Method for evaluating carbon sequestration and sink increase of farmland soil carbon pool in cold black soil region

Abstract

The application discloses a kind of cold region black soil area farmland soil carbon pool carbon fixation and increase assessment method, it is related to the technical field of agricultural ecological environment evaluation.Acquire target field current soil carbon pool reserves and three categories of indexes, at least including 4 cold region characteristic indexes and soil aggregate destruction rate and microbial carbon and normalization;Establish the freeze-thaw- leaching coupling carbon loss mechanism model distinguishing freezing period and thawing period, obtain the carbon pool reserves after freeze-thaw correction;According to the membership value of each index and weight, calculate comprehensive index, wherein the value of reserve type index is corrected carbon pool reserves, and the evaluation grade is output according to comprehensive index;When evaluation grade does not reach preset target grade, calculate the contribution of cold region characteristic index loss score, determine the short board index and match management measures, output management scheme, output compliance evaluation results when reaching.The evaluation result automatically drives management decision, realizes the closed loop connection of evaluation and management, is suitable for cold region black soil area farmland soil carbon pool accurate evaluation and carbon fixation and increase management.

Description

Technical Field

[0001] This invention relates to the field of agricultural ecological environment assessment technology. More specifically, this invention relates to a method for assessing carbon sequestration and carbon sink enhancement in farmland soils in cold black soil regions. Background Technology

[0002] Farmland soil carbon pools are a crucial component of terrestrial ecosystems, and their carbon sequestration capacity directly impacts soil fertility maintenance, crop production, and sustainable agricultural development. The cold-region black soil area is a vital commodity grain production base in my country, rich in soil organic matter and possessing enormous carbon sequestration potential. However, this region is located in a high-latitude, cold zone, where seasonal freeze-thaw cycles and spring snowmelt leaching result in poor soil carbon pool stability and a high risk of carbon loss. The dynamic changes in farmland soil carbon pools in this area differ significantly from those in conventional agricultural areas, necessitating the development of carbon pool assessment and management methods tailored to the characteristics of cold-region environments.

[0003] Existing methods for soil carbon pool assessment mainly include single-index assessment methods, static storage estimation models, and comprehensive assessments based on projection pursuit and genetic algorithms. Among these, the projection pursuit assessment model based on accelerated genetic algorithms has been applied to soil quality grading. By optimizing the projection direction through genetic algorithms, multi-dimensional evaluation indicators are projected into a low-dimensional space, achieving objective weighting and grading. This type of method represents the mainstream technical route for current comprehensive soil quality assessment and can reflect soil quality status to a certain extent. However, when applied to the assessment of carbon sequestration and enhancement in farmland soils in cold black soil regions, it still has the following shortcomings:

[0004] First, the evaluation results stop at the level classification and cannot be automatically transformed into actionable carbon sequestration and carbon sink enhancement management measures. The evaluation is disconnected from farmland management, and the evaluation results are difficult to directly guide production practices.

[0005] Second, most methods for quantifying carbon loss rely on static storage estimation or purely empirical fitting, failing to distinguish between the two distinct carbon release pathways of the freezing and thawing periods. While existing research has revealed the mechanisms by which freeze-thaw processes disrupt soil aggregates and release active carbon, and some studies have quantitatively analyzed the turnover of black soil aggregates and changes in organic carbon under freeze-thaw conditions using rare earth element tracer methods, these studies remain at the level of mechanistic understanding and have not yet developed a complete technical solution for farmland carbon pool evaluation and management. Prior patent CN105572318A discloses an in-situ monitoring device for soil freeze-thaw and a method for measuring soil carbon loss, where the total carbon loss Closs during the freeze-thaw process is expressed as Closs = DOC+ CH4+ A single-channel empirical estimate of CO2, where... DOC is the difference in soluble organic carbon in the soil before and after freeze-thaw cycles. CH4 represents the total methane emission flux throughout the entire freeze-thaw period. CO2 represents the total carbon dioxide emission flux during the entire freeze-thaw period. However, it does not distinguish between different release pathways during the freezing and thawing periods, making it difficult to accurately characterize the nonlinear process of carbon release during freeze-thaw in cold regions. This leads to a systematic underestimation of freeze-thaw carbon loss.

[0006] Third, the weight optimization adopts a single-objective optimization approach, which only aims to maximize the carbon sink function or the carbon storage. It cannot simultaneously take into account the two conflicting objectives of carbon sink function and carbon loss risk, resulting in fields with high carbon storage but high carbon loss risk being misjudged as superior.

[0007] In summary, existing technologies have not yet formed a complete method for cold black soil regions that can integrate the freeze-thaw-leaching coupling mechanism into carbon pool evaluation, achieve multi-objective weight optimization, and connect the evaluation results to management measures. Summary of the Invention

[0008] This invention provides a method for assessing carbon sequestration and carbon sink enhancement in farmland soil in cold black soil regions. It can solve the problem of the disconnect between assessment and farmland management, and is applicable to the accurate assessment and carbon sequestration and carbon sink enhancement management of farmland soil in cold black soil regions.

[0009] To achieve these objectives and other advantages according to the present invention, a method for assessing carbon sequestration and enhancement in farmland soils in cold black soil regions is provided, comprising the following steps:

[0010] The current soil carbon pool, soil physicochemical properties, soil carbon pool capacity, and soil carbon pool dynamics of the target field are obtained. The current soil carbon pool is characterized by the total organic carbon content of the soil in the target field, and includes at least four cold-region-specific indicators: seasonal freeze-thaw cycle intensity, spring snowmelt leaching coefficient, freeze-thaw carbon emission pulse, and DOC leaching flux, as well as soil aggregate destruction rate and microbial biomass carbon. The seasonal freeze-thaw cycle intensity is the product of the number of freeze-thaw cycles during the freeze-thaw period and the average temperature difference per single freeze-thaw cycle. The average temperature difference per single freeze-thaw cycle is the arithmetic mean of the difference between the highest and lowest soil temperatures during each complete freeze-thaw cycle. The spring snowmelt leaching coefficient is the product of snow water equivalent and snowmelt rate divided by... Soil saturated hydraulic conductivity and the carbon emission pulse during the freeze-thaw cycle were used as the peak value of soil carbon dioxide emission flux during the freeze-thaw cycle. DOC leaching flux was the integral of the product of soluble organic carbon concentration and runoff during the snowmelt period over time. Soil aggregate destruction rate was the ratio of broken soil aggregates to the total aggregates. The soil aggregates were determined by soil aggregate sieving method. Each index was normalized to a dimensionless membership value by trapezoidal membership function to calculate the comprehensive index of soil carbon sequestration and carbon sink enhancement and the contribution of loss. Seasonal freeze-thaw cycle intensity, spring snowmelt leaching coefficient, carbon emission pulse during the freeze-thaw period, and DOC leaching flux were used as negative indicators. A descending trapezoidal membership function was used. The inflection point threshold of the trapezoidal membership function was determined by the distribution characteristics of historical monitoring data of the target area.

[0011] A freeze-thaw-leaching coupled carbon loss mechanism model was established. The amount of active carbon released in the freezing channel was determined by the microbial biomass carbon, the number of freeze-thaw cycles, and the minimum temperature-related factors. The amount of active carbon released in the thawing channel was determined by the protected active carbon background, the soil aggregate destruction rate, and the leaching partition coefficient. The annual net change in the carbon pool was calculated as carbon input minus respiration consumption minus the amount of active carbon released in both channels. The freeze-thaw corrected carbon pool was calculated as the sum of the current soil carbon pool and the annual net change in the carbon pool. The units for both the current soil carbon pool and the annual net change in the carbon pool are g / kg.

[0012] The comprehensive index of soil carbon sequestration and carbon sink enhancement is calculated based on the normalized membership values ​​and weights of each indicator. Among them, the soil carbon pool storage index uses the freeze-thaw corrected carbon pool storage as its index value. The carbon sequestration and carbon sink enhancement level classification is carried out according to the preset carbon sequestration and carbon sink enhancement level classification rules and the evaluation level is output.

[0013] When the evaluation level does not reach the preset target level, the contribution of the score loss is calculated for the four cold-region characteristic indicators. The contribution of the score loss is the product of the indicator weight and 1 minus the indicator membership value. When the contribution of the score loss of each cold-region characteristic indicator is less than the preset score loss threshold, the limiting factor for carbon sequestration and carbon sink enhancement in the target field is determined to be a non-cold-region characteristic process, and general carbon sequestration and carbon sink enhancement management suggestions are output. When one or more of the contribution of the score loss of each cold-region characteristic indicator exceeds the preset score loss threshold, the cold-region characteristic indicator with the largest contribution of the score loss is determined to be the weak link indicator. According to the type of the weak link indicator, the management measures corresponding to the mechanism of action of the weak link indicator are matched from the preset carbon sequestration and carbon sink enhancement management measure library. When the weak link indicator is the seasonal freeze-thaw cycle intensity or the carbon emission pulse during the freeze-thaw period, management measures to reduce freeze-thaw disturbance are matched. When the weak link indicator is the spring snowmelt leaching coefficient or DOC leaching flux, management measures to reduce snowmelt leaching are matched. The management plan containing the weak link indicator, recommended management measures and measure parameters is output.

[0014] Preferably, the amount of activated carbon released during freezing is calculated using the following formula:

[0015] ,

[0016] in, Microbial biomass carbon, in g / kg. Here, is the freeze-thaw pyrolysis coefficient, and is a dimensionless coefficient. This refers to the number of freeze-thaw cycles. As a factor related to the lowest temperature, when the lowest temperature of the year is not lower than -10℃ Take 1.0, when the lowest temperature of the year is not higher than -30℃. The value is set to 0.5, with linear interpolation in between. The freezing channel corresponds to the microbial cell lysis and release process during the freezing period, driven by the number of freeze-thaw cycles.

[0017] Preferably, the amount of activated carbon released from the melting channel is calculated using the following formula:

[0018] ,

[0019] in, The active carbon background, physically protected by the aggregates, is expressed in g / kg. It is obtained by determining the organic carbon composition within the micro-aggregates after classifying soil aggregates by particle size using a wet sieving method. Seasonal freeze-thaw cycle intensity, The soil aggregate destruction rate, , The maximum aggregate destruction rate is determined by the maximum observed aggregate destruction rate in the historical monitoring data of the target area. The value is greater than 0 and less than 1. The destruction rate coefficient is expressed in °C. -1 ·Second-rate -1 , Greater than 0, This represents the leaching coefficient of snowmelt in spring. Leaching strength, The dimensionless value obtained by normalizing the historical monitoring data range of the target area. The values ​​are positively correlated. This is the mineralization distribution coefficient. The leaching partition coefficient, and All are non-negative coefficients and and The sum does not exceed 1. The remaining part of the activated carbon released by the breakup of aggregates, after deducting the mineralized and leached portions, remains in the soil. The melting channel corresponds to the physical breakup and release process of aggregates during the melting period, driven by the intensity of freeze-thaw cycles.

[0020] The freeze-thaw corrected carbon pool reserves are calculated based on the annual net change in carbon pool reserves, which is calculated using the following formula:

[0021] ,

[0022] in, For carbon input, This represents the normal soil respiration consumption, excluding the mineralization consumption of activated carbon released from the breakup of aggregates during freeze-thaw cycles. , , , , The unit is g / kg.

