Soil, grass and livestock carbon cycle regulation and control method and system based on grazing management

Abstract

The invention provides a soil, grass and livestock carbon cycle regulation and control method and system based on grazing management, and the method comprises the steps: collecting soil, grass and livestock multi-source data of a preset region, and forming a carbon flow pool; establishing a carbon circulation system coupling degree evaluation model, and combining the carbon flow pool to obtain a coupling degree rating result of the soil, grass and livestock carbon circulation system; based on the coupling degree rating result, the supporting level of the carbon circulation state of the ecological system on the grassland productivity and the livestock productivity is diagnosed, and a key carbon flow unbalance path causing decoupling of the soil, grass and livestock carbon circulation system is positioned; constructing a grazing and biological / non-biological factor threshold model based on the key carbon flow imbalance path; based on a grazing and biological / non-biological factor threshold model, grazing management is executed, a soil-grass-livestock carbon circulation system is driven to be recoupled, and cooperative improvement of grassland productivity and livestock productivity is achieved. According to the technical scheme, the conversion from blind grazing to intelligent carbon management is realized.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of carbon monitoring, and particularly relates to a soil-grass-livestock carbon cycle regulation method and system based on grazing management. BACKGROUND

[0002] At present, grazing management is mainly focused on short-term economic indicators such as grassland yield and livestock productivity, and lacks overall consideration of the carbon cycle process of the soil-grass-livestock system. Such one-sided management mode is easy to lead to overgrazing, causing vicious cycles such as grassland degradation, soil carbon loss and decline of ecosystem carbon sink function. Although the existing technology can monitor part of the carbon flux, it still lacks a systematic diagnosis method, and cannot quantitatively evaluate the coupling state of soil-grass-livestock carbon cycle, nor can it accurately identify the key carbon imbalance path and decoupling mechanism. Due to the failure to establish a grazing and environmental factor threshold system linked to ecological goals, traditional management often lags behind changes in the ecosystem, making it difficult to achieve the transition from passive response to active regulation, and restricting the coordinated improvement of grassland ecological function and economic benefits of animal husbandry. SUMMARY

[0003] To solve the problems existing in the prior art, the application provides a soil-grass-livestock carbon cycle regulation method and system based on grazing management, which quantifies the coupling state of the system, identifies the decoupling mechanism, and provides accurate regulation thresholds accordingly.

[0004] To achieve the above-mentioned purpose, the application provides the following solutions: A soil-grass-livestock carbon cycle regulation method based on grazing management, comprising: Collecting soil-grass-livestock multi-source data in a predetermined area, and respectively constructing plant carbon migration, livestock carbon migration, soil carbon migration and ecosystem carbon cycle to quantify the flux and stock of carbon in the formation process of grassland productivity and livestock productivity, and to form a carbon flow pool; Establishing a carbon cycle system coupling degree evaluation model, combining the carbon flow pool to obtain the coupling degree rating result of the soil-grass-livestock carbon cycle system; based on the coupling degree rating result, diagnosing the support level of the carbon cycle state of the ecosystem to the grassland productivity and the livestock productivity, and positioning the key carbon imbalance path leading to decoupling of the soil-grass-livestock carbon cycle system; Based on the key carbon imbalance path, a grazing and biological / non-biological factor threshold model is constructed; Based on the grazing and biological / non-biological factor threshold model, grazing management is performed to drive the recoupling of the soil-grass-livestock carbon cycle system, and to realize the coordinated improvement of the grassland productivity and the livestock productivity.

[0005] Preferably, in the carbon flow pool: In the plant carbon migration, the plant carbon input includes plant carbon assimilation product, carbon assimilation product carbon allocation to aboveground / belowground biomass, aboveground biomass carbon allocation to livestock / aboveground litter carbon, belowground biomass carbon allocation to live root / root exudate / belowground litter carbon; the plant carbon output includes plant aboveground part respiration and root respiration, aboveground / belowground litter decomposition, root exudate, and livestock feed intake carbon; In the livestock carbon migration, the livestock carbon input includes livestock feed intake carbon; the livestock carbon output includes livestock respiration carbon, intestinal methane carbon, fecal carbon, and livestock product sales carbon; In the soil carbon migration, the soil carbon input includes plant aboveground / belowground litter transferred into soil organic carbon, plant root exudate carbon, fecal transferred into soil organic carbon, and microbial carbon; the soil organic carbon pool mainly includes soil plant-derived carbon, soluble organic carbon, particulate organic carbon, and mineral-bound carbon; the soil carbon output includes autotrophic and heterotrophic respiration; The ecosystem carbon cycle includes carbon exchange between grassland, atmosphere, human society, and other ecosystems.

