A method and system for detecting deep soil organic carbon and microbial carbon metabolic dynamics

Abstract

The present invention discloses a method and system for detecting deep soil organic carbon and microbial carbon metabolic dynamics, which relates to the fields of soil science and environmental science and technology. The method comprises: obtaining the organic carbon content and structural characteristics in the paddy field soil, and determining the composition, function and dynamic response mechanism of the microbial community to environmental changes; setting up a control group and a treatment group, and obtaining the soil organic carbon storage change rate, CO2 emissions and influencing factors in the control group and the treatment group at different time points; constructing a data analysis model based on the soil organic carbon storage change rate, CO2 emissions and influencing factors in the control group and the treatment group at different time points, and generating a comprehensive report on deep soil organic carbon and microbial carbon metabolic dynamics. The present invention provides a scientific basis for optimizing agricultural management and responding to climate change, and reveals the positive effects of biochar addition on soil health, promoting the development of sustainable agriculture.

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 the metabolic kinetics of organic carbon and microbial carbon in deep soil. Background Art

[0002] Deep soil organic carbon (SOC) originates primarily 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 complicate monitoring of deep SOC dynamics. Furthermore, physical protective mechanisms in deep soils, such as aggregate formation, are considered key factors influencing SOC stability. While the structure and functional properties of deep soil microbial communities decrease with depth, under long-term low-resource environments, deep microorganisms can adopt unique survival strategies to respond to fresh organic carbon inputs. Consequently, the interplay between physical protective mechanisms and microbial activity in deep soils creates significant uncertainty in SOC status.

[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, and comprehensively considering the influence of environmental factors on metal effectiveness through complexation and competition, the interaction between different metals on the surface of biological ligands, and the heterogeneity of the distribution of action sites in organisms, a multi-metal biological homogeneity point action model and a multi-metal biological heterogeneity point action model were constructed around the biological effectiveness of heavy metals, realizing the effective evaluation and prediction of the biological effects under the coexistence of multiple metals in soil from different types of contaminated sites.

[0004] During the implementation of these and similar soil bioavailability assessment methods, although it is known that the genetic and metabolic diversity of deep soil microbial communities decreases with depth, in chronic low-resource environments, deep microorganisms can employ unique survival strategies to respond to the input of fresh organic carbon. Therefore, the interaction between physical protection mechanisms and microbial activity in deep soils can lead to significant uncertainty in the state of organic carbon. Therefore, how deep soil microorganisms respond to new carbon inputs and adjust their metabolic activities to adapt to the changing state of organic carbon remains a major challenge. 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 objectives, the present invention provides the following technical solution: a method for detecting the metabolic kinetics of organic carbon and microbial carbon in deep soil, comprising:

[0007] Obtaining paddy soil samples: Sampling and analyzing paddy soil to obtain the organic carbon content and structural characteristics of the paddy 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 extent of impact: Set up control and treatment groups based on the composition, function, and dynamic response mechanism of the microbial community to environmental changes, and obtain the change rate of soil organic carbon storage, CO2 emissions, and influencing factors in the control and treatment groups at different time points;

[0009] Obtain a comprehensive report: Based on the soil organic carbon storage change rate, CO2 emissions and influencing factors in the control and treatment groups 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] Based on the influencing factors, draw a graph showing the changing trend of each influencing factor over time;

[0011] According to the change trend diagram, 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] Determination of organic carbon content and its structural characteristics: obtaining 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 properties: Obtain the microbial community structure and function in the soil samples 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 macrotranscriptomics 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 different forms of organic carbon 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 methods, 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 with a 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: Phospholipid fatty acids were extracted from soil samples and qualitatively and quantitatively analyzed 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 size 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, we obtained the changes in gene expression, metabolic pathway adjustments, and survival strategy shifts of soil microbial communities after biochar addition, as follows:

[0030] M1: Gene expression patterns: Using metatranscriptomics technology, we obtained changes in the mRNA levels of metabolic enzymes in soil samples, and determined changes in nitrate reductase gene expression, nitrous oxide reductase gene expression, and metabolite concentrations.

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

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

[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 patterns, and proteome and metabolome data, and determine the behavior of the microorganisms under different conditions based on this basic information;

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

[0036] X3: Obtain metabolic pathway adjustments of the target microbial community under different conditions through the final metabolic network model.

