Tea garden whole-process carbon metering system based on LCA

Abstract

The invention relates to the technical field of carbon sink prediction management, and discloses an LCA-based tea garden full-flow carbon metering system, which comprises the following steps: analyzing a tea garden into ladder surfaces, ladder walls and inter-row landform units, and constructing a runoff transportation network; calculating chemical activity parameters representing residual polyphenol release potential and mineral interface reaction sensitivity parameters representing interaction of organic carbon and iron; the primary soil carbon mineralization is checked based on the positive excitation effect in the vertical dimension, and the lateral organic mineral leaching flux which is driven by a ligand dissolution promoting mechanism and has a nonlinear amplification attribute is quantified in the lateral dimension; and generating a management optimization scheme for avoiding the lateral leaching risk. According to the method, the problem of carbon footprint accounting distortion caused by neglecting a tea garden microtopography hydrological process and a soil chemical mechanism in the prior art is solved, and accurate prediction and low-carbon management of carbon emission in a complex mountain tea garden scene are realized.

Description

Technical Field

[0001] This invention relates to the field of carbon sink forecasting and management technology, and more specifically, to a full-process carbon metering system for tea gardens based on LCA. Background Technology

[0002] With increasing global emphasis on addressing climate change, product carbon footprint has become a key indicator for measuring agricultural sustainability. In the tea industry, carbon measurement based on life cycle assessment (LCA) is not only a necessary means to meet international green trade barriers, but also a fundamental tool to guide low-carbon planting management. Existing general agricultural LCA standards typically cover agricultural input production, field planting, processing, and transportation, aiming to assess the environmental cost per unit of product by quantifying greenhouse gas emissions throughout the entire process, and to formulate emission reduction strategies accordingly.

[0003] However, existing LCA carbon estimating methods for tea gardens mostly directly adopt general farmland models, treating tea gardens as homogeneous plains, and have significant mechanistic deficiencies in soil carbon flux accounting. First, most tea gardens in my country are located in the hilly red soil region of southern China, where terracing is widely used. Existing homogeneous models ignore the significant differences in hydrological connectivity and microbial function among micro-topographic units such as terrace surface, terrace walls, and inter-row areas, failing to capture the lateral migration of materials caused by topography. Second, existing models typically treat the return of pruned residues to the field as a simple linear process of soil organic carbon sequestration, without considering the unique biochemical interactions between the strongly acidic, iron-rich soils of tea gardens and the polyphenol-rich residues. In a strongly acidic environment, the input of exogenous organic matter triggers a strong positive excitation effect (i.e., accelerates the carbon mineralization of the native soil), and the complexation of polyphenols with iron oxides under heavy rainfall pulses easily induces the lateral loss of dissolved organic carbon. This complex nonlinear coupling mechanism is completely ignored in traditional static inventory models.

[0004] Due to a lack of consideration for the aforementioned micro-topographical hydrological processes and unique soil biochemical mechanisms, existing econometric systems often severely underestimate the actual carbon loss in tea gardens, and may even lead to incorrect management practices. For example, traditional models may predict that increasing the amount of pruned material returned to the field will linearly increase carbon sequestration, but in actual tea garden scenarios with high slopes, heavy rainfall, and iron richness, excessive polyphenol input may instead lead to the decapsulation and significant loss of mineral-protected carbon pools. Summary of the Invention

[0005] This invention provides a carbon metering system for the entire tea garden process based on LCA, which solves the technical problems mentioned in the background art.

[0006] This invention provides a full-process carbon metering system for tea gardens based on LCA, comprising:

[0007] The micro-topography and hydrological unit construction module is used to analyze the spatial structure of terraced fields, divide the tea garden into discrete landform units including terrace surfaces, terrace walls and rows, and construct a runoff transport network connecting each discrete landform unit based on drainage density data. The soil biochemical reaction parameter calculation module is configured to respond to the input of pruned residue returning to the field, calculate the chemical activity parameters of the residue that characterize the release potential of polyphenols in the residue, and solve the mineral interface reaction sensitivity parameters that characterize the binding stability of soil organic carbon and iron oxides based on soil acidity value and active iron content. The multidimensional carbon flux accounting module is used to perform bidirectional measurement by referencing the chemical activity parameters of the residues and the sensitivity parameters of the mineral interface reaction: in the vertical direction, it calculates the primary soil carbon mineralization flux induced by exogenous input based on the positive excitation effect; in the lateral direction, it quantifies the lateral organic mineral leaching flux driven by the ligand-promoted dissolution mechanism, and there is a nonlinear amplification relationship between the lateral organic mineral leaching flux and the amount of pruned residue input. The whole life cycle regulation module is used to superimpose the field carbon flux of the discrete geomorphic unit with the emissions from the tea processing and transportation process to generate the whole life cycle carbon footprint, and output a tea garden management scheme to avoid the nonlinear surge of the lateral organic mineral leaching flux.

[0008] The beneficial effects of this invention include: correcting the decision-making bias of traditional LCA models that simply treat organic inputs as linear carbon sinks in highly acidic mountain tea garden scenarios. By quantifying the lateral organic mineral leaching flux driven by ligand-induced deencapsulation mechanisms, this invention can accurately identify how excessive input of pruning residues leads to superlinear pulse loss of the mineral-protected carbon pool under conditions of terraced drainage connectivity and coupling with highly acidic and iron-rich soils. This enables management decisions to keenly capture the risk inflection point where increased carbon sinks turn into leaks, thereby outputting a spatiotemporally differentiated management scheme for terraced fields that can effectively avoid lateral carbon release and truly achieve full-process carbon reduction. Attached Figure Description

[0009] Figure 1 This is a block diagram of a tea garden carbon metering system based on LCA (Liquid Carbon Regulator) according to the present invention. Detailed Implementation

[0010] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, features described in some examples may be combined in other examples.

[0011] like Figure 1As shown, a tea garden end-to-end carbon metering system based on LCA includes: The micro-topography and hydrological unit construction module is used to analyze the spatial structure of terraced fields, divide the tea garden into discrete landform units including terrace surfaces, terrace walls and rows, and construct a runoff transport network connecting each discrete landform unit based on drainage density data. The soil biochemical reaction parameter calculation module is configured to respond to the input of pruned residue returning to the field, calculate the chemical activity parameters of the residue that characterize the release potential of polyphenols in the residue, and solve the mineral interface reaction sensitivity parameters that characterize the binding stability of soil organic carbon and iron oxides based on soil acidity value and active iron content. The multidimensional carbon flux accounting module is used to perform bidirectional measurement by referencing the chemical activity parameters of the residues and the sensitivity parameters of the mineral interface reaction: in the vertical direction, it calculates the primary soil carbon mineralization flux induced by exogenous input based on the positive excitation effect; in the lateral direction, it quantifies the lateral organic mineral leaching flux driven by the ligand-promoted dissolution mechanism, and there is a nonlinear amplification relationship between the lateral organic mineral leaching flux and the amount of pruned residue input. The whole life cycle regulation module is used to superimpose the field carbon flux of the discrete geomorphic unit with the emissions from the tea processing and transportation process to generate the whole life cycle carbon footprint, and output a tea garden management scheme to avoid the nonlinear surge of the lateral organic mineral leaching flux.

[0012] In a preferred embodiment, the micro-topographic hydrological unit construction module analyzes the spatial structure of the terraced fields, dividing the tea garden into discrete landform units comprising terrace surfaces, terrace walls, and rows, including: Build a node set The node types include stair surfaces, stair walls, and spaces between rows; For each node Calculate carbon cycle weights With nitrogen cycle weight :

[0013]

[0014] In the formula, Indicates the relative abundance of carbon-cycling microorganisms. Indicates the relative abundance of nitrogen-cycling microorganisms. This represents the summation of all nodes in the network.

[0015] Preferably, when the micro-topographic hydrological unit construction module analyzes the spatial structure of terraced fields, it first constructs a set of nodes. This set corresponds to discrete landform units within the tea garden, specifically covering three types of actual landform areas: terrace surfaces, terrace walls, and rows; subsequently, each node... Calculate carbon cycle weights With nitrogen cycle weight The intensity of carbon and nitrogen cycling based on geomorphic units is determined by the abundance of functional microorganisms in the corresponding area. Due to the differences in the microenvironment (such as the distribution of moisture and organic matter) on the surface, walls and between rows, the abundance of microorganisms participating in carbon and nitrogen cycling will be different in different units. Therefore, the contribution weight of each unit in carbon and nitrogen cycling is quantified by the proportion of relative microbial abundance.

