An accounting method based on full life cycle agricultural carbon emissions and emission reduction potential thereof

Abstract

The present application relates to a kind of accounting method based on whole life cycle agricultural carbon emissions and its emission reduction potential, it is related to data analysis field, after obtaining the target agricultural region identification sent by client, this method is not directly output single baseline carbon emissions, but from the preset database, the corresponding localized baseline parameter set of this area is called, and at least one emission reduction scenario is selected in response to client, the difference value operation of total carbon emissions determined based on baseline parameter set and total carbon emissions under emission reduction scenario is carried out, so as to obtain the carbon emission reduction of this area.This process will be organic link of localized baseline parameter and multi-scenario emission reduction simulation, so that the baseline carbon emission data that can only be static output originally is converted into quantifiable, comparable emission reduction effect evaluation result, to realize accurate evaluation carbon emission reduction effect.

Description

Technical Field

[0001] This invention relates to the field of data analysis and to an accounting method based on agricultural carbon emissions and their reduction potential throughout the entire life cycle. Background Technology

[0002] Agricultural carbon emission accounting is a crucial support for low-carbon agricultural development, widely applied in areas such as agricultural carbon trading, carbon labeling certification, carbon sink project development, and the promotion of low-carbon agricultural technologies. It is one of the core technical means for agricultural environmental management and low-carbon decision-making. In practical applications, even if some methods can obtain localized baseline parameters for the target area, such as data on fertilizer application, irrigation volume, and agricultural machinery fuel consumption for specific farmland obtained through field monitoring or farmer surveys, existing methods can only calculate a single baseline carbon emission based on these parameters.

[0003] However, the effectiveness of carbon reduction cannot be accurately assessed based solely on baseline carbon emissions. Summary of the Invention

[0004] In view of this, in order to solve the above problems, the present invention provides an accounting method for agricultural carbon emissions and their reduction potential based on the entire life cycle.

[0005] To achieve the above objectives, the present invention provides the following technical solution: Firstly, a method for calculating agricultural carbon emissions and their reduction potential based on the entire life cycle is provided, comprising: obtaining identification information of a target agricultural area sent by a client; retrieving a baseline parameter set corresponding to the target agricultural area from a preset database based on the identification information; wherein the baseline parameter set covers the direct and indirect carbon emissions of crops throughout their entire growth period from sowing to harvest; and, in response to at least one emission reduction scenario selected by the client, determining the difference between the total baseline carbon emissions and the total carbon emissions under at least one emission reduction scenario as the carbon emission reduction of the target agricultural area and outputting it to the client, wherein the total baseline carbon emissions are determined based on the baseline parameter set.

[0006] Therefore, this method, after obtaining the target agricultural area identifier sent by the client, does not directly output a single baseline carbon emission. Instead, it retrieves the corresponding localized baseline parameter set for that area from a pre-set database and, in response to the client's selection of at least one emission reduction scenario, calculates the difference between the total baseline carbon emission determined based on the baseline parameter set and the total carbon emission under the emission reduction scenario to obtain the carbon emission reduction for that area. This process organically connects the localized baseline parameters with multi-scenario emission reduction simulations, transforming the previously statically output baseline carbon emission data into quantifiable and comparable emission reduction effect assessment results, thereby achieving an accurate evaluation of carbon emission reduction effectiveness.

[0007] Optionally, direct carbon emissions include the sum of N2O emissions from farmland soil and carbon emissions from CH4 emissions from farmland; wherein, Farmland soil N2O carbon emissions are calculated based on the carbon emissions from farmland soil N2O, total nitrogen input, direct nitrogen fertilizer emission factors, crop drainage organic soil area, direct nitrous oxide emission factors, and N2O conversion to CO2. 2e The coefficients are determined; CH4 carbon emissions from farmland are determined based on planting area, crop growth period, baseline emission factor, growth period water management scaling factor, pre-season water management scaling factor, organic material application scaling factor, soil type scaling factor, and the coefficient for CH4 to CO2 conversion.

[0008] Therefore, direct carbon emissions are further defined as the sum of N2O emissions from farmland soil and CH4 emissions from farmland, with specific parameters clearly defined for each. Farmland soil N2O carbon emissions are determined by parameters such as total nitrogen input, area of ​​organic soil drained from crops, and direct emission factors for dryland or paddy fields, covering two key emission sources: nitrogen fertilizer application and organic soil drainage. Farmland CH4 carbon emissions are determined by multi-dimensional scaling factors including planting area, crop growth period, baseline emission factors, water management, organic material application, and soil type, fully incorporating core variables affecting methane emissions from paddy fields. This definition ensures that the accounting boundary for direct carbon emissions fully covers the main pathways of greenhouse gas production in farmland, providing a clear parameter basis for subsequent formulaic calculations.

[0009] Alternatively, the N2O carbon emissions from farmland soil may satisfy the following relationship: ; Where K represents the total K types of crops in the target agricultural area, K is an integer greater than or equal to 1, and i represents the i-th type of crop among the crop types; This represents the total carbon emissions from N2O emissions from farmland soil of K crops in the target agricultural area; This represents the total nitrogen input for the i-th crop type; It is a direct emission factor of nitrogen fertilizer; This represents the area of ​​the drained organic soil for the i-th crop type; Indicates the direct nitrous oxide emission factor of agricultural drainage organic soil; To convert N2O to CO 2e The coefficient; CH4 carbon emissions from farmland satisfy the following relationship: ; in, This represents the total carbon emissions from CH4 emissions from farmland of all K crops in the target agricultural region. This represents the planting area of ​​the i-th crop type. This represents the growth period of the i-th crop type; Indicates the emission factor on the baseline date; Scale factor for water management during the reproductive period; Scale factor for preseason water management; Apply scaling factors to organic materials; 27 represents the soil type scaling factor; 27 represents the conversion of CH4 to CO2. 2e The coefficient.

