Soil microbial community and chemical properties response to blueberry–soybean intercropping system

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Methods we designed a potted blueberry–soybean intercropping system to evaluate its impacts on crop yield, disease occurrence, and soil microbial community composition using survey statistics, high-throughput sequencing, and correlation analysis. Results The results demonstrate that the system is a feasible solution for obtaining additional soybean yield. Blueberry pot soil (BPS) sampled and rhizosphere soil sampled from intercropped Huayan 1 soybean plants (HYS) showed significantly higher fungal and bacterial diversity than control bulk soil (CK) with no cultivation history. Microbial communities and unique OTUs were differentially enriched in BPS and HYS, respectively, and the latter effect was more pronounced. pH, organic matter, and total N were the main factors driving soil chemistry-mediated microbial differences in the community between CK and both HYS and BPS. The significantly lower microbial abundance in BPS was likely related to N fixation, whereas significantly enriched bacteria in HYS were related to the N regulatory protein C protein family, N regulatory IIA and P-II2 proteins, N fixation regulation proteins, and other N-related functions ( p < 0.05), indicating that blueberry–soybean intercropping significantly improves microbial function in the soil. Conclusion These findings demonstrate that intercropping system could improve the acidification of soil and reduce the depletion of soil functional microorganisms caused by continuous monoculture of blueberries. Intercropping could help coordinated development of grain and fruit production, particularly in regions facing both food shortages and limited arable land in the world. Blueberry–soybean intercropping coordinated development nitrogen regulation soil microbes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Blueberries are an economically important crop worldwide; they are rich in anthocyanins and vitamins and are among the top fruits recommended by the United Nations Food and Agriculture Organization for their health benefits (Routray and Orsat, 2011 ). Blueberries have been introduced to Yunnan since 2004. The region has emerged as a key hub for early fresh blueberry production, gaining recognition for its high-quality berries in China (Jiafeng et al., 2019 ). However, in 2020, a policy proposed to ensure grain production in China firmly prohibited any use of cropland for non-agricultural purposes, such that fruit production was forbidden on basic farmland. This policy created a need for innovative methods for coordinated agricultural development between grain and fruit crops. Blueberry plants are adapted to acidic soil, with an optimal soil pH of 4.5–5.5. Excessively high or low pH values can affect their growth and development. Soil pH is a major driver of bacterial community structure (Che et al., 2022 ). Blueberry farmers typically use large amounts of acidic fertilizer during cultivation to adjust the soil pH, which intensifies soil acidification in field production (Ochmian et al., 2018 ; Yadav et al., 2020 ). Microbiomes associated with plant roots play crucial roles in the adaptation of plants to environmental stresses, enhancing fruit productivity and optimizing soil nutrient cycling (Kumar and Dubey, 2020 ; Liu et al., 2020 ; Saleem et al., 2019 ). The consumption of acidic substances in soil by blueberry plants further reduces soil biodiversity, resulting in soil ecosystem imbalances, which are characterized by deficiencies in essential elements for crops and increased heavy metal solubility (Chen et al., 2022 ; Haynes and Swift, 1986 ; Pan et al., 2020 ; Zhou et al., 2021 ). Blueberry plant growth is closely related to organic matter (OM) levels, enzyme activity, and microbial community diversity in soils (Zhou et al., 2022 ). Variation in the structure of blueberry roots also has a significant impact on the microbial diversity at roots. Microbial diversity is significantly lower in the blueberry rhizosphere than in other soils, and there are also less-complex interactions between bacteria and fungi (Che et al., 2023 ). Therefore, the development of effective methods to mitigate the impact of blueberry cultivation on the soil environment is crucial, particularly in countries with limited arable land resources. Interplanting is widely implemented in modern agriculture to address shortages of arable land and promote sustainable agriculture (Yang et al., 2021 ). A previous study analyzed 226 interplanting field trials conducted in various regions worldwide and found that this approach was effective for controlling pests, diseases, and weeds, improving nitrogen (N) use efficiency and achieving sustainable intensive agriculture (Li et al., 2023 ). Compared to monoculture, interplanting better utilizes land, light, and water resources, thereby improving yield per unit area (Li et al., 2020 , 2021 ). Interplanting different crops helps improve the microbial diversity, OM levels, and fertility of soils. Soybeans are among the preferred grain crops for interplanting, as they have high N fixation and soil fertility improvement abilities (Ablimit et al., 2022 ; Stefan et al., 2021 ). Legumes have been successfully interplanted with several other crops, such as corn, sorghum, and tea trees (Du et al., 2018 ; Wang et al., 2021 ; Xu et al., 2020 ). Various bacterial genera including Rhizobium , Novosphingobium , Phenylobacterium , Streptomyces , and Nocardioides are greatly enriched in the soybean rhizosphere, irrespective of soil type (Liu et al., 2019 ). Soybean roots can penetrate the sugarcane rhizosphere in intercropping systems, allowing sugarcane roots to accumulate more N-fixing bacteria, resulting in higher nutrient levels and increased biological activity in soils. Thus, intercropping is a beneficial measure that effectively reduces the excessive use of chemical fertilizers (Liu et al., 2021 ; Solanki et al., 2019 ). Food security has long been considered an important national security issue worldwide (Muluneh, 2021 ; Wheeler and Von Braun, 2013 ). In this study, we designed a blueberry–soybean intercropping system in Yunnan Province, China, and evaluated its impact on crop yield, disease occurrence, and the soil environment using survey statistics, high-throughput sequencing, and correlation analysis. We explored a method for maintaining the supply–demand balance between grain and fruit production, and cultivated land protecting. Our findings will be particularly useful for regions facing both food shortages and limited arable land. 2. Materials and Methods 2.1. Experimental design and soil sampling Experiments were conducted at a blueberry plantation of the Suninfinit Agriculture Group located in Donghui, Lancang County, Pu’er, southwestern Yunnan Province, China (22.42°N, 99.79°E; elevation: 1,297 m a.s.l.; Fig. 1 a). Blueberry plants were transplanted into a 25 L pots filled with substrate. Nutrients were supplied by drip irrigation at a flow rate of 2 L/h. The optimum soil pH range for blueberry growth is 4.5–5.5. The soybean variety Huayan 1 was provided by the College of Agronomy and Biotechnology at Yunnan Agricultural University. Soybean seeds were sown between blueberry plant rows, at a spacing of 0.30 m × 0.30 m in May 2022 (Fig. 1 b). In September 2022, soil samples were collected from the rhizosphere of Huayan 1 plants (Huayan 1 soil, HYS) and from the blueberry pots at a depth of 5 cm (blueberry pot soil, BPS). Bulk soil without cultivation history was used as the control (CK). Briefly, each soil sample was randomly collected from five pots and mixed into four biological replicates. All samples were placed in 5 mL centrifuge tubes and stored at − 80°C for DNA extraction and high-throughput sequencing. 2.2. Soybean yield and disease occurrence analyses We analyzed soybean yield and disease occurrence in leaves sampled from the blueberry–soybean intercropping system. Briefly, leaves were collected from a total of 120 plants and examined for signs of soybean powdery mildew. Disease was graded from 0 to IX according to the area of leaf spots, as follows: 0, healthy; I, 50%. The disease index was calculated as Σ (Disease class frequency × Score of disease class) × 100/Total number of plants sampled/Maximum disease index. In all, 30 soybean plants were measured to characterize yield in terms of plant height and numbers of pods, grains, and nodules. The 100-grain weight was measured in randomly selected plants, with three independent biological replicates. 2.3. DNA extraction and high-throughput sequencing Total genomic DNA from each soil sample was extracted using the Fast DNA Spin Kit for soil (MP Biomedicals, Solon, OH, USA), following the manufacturer’s instructions. The internal transcribed spacer 1 (ITS) region targeting fungi was amplified using the primer pairs ITS1/ITS4 (5’-CTTGGTCATTTAGAGGAAGTAA-3’ and 5’-TCCTCCGCTTATTGATATGC-3’). The V4–V5 regions of the bacterial 16S rRNA gene were amplified using the primer sets 27F (5’-AGRGTTTGATYNTGGCTCAG-3’) and 1492R (5’-TASGGHTACCTTGTTASGACTT-3’), under the following thermal conditions: initial denaturation at 95°C for 3 min, followed by 27 cycles at 95°C for 30 s, 55°C for 30 s, 72°C for 45 s, and 72°C for 10 min. The purified amplicons were sequenced using single molecule real-time sequencing (SMRT) and the PacBio Sequel sequencing platform (PacBio, Menlo Park, CA, USA), according to the standard protocol. Data preprocessing was conducted using Lima v1.7.0, Cutadapt v1.9.1, and UCHIME v4.2. These operations were completed by Biomarker Technologies Corp. (Beijing, China). All ITS and 16S rRNA gene sequences may be found in the National Center for Bioinformation Technology Short Read Archive ( https://trace.ncbi.nlm.nih.gov/traces/sra ) under accession number PRJNA1036779. 