Quantitative evaluation method for influence of pesticide residues on farmland ecosystem

Abstract

The invention discloses a quantitative evaluation method for the influence of pesticide residues on a farmland ecosystem, which comprises the following steps: collecting a farmland soil sample, separating and culturing soil microbial colonies, and constructing a microbial community fingerprint spectrum; determining a microbial characteristic marker set sensitive to pesticide residues based on a function response and structure change dual screening strategy; establishing a quantitative response model of the microbial characteristic marker set to different concentrations and different types of pesticide residues; according to the change state of the characteristic marker in the actual farmland soil sample, the ecological influence index of the pesticide residue is calculated in combination with the response model; according to the method, a quantitative evaluation system for the ecological influence of the pesticide residues is established through microflora analysis, the health condition of the pesticide residues on a farmland ecological system can be objectively evaluated, and a scientific basis is provided for safe use of pesticides and protection of the ecological system.

Description

Technical Field

[0001] The present invention belongs to the field of ecological impact assessment, and in particular relates to a quantitative assessment method for the impact of pesticide residues on farmland ecosystems. Background Art

[0002] Pesticide residue assessments primarily focus on detecting chemical residues, typically using instrumental analysis 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. 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 are unable to assess the synergistic effects of combined pesticides; third, they lack adequate assessments of the ecotoxicity of degradation metabolites; and fourth, they lack a standardized, quantitative indicator system for ecological risk assessment.

[0003] The structure and function of soil microbial communities respond significantly to pesticide stress and can serve as important parameters for assessing the ecological impact of pesticides. However, existing microbial-based assessment methods often remain at the qualitative description stage, 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 responses to make up for the shortcomings of existing technologies and provide a 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 the present invention is to provide a quantitative assessment method for the impact of pesticide residues on farmland ecosystems, so as to solve the technical problems that existing pesticide residue assessments mainly focus on chemical residues while ignoring the overall impact on the ecosystem, and the assessment process lacks a standardized and quantitative indicator system.

[0006] In order to achieve the above-mentioned purpose of the invention, the specific technical solutions are as follows: A quantitative assessment method for the impact of pesticide residues on farmland ecosystems, comprising: Step S1: Collect farmland soil samples, isolate and culture soil microbial colonies, and construct a microbial community fingerprint.

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

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

[0009] Step S4, calculating the ecological impact index of pesticide residues based on the change state of characteristic markers in actual farmland soil samples in combination with the response model.

[0010] Furthermore, the microbial community fingerprint map 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 map based on principal coordinate analysis (PCoA), non-metric multidimensional scaling analysis (NMDS) or heat map analysis.

[0011] Furthermore, the step of determining the microbial characteristic marker set based on the dual screening strategy of functional response and structural change includes: In step S21 , based on the structural changes, the microbial diversity index change rate is used to determine the microbial groups that are significantly sensitive to the pesticide.

[0012] Step S22: Based on the functional response, soil enzyme activity spectrum, microbial community metabolic spectrum (BIOLOG), and functional gene abundance changes are measured to determine the microbial groups with significant functional changes under the action of pesticides.

[0013] Step S23 , taking the intersection of the results of the screening based on structural changes and the screening based on functional responses as a candidate feature marker set.

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

[0015] Furthermore, each marker bacterial community is equipped with a sensitivity weight, ranging from 0 to 1, where a larger value indicates a higher sensitivity, which is determined based on a large-sample pesticide exposure response experiment.

[0016] Furthermore, the step of establishing a quantitative response model of the microbial characteristic marker set to pesticide residues includes: Construct a microcosm experimental system with gradient pesticide concentrations, culture under the same conditions, and sample at set time intervals; Determine the changes in characteristic markers under each concentration gradient and establish a dose-response curve; For each characteristic marker , establish response strength Function: ,in, is the pesticide concentration, is the response intensity coefficient, is the sensitivity amplification factor; the marker response rate calculation formula is: ,in, is the abundance after exposure, is the initial abundance; For different types of pesticides, a characteristic marker synergistic response model is established: ,in, For the The sensitivity weight of each marker, n is the total number of markers, is the correction factor for the interaction between markers.

