Method for quantitatively evaluating influence of pesticide residue on farmland ecosystem

Abstract

The application discloses a kind of quantitative evaluation method of the influence of pesticide residue on farmland ecosystem, comprising: collecting farmland soil sample, separating and culturing soil microbial colony, and constructing microbial community fingerprint;Based on the double screening strategy of functional response and structural change, determine the set of microbial characteristic markers sensitive to pesticide residue;Establish the quantitative response model of the set of microbial characteristic markers to different concentrations, different types of pesticide residues;According to the change state of characteristic markers in actual farmland soil sample, combined with response model, calculate the ecological impact index of pesticide residue;The method can objectively evaluate the health status of farmland ecosystem by microbial community analysis and establish a quantitative evaluation system for the ecological impact of pesticide residue, and provide a scientific basis for safe use of pesticides and ecosystem protection.

Description

Technical Field

[0001] This invention belongs to the field of ecological impact assessment, and specifically relates to a quantitative assessment method for the impact of pesticide residues on farmland ecosystems. Background Technology

[0002] Pesticide residue assessment primarily focuses on the detection of chemical residues, typically employing instrumental analytical methods such as gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) to determine pesticide residue concentrations in soil, crops, and water bodies. While these traditional assessment methods can accurately quantify the residue levels of specific pesticides, they have significant limitations: first, they struggle to reflect the impact of pesticide residues on the overall function and structure of farmland ecosystems; second, they cannot assess the synergistic effects of compound pesticides; third, they lack sufficient assessment of the ecotoxicity of degradation metabolites; and fourth, they lack a standardized and quantitative ecological risk assessment indicator system.

[0003] Soil microbial community structure and function have a significant response to pesticide stress and can serve as an important parameter for assessing the ecological impact of pesticides. However, existing microbial-based assessment methods often remain at the stage of qualitative description, lacking systematically screened characteristic markers and quantitative response models, making it difficult to provide objective and comparable assessment results.

[0004] Therefore, there is an urgent need to establish a quantitative assessment method for the ecological impact of pesticide residues based on microbial response, in order to make up for the shortcomings of existing technologies and provide theoretical basis and technical support for the scientific use of pesticides and the protection of farmland ecosystems. Summary of the Invention

[0005] The purpose of this invention is to provide a quantitative assessment method for the impact of pesticide residues on farmland ecosystems, addressing the technical problems of existing pesticide residue assessments that mainly focus on chemical residue levels while neglecting the overall impact on the ecosystem, and the lack of standardized and quantitative indicator systems in the assessment process.

[0006] To achieve the above-mentioned objectives, the specific technical solution is as follows: A method for quantitatively assessing the impact of pesticide residues on farmland ecosystems, the method comprising: Step S1: Collect farmland soil samples, isolate and culture soil microbial colonies, and construct a microbial community fingerprint map.

[0007] Step S2: Based on a dual screening strategy of functional response and structural change, a set of microbial biomarkers sensitive to pesticide residues is determined.

[0008] Step S3: Establish a quantitative response model of the microbial biomarker set to different concentrations and types of pesticide residues.

[0009] Step S4: Based on the changes in characteristic markers in actual farmland soil samples, calculate the ecological impact index of pesticide residues using a response model.

[0010] Furthermore, the microbial community fingerprint is constructed using high-throughput sequencing technology, specifically including: extracting total DNA from farmland soil samples and amplifying the 16S rRNA / ITS region; obtaining microbial taxonomic composition data through high-throughput sequencing; performing bioinformatics analysis on the sequencing data to obtain species composition, abundance distribution, and diversity index; and constructing the microbial community fingerprint based on principal coordinate analysis (PCoA), non-metric multidimensional scaling (NMDS), or heatmap analysis.

[0011] Furthermore, the step of determining the set of microbial biomarkers based on the dual screening strategy of functional response and structural change includes: Step S21: Based on structural changes, the microbial groups with significant sensitivity to pesticide action are determined using the rate of change of the microbial diversity index.

[0012] Step S22: Based on functional response, determine soil enzyme activity profile, microbial community metabolic profile (BIOLOG), and changes in functional gene abundance to identify microbial groups with significant functional changes under pesticide treatment.

[0013] Step S23: Take the intersection of the results based on structural change and the results based on functional response as the candidate feature marker set.

[0014] Step S34: Through indoor microcosm experiments and field verification experiments, 15-20 microbial groups with the highest response stability are screened to form the final set of characteristic markers, and a sensitivity weight coefficient is assigned to each marker.

[0015] Furthermore, each biomarker microbial community is assigned a sensitivity weight, with a value ranging from 0 to 1. The larger the value, the higher the sensitivity, determined based on a large-sample pesticide exposure response experiment.

[0016] Furthermore, the step of establishing a quantitative response model of microbial biomarker set to pesticide residues includes: A microcosm experimental system with gradient pesticide concentrations was constructed, cultured under identical conditions, and sampled at set time intervals. The changes in characteristic biomarkers at different concentration gradients were measured, and dose-response curves were established. For each feature marker Establish response strength Functions: ,in, This refers to pesticide concentration. For the response intensity coefficient, Sensitivity amplification factor; formula for calculating marker response rate: ,in, For post-exposure abundance, Initial abundance; Establish synergistic response models for characteristic biomarkers for different types of pesticides: ,in, For the first The sensitivity weights of each marker, where n is the total number of markers. This is a correction factor for interactions between markers.

