Method and system for estimating carbon-nitrogen coupling-based emission reduction potential of paddy field soil, and medium

Abstract

The present application belongs to the technical field of data processing, and particularly relates to a rice field soil emission reduction potential estimation method and system based on carbon-nitrogen coupling, and a medium. The method comprises collecting rice field nitrogen application information in a target area through literature statistics, classifying rice fields with similar nitrogen application into the same type, and selecting several typical areas from each type; soil sampling is performed at fixed intervals in the typical rice field area, soil nitrogen concentration levels are analyzed, and representative samples are obtained; soil column experiments are simulated using the representative samples to estimate the average emission of specific gases; significant influencing factors are determined, different carbon-nitrogen coupling scenarios are set for orthogonal simulation experiments, the importance of the factors is quantified, and the comprehensive emission estimation value is obtained by weighted accumulation to evaluate the carbon-nitrogen coupling-based biological carbon rice field emission reduction capacity. The present application can effectively estimate the rice field soil emission reduction potential.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of data processing, and particularly relates to a rice field soil emission reduction potential estimation method and system based on carbon-nitrogen coupling and a medium. BACKGROUND

[0002] It is of great significance to study the effect of biochar on reducing CH4 and N2O emissions in rice fields in response to climate change. Since rice fields are an important source of global greenhouse gas emissions, the use of nitrogen fertilizer and biochar can affect carbon and nitrogen cycles in the soil, thereby affecting the emission of these gases. In China, due to large differences in soil fertility and different uses of nitrogen fertilizer, it is essential to study the interaction between nitrogen fertilizer, biochar and soil nitrogen to improve the emission reduction effect of biochar.

[0003] Similar prior art is Chinese patent application No. CN118628278A, which provides a rice field methane weekly emission flux prediction method based on real-time sensor monitoring data. The method fuses a series of real-time soil sensing devices to collect rice field basic data, real-time climate data, real-time (inter) zone soil physical and chemical property data, and real-time rice growth, and combines a machine learning algorithm to develop a detection system that can realize real-time methane emission detection in rice fields at a small scale.

[0004] Similar prior art is Chinese patent application No. CN118798433A, which discloses a multi-source nitrogen oxide emission prediction method and system. The method includes obtaining multi-source data of the area to be predicted, including remote sensing column concentration data, concentration reanalysis data, and meteorological reanalysis data; pre-processing the multi-source data through unification and matching in spatial resolution; inputting the pre-processed multi-source data into the trained multi-source prediction model to predict the nitrogen oxide emissions of different emission sources; wherein the different emission sources include total nitrogen oxide emissions, human-caused nitrogen oxide emissions, nitrogen oxide emissions from biomass combustion, nitrogen oxide emissions from lightning, and nitrogen oxide emissions from soil.

[0005] However, the above two documents focus on the qualitative influence of nitrogen fertilizer types and application rates on emission reduction effects, but lack understanding of the micro-process and quantitative influence of biochar coupling with different nitrogen. Therefore, the present application provides a rice field soil emission reduction potential estimation method and system based on carbon-nitrogen coupling and a medium. SUMMARY

[0006] The present application quantifies the influence of carbon-nitrogen coupling on the emission reduction effect of biochar in rice fields through literature classification, soil sampling analysis, soil column experiment simulation, and orthogonal simulation experiment, in order to evaluate and optimize the emission reduction potential of biochar in rice fields based on carbon-nitrogen coupling.

[0007] In order to achieve the above-mentioned application purposes, the application provides a carbon-nitrogen coupling-based paddy field soil emission reduction potential estimation method as follows:

[0008] Step S1, collecting nitrogen application information of paddy fields in a target region by using a literature statistical method, dividing paddy field planting regions with similar nitrogen application information into the same type, and selecting several paddy field planting regions from each type of paddy field planting region as typical paddy field planting regions;

[0009] Step S2, sampling soil at a preset fixed interval in each of the typical paddy field planting regions, keeping a consistent sampling depth at all sampling points, performing experimental analysis on the sampled soil samples, obtaining soil nitrogen concentration levels of the typical paddy field planting regions, and analyzing and obtaining representative soil samples based on the soil nitrogen concentration levels;

[0010] Step S3, using the representative soil samples to perform soil injection experiment simulation to obtain soil injection experiment simulation results, estimating average emission of a specific gas of the soil of the typical paddy field planting region based on the soil injection experiment simulation results, and analyzing the soil injection experiment simulation results to obtain analysis results;

[0011] Step S4, determining significant influencing factors based on the analysis results, setting different carbon-nitrogen coupling scenarios based on the significant influencing factors, performing orthogonal simulation experiments, quantifying the factor importance of each of the significant influencing factors based on the orthogonal simulation experiment results using a statistical method, taking the factor importance as a weight, performing weighted accumulation on interpolation estimation results under each of the significant influencing factors to obtain a comprehensive emission estimation value of the specific gas, and obtaining the emission reduction capacity of the biological carbon paddy field based on carbon-nitrogen coupling based on the comprehensive emission estimation value.