[0010] Therefore, the above relationship accurately reflects the emission differences under different soil types and moisture conditions, and achieves a refined characterization of methane emission differences under different crop management practices.

[0011] Optionally, indirect carbon emissions are determined based on carbon emissions from the extraction of agricultural input raw materials used in planting in the target agricultural area, carbon emissions from the production of agricultural inputs used in planting in the target agricultural area, carbon emissions from field management during crop planting in the target agricultural area, carbon emissions from transportation links related to the target agricultural area, and N2O emissions directly emitted from the application of chemical and organic fertilizers in the target agricultural area.

[0012] Therefore, the above relationship extends the accounting boundary from the farmland site to the upstream agricultural input mining and production links of the industrial chain, as well as the downstream transportation links. At the same time, it includes the emissions from the application of chemical fertilizers and organic fertilizers during field management into the category of indirect carbon emissions, forming a complete indirect emission accounting chain and avoiding the problem of carbon emission underestimation caused by omissions in the accounting boundary.

[0013] Alternatively, indirect carbon emissions may satisfy the following relationship: ; in, Indicates indirect carbon emissions; This refers to the carbon emissions generated during the extraction of agricultural raw materials used in planting within the target agricultural region. Carbon emissions from the production process of agricultural inputs used in planting within the target agricultural region. This refers to the carbon emissions from field management during crop cultivation in the target agricultural region. Carbon emissions from transportation links related to the target agricultural region. The amount of N2O directly emitted from the application of chemical and organic fertilizers in the target agricultural area; in, K represents the total number of K crop types in the target agricultural region, where K is an integer greater than or equal to 1. This represents the amount of agricultural inputs used for the i-th crop type in the j-th environmental characteristics. denoted as the carbon emission factor of agricultural inputs corresponding to the i-th crop type under environmental characteristics j; in, ; E represents the amount of N2O produced by nitrogen from chemical and organic fertilizers in the target agricultural area through atmospheric nitrogen deposition; The amount of N2O indirectly generated from nitrogen in chemical and organic fertilizers through leaching and runoff in the target agricultural area; in, ; The amount of nitrogen applied in chemical fertilizers in the target agricultural area. The nitrogen application rate in organic fertilizer in the target agricultural area is 0.0109; the emission factor for direct nitrogen emissions is 0.0109. To convert N2O to CO 2e The coefficient; in, 20% and 10% are assumed values ​​for the volatilization of nitrogen in organic fertilizer and chemical fertilizer, respectively; 0.0075 is the factor for N2O emissions caused by leaching and runoff loss; and 0.01 is the factor for N2O emissions caused by atmospheric nitrogen deposition. in, 20% represents the nitrogen loss rate in the leaching and runoff processes, and 0.0075 represents the factor contributing to N2O emissions caused by leaching and runoff losses.

[0014] Therefore, the above relationships enable the differentiation of emissions from agricultural inputs under different environmental conditions. Carbon emissions from the application of chemical and organic fertilizers are further broken down into three sub-items: direct emissions, atmospheric nitrogen deposition emissions, and leaching runoff emissions. These are calculated using three different relationships. In each relationship, parameters such as the assumed value of nitrogen application, volatilization, and loss rate are all localized, making the indirect carbon emission accounting results more consistent with the actual situation in the target area.

[0015] Optionally, at least one emission reduction scenario corresponds to at least one of the following carbon emission reductions: carbon emissions from fuel or electricity consumption during agricultural machinery operations in the target agricultural area, carbon emissions from fertilizer production and input in the target agricultural area, carbon emissions from pesticide and agricultural film production and input in the target agricultural area, carbon emissions from irrigation energy consumption in the target agricultural area, carbon emissions of soil greenhouse gas CH4 in the target agricultural area, and carbon emissions from livestock and poultry manure management in the target agricultural area.

[0016] Therefore, the carbon emissions corresponding to the emission reduction scenarios are further refined into six categories: agricultural machinery operation energy consumption, fertilizer production and input, pesticide and agricultural film production and input, irrigation energy consumption, soil greenhouse gas CH4, and livestock and poultry manure management. This classification method establishes a correspondence between emission reduction measures and specific emission sources, enabling each emission reduction scenario selected by the user to be mapped to a specific emission accounting model, avoiding the problem of distorted emission reduction calculations due to the disconnect between emission reduction scenarios and emission sources.

[0017] Alternatively, the carbon emissions generated by fuel or electricity consumption during agricultural machinery operation shall satisfy the following relationship: ; in, This indicates the carbon emissions generated by fuel or electricity consumption during agricultural machinery operations. Let u be the area of ​​the agricultural machinery operation in the target agricultural region; For the uth item, the fuel consumption or electricity consumption of agricultural machinery operation; Carbon emission factors for fuel oil or electricity.

[0018] Therefore, the above relationship allows for substitution. Parameter types (such as replacing diesel consumption with electricity consumption) and corresponding adjustments The value of can be flexibly adapted to different emission reduction scenarios such as energy-saving agricultural machinery replacement, operation frequency optimization, and new energy agricultural machinery replacement, so that the emission reduction effect of agricultural machinery can be quantitatively compared under a unified calculation framework.

