Cunninghamia lanceolata forest soil plant silicon body carbon sink potential estimation method

Abstract

The invention discloses a cunninghamia lanceolata forest soil plant silicon body carbon sink potential estimation method, and relates to the field of data analysis, and the method comprises the steps: selecting a sampling land based on a cunninghamia lanceolata forest distribution region, a site condition type and stand development integrity, and dividing the sampling land into an initial stage forest, a middle stage forest, a mature stage forest and a special regeneration forest according to stand development stages, selecting grids in the sample plot based on three-dimensional grid division and random sampling so as to collect soil samples of soil layers with different depths; through the design of sample plot selection and three-dimensional grid sampling, the representativeness and uniformity of soil sample collection are improved, the accuracy of detection indexes is ensured through standard pretreatment and an efficient plant silicon body extraction technology, meanwhile, core driving factors are analyzed and identified in combination with multi-dimensional coupling, and the accuracy of the detection indexes is improved. Forest stand development and environmental factors are integrated into the constructed dynamic correlation model, and the carbon sink scale of each stage can be accurately deduced.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, specifically to a method for estimating the carbon sequestration potential of phytoliths in Chinese fir forest soil. Background Technology

[0002] The estimation of carbon sequestration potential of phytoliths in Chinese fir forests takes rhizosphere soil at different growth stages of Chinese fir forests as the research object. Through phytolith extraction, carbon content measurement and area normalization calculation, the rate and total amount of organic carbon sequestration by phytoliths are quantified, the impact of stand age and site conditions on phytolith carbon sequestration is clarified, and data support is provided for the assessment of the ecological carbon sequestration potential of Chinese fir forests.

[0003] Patent application No. 202410118615.8 discloses a method and system for estimating grassland carbon sequestration potential. This application aims to address the problem that "currently, the calculation of grassland carbon sequestration potential involves collecting carbon emissions from a predetermined distance range near the grassland area from government departments, detecting the carbon density in the air within that predetermined distance range, and then obtaining the remaining carbon in the air based on the carbon density and the area of ​​that predetermined distance range. Furthermore, the grassland carbon sequestration potential can be obtained based on the carbon emissions and the remaining carbon in that predetermined distance range. Understandably, the accuracy and reliability of the grassland carbon sequestration potential obtained using this method are low."

[0004] However, there is currently no specific estimation method for estimating the carbon sequestration potential of phytoliths in Chinese fir forest soils.

[0005] Therefore, we propose a method for estimating the carbon sink potential of soil phytoliths in Chinese fir forests. Summary of the Invention

[0006] In view of the above-mentioned shortcomings of the existing technology, the present invention provides a method for estimating the carbon sequestration potential of phytoliths in Chinese fir forest soil, which can effectively solve the problems of the existing technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions;

[0008] This invention discloses a method for estimating the carbon sequestration potential of phytoliths in Chinese fir forest soil, comprising:

[0009] Based on the distribution area, site conditions, and stand development integrity of Chinese fir forests, sample plots were selected and divided into early-stage, mid-stage, mature, and special regenerated forests according to their developmental stages. Soil samples were collected from different depths within the sample plots using a three-dimensional grid system and random sampling. The collected soil samples underwent pretreatment: non-soil components were removed, and the samples were air-dried, homogenized, ground, and sieved through a pre-set aperture sieve to obtain uniformly sized analytical samples. Each analytical sample was then independently labeled with the sample collection time and grid coordinates. Phytolith extraction technology was used to separate phytoliths from the soil. The study focuses on phytoliths, measuring phytolith content, phytolith-sequestered organic carbon content, and key physicochemical indicators such as soil silicon speciation, organic carbon, total nitrogen, total phosphorus, and pH characteristics. Based on soil bulk density, soil layer characteristics, and corresponding phytolith-sequestered organic carbon content, stratified calculations are used to obtain the phytolith carbon storage in Chinese fir forests. Based on stand development characteristics and environmental factors, multi-dimensional coupling analysis is used to identify core driving factors influencing phytolith carbon accumulation, and a dynamic correlation model between phytolith carbon storage and key driving factors is constructed. Based on this dynamic correlation model, the scale of phytolith carbon sinks at different stand development stages is extrapolated to quantitatively characterize the phytolith carbon sink potential of Chinese fir forests.

[0010] Furthermore, the three-dimensional grid division adopts a three-dimensional spatial equal division method. The grid side length is preset according to the sample plot area and soil heterogeneity. During random sampling, the number of sampling grids for each forest stand development stage is not less than the preset minimum value, and the sampling grids are evenly distributed within the sample plot.

[0011] Among the sample plots, the Chinese fir trees with the closest average diameter at breast height were selected, and rhizosphere soil at different soil depths was collected using the "S-shaped" sampling method.

[0012] in, Represented as the side length of the 3D grid. This represents the actual effective area of ​​a single sample plot. This indicates the total number of preset sampling grids for a single sample plot. Indicates the heterogeneity-corrected weights. This represents the soil heterogeneity coefficient.

[0013] Furthermore, the special regenerated forests include natural regeneration forests after clear-cutting, artificial regeneration forests on clear-cut sites, and restored forests of damaged stands.

[0014] Furthermore, in the soil sample pretreatment, non-soil components are removed manually by filtering through a sieve with a preset aperture. Natural air drying is carried out in a preset ventilated, light-proof, and constant-temperature environment. The air drying time is such that the sample quality remains constant within a preset time. Homogenization is performed using a stepwise grinding and mixing method. After grinding, the sample is screened through a sieve with a continuous gradient aperture to ensure that the particle size variation coefficient of the analyzed sample does not exceed a preset threshold. The marking information is recorded using both QR codes and text labels. The QR code contains data related to the sample collection time, grid coordinates, stand development stage, and site condition type.

