Grassland bearing capacity dynamic evaluation method and system considering grassland plant compensatory growth mechanism

Abstract

The invention discloses a grassland bearing capacity dynamic evaluation method and system considering a grassland plant compensatory growth mechanism, and relates to the technical field of ecology, grazing management and grassland productivity evaluation. The method comprises the steps of collecting data, preprocessing remote sensing data, meteorological data and the like, and simulating grassland productivity; constructing a grassland productivity-grazing intensity response model fused with a compensatory growth mechanism; and establishing a constraint equation based on the theoretical bearing capacity model, and solving the dynamic bearing capacity through a numerical method. The grassland bearing capacity evaluation method overcomes the defect that a traditional static evaluation method does not consider dynamic response of vegetation, remarkably improves scientificity and accuracy of grassland bearing capacity estimation, and is suitable for precise management and ecological protection of various grassland types such as alpine grassland and desert grassland.

Description

Technical Field

[0001] This invention relates to the fields of ecology, grazing management and grassland productivity assessment, and in particular to a method and system for dynamic assessment of grassland carrying capacity that takes into account the compensatory growth mechanism of grassland plants. Background Technology

[0002] With the increasing severity of climate change and human disturbance, grassland ecosystems are facing significant risks of structural and functional degradation, seriously threatening their ability to provide ecosystem services and consequently impacting human well-being. Grazing, as one of the most widespread human uses of grassland ecosystems, is a fundamental productive activity upon which hundreds of millions of people worldwide depend for survival. However, overgrazing leads to grassland degradation, biodiversity loss, and ecosystem dysfunction, even triggering regional and global ecological and environmental problems. Therefore, scientifically assessing grassland carrying capacity and rationally planning grazing intensity are crucial prerequisites for achieving sustainable use of grassland resources and regional ecological security.

[0003] Currently, grassland carrying capacity is generally defined as the number of grazing livestock that a grassland can support within a certain time and area, under the premise of maintaining ecological stability and normal livestock growth and development. Numerous studies have improved the accuracy of carrying capacity assessments by constructing or optimizing grassland production and yield models (such as the Carnegie-Ames-Stanford Approach for light energy use efficiency, the BIOME-BGC ecosystem process model, the Thornthwaite-Memorial climate productivity model, and the PROSAIL radiative transfer model). Simultaneously, assessment parameters are becoming increasingly refined, such as incorporating factors like forage intake by herbivorous wild ungulates, pika intake, available forage rate, root-to-shoot ratio, and supplemental feeding into the model system, enriching the carrying capacity assessment framework and improving the accuracy of grassland carrying capacity assessments to some extent.

[0004] However, most existing grassland carrying capacity assessment techniques are based on the assumption of static grassland growth processes, failing to fully consider the dynamic response of grassland vegetation to grazing disturbance. Studies have shown that under certain levels of foraging disturbance, plants can rapidly regenerate and recover, achieving a rebound in forage yield, and even exhibiting a "supercompensatory growth" effect. At the ecosystem scale, this mechanism manifests as the "grazing optimization hypothesis," meaning that moderate grazing may enhance grassland ecosystem productivity, while overgrazing inhibits forage growth. Traditional grassland carrying capacity assessment methods only statically consider the growth capacity and yield of grassland under the current grazing intensity. As grazing intensity changes, grassland forage production capacity and yield inevitably change accordingly, leading to overestimation or underestimation of grassland carrying capacity, resulting in mismanagement of grazing and potentially causing economic losses or grassland ecosystem degradation. Therefore, incorporating the compensatory growth mechanism into the grassland carrying capacity assessment system helps to more accurately reflect the grassland's response to grazing pressure and its ecological capacity. However, there is currently a lack of dynamic assessment methods for grassland carrying capacity that can incorporate compensatory growth mechanisms, making it difficult to meet the precise management needs of highly dynamic and disturbed grassland systems.

