Rapid diagnosis method for blueberry continuous cropping soil obstacle factors

Abstract

The invention discloses a rapid diagnosis method for blueberry continuous cropping soil obstacle factors, and belongs to the technical field of soil environment analysis. Based on combined analysis of soil physical and chemical factors, metagenome and metabolome, pathogenic bacteria types and key metabolites significantly related to the blueberry continuous cropping obstacle are extracted, and a characteristic diagnostic index system is formed. The index system comprises main pathogenic bacteria types, typical harmful metabolites, key soil physical and chemical factors and appropriate or risk threshold ranges thereof, and provides a scientific basis for rapid identification of soil continuous cropping obstacles. Comparative analysis is carried out by establishing a standardized index library, rapid comparison of related indexes and standard indexes in a newly sampled soil sample is realized, and whether the soil has continuous cropping obstacle risks or not is judged in an auxiliary manner by integrating the deviation degree of physical and chemical factors and biological factors.

Description

Technical Field

[0001] This invention belongs to the field of soil environmental analysis technology, specifically relating to a rapid diagnostic method for soil obstacle factors in blueberry continuous cropping. Background Technology

[0002] In the continuous cropping of perennial fruit trees such as blueberries, the soil environment is often under high-intensity, low-rotation-rate management conditions, which can easily lead to "continuation cropping obstacles," such as slowed plant growth, restricted root development, reduced yield, and poor fruit quality. Studies have shown that these obstacles are closely related to multiple factors, including disordered soil microbial community structure, pathogen accumulation, and imbalance of root metabolites. Currently, the diagnosis of continuous cropping obstacles in fruit trees mainly focuses on the following technical methods: first, soil physicochemical index testing (such as pH, organic matter, available phosphorus, and nitrogen forms); second, the isolation and quantitative detection of disease-related pathogens (such as root rot pathogens and wilt pathogens); and third, indirect assessment of soil continuous cropping risk through planting experiments or plant phenotypic observation.

[0003] These methods have some practical value, but they suffer from the following shortcomings: limited diagnostic indicators and a lack of systematic analytical capabilities. Physicochemical analysis can only reflect the basic state of the soil and cannot reveal complex biological barrier mechanisms; pathogen detection is mostly targeted at known species and struggles to capture changes in community structure. The combined effects of multiple factors are difficult to identify. Continuous cropping obstacles often result from the combined effects of pathogen accumulation, reduced antagonistic bacteria, and the accumulation of harmful metabolites, while traditional methods are mostly single-factor detections, making it difficult to construct causal networks. Diagnostic timeline: Most detection methods are only implemented after plants have shown obvious adverse growth symptoms, often missing the optimal intervention window, making early warning difficult, and limiting information throughput and resolution.

[0004] How to provide a comprehensive diagnostic solution that can simultaneously acquire information on soil physicochemical factors, active microorganisms, and metabolic status has become an important issue that urgently needs to be addressed. Summary of the Invention

[0005] Therefore, the purpose of this invention is to provide a rapid diagnostic method for soil obstacle factors in continuous cropping of blueberries. This method uses soil physicochemical factors, metagenomics, and metabolomics in combination to obtain physicochemical information, microbial population structure, functional gene characteristics, and metabolite distribution information in the soil. From this information, key physicochemical factors, pathogenic bacteria, and metabolic markers closely related to continuous cropping obstacles are screened out, thereby achieving accurate identification of the causes of obstacles and risk warning.

[0006] To achieve the above objectives, the present invention provides the following technical solution: This invention provides a rapid diagnostic method for soil obstacle factors in blueberry continuous cropping, comprising the following steps: S1, soil samples were collected from the continuous cropping obstacle area and the control area, respectively; S2, using the soil sample as the object, perform soil physicochemical factor measurement, metagenomic sequencing analysis and metabolomics analysis to obtain physicochemical factor data, metagenomic data and metabolomics data; S3, compare the metagenomic and metabolomics data of the continuous cropping obstacle manifestation area and the control area to obtain significant microbial data and significant metabolite data; S4, perform environmental factor analysis on the physicochemical factor data, the significant microbial data, and the significant metabolite data respectively to obtain the main environmental factor data; S5. Based on the significant microbial data, the significant metabolite data, and the major environmental factor data, establish an indicator library; S6. The microbial data, metabolite data, and physicochemical factor data of the soil to be diagnosed are compared with the index library to obtain the diagnostic results.

[0007] Based on the above technical solution, the soil samples further include rhizosphere soil samples and non-rhizosphere soil samples.

[0008] Based on the above technical solution, the physicochemical factor data further includes soil pH, electrical conductivity, organic matter content, nutrient status, heavy metal content, texture composition, moisture content, and bulk density.

[0009] Based on the above technical solution, the metagenomic data further includes the species composition, functional gene distribution, pathogenic bacteria, resistance genes, detoxification genes, and volatile synthesis pathways of the soil microbial community.

[0010] Based on the above technical solution, the metabolomics data further includes metabolite profiles, root exudates, toxic or beneficial metabolites, and characteristic small molecules related to the obstacle.

[0011] Based on the above technical solution, further, the significant microbial data refers to microbial-related data whose abundance changes significantly under continuous cropping obstacle conditions, and the significant metabolite data refers to metabolite data whose abundance changes significantly under continuous cropping obstacle conditions.

