Life cycle carbon footprint metering method and device for crop planting system and medium

Abstract

The invention discloses a crop planting system life cycle carbon footprint metering method and device and a medium, and relates to the technical field of crop planting systems.The method comprises the steps that the yield per unit area of agricultural products of a crop planting system is simulated through a hydrological-biogeochemical process model; a first carbon emission parameter of the life cycle of the crop planting system; determining a second carbon emission parameter of the life cycle of the crop planting system by using an emission factor method; based on the first carbon emission parameter and the second carbon emission parameter, the total greenhouse gas emission amount of the crop planting system in the unit area life cycle is determined; and determining the ratio of the total emission of greenhouse gases in the unit area life cycle of the crop planting system to the unit area yield of agricultural products, wherein the ratio is the life cycle carbon footprint of the crop planting system. Based on the hydrological-biogeochemical process model and the emission factor method, the measurement precision of the life cycle carbon footprint of the crop planting system is improved.

Description

Technical Field

[0001] The present application relates to the technical field of crop planting systems, and in particular to a method, device and medium for measuring the life cycle carbon footprint of a crop planting system. Background Art

[0002] Against the backdrop of climate change and a growing world population, the contradiction between food security and greenhouse gas emission reduction has become increasingly prominent. Once global warming causes the global average temperature to exceed pre-industrial levels by 2°C, it will cause irreversible damage to the ecosystem, society, and economy on which human beings depend. Therefore, it is urgent for all walks of life to reduce greenhouse gas emissions. Agriculture contributes 10%-14% of global net anthropogenic greenhouse gas emissions and will undoubtedly play an important role. However, maintaining food security is a top priority and should not be at the expense of food security. The development of climate-smart cropping aims to effectively resolve this contradiction, that is, to strive to achieve high crop yields and low carbon footprints at the same time. From the progress of carbon footprint research and application, it can be seen that there are still some problems that need to be solved in the carbon footprint measurement technology methods to date. For example, some technical methods are based on static emission coefficients to quantify greenhouse gas emissions during the life cycle, which will undoubtedly make the carbon footprint measurement of agricultural products less accurate, because the crop planting system is always exposed to the unpredictable natural environment and the complex and diverse human activities, and its direct emissions of greenhouse gases such as carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) and even some indirect emissions will change significantly with environmental changes and human disturbances. These changes are difficult to be reflected in the static emission coefficients generated based on a few measurement data. For another example, some technical methods do not fully consider the greenhouse gas components or emission processes of the production system when measuring carbon footprints. For example, some methods ignore the changes in soil organic carbon pools, and some methods only measure CO2 emissions without taking into account direct and indirect emissions of non-CO2 greenhouse gases. Summary of the invention

[0003] The purpose of this application is to provide a method, device and medium for measuring the life cycle carbon footprint of a crop planting system, which can improve the measurement accuracy of the life cycle carbon footprint of the crop planting system.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In a first aspect, the present application provides a method for measuring the carbon footprint of a crop planting system life cycle, comprising:

[0006] Use hydrological-biogeochemical process models to simulate the yield of agricultural products per unit area in crop planting systems, as well as the first carbon emission parameters of the crop planting system life cycle;

[0007] Using the emission factor method, the second carbon emission parameter of the crop planting system life cycle is determined;

[0008] Determining the total greenhouse gas emissions per unit area of ​​the crop planting system during its life cycle based on the first carbon emission parameter and the second carbon emission parameter;

[0009] The ratio of total greenhouse gas emissions per unit area of ​​the crop planting system during its life cycle to the output of agricultural products per unit area is determined as the life cycle carbon footprint of the crop planting system.