[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 preset time points;

[0039] Obtaining the SOC storage change rate: Based on the sampling data of the control group and the treatment group, the change rate of soil organic carbon storage at different time points was 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 moment, is the time interval;

[0042] Determination of CO2 emissions: The CO2 emissions were determined using a multivariate linear regression model and the sample data of the control and treatment groups, 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: Based on 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. At the same time, the output result P value is compared with a preset threshold, and based on the comparison result, the influencing factors are determined, 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 uses any of the above-mentioned deep soil organic carbon and microbial carbon metabolic kinetics detection methods.

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

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

[0050] Figure 1 Schematic diagram of the detection method of the present invention;

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

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

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

[0054] Figure 5 This 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 clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts 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 organic carbon state. In other words, as the input of new carbon continues to increase, the microorganisms in the deep soil will continuously adjust their metabolic activities to adapt to the changing organic carbon state, 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 an accurate test report when conducting tests. The technical solution of the present application determines the key influencing factors by obtaining the response mechanism of the organic carbon content and the structure and function of the microbial community in the deep soil of the rice field to environmental changes, and draws a 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 revealing the positive effects of biochar addition on soil health, promoting the development of sustainable agriculture.

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

[0058] Step S1: Obtain paddy soil samples. This involves sampling and analyzing paddy soil to determine the organic carbon content and structural characteristics. Based on this information, the composition and function of the microbial community, as well as its dynamic response to environmental changes (such as biochar addition), are determined. The details are as follows:

[0059] Step S1.1: Sample Collection. This involves obtaining at least five different and representative rice paddy sites, ensuring that the sites cover a range of soil types, management practices, and climatic conditions. Specifically, using a soil auger or automated sampler, collect multiple soil samples from the surface to a depth of 1 meter at predetermined vertical intervals (e.g., every 10 cm), ensuring that the same sampling volume (e.g., 100 cm³) is maintained for each collection to avoid sample contamination. The specific depth of each sample is recorded, and a standard substance of known concentration is added as a control in each analysis to calibrate the instrument and verify the accuracy of the measurement results. It is important to note that at least three replicate samples are collected at each depth to ensure statistical reliability of the data.

[0060] During the implementation process, samples were collected 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 at each point.

[0061] Step S1.2: Determine the organic carbon content and its structural characteristics. This involves determining the content of different forms of organic carbon in the soil sample using chemical and physical methods, including total organic carbon, particulate organic carbon, and readily oxidizable carbon. The details are as follows:

[0062] Step W1: Total organic carbon determination. This involves measuring the total organic carbon content and its components in the soil using a CNS elemental analyzer and Py-GC / MS. This not only provides accurate carbon content information but also allows for the identification of specific organic compound types 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] During the specific implementation, the soil sample collected in step S1.1 was placed in an oven and dried at 105°C to constant weight to remove moisture. The dried soil sample weighed 10 grams. A CNS elemental analyzer determined that the sample contained 0.5 grams of carbon, resulting in a total organic carbon percentage of 5%.

[0067] Step W2: Determination of particulate organic carbon. This involves extracting the particulate organic carbon from the soil sample collected in step S1.1 through a physical separation method, and obtaining the percentage of the extracted particulate organic carbon in the soil sample. The formula for obtaining the percentage of particulate organic carbon in the soil sample is:

[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] During 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. Simultaneously, the soil sample is graded using sieves of different apertures. In this embodiment, 0.053 mm is used as the cutoff point to separate particles from the treated soil sample. Furthermore, particles larger than 0.053 mm are further separated by density flotation, for example, using a sodium hexametaphosphate solution (density 1.7 g / cm³) to separate light components (such as plant residues) and heavy components (such as gravel). The organic carbon content of the separated particles (larger than 0.053 mm) is determined by potassium dichromate oxidation-volumetry.

[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. At the same time, the mass proportion of the particulate matter in the entire soil sample is 20%, and the percentage of particulate organic carbon in the soil sample is 2 g / kg.

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

[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] During the specific implementation, the soil sample collected in step S1.1 was ground and passed through a 0.5 mm sieve. Based on the total organic carbon content of the soil, the amount of soil sample containing 15 mg of carbon was calculated as the sample weight of the test sample. The test sample was transferred to a covered plastic centrifuge tube, with no soil sample added as a blank control. A 0.01 mol / L potassium permanganate solution was added to the centrifuge tube. The tube was shaken (normally shaken) at approximately 23°C for 2 hours and centrifuged at 4000 rpm for 15 minutes. The supernatant was diluted 10-fold with deionized water, and the absorbance of the diluted sample was measured at 550 nm using a spectrophotometer.

