Analysis method for rapidly accounting rice production carbon nitrogen footprint and sensitivity and uncertainty

Abstract

The invention discloses an analysis method for rapidly accounting rice production carbon nitrogen footprint, sensitivity and uncertainty, which comprises the following steps of: 1, setting a plurality of straw returning gradients and nitrogen fertilizer application gradients through field trials, a matrix management combination is formed, key management measures of the whole rice growth process and key parameters including soil, crops and weather are recorded, and the matrix management combination is used for driving a dynamic simulation emission DNDC model so as to simulate field emission; 2, counting agricultural material investment and determining a reasonable emission factor range; 3, comprehensively calculating carbon and nitrogen footprints; 4, determining a parameter input range and analyzing the uncertainty and sensitivity of a result; 5, identifying a key driving factor; and 6, determining an optimal management scheme. According to the method, the field carbon and nitrogen cycle process under specific management measures can be dynamically simulated, the evaluation result is more accurate and more targeted than that of a traditional emission factor method, and the scientific level of agricultural environment influence evaluation is improved.

Description

TECHNICAL FIELD

[0001] The application relates to the field of intelligent agriculture, and particularly relates to a method for rapidly calculating carbon and nitrogen footprints of rice production and sensitivity and uncertainty analysis. BACKGROUND

[0002] Rice is a major food crop in the world, providing 20% of food energy, but rice paddies emit about 48% of agricultural greenhouse gases, mainly including methane (CH4) and nitrous oxide (N2O), and excessive use of nitrogen fertilizer enters the environment through leaching, runoff, ammonia volatilization and other ways, which seriously endangers human health due to active nitrogen pollution. In the rice-wheat rotation area of China, straw returning and chemical fertilizer application are generally implemented. Straw returning can improve soil organic carbon sequestration and soil structure, but it increases CH4 emission in the short term; nitrogen fertilizer application increases yield, but leads to N2O emission and Nr loss.

[0003] Traditional life cycle assessment (LCA) models often use fixed emission factor coefficients when calculating carbon and nitrogen emissions in the production, transportation and use links, but with the improvement of production processes, the emission factor has an uncertainty interval; although process models (such as DNDC) can quickly simulate the carbon and nitrogen emission conditions in the field, it is also difficult to comprehensively obtain the model input parameters in a large area.

[0004] Chinese patent 202210627434.9 proposes a global sensitivity analysis and uncertainty analysis method for crop production carbon footprint evaluation. The method considers the influence of the interaction between parameters on the carbon footprint evaluation result, identifies parameters with high sensitivity from numerous input parameters, and improves the efficiency and accuracy of model evaluation by simplifying or fixing low-sensitive parameters. However, this method simply uses LCA combined with emission factors for carbon footprint accounting, and lacks dynamic model simulation process. Therefore, the accounting method is difficult to meet the application under different cultivation conditions. SUMMARY

[0005] The present application aims to solve the problems of the present stage rice production carbon and nitrogen footprint accounting method, which mainly relies on field observation or static emission factor method for calculation, has great difficulty in experiment, high uncertainty of results, and difficulty in fully considering the interaction of soil-crop-climate. The present application aims to solve the drawbacks of traditional accounting models relying on linear empirical coefficients, and establish a comprehensive carbon and nitrogen footprint evaluation method based on biogeochemistry, which can realize rapid accounting of carbon and nitrogen footprints of rice production under the interaction of straw and nitrogen fertilizer and sensitivity and uncertainty analysis.