2.4. Soil chemistry analyses Soil samples were sieved to a diameter of 2 mm and dried in an oven. Soil pH was determined using a pH meter (PHS-3E, Shanghai, China) in a soil/water suspension (1:2.5, w/v) according to standard NY/T 1121.2–2006. OM was assayed using dichromate wet combustion according to standard NY/T 1121.6–2006. Total N (TN) content was analyzed using an Azotometer (SKD-1000, Kjeldahl, Shanghai, China) according to standard NY/T 1121.24–2012. Available phosphorus (AP) was analyzed using an ultraviolet–visible spectrophotometer (T6 series; Persee Analytics, Auburn, CA, USA) according to standard NY/T 1121.7–2014, and available potassium (AK) was analyzed by flame atomic absorption spectrophotometry (Z-2310; Hitachi, Tokyo, Japan) according to standard NY/T 889–2004. Heavy metals were also quantified. Arsenic (As) levels were measured using an atomic fluorescence spectrometer (AFS-8520, Haiguang, Beijing, China) according to standard GB/T 22105.2–2008, and cadmium (Cd), chromium (Cr), and lead (Pb) were measured using inductively coupled plasma–mass spectrometry (ICAP RQ, Thermo Fisher Scientific, Waltham, MA, USA) according to standards DZ/T 0279.5–2016, DZ/T 0279.2–2016, and DZ/T 0279.2–2016, respectively. All soil samples from each treatment were tested with three replications. 2.5. Microbial community diversity analysis To mitigate the potential impact of sequencing depth, we used the rarefy function in the vegan v2.6.4 package of the R software (R Core Team, Vienna, Austria). To investigate the beta diversity of bacterial and fungal communities across different treatments, we initially computed community distance using the vegdist function in the R vegan package using the bray distance metric. Then we conducted principal coordinate analysis (PCoA) using the pcoa function in the ape v5.7.1 package in R. Finally, we used the ggplot2 v3.4.2 package in R to visually represent the PCoA results. 2.6. Differential abundance analysis of microbial phyla Phylum-level analysis is essential to assess variation in the microbial community. We summed the abundances of microorganisms found in the soil samples at the phylum level, excluding unclassified phyla, and then ranked the summed values in descending order. Phyla ranked 10 or lower were collectively labeled as others. We performed analysis of variance (ANOVA) using the anova_test function in the rstatix v0.7.2 package in R ( https://github.com/kassambara/rstatix ). Then we used the glht function in the multcomp v1.4.23 package in R to perform multiple comparisons among the ANOVA results based on Tukey’s method. We visualized the data using the ggplot2 R package. 2.7. Differential abundance analysis of the microbiome Differential microbial abundance can be analyzed through various methods. In this study, we compared microbial abundance in the BPS and HYS samples to that of the control group (CK) using the DESeq2 v1.38.3 package in R. The input data were a matrix of operational taxonomic unit (OTUs). Differentially abundant microbes were screened using the criteria of |log 2 fold change (FC)| > 1 and adjusted P ( P adj ) < 0.05. Microbes fulfilling these criteria were categorized as enriched, those with |log 2 FC| < 1 and P adj < 0.05 were categorized as depleted, and the remaining microorganisms were categorized as nonsignificantly different (NS). The ggplot2 and pheatmap v1.0.12 packages in R were used to visualize the overall results and the relative abundances of differentially abundant microorganisms, respectively. 2.8. Redundancy analysis (RDA) To analyze the relationship between the physicochemical properties of the soil and microbial communities, we conducted RDA using the rda function in the R vegan package (Dixon 2003) based on indicators of physical and chemical properties in the soil as input data. To obtain the corrected R 2 value, we used the RsquareAdj function in the vegan package. The results were visualized using the ggplot2 package. 2.9. Mantel test To examine the relationship between enriched and depleted microorganisms and the physicochemical properties of soils within each treatment, we performed a Mantel test using the mantel_test function in the R package linkET v0.0.6 ( https://github.com/Hy4m/linkET ), with microbial community and soil physicochemical property data as input. The qcorrplot and geom_couple functions of the linkET package were used to visualize the results. To compute correlations among the differential abundances of microorganisms and the physicochemical properties of soil, we used the R package WGCNA v1.72-1, and to visualize the correlation matrix, we used the pheatmap package. 3. Results 3.1. Intercropping soybean yield characteristics As shown in Fig. 1 c, soybeans approached maturation 114 days after transplanting. Powdery mildew disease occurred, with a disease index of 3.92 (Fig. 1 d). The average height of soybean plants was 53.76 ± 7.51 cm, with averages of 37.14 ± 4.99 pods per plant, 70.86 ± 6.44 grains per plant, and 4.07 ± 1.05 nodules per plant. The 100-grain weight was 54.84 ± 1.39 g (Fig. 1 e). 3.2. Microbial community characteristics As shown in Fig. 2 , fungal and bacterial diversity were significantly higher in the BPS and HYS samples than in CK ( P < 0.05). BPS showed significantly higher abundance-based coverage estimator (ACE) and Chao1 indices than both HYS and CK ( P < 0.01; Fig. 2 b, Supplementary Table 1). However, the fungal community was more enriched in HYS, with significantly higher α-diversity compared to BPS and CK ( P < 0.05; Fig. 2 c, Supplementary Table 2). The overall explanation rates (sum of principle coordinate axes 1 and 2) for the samples were 60.31% for the bacterial community (axis 1, 32.23%; axis 2, 28.08%) and 38.40% for the fungal community (axis 1, 20.44%; axis 2, 17.96%). This suggests that different crops in this interplanting system affected the composition of soil microorganisms (Fig. 2 d-e). The nine most abundant phyla in the bacterial communities of the soil samples were Acidobacteriota, Actinobacteriota, Bacteroidota, Myxococcota, Patescibacteria, Chloroflexota, Gemmatimonadetes, Proteobacteria, and Verrucomicrobia, with significant differences in the abundances of the first five among the three groups (Fig. 2 f). Acidobacteriota was significantly enriched in BPS ( P = 0.01), whereas Actinobacteriota and Patescibacteria were significantly enriched in HYS ( P = 0.000233, P = 0.008, respectively; Supplementary Table 3). The nine most abundant phyla in the fungal communities were Ascomycota, Basidiomycota, Calcarisporiellomycota, Chytridiomycota, Glomeromycota, Kickxellomycota, Mortierellomycota, Mucoromycota, and Rozellomycota (Fig. 2 g). 3.3. Differential abundance of OTUs in the intercropping system In the BPS bacterial community, 36 OTUs were enriched and 93 OTUs were depleted compared to CK ( P < 0.05; Fig. 3 a, Supplementary Table 4), whereas in the HYS bacterial community, 91 OTUs were enriched and 53 OTUs were depleted ( P < 0.05; Fig. 3 b, Supplementary Table 5). In the BPS fungal community, 4 OTUs were enriched and 6 OTUs were depleted, whereas in the HYS fungal community, 15 OTUs were enriched and 9 OTUs were depleted ( P < 0.05; Fig. 3 d-e). These results suggest that microorganism enrichment was more pronounced in HYS than in BPS, with BPS tending toward microbial depletion. Unique OTUs were enriched in both bacterial and fungal communities of all three groups; however, fewer microorganisms were significantly enriched in BPS than in HYS (Fig. 3 c, f). These results indicate that different crops selectively alter microbial community structure during intercropping. 3.4. Regional variation in soil chemistry in the intercropping system Except for a significant decrease in soil pH after blueberry cultivation, all other tested indicators were significantly higher in both BPS and HYS samples than in CK (Fig. 4 a). Soil pH was also significantly lower in BPS (5.17 ± 0.35) than in HYS (6.65 ± 0.23) and CK (5.98 ± 0.39). Among soil nutrients, HYS, BPS, and CK had TN contents of 1433.500 ± 147.725, 1100.750 ± 93.415, and 1003.083 ± 118.948 mg/kg, respectively. The average AP contents of BPS, HYS, and CK were 22.972 ± 6.927, 16.001 ± 3.321, and 8.626 ± 2.680 mg/kg, and those of AK were 108.182 ± 44.088, 72.350 ± 12.580, and 59.622 ± 17.943 mg/kg, respectively. The OM contents of HYS, BPS, and CK were 24.075 ± 4.279, 17.083 ± 2.181, and 13.425 ± 0.676 g/kg, respectively. Among heavy metals, As, Cd, Cr, and Pb levels in HYS and BPS samples significantly increased throughout the intercropping period, compared to CK. 3.5. Soil chemistry mediates changes in the structure of the microbial community In BPS, bacterial and fungal enrichment were positively correlated with As, Cr, Pb, AP, and AK contents, whereas in HYS, OM, TN, and Cd levels and pH were positively correlated with microbial enrichment (Fig. 4 b-c). Mantel analysis results showed that in HYS, significant differential enrichment of the bacterial phyla Proteobacteria, Acidobacteriota, Myxococcota, Bacteroidota, Actinobacteriota, and Chloroflexi was primarily influenced by As, OM, and TN contents ( P < 0.05; Supplementary Tables 6 and 7). In BPS, significant differential enrichment of the fungal phyla Ascomycota, Rozellomycota, and Mortierellomycota was influenced by AP and AK content and pH ( P 0.05; Fig. 4 e, Supplementary Table 8). The correlation heatmap showed that OM and TN contents and pH were the main factors associated with differential microbial community structure between HYS and BPS, compared to CK (Fig. 4 f-g). 