[0015] Furthermore, the phytolith separation and extraction stage in the soil includes the following steps:

[0016] S1: Take the homogenized analytical sample, add a 15%~25% hydrogen peroxide solution, and oxidize it at a constant temperature of 30℃~38℃ for 3~5 hours to remove easily oxidizable organic components in the sample. The oscillation rate is controlled at 180~220r / min to ensure uniform oxidation.

[0017] S2: After oxidation, add 8%~12% hydrochloric acid solution and let stand at room temperature for 1.5~2.5 hours to dissolve carbonate inorganic impurities in the sample. Then, remove the supernatant by centrifugation and collect the precipitate.

[0018] S3: Add sodium polytungstate heavy liquid with a density of 2.5~2.7 g / cm³ to the precipitate, disperse it by ultrasonication for 15~25 minutes, and then separate it by gradient centrifugation. The centrifugation speed is gradually adjusted from 1000 r / min to 4000~6000 r / min. Collect the crude phytolith extract in the upper suspension.

[0019] S4: The crude extract of phytoliths is purified by washing with deionized water and 2%~4% dilute nitric acid solution alternately 3~5 times to remove residual heavy liquid components and trace metal impurities. Finally, the phytolith sample is obtained by freeze drying.

[0020] The phytolith content was determined by gravimetric method, which is the ratio of the dry weight of the purified phytolith sample to the dry weight of the sample taken for analysis; the organic carbon content of the phytolith was determined by elemental analyzer, and the phytolith sample was subjected to secondary acid washing to remove impurities before the determination.

[0021] Furthermore, the formula for calculating the carbon storage of phytoliths in the soil of the Chinese fir forest is as follows:

[0022] ;

[0023] In the formula: The total carbon storage of soil phytoliths in Chinese fir forests; This refers to the total number of forest stand development stages and the number of soil layers at a single sampling point. Let J be the bulk density of the j-th soil layer during the i-th stand development stage; The thickness of the j-th soil layer in the i-th stand development stage; The content of organic carbon sequestrated by phytoliths in the soil layer j of the i-th stand development stage; is the spatial weighting coefficient of the j-th soil layer in the i-th forest stand development stage.

[0024] Furthermore, the stand development characteristics include stand age, average diameter at breast height (DBH), stand density, and biomass accumulation rate. Environmental factors include mean annual temperature, annual precipitation, soil texture, pH value, total nitrogen content, total phosphorus content, and silicon availability, among which silicon availability... ,in Indicates the silicon availability index. , These represent the weighting coefficients for active silicon and slow-release silicon, respectively. Indicates the active silicon content of the soil. Indicates the content of slow-release silicon in the soil. Indicates the total silicon content of the soil. express Influence coefficient This indicates the measured values ​​of soil acidity and alkalinity.

[0025] The expression for the dynamic association model is:

[0026] ;

[0027] In the formula: denoted as the carbon storage of phytoliths at time t during the i-th stand development stage; , For the nonlinear coefficients of the i-th stage sub-model; The total number of core driving factors; For the k-th core driving factor in the i-th stage; Standardized data for the k-th core driving factor; It is a non-linear exponent; As a key core driving factor; For machine learning correction coefficients; These are the model correction values ​​based on the random forest algorithm.

[0028] Furthermore, when extrapolating the scale of phytolith carbon sinks based on the dynamic correlation model, a stand development rate correction factor is introduced, and the formula for calculating the carbon sink rate at different stand development stages is as follows:

[0029] ;

[0030] In the formula: Let be the phytolith carbon sink rate at time t during the i-th stand development stage. is the carbon sink rate coefficient for the i-th stand development stage; denoted as the carbon storage of phytoliths at time t during the i-th stand development stage; This represents the upper limit of phytolith carbon storage at the i-th stand development stage; is the carbon sink attenuation coefficient for the i-th stand development stage; This marks the beginning of the developmental stage of the forest stand.

[0031] Furthermore, the quantitative formula used to quantitatively characterize the carbon sequestration potential of phytoliths in Chinese fir forests is as follows:

[0032] ;

[0033] In the formula: For carbon sink sustainability index; This represents the average carbon sequestration rate throughout the entire stand development cycle. The initial carbon sequestration rate of the initial forest stage; It is the reciprocal of the coefficient of variation of the core driving factor; This represents the sustainability coefficient of forest stand development.

[0034] Compared with the known prior art, the technical solution provided by this invention has the following beneficial effects:

[0035] This invention provides a method for estimating the carbon sequestration potential of phytoliths in Chinese fir forests. During implementation, the method enhances the representativeness and uniformity of soil sample collection through plot selection and three-dimensional grid sampling design. Standardized preprocessing and efficient phytolith extraction techniques ensure the accuracy of detection indicators. Simultaneously, multi-dimensional coupling analysis identifies core driving factors, and the constructed dynamic correlation model incorporates forest stand development and environmental factors, accurately predicting the scale of carbon sequestration at each stage. A dual-index approach quantifies carbon sequestration potential and sustainability. Furthermore, optimized sampling and factor analysis are applied to specific regenerated forests, adapting to different site conditions and disturbance scenarios, effectively reducing errors caused by soil heterogeneity and environmental fluctuations. This provides a reliable method for assessing phytolith carbon sequestration resources in Chinese fir forests and offers decision support for carbon sequestration accounting and forestry carbon sequestration management. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0037] Figure 1 This is a flowchart illustrating a method for estimating the carbon sequestration potential of phytoliths in Chinese fir forest soil. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0039] The present invention will be further described below with reference to embodiments.

[0040] Example:

[0041] This embodiment presents a method for estimating the carbon sequestration potential of phytoliths in Chinese fir forest soil, such as... Figure 1 As shown, it includes:

[0042] Based on the distribution area of ​​Chinese fir forest, site conditions and stand development integrity, sample plots were selected. The stand development stages were divided into early forest, mid-stage forest, mature forest and special regenerated forest. Within the sample plots, soil samples were collected from different soil layers by dividing the grid into three-dimensional grids and randomly sampling grids.