[0006] TECHNICAL SCHEME

[0007] A method for rapidly calculating carbon and nitrogen footprints of rice production and sensitivity and uncertainty analysis, which comprises the following steps:

[0008] Step one: Through field experiments, set up multiple straw returning gradients and nitrogen fertilizer application gradients, form matrix management combinations, record the whole process of rice growth key management measures and key parameters including soil, crops, weather, for driving dynamic simulation emission DNDC model to simulate field emissions;

[0009] Step two: Statistics of agricultural inputs and determination of reasonable emission factor range;

[0010] Step three: Based on life cycle assessment method, comprehensive calculation of carbon and nitrogen footprint;

[0011] Step four: Determine the parameter input range and the uncertainty and sensitivity analysis of the results, including:

[0012] (1) Constructing the uncertainty input parameter space: selecting the key input parameters affecting carbon and nitrogen footprint, including but not limited to: DNDC model soil physical and chemical parameters, crop parameters, management parameters, and agricultural inputs and emission factors in LCA accounting; Set a reasonable probability distribution range for each parameter;

[0013] (2) Parameter sampling: generate Sobol sample sequence by Saltelli sampling method;

[0014] (3) Batch simulation and calculation: use the generated Sobol sample sequence as batch input, repeat step two and step three, get thousands of pairs of "input parameter-output footprint" result set;

[0015] (4) Perform Sobol global sensitivity analysis: based on the above result set, calculate the first order sensitivity index S1 and total effect index ST of each input parameter on the final carbon and nitrogen footprint output; S1 represents the independent contribution of a single parameter change to the output variance; ST represents the total contribution of a single parameter change and its interaction with other parameters to the output variance;

[0016] Step five: Identify key driving factors: sort all input parameters according to the size of ST index; The parameter with the highest ST value is identified as the key driving factor affecting the variation of carbon footprint or nitrogen footprint;

[0017] Step six: Based on the footprint calculation results of step three and the key driving factors identified in step five, determine the optimal management scheme.

[0018] Preferably, in step one, the CH4 and N2O gas emissions in rice field are measured by static dark box-gas chromatography method, and the test data obtained are used to calibrate and verify the availability of DNDC model.

[0019] Preferably, the emission fluxes of CH4 and N2O are calculated by the following formula:

[0020]

[0021] where F is the CH4 or N2O emission flux, p is the density of the gas at standard state, V is the static chamber volume, A is the cross-sectional area of the chamber, dC / dt is the emission rate of CH4 or N2O, and T is the average temperature in the chamber during sampling;

[0022] The cumulative emission fluxes of CH4 and N2O are calculated as follows:

[0023]

[0024] where T is the total cumulative emission of the gas, F i and F i+1 are the average emission fluxes at the i-th and i+1-th sampling, D i and D i+1 are the i-th and i+1-th sampling times.

[0025] Preferably, the agricultural inputs in the rice production process include urea, straw, phosphate and potash fertilizer, agricultural film, pesticide, seed, fuel oil and electricity. The amounts of the agricultural inputs are determined according to the usage records during the experiment, and the specific values are as follows:

[0026]

[0027] where S0, S1 and S2 represent the straw returning to field gradient, and N0, N1 and N2 represent the nitrogen fertilizer application gradient. For straw and urea, since they have a significant impact on carbon and nitrogen emissions, the amount range is set to cover from zero (Min) to the maximum amount used in the experiment (Max) to ensure that the simulation process covers all input gradients. For the remaining seven inputs such as phosphate fertilizer, potash fertilizer and pesticide, the amount input range is set to an interval (Min, Max) with the average amount used in the experiment as the center and a 50% fluctuation up and down. This setting aims to simulate the common variation range of these inputs in actual production.

[0028] Preferably, the carbon and nitrogen emissions during the use of agricultural products are calculated using the relevant carbon and nitrogen emission factors, as follows:

[0029]

[0030] Preferably, the footprint calculation formula is as follows:

[0031]

[0032] where CF is the carbon footprint per unit yield; Y is the rice yield; Q i is the amount of the i-th agricultural input, and Q is the corresponding carbon emission factor; QCH4 and QN2O are the cumulative emissions of CH4 and N2O during the planting season.

[0033] All emissions are summed up after being converted into CO2 equivalent, and normalized by unit area yield, to obtain the carbon emissions per ton of rice production.