[0005] Therefore, proposing a dynamic assessment method and system for grassland carrying capacity that considers the compensatory growth mechanism of grassland plants to solve the problems existing in the prior art is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] In view of this, the present invention provides a method and system for dynamic assessment of grassland carrying capacity that takes into account the compensatory growth mechanism of grassland plants, aiming to solve the problem that traditional static assessment of grassland carrying capacity fails to consider the response capacity of grassland ecosystems to grazing disturbance, resulting in deviations in carrying capacity estimation.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: A method for dynamic assessment of grassland carrying capacity that considers the compensatory growth mechanism of grassland plants includes the following steps: S1 Data Acquisition Steps: Collect the first data according to the data requirements of the selected grassland productivity model, including remote sensing data, meteorological data, and solar radiation data; S2 Data Processing Steps: Clean the first data to obtain the second data; S3 Grassland Productivity Simulation Steps: Input the second data into the selected grassland productivity model, simulate the grassland productivity data, and convert the simulation results into predetermined productivity measurement indicators; S4 Steps for constructing a grassland productivity-grazing intensity response model: Supplement the collection of data related to compensatory growth effects, including grazing intensity data, topographic data, soil data, and human activity data; use grassland productivity data as the dependent variable and the supplemented data as the independent variable, and use machine learning or regression analysis methods to construct a grassland productivity-grazing intensity response model. S5 Steps for dynamic assessment of grassland carrying capacity based on compensatory growth: Based on the grassland productivity-grazing intensity response model, with the grassland production equal to the amount of grass eaten by livestock as a constraint, establish the grassland carrying capacity constraint equation, and solve the constraint equation by numerical solution method to obtain the grassland dynamic carrying capacity expressed in terms of grazing intensity.

[0008] Optionally, in the above method, the data acquisition step S1 may collect remote sensing data including normalized difference vegetation index, surface moisture index, and photosynthetically active radiation absorption ratio, and meteorological data including precipitation, air temperature, potential evapotranspiration, and solar radiation data.

[0009] Optionally, in the above method, the grassland productivity simulation step S3 uses an improved CASA model. The improved CASA model calculates the actual light energy utilization rate by introducing the optimal temperature for vegetation growth, low and high temperature stress factors, and water stress coefficient. It also calculates the net primary productivity of grassland by combining light and effective radiation absorptivity and solar radiation data.

[0010] Optionally, in the above method, the productivity measurement indicators in the S3 grassland productivity simulation step can be net primary productivity, aboveground biomass, underground biomass, or edible forage yield.

[0011] Optionally, in the above method, the grassland productivity-grazing intensity response model in step S4, which is the construction step, is a random forest model.

[0012] Optionally, in step S5, the dynamic assessment of grassland carrying capacity based on compensatory growth, the constraint equations are established based on a theoretical grassland carrying capacity model, specifically in the following form:

[0013] in, This represents the grassland productivity simulated by the grassland productivity-grazing intensity response model. This refers to grazing intensity.

[0014] A dynamic assessment system for grassland carrying capacity that considers the compensatory growth mechanism of grassland plants, implemented using any of the above-mentioned methods for dynamic assessment of grassland carrying capacity that considers the compensatory growth mechanism of grassland plants, includes a data acquisition module, a data processing module, a grassland productivity simulation module, a grassland productivity-grazing intensity response model construction module, and a grassland carrying capacity dynamic assessment module based on compensatory growth, connected in sequence. The data acquisition module is used to collect primary data based on the data requirements of the selected grassland productivity model, including remote sensing data, meteorological data, and solar radiation data. The data processing module is used to clean and process the first data to obtain the second data; The grassland productivity simulation module is used to input the second data into the selected grassland productivity model, simulate grassland productivity data, and convert the simulation results into a predetermined productivity measurement index. The grassland productivity-grazing intensity response model construction module is used to supplement the collection of data related to compensatory growth effects, including grazing intensity data, topographic data, soil data, and human activity data; using grassland productivity data as the dependent variable and the supplemented data as the independent variable, the grassland productivity-grazing intensity response model is constructed using machine learning or regression analysis methods. The grassland carrying capacity dynamic assessment module based on compensatory growth is used to establish a grassland carrying capacity constraint equation based on the grassland productivity-grazing intensity response model, with the grassland production equal to the amount of grass eaten by livestock as a constraint condition. The equation is then solved by numerical solution method to obtain the grassland dynamic carrying capacity expressed in terms of grazing intensity.