[0017] Furthermore, the quantitative response model further includes a synergistic effect model of pesticide residue combined effects, expressed as: ; in, Mixed pesticides The overall response intensity of pesticide residues This represents the total amount of mixed pesticides. For the first The concentration of pesticides, For the first The basic toxicity weight of pesticides For the first species and first Synergistic effect coefficient of pesticides as a marker For the first The response function of pesticides; The synergistic effect coefficient The results were determined through orthogonal design of mixed pesticide residue experiments, when... When it represents a positive synergistic effect, when The time indicates an antagonistic effect.

[0018] Furthermore, the step of calculating the ecological impact index of pesticide residues based on the changing state of characteristic markers includes: The microbial community data of the farmland soil samples to be evaluated were compared with those of the control samples, and the response rate of each characteristic marker was calculated. ; Based on the response rate and sensitivity weight of characteristic biomarkers, the ecological impact index of pesticide residues is calculated: Among them, when the abundance of the marker decreases When abundance increases The value is set at 0.2-0.8 based on the ecological indicative significance of the marker. Introducing environmental factor correction: ,in, For the first The influence coefficient of each environmental factor This refers to the degree to which environmental factors deviate from the normal range.

[0019] Furthermore, the ecological impact index is further evaluated in conjunction with ecosystem function parameters, and its calculation expression is as follows: ;in, To provide a comprehensive ecological impact index, The weights for microbial biomarkers range from 0.6 to 0.8. For the first The weights of each ecosystem function parameter This is the current function parameter value. These are the function parameter values ​​under reference conditions. The ecosystem function parameters include at least: soil respiration intensity, nutrient cycling efficiency, biomass accumulation rate, and organic matter decomposition rate.

[0020] Furthermore, the health status of farmland ecosystems is assessed in a tiered manner based on the ecological impact index: Establish standards for assessing the health of farmland ecosystems: For a healthy state, The condition is slightly affected. The impact is moderate. The condition is severely affected. This is a state of extremely severe impact.

[0021] Furthermore, construct indicators for assessing the resilience of farmland ecosystems: ,in, For the first The recovery indicator weight of each marker To restore the potential coefficient; when The system has self-recovery capabilities; when In such cases, manual intervention and repair are recommended.

[0022] Compared with the prior art, the beneficial effects of this invention are: This invention establishes a set of microbial biomarkers and a quantitative response model to achieve accurate assessment of the ecological impact of pesticide residues. It can objectively reflect the health status of pesticide residues on farmland ecosystems and provide resilience assessment indicators, thus providing a scientific basis for the safe use of pesticides and the protection of farmland ecosystems. Attached Figure Description

[0023] Figure 1 This is a flowchart of a method for quantitatively assessing the impact of pesticide residues on farmland ecosystems according to the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention are described clearly and completely below. Obviously, the described embodiments are only a part of the embodiments of this invention, not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0025] This invention provides a quantitative assessment method for the impact of pesticide residues on farmland ecosystems based on microbial colony screening. The method first collects farmland soil samples and isolates and cultures microbial colonies. A microbial community fingerprint is constructed using high-throughput sequencing. A dual screening strategy of functional response and structural change is employed to identify sensitive biomarkers. A quantitative response model is established, and finally, an ecological impact index is calculated and a graded assessment is performed. The overall process of this method is as follows: Figure 1 As shown, the method includes: Step S1: Collect farmland soil samples, isolate and culture soil microbial colonies, and construct a microbial community fingerprint map.

[0026] A combination of systematic and random sampling methods was used. Within the target farmland, 5-10 main sampling points were set up in an S or Z-shaped pattern, depending on the area size. Each main sampling point was surrounded by 4-5 sub-sampling points to form a sampling unit.

[0027] For topsoil of cultivated land, the sampling depth is 0-20 cm; depending on the research needs, soil samples can be collected in stratified layers of 0-10 cm and 10-20 cm to study the impact of pesticide vertical distribution. Sampling should be conducted at different time points after pesticide application (e.g., 1 day, 7 days, 14 days, 30 days, and 60 days after application) to monitor the dynamic impact of pesticide residues on the microbial community. Control samples should also be collected before pesticide application. 3-5 parallel samples should be collected from each sampling unit for replication validation and statistical analysis.

[0028] The following tools were used: a stainless steel soil drill (5cm inner diameter), a shovel, sample bags, labels, an ice box, and a GPS locator. All tools were disinfected with 75% alcohol before use. The sampling method was as follows: remove plant debris and other contaminants from the surface of the sampling point; insert the soil drill to the target depth and extract the soil core; remove contaminants from the surface of the soil core and take the central soil sample; mix the subsamples from the same sampling unit to form a mixed sample; divide the mixed sample into two parts using the quartering method, one part for physicochemical property determination and the other part for microbiological analysis.

[0029] Samples for microbial analysis should be placed in sterile sampling bags, labeled with information such as sampling location, time, and depth, and stored in a 4°C refrigerator. They should be transported back to the laboratory within 24 hours. Samples for microbiome analysis can be stored in an ultra-low temperature freezer at -80°C for long-term storage.

[0030] At the same time, environmental parameters at the sampling points were recorded, including: meteorological conditions: temperature, humidity, light intensity, and rainfall; soil physicochemical properties: pH value, organic matter content, total nitrogen, total phosphorus, and total potassium content; and pesticide application information: pesticide type, application amount, application method, and application time.

[0031] Soil sample preparation: Remove stones and plant debris from the soil sample by passing it through a 2 mm sieve; weigh 50 g of the pretreated soil sample and air dry or keep it in its original state under sterile conditions; take 10 g of the treated soil, add 90 mL of sterile water, and shake at 180 rpm for 30 minutes on a shaker; after standing for 10 minutes, take the supernatant and perform a 10-fold serial dilution to prepare dilutions from 10^-1 to 10^-7.