[0017] Furthermore, the quantitative response model further includes a synergistic effect model of the combined action of pesticide residues, which is expressed as: ; in, For mixed pesticides The comprehensive response intensity of pesticide residues, is the total amount of mixed pesticides, For the The concentration of pesticides, For the The basic toxicity weight of the pesticides, For the Species and The synergistic effect coefficient of the pesticides, As a landmark For the first Response function of the pesticide; The synergy coefficient The orthogonal design of mixed pesticide residue experiments determined that It indicates positive synergistic effect when antagonistic effect.

[0018] Furthermore, the step of calculating the ecological impact index of pesticide residues according to the change state of the characteristic markers includes: Compare the microbial community data of the evaluated farmland soil samples with those of the control samples and calculate the response rate of each characteristic marker ; The ecological impact index of pesticide residues is calculated based on the response rate and sensitivity weight of the characteristic markers: , where when the abundance of the marker decreases , when abundance increases According to the ecological significance of this marker, it is taken as 0.2-0.8; Introducing environmental factor correction: ,in, For the The influence coefficient of each environmental factor, It is the degree to which environmental factors deviate from the normal range.

[0019] Furthermore, the ecological impact index is further combined with ecosystem function parameters for comprehensive evaluation, and the calculation expression is: ;in, is the comprehensive ecological impact index, is the weight of microbial markers, ranging from 0.6 to 0.8, For the The weights of the ecosystem function parameters, is the current function parameter value, is the functional parameter value under the reference state; The ecosystem function parameters include at least: soil respiration intensity, nutrient cycle efficiency, biomass accumulation rate and organic matter decomposition rate.

[0020] Furthermore, the health status of farmland ecosystems is graded and assessed based on the ecological impact index: Establishing standards for assessing farmland ecosystem health: For health status, Mildly affected state. Moderate impact status, Severely affected state, It is a very severely affected state.

[0021] Furthermore, we constructed the following indicators to assess the resilience of farmland ecosystems: ,in, For the The recovery indicator weight of each marker, is the recovery potential coefficient; when When It is recommended to perform manual intervention to repair.

[0022] Compared with the prior art, the present invention has the following beneficial effects: By establishing a microbial marker set and a quantitative response model, the present invention achieves an accurate assessment of the ecological impact of pesticide residues, objectively reflects the health status of pesticide residues on farmland ecosystems, and provides resilience assessment indicators, providing a scientific basis for the safe use of pesticides and the protection of farmland ecosystems. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 The present invention is a flow chart of a quantitative assessment method for the impact of pesticide residues on farmland ecosystems. DETAILED DESCRIPTION

[0024] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention are described clearly and completely below. Obviously, the embodiments described are only part of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0025] The present 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 through high-throughput sequencing. A dual screening strategy of functional response and structural change is used to determine sensitive markers. A quantitative response model is established. Finally, an ecological impact index is calculated and a graded assessment is performed. The overall process of the 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.

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

[0027] For cultivated surface soil, sampling depth is 0-20 cm. Depending on research needs, soil samples can be collected at 0-10 cm and 10-20 cm depths to investigate the impact of pesticide vertical distribution. Sampling should be conducted at different time points after pesticide application (e.g., 1, 7, 14, 30, and 60 days after application) to monitor the dynamic effects of pesticide residues on microbial communities. Control samples should also be collected before pesticide application. Three to five replicate samples should be collected from each sampling unit for replicate validation and statistical analysis.

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

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

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

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

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

[0033] Culture medium for fungal isolation: Potato Dextrose Agar (PDA); recipe: 200 g potatoes (boil to extract juice), 20 g glucose, 15-20 g agar, 1000 mL distilled water, pH 5.6-6.0, autoclave at 121°C for 20 min.

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

[0035] Use a colony counter to count the number of colonies (CFU) on each plate and the number of microorganisms in the soil (CFU / g dry soil); perform preliminary classification based on colony morphology, color, size, and other characteristics; select and purify colonies with obvious characteristics to establish pure cultures.

