Dry farmland greenhouse gas emission reduction method based on Meta analysis and system dynamics

Abstract

The invention discloses a dry farmland greenhouse gas emission reduction method based on Meta analysis and system dynamics, which comprehensively evaluates influence factors of dry farmland greenhouse gas emission through combination of Meta analysis and a system dynamics model, and dynamically simulates greenhouse gas emission trends under different management measures by using the system dynamics model. And a scientific basis and a systematic emission reduction scheme are provided for long-term emission reduction of greenhouse gases in dry farmland. Long-term monitoring and dynamic adjustment are carried out on an emission reduction scheme, the continuity and stability of the emission reduction effect are ensured, and sustainable development of the dry farmland in the irrigated area is promoted. The optimized management measure for emission reduction of the greenhouse gas in the dry farmland has high operability and can be popularized and applied in the actual farmland, and effective emission reduction of the greenhouse gas is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural environmental engineering, and is particularly applicable to a method for reducing greenhouse gas emissions from dryland farmland based on Meta analysis and system dynamics. Background Art

[0002] As global climate change becomes increasingly severe, agriculture, as a major source of greenhouse gas emissions, has drawn considerable attention for its potential for emission reduction. Dryland farmland, due to water scarcity and limited soil conditions, poses a significant greenhouse gas emission risk. Traditional emission reduction methods often rely on single management measures, lacking systematic and dynamic approaches, making them difficult to achieve long-term, effective reductions.

[0003] Meta-analysis is a statistical method that integrates the results of multiple independent studies to provide more comprehensive and reliable conclusions. System dynamics, a method for simulating the dynamic behavior of complex systems, can be used to model and predict how the system will behave under different conditions. Combining these two approaches could provide a more scientific and systematic solution for reducing greenhouse gas emissions from dryland farmland. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for reducing greenhouse gas emissions from dryland farmland based on meta-analysis and system dynamics, which optimizes dryland farmland management measures and reduces greenhouse gas emissions through comprehensive analysis and dynamic simulation.

[0005] To achieve the above object, the present invention adopts the following technical solutions: The method for reducing greenhouse gas emissions from dryland farmland based on meta-analysis and system dynamics of the present invention comprises the following steps: S1, collect research data on greenhouse gas emissions from dryland farmland; S2, screen out valid sample data related to factors affecting greenhouse gas emissions from dryland farmland; S3: Build a meta-analysis database of greenhouse gas emissions and influencing factors from dryland farmland to identify key factors affecting greenhouse gas emissions from dryland farmland; S4, constructing a mathematical quantitative relationship between key factors and greenhouse gas emissions; S5, construct a multi-factor coupled system dynamics model of greenhouse gas emissions from dryland farmland; S6, simulate the differences in greenhouse gas emissions under different irrigation, fertilization and agronomic practices to obtain the optimal management measures for greenhouse gas emission reduction in dryland farmland; S7, real-time tracking of changes in influencing factors, simulation and dynamic adjustment of farmland greenhouse gas emission reduction management measures.

[0006] Furthermore, the influencing factors in step S2 include soil type, climate conditions, crop type, fertilization method, fertilizer type, and irrigation method.

[0007] Furthermore, the valid sample data should include the N2O emissions of the experimental group and the control group and the corresponding number of replicates, the standard deviation of the experimental group and the control group, soil texture, soil bulk density, organic matter, total nitrogen, pH, fertilization method, nitrogen application rate, potassium fertilizer application rate, phosphorus fertilizer application rate, irrigation method and rainfall.

[0008] Furthermore, in step S3, a random effects model was used to determine the cumulative effect value of each influencing factor through meta-analysis. According to the range of the 95% confidence interval of the cumulative effect value, the impact of each influencing factor on greenhouse gas emissions from dryland farmland was judged, and the key factors affecting greenhouse gas emissions from dryland farmland were determined.

[0009] Furthermore, the key factors include crop type, soil texture, phosphate fertilizer application amount, potassium fertilizer application amount, chemical nitrogen fertilizer application amount, organic nitrogen fertilizer application amount, straw return amount, biochar application amount, rainfall and irrigation amount.

[0010] The advantage of the present invention is that, through the combination of meta-analysis and system dynamics modeling, it comprehensively evaluates the factors affecting greenhouse gas emissions from dryland farmland, uses the system dynamics model to dynamically simulate greenhouse gas emission trends under different management measures, and provides a scientific basis and systematic emission reduction plan for the long-term reduction of greenhouse gas emissions from dryland farmland. Long-term monitoring and dynamic adjustment of emission reduction plans ensure the continuity and stability of emission reduction effects, and promote the sustainable development of dryland farmland in irrigated areas. In addition, the optimized greenhouse gas emission reduction management measures for dryland farmland are highly operational and can be promoted and applied in actual farmland to achieve effective greenhouse gas emission reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 This is a flow chart of the method for reducing greenhouse gas emissions from dryland farmland based on Meta analysis and system dynamics of the present invention.