[0019] Alternatively, the carbon emissions from fertilizer production and input can satisfy the following relationship: ; in, This indicates the carbon emissions from fertilizer production and input. , , These represent the application rates of nitrogen fertilizer, phosphorus fertilizer, and potassium fertilizer per unit area, respectively. , , These are the production emission factors for nitrogen fertilizer, phosphorus fertilizer, and potassium fertilizer, respectively. The carbon emissions from pesticide and agricultural film production and input satisfy the following relationship: ; in, This indicates the carbon emissions from the production and input of pesticides and agricultural films. This refers to the amount of pesticide applied per unit area. This refers to the amount of agricultural film used per unit area. Carbon emission factors for pesticide production; Carbon emission factors in agricultural film production.

[0020] Therefore, carbon emissions from fertilizer production and inputs are calculated by multiplying the application rates of nitrogen, phosphorus, and potassium fertilizers by their respective production emission factors and then summing the results. Carbon emissions from pesticides and agricultural films are calculated by multiplying the application rates by their corresponding emission factors. This separate accounting structure allows different emission reduction measures, such as nitrogen fertilizer reduction, organic fertilizer substitution, and slow-release fertilizer application, to independently adjust their corresponding parameters (e.g., reducing M or replacing emission factors) without affecting the accounting of other agricultural inputs, thus accurately reflecting the actual effect of individual agricultural input emission reduction measures.

[0021] Alternatively, the carbon emissions from irrigation energy consumption can satisfy the following relationship: ; in, Carbon emissions from irrigation energy consumption The planting area within the target agricultural region. This refers to the amount of irrigation water per unit area. Energy consumption for water lifting; Carbon emission factors for fuel oil or electricity; Soil greenhouse gas CH4 carbon emissions satisfy the following relationship: ; in, M5 represents the amount of CH4 carbon emissions from the soil greenhouse gas, and M5 represents the area of ​​paddy fields planted in the target agricultural region. Indicates the rice growth period, Indicates the emission factor on the baseline date; , , , Water management during the growing season, pre-season water management, application of organic materials, and soil type scaling factors; 27 is the conversion of CH4 to CO. 2e The coefficient.

[0022] Therefore, irrigation energy consumption carbon emissions are calculated by multiplying the planting area, irrigation water volume per unit area, water lifting energy consumption, and carbon emission factors. Soil greenhouse gas carbon emissions are calculated by multiplying the paddy field area, the number of days in the growing season, the baseline emission factor, and multiple scaling factors. When users select emission reduction scenarios such as sprinkler irrigation, drip irrigation, or intermittent irrigation, they can adjust parameters such as irrigation water volume or water management scaling factors to simultaneously affect both irrigation energy consumption and soil methane emissions, thus achieving simultaneous quantification of the dual emission reduction effects of irrigation management measures: "energy saving + methane reduction."

[0023] Alternatively, the carbon emissions from livestock and poultry manure management satisfy the following relationship: ; in, Carbon emissions from livestock and poultry manure management This refers to the amount of nitrogen excreted from livestock and poultry manure per unit area of ​​farmland. This refers to the volatile solids content of fecal waste; For the maximum potential of CH4 production, 27 is for the conversion of CH4 to CO. 2e The coefficient.

[0024] Therefore, carbon emissions from livestock and poultry manure management are calculated by multiplying nitrogen emissions per unit area, volatile solids content, maximum production potential, and methane conversion factor. The parameters in the formula can take different values ​​under different emission reduction scenarios, such as closed storage, aerobic composting, and anaerobic digestion to produce biogas. For example, aerobic composting can reduce the volatile solids content (VS), while anaerobic digestion can affect the methane conversion factor (MCF) through biogas recovery. This allows the emission reduction effects of different manure treatment methods to be compared and calculated under a unified calculation model, providing a quantitative basis for the optimal selection of livestock and poultry manure resource utilization pathways.

[0025] The objectives and other advantages of the present invention can be realized and obtained through the following detailed description. Attached Figure Description

[0026] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein: Figure 1 A flowchart illustrating a method for calculating agricultural carbon emissions and their reduction potential based on the entire life cycle, provided by this invention; Figure 2 This is a schematic diagram of the structure of a processing device provided by the present invention. Detailed Implementation

[0027] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] This embodiment provides a method for calculating agricultural carbon emissions and their reduction potential based on the entire life cycle. This method is executed by processing equipment, such as... Figure 1 As shown, the process of this method is as follows: S101, Obtain the identification information of the target agricultural area sent by the client.

[0029] In this embodiment, the client can be a mobile terminal, personal computer, or other interactive device with network communication capabilities deployed on the side of the agricultural production and management entity. The identification information of the target agricultural area refers to a data field that can uniquely identify the area to be calculated.

[0030] In one implementation, the identification information may include farmland plot numbers, farm registration codes, administrative region codes, geofence identifiers, or any combination of the above. For example, when the target agricultural area is a specific plot managed by an agricultural cooperative, the identification information may adopt a multi-level coding structure of "provincial administrative division code + county-level administrative division code + cooperative code + plot number," such as "3201-0118-NK031-005," corresponding to Province A, City B, Cooperative C, and Plot No. 5, respectively, thereby achieving step-by-step positioning from the provincial level to the plot level. As another example, when the target agricultural area is a contiguous farmland covered by a carbon sequestration project, the identification information may use geofence identifiers of the project boundary, such as a GeoJSON object defined by a sequence of polygon vertex coordinates, accurately describing the spatial extent of the area to be calculated.