[0036] Pick a single colony and purify it by streak separation on the corresponding culture medium, subcultured at least three times; observe the purified strain under a microscope to confirm its purity; prepare glycerol cryovials (30% glycerol), make a suspension of the pure culture, and store it in a -80°C ultra-low temperature freezer; some strains can be prepared as slant cultures and stored at 4°C.

[0037] The microbial community fingerprint map 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 map based on principal coordinate analysis (PCoA), non-metric multidimensional scaling analysis (NMDS) or heat map analysis.

[0038] Extract total soil DNA using a commercial soil DNA extraction kit (e.g., FastDNA SPIN Kit for Soil, MPBiomedicals). The specific steps are as follows: (1) Weigh 0.5 g of fresh soil sample and place it into a lysis tube containing lysis beads; (2) Add 978 μL sodium phosphate buffer and 122 μL MT buffer; (3) Place the lysis tube in the FastPrep instrument and shake 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, mix by manual inversion 10 times, and centrifuge at 12,000 × g for 5 minutes; (6) Transfer the supernatant to a new tube, add 1 mL of the binding matrix suspension, and mix by inversion 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 min, and discard the filtrate; (9) Repeat the SEWS-M washing step once; (10) Place the SPIN filter column in a new collection tube and air dry at room temperature for 5 minutes; (11) Add 100 μL of DES water, gently resuspend the matrix, 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; an A260 / A280 ratio between 1.8 and 2.0 indicated high-quality DNA. For integrity testing, 5 μL of DNA sample was electrophoresed on a 1% agarose gel at 80 V for 30 minutes to check DNA integrity. Purity was verified 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) included 10-20 ng of template DNA, 1 μL of 10 μM forward primer, 1 μL of 10 μM reverse primer, 25 μL of 2× Phanta Max Master Mix, and ddH2O to 50 μL. PCR reaction conditions included initial denaturation at 95°C for 3 min, denaturation at 95°C for 30 s, annealing at 55°C for 30 s, extension at 72°C for 45 s, 30 cycles, and final extension at 72°C for 10 min, followed by storage at 4°C.

[0042] Forward primer for amplification of the fungal ITS region: ITS1F (5'-CTTGGTCATTTAGAGGAAGTAA-3'), reverse primer: ITS2R (5'-GCTGCGTTCTTCATCGATGC-3'); PCR reaction system (50 μL): 10-20 ng of template DNA, 1 μL of forward primer (10 μM), 1 μL of reverse primer (10 μM), 25 μL of 2× Phanta Max Master Mix, and ddH2O to 50 μL. PCR reaction conditions: initial denaturation at 95°C for 3 minutes, denaturation at 95°C for 30 seconds, annealing at 52°C for 30 seconds, and extension at 72°C for 45 seconds, for 35 cycles; final extension at 72°C for 10 minutes, and storage at 4°C.

[0043] PCR product purification: 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 the beads were incubated at room temperature for 5 minutes. The beads were placed on a magnetic stand for 2 minutes and the supernatant was discarded. The beads were washed twice with 80% ethanol and dried at room temperature for 5 minutes. The DNA was eluted by adding 30 μL of TE buffer. The beads were placed on a magnetic stand again for 2 minutes and the supernatant was collected.

[0044] The Illumina TruSeq DNA PCR-Free Library Preparation Kit was used to construct sequencing libraries. The purified PCR products were end-repaired and A-tailed, and sequencing adapters were ligated. Specific barcodes were added. The library concentration was accurately quantified using a Qubit 3.0 Fluorometer, and the library fragment size distribution was detected using an Agilent 2100 Bioanalyzer. The effective concentration of the library was verified by qPCR.

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

[0046] The quality of 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 Q < 20 did not exceed 20% of the sequence length, sequences containing N bases were removed, and reads with a sequence length of less than 200 bp were discarded.