3.6. Functions of differentially enriched microorganisms in the intercropping system Next, we examined the evolutionary relationships among significantly differentially enriched microorganisms in the three soil groups. The 10 bacterial phyla with the greatest significant differences were Acidobacteriota, Actinobacteriota, Bacteroidota, Chloroflexi, Gemmatimonadota, Patescibacteria, Proteobacteria, Verrucomicrobiota, unclassified, and other bacteria (Supplementary Table 3). The 10 fungal phyla with the greatest significant differences were Ascomycota, Basidiomycota, Calcarisporiellomycota, Chytridiomycota, Glomeromycota, Mortierellomycota, Mucoromycota, Rozellomycota, unclassified, and other fungi (Supplementary Table 9). Most of the microorganisms that were differentially affected by the soil environment were bacteria, and the vast majority of these were Proteobacteria, Actinobacteriota, and Bacteroidota (Fig. 5 a). Potentially functional bacteria related to N fixation were examined and their abundances were compared to CK. HYS samples were significantly enriched in bacteria with functions related to the N regulatory protein C (NtrC) family, N regulatory proteins IIA and P-II 2, N fixation regulation proteins, and other N-related functions ( P < 0.05). CK samples were enriched in N regulatory proteins P-Ⅱ 1 and A, N fixation protein NifU, and related proteins, as well as N assimilation regulatory proteins. Notably, BPS samples had significantly lower abundance of microbes potentially related to N fixation ( P < 0.05; Fig. 5 b). 4. Discussion As a result of global climate change, urban expansion, and farmland resource limitations, the protection and utilization of cultivated land have become important priorities toward sustainable global agricultural development and ensuring food security(Liu and Zhou, 2021 ; Ramankutty et al., 2018 ; Smith and Gregory, 2013 ). Intercropping has resolved crop production problems in many countries with limited agricultural land resources, and has been widely promoted and applied worldwide. Reasonable crop rotation achieves diversification in agricultural planting systems, in terms of both time and space, and addresses issues such as limited arable land, plant disease outbreaks, and soil degradation, which are commonly encountered in monoculture production. In this study, we demonstrate that intercropping could contribute to the coordinated development of grain and fruit production, importantly, the intercropping system could improve the acidification of soil as well as reduce the depletion of soil functional microorganisms caused by continuous monoculture of blueberries. Our findings are important for regions struggling to address both food shortages and limitations to arable land area. Blueberry plants belong to the genus Vaccinium (family Ericaceae). These short, perennial shrubs are typically transplanted to a location where they will remain for several years; they require a row spacing of at least 1.5–2.0 m. In traditional blueberry field cultivation, the cultivated land between blueberry rows is vacant, providing no additional yield, which reduces the effective utilization of farmland. Our blueberry–soybean intercropping system did not affect blueberry yield, because the blueberry plants were in the seedling stage when the soybeans were sowed, and soybeans were harvested prior to the blueberry fruiting period. Although monoculture soybean yield was not investigated, our crop spatial allocation design led to increased soybean production within a single season. Notably, soybean powdery mildew does not occur easily in ordinary Huayan 1 crop fields, but showed occurrence in the intercropping system. This may be related to the dry climate during blueberry growth, or to an influence of blueberry plant growth on soybean resistance to powdery mildew; further research is required to elucidate these relationships. In the blueberry-soybean intercropping system, microbial community structure significantly differed between the BPS and HYS samples, with BPS exhibiting higher levels of bacteria α-diversity and lower levels of fungi α-diversity, which may be explained by plant genotype effects on belowground microorganisms (Park et al., 2023 ). OTU enrichment analysis showed significant enrichment of 91 bacterial OTUs and 15 fungal OTUs in HYS samples, whereas 36 bacterial OTUs and 4 fungal OTUs were significantly enriched in BPS samples, suggesting that soybean plants may have a greater ability to utilize underground microorganisms in intercropping systems. Soil pH is a major factor influencing microbial populations in soil. Previous studies have demonstrated that variation in pH can impact the activity of pathogenic bacteria, with acidic soil being more favorable for their development and infection (X. Li et al., 2023 ; Y. Yang et al., 2021 ). Apple and peach trees, potatoes, broad beans, and other food crops can become infected with the potentially harmful bacteria Basidiomycota, Chytridiomycota, and Mucoromycota (Dai et al., 2015 ; Zhao et al., 2023 ). In this study, nine Basidiomycota OTUs were upregulated in BPS compared to CK, which could cause harm to crop plants with the extension of blueberry cultivation years, because many types of root rot disease are related to Basidiomycota (Tan et al., 2017 ). Blueberry cultivation also resulted in acidic soil, which led to dramatic decreases in OM and primary microbial groups including Firmicutes, Actinobacteria, Bacillus, and associated microorganisms that break down proteins; these changes could have negative impacts on soil nutrient cycles (Zhang et al., 2022 ). In BPS, Firmicutes (14 OTUs), Actinobacteriota (29 OTUs), and Gemmatimonadota (2 OTUs) were considerably downregulated compared to CK. Following soybean intercropping, Patescibacteria (13 OTUs) and Bacteroidota (25 OTUs) were significantly enriched in soybean roots compared to CK. In a previous study, Patescibacteria and soil N were strongly positively correlated, whereas Bacteroidota were associated with the use of N-containing materials (Ren et al., 2020 ). Blueberry production significantly reduced soil pH (Fig. 4 ). In addition to pH, OM and TN content are important factors that affect bacteria and fungi in the underground parts of plants, which may significantly contribute to soil deterioration induced by blueberry farming (Ma et al., 2020 ; Wang et al., 2022 ). In a tomato-onion intercropping system, chemical signals released by onion root exudates altered recruitment in the rhizosphere microbiome, thereby enhancing disease resistance in tomato plants (Zhou et al., 2023 ). Intercropping cotton and alfalfa effectively improved soil health by reducing soil bulk density, enhancing salt stress tolerance, and increasing the OM levels and porosity of soil (Wheeler and Von Braun, 2013 ). Burkholderiales significantly influence plant growth and development. In this study, 6 Burkholderiales OTUs were upregulated and 4 were downregulated in HYS, whereas 7 were downregulated and 3 were upregulated in BPS, which demonstrates that interplanting soybeans may help reverse soil damage induced blueberry cultivation (Huang et al., 2019 ). The blueberry-soybean intercropping system increased soil TN, AP, AK, and OM levels, which enhanced nutrient recycling and utilization, and increased the land utilization rate compared to blueberry cultivation alone, as the additional soybean yield benefited grain production. Previous studies have shown that soil pH and heavy metal mobility are negatively correlated, e.g., a substantial increase in the solubility of Cd, Pb, and Zn has been observed as pH decreased (Du Laing et al., 2007 ; Sukreeyapongse et al., 2002 ). Our study also showed that both BPS and HYS samples had higher concentrations of As, Cd, Cr, and Pb than detected in CK, possibly due to either soil acidity or changes in the soil microenvironment caused by root exudates generated after crop planting. 5. Conclusion This study explored a coordinated development planting model consisting of grain and fruit cultivation. Compared to bulk soil without cultivation history, blueberry cultivation led to soil acidification and damaged the structure of the soil microbial community. In contrast, intercropping blueberry and soybean plants more fully utilized resources such as space, light, and heat, while increasing the abundance of beneficial soil microorganisms. Considering the recent implementation of land use limitation policies in China, the coordinated development of grain and fruit crops may be a key trend in future agricultural systems. Thus, in regions with land resource shortages, intercropping offers a potential solution for balancing grain and fruit crop production. Declarations Declaration of competing interest The authors declare that they have no competing interests. Authors' contributions Youyong Zhu proposed the blueberry-soybean intercropping model. Yingbin Li designed and conducted the experiments. Linna Ma, Xiang Li, and Yingbin Li analyzed the data and wrote the manuscript. Manuscript preparation under the advisory of Zhiping Zhang, Huichuan Huang, Yixiang Liu, and Shusheng Zhu. Ting Zhang and Haibin Duan provided seed/seedling resources and field management, respectively. All authors read and approved the manuscript. Acknowledgements This work was supported by the Young Talent Project of Yunnan Revitalization Talent Support Program (XDYC-QNRC-2022-0719), the Basic Research Program for Youths in Yunnan Province (202201AU070182), and the Expert Workstation Project in Yunnan Province (202105AF150046). 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J Soils Sediments 19:1911–1927. https://doi.org/10.1007/s11368-018-2156-3 Stefan L, Hartmann M, Engbersen N, Six J, Schöb C (2021) Positive effects of crop diversity on productivity driven by changes in soil microbial composition. Front Microbiol 12:660749. https://doi.org/10.3389/fmicb.2021.660749 Sukreeyapongse O, Holm PE, Strobel BW, Panichsakpatana S, Magid J, Hansen HCB (2002) pH-dependent release of cadmium, copper, and lead from natural and sludge-amended soils. J Environ Qual 31:1901–1909. https://doi.org/10.2134/jeq2002.1901 Tan Y, Cui Y, Li H, Kuang A, Li X, Wei Y, Ji X (2017) Rhizospheric soil and root endogenous fungal diversity and composition in response to continuous Panax notoginseng cropping practices. 