[0006] Preferably, the method for establishing the coupling degree evaluation model of the carbon cycle system comprises the following steps: Based on the carbon flow pool, the core carbon flow intensity, the core carbon pool capacity, and the system comprehensive efficiency are calculated to construct evaluation indexes; wherein, the system comprehensive efficiency includes ecological efficiency and system stability; The core carbon flow intensity is standardized, and the carbon flow weight of the standardized core carbon flow intensity is dynamically determined by using the entropy weight method in combination with the evaluation indexes; Based on the carbon flow weight and the standardized core carbon flow intensity, the weighted coupling degree between the soil-grass-livestock subsystems is calculated; Based on the measured net carbon balance of the ecosystem and the ideal carbon sink value, a carbon balance adjustment factor is established; Based on the weighted coupling degree, the system comprehensive efficiency, and the carbon balance adjustment factor, the coordination degree between the soil-grass-livestock subsystems is calculated; Based on the coordination degree, the coupling degree rating result of the soil-grass-livestock carbon cycle system is obtained.

[0007] Preferably, the method for positioning the key carbon flow imbalance path comprises the following steps: Based on the coupling degree rating result, the parameters of the carbon flow pool are subjected to sensitivity analysis to identify the dominant factor causing the state change of the soil-grass-livestock carbon cycle system; The dominant factor is mapped to the soil-grass-livestock carbon flow path to position the key carbon flow imbalance path.

[0008] Preferably, the threshold values in the grazing and biological / non-biological factor threshold model include the maximum sustainable grazing rate, the key biological factor threshold value, and the key non-biological factor threshold value; The key biological factor threshold value includes minimum vegetation coverage and dominant plant population proportion. The key non-biological factor threshold value includes soil water content critical value and growth season effective accumulated temperature range.

[0009] The application also provides a grazing management-based grassland livestock carbon cycle regulation system for realizing the method, comprising: A carbon flow pool construction module is configured to collect grassland livestock multi-source data of a preset area, and construct plant carbon migration, livestock carbon migration, soil carbon migration and ecosystem carbon cycle respectively, so as to quantify the flux and stock of carbon in the process of grassland productivity and livestock productivity formation, and form a carbon flow pool. A coupling degree rating module is configured to establish a carbon cycle system coupling degree evaluation model, and obtain a coupling degree rating result of the grassland livestock carbon cycle system in combination with the carbon flow pool. A carbon imbalance path positioning module is configured to diagnose the support level of the ecosystem carbon cycle state to the grassland productivity and livestock productivity based on the coupling degree rating result, and position a key carbon flow imbalance path causing decoupling of the grassland livestock carbon cycle system. A threshold model construction module is configured to construct a grazing and biological / non-biological factor threshold model based on the key carbon flow imbalance path. A grazing management module is configured to perform grazing management based on the grazing and biological / non-biological factor threshold model, drive recoupling of the soil-grass-livestock carbon cycle system, and realize coordinated improvement of the grassland productivity and the livestock productivity.

[0010] Preferably, in the carbon flow pool of the carbon flow pool construction module: In the plant carbon migration, the plant carbon input includes plant carbon assimilation products, carbon assimilation product allocation to aboveground / belowground biomass carbon, aboveground biomass carbon allocation to livestock / aboveground litter carbon, and belowground biomass carbon allocation to live roots / root exudates / belowground litter carbon; the plant carbon output includes plant aboveground part respiration and root respiration, aboveground / belowground litter decomposition, root exudates, and livestock feed intake carbon; In the livestock carbon migration, the livestock carbon input includes livestock feed intake carbon; the livestock carbon output includes livestock respiration carbon, intestinal methane carbon, fecal carbon and sold livestock product carbon; In the soil carbon migration, the soil carbon input includes plant aboveground / belowground litter transferred into soil organic carbon, plant root exudate carbon, fecal transferred into soil organic carbon and microbial carbon; the soil organic carbon pool mainly includes soil plant-derived carbon, soluble organic carbon, particulate organic carbon and mineral-bound carbon; the soil carbon output includes autotrophic and heterotrophic respiration of soil; The ecosystem carbon cycle includes carbon exchange between grassland, atmosphere, human society and other ecosystems.

[0011] Preferably, the coupling degree rating module comprises: Based on the carbon flow pool, the core carbon flow intensity, the core carbon storage capacity and the system comprehensive efficiency are calculated to construct evaluation indexes, wherein the system comprehensive efficiency comprises ecological efficiency and system stability; The core carbon flow intensity is standardized, and the carbon flow weight of the standardized core carbon flow intensity is dynamically determined by using an entropy weight method in combination with the evaluation indexes; Based on the carbon flow weight and the standardized core carbon flow intensity, the weighted coupling degrees between the grass and livestock sub-systems are calculated; Based on the measured net carbon balance of the ecosystem and the ideal carbon sink value, a carbon balance adjustment factor is established; Based on the weighted coupling degrees, the system comprehensive efficiency and the carbon balance adjustment factor, the coordination degrees between the grass and livestock sub-systems are calculated; Based on the coordination degrees, the coupling degree rating results of the grass and livestock carbon cycle system are obtained.

[0012] Compared with the prior art, the present application has the following beneficial effects: By establishing a technical chain of "carbon flow pool-coupling diagnosis-path identification-threshold regulation", the abstract carbon cycle process is first quantitatively associated with specific grassland productivity and livestock productivity indexes. The system can accurately identify the key carbon flow imbalance path that restricts the improvement of productivity, and target intervention is carried out through grazing management, which fundamentally changes the current situation of fuzzy understanding of the relationship between carbon cycle and productivity in traditional management.

