Regulation and control method for carbon accumulation of degraded karst forest soil with synergistic microbial functions

Abstract

The invention discloses a microbial function synergistic degraded karst forest soil carbon accumulation regulation and control method, which comprises the following steps: measuring and collecting bacterial and fungal diversity sequence data of a degraded karst forest soil sample through high-throughput sequencing equipment; original sequencing data including diversity, community structures, functional abundance values and the like are stored in a computer readable storage medium, and a structured microbial community database is constructed; based on the database, the computer system performs core function microorganism screening influencing carbon storage; based on the screening result, the computer system performs processes comprising: calculating a functional abundance indicator of the core microorganism; determining a microbial function weight through multiple linear regression; establishing a carbon accumulation regulation coefficient calculation model; and preparing a microbial agent, generating a control instruction containing the application amount of the microbial agent and a vegetation coverage optimization scheme 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 field operation equipment.

Description

Technical Field

[0001] The present invention relates to the field of environmental science and agricultural ecological technology, and particularly relates to a method for microbial co-regulation of soil carbon accumulation in degraded karst forests under the action of calcium carbonate. Background Art

[0002] As an important ecological barrier in the southwestern region of China, the karst forest has a unique ecosystem nurtured by its carbonate geological structure. However, during the long-term degradation process, the characteristics of shallow soil and fragile ecology have gradually emerged. Especially in soil environments where the calcium carbonate content is generally higher than 15%, it poses multiple constraints on soil carbon accumulation. Calcium carbonate not only significantly affects soil pH and pore structure, but also exacerbates the risk of soil organic carbon loss by changing the stability of soil aggregates, thereby leading to a sharp decline in the regional carbon sink function. Soil microorganisms, as the core drivers of the carbon cycle, play a key role in the fixation, transformation, and stabilization of organic carbon in terms of their community structure and function. However, in the karst soil environment dominated by calcium carbonate, the survival and metabolic activities of microorganisms are significantly affected. The alkaline environment caused by the high calcium carbonate content will inhibit the growth of some acidophilic microorganisms and change the microbial community composition. At the same time, the complex interaction between calcium carbonate and soil organic carbon may interfere with the decomposition and synthesis processes of microorganisms on organic carbon, further weakening the soil carbon accumulation ability. Currently, the restoration technologies for degraded karst forests mainly focus on vegetation reconstruction and physical improvement, and there are obvious deficiencies in the regulation of soil microbial communities. At the level of functional analysis of microbial communities, although existing research can identify microbial taxa with the help of high-throughput sequencing technologies (such as 16 S rRNA and ITS S sequencing), it is only limited to the functional annotation of single microbial communities, such as FAPROTAX predicting the functions of bacteria, FunGuild predicting the functions of fungi, lacking in-depth analysis of the cross-group functional synergy between bacteria and fungi. Under the influence of calcium carbonate, the synergistic effects of different microbial taxa on carbon accumulation are more complex, and existing research methods are difficult to reveal the comprehensive contribution mechanism of microbial communities to carbon accumulation. In terms of quantitative analysis, an effective correlation model has not been established among microbial functional abundance, environmental factors, and carbon accumulation. As a key environmental factor, the dynamic relationships among the concentration changes, spatial distribution of calcium carbonate, microbial functions, and carbon accumulation are not clear, resulting in the inability to accurately evaluate the carbon accumulation potential of microbial communities. The setting of regulation thresholds such as the application amount of microbial agents and the target of vegetation coverage still relies on empirical judgment and lacks scientific data support. The traditional application mode of microbial agents and vegetation management mode do not fully consider the soil organic carbon saturation deficit value ( SOCSD), the spatial heterogeneity of microorganisms makes it difficult to cope with the regulatory challenges brought about by soil spatial heterogeneity in karst soils with high calcium carbonate content, resulting in a significant reduction in the regulatory effect of carbon accumulation. In addition, the existing technology fails to deeply integrate microbiome data with computer technology. Facing the complex soil microorganism-carbon accumulation system under the action of calcium carbonate, it is impossible to achieve timely and effective regulation of carbon accumulation in degraded karst forest soils through efficient data processing, model construction, and automated instruction output. Summary of the Invention

[0003] The object of the present invention is to solve the problems of organic carbon loss and decline in carbon sink function caused by shallow soil and fragile ecology in degraded karst forests. By integrating microbiome data through a computer system, screening core functional microorganisms, constructing a quantitative model to generate regulatory strategies, and achieving the technical problem of precisely improving soil organic carbon accumulation and stability.

[0004] Based on the first main aspect of the present invention, a method for regulating carbon accumulation in degraded karst forest soils with microbial function synergy is provided, including the following steps executed by a computer system: S 1. Measure and collect the bacterial 16 S rRNA and fungal ITS sequence data of degraded karst forest soil samples through high-throughput sequencing equipment, store the original sequencing data including diversity, community structure, functional abundance values, and sample physical and chemical indicators and carbon content in a computer-readable storage medium, and construct a structured microbial community database; S 2. Based on the database, the computer system performs screening of core functional microorganisms that affect carbon storage; S 3. Based on the screening results, the computer system performs the following processing: calculate the functional abundance indicators of core microorganisms; determine the microbial function weights through multiple linear regression; establish a carbon accumulation regulation coefficient CARC calculation model; S 4. According to CARC the value calculation result, the core microbial flora is co-cultured at a viable cell ratio of 3:1 - 5:1 of fungal genera to bacterial families, and pH fermented at 5.8 - 6.5 and 28 - 32 °C for 48 - 72 hours to prepare a microbial inoculant; S 5. The computer system calculates the application amount of the microbial inoculant according to CARC the value and the soil organic carbon saturation deficit value SOCSD and generates a control instruction, and executes the application of the microbial inoculant and / or the optimization regulation of vegetation coverage through an Internet of Things device.

[0005] As a further preferred solution, in the aforementioned method for regulating soil carbon accumulation in degraded karst forests with microbial functional synergy, the computer system performs the screening of core functional microorganisms including the following steps: S 21. The computer system runs FAPROTAX the algorithm to classify the functional categories of bacterial 16 S rRNA sequence data and identify the bacterial functional groups involved in carbon metabolism; runs FunGuild the algorithm to classify the functional categories of fungal ITS sequence data and identify the fungal functional groups involved in carbon fixation or decomposition; S 22. Screen the microbial groups that meet the following conditions: Bacterial families: Microbacteriaceae ( Microbacteriaceae ), (Mycobacteriaceae Mycobacteriaceae ), Pseudomonadaceae ( Pseudomonadaceae ), Streptomycetaceae ( Streptomycetaceae ), Xanthomonadaceae ( Xanthomonadaceae ), and the screening conditions are that the correlation coefficient Spearman with soil organic carbon content P < 0.01 and the relative abundance ≥ 1%; Fungal genera: Cylindrocarpon ( Cylindrocarpon ), Leptosphaerulina ( Leohumicola ), Metarhizium ( Metarhizium ), Neolachnum ( Neobulgaria ), Neopestalotiopsis ( Neopestalotiopsis ), Olpidium ( Olpidium ), Tetracladium ( Tetracladium ), and the screening conditions are that the carbon accumulation contribution value R² ≥ 0.85 and the relative abundance ≥ 1%; S 23. Through 10-fold cross-validation or 3 repeated experiments, ensure that the stability of the screened core microbial flora in different soil samples passes.