[0023] Preferably, the weights of each indicator are determined using a combination of the NSGA-II multi-objective genetic algorithm and projection pursuit:

[0024] The Pareto optimal solution set is solved with the dual objectives of maximizing the carbon sink function value and minimizing the carbon loss risk index. The carbon sink function value is equal to the product of the carbon pool storage after freeze-thaw correction and normalized by the trapezoidal membership function, and the soil carbon fixation efficiency. The soil carbon fixation efficiency is the proportion of carbon input that is converted into a stable carbon pool. The carbon loss risk index is equal to the product of the spring snowmelt leaching coefficient normalized according to the historical monitoring data range of the target area, the soil aggregate destruction rate normalized according to the historical monitoring data range of the target area, and the DOC leaching flux normalized according to the historical monitoring data range of the target area. The normalized values ​​are positively correlated with the corresponding index values.

[0025] Projection pursuit is performed on the Pareto optimal solution set to determine the unique projection direction. The index weights are determined according to the percentage of the absolute value of the projection direction components. The sum of the weights of four cold-region characteristic indicators, namely, seasonal freeze-thaw cycle intensity, spring snowmelt leaching coefficient, freeze-thaw period carbon emission pulse, and DOC leaching flux, is constrained to be no less than 0.30.

[0026] Preferably, a freeze-thaw intensity correction factor is introduced to dynamically adjust the membership threshold when calculating the comprehensive index of soil carbon sequestration and carbon sink enhancement.

[0027] When the seasonal freeze-thaw cycle intensity is no greater than 25°C / cycle, the freeze-thaw intensity correction factor Take 1.0, when the seasonal freeze-thaw cycle intensity is greater than 25℃·cycle and not greater than 35℃·cycle, Calculate, where, The seasonal freeze-thaw cycle intensity is defined as follows: when the seasonal freeze-thaw cycle intensity is greater than 35°C / cycle, The basic membership threshold is set to 0.85, which is the inflection point of the trapezoidal membership function without freeze-thaw correction. This threshold is determined by the distribution characteristics of historical monitoring data for the target area. For negative indices using a descending trapezoidal membership function, the basic membership threshold is multiplied by [the threshold value]. To obtain the dynamic membership threshold, for positive indices using the ascending trapezoidal membership function, divide the basic membership threshold by... The comprehensive index of soil carbon sequestration and carbon sink enhancement, based on the dynamic membership threshold, is calculated using the following formula:

[0028] ,

[0029] in, For the first Item weight, To take the value of the indicator Substitute The membership values ​​obtained by the trapezoidal membership function after threshold correction are negative indices. Multiply by the basic membership threshold to adjust the threshold, and the positive index is... Divide by the basic membership threshold to correct the threshold. For the first The values ​​of each indicator.

[0030] Preferably, the methods for obtaining the four cold-region-specific indicators include:

[0031] The seasonal freeze-thaw cycle intensity is obtained by statistically analyzing the soil temperature data continuously monitored by a multi-layer soil temperature sensor array. The product of the number of cycles across 0℃ within the year and the average temperature difference per cycle is calculated. The average temperature difference per cycle is the average of the difference between the lowest and highest soil temperature values ​​in each freeze-thaw cycle.

[0032] The leaching coefficient of spring snowmelt water is obtained by inverting snow water equivalent and snowmelt rate from meteorological stations and remote sensing. It is calculated by dividing the product of snow water equivalent and snowmelt rate by the soil saturated hydraulic conductivity. The dimension is length and the unit is mm.

[0033] The freeze-thaw carbon emission pulse was obtained by continuously monitoring soil carbon dioxide emission flux during the freeze-thaw cycle using an in-situ carbon dioxide flux monitor with an automatic gas chamber method, and the peak value was recorded. The unit is g C·m. -2 ·d -1 ;

[0034] DOC leaching flux was obtained by monitoring the concentration of soluble organic carbon and runoff during the snowmelt period using a runoff collection device and an automated water quality analyzer, and then integrating the product of the two over the snowmelt period. The unit is kg C·ha. -1 ·a -1 .

[0035] Preferably, after outputting the management plan, the indicator data after implementing the management measures are re-acquired, and the comprehensive index of soil carbon sequestration and carbon sink enhancement is compared with the measured carbon loss membership degree after being transformed by the descending trapezoidal membership degree function. The larger the measured loss membership degree, the smaller the measured carbon loss. When the difference between the comprehensive index and the measured loss membership degree is less than 0.1, it is judged as consistent. The overall accuracy rate is the proportion of sample points judged as consistent.

[0036] The predicted freeze-thaw carbon release from the mechanistic model is compared with the measured carbon release. When the overall accuracy is below 85% or the predicted freeze-thaw carbon release deviation exceeds 20%, the soil aggregate destruction rate and the background of active carbon protected by the aggregate are updated first. If the predicted freeze-thaw carbon release deviation still exceeds 20% after the update, the freeze-thaw cracking coefficient, mineralization partition coefficient, and leaching partition coefficient are corrected with the goal of minimizing the mean square error between the measured and predicted values.

[0037] Preferably, when the contributions of multiple cold-region characteristic indicators are similar, the identification of collaborative weaknesses and the matching of collaborative measures are implemented, specifically including:

[0038] Calculate the mean and range of the contribution of the four cold-region characteristic indicators to the score loss. When the ratio of the range to the mean is less than 0.2, it is judged that there are multiple shortcomings.

[0039] For cold-region characteristic indicators with multiple shortcomings, a synergistic grouping rule is established based on the physical causal relationship between the indicators. Indicators with mutual amplification effects are merged into a synergistic shortcoming group. The physical causal relationship is the basis for determining the synergistic shortcoming group. The seasonal freeze-thaw cycle intensity forms a freeze-thaw synergistic shortcoming group by driving the carbon emission pulse during the freeze-thaw period, and the spring snowmelt leaching coefficient forms a leaching synergistic shortcoming group by driving the DOC leaching flux. When the correlation coefficient of two cold-region characteristic indicators in the historical monitoring sequence is greater than 0.6, it is determined that the two indicators have a synergistic amplification effect and the degree of synergy is strengthened. When the correlation coefficient is less than 0.6, the synergistic shortcoming group is still determined based on the physical causal relationship.

[0040] For the freeze-thaw synergy weakness group, a first set of management measures aimed at reducing freeze-thaw disturbance is matched; for the leaching synergy weakness group, a second set of management measures aimed at reducing snow leaching is matched. When the first and second sets of management measures contain the same management measures, the same management measures are output first. The maximum value of the recommended parameters of the same management measure in the first and second sets of management measures is multiplied by a synergy enhancement coefficient of 1.2 as the execution parameter. When the two sets of management measures do not overlap, the management measures in the first and second sets of management measures are output in descending order of contribution to the loss of points, forming a combined management scheme.

[0041] Preferably, when multiple management measures are matched, the management measure combination optimization is performed, specifically including:

[0042] A synergy-antagonism relationship matrix of management measures is pre-established. The matrix records the synergy gain coefficient or antagonism loss coefficient when any two management measures are combined. The synergy gain coefficient represents the gain ratio of the carbon sequestration and carbon sink enhancement effect when the two management measures are combined to the sum of the effects of the two management measures implemented individually. The antagonism loss coefficient represents the loss ratio of the carbon sequestration and carbon sink enhancement effect when the two management measures are combined. The synergy gain coefficient and the antagonism loss coefficient are determined based on historical experimental data or expert experience.

[0043] With the goal of maximizing the overall carbon sequestration and carbon sink enhancement effect and the constraint that the resource input for implementing measures does not exceed the upper limit of the management resource input for the target field, a combinatorial optimization model is constructed. The upper limit of the management resource input for the target field is set by the user based on the actual available resources. The overall carbon sequestration and carbon sink enhancement effect is composed of the weighted sum of the carbon sequestration and carbon sink enhancement effects of each management measure and the synergistic gain or antagonistic loss of any combination of two management measures. The weight of each management measure is determined based on the carbon sequestration and carbon sink enhancement effect contribution coefficient preset in the management measure library. The synergistic gain or antagonistic loss of any two management measures is the product of the corresponding coefficient obtained by querying the synergistic-antagonistic relationship matrix and the carbon sequestration and carbon sink enhancement effects of the two management measures. The constraint is that the sum of the resource input for implementing each management measure does not exceed the upper limit of the management resource input for the target field. The solution is to find the subset of management measures that maximizes the overall carbon sequestration and carbon sink enhancement effect.

[0044] The subset of management measures obtained from the solution is sorted from high to low according to the contribution coefficient of each management measure to carbon sequestration and carbon sink enhancement, and the sorted combination of management measures and corresponding parameters are output.

[0045] Preferably, when the model parameters of the freeze-thaw-leaching coupled carbon loss mechanism model are first applied, they are initially calibrated based on historical monitoring data or indoor freeze-thaw simulation experimental data of the target field area. The initial values ​​of the freeze-thaw cracking coefficient, mineralization partition coefficient, and leaching partition coefficient are determined according to the principle of minimizing the fitting residual between the measured carbon release from historical monitoring data and the carbon release predicted by the model.

[0046] The present invention has at least the following beneficial effects:

[0047] First, this invention constructs and normalizes a cold-region-specific indicator system, establishes a freeze-thaw-leaching coupled carbon loss mechanism model to correct carbon pool reserves, determines the weak indicators by the contribution of loss points and matches carbon sequestration and enhancement management measures, realizes the automatic driving of management decisions by evaluation results, and solves the problem of the disconnect between evaluation and farmland management.

[0048] Secondly, this invention quantifies the amount of activated carbon released by distinguishing between two channels: microbial cell lysis during the freezing period and physical fragmentation of aggregates during the thawing period. This clarifies the physical mechanism of freeze-thaw carbon release, improves the quantitative accuracy of freeze-thaw carbon loss, and overcomes the shortcomings of traditional empirical fitting, which cannot characterize the nonlinear process of freeze-thaw carbon release.

[0049] Third, this invention uses the NSGA-II multi-objective genetic algorithm coupled with projection pursuit to solve the Pareto optimal solution set with the dual objectives of maximizing carbon sink function and minimizing carbon loss risk. It objectively determines the unique weight vector that takes into account both objectives, thus avoiding misjudgment of fields with high reserves and high loss risk.

[0050] Fourth, this invention achieves closed-loop correction of model parameters by comparing the consistency of evaluation results with measured data, and avoids antagonistic conflicts between multiple measures by identifying collaborative weaknesses and optimizing the combination of measures, thereby achieving continuous improvement of carbon sequestration and carbon sink enhancement management schemes.

[0051] Other advantages, objectives and features of the present invention will become apparent in part from the following description, and in part from those skilled in the art through study and practice of the invention. Detailed Implementation

[0053] The present invention will now be described in further detail to enable those skilled in the art to implement it based on the description. It should be understood that terms such as "having," "comprising," and "including" as used herein do not exclude the presence or addition of one or more other elements or combinations thereof. It should be noted that the experimental methods described in the following embodiments should not be construed as limiting the present invention.

