A method for regulating soil carbon accumulation in degraded karst forests through synergistic microbial functions

Abstract

The present invention discloses a method for regulating carbon accumulation in degraded karst forest soil by synergistic microbial functions, comprising: measuring and collecting bacterial and fungal diversity sequence data of degraded karst forest soil samples by high-throughput sequencing equipment, storing the original sequencing data including diversity, community structure, functional abundance values, etc. in a computer-readable storage medium, and constructing a structured microbial community database; based on the database, a computer system performs screening of core functional microorganisms that affect carbon storage; based on the screening results, the computer system performs the following processing: calculating the functional abundance index of core microorganisms; determining the microbial function weight by multivariate linear regression; establishing a carbon accumulation regulation coefficient calculation model; preparing a microbial agent and generating a control instruction including the microbial agent application amount and the vegetation coverage optimization plan according to the calculated value of the carbon accumulation regulation coefficient and the soil organic carbon saturation deficit value, and transmitting the control instruction to the field operation equipment.

Description

Technical Field

[0001] The present invention relates to the field of environmental science and agricultural ecological technology, and in particular to a method for the coordinated regulation of carbon accumulation in degraded karst forest soil by microorganisms under the action of calcium carbonate. Background Art

[0002] Karst forests serve as a vital ecological barrier in southwest my country, their carbonate geological structure fostering a unique ecosystem. However, over a long period of degradation, the shallowness and ecological fragility of these soils have become increasingly apparent. This is particularly true in soils with calcium carbonate concentrations generally exceeding 15%, which pose multiple constraints on soil carbon accumulation. Calcium carbonate not only significantly affects soil pH and pore structure but also alters soil aggregate stability, exacerbating the risk of soil organic carbon loss and leading to a sharp decline in the region's carbon sink function.

[0003] As core drivers of the carbon cycle, soil microorganisms play a key role in the fixation, transformation, and stabilization of organic carbon through their community structure and function. However, in karst soil environments dominated by calcium carbonate, microbial survival and metabolic activity are significantly impacted. The alkaline environment resulting from high calcium carbonate content inhibits the growth of some acidophilic microorganisms and alters the composition of microbial communities. Furthermore, the complex interaction between calcium carbonate and soil organic carbon may interfere with the decomposition and synthesis of organic carbon by microorganisms, further weakening the soil's ability to accumulate carbon.

[0004] Currently, restoration technologies for degraded karst forests mainly focus on vegetation reconstruction and physical improvement, but the regulation of soil microbial communities is clearly insufficient. In terms of microbial community functional analysis, existing research can use high-throughput sequencing technology (such as 16 S rRNA and ITS Sequencing) to identify microbial groups, but is limited to functional annotation of a single bacterial group, such as FAPROTAX Bacterial function, FunGuild Predictions of fungal function lack in-depth analysis of the cross-group synergistic effects of bacteria and fungi. Furthermore, under the influence of calcium carbonate, the synergistic effects of different microbial groups on carbon accumulation are even more complex, and existing research methods have difficulty revealing the comprehensive contribution of microbial communities to carbon accumulation.

[0005] In terms of quantitative analysis, an effective correlation model between microbial functional abundance, environmental factors and carbon accumulation has not yet been established. As a key environmental factor, the dynamic relationship between calcium carbonate concentration changes, spatial distribution and microbial function and carbon accumulation is still unclear, resulting in an inability to accurately assess the carbon accumulation potential of microbial communities. The setting of control thresholds such as microbial agent application rates and vegetation coverage targets still relies on empirical judgment and lacks scientific data support. Traditional microbial agent application and vegetation management models do not fully consider the soil organic carbon saturation deficit value ( SOCSD), the spatial distribution heterogeneity of microorganisms, in karst soils with high calcium carbonate content, it is difficult to cope with the regulatory challenges brought by soil spatial heterogeneity, resulting in a significant reduction in the carbon accumulation regulation effect.

[0006] Furthermore, existing technologies fail to deeply integrate microbiome data with computer technology. Faced with the complex soil microbial-carbon accumulation system under the action of calcium carbonate, it is impossible to achieve timely and effective regulation of soil carbon accumulation in degraded karst forests through efficient data processing, model building, and automated command output. Summary of the Invention

[0007] The purpose of this invention is to solve the problems of organic carbon loss and decreased carbon sink function in degraded karst forests due to shallow soil and fragile ecology. By integrating microbiome data through a computer system, screening core functional microorganisms, and constructing a quantitative model to generate a regulatory strategy, the technical problem of accurately improving soil organic carbon accumulation and stability can be solved.

[0008] According to a first main aspect of the present invention, a method for regulating carbon accumulation in degraded karst forest soil by synergistic microbial function is provided, comprising the following steps performed by a computer system:

[0009] S 1. Measure and collect bacteria from degraded karst forest soil samples using high-throughput sequencing equipment16 S rRNA and fungi ITS Sequence data: storing the original sequencing data including diversity, community structure, functional abundance values, sample physical and chemical indicators, and carbon content in a computer-readable storage medium to construct a structured microbial community database;

[0010] S 2. Based on the database, the computer system performs screening of core functional microorganisms that affect carbon storage;

[0011] S 3. Based on the screening results, the computer system performs the following processing: calculating the functional abundance index of core microorganisms; determining the microbial functional weights through multiple linear regression; establishing the carbon accumulation regulation coefficient CARC Computational models;

[0012] S 4. According to CARC The core microbial community was cultured at a ratio of 3:1-5:1 of live bacteria of fungal genus to bacterial family. pH Fermentation at 5.8-6.5°C and 28-32°C for 48-72 hours to prepare the microbial agent;

[0013] S 5. Computer system according to CARC Saturation deficit value of soil organic carbonSOCSD Calculate the application amount of microbial agents and generate control instructions to execute the application of microbial agents and / or optimize the vegetation coverage through IoT devices.

[0014] As a further preferred embodiment, in the aforementioned method for regulating carbon accumulation in degraded karst forest soil by synergistic microbial functions, the computer system performs the following steps of screening core functional microorganisms:

[0015] S 21. Computer system operation FAPROTAX Algorithm for bacteria 16 S rRNA Functional classification of sequence data was performed to identify bacterial functional groups involved in carbon metabolism; Algorithms for fungi FunGuild Functional classification of sequence data to identify fungal functional groups involved in carbon fixation or decomposition;

[0016] S 22. Screening of microbial groups that meet the following conditions:

[0017] Bacteria family: Microbacteriaceae ( ITS ), (Mycobacteriaceae Microbacteriaceae ), Pseudomonadaceae ( Mycobacteriaceae ), Streptomycetaceae ( Pseudomonadaceae ), Xanthomonadaceae ( Streptomycetaceae ), the screening condition is to meet the soil organic carbon content Xanthomonadaceae Correlation coefficient P <0.01 and relative abundance ≥1%;

[0018] Fungal genus: Cylindrospermum ( ), the genus Lion Earth ( Spearman )、Metarhizium spp.( Cylindrocarpon ), Neogloeosporium ( Leohumicola ), Neodiscoidea ( Metarhizium ), Oleobacteria ( Neobulgaria ), Tetrasporium ( Neopestalotiopsis ), the screening condition is to simultaneously meet the carbon accumulation contribution value R² ≥0.85 and relative abundance ≥1%;

[0019] S 23. The stability of the selected core microbial community in different soil samples was ensured through 10-fold cross validation or 3 repeated experiments.