[0043] Plot selection includes regional suitability screening, site condition classification, and developmental integrity verification;

[0044] Regional suitability screening is based on the degree of suitability of the temperature and precipitation conditions required for the growth of Chinese fir forests, and priority is given to areas with no continuous human damage, no major natural damage and continuous vegetation distribution.

[0045] Site conditions are classified into units based on the combination of topographic features and basic soil properties. Topographic features include slope, aspect, and elevation, while basic soil properties include soil type, texture, and initial fertility.

[0046] The verification of developmental integrity is based on a comprehensive assessment of the rationality of the stand's age structure, the degree of variation in the diameter at breast height of dominant trees, and the degree of understory vegetation coverage. All three must meet the preset adaptation requirements, and any area that does not meet the standards will not be selected. The selection process is completed by manually quantifying various indicators and thresholds.

[0047] The initial stage of the forest is defined as the average age of the stand reaching the age at which the Chinese fir begins to grow rapidly. The rate of biomass accumulation in the stand is increasing, the diameter at breast height of the dominant trees is not lower than the preset standard, the understory vegetation has not formed a stable structure, and the stand density is higher than the preset initial value.

[0048] The average age of the mid-term forest stands is between the starting age of rapid growth and the starting age of maturity of Chinese fir. The biomass accumulation rate of the forest stands reaches its peak and then remains stable. The diameter at breast height growth rate of the dominant trees remains at a preset stable level. The understory vegetation forms a stable community, and the canopy closure of the forest stands reaches the preset standard.

[0049] The average age of mature forest stands exceeds the maturity initiation age of Chinese fir, the biomass accumulation rate of forest stands drops to the preset low level, the diameter at breast height growth of dominant trees tends to be slow, the age structure of forest stands is aging, the understory vegetation coverage is stable and the species diversity reaches the preset peak.

[0050] Special regenerated forests are defined by the start of the recovery phase after the stand has been disturbed. The determination is based on the type of disturbance, the duration of recovery, and the degree of biomass recovery. The recovery process must meet the requirements of continuous biomass growth and gradual optimization of soil physical and chemical properties.

[0051] It should be noted that the selection of sample plots and the division of forest stand development stages can also be performed using any applicable user-defined method other than the methods mentioned above.

[0052] The three-dimensional grid division adopts a three-dimensional space equal division method. The grid side length is preset according to the sample plot area and soil heterogeneity. When randomly sampling, the number of sampling grids for each forest stand development stage is not less than the preset minimum value, and the sampling grids are evenly distributed within the sample plot.

[0053] Among the sample plots, the Chinese fir trees with the closest average diameter at breast height were selected, and rhizosphere soil at different soil depths was collected using the "S-shaped" sampling method.

[0054] in, Represented as the side length of the 3D grid. This represents the actual effective area of ​​a single sample plot, after deducting the area within the plot that cannot be sampled. This indicates the total number of preset sampling grids for a single sample plot. Indicates the heterogeneity-corrected weights. Indicates the soil heterogeneity coefficient;

[0055] The above formula fully considers key factors such as the actual effective area of ​​a single plot, the total number of pre-set sampling grids, the soil heterogeneity correction weight, and the soil heterogeneity coefficient. By taking the square root of the effective area and relating it to the total number of sampling grids, and then adjusting it in combination with the heterogeneity correction weight and the soil heterogeneity coefficient, the grid side length can be adapted to the area size and soil heterogeneity of different plots. This ensures that the three-dimensional grid division is both scientific and reasonable and meets the requirements of uniform grid distribution and sufficient quantity in random sampling, thus providing a basis for accurate soil sample collection.

[0056] ∈[0.3,1.2], its value increases with the increase of soil type sensitivity to phytolith carbon accumulation in Chinese fir forest, and decreases with the decrease of this sensitivity; The results were obtained by weighted calculation of the coefficients of variation of soil phytolith content, organic carbon content, total nitrogen content, total phosphorus content, and acid-base properties obtained from pre-sampling within the sample plot.

[0057] Special regeneration forests include natural regeneration forests after clear-cutting, artificial regeneration forests on clear-cut sites, and restoration forests of damaged stands. For the selection of sample plots for these forest stands, it is necessary to specify three key parameters: disturbance type, disturbance intensity, and restoration period.

[0058] During soil sample collection, the sampling density should be appropriately increased for specific soil layers at different depths affected by disturbance. When analyzing the core driving factors of phytolith carbon accumulation, the disturbance recovery rate index is specifically included. This index is calculated by weighted summation of stand biomass recovery rate and soil physicochemical property recovery rate.

[0059] The collected soil samples were pretreated to remove non-soil components and air-dried. After homogenization, they were ground and screened through a sieve with a preset aperture to obtain analytical samples with uniform particle size. Each analytical sample was then independently labeled with the sample collection time and grid coordinates.

[0060] In soil sample pretreatment, non-soil components were removed manually by filtering through a sieve with a preset aperture. Natural air drying was carried out in a preset ventilated, light-proof, and constant-temperature environment. The air drying time was such that the sample quality remained constant within a preset time. Homogenization was performed using a stepwise grinding and mixing method. After grinding, the samples were screened through a sieve with a continuous gradient aperture to ensure that the particle size variation coefficient of the analyzed samples did not exceed a preset threshold. The labeling information was recorded using both QR codes and text labels. The QR code contained data related to the sample collection time, grid coordinates, stand development stage, and site condition type.

[0061] Phytoliths in soil were separated using phytolith extraction technology. Phytolith content, organic carbon content of phytoliths, and key physicochemical indicators such as soil silicon speciation, organic carbon, total nitrogen, total phosphorus, and acid-base properties were determined.