[0054] To achieve accurate assessment of the soil carbon pool in farmland in cold black soil regions, this invention first constructs an indicator system and selects core indicators:

[0055] First, a candidate indicator system was constructed. Based on the general indicator system for evaluating farmland soil carbon pools, and considering the unique loss processes caused by seasonal freeze-thaw cycles and snowmelt leaching in the cold black soil region, a candidate indicator system comprising 18 items in three main categories was constructed. These include soil physicochemical properties, soil carbon pool capacity, and soil carbon pool dynamics. Soil physicochemical properties reflect the basic physicochemical environment of the soil; soil carbon pool capacity reflects the size and composition of the soil carbon pool; and soil carbon pool dynamics reflects the input, output, and loss processes of the soil carbon pool. Specifically, the system includes the following indicators: The first level is soil physicochemical properties. The first level consists of six indicators: pH value, soil water-filled pore space, seasonal freeze-thaw cycle intensity, spring snowmelt leaching coefficient, average weight diameter of soil aggregates, and soil aggregate destruction rate. The second level consists of six indicators: total organic carbon, soluble organic carbon, microbial biomass carbon, particulate organic carbon, mineral-bound organic carbon, and light organic carbon. The third level consists of six indicators: carbon input-output ratio, soil carbon fixation efficiency, organic matter decomposition rate, soil respiration rate, freeze-thaw carbon emission pulse, and DOC leaching flux.

[0056] Second, six core indicators were selected from 18 candidate indicators. The selection methods included: (1) First, the two dominant loss processes that distinguish the cold black soil region from the conventional agricultural region were identified, namely the microbial cell lysis process during the freezing period and the physical breakage of aggregates and leaching process during the melting period. Then, the key control parameters were determined by the target of each process. The target of the freezing channel is microbial cells, so microbial biomass carbon is the key carbon source parameter for controlling carbon release in the freezing channel. The target of the melting channel is the aggregate structure, so the soil aggregate destruction rate is the key state parameter for controlling carbon release in the melting channel. The target of the leaching process is the transport of soil carbon by snowmelt, so the spring snowmelt leaching coefficient is the key driving parameter for controlling leaching intensity. (2) The direct driving and indirect influences were distinguished according to the level of the parameter's role in the physical process. The release amount of the freezing channel is physically equal to the product of the carbon pool size of the microbial cells and the cell lysis ratio. The release amount of the melting channel is physically equal to the protected active carbon. The product of the ratio of bottom and aggregate breakage exposure and the distribution ratio of destination, microbial biomass carbon and soil aggregate destruction rate are respectively in the direct multiplier position of the two product relationships. Their values ​​directly determine the scale of channel release and are necessary direct inputs of the mechanism model. Parameters such as seasonal freeze-thaw cycle intensity and spring snowmelt leaching coefficient are used as process intensity adjustment quantities to calculate channel release. Seasonal freeze-thaw cycle intensity acts on the freezing channel and melting channel respectively through the number of freeze-thaw cycles and the destruction rate process. Spring snowmelt leaching coefficient acts on the melting channel through leaching intensity and is a driving-regulating parameter. (3) Soil aggregate destruction rate represents the dynamic breakage degree of aggregates under freeze-thaw disturbance and directly reflects the structural exposure scale of the melting channel. It cannot be replaced by the average weight diameter of soil aggregates (static structure description), which is also an aggregate representation. Microbial biomass carbon represents the carbon pool of living cells and belongs to the two levels of activity and total amount, respectively, with total organic carbon (total carbon pool). They cannot be replaced by each other.

[0057] Third, the six core indicators are divided as follows: soil aggregate destruction rate and microbial biomass carbon are used as direct inputs to the dual-channel mechanism model, and four cold-region characteristic indicators are used as objects for score loss and weakness diagnosis. All indicators participate in the comprehensive index calculation.

[0058] This invention provides a method for assessing carbon sequestration and enhancement in farmland soils in cold black soil regions, comprising the following steps:

[0059] The current soil carbon pool storage, soil physicochemical properties, soil carbon pool capacity, and soil carbon pool dynamics indicators for the target field are obtained. The current soil carbon pool storage is characterized by the total organic carbon content of the soil in the target field. Soil physicochemical properties reflect the basic physicochemical environment of the soil. Soil carbon pool capacity reflects the size and composition of the soil carbon pool. Soil carbon pool dynamics indicators reflect the input, output, and loss processes of the soil carbon pool, including at least the seasonal freeze-thaw cycle intensity, spring snowmelt leaching coefficient, freeze-thaw period carbon emission pulse, and DOC (Dissolved Organic Carbon) index selected specifically for the unique environment of the cold black soil region. Carbon (soluble organic carbon) leaching flux, four cold-region-specific indicators, soil aggregate destruction rate, and microbial biomass carbon are used. These four cold-region-specific indicators are directly related to the two dominant loss processes of freeze-thaw and leaching. Microbial biomass carbon is a key carbon source parameter for release through freezing channels. Seasonal freeze-thaw cycle intensity characterizes the severity of repeated freezing and thawing of the soil within a year. Seasonal freeze-thaw cycle intensity is the product of the number of freeze-thaw cycles during the freeze-thaw period and the average temperature difference per single freeze-thaw cycle. The average temperature difference per single freeze-thaw cycle is the difference between the highest and lowest soil temperatures during each complete freeze-thaw cycle within the freeze-thaw period. The arithmetic mean of the temperature difference indicates a more severe freeze-thaw process and greater disturbance to soil structure. The spring snowmelt leaching coefficient represents the leaching potential of soil carbon from snowmelt water; it is the product of snowmelt water equivalent and snowmelt rate divided by the soil's saturated hydraulic conductivity. A larger snowmelt water equivalent, a faster snowmelt rate, lower soil saturated hydraulic conductivity, and longer snowmelt water retention time in the soil all contribute to a greater leaching potential. The freeze-thaw carbon emission pulse is the peak value of soil carbon dioxide emission flux during the freeze-thaw cycle. The freeze-thaw process disrupts soil structure and releases active carbon, which is then mineralized by microorganisms to form carbon dioxide emissions. The pulse emission and DOC leaching flux characterize the cumulative intensity of soluble organic carbon loss during snowmelt runoff, and are expressed as the integral of the product of soluble organic carbon concentration and runoff volume over time. Soil aggregate destruction rate characterizes the degree of damage to soil aggregates caused by freeze-thaw processes. It is the ratio of broken soil aggregates to the total aggregate volume, determined by soil aggregate sieving. This involves sieving soil samples according to particle size and counting the percentage of aggregates with a particle size smaller than a set threshold after crushing. A higher percentage indicates more severe aggregate destruction and a greater likelihood of loss of physically protected activated carbon. Exposure, specifically microbial biomass carbon, characterizes the carbon content of living microorganisms in soil and is the source of active carbon released through freezing channels. It can be measured using conventional methods such as chloroform fumigation extraction. Specifically, it includes the following indicators: The first level consists of soil physicochemical properties, including pH value, soil water-filled pore space, seasonal freeze-thaw cycle intensity, spring snowmelt leaching coefficient, average weight diameter of soil aggregates, and soil aggregate destruction rate (6 items in total). The second level consists of soil carbon pool capacity indicators, including total organic carbon, soluble organic carbon, microbial biomass carbon, particulate organic carbon, mineral-bound organic carbon, and light organic carbon (6 items in total).The third level consists of dynamic indicators of the soil carbon pool, including six items: carbon input-output ratio, soil carbon fixation efficiency, organic matter decomposition rate, soil respiration rate, freeze-thaw carbon emission pulse, and DOC leaching flux. The weights of these indicators are allocated across the three levels: a total weight of 25 for the first level, 40 for the second level, and 35 for the third level. The specific weights and design basis for each indicator are shown in Table 1.

[0060] Table 1

[0061]

[0062] The inflection point threshold of the trapezoidal membership function is determined based on the distribution characteristics of historical monitoring data of the target area. An example threshold is shown in Table 2. A six-level classification is applied based on the comprehensive index: Excellent (Level 1). >0.9, Excellent Level 2 ≤ 0.85 <0.9, Good Level 3 0.8≤ <0.85, Good Level 4 ≤ 0.75 <0.8, difference of five levels 0.7≤ <0.75, a difference of six levels <0.7;

[0063] Table 2

[0064]

[0065] Because the dimensions of the above indicators are different, they cannot be directly used in unified calculations. Each indicator is normalized to a dimensionless membership value using a trapezoidal membership function. During comprehensive evaluation, the performance of each indicator is measured to calculate the comprehensive index of soil carbon sequestration and carbon sink enhancement, as well as the contribution to carbon loss. Seasonal freeze-thaw cycle intensity, spring snowmelt leaching coefficient, freeze-thaw period carbon emission pulse, and DOC leaching flux are considered negative indicators; the higher the value, the higher the risk of carbon loss and the worse the carbon sequestration capacity. A descending trapezoidal membership function is used so that the larger the indicator value, the smaller the membership value, thus unifying the direction of each indicator as "the larger the value, the better." The inflection point threshold of the trapezoidal membership function is determined by the target... The distribution characteristics of regional historical monitoring data are determined. Specifically, the historical monitoring sequences are sorted by numerical value, and the values ​​of low, medium, and excellent inflection points are determined according to the set quantiles. This ensures that the normalization standard is adapted to the actual carbon pool level of the target area and avoids evaluation distortion caused by using fixed thresholds. Taking the seasonal freeze-thaw cycle intensity as an example, the mathematical form of its descending trapezoidal membership function is as follows: when the index value does not exceed the excellent inflection point, the membership degree is 1.0; between the excellent and poor inflection points, it decreases linearly; and when it exceeds the poor inflection point, the membership degree is 0.2. All indicators are mapped to the range of 0.2 to 1.0, and the direction is unified so that the larger the value, the better.

[0066] A freeze-thaw-leaching coupled carbon loss mechanism model was established. The impact of freeze-thaw processes on soil carbon in cold regions is realized through two relatively independent physical channels. The freezing channel corresponds to the microbial cell lysis and release process during the freezing period. During freezing, soil pore water freezes, and the increased ice crystal volume mechanically compresses the microbial cells, leading to cell lysis and the release of intracellular active carbon. The amount of active carbon released in the freezing channel is determined by factors related to microbial biomass carbon, the number of freeze-thaw cycles, and the minimum temperature. The more freeze-thaw cycles, the more repeatedly the microbial cells are compressed, the more complete the lysis, and the more active carbon is released. The thawing channel corresponds to the physical breakup and release process of soil aggregates during the thawing period. Freeze-thaw cycles cause soil aggregates to repeatedly expand and contract, breaking them up and exposing the active carbon that was physically protected by the aggregates. A portion of the exposed active carbon... Part of the carbon dioxide emitted is mineralized by microorganisms, and part of it is lost as DOC through leaching by snowmelt. The amount of active carbon released through the melting channel is determined by the protected active carbon background, the soil aggregate destruction rate, and the leaching distribution coefficient. After the amount of active carbon released through the two channels is determined, the annual net change in the carbon pool is the carbon input minus the respiration consumption minus the amount of active carbon released through the two channels. The carbon pool storage after freeze-thaw correction is the sum of the current soil carbon pool storage and the annual net change in the carbon pool. The units of the current soil carbon pool storage and the annual net change in the carbon pool are both g / kg. Through this model, the carbon loss caused by freeze-thaw and leaching processes can be corrected from the static carbon pool storage, resulting in a carbon pool storage that is closer to the actual state, thus overcoming the systematic underestimation of freeze-thaw carbon loss in static evaluation.