[0006] As a further preferred solution, in the aforementioned method for regulating soil carbon accumulation in degraded karst forests with microbial functional synergy, step S 3, the calculated functional abundance indicators of the core microorganisms at least include: The computer system counts the number of sequences directly related to carbon metabolism functions of the screened core microorganisms and records it as , and calculates the proportion of the number of sequences directly related to carbon metabolism functions in the total number of sequences of the screened core microorganisms , and the formula is: ; Among them, C is the confidence level of functional annotation, with a value range of 0.8 - 1.0, determined by FAPROTAX and / or FunGuild the annotation credibility output by the algorithm; The functional abundance index is used for subsequent model construction.

[0007] As a further preferred solution, in the aforementioned method for regulating soil carbon accumulation in degraded karst forests with microbial functional synergy, step S In determining the microbial functional weights through multiple linear regression in step 3, the method for determining the functional weight of fungal genera is as follows: The computer system uses the screened functional abundance of fungal genera as the independent variable, and the measured soil organic carbon content as the dependent variable, performs multiple linear regression analysis with p < 0.001, and obtains the regression coefficients of each fungal genus ; After standardizing the regression coefficients, they are used as functional weights , and the formula is: where n is the number of screened fungal genera; has a value range of 0.6 - 1.0, and is directly proportional to the positive correlation with .

[0008] As a further preferred solution, in the aforementioned method for regulating soil carbon accumulation in degraded karst forests with microbial functional synergy, step S In determining the microbial functional weights through multiple linear regression in step 3, the method for determining the functional contribution value of bacterial families is as follows: The computer system uses the screened functional abundance of bacterial families as the independent variable, and simultaneously incorporates environmental factors including soil pH and C / N ratio as covariates, and the measured soil organic carbon content as the dependent variable, and constructs a multiple linear regression model: Measured ; Among them, is the functional contribution value of the bacterial family, is the influence coefficient of the environmental factor, is the intercept term; Optimize the model parameters through 10-fold cross-validation to ensure that the root mean square error RMSE ≤3%, and finally output the as the functional contribution value of the bacterial family.

[0009] As a further preferred solution, in the aforementioned method for regulating soil carbon accumulation in degraded karst forests with microbial functional synergy, stepS Establish the carbon accumulation regulation coefficient in 3 CARC The specific formula of the calculation model is as follows: ; Among them, is the functional abundance of the i-th fungal genus screened out, is the corresponding functional weight; is the functional abundance of the j -th bacterial family screened out, is the corresponding functional contribution value; n is the number of fungal genera, m is the number of bacterial families; The said CARC value is used to quantify the comprehensive contribution degree of the microbial community to soil carbon accumulation.

[0010] As a further preferred solution, in the above-mentioned method for regulating soil carbon accumulation in degraded karst forests with microbial functional synergy, before constructing the structured microbial community database in step S 1, the computer system performs the following processing steps on the original sequencing data: Use QIIME 2 software's DADA 2 plug-in to remove low-quality sequences with a quality score < 20 in the sequencing data and filter short sequences with a length < 200 bp ; identify and remove chimeric sequences through the VSEARCH algorithm to ensure the biological authenticity of the effective sequences; Perform a deduplication operation on the remaining sequences, merge the same sequences and record their occurrence times to generate a non-redundant sequence list; Unify the number of effective sequences of each sample to 10,000 to eliminate the influence of sequencing depth differences on abundance calculation; the processed sequence data is associated and stored with the abundance values and sampling site coordinates to form a structured microbial community database.

[0011] As a further preferred solution, in the above-mentioned method for regulating soil carbon accumulation in degraded karst forests with microbial functional synergy, the preparation parameters of the microbial inoculant in step S4 are dynamically adjusted according to the CARC value, specifically including: When CARC < 60, the viable cell ratio of fungal genera to bacterial families 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 metabolic activity; When CARC ≥ 60, the viable cell ratio is adjusted to 3:1 - 4:1, and the water content of the medium for fungal solid fermentation is reduced to 55% - 60% to inhibit the functional attenuation caused by excessive sporulation.

[0012] As a further preferred solution, in the aforementioned method for regulating soil carbon accumulation in degraded karst forests with microbial functional synergy, step S The calculation method of the application amount of the microbial inoculant described in 5 is as follows: The computer system is based on the soil organic carbon saturation deficit value SOCSD and the carbon accumulation regulation coefficient CARC value, and calculates the application amount of the inoculant through the following formula: ; wherein, the application amount unit is kg / mu; the calculation result is used to guide the precise application of the inoculant to ensure that the promotion effect of exogenous microorganisms on carbon accumulation matches the degree of soil carbon deficit; Step S The generation rule of the vegetation coverage optimization plan described in 5 is as follows: When CARC ≥60, generate a vegetation coverage optimization instruction, and the target coverage range is 50%-70%; set the carbon-nitrogen ratio C / N of vegetation litter to 15-20 to inhibit the excessive decomposition of saprophytic fungi; Moreover, the coverage optimization plan includes the vegetation planting density and the mixing ratio to ensure the dynamic balance between soil carbon input and microbial decomposition.

[0013] Based on the second main aspect of the present invention, there is provided an application of the aforementioned method for regulating soil carbon accumulation in degraded karst forests with microbial functional synergy in the soil organic carbon accumulation of degraded karst forests, including: applying the method to a degraded forest area in karst landforms where the carbonate content > 15% and the soil organic carbon content < 10 g / kg , and improving the soil carbon sink capacity through the following steps: Use the computer system to execute step S 1 to S 5 of the method for regulating soil carbon accumulation in degraded karst forests with microbial functional synergy to realize the functional analysis of soil microbial communities and the evaluation of carbon accumulation potential; Drive the field operation equipment to execute the application of exogenous inoculant or vegetation optimization operation through the control instruction, the regulation period is 2-3 years, and the goal is to increase the annual soil organic carbon accumulation rate by ≥15% and the carbon stability index Kos by ≥10%.

[0014] Compared with the prior art, the present invention has achieved remarkable beneficial effects in the regulation of soil carbon accumulation in degraded karst forests through innovative technical means, which are specifically reflected in the following five aspects: First of all, the prior art only stays at the functional annotation of single bacterial communities and lacks the collaborative analysis of bacteria and fungi. The present invention combines high-throughput sequencing with FAPROTAX ,FunGuild The algorithm first integrates the carbon metabolic functions of bacterial families (such as Microbacteriaceae and Pseudomonadaceae) and fungal genera (such as Metarhizium and Cylindrocarpon), and through Spearman double screening of the correlation coefficient (P < 0.01), contribution value (R² ≥ 0.85), and relative abundance threshold (≥ 1%), and ensuring stability through 10-fold cross-validation, accurately identifies the core microbial communities strongly correlated with carbon accumulation, solving the problem of fragmented functional analysis in the prior art.

[0015] Secondly, the prior art lacks a quantitative correlation model between microbial functions and carbon accumulation, and the regulation threshold depends on empirical settings. The present invention constructs a carbon accumulation regulation coefficient ( CARC ) model, determines the fungal function weights and bacterial function contribution values through the functional abundance calculation formula and multiple linear regression, and incorporates environmental factors such as soil pH and C / N ratio, and ensures that the root mean square error ≤ 3% through 10-fold cross-validation. This model realizes the quantification ( CARC value) and dynamic evaluation of the carbon accumulation potential of the microbial community, changing the traditional subjective judgment mode.

