Agricultural water-grain-energy-carbon correlation strategy analysis method and platform and storage medium

Abstract

The invention discloses an agricultural water-grain-energy-carbon association strategy analysis method and system, and a storage medium. The method comprises the steps of constructing a water-grain link module comprising a blue water footprint and a green water footprint; according to the water volume data and the blue water footprint, constructing a water-energy link module containing total irrigation energy consumption; obtaining an energy footprint of the target area according to the crop data and the total irrigation energy consumption, and constructing a grain-energy link module; calculating carbon emission data of the target area based on the energy footprint, and constructing an energy-carbon link module; coupling the water-grain link module, the water-energy link module, the grain-energy link module and the energy-carbon link module to obtain an agricultural water-grain-energy-carbon correlation model; and inputting the expected agricultural strategy of the target area into the agricultural water-grain-energy-carbon correlation model to obtain feasible information of the expected agricultural strategy. According to the method, modularized and automatic agricultural water-grain-energy-carbon link modeling can be realized, and the feasibility of an agricultural strategy is analyzed in a multi-scale manner.

Description

Technical Field

[0001] The present invention relates to the field of agricultural management technology, and in particular to an agricultural water-grain-energy-carbon correlation strategy analysis method, system and storage medium. Background Art

[0002] As strategic and basic resources, water, energy and food are the fundamental guarantee for maintaining life and ensuring sustainable development of social economy, and they have a high degree of commonality and internal correlation. At present, the shortage of water, energy and food has become one of the great challenges facing the world, and the contradiction of imbalance between supply and demand has hindered the green and sustainable development of the economy and society. In addition, with the rapid population growth and economic development, the over-exploitation and irrational use of resources have led to the shortage of water, energy and food resources, the excessive consumption of irrigation and fertilizers, and the generation of a large amount of greenhouse gas emissions, which has greatly affected the sustainable development of agriculture and aggravated the contradiction between water, energy and food in the agricultural system. According to statistics from the Food and Agriculture Organization of the United Nations, greenhouse gases released by agricultural land exceed 30% of the total global anthropogenic greenhouse gas emissions, equivalent to 15 billion tons of carbon dioxide per year. Therefore, the development of an agricultural water-grain-energy-carbon nexus analysis method, device, medium and equipment will not only help to comprehensively evaluate the potential for agricultural greenhouse gas emission reduction from the perspective of production source and resource coupling development, but also have important significance for maximizing the synergy between different resource sectors, ensuring food security and responding to climate change.

[0003] In the face of current development requirements, agricultural production development, sustainable use of water resources and energy, and agricultural carbon reduction have a high and complex correlation, which needs further consideration. Previous agricultural nexus analysis rarely took greenhouse gas emissions into account, and there were problems such as a single research scale and incomplete process description. There was a lack of scientific quantitative description of resource conflicts and feedback mechanisms within the nexus system. Therefore, analyzing and coordinating the relationship between the water-grain-energy-carbon nexus system will play a guiding role in ensuring the dual security of sustainable use of agricultural resources and food production, while achieving low-carbon emission reduction in agriculture.

[0004] The present invention is closely centered around the analysis of the agricultural water-grain-energy-carbon nexus, and can realize modular, automated, and intelligent agricultural water-grain-energy-carbon nexus data processing, modeling, and analysis, and judge the feasibility of various agricultural strategies. It provides decision-making support for coordinating the relationship between water-grain-energy-carbon elements, promoting the green and low-carbon transformation of agriculture, enhancing the ability to cope with climate change governance, and ensuring food security. Summary of the invention

[0005] The embodiments of the present invention provide an agricultural water-grain-energy-carbon linkage strategy analysis method, system and storage medium, which can realize modular and automated agricultural water-grain-energy-carbon linkage modeling and analyze the feasibility of agricultural strategies at multiple scales.