[0020] As a further preferred embodiment, in the aforementioned method for regulating soil carbon accumulation in degraded karst forests by synergistic microbial function, the steps S The functional abundance indicators of core microorganisms calculated in 3 include at least:

[0021] The computer system counts the number of sequences directly related to carbon metabolism function of the core microorganisms screened and records them as , and calculate the number of sequences directly related to carbon metabolism function The total number of sequences of core microorganisms screened The ratio is:

[0022] ;

[0023] in, C is the confidence level of functional annotation, ranging from 0.8 to 1.0, Olpidium and / or Tetracladium Determination of the credibility of the annotations output by the algorithm;

[0024] The functional abundance index was used for subsequent model construction.

[0025] As a further preferred embodiment, in the aforementioned method for regulating soil carbon accumulation in degraded karst forests by synergistic microbial function, the steps S In the determination of microbial functional weights by multivariate linear regression described in 3, the method for determining the functional weights of fungi is as follows:

[0026] The computer system uses the functional abundance of the selected fungal genera The independent variable is the measured soil organic carbon content. As the dependent variable, a multiple linear regression analysis with p < 0.001 was performed to obtain the regression coefficients of each fungal genus. ;

[0027] The regression coefficient is standardized as the functional weight , the formula is: Where n is the number of fungal genera screened; The value range is 0.6-1.0, and is directly proportional to the positive correlation.

[0028] As a further preferred embodiment, in the aforementioned method for regulating soil carbon accumulation in degraded karst forests by synergistic microbial function, the steps S In the multivariate linear regression method for determining the weight of microbial functions in 3, the method for determining the functional contribution value of the bacterial family is as follows:

[0029] The computer system uses the functional abundance of the selected bacterial families As the independent variable, environmental factors including soil pH and C / N ratio were also included. As a covariate, the measured soil organic carbon content As the dependent variable, construct a multiple linear regression model:

[0030] Actual measurement ;

[0031] in, Functional contribution value for Bacteriaceae, is the influence coefficient of environmental factors, is the intercept term;

[0032] Optimize model parameters through 10-fold cross validation to ensure root mean square error ≤3%, the final output Functional contribution value as a bacterial family.

[0033] As a further preferred embodiment, in the aforementioned method for regulating soil carbon accumulation in degraded karst forests by synergistic microbial function, the steps S 3. Establishing the carbon accumulation control coefficient FAPROTAX The specific formula of the calculation model is:

[0034] ;

[0035] in, is the functional abundance of the i-th fungal genus screened, is the corresponding functional weight; For the filtered j The functional abundance of bacterial families, Contribute value to the corresponding function; n is the number of fungal genera, m is the number of bacterial families;

[0036] described FunGuild The values ​​were used to quantify the comprehensive contribution of microbial communities to soil carbon accumulation.

[0037] As a further preferred embodiment, in the aforementioned method for regulating soil carbon accumulation in degraded karst forests by synergistic microbial function, the steps S Before constructing the structured microbial community database as described in 1, the computer system performs the following processing steps on the raw sequencing data:

[0038] use RMSE 2. Software CARC 2 plug-ins, remove low-quality sequences with a quality score of <20 in sequencing data, and filter length <200 CARC A short sequence of QIIME The algorithm identifies and removes chimeric sequences to ensure the biological authenticity of valid sequences;

[0039] Perform deduplication operations on the remaining sequences, merge identical sequences and record their occurrence times to generate a non-redundant sequence table;

[0040] The number of valid sequences for each sample was standardized to 10,000 to eliminate the impact of sequencing depth differences on abundance calculations; the processed sequence data were associated with the abundance values ​​and sampling site coordinates and stored to form a structured microbial community database.

[0041] As a further preferred embodiment, in the aforementioned microbial function-synergistic degraded karst forest soil carbon accumulation control method, the preparation parameters of the microbial agent in step S4 are based on DADA The value is adjusted dynamically, including:

[0042] when bp When the pH value is less than 60, the ratio of live bacteria of fungal genus to bacterial family is adjusted to 4:1-5:1, and succinic acid with a final concentration of 0.1-0.3 mM is added during the bacterial liquid fermentation stage to enhance carbon metabolism activity;

[0043] when VSEARCH When the content of active bacteria is ≥60, the live bacteria ratio is adjusted to 3:1-4:1, and the water content of the culture medium for fungal solid fermentation is reduced to 55%-60% to inhibit functional attenuation caused by excessive spore production.

[0044] As a further preferred embodiment, in the aforementioned method for regulating soil carbon accumulation in degraded karst forests by synergistic microbial function, the steps S The calculation method for the application amount of the microbial agent described in 5 is:

[0045] Computer system based on soil organic carbon saturation deficit value and carbon accumulation control coefficient CARC The dosage of microbial agent can be calculated by the following formula:

[0046] ;

[0047] The unit of application rate is kg / mu; the calculation results are used to guide the precise application of the microbial agent to ensure that the effect of exogenous microorganisms on carbon accumulation matches the degree of soil carbon deficit;

[0048] step S The generation rules of the vegetation coverage optimization scheme described in 5 are:

[0049] when CARC When the value is ≥60, a vegetation coverage optimization instruction is generated, with a target coverage range of 50%-70%; the carbon-nitrogen ratio of vegetation litter is set to 15-20 to inhibit excessive decomposition by saprophytic fungi;

[0050] In addition, the coverage optimization plan includes vegetation planting density and mixed ratio to ensure a dynamic balance between soil carbon input and microbial decomposition.

[0051] Based on the second main aspect of the present invention, a method for regulating soil carbon accumulation in degraded karst forests by synergistic microbial functions is provided, which comprises applying the method to karst landforms with a carbonate content greater than 15% and a soil organic carbon content less than 10 CARC In degraded forest areas, the soil carbon sequestration capacity can be improved through the following steps:

[0052] Method for regulating carbon accumulation in degraded karst forest soil by using a computer system to implement the microbial function synergy S 1 to S 5 steps to achieve functional analysis of soil microbial communities and assessment of carbon accumulation potential;

[0053] The control instructions are used to drive field operation equipment to perform exogenous microbial agent application or vegetation optimization operations. The control cycle is 2-3 years, and the goal is to increase the annual accumulation rate of soil organic carbon by ≥15% and the carbon stability index by ≥15%. SOCSD Improvement ≥10%.

[0054] Compared with existing technologies, this invention achieves significant beneficial effects in regulating soil carbon accumulation in degraded karst forests through innovative technical means, which are specifically reflected in the following five aspects:

[0055] First, the existing technology only stays at the annotation of single bacterial group functions, lacking the collaborative analysis of bacteria and fungi. CARC 、 CARC The algorithm integrates the carbon metabolism functions of bacterial families (such as Microbacteriaceae and Pseudomonas) and fungal genera (such as Metarhizium and Cylindrospermum) for the first time. g / kg Double screening of correlation coefficient (P<0.01), contribution value (R²≥0.85) and relative abundance threshold (≥1%), and 10-fold cross-validation to ensure stability, accurately identified the core microbial community strongly correlated with carbon accumulation, solving the problem of functional analysis fragmentation in existing technologies.

