CN114781135A - Comprehensive estimation method and system for net greenhouse gas emission of regional agricultural planting system - Google Patents
Comprehensive estimation method and system for net greenhouse gas emission of regional agricultural planting system Download PDFInfo
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Abstract
The invention discloses a comprehensive estimation method and a comprehensive estimation system for net greenhouse gas emission of a regional agricultural planting system, wherein the method comprises the following steps of S1: acquiring relevant information of an agricultural planting system in a research area, and localizing an agricultural system model; s2: after localization, life cycle methods are used to evaluate CO caused by agricultural management2Discharge ECO2S3-mechanism model CH based on Process4MOD or DNDC models to simulate Rice-based farming systems for methane emissions ECH4(ii) a S4: calculating the total nitrous oxide emission amount of each agricultural planting system in the research area; s5, adopting a RothC or DAYCENT model based on the process to simulate the annual variation quantity delta SOC of the organic carbon on the surface layer (0-30cm) of the soil, and obtaining the solid carbon quantity of the organic carbon input quantity; and S6: and (4) comprehensively fixing carbon and discharging greenhouse gases to obtain the regional clean greenhouse gas discharge.
Description
Technical Field
The invention relates to the technical field of agricultural information, in particular to a comprehensive estimation method and a comprehensive estimation system for net greenhouse gas emission of a regional agricultural planting system.
Background
Zhengzheng promise of China in 2020, CO2Emissions were strived to reach a peak by 2030 and strive to achieve carbon neutralization by 2060. Carbon peaking and carbon neutralization were written in government work reports in 2021. Thus, carbon peaking and carbon neutralization have become a significant social and technical task.
Agricultural production consumes a large amount of fertilizers, pesticides, water resources and the like, and is an important greenhouse gas (GHG) emission source. Agricultural GHG emissions account for approximately 10-12% of the total GHG emissions worldwide. Meanwhile, agriculture plays an important role in carbon sequestration. Therefore, the method can be used for enhancing carbon fixation and emission reduction of an agricultural production system, developing green and low-carbon agriculture and meeting the requirement of realizing the aim of 'double carbon'. In order to achieve the aim, the net greenhouse gas emission estimation integrating carbon fixation and greenhouse gas emission of an agricultural production system is the basis and key for determining carbon fixation and emission reduction priority areas, agricultural planting systems and agricultural management measures. However, at present, aiming at carbon sequestration of an agricultural production system, the carbon sequestration of the agricultural production system is mainly measured by using a soil sample on a site scale. The method needs a long time sequence of soil samples to be capable of determining the annual change amount of soil carbon sequestration. On a regional scale, carbon sequestration of cultivated land is mainly simulated by adopting a carbon sequestration model, and different agricultural production systems are not distinguished. Aiming at the problem, the greenhouse gas emission evaluation mainly adopts an emission factor method, which is over simplified and has larger uncertainty. And the carbon sequestration and greenhouse gas emission assessment of the agricultural production system are usually carried out separately, and the two assessments are not combined, so that the carbon emission of the agricultural production system is difficult to be assessed comprehensively and objectively.
Accordingly, there is a need for new technical approaches to at least partially address the above-mentioned limitations of the prior art.
Disclosure of Invention
Aiming at the defects of the prior art, the invention firstly integrates various advanced models, methods and data, develops an advanced net greenhouse gas emission estimation method and system for integrated carbon fixation and greenhouse gas emission of regional agricultural planting systems, and scientifically determines the net greenhouse gas emission of different regional agricultural planting systems. The method can be used for determining areas with priority on carbon sequestration and emission reduction, agricultural planting systems and agricultural management measures, and provides important method technical support for carbon sequestration and emission reduction of agricultural production systems, development of green low-carbon agriculture and realization of a double-carbon target.