[0047] FLASH software was used to splice the double-end sequencing data into complete sequences. The UCHIME algorithm of USEARCH software was used to detect and remove chimeric sequences. The sequences were clustered into OTUs (Operational Taxonomic Units) using the UPARSE algorithm with a similarity threshold of 97%. The OTU representative sequences were annotated with species classification 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 map is constructed, including: Species composition analysis: generate stacked bar charts of species relative abundance at each taxonomic level (phylum, class, order, family, genus, and species), select the top 20 dominant genera to generate relative abundance heat maps to show the differences between samples, construct microbial community co-occurrence networks, and analyze the relationships between species; Diversity pattern analysis and NMDS / PCoA two-dimensional scatter plots show the differences in community structure between samples. Hierarchical clustering dendrograms based on Bray-Curtis distance intuitively display the similarities between samples. Microbial diversity gradient distribution maps are constructed to show the changing trends of diversity indices with environmental factors.

[0049] Functional prediction analysis: PICRUSt2 software was used 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 heat map was constructed to show the functional differences between different samples.

[0050] Environmental factor association analysis uses ranking analysis methods such as CCA / RDA to explore the correlation between environmental factors and microbial community structure, generate correlation heat maps between environmental factors and key microbial groups, construct scatter plots between environmental factors and microbial diversity indices, and analyze related trends.

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

[0052] The step of determining the microbial characteristic marker set based on the dual screening strategy of functional response and structural change includes: In step S21 , based on the structural changes, the microbial diversity index change rate is used to determine the microbial groups that are significantly sensitive to the pesticide.

[0053] Step S22: Based on the functional response, soil enzyme activity spectrum, microbial community metabolic spectrum (BIOLOG), and functional gene abundance changes are measured to determine the microbial groups with significant functional changes under the action of pesticides.

[0054] Step S23 , taking the intersection of the results of the screening based on structural changes and the screening based on functional responses as a candidate feature marker set.

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

[0056] In order to screen characteristic markers of microorganisms sensitive to pesticide residues, it is necessary to build a controllable microcosm experimental system: Preparation of experimental containers: Use 500 mL brown wide-mouth glass bottles as microcosm containers. Add 200 g of homogenized soil passed through a 2 mm sieve to each container, adjust the soil moisture content to 60% of the maximum water holding capacity, and cover the top of the container with a breathable sterile film to ensure gas exchange and prevent contamination.

[0057] Environmental conditions were controlled as follows: temperature: 25 ± 1 °C, light: 12 h light / 12 h dark cycle, humidity: relative humidity 60 ± 5%, soil moisture content was monitored regularly, and a stable moisture content was maintained 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 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 analytically pure standards using acetone as the solvent. The control group was treated with an equal amount of acetone, and 4 replicates were set for each treatment.

[0060] Spray the standard pesticide solution evenly onto the soil surface and gently stir the soil to ensure even distribution of the pesticide. After adding the pesticide, let it sit for 24 hours to allow the solvent to 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 served as the baseline control.

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

[0062] Screening sensitive structural markers by analyzing the response of microbial community structure to pesticide stress: Calculation of the change rate of the Alpha diversity index: Calculate the change rate of the Shannon index, Simpson index, and Chao1 index: Screening criteria: Time points with an absolute value of the change rate >20% were considered to have significant responses. A response curve of the diversity index over time was constructed to identify diversity patterns sensitive to different pesticides.

[0063] Beta diversity analysis: PERMANOVA analysis was used to test the significant differences in community structure between the treatment group and the control group. The dissimilarity between groups was calculated based on the Bray-Curtis distance, and a curve of dissimilarity versus 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 groups at each taxonomic level (phylum, class, order, family, genus), and calculate the response rate for each dominant group (relative abundance > 1%): ,Screening criteria: Clusters with an absolute response rate > 50% and statistical analysis p < 0.05 were considered to be sensitive and dominant clusters.

[0065] The Jaccard index and Sorensen index were used to calculate the similarity of community composition between the treatment group and the control group, and a similarity curve was constructed as a function of pesticide concentration and time. The screening criteria were: treatments with a similarity index < 0.7 were considered to produce significant changes in community composition.