[0012] Figure 2 Schematic diagram of the multi-factor coupling system dynamics model in the basic method of the present invention. DETAILED DESCRIPTION

[0013] The following is a clear and complete description of the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts are within the scope of protection of the present invention.

[0014] like Figure 1As shown, the method for reducing greenhouse gas emissions from dryland farmland based on meta-analysis and system dynamics of the present invention includes the following steps: S1. Collect research data on greenhouse gas emissions from dryland farmland.

[0015] This study collected research data on greenhouse gas emissions from dryland farmland using the CNKI, Wanfang, VIP, and Web of Science Core Collection databases. Using keywords such as "dryland farmland," "greenhouse gas emissions," "nitrous oxide," "N2O," "agricultural management," "wheat," "maize," "sunflower," "fertilization," "irrigation," "GHG emissions," "wheat," "maize," "sunflowers," "tillage method," and "fertilizer," we retrieved research literature published in both Chinese and English over the past 20 years on greenhouse gas emissions from dryland farmland.

[0016] S2. Filter out valid sample data related to factors affecting greenhouse gas emissions from dryland farmland. Set screening conditions based on the collected literature, and screen the experimental sample data of factors affecting greenhouse gas emissions from dryland farmland, mainly nitrous oxide (N2O) emissions, from the relevant literature information, including but not limited to soil type, climate conditions, crop types, fertilization methods, fertilizer types, irrigation methods, etc. The literature collected on greenhouse gas emissions from irrigated farmland is required to include not only the N2O emissions and the corresponding number of replicates for the experimental and control groups, but also the standard deviation, soil texture, soil bulk density, organic matter, total nitrogen, pH, whether fertilization is applied, fertilization methods, nitrogen application rate, potassium application rate, phosphorus application rate, irrigation methods, and rainfall for the experimental and control groups.

[0017] S3. Based on the experimental sample data between greenhouse gas emissions from dryland farmland and influencing factors, a meta-analysis database of greenhouse gas emissions from dryland farmland and influencing factors was constructed to determine the key factors affecting greenhouse gas emissions from dryland farmland.

[0018] 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 experimental area is irrigated dryland farmland across the country; (2) The experiment included at least one control treatment; (3) The experimental results provided in the literature include the soil greenhouse gas nitrous oxide (N2O) emission flux or cumulative emission during the dryland crop growing season (in the form of data or images) or can be calculated from the data provided in the paper; (4) The experiment has a clear number of replicates and the number of replicates is at least 3; (5) The experiment was a field-based positioning experiment; (6) The greenhouse gas emissions were measured using the "closed chamber gas chromatography method".

[0019] Based on the Meta-analysis database, the effects of different influencing factors on greenhouse gas emissions from dryland farmland were quantified, the key factors and main driving mechanisms affecting greenhouse gas emissions from dryland farmland were explored, and data verification was performed using the collected data.

[0020] Specifically, the present invention uses MetaWin2.1 software, and inputs the mean (Xe), number of replicates (Ne), and standard deviation (Se) of the experimental group (amount of nitrogen fertilizer applied) and the mean (Xc), number of replicates (Nc), and standard deviation (Sc) of the control group (no nitrogen fertilizer applied / conventional fertilization). The ratio of the experimental group to the control group is the response ratio (RR), and the natural logarithm of the response ratio is the effect value y. The calculation formula is as follows: ; To improve its accuracy and effectively reduce heterogeneity, a random effects model was used to calculate the cumulative effect value of each influencing factor. Based on the range of the 95% confidence interval of the cumulative effect value, the impact of each influencing factor on greenhouse gas emissions from dryland farmland was determined, and the key factors affecting greenhouse gas emissions from dryland farmland were identified.

[0021] In MetaWin2.1 software, the 95% confidence interval (CI) of the cumulative effect value is: ; If the 95% confidence interval of the calculated cumulative effect value of a certain influencing factor on soil N2O emissions contains 0, it means that the treatment of the influencing factor has no significant effect on soil N2O emissions (P<0.05); if the 95% confidence interval of the cumulative effect value of a certain influencing factor on soil N2O emissions is greater than 0, it means that the treatment of the influencing factor has a significant enhancing effect on soil N2O emissions (P<0.05); if the 95% confidence interval of the cumulative effect value of a certain influencing factor on soil N2O emissions is less than 0, it means that the treatment of the influencing factor has a significant weakening effect on soil N2O emissions (P<0.05).

[0022] In a specific embodiment of the present invention, Excel can be used to organize and classify valid data from the literature. The plotting function of MetaWin2.1 software can be used to draw a normal percentile plot to test the data bias. The effect value of each data group is calculated and a forest plot is drawn to analyze the data heterogeneity. Finally, the cumulative effect value and the corresponding 95% confidence interval are calculated. The resulting data are then plotted using R software to more intuitively analyze the N2O emission effects of dryland farmland under different farmland management measures.