[0034] Preferably, the nitrogen footprint calculation formula is as follows:

[0035]

[0036] In the formula, NF is the unit yield nitrogen footprint; Y is the yield of rice; Q m represents the application amount of the mth agricultural input in a planting season; f m is the corresponding nitrogen emission factor; NH3, N2O, NO, NH4 + respectively represent the ammonia volatilization, nitrous oxide, nitric oxide emission and ammonium nitrogen loss caused by field processes, which are simulated by the DNDC model;

[0037] All active nitrogen emissions are summed up and normalized by unit yield to obtain the nitrogen emissions per ton of rice production.

[0038] Preferably, the soil physical and chemical parameters of the DNDC model include pH and bulk density; the crop parameters include yield and growth accumulated temperature; and the management parameters include fertilizer application amount and straw returning amount.

[0039] Preferably, step six is specifically: based on the footprint calculation result of step four, and referring to the key driving factors identified in step five, a comprehensive evaluation is performed on all management scenarios; the management scenario that can effectively control the key driving factors while maintaining a high yield level, and makes the unit yield CF and NF at a low level, is determined as the optimal management scheme.

[0040] Advantages of the present application

[0041] The "DNDC+LCA" evaluation framework relied on by the present application can dynamically simulate the field carbon and nitrogen circulation process under specific management measures, and the evaluation result is more accurate and more targeted than the traditional emission factor method, thereby improving the scientific level of agricultural environmental impact assessment. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 It is a rice test design diagram for data collection carried out in the present application.

[0043] Figure 2 It is a verification result diagram of the DNDC model used in the present application for simulating the carbon and nitrogen footprint components.

[0044] Figure 3 It is a result diagram of the analysis of the sensitivity of carbon and nitrogen footprint in the present application.

[0045] Figure 4 This is a graph showing the analysis results regarding the uncertainty of carbon and nitrogen footprint in this invention. Detailed Implementation

[0046] The present invention will be further described below with reference to embodiments, but the scope of protection of the present invention is not limited thereto:

[0047] This invention addresses the shortcomings of existing technologies by providing a method for calculating the carbon and nitrogen footprint of paddy field production based on a biogeochemical and life cycle framework. It combines statistical data with model simulation and rapidly achieves sensitivity and uncertainty analysis through scenario simulation, thus solving the problem of traditional carbon and nitrogen footprint calculations relying on empirical coefficients and data collection.

[0048] Step 1: Construct a multi-scenario input database to simulate field emissions.

[0049] Measured data on rice plant carbon and nitrogen accumulation and cumulative greenhouse gas emission fluxes were obtained through multiple combined experiments of straw return to the field (S0, S1, S2) and nitrogen fertilizer application gradients (N0, N1, N2). Information on rice management practices throughout its growth process, combined with key parameters of soil, crop, and meteorology, was used to drive a DNDC model to simulate carbon and nitrogen emissions from paddy fields. Specifically, the model simulated rice plant carbon and nitrogen accumulation, and cumulative CH4 and N2O emission fluxes by inputting daily meteorological data, measured soil physicochemical properties, local rice variety parameters, and precise field management information. 2021 experimental data was used as a calibration benchmark. Key crop growth and soil carbon and nitrogen cycle parameters within the model were iteratively adjusted until the error between the model's output of plant carbon and nitrogen accumulation, CH4, and N2O cumulative emission fluxes and the measured values ​​was minimized. Finally, the calibrated model was validated using independent 2022 data, and the coefficient of determination (R²) was used to determine the model's performance. 2 Performance was evaluated using statistical indicators such as root mean square error (RMSE). The results confirmed that the model has the ability to accurately simulate the carbon and nitrogen dynamics of local paddy fields. Finally, based on the model simulation results, carbon and nitrogen footprint components that could not be directly measured during the field production stage due to experimental limitations were supplemented, thus improving the entire evaluation dataset. The emission fluxes of CH4 and N2O were calculated using the following formulas:

[0050]

[0051] In the formula, F is the emission flux of CH4 or N2O (mg / m³). -2 h -1 ), where ρ is the density of the gas under standard conditions (kg m³). -3 V is the static volume of the box (m³). 3 A is the cross-sectional area of ​​the box (m²). 2), dC / dt is the emission rate of CH4 or N2O (ppmv min). -1 T represents the average temperature inside the chamber during the sampling period (°C).