[0066] The analysis parameter settings of differential abundance analysis (LEfSe) were as follows: LDA score threshold: 2.5, Wilcoxon rank sum test p value threshold: 0.05, and consistency of differences between groups: 75%.

[0067] The microbial groups that were significantly enriched or reduced under each pesticide treatment were extracted, and an abundance box plot was drawn for each differential group to intuitively display the trend of change. The screening criteria were: groups that continued to show significant differences at at least three sampling time points were considered to have stable responsiveness.

[0068] Calculation of biomarker effect value (LEfSe-score): Calculate the effect value (LEfSe-score) for each differential cluster, rank the effect values ​​under different pesticide treatments, and screen the top 30 clusters with the highest absolute effect values ​​as candidate structural markers.

[0069] Screening of sensitive functional markers by analyzing the response of microbial community functions to pesticide stress: 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-glucoside colorimetric method, expressed as p-nitrophenol μg / g·h.

[0070] Enzyme activity response analysis: Calculate the response rate of each enzyme activity: , construct the response curve of enzyme activity to pesticide concentration and time, and calculate the comprehensive sensitivity index of each enzyme activity: , where the time weight reflects the importance of the 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 function indicators.

[0071] Microbial community metabolic profile (BIOLOG) analysis, BIOLOG-ECO microplate analysis: Preparation of soil suspension: 5 g of fresh soil was added to 45 mL of sterile water, shaken for 30 minutes, and allowed to stand for 30 minutes. The supernatant was diluted 10-fold and added to the ECO microplate, 150 μL per well, and incubated at 28°C. The OD590 value was measured every 24 hours for 96 hours, and 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 PCA / NMDS plot of carbon source utilization was constructed to intuitively display the differences in metabolic functions. The dose-response relationship between the utilization rate of each carbon source and the pesticide concentration was analyzed. Screening criteria: Carbon source types with an absolute value of response rate >40% and showing obvious gradient changes between treatment groups were considered sensitive metabolic indicators.

[0073] Functional gene detection: Key functional genes related to carbon, nitrogen and phosphorus cycles were selected for fluorescence quantitative PCR analysis, including 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 (phosphatase), 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°C pre-denaturation for 3 min, 95°C denaturation for 15 s, specific annealing temperature for 30 s, extension at 72°C for 30 s, 40 cycles, melting curve analysis: 65°C to 95°C, step size 0.5°C, dwell time 5 s.

[0075] Functional gene analysis uses calculations of the relative abundance changes of each functional gene to construct a functional gene abundance heat map, intuitively display the functional change patterns under different treatments, and calculate the response sensitivity index of the functional gene: , where the concentration gradient weight reflects 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 to be sensitive functional indicators.

[0076] The results of structural change screening and functional response screening were analyzed in an intersection manner to determine the candidate feature marker set: The Spearman correlation coefficient between structural markers (microbial groups) and functional indicators (enzyme activity, metabolic function, functional genes) was calculated, and a correlation network diagram was constructed to intuitively display the association pattern between structure and function. The screening criteria were: microbial groups with an absolute value of correlation coefficient > 0.6 and p < 0.05 were considered to be closely related to functional responses; the top 20 most sensitive microbial groups were selected from the structural change analysis, and the top 20 microbial groups highly correlated with sensitive functional indicators were selected from the functional association analysis. The intersection of the two was taken to form a set of candidate characteristic markers. If the intersection was too small (less than 10 groups), the highest-ranked groups in the separate structural or functional analyses were additionally screened.

[0077] Repeat the microcosm experiment using agricultural soils from different sources (at least three different soil types), testing only the pesticide treatment at a medium concentration (1 times the recommended dose), and evaluate the consistency of the responses of candidate markers: , Screening criteria: Markers with a consistency index > 70% were considered to have good response stability.