[0012] As a preferred technical solution of the application, the paddy field planting regions with similar nitrogen application information are divided into the same type, including the following steps:

[0013] Step S11, performing data preprocessing on the collected nitrogen application information, removing outliers, filling in missing values, and normalizing each data parameter in the nitrogen application information, and presetting a first number;

[0014] Step S12, classifying the nitrogen application information using a clustering algorithm, dividing the nitrogen application information into the first number of types, calculating the characteristic nitrogen application information of each type for each type of the nitrogen application information, calculating a first deviation difference value of each of the nitrogen application information belonging to the same type and the corresponding characteristic nitrogen application information, and adding all the first deviation difference values corresponding to the types as a first evaluation value corresponding to the current classification;

[0015] Step S13, adding one to the first number, repeating the step S12, reclassifying the nitrogen application information, calculating a second evaluation value corresponding to the first evaluation value, judging whether the classification result meets the preset condition based on the first evaluation value and the second evaluation value, if it meets, ending the step, otherwise, repeating the step until the classification result meets the preset condition.

[0016] As a preferred technical solution of the present application, judging whether the classification result meets the preset condition based on the first evaluation value and the second evaluation value comprises the following steps:

[0017] Judging whether the second evaluation value is less than or equal to the first evaluation value, if it is less, calculating a first difference value of the first evaluation value and the second evaluation value, and judging whether the first difference value is greater than or equal to a preset second threshold value, if it is greater, judging that it does not meet the preset condition, otherwise, judging that it meets the preset condition.

[0018] As a preferred technical solution of the present application, estimating the average emission amount of a specific gas of the soil in the typical rice planting area based on the soil injection experiment simulation result comprises the following steps:

[0019] Using multiple soil injections, numbering each soil injection, filling the collected soil samples into the soil injections, controlling the environmental factors of the soil injections to simulate the field conditions of each typical rice planting area, also obtaining multiple influencing factors, presetting multiple first processing conditions for each of the influencing factors, obtaining a first number of the first processing conditions, obtaining a second number of all soil injections, dividing the second number by the first number to obtain a first result value, dividing the multiple soil injections into first result value groups, setting the same first processing condition for each group of soil injections, periodically collecting gas samples from the soil injections, measuring the concentration of the specific gas using an analysis device, recording and saving the corresponding soil injection number, sampling time, sampling date and concentration data in a first list, adding up the specific gas concentration of all measurement times of each group of soil injections corresponding to each of the first processing conditions to obtain the total emission amount of the specific gas during the experiment, and calculating the average emission amount corresponding to each of the first processing conditions based on the total emission amount.

[0020] As a preferred technical solution of the present application, analyzing the soil injection experiment simulation result to obtain an analysis result comprises the following steps:

[0021] For each of the first processing conditions, the average emission of each group of soil injections under the first processing condition is calculated, and the total average emission of all groups is also calculated, the inter-group variance under the first processing condition is calculated based on the group average emission and the total average emission, the intra-group variance of each group of soil injections is also calculated, the inter-group mean square is calculated based on the inter-group variance, the intra-group mean square is calculated based on the intra-group variance, and the value obtained by dividing the inter-group mean square by the intra-group mean square is taken as a first value, and a second value is determined using statistical software based on the first value, the number of soil injections in each group, and the number of soil injection groups.

[0022] As a preferred technical solution of the present application, the significant influencing factors are determined based on the analysis results, including the following steps:

[0023] The second value corresponding to the first processing condition is obtained, and if the second value is less than a preset first threshold value, the influencing factor corresponding to the first processing condition is taken as the significant influencing factor.

[0024] As a preferred technical solution of the present application, different carbon-nitrogen coupling scenarios are set based on the significant influencing factors, including the following steps:

[0025] Different levels are set for each of the significant influencing factors, each level of each of the significant influencing factors is combined with each level of at least one of the other significant influencing factors at least once, a plurality of influencing factor combinations are generated, and different carbon-nitrogen coupling scenarios are set based on the plurality of influencing factor combinations.

[0026] As a preferred technical solution of the present application, the factor importance of each of the significant influencing factors is quantified using a statistical method based on the results of the orthogonal simulation experiment, including the following steps:

[0027] Each of the specific gas emission amounts is taken as an observed variable, and the significant influencing factors are taken as latent variables, a structural equation model is constructed based on the relationship between the observed variables and the latent variables, relevant data are collected based on the experimental results of the orthogonal simulation experiment, the relevant data include different levels of significant influencing factors and corresponding specific gas emission amounts, model parameters are estimated by fitting the structural equation model using statistical software, the goodness of fit of the structural equation model is evaluated using a goodness of fit test method, the structural equation model is modified when the goodness of fit is less than a preset fitting threshold value, until the goodness of fit is greater than or equal to the fitting threshold value, a first influence parameter and a second influence parameter of each of the significant influencing factors on the specific gas emission amount are calculated according to the model parameters, a comprehensive influence parameter is calculated based on the first influence parameter and the second influence parameter, and the comprehensive influence parameter is the factor importance of each of the significant influencing factors

[0028] The application further provides a carbon-nitrogen coupling-based rice field soil emission reduction potential estimation system, which comprises the following modules.

[0029] The classification module collects nitrogen application information of the rice fields in the target region by using a literature statistical method, divides rice planting regions with similar nitrogen application information into the same category, and selects several rice planting regions from each type of rice planting region as typical rice planting regions.

[0030] The sampling module is used for soil sampling at a preset fixed interval in each typical rice planting region, maintaining a consistent sampling depth at all sampling points, performing experimental analysis on the sampled soil samples, obtaining the soil nitrogen concentration level of each typical rice planting region, and obtaining representative soil samples based on the soil nitrogen concentration level.

[0031] The first analysis module uses the representative soil samples to perform soil injection experiment simulation to obtain soil injection experiment simulation results, estimates the average emission amount of a specific gas of the soil of the typical rice planting region based on the soil injection experiment simulation results, and analyzes the soil injection experiment simulation results to obtain analysis results.