[0016] Among them, formula middle, It is a node The relative abundance of carbon-cycling microorganisms, i.e. the proportion of microbial groups involved in carbon metabolism (such as organic matter decomposition and carbon fixation) in the total microbial population of the unit, is obtained by high-throughput sequencing technology to detect soil samples of the corresponding landform unit, based on the microbial community sequencing database of different landform units in the tea garden terraces. It is a set of nodes The sum of the relative abundance of carbon-cycling microorganisms at all nodes represents the total relative abundance of carbon-cycling microorganisms in the entire tea garden system.

[0017] Correspondingly, the formula middle, It is a node The corresponding relative abundance of nitrogen-cycling microorganisms, that is, the proportion of microbial groups involved in nitrogen metabolism (such as nitrification and denitrification) in the total microbial population of the unit, is also obtained through high-throughput sequencing technology, based on the distribution data of nitrogen-cycling microbial communities in different landform units of tea garden terraces. It is a set of nodes The summation of the relative abundance of nitrogen-cycling microorganisms at all nodes represents the total relative abundance of nitrogen-cycling microorganisms within the entire tea garden system. This method allows for the precise quantification of the weights of different geomorphic units in the carbon-nitrogen cycle, with each weight taking values ​​within a specific range. The sum of the weights of all nodes of the same type is 1.

[0018] In a preferred embodiment, the micro-topographic hydrological unit construction module constructs a runoff transport network connecting each of the discrete geomorphic units based on drainage density data, including: Based on the set of directed edges Constructing a normalized connected matrix Its matrix elements The calculation is as follows:

[0019] In the formula, For nodes The drainage density index For nodes The slope factor, Indicates from node Pointing to node All valid water flow paths.

[0020] Preferably, when constructing the runoff transport network using the micro-topographic hydrological unit construction module, the set of directed edges is first determined. The set of directed edges corresponds to the actual water flow paths between discrete landform units in the tea garden. Subsequently, a normalized connectivity matrix is ​​constructed based on this set. Specifically, the intensity of runoff transport between different geomorphic units is determined by the unit's own drainage capacity and slope. The runoff transport relationship needs to be accurately characterized by quantifying the water flow distribution ratio of each unit to each downstream path.

[0021] Among them, matrix elements The calculation formula is: (satisfy ), in the formula It is a node The drainage density index refers to the node The optimal value for the length of the drainage channel per unit area within the corresponding region is obtained by scanning and measuring the corresponding unit of the tea garden with a high-precision terrain scanning device. The value range matches the distribution of drainage facilities in the tea garden topography. It is a node The slope factor refers to the node The actual slope value of the corresponding area is preferably obtained by a terrain slope measurement tool, and the value is consistent with the actual terrain inclination of the unit. It is for all satisfaction nodes corresponding Summation is performed to represent nodes. The total flow contribution output to all downstream effective flow paths. In this way, Nodes can be accurately represented runoff towards nodes The proportion of transport, and the same node All corresponding The sum of these values ​​is 1, thus providing a complete characterization of the runoff transport network between discrete geomorphic units.

[0022] In a preferred embodiment, the soil biochemical reaction parameter calculation module responds to the input of pruned residue returning to the field and calculates the chemical activity parameters of the residue, characterizing the release potential of polyphenols in the residue, including: The nodes are calculated using the following formula. Chemical activity parameters of the residue :

[0023] In the formula, The baseline activity coefficient; To reduce the polyphenol content in the pruning residue, For reference polyphenol content; For nodes The humification index of dissolved organic carbon in soil, For reference, the humification index; among which Characterizes the degree of low humification of dissolved organic carbon.

[0024] Preferably, when the soil biochemical reaction parameter calculation module responds to the input of pruned residue returning to the field, the calculation node... Chemical activity parameters of the residue Specifically, the release potential of polyphenols in the residue is affected by both the polyphenol content of the residue itself and the humification state of dissolved organic carbon in the soil. It is necessary to combine these two factors to quantify the chemical activity of the residue in order to reflect the actual release capacity of polyphenols.

[0025] Among them, the chemical activity parameters of the residue The calculation formula is: , in the formula It is the baseline activity coefficient, with a preferred value of 1.0 (corresponding to the activity level under reference conditions), determined by basic experimental data on the release of polyphenols from tea garden residues; The optimal value is the polyphenol content in the pruned residue. The optimal value is obtained by detecting the pruned residue sample using liquid chromatography, and the value matches the actual polyphenol content of the residue. The reference polyphenol content is the average polyphenol content of common tea garden pruning residues, which is obtained by statistical analysis of test data from multiple groups of tea garden residue samples. It is a node The soil dissolved organic carbon humification index, and its optimal value was determined by fluorescence spectroscopy at nodes. Soil solution samples were tested to obtain values ​​that corresponded to the degree of humification of dissolved organic carbon in the area. The reference humification index is the preferred value, which is the average humification index of dissolved organic carbon in tea garden soil, obtained through statistical analysis of data from multiple tea garden soil samples. This method... It can characterize the release potential of polyphenols in the residue, and its value increases with the increase of polyphenol content in the residue and the increase of low humification of dissolved organic carbon in the soil.

[0026] In a preferred embodiment, the soil biochemical reaction parameter calculation module calculates mineral interface reaction sensitivity parameters characterizing the binding stability of soil organic carbon and iron oxides based on soil acidity and active iron content, including: The nodes are calculated using the following formula. Mineral interface reaction sensitivity parameters :

[0027] In the formula, For nodes Soil pH value, numerical value This is the set acidity threshold constant; For nodes The content of active iron in the soil; This is the acidity influence coefficient. The coefficient representing the influence of iron content; This represents an exponential function with the natural constant as its base.

[0028] In this preferred embodiment, the soil biochemical reaction parameter calculation module solves the node. Mineral interface reaction sensitivity parameters Specifically, the stability of the combination of soil organic carbon and iron oxides is jointly regulated by soil acidity and active iron content. When acidity is below the threshold, it will enhance the sensitivity of interfacial reactions, while the increase in active iron content will also change the combination state. Therefore, it is necessary to characterize the nonlinear effect of acidity through an exponential term and reflect the role of active iron through a linear term in order to quantify the degree of change in this stability.

[0029] Among them, the mineral interface reaction sensitivity parameter The calculation formula is: , in the formula It is a node The optimal soil pH value is determined by measuring the pH at each node. The values ​​were obtained by testing the soil sample extract and corresponded to the actual acid-base status of the soil in the area. It is the acidity influence coefficient, with an optimal value of 0.3, determined by experimental data on the correlation between soil acidity and the organic carbon-iron binding state in tea gardens; It is a node The optimal value for determining the active iron content in the soil was obtained by extraction of the nodes using a sodium dithionite-sodium citrate-sodium bicarbonate method. Soil samples were tested and the values ​​were matched with the actual content of active iron in the soil of the area. This is the influence coefficient of iron content, with an optimal value of 0.2, determined through experimental data on the correlation between active iron content in tea garden soil and the stability of organic carbon-iron binding. Through this method, It can characterize the binding stability of soil organic carbon and iron oxides, and its value increases with the increase of soil acidity below the threshold and the increase of active iron content.

[0030] In a preferred embodiment, the multidimensional carbon flux accounting module calculates the primary soil carbon mineralization flux induced by exogenous input in the vertical direction based on the positive excitation effect, including: The nodes are calculated using the following formula. exist positive excitation intensity at time :

[0031] And calculate the carbon mineralization flux of the primary soil. :

[0032] In the formula, Input amount for trimming residual parts; This refers to the soil pH value. The soil carbon-nitrogen ratio; This represents the current soil organic carbon storage. Baseline breathing; The contribution ratio coefficient for primary carbon is set to a value greater than 0.9. All are model fitting coefficients.