[0067] The comprehensive index of soil carbon sequestration and enhancement is calculated based on the normalized membership values ​​and weights of each indicator. For soil carbon pool storage indicators, the freeze-thaw corrected carbon pool storage is used as the indicator value. Carbon sequestration and enhancement level classification is performed according to a pre-defined rule, and the evaluation level is output. The weights of each indicator reflect their influence on the carbon sequestration and enhancement status of the carbon pool. The comprehensive index is obtained by weighting the normalized values ​​of each indicator with their corresponding weights. ,in, For the first Item weight, For the first The membership degree value of each indicator after normalization by the trapezoidal membership function. The larger the value of the comprehensive index, the better the carbon sequestration and absorption status of the field. The weight of the indicator can be determined by an objective weighting method based on data distribution or by combining expert experience. This implementation method does not limit this.

[0068] The preset target level is the level boundary that triggers the output of the shortcoming diagnosis and management plan. It is determined based on a six-level classification. By default, level three and above (good) will be selected. ≥0.8) is set as the compliance level: When the evaluation level reaches Good Level III or above, the overall status of the soil carbon sequestration and carbon sink enhancement of the target field is determined to meet the management objectives, and the compliance evaluation result is output. The contribution of lost points is no longer calculated, and the deficiency diagnosis and measure matching are no longer executed. When the evaluation level is lower than Good Level III (i.e., the evaluation level is Good Level IV, Poor Level V, or Poor Level VI), the overall status is determined to not meet the management objectives, triggering the calculation of lost points and deficiency diagnosis. The preset target level can be adjusted according to the carbon pool background level and management objectives of the target field: For fields with higher requirements for carbon sequestration and carbon sink enhancement management, the preset target level can be upgraded to Excellent Level II or above. ≥0.85); for fields with low carbon baseline and whose main goal in the short term is to maintain the status quo, the rating can be downgraded to good level four or above. (≥0.75) Once the preset target level is set, it remains unchanged within the same evaluation period to ensure consistency between the evaluation and diagnosis.

[0069] This invention selects four cold-region-specific indicators as the calculation objects for contribution to the score loss and as the basis for judging the shortcomings. All four indicators characterize unique processes in the cold-region black soil area that distinguish it from conventional farmland ecosystems. Although indicators such as total organic carbon and mineral-bound organic carbon have higher weights, their score loss reflects the long-term accumulation of carbon pool reserves. The corresponding intervention measures are general measures such as increasing the application of organic materials and conservation tillage, which are not specific to cold-region-specific processes. Furthermore, their status has already been reflected in the overall evaluation through the comprehensive index and carbon sink function value, and therefore are no longer considered as contribution objects for score loss. This avoids overlap between diagnostic and comprehensive evaluation functions and dilutes the specificity of the shortcomings. The seasonal freeze-thaw cycle intensity and the formation of freeze-thaw carbon emission pulses during the freeze-thaw period are also considered. The driving-output combination of the process, the former characterizes the driving force of the freeze-thaw process, and the latter characterizes the measured correspondence of mineralization output of the freeze-thaw process. The spring snowmelt leaching coefficient and DOC leaching flux form the driving-output combination of the leaching process. The former characterizes the driving force of the leaching process, and the latter characterizes the measured correspondence of carbon output of the leaching process. The diagnostic results of the loss contribution can be directly transformed into targeted management measures. The intensity of the seasonal freeze-thaw cycle can be reduced by winter snow cover and straw cover. The carbon emission pulse during the freeze-thaw period can be reduced by reducing freeze-thaw disturbance and regulating carbon input. The spring snowmelt leaching coefficient can be reduced by reducing tillage and increasing surface cover. The DOC leaching flux can be reduced by intercepting snowmelt runoff and increasing soil carbon interception.

[0070] When the evaluation level reaches the preset target level, the evaluation result is output, and the contribution of lost points is no longer calculated, nor is the weakness diagnosis and measure matching performed. When the evaluation level does not reach the preset target level, the contribution of lost points is calculated for the four cold-region characteristic indicators, and the contribution of lost points is... Calculate, where, This refers to the degree of point deduction. The greater the point deduction contribution, the greater the drag on the overall evaluation of the field. When the point deduction contribution of each cold-region characteristic indicator is less than the preset point deduction threshold (the preset point deduction threshold is the dividing value for determining whether a cold-region characteristic indicator constitutes a significant point deduction shortcoming, set by the user according to management objectives or determined based on the distribution characteristics of historical monitoring data of the target area), the limiting factor for carbon sequestration and carbon sink enhancement of the target field is determined to be a non-cold-region characteristic process, and general carbon sequestration and carbon sink enhancement management suggestions are output. When one or more of the point deduction contributions of each cold-region characteristic indicator exceed the preset point deduction threshold, the cold-region characteristic indicator with the largest point deduction contribution is determined to be the shortcoming indicator, that is, the main limiting factor for the carbon sequestration and carbon sink enhancement status of the field. Identifying the shortcoming indicator enables the matching of subsequent management measures to be more targeted. According to the type of shortcoming indicator, the corresponding shortcoming indicator with the mechanism of action is matched from the preset carbon sequestration and carbon sink enhancement management measure library. The target management measures library contains a variety of pre-stored management measures. Each management measure is labeled with its mechanism of action and the type of bottleneck indicator it is applicable to. The measure library can be pre-established based on agronomic practices in cold black soil regions before implementation. When the bottleneck indicator is the seasonal freeze-thaw cycle intensity or the carbon emission pulse during the freeze-thaw period, it indicates that the freeze-thaw process is the main limiting factor. Management measures to reduce freeze-thaw disturbance are matched, such as reducing the freeze-thaw cycle intensity of the topsoil through winter snow cover and straw cover. When the bottleneck indicator is the spring snowmelt leaching coefficient or DOC leaching flux, it indicates that snowmelt leaching is the main limiting factor. Management measures to reduce snowmelt leaching are matched, such as reducing tillage and increasing surface cover to reduce the leaching of soil carbon by snowmelt. The output includes a management plan including bottleneck indicators, recommended management measures, and measure parameters to guide the carbon sequestration and enhancement management practices of the target field.

[0071] In the above technical solution, by incorporating the freeze-thaw and leaching processes into the correction calculation of carbon pool storage, the accuracy of farmland soil carbon pool evaluation in cold black soil regions is improved, avoiding the systematic underestimation of freeze-thaw carbon loss in static evaluation. By calculating the contribution of loss, the shortcomings of field carbon sequestration and carbon sink enhancement are identified, enabling the evaluation results to pinpoint specific problem areas. Through the automatic matching of shortcomings and management measures, the evaluation results are directly transformed into an executable carbon sequestration and carbon sink enhancement management plan. This solves the technical problems of existing evaluation methods stopping at grade classification and the disconnect between evaluation and farmland management, realizing a closed-loop drive from evaluation to management, and providing a practical technical means for the precise management and carbon sequestration and carbon sink enhancement of farmland soil in cold black soil regions.

[0072] In another technical solution, the amount of activated carbon released from the freezing channel is expressed as an exponentially decaying function, and the amount of activated carbon released from the freezing channel is calculated using the following formula:

[0073] ,

[0074] in, Microbial biomass carbon, determined by chloroform fumigation extraction, is expressed in g / kg. The freeze-thaw lysis coefficient represents the lysis intensity of a single freeze-thaw cycle on microbial cells; it is a dimensionless coefficient. The larger the value, the fewer cycles are required to achieve the same lysis ratio. N represents the number of freeze-thaw cycles. When the number of freeze-thaw cycles is small, the lysis ratio increases approximately linearly with the number of cycles. When the number of freeze-thaw cycles is large, the lysis ratio tends to saturate, which is consistent with the characteristic that the total amount of microbial cell lysis is limited. The lowest temperature-related factor, i.e., the effect of freezing depth on the lysis process, indicates that when the annual lowest temperature is not lower than -10℃, the freezing depth is shallower, and the compression effect of ice crystals on cells is intact. Taking a value of 1.0, when the lowest annual temperature does not exceed -30℃, the freezing depth is deeper, but microbial activity is inhibited under extreme low temperatures, resulting in decreased lysis efficiency. A value of 0.5 is used, and linear interpolation is used when the value is between the two values. The freezing channel corresponds to the microbial cell lysis and release process during the freezing period, which is driven by the number of freeze-thaw cycles.

[0075] In the above technical solution, the cumulative effect of the number of freeze-thaw cycles on microbial cell lysis is characterized by an exponential decay function, and the influence of freezing depth is corrected by a minimum temperature-related factor. This makes the calculation of the amount of activated carbon released in the freezing channel more precise, avoids the arbitrariness of pure empirical fitting, and improves the quantitative accuracy of carbon release during freezing.

[0076] In another technical solution, the amount of activated carbon released through the melting channel is determined by three parts: the background activated carbon protected by aggregates, the soil aggregate destruction rate, and the distribution coefficient. The amount of activated carbon released through the melting channel is calculated using the following formula:

[0077] ,

[0078] in, To determine the background of active carbon physically protected by soil aggregates, the organic carbon content within the micro-aggregates was measured after classifying soil aggregates by particle size using a wet sieving method. The unit is g / kg. Seasonal freeze-thaw cycle intensity, The soil aggregate destruction rate varies with the intensity of seasonal freeze-thaw cycles. It increases with increasing, and can be expressed using a saturation function. , For the maximum aggregate destruction rate, A value greater than 0 and less than 1 indicates that the aggregate cannot be completely broken down, and there is an upper limit to the breakage rate. Determined by the maximum observed value of aggregate destruction rate in historical monitoring data of the target area. The destruction rate coefficient is expressed in °C. -1 ·Second-rate-1 , A value greater than 0 ensures that the damage rate increases with the intensity of freeze-thaw cycles. The predicted rate is determined by regression analysis based on the observation sequences of different freeze-thaw cycle intensities and corresponding aggregate destruction rates from historical monitoring data of the target area, following the principle of minimizing the fitting residuals of the destruction rate prediction formula. This represents the leaching coefficient of snowmelt in spring. To quantify the leaching intensity, the contribution of snowmelt to soil carbon leaching is determined. The dimensionless value obtained by normalizing the historical monitoring data range of the target area, and The values ​​are positively correlated. The larger the value, the greater the leaching intensity. This is the mineralization distribution coefficient. The leaching partition coefficient, and The proportion of activated carbon exposed by crushing and leaching is determined by the mineralization release of carbon dioxide and the leaching loss of DOC. and All are non-negative coefficients and and The sum does not exceed 1. The remaining part of the activated carbon released by the breakup of aggregates, after deducting the mineralized and leached parts, remains in the soil, ensuring the carbon destination is closed. The melting channel corresponds to the physical breakup and release process of aggregates during the melting period, driven by the intensity of the freeze-thaw cycle.

[0079] The freeze-thaw corrected carbon pool reserves are calculated based on the annual net change in carbon pool reserves, which is calculated using the following formula:

[0080] ,

[0081] in, Carbon inputs mainly include inputs from straw return to the field and root exudates. This represents the typical soil respiration consumption, occurring within the year outside of the freeze-thaw period, excluding the mineralization consumption of activated carbon released from the breakup of aggregates during the freeze-thaw period. , , , , The unit is uniformly g / kg.

[0082] In the above technical solution, the nonlinear response of the aggregate destruction rate with the intensity of freeze-thaw cycle is characterized by a saturation function, the destination of broken activated carbon is clarified by the mineralization and leaching distribution coefficients, and the carbon double deduction is avoided by distinguishing between conventional respiration and freeze-thaw mineralization. This makes the physical meaning of the calculated carbon release in the melting channel clear and the carbon destination closed, thus improving the quantitative accuracy of carbon loss during the melting period.