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Field Crops Res 246:107661. https://doi.org/10.1016/j.fcr.2019.107661 Yadav DS, Jaiswal B, Gautam M, Agrawal M (2020) Soil acidification and its impact on plants. Plant Responses Soil Pollut 1–26. https://doi.org/10.1007/978-981-15-4964-9_1 Yang H, Zhang W, Li L (2021) Intercropping: Feed more people and build more sustainable agroecosystems. Front Agric Sci Eng 8:373–386. https://doi.org/10.15302/J-FASE-2021398 Zhang S, Liu X, Zhou L, Deng L, Zhao W, Liu Y, Ding W (2022) Alleviating soil acidification could increase disease suppression of bacterial wilt by recruiting potentially beneficial rhizobacteria. Microbiol Spectr 10:e0233321. https://doi.org/10.1128/spectrum.02333-21 Zhao P, Li Y, Li Y, Liu F, Liang J, Zhou X, Cai L (2023) Applying early divergent characters in higher rank taxonomy of Melampsorineae (Basidiomycota, Pucciniales). Mycology 14:11–36. https://doi.org/10.1080/21501203.2022.2089262 Zhou X, Khashi U, Rahman M, Liu J, Wu F (2021) Soil acidification mediates changes in soil bacterial community assembly processes in response to agricultural intensification. Environ Microbiol 23:4741–4755. https://doi.org/10.1111/1462-2920.15675 Zhou X, Zhang J, Khashi U, Rahman M, Gao D, Wei Z, Wu F, Dini-Andreote F (2023) Interspecific plant interaction via root exudates structures the disease suppressiveness of rhizosphere microbiomes. Mol Plant 16:849–864. https://doi.org/10.1016/j.molp.2023.03.009 Zhou Y, Liu Y, Zhang X, Gao X, Shao T, Long X, Rengel Z (2022) Effects of soil properties and microbiome on highbush blueberry ( Vaccinium corymbosum ) Growth. Agronomy 12, 1263. https://doi.org/10.3390/agronomy12061263 Cite Share Download PDF Status: Published Journal Publication published 15 Jun, 2024 Read the published version in Plant and Soil → Version 1 posted Editorial decision: Major revisions 16 Feb, 2024 Reviewers agreed at journal 31 Jan, 2024 Reviewers invited by journal 03 Jan, 2024 Editor invited by journal 14 Dec, 2023 Editor assigned by journal 14 Dec, 2023 First submitted to journal 14 Dec, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3761618","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":265285846,"identity":"54359777-ae91-4bb4-91c5-9be7d41138d3","order_by":0,"name":"Linna Ma","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Linna","middleName":"","lastName":"Ma","suffix":""},{"id":265285847,"identity":"2c3c4081-26ae-491a-b7c4-9ef5d17b228d","order_by":1,"name":"Xiang Li","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Li","suffix":""},{"id":265285848,"identity":"2aa3c64a-8bdf-4ba2-939d-9785379e5b30","order_by":2,"name":"Zhiping Zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhiping","middleName":"","lastName":"Zhang","suffix":""},{"id":265285849,"identity":"c0b06e48-ceaa-447a-a59c-4b86d004c2de","order_by":3,"name":"Ting Zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Zhang","suffix":""},{"id":265285850,"identity":"2b7b48df-c6ff-407b-bec7-a977f01c1326","order_by":4,"name":"Haibin Duan","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Haibin","middleName":"","lastName":"Duan","suffix":""},{"id":265285851,"identity":"84d184b3-b537-4ed0-a05f-d27cba5dbfc8","order_by":5,"name":"Huichuan Huang","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Huichuan","middleName":"","lastName":"Huang","suffix":""},{"id":265285852,"identity":"d02d6783-98cc-4660-b2e5-032291275941","order_by":6,"name":"Yixiang Liu","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yixiang","middleName":"","lastName":"Liu","suffix":""},{"id":265285853,"identity":"a29bc6e6-707c-42c5-b417-794aee01298f","order_by":7,"name":"Shusheng Zhu","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shusheng","middleName":"","lastName":"Zhu","suffix":""},{"id":265285854,"identity":"543a9b53-cc0e-4291-bcbf-78290f6a1141","order_by":8,"name":"Youyong Zhu","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Youyong","middleName":"","lastName":"Zhu","suffix":""},{"id":265285855,"identity":"1a1924e4-570d-4268-b526-29b0835b86b8","order_by":9,"name":"Yingbin Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYBACPmYGNsYPBjY8/PwNRGphA2phlqhIk5GccYBYLSDEc+awjUFDArFa2HnMHki2necxYDjA+OFjDlEO4zE3KGy7zWPO3MAsOXMbcVrMJCSBWiwbDrAx8xKthbftHI/BgQRStPCcOUCSFrZyY4mKZB7JGQebifMLP//hbQ8/GNjZ8/M3H/zwkRgtDAwcBlAGYwNR6oGA/QGxKkfBKBgFo2CkAgDfmC0NFw3FOAAAAABJRU5ErkJggg==","orcid":"","institution":"Yunnan Agricultural University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yingbin","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2023-12-16 04:24:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3761618/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3761618/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11104-024-06775-8","type":"published","date":"2024-06-15T15:03:41+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":49301324,"identity":"c0c8f465-f65f-40d6-ae17-153b1b97d017","added_by":"auto","created_at":"2024-01-08 09:43:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4316533,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Map of the test site. (b) Schematic diagram of the blueberry–soybean intercropping system. (c) Soybean plants approaching maturation at 114 days after transplanting. (d) Symptoms of powdery mildew on soybean plant leaves. (e) Crop yield and disease occurrence indicators including plant height, pod number, bean number, and 100-grain weight (n = 120 for disease index calculations; n = 30 for soybean yield indicators). Pcs, pieces.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3761618/v1/5d0541a9a14de4fd66bca50c.png"},{"id":49301328,"identity":"14cb9d43-9c68-4edf-a859-8e3e7e7db187","added_by":"auto","created_at":"2024-01-08 09:43:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":16031249,"visible":true,"origin":"","legend":"\u003cp\u003eMicrobial community enrichment analysis. (a) Blueberry pot soil (BPS) sampled at a depth of 5 cm and rhizosphere soil sampled from Huayan 1 soybean plants intercropped with blueberry (HYS) were collected separately in September, 2022. Bulk soil without cultivation history was used as control (CK) (n = 4 for each group). Data are means ± standard deviations (SDs), compared using Fisher’s least significant difference (LSD) test (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01). The α-diversity of (b) bacterial and (c) fungal communities, determined according to the abundance-based coverage estimators (ACE) and Chao1 indexes, respectively (n = 4). The β-diversity of (d) bacterial and (e) fungal communities (n = 4). The 10 most abundant (f) bacterial and (g) fungal phyla in each soil group.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3761618/v1/a729d1e2c77d8e1768154fdb.png"},{"id":49301327,"identity":"582e8979-fd8b-4e13-a6a3-e1c1a176ea23","added_by":"auto","created_at":"2024-01-08 09:43:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":8064426,"visible":true,"origin":"","legend":"\u003cp\u003eMicroorganism enrichment and depletion in soil sampled from different crops. Bacteria in (a) BPS and (b) HYS compared to CK. (c) Heatmap of significantly enriched bacterial operational taxonomic units (OTUs) in BPS, HYS, and CK samples. Fungi in (d) BPS and (e) HYS samples compared to CK. (f) Heatmap of significantly enriched fungal OTUs in BPS, HYS, and CK samples. Each point represents an individual OTU, and positions along the y-axis represent changes in abundance compared to CK. B1 to B4 represent four biological replicates of BYS; CK1 to CK4 represent four biological replicates of CK; HY1 to HY4 represent four biological replicates of HYS.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3761618/v1/a5be3191cee96e17d7e86bc6.png"},{"id":49301326,"identity":"cce32d86-b1ed-4a28-8542-9672f1533b58","added_by":"auto","created_at":"2024-01-08 09:43:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":241294,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Physicochemical factors of soil influencing changes in microbial community structure among soil types, including heavy metal levels. Redundancy analysis of (b) bacterial and (c) fungal communities and the chemical properties of soils. Mantel analysis of differences in (d) bacteria and (e) fungi and the chemical properties of soils. Orange, green, and gray lines indicate \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, 0.01 \u0026lt; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, and \u003cem\u003eP\u003c/em\u003e ≥ 0.05, respectively; line width represents the R value. Correlation heatmaps of differences in (f) bacterial and (g) fungal communities and soil environmental factors.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3761618/v1/f16dd33869632601412beca9.png"},{"id":49301770,"identity":"b7c65e39-3f5b-4be8-99a0-54ade4d81d4f","added_by":"auto","created_at":"2024-01-08 09:51:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4486016,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional analysis of differentially enriched soil microorganisms. (a) Phylogenetic tree. Red, green, and blue indicate BPS, HYS, and CK, respectively. Outer circle indicates the relative abundance of microorganisms in the three treatments. Darker colors indicate greater enrichment of microorganisms within a soil group. In the inner circle, circles and triangles indicate bacteria and fungi; colors indicate the 10 most abundant phyla. (b) Results of PICRUST2-based function prediction and analysis of variance (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01) for nitrogen-related bacteria with differential abundance.