[0012] Based on the above technical solution, the environmental factor analysis further includes RDA, CCA, Mantel test or environmental factor significance screening, the physicochemical factor data are explanatory variables, and the significant microbial data and the significant metabolite data are response variables.

[0013] Based on the above technical solution, the nutrient status further includes the content of ammonium nitrogen, nitrate nitrogen, available phosphorus, and available potassium.

[0014] Based on the above technical solution, the diagnostic results are further presented in the form of a risk grading threshold map or judgment table for continuous cropping obstacles.

[0015] Based on the above technical solution, it further includes S3.1, the specific steps of which are as follows: The significant microbial data and significant metabolite data in S3 are used to construct a co-occurrence network to obtain core pathogenic species data and key metabolite data. The core pathogenic species data and key metabolite data are used as the significant microbial data and significant metabolite data in S4 and S5.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention, based on the combined analysis of soil physicochemical factors, metagenomics, and metabolomics, extracts pathogenic bacteria and key metabolites significantly associated with blueberry continuous cropping obstacles, forming a set of characteristic diagnostic indicator systems. This indicator system includes major pathogenic bacteria species, typical harmful metabolites, key soil physicochemical factors, and their suitable or risk threshold ranges, providing a scientific basis for the rapid identification of soil continuous cropping obstacles.

[0017] 2. This invention establishes a standardized index library for comparative analysis, enabling rapid comparison of relevant indicators in newly sampled soil samples with standard indicators. It also comprehensively analyzes the deviation of physicochemical and biological factors to help determine whether the soil is at risk of continuous cropping obstacles.

[0018] 3. This invention not only has the advantages of high throughput, no target, and no reliance on prior knowledge, but also integrates multidimensional biological information to improve the scientific, systematic and forward-looking nature of continuous cropping disorder diagnosis.

[0019] 4. The output results of this invention are visualized, making it easy to promote and use. The constructed risk classification threshold map or judgment table of continuous cropping obstacles can be presented intuitively on mobile devices or paper tools, which can assist in diagnosis and judgment without professional background, making it easy for grassroots agricultural technicians or growers to operate. Attached Figure Description

[0020] To more clearly illustrate the embodiments of the present invention, the accompanying drawings involved in the embodiments will be briefly described below.

[0021] Figure 1 This is an overall flowchart of the present invention; Figure 2 This is a flowchart of the technology of the present invention; Figure 3 This is a bar chart showing the percentage of fungi in this invention. Figure 4 This is a bar chart showing the percentage of bacteria in this invention. Figure 5This is a graph showing the overall difference in metabolomics levels among the groups in this invention. Detailed Implementation

[0022] This invention provides a rapid diagnostic method for soil obstacle factors in continuous blueberry cropping. By combining soil physicochemical factors, metagenomics, and metabolomics, it obtains physicochemical information, microbial population structure, functional gene characteristics, and metabolite distribution information in the soil. From this, it screens out key physicochemical factors, pathogenic bacteria, and metabolic markers closely related to continuous cropping obstacles, thereby achieving accurate identification of the causes of obstacles and risk warning.

[0023] like Figure 1 As shown, a rapid diagnostic method for soil obstacle factors in blueberry continuous cropping includes the following steps: S1, soil samples were collected from the continuous cropping obstacle area and the control area, respectively; S2, using soil samples as the object, performs soil physicochemical factor measurement, metagenomic sequencing analysis and metabolomics analysis to obtain physicochemical factor data, metagenomic data and metabolomics data; S3, by comparing metagenomic and metabolomics data of the continuous cropping obstacle performance area and the control area, significant microbial and metabolite data were obtained; S4. Environmental factor analysis was performed by comparing the physicochemical factor data with the significant microbial data and significant metabolite data to obtain the main environmental factor data. S5. An indicator library is established based on significant microbial data, significant metabolite data, and major environmental factor data. S6 compares the microbial data, metabolite data, and physicochemical factor data of the soil to be diagnosed with the indicator database to obtain the diagnostic results.

[0024] By comparing the differences in the composition of microbial communities and metabolites between healthy soil and soil with continuous cropping obstacles, it is possible to identify microorganisms and metabolites whose abundance changes significantly under continuous cropping obstacles.

[0025] Among them, since pathogenic microorganisms usually proliferate in soils with continuous cropping obstacles and cause damage to plant health, changes in their abundance can reflect their association with the occurrence of diseases.

[0026] Among them, metabolites, as products of microbial metabolic activities, can reflect the metabolic characteristics of pathogenic microorganisms and the dynamic changes in the soil environment through changes in their concentration.

[0027] Among them, soil physicochemical properties are important environmental factors that affect the growth and metabolic activities of microorganisms. Their changes not only directly affect the structure of soil microbial communities, but also indirectly affect the types and abundance of metabolites.

[0028] Among them, significant microbial data, significant metabolite data, and major environmental factor data all include their suitable or risk threshold ranges.