[0083] In another technical solution, the weights of each indicator are determined by coupling the NSGA-II (Non-dominated Sorting Genetic Algorithm II) multi-objective genetic algorithm with projection pursuit:

[0084] Since the objectives of carbon sequestration function and carbon loss risk are conflicting, it is difficult to achieve the optimal solution simultaneously through single-objective optimization. Therefore, the NSGA-II multi-objective genetic algorithm is used to solve for the Pareto optimal solution set. The NSGA-II algorithm parameters can be set as follows: parent population size 400, crossover probability 0.8, mutation probability 0.2, and 200 iterations. Each solution in the solution set represents a trade-off between carbon sequestration function and carbon loss risk. The goal is to solve for the Pareto optimal solution set with the dual objectives of maximizing the carbon sequestration function value and minimizing the carbon loss risk index. The carbon sequestration function value is equal to the freeze-thaw corrected carbon pool storage normalized by the trapezoidal membership function. The carbon loss risk index is the product of the normalized value and the soil carbon fixation efficiency, which is the proportion of carbon input converted into a stable carbon pool. The carbon loss risk index is equal to the product of the normalized value of the spring snowmelt leaching coefficient, the normalized value of the soil aggregate destruction rate, and the normalized value of the DOC leaching flux. The normalized value is positively correlated with the corresponding index value. The larger the index value, the higher the loss risk it reflects. The soil aggregate destruction rate is the ratio of the broken part of the soil aggregate to the total amount of aggregate, which is determined by the soil aggregate sieving method.

[0085] The Pareto optimal solution set contains multiple non-dominant solutions, while a unique weight vector is required for comprehensive evaluation. Therefore, projection pursuit is performed on the Pareto optimal solution set to determine a unique projection direction. This means finding the unique projection direction in the high-dimensional target space that makes the combined projection values ​​of the two targets optimal. The weights of the indicators are determined according to the proportion of the absolute value of the projection direction components. Projection pursuit can find the optimal projection direction from the data itself, avoiding the human interference of subjective weighting. It also constrains the sum of the weights of four cold-region characteristic indicators—seasonal freeze-thaw cycle intensity, spring snowmelt leaching coefficient, freeze-thaw period carbon emission pulse, and DOC leaching flux—to be no less than 0.30, ensuring the weight proportion of freeze-thaw and leaching-related indicators in the evaluation, so that the evaluation results fully reflect the impact of cold-region characteristic environmental processes.

[0086] In the above technical solution, the Pareto optimal solution set is solved by the NSGA-II multi-objective genetic algorithm. The dual objectives are to maximize the carbon sink function value and minimize the carbon loss risk index. The unique projection direction is objectively determined from the solution set through projection pursuit and weights are assigned accordingly. Both objective functions are dimensionless and can be solved directly. The determination of weights avoids subjective human interference. Furthermore, the evaluation status of cold-region characteristic indicators is guaranteed by the lower limit constraint of weights, avoiding misjudgment of fields with high reserves and high loss risk.

[0087] Under high-intensity freeze-thaw conditions, the active components of the soil carbon pool are more easily lost, and the stability of the carbon pool decreases. If a uniform evaluation threshold is used, the carbon sequestration status of the carbon pool will be overestimated. In another technical solution, a freeze-thaw intensity correction factor is introduced to dynamically adjust the membership threshold when calculating the comprehensive index of soil carbon sequestration and carbon sink enhancement.

[0088] When the seasonal freeze-thaw cycle intensity is no greater than 25°C / cycle, the freeze-thaw disturbance is at a normal level, and the freeze-thaw intensity correction factor... Taking a value of 1.0, when the seasonal freeze-thaw cycle intensity is greater than 25°C·cycle but not greater than 35°C·cycle, the freeze-thaw disturbance intensifies. Calculate, where, This refers to the seasonal freeze-thaw cycle intensity. When the seasonal freeze-thaw cycle intensity exceeds 35°C per cycle, the freeze-thaw disturbance is severe. The basic membership threshold is set to 0.85, which is the inflection point of the trapezoidal membership function without freeze-thaw correction. This threshold is determined by the distribution characteristics of historical monitoring data for the target area. For negative indices using a descending trapezoidal membership function, the basic membership threshold is multiplied by [the threshold value]. The dynamic membership threshold is obtained. Lowering the threshold tightens the evaluation criteria, amplifying the risk exposure reflected by negative indicators when freeze-thaw disturbances intensify. For positive indicators using an ascending trapezoidal membership function, the basic membership threshold is divided by... The dynamic membership threshold is obtained. As the threshold increases, the evaluation criteria are also tightened to avoid positive indicators being overscored due to the lowering of the threshold when freeze-thaw disturbances intensify. By distinguishing the above directions, the stronger the freeze-thaw disturbance, the tighter the evaluation criteria of each indicator are. The comprehensive index can truly reflect the actual situation of the carbon pool's active components being easily lost and the carbon fixation state declining under high-intensity freeze-thaw conditions.

[0089] The comprehensive index of soil carbon sequestration and carbon sink enhancement is calculated using the following formula:

[0090] ,

[0091] in, Let j be the weight of the j-th indicator. To take the value of the indicator Substituting the membership values ​​obtained by the trapezoidal membership function after threshold correction, the negative index is... Multiply by the basic membership threshold to adjust the threshold, and the positive index is... Divide by the basic membership threshold to correct the threshold. Let be the value of the j-th indicator. The larger the value of the comprehensive index, the better the carbon sequestration and absorption status of the field.

[0092] In the above technical solution, the freeze-thaw strength correction factor is used. The piecewise linear function enables dynamic adjustment of the membership threshold. As the freeze-thaw cycle intensity changes continuously and smoothly transitions with adjacent intervals, the negative index multiplication is distinguished. Positive indicators The directional correction tightens the evaluation criteria for each indicator as the freeze-thaw disturbance intensifies, and the comprehensive index can truly reflect the actual situation of easy loss of active components in the carbon pool and decline in carbon fixation under high-intensity freeze-thaw conditions, thus avoiding the evaluation results being artificially inflated.

[0093] In another technical solution, the four cold-region-specific indicators are obtained through the following methods:

[0094] The seasonal freeze-thaw cycle intensity is obtained by multiplying the number of cycles crossing 0℃ within the year by the average temperature difference per cycle, which is the average difference between the lowest and highest soil temperatures in each freeze-thaw cycle. Specifically, the temperature is obtained by multi-layer soil temperature sensor arrays, with sensors placed at different soil depths to continuously record soil temperatures. The number of cycles crossing 0℃ within the year is counted, and the difference between the lowest and highest temperatures in each cycle is counted. The average value is taken as the average temperature difference per cycle, and the product of the two is the seasonal freeze-thaw cycle intensity.

[0095] The leaching coefficient of spring snowmelt water is obtained by inverting snow water equivalent and snowmelt rate from meteorological stations and remote sensing. It is calculated by dividing the product of snow water equivalent and snowmelt rate by the soil saturated hydraulic conductivity. The dimension is length and the unit is mm.

[0096] The freeze-thaw carbon emission pulse was obtained by continuously monitoring soil carbon dioxide emission flux during the freeze-thaw cycle using an in-situ carbon dioxide flux monitor with an automatic gas chamber method, and the peak value was recorded. The unit is g C·m. -2 ·d -1 Specifically, in-situ carbon dioxide flux monitoring is continuously monitored using an automatic gas chamber method. The automatic gas chamber is opened and closed periodically to continuously record the carbon dioxide emission flux in the soil. During the freeze-thaw cycle, the flux reaches a peak, which is taken as the carbon emission pulse during the freeze-thaw period. The carbon emission pulse during the freeze-thaw period is an instantaneous process, and manual sampling has a time blind spot. In-situ automatic continuous monitoring can completely cover the entire freeze-thaw cycle process and avoid missing peak values.

[0097] DOC leaching flux was obtained by monitoring the concentration of soluble organic carbon and runoff during the snowmelt period using a runoff collection device and an automated water quality analyzer, and then integrating the product of the two over the snowmelt period. The unit is kg C·ha. -1 ·a -1 Specifically, the water quality is obtained through a runoff collection device in conjunction with an automatic water quality analyzer. The runoff collection device collects surface runoff during the snowmelt period, and the automatic water quality analyzer measures the concentration of soluble organic carbon in the runoff. The product of the concentration and the runoff volume is integrated over the snowmelt period.

[0098] In the above technical solution, through monitoring methods such as multi-layer soil temperature sensor array, in-situ carbon dioxide flux monitor, runoff collection device and automatic water quality analyzer, the automatic, continuous and in-situ acquisition of cold region characteristic indicators is realized. In particular, the continuous monitoring of instantaneous freeze-thaw carbon pulses avoids the defects of missed peak values ​​in manual sampling, and provides a reliable data foundation for the accurate calculation of the dual-channel mechanism model.

[0099] In another technical solution, after outputting the management plan, the indicator data after implementing the management measures are re-acquired to verify the consistency between the evaluation results and the measured carbon loss. Since the comprehensive index of soil carbon sequestration and carbon sink enhancement is a dimensionless value and the measured carbon loss is a dimensional physical quantity, the two cannot be directly compared. Therefore, the measured carbon loss is transformed into a measured loss membership degree through a descending trapezoidal membership function. The comprehensive index of soil carbon sequestration and carbon sink enhancement is compared with the measured loss membership degree of the measured carbon loss after the descending trapezoidal membership function transformation. The consistency is determined by the difference threshold. The state quantity is updated first, and then the background parameters are corrected. The state quantity is the soil aggregate destruction rate and the background of active carbon protected by the aggregate. The background parameters are the freeze-thaw cracking coefficient, mineralization partition coefficient and leaching partition coefficient. The larger the measured loss membership degree, the smaller the measured carbon loss. When the difference between the comprehensive index and the measured loss membership degree is less than 0.1, it is considered consistent. The overall accuracy rate is the proportion of sample points that are considered consistent.

[0100] The predicted freeze-thaw carbon release from the mechanistic model is compared with the measured carbon release. When the overall accuracy is below 85% or the predicted freeze-thaw carbon release deviation exceeds 20%, it indicates that there is a deviation between the evaluation or model and the actual state of the target field, and parameter updates are required. Since the implementation of management measures changes the physical state of the soil, including the soil aggregate destruction rate and the background of active carbon protected by the aggregates, the soil aggregate destruction rate and the background of active carbon protected by the aggregates are updated first. This indicates that there is a deviation in the regional background parameters. If the predicted freeze-thaw carbon release deviation still exceeds 20% after the update, the freeze-thaw cracking coefficient, mineralization partition coefficient, and leaching partition coefficient are corrected with the goal of minimizing the mean square error between the measured and predicted values.

[0101] In the above technical solution, the problem of consistent comparison of physical quantities with different dimensions is solved by transforming the measured carbon loss into a measured loss membership degree with the same scale as the comprehensive index through a descending trapezoidal membership function. By adopting a two-stage strategy of prioritizing the updating of state quantities and finally correcting the background parameters, the parameter update is consistent with the physical mechanism, avoiding confusion between state changes caused by management measures and constant calibration deviations, and realizing closed-loop continuous improvement of the evaluation method.