[0015] As can be seen from the above technical solution, compared with the prior art, the present invention provides a method and system for dynamic assessment of grassland carrying capacity that considers the compensatory growth mechanism of grassland plants, and has the following beneficial effects: (1) It effectively solves the problem that traditional assessment methods fail to consider the response of vegetation to grazing disturbance, thereby improving the scientificity and adaptability of carrying capacity estimation; (2) Based on the grassland productivity model, this method integrates remote sensing and meteorological, topographic, soil and human activity data to construct a grassland productivity-grazing intensity response model, which can dynamically simulate the grassland productivity change process under different grazing intensities; (3) By solving the constraint condition of balancing grassland production and livestock grazing, a higher precision quantitative estimation of grassland carrying capacity is achieved; (4) The dynamic assessment framework proposed in this invention is applicable to the grassland management needs of ecologically sensitive areas such as the Qinghai-Tibet Plateau, and can also provide a reference for other grazing areas, with good prospects for promotion and application. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0017] Figure 1 This invention discloses a flowchart of a dynamic assessment method for grassland carrying capacity that considers the compensatory growth mechanism of grassland plants; Figure 2 This is a complete evaluation flowchart of the overall evaluation framework and specific implementation methods disclosed in this invention; Figure 3 This is a structural block diagram of a dynamic assessment system for grassland carrying capacity that considers the compensatory growth mechanism of grassland plants, as disclosed in this invention. Detailed Implementation

[0018] 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.

[0019] In this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0020] See Figure 1 As shown, this invention discloses a dynamic assessment method for grassland carrying capacity that considers the compensatory growth mechanism of grassland plants, characterized by comprising the following steps: S1 Data Acquisition Steps: Collect the first data according to the data requirements of the selected grassland productivity model, including remote sensing data, meteorological data, and solar radiation data; S2 Data Processing Steps: Clean the first data to obtain the second data; S3 Grassland Productivity Simulation Steps: Input the second data into the selected grassland productivity model, simulate the grassland productivity data, and convert the simulation results into predetermined productivity measurement indicators; S4 Steps for constructing a grassland productivity-grazing intensity response model: Supplement the collection of data related to compensatory growth effects, including grazing intensity data, topographic data, soil data, and human activity data; use grassland productivity data as the dependent variable and the supplemented data as the independent variable, and use machine learning or regression analysis methods to construct a grassland productivity-grazing intensity response model. S5 Steps for dynamic assessment of grassland carrying capacity based on compensatory growth: Based on the grassland productivity-grazing intensity response model, with the grassland production equal to the amount of grass eaten by livestock as a constraint, establish the grassland carrying capacity constraint equation, and solve the constraint equation by numerical solution method to obtain the grassland dynamic carrying capacity expressed in terms of grazing intensity.

[0021] Furthermore, in the S1 data acquisition step, remote sensing data collected include normalized vegetation index, surface moisture index, and photosynthetically active radiation absorption ratio, while meteorological data include precipitation, air temperature, potential evapotranspiration, and solar radiation data.

[0022] Furthermore, in the S3 grassland productivity simulation step, the grassland productivity model is an improved CASA model. The improved CASA model calculates the actual light energy utilization rate by introducing the optimal temperature for vegetation growth, low temperature and high temperature stress factors, and water stress coefficient. It also calculates the net primary productivity of grassland by combining the light and effective radiation absorption ratio and solar radiation data.

[0023] The specific content of S3 is as follows: Based on the collected data and the requirements of the productivity model, the second data is input into the selected productivity model to simulate grassland productivity. Then, according to different research requirements (such as time scale, research object, etc.), appropriate productivity measurement indicators (such as net primary productivity, aboveground and belowground biomass, edible forage yield, etc.) are used to transform the simulation results.

[0024] Furthermore, in the S3 grassland productivity simulation step, the productivity metrics are net primary productivity, aboveground biomass, belowground biomass, or edible forage yield.

[0025] Furthermore, in the S4 grassland productivity-grazing intensity response model construction step, the grassland productivity-grazing intensity response model is a random forest model.

[0026] The specific content of S4 is as follows: Construction of a grassland productivity-grazing response model: Based on existing research results and requirements related to compensatory growth effects, supplementary remote sensing data, meteorological data, land use data, vegetation data, topographic data, soil data, and human activity data (including grazing intensity data) will be collected and cleaned. Using grassland productivity data obtained through simulation or direct field measurement as the dependent variable (target variable), and the remaining data as independent variables (feature variables), a grassland productivity-grazing intensity response model will be constructed by combining appropriate methods such as geographically weighted regression, partial least squares, neural networks, and machine learning.