[0032] The second analysis module determines significant influencing factors based on the analysis results, sets different carbon-nitrogen coupling scenarios based on the significant influencing factors, performs orthogonal simulation experiments, quantifies the factor importance of each significant influencing factor based on the orthogonal simulation experiment results using a statistical method, takes the factor importance as a weight, performs weighted accumulation on the interpolation estimation results under each significant influencing factor to obtain a comprehensive emission estimation value of the specific gas, and obtains the emission reduction capacity of the biological carbon rice field based on the carbon-nitrogen coupling based on the comprehensive emission estimation value.

[0033] The application further provides a medium storing program instructions, wherein the program instructions control a device where the medium is located to perform the carbon-nitrogen coupling-based rice field soil emission reduction potential estimation method.

[0034] Compared with the prior art, the application has at least the following advantages:

[0035] In the present application, first, the nitrogen application information in the target area is collected by literature statistics, the rice fields with similar nitrogen application are divided into a type, and several typical areas are selected from each type, which facilitates the selection of representative soil samples based on the typical rice field planting area, and further facilitates the study of the emission reduction potential of different rice field soil types; the typical rice field area is sampled at a fixed interval, the soil nitrogen concentration level is analyzed, and the representative samples are obtained, which ensures the representativeness of the soil sample collection; the representative samples are used for soil column experiment simulation, the average emission of a specific gas is estimated based on the experimental results, and the average emission helps researchers evaluate the influence of specific gas emission under different control conditions; the significant influencing factors are determined, the orthogonal simulation experiment is set under different carbon-nitrogen coupling conditions, the importance of the factors is quantified, and the comprehensive emission estimation value is obtained by weighted accumulation, so as to evaluate the emission reduction capacity of the carbon-nitrogen coupling based biochar rice field, and help us understand the potential change of the specific gas emission of the rice field soil under different factor combinations, and provide a scientific basis for agricultural management. BRIEF DESCRIPTION OF DRAWINGS

[0036] Figure 1 The step flow chart of the carbon-nitrogen coupling based rice field soil emission reduction potential estimation method of the present application is shown in the figure.

[0037] Figure 2 The CH4 emission data chart of the present application is shown in the figure.

[0038] Figure 3 The orthogonal table of the plurality of significant influencing factors of the present application is shown in the figure.

[0039] Figure 4 The composition structure diagram of the carbon-nitrogen coupling based rice field soil emission reduction potential estimation system of the present application is shown in the figure. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical scheme and advantages of the present application clearer and more apparent, the present application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0041] It can be understood that the terms "first", "second" and the like used in the present application can be used herein to describe various elements, but unless specifically stated, these elements are not limited by these terms. These terms are only used to distinguish the first element from another element. For example, without departing from the scope of the present application, the first xx script can be referred to as the second xx script, and similarly, the second xx script can be referred to as the first xx script.

[0042] The present application provides a carbon-nitrogen coupling based rice field soil emission reduction potential estimation method as shown in the figure, which is realized by performing the following step process: Figure 1 The present application provides a carbon-nitrogen coupling based rice field soil emission reduction potential estimation method as shown in the figure, which is realized by performing the following step process:

[0043] Step S1, collect the nitrogen application information of the rice fields in the target area by using the method of literature statistics, divide the rice field planting areas with similar nitrogen application information into the same type, and select several rice field planting areas from each type of rice field planting area as typical rice field planting areas.

[0044] Specifically, in order to estimate the soil emission reduction potential of rice fields, first, the nitrogen application information of the rice fields is collected by using the method of literature statistics, including the type of nitrogen fertilizer, the concentration of nitrogen fertilizer, the amount of fertilizer, the method of fertilization and the frequency of fertilization, etc. The nitrogen application information of the rice fields is similar, and the nitrogen content of the soil is probably similar. By understanding how the rice fields use nitrogen fertilizer through the nitrogen application information, such as what type of fertilizer is used and how much is used, the rice fields are divided into different types according to the similarity of the nitrogen application information. For example, rice fields with similar nitrogen fertilizer types and concentrations can be classified into the same type. Several rice fields are selected from each type of rice field as typical rice field planting areas, which are convenient for subsequent selection of representative soil samples based on typical rice field planting areas, and further facilitate the study of the emission reduction potential of different types of rice field soil.

[0045] Step S2, soil sampling is performed in each typical rice field planting area according to a predetermined fixed interval, a consistent sampling depth is maintained at all sampling points, and experimental analysis is performed on the sampled soil samples to obtain the soil nitrogen concentration level of each typical rice field planting area, and a representative soil sample is obtained based on the soil nitrogen concentration level.

[0046] Specifically, in order to ensure the representativeness of the soil sample collection, soil sampling is performed in the typical rice field planting area according to a predetermined fixed interval. In order to ensure that representative soil samples can be selected subsequently, the fixed interval is generally set to be small. Since the nitrogen concentration of the soil may vary with depth, a consistent sampling depth is maintained at all sampling points. Then, the collected soil samples are analyzed to obtain the soil nitrogen concentration level of each typical rice field planting area. The soil nitrogen concentration level refers to the content of nitrogen in the soil, such as the nitrogen content per kilogram of soil (mg / kg). The soil nitrogen concentration level helps to understand the influence of soil nitrogen on crop growth and the environment, and how to optimize the use of nitrogen through management measures. By the difference in soil nitrogen concentration level, a representative soil sample is obtained. Subsequently, through experimental analysis, the soil characteristics and specific gas emission of the entire area can be better understood. The specific gas refers to CH4 and N2O.