[0102] When management measures are matched based solely on a single weakness indicator, if the contribution of multiple cold-region-specific indicators to the score loss is similar, the determination of the weakness indicator may be unstable between different evaluation periods, leading to repeated switching of the matched management measures. In another technical solution, when the contribution of multiple cold-region-specific indicators to the score loss is similar, collaborative weakness identification and collaborative measure matching are performed, specifically including:

[0103] Calculate the mean and range of the contribution of the four cold-region characteristic indicators. When the ratio of the range to the mean is less than 0.2, it indicates that the contribution of each indicator is not significantly different, and it is judged that there are multiple weaknesses.

[0104] For cold-region characteristic indicators with multiple shortcomings, a synergistic grouping rule is established based on the physical causal relationship between the indicators. Indicators with mutual amplification effects are merged into a synergistic shortcoming group. The physical causal relationship is the basis for determining the synergistic shortcoming group. The seasonal freeze-thaw cycle intensity forms a freeze-thaw synergistic shortcoming group by driving the carbon emission pulse during the freeze-thaw period, and the spring snowmelt leaching coefficient forms a leaching synergistic shortcoming group by driving the DOC leaching flux. When the correlation coefficient between two cold-region characteristic indicators in the historical monitoring sequence is greater than 0.6, it is determined that the two indicators have a synergistic amplification effect and the degree of synergy is strengthened. Since the relationship between freeze-thaw intensity and carbon emission pulse may be nonlinear under different climate years, when the correlation coefficient is less than 0.6, the synergistic shortcoming group is still determined based on the physical causal relationship to avoid statistical lag leading to missed judgment of synergistic relationship.

[0105] For the freeze-thaw synergy bottleneck group, a first set of management measures aimed at reducing freeze-thaw disturbance is matched; for the leaching synergy bottleneck group, a second set of management measures aimed at reducing snow leaching is matched. When the first and second sets of management measures contain the same management measures, the same management measures are output first. The maximum value of the recommended parameters of the measure in the first and second sets of management measures is multiplied by a synergy enhancement coefficient of 1.2 as the execution parameter to ensure that the intensity of the measures meets the needs of both bottleneck groups. When there is no overlap, the management measures in the first and second sets of management measures are output in descending order of contribution to the score loss, forming a combined management scheme so that the management measures cover multiple interrelated bottleneck links at the same time.

[0106] In the above technical solution, the coexistence of multiple shortcomings is determined by the ratio of the range to the mean. The collaborative judgment based on physical causal relationship and assisted by correlation coefficient avoids the collaborative omission caused by statistical lag. The execution parameters are determined by taking the maximum value of the intersection measures and multiplying it by the synergy enhancement coefficient. This ensures that the strength of the measures meets the needs of multiple shortcoming groups at the same time, so that the management measures can cover the interrelated shortcoming links and improve the pertinence and stability of the transformation of evaluation results into management decisions.

[0107] When multiple management measures are implemented simultaneously, there may be synergistic gains or antagonistic losses between them. If they are simply superimposed, the carbon sequestration effects may cancel each other out due to mutual interference. In another technical solution, when multiple management measures are matched, management measure combination optimization is performed, specifically including:

[0108] A synergy-antagonism relationship matrix of management measures is pre-established. The matrix records the synergy gain coefficient or antagonism loss coefficient when any two management measures are combined. The coefficients are determined based on historical experimental data or expert experience. The synergy gain coefficient represents the gain ratio of the carbon sequestration and carbon sink enhancement effect when the two management measures are combined to the sum of the effects of the two management measures implemented individually. The antagonism loss coefficient represents the loss ratio of the carbon sequestration and carbon sink enhancement effect when the two management measures are combined. The synergy gain coefficient and the antagonism loss coefficient are determined based on historical experimental data or expert experience.

[0109] With the goal of maximizing the overall carbon sequestration effect and the constraint that the resource input for implementing measures does not exceed the upper limit of the target field management resource input, a combinatorial optimization model is constructed. The upper limit of the target field management resource input is set by the user based on the actual available resources. The overall carbon sequestration effect is composed of the weighted sum of the carbon sequestration and carbon sink enhancement effects of each management measure and the synergistic gain or antagonistic loss of any combination of two management measures. The weight of each management measure is determined based on the carbon sequestration and carbon sink enhancement effect contribution coefficient preset in the management measure library. The synergistic gain or antagonistic loss of any two management measures is the product of the corresponding coefficient obtained by querying the synergistic-antagonistic relationship matrix and the carbon sequestration and carbon sink enhancement effects of the two management measures. That is, the interaction term is quantified by multiplying the matrix coefficient with the measure effect, so that the synergistic-antagonistic relationship matrix truly participates in the optimization solution, rather than just being used as a post-selection condition. The constraint is that the sum of the resource input for implementing each management measure does not exceed the upper limit of the target field management resource input. The solution is to find the subset of management measures that maximizes the overall carbon sequestration effect, and to achieve the global optimal configuration of the carbon sequestration and carbon sink enhancement effect under resource constraints. Antagonistic combinations are not prohibited outright, but are uniformly weighed by the objective function.

[0110] The subset of management measures obtained from the solution is sorted from high to low according to the contribution coefficient of each management measure to carbon sequestration and carbon sink enhancement, and the sorted combination of management measures and corresponding parameters are output.

[0111] In the above technical solution, the gain and loss relationship between each pair of measures is recorded by the synergy-antagonism relationship matrix of management measures. The synergy gain and antagonism loss terms are included in the combined optimization objective function, so that the matrix really participates in the optimization solution. Antagonistic combinations are uniformly weighed by the objective function rather than being prohibited outright. This avoids the problem of local antagonism judgment replacing global resource optimization. Under resource constraints, the global optimal configuration of carbon sequestration and carbon sink enhancement effect is achieved, which improves the scientific nature of the multi-measure combination scheme.

[0112] In the dual-channel mechanism model, the freeze-thaw cracking coefficient, mineralization partitioning coefficient, and leaching partitioning coefficient lack initial values ​​for their first application and cannot be directly calculated. In another technical solution, the model parameters of the freeze-thaw-leaching coupled carbon loss mechanism model are initially calibrated based on historical monitoring data or indoor freeze-thaw simulation experimental data of the target field area for the first application. The initial calibration process is as follows: collect historical monitoring data of the target area, set the initial search range for each coefficient, and adjust the freeze-thaw cracking coefficient, mineralization partitioning coefficient, and leaching partitioning coefficient according to the principle of minimizing the fitting residual between the measured carbon release from historical monitoring data and the carbon release predicted by the model, so that the carbon release predicted by the model is as close as possible to the measured carbon release, thereby determining the initial values ​​of each coefficient. If the prediction deviation exceeds the limit, expand the search range and recalibrate until the accuracy requirements are met. The calibrated coefficients are used for the first evaluation to ensure that the model can achieve usable prediction accuracy in the first application. Subsequently, the state variables are updated, and the initial values ​​of the freeze-thaw cracking coefficient, mineralization partitioning coefficient, and leaching partitioning coefficient are determined according to the principle of minimizing the fitting residual between the measured carbon release from historical monitoring data and the carbon release predicted by the model.

[0113] In the above technical solution, the initial values ​​of the model coefficients are determined by minimizing the fitting residuals based on historical monitoring data or indoor freeze-thaw simulation experimental data. The model prediction accuracy is verified by dividing the calibration period and the validation period, ensuring that the model can achieve usable prediction accuracy on the first application. This forms a two-stage connection between initial calibration and adaptive update with the closed-loop correction mechanism, providing a parameter basis for the engineering application of the dual-channel mechanism model.

[0114] <Example 1>

[0115] An experimental field was established in a typical black soil region in central Heilongjiang Province, using a maize-soybean rotation system, and monitoring was conducted continuously for three years from 2022 to 2024. Routine static sampling was carried out each year after crop harvest in October, measuring the total organic carbon content in three soil layers: 0–20 cm, 20–40 cm, and 40–60 cm. The three-year average was 24.6 g / kg. According to traditional static evaluation methods, the carbon pool of this field was rated as good.

[0116] The values, weights, calculated values, and parameter sources for each indicator in this field in 2023 are shown in Table 3. The calculated values ​​in Table 3 are the product of the membership degree value of each indicator after normalization by the trapezoidal membership function and the weight. The membership degree is determined according to the threshold in Table 2. The total organic carbon is taken as the freeze-thaw corrected carbon pool reserve of 24.45 g / kg, which is calculated by the dual-channel mechanism model.

[0117] Table 3

[0118]

[0119] The input parameters for the mechanistic model obtained in 2023 were: number of freeze-thaw cycles N = 22 (obtained by counting the number of cycles in which the soil temperature crossed 0℃ during the year using a multi-layer soil temperature sensor array), annual minimum temperature of -16℃, and minimum temperature correlation factors. =0.85 (taken as 1.0 when the annual minimum temperature is not lower than -10℃, and 0.5 when it is not higher than -30℃, obtained by linear interpolation between the two), leaching strength =0.55 (obtained by normalizing the spring snowmelt leaching coefficient according to the historical monitoring data range of the target area), freeze-thaw pyrolysis coefficient =0.075, mineralization distribution coefficient =0.18, leaching partition coefficient =0.41 (obtained based on historical monitoring data of the target area or indoor freeze-thaw simulation experiments, calibrated according to the principle of minimizing the fitting residual between the measured carbon release and the model-predicted carbon release), the background of activated carbon protected by aggregates. =3.0 g / kg (obtained by measuring the organic carbon content within the aggregates), annual carbon input =1.5 g / kg (estimated by multiplying the amount of straw returned to the field by the carbon content of the straw and the input of root exudates), conventional respiratory consumption =1.07 g / kg (annual integral value of soil respiration flux during non-freeze-thaw period).

[0120] Calculate the amount of activated carbon released from the frozen channel. ≈0.24 g / kg, calculate the amount of activated carbon released from the melting channel. The total DOC concentration was approximately 0.34 g / kg, with about 0.15 g / kg being mineralized and about 0.19 g / kg being leached. The measured DOC leaching flux during the snowmelt season, measured using a snowmelt runoff collection device and an automatic water quality analyzer, was 38 kg C·ha. -1 ·a -1 Based on the weight of the 0~20 cm soil layer, 1500 t·ha -1The calculated leaching amount was 0.025 g / kg; the ratio of the model-calculated leaching amount of 0.19 g / kg to the measured value was approximately 7.6, which is consistent with the regional hydrological characteristics where the contribution of mid-flow to DOC leaching during the snowmelt period in cold regions is typically 5–8 times that of surface runoff, thus verifying the order-of-magnitude rationality of the calculated leaching amount through the melting channel. The annual net change in the carbon pool was calculated. = -0.15 g / kg, the freeze-thaw corrected carbon pool is 24.6 + (-0.15) = 24.45 g / kg; if only carbon input and respiration consumption are considered without considering freeze-thaw-leaching carbon loss, the corresponding reserve is 24.6 + 1.5 - 1.07 = 25.03 g / kg, the corrected reserve is 2.3% lower than that, and 0.6% lower than the static value of 24.6 g / kg.