[0027] Furthermore, in step S5, the dynamic assessment of grassland carrying capacity based on compensatory growth, the constraint equations are established based on the theoretical grassland carrying capacity model, specifically in the following form:

[0028] in, This represents the grassland productivity simulated by the grassland productivity-grazing intensity response model. This refers to grazing intensity.

[0029] In one specific embodiment, see Figure 2 The diagram shown is a complete evaluation flowchart of the overall evaluation framework and specific implementation methods disclosed in this invention. In this embodiment, the grassland productivity model selected is the improved Carnegie-Ames-Stanford Approach (CASA) model, and the grassland productivity-grazing intensity response model is the random forest model. The model was tested in the grassland area of ​​the Qinghai-Tibet Plateau.

[0030] First, various types of data required for model input are collected, including meteorological data, remote sensing data, vegetation data, and solar radiation data. After data cleaning, the data is input into the CASA model to calculate grassland net primary productivity data. Second, other data required for establishing the grassland productivity-grazing intensity response model are collected, including topography, grazing intensity data, pasture type data, and human activity data, in addition to the above data. Then, a random forest model is trained with grassland net primary productivity as the dependent variable and the remaining data as independent variables. Finally, the random forest model is combined with the theoretical grassland carrying capacity model and solved using the bisection method to obtain the dynamic assessment results of grassland carrying capacity.

[0031] The complete evaluation process for specific implementation methods is as follows: Figure 2 As shown, this paper presents the overall assessment framework and specific implementation method of the dynamic assessment method for grassland carrying capacity based on compensatory growth, including the following steps: Step 1: Collect the necessary data for the improved CASA model used in this embodiment, including solar radiation data, remote sensing data (surface moisture index, normalized difference vegetation index), vegetation classification data, temperature data, and photosynthetic radiation effective absorptivity (FPAR) data, and perform data cleaning. Input the obtained data into the improved CASA model to calculate the monthly net primary productivity (NPP, unit: g·C·K·L). ²). The specific steps for improving the CASA model are as follows: Figure 2 The basic formula for the simulation module of net primary productivity of grassland is shown below:

[0032] Among them, photosynthetically active radiation (APAR, unit: MJ·m) -2 Total solar global radiation (SOL, unit: MJ·mJ·mJ) is the total solar radiation. ²) is jointly determined by the efflux of photosynthetically active radiation (FPAR, unit: %); ε is the actual light energy utilization rate, the calculation process of which is shown below:

[0033] T ε1 and T ε2 W represents the inhibitory effect of low temperature and high temperature stress factors on light energy utilization. ε ε is the water stress coefficient. max This represents the maximum light energy utilization rate under ideal conditions. opt (Unit: °C) represents the optimal temperature for vegetation growth, defined as the average temperature of the month in which the NDVI of the region reaches its annual maximum; T represents the average temperature of the current month; LSWI (Land Surface Water Index) is the vegetation moisture index. max ε represents the maximum value of LSWI during the growth phase of a single pixel; max It can be set based on actual measurement data or literature.

[0034] Step 2: Based on the domain knowledge gained from existing literature on grassland plant compensatory growth, supplement the data needed to construct the grassland net primary productivity-grazing intensity response model. The supplementary data used in this example includes: elevation data, grazing intensity data, soil data, climate data (various temperature data, precipitation data, and potential evapotranspiration data), pasture classification data, remote sensing data (soil moisture index), and human activity data (nighttime light data). Using grassland net primary productivity as the dependent variable (target variable) and the remaining data as independent variables (feature variables), the data is preprocessed and input into a random forest model for training, thus constructing a grassland productivity-grazing intensity response model based on the random forest model.

