Method and system for detecting metabolism kinetics of organic carbon and microbial carbon in deep soil

Abstract

The invention discloses a method and a system for detecting metabolism kinetics of organic carbon and microbial carbon in deep soil, and relates to the technical field of soil science and environmental science. Comprising the following steps: acquiring the content and structural characteristics of organic carbon in the soil of the rice field, and determining the composition, function and dynamic response mechanism of microbial communities to environmental change; setting a control group and a treatment group, and obtaining soil organic carbon reserve change rates, CO2 emissions and influence factors in the control group and the treatment group at different time points; and constructing a data analysis model according to the soil organic carbon reserve change rate, CO2 emission and influence factors in the control group and the treatment group at different time points, and generating a comprehensive report of deep soil organic carbon and microbial carbon metabolism kinetics. According to the invention, a scientific basis is provided for optimizing agricultural management and coping with climate change, the positive influence of biomass charcoal addition on soil health is disclosed, and the development of sustainable agriculture is promoted.

Description

Technical Field

[0001] The present invention relates to the field of soil science and environmental science technology, and in particular to a method and system for detecting deep soil organic carbon and microbial carbon metabolic dynamics. Background Art

[0002] Deep soil organic carbon mainly comes from the vertical migration and deposition of dissolved organic carbon (DOC), particulate organic carbon and insoluble components in the surface soil. These complex source pathways increase the difficulty of dynamic monitoring of deep SOC. In addition, physical protection mechanisms in deep soils (such as aggregate formation) are considered to be one of the key factors affecting the stability of organic carbon. At the same time, the structure and functional characteristics of deep soil microbial communities decrease with depth, but in a long-term low-resource environment, deep microorganisms can adopt unique survival strategies to respond to the input of fresh organic carbon. Therefore, the interaction between physical protection mechanisms and microbial activities in deep soils makes the state of organic carbon uncertain.

[0003] The Chinese invention patent with publication number CN115032363A discloses a method for evaluating the biological effects of soil in multi-metal contaminated sites and its application. By conducting an assessment of the toxic effects of soil in heavy metal contaminated sites on model organisms, the influence of environmental factors on metal effectiveness through complexation and competition, the interaction of different metals on the surface of biological ligands, and the heterogeneity of the distribution of action sites in organisms are comprehensively considered. Focusing on the biological effectiveness of heavy metals, a multi-metal biological homogeneity point action model and a multi-metal biological heterogeneity point action model are constructed, which realizes the effective evaluation and prediction of the biological effects of multi-metal coexistence in soil of different types of contaminated sites.

[0004] In the process of implementing the above and similar soil biological effect evaluation methods, although it is known that the genetic and metabolic diversity of deep soil microbial communities decreases with depth, in a long-term low-resource environment, deep microorganisms can use their unique survival strategies to respond to the input of fresh organic carbon. Therefore, the interaction between the physical protection mechanism and microbial activities in deep soil will make the organic carbon state have many uncertainties. Therefore, how microorganisms in deep soil respond to new carbon inputs and adjust their own metabolic activities to adapt to the changing organic carbon state is a major problem at present. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for detecting the metabolic kinetics of deep soil organic carbon and microbial carbon to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a method for detecting deep soil organic carbon and microbial carbon metabolic kinetics, comprising:

[0007] Obtain paddy field soil samples: sampling and analyzing paddy field soil to obtain the organic carbon content and structural characteristics of the paddy field soil, and determining the composition, function and dynamic response mechanism of the microbial community to environmental changes based on the organic carbon content and structural characteristics;

[0008] Determine the degree of impact: set up a control group and a treatment group according to the composition, function and dynamic response mechanism of the microbial community to environmental changes, and obtain the soil organic carbon storage change rate, CO2 emission and influencing factors in the control group and the treatment group at different time points;

[0009] Obtain comprehensive report: Based on the soil organic carbon storage change rate, CO2 emissions and influencing factors in the control group and treatment group at different time points, a data analysis model is constructed to generate a comprehensive report on deep soil organic carbon and microbial carbon metabolic dynamics, including:

[0010] According to the influencing factors, draw a trend chart of each influencing factor changing over time;

[0011] According to the change trend chart, the influencing factors are used as input variables, the soil organic carbon storage change rate and CO2 emissions are used as output variables, and the data analysis model is constructed through multivariate linear regression to output a comprehensive report on the deep soil organic carbon and microbial carbon metabolic dynamics.

[0012] Furthermore, the composition, function and dynamic response mechanism of microbial communities to environmental changes were determined as follows:

[0013] Sample collection: Obtain soil samples at different depths at multiple rice field locations;

[0014] Determine the content of organic carbon and its structural characteristics: obtain the content of organic carbon in different forms in the soil sample by chemical and physical methods;

[0015] Determine the microbial community structure and functional characteristics analysis: Obtain the microbial community structure and function in the soil sample through phospholipid fatty acid analysis, high-throughput sequencing and fluorescence in situ hybridization combined with confocal microscopy;

[0016] Dynamic monitoring of deep soil microbial response mechanisms: Through metatranscriptomics technology, metabolic network models and molecular biology techniques, the gene expression changes, metabolic pathway adjustments and survival strategy changes of soil microbial communities after biochar addition are obtained.