[0016] Thirdly, the preparation of existing microbial inoculants mostly adopts fixed ratios and cannot respond to the differences in soil microbial functions. The present invention dynamically adjusts the inoculant preparation parameters according to the CARC value: when CARC < 60, increase the proportion of carbon-fixing fungi to 4:1 - 5:1 and add succinic acid to enhance metabolic activity; when CARC ≥ 60, optimize the viable bacteria ratio to 3:1 - 4:1 and reduce the water content of the fungal culture medium to maintain the balance of the microbial community.

[0017] Fourthly, the existing regulation strategies rely on manual operation, with a lagging response and a lack of spatial precision. The present invention automatically generates control instructions by a computer system according to the CARC value and the soil organic carbon saturation deficit value, so that it can be transmitted to Internet of Things devices (such as intelligent fertilizer applicators and drones) by using LoRa , NB- IoT or satellite communication technology, realizing the automatic execution of stratified application of the inoculant (surface spraying and deep injection) and optimization of vegetation coverage. The response speed is increased by more than 95% compared with manual regulation, and the application amount can be dynamically calculated according to the degree of soil carbon deficit, saving the consumption of the inoculant, and solving the problems of untimely regulation and insufficient response to spatial heterogeneity in the prior art.

[0018] Finally, the existing restoration technologies for degraded karst forests focus on vegetation and physical improvement, and the microbial regulation lacks systematicness. Through the coordinated regulation of microbial functions, the present invention can increase the annual carbon accumulation rate of soil organic carbon by ≥ 15% and the carbon stability index ( KosThe improvement is ≥ 10%, significantly enhancing the carbon sequestration capacity. Meanwhile, by constructing a complete technical chain of "data collection - function analysis - model construction - intelligent regulation", the present invention deeply integrates microbiomics, computer technology and ecological restoration, forming a new paradigm of "function - oriented + quantitative model - driven", which can be replicated to other fragile ecological areas such as rocky desertification areas and saline - alkali lands, providing an interdisciplinary systematic solution for enhancing the carbon sink of degraded ecosystems, and having significant ecological benefits and broad technical application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Shows the workflow diagram in one embodiment of the present invention; Figure 2 Shows the relationship diagram between bacterial diversity and saturation deficit in one embodiment of the present invention; Figure 3 Shows the Shannon curve graph of the microbial community in one embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] The preferred embodiments of the present invention will be described in detail below so as to more clearly understand the purpose, features and advantages of the present invention. It should be understood that the following embodiments are not a limitation on the scope of the present invention, but only illustrate the essential spirit of the technical solution of the present invention.

[0021] In the following description, certain specific details are set forth for purposes of explaining various disclosed embodiments 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 instances, well - known technologies associated with the present application may not be shown or described in detail so as to avoid unnecessarily obscuring the description of the embodiments.

[0022] References to "one embodiment" or "an embodiment" throughout the specification mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, appearances of "in one embodiment" or "in an embodiment" throughout the specification are not necessarily all referring to the same embodiment. Additionally, the particular features, structures, or characteristics may be combined in any manner in one or more embodiments.

[0023] In the following content, any technical means related to the implementation of the present invention that is not elaborated in detail is prior art, that is, those skilled in the art can understand its meaning and conventional operation process according to its technical name.

[0024] Such as Figure 1As shown in the figure, an embodiment of the present invention provides a method for regulating soil carbon accumulation in degraded karst forests with microbial functional synergy, including the following steps executed by a computer system: S 1. Measure and collect the bacterial 16 S rRNA and fungal ITS sequence data of the degraded karst forest soil samples, store 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, and construct a structured microbial community database; In some embodiments, Illumina MiSeq or NovaSeq a high-throughput sequencing platform is used to set a sampling depth of 0-30 cm for the degraded karst forest soil samples, multi-point mixed sampling is carried out, and bacterial 16 S rRNA gene V 3- V 4 region and fungal ITS 1 / ITS 2 region amplicon sequencing. During sampling, each soil sample is associated with a unique identifier, and the geographical coordinates of the sampling site, sampling time, and sample type (such as topsoil, deep soil) are recorded.

[0025] In some embodiments, it is necessary to preprocess the original sequencing data. For example, the following data cleaning process is executed using a computer system: Use the Divisive Amplicon Denoising Algorithm 2 (DADA2) plugin of QIIME2 software, set the quality filtering parameters, remove low-quality sequences with an average quality score <20, filter short sequences with a length <200bp, and retain high-quality single-end / double-end sequences. In some embodiments, chimera identification can be performed on the high-quality sequences through the Process Amplicon Sequencing Data command (VSEARCH command), and chimeric sequences are removed to ensure that the effective sequences are real biological sequences, and the chimeric sequence removal rate ≥95%.

[0026] Then, perform a deduplication operation on the remaining sequences, merge exactly the same sequences and record their occurrence times to generate a non-redundant sequence list (feature table), and each sequence corresponds to a unique feature ID. In some embodiments, the effective sequence numbers of each sample can be QIIME2 through the "sample rarefaction (sample rarefaction)” function to uniformly standardize to 10,000 to eliminate the influence of sequencing depth differences on microbial abundance calculation, and the data error after standardization ≤ ±2%.

[0027] In one embodiment, the storage medium can be selected to use a solid-state drive (SSD, capacity ≥512GB) or a distributed file system (such as HDFS) to store data, ensuring a read and write speed ≥500MB / s and supporting fast retrieval and call.

[0028] In one embodiment, the database structure shall include diversity metrics in the microbiome data, such as Shannon indices, Simpson indices, ACE indices, etc., which are calculated by the " α diversity (alpha diversity)" plugin of QIIME2.

[0029] In one embodiment, it also includes the community structure, which stores the relative abundances of various microbial taxa (such as phylum, family, genus) in the form of a feature table, accurate to 4 decimal places; In one embodiment, it also includes functional abundance values, which, after being annotated by the bacterial function prediction (FAPROTAX) and fungal functional group annotation tool (FunGuild) algorithms, statistically calculate the proportion of sequences in the functional groups related to carbon metabolism and record the confidence level of the functional annotation output by the algorithms ( C value, 0.8 - 1.0).

[0030] In one embodiment, it also includes physical and chemical indicators, including soil pH (accuracy ±0.1), C / N ratio (accuracy ±0.5), bulk density ( g / cm g / cm³, accuracy ±0.01), clay content (%, accuracy ±1%), and the above indicators can be measured by conventional laboratory methods (such as potentiometry, elemental analysis); In one embodiment, it also includes a carbon content indicator, where the soil organic carbon content ( g / kg ) is measured by the potassium dichromate oxidation method, and the detection accuracy is ≤±5%. At the same time, the theoretical saturation value required for calculating the soil organic carbon saturation deficit value SOCSD is calculated. Among them, the calculation method of the soil organic carbon saturation deficit value SOCSD is an existing technology, and a simple algorithm is provided below: Theoretical saturation value Measured value

[0031] In one embodiment, the theoretical saturation value is determined through the following steps: Based on the karst soil type (such as limestone soil) and vegetation type (such as Quercus forest), query the regional soil organic carbon saturation value database to obtain the reference value; combine the current soil bulk density ( g / cm g / cm³), clay content (%), and pH value, and correct it through the formula: Correct; In the art, the measured value is measured by the potassium dichromate oxidation method to ensure that the calculation accuracy is ≤±5%.

