Biochar rice field water carbon and nitrogen cycle process simulation method based on DNDC model

Abstract

The invention discloses a biochar rice field water carbon and nitrogen cycle process simulation method based on a DNDC model, which comprises the following steps: firstly obtaining basic data of rice growth and test monitoring data of a rice ecosystem under biochar treatment, and then constructing a biochar organic fertilizer module to improve the DNDC model; the method comprises the following steps: establishing a Biochar-DNDC model according to basic data of rice growth and test monitoring data, then calibrating and verifying the Biochar-DNDC model by adopting the test monitoring data, and finally simulating the water carbon and nitrogen cycle process of the biochar rice field by adopting the verified Biochar-DNDC model to obtain the water carbon and nitrogen cycle process of a rice field ecosystem under the condition that biochar is applied to a target area. The simulation accuracy of the DNDC model on the charcoal rice field water carbon nitrogen cycle process is improved, and the simulation result of the charcoal rice ecosystem carbon sequestration emission reduction synergy potential is more real and accurate.

Description

Technical Field

[0001] This invention belongs to the field of simulation technology for carbon and nitrogen cycle processes in paddy fields, specifically involving the design of a biochar simulation method for carbon and nitrogen cycle processes in paddy fields based on the DNDC model. Background Technology

[0002] Rice, as one of the world's staple crops, is crucial for ensuring global food security. In pursuit of high yields, frequent irrigation and fertilization of rice paddies have led to a significant increase in greenhouse gas emissions, exacerbating global warming. my country, as a major agricultural country and rice producer, has greenhouse gas emissions from rice paddies reaching a staggering 196 million tons of CO2e. Against the backdrop of food security and global warming, reducing greenhouse gas emissions from my country's rice systems and coordinating the sustainability of rice production with the potential for greenhouse gas emission reduction is of significant practical importance. Therefore, in recent years, carbon sequestration and emission reduction in rice paddies have attracted increasing attention from scholars, resulting in a proliferation of measures and methods. However, most commonly used rice paddy carbon sequestration and emission reduction measures focus on single aspects such as rice varieties, cultivation patterns, farming methods, and management fertilization, without comprehensively considering the carbon source and sink functions of the rice ecosystem, making it difficult to form a "low-emission-high-sink-high-yield-high-efficiency" rice system. Biochar, a high-carbon and difficult-to-decompose material obtained by anaerobic high-temperature decomposition and carbonization of agricultural waste (such as straw), has a "carbon negative effect". It can be applied to the soil to fix active carbon in the soil into a stable state and prevent it from being mineralized into greenhouse gases. It is considered to be the most promising and greenest solution for carbon sequestration and emission reduction in paddy fields.

[0003] Carbon sequestration and emission reduction in paddy field ecosystems are closely related to their water, carbon, and nitrogen cycles. These processes are complex and fragile, easily influenced by soil properties, farming practices, and climate. While research on biochar addition for carbon sequestration, emission reduction, yield increase, and efficiency improvement in paddy fields has some foundation, most studies remain in the exploratory stage of short-term field trials and indoor cultivation experiments. Rice production is sensitive to human activities and climate change; relying solely on short-term field trials cannot quickly determine the current status and trends of rice agricultural production under multiple scenarios. Currently, numerous biogeochemical process models for simulating water, carbon, and nitrogen cycles in farmland systems have been developed both domestically and internationally, such as the DNDC model. This model can simulate the migration and transformation of water, carbon, and nitrogen in paddy fields under different soil, climate, and management conditions, outputting various results such as carbon and nitrogen footprints, greenhouse gas emissions, and yield, demonstrating significant simulation advantages. However, like other biogeochemical process models, the DNDC model lacks a dedicated biochar module, making it difficult to accurately describe the absorption rate of carbon and nitrogen substrates in paddy field water by added biochar and its life cycle in the soil, thus hindering accurate simulation of the biochar-based water, carbon, and nitrogen cycle in paddy fields. Summary of the Invention

[0004] The purpose of this invention is to address the problem that existing DNDC models cannot accurately describe the impact of biochar addition on the carbon and nitrogen cycle of farmland water, and to propose a method for simulating the carbon and nitrogen cycle of biochar in paddy fields based on the DNDC model.

[0005] The technical solution of this invention is: a method for simulating the carbon and nitrogen cycle process of biochar paddy field water based on the DNDC model, comprising the following steps: S1. Obtain basic data on rice growth.

[0006] S2. Conduct experiments on adding biochar to paddy fields and obtain experimental monitoring data on the rice ecosystem under biochar treatment.

[0007] S3. Construct a biochar organic fertilizer module based on the DNDC model.

[0008] S4. The DNDC model was improved based on the biochar organic fertilizer module, and the Biochar-DNDC model was constructed based on the basic data of rice growth and experimental monitoring data.

[0009] S5. The Biochar-DNDC model was calibrated and validated using experimental monitoring data.

[0010] S6. The validated Biochar-DNDC model was used to simulate the carbon and nitrogen cycle process in biochar paddy fields.