[0010] Optionally, the first carbon emission parameter includes: net change in farmland soil organic carbon ΔSOC, net methane emission from biomass in farmland CH 4b and direct and indirect biogenic nitrous oxide emissions from farmland b ;

[0011] The second carbon emission parameter includes: non-biological greenhouse gas emissions GHG caused by energy consumption of machinery used in farmland ono , net non-biological carbon dioxide emissions IC caused by the application of exogenous substances to farmland, and methane emissions CH caused by the combustion of biomass in farmland 4bb , Nitrous oxide emissions from biomass burning in farmland bb , leaching runoff caused by farmland fertilization and nitrous oxide emissions outside farmland caused by gas deposition ofbi Indirect greenhouse gas emissions from mechanical and electrical storage and transportation outside farmland ofi .

[0012] Optionally, determining the total greenhouse gas emissions per unit area of ​​the crop planting system during its life cycle based on the first carbon emission parameter and the second carbon emission parameter includes:

[0013] Non-biogenic greenhouse gas emissions (GHG) from energy consumption of machinery used in farmland ono , net change of soil organic carbon in farmland △SOC, direct and indirect biogenic nitrous oxide emissions from farmland N2O b , net non-biological carbon dioxide emissions IC caused by the application of exogenous substances to farmland, net biogenic methane emissions CH4 from farmland 4b and CH4 emissions from biomass burning in farmland 4bb , determine greenhouse gas emissions from farmland;

[0014] Based on the leaching runoff caused by farmland fertilization and the nitrous oxide emissions outside the farmland caused by gas deposition, N2O ofbi Indirect greenhouse gas emissions from mechanical and electrical storage and transportation outside farmland ofi , determine greenhouse gas emissions outside of farmland;

[0015] The sum of greenhouse gas emissions within farmland and greenhouse gas emissions outside farmland is determined as the total greenhouse gas emissions.

[0016] Optionally, the greenhouse gas emissions in the farmland are:

[0017] GHG on =GHG ono -44 / 12(△SOC-IC)+16 / 12CH 4b · GWP m +44 / 28N2O b · GWP n ;

[0018] Among them, GHG on is the greenhouse gas emissions from farmland, GWP m is the global warming potential of methane, GWP n is the global warming potential of nitrous oxide.

[0019] Optionally, the greenhouse gas emissions outside the farmland are:

[0020] GHG of =GHG ofi +44 / 28N2O ofbi · GWP n ;

[0021] Among them, GHG of is the greenhouse gas emissions outside farmland.

[0022] Optionally, the non-biological greenhouse gas emissions caused by energy consumption of machinery used in the farmland are:

[0023]

[0024] in, is the energy consumption level of the i1th machine, is the emission factor of the i1th type of machinery, and n1 is the number of machinery types.

[0025] Optionally, the net non-biological carbon dioxide emissions caused by applying the exogenous substances to farmland are:

[0026] IC=L·EF L +D·EF D +U·EF U ;

[0027] Among them, L is the amount of lime applied, EF L is the emission factor of lime, D is the amount of dolomite applied, EF Dis the emission factor of dolomite, U is the amount of urea applied, EF U is the emission factor of urea.

[0028] Optionally, the nitrous oxide emissions outside the farmland caused by leaching runoff and gas deposition caused by farmland fertilization are:

[0029] N2O ofbi =N2O ofbil +N2O ofbig -0.15N2O bb ;

[0030] Among them, N2O ofbil N2O emissions from leaching runoff caused by fertilization on farmland, N2O ofbig N2O emissions from gas deposition caused by fertilizing agricultural land.

[0031] In a second aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned method for measuring the life cycle carbon footprint of a crop planting system.

[0032] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for measuring the life cycle carbon footprint of a crop planting system.