[0032] In the constructed structured microbial community database, microbial diversity data is used to identify the basis of species composition, that is, the diversity data of bacterial 16 S rRNA and fungal ITS sequence (such as OTU quantity, Shannon index) is used to characterize the species richness and evenness of soil microbial communities.

[0033] At the same time, microbial diversity data is also a prerequisite for screening core microorganisms, because diversity data is the basis for subsequent functional annotation and statistical analysis. For example, when screening core groups such as Microbacteriaceae and Metarhizium, it is necessary to identify their presence and relative abundance in the community based on diversity data. And through the temporal and spatial changes of diversity indicators, it is used to assist in judging the universality of core microbial flora.

[0034] In the constructed structured microbial community database, the role of community structure data can first clarify the distribution of dominant groups. For example, community structure data (such as the relative abundance ranking of each microbial group) is used to locate the bacterial families and fungal genera that play a leading role in carbon metabolism. For example, the 5 selected bacterial families and 7 fungal genera are all based on their dominant positions in the community structure (such as the relative abundance of Pseudomonadaceae ≥ 1%).

[0035] In addition, community structure data is also the basis for functional co-analysis. Through the composition ratio of bacterial and fungal groups in the community structure, it provides a structural basis for the weighted summation of the functional weights of fungal genera and the functional contribution values of bacterial families in the subsequent CARC model.

[0036] Moreover, community structure data is also convenient for subsequent spatial heterogeneity analysis. In implementation, it can be combined with sampling site coordinates (stored in the database) to analyze the spatial distribution law of community structure, providing data support for the zonal regulation of field operation equipment, such as spatial differential instructions for optimizing vegetation coverage.

[0037] In the constructed structured microbial community database, the role of functional abundance value data is first used to quantify the microbial carbon metabolism activity. The functional abundance value (such as the sequence proportion of carbon metabolism-related genes or pathways) is calculated by the following formula, which directly reflects the ability of core microorganisms to participate in processes such as carbon fixation and decomposition.

[0038]

[0039] Among them, C is the confidence level of functional annotation, with a value range of 0.8 - 1.0, which is determined by the annotation credibility output by FAPROTAX and / or FunGuild algorithm.

[0040] 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 ; Finally passed CARC The calculation formula was integrated into a comprehensive indicator to quantify the overall contribution of the microbial community to carbon accumulation.

[0041] 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 functions match soil requirements.

[0042] 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 pH 、 C / N 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 .

[0043] 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 ( SOCSD ) calculation depends on physical and chemical indicators; in the vegetation coverage optimization scheme, litter C / N The setting of the ratio needs to be combined with the soil C / N than, inhibiting excessive decomposition of saprophytic fungi.

[0044] 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 pH And other physical and chemical indicators to ensure the activity of the bacterial agent in the target environment.

[0045] The role of carbon content data in the constructed structured microbial community database is first as a core correlation indicator, as the Spearman ( Spearman ) 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).

[0046] Secondly, you can be used as a model verification benchmark and as a dependent variable in the regression model. , verifying the direct correlation between microbial functions and carbon accumulation, such as the positive or negative regression coefficient of the fungal genus β i reflecting promotion or inhibition; In addition, the carbon content data is also an important quantitative target for regulation effects. By comparing the changes in carbon content before and after regulation, the actual effects of microbial agent application and vegetation optimization are evaluated.

[0047] Generally speaking, the above data in the database constitute an important chain in each link of the present invention's solution. The diversity and community structure data are used to lock in the core microbial groups, the functional abundance data quantifies their carbon metabolism capabilities, the physical and chemical index data excludes environmental interference and optimizes regulation conditions, and the carbon content data serves as the final correlation target, driving model construction and effect verification.

[0048] In some embodiments, data association can be performed in the system, that is, through a unique sample ID to establish associations among sequencing data, diversity indices, community structure, functional abundance, and sample physical and chemical indices, carbon content, sampling site coordinates, etc., to form a structured database table.

[0049] S 2. Based on the database, the computer system performs screening of core functional microorganisms that affect carbon storage; including the following steps: S 21. The computer system runs FAPROTAX an algorithm to perform functional classification on bacterial 16 S rRNA sequence data to identify bacterial functional groups involved in carbon metabolism; runs FunGuild an algorithm to perform functional classification on fungal ITS sequence data to identify fungal functional groups involved in carbon fixation or decomposition; S 22. Screen microbial groups that meet the following conditions: Bacterial families: Microbacteriaceae ( Microbacteriaceae ), Mycobacteriaceae ( Mycobacteriaceae ), Pseudomonadaceae ( Pseudomonadaceae ), Streptomycetaceae ( [[ID= ), Xanthomonadaceae ( ​ ), and the screening condition is that the correlation coefficient ​ with soil organic carbon content P < 0.01 and relative abundance ≥ 1%; Fungal genera: Cylindrocarpon ( ​ ), Leptosphaerulina ( ​ ), Metarhizium ( ​ ), Neolentinus (​ ), Neopestalotiopsis ​ ), Olpidium ​ ), Tetracladium ​ ), and the screening criteria are that the carbon accumulation contribution value R² ≥0.85 and the relative abundance ≥1%; S 23. Through 10-fold cross-validation or 3 repeated experiments, ensure that the stability of the screened core microbiota in different soil samples passes.

[0050] In one embodiment, the computer system passes ​ language script calls ​ (v1.2.12) and ​ (v1.1.0) algorithms to perform functional classification on the preprocessed bacterial 16 ​ and fungal ​ sequence data. The input data is a standardized non-redundant sequence list, with each row corresponding to a sample and each column corresponding to a microbial feature, and the functional annotation results are associated. The system automatically generates a carbon metabolism functional classification table for bacterial data and a carbon fixation / decomposition functional classification table for fungal data, and the output results include the functional categories of each microbial group and the algorithm confidence score.

[0051] ​ The algorithm is based on ​ database to perform functional annotation on bacterial sequences, and identify functional groups involved in carbon metabolism through sequence alignment ( ​ , E value ≤ 1e-5). The computer system extracts bacterial features annotated as "carbon metabolism ( ​ ​ )", "glycolysis ( ​ )", "methane oxidation ( ​ )", etc., maps them to the family-level classification, and generates a bacterial family-carbon metabolism function association table. For multiply annotated groups, the functions directly related to carbon metabolism are preferentially retained to ensure the specificity of functional classification.

[0052] ​ The algorithm performs functional prediction on fungal ​ sequences through a random forest model, combines ​ database and ​ literature knowledge base to identify carbon fixation or carbon decomposition functional groups. The computer system extracts fungal features with a functional classification probability ≥0.7, maps them to the genus-level classification, and marks the niche specificity. For mixed functional groups, they are classified according to the main function probability output by the algorithm to ensure the accuracy of functional analysis.

[0053] In one embodiment, the computer system performs a dual screening on bacterial taxa mapped to the family level. First, through ​ correlation analysis, using R the language " ​ " function to calculate the ​ correlation coefficient between the relative abundance of each bacterial family and the soil organic carbon content, and screening P taxa with < 0.01; secondly, perform relative abundance filtering, retaining bacterial families with a relative abundance ≥ 1% in ≥ 50% of the samples to exclude the interference of rare taxa.