[0011] Furthermore, the basic data for rice growth in step S1 includes meteorological data of the rice growing area, soil data of paddy fields, rice crop parameter data, farmland management data, global warming potential data, and carbon emission intensity data.

[0012] Meteorological data for rice-growing areas include daily maximum temperature, daily minimum temperature, daily precipitation, wind speed, sunshine duration, relative humidity, nitrogen concentration in precipitation, atmospheric nitrogen concentration, atmospheric carbon dioxide concentration, and annual growth rate of atmospheric carbon dioxide.

[0013] Paddy field soil data include soil texture, clay content, bulk density, field water holding capacity, wilting coefficient, water conductivity, porosity, pH, soil organic matter content, soil ammonia nitrogen content, and soil nitrate nitrogen content.

[0014] Rice crop parameter data include crop type, maximum crop biomass, accumulated temperature for growth, crop water requirement, nitrogen fixation coefficient, and the biomass distribution ratio and C / N ratio of the four parts of the crop: seeds, leaves, stems and roots.

[0015] Farmland management data includes rice harvest time, nitrogen fertilizer application, straw return ratio, plowing method, number of plowings, number of fertilizations, amount of fertilizer, fertilization date, fertilization depth, biochar application amount and depth, number of irrigations, start date of irrigation, rate of water seepage at the bottom of the farmland, harvest date and number of harvests.

[0016] Furthermore, the biochar addition experiment in paddy fields in step S2 includes a micro-plot experiment of different biochar application levels. The area of ​​the micro-plot is 1m × 1m, and the different biochar application levels include: CK: represents the blank control without biochar application, corresponding to 0 kg / m³. 2 Biochar application level.

[0017] BC1.5: corresponds to 1.5 kg / m 2 Biochar application level.

[0018] BC3: corresponds to 3 kg / m 2 Biochar application level.

[0019] BC4.5: corresponds to 4.5 kg / m 2 Biochar application level.

[0020] BC6: corresponds to 6 kg / m 2 Biochar application level.

[0021] Furthermore, the experimental monitoring data in step S2 includes physiological indicators, soil indicators, yield indicators, and greenhouse gas indicators at different rice growth stages.

[0022] The rice growth period includes the greening stage, tillering stage, jointing and heading stage, grain filling and milk stage, and yellow ripening and harvesting stage.

[0023] Physiological indicators include number of clumps, number of tillers, plant height, stem diameter, leaf area, ear length, and number of ears.

[0024] Soil indicators include paddy field soil moisture content, field capacity, saturated water content, soil organic carbon content, soil ammonia nitrogen content, soil nitrate nitrogen content, soil total nitrogen content, and soil soluble organic carbon content.

[0025] Yield indicators include the number of rice panicles per clump, thousand-grain weight, and total grain weight in the experimental farmland micro-plots.

[0026] Greenhouse gas indicators include carbon dioxide, methane, and nitrous oxide.

[0027] Furthermore, the soil moisture content, field water holding capacity, and saturated moisture content of paddy fields were determined by the drying method; the soil organic carbon content was determined by the potassium dichromate-external heating method; the soil ammonia nitrogen content and soil nitrate nitrogen content were determined by the KCl extract-spectrophotometer method; the soil total nitrogen content was determined by the Kjeldahl method; and the soil soluble organic carbon content was determined by the total organic carbon analyzer.

[0028] Furthermore, the emission fluxes and cumulative emissions of carbon dioxide, methane, and nitrous oxide were monitored using a manual static dark chamber-gas chromatography method, specifically as follows: A1. Design and manufacture an artificial static darkroom. The artificial static darkroom includes a cylindrical box body and a circular base made of 5mm thick PVC material. The diameter of the bottom surface of the cylindrical box body is 32cm and the height is 120cm. The top of the circular base is provided with a sealing groove that is 20mm wide and 50mm deep. A miniature electric fan is installed in the top of the cylindrical box body. A thermometer probe is inserted into the hole next to the miniature electric fan. A 20cm long gas sampling tube, a three-way valve and a 50ml syringe are installed on one side of the cylindrical box body. Two holes of the three-way valve are connected to the gas sampling tube and the syringe respectively. The outer layer of the cylindrical box body is covered with tin foil.

[0029] A2. Before gas sampling, fill the sealing groove of the circular base with water to ensure that the cylindrical box can be sealed after placement, and let it stand for 2-3 minutes.

[0030] A3. During the critical growth period of rice, an artificial static dark box was placed in the rice field every 5-7 days. Gas was collected between 8:00 and 11:00. A sample was taken every 10 minutes for a total of 3 samplings. At the same time, the water depth, soil temperature and air temperature were recorded.

[0031] A4. Analyze the gas collected in the gas sampling cylinder using a gas chromatograph to calculate the emission flux and cumulative emissions of carbon dioxide, methane, and nitrous oxide: in F Indicates the emission flux of carbon dioxide, methane, or nitrous oxide. ρ This indicates the density of carbon dioxide, methane, or nitrous oxide under standard conditions. H This indicates the height of the cylindrical box. DC / dt This indicates the rate of change in the concentration of carbon dioxide, methane, or nitrous oxide within the chamber during the sampling process. T This represents the average temperature inside the cylindrical box. P This indicates the air pressure inside the cylindrical box. P 0 represents standard atmosphere. This indicates the cumulative emissions of carbon dioxide, methane, or nitrous oxide. Indicates the first i The emission flux of carbon dioxide, methane, or nitrous oxide at the time of the next sampling. Indicates the first i Sampling time, , n Indicates the number of samples.