[0032] Preparation of selective culture medium: Culture medium for bacterial isolation: Beef Extract Peptone Medium (BPM); Formula: 3g beef extract, 10g peptone, 5g NaCl, 15-20g agar, 1000mL distilled water, pH 7.2-7.4, autoclaved at 121℃ for 20 minutes; Culture medium for actinomycete isolation: Gause's No.1 Medium; formula: 20g soluble starch, 1g KNO3, 0.5g K2HPO4, 0.5g MgSO4·7H2O, 0.5g NaCl, 0.01g FeSO4·7H2O, 20g agar, 1000mL distilled water, pH 7.2-7.4, autoclaved at 121℃ for 20 minutes.

[0033] Culture medium for fungal isolation: Potato Dextrose Agar (PDA); Formula: 200g potato (boiled extract), 20g glucose, 15-20g agar, 1000mL distilled water, pH 5.6-6.0, autoclaved at 121℃ for 20 minutes.

[0034] Microbial isolation and culture were performed using the plate spread method: 100 μL of appropriately diluted soil suspension was evenly spread onto the corresponding selective culture medium plate; bacterial culture: incubation at 28-30℃ for 24-48 hours; actinomycete culture: incubation at 28℃ for 5-7 days; fungal culture: incubation at 25℃ for 5-7 days; 3-5 replicate plates were set up for each sample.

[0035] The number of colonies (CFU) on each plate was calculated using a colony counter, and the number of microorganisms in the soil was calculated (CFU / g dry soil). Preliminary classification was performed based on characteristics such as colony morphology, color, and size. Colonies with distinctive characteristics were selected and purified to establish pure cultures.

[0036] Select a single colony and purify it on the appropriate culture medium by streak plating, subculturing at least 3 times; observe the purified strain under a microscope to confirm its purity; prepare glycerol cryovials (30% glycerol), make the pure culture into a suspension, and store it in an ultra-low temperature freezer at -80℃; some strains can be prepared into slant cultures and stored at 4℃.

[0037] The microbial community fingerprint was constructed using high-throughput sequencing technology, specifically including: extracting total DNA from farmland soil samples and amplifying the 16S rRNA / ITS region; obtaining microbial taxonomic composition data through high-throughput sequencing; performing bioinformatics analysis on the sequencing data to obtain species composition, abundance distribution, and diversity index; and constructing the microbial community fingerprint based on principal coordinate analysis (PCoA), non-metric multidimensional scaling (NMDS), or heatmap analysis.

[0038] Total DNA was extracted from the soil using a commercial soil DNA extraction kit (such as the FastDNA SPIN Kit for Soil, MPBiomedicals). The specific steps are as follows: (1) Weigh 0.5g of fresh soil sample and place it into a pyrolysis tube containing pyrolysis beads; (2) Add 978 μL of sodium phosphate buffer and 122 μL of MT buffer; (3) Place the pyrolysis tube into the FastPrep instrument and oscillate it at a speed of 6.0 m / s for 40 seconds. Repeat twice with a 5-minute interval. (4) Centrifuge at 12,000×g for 5 minutes and collect the supernatant; (5) Add 250 μL of PPS solution, manually invert and mix 10 times, and centrifuge at 12,000 × g for 5 minutes; (6) Transfer the supernatant to a new tube, add 1 mL of binding matrix suspension, and mix by inverting for 2 minutes; (7) Let stand for 5 minutes, discard the supernatant, and add 1 mL of SEWS-M solution to resuspend the precipitate; (8) Transfer to a SPIN filter column, centrifuge at 14,000×g for 1 minute, and discard the filtrate; (9) Repeat the SEWS-M washing step once; (10) Place the SPIN filter column in the new collection tube and let it air dry at room temperature for 5 minutes; (11) Add 100 μL of DES water, gently resuspend the substrate, and incubate at room temperature for 5 minutes; (12) Centrifuge at 14,000×g for 1 minute and collect the filtrate containing DNA.

[0039] DNA concentration was determined using a NanoDrop 2000 spectrophotometer. A 260 / A280 ratio between 1.8 and 2.0 indicates high-quality DNA. For integrity testing, 5 μL of DNA sample was taken and DNA integrity was assessed by 1% agarose gel electrophoresis at 80V for 30 minutes. Purity verification was performed by PCR amplification of 16S rRNA or ITS fragments to verify the usability of the extracted DNA.

[0040] Before constructing the microbial community fingerprint, PCR amplification and fungal ITS region amplification were performed.

[0041] The forward primer for amplifying the V3-V4 region of the bacterial 16S rRNA gene was 341F (5'-CCTACGGGNGGCWGCAG-3'); the reverse primer was 805R (5'-GACTACHVGGGTATCTAATCC-3'); the PCR reaction system (50 μL) consisted of 10-20 ng template DNA, 1 μL forward primer (10 μM), 1 μL reverse primer (10 μM), 25 μL 2×Phanta Max Master Mix, and ddH2O to a final volume of 50 μL; the PCR reaction conditions were: 95℃ pre-denaturation for 3 min, 95℃ denaturation for 30 s, 55℃ annealing for 30 s, 72℃ extension for 45 s, 30 cycles, 72℃ final extension for 10 min, and storage at 4℃.

[0042] Forward primers for fungal ITS region amplification: ITS1F (5'-CTTGGTCATTTAGAGGAAGTAA-3'), reverse primer: ITS2R (5'-GCTGCGTTCTTCATCGATGC-3'); PCR reaction system (50μL): template DNA 10-20ng, forward primer (10μM) 1μL, reverse primer (10μM) 1μL, 2×PhantaMax Master Mix 25μL, ddH2O to 50μL; PCR reaction conditions: 95℃ pre-denaturation for 3 min, 95℃ denaturation for 30 s, 52℃ annealing for 30 s, 72℃ extension for 45 s, 35 cycles; final extension at 72℃ for 10 min, stored at 4℃.