[0013] The maximum sustainable grazing rate is dynamically coupled with key biological / non-biological factors such as vegetation coverage and soil moisture content, and a threshold model is established to ensure the benign coupling of system carbon cycle and the sustainability of productivity. The system can adaptively adjust the management strategy according to the changes in environmental conditions, realizing the transformation from empirical grazing to data-driven precision management, and providing scientific guarantee for the continuous improvement of productivity.

[0014] By promoting the recoupling of soil-grass-livestock system carbon cycle, the soil carbon sink and the ability of grassland to sequester carbon are improved, and the efficiency of grassland output and the productivity of livestock are effectively improved. This method breaks through the dilemma of the opposition between ecological protection and production development in traditional management, forms a virtuous cycle of "carbon cycle optimization-system health-productivity improvement", and provides a reliable technical path for the sustainable development of grassland. BRIEF DESCRIPTION OF DRAWINGS

[0015] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of a method for regulating the carbon cycle of livestock and grassland based on grazing management, as described in an embodiment of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] Example 1 like Figure 1 As shown, a method for regulating the soil-grass-livestock carbon cycle based on grazing management includes: S1: Collect multi-source data on soil, grass, and livestock in a preset area, and construct carbon migration data for plants, livestock, soil, and ecosystem carbon cycling to quantify the flux and stock of carbon in grassland and livestock productivity formation processes, forming a carbon flow pool; a further implementation method is that, in the carbon flow pool: In plant carbon migration, plant carbon input includes plant carbon assimilation products, allocation of carbon assimilation products to aboveground / underground biomass carbon, allocation of aboveground biomass carbon to livestock / aboveground litter carbon, and allocation of underground biomass carbon to living roots / root exudates / underground litter carbon; plant carbon output includes aboveground respiration and root respiration, decomposition of aboveground / underground litter, root exudates, and carbon from livestock feed intake.

[0020] Specifically, a photosynthetic physiology-remote sensing collaborative model is adopted to overcome the limitations of traditional vegetation index estimation. This model uses photosynthetically active radiation, leaf area index, air temperature, and soil moisture as driving factors. By combining the Farquhar photosynthetic biochemistry model with light response curves, it dynamically simulates the net photosynthetic rate at the leaf scale and extrapolates it to the canopy scale, achieving mechanistic and high-precision estimation of plant carbon assimilation products.

[0021] A dynamic allocation model based on phenology and source-sink relationship is introduced. This model takes carbon assimilation products as total resources, and sets different allocation priorities and coefficients according to different phenological stages of grass growth (such as green-up period, rapid growth period, and senescence period). For example, in the green-up period, the allocation is preferentially given to new leaves; in the rapid growth period, the allocation to roots is increased to enhance nutrient uptake; and in the reproductive period, the allocation to reproductive organs is considered. This mechanism dynamically adjusts by coupling growing degree days and water stress factors, making the allocation ratio of aboveground / underground biomass carbon more consistent with the real physiological and ecological strategies of plants.

[0022] For aboveground / underground litter carbon, instead of using a fixed turnover rate, a dynamic litter model related to plant phenology, tissue carbon-nitrogen ratio, and environmental temperature is established. Aging tissues are converted to litter at a certain rate. For root exudate carbon, it is modeled as a function of a fixed proportion of photosynthetic product allocation to underground carbon and microbial priming effect, thus more accurately depicting this often overlooked but crucial carbon input pathway.

[0023] In livestock carbon migration, livestock carbon input includes livestock carbon intake; livestock carbon output includes livestock respiration carbon, intestinal methane carbon, fecal carbon, and sales of livestock products carbon.

[0024] Specifically, combining animal individual energy demand model and grassland available biomass remote sensing inversion results. First, according to the type of livestock, body weight, and physiological stage (maintenance, growth, lactation), the daily metabolic energy demand is calculated, then combined with the nutrient composition and digestibility of grassland pasture, the required dry matter intake is back calculated, and finally the carbon intake is converted according to the carbon content of the grass. This method directly links the intake to the actual situation of the livestock physiological needs and grassland, with much higher accuracy than empirical estimation.

[0025] Livestock respiration carbon and intestinal methane carbon are calculated using the respiration entropy model based on intake and feed composition and the IPCC Tier2 methane emission factor method, rather than a general total emission.

[0026] Sales of livestock products carbon: Establish a database of carbon content in livestock products (such as meat, dairy products, and fur), and accurately calculate the carbon content according to the number of animals sold or the yield, to realize the carbon flow traceability from production to sales.

[0027] Fecal carbon: estimated by mass conservation principle (intake carbon - respiratory methane carbon - carbon fixed in weight gain - product carbon = fecal carbon), and considering the carbon loss rate during manure management, to more accurately assess the amount of carbon returned to the soil.