[0032] Furthermore, the parameters of the biochar organic fertilizer module in step S3 include basic parameters and characteristic parameters.

[0033] The basic parameters include the carbon-to-nitrogen ratio (C / N), organic carbon, organic nitrogen, ammonia nitrogen, and nitrate nitrogen.

[0034] Characteristic parameters include biochar pH, substrate absorption rate, decay rate and turnover residence time in stable biochar tanks, and decay rate and turnover residence time in unstable biochar tanks.

[0035] Furthermore, the decay rate and turnover residence time of the stable biochar tank and the decay rate and turnover residence time of the unstable biochar tank were determined using a dual-tank exponential decay model, with the specific formula as follows: in Indicates the time elapsed since biochar was applied to the soil. t The remaining inventory after that, Indicates the initial biochar inventory. This indicates a stable biochar pond inventory. This indicates the inventory of unstable biochar ponds. This indicates the decay rate of the stable biochar pond. This indicates the decay rate of the unstable biochar pond. This indicates the turnover residence time in the stabilized biochar tank. This indicates the turnover residence time in the unstable biochar tank.

[0036] Furthermore, the indicators used in step S5 to calibrate and validate the Biochar-DNDC model include rice yield, soil organic carbon, soil soluble organic carbon, ammonia nitrogen, nitrate nitrogen, methane, and nitrous oxide.

[0037] Furthermore, in step S5, when calibrating and validating the Biochar-DNDC model, the mean absolute error, root mean square error, normalized root mean square error, relative root mean square error, and coefficient of determination are used to evaluate the degree of agreement between the simulation results of the Biochar-DNDC model and the actual data.

[0038] The beneficial effects of this invention are: (1) The present invention sets up a biochar organic fertilizer module, and uses biochar stability parameters such as biochar pH, substrate uptake ratio, and biochar dual-pool decay index model to fully consider the real impact of biochar addition on the rice system, realizes the parameter optimization of newly added biochar fertilizer in the DNDC model, and thus forms a Biochar-DNDC model that can simulate the carbon and nitrogen cycle process of biochar paddy water. This effectively solves the defect of the original model that cannot accurately describe the degree of influence of biochar addition on the carbon and nitrogen cycle process of farmland water, and improves the simulation accuracy of the established model.

[0039] (2) In the simulation, this invention considers various changing scenarios such as biochar application, nitrogen fertilizer application, rice irrigation mode, and future climate, and fully predicts the carbon sequestration, emission reduction and yield increase potential of biochar rice ecosystem under different climate, fertilization mode and irrigation management mode in the future. It provides a scientific basis for optimizing and proposing a green management mode with the highest comprehensive benefits and lowest risks for carbon sequestration, emission reduction and efficiency improvement of future rice system. Attached Figure Description

[0040] Figure 1 The diagram shown is a flowchart of a method for simulating the carbon and nitrogen cycle process in paddy water based on a DNDC model, as provided in an embodiment of the present invention.

[0041] Figure 2 The diagram shown is a schematic diagram of the DNDC model structure provided in an embodiment of the present invention.

[0042] Figure 3 The diagram shows a schematic of the Biochar-DNDC model construction process provided in an embodiment of the present invention. Detailed Implementation

[0043] Exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be understood that the embodiments shown and described in the drawings are merely exemplary and are intended to illustrate the principles and spirit of the invention, and are not intended to limit the scope of the invention.

[0044] In this embodiment of the invention, the experimental site was an experimental field at the Hubei Provincial Irrigation Experimental Center Station, and the crop planted was rice, with the tested variety being "Zuanliangyou Chaozhan". The irrigation method was conventional flooding, followed by mid-term field drying. Biochar application rates were 0, 1.5, 3, 4.5, and 6 kg / m², respectively. 2 (Designated as CK, BC1.5, BC3, BC4.5, and BC6), where CK is the blank control without biochar application. Fertilizer application rate: Nitrogen fertilizer 180 kg / hm² 2 (Urea 46.4% N), Phosphate fertilizer 115 kg / hm 2(P2O5, superphosphate), potassium fertilizer 72 kg / hm 2 (K2O, potassium sulfate), with nitrogen applied to the soil in two ways: 50% as basal fertilizer and 50% as topdressing. Basal fertilizer was applied to the soil two days before transplanting and thoroughly mixed. Topdressing was applied topically during the tillering stage. Drainage was not carried out for 3-5 days after applying both basal and topdressing fertilizers. Before rice transplanting, biochar was evenly mixed with the top 15-20cm of soil in the micro-plot, then the field was leveled and soaked for about 7 days. Rice seedlings, approximately 27 days old, were then transplanted with soil attached, 3 seedlings per clump. Routine pest and disease control and weeding during the rice growth period were conducted according to local practices, maintaining consistency across different biochar treatments. A two-year trial was conducted from 2022 to 2023, with rice sowing dates of May 19, 2022, and May 27, 2023, and harvest dates of September 17, 2022, and September 3, 2023, respectively.