[0043] PCR products were purified using AMPure XP magnetic beads to remove primer dimers and nonspecific products. An equal volume of AMPure XP magnetic beads was added and incubated at room temperature for 5 minutes. The product was then placed on a magnetic rack for 2 minutes, and the supernatant was discarded. The magnetic beads were washed twice with 80% ethanol. The product was dried at room temperature for 5 minutes, and 30 μL of TE buffer was added to elute the DNA. The product was then placed on a magnetic rack again for 2 minutes, and the supernatant was collected.

[0044] Sequencing libraries were constructed using the Illumina TruSeq DNA PCR-Free Library Preparation Kit. The purified PCR products underwent end repair and A-tail addition, sequencing adapters were ligated, and specific tags (barcodes) were added. Library concentration was precisely quantified using a Qubit 3.0 Fluorometer, and the size distribution of library fragments was detected using an Agilent 2100 Bioanalyzer. The effective concentration of the library was verified using qPCR.

[0045] Sequencing was performed using the Illumina MiSeq / NovaSeq platform: The library was diluted to a final concentration of 4 nM, denatured with an equal volume of 0.2 N NaOH solution, and then diluted to a final concentration of 12 pM. At least 5% PhiXControl was added as an internal control. The MiSeq Reagent Kit v3 (600 cycles) was used with paired-end sequencing (2 × 300 bp) mode to ensure that each sample reached a sequencing depth of at least 30,000 valid sequences.

[0046] The quality of the raw sequencing data was checked using FastQC software, and low-quality sequences and adapter sequences were removed using Trimmomatic software. The filtering criteria were: the proportion of bases with a quality value of Q < 20 should not exceed 20% of the sequence length, sequences containing N bases were removed, and reads with a length of less than 200 bp were discarded.

[0047] The paired-end sequencing data were assembled into complete sequences using FLASH software. Chimeric sequences were detected and removed using the UCHIME algorithm in USEARCH software. The sequences were clustered into OTUs (Operational Taxonomic Units) using the UPARSE algorithm with a similarity threshold of 97%. The species classification annotation of the OTU representative sequences was performed using RDP Classifier. Bacterial sequences were annotated using the Silva database with a confidence threshold of 0.7, and fungal sequences were annotated using the UNITE database with a confidence threshold of 0.7.

[0048] Based on high-throughput sequencing data, a microbial community fingerprint was constructed, specifically including: Species composition analysis was performed to generate stacked bar charts of relative abundance of species at each taxonomic level (phylum, class, order, family, genus, species). The top 20 dominant genera were selected to generate relative abundance heatmaps to show the differences between samples. A microbial community co-occurrence network was constructed to analyze the relationships between species. Diversity pattern analysis, NMDS / PCoA two-dimensional scatter plots, show the differences in community structure among samples, hierarchical clustering dendrograms based on Bray-Curtis distance, visually display the similarity among samples, and a gradient distribution map of microbial diversity is constructed to show the changing trend of diversity index with environmental factors.

[0049] Functional prediction analysis was performed using PICRUSt2 software to predict the functional genome of the microbial community. Based on the KEGG database, the relative abundance of microbial functional metabolic pathways was analyzed, and a functional genome heatmap was constructed to show the functional differences among different samples.

[0050] Correlation analysis of environmental factors was conducted using ordination analysis methods such as CCA / RDA to explore the correlation between environmental factors and microbial community structure. A heatmap of the correlation between environmental factors and key microbial taxa was generated, and a scatter plot of environmental factors and microbial diversity index was constructed to analyze the correlation trends.

[0051] Step S2: Based on a dual screening strategy of functional response and structural change, a set of microbial biomarkers sensitive to pesticide residues is determined.

[0052] The steps for determining the set of microbial biomarkers based on a dual screening strategy of functional response and structural change include: Step S21: Based on structural changes, the microbial groups with significant sensitivity to pesticide action are determined using the rate of change of the microbial diversity index.

[0053] Step S22: Based on functional response, determine soil enzyme activity profile, microbial community metabolic profile (BIOLOG), and changes in functional gene abundance to identify microbial groups with significant functional changes under pesticide treatment.

[0054] Step S23: Take the intersection of the results based on structural change and the results based on functional response as the candidate feature marker set.

[0055] Step S34: Through indoor microcosm experiments and field verification experiments, 15-20 microbial groups with the highest response stability are screened to form the final set of characteristic markers, and a sensitivity weight coefficient is assigned to each marker.

[0056] To screen for microbial biomarkers sensitive to pesticide residues, it is necessary to construct a controllable microcosm experimental system: Experimental container preparation: Use 500mL brown wide-mouth glass bottles as microcosm containers. Add 200g of homogeneous soil that has passed through a 2mm sieve to each container and adjust the soil moisture content to 60% of the maximum water holding capacity. Cover the top of the container with a breathable sterile membrane to ensure gas exchange while preventing contamination.

[0057] Environmental conditions control: Temperature: 25±1℃, Light: 12 hours light / 12 hours dark cycle, Humidity: Relative humidity 60±5%, Regularly monitor soil moisture content and maintain stable moisture content by adding sterile water.

[0058] Three typical pesticides were selected for the experiment, representing different mechanisms of action: organophosphates: chlorpyrifos, pyrethroids: lambda-cyhalothrin, and neonicotinoids: imidacloprid.

[0059] Five concentration gradients were set up for each pesticide, including 0.1 times, 0.5 times, 1 times, 2 times and 5 times the recommended dose. The pesticides were prepared from analytical grade standards and acetone was used as the solvent. The control group was treated with an equal amount of acetone. Each treatment was replicated in 4 places.