According to one aspect of the invention, a comprehensive estimation method for net greenhouse gas emission of a regional agricultural planting system is provided, which comprises the following steps:
s1: acquiring relevant information of an agricultural planting system in a research area, including agricultural management, remote sensing, soil and climate information, and performing parameter optimization and verification on an agricultural system model according to experimental observation data of crops in the research area to realize localization of the model;
s2: after localization, life cycle methods are used to evaluate CO caused by agricultural management2Discharge ECO2CO involving straw combustion2Discharging amount; CO produced in the production and use of nitrogenous, phosphatic and potash fertilizers2Discharging amount; diesel oil, plastic film, pesticide and CO produced by irrigation2Discharge capacity:
s3, adopting a mechanism model CH based on the process4MOD or DNDC models to simulate the methane emissions E of rice-based farming systemsCH4；
S4: coupling a global model for crop water use and a spatial reference non-linear model (SRNM) or DNDC (De-Nitrification)&De-Composition) model to calculate the nitrous oxide (N) of each agricultural planting system in the area2O) emission amount, and calculating indirect N caused by nitrogen fertilizer application by adopting emission factor method2O emissions and N resulting from straw combustion2Discharge amount of O, thereby obtaining the whole N2Total amount N of O discharged2O-N；
S5, adopting a RothC (Rothamsted Carbon model) or DAYCENT (Daily centre model) model based on the process to simulate the annual variation quantity delta SOC of the organic Carbon on the surface layer (0-30cm) of the soil and obtain the Carbon fixation quantity of the organic Carbon input quantity; and
s6: and (3) calculating the net greenhouse gas emission NGE of each agricultural planting system according to the following formula by combining the carbon sequestration and greenhouse gas emission:
NGE＝GHG-ΔSOC*44/12
wherein GHG is the amount of greenhouse gas emitted,
and GWPN2OAre each CH4And N2The global warming potential of O at the level of 100 years is respectively 28 and 265; delta SOC is the annual change in soil organic carbon for each agricultural planting system; n is a radical of2O-N being N2Total O emission; eCH4Is the methane emission; eCO2Is CO2And (4) discharging the amount.
According to the embodiment of the present invention, in S1, the agricultural management information includes the planting area of the agricultural planting systems that can be planted in the area, the planting area of various agricultural planting systems, the irrigation ratio, the pure nitrogen fertilizer, phosphate fertilizer and potash fertilizer amount, the yield, the farmyard manure input, the straw direct returning ratio and the straw combustion returning ratio; the climate information comprises climate driving data; the soil information includes soil properties of the respective agricultural planting systems.
In S3, CH according to an embodiment of the present invention4The input data of the MOD or DNDC model comprises soil sand grain proportion, daily temperature, pre-season crop yield, current-season rice yield, rice phenology, a water management system and farmyard manure usage.
According to an embodiment of the present invention, S4 includes: simulating the irrigation quantity of each agricultural planting system in the region under the irrigation scene by adopting a crop water model; inputting the precipitation, temperature, irrigation volume and soil properties of each agricultural planting system during the growing season into the SRNM or DNDC to obtain N for each agricultural planting system under irrigation and non-irrigation scenarios2O emission; then, the irrigation proportion is applied to carry out weighted average, and N of each agricultural planting system in the area is calculated2And (4) discharging the O.
According to an embodiment of the present invention, in S5, the model input data includes soil sand ratio, daily air temperature, pre-season crop yield, current season rice yield, rice phenology, moisture management regime, and farmyard manure usage.
According to an embodiment of the present invention, the organic carbon input amount of each agricultural production system in S6 includes straw returning organic carbon input, root system organic carbon input, and farmyard manure organic carbon input amount.
According to an embodiment of the invention, the agricultural planting system comprises a wheat, corn, soybean, rice planting system.
According to another aspect of the present invention, there is also provided a system for integrated estimation of net greenhouse gas emission from regional farming systems, comprising: one or more processors; and a memory storing instructions executable by the one or more processors to cause the system to perform the method according to the invention.
The invention integrates a plurality of agricultural system models, integrates multi-source data and information, and determines the net greenhouse gas emission of the agricultural planting system. The beneficial effects can be realized:
1) and (3) evaluating the net greenhouse gas emission of each planting system aiming at a certain region.
2) And identifying a main process in the emission process of the clean greenhouse gas, and taking a targeted measure aiming at the process to achieve the purpose of emission reduction.
3) The reduction of greenhouse gas emission in the whole or in regions of China is promoted, so that carbon peak reaching and carbon neutralization are realized.
