacterial taxonomic differences (Fig. 6A) (permutation test, P = 0.001). Notably, Sphingomonas exhibited a positive correlation with total phosphorus and the SEC; however, it exhibited a negative correlation with OM, pH, and NH₄⁺-N. Pseudomonas, Nitrospira, and Bacillus were strongly associated with the pH and NH₄⁺-N (Fig. 6A). Regarding fungi and soil physicochemical parameters, the redundancy analysis 1 and redundancy analysis 2 axes explained 42.24% and 23.03% of the variance, respectively (permutation test, P = 0.006) (Fig. 6B). Strong positive correlations were also found between Mortierella and NH₄⁺-N. Significant positive correlations were observed between Chrysosporium, Pseudogymnoascus, and Tetracladium, as well as between NO₃⁻-N, available phosphorus, OM, and available kalium (Fig. 6B).

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Correlation between microbes [operational taxonomic unit (OTU) >0.1%] and physicochemical characteristics of soil. A redundancy analysis (RDA) predicted correlations between soil characteristics and (A) bacterial and (B) fungal genera. The Spearman correlation analysis revealed soil characteristics and (C) bacterial and (D) fungal genera (Spearman correlation coefficient, |r| ≥ 0.6; P < 0.05). *** P ≤ 0.001; ** 0.001 < P ≤ 0.01; * 0.01 < P ≤ 0.05. n = 48.

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Spearman’s correlation analysis elucidated the effect of specific environmental factors on the key microbial communities. Thus, the SEC and available kalium displayed strong positive correlations with Steroidobacter and Escherichia–Shigella, as well as a significant negative correlation with Salinimicrobium. Notably, NH₄⁺-N showed significant negative correlations with Stenotrophobacter, Escherichia–Shigella, Pirellula, and Gaiella, as well as a positive correlation with Salinimicrobium (Fig. 6C). A significant negative correlation was observed between Bryobacter abundance and pH, concomitant with a significant positive correlation with the SEC. In addition, Lysobacter was significantly negatively correlated with OM (P < 0.01) (Fig. 6C). In contrast to the bacterial community, intricate associations were found between the dominant fungal species and soil physicochemical parameters. Thus, available phosphorus and available kalium showed significant negative correlations with Alternaria and positive correlations with Pratylenchus and Capsicum (P < 0.001) (Fig. 6D). Furthermore, NH₄⁺-N was positively correlated with Mortierella and Fusarium; however, it was negatively correlated with Cirsium, Brassica, Meloidogyne, and Lobomonas (P < 0.01) (Fig. 6D). Additionally, NO₃⁻-N was positively correlated with Chrysosporium, Pseudogymnoascus, Cladosporium, and Lobomonas; however, it was significantly negatively correlated with Fusarium (Fig. 6D).

Exploring soil microbial functionality in pepper under oats and biochar combined treatments.

To gain a better understanding of the significant effect of the cultivation type on the soil microbial community of pepper, the PICRUSt program was used to predict functional metabolic pathways based on 16S rRNA and ITS sequencing data from the direct homologous group (COG) database. The functions of the bacterial genes were primarily related to metabolism (56.55%), genetic information processing (23.41%), cellular processes (13.95%), and environmental information processing (5.13%) (Supplemental Table 2). These functional genes were annotated to 20 pathways (Fig. 7A). Genes involved in carbohydrates, terpenoid metabolism, polyketide metabolism, and amino acid metabolism were clearly more abundant in T1 than in the other treatment groups (Fig. 7A). The bacterial community in the soil in the CK treatment had enriched lipid metabolism, transport, and catabolism. Furthermore, T3 resulted in relatively active signal transduction and membrane transport pathways in the bacterial community (Fig. 7A).

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Kyoto Encyclopedia of Genes and Genomes (KEGG) functional annotation of (A) bacterial and (B) fungal communities during pepper development under various cultivation types. CK = pepper monoculture; T1 = oat + biochar; T2 = biochar amendment; T3 = oat intercropping. n = 48.

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The metabolic pathways of the inter-root fungal community in pepper were projected to be abundant in 20 pathways (Fig. 7B), with the phospholipase pathway showing the highest abundance in the CK group. The L-methionine biosynthesis III pathway was most abundant in T1; in this case, the abundance of the pathway was at least 41.33% higher than that in the other treatment groups (Supplemental Table 3). Glycolysis and CoA biosynthesis-related gene expression were significantly enhanced by treatment T2, and the extent of enhancement was 75.13% higher and 81.91% higher, respectively, in plants under T2 than in those under CK (Supplemental Table 3). In addition, saturated fatty acid elongation pathway-related genes increased under T1, T2, and T3, compared with those under CK, with fold increases of 0.89, 1.24, and 0.48, respectively (Fig. 7B).

Enhancing soil and pepper nutrients under continuous pepper cultivation in combination with oats and biochar.