[0078] For the field validation test, select 3-5 typical farmland test sites, set up pesticide treatment plots (recommended dose) and control plots, collect soil samples at key time points (1 day, 7 days, 14 days, and 28 days) after pesticide application, analyze the response of candidate markers under field conditions, and calculate the consistency of the response between the indoor microcosm and the field test: , screening criteria: Candidate marker sets with a consistency index > 60% are considered to have good application potential under field conditions A composite sensitivity score was calculated for each candidate marker: , where W1, W2, and W3 are the weights of structure, function, and stability, respectively, and are set to 0.3, 0.3, and 0.4. Structural sensitivity is calculated based on the LEfSe effect value and response rate. Functional relevance 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] The candidate markers were ranked by comprehensive sensitivity scores, and the 15-20 microbial groups with the highest scores were selected as the final characteristic marker set. This ensured that the final marker set contained representative groups at different taxonomic levels (phylum, class, order, family, and genus). A standard sensitivity coefficient was assigned to each marker, and the following was calculated based on the normalized sensitivity score: Each marker bacterial community is equipped with a sensitivity weight, ranging from 0 to 1. The larger the value, the higher the sensitivity. This weight is determined based on a large-sample pesticide exposure response experiment.

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

[0081] The step of establishing a quantitative response model of a microbial characteristic marker set to pesticide residues comprises: Construct a microcosm experimental system with gradient pesticide concentrations, culture under the same conditions, and sample at set time intervals; Determine the changes in characteristic markers under each concentration gradient and establish a dose-response curve; For each characteristic marker , establish response strength Function: ,in, is the pesticide concentration, is the response intensity coefficient, is the sensitivity amplification factor; the marker response rate calculation formula is: ,in, is the abundance after exposure, is the initial abundance; For different types of pesticides, a characteristic marker synergistic response model is established: ,in, For the The sensitivity weight of each marker, n is the total number of markers, is the correction factor for the interaction between markers.

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

[0083] The microcosm experiments were performed in 250 mL glass bottles, and 100 g of standardized soil was added. The standardized soil was passed through a 2 mm sieve and the moisture content was adjusted to 60% of the maximum water holding capacity. The microbial community was stabilized by pre-incubation for 7 days at a temperature of 25 ± 0.5 °C, a relative humidity of 60 ± 3%, and a photoperiod of 12 h:12 h.

[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 key markers for qPCR analysis to improve quantitative accuracy. Three technical replicates were set for each sample to reduce the impact of sequencing errors.

[0085] The quantitative response model further includes a synergistic effect model of the combined action of pesticide residues, which is expressed as: ; in, For mixed pesticides The comprehensive response intensity of pesticide residues, is the total amount of mixed pesticides, For the The concentration of pesticides, For the The basic toxicity weight of the pesticides, For the Species and The synergistic effect coefficient of the pesticides, As a landmark For the first Response function of the pesticide; The synergy coefficient The orthogonal design of mixed pesticide residue experiments determined that It indicates positive synergistic effect when antagonistic effect.

[0086] Three typical pesticides (chlorpyrifos, cypermethrin, and imidacloprid) were selected for pairwise combination and three-mixture experiments. The concentration combinations were designed using the central composite design (CCD) method to ensure coverage of main effects and interaction effects. Four replicates were set for each combination. Microcosm experiments were carried out and marker responses were monitored.

[0087] Step S4, calculating the ecological impact index of pesticide residues based on the change state of characteristic markers in actual farmland soil samples in combination with the response model.

[0088] The steps for calculating the ecological impact index of pesticide residues based on the changing state of characteristic markers include: Compare the microbial community data of the evaluated farmland soil samples with those of the control samples and calculate the response rate of each characteristic marker ; The ecological impact index of pesticide residues is calculated based on the response rate and sensitivity weight of the characteristic markers: , where when the abundance of the marker decreases , when abundance increases According to the ecological significance of this marker, it is taken as 0.2-0.8; Introducing environmental factor correction: ,in, For the The influence coefficient of each environmental factor, It is the degree to which environmental factors deviate from the normal range.