Drawings
The same reference numbers in the drawings identify the same or similar elements or components. The objects and features of the present invention will become more apparent upon consideration of the following description taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a schematic flow diagram of a method for integrated estimation of net greenhouse gas emissions from a regional farming system according to an embodiment of the present invention.
FIG. 2 is a simulated unit CO for agricultural management in a research area, according to an embodiment of the present invention2A discharge variation trend chart;
FIG. 3 is a graph of simulated agricultural management-related CO applied to a research area according to an embodiment of the present invention2A trend graph of annual emission total variation;
FIG. 4 is a graph of the simulated trend of the unit methane emission (left) and the total annual methane emission (right) for a certain area of study for a method according to an embodiment of the present invention;
FIG. 5 is a graph of the direct N units resulting from nitrogen fertilizer input for agricultural systems simulated for use in a research area according to an embodiment of the present invention2A trend graph of O emissions;
FIG. 6 is a graph of indirect N for each agricultural system unit simulated for use in a research area by a method according to an embodiment of the present invention2A trend graph of O emissions;
FIG. 7 is a graph of simulated agricultural system units N applied to a research area according to a method of an embodiment of the present invention2A change trend graph of the total emission in O years;
FIG. 8 is an annual graph of carbon fixation for various agricultural systems simulated for use in a research area in accordance with an embodiment of the present invention;
FIG. 9 is a graph of net greenhouse gas unit emissions trend for various agricultural systems simulated for a research area according to methods of embodiments of the present invention; and
FIG. 10 is a graph of net greenhouse gas annual total emissions trend for various agricultural systems simulated for a research area according to methods of embodiments of the present invention.
Detailed Description
For the purpose of clearly illustrating the aspects of the present invention, preferred embodiments are provided below in conjunction with the detailed description of the drawings. The following description is merely exemplary in nature and is not intended to limit the present disclosure, application, or uses.
It should be understood that the various agricultural system models (crop models) referenced in the present disclosure are known per se, such as the various sub-modules of the model, various parameters, operating mechanisms, simulation processes such as localization, etc., and therefore the present disclosure focuses on a process of how to integrate multiple crop models and multi-source information to quantify the net greenhouse gas emissions of multiple agricultural growth systems within a research area.
FIG. 1 is a schematic flow diagram of a method for the integrated estimation of net greenhouse gas emissions from a regional farming system, according to an embodiment of the present invention. As shown in fig. 1, the method for comprehensively estimating net greenhouse gas emission of a regional farming system of an embodiment may include the following steps S1-S6:
and S1, acquiring relevant information of the agricultural planting system in the research area, including agricultural management, remote sensing, soil and climate information, and performing parameter optimization and verification on the agricultural system model according to experimental observation data of crops in the research area to realize localization of the model. Agricultural growing systems may include, for example, wheat, corn, soybean, rice growing systems, and the like.
More specifically, the agricultural management information may include agricultural planting systems that can be planted in the area, planting areas of various agricultural planting systems, irrigation proportions, depurated nitrogen fertilizer, phosphate fertilizer and potash fertilizer amounts, yield, farmyard manure input, straw direct returning and straw combustion returning proportions, and the like; climate information may include climate actuation data, etc.; the soil information includes soil properties of the respective agricultural planting systems, and the like. For example, the planting area, irrigation proportion, pure nitrogen fertilizer, phosphate fertilizer and potassium fertilizer content, yield, farmyard manure input, straw direct returning and straw burning returning proportion and the like of an agricultural planting system which can be planted in a region (such as province and city) can be determined according to the annual agricultural statistics of the region for many years, the climate driving data and the soil property of each agricultural planting system are collected from a public high-quality data set, and the phenological period of each agricultural system is collected from an agricultural observation station of the region.
In the present invention, the system model (crop model) may adopt a lifecycle method model, CH4MOD or DNDC model, global crop water model, spatial reference non-linear model (SRNM) or DNDC (De-Nitrification)&De-Composition) model, RothC (Rothamsted Carbon model), and DAYCENT (Daily centre model), among others. Other suitable models may of course be used. These models are known per se in the art, and before being applied to a specific region, they should generally be optimized, i.e. localized, to be suitable for the local region, using local specific observations (experimental data), for example, recorded by local agricultural observers.