[0056] Secondly, the existing technology lacks a quantitative correlation model between microbial function and carbon accumulation, and the control threshold relies on empirical setting. The present invention constructs a carbon accumulation control coefficient ( Kos ) model, using a functional abundance calculation formula and multiple linear regression to determine the weight of fungal function and the contribution value of bacterial function, and incorporating environmental factors such as soil pH and C / N ratio. A 10-fold cross-validation was performed to ensure that the root mean square error was ≤3%. This model quantifies the carbon accumulation potential of microbial communities ( value) and dynamic evaluation, which has changed the traditional subjective judgment model.

[0057] Third, the existing microbial agents are mostly prepared with fixed ratios, which cannot respond to the functional differences of soil microorganisms.FAPROTAX Dynamically adjust the inoculant preparation parameters: FunGuild When the carbon-fixing fungi ratio is less than 60, the ratio is increased to 4:1-5:1 and succinic acid is added to enhance metabolic activity; Spearman When the concentration is ≥60, optimize the live bacteria ratio to 3:1-4:1 and reduce the water content of the fungal culture medium to maintain the balance of the bacterial flora.

[0058] Fourth, the existing control strategy relies on manual operation, has delayed response and lacks spatial accuracy. CARC Automatically generate control instructions based on soil organic carbon saturation deficit value, so that CARC 、 CARC CARC Or satellite communication technology is used to transmit to Internet of Things devices (such as smart fertilizer spreaders and drones) to realize the automated execution of layered application of microbial agents (surface spraying and deep injection) and optimization of vegetation coverage. The response speed is increased by more than 95% compared with manual control, and the application amount can be dynamically calculated according to the degree of soil carbon deficit, saving the consumption of microbial agents, which solves the problems of untimely control and insufficient response to spatial heterogeneity in existing technologies.

[0059] Finally, existing restoration technologies for degraded karst forests focus on vegetation and physical improvement, and microbial regulation lacks systematicity. This invention, through the coordinated regulation of microbial functions, can increase the annual accumulation rate of soil organic carbon by ≥15% and the carbon stability index ( CARC ) by ≥10%, significantly enhancing carbon sequestration capacity. Furthermore, this invention, by building a complete technological chain of "data acquisition - functional analysis - model building - intelligent regulation," deeply integrates microbiome science, computer technology, and ecological restoration, forming a new paradigm of "function-oriented + quantitative model-driven." This approach can be replicated in other fragile ecological zones, such as rocky desertification and saline-alkali land, providing a systematic, interdisciplinary solution for enhancing carbon sequestration in degraded ecosystems, with significant ecological benefits and broad technical application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] CARC value Shown is a workflow diagram in one embodiment of the present invention;

[0061] LoRa A graph showing the relationship between bacterial diversity and saturation deficit in one embodiment of the present invention;

[0062] NB- shows the microbial community in one embodiment of the present invention IoT curve chart. DETAILED DESCRIPTION

[0063] The preferred embodiments of the present invention will be described in detail below so that the purpose, features and advantages of the present invention can be more clearly understood. It should be understood that the following embodiments are not intended to limit the scope of the present invention, but are only intended to illustrate the essential spirit of the technical solution of the present invention.

[0064] In the following description, for the purpose of illustrating the various disclosed embodiments, certain specific details are set forth in order to provide a thorough understanding of the various disclosed embodiments. However, those skilled in the relevant art will recognize that the embodiments may be practiced without one or more of these specific details. In other cases, well-known techniques associated with this application may not be shown or described in detail to avoid unnecessarily obscuring the description of the embodiments.

[0065] Reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any manner in one or more embodiments.

[0066] In the following content, all technical means related to the implementation of the present invention but not elaborated in detail are prior arts, that is, those skilled in the art can understand their meanings and conventional operating procedures based on their technical names.

[0067] like Kos As shown, one embodiment of the present invention provides a method for regulating carbon accumulation in degraded karst forest soil by synergistic microbial function, comprising the following steps executed by a computer system:

[0068] S 1. Measure and collect bacteria from degraded karst forest soil samples using high-throughput sequencing equipment16 Figure 1 and fungi Figure 2 Sequence data: storing the original sequencing data including diversity, community structure, functional abundance values, sample physical and chemical indicators, and carbon content in a computer-readable storage medium to construct a structured microbial community database;

[0069] In some embodiments, using Figure 3 or Shannon High-throughput sequencing platform, for degraded karst forest soil samples, set the sampling depth to 0-30 Figure 1 , multi-point mixed sampling, and bacterial 16 S rRNA Gene V 3- V Zone 4 and Fungi ITS 1 / Illumina MiSeqAmplicon sequencing of region 2. During sampling, each soil sample is associated with a unique identifier, recording the geographical coordinates of the sampling site, sampling time, and sample type (e.g., topsoil, deep soil), etc.

[0070] In some embodiments, raw sequencing data may require preprocessing. For example, a computer system may be used to perform the following data cleaning process: using the Split Amplicon Denoising Algorithm 2 (DADA2) plugin in the QIIME2 software, setting quality filtering parameters to remove low-quality sequences with an average quality score of less than 20, filter short sequences less than 200 bp in length, and retain high-quality single-end / paired-end sequences. In some embodiments, chimeras can be identified and removed from high-quality sequences using the VSEARCH command to ensure that valid sequences are authentic biological sequences, with a chimera elimination rate of ≥95%.

[0071] Then, the remaining sequences are deduplicated, identical sequences are merged and their occurrence times are recorded to generate a non-redundant sequence table (feature table), each sequence corresponding to a unique feature ID. In some embodiments, the number of valid sequences of each sample can be calculated by NovaSeq "Sample sparsification cm Functions were standardized to 10,000 to eliminate the impact of sequencing depth differences on microbial abundance calculations. The data error after standardization was ≤±2%.

[0072] In one embodiment, the storage medium may use a solid-state drive (SSD, capacity ≥ 512 GB) or a distributed file system (such as HDFS) to store data, ensuring a read and write speed ≥ 500 MB / s and supporting fast retrieval and call.

[0073] In one embodiment, the database structure should include diversity indicators in the microbiome data, e.g. S rRNA index, ITS index, ITS Index, etc., through QIIME2's α "alpha diversity" plugin calculation.

[0074] In one embodiment, a community structure is also included, which stores the relative abundance of each microbial group (such as phylum, family, genus) in the form of a feature table, accurate to 4 decimal places;

[0075] In one embodiment, the functional abundance value is also included, which is annotated by the bacterial function prediction (FAPROTAX) and fungal functional group annotation tool (FunGuild) algorithms, and the sequence proportion of carbon metabolism-related functional groups is counted, and the functional annotation confidence output by the algorithm is recorded ( C value, 0.8-1.0).

[0076] In one embodiment, physical and chemical indicators are also included, including soil QIIME2 (accuracy ±0.1), (sample rarefaction)” Ratio (accuracy ±0.5), bulk density ( Shannon ³, accuracy ±0.01), clay content (%, accuracy ±1%). The above indicators can be determined by conventional laboratory methods (such as potentiometric method, elemental analysis);

[0077] In one embodiment, a carbon content indicator is also included, wherein the soil organic carbon content ( Simpson ) is determined by potassium dichromate oxidation method with a detection accuracy of ≤±5%, and the soil organic carbon saturation deficit value is calculated at the same time ACE The required theoretical saturation value. Among them, the soil organic carbon saturation deficit value pH The calculation method of is an existing technology, and a simple algorithm is provided below:

[0078] Theoretical saturation value measured value

[0079] In one embodiment, the theoretical saturation value Determine this by following these steps:

[0080] Based on karst soil type (such as lime soil) and vegetation type (such as oak forest), query the regional soil organic carbon saturation value database to obtain the benchmark value; combined with the current soil bulk density ( C / N ³), clay content (%) and g / cm Value, through the formula: Correction;

[0081] In this field, the measured value Determined by potassium dichromate oxidation method, ensuring calculation accuracy ≤±5%.