[0031] In one implementation, users specify target agricultural areas on the client interface via map selection, drop-down list selection, scanning of plot QR codes, or manual input. Taking map selection as an example, the client interface integrates a geographic information system (GIS) component, displaying an overlay view of satellite remote sensing base maps and plot vector layers. Users can select target areas by clicking, selecting with a box, or tracing along plot boundaries. The client automatically extracts the preset identifier code for the plot based on the user's actions. Taking drop-down lists as an example, the client pre-synchronizes a list of areas within the user's authorized scope from the server, displaying it in a tree structure according to administrative level or organizational affiliation. After the user expands and selects target areas, the client obtains the corresponding identifier information. Taking QR code scanning as an example, QR code signs deployed in the fields pre-store the plot's identifier code. After the user scans the QR code with the client, the client automatically parses and fills in the identifier information.

[0032] In one implementation, after obtaining the identification information, the client generates a calculation request carrying that identification information. The calculation request can use an HTTP POST message or other custom protocol format. The identification information is encapsulated as a key-value pair in the request body, for example, using "region_id" as the key and the aforementioned identification code as the key value. The client sends the calculation request to the server executing this method via a wired or wireless network. After receiving the calculation request, the server parses the identification information in the request body and uses it as the index for calling the corresponding baseline parameter set in subsequent steps.

[0033] In one implementation, the identification information may also include a timestamp or crop season identifier to distinguish the accounting needs of the same plot in different planting seasons. For example, the identification information may take the form of a combination of "plot code + planting year + season", such as "3201-0118-NK031-005-2026-S1", which indicates an accounting request for the first season of 2026. This allows the system to accurately match the baseline parameter records corresponding to that planting season and avoid data confusion across multiple seasons.

[0034] S102, Based on the identification information, retrieve the baseline parameter set corresponding to the target agricultural area from the preset database; wherein, the baseline parameter set covers the entire growth period of crops from sowing to harvest, including direct carbon emissions and indirect carbon emissions.

[0035] Direct carbon emissions include the sum of carbon emissions from N2O emissions from farmland soil and CH4 emissions from farmland; among which, Farmland soil N2O emissions are calculated based on the carbon emissions from farmland soil N2O emissions, total nitrogen input, direct nitrogen fertilizer emission factors, crop drainage organic soil area, direct nitrous oxide emission factors, and N2O conversion to CO2. 2e The coefficient is determined.

[0036] For example, N2O emissions from farmland soil satisfy the following relationship: ; Where K represents the total K types of crops in the target agricultural area, K is an integer greater than or equal to 1, and i represents the i-th type of crop among the crop types; This represents the total carbon emissions (CO2 equivalent) from N2O emissions from farmland soil of K crops in the target agricultural area. 2e (Unit: kg) This represents the total nitrogen input for the i-th crop type (including chemical fertilizers, organic fertilizers, etc., in kgN / ha). As a direct emission factor of nitrogen fertilizer, the concentration is 0.01 kg N2O-N / kg in dryland and 0.003 kg N2O-N / kg in paddy fields. The area of ​​the crop drainage organic soil for the i-th crop type is represented in ha. The direct emission factor of nitrous oxide from organic soil in agricultural drainage is represented by a value of 5 kg N2O-N / ha / year. To convert N2O to CO 2e The coefficient.

[0037] CH4 emissions from farmland are determined based on planted area, crop growth period, baseline emission factor, growth period water management scaling factor, pre-season water management scaling factor, organic matter application scaling factor, soil type scaling factor, and the CH4-to-CO2 conversion coefficient. For example, farmland CH4 emissions satisfy the following relationship: ; in, This represents the carbon emissions (kg CO) from CH4 emissions from farmland of all K crops in the target agricultural area. 2e ); This represents the planting area of ​​the i-th crop type. This represents the crop growth period (planting cycle) of the i-th crop type, in days (d). This represents the emission factor on the baseline date (continuous flooding, no organic amendments, and mineral soils), in kgCH4 / (ha·d). The IPCC 2019 global default value is 1.19 kgCH4 / (ha·d) for continuous flooding, no organic amendments, and mineral soils. The scaling factor for water management during the reproductive period is shown in Table 1. The scaling factor for preseason water management is 1.0 for preseason flooding (>30 days) and 0.8 for preseason drainage / dry farming. The scaling factor for applying organic materials is 1.0 when no organic fertilizer is applied and 2.0 when straw / fertilizer / green manure is applied. The soil type scaling factor is 1.0 when no organic fertilizer is applied and 2.0 when straw / fertilizer / green manure is applied; 27 represents the conversion of CH4 to CO. 2e The coefficient.

[0038] Table 1. Scale Factors for Water Management Indirect carbon emissions are determined based on carbon emissions from the extraction of agricultural raw materials used in planting in the target agricultural area, carbon emissions from the production of agricultural inputs used in planting in the target agricultural area, carbon emissions from field management during crop planting in the target agricultural area, carbon emissions from transportation links related to the target agricultural area, and N2O emissions directly emitted from the application of chemical and organic fertilizers in the target agricultural area.

[0039] For example, indirect carbon emissions satisfy the following relationship: ; in, Indirect carbon emissions (kg CO) 2e ).