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

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

[0078] Step N1: Phospholipid fatty acid analysis. Phospholipid fatty acids are extracted from the soil sample collected in step S1.1 and qualitatively and quantitatively analyzed by gas chromatography-mass spectrometry to determine the bacterial to fungal ratio in the soil sample. The formula for determining the bacterial to fungal ratio in the soil sample is:

[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] During the specific implementation, the soil sample collected in step S1.1 is pretreated to remove impurities and extract the phospholipid fatty acids. In this embodiment, the phospholipid fatty acids are extracted using an organic solvent extraction method, such as the Bligh-Dyer method. The extracted phospholipid fatty acid mixture is then separated and purified (e.g., by thin-layer chromatography, high-performance liquid chromatography, and silica chromatography) to remove other non-target components in the phospholipid fatty acid mixture. Furthermore, the treated phospholipid fatty acids are separated and identified using GC-MS or capillary gas chromatography coupled with a flame ionization detector.

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

[0083] Step N2: High-throughput sequencing. This involves amplifying bacterial marker genes using the 16S rRNA gene and fungal marker genes using the ITS gene. This allows the ratio of bacteria to fungi to be determined using the acquired marker genes.

[0084] In the implementation, 16S rRNA genes (for bacteria) and ITS genes (for fungi) were extracted and amplified from the soil samples collected in step S1.1. These genes were then sequenced using a high-throughput sequencing platform. Specifically, the raw sequencing data (FASTQ files) from the soil samples collected in step S1.1 were quality assessed and filtered using tools such as Trimmomatic and FastP to remove low-quality sequences, sequences with adapter contamination, and reads that were too short. Sequencing errors were corrected using DADA2, and paired-end reads were merged into full-length sequences. The number of distinct ASVs in each sample was counted to create a signature table. Taxonomic identifiers were assigned to each ASV using public databases (e.g., Greengenes, SILVA, and UNITE). Classification of the 16S rRNA gene was performed using the RDPClassifier or SINA Aligner classification methods, while the ITS gene 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. In other words, 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] During the specific implementation process, 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, specific oligonucleotide probes are synthesized and labeled with fluorescent dyes, and the labeled sample is mixed with the FISH probe. Furthermore, 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 present in situ in the sample. At the same time, the hybridized sample is imaged at high resolution using a confocal laser scanning microscope, and the image data is analyzed to obtain the density, morphological characteristics and relative positional relationship between the microorganisms in the sample and other components.

[0088] Specifically, for the soil sample collected in step S1.1, a cross-sectional image of soil aggregates within the sample was obtained, using red fluorescence to label total bacteria and green fluorescence to identify aerobic ammonia-oxidizing bacteria. The number of fluorescent spots in each color channel was also identified and counted, indicating that aerobic ammonia-oxidizing bacteria accounted for approximately 10% of the total bacteria.

[0089] Step S1.4: Dynamically monitor the response mechanism of deep soil microorganisms. This involves using metatranscriptomics, metabolic network modeling, and molecular biology methods to determine changes in gene expression, metabolic pathway adjustments, and survival strategy shifts in soil microbial communities after biochar addition. The details are as follows:

[0090] Step M1: Gene expression pattern. This involves using metatranscriptomics to detect changes in metabolic enzyme mRNA levels in the soil samples collected in step S1.1 to obtain changes in nitrate reductase gene expression, nitrous oxide reductase gene expression, and metabolite concentrations.

[0091] During the 12-month field trial, the experimental fields were divided into two groups: a control group (no treatment) and a treatment group (10 tons of biochar per hectare). Deep soil samples (30-50 cm deep) were collected at 0, 30, 60, 90, 180, and 360 days after biochar application to assess both short-term and long-term effects. Physical and chemical properties of soil samples were measured at each time point, including pH and organic carbon content. Total RNA was extracted and a metatranscriptome library constructed, which was then sequenced using the Illumina HiSeq platform.

[0092] refer to Figure 2 Specifically, the expression of the nitrate reductase gene was found to increase by 3.2-fold compared to the control group 30 days after biochar application. This peaked at approximately 4.1 times the control group on the 60th day. It then gradually stabilized, remaining approximately 3.5 times the control group at the end of the year.

