A method for synergistic regulation of nitrous oxide emission reduction in farmland and soil nitrogen leaching

Abstract

This invention discloses a system and method for synergistic regulation of nitrous oxide emission reduction and soil nitrogen leaching in farmland, relating to the field of farmland nitrogen management technology. The method integrates meta-analysis and enhanced regression tree models. Through steps such as collecting relevant research data on biochar application, screening effective sample data, constructing a meta-analysis database, identifying key influencing factors, establishing mathematical quantitative relationships, constructing an enhanced regression tree model, and developing simulation optimization schemes, it achieves accurate prediction and synergistic regulation of N2O emissions and soil nitrogen leaching under different farmland conditions. This invention can quantify the relative importance of each influencing factor, dynamically generate quantitative and operable field management plans adapted to different production scenarios, and ensure the stability of emission reduction effects through long-term monitoring and dynamic feedback mechanisms. It promotes efficient nitrogen utilization and sustainable development in farmland, possesses broad application value, is easy to implement and promote in large-scale farmland, and provides a reliable path for effective greenhouse gas emission reduction.

Description

Technical Field

[0001] This invention relates to the field of farmland nitrogen management technology, specifically to a method for synergistic regulation of farmland nitrous oxide emission reduction and soil nitrogen leaching. Background Technology

[0002] Against the backdrop of global climate change, reducing greenhouse gas emissions from agriculture has become an international consensus. In order to ensure food security, my country has maintained a high level of nitrogen fertilizer application for a long time, leading to increased N2O emissions from farmland and prominent problems of nitrogen leaching from soil. This not only wastes resources but also causes environmental pollution. Existing research mostly focuses on the effects or mechanisms of single emission reduction measures, lacking systematic technical solutions that integrate multi-source field trial data and can directly guide the coordinated management of nitrogen loss under different production conditions. Agricultural practitioners find it difficult to obtain actionable decision support from a vast amount of literature, resulting in a disconnect between research findings and field practice. There is an urgent need to establish a standardized technical pathway from data integration to solution generation.

[0003] Meta-analysis can quantitatively integrate multiple independent studies to draw universally applicable conclusions; enhanced regression tree (BRT) models can fuse multiple variables for high-precision prediction and accurately identify the influence weights of each factor on emission reduction effects. The synergistic application of these two methods can achieve a closed loop of "data integration - pattern analysis - scheme optimization," transforming scattered scientific research conclusions into practical management schemes adapted to different scenarios, and providing new technical support for the coordinated regulation of nitrogen loss in farmland. Summary of the Invention

[0004] The purpose of this invention is to provide a system and method for synergistic regulation of nitrous oxide emission reduction in farmland and soil nitrogen leaching, so as to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for synergistic regulation of nitrous oxide emission reduction in farmland and soil nitrogen leaching includes the following steps: Step 1: Collect research data on N2O emissions and nitrogen leaching in farmland with biochar application; Step 2: Screen out valid sample data related to factors influencing farmland N2O emissions and nitrogen leaching; Step 3: Construct a meta-analysis database of farmland N2O emissions and nitrogen leaching under biochar application conditions and influencing factors to identify key factors affecting farmland nitrogen loss; Step 4: Construct a mathematical quantitative relationship between key factors and nitrogen loss in farmland; Step 5: Construct an enhanced regression tree model of N2O emissions and nitrogen leaching under biochar application conditions; Step 6: Combining the N2O emissions and nitrogen leaching influencing factors under different agronomic measures, the optimal farmland greenhouse gas emission reduction management measures are obtained.

[0006] Furthermore, the influencing factors mentioned in step 2 include climate conditions, soil physicochemical properties, fertilization methods, and biochar properties.

[0007] Furthermore, the effective sample data should include N2O emissions and nitrogen leaching amounts from farmland in the experimental and control groups and the corresponding number of replicates, the standard deviations of the experimental and control groups, soil organic matter, total nitrogen, pH, fertilization method, nitrogen application rate, biochar application rate, pH, pyrolysis temperature, carbon-nitrogen ratio, rainfall, and air temperature.

[0008] Furthermore, in step 3, a random effects model is used to determine the cumulative effect value of each influencing factor through meta-analysis. Based on the range of the 95% confidence interval of the cumulative effect value, the impact of each influencing factor on farmland N2O emissions and nitrogen leaching is determined, and the key factors affecting farmland N2O emissions and nitrogen leaching are identified.