[0054] For the screening of fungal genera, regression model fitting verification is adopted. First, calculate the carbon accumulation contribution value ( R ²). Using the relative abundance of each fungal genus as the independent variable and the soil organic carbon content as the dependent variable, construct a unary linear regression model, and screen R fungal genera with ² ≥ 0.85 (indicating that the abundance change can explain more than 85% of the carbon content fluctuation). Then perform spatial heterogeneity filtering, requiring that the coefficient of variation of the relative abundance of the target fungal genus at different sampling sites ≤ 20% to ensure its distribution stability.

[0055] 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 function weights through multiple linear regression; establishing a carbon accumulation regulation coefficient ​ calculation model; In the above solution, the calculation of the functional abundance index of core microorganisms includes at least: The computer system counts the number of sequences directly related to the carbon metabolism function of the screened core microorganisms and records it as and calculates its proportion in the total number of sequences of the microorganism. The formula is: ; where C is the confidence level of functional annotation, with a value range of 0.8 - 1.0, determined by the annotation confidence ​ and / or ​ output by the algorithm; the functional abundance index is used for subsequent model construction.

[0056] In one embodiment, when using ​ algorithm to perform functional annotation on bacterial sequences, the confidence level is evaluated through sequence alignment consistency and E E value. When the sequence alignment consistency ≥ 90% and E ≤ 1e-5, it is determined to be of high confidence, and the functional annotation confidence C E value is assigned 1.0; if the consistency is between 80% - 90% or the E value is between 1e-5 - 1e-4, it is regarded as medium confidence,C The value is 0.9, and manual verification is required; when the consistency ≥ 70% and the number of supporting documents for the algorithm output function ≥ 2, it is defined as low confidence. C The value is 0.8 and is only used for secondary analysis. Through the above rules, the comparison results output by the algorithm are converted into quantifiable confidence indicators to ensure the reliability of bacterial function annotation.

[0057] For ​ the fungal function annotation of the algorithm, the value is determined based on the function classification probability and niche specificity. C If the function classification probability ≥ 0.9 and the niche specificity ≥ 80% (such as the carbon fixation function of Metarhizium in karst soil), C the value is assigned 1.0; when the probability is 0.8 - 0.9 or the specificity is 60% - 80%, C the value is 0.9, and cross - validation with samples of the same taxonomic group is required; when the probability ≥ 0.7 and there is at least 1 study on karst habitats to support it (such as the carbon decomposition function of Olpidium), C the value is 0.8. This rule combines the algorithm probability output and niche data to achieve precise quantification of the credibility of fungal function annotation.

[0058] When the same microbial taxonomic group is annotated by multiple algorithms, the value is calculated by weighted average. C For C low - confidence annotations with < 0.8, the system automatically marks and excludes them and does not include them in the calculation of functional abundance. Through the dynamic verification and filtering mechanism, the interference of data errors to the model is reduced, ensuring the accuracy of the core microbial function analysis and laying a reliable foundation for the construction of the subsequent carbon accumulation regulation coefficient ( ​ ) model.

[0059] In one embodiment, in the method for regulating carbon accumulation in degraded karst forest soil with the aforementioned microbial function synergy, in step S 3, the method for determining the function weight of the fungal genus in determining the microbial function weight through multiple linear regression is as follows: The computer system uses the screened functional abundance of the fungal genus as the independent variable and the measured soil organic carbon content as the dependent variable, and performs a multiple linear regression analysis with p < 0.001 to obtain the regression coefficient of each fungal genus; After standardizing the regression coefficient, it is used as the function weight , and the formula is: where n is the number of screened fungal genera; The value range is 0.6 - 1.0, and it is directly proportional to the positive correlation of .

[0060] In one embodiment, in the aforementioned method for regulating soil carbon accumulation in degraded karst forests with microbial functional synergy, step S In step 3, in the determination of the microbial functional weights by multiple linear regression, the method for determining the functional contribution value of bacterial families is as follows: The computer system uses the screened functional abundances of bacterial families as independent variables, and simultaneously incorporates environmental factors including soil pH and C / N ratio as covariates, and uses the measured soil organic carbon content as the dependent variable to construct a multiple linear regression model:

[0061] where, is the functional contribution value of the bacterial family, is the influence coefficient of the environmental factor, is the intercept term; m represents the number of core bacterial families screened out, j is the index variable of the bacterial family, and its value range is from 1 to m ; p is the number of environmental factors incorporated into the model, k is the index variable of the environmental factor, and its value range is from 1 to p .

[0062] Among them, the intercept term is a mathematically necessary parameter of the multiple linear regression model, which is automatically determined by data fitting, and its value is jointly determined by the sample mean and the independent variable coefficients. In the present invention, the intercept term does not directly correspond to a specific microbial function, but serves as a comprehensive representation of abiotic factors and unexplained variables of the model, and ensures the prediction accuracy of the model for soil carbon content through statistical optimization.

[0063] Optimize the model parameters through 10-fold cross-validation to ensure that the root mean square error ​ ≤3%, and finally output the as the functional contribution value of the bacterial family.

[0064] 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, trains the screening model with 9 subsets each time, and validates with the remaining 1 subset. After repeating 10 times, calculate the reproducibility rate of the core microbiota; perform the screening process on 3 independent soil samples in the same area respectively, and compare the consistency of the core microbial groups.

[0065] After verification, the system generates a list of core microbiota containing 5 bacterial families and 7 fungal genera, and its stability indicators in different verification scenarios (such as ​ correlation coefficient P value,R The fluctuation range of the ² value) ≤ 5% to ensure the reliability of subsequent model construction and microbial agent preparation. At the same time, a relationship diagram of bacterial diversity and saturation deficit can be drawn (as Figure 2 shown) or Shannon curve (as Figure 3 shown) etc. for intuitive display.

[0066] As a further preferred solution, in the aforementioned method for regulating soil carbon accumulation in degraded karst forests with microbial functional synergy, step S 3 to establish a carbon accumulation regulation coefficient CARC The specific formula for calculating the model is: [[ID=**16**]] [[ID=**17**]] [[ID=**18**]]

[0067] [[ID=**19**]]where [[ID=**20**]] [[ID=**21**]]is the functional abundance of the i-th fungal genus screened out, [[ID=**22**]] [[ID=**23**]]is the corresponding functional weight; [[ID=**24**]] [[ID=**25**]]is the functional abundance of the [[ID=**26**]] j [[ID=**27**]]-th bacterial family screened out, [[ID=**28**]] [[ID=**29**]]is the corresponding functional contribution value; [[ID=**30**]] n [[ID=**31**]]is the number of fungal genera, [[ID=**32**]] m [[ID=**33**]]is the number of bacterial families; the [[ID=**34**]] CARC [[ID=**35**]]value is used to quantify the comprehensive contribution degree of the microbial community to soil carbon accumulation. [[ID=**36**]] [[ID=**37**]]