[0040] This refers to the carbon emissions generated during the extraction of agricultural raw materials used in planting within the target agricultural region. Carbon emissions from the production process of agricultural inputs used in planting within the target agricultural region. This refers to the carbon emissions from field management during crop cultivation in the target agricultural region. Carbon emissions from transportation links related to the target agricultural region. The amount of N2O directly emitted from the application of chemical and organic fertilizers in the target agricultural area; in, K represents the total number of K crop types in the target agricultural region, where K is an integer greater than or equal to 1. This represents the amount of agricultural inputs used for the i-th crop type in the j-th environmental characteristics. denoted as the carbon emission factor of agricultural inputs corresponding to the i-th crop type under environmental characteristics j; in, ; E represents the amount of N2O produced by nitrogen from chemical and organic fertilizers in the target agricultural area through atmospheric nitrogen deposition; The amount of N2O indirectly generated from nitrogen in chemical and organic fertilizers through leaching and runoff in the target agricultural area; in, ; The amount of nitrogen applied in chemical fertilizers in the target agricultural area. The nitrogen application rate in organic fertilizer in the target agricultural area is 0.0109; the emission factor for direct nitrogen emissions is 0.0109. To convert N2O to CO 2e The coefficient; in, 20% and 10% are assumed values ​​for the volatilization of nitrogen in organic fertilizer and chemical fertilizer, respectively; 0.0075 is the factor for N2O emissions caused by leaching and runoff loss; and 0.01 is the factor for N2O emissions caused by atmospheric nitrogen deposition. in, 20% represents the nitrogen loss rate in the leaching and runoff processes, and 0.0075 represents the factor contributing to N2O emissions caused by leaching and runoff losses.

[0041] thus, ;in, This represents the baseline total carbon emissions.

[0042] S103, in response to at least one emission reduction scenario selected by the client, the difference between the baseline total carbon emissions and the total carbon emissions under at least one emission reduction scenario is determined as the carbon emission reduction for the target agricultural area and output to the client, wherein the baseline total carbon emissions are determined based on the baseline parameter set.

[0043] In this embodiment, the processing terminal receives scenario selection parameters sent by the client. These scenario selection parameters indicate at least one emission reduction scenario selected by the user through the client interface. Each emission reduction scenario corresponds to at least one of the following carbon emission reductions: carbon emissions from fuel or electricity consumption during agricultural machinery operations in the target agricultural area; carbon emissions from fertilizer production and input in the target agricultural area; carbon emissions from pesticide and agricultural film production and input in the target agricultural area; carbon emissions from irrigation energy consumption in the target agricultural area; carbon emissions of CH4 greenhouse gases from soil in the target agricultural area; and carbon emissions from livestock and poultry manure management in the target agricultural area.

[0044] In one implementation, the scenario selection parameter can be a set of flag bits, with one flag bit corresponding to each emission reduction scenario. The processing terminal determines whether the user has selected a scenario by reading the value of the flag bit. For example, a flag bit value of "1" indicates selection, and a value of "0" indicates non-selection. The processing terminal iterates through each flag bit and includes the selected emission reduction scenario in the current calculation. In another implementation, the scenario selection parameter can be a scenario code list, which directly contains the preset codes of the emission reduction scenarios selected by the user. The processing terminal determines the type of emission reduction scenario to be executed by parsing this list.

[0045] After the processing terminal determines the selected emission reduction scenario, it corrects the parameters associated with the scenario in the baseline parameter set according to the preset parameter adjustment rules corresponding to each scenario, so as to generate the emission reduction scenario parameter set under the scenario, and then calculates the corresponding carbon emissions. The details are as follows.

[0046] 1) The carbon emissions generated by fuel or electricity consumption during agricultural machinery operations satisfy the following relationship: ; in, This indicates the carbon emissions generated by fuel (diesel) consumption (kgdiesel / ha) or electricity consumption (kWh / ha) during agricultural machinery operations. Traditional tillage consumes 8-12 kgdiesel / ha, no-till / reduced tillage consumes 3-6 kgdiesel / ha, and electric agricultural machinery consumes 15 kWh / h per unit area. Let u be the area of ​​the agricultural machinery operation in the target agricultural region; For the uth item, the fuel consumption or electricity consumption of agricultural machinery operation; The carbon emission factors are for fuel oil or electricity, with the diesel carbon emission factor set at 3.15 kgCO2e / kgdiesel (IPCC 2019, Provincial Greenhouse Gas Inventory Guidelines) and the average electricity carbon emission factor set at 0.582 kgCO2e / kWh (Ministry of Agriculture and Rural Affairs Carbon Emission Reduction Technology Manual). All of these parameters can be pre-collected and recorded in a preset database.

[0047] The following scenarios are set for reducing agricultural machinery energy consumption and emissions: 1) Scenario 1 (Energy-saving agricultural machinery replacement): Replace low-fuel-consumption tractors, combine harvesters, and other agricultural machinery to reduce fuel consumption per unit area. 2) Scenario 2 (Operation frequency optimization): Adopt no-till and reduced-till technologies to reduce redundant operations such as plowing and rotary tillage, thereby reducing agricultural machinery operation energy consumption. 3) Scenario 3 (New energy agricultural machinery replacement): Use electric tractors, electric plant protection machines, and other new energy agricultural machinery to replace traditional diesel agricultural machinery, thereby reducing fossil fuel consumption and carbon emissions.