[0045] Based on this, embodiments of the present invention provide a method for simulating the carbon and nitrogen cycle process of biochar paddy field water based on the DNDC model, including the following steps S1~S6: S1. Obtain basic data on rice growth.

[0046] In this embodiment of the invention, the basic data for rice growth includes meteorological data of the rice growing area, soil data of paddy fields, rice crop parameter data, farmland management data, global warming potential data, and carbon emission intensity data.

[0047] Meteorological data for rice-growing areas include daily maximum temperature, daily minimum temperature, daily precipitation, wind speed, sunshine duration, relative humidity, nitrogen concentration in precipitation, atmospheric nitrogen concentration, atmospheric carbon dioxide concentration, and annual growth rate of atmospheric carbon dioxide.

[0048] Paddy field soil data include soil texture, clay content, bulk density, field water holding capacity, wilting coefficient, water conductivity, porosity, pH, soil organic matter content, soil ammonia nitrogen content, and soil nitrate nitrogen content.

[0049] Rice crop parameter data include crop type, maximum crop biomass, accumulated temperature for growth, crop water requirement, nitrogen fixation coefficient, and the biomass distribution ratio and C / N ratio of the four parts of the crop: seeds, leaves, stems and roots.

[0050] Farmland management data includes rice harvest time, nitrogen fertilizer application, straw return ratio, plowing method, number of plowings, number of fertilizations, amount of fertilizer, fertilization date, fertilization depth, biochar application amount and depth, number of irrigations, start date of irrigation, rate of water seepage at the bottom of the farmland, harvest date and number of harvests.

[0051] S2. Conduct experiments on adding biochar to paddy fields and obtain experimental monitoring data on the rice ecosystem under biochar treatment.

[0052] In this embodiment of the invention, the biochar addition experiment in paddy fields includes different application levels of biochar in a farmland micro-plot experiment. The area of ​​the farmland micro-plot is 1m×1m, and the different application levels of biochar include: CK: represents the blank control without biochar application, corresponding to 0 kg / m³. 2 Biochar application level.

[0053] BC1.5: corresponds to 1.5 kg / m 2 Biochar application level.

[0054] BC3: corresponds to 3 kg / m 2 Biochar application level.

[0055] BC4.5: corresponds to 4.5 kg / m 2 Biochar application level.

[0056] BC6: corresponds to 6 kg / m 2 Biochar application level.

[0057] In this embodiment of the invention, the experimental monitoring data includes physiological indicators, soil indicators, yield indicators, and greenhouse gas indicators at different rice growth stages.

[0058] The rice growth period includes the greening stage, tillering stage, jointing and heading stage, grain filling and milk stage, and yellow ripening and harvesting stage.

[0059] Physiological indicators include number of clumps, number of tillers, plant height, stem diameter, leaf area, ear length, and number of ears.

[0060] Soil indicators include paddy field soil moisture content, field capacity, saturated water content, soil organic carbon content, soil ammonia nitrogen content, soil nitrate nitrogen content, soil total nitrogen content, and soil soluble organic carbon content.

[0061] In this embodiment of the invention, the soil moisture content, field water holding capacity, and saturated moisture content of paddy fields were determined by the drying method; the soil organic carbon content was determined by the potassium dichromate-external heating method; the soil ammonia nitrogen content and soil nitrate nitrogen content were determined by the KCl extract-spectrophotometer method; the soil total nitrogen content was determined by the Kjeldahl nitrogen determination method; and the soil soluble organic carbon content was determined by a total organic carbon analyzer (TOC-L analyzer).

[0062] Yield indicators include the number of rice panicles per clump, thousand-grain weight, and total grain weight in the experimental farmland micro-plots.

[0063] Greenhouse gas indicators include carbon dioxide, methane, and nitrous oxide.

[0064] In this embodiment of the invention, the emission fluxes and cumulative emissions of carbon dioxide, methane, and nitrous oxide are monitored using a manual static dark chamber-gas chromatography method. The specific method is as follows: A1. Design and fabricate an artificial static darkroom. The artificial static darkroom consists of a cylindrical box body made of 5mm thick PVC material and a circular base. The diameter of the bottom surface of the cylindrical box body is 32cm, and the height is 120cm. The top of the circular base has a sealing groove 20mm wide and 50mm deep. The box body is sealed with water or soil during gas sampling. A miniature electric fan is installed at the top of the cylindrical box body. A thermometer probe is inserted into an opening next to the miniature electric fan to correct for sampling errors caused by temperature rise inside the box. A 20cm long gas sampling tube, a three-way valve, and a 50ml syringe are installed on one side of the cylindrical box body. Two holes of the three-way valve are connected to the gas sampling tube and the syringe, respectively. The three-way valve is used to first establish a gas sampling state, ensuring that the syringe collects gas from the darkroom. After the syringe collects gas, the three-way valve is used to close the box, the syringe is removed, and the gas collected in the syringe is injected into the gas sampling bottle. Finally, a new syringe is used to collect gas from the darkroom for the next time period. The cylindrical box is covered with tin foil to reduce the impact of solar radiation on the internal temperature and achieve the purpose of heat insulation.