[0047] Step S3, using the representative soil sample to perform soil injection experiment simulation to obtain soil injection experiment simulation results, estimating the average emission of specific gas of the soil of the typical rice field planting area based on the soil injection experiment simulation results, and analyzing the soil injection experiment simulation results to obtain analysis results.

[0048] Specifically, the collected soil samples are subjected to soil column experiment simulation, by controlling different environmental factors such as temperature, humidity, PH value and other environmental factors in the soil column, the field conditions of typical rice planting areas are simulated, gas samples are collected from the top or side of the soil column at regular intervals, the concentration of specific gas is measured using a gas chromatograph or other suitable analysis equipment, and the time, date and concentration data of each gas sampling are recorded, the total emission of specific gas during the experiment is calculated, the average emission of specific gas of the soil in the typical rice planting area is calculated based on the total emission, and the average emission can help researchers evaluate the influence of specific gas emission under different control conditions and the long-term influence of these control conditions on soil carbon and nitrogen cycle, and the specific gas refers to CH4 and N2O.

[0049] Step S4, determining the significant influencing factors based on the analysis results, setting different carbon and nitrogen coupling situations based on the significant influencing factors, performing orthogonal simulation experiment, quantifying the factor importance of each significant influencing factor based on the results of the orthogonal simulation experiment using statistical methods, taking the factor importance as the weight, weighting and accumulating the interpolation estimation results under each significant influencing factor to obtain the comprehensive emission estimation value of the specific gas, and then obtaining the emission reduction capacity of the biological carbon rice field based on the carbon and nitrogen coupling.

[0050] Specifically, in order to more accurately evaluate and optimize the carbon sequestration and emission reduction capacity of farmland soil, thereby achieving the effect of improving crop yield and soil fertility, combined with nitrogen application information, different carbon and nitrogen coupling situations are set, and by setting different carbon and nitrogen coupling situations such as biological carbon temperature, nitrogen fertilizer type, nitrogen fertilizer concentration, orthogonal simulation experiment is performed, which can systematically evaluate the influence of these influencing factors on the specific gas emission reduction potential of rice field soil, help to identify the most critical factors and optimize the emission reduction strategy, and use statistical methods such as structural equation model or regression analysis method to quantify the influence of influencing factors on emission reduction effect, which can determine which significant influencing factors contribute most to emission reduction, and help to provide specific guidance for farmland management, such as selecting the best biological carbon use temperature and nitrogen fertilizer use strategy, then using interpolation method to estimate the specific gas emission under different levels for each level of each influencing factor, multiplying the interpolation estimation results of each influencing factor by the corresponding influencing factor importance, and then accumulating these weighted results to obtain the comprehensive emission estimation value, which considers the contribution difference of different influencing factors to the emission reduction effect, making the final emission estimation value more accurate and comprehensive. The above method helps us understand the potential changes of specific gas emission of rice field soil under different factor combinations, and provides scientific basis for agricultural management.

[0051] Further, rice planting areas with similar nitrogen application information are divided into the same category, including the following steps:

[0052] Step S11, data preprocessing is performed on the collected nitrogen application information, outliers are removed, missing values are filled, and each data parameter in the nitrogen application information is normalized, and a first number is preset;

[0053] Step S12, using a clustering algorithm to classify the nitrogen application information into a first number of types, for each type of nitrogen application information, the corresponding characteristic nitrogen application information is obtained by calculation, the first deviation value of each nitrogen application information belonging to the same type and the corresponding characteristic nitrogen application information is calculated, and the sum of the first deviation values of all types is added as the first evaluation value corresponding to the current classification;

[0054] Step S13, adding one to the first number, repeating step S12, reclassifying the nitrogen application information, calculating the corresponding second evaluation value, and judging whether the classification result meets the preset condition based on the first evaluation value and the second evaluation value, if it meets, ending the step, otherwise, repeating the step until the classification result meets the preset condition.

[0055] Specifically, in order to scientifically and reasonably classify the rice planting area, a first number is preset, and the existing clustering algorithm such as K-means is used to classify the nitrogen application information for the first time, for example, the first number is 2, and the nitrogen application information is classified into 2 types, in order to judge whether the classified types are reasonable, the corresponding characteristic nitrogen application information of each type of nitrogen application information is calculated, for example, the average value of each data parameter in the nitrogen application information belonging to the same type is calculated, and the combination of the average values is taken as the characteristic nitrogen application information, then the first deviation value of the characteristic nitrogen application information of each type and the nitrogen application information in the same type is calculated, the sum of the first deviation values of all types in the current classification is taken as the first evaluation value of the corresponding type, the first evaluation value can represent the aggregation degree of the current classification, the larger the first evaluation value, the smaller the clustering density of the current classification, then the first number is added by 1, at this time the value of the first number is 3, the nitrogen application information is classified again using the clustering algorithm, at this time the nitrogen application information is classified into 3 types, then step S12 is repeated to calculate the second evaluation value corresponding to the classification into 3 types, based on the first evaluation value and the second evaluation value, whether the classification result meets the preset condition is judged, if it meets, the step is ended, otherwise, the step is repeated until the classification result meets the preset condition, the specific method of judging whether it meets the preset condition will be explained in detail later.

[0056] Among them, the variance of the characteristic nitrogen application information and the nitrogen application information in the same type can be taken as the first deviation value.