[0009] Furthermore, the key factors include standard deviation, soil organic matter, total nitrogen, pH, fertilization method, nitrogen application rate, rainfall, raw materials for biochar, application rate, carbon-nitrogen ratio, and pH.

[0010] The advantage of this invention lies in its systematic analysis of key driving factors for N2O emissions and nitrogen leaching from farmland by integrating meta-analysis and enhanced regression tree (BRT) models. This method can quantify the relative importance of each moderating variable on N2O emissions and nitrogen leaching under soil amendment application conditions, as well as the dynamic response of changes in different influencing factors to N2O emissions and nitrogen leaching, thus providing a scientific basis for formulating long-term emission reduction strategies. The emission reduction scheme built upon this foundation emphasizes long-term monitoring and dynamic optimization to ensure the sustainability and stability of emission reduction effects and promote sustainable farmland development. Furthermore, this series of management measures is highly operable and easy to implement and promote in large-scale farmland, providing a reliable path to achieve effective greenhouse gas emission reduction.

[0011] Due to the adoption of the above technical solution, the technical progress achieved by this invention compared to the prior art is as follows: 1. This invention provides a synergistic regulation system for reducing nitrous oxide emissions in farmland and nitrogen leaching in soil. Through deep integration of meta-analysis and enhanced regression tree model, key driving factors such as biochar application rate, pH value, and nitrogen application rate are accurately identified. The generated synergistic regulation scheme can simultaneously achieve efficient control of N2O emissions and nitrogen leaching in farmland. Field verification shows that the N2O emission reduction rate can reach 37.9%-40.8%, and the nitrogen leaching reduction rate can reach 34.2%-38.5%. This effectively solves the limitation of single measures that can only control a single nitrogen loss pathway, reduces greenhouse gas emissions, reduces soil nitrogen loss and protects the water environment, and achieves dual environmental benefits.

[0012] 2. This invention provides a system and method for synergistic regulation of nitrous oxide emission reduction in farmland and soil nitrogen leaching. Based on the integration of multi-source literature data and machine learning prediction, it can adapt to the farmland production needs of different climate zones, soil types and planting patterns. By quantifying the dynamic response laws of key factors, it can generate customized solutions to avoid the problem of unstable emission reduction effect caused by "one-size-fits-all" management. It has a wide range of applications.

[0013] 3. This invention provides a system method for synergistic regulation of nitrous oxide emission reduction in farmland and soil nitrogen leaching. Meta-analysis is used to denoise, integrate, and statistically validate multi-source independent research data, providing a high-quality input dataset for model construction. The enhanced regression tree model has strong multivariate fitting and prediction capabilities, with a prediction determination coefficient (R²) of over 0.80 and a root mean square error (RMSE) of less than 0.04, significantly improving the scientific rigor and accuracy of the proposed solution.

[0014] 4. This invention provides a system and method for synergistic regulation of nitrous oxide emission reduction in farmland and soil nitrogen leaching. This invention can quantify the relative importance of each influencing factor, dynamically generate quantitative and operable field management plans adapted to different production scenarios, and ensure the stability of emission reduction effect through long-term monitoring and dynamic feedback mechanisms, thereby promoting the efficient use of nitrogen in farmland and sustainable development. It has broad application value. Attached Figure Description

[0015] Figure 1 This is a flowchart of the method for synergistic emission reduction of farmland N2O emissions and nitrogen leaching based on meta-analysis and enhanced regression tree model, which is based on the present invention.

[0016] Figure 2 This invention is based on the results of biochar application on N2O emissions from farmland using an enhanced regression tree model.

[0017] Figure 3 This invention relates to the results of soil nitrogen leaching applied using biochar based on an enhanced regression tree model. Detailed Implementation

[0018] The present invention will be further described in detail below with reference to embodiments:

[0019] Example 1 This invention provides a method for synergistic regulation of nitrous oxide emission reduction in farmland and soil nitrogen leaching, comprising the following steps: Collect research data on the effects of biochar on N2O emissions from farmland and soil nitrogen leaching: This invention collects research data on the effects of biochar application on N2O emissions and nitrogen leaching in farmland from CNKI, Wanfang, and Web of Science Core Collection databases. Published research literature on the effects of biochar on N2O emissions and nitrogen leaching in farmland was retrieved using keywords such as ("N2O" OR "nitrious oxide", "N2O emission" OR "nitrious oxide emission"), ("Biochar" OR "hydrochar" OR "black carbon"), "biochar", "greenhouse gas emissions", and "leaching". Valid sample data related to the influencing factors of soil conditioners on N2O emissions and nitrogen leaching methods in farmland were screened. Based on collected literature, screening criteria were set to select experimental sample data related to influencing factors of N2O emissions and nitrogen leaching in farmland from relevant literature information, including but not limited to soil type, climate conditions, crop type, fertilization method, fertilizer type, and irrigation method. The collected literature on farmland greenhouse gas emissions research is required to include, in addition to the necessary experimental and control group N2O emissions, nitrate nitrogen content of nitrogen leaching, and corresponding replication numbers, the standard deviation of the experimental and control groups, soil organic matter, total nitrogen, pH, fertilization method, nitrogen application rate, rainfall, temperature, biochar raw materials, application rate, carbon-nitrogen ratio, and pH, etc. Based on experimental sample data on greenhouse gas emissions from farmland and their influencing factors, a meta-analysis database of greenhouse gas emissions from farmland and their influencing factors was constructed to identify key factors affecting greenhouse gas emissions from dryland farmland. To ensure the accuracy of the selected data and effectively reduce heterogeneity, a series of more stringent screening criteria were established to conduct a secondary screening of the collected literature. The specific screening criteria are as follows: (1) The experiment includes at least one control treatment. (2) The experimental results provided in the literature include the emission flux and cumulative emission of soil greenhouse gas nitrous oxide (N2O) during the growing season of farmland crops and the nitrate nitrogen content of nitrogen leaching (in the form of data or images) or can be calculated from the data provided in the paper; (3) The experiment has a defined number of replicates and at least 3 replicates; (4) The experiment was a field-based, location-based experiment; (5) The greenhouse gas emission measurement method is “closed chamber-gas chromatography”.

[0020] Based on the meta-analysis database, this study quantifies the impact of different influencing factors on farmland N2O emissions and nitrogen leaching, explores the key factors and main driving mechanisms affecting farmland greenhouse gas emissions, and uses the collected data for data verification.

[0021] Specifically, this invention uses the "metafor" software package in R4.1.3, inputting the mean (Xe), number of replicates (Ne), and standard deviation (Se) of the experimental group, and the mean (Xc), number of replicates (Nc), and standard deviation (Sc) of the control group (without biochar / with biochar). The ratio of the experimental group to the control group is used as the response ratio (RR), and the natural logarithm of the response ratio is used as the effect value y. The calculation formula is as follows: .

[0022] The variance (vi) within a single case is calculated as follows:

[0023] In the formula, Sc and St represent the standard deviations of the biochar treatment group and the control group, respectively. nc and nt represent the biochar treatment... The number of replicates in the control and control groups. The weight (wi) of the effect size for each observed case is calculated using the reciprocal of the variance, as follows: The equation is shown. It considers both the variance within a single case (vi) and the variance between cases (τ2), where τ2 is estimated using the maximum likelihood method.

[0024]

[0025] The average cumulative effect size and 95% confidence interval of soil nitrogen leaching and N2O emissions were calculated using a random effects model. The calculated effect size (lnRR) and 95% CI were converted into percentage form using the formula %change=(eln(RR)-1)×100%. The effect size was considered significant when the 95% confidence interval did not overlap with the zero line.

[0026] In a specific embodiment of the present invention, Excel can be used to organize and classify the valid data from the literature, and the plotting function of R3.1.4 software can be used to draw a normal percentile plot to test the bias of the data. The effect value of each group of data can be calculated and a forest plot can be drawn to perform heterogeneity analysis of the data. Finally, the cumulative effect value and the corresponding 95% confidence interval can be calculated to intuitively analyze the N2O emission and soil nitrogen leaching effect of farmland under different farmland management measures.

[0027] We construct a mathematical quantitative relationship between key factors and greenhouse gas emissions, and assign different weight values ​​to each key factor.

[0028] An advanced machine learning algorithm—the Enhanced Regression Tree (BRT) model—is employed to continuously fit and optimize the model by combining regression tree construction and enhancement performance, thereby improving overall performance and predictive ability. This allows for the calculation of the relative importance of each moderating variable in the impact of biochar application on farmland N2O emissions and soil nitrogen leaching. The relative importance of each moderating factor is measured based on the number of times it is selected for model calculation, and is further enhanced through a weighted average. The sum of the relative importance of the moderating variables is 100, with higher values ​​indicating a greater impact on the response variable. The BRT model is run using the "gbm" and "dismo" packages in R 4.1.3, with a Gaussian error structure selected. The tree complexity is set to 5, and 10x cross-validation is used to estimate the optimal number of trees. The learning rate (0.001, 0.005, 0.01, and 0.05) and bag-of-slot fraction (0.5, 0.6, and 0.75) are adjusted to obtain the optimal model with the minimum prediction error. Observations and corresponding predicted values ​​are obtained using the "openair" package in R for further evaluation of the model. The coefficient of determination (R²) and root mean square error (RMSE) of a linear regression model are used to assess the accuracy of the model's predictions.