[0017] Furthermore, the content of organic carbon in different forms in soil samples was determined by chemical and physical methods, including:

[0018] W1: Total organic carbon determination: The total organic carbon content and its components in soil samples were obtained by CNS elemental analyzer method and Py-GC / MS technology;

[0019] W2: Determination of particulate organic carbon: Obtain particulate organic carbon from the soil sample by physical separation method, and determine the percentage of particulate organic carbon in the soil sample, specifically:

[0020]

[0021] in: is the mass concentration of particulate organic carbon, is the mass concentration of soil organic carbon in particulate matter, is the mass ratio of particulate matter to the entire soil sample;

[0022] W3: Determination of easily oxidizable carbon: The soil sample is oxidized by KMnO4 solution to obtain the easily oxidizable carbon content, specifically:

[0023]

[0024] in: is the mass of carbon oxidized, is the difference in potassium permanganate concentration before and after the experiment, is the molar mass of carbon.

[0025] Furthermore, the microbial community structure and function in the soil sample are obtained as follows:

[0026] N1: Phospholipid fatty acid analysis: extract phospholipid fatty acids from soil samples, and perform qualitative and quantitative analysis on the phospholipid fatty acids by gas chromatography-mass spectrometry to obtain the ratio of bacteria to fungi in the soil samples;

[0027] N2: High-throughput sequencing: amplify specific marker genes of bacteria and fungi to obtain the ratio of bacteria and fungi;

[0028] N3: Fluorescence in situ hybridization and confocal microscopy: The microbial community structure and function in the soil samples were determined by fluorescence in situ hybridization and confocal microscopy.

[0029] Furthermore, the changes in gene expression, metabolic pathway adjustments, and survival strategy changes of soil microbial communities after biochar addition were obtained as follows:

[0030] M1: Gene expression pattern: Through the metatranscriptomics technology, the changes in the mRNA levels of metabolic enzymes in soil samples were obtained to determine the changes in nitrate reductase gene expression, nitrous oxide reductase gene expression and metabolite concentrations;

[0031] M2: Metabolic pathway adjustment: obtain the basic information of the microbial community in the soil sample, build a metabolic network model, and determine the adjustment direction of the metabolic pathway;

[0032] M3: Changes in survival strategies: Through molecular biology techniques, the changes in the diversity, abundance and activity of microorganisms in the microbial community are obtained.

[0033] Furthermore, the adjustment direction of metabolic pathways is determined, including:

[0034] X1: Obtain basic information of the target microbial community, including but not limited to genome sequence, transcriptome expression pattern, and proteome and metabolome data, and determine the behavior of the microorganisms under different conditions based on the basic information;

[0035] X2: Establish an initial metabolic network model based on the basic information of the target microbial community and the genome-scale metabolic model, and calibrate the parameters of the initial metabolic network model by integrating multi-omics data to obtain the final metabolic network model;

[0036] X3: Through the final metabolic network model, the metabolic pathway adjustment of the target microbial community under different conditions is obtained.

[0037] Furthermore, the soil organic carbon storage change rate, CO2 emission and influencing factors in the control group and the treatment group at different time points were obtained, including:

[0038] Setting up a control group and a treatment group: setting up the control group and the treatment group according to the microbial response mechanism, and sampling the control group and the treatment group at a preset time point;

[0039] Obtaining the SOC reserve change rate: According to the sampling data of the control group and the treatment group, the change rate of soil organic carbon reserve at different time points is obtained to determine the organic carbon fixation efficiency, specifically:

[0040]

[0041] in: is the change rate of soil organic carbon storage at different time points, is the soil organic carbon content at the final moment, is the soil organic carbon content at the initial time, is the time interval;

[0042] Determination of CO2 emissions: The CO2 emissions were determined by using a multiple linear regression model and the sampling data of the control group and the treatment group, specifically:

[0043]

[0044] in: is CO2 emissions, is the intercept term, is the influence of pH value on CO2 emissions, is the pH value, is the influence of temperature on CO2 emissions, is the temperature, is the influence of soluble organic carbon concentration on CO2 emissions, is the concentration of dissolved organic carbon, is the error term;

[0045] Determine the influencing factors: According to the SOC reserve change rate and CO2 emissions, a prediction model is constructed through a multivariate linear regression statistical model to obtain an output result P value, and the output result P value is compared with a preset threshold value, and the influencing factors are determined according to the comparison result, specifically:

[0046] When the output result P value is less than a preset threshold, the factor corresponding to the output result P value is an influencing factor; otherwise, the factor corresponding to the output result P value is not an influencing factor.