[0029] In S3, after comparison, multi-omics association analysis was conducted, using Spearman correlation, co-occurrence network, or Mantel test methods to integrate metagenomic and metabolomics data, screening for pathogenic microorganisms and key metabolites closely related to continuous cropping obstacles. Further correlation analysis between differentially expressed microorganisms and metabolites and plant health indicators and soil physicochemical properties revealed their direct or indirect links to continuous cropping obstacles. Co-occurrence network analysis helped discover the interactions and potential functions of these key microorganisms and metabolites in the soil ecosystem, further elucidating their mechanisms of action in the occurrence and development of continuous cropping obstacles. By comprehensively considering differential expression, correlation, and network structure characteristics, pathogenic microorganisms and metabolites highly associated with continuous cropping obstacles can be effectively screened, providing a scientific basis for subsequent functional verification and management strategies.

[0030] This invention combines soil physicochemical factors with microbial community structure and function analysis and metabolite profiling to systematically identify pathogenic groups, key metabolic characteristics, and environmental drivers associated with continuous cropping obstacles. Compared to traditional diagnostic methods that rely solely on physicochemical properties or single pathogens, this comprehensive approach can more fully reveal the disease pathogenesis mechanism and significantly improve the accuracy and stability of diagnosis.

[0031] This invention employs a standard sampling procedure combined with high-throughput sequencing and metabolomics platforms. Sample processing and data analysis are completed within 7 working days, significantly shortening the observation period compared to traditional manual observation of continuous cropping obstacles and greatly improving detection efficiency. It enables early warning and prevention. This invention can assess the risk of continuous cropping obstacles based on soil microecological characteristics before obvious plant symptoms appear, allowing for early warning intervention one growing season in advance, effectively reducing the risk of losses.

[0032] In some embodiments, soil samples include rhizosphere soil samples and non-rhizosphere soil samples.

[0033] The collected soil samples were sieved through a 2mm sieve and immediately frozen for subsequent microbial and metabolomics analysis.

[0034] In some embodiments, physicochemical data include soil pH, electrical conductivity, organic matter content, nutrient status, heavy metal content, texture composition, moisture content, and bulk density.

[0035] In some embodiments, metagenomic data include the species composition, functional gene distribution, pathogenic flora, resistance genes, detoxification genes, and volatile synthesis pathways of soil microbial communities.

[0036] Among them, metagenomic sequencing analysis utilizes a high-throughput sequencing platform to perform metagenomic sequencing and analysis on soil samples.

[0037] In some embodiments, metabolomics data include metabolite profiles, root exudates, toxic or beneficial metabolites, and characteristic small molecules associated with the disorder.

[0038] Metabolomics analysis involves performing non-targeted metabolomics analysis on the same soil sample using an LC-MS / MS or GC-MS platform.

[0039] In some embodiments, significant microbial data refers to microbial-related data whose abundance changes significantly under continuous cropping obstacles, and significant metabolite data refers to metabolite data whose abundance changes significantly under continuous cropping obstacles.

[0040] In some embodiments, environmental factor analysis includes RDA, CCA, Mantel test, or environmental factor significance screening, with physicochemical factor data as explanatory variables and significant microbial data and significant metabolite data as response variables.

[0041] In some embodiments, nutrient status includes the content of ammonium nitrogen, nitrate nitrogen, available phosphorus, and available potassium.

[0042] In some embodiments, the diagnostic results are presented in the form of a risk grading threshold map or decision table for continuous cropping obstacles.

[0043] The diagnostic results of this invention are converted into a risk grading threshold map or judgment table for continuous cropping obstacles. Combined with a simplified risk grading system, this provides farmers and grassroots technicians with intuitive risk warnings regarding continuous cropping obstacles in the soil. Simultaneously, corresponding agricultural management suggestions can be provided based on the risk level, such as selecting disease-resistant rootstocks and applying biological agents appropriately, to assist in achieving precision management.

[0044] Develop visual charts or threshold judgment tables to facilitate on-site operation and result interpretation. Output results and agricultural guidance suggestions.

[0045] The visualized output results facilitate widespread use. The constructed risk classification threshold map or judgment table for continuous cropping obstacles is presented intuitively on mobile devices or paper tools, which can assist in diagnosis and judgment without professional background, making it easy for grassroots agricultural technicians or growers to operate.

[0046] In some embodiments, S3.1 is further included, and the specific steps of S3.1 are as follows: Co-occurrence networks were constructed using the significant microbial and metabolite data in S3 to obtain core pathogenic species data and key metabolite data. The core pathogenic species data and key metabolite data were then used as the significant microbial and metabolite data in S4 and S5.

[0047] The present invention will be described in detail below with reference to the embodiments. However, the implementation of the present invention is not limited thereto. Obviously, the embodiments described below are only some embodiments of the present invention. For those skilled in the art, other similar embodiments can be obtained without creative effort and all fall within the protection scope of the present invention.

[0048] Example 1 In this embodiment, soil with 6 years of continuous cropping and the resulting plant wilting and yield reduction were used as the soil to be diagnosed. The area where the diseased plants were located was designated as the continuous cropping obstacle area, and the area where the healthy plants were located was designated as the control area. Rapid diagnosis of soil obstacle factors in blueberry continuous cropping was carried out to verify the rapid diagnosis method for soil obstacle factors in blueberry continuous cropping provided by this invention.

[0049] 1. Soil sample collection: Rhizosphere and non-rhizosphere soil samples were collected at a depth of 0-20 cm, and three mixed soil samples were collected within a 10 cm radius of each sampling point.