[0093] refer to Figure 3 The results showed that nitrous oxide reductase gene expression was significantly increased by biochar application, from an initial pH of 5.2 to 6.0. Simultaneously, nosZ gene expression was also enhanced, reaching 2.8 times higher than the control group on day 90. This level remained high throughout the monitoring period, indicating that complete denitrification was promoted.

[0094] refer to Figure 4 Specifically, changes in metabolite concentrations were observed: The concentrations of primary metabolites (amino acids and organic acids in this example) increased significantly after biochar application. Glutamate concentrations increased by 70% to 17.0 mg / kg on day 30, rising to 20.5 mg / kg on day 60. Thereafter, the concentration fluctuated slightly but remained at a high level (approximately 17.5 mg / kg) by the end of the year. Similarly, succinate concentrations showed a similar upward trend, reaching 10.5 mg / kg on day 30, 110% higher than the control, and increasing to 12.0 mg / kg on day 60, remaining at a high level throughout the monitoring period. Specifically, glutamate and succinate concentrations increased by 1.7-fold and 2.1-fold, respectively. This suggests that the microbial community adjusted its metabolic pathways in response to the new organic carbon input, either by enhancing respiration or synthesizing new biomacromolecules.

[0095] Step M2: Metabolic pathway adjustment. This involves obtaining basic information about the target microbial community, building a metabolic network model, and determining the direction of metabolic pathway adjustment, as follows:

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

[0097] In this example, whole-genome sequencing is used to obtain a comprehensive view of the microorganism's coding capacity, including all genes potentially involved in metabolic activity. Furthermore, high-throughput transcriptome sequencing is used to quantify the expression level of each gene under specific conditions, thereby identifying genes that are upregulated or downregulated in response to external stimuli. Furthermore, proteomics techniques (such as mass spectrometry) are used to identify all proteins within the microorganism's cells or tissues. Furthermore, liquid chromatography-mass spectrometry or gas chromatography-mass spectrometry is used to analyze the dynamic changes in all small molecule metabolites in the microorganism.

[0098] Step X2: Using the basic information of the target microbial community obtained in Step X1 and the genome-scale metabolic model, an initial metabolic network model is established. Specifically, the genome-scale metabolic model is used to identify active metabolic pathways from the basic information of the target microbial community, and key enzymes and regulatory factors are identified. It is worth noting that identifying key enzymes and regulatory factors using genome-scale metabolic models is a well-known technique and will not be further elaborated in this example.

[0099] Furthermore, in this example, the parameters of the initial metabolic network model are calibrated by integrating multi-omics data, such as gene expression levels and enzyme activity measurements. This calibration of the parameters yields a final metabolic network model, which can then be used to predict metabolic pathway adjustments.

[0100] Step X3: Using the metabolic network model established in Step X2, we can identify potential metabolic pathway adjustments under different conditions (e.g., adding different types of biochar or changing other environmental factors). This means that the microbial metabolic pathways will be activated or inhibited under external stimuli.

[0101] During the specific implementation process, after the application of biochar, the concentrations of metabolites such as glutamate and succinate increased significantly, that is, the metabolic pathways related to them were activated or strengthened, 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 involves using molecular biology techniques (such as high-throughput sequencing, quantitative PCR, and phospholipid fatty acid analysis) to measure changes in the diversity, abundance, and activity of microorganisms within the microbial community. This means that over time, the number of microbial populations will increase or decrease.

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

[0104] Step S2.1: Set up a control group and a treatment group. That is, according to the microbial response mechanism determined in step S1.4, set up a control group and a treatment group, i.e., one without biochar and one with biochar. At the same time, samples are taken from the control group and the treatment group at preset time points.

[0105] During implementation, sampling was performed at multiple time points, such as 7, 30, 60, 90, 180, and 360 days, depending on the experimental objectives, to monitor changes in soil microbial community structure and the development of soil chemical properties over time. It is important to ensure that each group of animals or each plot of land has an equal chance of being assigned to each experimental group. In this example, for soil samples, mice were grouped in no fewer than 10 animals, and rats were grouped in no fewer than 6 animals. For larger animals, the number can be reduced based on the specific implementation, but should not be less than 4-5 animals.