The above-mentioned processes and principles of model parameter optimization (localization) and simulation prediction are well known to those skilled in the art and will not be described herein.
S2: after localization, life cycle methods are used to evaluate CO caused by agricultural management2Emission ECO2In particular CO of straw combustion2Discharging amount; CO produced in the production and use of nitrogenous fertilizer, phosphate fertilizer and potash fertilizer2Discharge capacity; diesel oil, plastic film, pesticide and CO produced by irrigation2And (4) discharging the amount. For example, the lifecycle method recommended by IPCC2019 greenhouse gas emissions can be used to evaluate CO caused by agricultural management2And (4) discharging the amount.
S3 adopting a mechanism model CH based on the process4MOD or DNDC models to simulate the methane emissions E of rice-based farming systemsCH4(ii) a Studies have shown that the major contribution to methane emissions in agricultural systems comes from rice. Accordingly, the present invention is primarily directed to rice-based farming systems. The input data of the model can comprise soil sand grain proportion, daily temperature, pre-season crop yield, current-season rice yield, rice phenology, a moisture management system, farmyard manure usage and the like.
S4: coupling a global model for crop water use and a spatial reference non-linear model (SRNM) or DNDC (De-Nitrification)&De-Composition) model to calculate the nitrous oxide (N) of each agricultural planting system in the area2O) discharge amount and adopting discharge factor method to calculate indirect N caused by nitrogen fertilizer use2O emissions and N resulting from straw combustion2Discharge amount of O, thereby obtaining the whole N2Total amount of O emitted N2O-N。
More specifically, a global crop water model can be adopted to simulate the irrigation amount of each agricultural planting system in a region under the irrigation situation; inputting the precipitation, temperature, irrigation quantity and soil property of each agricultural planting system in the growing season into SRNM or DNDC to obtain N of each agricultural planting system in the irrigation scene2And (4) discharging the O. The irrigation quantity under the non-irrigation condition is 0, and only precipitation, temperature and soil property are required to be input into SRNM or DNDC to obtain N of each agricultural system under the non-irrigation condition2O rowReleasing quantity; taking a grade city or province as a statistical unit, respectively counting the average N2O of the lattice points under the irrigation situation and the N under the non-irrigation situation2O, then the irrigation proportion of each agricultural system in the grade city or province is applied to carry out weighted average, and the N of each agricultural planting system in the region is calculated2And (4) discharging the O. N is a radical of hydrogen2The O emission should also include indirect N due to nitrogen fertilizer use2O emissions and N resulting from straw combustion2The amount of O discharged. The emission factor method recommended by the IPCC2019 national greenhouse gas emission list can be adopted to calculate the indirect N caused by the use of the nitrogen fertilizer2O emissions and N resulting from straw combustion2The amount of O discharged. For example, theoretically, the corresponding N can be obtained by multiplying the nitrogen fertilizer usage amount and the straw combustion amount by the respective emission factors2And (4) discharging the O.
S5, adopting a RothC (Rothamsted Carbon model) or DAYCENT (Daily Century model) model based on the process to simulate the annual variation quantity delta SOC of the organic Carbon on the soil surface layer (0-30cm), and obtaining the Carbon fixation quantity of the organic Carbon input quantity. For example, a rothc (rothamsted Carbon model) model based on the process) can be used to estimate the decomposition process of the organic Carbon on the surface layer (0-30cm) of the soil, so that a fixed amount of the organic Carbon input can be obtained. The organic carbon input amount of each agricultural production system mainly comprises straw returning organic carbon input, root system organic carbon input and farmyard manure organic carbon input amount. During simulation, the simulation is separately carried out on each agricultural planting system.