[0050] Sample groups: healthy plant area (5 plants) and diseased plant area (5 plants), with 3 replicates per group.

[0051] 2. Determination of soil physicochemical factors: pH value: Glass electrode method, with a water-to-soil ratio of 1:2.5, the measured value range is 4.0-4.5 (soil in diseased plant areas is slightly acidic).

[0052] Redox potential (Eh): The value measured on-site using the potentiometric method is 150-180mV (the soil in the diseased plant area is 200-250mV lower than that in the healthy plant area).

[0053] Organic matter content: The value was measured using the potassium dichromate oxidation method, ranging from 1.0 to 1.2 g / kg (the soil in the diseased plant area was 30% lower than that in the healthy plant area).

[0054] Available potassium content: The value was determined by NH4OAc extraction-flame photometry, ranging from 80 to 100 mg / kg (critical value is 120 mg / kg).

[0055] 3. Soil activity factor analysis: 3.1 Sample DNA extraction and sequencing: 3.1.1 Sample DNA extraction: Total genomic DNA was extracted from the microbial community. After extraction, DNA concentration and purity were assessed, and DNA integrity was checked using 1% agarose gel electrophoresis. The DNA was fragmented using a Covaris M220 (Genetron Health, China), and fragments of approximately 350 bp were selected for constructing PE libraries.

[0056] 3.1.2 Building the PE library: Library construction was performed using NEXTFLEX Rapid DNA-Seq (Bioo Scientific, USA). The specific process is as follows: (1) Connector link; (2) Use magnetic beads to screen and remove self-connected segments at the joints; (3) Enrichment of library templates using PCR amplification; (4) PCR products were recovered by magnetic beads to obtain the final library.

[0057] 3.1.3 Bridge PCR and Sequencing: Metagenomic sequencing was performed using the Illumina NovaSeq™ X Plus (Illumina, USA) sequencing platform (Shanghai Meiji Biopharmaceutical Technology Co., Ltd.). The specific workflow is as follows: (1) One end of the library molecule is complementary to the primer bases. After one round of amplification, the template information is fixed on the chip; (2) The other end of the molecule fixed on the chip is randomly complementary to another nearby primer and is also fixed, forming a "bridge"; (3) PCR amplification produces DNA clusters; (4) DNA amplicon linearizes into a single strand; (5) Add modified DNA polymerase and dNTPs with 4 fluorescent labels, and synthesize only one base per cycle; (6) Use a laser to scan the surface of the reaction plate and read the types of nucleotides that were polymerized in the first round of reaction for each template sequence; (7) Chemically cleave the "fluorescent group" and "terminator group" to restore the 3' end stickiness and continue to polymerize the second nucleotide; (8) Statistically analyze the fluorescence signal results collected in each round to obtain the sequence of the template DNA fragment.

[0058] 3.2 Data Processing and Analysis Workflow: 3.2.1 Data Quality Control (1) Use fastp (https: / / github.com / OpenGene / fastp, version 0.20.0) to cut the adapter sequence at the 3' and 5' ends of the reads; (2) Use fastp (https: / / github.com / OpenGene / fastp, version 0.20.0) to remove reads with a length less than 50bp after cutting and an average base quality value less than 20, and retain high-quality sequences; (3) Use the software BWA (http: / / bio-bwa.sourceforge.net, version 0.7.17) to align reads with the host DNA sequence and remove contaminating reads with high alignment similarity.

[0059] 3.2.2 Assembly and Gene Prediction: The optimized sequences were assembled using the software MEGAHIT (https: / / github.com / voutcn / megahit, version 1.1.2). Contigs ≥300 bp (the specific length depends on the filter used) were selected from the assembly results as the final assembly. ORFs were predicted for the contigs in the assembly results using Prodigal (https: / / github.com / hyattpd / Prodigal, version 2.6.3). Genes with a nucleic acid length greater than or equal to 100 bp were selected and translated into amino acid sequences.

[0060] 3.2.3 Construction of non-redundant gene sets: The gene sequences predicted from all samples were clustered using CD-HIT (http: / / weizhongli-lab.org / cd-hit / , version 4.7) with parameters of 90% identity and 90% coverage. The longest gene in each cluster was taken as the representative sequence to construct a non-redundant gene set.

[0061] 3.2.4 Gene abundance calculation: Using SOAPaligner software (https: / / github.com / ShujiaHuang / SOAPaligner, version soap2.21 release), the high-quality reads of each sample were compared with the non-redundant gene set (95% identity), and the abundance information of genes in the corresponding samples was statistically analyzed.

[0062] 3.2.5 Species and Functional Notes: 3.2.5.1 Species Taxonomy Notes: Diamond (https: / / github.com / bbuchfink / diamond, version 2.0.13) was used to align the amino acid sequences of the non-redundant gene set with the NR database (BLASTP alignment parameter was set to the expected value e-value of 1e-5), and species annotations were obtained from the taxonomic information database corresponding to the NR database. Then, the abundance of the species was calculated using the sum of the gene abundances corresponding to the species.

[0063] 3.2.5.2 KEGG Function Comments: The amino acid sequences of the non-redundant gene set were aligned with the KEGG database using Diamond (https: / / github.com / bbuchfink / diamond, version 2.0.13) (BLASTP alignment parameters were set with an expected e-value of 1e-5). The KEGG functions corresponding to the genes were obtained. The abundance of the corresponding functional categories was calculated by summing the gene abundance for KO, Pathway, EC, and Module.