[0052] The cumulative emission fluxes of CH4 and N2O are calculated using the following formulas:

[0053]

[0054] In the formula, T represents the total cumulative gaseous emissions (mg / m³). -2 ), F i and F i+1 These are the average emission fluxes (mg / m³) at the i-th and (i+1)-th sampling times, respectively. -2 h -1 ), D i and D i+1 These are the sampling times for the i-th and i+1-th times, respectively.

[0055] Step 2: Statistically analyze agricultural inputs and determine a reasonable range of emission factors.

[0056] Agricultural inputs used in rice production include urea, straw, phosphate and potassium fertilizers, agricultural film, pesticides, seeds, fuel oil, and electricity. Data are derived from experimental records and are detailed in Table 1.

[0057] Table 1:

[0058]

[0059]

[0060] Carbon and nitrogen emissions from agricultural inputs will be calculated using relevant carbon and nitrogen emission factors. Considering the differences in emission factors in different databases, the maximum and minimum values ​​of emission factors for each input were summarized through literature review for carbon and nitrogen footprint uncertainty analysis. The emission factor parameters are detailed in Table 2.

[0061] Table 2:

[0062]

[0063] Step 3: Calculate the carbon and nitrogen footprints.

[0064] Based on the life cycle assessment method, the boundary of the rice production system is defined as the entire growth period from sowing to harvest. The carbon footprint includes: (1) greenhouse gas emissions (CH4, N2O) directly emitted by the crop system; and (2) greenhouse gas emissions indirectly generated during the production and use of agricultural inputs, including inorganic fertilizers (nitrogen, phosphorus, and potassium fertilizers), mulch film, pesticides, seeds, electricity, diesel, etc. The carbon footprint calculation formula is as follows:

[0065]

[0066] In the formula, CF represents the carbon footprint per unit output (kg CO2 - eq t-1seas). o n-1); Y is the rice yield (t ha -1 Qi represents the amount of the i-th agricultural input used, and q represents the corresponding carbon emission factor (kg CO2-eq unit). -1 QCH4 and QN2O represent the cumulative emissions of CH4 and N2O during the growing season (kg ha). -1 ); 27 and 273 are the global warming potentials of CH4 and N2O relative to CO2, respectively. All emissions were converted to CO2 equivalents, summed, and normalized by yield per unit area to obtain the carbon emissions per ton of rice produced.

[0067] The nitrogen footprint of paddy field production is defined as the total reactive nitrogen emissions from the rice production system. This includes reactive nitrogen emissions directly and indirectly generated during crop system and agricultural input use. Eutrophication potential is used to characterize eutrophic releases from air, water, and soil. The nitrogen footprint calculation formula is as follows:

[0068]

[0069] In the formula, NF represents the nitrogen footprint per unit yield (kg N-eq t) -1 season -1 Y represents rice yield (t ha). -1 );Q m This indicates the application rate (kg ha) of agricultural input of type m during a growing season. -1 season -1 );f m The corresponding nitrogen emission factor (kgN-eq unit) -1 ,); NH3, N2O, NO, NH4 + These figures represent the emissions of ammonia volatilization, nitrous oxide, nitric oxide, and ammonium nitrogen loss caused by field processes (kg N ha). -1 The values ​​were obtained from the DNDC model simulation; 0.833, 0.476, 0.820, and 0.786 represent NH3, N2O, NO, and NH4, respectively. + Eutrophication potential factor (kg N-eq kg) -1 The total amount of reactive nitrogen emissions was summed and normalized to the amount of nitrogen emitted per ton of rice produced.