[0048] 2) The carbon emissions from fertilizer production and input satisfy the following relationship: ; in, This indicates the carbon emissions from fertilizer production and input (kg CO2). 2e ); , , These represent the application rates of nitrogen, phosphorus, and potassium fertilizers per unit area, in kg / ha. , , The emission factors for nitrogen fertilizer, phosphate fertilizer, and potash fertilizer are respectively set at 6.6 kg CO2e / kg N, 1.0 kg CO2e / kg P2O5, and 0.8 kg CO2e / kg K2O. All these parameters can be pre-collected and recorded in a preset database.

[0049] 3) The carbon emissions from pesticide and agricultural film production and input satisfy the following relationship: ; in, This indicates the carbon emissions (kg CO) from pesticide and agricultural film production and input. 2e ), The pesticide application rate per unit area (based on active ingredient), kga.i. / ha. The amount of agricultural film used per unit area is expressed in kg / ha. For the carbon emission factor of pesticide production, take 18 kg CO2e / kg g a.i; To determine the carbon emission factor in agricultural film production, take 2.0 kg of CO2. 2e / kg.

[0050] The carbon emissions from fertilizer production and input, as well as from pesticide and agricultural film production and input, meet the following scenarios: 1) Scenario 1 (Nitrogen Fertilizer Reduction): While ensuring crop yield, reduce conventional nitrogen fertilizer application by 10%-30% and optimize nitrogen input structure. 2) Scenario 2 (Organic Fertilizer Substitution): Replace 20%-50% of chemical fertilizer nitrogen with organic fertilizer to achieve fertilizer reduction and nutrient recycling. 3) Scenario 3 (Low-Carbon Agricultural Input Application): Apply slow-release fertilizers and stable nitrogen fertilizers to reduce nitrogen loss; use biodegradable agricultural films in conjunction with pesticide reduction technologies to reduce emissions from agricultural input production and use.

[0051] 4) Irrigation energy consumption and carbon emissions satisfy the following relationship: ; in, Carbon emissions from irrigation energy consumption (kg CO) 2e ), The planting area in the target agricultural region, ha. The amount of irrigation water per unit area, in meters. 3 / ha; Energy consumption for water extraction is calculated in kWh / m³ (for electromechanical wells, it is generally taken as 0.25-0.4 kWh / m³). Carbon emission factors for fuel oil or electricity.

[0052] 5) Soil greenhouse gas CH4 carbon emissions satisfy the following relationship: ; in, Soil greenhouse gas CH4 carbon emissions (kg CO) 2e M5 represents the rice paddy planting area in the target agricultural region. This indicates the rice growing season (planting cycle), expressed in days (d). Indicates the emission factor on the baseline date; , , , The text discusses water management during the growing season, pre-season water management, organic material application, and soil type scaling factors, including continuous flooding. =1.0, intermittent irrigation =0.5-0.7, pre-season flooding =1.0, no organic materials applied =1.0, mineral soil =1.0; 27 represents the conversion of CH4 to CO. 2e The coefficient.

[0053] The above-mentioned carbon emissions from irrigation energy consumption and soil greenhouse gas CH4 emissions are applicable to the following scenarios: 1) Scenario 1 (Sprinkler Irrigation Replacement): Using sprinkler irrigation technology reduces irrigation water volume, lowers pumping energy consumption, and simultaneously reduces soil greenhouse gas emissions. 2) Scenario 2 (Drip Irrigation Replacement): Using drip irrigation technology provides precise water supply, minimizing irrigation water volume and pumping energy consumption, improving water resource utilization efficiency and emission reduction effects. 3) Scenario 3 (Paddy Field Water Optimization): Paddy fields adopt an intermittent irrigation (AWD) model to replace the traditional continuous flooding model, reducing methane (CH4) emissions from paddy fields; dryland farmland reduces irrigation frequency and implements water control management.

[0054] 6) The carbon emissions generated from livestock and poultry manure management satisfy the following relationship: ; in, Carbon emissions (kg CO) from livestock and poultry manure management 2e ), The nitrogen discharge from livestock and poultry manure per unit area of ​​farmland, expressed as kg N / ha. The volatile solids content of feces is expressed in kg VS / kg feces (the conventional value is 0.2-0.3). The maximum CH4 production potential is expressed in kg CH4 / kg VS (the conventional value for livestock and poultry manure is 0.25-0.35), where 27 represents the conversion of CH4 to CO. 2e The coefficient.

[0055] The carbon emissions from livestock and poultry manure management are applicable to the following scenarios: 1) Scenario 1 (Closed Storage): Using closed manure storage facilities reduces methane (CH4) volatilization emissions during storage. 2) Scenario 2 (Aerobic Composting): Aerobic composting of livestock and poultry manure kills harmful microorganisms, reduces CH4 emissions, and produces organic fertilizer for farmland, achieving nutrient cycling. 3) Scenario 3 (Anaerobic Digestion for Biogas): Constructing anaerobic digestion projects to anaerobic ferment livestock and poultry manure to produce biogas, recovering and utilizing biomass energy to replace fossil fuels, while reducing greenhouse gas emissions from manure. 4) Scenario 4 (Full Return of Manure to the Field): Returning all treated livestock and poultry manure to the field to replace part of the chemical fertilizers, reducing fertilizer production and soil nitrous oxide (N2O) emissions.

[0056] In summary, after obtaining the target agricultural region identifier sent by the client, this method does not directly output a single baseline carbon emission. Instead, it retrieves the corresponding localized baseline parameter set for that region from a pre-set database. Responding to the client's selection of at least one emission reduction scenario, it calculates the difference between the total baseline carbon emissions determined based on the baseline parameter set and the total carbon emissions under the emission reduction scenario, thus obtaining the carbon emission reduction for that region. This process organically connects the localized baseline parameters with multi-scenario emission reduction simulations, transforming the previously statically output baseline carbon emission data into quantifiable and comparable emission reduction effect assessment results, thereby achieving an accurate evaluation of carbon emission reduction effectiveness.