[0064] 3.3 Metabolomics assay: This study employed LC-MS technology for non-targeted metabolomics analysis of soil samples to reveal the changes in soil metabolites under different treatment conditions. Workflow: In this study, soil samples were pretreated and directly analyzed using a liquid chromatography-mass spectrometry (LC-MS) system. Various metabolites in the mixture were separated by LC-MS and detected using a high-resolution mass spectrometry platform (such as Q-TOF or Orbitrap) to obtain key information such as mass-to-charge ratio and retention time. The raw mass spectrometry data were processed using specialized software, peak information was extracted, and noise reduction, alignment, and normalization were performed. Metabolite annotation was then performed using public databases (such as KEGG). Multivariate statistical methods such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to identify metabolic differences between different treatment groups. Furthermore, functional enrichment analysis of differentially expressed metabolites was conducted to help elucidate the potential metabolic regulatory mechanisms among the treatment groups.

[0065] 4. Correlation analysis and indicator selection: 4.1 Multi-omics data integration and analysis: Spearman correlation analysis was used to calculate the correlation coefficient between microbial abundance and metabolite content in blueberry samples with continuous cropping obstacles. Microbial-metabolite pairs with significant correlation coefficients (p<0.05) and absolute values ​​greater than 0.6 were selected to provide candidate indicators for subsequent construction of the association network.

[0066] 4.2 Co-occurrence Network Construction: Metagenomic and metabolomical data were compared between the continuous cropping obstacle region and the control region to obtain significant microbial and metabolite data. Based on the screened significantly related microorganisms and metabolites, a microbial-metabolite co-occurrence network was constructed using the SparCC method, with the threshold set as an absolute correlation coefficient > 0.6 and p < 0.05. Key nodes were identified through network topology parameters (such as degree centrality and module partitioning) to pinpoint core pathogenic species and key metabolites.

[0067] 4.3 Mantel Test: The Mantel test was used to assess the correlation between soil physicochemical parameters (pH, organic matter, redox potential, etc.) and microbial community structure and metabolome data. The Bray-Curtis distance matrix was used to calculate sample similarity, and significance was determined through a 999-permutation test.

[0068] 4.4 Screening of Barrier Factors: Based on the above analysis results, pathogenic microorganisms and metabolites significantly associated with continuous cropping obstacles were extracted, and a set of key obstacle factors for blueberry continuous cropping obstacles was constructed. The indicator set includes species name, abundance range, metabolite type, and concentration range.

[0069] 5. Extraction and comparative analysis of diagnostic indicators for obstacle factors: 5.1 Construction of the indicator system: Based on the combined analysis of metagenomics and metabolomics, a diagnostic index system for continuous cropping obstacles was formed by summarizing the main pathogenic species (such as specific fungi and bacterial genera) and typical harmful metabolites (such as organic acids and secondary metabolites) and their abundance variation range.

[0070] 5.2 Establishment of Standard Indicator Library: The above-mentioned soil physicochemical factors, species names, abundance ranges, metabolite categories, and concentration ranges were standardized to establish a diagnostic database for blueberry continuous cropping obstacles. Threshold standards and reference ranges were provided for each indicator for rapid comparison in subsequent sample diagnosis. The threshold standards and reference ranges for soil physicochemical factors are shown in Table 1. The threshold standards and reference ranges for microbial indicators are shown in Table 2. The threshold standards and reference ranges for metabolite indicators are shown in Table 3.

[0071] Table 1: .

[0072] Table 2: .

[0073] Table 3: .

[0074] 5.3 Comparison and Analysis Process: Develop an automated analysis script based on the R language to quickly compare metagenomic and metabolomics data of newly collected soil samples with a standard index library, calculate the matching degree and generate a risk score to help determine the existence and extent of continuous cropping obstacles in soil.

[0075] The standard index library is shown in Table 4. The soil observation values ​​to be diagnosed are shown in Table 5. The diagnostic results are shown in Table 6.

[0076] Table 4: .

[0077] Table 5: .

[0078] Table 6: .

[0079] According to the diagnostic results, the rapid diagnostic method for soil obstacle factors in blueberry continuous cropping provided by this invention can accurately diagnose soil continuous cropping obstacles.

[0080] 5.4 Visualization Tool Design: The design includes a visualization module for the comparison results of indicators, including heatmaps, radar charts, and risk level assessment tables, which facilitates technicians to quickly and intuitively understand the diagnostic results, improving practicality and efficiency in field applications.

[0081] 6. Results Output and Agricultural Guidance Suggestions: 6.1 Report Generation: Based on indicator comparison and risk scoring, a detailed diagnostic report on blueberry continuous cropping obstacles is generated. The report includes abundance analysis of major pathogenic fungi and harmful metabolites, risk level classification, and scientific interpretation. The report content is as follows.