[0033] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0034] The present application provides a method, equipment and medium for measuring the carbon footprint of a crop planting system during its life cycle. It realizes the hydrological-biogeochemical process model simulation technology based on the fine characterization of the full coupling process of the carbon, nitrogen, phosphorus and water cycles, combined with the life cycle assessment method, and develops a carbon footprint measurement technology method with a more sufficient scientific basis, thereby overcoming the above two defects, greatly reducing the error in the carbon footprint accounting of agricultural and forestry products, and providing scientific and reasonable carbon accounting, carbon auditing and other carbon management systems by improving the accuracy of carbon footprint data and providing more scientific and applicable carbon footprint accounting tools. Provide scientific and technological support. The system that integrates the carbon footprint measurement method for the life cycle of a crop planting system provided by this application is a software system for calculating and evaluating the carbon footprint of a crop planting system during its life cycle that is universal, easy to operate, and can be operated in a business-like manner or applied in a market-oriented manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 This is a flow chart of a method for measuring the carbon footprint of a crop planting system life cycle in one embodiment of the present application;

[0037] Figure 2 This is a schematic diagram of a method for measuring the carbon footprint of a crop planting system during its life cycle in one embodiment of the present application;

[0038] Figure 3 This is a flow chart of error calculation in the simulation output link of the hydrological-biogeochemical process model in one embodiment of the present application;

[0039] Figure 4 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0041] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0042] In an exemplary embodiment, Figure 1 and Figure 2 As shown, a method for measuring the carbon footprint of a crop planting system life cycle is provided, including:

[0043] Step 101: Use the hydrological-biogeochemical process model (CNMM-DNDC) to simulate the yield of agricultural products per unit area of ​​the crop planting system and the first carbon emission parameter of the crop planting system life cycle. The first carbon emission parameter includes: the net change of organic carbon in farmland soil △SOC, the net methane emission of biogenic sources in farmland CH 4b and direct and indirect biogenic nitrous oxide emissions from farmland b .

[0044] Step 102: Determine the second carbon emission parameter of the crop planting system life cycle using the emission factor method. The second carbon emission parameter includes: non-biological greenhouse gas emissions GHG caused by energy consumption of machinery used in farmland ono , net non-biological carbon dioxide emissions IC caused by the application of exogenous substances to farmland, and methane emissions CH caused by the combustion of biomass in farmland 4bb , Nitrous oxide emissions from biomass burning in farmland bb , leaching runoff caused by farmland fertilization and nitrous oxide emissions outside farmland caused by gas deposition ofbi Indirect greenhouse gas emissions from mechanical and electrical storage and transportation outside farmland ofi .

[0045] Step 103: Based on the first carbon emission parameter and the second carbon emission parameter, determine the total greenhouse gas emissions per unit area of ​​the crop planting system during its life cycle.

[0046] Step 104: Determine the ratio of the total greenhouse gas emissions per unit area of ​​the crop planting system during its life cycle to the yield of agricultural products per unit area, which is the life cycle carbon footprint of the crop planting system.

[0047] Step 103 includes:

[0048] Step 103-1: GHG emissions from non-biological sources caused by energy consumption of machinery used in farmland ono , net change of soil organic carbon in farmland △SOC, direct and indirect biogenic nitrous oxide emissions from farmland N2O b , net non-biological carbon dioxide emissions IC caused by the application of exogenous substances to farmland, net biogenic methane emissions CH4 from farmland 4b and methane emissions from biomass burning 4bb , determine greenhouse gas emissions from farmland.

[0049] Step 103-2: Based on the leaching runoff caused by farmland fertilization and the nitrous oxide emissions outside the farmland caused by gas deposition, N2O ofbi Indirect greenhouse gas emissions from mechanical and electrical storage and transportation outside farmland ofi , determine greenhouse gas emissions outside of farmland.

[0050] Step 103-3: Determine the sum of greenhouse gas emissions within the farmland and greenhouse gas emissions outside the farmland, which is the total greenhouse gas emissions.

[0051] Among them, greenhouse gas emissions from farmland are:

[0052] GHG on =GHG ono-44 / 12(△SOC-IC)+16 / 12CH 4b · GWP m +44 / 28N2O b · GWP n .