[0057] Further, based on the first evaluation value and the second evaluation value, whether the classification result meets the preset condition is judged, including the following steps:

[0058] If the second evaluation value is less than or equal to the first evaluation value, a first difference value between the first evaluation value and the second evaluation value is calculated, and it is determined whether the first difference value is greater than or equal to a preset second threshold value. If the first difference value is greater than the second threshold value, it is determined that the preset condition is not met. Otherwise, it is determined that the preset condition is met.

[0059] Specifically, since the first evaluation value and the second evaluation value represent the clustering tightness of the current classification result, the greater the evaluation value, the smaller the clustering tightness of the current classification result. Therefore, by comparing the evaluation values of the adjacent two classification results, if the second evaluation value is less than or equal to the first evaluation value, it indicates that the clustering tightness of the classification result after re-classification is better than that of the last classification. Therefore, the classification result after re-classification is relatively scientific. However, in order to determine whether there is still optimization space for classification, the first difference value between the two evaluation values and the preset second threshold value are compared. If the first difference value is greater than the second threshold value, it indicates that the clustering tightness of the two classifications differs greatly, indicating that there is still optimization space for classification. Therefore, it is determined that the preset condition is not met, and the first number is increased by one for re-classification. With the increase of the classification number, the clustering tightness of the classification result will be better and better, and the first difference value of the evaluation values of the adjacent two classifications will be smaller and smaller. Therefore, when the first difference value is less than the second threshold value, it is determined that the preset condition is met, and the classification is stopped.

[0060] Further, based on the simulation results of the soil injection experiment, the average emission amount of a specific gas of the soil in a typical rice planting area is estimated, including the following steps:

[0061] A plurality of soil injections are used, each soil injection is numbered, the collected soil samples are filled into the soil injections, the environmental factors of the soil injections are controlled to simulate the field conditions of each typical rice planting area, a plurality of influencing factors are obtained, a plurality of first processing conditions are preset for each influencing factor, a first number of the first processing conditions is obtained, a second number of all soil injections is obtained, the first number is divided by the second number to obtain a first result value, the plurality of soil injections are divided into first result value groups, the same first processing condition is set for each group of soil injections, gas samples are periodically collected from the soil injections, the concentration of the specific gas is measured using an analysis device, the corresponding soil injection number, sampling time, sampling date and concentration data are recorded and saved in a first list, for each first processing condition, the specific gas concentrations of all measurement times of the corresponding each group of soil injections are added to obtain the total emission amount of the specific gas during the experiment, and the average emission amount corresponding to each first processing condition is calculated based on the total emission amount.

[0062] Specifically, the influencing factors include nitrogen fertilizer type, nitrogen fertilizer addition amount, and biochar addition amount, etc. The first treatment condition refers to using different nitrogen fertilizers, adding different amounts of nitrogen fertilizers, and adding different types and amounts of biochar in the soil injection. The soil injection experiment usually focuses on a single environmental factor, such as the effect of a single nitrogen fertilizer amount on the results. In order to clearly explain the calculation method of the average emission amount, the following examples are only simple and small examples. In fact, a very large number of first treatment conditions can be set. Assuming that the first treatment condition is different nitrogen fertilizer application levels (0 kg N / ha, 60 kg N / ha, 120 kg N / ha), taking CH4 as an example, in order to study the effect of different nitrogen fertilizer application levels on CH4 emission from paddy soil, 3 soil injections are configured for each nitrogen fertilizer application level, and the experimental period is 30 days. CH4 emission is measured every 5 days, and the CH4 emission of each soil injection is recorded at each measurement time point (5th day, 10th day, 15th day, 20th day, 25th day, 30th day), as shown in Figure 2 The CH4 emission data chart we obtained is shown in Figure 2 The total emission amount of each group of soil injections is obtained by adding the CH4 emission amounts of all measurement time points of each group of soil injections during the experiment. The average value of the total emission amount of all soil injections is calculated for each nitrogen fertilizer application level. The average emission amount under the treatment of 0 kg N / ha is (0.1+0.2+0.15+0.2+0.18+0.22)+(0.12+0.22+0.13+0.21+0.19+0.23)+(0.11+0.21+0.14+0.2+0.17+0.2) / (3*6) which is approximately equal to 0.21 g CH4 / m2. The average emission amount under the treatment of 60 kg N / ha is 0.34 g CH4 / m2, and the average emission amount under the treatment of 120 kg N / ha is 0.39 g CH4 / m2. The average emission amount of CH4 and the average emission amount of N2O of the paddy soil under each first treatment condition can be calculated by the above method, and the effect of different first treatment conditions on CH4 and N2O emission can be evaluated.

[0063] Further, the simulation results of the soil injection experiment are analyzed to obtain analysis results, including the following steps:

[0064] For each first treatment condition, the group emission mean value corresponding to each group of soil injections under the first treatment condition is calculated, and the total emission mean value of all groups is also calculated. The inter-group variance under the first treatment condition is calculated based on the group emission mean value and the total emission mean value. The intra-group variance of each group of soil injections is also calculated. The inter-group mean square is calculated based on the inter-group variance. The intra-group mean square is calculated based on the intra-group variance. The value obtained by dividing the inter-group mean square by the intra-group mean square is taken as the first value. Based on the first value, the number of soil injections included in each group, and the number of soil injection groups, a second value is determined using statistical software.