[0060] The pesticide standard solution was evenly sprayed onto the soil surface, and the soil was gently stirred to ensure uniform distribution of the pesticide. After adding the pesticide, the soil was left to stand for 24 hours to allow the solvent to fully evaporate. Seven sampling time points were set: day 0 (before treatment), day 1, day 3, day 7, day 14, day 28, and day 56. The sample on day 0 was used as a baseline control.

[0061] At each sampling time point, 10g of soil was taken from each microcosm container and divided into two portions: 5g for DNA extraction and high-throughput sequencing, and 5g for enzyme activity and biological analysis. The samples were processed immediately or stored in a -80°C freezer.

[0062] By analyzing the response of microbial community structure to pesticide stress, sensitive structural biomarkers were screened: Alpha diversity index change rate calculation: Calculate the change rates of the Shannon index, Simpson index, and Chao1 index: Screening criteria: Time points with an absolute change rate > 20% are considered to have a significant response. A response curve of the diversity index over time is constructed to identify diversity patterns sensitive to different pesticides.

[0063] Beta diversity analysis: Permanente analysis was used to test the significant differences in community structure between the treatment group and the control group. The dissimilarity between the groups was calculated based on Bray-Curtis distance, and the dissimilarity curve as a function of pesticide concentration was constructed. The screening criteria were: treatments with p < 0.05 and dissimilarity > 0.6 were considered to produce significant structural changes.

[0064] Calculate the relative abundance changes of dominant taxa at each taxonomic level (phylum, class, order, family, genus), and calculate the response rate for each dominant taxa (relative abundance > 1%): Screening criteria: Groups with an absolute response rate > 50% and a statistical analysis p < 0.05 are considered to be sensitive dominant groups.

[0065] The similarity of community composition between the treatment group and the control group was calculated using the Jaccard index and the Sorensen index. Curves of similarity change with pesticide concentration and time were constructed. Screening criteria: treatments with a similarity index <0.7 were considered to produce significant changes in community composition.

[0066] Differential abundance analysis (LEfSe) parameter settings: LDA score threshold: 2.5, Wilcoxon rank-sum test p-value threshold: 0.05, intergroup consistency: 75%.

[0067] Microbial taxa that were significantly enriched or reduced under each pesticide treatment were extracted, and abundance box plots were drawn for each differential taxa to visually display the changing trends. The screening criteria were: taxa that showed significant differences at at least 3 sampling time points were considered to have stable responsiveness.

[0068] Biomarker effect value (LEfSe-score) calculation: The effect value (LEfSe-score) is calculated for each differential group. The effect values ​​under different pesticide treatments are ranked, and the top 30 groups with the highest absolute effect values ​​are selected as candidate structural biomarkers.

[0069] By analyzing the response of microbial community function to pesticide stress, sensitive functional biomarkers were screened: Soil enzyme activity determination: Urease: Phenol-sodium hypochlorite colorimetric method, with NH4+ + -N μg / g·24h, sucrase: 3,5-dinitrosalicylic acid colorimetric method, expressed as glucose mg / g·24h, phosphatase: disodium p-nitrophenyl phosphate colorimetric method, expressed as phenol μg / g·h, catalase: potassium permanganate titration method, expressed as 0.1N KMnO4 mL / g·h, dehydrogenase: 2,3,5-triphenyltetrazolium (TTC) colorimetric method, expressed as TPF μg / g·24h, β-glucosidase: p-nitrophenyl-β-D-glucosidase colorimetric method, expressed as p-nitrophenol μg / g·h.

[0070] Enzyme activity response analysis: Calculate the response rate of each enzyme activity: Response curves of enzyme activity to pesticide concentration and time were constructed, and the comprehensive sensitivity index of each enzyme activity was calculated. In this context, time weight reflects the importance of early response, n is the number of sampling time points, and the screening criteria are: enzymes with a sensitivity index >30% are considered to be sensitive functional indicators.

[0071] Microbial community metabolomics (BIOLOG) analysis, BIOLOG-ECO microplate analysis: Soil suspension preparation: 5g fresh soil was added to 45mL sterile water, shaken for 30 minutes, allowed to stand for 30 minutes, and the supernatant was diluted 10 times and added to ECO microplates, 150μL per well, and incubated at 28℃. The OD590 value was measured every 24 hours and monitored continuously for 96 hours. The average color change rate (AWCD), carbon source utilization diversity, and carbon source utilization pattern were calculated.

[0072] Metabolic function analysis: The utilization rates of six types of carbon sources (carbohydrates, amino acids, carboxylic acids, phenolic compounds, amines, and polymers) were calculated, and a carbon source utilization PCA / NMDS plot was constructed to visually display the differences in metabolic function. The dose-response relationship between the utilization rate of each carbon source and pesticide concentration was analyzed. Screening criteria: Carbon source types with an absolute response rate >40% and showing a significant gradient change among treatment groups were considered sensitive metabolic indicators.

[0073] Functional gene detection: Key functional genes related to the carbon, nitrogen, and phosphorus cycles were selected for quantitative real-time PCR analysis. Carbon cycle related genes: cbbl (RubisCO), pmoA (methane monooxygenase), mcrA (methane synthase); nitrogen cycle related genes: nifH (nitrogenase), amoA (ammonia monooxygenase), nirK and nirS (denitrification), narG (nitrification); phosphorus cycle related genes: phoD and phoN (phosphatases); resistance genes: tetW (tetracycline resistance), sulI (sulfonamide resistance), strB (streptomycin resistance).