[0047] A deep soil organic carbon and microbial carbon metabolic kinetics detection system, the detection system uses any of the deep soil organic carbon and microbial carbon metabolic kinetics detection methods described above.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] The present invention uses a variety of methods such as chemistry, physics, biology and molecular biology to deeply analyze the soil carbon cycle process. At the same time, through long-term dynamic monitoring of soil samples under different treatment conditions, the changing trend of organic carbon storage change rate, CO2 emissions and microbial community structure over time is obtained, and the statistical method of multivariate linear regression model is used to identify the influencing factors, thereby providing a scientific basis for optimizing agricultural management and responding to climate change, and revealing the positive impact of biochar addition on soil health, promoting the development of sustainable agriculture. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a schematic diagram of the process of the detection method of the present invention;

[0051] Figure 2 This is a comparison chart of the narG expression levels of the control group and the biochar treatment group in the present invention;

[0052] Figure 3This is a comparison chart of soil pH value and nosZ expression level between the control group and the biochar treatment group in the present invention;

[0053] Figure 4 It is a comparison chart of the concentrations of amino acids and organic acids between the control group and the biochar treatment group in the present invention;

[0054] Figure 5 It is a comparison chart of the SOC reserve change rate in farmland affected by different agricultural management measures in the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments 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 creative work are within the scope of protection of the present invention.

[0056] In the process of implementing the existing soil biological effect evaluation methods, although it is known that the deep soil microbial community decreases with depth in terms of genetic and metabolic diversity, in a long-term low-resource environment, deep microorganisms can adopt their unique survival strategies to respond to the input of fresh organic carbon. Therefore, the interaction between the physical protection mechanism and microbial activity in the deep soil will cause many uncertainties in the state of organic carbon. In other words, with the continuous increase of new carbon input, the microorganisms in the deep soil will constantly adjust their metabolic activities to adapt to the changing state of organic carbon, which will make the relationship between organic carbon and microbial carbon in the deep soil more complicated, and it will be more difficult to obtain accurate test reports when conducting tests. The technical solution of this application obtains the response mechanism of the organic carbon content in the deep soil of the paddy field and its microbial community structure and function to environmental changes, determines the key influencing factors, and draws the change trend diagram corresponding to each influencing factor to construct a data analysis model and generate a comprehensive report, thereby providing a scientific basis for optimizing agricultural management and responding to climate change, and reveals the positive effects of biochar addition on soil health, and promotes the development of sustainable agriculture.

[0057] refer to Figure 1-Figure 5 This embodiment provides a method for detecting deep soil organic carbon and microbial carbon metabolic dynamics, the detection method comprising the following steps:

[0058] Step S1: Obtain paddy field soil samples. That is, by sampling and analyzing paddy field soil, the organic carbon content and its structural characteristics in the paddy field soil are obtained, and based on the obtained organic carbon content and its structural characteristics, the composition and function of the microbial community and its dynamic response mechanism to environmental changes (such as biochar addition) are determined. The details are as follows:

[0059] Step S1.1: Sample collection. That is, obtain at least five different and representative rice field sites, and ensure that the rice field sites obtained cover different soil types, management practices and climatic conditions. Specifically, use a soil drill or an automated sampler to collect multiple soil samples from the surface to a depth of 1 meter at preset vertical intervals (for example, every 10 cm), and ensure that the same sampling volume (for example, 100 cm³) is maintained during each collection to avoid sample contamination. At the same time, record the specific depth information of each sample, and add a standard substance of known concentration as a control in each analysis to calibrate the instrument and verify the accuracy of the measurement results. It is worth noting that at least three replicate samples are collected at each depth to ensure the statistical reliability of the data.

[0060] During the implementation process, samples were collected at each point at ten depths of 0-10cm, 10-20cm, 20-30cm, 30-40cm, 40-50cm, 50-60cm, 60-70cm, 70-80cm, 80-90cm and 90-100cm at intervals of 10cm.

[0061] Step S1.2: Determine the organic carbon content and its structural characteristics. That is, determine the content of organic carbon in different forms in the soil sample by chemical and physical methods, including total organic carbon, particulate organic carbon and easily oxidizable carbon. The details are as follows:

[0062] Step W1: Total organic carbon determination. That is, the total organic carbon content and its components in the soil are measured by CNS element analyzer method and Py-GC / MS technology, which can not only provide accurate carbon content information, but also further obtain the specific types of organic compounds through pyrolysis gas chromatography-mass spectrometry.

[0063] In this embodiment, the CNS elemental analyzer method is used to measure the content of elements such as carbon, nitrogen and sulfur in the sample, which obtains the total organic carbon in the soil sample collected in step S1.1. The Py-GC / MS technology can identify and quantify the types of organic compounds contained in the soil sample collected in step S1.1. The formula for obtaining the total organic carbon content is specifically:

[0064]

[0065] in: is the percentage of total organic carbon, is the mass of carbon element actually measured, is the mass of the soil sample after drying.

[0066] In the specific implementation process, the soil sample collected in step S1.1 is placed in an oven and dried to constant weight at 105°C to remove the influence of moisture. The mass of the dried soil sample is 10 grams. At the same time, it is determined by a CNS element analyzer that the sample contains 0.5 grams of carbon, so its total organic carbon percentage is 5%.

[0067] Step W2: Determination of particulate organic carbon. That is, the particulate organic carbon in the soil sample collected in step S1.1 is extracted by a physical separation method, and its percentage in the soil sample is obtained based on the extracted particulate organic carbon. The formula for obtaining the percentage of particulate organic carbon in the soil sample is specifically:

[0068]

[0069] in: is the mass concentration of particulate organic carbon, is the mass concentration of soil organic carbon in particulate matter, is the mass ratio of particulate matter to the entire soil sample.