[0028] In the process of soil carbon migration, the input of soil carbon includes organic carbon transferred into soil by plant aboveground / underground litter, plant root exudates, feces and microorganism carbon; the soil organic carbon pool mainly includes soil plant-derived carbon (lignin phenol), soluble organic carbon, particulate organic carbon and mineral-bound carbon; and the output of soil carbon includes autotrophic and heterotrophic respiration.

[0029] Specifically, the relative contents of particulate organic carbon (POC, physical protection) and mineral-associated organic carbon (MAOC, chemical protection) in surface soil are inversed by using hyperspectral remote sensing and soil visible-near infrared spectroscopy and through machine learning algorithm. This realizes the spatial identification of functional components of soil carbon pool and provides a new dimension for evaluating carbon stability.

[0030] The carbon inputs of different sources (litter, root exudates and fecal carbon) are set with different stoichiometric ratios (such as C:N) and degradation rate constants in the model. For example, the decomposition rate of fecal carbon is significantly higher than that of litter with high lignification. This differential treatment enables the model to more realistically simulate the contribution differences of different carbon sources to each component of soil carbon pool.

[0031] In the soil carbon output (heterotrophic respiration) module, a simplified model of the microbial carbon pump concept framework is used to express heterotrophic respiration as a function of microbial biomass, soil temperature, humidity and substrate availability, thereby upgrading the simple physical and chemical process to a biogeochemical process regulated by microbial life activities, greatly improving the mechanism and accuracy of soil carbon turnover simulation.

[0032] Ecosystem carbon cycle includes carbon exchange between grassland, atmosphere, human society and other ecosystems. Specifically, the system boundary is clearly defined, and the net carbon exchange (NEE) between grassland and atmosphere is taken as the final criterion for system carbon balance, and its value can be cross-verified by mass conservation of carbon flow pools. The carbon output to human society system (carbon in livestock products) and the carbon input from other ecosystems (such as supplementary feeding) are calculated separately.

[0033] S2: Establish a carbon cycle system coupling degree evaluation model, combine the carbon flow pool, and obtain the coupling degree rating results of the soil-grass-livestock carbon cycle system; a further implementation manner is that the method for establishing the carbon cycle system coupling degree evaluation model comprises: Based on the carbon flow pool, the core carbon flow intensity, the core carbon pool capacity and the system comprehensive efficiency are calculated to construct the evaluation index; wherein, the system comprehensive efficiency includes the ecological efficiency and the system stability; specifically, the quantitative parameters with clear ecological direction are extracted and calculated. The core carbon flow intensity not only quantifies the flux of "grass-soil carbon flow", "grass-livestock carbon flow" and "livestock-soil carbon flow", but more importantly, it explains the direct relationship with productivity: the "grass-livestock carbon flow" intensity is used to reflect the "grassland pasture supply capacity" and "livestock carbon intake capacity", which is the direct carbon basis of livestock productivity. The "grass-soil carbon flow" intensity reflects the ability of the system to maintain its own fertility, and its strength determines the foundation of the sustainability of grassland productivity. The "livestock-soil carbon flow" intensity represents the efficiency of returning livestock carbon to the soil through grazing management, which is the key path to synergistically improve soil fertility and reduce external fertilization.

[0034] The core carbon pool capacity is used to evaluate the carbon capital stock to maintain productivity. The soil carbon pool reserves are not only an ecological carbon sink, but also a "soil fertility carbon pool", and its capacity directly determines the grassland production potential and disturbance resistance. The vegetation carbon pool reserves are "renewable feed carbon pool", and its current situation reflects the current level of feed carbon reserves available for livestock production.

[0035] The system comprehensive efficiency is a comprehensive criterion for diagnosing the sustainability of productivity. The ecological efficiency is represented by the "net ecosystem carbon balance" to characterize the carbon sink intensity of the system. The system stability is reflected by the approximation degree of actual NPP and potential NPP to reflect the pressure on the system.

[0036] The core carbon flow intensity is standardized, and the carbon flow weight of the standardized core carbon flow intensity is dynamically determined by using the entropy weight method combined with the evaluation index; Based on the carbon flow weight and the standardized core carbon flow intensity, the weighted coupling degree between the soil, grass and livestock subsystems is calculated; specifically, in the process of determining the carbon flow weight and calculating the weighted coupling degree, the invention discards the fixed or subjective weighting of the subsystem in the traditional model, and introduces a dynamic weight determination mechanism based on the entropy weight method. First, the core carbon flow intensity is standardized, and then the entropy weight of each carbon flow path is calculated according to the historical data distribution under different coupling levels. For example, in the data set of a system in a state of serious decoupling, "grass-soil carbon flow" usually shows higher information entropy, so it is given a greater weight, which accurately reflects that in this state, the recovery of soil carbon input is the primary contradiction. Then, these dynamic weights are substituted into the improved coupling degree formula to calculate the weighted coupling degree, which can better reveal the real interaction strength between each carbon flow path under the current specific system state.