[0065] A2. Before gas sampling, fill the sealing groove of the circular base with water to ensure that the cylindrical box can be sealed after placement. Let it stand for 2-3 minutes to allow CO2, CH4 and N2O in the box to mix thoroughly and evenly.

[0066] A3. During the critical growth period of rice, artificial static dark boxes were placed in the rice field every 5-7 days. Gas collection was carried out between 8:00 and 11:00, which best represents the average level of CO2, CH4 and N2O emissions on that day. Samples were taken once every 10 minutes for a total of 3 samplings. At the same time, the water depth, soil temperature and air temperature were recorded.

[0067] In this embodiment of the invention, if abnormal temperature occurs during the rice growing season, the number of samplings should be increased appropriately, while if heavy rainfall occurs, sampling should be postponed.

[0068] A4. The gas collected in the gas sampling cylinder is analyzed using a gas chromatograph (GC, 7890A), and the emission fluxes and cumulative emissions of carbon dioxide, methane, and nitrous oxide are calculated: in F This indicates the emission flux of carbon dioxide, methane, or nitrous oxide, expressed in mg / (m³). 2 / h), ρ This indicates the density of carbon dioxide, methane, or nitrous oxide under standard conditions. H This indicates the height of the cylindrical box, in meters (m). DC / dt This indicates the rate of change in the concentration of carbon dioxide, methane, or nitrous oxide within the chamber during the sampling process, expressed in ml / (m³). 3 / h), T This indicates the average temperature inside the cylindrical box, expressed in °C. P This indicates the air pressure inside the cylindrical box. P 0 represents standard atmospheric pressure, which is taken in this embodiment of the invention. P = P 0, This indicates the cumulative emissions of carbon dioxide, methane, or nitrous oxide, expressed in kg / hm². 2 , Indicates the first i The emission flux of carbon dioxide, methane, or nitrous oxide at each sampling time, in mg / (m³). 2 / h), Indicates the first i The sampling time is expressed in days (d). , n Indicates the number of samples.

[0069] On a 100-year timescale, the warming potential of methane and nitrous oxide is 27.9 times and 273 times that of nitrogen dioxide, respectively. The formula for calculating the Global Warming Potential (GWP) is as follows: The formula for calculating Greenhouse Gas Emission Intensity (GHGI) is as follows: in GWP This represents global warming potential, expressed in kgCO2 / hm². 2 ; , , These represent the cumulative emissions of CH4, N2O, and CO2 during the growing season, expressed in kg / hm². 2 27.9 and 273 represent the values ​​per unit mass of CH4 and N2O on a 100-year timescale. GWP A multiple of CO2. GHGI Greenhouse gas emission intensity is expressed in units of CO2e. Y This indicates rice yield, expressed in kg / hm². 2 .

[0070] S3. Construct a biochar organic fertilizer module based on the DNDC model.

[0071] like Figure 2As shown, the DNDC model primarily uses ecological factors (climate, soil, vegetation, and human activities) to drive the three sub-models of the first component, predicting soil temperature, humidity, pH, Eh, and substrate concentration, respectively. Then, based on soil environmental conditions (soil temperature, humidity, pH, Eh, and substrate concentration), it simulates relevant geochemical and biochemical reactions. The three sub-models of the second component are used to predict carbon and nitrogen gas fluxes (CH4, NO, N2O, NH3) in the farmland ecosystem. The six sub-models in the DNDC model can communicate and transfer data with each other, thus effectively describing and tracking the carbon and nitrogen cycling processes in the farmland ecosystem.

[0072] In embodiments of the present invention, such as Figure 3 As shown, the parameters of the biochar organic fertilizer module include basic parameters and characteristic parameters.

[0073] The basic parameters include the carbon-to-nitrogen ratio (C / N), organic carbon, organic nitrogen, ammonia nitrogen, and nitrate nitrogen.

[0074] Characteristic parameters include biochar pH, substrate absorption rate (the absorption rate of ammonia nitrogen, nitrate nitrogen and soluble organic carbon in soil due to the porous nature of biochar), decay rate and turnover residence time of stable biochar ponds, and decay rate and turnover residence time of unstable biochar ponds.

[0075] In embodiments of the present invention, such as Figure 3 As shown, the decay rate and turnover residence time of stable and unstable biochar ponds were determined using a two-pond exponential decay model. The two-pond exponential decay model defines biochar as consisting of two parts: a stable biochar pond and an unstable biochar pond. The biochar in the stable biochar pond decays slowly, while the biochar in the unstable biochar pond is easily mineralized and decays faster. The unstable biochar pond constitutes a small proportion, with the remainder being stable biochar that can remain in the soil for a long time. The two biochar ponds decay in parallel without energy exchange. The specific formula for the two-pond exponential decay model is: in Indicates the time elapsed since biochar was applied to the soil. t The remaining inventory after that, Indicates the initial biochar inventory. This indicates a stable biochar pond inventory. This indicates the inventory of unstable biochar ponds. This indicates the decay rate of the stable biochar pond. This indicates the decay rate of the unstable biochar pond. This indicates the turnover residence time in the stabilized biochar tank. This indicates the turnover residence time in the unstable biochar tank.