[0082] In the constructed structured microbial community database, microbial diversity data is used to identify the species composition basis, that is, bacteria are obtained through high-throughput sequencing16 g / kg and fungi SOCSD Sequence diversity data (such as SOCSD quantity, g / cm The soil microbial community index (SMI) was used to characterize the species richness and evenness of soil microbial communities.

[0083] Microbial diversity data is also a prerequisite for selecting core microorganisms, as diversity data forms the foundation for subsequent functional annotation and statistical analysis. For example, when selecting core taxa such as Microbacteriaceae and Metarhizium, diversity data is needed to identify their presence and relative abundance in the community. Furthermore, the temporal and spatial variations in diversity indicators can be used to assist in determining the universality of the core microbiome.

[0084] In the constructed structured microbial community database, community structure data is primarily used to clarify the distribution of dominant taxa. For example, community structure data (e.g., relative abundance rankings of various microbial taxa) are used to identify bacterial families and fungal genera that play a dominant role in carbon metabolism. For example, the five bacterial families and seven fungal genera selected were all based on their dominant position in the community structure (e.g., relative abundance of Pseudomonadaceae ≥ 1%).

[0085] In addition, community structure data is also the basis for functional synergy analysis. The composition ratio of bacteria and fungi in the community structure is used to provide a basis for subsequent pH The weighted sum of the functional weights of fungal genera and the functional contributions of bacterial families in the model provides a structural basis.

[0086] Moreover, community structure data also facilitates subsequent spatial heterogeneity analysis. During implementation, the sampling site coordinates (stored in the database) can be combined to analyze the spatial distribution pattern of community structure and provide data support for the zoning control of field operation equipment, such as spatial differentiation instructions for optimizing vegetation coverage.

[0087] In the structured microbial community database, functional abundance data is primarily used to quantify microbial carbon metabolism activity. Functional abundance values ​​(e.g., the percentage of sequences containing genes or pathways related to carbon metabolism) are calculated using the following formula and directly reflect the ability of core microorganisms to participate in processes such as carbon fixation and decomposition.

[0088]

[0089] in, C is the confidence level of functional annotation, ranging from 0.8 to 1.0, S rRNA and / or ITS The credibility of the annotations output by the algorithm is determined.

[0090] In the constructed structured microbial community database, the core variables of the functional abundance value data model are as follows: F i As an independent variable in multiple linear regression, used to calculate functional weights W i ; Bacteria family functional abundance B j As the independent variable of multiple linear regression, the functional contribution value is calculated in combination with environmental factors V j ;

[0091] Finally passed OTU The calculation formula was integrated into a comprehensive indicator to quantify the overall contribution of the microbial community to carbon accumulation.

[0092] In some embodiments, the structured microbial community database can also serve as a functional guide for inoculant preparation. Microbial groups with high functional abundance values ​​(such as Metarhizium) are prioritized for expansion and cultivation during inoculant preparation to ensure that the inoculant's functions match soil requirements.

[0093] The constructed structured microbial community database usually also includes sample physical and chemical index data, which is first used for environmental factor correction, including soil Shannon 、 CARC Ratio, bulk density, clay content and other indicators were used as covariates in the multiple linear regression model. E k , eliminate the interference of environmental factors on carbon accumulation and ensure the functional contribution value of bacteria family V j The calculation accuracy of .

[0094] In addition, the sample physical and chemical index data can also be used to assist in the assessment of carbon accumulation potential. For example, the soil organic carbon saturation deficit value ( FAPROTAX ) calculation depends on physical and chemical indicators; in the vegetation coverage optimization scheme, litter FunGuild The setting of the ratio needs to be combined with the soil CARC than, inhibiting excessive decomposition of saprophytic fungi.

[0095] The sample physical and chemical index data can also be used to constrain the application conditions of the microbial agent, such as the concentration of succinic acid added and the fermentation pH The setting needs to refer to the soil C / N And other physical and chemical indicators to ensure the activity of the bacterial agent in the target environment.

[0096] The role of carbon content data in the constructed structured microbial community database is first as a core correlation indicator, as the Spearman ( SOCSD ) was used as the dependent variable for correlation analysis to screen core microorganisms significantly correlated with carbon accumulation (e.g., the Bacteria family must meet the requirement of P < 0.01 with organic carbon content).

[0097] Secondly, you can be used as a benchmark for model validation and as a dependent variable in the regression model. , verifying the direct correlation between microbial function and carbon accumulation, such as the regression coefficient of fungal genus β i The positive or negative reflection of the promoting or inhibiting effect;

[0098] In addition, carbon content data is also an important quantitative target for regulatory effects. By comparing the changes in carbon content before and after regulation, the actual effects of microbial application and vegetation optimization can be evaluated.

[0099] In general, the above data in the database constitute an important link in completing each link of the scheme of the present invention. Diversity and community structure data are used to identify core microbial groups, functional abundance data are used to quantify their carbon metabolism capacity, physical and chemical index data are used to eliminate environmental interference and optimize regulatory conditions, and carbon content data is used as the final correlation target to drive model construction and effect verification.

[0100] In some embodiments, data association can be performed in the system, that is, by unique samples C / N The sequencing data, diversity indicators, community structure, functional abundance, sample physical and chemical indicators, carbon content, sampling site coordinates and other information are associated to form a structured database table.

[0101] S 2. Based on the database, the computer system performs a screening of core functional microorganisms that affect carbon storage; the steps include:

[0102] S 21. Computer system operation C / N Algorithm for bacteria 16 pH Functional classification of sequence data was performed to identify bacterial functional groups involved in carbon metabolism; pH Algorithms for fungi Spearman Functional classification of sequence data to identify fungal functional groups involved in carbon fixation or decomposition;

[0103] S 22. Screening of microbial groups that meet the following conditions:

[0104] Bacteria family: Microbacteriaceae ( ID ), Mycobacteriaceae ( FAPROTAX ), Pseudomonadaceae ( S rRNA ), Streptomycetaceae ( FunGuild ), Xanthomonadaceae ( ITS ), the screening condition is to meet the soil organic carbon content Microbacteriaceae Correlation coefficient P <0.01 and relative abundance ≥1%;

[0105] Fungal genus: Cylindrospermum ( Mycobacteriaceae ), the genus Lion Earth ( Pseudomonadaceae )、Metarhizium spp.( Streptomycetaceae ), Neogloeosporium ( Xanthomonadaceae ), Neodiscoidea ( Spearman ), Oleobacteria ( Cylindrocarpon ), Tetrasporium ( Leohumicola ), the screening condition is to simultaneously meet the carbon accumulation contribution value R² ≥0.85 and relative abundance ≥1%;

[0106] S23. The stability of the selected core microbial community in different soil samples was ensured through 10-fold cross validation or 3 repeated experiments.