[0070] Step 4: Determine the parameter input range and perform uncertainty and sensitivity analysis on the results.

[0071] (1) Constructing an uncertain input parameter space: Select key input parameters that affect the carbon and nitrogen footprint, including but not limited to: soil physicochemical parameters (such as pH and bulk density), crop parameters (such as yield and accumulated temperature), management parameters (such as fertilizer application and straw return), and agricultural input and emission factors in LCA accounting. Set a reasonable probability distribution range for each parameter.

[0072] (2) Parameter sampling: Sobol sample sequences were generated using the Saltelli sampling method. This method can efficiently explore the entire parameter space and is a standard method for global sensitivity analysis.

[0073] (3) Batch simulation and calculation: The generated Sobol sample sequence is used as batch input to drive the comprehensive evaluation framework of the present invention (i.e., repeat steps two to four) to obtain thousands of pairs of "input parameter-output footprint" result sets.

[0074] (4) Perform Sobol global sensitivity analysis: Based on the above result set, calculate the first-order sensitivity index (S1) and total effect index (ST) of each input parameter to the final carbon and nitrogen footprint output.

[0075] S1 represents the independent contribution of a single parameter variation to the output variance.

[0076] ST represents the total contribution of a single parameter variation and its interaction with other parameters to the output variance.

[0077] Step 5: Identify key drivers: Sort all input parameters according to the magnitude of the ST index. The parameter with the highest ST value is identified as a key driver affecting the variation of carbon or nitrogen footprint (e.g., in this study, CH4 emissions were identified as a key driver of CF, and NH3 volatilization as a key driver of NF).

[0078] Quantifying the uncertainty of the output: Statistical analysis is performed on all carbon and nitrogen footprint results calculated in batches, calculating their mean and standard deviation, and determining their 95% confidence interval (CI). This provides a quantitative basis for assessing the reliability range of the results.

[0079] Step Six: Determine the optimal management plan.

[0080] Based on the footprint calculation results from step three, and with reference to the key driving factors identified in step five, all management scenarios are comprehensively evaluated. The optimal management scenario is selected as one that can effectively control the emissions of key driving factors (such as CH4 and NH3) while maintaining a high production level, and keeps both CF and NF at low levels (with acceptable uncertainty ranges) per unit of output.

[0081] Figure 1 The data foundation of this invention's methodology stems from a scientific, rigorous, and standardized field trial design. The figure clearly illustrates a multi-gradient (S0, S1, S2 represent straw; N0, N1, N2 represent nitrogen fertilizer) and multi-repeated matrix plot layout. This design ensures the reliability, systematicity, and comparability of the collected data. Unlike existing techniques that rely on literature data or empirical estimations, the data in this invention is rooted in real field practice. This high-quality data source is the prerequisite and guarantee for the effective calibration and validation of the subsequent DNDC model, ensuring the scientific validity and accuracy of the entire evaluation framework of this invention from the source, and solving the problem of large deviations in evaluation results caused by the uncertainty of data sources in existing techniques.

[0082] To verify the accuracy of the DNDC model in simulating carbon and nitrogen dynamics and greenhouse gas emissions in paddy fields, regression analysis was performed between the simulation results and measured data. The results are as follows: Figure 2 As shown, the model has high accuracy in simulating cumulative N2O emissions, R0 2 =0.60, RMSE=0.54. The simulation accuracy for CH4 emissions is higher. 2 =0.71, RMSE=30.22. The scatter points in the graph are closely distributed near the 1:1 line (red dashed line), indicating a high degree of consistency between the simulated and measured values. This graph strongly demonstrates the accuracy and reliability of the core technical aspect of this invention—dynamic simulation using the DNDC model. It directly solves the fundamental deficiency of existing technologies (such as the simple LCA emission factor method) in failing to capture the dynamic interaction process of soil-crop-climate. Verification shows that the method of this invention is not a "black box" operation, but can scientifically and accurately reproduce the actual emission process in the field. Furthermore, through continuous calculation and verification with measured data, the good simulation effect of the DNDC model is proven. With the help of the powerful functions of the model, it can effectively simulate other carbon and nitrogen footprint components that have not been actually measured. Therefore, the carbon and nitrogen footprint results calculated based on this model are more realistic, more reliable, and more instructive than existing technologies.