[0033] Preferably, when the multidimensional carbon flux calculation module calculates the carbon mineralization flux of native soil in the vertical direction, specifically, the input of exogenous pruning residues will induce native soil carbon mineralization through a positive excitation effect. The intensity of this effect is jointly regulated by the amount of residue input, soil acidity, carbon-nitrogen ratio and current organic carbon storage. It is necessary to combine these factors to quantify the excitation intensity and then obtain the actual mineralization flux based on the contribution ratio of native carbon.

[0034] First calculate the nodes. exist positive excitation intensity at time Its calculation formula is , in the formula It is the model fitting coefficient, with an optimal value of 0.02, determined by the correlation experimental data of tea garden residue input and stimulating effect; This is the input amount of pruned crop residue, and its optimal value is obtained through the weighing record of actual crop residue returned to the field. The value is related to the node. exist The amount of residual body thrown at any given moment corresponds to; It is the model fitting coefficient, with an optimal value of 0.25, determined by experimental data on the correlation between soil acidity and the intensity of the activating effect; It is the model fitting coefficient, with an optimal value of 0.4, which is determined by experimental data on the correlation between soil carbon-nitrogen ratio and the intensity of the activating effect. It refers to the soil carbon-nitrogen ratio, and its optimal value is determined by elemental analysis of the nodes. Soil samples were tested to obtain values ​​that corresponded to the actual ratio of carbon and nitrogen in the soil of that area. This represents the current soil organic carbon storage, and its optimal value is determined by the potassium dichromate oxidation method at the nodes. exist Soil samples were collected at specific times, and the values ​​were determined to correspond to the actual amount of organic carbon stored in the soil in that area.

[0035] Subsequently, the carbon mineralization flux of the native soil was calculated. Its calculation formula is , in the formula This is baseline respiration, and its optimal value is obtained from soil respiration monitoring data under conditions of no residual input. The value is related to the node. exist The baseline breathing level at any given time corresponds to; This is the primary carbon contribution ratio coefficient, with an optimal value of 0.95, determined through experimental data distinguishing primary soil carbon from exogenous residual carbon mineralization. This method allows for the calculation of the primary soil carbon mineralization flux induced by exogenous inputs; its value increases with increasing residual input, greater soil acidity below the threshold, and a decreasing carbon-to-nitrogen ratio.

[0036] In a preferred embodiment, the multidimensional carbon flux accounting module quantifies the lateral organic mineral leaching flux driven by the ligand-induced dissolution mechanism in the lateral direction. The lateral organic mineral leaching flux exhibits a non-linear amplification relationship with the pruned residue input, including: Calculate the amount of mobile dissolved organic carbon Mobilization amount of mineral-bound carbon :

[0037]

[0038] compute nodes Flow to Node The lateral organic mineral leaching flux :

[0039] In the formula, The chemical activity parameters of the residue; This refers to the sensitivity parameter of the mineral interface reaction; Input amount for trimming residual parts; This refers to the storage of iron-bound organic carbon. The mobilization rate constant; Elements of a normalized connected matrix; The intensity of the rainfall pulse; where The term establishes the aforementioned nonlinear amplification relationship.

[0040] Preferably, when the multidimensional carbon flux accounting module quantifies the lateral organic mineral leaching flux, specifically, under the ligand-induced dissolution mechanism, the pruned residue input will first generate mobile dissolved organic carbon. This organic carbon, as ligands, will mobilize mineral-bound carbon, and the increase in ligand concentration will nonlinearly enhance the mobilization capacity of mineral carbon. At the same time, the leaching flux is also affected by runoff transport and rainfall intensity. Therefore, it is necessary to quantify the amount of dissolved and mineral-bound carbon in steps, and then combine hydrological factors to obtain the final lateral flux.

[0041] First, calculate the amount of mobile dissolved organic carbon. Its calculation formula is , in the formula These are the chemical activity parameters of the residue. This involves calculating the input amount of the pruned residue; next, the mobilization amount of mineral-bound carbon is calculated. Its calculation formula is ,in It is the mobilization rate constant, with an optimal value of 0.01, determined by experimental data on the correlation between ligand concentration and mineral carbon mobilization. It is a parameter related to the sensitivity of mineral interface reactions; It is the iron-bound organic carbon storage, and its preferred value is determined by the sodium dithionite extraction method for node... exist Soil samples were collected at specific times, and the values ​​were correlated with the actual storage of iron-bound organic carbon in the area. The term reflects the nonlinear amplification effect of ligand concentration on mineral carbon mobilization through a quadratic relationship.

[0042] Then calculate the nodes. Flow to Node Lateral organic mineral leaching flux Its calculation formula is , in the formula These are elements of a normalized connected matrix; It is the intensity of the rainfall pulse, and its optimal value is determined by rainfall monitoring equipment. The measured data at each time point are obtained, and the values ​​correspond to the actual rainfall intensity during that period. This method allows for the quantification of lateral organic mineral leaching flux driven by the ligand-induced dissolution mechanism, and this flux exhibits a non-linear amplification trend with increasing input of pruned residues.

[0043] In a preferred embodiment, the full life cycle regulation module superimposes the field carbon flux of the discrete geomorphic unit with the emissions from tea processing and transportation to generate a full life cycle carbon footprint, and outputs a tea garden management scheme to avoid the nonlinear surge in lateral organic mineral leaching flux, including: Construct the optimization objective function :

[0044] In the formula, Input the strategy vector for pruning the remnants; The predicted value of carbon footprint over the entire life cycle consists of the sum of the predicted value of field carbon flux and the predicted value of process emissions; As a functional unit; For penalty weighting; The target is the soil organic carbon storage; To predict the total organic carbon storage in the soil of the entire orchard at the end of the period; The optimal management scheme is solved using the following iterative formula:

[0045] In the formula, For the first The policy vector for the next iteration; Step size; The gradient of the objective function; This is a projection operation on the constraint set; because The derivative term, which includes the aforementioned nonlinear amplification relationship, will be included in the optimization process. The process will automatically limit the input to avoid the nonlinear surge in the lateral organic mineral leaching flux.

[0046] Preferably, when the whole life cycle regulation module generates the whole life cycle carbon footprint and outputs the tea garden management plan, specifically, tea garden management needs to simultaneously consider the reduction of the whole life cycle carbon footprint and the stability of soil organic carbon storage. The nonlinear surge of lateral organic mineral leaching is related to the input of pruning residues. Therefore, by constructing an objective function that includes carbon footprint and carbon storage constraints, and combining iterative solution with the projection gradient method, the amount of residue input can be limited while optimizing the carbon footprint, thereby avoiding the nonlinear surge of leaching flux.

[0047] First, construct the optimization objective function. Its calculation formula is , in the formula It is the input strategy vector for pruning the remnants; It is the predicted value of carbon footprint throughout the entire life cycle; It is a functional unit, with the preferred value being per kilogram of dry tea, set according to the standard functional units for tea production. It is the penalty weight, with an optimal value of 5, determined by experimental data on the importance weights of carbon footprint and carbon storage. The target soil organic carbon storage is determined by the optimal value of the tea garden soil fertility maintenance requirement data. This is the total organic carbon storage of the entire garden soil at the end of the prediction period. This part constrains the deviation between the carbon storage and the target value through the square term.

[0048] The optimal management scheme is then solved using an iterative formula, which is: , in the formula It is the first The policy vector for the next iteration; The step size is preferably 0.1, and is determined by experimental data on iterative convergence. It is the gradient of the objective function. Since this gradient contains the derivative term of the aforementioned lateral leaching nonlinear amplification relationship, the gradient will increase significantly when the residual input is close to the threshold that triggers the nonlinear surge. This is a projection operation on the constraint set, its function being to restrict the iteratively obtained policy vector within a reasonable range of the residual input. Through this iterative solution process, the final output is the optimal policy vector. It automatically limits the input of pruned residues, thereby avoiding the nonlinear surge in lateral organic mineral leaching flux, while achieving synergistic optimization of the life cycle carbon footprint and soil organic carbon storage.

[0049] It should be noted that the node set It is a discretized modeling result of the spatial structure of tea garden terraces, specifically adapted to the three core landform units of tea garden terraces: terrace surface, terrace wall, and row space. Each node corresponds to a real area with a uniform microenvironment. Through this set, the complex terrace landform is transformed into a quantifiable object.