[0074] qPCR reaction system and conditions: Reaction system (20 μL): Template DNA 2 μL (10 ng), forward primer (10 μM) 0.8 μL, reverse primer (10 μM) 0.8 μL, 2×SYBR Green qPCR Mix 10 μL, ddH2O 6.4 μL. qPCR program: 95℃ pre-denaturation for 3 min, 95℃ denaturation for 15 s, specific annealing temperature for 30 s, 72℃ extension for 30 s, 40 cycles. Melting curve analysis: 65℃ to 95℃, step size 0.5℃, pause for 5 s.

[0075] Functional gene analysis calculates the relative abundance changes of each functional gene, constructs a functional gene abundance heatmap, visually displays the functional change patterns under different treatments, and calculates the response sensitivity index of functional genes. In this context, the concentration gradient weights reflect the importance of dose dependence, n is the number of concentration gradients, and the screening criteria are: functional genes with a sensitivity index > 1.5 and showing obvious dose dependence are considered as sensitive functional indicators.

[0076] The results of structural change screening and functional response screening are intersected to determine the set of candidate feature markers: Spearman correlation coefficients were calculated between structural markers (microbial taxa) and functional indicators (enzyme activity, metabolic function, functional genes), and correlation network diagrams were constructed to visually demonstrate the association patterns between structure and function. Screening criteria: microbial taxa with an absolute correlation coefficient > 0.6 and p < 0.05 were considered closely related to functional responses. The top 20 most sensitive microbial taxa were selected from structural change analysis, and the top 20 microbial taxa highly correlated with sensitive functional indicators were selected from functional association analysis. The intersection of these two selections formed a candidate feature marker set. For cases where the intersection was too small (less than 10 taxa), the highest-ranking taxa in either the structural or functional analysis were further screened.

[0077] The microcosm experiment was repeated using farmland soils from different sources (at least three different soil types), testing only moderate concentrations (1x the recommended dose) of pesticides to evaluate the consistency of responses to candidate biomarkers: Screening criteria: Markers with a consistency index >70% are considered to have good response stability.

[0078] Field validation trials were conducted at 3-5 typical farmland sites, with pesticide treatment plots (recommended dosage) and control plots. Soil samples were collected at key time points after pesticide application (1 day, 7 days, 14 days, and 28 days) to analyze the response of candidate biomarkers under field conditions and calculate the consistency of responses between the indoor microcosm and the field trials. Screening criteria: Candidate biomarkers with a consistency index >60% are considered to have good application potential under field conditions. Calculate a comprehensive sensitivity score for each candidate biomarker: Where W1, W2, and W3 are the weights of structure, function, and stability, respectively, set to 0.3, 0.3, and 0.4. Structural sensitivity is calculated based on the LEfSe effect value and response rate. Functional correlation is calculated based on the correlation coefficient with key functional indicators. Response stability is calculated based on the consistency index of indoor cross-validation and field validation.

[0079] Candidate biomarkers were ranked according to their comprehensive sensitivity scores, and the 15-20 microbial groups with the highest scores were selected as the final set of biomarkers. This ensured that the final biomarker set included representative groups at different taxonomic levels (phylum, class, order, family, genus). A standard sensitivity coefficient was assigned to each biomarker, calculated based on its sensitivity score and normalized. Each biomarker microbial community is assigned a sensitivity weight, with a value ranging from 0 to 1. The larger the value, the higher the sensitivity, which is determined based on a large-sample pesticide exposure response experiment.

[0080] Step S3: Establish a quantitative response model of the microbial biomarker set to different concentrations and types of pesticide residues.

[0081] The steps for establishing a quantitative response model of microbial biomarker set to pesticide residues include: A microcosm experimental system with gradient pesticide concentrations was constructed, cultured under identical conditions, and sampled at set time intervals. The changes in characteristic biomarkers at different concentration gradients were measured, and dose-response curves were established. For each feature marker Establish response strength Functions: ,in, This refers to pesticide concentration. For the response intensity coefficient, Sensitivity amplification factor; formula for calculating marker response rate: ,in, For post-exposure abundance, Initial abundance; Establish synergistic response models for characteristic biomarkers for different types of pesticides: ,in, For the first The sensitivity weights of each marker, where n is the total number of markers. This is a correction factor for interactions between markers.

[0082] Eight concentration gradients were set for each pesticide to cover a wider range: 0.01, 0.05, 0.1, 0.5, 1, 2, 5, and 10 times the recommended dose; the concentration gradients were ensured to be logarithmically distributed to facilitate the establishment of dose-response curves; and five replicates were set for each concentration gradient to improve data reliability.

[0083] The microcosm experiment used a 250mL glass bottle, with 100g of standardized soil added. Standardization treatment: passing through a 2mm sieve, adjusting the moisture content to 60% of the maximum water holding capacity, pre-culturing for 7 days to stabilize the microbial community, temperature 25±0.5℃, relative humidity 60±3%, and light cycle 12h:12h.

[0084] At least 50,000 high-quality sequences were obtained for each sample to improve the detection sensitivity of rare groups. Specific primers were designed for qPCR analysis targeting key biomarkers to improve quantitative accuracy. Three technical replicates were set up for each sample to reduce the impact of sequencing errors.

[0085] The quantitative response model further includes a synergistic effect model of pesticide residue combined effects, expressed as: ; in, Mixed pesticides The overall response intensity of pesticide residues This represents the total amount of mixed pesticides. For the first The concentration of pesticides, For the first The basic toxicity weight of pesticides For the first species and first Synergistic effect coefficient of pesticides as a marker For the first The response function of pesticides; The synergistic effect coefficient The results were determined through orthogonal design of mixed pesticide residue experiments, when... When it represents a positive synergistic effect, when The time indicates an antagonistic effect.