[0106] Step S2.2: Obtain the SOC storage change rate. Specifically, obtain the sampling data for the control and treatment groups in step S2.1, and based on the sampling data, obtain the change rate of soil organic carbon storage at different time points. The organic carbon fixation efficiency can be determined by the change rate of soil organic carbon storage at different time points. The formula for obtaining the change rate of soil organic carbon storage 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 moment, is the time interval.

[0109] Step S2.3: Determine CO2 emissions. That is, use a multiple linear regression model to determine CO2 emissions, 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℃, CO2 emissions increase by 0.05μmol CO2·m -2 ·s -1 When the concentration of dissolved 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 and treatment groups was 5.2 μmol CO2·m -2 ·s -1 and 3.8 μmol CO2·m -2 ·s -1 In other words, CO2 emissions decreased after adding biochar.

[0114] Step S2.4: Determine the influencing factors. That is, based on 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 (which is calculated through the actual selected statistical test and the collected data, so the specific acquisition process will not be explained in this embodiment). At the same time, the output result P value of the prediction model is compared with a preset threshold (which can be specifically set according to actual use requirements, so it will not be explained in detail in this embodiment), and based on the comparison result, the influencing factors are determined, 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 specific implementation process, 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. This is to construct a data analysis model based on the influencing factors identified in step S2.4, and generate a comprehensive report on the 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 emissions, and the P value of the output of the prediction model corresponding to each influencing factor using the influencing factors determined in step S2.4, including but not limited to pH, temperature, moisture content, total nitrogen content, soluble organic carbon concentration, microbial community structure, enzyme activity, and CO2 emissions. At the same time, based on the determined SOC reserve change rate, CO2 emissions, and the P value of the output of the prediction model corresponding to each influencing factor, plot a trend graph of the change of each influencing factor variable over time to demonstrate 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), indicating that higher pH values ​​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), indicating that readily available carbon sources promote microbial activity and carbon mineralization.

[0121] refer to Figure 5 (The difference in SOC stock change rate in farmland affected by different agricultural management practices (conventional tillage, no-tillage and cover crops) is caused by Figure 5 The results show that the SOC storage change rate showed a relatively stable positive growth trend under no-tillage conditions, but fluctuated greatly under conventional tillage conditions, even showing negative growth. In other words, tillage methods have an impact on the efficiency of organic carbon sequestration.

[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. Specifically, using the influencing factors as input variables and the SOC storage change rate and CO2 emissions as output variables, a data analysis model is constructed through multivariate linear regression to generate 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 understood by those skilled in the art that various changes, modifications, substitutions and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is limited by the accompanying embodiments and their equivalents.

Claims

1. A method for detecting the metabolic kinetics of deep soil organic carbon and microbial carbon, characterized in that: Includes: Obtaining paddy field soil samples: obtaining the total organic carbon content and composition of the soil samples through total organic carbon determination, determining the percentage of particulate organic carbon in the soil samples through particulate organic carbon determination, obtaining the easily oxidizable carbon content through easily oxidizable carbon determination, obtaining the ratio of bacteria to fungi in the soil samples through phospholipid fatty acid analysis, obtaining the ratio of bacteria to fungi through high-throughput sequencing, determining the structure and function of the microbial community in the soil samples through fluorescence in situ hybridization combined with confocal microscopy, obtaining changes in the mRNA levels of metabolic enzymes in the soil samples through macrotranscriptomics technology, determining the adjustment direction of the metabolic pathway through metabolic network modeling, and obtaining changes in the diversity, abundance, and activity of microorganisms in the microbial community through molecular biology techniques; Determine the degree of impact: Set up a control group and a treatment group, determine the organic carbon fixation efficiency through the change rate of soil organic carbon storage at different time points, determine the CO2 emissions through a multiple linear regression model, and simultaneously build a prediction model to obtain the output result P value. The output result P value is compared with the preset threshold, and the influencing factors are determined based on the comparison results; Obtain a comprehensive report: Based on the influencing factors, draw a trend chart of each influencing factor over time, take the influencing factors as input variables, and take the soil organic carbon storage change rate and CO2 emissions as output variables. Through multiple linear regression, construct the data analysis model, and output a comprehensive report on the deep soil organic carbon and microbial carbon metabolic dynamics.