[0050] It should be noted that carbon cycle weight It is a core parameter for quantifying the contribution of a single node to the overall carbon cycle of the park. Its value reflects the activity level of carbon metabolism-related microorganisms at that node (corresponding to the surface, wall, or row spacing). The values ​​of surface and wall nodes... The height is above the inter-row nodes, which is consistent with the characteristic that the terrace surface and terrace wall are more conducive to the colonization of carbon cycling microorganisms in the actual microenvironment of tea gardens, and can distinguish the differences in carbon cycling function of different landform units.

[0051] It should be noted that the nitrogen cycle weight Corresponding to the carbon cycle weight, it is used to characterize the proportion of contribution of a single node to the overall nitrogen cycle of the park, and the inter-row nodes' The value is higher, which aligns with the fact that the relative abundance of nitrogen-cycling microorganisms is higher in the inter-row area. This parameter can be used to characterize the spatial heterogeneity of carbon and nitrogen cycling in tea gardens.

[0052] It should be noted that the carbon cycle weight calculation formula The carbon cycling weight of a node is determined by the proportion of the relative abundance of carbon-cycling microorganisms in a single node to the total abundance of the entire garden. The denominator of the formula ensures that the sum of the carbon cycling weights of all nodes is 1, which satisfies the normalization requirement of weight allocation and makes the carbon cycling contributions of different nodes comparable.

[0053] It should be noted that the nitrogen cycle weight calculation formula... The nitrogen cycle weights are allocated by the proportion of the relative abundance of nitrogen-cycling microorganisms at each node, which also satisfies the normalization condition and enables the quantification of the spatial heterogeneity of the nitrogen cycle.

[0054] It should be noted that, Represents a set of nodes The sum of the relative abundance of carbon-cycling microorganisms at all nodes is the normalization basis for calculating carbon cycling weights. This parameter integrates the distribution information of carbon-cycling microorganisms throughout the park, ensuring that the weight of a single node can objectively reflect its position in the overall carbon cycle.

[0055] It should be noted that, The sum of the relative abundance of nitrogen-cycling microorganisms at all nodes serves as the normalization basis for calculating nitrogen cycling weights. By integrating the distribution data of nitrogen-cycling microorganisms across the entire park, the nitrogen cycling weights of individual nodes have global reference significance.

[0056] It should be noted that the relative abundance of carbon-cycling microorganisms Pointer node Within a given region, the proportion of microbial groups involved in carbon metabolism processes such as organic matter decomposition and carbon fixation within the total number of microorganisms in that region is as follows: for example, the proportion of microorganisms involved in cellulose decomposition in tiered nodes is higher than that in inter-row nodes. This parameter reflects the strength of the node's carbon cycle function.

[0057] It should be noted that the relative abundance of nitrogen-cycling microorganisms Pointer node The proportion of microbial groups involved in nitrogen metabolism processes such as nitrification and denitrification in the total microbial population of the region is higher, for example, the proportion of microorganisms involved in nitrification is higher in inter-row nodes.

[0058] It should be noted that the microbial community sequencing database for different landform units in tea garden terraces is a collection of microbial community data constructed using high-throughput sequencing technology specifically for three types of landform units: terrace surface, terrace wall, and inter-row. It includes information on the types, quantities, and relative abundance of carbon and nitrogen cycle-related microorganisms in different landform units.

[0059] It should be noted that the nitrogen cycling microbial community distribution data of different landform units in the tea garden terraces is a special data set focusing on nitrogen cycling microorganisms in different micro-topographic units of the tea garden, which records in detail the distribution characteristics and relative abundance differences of various nitrogen cycling microorganisms on the terrace surface, terrace walls, and between rows.

[0060] It should be noted that the set of directed edges It is an abstract description of the water flow path between discrete landform units in a tea garden. Each directed edge corresponds to the water flow channel from one node (landform unit) to another node in the actual tea garden. For example, the directed edge from the terrace surface node to the terrace wall node corresponds to the actual path of rainwater flowing from the terrace surface to the terrace wall. This set can completely depict the transport path network of tea garden runoff.

[0061] It should be noted that the normalized connected matrix It is a core tool for quantifying the proportion of runoff transport among discrete geomorphic units in a tea garden. The rows and columns of the matrix correspond to the nodes of the tea garden, and the matrix elements... Reflecting nodes To the node The mass transport ratio is calculated by normalizing the matrix to ensure that the sum of the transport ratios of all output paths of a single node is 1.

[0062] It should be noted that matrix elements Representative node To the node The distribution ratio of transported runoff and carried matter is determined by the nodes. The drainage capacity and slope are jointly determined; for example, the proportion of water transported from the surface nodes to the wall nodes is determined by the high drainage density and steep slope. higher.

[0063] It should be noted that the formula for calculating matrix elements... ( The proportion of material transport is determined by the product of the node drainage density and the slope factor. The numerator of the formula reflects the node. To the node The potential for transfer, the denominator integrates the nodes. The total migration potential to all downstream nodes ensures the rationality and normalization of migration ratios, for example, for nodes. The drainage density is 2.5 km / km², the slope factor is 0.8, and the drainage density is towards the node. and nodes The total transport potential is 4.0, then The value is (2.5 × 0.8) / 4.0 = 0.5, meaning 50% of the material migrates to the nodes. Transport.

[0064] It should be noted that, Represents a node The total flow contribution output to all downstream effective flow paths; this parameter integrates node parameters. The combined effects of drainage capacity and slope on runoff transport, such as nodes. With a drainage density of 3.0 km / km² and a slope factor of 0.7, and three effective flow paths downstream, the total flow contribution is 3.0 × 0.7 = 2.1.

[0065] It should be noted that, It is a logical definition of the effective water flow path, representing the path from the node. Pointing to node The water flow channels belong to a set of directed edges. That is, the path is an actual runoff transport channel, such as (step surface node → step wall node) ∈ This indicates that the water flow path from the ladder surface to the ladder wall is an effective transport path.

[0066] It should be noted that the drainage density index Pointer node Within a given area, the total length of drainage channels per unit area (typically km²) is an indicator of the area's drainage capacity. For example, areas near ladder wall nodes tend to have densely packed drainage ditches. The value may be 3.2 km / km², and this parameter affects the transport efficiency and distribution ratio of nodal runoff.

[0067] It should be noted that the slope factor Pointer node The actual slope value of the corresponding area reflects the degree of inclination of the terrain. For example, the slope factor of the upslope terrace node may be 0.9, and that of the downslope terrace node may be 0.6. The larger the slope factor, the stronger the potential energy of runoff transport and the more significant the impact on the proportion of material transport.

[0068] It should be noted that the distribution of drainage facilities in tea garden topography refers to the spatial layout of drainage ditches, canals, and other drainage facilities within the tea garden. For example, longitudinal drainage ditches are usually set between the terrace surfaces and walls, while transverse drainage canals are set between rows. This distribution characteristic determines the nodes. Drainage density index Range of values.

[0069] It should be noted that the chemical activity parameters of the residue... It is a dedicated quantization node The parameter representing the potential for polyphenol release from pruned plant debris is determined by a combination of the polyphenol content of the debris itself and the humification state of dissolved organic carbon in the soil, reflecting the ability of the debris to release highly reactive ligands in the soil environment.

[0070] It should be noted that the baseline activity coefficient This is the baseline value for the chemical activity of pruning residues under the established reference conditions, with a preferred value of 1.0. This parameter provides a unified reference standard for comparing the chemical activity of pruning residues from different tea gardens and different batches, ensuring... The calculations are consistent and comparable; for example, under standard polyphenol content and standard humification index conditions, the chemical activity of the residue is equal to... .

[0071] It should be noted that the formula for calculating the chemical activity parameters of the residue is... This is a quantitative model that integrates the characteristics of the residue itself with soil environmental conditions. It uses the polyphenol content ratio to reflect the residue's own ligand supply potential and the non-humification ratio to reflect the soil's influence on ligand activity. The product of these two ratios is then combined with a baseline activity coefficient to achieve a precise characterization of the residue's chemical activity. For example, when the residue's polyphenol content is 1.2 times the reference value and the non-humification ratio is 1.1 times the reference value... This indicates that the residue has a higher-than-average potential to release polyphenols.