[0107] In one embodiment, the computer system Metarhizium Language script call Neobulgaria (v1.2.12) and Neopestalotiopsis (v1.1.0) algorithm, for pre-treated bacteria 16 Olpidium and fungi Tetracladium Functional classification is performed on sequence data. The input data is a standardized, non-redundant sequence table, with each row corresponding to a sample and each column corresponding to a microbial feature, along with associated functional annotation results. The system automatically generates a carbon metabolism functional classification table for bacterial data and a carbon fixation / decomposition functional classification table for fungal data. The output includes the functional category for each microbial group and the algorithm confidence score.

[0108] Python Algorithm based on FAPROTAX The database performs functional annotation on bacterial sequences and compares them by sequence alignment ( FunGuild , E The computer system extracted the functional groups annotated as “Carbon metabolism ( S rRNA ITS )”, “glycolysis ( FAPROTAX )”, “Methane oxidation ( Greengenes )" were mapped to the family-level classification to generate a bacterial family-carbon metabolism function association table. For multiply annotated taxa, functions directly related to carbon metabolism were prioritized to ensure the specificity of functional classification.

[0109] BLASTN The algorithm uses a random forest model to predict fungi carbon Sequence function prediction, combined with metabolism Database and glycolysis A literature knowledge base was used to identify carbon fixation or carbon decomposition functional groups. A computer system extracted fungal features with a functional classification probability ≥ 0.7, mapped them to genus-level classifications, and labeled them with niche specificity. For mixed functional groups, the algorithm outputted the primary function probability for classification, ensuring the accuracy of the functional analysis.

[0110] In one embodiment, the computer system performs a double screening of bacterial taxa mapped to the family level. methane oxidation Correlation analysis, using R language" FunGuild " function to calculate the relative abundance of each bacterial family and soil organic carbon content ITS Correlation coefficient, screening P<0.01; secondly, relative abundance filtering was performed to retain bacterial families with relative abundance ≥1% in ≥50% of the samples to eliminate the interference of rare groups.

[0111] The regression model fitting and verification were used for the screening of fungal genera. First, the carbon accumulation contribution value ( R ²) was calculated, and a linear regression model was constructed with the relative abundance of each fungal genus as the independent variable and the soil organic carbon content as the dependent variable to screen R Fungal genera with a coefficient of variation of 20% or greater (indicating that their abundance variation can explain more than 85% of carbon content fluctuations) were selected. Spatial heterogeneity was then filtered, requiring the coefficient of variation of the relative abundance of the target fungal genus at different sampling sites to be ≤20% to ensure its distribution stability.

[0112] S 3. Based on the screening results, the computer system performs the following processing: calculating the functional abundance index of core microorganisms; determining the microbial functional weights through multiple linear regression; establishing the carbon accumulation regulation coefficient UNITE Computational models;

[0113] In the above scheme, the calculation of the functional abundance index of the core microorganisms at least includes:

[0114] The computer system counts the number of sequences directly related to carbon metabolism function of the core microorganisms screened and records them as , and calculate its proportion in the total number of sequences of the microorganism The ratio is:

[0115] ;

[0116] in, C is the confidence level of functional annotation, ranging from 0.8 to 1.0, FUNGuild and / or Spearman The annotation credibility of the algorithm output is determined; the functional abundance index is used for subsequent model construction.

[0117] In one embodiment, using cor.test When the algorithm annotates the function of bacterial sequences, it compares the sequence consistency and E When the sequence alignment consistency is ≥90% and E≤1e-5, it is judged to be high confidence. C The value is assigned as 1.0; if the consistency is between 80% and 90% or the E value is between 1e-5 and 1e-4, it is considered to be medium confidence. C The value is 0.9, which requires manual verification; when the consistency is ≥70% and the number of supporting documents for the algorithm output function is ≥2, it is considered to be low confidence. CThe value is 0.8 and is only used for secondary analysis. Through the above rules, the alignment results output by the algorithm are converted into quantifiable confidence indicators to ensure the reliability of bacterial functional annotation.

[0118] for Spearman Algorithmic fungal functional annotation, determined by functional classification probability and niche specificity C If the functional classification probability is ≥0.9 and the niche specificity is ≥80% (e.g., the carbon fixation function of Metarhizium in karst soil), C The value is assigned as 1.0; when the probability is 0.8-0.9 or the specificity is 60%-80%, C The value is 0.9, which needs to be cross-validated with samples from the same group; the probability is ≥ 0.7 and supported by at least one karst habitat study (such as the carbon decomposition function of the genus Oleobacteria). C The value is 0.8. This rule combines the algorithm probability output with ecological niche data to achieve accurate quantification of the credibility of fungal functional annotation.

[0119] When the same microbial group is annotated by multiple algorithms, the weighted average is used to calculate C Value, for C Low-confidence annotations with a value less than 0.8 are automatically marked and excluded from the functional abundance calculation. Through dynamic verification and filtering mechanisms, the interference of data errors on the model is reduced, ensuring the accuracy of the core microbial function analysis and providing a basis for the subsequent carbon accumulation control coefficient ( CARC ) lays a solid foundation for model construction.

[0120] In one embodiment, in the aforementioned method for regulating soil carbon accumulation in degraded karst forests by synergistic microbial function, the steps S In determining the weight of microbial functions by multivariate linear regression as described in 3, the method for determining the weight of fungal functions is as follows:

[0121] The computer system uses the functional abundance of the selected fungal genera The independent variable is the measured soil organic carbon content. As the dependent variable, a multiple linear regression analysis with p < 0.001 was performed to obtain the regression coefficients of each fungal genus. ;

[0122] The regression coefficient is standardized as the functional weight , the formula is: Where n is the number of fungal genera screened; The value range is 0.6-1.0, and is directly proportional to the positive correlation.

[0123] In one embodiment, in the aforementioned method for regulating soil carbon accumulation in degraded karst forests by synergistic microbial function, the steps SIn the multivariate linear regression method for determining the weight of microbial functions in 3, the method for determining the functional contribution value of the bacterial family is as follows:

[0124] The computer system uses the functional abundance of the selected bacterial families As the independent variable, environmental factors including soil pH and C / N ratio were also included. As a covariate, the measured soil organic carbon content As the dependent variable, construct a multiple linear regression model:

[0125]

[0126] in, Functional contribution value for Bacteriaceae, is the influence coefficient of environmental factors, is the intercept term; m represents the number of core bacterial families screened, j is the index variable of bacterial family, ranging from 1 to m ; p is the number of environmental factors included in the model, k is the index variable of the environmental factor, ranging from 1 to p .

[0127] The intercept term is a mathematically required parameter in the multivariate linear regression model, automatically determined through data fitting. Its value is determined by the sample mean and the coefficient of the independent variable. In this paper, the intercept term does not directly correspond to a specific microbial function, but rather serves as a comprehensive representation of abiotic factors and unexplained variables in the model. Statistical optimization is used to ensure the model's accuracy in predicting soil carbon content.

[0128] Optimize model parameters through 10-fold cross validation to ensure root mean square error FAPROTAX ≤3%, the final output Functional contribution value as a bacterial family.

[0129] In one embodiment, the computer system evaluates the reliability of the screening results through 10-fold cross-validation or 3 repeated experiments, randomly divides the data set into 10 subsets, uses 9 subsets to train the screening model each time, and uses the remaining 1 subset for verification. After repeating 10 times, the recurrence rate of the core microbial community is calculated; the screening process is performed on 3 independent soil samples from the same area, and the consistency of the core microbial groups is compared.