[0037] Based on the measured net carbon balance of the ecosystem and the ideal carbon sink value, a carbon balance adjustment factor is established; based on the weighted coupling degree, the system comprehensive efficiency and the carbon balance adjustment factor, the coordination degree between each subsystem of the soil-grass-livestock is calculated; based on the coordination degree, the coupling degree rating result of the soil-grass-livestock carbon cycle system is obtained. Specifically, a nonlinear carbon balance adjustment factor is introduced, which is constructed based on the ratio of the measured net carbon balance of the ecosystem to the regional ideal carbon sink value: when the system is a carbon sink, the factor > 1, which has an amplification effect on the coordination degree result; when the system is a carbon source, the factor < 1, which has a punitive discount on the coordination degree result. Then, the linear weighted sum of the weighted coupling degree and the system comprehensive efficiency is combined with the carbon balance adjustment factor to calculate the final adjusted coordination degree. Based on this coordination degree value, the final output of the four-level coupling degree rating result D not only reflects the internal coordination of the system, but also fundamentally marks its functional role in global carbon cycle.

[0038] The four-level rating standard is as follows: I level (high-quality coupling): 0.8 < D ≤ 1.0 (high carbon sink, high output, and stable system); II level (good coupling): 0.6 < D ≤ 0.8 (carbon sink and output balance, and relatively stable system); III level (critical decoupling): 0.4 < D ≤ 0.6 (carbon sink or output is blocked, and the system appears pressure); IV level (serious decoupling): 0 ≤ D ≤ 0.4 (carbon sink function loss, grassland degradation, and low productivity).

[0039] S3: Based on the coupling degree rating result, the support level of the carbon cycle state of the ecosystem to the grassland productivity and the livestock productivity is diagnosed, and the key carbon flow imbalance path leading to the decoupling of the soil-grass-livestock carbon cycle system is located; a further implementation is that the method for locating the key carbon flow imbalance path comprises: Based on the coupling degree rating result, the parameters of the carbon flow pool are subjected to sensitivity analysis to identify the dominant factor leading to the change of the state of the soil-grass-livestock carbon cycle system; specifically, the conventional method of indiscriminately analyzing the sensitivity of all carbon flow parameters is abandoned, and the target function of the sensitivity analysis is set according to the coupling level diagnosed preliminarily. For example, when the system is diagnosed as IV level (serious decoupling), the core target of the analysis is to find out the parameter that has the greatest impact on the "system stability index" and "ecological efficiency"; when the system is at II level (good coupling), the focus is on analyzing the parameter most sensitive to "economic efficiency". This prior orientation based on rating makes the analysis process more efficient and targeted.

[0040] The global sensitivity analysis method based on variance is used to quantitatively evaluate the influence of key parameters in the carbon flow pool. This method not only measures the direct influence (first-order sensitivity index) of a single parameter (such as forage intake, soil respiration rate) on the coupling degree of the system (i.e. the objective function), but also reveals the complex nonlinear influence (total sensitivity index) of the interaction between parameters (such as the synergistic effect of grazing rate and precipitation) on the system. Through Monte Carlo simulation, thousands of sampling operations are performed within the predetermined reasonable range of parameters, thereby accurately quantifying the contribution of each carbon flow parameter to the change in system state, and selecting the top-ranked dominant factor set accordingly.

[0041] The dominant factors are mapped to the soil-grass-livestock carbon flow path to locate the key carbon flow imbalance path. Specifically, in the key step of mapping dominant factors to specific carbon flow imbalance paths, a "carbon flow path-ecological function" correlation matrix is constructed. This matrix pre-defines the ecological process corresponding to each type of dominant factor and its specific link in the soil-grass-livestock cycle. For example, when "soil heterotrophic respiration rate" is identified as a high-sensitivity dominant factor, it is directly mapped to the "soil carbon pool→atmosphere" carbon emission path through the correlation matrix, and its imbalance mechanism points to "soil-grass" decoupling, i.e. the carbon input of grassland vegetation is insufficient to compensate for the carbon output of soil microorganisms. Similarly, if "livestock foraging efficiency" is a dominant factor, it is mapped to the "grassland→livestock" carbon flow path, and its imbalance mechanism is "grass-livestock" decoupling. Through this standardized mapping from "numerical factors" to "ecological paths", we not only locate the key carbon flow imbalance path, but also simultaneously diagnose the internal mechanism that leads to the decoupling of the system, providing unarguable mathematical basis and action targets for subsequent formulation of precise threshold regulation strategies.

[0042] S4: Based on the key carbon flow imbalance path, a grazing and biological / non-biological factor threshold model is constructed; further embodiments are that the thresholds in the grazing and biological / non-biological factor threshold model include the maximum sustainable grazing rate, key biological factor threshold, and key non-biological factor threshold; Wherein, the key biological factor threshold includes the minimum vegetation coverage and the proportion of dominant plant population; The key non-biological factor threshold includes the critical value of soil moisture content and the effective accumulated temperature range in the growing season.