Biochar and oats have been shown to enhance soil properties (Gao and Zhang 2023; Qu et al. 2023). In this study, limitations to continuous pepper cropping were characterized by three parameters: NO₃⁻-N, NH₄⁺-N, and SEC (Fig. 3B). Soil microbes play a crucial role in nitrogen assimilation by oxidizing NH₄⁺-N to NO₃⁻-N (Li et al. 2023b). Furthermore, biochar increases NH₄⁺-N adsorption and reduces the oxidation of NH₄⁺-N to NO₃^--N, thereby contributing to the reduction in the soil nitrate content (Zhang et al. 2023c). However, no decrease in NO₃⁻-N was observed in the T2 treatment. Additionally, NH₄⁺-N in the soil nitrogen pool reportedly contributes significantly to plant nitrogen uptake and enhances soluble sugars (Beuters et al. 2015; Li et al. 2023b). Under T3 treatment, the soil NH₄⁺-N increased throughout the pepper growth cycle, which likely reflected the interaction between biochar and pepper cultivation. Additionally, the soil SEC is commonly used to assess soil salinity and alkalinity, thus indicating its ability to conduct electrical currents (Wu et al. 2021). Biochar can enhance microbial survival by providing habitats with high porosity (Warnock et al. 2007), thus potentially affecting the soil SEC. During the S2 growth stage, SEC decreased under the oat/biochar treatment compared with that during S1. In contrast, SEC increased during S2 in the pepper–monoculture control system (Table 2). However, the increase in SEC in CK pots suggested the sensitivity and adaptability of pepper plants to soil salinity and alkalinity (Qu et al. 2023); therefore, further research is warranted.

As a soil amendment, biochar is widely recognized for its effectiveness in enhancing crop growth in acidic soils because of the resulting increase in soil pH following its application to the soil (Gao and Zhang 2023; Syuhada et al. 2016; Yao et al. 2017; Zhong et al. 2024). Nevertheless, the increase in soil pH, particularly during S1 and S2, following the application of the T2 biochar treatment was relatively small (Table 2). An alternative explanation for the increase in pH attributable to the biochar amendment might be the conversion of alkaline cations (Ca²⁺, Mg²⁺, and K⁺) present in the biochar into alkaline substances such as oxides, hydroxides, and carbonates during pyrolysis (Singh et al. 2022). In this study, we found that treatment companion oat/biochar did not significantly alter the soil pH, available phosphorus, OM, or available kalium contents (Table 2). These results suggest that the role of biochar in regulating soil chemical properties in the pepper–monoculture system requires further confirmation and is possibly related to soil enzyme activity and microbial communities (Gao and Zhang 2023; Zhang et al. 2023c). Previous studies have indicated that biochar can absorb and retain nutrients such as NO₃⁻-N, NH₄⁺-N, and phosphate, thus reducing soil nutrient losses and increasing phosphorus availability, thereby enhancing plant uptake (He et al. 2021; Thangarajan et al. 2018). Thus, the dynamics of the soil available kalium content may be linked to the soil NO₃⁻-N and NH₄⁺-N contents. Moreover, T3 led to a significant increase in available phosphorus during S3 (P < 0.05) (Table 1), suggesting that the application of oats may facilitate nutrient uptake in peppers during later growth stages. These findings agree with those of a previous study that reported that intercropping with common vetch significantly improved soil available phosphorus and available kalium contents (Qu et al. 2023).

Intercropping of two different plant species, in combination with biochar addition, plays vital roles in promoting vigorous plant growth and mitigating the accumulation of toxic substances, thereby reducing the risk of plant toxicity (Brooker et al. 2015; He et al. 2021; Thangarajan et al. 2018). Both T1 and T2 led to significant increases in ascorbic acid and β-carotene contents in peppers (P < 0.05) (Table 1), suggesting that the application of biochar in combination with oats intercropping with pepper may have produced a synergistic effect. Biochar application stimulates soil microbial activity, enhances the soil microbial environment, and promotes crop root growth and yield (Gao and Zhang 2023; Yao et al. 2017; Zhang et al. 2023c). Additionally, biochar improves fruit quality because it increases the leaf photosynthetic rate and promotes the transport of photosynthates to the growing fruit (Zhang et al. 2023c). Conversely, T3 treatment led to higher soluble solids and soluble sugar contents in the developing fruits (Table 1). These results suggest that the improvement in the pepper fruit quality mediated by oats intercropping may involve more complex interactive mechanisms that require validation by further research.

Interplay of soil microbes and physicochemical factors in pepper cultivation under oats intercropping and biochar treatments.