[0089] The ecological impact index is further combined with ecosystem function parameters for comprehensive evaluation, and the calculation expression is: ;in, is the comprehensive ecological impact index, is the weight of microbial markers, ranging from 0.6 to 0.8, For the The weights of the ecosystem function parameters, is the current function parameter value, is the functional parameter value under the reference state; The ecosystem function parameters include at least: soil respiration intensity, nutrient cycle efficiency, biomass accumulation rate and organic matter decomposition rate.

[0090] Grading assessment of farmland ecosystem health status based on ecological impact index: Establishing standards for assessing farmland ecosystem health: For health status, Mildly affected state. Moderate impact status, Severely affected state, It is a very severely affected state.

[0091] Constructing indicators for assessing farmland ecosystem resilience: ,in, For the The recovery indicator weight of each marker, is the recovery potential coefficient; when When It is recommended to perform manual intervention to repair.

[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 usage: mainly using pesticides such as chlorpyrifos, chlorpyrifos, and imidacloprid to control pests and diseases.

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

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

[0095] Based on the marker library established in the early stage, 18 characteristic markers applicable to the region were screened out, the response rate of each characteristic marker was calculated, and the correlation between the marker response and the pesticide use history was analyzed.

[0096] Calculate the initial ecological impact index EI, consider soil pH, organic matter content and other environmental factors for correction, and get , combined with soil respiration intensity, nitrogen mineralization rate and other functional parameters, calculate the comprehensive impact index .

[0097] Among the 16 conventional greenhouses, 3 were in a healthy state ( ), 6 were in mild impact status ( ), 5 were moderately impacted ( ), 2 were severely affected ( ), no serious impact. All 8 organic greenhouses are in a healthy state ( ).

[0098] Frequency of pesticide use and There was a significant positive correlation (r=0.78, p<0.01). The greenhouses that used the same type of pesticides for a long time had more significant characteristic marker responses. The greenhouses that used compound pesticides had a more significant characteristic marker response. The value was significantly higher than that in greenhouses using only one pesticide.

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

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

[0101] Soil sampling was carried out on the 1st, 3rd, 7th, 14th, 21st, 28th, 42nd and 56th day after pesticide application. Three repeated sampling points were set up in each treatment area, and soil from the 0-20 cm soil layer was collected. The soil environmental parameters were also recorded.

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

[0103] Calculate the ecological impact index at each time point ,draw The curve of change over time, determine Time required to drop below 0.2 (health status).

[0104] The results and safety interval recommendations are as follows: Impact dynamic analysis: Imidacloprid treatment: Peak =0.43 (moderate impact), falling below 0.2 after 21 days; Chlorpyrifos treatment: Peak =0.55 (moderate impact), falling below 0.2 after 28 days; Mixing Process: Peak =0.73 (severe impact), and dropped to below 0.2 after 42 days.

[0105] Recovery feature analysis: Imidacloprid treatment: The recovery pattern was exponential decay, with a resilience index RI = 1.32; Chlorpyrifos treatment: The recovery pattern was linear attenuation, with a resilience index RI = 0.97; Mixed treatment: The recovery mode is delayed response type, and the resilience index RI=0.68.

[0106] Safety interval recommendations: Imidacloprid: The recommended safety interval is ≥ 28 days (21 days of recovery period + 7 days of safety margin); Chlorpyrifos: The recommended safety interval is ≥ 35 days (28 days of recovery period + 7 days of safety margin); Mixed treatment: A safe interval of ≥49 days (42 days of recovery + 7 days of safety margin) is recommended, and soil improvement measures are recommended to promote recovery.

[0107] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method 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 in the scope of protection of the present invention.

Claims

1. A quantitative assessment method for the impact of pesticide residues on farmland ecosystems, characterized in that: The method comprises: Step S1, collecting farmland soil samples, isolating and culturing soil microbial colonies, and constructing a microbial community fingerprint; Step S2, based on the dual screening strategy of functional response and structural change, determine the set of microbial characteristic markers sensitive to pesticide residues; Step S3, establishing a quantitative response model of the microbial characteristic marker set to different concentrations and types of pesticide residues; Step S4, calculating the ecological impact index of pesticide residues based on the change state of characteristic markers in actual farmland soil samples in combination with the response model.