[0062] The phytolith separation and extraction stage in soil includes the following steps:

[0063] S1: Take the homogenized analytical sample, add a 15%~25% hydrogen peroxide solution, and oxidize it at a constant temperature of 30℃~38℃ for 3~5 hours to remove easily oxidizable organic components in the sample. The oscillation rate is controlled at 180~220r / min to ensure uniform oxidation.

[0064] S2: After oxidation, add 8%~12% hydrochloric acid solution and let stand at room temperature for 1.5~2.5 hours to dissolve carbonate inorganic impurities in the sample. Then, remove the supernatant by centrifugation and collect the precipitate.

[0065] S3: Add sodium polytungstate heavy liquid with a density of 2.5~2.7 g / cm³ to the precipitate, disperse it by ultrasonication for 15~25 minutes, and then separate it by gradient centrifugation. The centrifugation speed is gradually adjusted from 1000 r / min to 4000~6000 r / min. Collect the crude phytolith extract in the upper suspension.

[0066] S4: The crude extract of phytoliths is purified by washing with deionized water and 2%~4% dilute nitric acid solution alternately 3~5 times to remove residual heavy liquid components and trace metal impurities. Finally, the phytolith sample is obtained by freeze drying.

[0067] The phytolith content was determined by gravimetric method, which is the ratio of the dry weight of the purified phytolith sample to the dry weight of the sample taken for analysis. The organic carbon content of the phytolith was determined by elemental analyzer. Before the determination, the phytolith sample was acid washed twice to remove impurities. It was soaked in 6%~8% phosphoric acid solution for 45~75 minutes to completely remove the interference of residual inorganic carbon. After acid washing, it was washed with deionized water until neutral and dried for later use.

[0068] Based on soil bulk density, soil layer characteristics and corresponding soil layer phytolith sequestration organic carbon content, the carbon storage of phytoliths in Chinese fir forest soil was obtained through stratified accounting.

[0069] Soil bulk density was determined using the ring sampler method. At least three repeated sampling points were set at different depths in each sampling grid. Soil characteristics included soil thickness, texture type, and porosity. The formula for calculating the carbon storage of phytoliths in the stratified Chinese fir forest soil is as follows:

[0070] ;

[0071] In the formula: The total carbon storage of soil phytoliths in Chinese fir forests; This refers to the total number of forest stand development stages and the number of soil layers at a single sampling point. Let J be the bulk density of the j-th soil layer during the i-th stand development stage; The thickness of the j-th soil layer in the i-th stand development stage; The content of organic carbon sequestrated by phytoliths in the soil layer j of the i-th stand development stage; The spatial weighting coefficient of the j-th soil layer at the i-th stand development stage; spatial weighting coefficient The method for determining the weighting coefficient is as follows: taking into account the proportion of soil layer distribution area in the sample plot, the vertical distribution pattern of phytoliths, and the soil heterogeneity correction coefficient. Specifically: (1) The phytolith accumulation in the topsoil (0-20cm) is usually high and easily affected by plant roots and litter decomposition, with a weighting coefficient ranging from 0.8 to 1.0; (2) The phytolith content in the middle soil (20-40cm) shows a decreasing trend, with a weighting coefficient ranging from 0.5 to 0.8; (3) The phytolith content in the deep soil (>40cm) is further reduced, with a weighting coefficient ranging from 0.3 to 0.6; (4) For sampling points with a spatial variation coefficient greater than 30%, the influence of outliers is weakened by reducing the weighting coefficient (multiplying by a correction factor of 0.7-0.9). The weighting coefficient of each soil layer directly reflects the actual contribution of the soil layer to the total carbon storage and does not require mandatory normalization.

[0072] The above formula adopts a hierarchical accounting approach, comprehensively incorporating parameters such as soil bulk density, soil layer thickness, phytolith-sequestered organic carbon content, and spatial weighting coefficients for different forest stand development stages and soil layers. It achieves accurate accounting of total carbon storage through double summation. The spatial weighting coefficient is determined based on the distribution area ratio of soil layers in the sample plot and the soil heterogeneity correction coefficient, which can fully reflect the differences in the contribution of different soil layers to carbon storage, avoid errors caused by single-dimensional accounting, and improve the accuracy and reliability of carbon storage calculation.

[0073] in, ∈ (0,1), its value increases as the proportion of the distribution area of ​​the j-th soil layer in the sample plot increases during the i-th forest stand development stage and the soil heterogeneity correction coefficient approaches 1, and decreases as the proportion of the distribution area of ​​the soil layer decreases and the soil heterogeneity correction coefficient decreases.

[0074] Based on stand development characteristics and environmental factors, we identified the core driving factors affecting carbon accumulation in phytoliths through multi-dimensional coupling analysis, and constructed a dynamic correlation model between phytolith carbon storage and key driving factors.

[0075] Forest stand development characteristics include stand age, mean diameter at breast height (DBH), stand density, and biomass accumulation rate. Environmental factors include mean annual temperature, annual precipitation, soil texture, pH, total nitrogen content, total phosphorus content, and silicon availability, among which silicon availability... ,in Indicates the silicon availability index. , These represent the weighting coefficients for active silicon and slow-release silicon, respectively. Indicates the active silicon content of the soil. Indicates the content of slow-release silicon in the soil. Indicates the total silicon content of the soil. express Influence coefficient This indicates the measured values ​​of soil acidity and alkalinity.

[0076] The above formula is based on the different contributions of different forms of silicon (active silicon and slow-release silicon) in the soil. It sets corresponding weighting coefficients to reflect the direct contribution of active silicon and the indirect supporting role of slow-release silicon. Simultaneously, it introduces the total soil silicon content and pH influence coefficient to consider the inhibitory effect of soil acidity and alkalinity on silicon dissolution and release, comprehensively taking into account both silicon morphology and soil environmental conditions to achieve a reasonable quantification of silicon availability. In actual calculations, when the measured soil pH value is in the range of [6.95, 7.05], to avoid numerical singularities caused by |pH-7.0| approaching 0 in the denominator, the lower limit of |pH-7.0| is set to 0.1.