Because of the long-term accumulation of root exudates from the same crop, continuous cropping often leads to alterations in the rhizosphere, thereby limiting microbial diversity (Bowles et al. 2014; Lehmann et al. 2020; Li et al. 2024). Therefore, healthy soil–microbial communities are vital to successful crop growth (Chen et al. 2022; Gao and Zhang 2023; Li et al. 2024; Zhong et al. 2024). This study revealed significant effects on the microbial community structure across fruit growth stages and treatments (Table 3), thus highlighting the influence of soil physicochemical properties on microbial ecology. Amendments with biochar and oats–pepper cocultivation can alter and regulate the structure and distribution of the soil microbial community (Gao and Zhang 2023; Yang et al. 2021; Yao et al. 2017; Zhang et al. 2023c). In addition, our study revealed an association between soil physicochemical properties and microbial communities (Fig. 6). Furthermore, the pepper cultivation type and fruit development stage affected soil physicochemical properties (Table 2). Biochar and oats–pepper cocultivation can alter soil nutrient availability, thereby affecting microbial abundance and diversity (He et al. 2021; Li et al. 2024). Notably, the effects of biochar (T1 and T2) on bacterial and fungal diversity showed distinct trends (Supplemental Table 1). Similar results of the cultivation of sweet peppers with biochar amendment were observed (Li et al. 2024). This was attributed to increased mineral–nutrient absorption and utilization by bacteria because such minerals are readily adsorbed on the surface of the biochar or colonize its pores (He et al. 2021; Nguyen et al. 2018). In addition, biochar provides soil bacteria with an appropriate habitat and nutrition, thus expanding the bacterial ecological niche (Zhong et al. 2024).

Environmental factors such as pH, soil organic carbon, and available phosphorus control soil microbial communities in different ecosystems (Gao and Zhang 2023; Saldanha et al. 2019). In this study, bacterial taxa identified in different pepper cultivation ecosystems encompassed Sphingomonas, Pseudomonas, Bacillus, and others, whereas fungal taxa included Chrysosporium, Mortierella, Pratylenchus, Pseudogymnoascus, as well as others (Fig. 5); this finding was consistent with those of previous studies (Gao and Zhang 2023; Li et al. 2023a; Obieze et al. 2023). These dominant microbes primarily belong to Proteobacteria, Actinobacteria, Chloroflexi, and Ascomycota, which represent essential components of diverse plant–soil microbial communities (Chen et al. 2020; He et al. 2021; Li et al. 2023b). Members of the dominant fungal genus Sphingomonas participate in nutrient cycling and promote plant growth. This has been confirmed in tea plants treated with biochar (Yang et al. 2021). Interactions with environmental factors can disrupt or enhance the activities of beneficial microbial communities (Bowles et al. 2014; Liu et al. 2022; Qu et al. 2023). Within the scope of this investigation, specific taxa, namely, Stenotrophobacter, Pseudomonas, Nitrospira, Bacillus, Cladosporium, Pratylenchus, and Capsicum, were identified as pivotal entities that exhibit strong potential correlative relationships with distinct soil physicochemical characteristics (Fig. 6). Notably, soil attributes such as NO₃⁻-N and SEC were positively correlated with a multitude of fungal genera (Fig. 6B). He et al. (2021) found that NO₃⁻-N and SEC were significantly and positively correlated with inter-root soil fungal species in tomatoes. In a northeastern black soil, biochar amendment was linked to shifts in the fungal community structure, with a notable correlation with the NO₃⁻-N content (Yao et al. 2017). However, fungal community diversity was not significantly altered by biochar amendment (Yao et al. 2017). These findings further highlight the importance of soil chemical properties to determining the composition and functionality of microbial communities and provide perspective regarding how soil microbes adapt to environmental alterations.

Changes in the bacterial community structure are strongly correlated with biochar-induced shifts in the SEC (Chen et al. 2017; Qu et al. 2023), which may be related to improvements in the soil properties required for microbial colonization (He et al. 2021, 2023). In particular, in this study, core bacterial genera, such as Steroidobacter and Bryobacter, were significantly and positively correlated with the soil SEC (Fig. 6C), suggesting that the activity of these bacteria in the soil may be influenced by changes in the SEC. The capacity of Steroidobacter to remove polycyclic aromatic hydrocarbons and adsorb heavy metals present in the soil (Shang et al. 2024) as well as the reliance of Bryobacter on low soil nitrogen or phosphorus (He et al. 2023) underscore the importance of these microbes to soil health. In addition, Pseudomonas, Nitrospira, and Bacillus were strongly correlated with the pH and NH₄⁺-N (Fig. 6C), which can alter the soil physicochemical status (Bowles et al. 2014). Additionally, available phosphorus and available kalium were significantly and negatively correlated with Alternaria spp. (Fig. 5D), which are widely distributed in soils and are producers of exudates rich in nitrogen metabolites, steroids, and terpenoids (Lou et al. 2013). Moreover, Fusarium was closely correlated with various physicochemical factors (Fig. 6D). In particular, Fusarium, a highly pathogenic fungus, is known for causing Fusarium wilt and having severe detrimental effects on plants, leading to high plant mortality rates (Li et al. 2018). In this study, Fusarium was not a dominant fungal species, indicating that cocultivation and biochar addition may enhance soil resistance and reduce pathogenicity (Gao and Zhang 2023; Saldanha et al. 2019; Shang et al. 2024). These results are significant to the improvement of soil health. Subsequent research of functional genes is necessary to further understand the mechanisms by which cocultivation schemes affect soil fertility and health in continuous pepper cropping systems (Deng et al. 2020; Gao and Zhang 2023).