[0065] Specifically, the inter-group variance is divided by the number of soil injection groups minus one to obtain the inter-group mean square, and the intra-group variance is divided by the number of intra-group soil injections minus the number of soil injection groups to obtain the intra-group mean square. The first value can be used to indicate the difference between the inter-group difference and the intra-group difference. If the first value is large, it means that the difference is large, which means that the mean of at least one group is significantly different from other groups. The second value is used to compare with the third threshold value, which is a preset significance level. If the second value is less than the third threshold value, it is considered that there is a significant difference between at least two groups under the first treatment condition, which means that the first treatment condition has a significant effect on the emission of a specific gas. The influence factor corresponding to the first treatment condition is taken as the significant influence factor, and the statistical software can use AMOS software.

[0066] Further, based on the analysis result, the significant influence factor is determined, including the following steps:

[0067] The second value corresponding to the first treatment condition is obtained. If the second value is less than the third threshold value, the influence factor corresponding to the first treatment condition is taken as the significant influence factor.

[0068] Further, based on the significant influence factor, different carbon-nitrogen coupling scenarios are set, including the following steps:

[0069] Different levels are set for each significant influence factor. Each level of each significant influence factor is combined with each level of at least one other significant influence factor to generate a plurality of influence factor combinations. Different carbon-nitrogen coupling scenarios are set based on the plurality of influence factor combinations.

[0070] Specifically, for example, there are three significant influence factors, namely nitrogen fertilizer type A: no nitrogen fertilizer (1), urea (2), and ammonia water (3), nitrogen fertilizer concentration (B): low (1), medium (2), and high (3), and biochar addition amount (C): 0% (1), 1% (2), and 2% (3). The generated plurality of influence factor combinations are as shown in Table 1. Figure 3 Figure 3 It is also an orthogonal table of a plurality of significant influence factors. Based on the orthogonal table, different carbon-nitrogen coupling scenarios are set for orthogonal simulation experiments. Through the design of the orthogonal table, the number of required experiments is reduced, making the experiment more efficient, while still providing sufficient information to evaluate a plurality of influence factors.

[0071] Further, based on the results of the orthogonal simulation experiment, a statistical method is used to quantify the factor importance of each of the influence factors, including the following steps:

[0072] ​Using specific gas emissions as observed variables and significant influencing factors as latent variables, a structural equation model was constructed based on the assumed relationship between the observed and latent variables. Relevant data were collected based on the results of orthogonal simulation experiments, including different levels of significant influencing factors and their corresponding specific gas emissions. Statistical software was used to fit the structural equation model and estimate its parameters. The goodness-of-fit test was used to evaluate the goodness-of-fit of the structural equation model. If the goodness-of-fit was less than a preset threshold, the structural equation model was revised until the goodness-of-fit was greater than or equal to the threshold. Based on the model parameters, the first and second influence parameters of each significant influencing factor on the specific gas emissions were calculated. Based on the first and second influence parameters, a comprehensive influence parameter was calculated, representing the factor importance of each significant influencing factor.

[0073] Specifically, to quantify the influence of significant factors on specific gas emissions, structural equation modeling (SEM) is used. SEM is a multivariate statistical analysis technique used to analyze complex relationships between variables. First, significant influencing factors (such as nitrogen fertilizer application) are treated as latent variables, and other specific emissions are treated as observed variables. The relationship between the observed and latent variables is hypothesized. Then, experimental results from orthogonal simulations are collected as relevant data. Based on this data, statistical software such as AMOS is used to estimate model parameters, and goodness-of-fit testing methods such as chi-square tests are used to evaluate the goodness of fit of the SEM. If the goodness of fit is less than a preset threshold, the SEM is modified, i.e., the model parameters are adjusted, until… If the goodness of fit is greater than or equal to the goodness of fit threshold, then the first and second influence parameters are calculated based on the model parameters. The first influence parameter refers to the direct impact of the significant influencing factor on the emission of a specific gas, and the second influence parameter refers to the indirect impact of the significant influencing factor on the emission of a specific gas through at least one intermediate variable. The comprehensive influence parameter refers to the overall impact of the significant influencing factor on the emission of a specific gas. Assuming the first influence parameter is 0.5 and the second influence parameter is 0.15, then the comprehensive influence parameter is 0.5 plus 0.15 = 0.65. Through the above method, the impact of different significant influencing factors on the emission of a specific gas can be fully understood, and the relationship between the two can be scientifically quantified, providing a scientific basis for subsequent decision-making.

[0074] According to another aspect of the embodiments of the present invention, reference is made to... Figure 4 As shown, a system for estimating the emission reduction potential of paddy field soil based on carbon-nitrogen coupling is also provided, including an integration module, a transmission module, a storage module, and a backup module, to implement the emission reduction potential estimation method of paddy field soil based on carbon-nitrogen coupling as described above. The specific functions of each module are as follows:

[0075] The classification module collects nitrogen application information of the rice fields in the target region by using a literature statistical method, divides rice field planting areas with similar nitrogen application information into the same type, and selects several rice field planting areas from each type of rice field planting area as typical rice field planting areas.

[0076] The sampling module is configured to sample soil in each typical rice field planting area at a preset fixed interval, keep a consistent sampling depth at all sampling points, perform experimental analysis on the sampled soil samples, obtain soil nitrogen concentration levels of the typical rice field planting areas, and analyze the soil nitrogen concentration levels to obtain representative soil samples.