[0077] ∈[0.6, 0.9], the faster the rate of conversion of active silicon in soil to phytoliths and the higher the proportion of its direct contribution to carbon accumulation in phytoliths, the better. The larger the value, the slower the conversion rate and the lower the direct contribution percentage. The smaller the value; ∈[0.1, 0.4], the higher the release rate of slow-release silicon in the soil and the higher the efficiency of its conversion to active silicon, the stronger its indirect supporting effect on carbon accumulation in phytoliths. The larger the value, the lower the release conversion efficiency and the weaker the indirect support effect. The smaller the value; ∈[0.05, 0.2], the greater the deviation of the soil pH from neutral, the stronger the inhibitory effect on silicon dissolution and release. The higher the value, the closer the soil pH is to neutral and the weaker the inhibitory effect. The smaller the value;

[0078] Multidimensional coupling analysis uses redundancy analysis combined with partial least squares path model. The core driving factors must satisfy the following conditions: the variance explained rate is not less than the preset threshold and the absolute value of the path coefficient is greater than the preset critical value.

[0079] The expression for the dynamic association model is:

[0080] ;

[0081] In the formula: denoted as the carbon storage of phytoliths at time t during the i-th stand development stage; , For the nonlinear coefficients of the i-th stage sub-model; The total number of core driving factors; For the k-th core driving factor in the i-th stage; Standardized data for the k-th core driving factor; It is a non-linear exponent; As a key core driving factor; For machine learning correction coefficients; These are the model calibration values ​​based on the random forest algorithm; standardized data. and The min-max normalization method is used to ensure that the standardized numerical range is [0, 1]. This method guarantees that the input value in the logarithmic function is always greater than 1, avoiding mathematical errors or negative values ​​in logarithmic operations; weighting coefficients... The weighted summation term is obtained by fitting the data from the training data using multiple linear regression or partial least squares regression, ensuring that it is positive; for the key driving factor exponent term... The nonlinear exponent d typically ranges from 1.2 to 2.5, and the optimal value is determined through cross-validation. It is used to characterize the nonlinear response of key driving factors to carbon reserves (such as diminishing marginal effects). Since the core driving factors have been standardized to the [0,1] interval, the exponent calculation will not produce numerical overflow or outliers.

[0082] The above formula combines the characteristics of forest stand development stages, integrates standardized data of core driving factors through nonlinear functions, introduces nonlinear exponential terms of key core driving factors, and optimizes the model prediction results by using machine learning correction coefficients and model correction values ​​based on random forest algorithms. This ensures that the model can accurately reflect the dynamic relationship between carbon storage and key driving factors, and improves the accuracy of carbon storage prediction at different forest stand development stages.

[0083] in, >0 indicates that the value is larger when the synergistic effect of the core driving factors in the stand carbon accumulation process is more complex and the nonlinear change is more obvious, and the value is smaller when the carbon accumulation process is closer to a linear trend. >0 indicates that the value is larger when the key driving factor plays a more prominent dominant role in this stage and has a more significant regulatory effect on carbon accumulation, and smaller when the regulatory effect of the key driving factor is more moderate and its impact on carbon accumulation is more gradual. ∈ (0,1), the stronger the limiting or promoting effect of the driving factor on carbon accumulation and the higher its proportion in multi-factor coupling, the larger the value; the lower the contribution and the smaller the coupling proportion, the smaller the value. >0 indicates that the larger the value is when a small change in the key driving factor causes more drastic fluctuations in carbon storage and the more the regulatory relationship deviates from linearity, the smaller the value is when the regulatory relationship is closer to linear and the carbon storage fluctuations are more gradual. ∈(0,1], the larger the deviation between the model prediction and the actual measured value, and the higher the heterogeneity of the forest stand environment, the larger the value; the higher the model fitting accuracy and the more stable the environmental conditions, the smaller the value. Obtained through iterative optimization using training and validation set data; machine learning correction value. The method for obtaining the carbon storage is as follows: First, the measured data (including stand characteristics, environmental factors, and corresponding phytolith carbon storage) at each stand development stage are used as the training set to construct a random forest regression model; then, for new sample plots or time points to be predicted, their stand characteristics and environmental factors are input into the random forest model to obtain the predicted carbon storage. ; Calculate the relative deviation ,in This refers to the calculated values ​​of the first part of the dynamic association model (the parameterized expression within square brackets); finally, [the values ​​will be...]. These correction coefficients are incorporated into the complete model. The value range is 0.1-0.3, which limits the magnitude of machine learning corrections and maintains the stability and interpretability of the main framework of the parameterized model. This method organically combines the mechanistic interpretability of the parameterized model with the data fitting accuracy of the machine learning model, overcoming the limitations of a single method.

[0084] During model training, the parameters are optimized using the 5-fold cross-validation method. The model fit is evaluated by a combination of the coefficient of determination, root mean square error, and mean absolute percentage error. The coefficient of determination is not lower than the preset fit standard, and the root mean square error and mean absolute percentage error do not exceed the preset error threshold.

[0085] The scale of phytolith carbon sinks at different forest stand development stages was extrapolated based on a dynamic correlation model to quantitatively characterize the carbon sink potential of phytoliths in Chinese fir forests.