[0057] Figure 2 This is a schematic diagram of the structure of a processing device provided in an embodiment of this application. Exemplarily, the processing device may be a terminal, or a chip (system) or other component or assembly that can be disposed on the terminal. Figure 2 As shown, the processing device 200 may include a processor 201. Optionally, the processing device 200 may also include a memory 202 and / or a transceiver 203. The processor 201 is coupled to the memory 202 and the transceiver 203, for example, via a communication bus.

[0058] The following is combined with Figure 2 A detailed description of each component of the processing equipment 200 is provided below: The processor 201 is the control center of the processing device 200. It can be a single processor or a collective term for multiple processing elements. For example, the processor 201 can be one or more central processing units (CPUs), application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement the embodiments of this application, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0059] Optionally, the processor 201 can perform various functions of the processing device 200 by running or executing software programs stored in the memory 202 and calling data stored in the memory 202, such as performing the above-mentioned functions. Figure 1 The method shown.

[0060] In a specific implementation, as one example, the processor 201 may include one or more CPUs, for example... Figure 2 CPU0 and CPU1 are shown in the diagram.

[0061] In a specific implementation, as one example, the processing device 200 may also include multiple processors, for example... Figure 2 The processor 201 shown is an example. Each of the processors 201 can be a single-core processor or a multi-core processor. Here, "processor" can refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0062] The memory 202 is used to store the software program that executes the solution of this application, and the processor 201 controls its execution. The specific implementation method can be referred to the above method embodiment, and will not be repeated here.

[0063] Optionally, the memory 202 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 202 may be integrated with the processor 201 or exist independently, and may be connected via the interface circuitry of the processing device 200. Figure 2 (Not shown in the image) is coupled to processor 201, but this embodiment does not specifically limit this.

[0064] Transceiver 203 is used for communication with other processing devices. For example, if processing device 200 is a terminal, transceiver 203 can be used to communicate with a network device or with another terminal device. As another example, if processing device 200 is a network device, transceiver 203 can be used to communicate with a terminal or with another network device.

[0065] Optionally, transceiver 203 may include a receiver and a transmitter. Figure 2 (Not shown separately). The receiver is used to implement the receiving function, and the transmitter is used to implement the transmitting function.

[0066] Optionally, the transceiver 203 can be integrated with the processor 201, or it can exist independently and be connected via the interface circuit of the processing device 200. Figure 2 (Not shown in the image) is coupled to processor 201, but this embodiment does not specifically limit this.

[0067] Understandable Figure 2 The structure of the processing device 200 shown does not constitute a limitation on the processing device. Actual processing devices may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0068] Furthermore, the technical effects of the processing device 200 can be referred to the technical effects of the method described in the above method embodiments, and will not be repeated here.

[0069] It should be understood that the processor in the embodiments of this application can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0070] It should also be understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0071] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0072] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0073] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0074] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0075] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0076] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0077] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0078] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0079] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0080] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0081] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for calculating agricultural carbon emissions and their reduction potential based on the entire life cycle, characterized in that, include: Obtain the identification information of the target agricultural area sent by the client; Based on the identification information, a baseline parameter set corresponding to the target agricultural area is retrieved from a preset database; wherein, the baseline parameter set covers the entire growth period of crops from sowing to harvest, including both direct and indirect carbon emissions; In response to at least one emission reduction scenario selected by the client, the difference between the baseline total carbon emissions and the total carbon emissions under the at least one emission reduction scenario is determined as the carbon emission reduction for the target agricultural area and output to the client, wherein the baseline total carbon emissions are determined based on the baseline parameter set.

2. The method according to claim 1, characterized in that, The direct carbon emissions include the sum of carbon emissions from N2O emissions from farmland soil and CH4 emissions from farmland; wherein, The N2O carbon emissions from farmland soil are calculated based on the carbon emissions from farmland soil N2O, total nitrogen input, direct nitrogen fertilizer emission factors, crop drainage organic soil area, direct nitrous oxide emission factors, and the conversion of N2O to CO. 2e The coefficients are determined; The CH4 carbon emissions from farmland are calculated based on planting area, crop growth period, baseline emission factor, growth period water management scaling factor, pre-season water management scaling factor, organic matter application scaling factor, soil type scaling factor, and CH4 converted to carbon dioxide equivalent (CO). 2e The coefficient is determined.

3. The method according to claim 2, characterized in that, The N2O carbon emissions from the farmland soil satisfy the following relationship: ； Wherein, K represents the total K types of crops in the target agricultural area, K is an integer greater than or equal to 1, and i represents the i-th type of crop among the crop types; This represents the total carbon emissions from N2O emissions from farmland soil of the K crops in the target agricultural area; This represents the total nitrogen input for the i-th crop type; The direct emission factor of the nitrogen fertilizer; This represents the area of ​​the drained organic soil of the i-th crop type; The direct emission factor of nitrous oxide from agricultural drainage organic soil; The N2O is converted into CO 2e The coefficient; The CH4 carbon emissions from the farmland satisfy the following relationship: ； in, This represents the carbon emissions from CH4 emissions from farmland of the total K crops in the target agricultural area; This represents the planting area of ​​the i-th crop type. This indicates the crop growth period of the i-th crop type; This represents the emission factor for the baseline day; The scaling factor for water management during the reproductive period; The scaling factor for preseason water management; Apply a scaling factor to the organic material; 27 is the soil type scaling factor; 27 is the CH4 to CO conversion factor. 2e The coefficient.