[0070] In the specific implementation process, after removing visible large plant residues and stones from the soil sample collected in step S1.1, the soil is air-dried or freeze-dried at low temperature. At the same time, the soil sample is graded by sieves of different apertures. In this embodiment, 0.053 mm is used as the dividing point to separate particles from the treated soil sample. Furthermore, for particles larger than 0.053 mm, density flotation is used for further separation, for example, light components (such as plant residues) and heavy components (such as gravel) are separated by sodium hexametaphosphate solution (density of 1.7 g / cm³). At the same time, the organic carbon content of the separated particles (particles larger than 0.053 mm) is determined by potassium dichromate oxidation-volume method.

[0071] Specifically, in this embodiment, the organic carbon content measured from the separated particulate matter (particles larger than 0.053 mm) is 10 g / kg, and the mass proportion of the particulate matter in the entire soil sample is 20%, so the percentage of particulate organic carbon in the soil sample is 2 g / kg.

[0072] Step W3: Determination of easily oxidizable carbon. The soil sample collected in step S1.1 is oxidized by KMnO4 solution, and the amount of KMnO4 consumed is measured by a spectrophotometer to obtain the easily oxidizable carbon content. The formula for obtaining the easily oxidizable carbon content is as follows:

[0073]

[0074] in: is the mass of carbon oxidized, is the difference in potassium permanganate concentration before and after the experiment, is the molar mass of carbon.

[0075] In the specific implementation process, the soil sample collected in step S1.1 is ground and passed through a 0.5 mm sieve. At the same time, according to the total organic carbon content of the soil, the amount of soil sample containing 15 mg of carbon is calculated as the sample weight of the sample to be tested. At the same time, the sample to be tested is transferred to a plastic centrifuge tube with a lid, and no soil sample is added as a blank control. A potassium permanganate solution with a concentration of 0.01 mol / L is added to the centrifuge tube, and the centrifuge tube is oscillated (conventional shaking) for 2 hours at about 23°C, and centrifuged at a speed of 4000 rpm for 15 minutes. The supernatant is diluted 10 times with deionized water, and the diluted sample is measured at 550 nanometers by a spectrophotometer. The absorbance value.

[0076] Specifically, the concentration change of potassium permanganate measured in this example is 0.001 mol / L, the molar mass of carbon is 12 g / mol, and thus the mass of oxidized carbon is 0.004 g.

[0077] Step S1.3: Determine the microbial community structure and functional characteristics analysis. That is, through three technical means, phospholipid fatty acid analysis, high-throughput sequencing and fluorescence in situ hybridization combined with confocal microscopy, comprehensively evaluate the microbial community structure and function in soil samples. The details are as follows:

[0078] Step N1: Phospholipid fatty acid analysis. That is, extract phospholipid fatty acids from the soil sample collected in step S1.1, and perform qualitative and quantitative analysis on the extracted phospholipid fatty acids by gas chromatography-mass spectrometry to obtain the ratio of bacteria to fungi in the soil sample. The formula for obtaining the ratio of bacteria to fungi in the soil sample is specifically:

[0079]

[0080] in: is the ratio of bacteria to fungi, is the total amount of all known bacterial-specific phospholipid fatty acids, It is the total amount of all known phospholipid fatty acids that are unique to fungi.

[0081] In the specific implementation process, the soil sample collected in step S1.1 is pretreated to remove impurities and extract phospholipid fatty acids. In this embodiment, phospholipid fatty acids are extracted by organic solvent extraction, such as fatty acid extraction by Bligh-Dyer method. At the same time, the extracted phospholipid fatty acid mixture is separated and purified (such as thin layer chromatography, high performance liquid chromatography and silica chromatography) to remove other non-target components in the phospholipid fatty acid mixture. Further, the treated phospholipid fatty acids are separated and identified by GC-MS or capillary gas chromatography combined with flame ionization detector.

[0082] Specifically, the soil sample of this example contains a total bacterial PLFAs of 50 nmol / g dry soil and a total fungal PLFAs of 20 nmol / g dry soil, so the ratio of bacteria to fungi is 2.5.

[0083] Step N2: high-throughput sequencing. That is, the specific marker genes of bacteria are amplified by 16S rRNA genes, and the specific marker genes of fungi are amplified by ITS genes. In other words, the proportion of bacteria and fungi is determined by obtaining the specific marker genes.

[0084] In the specific implementation process, 16SrRNA genes (for bacteria) and ITS genes (for fungi) were extracted and amplified from the soil samples collected in step S1.1, and the 16S rRNA genes and ITS genes were sequenced by a high-throughput sequencing platform. Specifically, the raw sequencing data (FASTQ files) in the soil samples collected in step S1.1 were quality evaluated and filtered by tools such as Trimmomatic and FastP to remove low-quality sequences, sequences containing adapter contamination, and too short reads, and sequencing errors were corrected by DADA2 to merge paired end reads into full-length sequences. At the same time, the number of different ASVs in each sample was counted to form a feature table. And a classification identifier was assigned to each ASV through public databases (such as Greengenes, SILVA, UNITE, etc.). Specifically, for the 16S rRNA gene, it was classified using the RDPClassifier or SINA Aligner classification method, and for the ITS gene, it was classified using the UTAX classification method.