[0077] The first analysis module uses the representative soil samples to perform soil column experiment simulation to obtain soil column experiment simulation results, estimates the average emission amount of a specific gas of the soil of the typical rice field planting area based on the soil column experiment simulation results, analyzes the soil column experiment simulation results to obtain analysis results, and estimates the average emission amount of the specific gas of the soil of the typical rice field planting area based on the analysis results.

[0078] The second analysis module determines significant influencing factors based on the analysis results, sets different carbon-nitrogen coupling scenarios based on the significant influencing factors, performs orthogonal simulation experiments, quantifies the factor importance of each significant influencing factor based on the orthogonal simulation experiment results using a statistical method, takes the factor importance as a weight, and performs weighted accumulation on the interpolation estimation results under each significant influencing factor to obtain a comprehensive emission estimation value of the specific gas, and obtains the carbon-nitrogen coupling-based biological carbon rice field emission reduction capacity based on the comprehensive emission estimation value.

[0079] According to another aspect of the embodiment of the present application, a medium is also provided, which stores program instructions, wherein the program instructions control the device where the medium is located to perform the carbon-nitrogen coupling-based rice field soil emission reduction potential estimation method in any one of the above.

[0080] In summary, the carbon-nitrogen coupling-based rice field soil emission reduction potential estimation method, system and medium of the present application include collecting nitrogen application information of rice fields in a target region by using a literature statistical method, dividing rice fields with similar nitrogen application into the same type, and selecting several typical areas from each type. Soil sampling is performed at a fixed interval in the typical rice field area, the soil nitrogen concentration level is analyzed, and representative samples are obtained. The representative samples are used for soil column experiment simulation to estimate the average emission amount of a specific gas. Significant influencing factors are determined, different carbon-nitrogen coupling scenarios are set for orthogonal simulation experiments, the factor importance is quantified, the comprehensive emission estimation value is obtained by weighted accumulation, and the carbon-nitrogen coupling-based biological carbon rice field emission reduction capacity is evaluated. The present application can effectively estimate the emission reduction potential of rice field soil.

[0081] It should be understood that, although the steps in the flowcharts of the embodiments of the present application are shown in a certain order according to the arrows, the steps are not necessarily executed in the order of the arrows. Unless otherwise specified herein, the execution of the steps is not strictly limited in order, and the steps can be executed in other orders. Moreover, at least some of the steps in the embodiments can include a plurality of sub-steps or a plurality of stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of the sub-steps or stages is not necessarily sequential, but can be round-robin or alternately executed with at least some of the other steps or sub-steps or stages of the other steps.

[0082] It can be understood by those skilled in the art that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware. The above-mentioned program can be stored in a non-volatile computer readable storage medium, and when the program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, storage, database or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0083] The technical features of the above-mentioned embodiments can be combined in any way. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described, but as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present application.

[0084] The above embodiments are only some embodiments of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present application. It should be pointed out that for ordinary skilled persons in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of protection of the present application. Therefore, the scope of protection of the patent of the present application should be subject to the appended claims.