[0053] Among them, GHG on is the greenhouse gas emissions from farmland, GWP m is the global warming potential of methane, GWP n is the global warming potential of nitrous oxide.

[0054] Greenhouse gas emissions outside farmland are:

[0055] GHG of =GHG ofi +44 / 28N2O ofbi · GWP n .

[0056] Among them, GHG of is the greenhouse gas emissions outside farmland.

[0057] The non-biological greenhouse gas emissions from energy consumption of machinery used in farmland are:

[0058]

[0059] in, is the energy consumption level of the i1th machine, is the emission factor of the i1th type of machinery, and n1 is the number of machinery types.

[0060] The net non-biological carbon dioxide emissions caused by the application of exogenous substances to farmland are:

[0061] IC=L·EF L +D·EF D +U·EF U .

[0062] Among them, L is the amount of lime applied, EF L is the emission factor of lime, D is the amount of dolomite applied, EF D is the emission factor of dolomite, U is the amount of urea applied, EF U is the emission factor of urea.

[0063] The amount of nitrous oxide emissions outside farmland caused by leaching runoff caused by farmland fertilization and gas deposition is:

[0064] N2O ofbi =N2O ofbil+N2O ofbig -0.15N2O bb .

[0065] Among them, N2O ofbil is the leaching runoff caused by farmland fertilization, N2O ofbig The amount of gas deposition caused by fertilizing farmland.

[0066] In an exemplary embodiment, a method for measuring the life cycle carbon footprint of a crop planting system is provided to clarify the links and boundaries of the life cycle carbon footprint accounting of the crop planting system, including greenhouse gas emissions in and outside the farmland; establish a supporting database for greenhouse gas emissions caused by mechanical operations such as the application of exogenous substances, irrigation, tillage, and harvesting of major crops in the farmland, as well as greenhouse gas emissions caused by the applied exogenous substances outside the farmland and their errors; clarify the accounting link variable values ​​provided by the process model CNMM-DNDC, including the yield per unit area of ​​agricultural products, the net change in organic carbon in farmland soil, the net CH4 emissions from biomass in farmland, and direct and indirect biomass. Source N2O emissions; Based on the zero-intercept univariate linear regression equation between the simulated values ​​and the observed values, the relative root mean square error, the simulation deviation and other statistical indicators, the simulation effect of CNMM-DNDC on the net CH4 emissions, N2O emissions, crop yields and net changes in soil organic carbon from farmland was tested, and the errors of variables in each link were determined; Through the simulation results of the process model CNMM-DNDC and the greenhouse gas emission factor support library based on spreadsheets and the crop planting system activity level database, a crop planting system life cycle carbon footprint accounting system was constructed, and the greenhouse gas emissions corresponding to each link and the unit area yield of agricultural products were calculated to measure the crop planting system life cycle carbon footprint.

[0067] The carbon footprint of the crop planting system throughout its life cycle (C fp ,kgCO2ekg -1 dm) is defined as the total cradle-to-gate greenhouse gas emissions of crop cultivation systems in the process of forming a unit product, calculated as the greenhouse gas emissions per unit area in the life cycle (GHG total , kgCO2e) and crop yield (Y, kgd.m.ha -1 )’s merchant.

[0068] C fp =GHG total / Y.

[0069] The boundary of the whole life cycle is defined as the "cradle to gate" category. The greenhouse gas measurement of this category ends when the marketable product is formed, and does not include greenhouse gas emissions generated during and after the product is used by consumers. The greenhouse gas emissions of the whole life cycle are measured as the sum of the direct and indirect emissions of the three greenhouse gases, including the inside and outside of the planting field (GHG on and GHG of ) Two-part emission.

[0070] GHG total =GHG on +GHG of .