[0083] The analytical results regarding carbon and nitrogen footprint sensitivity in this invention are as follows: Figure 3As shown, this figure clearly quantifies and identifies the key driving factors affecting carbon and nitrogen footprint variation. The carbon footprint analysis chart below reveals that the sensitivity index (total effect index ST as high as 0.36) for CH4 emissions is significantly higher than any other parameter, indicating that controlling carbon footprint hinges on controlling CH4 emissions. Similarly, the nitrogen footprint analysis chart above shows that the sensitivity index for NH3 volatilization (ST≈0.189) is the highest, making it crucial for controlling the nitrogen footprint. Existing technologies can only provide a general footprint value, while this invention, through sensitivity analysis, can precisely pinpoint the "critical factors" affecting the footprint. This function allows subsequent emission reduction strategies to be targeted, precise, and efficient, avoiding the blind spots in the formulation of emission reduction measures by existing technologies. For example, based on the conclusions of this chart, efforts can be focused on optimizing water management to reduce CH4 and optimizing fertilization methods to reduce NH3 volatilization, thereby achieving a more effective emission reduction.

[0084] The analytical results regarding the uncertainty of carbon and nitrogen footprints in this invention are as follows: Figure 4 As shown, this figure illustrates the possible distribution range of the final carbon and nitrogen footprints, considering all uncertainties in the input parameters. For example, the figure below shows a mean carbon footprint of 2297.56 kg CO2-eq t -1 However, its 95% confidence interval (CI) is [474.35–6339.30 kg CO2-eq t]. -1 The range of values ​​is quite large. This indicates that under certain conditions, there is a significant risk of a surge in carbon footprint. This invention addresses the shortcomings of existing technologies that provide a single, "pseudo-precise" value, achieving risk quantification of the assessment results. It not only provides a "most likely" average value, but more importantly, it gives the range of uncertainty for the results. This assessment result with a confidence interval is more robust and reliable for decision-makers (such as governments and farmers), helping them understand the potential extreme environmental risks behind different management strategies, thereby developing more resilient and safer agricultural production plans and avoiding erroneous decisions based on a single, uncertain assessment value.

[0085] The specific embodiments described herein are merely illustrative of the spirit of the invention. Those skilled in the art to which this invention pertains may make various modifications or additions to the described specific embodiments or use similar methods to substitute them, without departing from the spirit of the invention or exceeding the scope defined by the appended claims.

Claims

1. A rapid method for calculating the carbon and nitrogen footprint of rice production and analyzing its sensitivity and uncertainty, characterized in that... It includes the following steps: Step 1: Through field trials, multiple straw return gradients and nitrogen fertilizer application gradients are set up to form a matrix management combination. Key management measures and key parameters including soil, crop and meteorological parameters throughout the entire rice growth process are recorded to drive the dynamic emission simulation DNDC model to simulate field emissions. Step 2: Compile statistics on agricultural inputs and determine a reasonable range of emission factors; Step 3: Calculate the carbon and nitrogen footprints comprehensively based on the life cycle assessment method; Step 4: Determine the parameter input range and perform uncertainty and sensitivity analysis on the results, specifically including: (1) Construct an uncertain input parameter space: Select key input parameters that affect the carbon and nitrogen footprint, including but not limited to: soil physicochemical parameters, crop parameters, management parameters in the DNDC model, and agricultural input and emission factors in LCA accounting; set a reasonable probability distribution range for each parameter; (2) Parameter sampling: Sobol sample sequences were generated using the Saltelli sampling method; (3) Batch simulation and calculation: The generated Sobol sample sequence is used as batch input. Steps 2 and 3 are repeated to obtain thousands of pairs of "input parameter-output footprint" result sets; (4) Perform Sobol global sensitivity analysis: Based on the above result set, calculate the first-order sensitivity index S1 and the total effect index ST for each input parameter on the final carbon and nitrogen footprint output; S1 represents the independent contribution of a single parameter change to the output variance; ST represents the total contribution of a single parameter change and its interaction with other parameters to the output variance. Step 5: Identify key driving factors: Sort all input parameters according to the magnitude of the ST index; the parameter with the highest ST value is identified as the key driving factor affecting the variation of carbon or nitrogen footprint. Step Six: Based on the footprint calculation results from Step Three and the key driving factors identified in Step Five, determine the optimal management plan.