[0085] Specifically, 10,000 bacterial sequences and 2,000 fungal sequences were detected in the soil sample of this embodiment, so the ratio of bacteria to fungi was 5: 1. That is, in the soil sample of this embodiment, the number of bacteria was five times the number of fungi.

[0086] Step N3: Fluorescence in situ hybridization and confocal microscopy: Fluorescence in situ hybridization is combined with confocal microscopy to analyze the microbial community structure and function in the soil samples collected in step S1.1.

[0087] In the process of specific implementation, the soil sample collected in step S1.1 is pretreated, such as washing and fixing. At the same time, based on the known microbial gene sequence information, a specific oligonucleotide probe is synthesized, and it is labeled with a fluorescent dye, and the labeled sample is mixed with the FISH probe. Further, after the FISH probe fully penetrates into the sample and hybridizes with the target sequence, the unbound free probe is removed by washing, and the retained fluorescent signal is the specific microorganism existing in situ in the sample. At the same time, the hybridized sample is imaged with high resolution by a confocal laser scanning microscope, and the image data is analyzed to obtain the density of the microorganisms in the sample, morphological characteristics and their relative positional relationship with other components.

[0088] Specifically, for the soil sample collected in step S1.1, in the cross-sectional image of the soil aggregates in the sample, the total bacteria are marked by red fluorescence, and the aerobic ammonia oxidizing bacteria are marked by green fluorescence. At the same time, the number of fluorescent spots under each color channel is identified and counted, that is, the proportion of aerobic ammonia oxidizing bacteria to the total bacteria is about 10%.

[0089] Step S1.4: Dynamically monitor the response mechanism of deep soil microorganisms. That is, through macrotranscriptomics technology, metabolic network modeling and molecular biology methods, obtain the changes in gene expression, metabolic pathway adjustment and survival strategy transformation of soil microbial communities after the addition of biochar. The details are as follows:

[0090] Step M1: Gene expression pattern. That is, the changes in the mRNA levels of metabolic enzymes in the soil samples collected in step S1.1 are detected by macrotranscriptomics technology to obtain the changes in nitrate reductase gene expression, nitrous oxide reductase gene expression and metabolite concentration.

[0091] In the specific implementation process, in the 12-month field trial, the experimental field was divided into two groups: one group was the control group (no treatment was added) and the other group was the treatment group (biochar was applied at a dosage of 10 tons per hectare). Deep soil samples (depth of 30-50 cm) were collected on the 0th, 30th, 60th, 90th, 180th and 360th days after the application of biochar to obtain short-term and long-term effects. At the same time, the physicochemical properties of the soil samples collected at each time point were determined, including pH and organic carbon content. At the same time, total RNA was extracted and the metatranscriptome library was constructed, and sequenced using the Illumina HiSeq platform.

[0092] refer to Figure 2 It can be seen that the expression of nitrate reductase gene was obtained as follows: 30 days after the application of biochar, the relative expression of narG gene increased by 3.2 times compared with the control group. By the 60th day, the value reached the maximum increase, about 4.1 times that of the control group. After that, it gradually stabilized and remained at about 3.5 times that of the control group at the end of one year.

[0093] refer to Figure 3 It can be seen that the expression of nitrous oxide reductase gene was obtained specifically as follows: the application of biochar significantly increased the soil pH from the initial 5.2 to 6.0. At the same time, the expression of nosZ gene was also enhanced. On the 90th day, its expression level was 2.8 times higher than that of the control group, and maintained a high level throughout the monitoring period, that is, it was promoted in the complete denitrification process.

[0094] refer to Figure 4 It can be seen that the changes in the concentration of metabolites obtained are specifically as follows: the concentration of primary metabolites (amino acids and organic acids are selected in this embodiment) increased significantly after the application of biochar, among which the concentration of glutamate increased by 70% on the 30th day, reaching 17.0 mg / kg, and increased to 20.5 mg / kg on the 60th day, and then fluctuated slightly but remained at a high level (about 17.5 mg / kg) at the end of one year. Similarly, the concentration of succinic acid also showed a similar upward trend, reaching 10.5 mg / kg on the 30th day, which was 110% higher than the control group, and increased to 12.0 mg / kg on the 60th day, and remained at a high level throughout the monitoring period. Specifically, the concentrations of glutamate and succinic acid increased by 1.7 times and 2.1 times, respectively. In other words, the microbial community adjusted its metabolic pathway in response to the newly input organic carbon, that is, enhanced respiration or synthesized new biomacromolecules.

[0095] Step M2: Metabolic pathway adjustment. That is, by obtaining the basic information of the target microbial community, constructing a metabolic network model, and determining the adjustment direction of the metabolic pathway, as follows:

[0096] Step X1: Obtain basic information of the target microbial community, including but not limited to genome sequence, transcriptome expression pattern, and proteome and metabolome data to determine the behavior of the microorganisms under different conditions.