[0071] Greenhouse gas emissions from farmland include: 1) net change in soil organic carbon in farmland; 2) net CH4 emissions from biogenic sources in farmland; 3) direct and indirect N2O emissions from biogenic sources in farmland; 4) non-biogenic CO2, CH4 and N2O emissions caused by energy consumption of machinery used in farmland; 5) net non-biogenic CO2 emissions caused by the application of exogenous substances to farmland; 6) CH4 and N2O emissions caused by biomass burning.

[0072] GHG on =GHG ono -44 / 12(△SOC-IC)+16 / 12CH 4b · GWP m +44 / 28N2O b · GWP n .

[0073] Among them, the net change of farmland soil organic carbon is the change of soil organic carbon simulated by the CNMM-DNDC model; the net CH4 emission from farmland biomass is the net CH4 emission simulated by the CNMM-DNDC model; the direct and indirect biogenic N2O emission refers to the nitrogen application (N2O bdc ) and nitrogen application in previous years (N2O bdp ) and N2O emissions caused by irrigation / runoff / sedimentation nitrogen application, that is, N2O emissions simulated by the CNMM-DNDC model.

[0074] Non-biological CO2, CH4 and N2O emissions from energy consumption of farm machinery according to the energy consumption level of different machines and emission factors calculate.

[0075]

[0076] Exogenous substances include lime, dolomite and urea, and the net CO2 emissions from non-biological sources are calculated based on their application amounts and emission factors.

[0077] IC=L·EF L +D·EF D +U·EF U .

[0078] GWP m and GWP n These are the global warming potentials of CH4 and N2O provided by IPCC respectively.

[0079] Greenhouse gas emissions outside of farmland include two parts:

[0080] GHG of =GHG ofi +44 / 28N2O ofbi · GWP n .

[0081] Part I: Greenhouse gas emissions (GHG ofi ), including fertilizers (GHG ofis ), organic fertilizer (GHG ofim ), pesticides (GHG ofic ), fuel (GHG ofif ), electricity (GHG ofie ) and others (GHG ofio ).

[0082] GHG ofi =GHG ofis +GHG ofim +GHG ofic +GHG ofif +GHG ofie +GHG ofio .

[0083] Part II: Leaching runoff caused by agricultural fertilization (N2O ofbil ) and gas deposition (N2O ofbig ) caused by off-farm N2O emissions (N2O ofbi ). Each item of off-farm greenhouse gas emissions is calculated based on activity data and emission factors.

[0084] N2O ofbi =N2O ofbil +N2O ofbig -0.15N2O bb .

[0085] A supporting database of greenhouse gas emission factors and their errors on and off farmland caused by anthropogenic consumption of non-bioenergy for major crop planting systems.

[0086] The model simulation effect was verified by using relative root mean square error (RMSE), relative model bias (RMB) and observation value (O i ) and analog value (S i ) between the zero-intercept linear regression (ZIR) and the slope (S), significance level and determination coefficient (R 2 ) for quantitative evaluation. S and R of zero-intercept linear regression 2 The closer the value is to 1, the better the model performance is. RMSE is used to judge the consistency between simulated values ​​and observed values. The closer the RMSE is to 0, the better the model simulation effect is. i and O i represent the i-th pair of simulated values ​​and observed values ​​respectively.

[0087]

[0088] When RMB i The absolute value of (i.e., |RMB|) is smaller than the relative random error of the observed value within the 95% confidence interval (defined as twice the coefficient of variation CV), indicating that the ith simulation value has a better simulation effect on the ith observation value, i.e., an effective simulation.

[0089]

[0090] C fp The uncertainty of the value at the 95% confidence interval (approximately two standard deviations) is based on the estimated GHG total The uncertainty estimation of Y simulated by CNMM-DNDC follows the random error and systematic error transfer formula.