4. The method according to claim 1, characterized in that, The indirect carbon emissions are determined based on the carbon emissions from the extraction of agricultural raw materials used in planting in the target agricultural area, the carbon emissions from the production of agricultural inputs used in planting in the target agricultural area, the carbon emissions from field management during crop planting in the target agricultural area, the carbon emissions from transportation links related to the target agricultural area, and the amount of N2O directly emitted from the application of chemical and organic fertilizers in the target agricultural area.

5. The method according to claim 4, characterized in that, The indirect carbon emissions satisfy the following relationship: ； in, This indicates the amount of indirect carbon emissions; The carbon emissions during the extraction of agricultural raw materials used in planting in the target agricultural area. The carbon emissions from the production process of agricultural inputs used in planting in the target agricultural area. This refers to the carbon emissions from field management during crop cultivation in the target agricultural region. The carbon emissions from transportation in connection with the target agricultural region. The amount of N2O directly emitted from the application of chemical and organic fertilizers in the target agricultural area; in, K represents the total number of K crop types in the target agricultural region, where K is an integer greater than or equal to 1. This indicates the amount of agricultural inputs used for the i-th crop type among the crop types under environmental characteristics j; The carbon emission factor of the agricultural input corresponding to the i-th crop type under environmental characteristics j; in, ; E represents the amount of N2O generated by nitrogen from chemical and organic fertilizers in the target agricultural area through atmospheric nitrogen deposition; The amount of N2O indirectly generated from nitrogen in chemical and organic fertilizers through leaching and runoff in the target agricultural area; in, ; The amount of nitrogen applied in fertilizers in the target agricultural area. 0.0109 represents the amount of nitrogen applied in organic fertilizer in the target agricultural area; 0.0109 is the emission factor for direct nitrogen emissions. To convert N2O to CO 2e The coefficient; in, 20% and 10% are assumed values ​​for the volatilization of nitrogen in organic fertilizer and chemical fertilizer, respectively; 0.0075 is the factor for N2O emissions caused by leaching and runoff loss; 0.01 is the factor for N2O emissions caused by atmospheric nitrogen deposition. in, 20% represents the nitrogen loss rate in the leaching and runoff processes, and 0.0075 represents the factor contributing to N2O emissions caused by leaching and runoff losses.

6. The method according to claim 1, characterized in that, The at least one emission reduction scenario corresponds to at least one of the following carbon emission reductions: carbon emissions from fuel or electricity consumption during agricultural machinery operations in the target agricultural area; carbon emissions from fertilizer production and input in the target agricultural area; carbon emissions from pesticide and agricultural film production and input in the target agricultural area; carbon emissions from irrigation energy consumption in the target agricultural area; carbon emissions of soil greenhouse gas CH4 in the target agricultural area; and carbon emissions from livestock and poultry manure management in the target agricultural area.

7. The method according to claim 6, characterized in that, The carbon emissions generated by fuel or electricity consumption during agricultural machinery operations satisfy the following relationship: ； in, This indicates the carbon emissions generated by fuel or electricity consumption during agricultural machinery operations. Let u be the area of ​​the agricultural machinery operation in the target agricultural region; The fuel or electricity consumption of the u-th agricultural machinery operation; Carbon emission factors for fuel oil or electricity.

8. The method according to claim 6, characterized in that, The carbon emissions from fertilizer production and input satisfy the following relationship: ； in, This indicates the carbon emissions from the production and input of the fertilizer; , , These represent the application rates of nitrogen fertilizer, phosphorus fertilizer, and potassium fertilizer per unit area, respectively. , , These are the production emission factors for nitrogen fertilizer, phosphorus fertilizer, and potassium fertilizer, respectively. The carbon emissions from the production and input of pesticides and agricultural films satisfy the following relationship: ； in, This indicates the carbon emissions from the production and input of the aforementioned pesticides and agricultural films. This refers to the amount of pesticide applied per unit area. This refers to the amount of agricultural film used per unit area. Carbon emission factors for pesticide production; Carbon emission factors in agricultural film production.

9. The method according to claim 6, characterized in that, The carbon emissions from irrigation energy consumption satisfy the following relationship: ； in, The carbon emissions from the irrigation energy consumption are mentioned. The planting area is the area within the target agricultural region. This refers to the amount of irrigation water per unit area. Energy consumption for water lifting; Carbon emission factors for fuel oil or electricity; The soil greenhouse gas CH4 carbon emissions satisfy the following relationship: ； in, M5 represents the amount of CH4 greenhouse gas emitted from the soil, and M5 represents the rice paddy planting area in the target agricultural region. Indicates the rice growth period, Indicates the emission factor on the baseline date; , , , Water management during the growing season, pre-season water management, application of organic materials, and soil type scaling factors; 27 is the conversion of CH4 to CO. 2e The coefficient.

10. The method according to claim 6, characterized in that, The carbon emissions generated from the management of livestock and poultry manure satisfy the following relationship: ； in, The carbon emissions generated by the management of livestock and poultry manure. This refers to the amount of nitrogen excreted from livestock and poultry manure per unit area of ​​farmland. The content of volatile solids in fecal waste; For the maximum potential of CH4 production, 27 is for the conversion of CH4 to CO. 2e The coefficient.

Images