[0072] It should be noted that the proportion of dissolved organic carbon that has undergone non-humification... It is a representation node Parameters indicating the low degree of humification of dissolved organic carbon. The smaller the value, the larger the proportion, indicating that the dissolved organic carbon has a simpler structure, stronger reactivity, and is more likely to complex with iron oxides in the soil. It is an important soil environmental factor affecting the chemical activity of the residue.

[0073] It should be noted that the reference value for the non-humification ratio of dissolved organic carbon is... It is the average level of the proportion of dissolved organic carbon in tea garden soil that has undergone non-humification, serving as a benchmark. A benchmark of relatively high or low non-humification ratios ensures the comparability of the impact of soil environment on the chemical activity of residues across different nodes.

[0074] It should be noted that the content of polyphenols in the pruned residue... This refers to the total content of allelochemicals with ligand activity, such as tea polyphenols, caffeine, and catechins, in tea plantation pruning residues. These substances are important precursors to highly reactive DOM (domestic organic matter) in the soil, and their content directly determines the potential ability of the residues to release ligands. For example, freshly pruned residues that are not composted... It is usually higher than the residue after composting.

[0075] It should be noted that the reference polyphenol content The average polyphenol content is obtained through statistical analysis of a large number of tea garden pruning residue samples. It reflects the baseline level of ligand supply from tea garden pruning residues under normal conditions. For example, if the average polyphenol content is 12 g / kg, then... .

[0076] It should be noted that nodes Soil dissolved organic carbon humification index The nodes were detected by fluorescence spectroscopy. Soil solution indices reflect the degree of humification and structural complexity of dissolved organic carbon. The higher the value, the more complex the organic carbon structure, the higher the degree of humification, and the weaker the reactivity; conversely, the lower the value, the stronger the reactivity.

[0077] It should be noted that the reference humification index It is the average humification index based on statistical analysis of multiple sets of tea garden soil sample data. It represents the conventional level of dissolved organic carbon humification in tea garden soil. For example, it is obtained by analyzing the fluorescence spectral data of 50 tea garden soil samples. If it is 0.6, then .

[0078] It should be noted that the basic experimental data on the release of polyphenols from tea plantation residues is a set of data on the release patterns of polyphenols from residues obtained through controlled variable experiments. It includes the relationship between the release rate and amount of polyphenols from residues and the content of polyphenols from residues and the soil environment under different conditions.

[0079] It should be noted that the test data from multiple tea garden residue samples are datasets obtained by detecting the polyphenol content of pruning residues from tea gardens in different regions, of different varieties, and at different growth stages. Statistical analysis can yield reference polyphenol content. This ensures that the reference value is widely representative.

[0080] It should be noted that the test data of multiple sets of tea garden soil samples are datasets obtained by detecting the dissolved organic carbon humification index of soils from tea gardens with different geomorphic units and different planting years. The reference humification index was determined through statistical analysis. This ensures that it reflects the general characteristics of the tea garden soil.

[0081] It should be noted that the mineral interface reaction sensitivity parameter It is a representation node The core parameter for the stability of soil organic carbon-iron oxide binding is determined by a combination of soil acidity and active iron content, directly reflecting the ease with which ligands unlock the organic carbon-iron oxide complex in the soil. The larger the value, the weaker the binding stability, and the easier it is for ligands released from the residue to be mobilized.

[0082] It should be noted that the acidity threshold constant It is a key constant for adapting to the strongly acidic environment of tea gardens. The pH of tea garden soil is usually between 3.9 and 5.2. This threshold can characterize the catalytic effect of the strongly acidic environment on chemical complexation reactions. When the soil pH is close to or below this value, the effect of acidity on the sensitivity of interfacial reactions is more significant.

[0083] It should be noted that the acidity influence coefficient This is a fitting coefficient quantifying the nonlinear catalytic effect of soil acidity on chemical complexation reactions, with a preferred value of 0.3. Its magnitude determines the extent of the impact on the sensitivity of mineral interface reactions when soil pH deviates from the acidity threshold. The larger the value, the more the pH decreases by 0.1. The more significant the increase, the better.

[0084] It should be noted that the iron content influence coefficient This is a coefficient that quantifies the contribution of soil active iron content to the number of reaction sites. A preferred value is 0.2; the higher the active iron content, the more complexation reaction sites are provided. The larger the value, the more this coefficient reflects the enhanced effect of active iron on the sensitivity of mineral interface reactions.

[0085] It should be noted that the exponential function It is an expression specifically designed to characterize the nonlinear effect of soil acidity on the sensitivity of interfacial reactions. Since tea garden soil is highly acidic, The value is positive; the lower the pH, the larger the function value. The stronger the amplification effect, the better it aligns with the catalytic principle of acidity on complexation reactions under strongly acidic conditions.

[0086] It should be noted that the linear gain function It is a mathematical model characterizing the number of reaction sites provided by iron minerals, and the active iron content. The higher the value, the larger the function value, reflecting the linear gain effect of active iron on the reaction site, for example... When the function value is 1 + 0.2 × 20 = 5, it indicates that the iron minerals in the soil provide 5 times the reaction sites of the baseline level.

[0087] It should be noted that the formula for calculating the mineral interface reaction sensitivity parameter... It is a quantitative model that integrates the nonlinear catalytic effect of acidity with the linear gain effect of active iron. Through the product of an exponential function and a linear function, it achieves precise quantification of the stability of soil organic carbon-iron oxide binding, such as at nodes. pH=4.0 ,but .

[0088] It should be noted that the soil acidity item It is the difference obtained by subtracting the soil pH value at the node from the acidity threshold constant. It directly quantifies the degree to which the soil deviates from the acidity threshold. The larger the difference, the stronger the soil acidity and the more significant the catalytic effect on chemical complexation reactions. It is the core intermediate variable that affects the sensitivity of mineral interface reactions.

[0089] It should be noted that nodes Soil pH value Detection node via pH meter The values ​​obtained from soil sample extracts reflect the actual acid-base status of the soil in the area, including tea garden soil. Typically between 4.5 and 6.5, acidity is a typical characteristic of tea garden soils, affecting the stability of the combination of organic carbon and iron oxides.

[0090] It should be noted that nodes Soil active iron content The nodes were detected by the sodium dithionite-sodium citrate-sodium bicarbonate extraction method. The actual content of active iron in the soil, which exists in the form of iron oxides, is the core carrier for forming complexes with organic carbon. The level of its content determines the number of reaction sites.

[0091] It should be noted that the experimental data on the correlation between soil acidity and the organic carbon-iron binding state in tea gardens was obtained by controlling the soil acidity gradient and detecting changes in the binding stability of organic carbon-iron oxides. The dataset includes data such as the binding strength of organic carbon-iron and the complexation reaction rate under different pH conditions.

[0092] It should be noted that the experimental data on the correlation between active iron content in tea garden soil and the stability of organic carbon-iron binding was obtained by controlling the gradient of active iron content in the soil and studying the changing law of organic carbon-iron binding stability. The dataset includes data such as the decomposition difficulty of organic carbon-iron complex and ligand unlocking efficiency under different active iron contents.

[0093] It should be noted that the positive excitation intensity It is a quantization node exist The core parameter for determining the strength of the exogenous pruning residue's effect on the primary soil carbon mineralization is the value of the residue input, which reflects the degree to which the residue input promotes the mineralization of primary soil carbon (SOC).

[0094] It should be noted that the carbon mineralization flux in the primary soil It is a special representation node exist The CO2 flux generated by primary soil carbon mineralization, which is dominated by positive excitation effects, is a parameter that focuses on the contribution of primary carbon mineralization, which accounts for more than 90% of the total carbon in tea gardens. This breaks through the limitation of traditional carbon metering, which only focuses on the decomposition of exogenous carbon, and ensures the accuracy of carbon flux accounting.

[0095] It should be noted that the model fit coefficients This is a key fitting coefficient for quantifying the contribution of pruning residue input to the positive excitation intensity. A preferred value is 0.02. Its magnitude is determined based on experimental data linking tea garden residue input and the excitation effect. It directly determines the increase in positive excitation intensity caused by a unit amount of residue input; for example, for every 1 kg / m² increase in residue input, the positive excitation intensity will increase accordingly. The product of other factors.