[0097] In this embodiment, the whole genome sequence is obtained by sequencing technology to obtain a comprehensive view of the coding capacity of the microorganism, including all genes that may be involved in metabolic activities. Further, by high-throughput sequencing of the transcriptome, the expression level of each gene under specific conditions is quantified, thereby determining the genes that are upregulated or downregulated in response to external stimuli. Further, by proteomics technology (such as mass spectrometry), all proteins in cells or tissues in the microorganism are determined. Further, by liquid chromatography-mass spectrometry or gas chromatography-mass spectrometry technology, the dynamic changes of all small molecule metabolites in the microorganism are obtained.

[0098] Step X2: Establish an initial metabolic network model through the basic information of the target microbial community obtained in step X1 and the genome-scale metabolic model. That is, through the genome-scale metabolic model, the active metabolic pathways are determined from the basic information of the target microbial community, and key enzymes and regulatory factors are identified therefrom. It is worth noting that identifying key enzymes and regulatory factors through the genome-scale metabolic model is a technology known to the public, so it will not be specifically described in this embodiment.

[0099] Furthermore, in this embodiment, the parameters of the initial metabolic network model are calibrated by integrating multi-omics data, such as gene expression levels and enzyme activity measurement results. In other words, the final metabolic network model can be obtained by calibrating the parameters, so that the metabolic pathway adjustment can be predicted through the final metabolic network model.

[0100] Step X3: Through the metabolic network model established in step X2, obtain the potential metabolic pathway adjustments under different conditions (such as adding different types of biochar or changing other environmental factors). In other words, the metabolic pathways of microorganisms will be activated or inhibited under external stimuli.

[0101] In the specific implementation process, after the application of biochar, the concentrations of metabolites such as glutamate and succinic acid increased significantly, that is, the metabolic pathways related to them were activated or enhanced, that is, the microbial community adjusted the specific mechanism of its metabolic pathway when faced with new input of organic carbon.

[0102] Step M3: Changes in survival strategies. This is to obtain changes in the diversity, abundance, and activity of microorganisms in the microbial community through molecular biology techniques (such as high-throughput sequencing, quantitative PCR, and phospholipid fatty acid analysis). In other words, the number of microbial populations will increase or decrease over time.

[0103] Step S2: Determine the degree of impact. That is, according to the composition, function and dynamic response mechanism of the microbial community to environmental changes determined in step S1, set up a control group and a treatment group (adding biochar), and systematically monitor and analyze the soil organic carbon storage change rate, CO2 emissions and influencing factors at multiple preset time points. The details are as follows:

[0104] Step S2.1: Setting a control group and a treatment group. That is, according to the microbial response mechanism determined in step S1.4, a control group and a treatment group are set, that is, no biochar is added and biochar is added, and samples are taken from the set control group and treatment group according to preset time points.

[0105] In the process of specific implementation, multiple time points are set for sampling according to the experimental purpose, such as 7 days, 30 days, 60 days, 90 days, 180 days and 360 days, to monitor the changes in the soil microbial community structure and the development trend of soil chemical properties over time. It is worth noting that it is necessary to ensure that each group of animals or each plot has an equal chance of being assigned to each experimental group. In this embodiment, for soil samples, there are no less than 10 mice per group and no less than 6 rats per group. For larger animals, the number can be reduced according to the specific implementation situation, but not less than 4 to 5.

[0106] Step S2.2: Obtain the SOC reserve change rate. That is, obtain the sampling data of the control group and the treatment group in step S2.1, and obtain the change rate of soil organic carbon reserves at different time points based on the sampling data, so as to determine the organic carbon fixation efficiency through the change rate of soil organic carbon reserves at different time points. The formula for obtaining the change rate of soil organic carbon reserves at different time points is specifically:

[0107]

[0108] in: is the change rate of soil organic carbon storage at different time points, is the soil organic carbon content at the final moment, is the soil organic carbon content at the initial time, For the time interval.

[0109] Step S2.3: Determine CO2 emissions. That is, determine CO2 emissions through a multivariate linear regression model, specifically:

[0110]

[0111] in: is CO2 emissions, is the intercept term, is the influence of pH value on CO2 emissions, is the pH value, is the influence of temperature on CO2 emissions, is the temperature, is the influence of soluble organic carbon concentration on CO2 emissions, is the concentration of dissolved organic carbon, is the error term.

[0112] In the specific implementation process, the intercept term was set to 2.5, the influence of pH value on CO2 emissions was set to -0.4, the influence of temperature on CO2 emissions was set to 0.05, and the influence of soluble organic carbon concentration on CO2 emissions was set to 0.6. In other words, when the pH value increased by 1 unit, CO2 emissions decreased by 0.4 μmol CO2·m -2 ·s -1 When the temperature rises by 1°C, CO2 emissions increase by 0.05 μmol CO2·m -2 ·s -1 When the concentration of soluble organic carbon increases by 1 mg / L, CO2 emissions increase by 0.6 μmol CO2·m -2 ·s -1 .

[0113] Furthermore, the average CO2 emission of the control group and the treatment group was 5.2 μmol CO2·m -2 ·s -1 and 3.8 μmol CO2·m -2 ·s -1 That is to say, CO2 emissions were reduced after adding biochar.