[0086] When extrapolating the scale of phytolith carbon sinks based on a dynamic correlation model, a stand development rate correction factor is introduced. The formulas for calculating carbon sink rates at different stand development stages are as follows:

[0087] ;

[0088] In the formula: Let be the phytolith carbon sink rate at time t during the i-th stand development stage. is the carbon sink rate coefficient for the i-th stand development stage; denoted as the carbon storage of phytoliths at time t during the i-th stand development stage; This represents the upper limit of phytolith carbon storage at the i-th stand development stage; is the carbon sink attenuation coefficient for the i-th stand development stage; This represents the starting time of the stand's developmental stage; the upper limit of phytolith carbon storage for the i-th stand's developmental stage. The methods for determining the carbon storage include: (1) Long-term monitoring data method: Select the oldest and most mature sample plot in the forest development stage and take the 90th or 95th percentile of multiple sample plots as the upper limit value; (2) Ecological potential extrapolation method: Calculate the maximum biomass potential of Chinese fir forest in the region (refer to the regional forest resource inventory data or growth model prediction value) and phytolith accumulation coefficient (take 0.5%-2% of biomass, depending on tree species and environmental conditions); (3) Model extrapolation method: Use the dynamic correlation model of this invention to extrapolate the forest stand age to the theoretical maximum value (e.g., 80-100 years for mature forests), set the environmental factors as the optimal combination, and calculate the carbon storage as the theoretical upper limit value. In practical applications, appropriate methods can be selected according to data availability, or multiple methods can be combined and averaged to improve reliability. For special regenerated forests, their C max The value can be referenced to 80%-90% of that of mature forests under the same site conditions;

[0089] The above formula introduces a forest stand development rate correction factor, which comprehensively considers the carbon sink rate coefficient, carbon storage, upper limit of carbon storage, carbon sink decay coefficient and time factor at each forest stand development stage. By combining the logistic growth function and the exponential decay function, it simulates the change law of carbon sink rate over time. The carbon sink rate coefficient reflects the synergistic promoting effect of the core driving factors, the carbon sink decay coefficient reflects the influence of forest stand development stage, environmental fluctuations and changes in soil physicochemical properties, and the upper limit of carbon storage is determined by site conditions and forest stand potential, thereby accurately depicting the carbon sink rate characteristics of each forest stand development stage at different times.

[0090] in, ∈ (0,1), the larger the value is when the core driving factors synergistically promote carbon accumulation in phytoliths, the smaller the value is when the core driving factors are subject to limiting factors (such as unsuitable temperature and humidity, soil nutrient deficiency, and unreasonable forest stand structure) which weaken the carbon accumulation effect. ∈ (0,0.1), the larger the value is when the forest stand enters the late stage of maturity, the environment fluctuates frequently or the soil physicochemical properties deteriorate, and the smaller the value is when the forest stand is in the vigorous growth stage, the environmental conditions are stable and the soil fertility is maintained well. It is determined by both site conditions and stand potential;

[0091] The carbon sequestration potential stages of phytoliths in Chinese fir forests were quantitatively characterized using a dual approach: a carbon sequestration potential index and a carbon sequestration sustainability index. The carbon sequestration potential index is the ratio of the cumulative carbon sequestration scale at each stand development stage to the theoretical maximum carbon sequestration value. The carbon sequestration sustainability index was determined through a coupled analysis of the carbon sequestration rate decay trend, the stability of core driving factors, and the sustainability of stand development. The applied quantitative formula is as follows:

[0092] ;

[0093] In the formula: For carbon sink sustainability index; This represents the average carbon sequestration rate throughout the entire stand development cycle. The initial carbon sequestration rate of the initial forest stage; It is the reciprocal of the coefficient of variation of the core driving factor; The forest stand development sustainability coefficient;

[0094] The above formula comprehensively measures the sustainability of carbon sinks by combining the ratio of the average carbon sink rate to the initial carbon sink rate of the initial forest with the product of the inverse of the coefficient of variation of the core driving factors and the square root of the stand development sustainability coefficient. The coefficient of variation of the core driving factors is calculated by weighting the contribution of each factor, and the stand development sustainability coefficient reflects the rationality of the stand age structure and natural regeneration capacity. This formula achieves a coupled evaluation of the carbon sink rate decay trend, the stability of the core driving factors and the stand development sustainability, and finally comprehensively and scientifically quantifies the sustainability level of carbon sinks in Chinese fir phytoliths.

[0095] The coefficient of variation of the core driving factors is the average value calculated by weighting the standard deviation of each core driving factor to the mean of the corresponding factor, according to the contribution of each factor to carbon accumulation in phytoliths. ∈ (0,1), the value is larger when the stand age structure is more reasonable and the natural regeneration capacity is stronger, and the value is smaller when the stand age structure is unbalanced or the natural regeneration capacity is weak.

[0096] In this embodiment, the above method is applied to the scenario of estimating the carbon sink potential of phytoliths in Chinese fir forests. It can accurately capture the carbon sink characteristics of Chinese fir forests at different developmental stages, improve the accuracy of carbon storage accounting and the scientific nature of carbon sink potential assessment, and adapt to diverse site and disturbance restoration scenarios. It provides reliable support for the efficient utilization of carbon sink resources in Chinese fir forests, optimization of management and operation, and ecological carbon sink accounting, and helps to achieve precise control and green development of forestry carbon sinks.

[0097] Referring to the method in the above embodiments, the following is an application example of this method:

[0098] A typical Chinese fir forest distribution area in southern China was selected as the study area. This area has no continuous human-caused damage or major natural destruction, the vegetation is continuously distributed, and the temperature and precipitation conditions are highly compatible with the growth requirements of the Chinese fir forest. Based on topographic features (slope, aspect, altitude) and basic soil characteristics (soil type, texture, initial fertility), the study area was divided into three site condition units. By verifying the rationality of the stand age structure, the degree of variation in diameter at breast height of dominant trees, and the degree of understory vegetation cover, sample plots that met the requirements for developmental integrity were selected and divided into early-stage forest, mid-stage forest, mature forest, and special regenerated forest with natural regeneration after clear-cutting. Among them, the special regenerated forest had a clear-cutting disturbance type, moderate disturbance intensity, and a recovery period of 8 years.