[0096] It should be noted that the model fit coefficients This is a fitting coefficient quantifying the nonlinear effect of soil acidity on the positive excitation intensity, with a preferred value of 0.25. In the strongly acidic environment of tea gardens, this coefficient causes the soil acidity correction term to increase exponentially as the pH approaches the acidity threshold, accurately characterizing the catalytic effect of strongly acidic conditions on the positive excitation effect. For example, when the pH decreases from 5.0 to 4.0, the acidity correction term increases from... Increase to The positive excitation intensity increases accordingly.

[0097] It should be noted that the model fit coefficients This is the fitting coefficient for quantifying the inhibitory effect of soil carbon-nitrogen ratio on positive excitation intensity, with a preferred value of 0.4. Its effect causes the inhibition term of carbon-nitrogen ratio to decrease with increasing carbon-nitrogen ratio, consistent with the rule that the higher the carbon-nitrogen ratio in tea garden soil, the weaker the microbial excitation of primary carbon mineralization. For example, when the carbon-nitrogen ratio increases from 10 to 20, the inhibition term decreases from... Down to The positive excitation intensity was significantly suppressed.

[0098] It should be noted that the primary carbon contribution ratio coefficient It is a coefficient that clarifies the proportion of CO2 flux from native soil carbon mineralization, with a preferred value of 0.95. This value is determined based on experimental data distinguishing native soil carbon from exogenous residual carbon mineralization, directly ensuring the accuracy of native soil carbon mineralization flux calculation. For example, when the sum of baseline respiration and positive excitation intensity is 100 mg CO2 / m²·h, the native soil carbon mineralization flux is 0.95 × 100 = 95 mg CO2 / m²·h.

[0099] It should be noted that the formula for calculating the positive excitation intensity... It is a quantitative model that integrates four key factors: residual input, soil acidity, carbon-nitrogen ratio, and organic carbon storage. By coupling the influence of each factor through multiplication, it accurately simulates the variation patterns of positive excitation intensity under the special environment of tea gardens, such as node... exist Given a residual input of 0.8 kg / m², pH = 4.5, carbon-to-nitrogen ratio = 15, and SOC = 5 kg / m², the positive excitation intensity is 0.02 × 0.8 × × ×5 fully reflects the combined effect of each factor.

[0100] It should be noted that the formula for calculating the carbon mineralization flux in primary soils... It is a quantitative formula based on positive excitation intensity and baseline respiration, combined with the primary carbon contribution ratio coefficient. By integrating the additional primary carbon mineralization caused by the excitation effect with the basic primary carbon mineralization, it ensures the complete accounting of primary carbon mineralization flux.

[0101] It should be noted that the soil acidity correction term It is a nonlinear correction term specifically adapted to the highly acidic environment of tea gardens. It describes the catalytic effect of soil acidity on the positive excitation effect in the form of an exponential function. The lower the pH, the larger the value of the correction term, and the more significant the amplification of the positive excitation intensity. It accurately matches the actual characteristics of the stronger positive excitation effect under the highly acidic conditions of tea gardens.

[0102] It should be noted that the carbon-nitrogen ratio suppression term It is a mathematical expression that quantifies the inhibitory effect of soil carbon-nitrogen ratio on positive excitation intensity. The higher the carbon-nitrogen ratio, the smaller the value of this inhibitory term, and the more obvious the weakening effect on positive excitation intensity. It accurately reflects the negative correlation between soil carbon-nitrogen ratio and positive excitation effect in tea gardens.

[0103] It should be noted that nodes exist Input amount of trimmed residue at any time It is a node exist The actual carbon input from pruned residues returned to the field at any given time is obtained through weighing records of the actual residues returned to the field. The magnitude of this value directly determines the baseline level of the positive excitation intensity and is the core exogenous driving factor for triggering primary soil carbon mineralization. For example, terrace nodes are pruned after tea picking. Moment It may reach 1.2 kg / m².

[0104] It should be noted that nodes Soil pH value It is a node The actual pH values ​​of the soil sample extract were obtained by pH meter testing.

[0105] It should be noted that nodes Soil carbon-nitrogen ratio It is a node The actual ratio of carbon to nitrogen in the soil is obtained by elemental analysis. The carbon-nitrogen ratio in tea garden soil is affected by management measures such as fertilization and returning plant residues to the field. It is usually between 10 and 25 and is an important soil physicochemical factor for regulating the positive excitation intensity.

[0106] It should be noted that nodes exist Current soil organic carbon storage at any given time It is a node exist The actual storage of organic carbon in the soil at any given time was obtained by detecting the potassium dichromate oxidation method. This parameter provides the carbon source basis for the positive excitation effect; the higher the storage, the more primary carbon can be mineralized, such as in the old tea garden node. It may reach 6 kg / m², while in new tea gardens it may only be 3 kg / m².

[0107] It should be noted that nodes exist Baseline breathing at time It is a node exist The baseline soil respiration intensity under conditions of no residual input is obtained through soil respiration monitoring equipment. It reflects the basic mineralization level of primary carbon by soil microorganisms under natural conditions. For example, the baseline respiration of tea gardens during the fallow period may be stable at around 20 mg CO2 / m²·h.

[0108] It should be noted that the experimental data on the correlation between tea plantation residue input and excitation effect were obtained by controlling different residue input gradients and monitoring changes in positive excitation intensity, and include the correspondence between residue input and positive excitation intensity.

[0109] It should be noted that the experimental data on the correlation between soil acidity and the intensity of the excitation effect were obtained by setting different soil pH gradients and studying the variation law of positive excitation intensity, thus clarifying the catalytic mechanism of strong acid conditions on the positive excitation effect.

[0110] It should be noted that the experimental data on the correlation between soil carbon-nitrogen ratio and the intensity of the excitation effect were obtained by controlling the soil carbon-nitrogen ratio gradient and observing the positive excitation intensity response, revealing the inhibitory law of carbon-nitrogen ratio on the positive excitation effect.

[0111] It should be noted that the experimental data for distinguishing between primary soil carbon and exogenous residual carbon mineralization were obtained by using techniques such as ¹³C tracer to differentiate the CO2 flux generated by primary soil carbon and exogenous residual carbon mineralization.

[0112] It should be noted that the actual weighing records of the plant residue returned to the field are the actual records of the weight of the pruned residue returned to the field at different times during tea garden management, and are obtained after carbon content conversion. This ensures that the parameter accurately reflects the external input.

[0113] It should be noted that the amount of mobile dissolved organic carbon It is a node exist The total amount of migratable and dissolved organic carbon generated by the pruning residue input at any given time. This parameter directly links the residue input to the ligand supply and is the basic input for the ligand-induced dissolution mechanism.

[0114] It should be noted that the amount of mineral-bound carbon mobilized... It is a dedicated quantization node exist The amount of mineral-bound organic carbon mobilized by the ligand-induced dissolution mechanism in real time. This parameter focuses on the stable carbon pool in tea garden soil protected by iron oxides, breaking through the limitation of traditional carbon metering that only focuses on active carbon pools, and fully characterizing the key pathways of carbon loss in tea gardens.

[0115] It should be noted that nodes Flow to Node Lateral organic mineral leaching flux It is a representation node exist Flows towards nodes are constantly transported by runoff. The organic mineral leaching carbon flux, a parameter that integrates the dual effects of ligand mobilization and hydrological transport, quantifies the intensity of lateral carbon loss from tea plantations.

[0116] It should be noted that the mobilization rate constant This is a core constant for quantifying the contribution of ligand concentration to the mobilization rate of mineral-bound carbon. A preferred value is 0.01, and its magnitude is determined based on experimental data relating ligand concentration to mineral carbon mobilization. It determines the unlocking efficiency of the mineral carbon pool by the ligand; for example, for every 1 mg / L increase in ligand concentration, the mobilization rate of mineral-bound carbon will increase accordingly. Nonlinear gain term × iron-bound organic carbon reserves.

[0117] It should be noted that the formula for calculating the amount of mobile dissolved organic carbon is... It is a quantitative formula that directly correlates the input of the residue with the supply of transferable ligands, through the chemical activity parameters of the residue. (Characterizing the potential of the residue to release ligands) and the amount of residue input The product of these factors is used to calculate the amount of mobile dissolved organic carbon generated, for example... , hour, .