[0114] Step S2.4: Determine the influencing factors. That is, according to the SOC reserve change rate obtained in step S2.2 and the CO2 emissions obtained in step S2.3, a prediction model is constructed through a multivariate linear regression statistical model, and the output result P value of the prediction model is obtained (it is calculated through the actual selected statistical test and the collected data, so in this embodiment, its specific acquisition process will not be explained). At the same time, the output result P value of the prediction model is compared with a preset threshold value (which can be set according to actual use requirements, so it will not be explained in detail in this embodiment), and the influencing factors are determined according to the comparison results, specifically:

[0115] When the output result P value is less than a preset threshold (set to 0.05 in this embodiment), the factor corresponding to the output result P value is an influencing factor, otherwise the factor corresponding to the output result P value is not an influencing factor.

[0116] During the implementation, the SOC reserve change rate at different time points was -0.2g C / m2 / year, +0.5g C / m 2 / year and +0.8g C / m 2 / year. CO2 emissions are 5.2μmol CO2·m -2 ·s -1 、4.8μmol CO2·m -2 ·s -1 and 4.5 μmol CO2·m -2 ·s -1 .

[0117] According to the above data, there is a significant positive correlation between pH value and CO2 emissions (the output result P value is less than 0.05), so pH value is an influencing factor.

[0118] Step S3: Obtain a comprehensive report. That is, through the influencing factors determined in step S2.4, a data analysis model is constructed to generate a comprehensive report on deep soil organic carbon and microbial carbon metabolic dynamics. The details are as follows:

[0119] Step S3.1: Determine the SOC reserve change rate, CO2 emission and the output result P value of the prediction model corresponding to each influencing factor through the influencing factors determined in step S2.4, including but not limited to pH value, temperature, moisture content, total nitrogen content, soluble organic carbon concentration, microbial community structure, enzyme activity and CO2 emission. At the same time, according to the determined SOC reserve change rate, CO2 emission and the output result P value of the prediction model corresponding to each influencing factor, draw a trend chart of the change of each influencing factor variable over time to show the change pattern of each influencing factor variable over time.

[0120] In the specific implementation process, there is a significant positive correlation between pH and CO2 emissions (output result P value less than 0.05), and higher pH values ​​will promote organic carbon mineralization. Similarly, there is a strong positive correlation between DOC concentration and CO2 emissions (output result P value less than 0.01), and easily available carbon sources will promote microbial activity and carbon mineralization.

[0121] refer to Figure 5 (The difference in the change rate of SOC storage in farmland affected by different agricultural management practices (conventional tillage, no-tillage and cover crops) is determined by Figure 5 It can be seen that the SOC storage change rate shows a relatively stable positive growth trend under no-tillage conditions, but fluctuates greatly under conventional tillage conditions, and even shows negative growth. In other words, tillage methods have an impact on the efficiency of organic carbon fixation.

[0122] Step S3.2: Based on the trend graph drawn in step S3.1, a data analysis model is constructed to generate a comprehensive report on the deep soil organic carbon and microbial carbon metabolism dynamics. That is, with the influencing factors as input variables, the SOC storage change rate and CO2 emissions as output variables, a data analysis model is constructed through multiple linear regression to output a comprehensive report on the deep soil organic carbon and microbial carbon metabolism dynamics.

[0123] This embodiment also provides a deep soil organic carbon and microbial carbon metabolic kinetics detection system, which uses a deep soil organic carbon and microbial carbon metabolic kinetics detection method in the above embodiment.

[0124] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is limited by the attached embodiments and their equivalents.

Claims

1. A method for detecting the metabolic kinetics of organic carbon and microbial carbon in deep soil, characterized in that: Included are: Obtain paddy field soil samples: sampling and analyzing paddy field soil to obtain the organic carbon content and structural characteristics of the paddy field soil, and determining the composition, function and dynamic response mechanism of the microbial community to environmental changes based on the organic carbon content and structural characteristics; Determine the degree of impact: set up a control group and a treatment group according to the composition, function and dynamic response mechanism of the microbial community to environmental changes, and obtain the soil organic carbon storage change rate, CO2 emission and influencing factors in the control group and the treatment group at different time points; Obtain comprehensive report: Based on the soil organic carbon storage change rate, CO2 emissions and influencing factors in the control group and treatment group at different time points, a data analysis model is constructed to generate a comprehensive report on deep soil organic carbon and microbial carbon metabolic dynamics, including: According to the influencing factors, draw a trend chart of each influencing factor changing over time; According to the change trend chart, the influencing factors are used as input variables, the soil organic carbon storage change rate and CO2 emissions are used as output variables, and the data analysis model is constructed through multivariate linear regression to output a comprehensive report on the deep soil organic carbon and microbial carbon metabolic dynamics.

2. A method for detecting the metabolic kinetics of deep soil organic carbon and microbial carbon according to claim 1, characterized in that: The composition, function and dynamic response mechanisms of microbial communities to environmental changes were determined as follows: Sample collection: Obtain soil samples at different depths at multiple rice field locations; Determine the content of organic carbon and its structural characteristics: obtain the content of organic carbon in different forms in the soil sample by chemical and physical methods; Determine the microbial community structure and functional characteristics analysis: Obtain the microbial community structure and function in the soil sample through phospholipid fatty acid analysis, high-throughput sequencing and fluorescence in situ hybridization combined with confocal microscopy; Dynamic monitoring of deep soil microbial response mechanisms: Through metatranscriptomics technology, metabolic network models and molecular biology techniques, the gene expression changes, metabolic pathway adjustments and survival strategy changes of soil microbial communities after biochar addition are obtained.