[0082] Blueberry Continuous Crop Obstacle Soil Health Diagnosis Report Sample Information Sampling date: 2025-08-15 Sampling location: Rhizosphere soil in blueberry orchard (planted continuously for 3 years) Analytical methods: Physicochemical indicators (routine tests), metagenomic sequencing (Illumina), metabolomics (LC-MS / MS) Data Comparison Library Version: Blueberry Continuous Cropping Obstacle Standard Indicator Library v1.0 Comprehensive diagnostic results The sample exceeded the limits for 7 out of 7 indicators (5 of which were at the warning level and 2 at the alert level), and the overall risk score was 88 / 100 (high risk).

[0083] The main issues include: High soil acidity (pH 4.15) and high salinity (EC 1.35 dS / m) are detrimental to root ion balance and nutrient absorption. The pathogenic fungus Fusarium oxysporum was found to be in severely excessive levels, posing a high risk of root rot outbreaks. The beneficial microbial community (Pseudomonas fluorescens) was significantly deficient, resulting in a decrease in soil antagonism. The autotoxic substance p-coumaric acid accumulates significantly, inhibiting root growth and affecting the balance of the microbial community. High succinic acid concentration suggests metabolic abnormalities and enhanced oxidative stress.

[0084] Scientific Interpretation Blueberries are acid-loving crops, but a pH below 4.2 indicates a risk of aluminum and manganese toxicity, easily leading to root tip damage. Increased electrical conductivity signifies increased salt ion concentration, which can easily cause osmotic stress in the shallow root environment of blueberries. Metagenomic analysis showed that the relative abundance of *F. oxysporum* exceeded the warning threshold by more than 13 times, making it one of the common major pathogens in continuous cropping obstacles; the absence of beneficial bacteria *P. fluorescens* weakened the microbial defense barrier. Metabolomics analysis revealed a significant accumulation of phenolic acid autotoxic substances, indicating an imbalance between root exudates and their microbial degradation; elevated succinic acid levels further reflect soil microbial metabolic disorders.

[0085] Repair and Management Recommendations pH adjustment and buffering: Use acidic amendments (sulfur powder) with caution, and prioritize the application of neutral organic fertilizers (such as compost) to adjust buffering capacity; avoid further lowering of pH.

[0086] Salt reduction: Increase irrigation flushing, avoid high-salt fertilizers, and control EC below 0.75 dS / m.

[0087] Biocontrol: Introduce Pseudomonas fluorescens or other antagonistic agents to inhibit pathogenic fungi.

[0088] Degradation of autotoxic substances: Apply microbial agents rich in phenolic acid degrading agents (such as Bacillus subtilis) and combine with straw return to the field.

[0089] Organic matter enhancement: Add 3–5% well-rotted organic fertilizer or biochar to improve soil structure and microbial diversity.

[0090] 6.2 Risk Level Classification: By combining the abundance thresholds of diagnostic indicators, the risk of continuous cropping obstacles is divided into three levels: low, medium, and high. The threshold ranges corresponding to the risk levels are clearly defined, which makes it easier for users to quickly assess the soil health status.

[0091] 6.3 Management Recommendations: Based on different risk levels, targeted agricultural management recommendations are proposed, including recommending the use of disease-resistant rootstocks, the application of specific biological agents, optimization of fertilizer ratios, and soil improvement measures, to guide farmers in precise prevention and control of continuous cropping obstacles.

[0092] 6.4 Continuous monitoring recommendations: It is recommended to establish a regular sampling and monitoring mechanism, and dynamically adjust the management plan based on the changing trends of diagnostic indicators, so as to achieve early warning and scientific management of blueberry continuous cropping obstacles and ensure the continuous improvement of blueberry planting benefits.

[0093] Example 2 This embodiment uses blank control soil, rhizosphere soil from 4 consecutive years of cropping, soil from 4 consecutive years of cropping, rhizosphere soil from 6 consecutive years of cropping, soil from 6 consecutive years of cropping, and abandoned and restored soil as subjects to conduct metagenomic and metabolomics data analysis.

[0094] Among them, no obstacles were found in the soil and rhizosphere soil groups that had been continuously cropped for 4 years, while obstacles were found in the soil and rhizosphere soil groups that had been continuously cropped for 6 years.

[0095] Each soil type was replicated three times. Soil samples were randomly collected from different treatment plots in the orchard, with rhizosphere soil specifically collected near plant roots. After collection, stones, plant debris, and larger impurities were removed from the soil samples, and the samples were homogenized through a 2 mm sieve and mixed using a quartering method to reduce sampling error.

[0096] In metagenomics, the processed soil samples were cryopreserved. Total DNA was first extracted to remove impurities and ensure DNA purity and integrity. After passing quality testing, the extracted total DNA was fragmented, and then metagenomic sequencing libraries were constructed, including end repair, A-tailing, and adapter ligation, to ensure molecular homogeneity and sequenceability of the library.

[0097] The constructed libraries were subjected to whole-genome deep sequencing on a high-throughput sequencing platform (such as Illumina NovaSeq) to obtain high-quality raw sequences covering the soil microbial community. After quality control and removal of low-quality sequences and host DNA contamination, the sequencing data yielded high-quality metagenomic data that could be used for microbial abundance, function, and diversity analysis.