Thereafter, step S6 is performed to integrate the carbon sequestration and greenhouse gas emission, and the net greenhouse gas emission NGE (CO) of each farming system may be calculated according to the following formula2-eq ha-1yr-1)：
NGE＝GHG-ΔSOC*44/12
Among them, GHG is greenhouse gas emission (kgCO)2-eq ha-1yr-1)，
And GWPN2OAre each CH4And N2Global warming potential of O at 100 years level, values 28 and 265 (respectively)IPCC,2014) (ii) a Delta SOC is the annual variation of soil organic carbon (kg C ha) for each agricultural planting system-1yr-1)；N2O-N being N2Total O emission (kg Nha)-1yr-1)；ECH4Is the methane emission (kgCH)4 ha-1yr-1)；ECO2Is CO2Emission (kgCH)4 ha-1yr-1)。
Examples
Taking Heilongjiang province as an example, the comprehensive estimation of net greenhouse gas emission of agricultural planting systems in years of 1984 + 2018 in the research area is predicted and researched by using the technical method, wherein a life cycle method, an SRNM model, a CH4MOD model and a RothC model are used, and data information is observed by using years of experiments of climate, soil and site history. The specific process is as follows.
Acquiring data of all relevant information, and constructing a database for simulating net greenhouse gas emission of wheat, rice, corn and soybean, wherein the related data set is specifically shown in the following table 1:
TABLE 1
Then, a life cycle method recommended by an IPCC2019 greenhouse gas emission list is adopted to evaluate the emission of CO2 caused by agricultural management, the substances of the following formula model are input into nitrogen fertilizer, phosphate fertilizer, potash fertilizer, pesticide, agricultural film, agricultural diesel oil dosage, irrigation proportion and yield of each agricultural system in unit area, and related parameters comprise straw combustion proportion, harvest index, drying coefficient and CO of each substance2Emission factor:
Ei＝∑AIi×EFi+Eco2+266.48*Iratio
MB＝(Yieldi×Fidi/Hi-Yieldi×Fidi)×Rburi
wherein EFi is CO used in the dosage of nitrogenous fertilizer, phosphate fertilizer, potash fertilizer, pesticide, agricultural film and agricultural diesel oil2An emission factor; AIi represents the dosage of nitrogen fertilizer, phosphate fertilizer, potash fertilizer, pesticide, agricultural film and agricultural diesel; ECO2Indicating CO caused by straw combustion2Discharge capacity; m is a group ofBMass for combustion (kg ha)-1) The combustion amount of the straw is calculated by a straw combustion ratio (Rburi), a harvest index (Hi), a Yield (Yield) and a drying coefficient (Fid); comfIs the combustion ratio, Gefco2Is CO of straw combustion2Emission factor (available from IPCC2019 national greenhouse gas emission list); iratio indicates the proportion of the pesticide system irrigated on the province.
TCO2i＝Ei*Areai
Intermediate TCO2i represents the total amount of CO2 emissions (kg CO) caused by agricultural management of the agricultural production system i2eq/yr); ei represents the unit CO2Discharge (kg/ha); area denotes the Area ha. Total CO of four agricultural production systems of this province2The emissions are the sum of these four agricultural production systems. Agriculture-related Unit CO of four agricultural systems2The annual emission and the total annual emission are shown in fig. 2 and 3, respectively.
A CH4MOD model is adopted to simulate the methane emission on each rice lattice point in Heilongjiang province. The input of the model comprises soil sand grain proportion, daily temperature, exogenous organic matters, current-season rice yield, rice climates (transplanting period, jointing period and maturing period), and a water management system (the times and time of drying the rice fields are set to be 4-8 days after the jointing period of the rice, and then the rice fields are drained and harvested after continuous irrigation till 5-10 days before harvesting. Wherein the exogenous organic matters comprise the organic carbon content of straw returning, the organic carbon content of the root system of crops in the previous season and the organic carbon content of farmyard manure, and relevant data are well organized in the S1 database construction step. Each grid point simulates the methane emission under 30 moisture management regimes, and the average of the 30 samples is taken as the final methane emission. After the simulation is finished, the province is taken as a statistical unit to obtain the average CH4 emission of the rice in province level. FIG. 4 shows the variation trend of the total and unit methane emission in 1984 and 2018 of Heilongjiang province.