2. The method according to claim 1, characterized in that The microbial community fingerprint map 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 map based on principal coordinate analysis, non-metric multidimensional scaling analysis, or heat map analysis.

3. The method according to claim 1, characterized in that The step of determining the microbial characteristic marker set based on the dual screening strategy of functional response and structural change includes: Step S21, based on the structural changes, using the microbial diversity index change rate to determine the microbial groups that are significantly sensitive to the pesticide; Step S22, based on the functional response, measuring the soil enzyme activity spectrum, microbial community metabolic spectrum, and functional gene abundance changes, and determining the microbial groups with significant functional changes under the action of the pesticide; Step S23, taking the intersection of the results based on structural changes and the results based on functional responses as a candidate feature marker set; In step S34, 15-20 microbial groups with the highest response stability are screened through indoor microcosm experiments and field verification tests to form a final set of characteristic markers, and a sensitivity weight coefficient is assigned to each marker.

4. The method according to claim 3, characterized in that Each marker bacterial community is equipped with a sensitivity weight, ranging from 0 to 1. A larger value indicates a higher sensitivity. This weight is determined based on a large-sample pesticide exposure response experiment.

5. The method according to claim 4, characterized in that The step of establishing a quantitative response model of a microbial characteristic marker set to pesticide residues comprises: Construct a microcosm experimental system with gradient pesticide concentrations, culture under the same conditions, and sample at set time intervals; Determine the changes in characteristic markers under each concentration gradient and establish a dose-response curve; For each characteristic marker , establish response strength Function: ,in, is the pesticide concentration, is the response intensity coefficient, is the sensitivity amplification factor; the marker response rate calculation formula is: ,in, is the abundance after exposure, is the initial abundance; For different types of pesticides, a characteristic marker synergistic response model is established: ,in, For the The sensitivity weight of each marker, n is the total number of markers, is the correction factor for the interaction between markers.

6. The method according to claim 5, characterized in that The quantitative response model further includes a synergistic effect model of the combined action of pesticide residues, which is expressed as: ； in, For mixed pesticides The comprehensive response intensity of pesticide residues, is the total amount of mixed pesticides, For the The concentration of pesticides, For the The basic toxicity weight of the pesticides, For the Species and The synergistic effect coefficient of the pesticides, As a landmark For the first Response function of the pesticide; The synergy coefficient The orthogonal design of mixed pesticide residue experiments determined that It indicates positive synergistic effect when antagonistic effect.

7. The method according to claim 1, characterized in that The step of calculating the ecological impact index of pesticide residues according to the change state of the characteristic markers includes: Compare the microbial community data of the evaluated farmland soil samples with those of the control samples and calculate the response rate of each characteristic marker ; The ecological impact index of pesticide residues is calculated based on the response rate and sensitivity weight of the characteristic markers: , where when the abundance of the marker decreases , when abundance increases According to the ecological significance of this marker, it is taken as 0.2-0.8; Introducing environmental factor correction: ,in, For the The influence coefficient of each environmental factor, It is the degree to which environmental factors deviate from the normal range.

8. The method according to claim 7, characterized in that The ecological impact index is further combined with ecosystem function parameters for comprehensive evaluation, and the calculation expression is: ;in, is the comprehensive ecological impact index, is the weight of microbial markers, ranging from 0.6 to 0.8, For the The weights of the ecosystem function parameters, is the current function parameter value, is the functional parameter value under the reference state; The ecosystem function parameters include at least: soil respiration intensity, nutrient cycle efficiency, biomass accumulation rate and organic matter decomposition rate.

9. The method according to claim 1, characterized in that Grading assessment of farmland ecosystem health status based on ecological impact index: Establishing standards for assessing farmland ecosystem health: For health status, Mildly affected state. Moderate impact status, Severely affected state, It is a very severely affected state.

10. The method according to claim 9, characterized in that Constructing indicators for assessing farmland ecosystem resilience: ,in, For the The recovery indicator weight of each marker, is the recovery potential coefficient; when When It is recommended to perform manual intervention to repair.

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