[0043] In this embodiment, a dynamic, adaptive, and multi-factor collaborative constraint intelligent decision-making model is established. This model not only defines the thresholds, but more importantly, it clarifies the internal linkage mechanism between thresholds and their functional relationship with the ultimate goal of the system (coupling coordination degree). The specific technical process is as follows: Firstly, the solution of the maximum sustainable grazing rate (MSGR) is the cornerstone of the model construction. The invention adopts an innovative method combining reverse solving and machine learning. Traditional methods attempt to derive the carrying capacity from the grass quantity, while the invention takes "maintaining or improving the system coupling coordination degree to level II (0.6) and above" as the global optimization goal, and regards MSGR as a variable to be solved. The specific process is as follows: collect historical data sets covering different climate types, soil bases and grazing intensities, each data point of which contains the grazing rate, biological / non-biological factor observation values and the system coupling coordination degree calculated therefrom. Then, use integrated learning algorithms such as random forest or gradient boosting tree to train a prediction model with grazing rate, vegetation coverage, soil moisture, effective accumulated temperature and other factors as features and system coupling coordination degree as labels. After training, fix other environmental factors in a typical scenario, and use numerical iterative optimization algorithms (such as bisection method or Newton method) to inversely solve the maximum grazing rate under the constraint condition of system coupling coordination degree ≥ 0.6, which is the MSGR in this environmental scenario.

[0044] The determination of the threshold values of key biological and non-biological factors is not independent, but is a dynamic constraint condition for MSGR, which is a great creation of the model. We define the threshold values of the environmental factors by analyzing the critical state of each factor when the system is about to slide from "benign coupling (level II)" to "critical decoupling (level III)" in the above historical data set. For example, the threshold value of the minimum vegetation coverage is determined by analyzing the distribution of the vegetation index (such as NDVI) corresponding to the system coupling coordination degree value falling below 0.6 in history, and taking the lower limit of the confidence interval. The threshold value of the proportion of dominant plant population is determined by correlation analysis, which sets the warning line when the proportion of degradation indicator plants exceeds a certain critical point or the proportion of high-quality forage is below a certain critical point.

[0045] The creative integration of the model is reflected in the construction of a "state-response" query matrix. The matrix takes soil moisture and effective accumulated temperature in the growing season as the two main dimensions of the core environmental driving factors, and discretizes continuous environmental conditions into multiple scenario cells. In each cell, a set of threshold parameters corresponding to it, such as MSGR and minimum vegetation coverage, are stored through reverse solving and critical analysis described above. Thus, the threshold model upgrades from a isolated numerical table to a dynamic decision support system: the manager only needs to input the current measured soil moisture and accumulated temperature data, and the system can output the precise upper limit of grazing intensity under the current environmental carrying capacity and the red line of vegetation health that needs to be monitored through querying the matrix and interpolation calculation, realizing the fundamental change from "experience-driven" to "data and model-driven" in grazing management, and ensuring the scientific and achievable nature of ecological goals.

[0046] S5: Based on the grazing and biological / non-biological factor threshold model, grazing management is performed to drive the recoupling of the soil-grass-livestock carbon cycle system, and the synergistic improvement of grassland productivity and livestock productivity is realized.

[0047] Specifically, for the decoupling of "grass-livestock": strictly implement the MSGR, implement zoning and rotation grazing, and prohibit grazing and resting during the critical growth period.

[0048] For the decoupling of "soil-grass": supplementary planting of legume grass (increasing nitrogen fixation and improving litter quality), and implementing light harrowing or fertilization to promote vegetation restoration and carbon input.

[0049] For the decoupling of "livestock-soil": optimize the spatial distribution of livestock (such as the layout of salt points and drinking water points), and promote the uniform return of manure. Even the manure can be collected for composting and then returned to the field.

[0050] Utilize Internet of Things sensors (soil moisture, temperature), unmanned aerial vehicles and satellite remote sensing to continuously monitor key indicators. Real-time monitoring data is input into the rating model of the application, and the change of coupling coordination degree is dynamically tracked. If the coupling coordination degree does not improve as expected or decreases, the threshold system is automatically triggered to recalculate and adjust the control strategy, forming a self-adaptive and closed-loop intelligent management system.

[0051] Embodiment two: The application also provides a soil-grass-livestock carbon cycle regulation and control system based on grazing management, which is used to realize the method, comprising: A carbon flow pool construction module is configured to collect multi-source data of soil, grass and livestock in a preset area, and construct plant carbon migration, livestock carbon migration, soil carbon migration and ecosystem carbon cycle respectively, so as to quantify the flux and stock of carbon in the process of forming grassland productivity and livestock productivity, and form a carbon flow pool. A coupling degree rating module is configured to establish a coupling degree evaluation model of the carbon cycle system, and obtain a coupling degree rating result of the soil-grass-livestock carbon cycle system in combination with the carbon flow pool. A carbon imbalance path positioning module is configured to diagnose the support level of the state of the ecosystem carbon cycle to the grassland productivity and the livestock productivity based on the coupling degree rating result, and position a key carbon flow imbalance path causing the decoupling of the soil-grass-livestock carbon cycle system. A threshold model construction module is configured to construct a grazing and biological / non-biological factor threshold model based on the key carbon flow imbalance path. A grazing management module is configured to perform grazing management based on the grazing and biological / non-biological factor threshold model, drive the recoupling of the soil-grass-livestock carbon cycle system, and realize the synergistic improvement of the grassland productivity and the livestock productivity.