[0099] Three-dimensional grids were used to divide each type of sample plot into equal segments. The actual effective area of ​​a single sample plot (excluding unsampling areas) was 1000 square meters. The total number of sampling grids was preset to 20. Considering the soil heterogeneity in the area, the side length of the three-dimensional grid was calculated to be 5 meters. Five sampling grids were evenly distributed for each forest stand development stage. Soil samples were collected from three depths within each sampling grid: 0-20cm, 20-40cm, and 40-60cm. For the 20-40cm soil layer, which is significantly affected by disturbance in special regenerated forest sample plots, the sampling density was appropriately increased.

[0100] After collection, soil samples were manually screened to remove non-soil components such as stones and plant debris. They were then air-dried naturally in a ventilated, dark, and constant-temperature environment until their quality was constant. Homogenization was carried out using a stepwise grinding and mixing method. After grinding, the samples were screened through a continuously gradient aperture sieve to ensure that the particle size variation coefficient of the analytical samples met the requirements. Each analytical sample was marked with both a QR code and text label to clearly record information such as collection time, grid coordinates, stand development stage, and site condition type.

[0101] Phytoliths in soil were separated using phytolith extraction technology: After homogenization, the analytical sample was treated with 20% hydrogen peroxide solution and oxidized at 35℃ with constant oscillation at 200 r / min for 4 hours to remove easily oxidizable organic components. After oxidation, 10% hydrochloric acid solution was added, and the sample was allowed to stand at room temperature for 2 hours to dissolve carbonate inorganic impurities. The supernatant was removed by centrifugation, and the precipitate was collected. Sodium polytungstate heavy liquid with a density of 2.6 g / cm³ was added to the precipitate, and after ultrasonic dispersion for 20 minutes, a gradient centrifugation method was used for separation. The centrifugation speed was gradually adjusted from 1000 r / min to 5000 r / min, and the crude phytolith extract in the upper suspension was collected. The crude phytolith extract was washed four times alternately with deionized water and 3% dilute nitric acid solution, and then freeze-dried to obtain the phytolith sample. Phytolith content was determined by gravimetric method, and organic carbon content of phytoliths was determined by elemental analyzer (residual inorganic carbon was removed by soaking in 7% phosphoric acid solution for 60 minutes before measurement). Key physicochemical indicators such as soil silicon form, organic carbon, total nitrogen, total phosphorus, and acid-base properties were also measured.

[0102] The bulk density of soil layers at different depths in each sampling grid was determined using the ring cutter method. Combined with soil layer characteristics such as thickness, texture type, and porosity, as well as the organic carbon content of phytoliths in the corresponding soil layers, the total carbon storage of soil phytoliths in the Chinese fir forest in this area was calculated to be 8.6 tons / hectare using a stratified accounting method.

[0103] Stand development characteristics such as stand age, mean diameter at breast height (DBH), stand density, and biomass accumulation rate were selected, along with environmental factors including mean annual temperature, annual precipitation, soil texture, pH, total nitrogen content, total phosphorus content, and silicon availability. The silicon availability index was calculated to be 0.72. Through redundancy analysis combined with a partial least squares path model, multi-dimensional coupling analysis was conducted to identify biomass accumulation rate, mean annual temperature, total soil nitrogen content, and silicon availability as the core driving factors influencing phytolith carbon accumulation. A dynamic correlation model was constructed, and after parameter optimization using a 5-fold cross-validation method, the model's coefficient of determination, root mean square error, and mean absolute percentage error all met the preset standards. This model was used to extrapolate the phytolith carbon sink scale at each stand development stage.

[0104] Introducing a forest stand development rate correction factor, the calculated carbon sink rates for early-stage forests, mid-stage forests, mature forests, and special regenerated forests were 0.32 tons / (hectare·year), 0.58 tons / (hectare·year), 0.15 tons / (hectare·year), and 0.27 tons / (hectare·year), respectively. Carbon sink potential was characterized by both a carbon sink potential index and a carbon sink sustainability index, with the latter being 0.63 and the former 0.78. These results quantitatively indicate that the carbon sink potential of the phytoliths in the *Cunninghamia lanceolata* forests in this region is at a moderately high level, with the mid-stage forests making the most significant contribution to carbon sinking.

[0105] In summary, the methods described in the above embodiments improve the representativeness and uniformity of soil sample collection through plot selection and three-dimensional grid sampling design. Standardized preprocessing and efficient phytolith extraction technology ensure the accuracy of detection indicators. Furthermore, multi-dimensional coupling analysis identifies core driving factors, and the constructed dynamic correlation model incorporates forest stand development and environmental factors, enabling accurate prediction of carbon sink scale at each stage. A dual-index approach quantifies carbon sink potential and sustainability. In addition, optimized sampling and factor analysis for specific regenerated forests adapt to different site conditions and disturbance scenarios, effectively reducing errors caused by soil heterogeneity and environmental fluctuations. This provides a reliable method for assessing phytolith carbon sink resources in Chinese fir forests and offers decision support for carbon sink accounting and forestry carbon sink management.

[0106] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for estimating the potential of soil phytolith carbon sink in Chinese fir forest, characterized in that, The method comprises the following steps: Based on the distribution area of Chinese fir forest, site condition type and stand development integrity, sample plots are selected, and are divided into initial forest, medium-term forest, mature forest and special regeneration forest according to the stand development stage. Grids are selected in the sample plots based on three-dimensional grid division and random sampling to collect soil samples of different depths; The collected soil samples are pretreated, and non-soil components are removed and naturally dried. After homogenization treatment, grinding, screening through a pre-set aperture screen, uniform particle size analysis samples are obtained. Each analysis sample is independently labeled, and the label content is the sampling time and grid coordinates of the sample body sample; Phytolith extraction technology is used to separate phytolith from soil, and the content of phytolith, the content of phytolith-occluded organic carbon, and the key physicochemical indexes of soil silicon form, organic carbon, total nitrogen, total phosphorus and acid-base characteristics are determined; Based on the soil bulk density, soil layer characteristics and corresponding soil layer phytolith-occluded organic carbon content, the phytolith carbon storage of Chinese fir forest soil is obtained through stratified accounting; Based on the stand development characteristics and environmental factors, the core driving factors affecting phytolith carbon accumulation are identified through multi-dimensional coupling analysis, and a dynamic correlation model of phytolith carbon storage and key driving factors is established; Based on the dynamic correlation model, the phytolith carbon sink size of different stand development stages is deduced to quantitatively represent the phytolith carbon sink potential of Chinese fir forest.