[0068] [[ID=**38**]]In the above solution, [[ID=**39**]] CARC [[ID=**40**]]The derivation of the calculation model is based on the core theory in microbiomics that functional abundance determines the intensity of ecological processes, combined with the microbial synergy mechanism of soil carbon accumulation in degraded karst forests. First, bacterial families directly related to carbon metabolism (such as Microbacteriaceae) and fungal genera (such as Metarhizium) are identified through high-throughput sequencing and functional annotation, and two key variables are defined: [[ID=**41**]] [[ID=**42**]] (1) Functional abundance: It refers to the proportion of carbon metabolism-related sequences in the core microorganisms to their total sequences [[ID=**43**]] F [[ID=**44**]] i [[ID=**45**]], [[ID=**46**]] B [[ID=**47**]] j [[ID=**48**]], reflecting their potential activity in participating in carbon accumulation. [[ID=**49**]] [[ID=**50**]]

[0069] [[ID=**51**]] (2) Functional weight and contribution value: The functional weight of the fungal genus [[ID=**52**]] W [[ID=**53**]] i [[ID=**54**]]is obtained by standardizing the multiple linear regression coefficient of its abundance and soil organic carbon content, characterizing its direct contribution intensity to carbon accumulation; the functional contribution value of the bacterial family [[ID=**55**]] V [[ID=**56**]] j [[ID=**57**]]is calculated through a multiple linear regression model incorporating environmental factors such as soil [[ID=**58**]] pH [[ID=**59**]], [[ID=**60**]] C / N [[ID=**61**]]ratio, reflecting its actual action efficiency in a complex environment. [[ID=**62**]] [[ID=**63**]]

[0070] Model construction is divided into three key steps: First, determine the functional weights of fungal genera. Using the functional abundances of each fungal genus F i as independent variables and the measured carbon content C s as the dependent variable, perform p multiple linear regression with <0.001 to obtain the regression coefficients β i and then standardize them to weights, ensuring that the sum of the weights is 1 and is positively correlated with carbon accumulation. Then, calculate the functional contribution values of bacterial families. Using the functional abundances of bacterial families B j as independent variables and the carbon content as the dependent variable, and at the same time controlling the environmental factors E k construct a regression model. Optimize the parameters through 10-fold cross-validation to make the root mean square error (RMSE) ≤ 3%. Finally, extract the V j that reflects the net contribution of bacterial families to carbon accumulation.

[0071] Finally, linearly superimpose the quantification results of fungal genera and bacterial families to form CARC a formula to achieve the quantification of the cross-group functional synergistic effect. CARC The construction principle of the model lies in integrating the directness and environmental adaptability of microbial functions. The weights of fungal genera focus on the direct functional contributions. The standardization process eliminates the differences in variable dimensions, enabling direct comparison of the contributions of different fungal genera (e.g., the contribution of Metarhizium W i = 0.9 is significantly higher than that of Cylindrocarpon W i == 0.6). The contribution values of bacterial families V j focus on the actual functional expression under environmental constraints. By incorporating pH , C / N ratio and other covariates, it excludes the interference of abiotic factors on microbial functions and ensures the applicability of the model in karst highly heterogeneous soils.

[0072] CARC The linear superposition logic in the model is based on the functional complementarity hypothesis in microbial community ecology, that is, bacteria and fungi drive carbon accumulation together through the division of labor in the carbon metabolic pathway (e.g., bacteria decompose small-molecule organic matter and fungi promote carbon sequestration). By quantifying the synergistic effect between the two, the model provides a unified evaluation scale for the microbial regulation of degraded ecosystems.

[0073] Among them, the measured environmental parameters (such as soil pH , C / N ratio, bulk density, clay content, etc.) are accurately measured in the laboratory. First, data cleaning is performed on pH ,C / N Ratio and other numerical data adopt Z-scor the e-standardization method to convert it into dimensionless data with a mean of 0 and a standard deviation of 1, eliminating the interference of dimensional differences on the model. One-hot encoding is performed on categorical data such as soil texture and converted into binary numerical variables. At the same time, through Dixon methods such as inspection to identify and remove outliers to ensure data accuracy.

[0074] In one embodiment, the measured soil pH value is 6.5, and after standardization, it is converted into the corresponding Z value to make it comparable to other factors such as C / N ratio. The preprocessed environmental parameters are screened through statistical tests and incorporated into the model. First, calculate the Spearman correlation coefficient between each parameter and the soil organic carbon content, and retain the parameters with significant correlation ( P <0.05) and an absolute value ≥ 0.3 (such as pH , C / N ratio); then detect multicollinearity through the variance inflation factor ( VIF ), and remove the parameters with VIF ≥5 to avoid model distortion. The screened environmental parameters are used as covariates E k to enter the multiple linear regression model. The computer system fits the model by the least squares method and automatically calculates the E k influence coefficient , and finally participates in the calculation of the functional contribution value \(V_j\) of the bacterial family in the form of standardized values, realizing the transformation from measured parameters to model variables.

[0075] S 4. According to the CARC value calculation result, the core microbial flora is compound-cultured according to the viable bacteria ratio of 3:1 - 5:1 of fungal genera to bacterial families, and fermented at pH 5.8 - 6.5, 28 - 32 °C for 48 - 72 hours to prepare the microbial inoculum; In one embodiment, step S 4 uses the screened core microbial flora (5 bacterial families, 7 fungal genera) as the source of the strain, and performs compound culture according to the viable bacteria ratio of 3:1 - 5:1 of fungal genera to bacterial families. The fermentation process uses a constant-temperature controlled fermenter to adjust pH to 5.8 - 6.5, which can be calibrated in real time through a 1 M NaOH / HCl solution, the temperature is set at 28 - 32 ℃ , with an error of ±0.5 ℃ , and the fermentation time is 48 - 72 hours. Among them, the bacteria are fermented in liquid, and the culture medium contains 10 g / L peptone, 5 glucoseg / L For the fungi, solid fermentation is adopted, and the culture medium is wheat bran: corn flour = 7:3 by weight, with a water content of 60% - 70%. The activity of the microbial cells is ensured by the stirring rate (for bacteria, 150 - 200 rpm ).) and the aeration rate (for fungi, turning the pile once a day).

[0076] When the computer system determines that CARC 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: Increasing the viable cell ratio of the fungal genus to the bacterial family from the default 3:1 - 5:1 to 4:1 - 5: One example is to increase the proportion of viable cells of the genus Metarhizium from 30% to 40% - 50% to enhance the carbon fixation ability; adding succinic acid (an intermediate of the tricarboxylic acid cycle) with a final concentration of 0.1 - 0.3 mM at the mid-stage of liquid fermentation (24 hours after inoculation), to improve the decomposition efficiency of small-molecule organic matter by promoting the glyoxylate cycle pathway of bacteria, and increase the 600 value of the bacterial liquid from 1.2 - 1.5 to 1.8 - 2.0, and increase the activity of carbon metabolism-related enzymes (such as succinate dehydrogenase) by 20% - 30%. OD When the

[0077] value ≥ 60, it indicates that the soil microbial community already has a strong carbon accumulation ability, and the preparation of the microbial inoculum turns to maintaining the balance of the microbial community and functional stability: adjusting the viable cell ratio to 3:1 - These adjustments are made to avoid the over-competition of highly active bacteria (such as the family Pseudomonadaceae decreasing from 25% to 20%) from inhibiting the growth of fungi; reducing the water content of the solid fermentation medium from 60% - 70% to 55% - 60%, inhibiting the over-production of spores by fungi by restricting water, and promoting the growth of mycelia to enhance the secretion of carbon sequestration-related enzymes, with the enzyme activity maintained within the range of 50 - 80 CARC When the U / mg protein range.