[0052] Preferably, in the carbon flow pool of the carbon flow pool construction module: In the plant carbon migration, the plant carbon input includes plant carbon assimilation product, carbon assimilation product carbon allocation to aboveground / belowground biomass, aboveground biomass carbon allocation to livestock / aboveground litter carbon, belowground biomass carbon allocation to living root / root exudate / belowground litter carbon; the plant carbon output includes plant aboveground part respiration and root respiration, aboveground / belowground litter decomposition, root exudate, livestock feed intake carbon; In the livestock carbon migration, the livestock carbon input includes livestock feed intake carbon; the livestock carbon output includes livestock respiration carbon, intestinal methane carbon, fecal carbon and livestock product sale carbon; In the soil carbon migration, the soil carbon input includes plant aboveground / belowground litter transferred into soil organic carbon, plant root exudate carbon, fecal transferred into soil organic carbon and microbial carbon; the soil organic carbon pool mainly includes soil plant-derived carbon, soluble organic carbon, particulate organic carbon and mineral-bound carbon; the soil carbon output includes autotrophic and heterotrophic respiration; The ecosystem carbon cycle includes grassland carbon exchange with the atmosphere, human society and other ecosystems.

[0053] Preferably, the coupling degree rating module comprises: Based on the carbon flow pool, core carbon flow intensity, core carbon pool capacity and system comprehensive efficiency are calculated to construct evaluation indexes; wherein, the system comprehensive efficiency includes ecological efficiency and system stability; The core carbon flow intensity is standardized, and the evaluation indexes are combined to dynamically determine carbon flow weight of the standardized core carbon flow intensity using an entropy weight method; Based on the carbon flow weight and the standardized core carbon flow intensity, the weighted coupling degree between each subsystem of the soil-grass-livestock system is calculated; Based on the measured ecosystem net carbon balance and ideal carbon sink value, a carbon balance adjustment factor is established; Based on the weighted coupling degree, the system comprehensive efficiency and the carbon balance adjustment factor, the coordination degree between each subsystem of the soil-grass-livestock system is calculated; Based on the coordination degree, the coupling degree rating result of the soil-grass-livestock carbon cycle system is obtained.

[0054] The above-described embodiments are only descriptions of the preferred modes of the present application and do not limit the scope of the present application. Without departing from the design spirit of the present application, various modifications and improvements to the technical solutions of the present application made by those of ordinary skill in the art shall fall within the protection scope determined by the claims of the present application.

Claims

1. A method for regulating soil grass and livestock carbon cycle based on grazing management, characterized in that, The method comprises the following steps: Collecting multi-source data of soil, grass and livestock in a preset area, respectively constructing plant carbon migration, livestock carbon migration, soil carbon migration and ecosystem carbon cycle to quantify the flux and stock of carbon in the formation process of grassland productivity and livestock productivity, and forming a carbon flow pool; Establishing a carbon cycle system coupling degree evaluation model, combining the carbon flow pool to obtain the coupling degree rating result of the soil-grass-livestock carbon cycle system; Based on the coupling degree rating result, diagnosing the support level of the ecosystem carbon cycle state to the grassland productivity and the livestock productivity, and positioning the key carbon flow imbalance path leading to the decoupling of the soil-grass-livestock carbon cycle system; Based on the key carbon flow imbalance path, constructing a grazing and biological / non-biological factor threshold model; Based on the grazing and biological / non-biological factor threshold model, performing grazing management to drive the recoupling of the soil-grass-livestock carbon cycle system and realize the coordinated improvement of the grassland productivity and the livestock productivity.

2. The method of claim 1, wherein, In the carbon flow pool: In the plant carbon migration, the plant carbon input includes plant carbon assimilation products, carbon assimilation product allocation to aboveground / belowground biomass carbon, aboveground biomass carbon allocation to livestock / aboveground litter carbon, and belowground biomass carbon allocation to live roots / root exudates / belowground litter carbon; the plant carbon output includes plant aboveground part respiration and root respiration, aboveground / belowground litter decomposition, root exudates, and livestock feed intake carbon; In the livestock carbon migration, the livestock carbon input includes livestock feed intake carbon; the livestock carbon output includes livestock respiration carbon, intestinal methane carbon, fecal carbon and livestock product sales carbon; In the soil carbon migration, the soil carbon input includes plant aboveground / belowground litter transferred into soil organic carbon, plant root exudate carbon, fecal transferred into soil organic carbon and microbial carbon; the soil organic carbon pool mainly includes soil plant-derived carbon, soluble organic carbon, particulate organic carbon and mineral-bound carbon; the soil carbon output includes soil autotrophic and heterotrophic respiration; The ecosystem carbon cycle includes carbon exchange between grassland, atmosphere, human society and other ecosystems.