[0121] The freeze-thaw cycle intensity for this field is no greater than 25℃ per cycle and 22℃ per cycle. Freeze-thaw intensity correction factor. Set the value to 1.0 and calculate the composite index. =70.06 / 100≈0.70, rated as poor level 5 (0.7≤P<0.75). The preset target level is set as good level 3 (…). (≥0.8), the evaluation level of this field did not reach the preset target level. The contribution of the four cold-region characteristic indicators was calculated as follows: seasonal freeze-thaw cycle intensity 6×(1-0.86)=0.84, spring snowmelt leaching coefficient 5×(1-0.75)=1.25, freeze-thaw period carbon emission pulse 10×(1-0.44)=5.60, DOC leaching flux 10×(1-0.53)=4.70. The freeze-thaw period carbon emission pulse, which has the largest contribution to the score loss, was identified as the weakest indicator. Management measures to reduce freeze-thaw disturbance were matched from the carbon sequestration and carbon sink management measures library: retain more than 15 cm of snow cover in winter and return all corn stalks to the field (returning amount 6000~8000 kg·ha). -1 In spring, reduce tillage and output a management plan that includes shortcomings indicators, recommended management measures and measures parameters to guide the carbon sequestration and enhancement management of the field.

[0122] This embodiment shows that the static evaluation, based solely on total organic carbon content, rated the field as good. This invention, by incorporating carbon loss during freeze-thaw cycles and snowmelt, corrects the carbon pool to 24.45 g / kg, lower than the static result, indicating that the static evaluation is overly optimistic. The dual-channel model divides the annual carbon pool change into two parts: freeze release (0.24 g / kg) and thaw release (0.34 g / kg). Adding these to carbon input and respiration consumption yields a net annual change of -0.15 g / kg, with the figures at each step matching. The leaching amount calculated by the model differs from the measured DOC leaching by approximately 7.6 times, consistent with the significant contribution of interflow during the local snowmelt period, and the order of magnitude is reasonable.

[0123] <Example 2>

[0124] Twelve sampling sites (including the experimental field of Example 1) were selected in the same study area and evaluated using single-objective projection pursuit optimization and the NSGA-II multi-objective optimization method of this invention. Single-objective optimization aimed only at maximizing the carbon sequestration function. One sampling site received a high evaluation due to its high soil carbon storage (26.8 g / kg) and high soil carbon fixation efficiency (52%), with a comprehensive index of 0.91, and was rated as excellent (Level 1). However, this sampling site had a low leaching coefficient from spring snowmelt. The soil aggregate destruction rate was 0.68 mm. 0.35, DOC leaching flux 65 kg C·ha -1 ·a -1 All three loss indicators are at historically high levels.

[0125] Calculate the carbon loss risk index for this sample point: =0.68, after normalization according to the historical monitoring data range (0.2~1.0) of the target area, becomes (0.68-0.2) / (1.0-0.2)=0.60. =0.35, after normalization to the range (0.05~0.45), becomes 0.75. =65, after normalization to the range (5~75), is 0.86, and the carbon loss risk index is 0.387, which is significantly higher than the median value of 0.21 of the 12 sample points.

[0126] After incorporating the carbon loss risk index into the multi-objective optimization, the comprehensive projected characteristic value of the sample point was adjusted, the weight of freeze-thaw related indicators was increased to 0.35, and the comprehensive index was adjusted to 0.76, which did not meet the preset target level (good level 3, P≥0.8). The contribution of the four cold-region characteristic indicators was calculated, among which DOC leaching flux had the largest contribution and was identified as the weakest indicator. Management measures to reduce snowmelt leaching were matched from the carbon sequestration and enhancement management measures library: contour tillage to intercept snowmelt runoff, reduced plowing, and stubble cover (surface cover ≥60%). The output was a management plan including the weakest indicator, recommended management measures, and measure parameters. Validation data from 12 sample points showed that, measured by the Spearman rank correlation coefficient between the comprehensive index and the measured carbon loss in the following year, the correlation coefficient of the multi-objective optimization was 0.82, while that of the single-objective optimization was 0.54.

[0127] This embodiment verifies the beneficial effects of NSGA-II multi-objective optimization and projection pursuit coupled weighting: multi-objective optimization simultaneously takes into account maximizing carbon sink function and minimizing carbon loss risk. After incorporating carbon loss risk into the optimization objective, fields with high reserves and high loss risk are accurately identified and downgraded, avoiding misjudgment of such fields by single-objective optimization, and providing a more reliable technical means for the identification and early warning of high-risk areas.

[0128] <Example 3>

[0129] Six monitoring sites were established in the high-latitude region of northern Heilongjiang Province, where the average annual freeze-thaw cycle intensity exceeds 30°C. Evaluation was conducted using both fixed thresholds and the dynamic thresholds of this invention.

[0130] The fixed threshold was evaluated based on the exemplary membership threshold in Table 2, without considering the impact of freeze-thaw intensity on carbon pool stability. The consistency between the evaluation level of the 6 samples and the ranking of measured carbon loss in the following year was 33%.

[0131] Dynamic threshold introduces freeze-thaw strength correction factor Based on freeze-thaw cycle strength Taking 32℃·time as an example, calculate =0.895. For negative indices using a descending trapezoidal membership function (seasonal freeze-thaw cycle intensity, spring snowmelt leaching coefficient, freeze-thaw carbon emission pulse, DOC leaching flux), the basic membership threshold is multiplied by 0.895. The evaluation threshold is tightened; for positive indices using ascending trapezoidal membership functions (total organic carbon, average weight diameter of soil aggregates, etc.), the basic membership threshold is divided by... The evaluation thresholds are also tightened, so that the evaluation standards for all indicators are tightened in tandem with the freeze-thaw intensity.

[0132] After adopting a dynamic threshold, the consistency between the evaluation level of the six sampling points and the ranking of measured carbon loss in the following year improved to 83%. Taking the sampling point with the highest freeze-thaw cycle intensity as an example, its comprehensive index was significantly reduced after the dynamic threshold evaluation, falling below 0.8, failing to reach the preset target level (Good Level 3). (≥0.8), calculate the contribution of the four cold-region characteristic indicators. The seasonal freeze-thaw cycle intensity has the largest contribution and is identified as the weak link indicator. Match management measures to reduce freeze-thaw disturbance from the carbon sequestration and carbon sink management measures library: winter snow cover, straw cover, and reduced tillage. Output a management plan that includes the weak link indicator, recommended management measures and measure parameters.

[0133] This embodiment verifies the beneficial effects of dynamic threshold fuzzy comprehensive evaluation: freeze-thaw strength correction factor The threshold correction direction is continuously adjusted according to the freeze-thaw cycle intensity and differentiated by index type (negative multiplication). Positive division This allows for adaptive tightening of evaluation criteria in high-intensity freeze-thaw zones, increasing the consistency between evaluation results and measured carbon loss from 33% to 83%. This significantly improves the accuracy of evaluation results under varying regional environmental conditions and avoids the problem of overestimating carbon sequestration status in high-intensity freeze-thaw zones using fixed thresholds.

[0134] The number of devices and processing scale described herein are for the purpose of simplifying the description of the invention. Applications, modifications, and variations of the invention will be readily apparent to those skilled in the art.

[0135] Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention, and other modifications can be easily made by those skilled in the art.

Claims

1. A method for assessing carbon sequestration and enhancement in farmland soil carbon pools in cold black soil regions, characterized in that, Includes the following steps: The current soil carbon pool, soil physicochemical properties, soil carbon pool capacity, and soil carbon pool dynamics of the target field are obtained. The current soil carbon pool is characterized by the total organic carbon content of the soil in the target field, and includes at least four cold-region-specific indicators: seasonal freeze-thaw cycle intensity, spring snowmelt leaching coefficient, freeze-thaw carbon emission pulse, and DOC leaching flux, as well as soil aggregate destruction rate and microbial biomass carbon. The seasonal freeze-thaw cycle intensity is the product of the number of freeze-thaw cycles during the freeze-thaw period and the average temperature difference per single freeze-thaw cycle. The average temperature difference per single freeze-thaw cycle is the arithmetic mean of the difference between the highest and lowest soil temperatures during each complete freeze-thaw cycle. The spring snowmelt leaching coefficient is the product of snow water equivalent and snowmelt rate divided by... Soil saturated hydraulic conductivity and the carbon emission pulse during the freeze-thaw cycle were used as the peak value of soil carbon dioxide emission flux during the freeze-thaw cycle. DOC leaching flux was the integral of the product of soluble organic carbon concentration and runoff during the snowmelt period over time. Soil aggregate destruction rate was the ratio of broken soil aggregates to the total aggregates. The soil aggregates were determined by soil aggregate sieving method. Each index was normalized to a dimensionless membership value by trapezoidal membership function to calculate the comprehensive index of soil carbon sequestration and carbon sink enhancement and the contribution of loss. Seasonal freeze-thaw cycle intensity, spring snowmelt leaching coefficient, carbon emission pulse during the freeze-thaw period, and DOC leaching flux were used as negative indicators. A descending trapezoidal membership function was used. The inflection point threshold of the trapezoidal membership function was determined by the distribution characteristics of historical monitoring data of the target area. A freeze-thaw-leaching coupled carbon loss mechanism model was established. The amount of active carbon released in the freezing channel was determined by the microbial biomass carbon, the number of freeze-thaw cycles, and the minimum temperature-related factors. The amount of active carbon released in the thawing channel was determined by the protected active carbon background, the soil aggregate destruction rate, and the leaching partition coefficient. The annual net change in the carbon pool was calculated as carbon input minus respiration consumption minus the amount of active carbon released in both channels. The freeze-thaw corrected carbon pool was calculated as the sum of the current soil carbon pool and the annual net change in the carbon pool. The units for both the current soil carbon pool and the annual net change in the carbon pool are g / kg. The comprehensive index of soil carbon sequestration and carbon sink enhancement is calculated based on the normalized membership values ​​and weights of each indicator. Among them, the soil carbon pool storage index uses the freeze-thaw corrected carbon pool storage as its index value. The carbon sequestration and carbon sink enhancement level classification is carried out according to the preset carbon sequestration and carbon sink enhancement level classification rules and the evaluation level is output. When the evaluation level does not reach the preset target level, the contribution of the score loss is calculated for the four cold-region characteristic indicators. The contribution of the score loss is the product of the indicator weight and 1 minus the indicator membership value. When the contribution of the score loss of each cold-region characteristic indicator is less than the preset score loss threshold, the limiting factor for carbon sequestration and carbon sink enhancement in the target field is determined to be a non-cold-region characteristic process, and general carbon sequestration and carbon sink enhancement management suggestions are output. When one or more of the contribution of the score loss of each cold-region characteristic indicator exceeds the preset score loss threshold, the cold-region characteristic indicator with the largest contribution of the score loss is determined to be the weak link indicator. According to the type of the weak link indicator, the management measures corresponding to the mechanism of action of the weak link indicator are matched from the preset carbon sequestration and carbon sink enhancement management measure library. When the weak link indicator is the seasonal freeze-thaw cycle intensity or the carbon emission pulse during the freeze-thaw period, management measures to reduce freeze-thaw disturbance are matched. When the weak link indicator is the spring snowmelt leaching coefficient or DOC leaching flux, management measures to reduce snowmelt leaching are matched. The management plan containing the weak link indicator, recommended management measures and measure parameters is output.

2. The method for assessing carbon sequestration and enhancement in farmland soil carbon pools in cold black soil regions according to claim 1, characterized in that, The amount of activated carbon released during freezing is calculated using the following formula: ， in, Microbial biomass carbon, in g / kg. Here, is the freeze-thaw pyrolysis coefficient, and is a dimensionless coefficient. This refers to the number of freeze-thaw cycles. As a factor related to the lowest temperature, when the lowest temperature of the year is not lower than -10℃ Take 1.0, when the lowest temperature of the year is not higher than -30℃. The value is set to 0.5, with linear interpolation in between. The freezing channel corresponds to the microbial cell lysis and release process during the freezing period, driven by the number of freeze-thaw cycles.