[0078] After fermentation, quality testing is carried out on the microbial inoculum: the total viable cell count is determined by the plate counting method (required to be ≥ 1×10 8 CFU / g ), and the abundance of functional genes of core microorganisms is detected through qPCR detection, such as the 16 S rRNA gene of bacteria and the ITS gene copy number of fungi, ensuring that the matching degree with the predicted functional abundance of the model is ≥ 90%. At the same time, inoculate the microbial inoculum in soil containing 15% carbonate, and detect the soil organic carbon content after 30 days of cultivation, requiring that the carbon accumulation rate is increased by ≥ 15% compared with the control group. The finally prepared microbial inoculum is packaged according to different CARC value zones, marked with the applicable soil carbon deficit range, and transported to the field operation equipment through cold chain.

[0079] S 5. The computer system, according to CARC value and the soil organic carbon saturation deficit value SOCSD calculates the application rate of microbial inoculant and generates a control instruction, and executes the application of microbial inoculant and / or the optimization and regulation of vegetation coverage through Internet of Things devices.

[0080] The calculation method of the application rate of microbial inoculant is as follows: The computer system is based on the soil organic carbon saturation deficit value SOCSD and the carbon accumulation regulation coefficient CARC value, and calculates the application rate of the inoculant through the following formula:

[0081] wherein, the unit of the application rate is kg / mu; the calculation result is used to guide the precise application of the inoculant to ensure that the promotion effect of exogenous microorganisms on carbon accumulation matches the degree of soil carbon deficit; Step S The generation rule of the vegetation coverage optimization plan described in 5 is as follows: When CARC ≥60, a vegetation coverage optimization instruction is generated, and the target coverage range is 50%-70%; the carbon-nitrogen ratio of vegetation litter is set C / N to 15-20 to inhibit the excessive decomposition of saprophytic fungi; Moreover, the coverage optimization plan includes the vegetation planting density and the mixing ratio to ensure the dynamic balance between soil carbon input and microbial decomposition.

[0082] In one embodiment, the computer system retrieves in real time CARC value and SOCSD data, and generates multi-dimensional control instructions based on preset rules. When CARC <60 and SOCSD >10 [[ID= , it is determined as a high carbon deficit scenario, and a microbial inoculant application instruction is preferentially generated; when CARC≥60, the vegetation coverage optimization process is triggered. The instruction content includes: the coordinates of the microbial inoculant application area, the application method, the target vegetation coverage, and the proportion of mixed tree species. The instruction is encrypted and transmitted to the field Internet of Things gateway through ​ the communication protocol.

[0083] After receiving the microbial inoculant application instruction, the intelligent fertilizer applicator automatically navigates to the target area and adjusts the application depth according to the soil texture. Deep injection is used for sandy soil, and surface spraying is used for clay soil. The unmanned aerial vehicle is used for large-area uniform spraying. The vegetation density is scanned in real time through a multi-spectral sensor, and the flight height and spraying rate are dynamically adjusted to ensure that the coverage deviation of the inoculant is ≤5%. During the application process, the device synchronously transmits the operation data (such as the applied area and the remaining amount of inoculant) back to the computer system to form a closed-loop feedback.

[0084] When the system generates a vegetation coverage optimization instruction, the Internet of Things device starts a multi-stage execution plan. First, the seed coating seeder carried by the drone sows the carbon sequestration plant seeds according to the target coverage, and the seeding density error ≤ 3%; second, the intelligent irrigation system automatically starts and stops according to the data of the soil moisture sensor to promote the establishment of seedlings; finally, the image recognition camera regularly monitors the vegetation growth status, controls the carbon-nitrogen ratio of litter, and inhibits the excessive decomposition of saprophytic fungi. During the entire regulation period, the fluctuation of the vegetation coverage is controlled within the range of ± 5%, realizing the dynamic balance of carbon input and microbial decomposition.

[0085] In one embodiment, an application of the method for regulating soil carbon accumulation in degraded karst forests in the accumulation of soil organic carbon in degraded karst forests is provided. In a degraded karst forest area where the carbonate content > 15% and the soil organic carbon content < 10 ​ first, the computer system executes the acquisition of the bacterial 16 ​ and fungal ​ sequence data of soil samples by using a high-throughput sequencing device. After ​ data cleaning ​ and functional annotation, a structured database containing microbial diversity, community structure, functional abundance, and soil physical and chemical indicators is constructed. Based on this database, through ​ correlation analysis and multiple linear regression, the core microbial flora is screened, and a carbon accumulation regulation coefficient ​ model is established to quantify the carbon accumulation potential of the community.

[0086] The computer system generates a control instruction according to the ​ value and the soil organic carbon saturation deficit value ​ : when ​ < 60, drive the intelligent fertilizer applicator to apply the microbial inoculant fortified with succinic acid at a viable bacteria ratio of 4:1 - 5:1, and adopt the deep injection process to ensure that the inoculant contacts the roots; when ​ ≥ 60, sow the Broussonetia papyrifera-Zenia insignis mixed forest by drone, with a mixing ratio of 3:1, regulate the vegetation coverage to 50% - 70%, and maintain the litter ​ ratio at 15 - 20 through the intelligent irrigation system.

[0087] The regulation period is set to 2 - 3 years, and the application of the inoculant and vegetation pruning are carried out in spring and autumn every year. The computer system collects soil samples every quarter to monitor the organic carbon content and the carbon stability index ( ​ , measured by the combination of an elemental analyzer and an infrared spectrometer). The data shows that in the first year of regulation, the annual accumulation rate of soil organic carbon increases by 12% - 18%, ​ increases by 8% - 12%; in the second to third years, the rate stabilizes at 15% - 20%, ​There was a 10%-15% increase, all meeting the expected goals. By continuously iterating and optimizing the CARC model parameters, a systematic improvement in the soil carbon sequestration capacity of degraded karst forests was achieved, providing a replicable technical paradigm for rocky desertification ecological restoration.

[0088] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for regulating soil carbon accumulation in degraded karst forests with microbial functional synergy, characterized in that, Including the following steps executed by a computer system: S1. Measuring and collecting the bacterial 16S rRNA and fungal ITS sequence data of the degraded karst forest soil samples by a high-throughput sequencing device, storing the original sequencing data including diversity, community structure, functional abundance values, sample physical and chemical indexes, and carbon content in a computer-readable storage medium, and constructing a structured microbial community database; S2. Based on the said database, the computer system executes the screening of core functional microorganisms that affect carbon storage; S3. Based on the screening results, the computer system executes the following processing: calculating the functional abundance indexes of the core microorganisms; determining the microbial functional weights through multiple linear regression; Establishing a calculation model for the carbon accumulation regulation coefficient CARC; S4. According to the calculation results of the CARC value, the core microbial flora is co-cultured at a viable cell ratio of 3:1 - 5:1 of fungal genus to bacterial family, and fermented for 48 - 72 hours under the conditions of pH 5.8 - 6.5 and 28 - 32 °C to prepare a microbial inoculant; S5. The computer system calculates the application amount of the microbial inoculant according to the CARC value and the soil organic carbon saturation deficit value SOCSD, generates a control instruction, and executes the application of the microbial inoculant and / or the optimization regulation of the vegetation coverage through an Internet of Things device.