The method is characterized in that a global crop water model GCWM is adopted to simulate the irrigation quantity of four agricultural production systems of Heilongjiang province in growing seasons of each grid point, and main inputs comprise daily rainfall, daily potential evapotranspiration, crop related parameters (e.g. crop coefficient Kc and maximum root depth), soil hydrological properties, a seeding period and a maturation period, wherein the daily potential evapotranspiration is calculated by a P-M formula recommended by FAO 56. Then inputting irrigation quantity, soil property, average temperature of growing season, precipitation of growing season and nitrogen fertilizer usage amount to SRNM model on each grid point to calculate N of each grid point under irrigation situation2O direct discharge, then inputting other variables except irrigation water consumption into the SRNM model in each grid point, and calculating N of each grid point under non-irrigation condition2And O is directly discharged. Respectively taking provinces as statistical units to count N of irrigation scenes2O average and direct N of non-irrigation scenarios2O average value of discharge, and then N of the average unit area of each agricultural system of province can be obtained by applying provincial agricultural system irrigation proportion to carry out weighted average2O direct discharge (see FIG. 5), multiplying the respective areas and summing to obtain the total N for the four agricultural systems of the province2And O is directly discharged.
Then, calculating indirect discharge amount, including calculating indirect N caused by nitrogen fertilizer use by adopting a discharge factor method recommended by IPCC2019 national greenhouse gas discharge list2O emissions and N resulting from straw combustion2The amount of O discharged. The former includes nitrogen volatilization and nitrogen leaching. The input data includes various nitrogen inputs including nitrogen input in returning straw to field, nitrogen content in root system, nitrogen content in farmyard manure and inorganic nitrogen input, and the input data for calculating straw combustion includes direct straw combustion rate, yield and N for burning straw2O emission factor, etc. Final four agricultural linesSystematic N2O indirect N2The O-bleed is shown in FIG. 6.
To direct N2O bleed and Indirect N2Adding the O discharge to obtain a unit N of each agricultural system2And (4) discharging the O. Multiplying by the area to calculate N for each agricultural system2The total amount of emissions in O years is shown in FIG. 7.
The RothC model and the global crop water model GCWM were coupled to simulate the carbon fixation of various agricultural systems. Firstly, a global crop water model is adopted to simulate the irrigation quantity of the agricultural production system of Heilongjiang province on each grid point, and the irrigation quantity of each month is counted according to the month. And then inputting the historical average monthly latent evapotranspiration, monthly rainfall, monthly irrigation quantity, monthly temperature and annual average organic carbon input quantity, and inputting the initial soil organic carbon and soil layer thickness into a RothC model to preheat for 2000 years, so that each carbon reservoir reaches a balanced state. And then, the climate data and the organic carbon input amount in 1984-2018 are adopted to carry out simulation to obtain the soil organic carbon content in 2018, and the initial organic carbon amount is subtracted and is divided by 35 years to obtain the soil organic carbon variation in each year. The change is the soil organic carbon change under irrigation scenarios. And then, not inputting the monthly irrigation quantity, preheating and simulating RothC to obtain the soil organic carbon variation under the rainfed scene, averaging by taking the province as a statistical unit, and finally, carrying out weighted average by applying an irrigation proportion to obtain the average organic carbon variation of the province. The results are shown in FIG. 8.
Finally, based on the following formula, the greenhouse gas emission is unified according to the warming potential of each greenhouse gas, and the net greenhouse gas emission is calculated by integrating the greenhouse gas emission and carbon sequestration, and the variation trend of the unit net greenhouse gas emission and the annual total amount of net greenhouse gas emission is shown in fig. 9 and 10:
NGE＝GHG-ΔSOC*44/12
among them, GHG is greenhouse gas emission (kgCO)2-eq ha-1yr-1)，
And GWPN2OAre each CH4And N2The global warming potential of O at the level of 100 years is respectively 28 and 265; delta SOC is the annual change in soil organic carbon (kg Cha) for each agricultural planting system-1yr-1)；N2O-N being N2Total O emission (kg N ha)-1yr-1)；ECH4Is the emission of methane (kgCH)4 ha-1yr-1)；ECO2Is CO2Emission (kgCH)4ha-1yr-1)。
Additionally, embodiments of the present invention provide a regional farming system net greenhouse gas emission integrated estimation system that may include one or more processors and a memory storing instructions executable by the one or more processors to cause the automatic identification system to perform a method according to the present invention.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the specific embodiments in the present specification should not be construed as a limitation to the present invention.