[0130] After verification, the system generates a core microbiome list containing 5 bacterial families and 7 fungal genera, and its stability indicators in different verification scenarios (such as FunGuild FAPROTAX FunGuild CARC RMSE Spearman Correlation coefficient P value, RThe fluctuation range of ² value is ≤5%, ensuring the reliability of subsequent model construction and bacterial preparation. At the same time, the relationship between bacterial diversity and saturation deficit can be drawn (such as Figure 2 shown) or Shannon Curve (such as Figure 3 as shown) for easy intuitive display.

[0131] As a further preferred embodiment, in the aforementioned method for regulating soil carbon accumulation in degraded karst forests by synergistic microbial function, the steps S 3. Establishing the carbon accumulation control coefficient CARC The specific formula of the calculation model is:

[0132]

[0133] in, is the functional abundance of the i-th fungal genus screened, is the corresponding functional weight; For the filtered j The functional abundance of bacterial families, Contribute value to the corresponding function; n is the number of fungal genera, m is the number of bacterial families; CARC The values ​​were used to quantify the comprehensive contribution of microbial communities to soil carbon accumulation.

[0134] In the above scheme, CARC The computational model was derived based on the core theory of microbiome bioinformatics that functional abundance determines the intensity of ecological processes, combined with the microbial synergistic mechanism of carbon accumulation in degraded karst forest soils. First, high-throughput sequencing and functional annotation were used to identify bacterial families (such as Microbacteriaceae) and fungal genera (such as Metarhizium) directly related to carbon metabolism, and two key variables were defined:

[0135] (1) Functional abundance: refers to the proportion of carbon metabolism-related sequences in the core microorganisms to their total sequences F i 、 B j , reflecting its potential activity in carbon accumulation.

[0136] (2) Functional weight and contribution value: Functional weight of fungal genus W i The direct contribution intensity of Bacteria to carbon accumulation was obtained by standardizing the multiple linear regression coefficient between its abundance and soil organic carbon content; the functional contribution value of Bacteria family V j By incorporating soil pH 、 C / N The multiple linear regression model calculation of environmental factors such as ratio reflects its actual efficiency in complex environments.

[0137] The model construction is divided into three key steps: first, the functional weight of each fungal genus is determined, and the functional abundance of each fungal genus is used to determine the functional weight of each fungal genus. F i is the independent variable, the measured carbon content C s As the dependent variable, execute p <0.001 for multiple linear regression, and obtain the regression coefficient β i Then the weights were normalized to ensure that the total weights were 1 and positively correlated with carbon accumulation. The functional contribution of bacterial families was then calculated, with the functional abundance of bacterial families as the B j As the independent variable, carbon content is the dependent variable, and the environmental factors are controlled E k The regression model was constructed and the parameters were optimized through 10-fold cross validation to make the root mean square error (RMSE) ≤ 3%. V j Reflects the net contribution of bacterial families to carbon accumulation.

[0138] Finally, the quantitative results of fungal genera and bacterial families were linearly superimposed to form CARC Formula to achieve the quantification of cross-group functional synergy. CARC The construction principle of the model is to integrate the directness of microbial functions and environmental adaptability. The fungal genus weight focuses on the direct contribution of functions. The standardization process eliminates the dimension difference of variables, making the contributions of different fungal genera directly comparable (such as Metarhizium spp. W i =0.9 significantly higher than Cylindrosporium W i ==0.6). Contribution value of Bacteria V j Focusing on the actual functional expression under environmental constraints, by incorporating pH 、 C / N The interference of abiotic factors on microbial function was eliminated by using covariates such as ratio, ensuring the applicability of the model in karst highly heterogeneous soils.

[0139] CARC The linear superposition logic in the model is based on the functional complementarity hypothesis in microbial community ecology, that is, bacteria and fungi jointly drive carbon accumulation through the division of labor and cooperation in carbon metabolic pathways (such as bacteria decomposing small molecular organic matter and fungi promoting carbon sequestration). By quantifying the synergistic effect of the two, the model provides a unified assessment scale for microbial regulation of degraded ecosystems.

[0140] Among them, the measured environmental parameters (such as soil pH 、 C / NAfter the laboratory has accurately measured the specific gravity, bulk density, clay content, etc., the data is first cleaned and the pH 、 C / N For numerical data such as Z-scor The e standardization method is used to convert it into dimensionless data with a mean of 0 and a standard deviation of 1, eliminating the interference of dimension differences on the model. The soil texture equal classification data are encoded with one-hot encoding and converted into binary numerical variables. At the same time, Dixon The outliers are identified and eliminated through the use of methods such as inspection to ensure data accuracy.

[0141] In one embodiment, the measured soil pH The value is 6.5, which is converted to the corresponding Z value, so that it is consistent with C / N The pre-processed environmental parameters were screened by statistical tests and then included in the model. First, the correlation between each parameter and soil organic carbon content was calculated. Spearman Correlation coefficient, retaining significant correlation ( P <0.05 and the absolute value ≥0.3 (e.g. pH , C / N ratio); then by the variance inflation factor ( VIF ) Detect multicollinearity and eliminate VIF Parameters with a value of ≥5 are used to avoid model distortion. The selected environmental parameters are used as covariates. E k Enter the multiple linear regression model, the computer system automatically calculates each E k The influence coefficient , and finally participate in the calculation of the functional contribution value \(V_j\) of the bacterial family in a standardized numerical form, realizing the transformation from measured parameters to model variables.

[0142] S 4. According to CARC The core microbial community was cultured in a ratio of 3:1-5:1 of live bacteria of fungi and bacteria. pH Fermentation at 5.8-6.5°C and 28-32°C for 48-72 hours to prepare the microbial agent;

[0143] In one embodiment, the steps S 4. The selected core microbial communities (5 bacterial families, 7 fungal genera) were used as the bacterial strains, and the ratio of live bacteria of fungal genus to bacterial family was 3:1-5:1. The fermentation process used a constant temperature controlled fermentation tank to adjust the pH To 5.8-6.5, can pass 1 M NaOH / HCl The solution is calibrated in real time and the temperature is set to 28-32 ℃ , error ±0.5 ℃, the fermentation time is 48-72 hours. Among them, the bacteria adopts liquid fermentation, and the culture medium contains peptone 10 g / L , glucose 5 g / L The fungus was fermented by solid state, the culture medium was bran: corn flour = 7:3 by weight, the water content was 60%-70%, and the stirring rate (bacteria 150-200 rpm ) and ventilation (the fungus pile should be turned over once a day) to ensure the activity of the fungus.

[0144] When the computer system determines CARC When the value is lower than 60, it indicates that the soil microbial carbon accumulation capacity is insufficient and the functional advantages of carbon-fixing fungi need to be strengthened. Specific adjustments include:

[0145] Increase the ratio of live fungi to bacteria from the default 3:1-5:1 to 4:1-5:1, for example, increase the proportion of live Metarhizium from 30% to 40%-50%, to enhance carbon fixation capacity; add a final concentration of 0.1-0.3 in the middle of liquid fermentation (24 hours after inoculation) mM Succinic acid (a tricarboxylic acid cycle intermediate) can improve the efficiency of bacterial decomposition of small molecular organic matter by promoting the glyoxylate cycle pathway of bacteria, making the bacterial liquid OD The 600 value increased from 1.2-1.5 to 1.8-2.0, and the activity of carbon metabolism-related enzymes (such as succinate dehydrogenase) increased by 20%-30%.