[0029] By simulating the differences in farmland N2O emissions and soil nitrogen leaching under different biochar application and agronomic measures, the optimal management measures for synergistic reduction of farmland N2O emissions and soil nitrogen leaching were obtained.

[0030] The above experiments show that biochar can synergistically reduce emissions under conditions of high application rate and high pH.

[0031] The present invention has been described in detail above. However, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, any modifications or improvements that do not depart from the spirit of the present invention are within the scope of protection of the present invention.

Claims

1. A system and method for synergistically regulating nitrous oxide emission reduction and soil nitrogen leaching in an agricultural field, the method comprising: Includes the following steps ​ Step 1: Collect research data on N2O emissions from farmland and soil nitrogen leaching after biochar application; Step 2: Screen out valid sample data related to factors affecting N2O emissions and soil nitrogen leaching in farmland after biochar application; Step 3: Construct a meta-analysis database of farmland N2O emissions and soil nitrogen leaching and influencing factors to identify key factors affecting farmland N2O emissions and soil nitrogen leaching; Step 4: Construct mathematical quantitative relationships between key factors and farmland N2O emissions and soil nitrogen leaching; Step 5: Construct an enhanced regression tree model of farmland N2O emissions and soil nitrogen leaching after biochar application; Step 6: Simulate the differences in farmland N2O emissions and soil nitrogen leaching under different biochar and agronomic measures to obtain the optimal biochar application scheme for synergistic emission reduction of farmland N2O emissions and soil nitrogen leaching.

2. The method for synergistic regulation of nitrous oxide emission reduction and soil nitrogen leaching in farmland according to claim 1, characterized in that: The influencing factors mentioned in step 2 include climate conditions, soil physicochemical properties, fertilization methods, and biochar properties. Climate conditions include rainfall and temperature, soil physicochemical properties include soil organic matter, total nitrogen, and pH, and biochar properties include raw materials, application rate, carbon-nitrogen ratio, pH, and pyrolysis temperature.

3. The method for synergistic regulation of nitrous oxide emission reduction and soil nitrogen leaching in farmland according to claim 1, characterized in that: The valid sample data must include N2O emissions, nitrogen leaching amount and corresponding replicates and standard deviation for the experimental and control groups, and also cover basic parameters such as soil organic matter, total nitrogen, pH, nitrogen application rate, rainfall, temperature, biochar raw materials, application rate, carbon-nitrogen ratio, and pH.

4. The method for synergistic regulation of nitrous oxide emission reduction and soil nitrogen leaching in farmland according to claim 1, characterized in that: In step 3, a random effects model is used to calculate the cumulative effect value of each influencing factor through meta-analysis. The significance of the influence is judged based on whether the 95% confidence interval of the cumulative effect value overlaps with the zero line, and then the key factors are determined.

5. The method for a synergistic regulation system of farmland nitrous oxide emission reduction and soil nitrogen leaching according to claim 1, characterized in that: The key factors include crop type, soil texture, biochar properties, rainfall, temperature, nitrogen application rate, and soil pH, which can accurately predict the response patterns of different factors to farmland N2O emissions and soil nitrogen leaching.

6. The method for a synergistic regulation system of farmland nitrous oxide emission reduction and soil nitrogen leaching according to claim 1, characterized in that: In step 5, the enhanced regression tree model is constructed using the "gbm" and "dismo" packages in R software. The tree complexity is set to 5, the number of trees is optimized by 10x cross-validation, the learning rate is adjusted to 0.001-0.05 and the bag fraction to 0.5-0.75 to obtain the optimal prediction effect, and the coefficient of determination and root mean square error are used to evaluate the accuracy of the model.

7. The method for a synergistic regulation system of farmland nitrous oxide emission reduction and soil nitrogen leaching according to claim 1, characterized in that: The core of the optimal synergistic regulation scheme in step 6 is a combination of high application rate and high pH biochar application, combined with appropriate nitrogen application rate, fertilization method and irrigation management measures.

Images