Claims (7)
1. A comprehensive estimation method for net greenhouse gas emission of a regional agricultural planting system comprises the following steps:
s1: acquiring relevant information of an agricultural planting system in a research area, including agricultural management, remote sensing, soil and climate information, and performing parameter optimization and verification on an agricultural system model according to experimental observation data of crops in the research area to realize localization of the model;
s2: after localization, life cycle methods are used to evaluate CO caused by agricultural management2Discharge ECO2CO including straw combustion2Discharging amount; CO produced in the production and use of nitrogenous, phosphatic and potash fertilizers2Discharge capacity; diesel oil, plastic film, pesticide and CO produced by irrigation2Discharge capacity:
s3, adopting a mechanism model CH based on the process4MOD or DNDC models to simulate Rice-based farming systems for methane emissions ECH4；
S4: coupling a global model for crop water use and a spatial reference non-linear model (SRNM) or DNDC (De-Nitrification)&De-Composition) model to calculate nitrous oxide (N) for each agricultural growing system in the area of interest2O) direct discharge amount, and calculating indirect N caused by nitrogen fertilizer use by adopting discharge factor method2O emissions and N resulting from straw combustion2Discharge amount of O, thereby obtaining N2Total amount of O emitted N2O-N；
S5, adopting a RothC (Rothamsted Carbon model) or DAYCENT (Daily centre model) model based on the process to simulate the annual variation quantity delta SOC of the organic Carbon on the surface layer (0-30cm) of the soil and obtain the Carbon fixation quantity of the organic Carbon input quantity; and
s6: and (3) calculating the net greenhouse gas emission NGE of each agricultural planting system according to the following formula by combining the carbon sequestration and greenhouse gas emission:
NGE＝GHG-ΔSOC*44/12
wherein GHG is the amount of greenhouse gas emitted,
and GWPN2OAre each CH4And N2The global warming potential of O at the level of 100 years is respectively 28 and 265; delta SOC is the annual change of soil organic carbon of each agricultural planting system; n is a radical of2O-N being N2Total amount of O emitted; eCH4Is the methane emission; eCO2Is CO2Discharge capacity。
2. The method according to claim 1, wherein in S1, the agricultural management information includes agricultural planting systems that can be planted in the area, planting areas of various agricultural planting systems, irrigation proportion, pure nitrogen fertilizer, phosphate fertilizer and potassium fertilizer amount, yield, farmyard manure input, straw direct returning and straw combustion returning proportion; the climate information comprises climate actuation data; the soil information includes soil properties of the respective agricultural planting systems.
3. The method of claim 1, wherein in S3, CH4The input data of the MOD or DNDC model comprises soil sand grain proportion, daily temperature, pre-season crop yield, current-season rice yield, rice phenology, a water management system and farmyard manure usage.
4. The method according to claim 1, wherein S4 comprises: simulating the irrigation quantity of each agricultural planting system in the region under the irrigation scene by adopting a crop water model; inputting the precipitation, temperature, irrigation volume and soil properties of each agricultural planting system during the growing season into the SRNM or DNDC to obtain the N of each agricultural planting system under the irrigation and non-irrigation situations2O discharge amount; then, the irrigation proportion is applied to carry out weighted average, and N of each agricultural planting system in the area is calculated2And (4) discharging the O.
5. The method of claim 1, wherein the organic carbon input of each agricultural production system in S5 comprises straw returning organic carbon input, root system organic carbon input and farmyard manure organic carbon input.