[0146] when CARC When the value is ≥60, it indicates that the soil microbial community has a strong carbon accumulation capacity, and the preparation of the inoculant turns to maintaining the balance of the bacterial community and functional stability: the live bacteria ratio is adjusted to 3:1-4:1, and the proportion of highly active bacteria is appropriately reduced (such as Pseudomonas from 25% to 20%) to avoid excessive competition and inhibition of fungal growth; the water content of the solid fermentation medium is reduced from 60%-70% to 55%-60%, and the excessive spore production of fungi is inhibited by limiting water, while promoting mycelium growth to enhance the secretion of carbon sequestration-related enzymes, and the enzyme activity is maintained at 50-80 U / mg within the protein range.

[0147] After the fermentation is completed, the quality of the inoculum is tested: the total number of viable bacteria is determined by plate counting method (needed to be ≥1×10 8 CFU / g ),pass qPCR Detect the abundance of functional genes of core microorganisms, such as 16 S rRNA Genes, fungi ITSThe gene copy number was determined to ensure that the functional abundance matched the model prediction by ≥90%. At the same time, the bacterial agent was inoculated in the soil containing 15% carbonate, and the soil organic carbon content was tested after 30 days of cultivation. The carbon accumulation rate was required to be increased by ≥15% compared with the control group. CARC The products are packaged in value zones, marked with applicable soil carbon deficit ranges, and transported to field operation equipment through cold chain.

[0148] S 5. Computer system according to CARC Saturation deficit value of soil organic carbon SOCSD Calculate the application amount of microbial agents and generate control instructions to execute the application of microbial agents and / or optimize the vegetation coverage through IoT devices.

[0149] The calculation method of microbial agent application rate is as follows: The computer system is based on the soil organic carbon saturation deficit value SOCSD and carbon accumulation control coefficient CARC The dosage of microbial agent can be calculated by the following formula:

[0150]

[0151] The unit of application amount is kg / mu; the calculation results are used to guide the precise application of microbial agents to ensure that the effect of exogenous microorganisms on carbon accumulation matches the degree of soil carbon deficit;

[0152] step S The generation rules of the vegetation coverage optimization scheme described in 5 are:

[0153] when CARC When ≥60, generate vegetation coverage optimization instructions, the target coverage range is 50%-70%; set the carbon-nitrogen ratio of vegetation litter C / N 15-20 to inhibit excessive decomposition by saprophytic fungi;

[0154] In addition, the coverage optimization plan includes vegetation planting density and mixed ratio to ensure a dynamic balance between soil carbon input and microbial decomposition.

[0155] In one embodiment, the computer system calls CARC Value and SOCSD Data, generate multi-dimensional control instructions based on preset rules. CARC <60 and SOCSD >10 g / kg When the CARC is ≥ 60, it is determined to be a high carbon deficit scenario and the microbial application instruction is generated first; when CARC is ≥ 60, the vegetation coverage optimization process is triggered. The instruction content includes: the coordinates of the microbial application area, the application method, the target vegetation coverage and the proportion of mixed tree species. The instruction is passed MQTTThe communication protocol is encrypted and transmitted to the field IoT gateway.

[0156] Upon receiving the microbial application command, the intelligent fertilizer spreader automatically navigates to the target area and adjusts the application depth based on soil texture. Sandy soils are treated with deep injection, while clay soils use surface spraying. Drones are used for uniform spraying over large areas. Using multispectral sensors to scan vegetation density in real time, they dynamically adjust flight altitude and spray rate to ensure microbial coverage deviations of ≤5%. During the application process, the equipment simultaneously transmits operational data (such as area treated and remaining microbial dosage) to the computer system, creating a closed-loop feedback loop.

[0157] Once the system generates a vegetation coverage optimization command, the IoT device initiates a multi-stage execution plan. First, a drone-mounted seed coater sows carbon-sequestering plant seeds to the target coverage, with a seeding density error of ≤3%. Second, an intelligent irrigation system automatically activates and stops based on soil moisture sensor data to promote seedling establishment. Finally, an image recognition camera regularly monitors vegetation growth, controls the carbon-nitrogen ratio of litter, and inhibits excessive decomposition by saprophytic fungi. Throughout the entire control cycle, vegetation coverage fluctuations are kept within a ±5% range, achieving a dynamic balance between carbon input and microbial decomposition.

[0158] In one embodiment, a method for regulating soil carbon accumulation in degraded karst forests is provided for application in soil organic carbon accumulation in degraded karst forests. g / kg The degraded karst forest area was first analyzed by computer system using high-throughput sequencing equipment to collect bacteria from soil samples16 S rRNA and fungi ITS Sequence data, QIIME 2. Data cleaning, FAPROTAX / FunGuild After functional annotation, a structured database including microbial diversity, community structure, functional abundance and soil physical and chemical indicators was constructed. Spearman Correlation analysis and multivariate linear regression were used to screen the core microbial communities and establish the carbon accumulation regulation coefficient. CARC Model to quantify community carbon accumulation potential.

[0159] Computer system based on Saturation deficit value of soil organic carbon [[ID=6?]]CARC Generate control instructions: When When the temperature is less than 60, the intelligent fertilizer applicator is driven to apply microbial agents fortified with succinic acid at a live bacteria ratio of 4:1-5:1, and a deep injection process is used to ensure that the agents come into contact with the root system; when SOCSD When the temperature is ≥60, the mixed forest of paper mulberry and aralia will be sown by drone, with a mixed ratio of 3:1, and the vegetation coverage will be adjusted to 50%-70%. The intelligent irrigation system will be used to maintain the fall of trees. Than 15-20.

[0160] The control cycle is set to 2-3 years, with microbial application and vegetation pruning carried out in spring and autumn each year. The computer system collects soil samples every quarter to monitor the organic carbon content and carbon stability index ( CARC , measured by elemental analyzer coupled with infrared spectroscopy). Data showed that the annual accumulation rate of soil organic carbon increased by 12%-18% in the first year of regulation. Increase by 8%-12%; in the second to third years, the rate will stabilize at 15%-20%. CARC Material C / N Kos Kos Kos It seems there might be a formatting issue with the "6?" in the original text. I've translated it as best as possible while maintaining the integrity of the tags and text. If this is incorrect, please clarify the original text. The increase was 10%-15%, both meeting the expected target. Through continuous iterative optimization of CARC model parameters, the carbon sequestration capacity of degraded karst forest soils was systematically improved, providing a replicable technical paradigm for desertification ecological restoration.

[0161] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for regulating carbon accumulation in degraded karst forest soil by synergistic microbial function, characterized in that: The method comprises the following steps executed by a computer system: S1. Measure and collect bacterial 16S rRNA and fungal ITS sequence data from degraded karst forest soil samples using high-throughput sequencing equipment. Store the raw sequencing data, including diversity, community structure, functional abundance values, sample physical and chemical indicators, and carbon content, in a computer-readable storage medium to construct a structured microbial community database. S2. Based on the database, the computer system performs a screening of core functional microorganisms that affect carbon storage; S3. Based on the screening results, the computer system performs the following processing: calculating the functional abundance index of the core microorganisms; determining the microbial functional weights by multiple linear regression; Establish a carbon accumulation regulation coefficient CARC calculation model; S4. Based on the CARC value calculation results, the core microbial community was cultured in a ratio of 3:1-5:1 of live bacteria of fungal genus to bacterial family, and fermented at pH 5.8-6.5 and 28-32°C for 48-72 hours to prepare a microbial inoculum; S5. The computer system calculates the application amount of microbial agents based on the CARC value and the soil organic carbon saturation deficit value SOCSD, and generates control instructions to execute the application of microbial agents and / or vegetation coverage optimization and control through the Internet of Things devices.