2. The method according to claim 1, wherein, The three-dimensional grid division adopts a three-dimensional space equal division method, and the grid length is pre-set according to the sample plot area and soil heterogeneity. The number of sampling grids for each stand development stage is not less than the pre-set minimum value, and the sampling grids are uniformly distributed in the sample plot; In the extracted sample plot, the Chinese fir with the closest average diameter is selected, and the rhizosphere soil of different soil layer depths is collected according to the "S-shaped" sampling method; wherein, represents the length of the stereogrid edge, represents the actual effective area of a single plot, represents the total number of preset sampling grids of a single plot, represents the heterogeneity correction weight, represents the soil heterogeneity coefficient.

3. The method according to claim 1, wherein, The special regeneration forest includes natural regeneration forest after clear cutting, artificial regeneration forest on trace land and damaged stand recovery forest.

4. The method according to claim 1, wherein, In the soil sample pretreatment, non-soil components are removed by filtering through a screen with a pre-set aperture, and natural drying is performed in a pre-set ventilation, light-proof and constant temperature environment. The drying time is constant within a pre-set time, the homogenization treatment adopts a step-by-step grinding and mixing method, and the ground sample is screened through a continuous gradient aperture screen to ensure that the particle size variation coefficient of the analysis sample does not exceed a pre-set threshold. The marking information is recorded by a two-dimensional code and a text label, and the two-dimensional code includes sample collection time, grid coordinates, stand development stage and site condition type correlation data.

5. The method according to claim 1, wherein, The phytolith separation and extraction stage in the soil comprises the following steps: S1: Take the homogenized analysis sample, add a 15%-25% hydrogen peroxide solution, and oscillate at 30-38°C for 3-5 hours to remove easily oxidized organic components. The oscillation rate is controlled at 180-220 r / min to ensure uniform oxidation; S2: After oxidation, add an 8%-12% hydrochloric acid solution, and stand at room temperature for 1.5-2.5 hours to dissolve carbonate inorganic impurities in the sample. Then, remove the supernatant by centrifugal separation, and collect the precipitate; S3: adding sodium polytungstate heavy liquid with a density of 2.5-2.7 g / cm3 into the precipitate, dispersing for 15-25 minutes by ultrasonic wave, and then separating by gradient centrifugation, with the centrifugal speed gradually adjusted from 1000 r / min to 4000-6000 r / min, and collecting the phytolith crude extract in the upper suspension; S4: purifying the phytolith crude extract, washing with deionized water and 2%-4% dilute nitric acid solution alternately for 3-5 times, removing residual heavy liquid components and trace metal impurities, and finally obtaining the phytolith sample by freeze-drying; wherein the phytolith content is determined by weight method, i.e. the ratio of the dry weight of the purified phytolith sample to the dry weight of the analysis sample; and the sealed organic carbon content of the phytolith is determined by an elemental analyzer, and the phytolith sample is subjected to secondary acid washing to remove impurities before determination. 6.The method of estimating the potential of soil silicate carbon sink of Chinese fir forest according to claim 1, wherein, The formula for stratified accounting of the phytolith carbon storage in the Cunninghamia lanceolata forest soil is: ； In the formula: is the total carbon storage of soil phytolith in Chinese fir forest; is the total number of stand development stages and the number of soil layers of a single sampling point; is the bulk density of the jth layer of soil in the ith stand development stage; is the thickness of the jth layer of soil in the ith stand development stage; is the phytolith-occluded organic carbon content of the jth layer of soil in the ith stand development stage; is the spatial weight coefficient of the jth layer of soil in the ith stand development stage.

7. The method according to claim 1, wherein the method is characterized by, The stand development characteristics include stand age, average DBH, stand density and biomass accumulation rate, and the environmental factors include annual mean temperature, annual precipitation, soil texture, pH value, total nitrogen content, total phosphorus content and silicon availability, wherein the silicon availability wherein represents the silicon availability index, , represents the weight coefficient of active silicon and slow-release silicon respectively, represents the soil active silicon content, represents the soil slow-release silicon content, represents the soil total silicon content, represents the influence coefficient, represents the soil acid-base characteristic determination value; The dynamic correlation model expression is: ； wherein: is the phytolith carbon storage of the i-th stand development stage at time t; , is the nonlinear coefficient of the i-th stage sub-model; is the total number of core driving factors; is the weight coefficient of the i-th stage k-th core driving factor; is the normalized data of the k-th core driving factor; is the nonlinear index; is the key core driving factor; is the machine learning correction coefficient; is the model correction value based on the random forest algorithm.

8. The method according to claim 1, wherein the method is characterized by, When deducing the phytolith carbon sink scale based on the dynamic correlation model, a forest development rate correction factor is introduced, and the carbon sink rate calculation formula for different forest development stages is: ； wherein: is the phytolith carbon sink rate at time t for the i-th stand development stage; is the carbon sink rate coefficient for the i-th stand development stage; is the phytolith carbon stock at time t for the i-th stand development stage; is the upper limit of the phytolith carbon stock for the i-th stand development stage; is the carbon sink decay coefficient for the i-th stand development stage; is the start time for the stand development stage. 9.The method according to claim 1, wherein, The quantitative formula applied in the stage of quantitatively characterizing the phytolith carbon sink potential of the Cunninghamia lanceolata forest is: ； wherein: is the carbon sink sustainability index; is the average carbon sink rate over the entire stand development cycle; is the initial carbon sink rate of the initial forest; is the inverse of the coefficient of variation of the core drivers; is the stand development sustainability coefficient.

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