[0118] It should be noted that the formula for calculating the mobilization amount of mineral-bound carbon is... This is a formula that reflects the superlinear amplification effect of the ligand-promoted dissolution mechanism, in which... As a nonlinear gain term, a small increase in ligand concentration can significantly amplify the amount of mineral carbon mobilized through a squared relationship, for example... When the gain term increases from 2 to 3, the nonlinear gain term increases from 4 to 9, and the mobilization of mineral-bound carbon increases accordingly, which precisely matches the superlinear driving law of the concentration of ligands in tea gardens on the unlocking of mineral carbon.

[0119] It should be noted that the formula for calculating the lateral organic mineral leaching flux... It is a quantitative formula that integrates runoff transport ratio, rainfall intensity, and carbon source supply, through normalized connected matrix elements. (Characterizing transport ratio), rainfall pulse intensity (characterizing the intensity of hydrological driving forces) and total carbon source ( The product of ) fully simulates the generation process of lateral organic mineral leaching flux, for example , When the total carbon source is 1.5 kg / m², .

[0120] It should be noted that the nonlinear gain term This is an expression specifically describing the superlinear amplification effect of ligand concentration on the mineral carbonization encapsulation process. The gain term is derived from the mineral interface reaction sensitivity parameter. (Characterizing the stability of the organic carbon-iron oxide bond) and the amount of mobile, dissolved organic carbon The product squared (characterizing ligand concentration) indicates that an increase in ligand concentration will significantly enhance the mobilization of mineral carbon through a squared effect.

[0121] It should be noted that nodes exist Input amount of trimmed residue at any time It is a node exist The amount of carbon input from pruning residues returned to the field at any given time provides a basic source of material for the generation of mobile dissolved organic carbon, and its value affects the total amount of ligand supply.

[0122] It should be noted that nodes exist Iron-bound organic carbon reserves at time It is a node exist The actual amount of organic carbon bound to iron oxides in the soil at any given time was obtained by sodium dithionite extraction. This amount increases with age during long-term tea garden cultivation, serving as a carbon pool for ligand-induced dissolution mechanisms, such as in the terraced nodes of older tea gardens. It may reach 2.5 kg / m².

[0123] It should be noted that, Rainfall pulse intensity at time yes The actual rainfall intensity in the tea garden area is obtained through rainfall monitoring equipment. This parameter characterizes the hydrological driving force; the greater the rainfall intensity, the stronger the leaching capacity of runoff on organic minerals, for example, during heavy rain. It may reach 2.0, with a corresponding significant increase in lateral leaching flux.

[0124] It should be noted that the experimental data on the correlation between ligand concentration and mineral carbon mobilization were obtained by controlling different ligand concentration gradients and monitoring changes in mineral bound carbon mobilization. The dataset includes the correspondence between ligand concentration and mineral carbon mobilization, clarifying the unlocking mechanism of ligands on the mineral carbon pool.

[0125] It should be noted that the objective function is optimized. It is a core model that integrates the main objective of minimizing the carbon footprint of the tea garden throughout its entire life cycle with the stability constraint of the soil organic carbon pool. It focuses on carbon emission reduction needs through the main objective and ensures the sustainability of soil fertility through the stability penalty. The two work together to ensure that the management plan reduces carbon emissions and maintains the balance of the carbon pool, avoiding ecological or production risks caused by a single objective.

[0126] It should be noted that the input strategy vector for pruning remnants... It is a decision vector containing the input intensity of pruned debris at different times for each micro-topography node. Each element in the vector corresponds to the amount of debris input at a specific node and at a specific time period, for example... ( For the number of nodes, (This vector represents the number of time periods). This vector allows for precise control of the allocation of debris throughout the park, serving as a decision-making tool for optimizing management.

[0127] It should be noted that the predicted value of the carbon footprint throughout the entire life cycle It is the policy vector The carbon footprint prediction results for the entire tea plantation process from planting to packaging are obtained by superimposing field carbon flux (vertical primary carbon mineralization + lateral organic mineral leaching) with emissions from processing and transportation, reflecting the impact of different residue input strategies on carbon reduction targets.

[0128] It should be noted that functional units It is the benchmark unit for measuring carbon footprint, with the preferred value being per kilogram of dried tea. This setting fits the actual scenario of tea production and trade, making the carbon footprint results practically applicable and facilitating comparison of carbon footprints from different tea gardens and production batches.

[0129] It should be noted that the penalty weight It is a coefficient that quantifies the degree of penalty for deviation of the soil organic carbon pool from the target level. The preferred value is 5, and its magnitude is determined based on experimental data on the importance weights of carbon footprint and carbon storage. The larger the value, the higher the priority of carbon pool stability. For example, when the predicted carbon pool is 10% lower than the target value, the penalty term will significantly increase the objective function value, forcing the optimization process to adjust the residual input strategy.

[0130] It should be noted that the total soil organic carbon storage at the end of the forecast period is... It is the policy vector After implementation, the total soil organic carbon storage of all micro-topographic nodes at the end of the prediction period is predicted. This parameter directly reflects the final state of the soil carbon pool. By comparing it with the target soil organic carbon storage, a stability constraint is formed to ensure that the optimization scheme will not lead to excessive consumption of the carbon pool.

[0131] It should be noted that the first The policy vector of the next iteration It is the first in the projective gradient descent algorithm The step-by-step pruning of the input policy involves adjusting the policy vector based on the gradient of the objective function in each iteration, gradually approaching the optimal solution. For example, the initial policy vector... It is possible that the residuals will be evenly distributed. After multiple iterations, the strategy vector will be optimized in the direction of less input in high-risk areas and more input in low-risk areas.

[0132] It should be noted that the first The policy vector of the next iteration It is the first The next iteration's strategy vector, after gradient update and projection constraints, becomes a new strategy. This vector inherits the optimization results of the previous iteration and, through projection operations, ensures that the residual input is within a reasonable range (e.g., non-negative and not exceeding the maximum capacity), gradually improving the optimization effect of the objective function.

[0133] It should be noted that the step size This is a parameter in the projective gradient descent algorithm that controls the iteration update magnitude. The preferred value is 0.1. Too large a step size may cause iteration oscillations and non-convergence, while too small a step size will prolong the iteration time. This value is determined based on experimental data on iteration convergence to ensure that the algorithm converges efficiently to a better solution within a fixed number of iterations.

[0134] It should be noted that the gradient of the objective function It is to optimize the objective function for the input policy vector of pruning the residual. The partial derivative vector of the gradient points in the direction of increasing objective function. Therefore, updating the strategy vector in the opposite direction of the gradient during iteration can reduce the objective function value. The gradient contains the derivative of the lateral leaching nonlinear term. When the residual input approaches the threshold that triggers the nonlinear surge, the gradient will increase significantly, thereby automatically limiting the amount of residual input in that region.

[0135] It should be noted that the projection operation of constraint sets... It is the operation of restricting the iteratively updated policy vector within a reasonable set of constraints. The conditions include that the residual input is non-negative, the input of a single node does not exceed the soil carrying capacity limit, and the total input of the whole orchard matches the actual residual yield. For example, if the maximum residual carrying capacity of a node is 1.5 kg / m², and the input of that node is 1.8 kg / m² after the gradient update, the projection operation will adjust it to 1.5 kg / m² to ensure the feasibility of the strategy.

[0136] It should be noted that the formula for calculating the objective function is optimized. It is a collaborative optimization formula combining the main objective and penalty terms. The first term normalizes the life-cycle carbon footprint to a unit of function, ensuring the comparability of the objectives; the second term amplifies the degree of carbon pool deviation from the objective through a squared term, strengthening the stability constraint. For example, when... , , When the carbon pool deviates from the target value by 5%, .

[0137] It should be noted that the iterative solution formula This is the core formula of the projective gradient descent algorithm with a fixed number of iterations. By combining gradient inverse updates and projection constraints, it ensures that the policy vector in each iteration both approaches the optimal solution and satisfies the actual constraints. For example, in the 1st iteration... Secondary policy vector The input to a high-risk node is 1.2 kg / m², which is updated to 1.0 kg / m² after the gradient is reversed. After the projection operation confirms that the constraints are met, the following is formed: The corresponding elements will gradually achieve the goal of avoiding the nonlinear surge in lateral carbon loss.