3. A method for detecting deep soil organic carbon and microbial carbon metabolic kinetics according to claim 2, characterized in that: The content of organic carbon in different forms in soil samples is determined by chemical and physical methods, including: W1: Total organic carbon determination: The total organic carbon content and its components in soil samples were obtained by CNS elemental analyzer method and Py-GC / MS technology; W2: Determination of particulate organic carbon: Obtain particulate organic carbon from the soil sample by physical separation method, and determine the percentage of particulate organic carbon in the soil sample, specifically: ； in: is the mass concentration of particulate organic carbon, is the mass concentration of soil organic carbon in particulate matter, is the mass ratio of particulate matter to the entire soil sample; W3: Determination of easily oxidizable carbon: The soil sample is oxidized by KMnO4 solution to obtain the easily oxidizable carbon content, specifically: ； in: is the mass of carbon oxidized, is the difference in potassium permanganate concentration before and after the experiment, is the molar mass of carbon.

4. A method for detecting deep soil organic carbon and microbial carbon metabolic kinetics according to claim 2, characterized in that: The microbial community structure and function in the soil sample are obtained as follows: N1: Phospholipid fatty acid analysis: extract phospholipid fatty acids from soil samples, and perform qualitative and quantitative analysis on the phospholipid fatty acids by gas chromatography-mass spectrometry to obtain the ratio of bacteria to fungi in the soil samples; N2: High-throughput sequencing: amplify specific marker genes of bacteria and fungi to obtain the ratio of bacteria and fungi; N3: Fluorescence in situ hybridization and confocal microscopy: The microbial community structure and function in the soil samples were determined by fluorescence in situ hybridization and confocal microscopy.

5. A method for detecting the metabolic kinetics of deep soil organic carbon and microbial carbon according to claim 2, characterized in that: Obtain the changes in gene expression, metabolic pathway adjustments, and survival strategy changes of soil microbial communities after biochar addition, as follows: M1: Gene expression pattern: Through the metatranscriptomics technology, the changes in the mRNA levels of metabolic enzymes in soil samples were obtained to determine the changes in nitrate reductase gene expression, nitrous oxide reductase gene expression and metabolite concentrations; M2: Metabolic pathway adjustment: obtain the basic information of the microbial community in the soil sample, build a metabolic network model, and determine the adjustment direction of the metabolic pathway; M3: Changes in survival strategies: Through molecular biology techniques, the changes in the diversity, abundance and activity of microorganisms in the microbial community are obtained.

6. A method for detecting the metabolic kinetics of deep soil organic carbon and microbial carbon according to claim 5, characterized in that: Determine the direction of adjustment of metabolic pathways, including: X1: Obtain basic information of the target microbial community, including but not limited to genome sequence, transcriptome expression pattern, and proteome and metabolome data, and determine the behavior of the microorganisms under different conditions based on the basic information; X2: Establish an initial metabolic network model based on the basic information of the target microbial community and the genome-scale metabolic model, and calibrate the parameters of the initial metabolic network model by integrating multi-omics data to obtain the final metabolic network model; X3: Through the final metabolic network model, the metabolic pathway adjustment of the target microbial community under different conditions is obtained.

7. A method for detecting deep soil organic carbon and microbial carbon metabolic kinetics according to claim 1, characterized in that: Obtain the soil organic carbon storage change rate, CO2 emission and influencing factors in the control group and treatment group at different time points, including: Setting up a control group and a treatment group: setting up the control group and the treatment group according to the microbial response mechanism, and sampling the control group and the treatment group at a preset time point; Obtaining the SOC reserve change rate: According to the sampling data of the control group and the treatment group, the change rate of soil organic carbon reserve at different time points is obtained to determine the organic carbon fixation efficiency, specifically: ； in: is the change rate of soil organic carbon storage at different time points, is the soil organic carbon content at the final moment, is the soil organic carbon content at the initial time, is the time interval; Determination of CO2 emissions: The CO2 emissions were determined by using a multiple linear regression model and the sampling data of the control group and the treatment group, specifically: ； in: is CO2 emissions, is the intercept term, is the influence of pH value on CO2 emissions, is the pH value, is the influence of temperature on CO2 emissions, is the temperature, is the influence of soluble organic carbon concentration on CO2 emissions, is the concentration of dissolved organic carbon, is the error term; Determine the influencing factors: According to the SOC reserve change rate and CO2 emissions, a prediction model is constructed through a multivariate linear regression statistical model to obtain an output result P value, and the output result P value is compared with a preset threshold value, and the influencing factors are determined according to the comparison result, specifically: When the output result P value is less than a preset threshold, the factor corresponding to the output result P value is an influencing factor; otherwise, the factor corresponding to the output result P value is not an influencing factor.

8. A deep soil organic carbon and microbial carbon metabolic dynamics detection system, characterized in that: The detection system uses a deep soil organic carbon and microbial carbon metabolic kinetics detection method as described in any one of claims 1-7.

Images