[0098] For the metabolomics, the collected soil samples underwent metabolite extraction. Small molecule metabolites were fully extracted using an appropriate solvent system, while particulate impurities and interfering substances such as proteins were removed. The extract was centrifuged, filtered, and concentrated, then subjected to quality testing and quantification to prepare samples suitable for mass spectrometry analysis. Subsequently, the samples were introduced into a liquid chromatography-mass spectrometry (LC-MS / MS) platform for high-throughput analysis to obtain qualitative and quantitative information on soil metabolites. The acquired raw mass spectrometry data underwent noise removal, peak identification, peak integration, and correction to generate a high-quality data matrix for soil metabolomics analysis, which can be used for comparison with indicator libraries and analysis of metabolites related to continuous cropping obstacles.

[0099] Example 3 This embodiment uses the data provided in Example 2 as the object to analyze the relationship between fungi in the soil and continuous cropping obstacles.

[0100] The specific analysis process is as follows: Relative abundance data (percentages) at the genus level were analyzed using R software. Data processing and visualization utilized the R packages tidyverse (for data preparation and transformation), viridis (for color palette support), and scales (for scaling). The input data consisted of a table of relative abundance at the genus level for multiple samples, including multiple treatment groups and their replicates. First, the relative abundance of replicates within the same treatment group was averaged to obtain the average relative abundance of each genus in each treatment group. Then, the data was converted from a wide format to a long format for plotting. Genuses were sorted according to the average abundance of all samples, retaining the top 15 most abundant genera, and the remaining genera were categorized as "Others". In the plot, "Others" was fixed at the bottom of the stacked plot, and the remaining genera were arranged in descending order of average abundance. Finally, ggplot2 was used to create a percentage stacked bar chart, assigning a custom color to each genus, and adjusting the axis labels, scales, and legend styles to improve the readability and aesthetics of the graph.

[0101] The results are as follows Figure 3As shown, the results indicate that the fungal community structure also underwent significant changes in the continuous cropping obstacle group, characterized mainly by pathogen enrichment and the collapse of the symbiotic system. The relative abundance of *Fusarium* was significantly increased; it is an important pathogen of blueberry root rot, suggesting that it may have caused severe root disease in this group. Meanwhile, *Rhizopus* was detected for the first time in the continuous cropping obstacle group, potentially synergistically exacerbating the risk of root rot. On the other hand, the mycorrhizal fungus *Acaulospora*, which promotes plant nutrient absorption, completely disappeared in the continuous cropping obstacle group, indicating that the plant-fungus symbiotic system was disrupted, leading to plant nutritional deficiencies. Furthermore, the relative abundance of the "Others" group exceeded 30%, reflecting the disordered fungal community structure and decreased diversity. Notably, in the rhizosphere soil treatment without continuous cropping obstacles (rhizosphere of 4 years of continuous cropping), the stress-tolerant and antagonistic potential *Pseudogymnoascus* was significantly enriched, suggesting its potential value in biocontrol.

[0102] Example 4 This embodiment uses the data provided in Example 2 as the object to analyze the relationship between bacteria in the soil and continuous cropping obstacles.

[0103] The analysis process is the same as in Example 2.

[0104] The results are as follows Figure 4 As shown, the results indicate that the bacterial community in the soil treated with continuous cropping obstacles for 6 years exhibited significant dysregulation. The relative abundance of "Others" (unclassified genera) exceeded 40%, significantly higher than other soil treatments, indicating severe microbial community structure disorder and decreased community stability under this treatment. The abundance of several key functional bacteria significantly decreased or even disappeared completely. For example, the acid-tolerant bacterium *Candidatus Solibacter* was completely absent in the continuous cropping obstacle treatment, weakening the soil's buffering capacity against acidification stress; the abundance of the nitrogen-fixing bacterium *Bradyrhizobium* was extremely low, suggesting that its nitrogen input function was inhibited. Furthermore, *Sphingomonas*, which has the ability to degrade organic toxins, also decreased significantly in the continuous cropping obstacle treatment, leading to increased soil toxin accumulation. In contrast, in the non-continuous cropping obstacle treatment, *Sphingomonas* was significantly enriched, alleviating continuous cropping stress through its toxin degradation function.

[0105] Example 5 This embodiment uses the data provided in Example 2 as the object to analyze the relationship between metabolites in the soil and continuous cropping obstacles.

[0106] The specific analysis process is as follows: Pattern recognition analysis of metabolomics data was performed using R software. The ropls package was used for PLS-DA (Partial Least Squares Discriminant Analysis) modeling, and R extension packages such as ggplot2, dplyr, and stringr were used for result visualization and data processing. The input data was a matrix table containing metabolite peak areas or normalized abundance, where rows represent metabolites and columns represent different samples and their grouping information. First, experimental samples were selected and quality control (QC) samples were removed. Then, the data matrix was transposed to a format with samples as rows and metabolites as columns, and grouping information was extracted based on sample names. Pareto scaling was used in the PLS-DA modeling process, with the number of predicted components set to 2 (predI=2), orthogonal components not used (orthoI=0), and 200 permutation tests were performed to evaluate the stability and significance of the model. After the model calculation was completed, the score matrix was extracted for scatter plotting, and the explanatory power (R²) of the first two principal components on the X matrix was obtained. 2 X). Finally, ggplot2 was used to plot the PLS-DA scatter plot, and 95% confidence ellipses were added to each group to visually demonstrate the differences in metabolite profiles and clustering trends between different groups.