[0023] Meta-analysis identified the key factors affecting N2O emissions from farmland, including crop type, soil texture, phosphate fertilizer application, potassium fertilizer application, chemical nitrogen fertilizer application, organic nitrogen fertilizer application, straw return amount, biochar application, rainfall, and irrigation amount.

[0024] S4, construct a mathematical quantitative relationship between key factors and greenhouse gas emissions, and assign different weight values to each key factor.

[0025] S5, construct a multi-factor coupling system dynamics model of greenhouse gas emissions from dryland farmland. Based on the mathematical quantitative relationship between influencing factors and greenhouse gas emissions, the Vensim PLE software was used to construct a multi-factor coupling system dynamics model of greenhouse gas emissions from dryland farmland in irrigated areas, such as Figure 2 The collected data were used to verify and revise the reliability of the multi-factor coupled system dynamics model of greenhouse gas emissions from dryland farmland.

[0026] From Figure 2 It can be seen that the total nitrous oxide emissions in the irrigation area = average nitrous oxide emissions per mu * the planting area in the irrigation area.

[0027] Average nitrous oxide emissions per mu = average nitrous oxide production rate per mu * time.

[0028] The nitrous oxide production rate is related to the key factors that influence N2O emissions from farmland identified through meta-analysis. The specific formula, constructed using Vensim PLE software based on the mathematical relationship between influencing factors and greenhouse gas emissions, and after data verification and correction, is: Among them: α, γ, ζ, η, δ, θ, κ are weight factors, F N is the amount of chemical nitrogen fertilizer applied, in kg / ha; F orgis the amount of organic fertilizer nitrogen applied, in kg / ha; S is the amount of straw returned to the field, in kg / ha; P is the amount of phosphorus fertilizer applied, in kg / ha; K is the amount of potassium fertilizer applied, in kg / ha; B is the amount of biochar applied, in kg / ha; C is the crop type; T is the soil texture; I is the amount of irrigation, in mm; The specific weight coefficients are extracted based on the analysis of valid agricultural greenhouse gas emission factor datasets.

[0029] S6, simulates the differences in greenhouse gas emissions under different irrigation, fertilization and agronomic measures to obtain the optimal greenhouse gas emission reduction management measures for dryland farmland.

[0030] S7, real-time tracking of changes in influencing factors, simulation and dynamic adjustment of farmland greenhouse gas emission reduction management measures.

Claims

1. A method for reducing greenhouse gas emissions from dryland farmland based on meta-analysis and system dynamics, characterized by: The following steps are involved: S1, collect research data on greenhouse gas emissions from dryland farmland; S2, screen out valid sample data related to factors affecting greenhouse gas emissions from dryland farmland; S3: Build a meta-analysis database of greenhouse gas emissions and influencing factors from dryland farmland to identify key factors affecting greenhouse gas emissions from dryland farmland; S4, constructing a mathematical quantitative relationship between key factors and greenhouse gas emissions; S5, construct a multi-factor coupled system dynamics model of greenhouse gas emissions from dryland farmland; S6, simulate the differences in greenhouse gas emissions under different irrigation, fertilization and agronomic practices to obtain the optimal management measures for greenhouse gas emission reduction in dryland farmland; S7, real-time tracking of changes in influencing factors, simulation and dynamic adjustment of farmland greenhouse gas emission reduction management measures.

2. The method for reducing greenhouse gas emissions from dryland farmland based on meta-analysis and system dynamics according to claim 1, characterized in that: The influencing factors described in step S2 include soil type, climate conditions, crop type, fertilization method, fertilizer type, and irrigation method.

3. The method for reducing greenhouse gas emissions from dryland farmland based on meta-analysis and system dynamics according to claim 1, characterized in that: The valid sample data should include the N2O emissions of the experimental and control groups and the corresponding number of replicates, the corresponding standard deviations of the experimental and control groups, soil texture, soil bulk density, organic matter, total nitrogen, pH, fertilization method, nitrogen application rate, potassium fertilizer application rate, phosphorus fertilizer application rate, irrigation method and rainfall.

4. The method for reducing greenhouse gas emissions from dryland farmland based on meta-analysis and system dynamics according to claim 1, characterized in that: In step S3, a random effects model was 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 greenhouse gas emissions from dryland farmland was judged, and the key factors affecting greenhouse gas emissions from dryland farmland were determined.

5. The method for reducing greenhouse gas emissions from dryland farmland based on meta-analysis and system dynamics according to claim 1 or 4, characterized in that: The key factors include crop type, soil texture, phosphate fertilizer application rate, potassium fertilizer application rate, chemical nitrogen fertilizer application rate, organic nitrogen fertilizer application rate, straw return rate, biochar application rate, rainfall and irrigation amount.

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