[0091] The uncertainties in the net CH4 emissions, N2O emissions, crop yield Y, and net change in soil organic carbon provided by the CNMM-DNDC simulation are caused by errors in the model input data and insufficient scientific structure and / or inappropriate parameters of the model. First, since the activity level data such as field management practices in the area of ​​interest as model input variables are obtained through surveys, their uncertainties are considered to be minimal, and their errors have little impact on GHG total The contribution of uncertainty in the model simulation link or Y can be ignored. However, in the quantification of greenhouse gas emissions and the uncertainty in output Y, as well as C fp When the uncertainty is included, the errors in other model input variables are involved. Therefore, the systematic and random uncertainties in farm greenhouse gas emissions and yields Y, which are the model outputs, are caused by errors in model inputs (including meteorological variables and soil properties) and uncertain model scientific structure or parameters. The systematic error in the model output (ε s) is called the mean deviation of the simulation from the observation. It is expressed as the difference in the mean slope of the ZIR between the simulated and observed values, or as the mean of the RMBs produced by the model validation. When the ZIR between the simulated and observed values ​​is significant, it is expressed as the mean slope of the ZIR between the simulated and observed values; when the model validation fails to produce a significant ZIR, the mean of the RMBs is used as ε s The random error of the model output under the 95% confidence interval (ε r ) is expressed as two times the standard deviation of the significant ZIR slope or two times the standard deviation of the RMB generated by model validation. When there is no directly measured variable in the area of ​​interest used for simulation validation of the CNMM-DNDC model, the ε of the variable is assumed to be s and its corresponding ε r It is equal to the average error of the corresponding response variable in the existing study area, and the total error is about 30% of the observed value. Figure 3 The CNMM-DNDC simulation of greenhouse gas links and production Y is summarized. s and its corresponding ε r The calculation method of . Figure 3 In the figure, “SDofRMB” represents the standard deviation of the relative model deviation RMB; S represents the slope of the zero-intercept linear regression equation ZIR; “SDofS-1” represents the standard deviation of the slope S-1 of the zero-intercept linear regression equation ZIR.

[0092] For greenhouse gas emissions inside and outside farmland estimated using methods based on collected emission factors and their error support databases, the uncertainty is assumed to be random and is determined by the standard deviation SD value of the collected emission factor EF through error transfer calculation. Relying on the above calculation process, a spreadsheet-based calculation module is independently constructed, and a quantitative assessment system for the carbon footprint of the crop planting system throughout its life cycle with the process model as the core is established by combining the simulation results and errors of the CNMM-DNDC model, the collected emission factors of each link and their error support database, and activity level data. Among them, the unit area yield of agricultural products, the net change of organic carbon in farmland soil, the net CH4 emission of biogenic sources in farmland, and the direct and indirect biogenic N2O emission in farmland required for the calculation of the carbon footprint of the crop planting system life cycle are the algebraic sum of the 3-hour scale emissions provided by the CNMM-DNDC model simulation, and the emissions of the remaining links are calculated based on the specific emission factors given in the above support database. The universal software tool for measuring the carbon footprint of crop production systems constructed in this application can support its wide practical application in methodology and databases.

[0093] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 4As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for measuring the carbon footprint of a crop planting system life cycle is implemented.

[0094] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0095] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0096] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0097] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0098] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0099] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0100] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0101] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for measuring the carbon footprint of a crop planting system during its life cycle, characterized in that: include: Use hydrological-biogeochemical process models to simulate the yield of agricultural products per unit area in crop planting systems, as well as the first carbon emission parameters of the crop planting system life cycle; Using the emission factor method, the second carbon emission parameter of the crop planting system life cycle is determined; Determining the total greenhouse gas emissions per unit area of ​​the crop planting system during its life cycle based on the first carbon emission parameter and the second carbon emission parameter; The ratio of total greenhouse gas emissions per unit area of ​​the crop planting system during its life cycle to the output of agricultural products per unit area is determined as the life cycle carbon footprint of the crop planting system.