[0107] The results are as follows Figure 5 As shown, principal component analysis results indicate that Component 1 (horizontal axis) explains 54.10% of the metabolic variation among samples, constituting the main driving factor distinguishing each treatment group. The control group samples are mainly distributed on the right side of the coordinate axis (positive value region) and show a clear clustering trend, indicating that this treatment group has high metabolic stability, representing the metabolic homeostasis of healthy soil. Conversely, the distribution of the rhizosphere soil group with continuous cropping obstacles is significantly biased towards the negative value region of Component 1, and the degree of deviation is significant (sample score close to -80), suggesting that this group has significant soil metabolic disorder, possibly a direct manifestation of continuous cropping obstacles. The fallow recovery group and the non-obstacle continuous cropping soil group are distributed between the control and the continuous cropping obstacles / rhizosphere soil, and are closer to the blank control, indicating that their metabolic profile is closer to the healthy control, and they may still maintain a certain degree of metabolic functional stability.

[0108] Component 2 (vertical axis) explains 14.30% of the variation, mainly reflecting differences in minor metabolic characteristics among different treatment groups. The soil group with 6 years of continuous cropping showed a significant bias towards the negative region of Component 2, with a marked shift, suggesting significant soil metabolic disorder in this group, possibly a direct manifestation of continuous cropping obstacles. However, the soil group with 4 years of continuous cropping showed a more similar Component 2 result to the 6-year continuous cropping group, indicating the presence of some potential continuous cropping obstacles.

[0109] Regarding intragroup differences, the area of ​​the 95% confidence ellipse reflects the metabolic consistency of samples from different treatment groups. The control group had the smallest confidence ellipse, indicating a high degree of consistency in metabolic composition among its samples and strong intragroup stability. In contrast, the continuous cropping obstacle group had the largest confidence ellipse, which expanded to the right, reflecting the high dispersion and instability of the metabolic pattern in this group, further confirming the disruptive effect of continuous cropping obstacles on soil metabolic balance.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A rapid diagnostic method for soil obstacle factors in continuous blueberry cropping, characterized in that, Includes the following steps: S1, soil samples were collected from the continuous cropping obstacle area and the control area, respectively; S2, using the soil sample as the object, perform soil physicochemical factor measurement, metagenomic sequencing analysis and metabolomics analysis to obtain physicochemical factor data, metagenomic data and metabolomics data; S3, compare the metagenomic and metabolomics data of the continuous cropping obstacle manifestation area and the control area to obtain significant microbial data and significant metabolite data; S4, perform environmental factor analysis on the physicochemical factor data, the significant microbial data, and the significant metabolite data respectively to obtain the main environmental factor data; S5. Based on the significant microbial data, the significant metabolite data, and the major environmental factor data, establish an indicator library; S6. The microbial data, metabolite data, and physicochemical factor data of the soil to be diagnosed are compared with the index library to obtain the diagnostic results.

2. The rapid diagnostic method for soil obstacle factors in blueberry continuous cropping according to claim 1, characterized in that, The soil samples include rhizosphere soil samples and non-rhizosphere soil samples.

3. The rapid diagnostic method for soil obstacle factors in blueberry continuous cropping according to claim 3, characterized in that, The physicochemical data include soil pH, electrical conductivity, organic matter content, nutrient status, heavy metal content, texture composition, moisture content, and bulk density.

4. The rapid diagnostic method for soil obstacle factors in blueberry continuous cropping according to claim 3, characterized in that, The nutrient status includes the content of ammonium nitrogen, nitrate nitrogen, available phosphorus, and available potassium.

5. A rapid diagnostic method for soil obstacle factors in continuous blueberry cropping according to claim 1, characterized in that, The metagenomic data includes the species composition, functional gene distribution, pathogenic flora, resistance genes, detoxification genes, and volatile synthesis pathways of the soil microbial community.

6. The rapid diagnostic method for soil obstacle factors in blueberry continuous cropping according to claim 1, characterized in that, The metabolomics data includes metabolite profiles, root exudates, toxic or beneficial metabolites, and characteristic small molecules associated with the barrier.

7. The rapid diagnostic method for soil obstacle factors in blueberry continuous cropping according to claim 1, characterized in that, The significant microbial data refers to microbial data whose abundance changes significantly under continuous cropping obstacles, and the significant metabolite data refers to metabolite data whose abundance changes significantly under continuous cropping obstacles.

8. A rapid diagnostic method for soil obstacle factors in continuous blueberry cropping according to claim 1, characterized in that, The environmental factor analysis includes RDA, CCA, Mantel test, or environmental factor significance screening. The physicochemical factor data are explanatory variables, and the significant microbial data and significant metabolite data are response variables.

9. A rapid diagnostic method for soil obstacle factors in blueberry continuous cropping according to claim 1, characterized in that, The diagnostic results are presented in the form of a risk grading threshold map or judgment table for continuous cropping obstacles.

10. A rapid diagnostic method for soil obstacle factors in blueberry continuous cropping according to claim 1, characterized in that, It also includes S3.1, the specific steps of which are as follows: The significant microbial data and significant metabolite data in S3 are used to construct a co-occurrence network to obtain core pathogenic species data and key metabolite data. The core pathogenic species data and key metabolite data are used as the significant microbial data and significant metabolite data in S4 and S5.

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