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3761618/v1/8b770a2c946654357159c9dd.png"},{"id":58822659,"identity":"9a9569f2-f9cb-40f5-a86e-6b9bac6bbe95","added_by":"auto","created_at":"2024-06-21 16:46:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":33237140,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3761618/v1/f1c22691-c89b-4876-97ac-60e099207840.pdf"}],"financialInterests":"","formattedTitle":"Soil microbial community and chemical properties response to blueberry–soybean intercropping system","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBlueberries are an economically important crop worldwide; they are rich in anthocyanins and vitamins and are among the top fruits recommended by the United Nations Food and Agriculture Organization for their health benefits (Routray and Orsat, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Blueberries have been introduced to Yunnan since 2004. The region has emerged as a key hub for early fresh blueberry production, gaining recognition for its high-quality berries in China (Jiafeng et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, in 2020, a policy proposed to ensure grain production in China firmly prohibited any use of cropland for non-agricultural purposes, such that fruit production was forbidden on basic farmland. This policy created a need for innovative methods for coordinated agricultural development between grain and fruit crops.\u003c/p\u003e \u003cp\u003eBlueberry plants are adapted to acidic soil, with an optimal soil pH of 4.5\u0026ndash;5.5. Excessively high or low pH values can affect their growth and development. Soil pH is a major driver of bacterial community structure (Che et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Blueberry farmers typically use large amounts of acidic fertilizer during cultivation to adjust the soil pH, which intensifies soil acidification in field production (Ochmian et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Yadav et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Microbiomes associated with plant roots play crucial roles in the adaptation of plants to environmental stresses, enhancing fruit productivity and optimizing soil nutrient cycling (Kumar and Dubey, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Saleem et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The consumption of acidic substances in soil by blueberry plants further reduces soil biodiversity, resulting in soil ecosystem imbalances, which are characterized by deficiencies in essential elements for crops and increased heavy metal solubility (Chen et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Haynes and Swift, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Pan et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Blueberry plant growth is closely related to organic matter (OM) levels, enzyme activity, and microbial community diversity in soils (Zhou et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Variation in the structure of blueberry roots also has a significant impact on the microbial diversity at roots. Microbial diversity is significantly lower in the blueberry rhizosphere than in other soils, and there are also less-complex interactions between bacteria and fungi (Che et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, the development of effective methods to mitigate the impact of blueberry cultivation on the soil environment is crucial, particularly in countries with limited arable land resources.\u003c/p\u003e \u003cp\u003eInterplanting is widely implemented in modern agriculture to address shortages of arable land and promote sustainable agriculture (Yang et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A previous study analyzed 226 interplanting field trials conducted in various regions worldwide and found that this approach was effective for controlling pests, diseases, and weeds, improving nitrogen (N) use efficiency and achieving sustainable intensive agriculture (Li et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Compared to monoculture, interplanting better utilizes land, light, and water resources, thereby improving yield per unit area (Li et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Interplanting different crops helps improve the microbial diversity, OM levels, and fertility of soils. Soybeans are among the preferred grain crops for interplanting, as they have high N fixation and soil fertility improvement abilities (Ablimit et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Stefan et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Legumes have been successfully interplanted with several other crops, such as corn, sorghum, and tea trees (Du et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Various bacterial genera including \u003cem\u003eRhizobium\u003c/em\u003e, \u003cem\u003eNovosphingobium\u003c/em\u003e, \u003cem\u003ePhenylobacterium\u003c/em\u003e, \u003cem\u003eStreptomyces\u003c/em\u003e, and \u003cem\u003eNocardioides\u003c/em\u003e are greatly enriched in the soybean rhizosphere, irrespective of soil type (Liu et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Soybean roots can penetrate the sugarcane rhizosphere in intercropping systems, allowing sugarcane roots to accumulate more N-fixing bacteria, resulting in higher nutrient levels and increased biological activity in soils. Thus, intercropping is a beneficial measure that effectively reduces the excessive use of chemical fertilizers (Liu et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Solanki et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFood security has long been considered an important national security issue worldwide (Muluneh, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wheeler and Von Braun, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In this study, we designed a blueberry\u0026ndash;soybean intercropping system in Yunnan Province, China, and evaluated its impact on crop yield, disease occurrence, and the soil environment using survey statistics, high-throughput sequencing, and correlation analysis. We explored a method for maintaining the supply\u0026ndash;demand balance between grain and fruit production, and cultivated land protecting. Our findings will be particularly useful for regions facing both food shortages and limited arable land.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Experimental design and soil sampling\u003c/h2\u003e \u003cp\u003eExperiments were conducted at a blueberry plantation of the Suninfinit Agriculture Group located in Donghui, Lancang County, Pu\u0026rsquo;er, southwestern Yunnan Province, China (22.42\u0026deg;N, 99.79\u0026deg;E; elevation: 1,297 m a.s.l.; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Blueberry plants were transplanted into a 25 L pots filled with substrate. Nutrients were supplied by drip irrigation at a flow rate of 2 L/h. The optimum soil pH range for blueberry growth is 4.5\u0026ndash;5.5. The soybean variety Huayan 1 was provided by the College of Agronomy and Biotechnology at Yunnan Agricultural University. Soybean seeds were sown between blueberry plant rows, at a spacing of 0.30 m \u0026times; 0.30 m in May 2022 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). In September 2022, soil samples were collected from the rhizosphere of Huayan 1 plants (Huayan 1 soil, HYS) and from the blueberry pots at a depth of 5 cm (blueberry pot soil, BPS). Bulk soil without cultivation history was used as the control (CK). Briefly, each soil sample was randomly collected from five pots and mixed into four biological replicates. All samples were placed in 5 mL centrifuge tubes and stored at \u0026minus;\u0026thinsp;80\u0026deg;C for DNA extraction and high-throughput sequencing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Soybean yield and disease occurrence analyses\u003c/h2\u003e \u003cp\u003eWe analyzed soybean yield and disease occurrence in leaves sampled from the blueberry\u0026ndash;soybean intercropping system. Briefly, leaves were collected from a total of 120 plants and examined for signs of soybean powdery mildew. Disease was graded from 0 to IX according to the area of leaf spots, as follows: 0, healthy; I, \u0026lt;\u0026thinsp;5% of the leaf area; III, 6\u0026ndash;15%; V, 16\u0026ndash;25%; VII, 26\u0026ndash;50%; and Ⅸ, \u0026gt; 50%. The disease index was calculated as Σ (Disease class frequency \u0026times; Score of disease class) \u0026times; 100/Total number of plants sampled/Maximum disease index. In all, 30 soybean plants were measured to characterize yield in terms of plant height and numbers of pods, grains, and nodules. The 100-grain weight was measured in randomly selected plants, with three independent biological replicates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. DNA extraction and high-throughput sequencing\u003c/h2\u003e \u003cp\u003eTotal genomic DNA from each soil sample was extracted using the Fast DNA Spin Kit for soil (MP Biomedicals, Solon, OH, USA), following the manufacturer\u0026rsquo;s instructions. The internal transcribed spacer 1 (ITS) region targeting fungi was amplified using the primer pairs ITS1/ITS4 (5\u0026rsquo;-CTTGGTCATTTAGAGGAAGTAA-3\u0026rsquo; and 5\u0026rsquo;-TCCTCCGCTTATTGATATGC-3\u0026rsquo;). The V4\u0026ndash;V5 regions of the bacterial 16S rRNA gene were amplified using the primer sets 27F (5\u0026rsquo;-AGRGTTTGATYNTGGCTCAG-3\u0026rsquo;) and 1492R (5\u0026rsquo;-TASGGHTACCTTGTTASGACTT-3\u0026rsquo;), under the following thermal conditions: initial denaturation at 95\u0026deg;C for 3 min, followed by 27 cycles at 95\u0026deg;C for 30 s, 55\u0026deg;C for 30 s, 72\u0026deg;C for 45 s, and 72\u0026deg;C for 10 min. The purified amplicons were sequenced using single molecule real-time sequencing (SMRT) and the PacBio Sequel sequencing platform (PacBio, Menlo Park, CA, USA), according to the standard protocol. Data preprocessing was conducted using Lima v1.7.0, Cutadapt v1.9.1, and UCHIME v4.2. These operations were completed by Biomarker Technologies Corp. (Beijing, China). All ITS and 16S rRNA gene sequences may be found in the National Center for Bioinformation Technology Short Read Archive (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://trace.ncbi.nlm.nih.gov/traces/sra\u003c/span\u003e\u003cspan address=\"https://trace.ncbi.nlm.nih.gov/traces/sra\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) under accession number PRJNA1036779.