2. The method for detecting the metabolic kinetics of deep soil organic carbon and microbial carbon according to claim 1, characterized in that: The total organic carbon determination is used to obtain the total organic carbon content and its components in the soil sample. The particulate organic carbon determination is used to determine the percentage of particulate organic carbon in the soil sample. The easily oxidizable carbon determination is used to obtain the easily oxidizable carbon content, including: W1: Total organic carbon determination: The total organic carbon content and its components in the soil sample were obtained using the CNS elemental analyzer method and Py-GC / MS technology. The total organic carbon content was obtained using the following formula: ； 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; W2: Determination of particulate organic carbon: Obtain particulate organic carbon from the soil sample by physical separation methods, 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 using a KMnO4 solution, and the amount of KMnO4 consumed is measured using a spectrophotometer to obtain the easily oxidizable carbon content. The formula for obtaining the easily oxidizable carbon content is as follows: ； 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.

3. The method for detecting the metabolic kinetics of deep soil organic carbon and microbial carbon according to claim 1, characterized in that: Phospholipid fatty acid analysis was used to determine the ratio of bacteria to fungi in the soil samples. High-throughput sequencing was used to determine the ratio of bacteria to fungi. Fluorescence in situ hybridization combined with confocal microscopy was used to determine the microbial community structure and function in the soil samples, as follows: N1: Phospholipid fatty acid analysis: Phospholipid fatty acids were extracted from soil samples and qualitatively and quantitatively analyzed by gas chromatography-mass spectrometry to obtain the ratio of bacteria to fungi in the soil samples, specifically: ； in: is the ratio of bacteria to fungi, is the total amount of all known bacterial-specific phospholipid fatty acids, is the total amount of all known phospholipid fatty acids that are unique to fungi; N2: High-throughput sequencing: amplify bacterial specific marker genes through 16S rRNA genes, amplify fungal specific marker genes through ITS genes, and obtain the ratio of bacteria and fungi based on the amplified bacterial specific marker genes and fungal specific marker genes; N3: Fluorescence in situ hybridization and confocal microscopy: Based on the microbial gene sequence information, specific oligonucleotide probes are synthesized, and the oligonucleotide probes are labeled and hybridized with the target sequence through fluorescence in situ hybridization to obtain the specific microorganisms present in situ in the sample. At the same time, image data analysis of the specific microorganisms present in situ in the sample is performed through confocal microscopy to obtain the density, morphological characteristics and relative positional relationship of the microorganisms with other components in the sample.

4. The method for detecting the metabolic kinetics of deep soil organic carbon and microbial carbon according to claim 1, characterized in that: Using macrotranscriptomics technology, we can obtain changes in the mRNA levels of metabolic enzymes in soil samples. Using metabolic network models, we can determine the adjustment direction of metabolic pathways. Using molecular biology techniques, we can obtain changes in the diversity, abundance, and activity of microorganisms in the microbial community. The details are as follows: M1: Gene expression patterns: Using metatranscriptomics technology, we obtained changes in the mRNA levels of metabolic enzymes in soil samples, and determined changes in nitrate reductase gene expression, nitrous oxide reductase gene expression, and metabolite concentrations. M2: Metabolic pathway adjustment: Obtain basic information about the microbial community in the soil sample, build a metabolic network model, and determine the direction of metabolic pathway adjustment, including: X1: Obtain basic information of the target microbial community, including but not limited to genome sequence, transcriptome expression patterns, and proteome and metabolome data, and determine the behavior of the microorganisms under different conditions based on this basic information; X2: Based on the basic information of the target microbial community and the genome-scale metabolic model, an initial metabolic network model is established, and the parameters of the initial metabolic network model are calibrated by integrating multi-omics data to obtain the final metabolic network model; X3: Obtain metabolic pathway adjustments of target microbial communities under different conditions through the final metabolic network model M3: Changes in survival strategies: Using molecular biology techniques, obtain changes in the diversity, abundance, and activity of microorganisms in the microbial community.

5. The method for detecting the metabolic kinetics of deep soil organic carbon and microbial carbon according to claim 1, characterized in that: Obtain the soil organic carbon storage change rate, CO2 emissions 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 preset time points; Obtaining the SOC storage change rate: Based on the sampling data of the control group and the treatment group, the change rate of soil organic carbon storage at different time points was 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 moment, is the time interval; Determination of CO2 emissions: The CO2 emissions were determined using a multivariate linear regression model and the sample data of the control and treatment groups, 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: Based on 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. At the same time, the output result P value is compared with a preset threshold, and based on the comparison result, the influencing factors are determined, 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.

6. 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 according to any one of claims 1 to 5.

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