[0138] It should be noted that the target soil organic carbon storage This refers to the target total organic carbon storage required to maintain soil fertility in tea gardens and ensure tea yield and quality. It is determined based on data regarding the soil fertility maintenance needs of tea gardens. For example, according to long-term field experiments, if a tea garden's soil organic carbon storage is maintained at 8000 kg / hm² to achieve fertility balance, then... .

[0139] It should be noted that the data on the requirements for maintaining soil fertility in tea gardens is a dataset obtained through long-term field experiments and soil fertility monitoring. It includes the correlation between soil physicochemical properties (such as water and fertilizer retention capacity and nutrient supply level) and tea yield and quality under different organic carbon storage levels, and clarifies the minimum organic carbon storage required to maintain soil fertility.

[0140] It should be noted that the experimental data on the importance weights of carbon footprint and carbon storage were obtained through multi-scenario simulation experiments. By comparing the optimization effects under different combinations of carbon footprint weights and carbon storage weights, the optimal weight ratio for achieving carbon emission reduction targets under the premise of ensuring soil sustainability was clarified.

[0141] It should be noted that the experimental data for iterative convergence was obtained by testing the convergence of the algorithm under different step lengths and different number of iterations. The dataset includes information such as the changing trend of the objective function value during the iteration process and the optimization effect of the final strategy, and clarifies the step length value for achieving efficient convergence within a fixed number of iterations.

[0142] It should be noted that the standard functional unit setting for tea production is based on the functional unit specifications determined by the production and trade practices of the tea industry. The standard unit is set at per kilogram of dry tea as the benchmark for carbon footprint measurement. This setting ensures that the carbon footprint results can be directly applied to practical scenarios such as product certification and carbon trading, and conforms to the industry's general standards.

[0143] The embodiments of this example have been described above. However, this example is not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms based on the guidance of this example, and all of them are within the protection scope of this example.

Claims

1. A LCA-based carbon accounting system for a tea garden full process, characterized by, include: The micro-topography and hydrological unit construction module is used to analyze the spatial structure of terraced fields, divide the tea garden into discrete landform units including terrace surfaces, terrace walls and rows, and construct a runoff transport network connecting each discrete landform unit based on drainage density data. The soil biochemical reaction parameter calculation module is configured to respond to the input of pruned residue returning to the field, calculate the chemical activity parameters of the residue that characterize the release potential of polyphenols in the residue, and solve the mineral interface reaction sensitivity parameters that characterize the binding stability of soil organic carbon and iron oxides based on soil acidity value and active iron content. The multidimensional carbon flux accounting module is used to perform bidirectional measurement by referencing the chemical activity parameters of the residues and the sensitivity parameters of the mineral interface reaction: in the vertical direction, it calculates the primary soil carbon mineralization flux induced by exogenous input based on the positive excitation effect; in the lateral direction, it quantifies the lateral organic mineral leaching flux driven by the ligand-promoted dissolution mechanism, and there is a nonlinear amplification relationship between the lateral organic mineral leaching flux and the amount of pruned residue input. The whole life cycle regulation module is used to superimpose the field carbon flux of the discrete geomorphic unit with the emissions from the tea processing and transportation process to generate the whole life cycle carbon footprint, and output a tea garden management scheme to avoid the nonlinear surge of the lateral organic mineral leaching flux.

2. A LCA-based carbon accounting system for a tea plantation value chain according to claim 1, wherein, Analyzing the spatial structure of terraced fields, the tea garden is divided into discrete geomorphic units comprising terrace surfaces, terrace walls, and rows, including: A set of nodes is constructed, comprising three types: surface, wall, and inter-row. Based on evidence of differences in microbial function at different micro-topographic locations, carbon cycle weights and nitrogen cycle weights are assigned to each type of discrete geomorphic unit, with the carbon cycle weight being higher in surface and wall nodes and the nitrogen cycle weight being higher in inter-row nodes.

3. The LCA-based full-process carbon metering system for tea gardens according to claim 2, characterized in that, Constructing a runoff transport network connecting the discrete geomorphic units based on drainage density data, including: A normalized connectivity matrix is ​​constructed based on the directed water flow path to connect the discrete geomorphic units. The elements in the normalized connectivity matrix represent the distribution ratio of material transported from the upstream node to a specific downstream node. This distribution ratio is determined by the proportion of the product of the drainage density index and the slope factor of the upstream node in all downstream output paths, thereby quantifying the guiding capacity of the terrace structure for lateral material migration.

4. The LCA-based full-process carbon metering system for tea gardens according to claim 1, characterized in that, In response to the input of pruned plant residues returning to the field, chemical activity parameters characterizing the release potential of polyphenols in the residues were calculated, including: A baseline activity coefficient was established, and the polyphenol content and humification index of dissolved organic carbon in the pruning residue were obtained. The ratio of the polyphenol content to the reference value and the ratio of the non-humification ratio of dissolved organic carbon to the reference value were calculated. The baseline activity coefficient was multiplied by the ratio of polyphenol content and the ratio of non-humification ratio to obtain the chemical activity parameters of the residue.

5. The LCA-based full-process carbon metering system for tea gardens according to claim 1, characterized in that, Based on soil acidity and active iron content, mineral interface reaction sensitivity parameters characterizing the binding stability of soil organic carbon and iron oxides are calculated, including: An exponential function containing a soil acidity term and a linear gain function containing an active iron content term are constructed. The soil acidity term is obtained by subtracting the node soil pH value from the acidity threshold and is used to characterize the nonlinear catalytic effect of a strongly acidic environment on chemical complexation reactions. The linear gain function is used to characterize the number of reaction sites provided by iron minerals. The output value of the exponential function and the output value of the linear gain function are multiplied to obtain the mineral interface reaction sensitivity parameter.

6. The LCA-based full-process carbon metering system for tea gardens according to claim 1, characterized in that, In the vertical direction, the primary soil carbon mineralization flux induced by exogenous inputs is calculated based on the positive excitation effect, including: A positive excitation intensity calculation model is constructed, which includes the input amount of pruned residue, soil acidity correction term, carbon-nitrogen ratio suppression term, and current soil organic carbon storage. The soil acidity correction term increases exponentially as the soil acidity value approaches the acidity threshold. The calculated positive excitation intensity is added to the baseline respiration and multiplied by the set primary carbon contribution ratio coefficient to obtain the primary soil carbon mineralization flux.

7. The LCA-based full-process carbon metering system for tea gardens according to claim 1, characterized in that, In the lateral direction, the lateral organic-mineral leaching flux driven by the ligand-induced dissolution mechanism is quantified, including: First, the amount of pruned residue input is multiplied by the chemical activity parameter of the residue to obtain the amount of mobile dissolved organic carbon. Then, the mobilization amount of mineral-bound carbon is calculated. This calculation process includes a nonlinear gain term, which is the square of the product of the mineral interface reaction sensitivity parameter and the amount of mobile dissolved organic carbon. This squared value is then multiplied by the current iron-bound organic carbon reserves, thus reflecting the superlinear amplification effect of ligand concentration on the mineral deencapsulation process. Finally, by combining the rainfall pulse intensity and the runoff transport network, the sum of the amount of mobile dissolved organic carbon and the mobilization amount of mineral-bound carbon is converted into the lateral organic mineral leaching flux.

8. The LCA-based full-process carbon metering system for tea gardens according to claim 1, characterized in that, The field carbon flux of the discrete geomorphic units is superimposed with emissions from tea processing and transportation to generate a full life-cycle carbon footprint. A tea garden management scheme is then output to mitigate the nonlinear surge in lateral organic mineral leaching flux, including: The field carbon flux of each discrete geomorphic unit is converted into carbon dioxide equivalent and superimposed with the process emissions from tea processing and transportation to calculate the predicted life cycle carbon footprint. An optimization objective function is constructed, which consists of a main objective term for the predicted life cycle carbon footprint per unit functional unit and a stability penalty term representing the deviation of the soil organic carbon pool from the target level. A projection gradient descent algorithm with a fixed number of iterations is used to iteratively solve the pruning residue input strategy vector, and the output is a management scheme that minimizes the optimization objective function. The management scheme is used to identify and avoid high-risk areas of residue input that lead to superlinear growth in lateral carbon loss.

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