[0076] S4. The DNDC model was improved based on the biochar organic fertilizer module, and the Biochar-DNDC model was constructed based on the basic data of rice growth and experimental monitoring data.

[0077] S5. The Biochar-DNDC model was calibrated and validated using experimental monitoring data.

[0078] In this embodiment of the invention, the indicators used for calibration and validation of the Biochar-DNDC model include rice yield, soil organic carbon, soil soluble organic carbon, ammonia nitrogen, nitrate nitrogen, methane, and nitrous oxide.

[0079] In this embodiment of the invention, the mean absolute error is used when calibrating and validating the Biochar-DNDC model. MAE Root mean square error RMSE Normalized root mean square error NRMSE Relative root mean square error RRMSE and coefficient of determination R 2 The following formula is used to evaluate the good agreement between the simulation results of the Biochar-DNDC model and the actual data: in n For data number, O i , P i They are respectively i Real-time measured values ​​and simulated values; O m This is the average value observed. y max , y min These are the maximum and minimum values ​​of the measured values, respectively. MAE , RMSE , RRMSE The ideal value is 0; the smaller the value, the better the model simulation effect. R 2 The value ranges from 0 to 1, with the closer the value is to 1, the better the simulation effect. RRMSE It can not only reflect the prediction accuracy of the model, but also be used to compare the accuracy of different models. When NRMSE When it is less than 25%, it indicates that the model fits well; when NRMSE A result within the range of 25% to 30% indicates that the model fit is acceptable.

[0080] S6. The validated Biochar-DNDC model was used to simulate the carbon and nitrogen cycle process in biochar paddy fields.

[0081] Based on the Biochar-DNDC model, considering various scenarios such as biochar application, nitrogen fertilizer application, rice irrigation patterns, and future climate, multi-scenario simulations of water, carbon, and nitrogen cycles in paddy field soil were conducted. Details of the model's multi-scenario settings are shown in Table 1. Three representative radiative forcing scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) were selected (low, medium, and high), and the simulation model chosen was the BCC-CSM2-MR climate system model developed by the National Climate Center (Beijing) (BCC).

[0082] Table 1. Simulation settings for multiple scenarios in the Biochar-DNDC model. Table 2 shows the simulated statistical results of rice yield, soil organic carbon, soil soluble organic carbon, ammonia nitrogen, nitrate nitrogen, methane, and nitrous oxide during the rate-setting and verification periods in this embodiment of the invention.

[0083] Table 2. Statistical results of model simulation during the calibration and validation periods. For yield, compared to DNDC, the Biochar-DNDC model has higher R-values ​​during the periodic and validation phases. 2 The RRMSE and NRMSE values ​​are all significantly low, with NRMSE less than 11.09%, far below 25%, indicating a good model fit. Furthermore, the mean RRMSE of the Biochar-DNDC model is 0.085 kg / hm². 2 The value was significantly lower than that of the DNDC model (0.134 kg / hm). 2 This indicates that the Biochar-DNDC model improves accuracy by 36.95% compared to the original DNDC model. (Biochar-DNDC model R...) 2 The yield values ​​for the Biochar-DNDC model were 0.86 and 0.56 during the rate-setting and validation periods, respectively, both higher than those of the DNDC model. Therefore, the rice yield simulation results after adding biochar were more accurate in the Biochar-DNDC model than those in the DNDC model.

[0084] Regarding SOC content, both models performed well in the rate-period simulation of SOC. The Biochar-DNDC model had lower MAE, RMSE, and NRMSE than the DNDC model. 2The Biochar-DNDC model is better than the DNDC model. During the validation period, the simulation results of both models significantly decreased, R... 2 The values ​​were only 0.46 and 0.27, respectively. Comparing the mean values ​​of the RRMSE rate during the periodic and validation periods, the Biochar-DNDC model (0.067 g / kg) had a smaller value than the DNDC model (0.094 g / kg), indicating that the Biochar-DNDC model improved the accuracy of SOC simulation by 28.74% compared to the DNDC model.

[0085] For DOC content, the mean values ​​of MAE, RMSE, NRMSE, and RRMSE of the Biochar-DNDC model were all lower than those of the DNDC model. 2 The results are higher than those of the DNDC model, indicating that the Biochar-DNDC model simulates DOC better and more accurately than the DNDC model. Comparing RRMSE, the Biochar-DNDC model shows a 16.88% improvement in accuracy compared to the DNDC model, demonstrating that the Biochar-DNDC model has a more precise DOC simulation effect.

[0086] For NH4 + -N content, as shown in Table 2, indicates that the values ​​of MAE, RMSE, NRMSE, and RRMSE statistical indicators were lower during the rate-period period than during the validation period. The values ​​in the Biochar-DNDC model were also lower than those in the DNDC model. 2 The numerical values ​​are greater than those of the DNDC model; the Biochar-DNDC model is superior to the DNDC model NH4. + -N simulation accuracy improved by 21.86%. This indicates that the Biochar-DNDC model can better simulate soil NH4 during rice growth. + Changes in -N content.