[0086] Three typical pesticides (chlorpyrifos, cypermethrin, and imidacloprid) were selected for pairwise and three-component mixed experiments. The concentration combinations were designed using the central composite design (CCD) method to ensure coverage of the main effect and interaction effect. Each combination was set up with four replicates. Microcosm experiments were carried out and the biomarker response was monitored.

[0087] Step S4: Based on the changes in characteristic markers in actual farmland soil samples, calculate the ecological impact index of pesticide residues using a response model.

[0088] The steps for calculating the ecological impact index of pesticide residues based on changes in characteristic markers include: The microbial community data of the farmland soil samples to be evaluated were compared with those of the control samples, and the response rate of each characteristic marker was calculated. ; Based on the response rate and sensitivity weight of characteristic biomarkers, the ecological impact index of pesticide residues is calculated: Among them, when the abundance of the marker decreases When abundance increases The value is set at 0.2-0.8 based on the ecological indicative significance of the marker. Introducing environmental factor correction: ,in, For the first The influence coefficient of each environmental factor This refers to the degree to which environmental factors deviate from the normal range.

[0089] The ecological impact index is further evaluated in conjunction with ecosystem function parameters, and its calculation expression is as follows: ;in, To provide a comprehensive ecological impact index, The weights for microbial biomarkers range from 0.6 to 0.8. For the first The weights of each ecosystem function parameter This is the current function parameter value. These are the function parameter values ​​under reference conditions. The ecosystem function parameters include at least: soil respiration intensity, nutrient cycling efficiency, biomass accumulation rate, and organic matter decomposition rate.

[0090] A graded assessment of farmland ecosystem health status based on the ecological impact index: Establish standards for assessing the health of farmland ecosystems: For a healthy state, The condition is slightly affected. The impact is moderate. The condition is severely affected. This is a state of extremely severe impact.

[0091] Constructing indicators for assessing the resilience of farmland ecosystems: ,in, For the first The recovery indicator weight of each marker To restore the potential coefficient; when The system has self-recovery capabilities; when In such cases, manual intervention and repair are recommended.

[0092] To verify the practicality of this method, we selected a facility vegetable production base to conduct an ecological impact assessment of pesticide residues: Area: approximately 200 mu of facility vegetable greenhouses; main crops: tomatoes, cucumbers, eggplants, etc.; pesticide use: mainly using pesticides such as chlorpyrifos, cypermethrin, and imidacloprid to control pests and diseases.

[0093] Twenty-four greenhouses were selected for sampling, including 16 conventional greenhouses (using pesticides) and 8 organic greenhouses (not using chemical pesticides). Soil samples were collected from five sampling points in each greenhouse at a depth of 0-20 cm and mixed into a composite sample. Information such as pesticide use history, dosage, and frequency of application for each greenhouse was recorded. At the same time, environmental parameters such as soil physicochemical properties and microclimate conditions were measured.

[0094] High-throughput sequencing technology was used to analyze the microbial community structure of soil samples, identify the main microbial groups in 24 samples, and calculate the differences between organic greenhouses (control) and conventional greenhouses (treatment).

[0095] Based on the previously established biomarker library, 18 characteristic biomarkers applicable to the region were selected, the response rate of each characteristic biomarker was calculated, and the correlation between biomarker response and pesticide use history was analyzed.

[0096] The initial ecological impact index (EI) was calculated and corrected for environmental factors such as soil pH and organic matter content. The comprehensive impact index is calculated by combining functional parameters such as soil respiration intensity and nitrogen mineralization rate. .

[0097] Of the 16 conventional planting greenhouses, 3 were in good condition. ), 6 are in a state of mild impact ( ), 5 are in a state of moderate impact ( ), 2 of which are in a state of severe impact ( The plants are not severely affected. All eight organic greenhouses are in a healthy state. ).

[0098] Pesticide usage frequency and A significant positive correlation was observed (r=0.78, p<0.01). Greenhouses using the same type of pesticide for a long period showed a more significant response to the characteristic markers. Greenhouses using compound pesticides showed a significantly higher response. The value is significantly higher than that of greenhouses using only one pesticide.

[0099] The resilience index (RI) was calculated for each greenhouse. Among the greenhouses under moderate and severe impact, 3 had RI > 1, indicating self-recovery capability. For the 4 greenhouses with RI < 1, soil remediation measures were recommended.

[0100] This method can also be used to assess the safety interval after pesticide application. The following is an example of pesticide safety interval assessment in a wheat field: Area: 4 treatment zones, 0.5 mu each; Treatments: imidacloprid treatment (recommended dose), chlorpyrifos treatment (recommended dose), a mixture of the two treatments, and a control (no pesticide application).

[0101] Soil samples were taken on days 1, 3, 7, 14, 21, 28, 42, and 56 after pesticide application. Three replicate sampling points were set up in each treatment area to collect soil samples from the 0-20cm soil layer and record soil environmental parameters.

[0102] Microbial community analysis was performed on soil samples at each time point, and the abundance changes of 18 characteristic markers were tracked to construct the change curves of marker response over time.

[0103] Calculate the ecological impact index at each time point ,draw The curve of change over time is determined. The time required for the level to drop below 0.2 (healthy state).

[0104] The recommended results and safety intervals are as follows: Impact dynamic analysis: Imidacloprid treatment: peak value =0.43 (moderate impact), dropped below 0.2 after 21 days; Chlorpyrifos treatment: Peak =0.55 (moderate impact), dropped below 0.2 after 28 days; Hybrid processing: Peak =0.73 (severe impact), dropped below 0.2 after 42 days.