2. The method for regulating soil carbon accumulation in degraded karst forests with microbial functional synergy according to claim 1, characterized in that, The screening of the core functional microorganisms executed by the said computer system includes the following steps: S21. The computer system runs the FAPROTAX algorithm to classify the functions of the bacterial 16S rRNA sequence data, and identifies the bacterial functional groups participating in carbon metabolism; runs the FunGuild algorithm to classify the functions of the fungal ITS sequence data, and identifies the fungal functional groups participating in carbon fixation or decomposition; S22. Screening the microbial groups that meet the following conditions: Bacterial families: Microbacteriaceae, Mycobacteriaceae, Pseudomonadaceae, Streptomycetaceae, Xanthomonadaceae, and the screening conditions are that the Spearman correlation coefficient P with the soil organic carbon content is < 0.01 and the relative abundance ≥ 1% are satisfied simultaneously; Fungal genera: Cylindrocarpon, Leptosphaerulina, Metarhizium, Neolachnum, Neopestalotiopsis, Olpidium, Tetracladium, and the screening conditions are that the carbon accumulation contribution value R² ≥ 0.85 and the relative abundance ≥ 1% are satisfied simultaneously; S23. Through 10-fold cross-validation or 3 repeated experiments, ensure that the stability of the screened core microbial flora in different soil samples passes.

3. The method for regulating soil carbon accumulation in degraded karst forests with microbial functional synergy according to claim 1 or 2, characterized in that, The calculation of the functional abundance indexes of the core microorganisms in step S3 includes at least: The computer system counts the number of sequences directly related to carbon metabolism function for the selected core microorganisms and records it as , and calculates the number of sequences directly related to carbon metabolism function accounting for the total number of sequences of the selected core microorganisms The proportion is calculated by the formula: ; Where C is the functional annotation confidence level, and the value range is 0.8 - 1.0, which is determined by the annotation credibility output by the FAPROTAX and / or FunGuild algorithms; The said functional abundance indexes are used for subsequent model construction.

4. The method for regulating soil carbon accumulation in degraded karst forests with microbial functional synergy according to claim 3, wherein In the determination of the microbial functional weights through multiple linear regression in step S3, the determination method of the functional weights of the fungal genera is: The computer system uses the functional abundance of the selected fungal genera as the independent variable and the measured soil organic carbon content as the dependent variable, performs a multiple linear regression analysis with p < 0.001, and obtains the regression coefficients of each fungal genus ; The regression coefficients are standardized and used as functional weights , and the formula is: where n is the number of fungal genera selected; The value range is 0.6 - 1.0, and it is directly proportional to the positive correlation with ​ 5. The method for regulating soil carbon accumulation in degraded karst forests with microbial functional synergy according to claim 3, characterized in that, In the determination of the microbial functional weights through multiple linear regression in step S3, the determination method of the functional contribution values of the bacterial families is: The computer system uses the functional abundance of the selected bacterial families as the independent variable, and simultaneously incorporates environmental factors including soil pH and C / N ratio as covariates, and uses the measured soil organic carbon content as the dependent variable to construct a multiple linear regression model: ; Among them, is the functional contribution value of the bacterial family, is the influence coefficient of environmental factors, is the intercept term; Optimize the model parameters through 10-fold cross-validation to ensure that the root mean square error RMSE ≤ 3%, and finally output the As the functional contribution value of the bacterial family.

6. The method for regulating soil carbon accumulation in degraded karst forests with microbial functional synergy according to claim 3, characterized in that The specific formula for establishing the calculation model of the carbon accumulation regulation coefficient CARC in step S3 is: ; Among them, is the functional abundance of the i-th fungal genus screened out, is the corresponding functional weight; is the functional abundance of the j-th bacterial family screened out, is the corresponding functional contribution value; n is the number of fungal genera, and m is the number of bacterial families; The said CARC value is used to quantify the comprehensive contribution degree of the microbial community to soil carbon accumulation.

7. The method for regulating soil carbon accumulation in degraded Karst forests with microbial functional synergy according to claim 1, wherein Before constructing the structured microbial community database described in step S1, the computer system performs the following processing steps on the original sequencing data: Using the DADA2 plugin of QIIME2 software, low-quality sequences with a quality score < 20 in the sequencing data are removed, and short sequences with a length < 200 bp are filtered; chimeric sequences are identified and removed through the VSEARCH algorithm to ensure the biological authenticity of the effective sequences; The remaining sequences are de-duplicated, the same sequences are merged and the number of their occurrences is recorded to generate a non-redundant sequence list; The effective sequence numbers of each sample are uniformly standardized to 10,000 to eliminate the influence of sequencing depth differences on abundance calculation; the processed sequence data is associated and stored with abundance values and sampling site coordinates to form a structured microbial community database.

8. The method for regulating soil carbon accumulation in degraded karst forests with microbial functional synergy according to claim 1, wherein The preparation parameters of the microbial inoculant described in step S4 are dynamically adjusted according to the CARC value, specifically including: When CARC < 60, the viable cell ratio of the fungal genus to the 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 metabolic activity; When CARC ≥ 60, the viable cell ratio is adjusted to 3:1 - 4:1, and the water content of the medium for fungal solid fermentation is reduced to 55% - 60% to inhibit the functional attenuation caused by excessive sporulation.

9. The method for regulating soil carbon accumulation in degraded karst forests with microbial functional synergy according to claim 1, characterized in that The calculation method of the application amount of the microbial inoculant described in step S5 is: The computer system calculates the application amount of the inoculant based on the soil organic carbon saturation deficit value SOCSD and the carbon accumulation regulation coefficient CARC value through the following formula: ; Among them, the application amount unit is kg / mu; the calculation result is used to guide the precise application of the inoculant to ensure that the promotion effect of exogenous microorganisms on carbon accumulation matches the degree of soil carbon deficit; The generation rule of the vegetation coverage optimization plan described in step S5 is: When CARC ≥ 60, a vegetation coverage optimization instruction is generated, and the target coverage range is 50% - 70%; the carbon-nitrogen ratio C / N of the vegetation litter is set to 15 - 20 to inhibit the excessive decomposition of saprophytic fungi; Moreover, the coverage optimization plan includes the vegetation planting density and the mixed ratio to ensure the dynamic balance between soil carbon input and microbial decomposition.

10. Use of the method for regulating soil carbon accumulation in degraded karst forests with microbial functional synergy according to any one of claims 1-9 in the accumulation of soil organic carbon in degraded karst forests, characterized in that Applying the method to a degraded forest area in karst landform with a carbonate content > 15% and a soil organic carbon content < 10 g / kg, the soil carbon sequestration capacity is improved through the following steps: Using the computer system to execute steps S1 to S5 of the method for regulating soil carbon accumulation in degraded karst forest with microbial function coordination to realize the functional analysis of soil microbial communities and the assessment of carbon accumulation potential; Driving the field operation equipment to perform the application of exogenous inoculant or vegetation optimization operation through the control instruction, the regulation period is 2 - 3 years, and the goal is to increase the annual soil organic carbon accumulation rate by ≥ 15% and the carbon stability index Kos by ≥ 10%.

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