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Soil chemistry analyses\u003c/h2\u003e \u003cp\u003eSoil samples were sieved to a diameter of 2 mm and dried in an oven. Soil pH was determined using a pH meter (PHS-3E, Shanghai, China) in a soil/water suspension (1:2.5, w/v) according to standard NY/T 1121.2\u0026ndash;2006. OM was assayed using dichromate wet combustion according to standard NY/T 1121.6\u0026ndash;2006. Total N (TN) content was analyzed using an Azotometer (SKD-1000, Kjeldahl, Shanghai, China) according to standard NY/T 1121.24\u0026ndash;2012. Available phosphorus (AP) was analyzed using an ultraviolet\u0026ndash;visible spectrophotometer (T6 series; Persee Analytics, Auburn, CA, USA) according to standard NY/T 1121.7\u0026ndash;2014, and available potassium (AK) was analyzed by flame atomic absorption spectrophotometry (Z-2310; Hitachi, Tokyo, Japan) according to standard NY/T 889\u0026ndash;2004. Heavy metals were also quantified. Arsenic (As) levels were measured using an atomic fluorescence spectrometer (AFS-8520, Haiguang, Beijing, China) according to standard GB/T 22105.2\u0026ndash;2008, and cadmium (Cd), chromium (Cr), and lead (Pb) were measured using inductively coupled plasma\u0026ndash;mass spectrometry (ICAP RQ, Thermo Fisher Scientific, Waltham, MA, USA) according to standards DZ/T 0279.5\u0026ndash;2016, DZ/T 0279.2\u0026ndash;2016, and DZ/T 0279.2\u0026ndash;2016, respectively. All soil samples from each treatment were tested with three replications.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Microbial community diversity analysis\u003c/h2\u003e \u003cp\u003eTo mitigate the potential impact of sequencing depth, we used the rarefy function in the \u003cem\u003evegan\u003c/em\u003e v2.6.4 package of the R software (R Core Team, Vienna, Austria). To investigate the beta diversity of bacterial and fungal communities across different treatments, we initially computed community distance using the vegdist function in the R \u003cem\u003evegan\u003c/em\u003e package using the bray distance metric. Then we conducted principal coordinate analysis (PCoA) using the pcoa function in the \u003cem\u003eape\u003c/em\u003e v5.7.1 package in R. Finally, we used the \u003cem\u003eggplot2\u003c/em\u003e v3.4.2 package in R to visually represent the PCoA results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Differential abundance analysis of microbial phyla\u003c/h2\u003e \u003cp\u003ePhylum-level analysis is essential to assess variation in the microbial community. We summed the abundances of microorganisms found in the soil samples at the phylum level, excluding unclassified phyla, and then ranked the summed values in descending order. Phyla ranked 10 or lower were collectively labeled as others. We performed analysis of variance (ANOVA) using the anova_test function in the \u003cem\u003erstatix\u003c/em\u003e v0.7.2 package in R (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/kassambara/rstatix\u003c/span\u003e\u003cspan address=\"https://github.com/kassambara/rstatix\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Then we used the glht function in the \u003cem\u003emultcomp\u003c/em\u003e v1.4.23 package in R to perform multiple comparisons among the ANOVA results based on Tukey\u0026rsquo;s method. We visualized the data using the \u003cem\u003eggplot2\u003c/em\u003e R package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Differential abundance analysis of the microbiome\u003c/h2\u003e \u003cp\u003eDifferential microbial abundance can be analyzed through various methods. In this study, we compared microbial abundance in the BPS and HYS samples to that of the control group (CK) using the \u003cem\u003eDESeq2\u003c/em\u003e v1.38.3 package in R. The input data were a matrix of operational taxonomic unit (OTUs). Differentially abundant microbes were screened using the criteria of |log\u003csub\u003e2\u003c/sub\u003efold change (FC)| \u0026gt; 1 and adjusted \u003cem\u003eP\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eadj\u003c/sub\u003e)\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Microbes fulfilling these criteria were categorized as enriched, those with |log\u003csub\u003e2\u003c/sub\u003eFC| \u0026lt; 1 and \u003cem\u003eP\u003c/em\u003e\u003csub\u003eadj\u003c/sub\u003e \u0026lt; 0.05 were categorized as depleted, and the remaining microorganisms were categorized as nonsignificantly different (NS). The \u003cem\u003eggplot2\u003c/em\u003e and \u003cem\u003epheatmap\u003c/em\u003e v1.0.12 packages in R were used to visualize the overall results and the relative abundances of differentially abundant microorganisms, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Redundancy analysis (RDA)\u003c/h2\u003e \u003cp\u003eTo analyze the relationship between the physicochemical properties of the soil and microbial communities, we conducted RDA using the rda function in the R \u003cem\u003evegan\u003c/em\u003e package (Dixon 2003) based on indicators of physical and chemical properties in the soil as input data. To obtain the corrected R\u003csup\u003e2\u003c/sup\u003e value, we used the RsquareAdj function in the \u003cem\u003evegan\u003c/em\u003e package. The results were visualized using the \u003cem\u003eggplot2\u003c/em\u003e package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9. Mantel test\u003c/h2\u003e \u003cp\u003eTo examine the relationship between enriched and depleted microorganisms and the physicochemical properties of soils within each treatment, we performed a Mantel test using the mantel_test function in the R package \u003cem\u003elinkET\u003c/em\u003e v0.0.6 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/Hy4m/linkET\u003c/span\u003e\u003cspan address=\"https://github.com/Hy4m/linkET\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), with microbial community and soil physicochemical property data as input. The qcorrplot and geom_couple functions of the \u003cem\u003elinkET\u003c/em\u003e package were used to visualize the results. To compute correlations among the differential abundances of microorganisms and the physicochemical properties of soil, we used the R package \u003cem\u003eWGCNA\u003c/em\u003e v1.72-1, and to visualize the correlation matrix, we used the \u003cem\u003epheatmap\u003c/em\u003e package.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Intercropping soybean yield characteristics\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, soybeans approached maturation 114 days after transplanting. Powdery mildew disease occurred, with a disease index of 3.92 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). The average height of soybean plants was 53.76\u0026thinsp;\u0026plusmn;\u0026thinsp;7.51 cm, with averages of 37.14\u0026thinsp;\u0026plusmn;\u0026thinsp;4.99 pods per plant, 70.86\u0026thinsp;\u0026plusmn;\u0026thinsp;6.44 grains per plant, and 4.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05 nodules per plant. The 100-grain weight was 54.84\u0026thinsp;\u0026plusmn;\u0026thinsp;1.39 g (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Microbial community characteristics\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, fungal and bacterial diversity were significantly higher in the BPS and HYS samples than in CK (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). BPS showed significantly higher abundance-based coverage estimator (ACE) and Chao1 indices than both HYS and CK (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, Supplementary Table\u0026nbsp;1). However, the fungal community was more enriched in HYS, with significantly higher α-diversity compared to BPS and CK (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec, Supplementary Table\u0026nbsp;2). The overall explanation rates (sum of principle coordinate axes 1 and 2) for the samples were 60.31% for the bacterial community (axis 1, 32.23%; axis 2, 28.08%) and 38.40% for the fungal community (axis 1, 20.44%; axis 2, 17.96%). This suggests that different crops in this interplanting system affected the composition of soil microorganisms (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed-e). The nine most abundant phyla in the bacterial communities of the soil samples were Acidobacteriota, Actinobacteriota, Bacteroidota, Myxococcota, Patescibacteria, Chloroflexota, Gemmatimonadetes, Proteobacteria, and Verrucomicrobia, with significant differences in the abundances of the first five among the three groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef). Acidobacteriota was significantly enriched in BPS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01), whereas Actinobacteriota and Patescibacteria were significantly enriched in HYS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000233, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008, respectively; Supplementary Table\u0026nbsp;3). The nine most abundant phyla in the fungal communities were Ascomycota, Basidiomycota, Calcarisporiellomycota, Chytridiomycota, Glomeromycota, Kickxellomycota, Mortierellomycota, Mucoromycota, and Rozellomycota (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Differential abundance of OTUs in the intercropping system\u003c/h2\u003e \u003cp\u003eIn the BPS bacterial community, 36 OTUs were enriched and 93 OTUs were depleted compared to CK (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, Supplementary Table\u0026nbsp;4), whereas in the HYS bacterial community, 91 OTUs were enriched and 53 OTUs were depleted (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, Supplementary Table\u0026nbsp;5). In the BPS fungal community, 4 OTUs were enriched and 6 OTUs were depleted, whereas in the HYS fungal community, 15 OTUs were enriched and 9 OTUs were depleted (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed-e). These results suggest that microorganism enrichment was more pronounced in HYS than in BPS, with BPS tending toward microbial depletion. Unique OTUs were enriched in both bacterial and fungal communities of all three groups; however, fewer microorganisms were significantly enriched in BPS than in HYS (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, f). These results indicate that different crops selectively alter microbial community structure during intercropping.