[0035] Step 3: Solve the grassland productivity-grazing intensity response model using the theoretical grassland carrying capacity model. The theoretical grassland carrying capacity model and its parameters used in this embodiment refer to the national agricultural industry standard NY / T 635-2015 "Methods for Determining the Carrying Capacity of Natural Grasslands" and related published literature on theoretical carrying capacity calculation methods. The specific model is shown below: ; Where C represents the solved grassland dynamic carrying capacity, expressed as grazing intensity; NPP CG NPP is the net primary productivity of grassland corrected for compensatory growth effects; a: forage utilization rate; b: proportion of edible forage; SG: ratio of underground biomass to aboveground biomass for different grassland types; d: forage moisture content; 0.5 is the conversion factor between grassland biomass and net primary productivity; D: grazing days; I: daily feed intake per standard sheep unit. Based on this, the forage yield and forage consumption are shown below, where: NPP is f(x), and grazing intensity is x:

[0036] The above formula can be used to construct the grassland carrying capacity constraint equation. Since the random forest model is a non-parametric, nonlinear, and non-differentiable black-box model, its output cannot be directly inverted analytically. Considering the nonlinear characteristic of grassland productivity responding to grazing intensity with an initial increase followed by a decrease, the classic bisection method is used in this embodiment for inverse calculation. This method can achieve stable linear convergence within a known single-root interval, gradually approximating the optimal solution, thereby obtaining the grassland dynamic carrying capacity expressed in terms of grazing intensity. Based on this, the corresponding net primary productivity of the grassland is the grass production capacity (NPP) obtained after correction by incorporating compensatory growth effects. CG .

[0037] To verify the simulation effect of grassland net primary productivity (NPP), the simulation results of the improved CASA model were compared with those of the widely validated MODIS MOD17A3HGF product. The results show that the improved model has good simulation performance (R² = 0.8808, RMSE = 57.2193) and can meet the requirements for dynamic estimation of grassland carrying capacity. The dynamic assessment results of carrying capacity indicate that the overall average carrying capacity grazing intensity of grassland in the study area is 0.43 SU·h. ², among which shrub meadow has the highest carrying capacity (average 0.80 SU·h). ²), followed by alpine meadows (0.56 SU·h). ²), while alpine steppes and desert steppes have relatively lower values ​​(both approximately 0.17 SU·h). ²). Furthermore, a comparative analysis of the dynamic carrying capacity assessment method of this invention with the traditional static assessment method, which does not consider compensatory growth effects, reveals that at the grid scale, the traditional static assessment method exhibits varying degrees of overestimation or underestimation, with an average relative error of approximately 10.29%, and the deviation is more significant in some high-productivity areas. Based on the total amount across the entire Qinghai-Tibet Plateau, the traditional method's estimation deviation for the overall grassland carrying capacity is +0.33%. In contrast, the dynamic assessment method based on compensatory growth mechanisms proposed in this invention more realistically reflects the ecosystem's response mechanism to grazing pressure, and the assessment results are more scientific, accurate, and have greater application value.

[0038] and Figure 1 Corresponding to the method shown, this invention also discloses a dynamic assessment system for grassland carrying capacity that considers the compensatory growth mechanism of grassland plants, for use in... Figure 1 The implementation of a dynamic assessment method for grassland carrying capacity that considers the compensatory growth mechanism of grassland plants is shown in the diagram below. Figure 3 It includes a data acquisition module, a data processing module, a grassland productivity simulation module, a grassland productivity-grazing intensity response model construction module, and a grassland carrying capacity dynamic assessment module based on compensatory growth, which are connected in sequence. The data acquisition module is used to collect primary data based on the data requirements of the selected grassland productivity model, including remote sensing data, meteorological data, and solar radiation data. The data processing module is used to clean and process the first data to obtain the second data; The grassland productivity simulation module is used to input the second data into the selected grassland productivity model, simulate grassland productivity data, and convert the simulation results into a predetermined productivity measurement index. The grassland productivity-grazing intensity response model construction module is used to supplement the collection of data related to compensatory growth effects, including grazing intensity data, topographic data, soil data, and human activity data; using grassland productivity data as the dependent variable and the supplemented data as the independent variable, the grassland productivity-grazing intensity response model is constructed using machine learning or regression analysis methods. The grassland carrying capacity dynamic assessment module based on compensatory growth is used to establish grassland carrying capacity constraint equations based on the grassland productivity-grazing intensity response model, with grassland production equal to livestock grazing as the constraint condition. The constraint equations are then solved numerically to obtain the grassland dynamic carrying capacity expressed in terms of grazing intensity.