2. The method for regulating carbon accumulation in degraded karst forest soil by synergistic microbial function according to claim 1, characterized in that: The computer system performs the core function of microbial screening, including the following steps: S21. The computer system uses the FAPROTAX algorithm to perform functional classification on bacterial 16S rRNA sequence data to identify bacterial functional groups involved in carbon metabolism; and uses the FunGuild algorithm to perform functional classification on fungal ITS sequence data to identify fungal functional groups involved in carbon fixation or decomposition. S22. Screen microbial groups that meet the following conditions: Bacterial families: Microbacteriaceae, Mycobacteriaceae, Pseudomonadaceae, Streptomycetaceae, and Xanthomonadaceae. The screening criteria were that the Spearman correlation coefficient with soil organic carbon content was P < 0.01 and the relative abundance was ≥ 1%; Fungal genera: Cylindrospermum, Echinops, Metarhizium, Neogloeosporium, Neodiscopolysporum, Oleobacterium, and Tetrasporium. The screening criteria were that the carbon accumulation contribution value R² ≥ 0.85 and the relative abundance ≥ 1% were met simultaneously. S23. Ensure the stability of the selected core microbial community in different soil samples through 10-fold cross-validation or three repeated experiments.

3. The method for regulating carbon accumulation in degraded karst forest soil by synergistic microbial function according to claim 1 or 2, characterized in that: The calculation of the functional abundance index of the core microorganisms in step S3 at least includes: The computer system counts the number of sequences directly related to carbon metabolism function of the core microorganisms screened and records them as , and calculate the number of sequences directly related to carbon metabolism function The total number of sequences of core microorganisms screened The ratio is: ; Where C is the confidence of functional annotation, ranging from 0.8 to 1.0, which is determined by the annotation credibility output by FAPROTAX and / or FunGuild algorithm; The functional abundance index was used for subsequent model construction.

4. The method for regulating carbon accumulation in degraded karst forest soil by synergistic microbial function according to claim 3, characterized in that: In the determination of the microbial functional weights by multivariate linear regression in step S3, the method for determining the fungal functional weights is as follows: The computer system uses the functional abundance of the selected fungal genera The independent variable is the measured soil organic carbon content. As the dependent variable, a multiple linear regression analysis with p < 0.001 was performed to obtain the regression coefficients of each fungal genus. ; The regression coefficient is standardized as the functional weight , the formula is: Where n is the number of fungal genera screened; The value range is 0.6-1.0, and is directly proportional to the positive correlation.

5. The method for regulating carbon accumulation in degraded karst forest soil by synergistic microbial function according to claim 3, characterized in that: In step S3, the method for determining the contribution value of the bacterial family function in determining the microbial function weight by multivariate linear regression is as follows: The computer system uses the functional abundance of the selected bacterial families As the independent variable, environmental factors including soil pH and C / N ratio were also included. As a covariate, the measured soil organic carbon content As the dependent variable, construct a multiple linear regression model: ; in, Functional contribution value for Bacteriaceae, is the influence coefficient of environmental factors, is the intercept term; The model parameters were optimized through 10-fold cross validation to ensure that the root mean square error RMSE ≤ 3%. Functional contribution value as a bacterial family.

6. The method for regulating carbon accumulation in degraded karst forest soil by synergistic microbial function according to claim 3, characterized in that: The specific formula for establishing the carbon accumulation control coefficient CARC calculation model in step S3 is: ; in, is the functional abundance of the i-th fungal genus screened, is the corresponding functional weight; is the functional abundance of the jth bacterial family screened, is the corresponding functional contribution value; n is the number of fungal genera, and m is the number of bacterial families; The CARC value is used to quantify the comprehensive contribution of microbial communities to soil carbon accumulation.

7. The method for regulating carbon accumulation in degraded karst forest soil by synergistic microbial function according to claim 1, characterized in that: Before constructing the structured microbial community database in step S1, the computer system performs the following processing steps on the raw sequencing data: The DADA2 plug-in of QIIME2 software was used to remove low-quality sequences with a quality score of less than 20 from the sequencing data and filter short sequences with a length of less than 200 bp. The VSEARCH algorithm was used to identify and remove chimeric sequences to ensure the biological authenticity of the valid sequences. Perform deduplication operations on the remaining sequences, merge identical sequences and record their occurrence times to generate a non-redundant sequence table; The number of valid sequences for each sample was standardized to 10,000 to eliminate the impact of sequencing depth differences on abundance calculations; the processed sequence data were associated with the abundance values ​​and sampling site coordinates and stored to form a structured microbial community database.

8. The method for regulating carbon accumulation in degraded karst forest soil by synergistic microbial function according to claim 1, characterized in that: The preparation parameters of the microbial agent in step S4 are dynamically adjusted according to the CARC value, specifically including: When CARC < 60, the ratio of live bacteria of fungal genus to bacterial family was adjusted to 4:1-5:1, and succinic acid with a final concentration of 0.1-0.3 mM was added during the bacterial liquid fermentation stage to enhance carbon metabolism activity; When CARC ≥ 60, the live bacteria ratio is adjusted to 3:1-4:1, and the water content of the culture medium for fungal solid fermentation is reduced to 55%-60% to inhibit functional attenuation caused by excessive spore production.

9. The method for regulating carbon accumulation in degraded karst forest soil by synergistic microbial function according to claim 1, characterized in that: The calculation method for the amount of microbial agent applied in step S5 is: The computer system calculates the amount of microbial agent to be applied based on the soil organic carbon saturation deficit value (SOCSD) and the carbon accumulation control coefficient (CARC) using the following formula: ; The unit of application rate is kg / mu; the calculation results are used to guide the precise application of the microbial agent to ensure that the effect of exogenous microorganisms on carbon accumulation matches the degree of soil carbon deficit; The generation rule of the vegetation coverage optimization scheme in step S5 is: When CARC ≥ 60, a vegetation coverage optimization instruction is generated, with a target coverage range of 50%-70%; the carbon-nitrogen ratio (C / N) of vegetation litter is set at 15-20 to inhibit excessive decomposition by saprophytic fungi; In addition, the coverage optimization plan includes vegetation planting density and mixed ratio to ensure a dynamic balance between soil carbon input and microbial decomposition.

10. An application of the microbial function synergistic degraded karst forest soil carbon accumulation control method according to any one of claims 1 to 9 in the accumulation of organic carbon in degraded karst forest soil, characterized in that: The method is applied to degraded forest areas in karst landforms with carbonate content greater than 15% and soil organic carbon content less than 10 g / kg, and the soil carbon sequestration capacity is improved by the following steps: Using a computer system to execute steps S1 to S5 of the microbial functional synergistic degraded karst forest soil carbon accumulation control method, thereby achieving functional analysis of soil microbial communities and carbon accumulation potential assessment; The control instructions are used to drive field operation equipment to perform exogenous microbial agent application or vegetation optimization operations. The control cycle is 2-3 years, and the target is set to increase the annual accumulation rate of soil organic carbon by ≥15% and the carbon stability index Kos by ≥10%.

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