3. The method of claim 2, wherein, The method for establishing the carbon cycle system coupling degree evaluation model comprises: Based on the carbon flow pool, calculating the core carbon flow intensity, the core carbon pool capacity and the system comprehensive efficiency to construct evaluation indexes; wherein the system comprehensive efficiency includes ecological efficiency and system stability; Standardizing the core carbon flow intensity, combining the evaluation indexes and using the entropy weight method to dynamically determine the carbon flow weight of the standardized core carbon flow intensity; Based on the carbon flow weight and the standardized core carbon flow intensity, calculating the weighted coupling degree between each subsystem of soil, grass and livestock; Based on the measured ecosystem net carbon balance and the ideal carbon sink value, establishing a carbon balance adjustment factor; Based on the weighted coupling degree, the system comprehensive efficiency and the carbon balance adjustment factor, calculating the coordination degree between each subsystem of soil, grass and livestock; Based on the coordination degree, obtaining the coupling degree rating result of the soil-grass-livestock carbon cycle system.

4. The method of claim 1, wherein, The method for positioning the key carbon flow imbalance path comprises: Based on the coupling degree rating result, performing sensitivity analysis on the parameters of the carbon flow pool to identify the dominant factor leading to the state change of the soil-grass-livestock carbon cycle system; Mapping the dominant factor to the soil-grass-livestock carbon flow path to position the key carbon flow imbalance path.

5. The method of claim 1, wherein, The threshold in the grazing and biological / non-biological factor threshold model includes a maximum sustainable grazing rate, a key biological factor threshold, and a key non-biological factor threshold; The key biological factor threshold includes a minimum vegetation coverage and a dominant plant population ratio; The key non-biological factor threshold includes a soil water content critical value and a range of effective accumulated temperature in the growing season.

6. A grazing management-based soil grass livestock carbon cycle regulation system for implementing the method of any one of claims 1-5, characterized in that, It comprises: a carbon flow pool construction module for collecting multi-source data of soil, grass and livestock in a preset area, respectively constructing plant carbon migration, livestock carbon migration, soil carbon migration and ecosystem carbon cycle to quantify the flux and stock of carbon in the process of forming grassland productivity and livestock productivity, and forming a carbon flow pool; a coupling degree rating module for establishing a carbon cycle system coupling degree evaluation model, combining the carbon flow pool to obtain the coupling degree rating result of the soil, grass and livestock carbon cycle system; a carbon imbalance path positioning module for diagnosing the support level of the ecosystem carbon cycle state to the grassland productivity and livestock productivity based on the coupling degree rating result, and positioning the key carbon flow imbalance path causing the decoupling of the soil, grass and livestock carbon cycle system; a threshold model construction module for constructing a grazing and biological / non-biological factor threshold model based on the key carbon flow imbalance path; a grazing management module for performing grazing management based on the grazing and biological / non-biological factor threshold model, driving the recoupling of the soil-grass-livestock carbon cycle system, and realizing the coordinated improvement of the grassland productivity and livestock productivity.

7. The system of claim 6, wherein, In the carbon flow pool of the carbon flow pool construction module: In plant carbon migration, plant carbon input includes plant carbon assimilation products, carbon assimilation product allocation to aboveground / belowground biomass carbon, aboveground biomass carbon allocation to livestock / aboveground litter carbon, and belowground biomass carbon allocation to live roots / root exudates / underground litter carbon; plant carbon output includes plant aboveground part respiration and root respiration, aboveground / belowground litter decomposition, root exudates, and livestock feed intake carbon; In livestock carbon migration, livestock carbon input includes livestock feed intake carbon; livestock carbon output includes livestock respiration carbon, intestinal methane carbon, fecal carbon, and sold livestock product carbon; In soil carbon migration, soil carbon input includes plant aboveground / belowground litter transferred into soil organic carbon, plant root exudate carbon, fecal transferred into soil organic carbon, and microbial carbon; soil organic carbon pool mainly includes soil plant-derived carbon, soluble organic carbon, particulate organic carbon, and mineral-bound carbon; soil carbon output includes soil autotrophic and heterotrophic respiration; Ecosystem carbon cycle includes carbon exchange between grassland, atmosphere, human society and other ecosystems.

8. The system of claim 6, wherein, The coupling degree rating module comprises: Based on the carbon flow pool, calculate the core carbon flow intensity, core carbon pool capacity and system comprehensive efficiency to build evaluation indexes; wherein the system comprehensive efficiency includes ecological efficiency and system stability; Standardize the core carbon flow intensity, combine the evaluation indexes, and use the entropy weight method to dynamically determine the carbon flow weight of the standardized core carbon flow intensity; Based on the carbon flow weight and the standardized core carbon flow intensity, calculate the weighted coupling degree between each subsystem of soil, grass and livestock; Based on the measured net carbon balance of the ecosystem and the ideal carbon sink value, establish a carbon balance adjustment factor; Based on the weighted coupling degree, the system comprehensive efficacy and the carbon balance adjustment factor, a coordination degree between each subsystem of the grass-fed livestock is calculated; Based on the coordination degree, a coupling degree rating result of the grass-fed livestock carbon cycle system is obtained.

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