[0039] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for dynamic assessment of grassland carrying capacity considering the compensatory growth mechanism of grassland plants, characterized in that, Includes the following steps: S1 Data Acquisition Steps: Collect the first data according to the data requirements of the selected grassland productivity model, including remote sensing data, meteorological data, and solar radiation data; S2 Data Processing Steps: Clean the first data to obtain the second data; S3 Grassland Productivity Simulation Steps: Input the second data into the selected grassland productivity model, simulate the grassland productivity data, and convert the simulation results into predetermined productivity measurement indicators; S4 Steps for constructing a grassland productivity-grazing intensity response model: Supplement the collection of data related to compensatory growth effects, including grazing intensity data, topographic data, soil data, and human activity data; use grassland productivity data as the dependent variable and the supplemented data as the independent variable, and use machine learning or regression analysis methods to construct a grassland productivity-grazing intensity response model. S5 Steps for dynamic assessment of grassland carrying capacity based on compensatory growth: Based on the grassland productivity-grazing intensity response model, with the grassland production equal to the amount of grass eaten by livestock as a constraint, establish the grassland carrying capacity constraint equation, and solve the constraint equation by numerical solution method to obtain the grassland dynamic carrying capacity expressed in terms of grazing intensity.

2. The method for dynamic assessment of grassland carrying capacity considering the compensatory growth mechanism of grassland plants according to claim 1, characterized in that, In the S1 data acquisition step, remote sensing data collected include normalized vegetation index, surface moisture index, and photosynthetically active radiation absorption ratio, while meteorological data include precipitation, air temperature, potential evapotranspiration, and solar radiation data.

3. The method for dynamic assessment of grassland carrying capacity considering the compensatory growth mechanism of grassland plants according to claim 1, characterized in that, In the S3 grassland productivity simulation step, the grassland productivity model is an improved CASA model. The improved CASA model calculates the actual light energy utilization rate by introducing the optimal temperature for vegetation growth, low temperature and high temperature stress factors, and water stress coefficient. It also calculates the net primary productivity of grassland by combining the light and effective radiation absorptivity ratio and solar radiation data.

4. The method for dynamic assessment of grassland carrying capacity considering the compensatory growth mechanism of grassland plants according to claim 1, characterized in that, In the S3 grassland productivity simulation step, the productivity metric is net primary productivity.

5. The method for dynamic assessment of grassland carrying capacity considering the compensatory growth mechanism of grassland plants according to claim 1, characterized in that, In the S4 grassland productivity-grazing intensity response model construction steps, the grassland productivity-grazing intensity response model is a random forest model.

6. The method for dynamic assessment of grassland carrying capacity considering the compensatory growth mechanism of grassland plants according to claim 1, characterized in that, In step S5, the dynamic assessment of grassland carrying capacity based on compensatory growth, the constraint equations are established based on the theoretical grassland carrying capacity model, specifically in the following form: in, This represents the grassland productivity simulated by the grassland productivity-grazing intensity response model. This refers to grazing intensity.

7. A dynamic assessment system for grassland carrying capacity considering the compensatory growth mechanism of grassland plants, characterized in that, The implementation of the grassland carrying capacity dynamic assessment method considering the compensatory growth mechanism of grassland plants as described in any one of claims 1-6 includes a data acquisition module, a data processing module, a grassland productivity simulation module, a grassland productivity-grazing intensity response model construction module, and a grassland carrying capacity dynamic assessment module based on compensatory growth, connected in sequence. The data acquisition module is used to collect primary data based on the data requirements of the selected grassland productivity model, including remote sensing data, meteorological data, and solar radiation data. The data processing module is used to clean and process the first data to obtain the second data; The grassland productivity simulation module is used to input the second data into the selected grassland productivity model, simulate grassland productivity data, and convert the simulation results into a predetermined productivity measurement index. The grassland productivity-grazing intensity response model construction module is used to supplement the collection of data related to compensatory growth effects, including grazing intensity data, topographic data, soil data, and human activity data; using grassland productivity data as the dependent variable and the supplemented data as the independent variable, the grassland productivity-grazing intensity response model is constructed using machine learning or regression analysis methods. The grassland carrying capacity dynamic assessment module based on compensatory growth is used to establish grassland carrying capacity constraint equations based on the grassland productivity-grazing intensity response model, with grassland production equal to livestock grazing as the constraint condition. The constraint equations are then solved numerically to obtain the grassland dynamic carrying capacity expressed in terms of grazing intensity.

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