[0087] For NO3 - For -N content, the Biochar-DNDC model performed better than the DNDC model in simulation. The RRMSE of the Biochar-DNDC model (mean 0.353 mg / kg) was significantly lower than that of the original DNDC model (mean 0.735 mg / kg), representing a 51.91% improvement in accuracy. 2 The Biochar-DNDC model (0.81) is 23.85% higher than the DNDC model (0.65), therefore the Biochar-DNDC model is more effective for NO3. — -N content simulation is more accurate.

[0088] For CH4 and N2O, the Biochar-DNDC model showed lower mean values ​​for MAE, RMSE, and NRMSE compared to the DNDC model. Regarding NRMSE, only the Biochar-DNDC model achieved less than 25% during the calibration period, indicating good simulation accuracy; while the DNDC model exceeded 30% during the validation period, showing poor simulation accuracy; the others were within the 25%–30% range, with acceptable simulation results. Furthermore, in terms of RRMSE, the Biochar-DNDC model increased the simulation accuracy of CH4 and N2O by 28.58% and 42.06%, respectively. Therefore, the Biochar-DNDC model demonstrates good performance and relatively stable prediction results.

[0089] In summary, compared with the DNDC model, the Biochar-DNDC model provides better control over yield, SOC, DOC, and NH4. + -N, NO3 - The simulation results for indices such as -N, CH4, and N2O are better, with an average improvement in simulation accuracy of 28.42% compared to the DNDC model.

[0090] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. A method for simulating a process of water-carbon-nitrogen cycle in a rice field based on a DNDC model, characterized in that, The method comprises the following steps: S1, obtaining basic data of rice growth; S2, conducting a biochar addition experiment in a rice field and obtaining experimental monitoring data of a rice ecosystem under biochar treatment; S3, constructing a biochar organic fertilizer module based on a DNDC model; S4, improving the DNDC model based on the biochar organic fertilizer module, and constructing a Biochar-DNDC model according to the basic data of rice growth and the experimental monitoring data; S5, calibrating and verifying the Biochar-DNDC model by using the experimental monitoring data; S6, simulating the water-carbon-nitrogen cycle process in a biochar rice field by using the verified Biochar-DNDC model.

2. The DNDC model-based simulation method of the process of water-carbon-nitrogen cycle of biochar in a paddy field according to claim 1, characterized in that, The basic data of rice growth in step S1 includes meteorological data of a rice growth area, soil data of a rice field, crop parameter data of rice, farmland management data, global warming potential data, and carbon emission intensity data; The meteorological data of the rice growth area includes daily maximum temperature, daily minimum temperature, daily precipitation, wind speed, sunshine duration, relative humidity, nitrogen concentration in rainfall, nitrogen concentration in the atmosphere, carbon dioxide concentration in the atmosphere, and annual growth rate of carbon dioxide in the atmosphere; The soil data of the rice field includes texture, clay content, bulk density, field moisture capacity, wilting coefficient, water conductivity, porosity, pH, soil organic matter content, soil ammonia nitrogen content, and soil nitrate nitrogen content of the soil of the rice field; The crop parameter data of rice includes crop type, maximum biomass, accumulated temperature, water requirement, nitrogen fixation coefficient, and biomass allocation ratio and C / N ratio of four parts of the crop, i.e., seeds, leaves, stems, and roots; The farmland management data includes harvesting time, nitrogen fertilizer dosage, straw return ratio, plowing method, plowing frequency, fertilization frequency, fertilization amount, fertilization date, fertilization depth, biochar application amount and application depth, flooding frequency, flooding start date, farmland bottom water leakage rate, harvesting date, and harvesting frequency.

3. The DNDC model-based simulation method of the process of water-carbon-nitrogen cycle of biochar in a paddy field according to claim 1, characterized in that, The biochar addition experiment in a rice field in step S2 includes different biochar application levels in a farmland micro-area experiment, the area of the farmland micro-area is 1m x 1m, and the different biochar application levels include: CK: represents the blank control without biochar application, corresponding to 0 kg / m 2 of biochar application level; BC1.5: corresponds to 1.5 kg / m 2 of biochar application level; BC3: corresponds to 3 kg / m 2 of biochar application level; BC4.5: corresponds to 4.5 kg / m 2 of biochar application level; BC6: corresponds to 6 kg / m 2 of biochar application level.

4. The DNDC model-based simulation method of the process of water-carbon-nitrogen cycle of biochar in a paddy field according to claim 1, characterized in that, The experimental monitoring data in step S2 includes physiological indicators, soil indicators, yield indicators, and greenhouse gas indicators at different rice growth stages; The rice growth stages include the green return stage, the tillering stage, the jointing and heading stage, the grain filling and milk ripening stage, and the yellow ripening and harvesting stage; The physiological indicators include tiller number, tiller number, plant height, stem diameter, leaf area, panicle length, and panicle number; The soil indicators include soil moisture content, field moisture capacity, saturated water content, soil organic carbon content, soil ammonia nitrogen content, soil nitrate nitrogen content, soil total nitrogen content, and soil dissolved organic carbon content; The yield indicators include panicle number per tiller, thousand-grain weight, and total grain weight of each farmland micro-area in the experimental field; The greenhouse gas indicators include carbon dioxide, methane, and nitrous oxide.