[0085] The above is only a preferred embodiment of the present application, and does not limit the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for estimating the emission reduction potential of paddy field soil based on carbon-nitrogen coupling, characterized by, The method comprises the following steps: Step S1, collecting nitrogen application information of paddy fields in a target area by using a literature statistical method, and dividing paddy field planting areas with similar nitrogen application information into the same type, and selecting a plurality of paddy field planting areas from each type of paddy field planting area as typical paddy field planting areas; Step S2, soil sampling is performed at a predetermined fixed interval in each typical paddy field planting area, a consistent sampling depth is maintained at all sampling points, and experimental analysis is performed on the sampled soil samples to obtain the soil nitrogen concentration level of each typical paddy field planting area, and a representative soil sample is obtained based on the analysis of the soil nitrogen concentration level; Step S3, using the representative soil sample to perform a soil column experiment simulation to obtain a soil column experiment simulation result, estimating the average emission amount of a specific gas of the soil of the typical paddy field planting area based on the soil column experiment simulation result, and analyzing the soil column experiment simulation result to obtain an analysis result; Step S4, determining a significant influencing factor based on the analysis result, setting different carbon-nitrogen coupling scenarios based on the significant influencing factor, performing an orthogonal simulation experiment, quantifying the factor importance of each significant influencing factor based on the orthogonal simulation experiment result using a statistical method, taking the factor importance as a weight, and performing weighted accumulation on the interpolation estimation result under each significant influencing factor to obtain a comprehensive emission estimation value of the specific gas, and obtaining the carbon-nitrogen coupling-based biological carbon paddy field emission reduction capacity based on the comprehensive emission estimation value; In step S1, the paddy field planting areas with similar nitrogen application information are divided into the same type, which comprises the following steps: Step S11, data preprocessing is performed on the collected nitrogen application information, a first quantity A is preset, abnormal values are removed, missing values are filled, and each data parameter in the nitrogen application information is normalized; Step S12, the nitrogen application information is classified using a clustering algorithm, and is divided into A first quantity types, and the characteristic nitrogen application information of each type is obtained by calculation, and the first deviation difference value of each nitrogen application information belonging to the same type and the corresponding characteristic nitrogen application information is calculated, and the first deviation difference values of all types are added as the first evaluation value corresponding to the current classification; Step S13, the first quantity A is increased by one, and step S12 is repeated to classify the nitrogen application information again, and the corresponding second evaluation value is calculated, and whether the classification result meets the preset condition is judged based on the first evaluation value and the second evaluation value, if it meets, the step is ended, otherwise, the step is repeated until the classification result meets the preset condition; wherein, whether the classification result meets the preset condition is judged based on the first evaluation value and the second evaluation value, which comprises: judging whether the second evaluation value is less than or equal to the first evaluation value, if it is less, calculating the first difference value of the first evaluation value and the second evaluation value, and judging whether the first difference value is greater than or equal to a second threshold value, if it is greater, it is judged as not meeting the preset condition, otherwise it is judged as meeting the preset condition; In step S3, the average emission of a specific gas of the soil in a typical rice planting area is estimated based on the experimental simulation results, including: using multiple soil columns, numbering each soil column, filling the collected soil samples into the soil columns, controlling the environmental factors of the soil columns to simulate the field conditions of each typical rice planting area, obtaining multiple influencing factors, presetting multiple first processing conditions for each influencing factor, obtaining a first number B of first processing conditions, obtaining a second number of all soil columns, dividing the second number by the first number B to obtain a first result value, dividing the multiple soil columns into first result value groups, setting the same first processing condition for each group of soil columns, periodically collecting gas samples from the soil columns, using an analysis device to measure the concentration of the specific gas, recording and saving the corresponding soil column number, sampling time, sampling date and concentration data in a first list, adding the specific gas concentration of each group of soil columns at all measurement times corresponding to each first processing condition, obtaining the total emission of the specific gas during the test period, and calculating the average emission corresponding to each first processing condition based on the total emission; wherein the first processing condition refers to using different nitrogen fertilizers, adding different amounts of nitrogen fertilizers, adding different types and amounts of biochar in the soil columns. In step S4, the significant influencing factors are determined based on the analysis result, including: obtaining a second value corresponding to the first processing condition, if the second value is less than a preset first threshold value, the influencing factor corresponding to the first processing condition is taken as a significant influencing factor; analyzing the soil column experiment result to obtain an analysis result, including: for each first processing condition, calculating the group emission mean value corresponding to each group of soil columns under the first processing condition, also calculating the total emission mean value of all groups, calculating the inter-group variance under the first processing condition based on the group emission mean value and the total emission mean value, also calculating the intra-group variance of each group of soil columns, calculating the inter-group mean square based on the inter-group variance, calculating the intra-group mean square based on the intra-group variance, taking the value obtained by dividing the inter-group mean square by the intra-group mean square as a first value, and using statistical software to determine a second value based on the first value, the number of soil columns contained in each group, and the number of soil column groups; setting different carbon-nitrogen coupling scenarios based on the significant influencing factors, including: setting different levels for each significant influencing factor, combining each level of each significant influencing factor with each level of other significant influencing factors at least once to generate a plurality of influencing factor combinations, and setting different carbon-nitrogen coupling scenarios based on the plurality of influencing factor combinations; quantifying the factor importance of each significant influencing factor using statistical methods based on the orthogonal simulation experiment result, including: taking the specific gas emission amount as an observation variable, and taking the significant influencing factor as a latent variable, constructing a structural equation model based on the relationship between the observation variable and the latent variable, collecting relevant data based on the experimental results of the orthogonal simulation experiment, the relevant data including different levels of the significant influencing factors and corresponding specific gas emission amounts, using statistical software to fit the structural equation model to estimate the model parameters, using a fitting degree test method to evaluate the fitting degree of the structural equation model, and when the fitting degree is less than a preset fitting threshold value, modifying the structural equation model until the fitting degree is greater than or equal to the fitting threshold value, calculating a first influence parameter and a second influence parameter of each significant influencing factor on the specific gas emission amount according to the model parameters, calculating a comprehensive influence parameter based on the first influence parameter and the second influence parameter, and the comprehensive influence parameter is the factor importance of each significant influencing factor.

2. A carbon-nitrogen coupling-based paddy field soil emission reduction potential estimation system for implementing the carbon-nitrogen coupling-based paddy field soil emission reduction potential estimation method according to claim 1, characterized by The classification module collects nitrogen application information of rice fields in the target region by using a literature statistical method, divides rice planting areas with similar nitrogen application information into the same type, and selects a plurality of rice planting areas as typical rice planting areas from each type of rice planting area. The sampling module is configured to sample soil at a preset fixed interval in each typical rice planting area, maintain a consistent sampling depth at all sampling points, and perform experimental analysis on the sampled soil samples to obtain soil nitrogen concentration levels of the typical rice planting areas, and analyze the soil nitrogen concentration levels to obtain representative soil samples. The first analysis module uses the representative soil samples to perform soil column experiment simulation to obtain soil column experiment simulation results, estimates the average emission amount of a specific gas of the soil of the typical rice planting area based on the soil column experiment simulation results, and analyzes the soil column experiment simulation results to obtain an analysis result. ​ The second analysis module determines significant influence factors based on the analysis result, sets different carbon-nitrogen coupling scenarios based on the significant influence factors, performs an orthogonal simulation experiment, quantifies the factor importance of each significant influence factor using a statistical method based on the orthogonal simulation experiment result, takes the factor importance as a weight, performs weighted accumulation on the interpolation estimation result under each significant influence factor to obtain a comprehensive emission estimation value of the specific gas, and obtains the emission reduction capacity of the biological carbon paddy field based on carbon-nitrogen coupling based on the comprehensive emission estimation value.

3. A storage medium, characterized by The medium stores program instructions, wherein the program instructions control the device where the storage medium is located to perform the emission reduction potential estimation method of the paddy field soil based on carbon-nitrogen coupling according to claim 1 when the program instructions are executed.

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