3. The method for assessing carbon sequestration and enhancement in farmland soil in cold black soil regions according to claim 1, characterized in that, The amount of activated carbon released from the melting channel is calculated using the following formula: ， in, The active carbon background, physically protected by the aggregates, is expressed in g / kg. It is obtained by determining the organic carbon composition within the micro-aggregates after classifying soil aggregates by particle size using a wet sieving method. Seasonal freeze-thaw cycle intensity, The soil aggregate destruction rate, , The maximum aggregate destruction rate is determined by the maximum observed aggregate destruction rate in the historical monitoring data of the target area. The value is greater than 0 and less than 1. The destruction rate coefficient is expressed in °C. -1 ·Second-rate -1 , Greater than 0, This represents the leaching coefficient of snowmelt in spring. Leaching strength, The dimensionless value is obtained by normalizing the historical monitoring data range of the target area. The values ​​are positively correlated. This is the mineralization distribution coefficient. The leaching partition coefficient, and All are non-negative coefficients and and The sum does not exceed 1. The remaining part of the activated carbon released by the breakup of aggregates, after deducting the mineralized and leached portions, remains in the soil. The melting channel corresponds to the physical breakup and release process of aggregates during the melting period, driven by the intensity of freeze-thaw cycles. The freeze-thaw corrected carbon pool reserves are calculated based on the annual net change in carbon pool reserves, which is calculated using the following formula: ， in, For carbon input, This represents the normal soil respiration consumption, excluding the mineralization consumption of activated carbon released from the breakup of aggregates during freeze-thaw cycles. , , , , The unit is g / kg.

4. The method for assessing carbon sequestration and enhancement in farmland soil carbon pools in cold black soil regions according to claim 1, characterized in that, The weights of each indicator are determined using a combination of the NSGA-II multi-objective genetic algorithm and projection pursuit. The Pareto optimal solution set is solved with the dual objectives of maximizing the carbon sink function value and minimizing the carbon loss risk index. The carbon sink function value is equal to the product of the carbon pool storage after freeze-thaw correction and normalized by the trapezoidal membership function, and the soil carbon fixation efficiency. The soil carbon fixation efficiency is the proportion of carbon input that is converted into a stable carbon pool. The carbon loss risk index is equal to the product of the spring snowmelt leaching coefficient normalized according to the historical monitoring data range of the target area, the soil aggregate destruction rate normalized according to the historical monitoring data range of the target area, and the DOC leaching flux normalized according to the historical monitoring data range of the target area. The normalized values ​​are positively correlated with the corresponding index values. Projection pursuit is performed on the Pareto optimal solution set to determine the unique projection direction. The index weights are determined according to the percentage of the absolute value of the projection direction components. The sum of the weights of four cold-region characteristic indicators, namely, seasonal freeze-thaw cycle intensity, spring snowmelt leaching coefficient, freeze-thaw period carbon emission pulse, and DOC leaching flux, is constrained to be no less than 0.

30.

5. The method for assessing carbon sequestration and enhancement in farmland soil carbon pools in cold black soil regions according to claim 1, characterized in that, When calculating the comprehensive index of soil carbon sequestration and carbon sink enhancement, a freeze-thaw intensity correction factor is introduced to dynamically adjust the membership threshold: When the seasonal freeze-thaw cycle intensity is no greater than 25°C / cycle, the freeze-thaw intensity correction factor Take 1.0, when the seasonal freeze-thaw cycle intensity is greater than 25℃·cycle and not greater than 35℃·cycle, Calculate, where, The seasonal freeze-thaw cycle intensity is defined as follows: when the seasonal freeze-thaw cycle intensity is greater than 35°C / cycle, The basic membership threshold is set to 0.85, which is the inflection point of the trapezoidal membership function without freeze-thaw correction. This threshold is determined by the distribution characteristics of historical monitoring data for the target area. For negative indices using a descending trapezoidal membership function, the basic membership threshold is multiplied by [the threshold value]. To obtain the dynamic membership threshold, for positive indices using the ascending trapezoidal membership function, divide the basic membership threshold by... The comprehensive index of soil carbon sequestration and carbon sink enhancement, based on the dynamic membership threshold, is calculated using the following formula: ， in, For the first Item weight, To take the value of the indicator Substitute The membership values ​​obtained by the trapezoidal membership function after threshold correction are negative indices. Multiply by the basic membership threshold to adjust the threshold, and the positive index is... Divide by the basic membership threshold to correct the threshold. For the first The values ​​of each indicator.

6. The method for assessing carbon sequestration and enhancement in farmland soil carbon pools in cold black soil regions according to claim 1, characterized in that, The methods for obtaining the four cold-region-specific indicators include: The seasonal freeze-thaw cycle intensity is obtained by statistically analyzing the soil temperature data continuously monitored by a multi-layer soil temperature sensor array. The product of the number of cycles across 0℃ within the year and the average temperature difference per cycle is calculated. The average temperature difference per cycle is the average of the difference between the lowest and highest soil temperature values ​​in each freeze-thaw cycle. The leaching coefficient of spring snowmelt water is obtained by inverting snow water equivalent and snowmelt rate from meteorological stations and remote sensing. It is calculated by dividing the product of snow water equivalent and snowmelt rate by the soil saturated hydraulic conductivity. The dimension is length and the unit is mm. The freeze-thaw carbon emission pulse was obtained by continuously monitoring soil carbon dioxide emission flux during the freeze-thaw cycle using an in-situ carbon dioxide flux monitor with an automatic gas chamber method, and the peak value was recorded. The unit is g C·m. -2 ·d -1 ; DOC leaching flux was obtained by monitoring the concentration of soluble organic carbon and runoff during the snowmelt period using a runoff collection device and an automated water quality analyzer, and then integrating the product of the two over the snowmelt period. The unit is kg C·ha. -1 ·a -1 .

7. The method for assessing carbon sequestration and enhancement in farmland soils in cold black soil regions according to claim 1, characterized in that, After outputting the management plan, the indicator data after the implementation of the management measures are re-acquired. The comprehensive index of soil carbon sequestration and carbon sink enhancement is compared with the measured carbon loss membership degree after the measured carbon loss is transformed by the descending trapezoidal membership degree function. The larger the measured loss membership degree, the smaller the measured carbon loss. When the difference between the comprehensive index and the measured loss membership degree is less than 0.1, it is judged as consistent. The overall accuracy rate is the proportion of sample points judged as consistent. The predicted freeze-thaw carbon release from the mechanistic model is compared with the measured carbon release. When the overall accuracy is below 85% or the predicted freeze-thaw carbon release deviation exceeds 20%, the soil aggregate destruction rate and the background of active carbon protected by the aggregate are updated first. If the predicted freeze-thaw carbon release deviation still exceeds 20% after the update, the freeze-thaw cracking coefficient, mineralization partition coefficient, and leaching partition coefficient are corrected with the goal of minimizing the mean square error between the measured and predicted values.

8. The method for assessing carbon sequestration and enhancement in farmland soil carbon pools in cold black soil regions according to claim 7, characterized in that, When the contributions of multiple cold-region-specific indicators are similar, the identification of collaborative weaknesses and the matching of collaborative measures should be carried out, specifically including: Calculate the mean and range of the contribution of the four cold-region characteristic indicators to the score loss. When the ratio of the range to the mean is less than 0.2, it is judged that there are multiple shortcomings. For cold-region characteristic indicators with multiple shortcomings, a synergistic grouping rule is established based on the physical causal relationship between the indicators. Indicators with mutual amplification effects are merged into a synergistic shortcoming group. The physical causal relationship is the basis for determining the synergistic shortcoming group. The seasonal freeze-thaw cycle intensity forms a freeze-thaw synergistic shortcoming group by driving the carbon emission pulse during the freeze-thaw period, and the spring snowmelt leaching coefficient forms a leaching synergistic shortcoming group by driving the DOC leaching flux. When the correlation coefficient of two cold-region characteristic indicators in the historical monitoring sequence is greater than 0.6, it is determined that the two indicators have a synergistic amplification effect and the degree of synergy is strengthened. When the correlation coefficient is less than 0.6, the synergistic shortcoming group is still determined based on the physical causal relationship. For the freeze-thaw synergy weakness group, a first set of management measures aimed at reducing freeze-thaw disturbance is matched; for the leaching synergy weakness group, a second set of management measures aimed at reducing snow leaching is matched. When the first and second sets of management measures contain the same management measures, the same management measures are output first. The maximum value of the recommended parameters of the same management measure in the first and second sets of management measures is multiplied by a synergy enhancement coefficient of 1.2 as the execution parameter. When the two sets of management measures do not overlap, the management measures in the first and second sets of management measures are output in descending order of contribution to the loss of points, forming a combined management scheme.

9. The method for assessing carbon sequestration and enhancement in farmland soil carbon pools in cold black soil regions according to claim 8, characterized in that, When multiple management measures are matched, optimize the combination of management measures, which includes: A synergy-antagonism relationship matrix of management measures is pre-established. The matrix records the synergy gain coefficient or antagonism loss coefficient when any two management measures are combined. The synergy gain coefficient represents the gain ratio of the carbon sequestration and carbon sink enhancement effect when the two management measures are combined to the sum of the effects of the two management measures implemented individually. The antagonism loss coefficient represents the loss ratio of the carbon sequestration and carbon sink enhancement effect when the two management measures are combined. The synergy gain coefficient and the antagonism loss coefficient are determined based on historical experimental data or expert experience. With the goal of maximizing the overall carbon sequestration and carbon sink enhancement effect and the constraint that the resource input for implementing measures does not exceed the upper limit of the management resource input for the target field, a combinatorial optimization model is constructed. The upper limit of the management resource input for the target field is set by the user based on the actual available resources. The overall carbon sequestration and carbon sink enhancement effect is composed of the weighted sum of the carbon sequestration and carbon sink enhancement effects of each management measure and the synergistic gain or antagonistic loss of any combination of two management measures. The weight of each management measure is determined based on the carbon sequestration and carbon sink enhancement effect contribution coefficient preset in the management measure library. The synergistic gain or antagonistic loss of any two management measures is the product of the corresponding coefficient obtained by querying the synergistic-antagonistic relationship matrix and the carbon sequestration and carbon sink enhancement effects of the two management measures. The constraint is that the sum of the resource input for implementing each management measure does not exceed the upper limit of the management resource input for the target field. The solution is to find the subset of management measures that maximizes the overall carbon sequestration and carbon sink enhancement effect. The subset of management measures obtained from the solution is sorted from high to low according to the contribution coefficient of each management measure to carbon sequestration and carbon sink enhancement, and the sorted management measure combination and corresponding parameters are output.

10. The method for assessing carbon sequestration and enhancement in farmland soils in cold black soil regions according to claim 1, characterized in that, When the model parameters of the freeze-thaw-leaching coupled carbon loss mechanism model are first applied, they are initially calibrated based on historical monitoring data or indoor freeze-thaw simulation experimental data of the target field area. The initial values ​​of the freeze-thaw cracking coefficient, mineralization partition coefficient, and leaching partition coefficient are determined according to the principle of minimizing the fitting residual between the measured carbon release from historical monitoring data and the carbon release predicted by the model.

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