5. The DNDC model-based simulation method of the process of water-carbon-nitrogen cycle of biochar in paddy field according to claim 4, characterized in that, The soil water content, field water capacity and saturated water content of the paddy field are determined by the drying method, the soil organic carbon content is determined by the potassium dichromate-external heating method, the soil ammonia nitrogen content and soil nitrate nitrogen content are determined by the KCL extract-spectrophotometer method, the soil total nitrogen content is determined by the Kjeldahl method, and the soil soluble organic carbon content is determined by the total organic carbon analyzer.

6. The DNDC model-based simulation method of the process of water-carbon-nitrogen cycle of biochar in a paddy field according to claim 4, characterized in that, The emission fluxes and cumulative emissions of carbon dioxide, methane and nitrous oxide are monitored by the artificial static dark box-gas chromatography method, and the specific method is as follows: A1, design and make an artificial static dark box, the artificial static dark box includes a cylindrical box body made of PVC material with a thickness of 5mm and a circular base, the bottom surface diameter of the cylindrical box body is 32cm, the height is 120cm, the top of the circular base is provided with a sealing groove with a width of 20mm and a depth of 50mm, a micro electric fan is installed on the inner top of the cylindrical box body, a thermometer probe is inserted into the hole beside the micro electric fan, a gas sampling pipe with a length of 20cm, a three-way valve and a 50ml syringe are installed on one side of the cylindrical box body, two holes of the three-way valve are connected with the gas sampling pipe and the syringe respectively, and the outer layer of the cylindrical box body is covered with tin paper; A2, water is injected into the sealing groove of the circular base before gas sampling to ensure that the cylindrical box body can be sealed after being placed, and it is placed for 2-3min; A3, place the artificial static dark box in the paddy field every 5-7 days during the key growth period of rice, collect gas between 8:00-11:00, sample every 10 minutes, a total of 3 times, and record the water depth, ground temperature and air temperature at the same time; A4, the collected gas in the gas sampling bottle is detected by a gas chromatograph, and the emission fluxes and cumulative emissions of carbon dioxide, methane and nitrous oxide are calculated: wherein F represents the emission flux of carbon dioxide, methane or nitrous oxide, The parameters of the biochar organic fertilizer module in step S3 include basic parameters and characteristic parameters. represents the density of carbon dioxide, methane or nitrous oxide at standard state, H represents the height of the cylindrical chamber, The basic parameters include carbon-nitrogen ratio C / N, organic carbon, organic nitrogen, ammonia nitrogen and nitrate nitrogen. The characteristic parameters include biochar pH, substrate absorption rate, stable biochar pool decay rate and turnover residence time, and unstable biochar pool decay rate and turnover residence time. represents the rate of change of the concentration of carbon dioxide, methane or nitrous oxide in the chamber during sampling, T represents the average temperature in the cylindrical chamber, P represents the air pressure in the cylindrical chamber, P 0 represents standard atmospheric pressure, represents the cumulative emission of carbon dioxide, methane or nitrous oxide, represents the emission flux of carbon dioxide, methane or nitrous oxide at the i time of sampling, represents the sampling time at the i time, n represents the number of samplings.​​ 7. The DNDC model-based simulation method of the process of water-carbon-nitrogen cycle of biochar in a paddy field according to claim 1, characterized in that, The stable biochar pool decay rate and turnover residence time and the unstable biochar pool decay rate and turnover residence time are determined by a double-pool exponential decay model, and the specific formula is as follows: The indicators for calibrating and verifying the Biochar-DNDC model in step S5 include rice yield, soil organic carbon, soil soluble organic carbon, ammonia nitrogen, nitrate nitrogen, methane and nitrous oxide. When calibrating and verifying the Biochar-DNDC model in step S5, the average absolute error, root mean square error, normalized root mean square error, relative root mean square error and determination coefficient are used to evaluate the goodness of fit of the Biochar-DNDC model simulation results and actual data.

8. The DNDC model-based simulation method of the process of water-carbon-nitrogen cycle of biochar in a paddy field according to claim 7, characterized in that, ​ wherein represents the remaining stock of biochar applied to the soil over time, t represents the initial biochar stock, represents the stable biochar pool stock, represents the non-stable biochar pool stock, represents the decay rate of the stable biochar pool, represents the decay rate of the non-stable biochar pool, represents the turnover residence time of the stable biochar pool, represents the turnover residence time of the non-stable biochar pool.​ 9. The DNDC model-based simulation method of the process of carbon and nitrogen circulation of water in a rice field with biochar according to claim 1, characterized in that, ​ 10. The DNDC model-based simulation method of the process of carbon and nitrogen circulation of water in a rice field with biochar according to claim 1, characterized in that, ​

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