6. The method of claim 1, wherein the agricultural growing system comprises a wheat, corn, soybean, rice growing system.
7. A system for integrated estimation of net greenhouse gas emission from a regional farming system, comprising: one or more processors; and memory storing instructions executable by the one or more processors to cause the system to perform the method of any one of claims 1-6.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116090639A (en) * | 2023-01-17 | 2023-05-09 | 中国科学院地理科学与资源研究所 | Method and device for predicting total process greenhouse gas emission of agricultural land system |
| CN116681315A (en) * | 2023-08-03 | 2023-09-01 | 南京农业大学 | Method, device, equipment and storage medium for estimating net emission of greenhouse gases |
| CN116882637A (en) * | 2023-09-05 | 2023-10-13 | 北京建工环境修复股份有限公司 | Farmland carbon emission analysis method and system |
| CN117371854A (en) * | 2023-10-17 | 2024-01-09 | 中国科学院沈阳应用生态研究所 | Carbon-nitrogen footprint evaluation method and system for farming industry |
| CN117669899A (en) * | 2024-02-02 | 2024-03-08 | 航天宏图信息技术股份有限公司 | Agricultural carbon emission reduction potential analysis method, system, electronic equipment and medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106780078A (en) * | 2016-11-25 | 2017-05-31 | 中国农业大学 | A kind of cereal crops produce the measuring method of carbon emission amount |
| CN110826797A (en) * | 2019-11-04 | 2020-02-21 | 中国科学院地理科学与资源研究所 | Method for determining optimal agricultural planting system in research area based on multi-target comprehensive evaluation system |
| CN111178789A (en) * | 2020-02-17 | 2020-05-19 | 北京师范大学 | Agricultural greenhouse gas evaluation method oriented to water-soil-energy comprehensive management |
| CN112508333A (en) * | 2020-11-04 | 2021-03-16 | 西北农林科技大学 | Estimation system and method for greenhouse effect of biomass energy utilization |
| CN113822479A (en) * | 2021-09-22 | 2021-12-21 | 中国科学院地理科学与资源研究所 | Multi-objective optimization method for regional agricultural planting structure considering production, environment and economic benefits |
| CN114219330A (en) * | 2021-12-20 | 2022-03-22 | 深圳市润科环保应用技术研究有限公司 | Greenhouse planting process-based carbon emission assessment method and system and computer equipment |
-
2022
- 2022-04-06 CN CN202210357602.7A patent/CN114781135A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106780078A (en) * | 2016-11-25 | 2017-05-31 | 中国农业大学 | A kind of cereal crops produce the measuring method of carbon emission amount |
| CN110826797A (en) * | 2019-11-04 | 2020-02-21 | 中国科学院地理科学与资源研究所 | Method for determining optimal agricultural planting system in research area based on multi-target comprehensive evaluation system |
| CN111178789A (en) * | 2020-02-17 | 2020-05-19 | 北京师范大学 | Agricultural greenhouse gas evaluation method oriented to water-soil-energy comprehensive management |
| CN112508333A (en) * | 2020-11-04 | 2021-03-16 | 西北农林科技大学 | Estimation system and method for greenhouse effect of biomass energy utilization |
| CN113822479A (en) * | 2021-09-22 | 2021-12-21 | 中国科学院地理科学与资源研究所 | Multi-objective optimization method for regional agricultural planting structure considering production, environment and economic benefits |
| CN114219330A (en) * | 2021-12-20 | 2022-03-22 | 深圳市润科环保应用技术研究有限公司 | Greenhouse planting process-based carbon emission assessment method and system and computer equipment |
Non-Patent Citations (1)
| Title |
|---|
| 宋博: "《关于中国口粮生产和消费对资源和环境相关影响的研究》", 中国经济出版社 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116090639A (en) * | 2023-01-17 | 2023-05-09 | 中国科学院地理科学与资源研究所 | Method and device for predicting total process greenhouse gas emission of agricultural land system |
| CN116681315A (en) * | 2023-08-03 | 2023-09-01 | 南京农业大学 | Method, device, equipment and storage medium for estimating net emission of greenhouse gases |
| CN116681315B (en) * | 2023-08-03 | 2023-11-21 | 南京农业大学 | Method, device, equipment and storage medium for estimating net emission of greenhouse gases |
| CN116882637A (en) * | 2023-09-05 | 2023-10-13 | 北京建工环境修复股份有限公司 | Farmland carbon emission analysis method and system |
| CN116882637B (en) * | 2023-09-05 | 2023-11-21 | 北京建工环境修复股份有限公司 | Farmland carbon emission analysis method and system |
| CN117371854A (en) * | 2023-10-17 | 2024-01-09 | 中国科学院沈阳应用生态研究所 | Carbon-nitrogen footprint evaluation method and system for farming industry |
| CN117669899A (en) * | 2024-02-02 | 2024-03-08 | 航天宏图信息技术股份有限公司 | Agricultural carbon emission reduction potential analysis method, system, electronic equipment and medium |
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