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Regional variation in soil chemistry in the intercropping system\u003c/h2\u003e \u003cp\u003eExcept for a significant decrease in soil pH after blueberry cultivation, all other tested indicators were significantly higher in both BPS and HYS samples than in CK (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Soil pH was also significantly lower in BPS (5.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35) than in HYS (6.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23) and CK (5.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39). Among soil nutrients, HYS, BPS, and CK had TN contents of 1433.500\u0026thinsp;\u0026plusmn;\u0026thinsp;147.725, 1100.750\u0026thinsp;\u0026plusmn;\u0026thinsp;93.415, and 1003.083\u0026thinsp;\u0026plusmn;\u0026thinsp;118.948 mg/kg, respectively. The average AP contents of BPS, HYS, and CK were 22.972\u0026thinsp;\u0026plusmn;\u0026thinsp;6.927, 16.001\u0026thinsp;\u0026plusmn;\u0026thinsp;3.321, and 8.626\u0026thinsp;\u0026plusmn;\u0026thinsp;2.680 mg/kg, and those of AK were 108.182\u0026thinsp;\u0026plusmn;\u0026thinsp;44.088, 72.350\u0026thinsp;\u0026plusmn;\u0026thinsp;12.580, and 59.622\u0026thinsp;\u0026plusmn;\u0026thinsp;17.943 mg/kg, respectively. The OM contents of HYS, BPS, and CK were 24.075\u0026thinsp;\u0026plusmn;\u0026thinsp;4.279, 17.083\u0026thinsp;\u0026plusmn;\u0026thinsp;2.181, and 13.425\u0026thinsp;\u0026plusmn;\u0026thinsp;0.676 g/kg, respectively. Among heavy metals, As, Cd, Cr, and Pb levels in HYS and BPS samples significantly increased throughout the intercropping period, compared to CK.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Soil chemistry mediates changes in the structure of the microbial community\u003c/h2\u003e \u003cp\u003eIn BPS, bacterial and fungal enrichment were positively correlated with As, Cr, Pb, AP, and AK contents, whereas in HYS, OM, TN, and Cd levels and pH were positively correlated with microbial enrichment (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb-c). Mantel analysis results showed that in HYS, significant differential enrichment of the bacterial phyla Proteobacteria, Acidobacteriota, Myxococcota, Bacteroidota, Actinobacteriota, and Chloroflexi was primarily influenced by As, OM, and TN contents (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Supplementary Tables\u0026nbsp;6 and 7). In BPS, significant differential enrichment of the fungal phyla Ascomycota, Rozellomycota, and Mortierellomycota was influenced by AP and AK content and pH (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed, Supplementary Table\u0026nbsp;8). However, in HYS, the significant enrichment of fungal phyla was not significantly related to environmental factors (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee, Supplementary Table\u0026nbsp;8). The correlation heatmap showed that OM and TN contents and pH were the main factors associated with differential microbial community structure between HYS and BPS, compared to CK (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef-g).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Functions of differentially enriched microorganisms in the intercropping system\u003c/h2\u003e \u003cp\u003eNext, we examined the evolutionary relationships among significantly differentially enriched microorganisms in the three soil groups. The 10 bacterial phyla with the greatest significant differences were Acidobacteriota, Actinobacteriota, Bacteroidota, Chloroflexi, Gemmatimonadota, Patescibacteria, Proteobacteria, Verrucomicrobiota, unclassified, and other bacteria (Supplementary Table\u0026nbsp;3). The 10 fungal phyla with the greatest significant differences were Ascomycota, Basidiomycota, Calcarisporiellomycota, Chytridiomycota, Glomeromycota, Mortierellomycota, Mucoromycota, Rozellomycota, unclassified, and other fungi (Supplementary Table\u0026nbsp;9). Most of the microorganisms that were differentially affected by the soil environment were bacteria, and the vast majority of these were Proteobacteria, Actinobacteriota, and Bacteroidota (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003ePotentially functional bacteria related to N fixation were examined and their abundances were compared to CK. HYS samples were significantly enriched in bacteria with functions related to the N regulatory protein C (NtrC) family, N regulatory proteins IIA and P-II 2, N fixation regulation proteins, and other N-related functions (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). CK samples were enriched in N regulatory proteins P-Ⅱ 1 and A, N fixation protein NifU, and related proteins, as well as N assimilation regulatory proteins. Notably, BPS samples had significantly lower abundance of microbes potentially related to N fixation (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAs a result of global climate change, urban expansion, and farmland resource limitations, the protection and utilization of cultivated land have become important priorities toward sustainable global agricultural development and ensuring food security(Liu and Zhou, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ramankutty et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Smith and Gregory, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Intercropping has resolved crop production problems in many countries with limited agricultural land resources, and has been widely promoted and applied worldwide. Reasonable crop rotation achieves diversification in agricultural planting systems, in terms of both time and space, and addresses issues such as limited arable land, plant disease outbreaks, and soil degradation, which are commonly encountered in monoculture production. In this study, we demonstrate that intercropping could contribute to the coordinated development of grain and fruit production, importantly, the intercropping system could improve the acidification of soil as well as reduce the depletion of soil functional microorganisms caused by continuous monoculture of blueberries. Our findings are important for regions struggling to address both food shortages and limitations to arable land area.\u003c/p\u003e \u003cp\u003eBlueberry plants belong to the genus \u003cem\u003eVaccinium\u003c/em\u003e (family Ericaceae). These short, perennial shrubs are typically transplanted to a location where they will remain for several years; they require a row spacing of at least 1.5\u0026ndash;2.0 m. In traditional blueberry field cultivation, the cultivated land between blueberry rows is vacant, providing no additional yield, which reduces the effective utilization of farmland. Our blueberry\u0026ndash;soybean intercropping system did not affect blueberry yield, because the blueberry plants were in the seedling stage when the soybeans were sowed, and soybeans were harvested prior to the blueberry fruiting period. Although monoculture soybean yield was not investigated, our crop spatial allocation design led to increased soybean production within a single season. Notably, soybean powdery mildew does not occur easily in ordinary Huayan 1 crop fields, but showed occurrence in the intercropping system. This may be related to the dry climate during blueberry growth, or to an influence of blueberry plant growth on soybean resistance to powdery mildew; further research is required to elucidate these relationships.\u003c/p\u003e \u003cp\u003eIn the blueberry-soybean intercropping system, microbial community structure significantly differed between the BPS and HYS samples, with BPS exhibiting higher levels of bacteria α-diversity and lower levels of fungi α-diversity, which may be explained by plant genotype effects on belowground microorganisms (Park et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). OTU enrichment analysis showed significant enrichment of 91 bacterial OTUs and 15 fungal OTUs in HYS samples, whereas 36 bacterial OTUs and 4 fungal OTUs were significantly enriched in BPS samples, suggesting that soybean plants may have a greater ability to utilize underground microorganisms in intercropping systems.