[0105] Recovery Feature Analysis: Imidacloprid treatment: The recovery pattern showed an exponential decay, with a resilience index (RI) of 1.32; Chlorpyrifos treatment: The recovery pattern showed a linear decay, with a resilience index (RI) of 0.97; Hybrid treatment: The recovery mode exhibits a delayed response, with a resilience index RI of 0.68.

[0106] Recommended safe interval period: Imidacloprid: Recommended safety interval ≥ 28 days (21-day recovery period + 7-day safety margin); Chlorpyrifos: Recommended safety interval ≥ 35 days (28-day recovery period + 7-day safety margin); Combined treatment: It is recommended that the safety interval be ≥49 days (42-day recovery period + 7-day safety margin), and soil improvement measures be taken to promote recovery.

[0107] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for quantitatively assessing the impact of pesticide residues on farmland ecosystems, characterized in that, The method includes: Step S1: Collect farmland soil samples, isolate and culture soil microbial colonies, and construct a microbial community fingerprint map; Step S2: Based on a dual screening strategy of functional response and structural change, determine the set of microbial biomarkers sensitive to pesticide residues; The steps for determining a set of microbial biomarkers based on a dual screening strategy of functional response and structural change include: Step S21: Based on structural changes, the microbial groups with significant sensitivity to pesticide action are determined using the rate of change of the microbial diversity index. Step S22: Based on the functional response, determine the soil enzyme activity spectrum, microbial community metabolic spectrum, and changes in functional gene abundance to identify microbial groups that show significant functional changes under pesticide treatment. Step S23: Take the intersection of the results based on structural change and the results based on functional response as the candidate feature marker set; Step S24: Through indoor microcosm experiments and field verification experiments, 15-20 microbial groups with the highest response stability are screened to form the final set of characteristic markers, and a sensitivity weight coefficient is assigned to each marker. Step S3: Establish a quantitative response model of the microbial biomarker set to different concentrations and types of pesticide residues; A microcosm experimental system with gradient pesticide concentrations was constructed, cultured under identical conditions, and sampled at set time intervals. The changes in characteristic biomarkers at different concentration gradients were measured, and dose-response curves were established. For each feature marker Establish response strength Functions: ,in, This refers to pesticide concentration. For the response intensity coefficient, Sensitivity amplification factor; formula for calculating marker response rate: ,in, For post-exposure abundance, Initial abundance; Establish synergistic response models for characteristic biomarkers for different types of pesticides: ,in, For the first The sensitivity weights of each marker, where n is the total number of markers. This is a correction factor for interactions between markers; Step S4: Based on the changes in the characteristic markers in the actual farmland soil samples, and using the characteristic marker set and its sensitivity weights determined in Step S2, the ecological impact index of pesticide residues is calculated using the marker response rate defined in Step S3.

2. The method according to claim 1, characterized in that, The microbial community fingerprint was constructed using high-throughput sequencing technology, specifically including: extracting total DNA from farmland soil samples and amplifying the 16S rRNA / ITS region; obtaining microbial taxonomic composition data through high-throughput sequencing; performing bioinformatics analysis on the sequencing data to obtain species composition, abundance distribution, and diversity index; and constructing the microbial community fingerprint based on principal coordinate analysis, non-metric multidimensional scaling analysis, or heatmap analysis.

3. The method according to claim 2, characterized in that, Each biomarker microbial community is assigned a sensitivity weight, with a value ranging from 0 to 1. The larger the value, the higher the sensitivity, which is determined based on a large-sample pesticide exposure response experiment.

4. The method according to claim 3, characterized in that, The quantitative response model further includes a synergistic effect model of pesticide residue combined effects, expressed as: ； in, Mixed pesticides The overall response intensity of pesticide residues This represents the total amount of mixed pesticides. For the first The concentration of pesticides, For the first The basic toxicity weight of pesticides For the first species and first Synergistic effect coefficient of pesticides as a marker For the first The response function of pesticides; The synergistic effect coefficient The results were determined through orthogonal design of mixed pesticide residue experiments, when... When it represents a positive synergistic effect, when The time indicates an antagonistic effect.

5. The method according to claim 4, characterized in that, The ecological impact index of pesticide residues is calculated based on the changes in characteristic biomarkers, including: The microbial community data of the farmland soil samples to be evaluated were compared with those of the control samples, and the response rate of each characteristic marker was calculated. ; Based on the response rate and sensitivity weight of characteristic biomarkers, the ecological impact index of pesticide residues is calculated: Among them, when the abundance of the marker decreases When abundance increases The value is set at 0.2-0.8 based on the ecological indicative significance of the marker. Introducing environmental factor correction: ,in, For the first The influence coefficient of each environmental factor This refers to the degree to which environmental factors deviate from the normal range.

6. The method according to claim 5, characterized in that, The ecological impact index is further evaluated in conjunction with ecosystem function parameters, and its calculation expression is as follows: ;in, To provide a comprehensive ecological impact index, The weights for microbial biomarkers range from 0.6 to 0.

8. For the first The weights of each ecosystem function parameter This is the current function parameter value. These are the function parameter values ​​under reference conditions. The ecosystem function parameters include at least: soil respiration intensity, nutrient cycling efficiency, biomass accumulation rate, and organic matter decomposition rate.

7. The method according to claim 6, characterized in that, A graded assessment of farmland ecosystem health status based on the ecological impact index: Establish standards for assessing the health of farmland ecosystems: For a healthy state, The condition is slightly affected. The impact is moderate. The condition is severely affected. This is a state of extremely severe impact.

8. The method according to claim 7, characterized in that, Constructing indicators for assessing the resilience of farmland ecosystems: ,in, For the first The recovery indicator weight of each marker To restore the potential coefficient; when The system has self-recovery capabilities; when In such cases, manual intervention and repair are recommended.

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