2. The method according to claim 1, characterized in that... In step one, the emissions of CH4 and N2O gases from the paddy field were measured using static dark chamber gas chromatography. The experimental data obtained will be used to calibrate and verify the usability of the DNDC model.

3. The method according to claim 2, characterized in that... The emission fluxes of CH4 and N2O are calculated using the following formulas: In the formula, F is the emission flux of CH4 or N2O, ρ is the density of the gas under standard conditions, V is the volume of the static chamber, A is the cross-sectional area of ​​the chamber, dC / dt is the emission rate of CH4 or N2O, and T is the average temperature inside the chamber during the sampling period. The cumulative emission fluxes of CH4 and N2O are calculated using the following formulas: In the formula, T represents the total cumulative gas emissions, and F i and F i+1 D represents the average emission flux at the i-th and (i+1)-th sampling times, respectively. i and D i+1 These are the sampling times for the i-th and i+1th sampling times, respectively.

4. The method according to claim 1, characterized in that... Agricultural inputs used in rice production include urea, straw, phosphate and potassium fertilizers, agricultural film, pesticides, seeds, fuel oil, and electricity. The dosages of each input were determined based on usage records during the experiment, and the specific values ​​are as follows: Among them, S0, S1, and S2 represent the straw return gradient, and N0, N1, and N2 represent the nitrogen fertilizer application gradient.

5. The method according to claim 1, characterized in that... Carbon and nitrogen emissions from the input and use of agricultural products are calculated using relevant carbon and nitrogen emission factors, as detailed below:

6. The method according to claim 1, characterized in that... The formula for calculating footprints is as follows: In the formula, CF represents the carbon footprint per unit yield; Y represents the rice yield; Q i Let represent the usage of the i-th agricultural input, and represent the corresponding carbon emission factor; QCH4 and QN2O are the cumulative emissions of CH4 and N2O during the planting season. All emissions were converted to CO2 equivalents, summed, and normalized by yield per unit area to obtain the carbon emissions per ton of rice produced.

7. The method according to claim 1, characterized in that... The formula for calculating nitrogen footprint is as follows: In the formula, NF represents the nitrogen footprint per unit yield; Y represents the rice yield; Q m This indicates the amount of agricultural input of type m applied during a growing season; f m The corresponding nitrogen emission factors are: NH3, N2O, NO, and NH4. + These figures represent the emissions of ammonia volatilization, nitrous oxide, nitric oxide, and ammonium nitrogen, respectively, caused by field processes and were obtained from DNDC model simulations. The total amount of reactive nitrogen emissions is summed and normalized to the amount of nitrogen emitted per ton of rice produced.

8. The method according to claim 1, characterized in that... The soil physicochemical parameters of the DNDC model include pH and bulk density; crop parameters include yield and accumulated temperature for growth; and management parameters include fertilizer application and straw return to the field.

9. The method according to claim 1, characterized in that... Step six specifically involves: based on the footprint calculation results from step four and referring to the key driving factors identified in step five, conducting a comprehensive evaluation of all management scenarios; selecting the management scenario that can effectively control the emissions of key driving factors while maintaining a high output level, and keeping both CF and NF at low levels per unit output, and determining it as the optimal management plan.

Images