\u003c/p\u003e \u003cp\u003eSoil pH is a major factor influencing microbial populations in soil. Previous studies have demonstrated that variation in pH can impact the activity of pathogenic bacteria, with acidic soil being more favorable for their development and infection (X. Li et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Y. Yang et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Apple and peach trees, potatoes, broad beans, and other food crops can become infected with the potentially harmful bacteria Basidiomycota, Chytridiomycota, and Mucoromycota (Dai et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this study, nine Basidiomycota OTUs were upregulated in BPS compared to CK, which could cause harm to crop plants with the extension of blueberry cultivation years, because many types of root rot disease are related to Basidiomycota (Tan et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Blueberry cultivation also resulted in acidic soil, which led to dramatic decreases in OM and primary microbial groups including Firmicutes, Actinobacteria, Bacillus, and associated microorganisms that break down proteins; these changes could have negative impacts on soil nutrient cycles (Zhang et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In BPS, Firmicutes (14 OTUs), Actinobacteriota (29 OTUs), and Gemmatimonadota (2 OTUs) were considerably downregulated compared to CK. Following soybean intercropping, Patescibacteria (13 OTUs) and Bacteroidota (25 OTUs) were significantly enriched in soybean roots compared to CK. In a previous study, Patescibacteria and soil N were strongly positively correlated, whereas Bacteroidota were associated with the use of N-containing materials (Ren et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBlueberry production significantly reduced soil pH (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In addition to pH, OM and TN content are important factors that affect bacteria and fungi in the underground parts of plants, which may significantly contribute to soil deterioration induced by blueberry farming (Ma et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In a tomato-onion intercropping system, chemical signals released by onion root exudates altered recruitment in the rhizosphere microbiome, thereby enhancing disease resistance in tomato plants (Zhou et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Intercropping cotton and alfalfa effectively improved soil health by reducing soil bulk density, enhancing salt stress tolerance, and increasing the OM levels and porosity of soil (Wheeler and Von Braun, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Burkholderiales significantly influence plant growth and development. In this study, 6 Burkholderiales OTUs were upregulated and 4 were downregulated in HYS, whereas 7 were downregulated and 3 were upregulated in BPS, which demonstrates that interplanting soybeans may help reverse soil damage induced blueberry cultivation (Huang et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The blueberry-soybean intercropping system increased soil TN, AP, AK, and OM levels, which enhanced nutrient recycling and utilization, and increased the land utilization rate compared to blueberry cultivation alone, as the additional soybean yield benefited grain production. Previous studies have shown that soil pH and heavy metal mobility are negatively correlated, e.g., a substantial increase in the solubility of Cd, Pb, and Zn has been observed as pH decreased (Du Laing et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Sukreeyapongse et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Our study also showed that both BPS and HYS samples had higher concentrations of As, Cd, Cr, and Pb than detected in CK, possibly due to either soil acidity or changes in the soil microenvironment caused by root exudates generated after crop planting.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study explored a coordinated development planting model consisting of grain and fruit cultivation. Compared to bulk soil without cultivation history, blueberry cultivation led to soil acidification and damaged the structure of the soil microbial community. In contrast, intercropping blueberry and soybean plants more fully utilized resources such as space, light, and heat, while increasing the abundance of beneficial soil microorganisms. Considering the recent implementation of land use limitation policies in China, the coordinated development of grain and fruit crops may be a key trend in future agricultural systems. Thus, in regions with land resource shortages, intercropping offers a potential solution for balancing grain and fruit crop production.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthors' contributions\u003c/h2\u003e \u003cp\u003eYouyong Zhu proposed the blueberry-soybean intercropping model. Yingbin Li designed and conducted the experiments. Linna Ma, Xiang Li, and Yingbin Li analyzed the data and wrote the manuscript. Manuscript preparation under the advisory of Zhiping Zhang, Huichuan Huang, Yixiang Liu, and Shusheng Zhu. Ting Zhang and Haibin Duan provided seed/seedling resources and field management, respectively. All authors read and approved the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis work was supported by the Young Talent Project of Yunnan Revitalization Talent Support Program (XDYC-QNRC-2022-0719), the Basic Research Program for Youths in Yunnan Province (202201AU070182), and the Expert Workstation Project in Yunnan Province (202105AF150046).\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe original contributions presented in the study are publicly available. These data can be found in the Short Read Archive (SRA) at NCBI database under accession number PRJNA1036779. The datasets analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAblimit R, Li W, Zhang J, Gao H, Zhao Y, Cheng M, Meng X, An L, Chen Y (2022) Altering microbial community for improving soil properties and agricultural sustainability during a 10-year maize-green manure intercropping in Northwest China. J Environ Manage 321:115859. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jenvman.2022.115859\u003c/span\u003e\u003cspan address=\"10.1016/j.jenvman.2022.115859\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChe J, Wu Y, Yang H, Wang S, Wu W, Lyu L, Li W (2022) Long-term cultivation drives dynamic changes in the rhizosphere microbial community of blueberry. 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Agronomy 12, 1263. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/agronomy12061263\u003c/span\u003e\u003cspan address=\"10.3390/agronomy12061263\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"plant-and-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plso","sideBox":"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)","snPcode":"11104","submissionUrl":"https://submission.nature.com/new-submission/11104/3","title":"Plant and Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Blueberry–soybean intercropping, coordinated development, nitrogen regulation, soil microbes","lastPublishedDoi":"10.21203/rs.3.rs-3761618/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3761618/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eBackground and Aims\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCurrent global population growth and agricultural land resource limitations have led to intensifying conflicts between grain and fruit production.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMethods\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ewe designed a potted blueberry–soybean intercropping system to evaluate its impacts on crop yield, disease occurrence, and soil microbial community composition using survey statistics, high-throughput sequencing, and correlation analysis.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eResults\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe results demonstrate that the system is a feasible solution for obtaining additional soybean yield. Blueberry pot soil (BPS) sampled and rhizosphere soil sampled from intercropped Huayan 1 soybean plants (HYS) showed significantly higher fungal and bacterial diversity than control bulk soil (CK) with no cultivation history. Microbial communities and unique OTUs were differentially enriched in BPS and HYS, respectively, and the latter effect was more pronounced. pH, organic matter, and total N were the main factors driving soil chemistry-mediated microbial differences in the community between CK and both HYS and BPS. The significantly lower microbial abundance in BPS was likely related to N fixation, whereas significantly enriched bacteria in HYS were related to the N regulatory protein C protein family, N regulatory IIA and P-II2 proteins, N fixation regulation proteins, and other N-related functions (\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05), indicating that blueberry–soybean intercropping significantly improves microbial function in the soil.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConclusion\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThese findings demonstrate that intercropping system could improve the acidification of soil and reduce the depletion of soil functional microorganisms caused by continuous monoculture of blueberries. Intercropping could help coordinated development of grain and fruit production, particularly in regions facing both food shortages and limited arable land in the world.\u003c/p\u003e","manuscriptTitle":"Soil microbial community and chemical properties response to blueberry–soybean intercropping system","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-08 09:43:28","doi":"10.21203/rs.3.rs-3761618/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2024-02-16T20:26:54+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-01-31T14:40:17+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-04T02:37:05+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Plant and Soil","date":"2023-12-15T04:30:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-12-15T03:56:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant and Soil","date":"2023-12-14T21:43:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"plant-and-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plso","sideBox":"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)","snPcode":"11104","submissionUrl":"https://submission.nature.com/new-submission/11104/3","title":"Plant and Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"041a29e2-b526-42c0-9bbe-c5e7d95ac19a","owner":[],"postedDate":"January 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-06-21T15:03:42+00:00","versionOfRecord":{"articleIdentity":"rs-3761618","link":"https://doi.org/10.1007/s11104-024-06775-8","journal":{"identity":"plant-and-soil","isVorOnly":false,"title":"Plant and Soil"},"publishedOn":"2024-06-15 15:03:41","publishedOnDateReadable":"June 15th, 2024"},"versionCreatedAt":"2024-01-08 09:43:28","video":"","vorDoi":"10.1007/s11104-024-06775-8","vorDoiUrl":"https://doi.org/10.1007/s11104-024-06775-8","workflowStages":[]},"version":"v1","identity":"rs-3761618","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3761618","identity":"rs-3761618","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}