2. The method for measuring the life cycle carbon footprint of a crop planting system according to claim 1, characterized in that: The first carbon emission parameter includes: the net change of soil organic carbon in farmland △SOC, the net methane emission from biomass in farmland CH 4b and direct and indirect biogenic nitrous oxide emissions from farmland b ; The second carbon emission parameter includes: non-biological greenhouse gas emissions GHG caused by energy consumption of machinery used in farmland ono , net non-biological carbon dioxide emissions IC caused by the application of exogenous substances to farmland, and methane emissions CH caused by the combustion of biomass in farmland 4bb , Nitrous oxide emissions from biomass burning in farmland bb , leaching runoff caused by farmland fertilization and nitrous oxide emissions outside farmland caused by gas deposition ofbi Indirect greenhouse gas emissions from mechanical and electrical storage and transportation outside farmland ofi .

3. The method for measuring the carbon footprint of a crop planting system life cycle according to claim 2, characterized in that: Determining the total greenhouse gas emissions per unit area of ​​the crop planting system during its life cycle based on the first carbon emission parameter and the second carbon emission parameter includes: Non-biogenic greenhouse gas emissions (GHG) from energy consumption of machinery used in farmland ono , net change of soil organic carbon in farmland △SOC, direct and indirect biogenic nitrous oxide emissions from farmland N2O b , net carbon dioxide emissions from non-biological sources due to the application of exogenous substances to farmland IC, net methane emissions from biogenic sources in farmland CH 4b and CH4 emissions from agricultural biomass burning 4bb , determine greenhouse gas emissions from farmland; Based on the leaching runoff caused by farmland fertilization and the nitrous oxide emissions outside the farmland caused by gas deposition, N2O ofbi Indirect greenhouse gas emissions from mechanical and electrical storage and transportation outside farmland ofi , determine greenhouse gas emissions outside of farmland; The sum of greenhouse gas emissions within farmland and greenhouse gas emissions outside farmland is determined as the total greenhouse gas emissions.

4. The method for measuring the life cycle carbon footprint of a crop planting system according to claim 3, characterized in that: The greenhouse gas emissions from the farmland are: GHG on =GHG ono -44 / 12(△SOC-IC)+16 / 12CH 4b ·GWP m +44 / 28N2O b ·GWP n ; Among them, GHG on is the greenhouse gas emissions from farmland, GWP m is the global warming potential of methane, GWP n is the global warming potential of nitrous oxide.

5. The method for measuring the carbon footprint of a crop planting system life cycle according to claim 4, characterized in that: The greenhouse gas emissions outside the farmland are: GHG of =GHG ofi +44 / 28N2O ofbi ·GWP n ; Among them, GHG of is the greenhouse gas emissions outside farmland.

6. The method for measuring the life cycle carbon footprint of a crop planting system according to claim 4, characterized in that: The non-biological greenhouse gas emissions caused by energy consumption of machinery used in the farmland are: in, is the energy consumption level of the i1th machine, is the emission factor of the i1th type of machinery, and n1 is the number of machinery types.

7. The method for measuring the carbon footprint of a crop planting system life cycle according to claim 4, characterized in that: The net non-biological carbon dioxide emissions caused by the application of exogenous substances to farmland are: IC=L·EF L +D·EF D +U·EF U ; Among them, L is the amount of lime applied, EF L is the emission factor of lime, D is the amount of dolomite applied, EF D is the emission factor of dolomite, U is the amount of urea applied, EF U is the emission factor of urea.

8. The method for measuring the life cycle carbon footprint of a crop planting system according to claim 4, characterized in that: The nitrous oxide emissions outside the farmland caused by leaching runoff and gas deposition caused by farmland fertilization are: N2O ofbi = N2O ofbil +N2O ofbig -0.15N2O bb ; Among them, N2O ofbil is the leaching runoff caused by farmland fertilization, N2O ofbig The amount of gas deposition caused by fertilizing farmland.

9. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for measuring the life cycle carbon footprint of a crop planting system according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for measuring the life cycle carbon footprint of a crop planting system according to any one of claims 1 to 8 is implemented.

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