[0006] An embodiment of the present invention provides an agricultural water-grain-energy-carbon association strategy analysis method, comprising:

[0007] Obtain meteorological data, water volume data and crop data of the target area, and build a water-grain link module including blue water footprint and green water footprint;

[0008] constructing a water-energy nexus module including total irrigation energy consumption based on the water volume data and the blue water footprint;

[0009] Obtaining the energy footprint of the target area according to the crop data and the total irrigation energy consumption, and constructing a food-energy linkage module;

[0010] Calculate the carbon emission data of the target area based on the energy footprint, and build an energy-carbon linkage module;

[0011] The water-grain linkage module, the water-energy linkage module, the grain-energy linkage module and the energy-carbon linkage module are coupled to obtain an agricultural water-grain-energy-carbon linkage model;

[0012] The desired agricultural strategy of the target area is input into the agricultural water-grain-energy-carbon association model to obtain feasible information of the desired agricultural strategy.

[0013] As an improvement of the above scheme, the acquisition of meteorological data, water volume data and crop data of the target area and the construction of a water-grain link module including blue water footprint and green water footprint include:

[0014] Based on the crop surface net radiation, soil heat flux, average temperature, wind speed and water vapor pressure data in the meteorological data, the reference crop evapotranspiration is obtained using the Penman-Monteith equation;

[0015] Calculating actual evapotranspiration of crops based on the water stress coefficient and crop coefficient in the crop data;

[0016] Based on the effective rainfall in the water volume data, the blue water evapotranspiration and the green water evapotranspiration are calculated, and the blue water footprint and the green water footprint are obtained.

[0017] As an improvement of the above solution, the water-energy link module including the total irrigation energy consumption is constructed according to the water volume data and the blue water footprint, including:

[0018] Obtaining a groundwater irrigation ratio and a surface water irrigation ratio based on the water volume data;

[0019] Calculate groundwater irrigation energy consumption according to the groundwater irrigation ratio and the blue water footprint, and calculate surface water irrigation energy consumption according to the surface water irrigation ratio and the blue water footprint;

[0020] The total irrigation energy consumption is obtained according to the sum of the groundwater irrigation energy consumption and the surface water irrigation energy consumption.

[0021] As an improvement of the above solution, the energy footprint of the target area is obtained according to the crop data and the total irrigation energy consumption, and a grain-energy link module is constructed, including:

[0022] Calculating energy consumption during the use of agricultural machinery based on the crop data;

[0023] The indirect energy consumption in the production of seeds, pesticides, agricultural films and fertilizers was calculated using the Leontief inverse matrix model;

[0024] The energy footprint of the target area is obtained according to the sum of the energy consumption during the use of the agricultural machinery, the indirect energy consumption and the total irrigation energy consumption.

[0025] As an improvement of the above solution, the carbon emission data of the target area is calculated based on the energy footprint to construct an energy-carbon link module, including:

[0026] According to the crop data and the energy footprint, calculate the sum of CH4 emissions from rice fields, N2O emissions from agricultural land, CO2 emissions from direct energy use, CO2 emissions from indirect energy use, and CH4 and N2O emissions from straw burning, and use this as emission-side data;

[0027] According to the crop data, the photosynthetic carbon absorption of the crops and the increase in the soil organic carbon pool storage caused by returning the straw to the field are obtained, and the sum of the photosynthetic carbon absorption of the crops and the increase in the soil organic carbon pool storage caused by returning the straw to the field is used as the absorption side data;

[0028] The carbon emission data of the target area is obtained according to the difference between the emission side data and the absorption side data.

[0029] As an improvement of the above scheme, the coupling of the water-grain linkage module, the water-energy linkage module, the grain-energy linkage module and the energy-carbon linkage module to obtain an agricultural water-grain-energy-carbon association model includes:

[0030] Constructing constraint models of the water-grain linkage module, the water-energy linkage module, the grain-energy linkage module and the energy-carbon linkage module to obtain the agricultural water-grain-energy-carbon association model;

[0031] The agricultural water-grain-energy-carbon association model includes: crop production capacity constraints, groundwater irrigation ratio constraints, straw burning area constraints and carbon emission constraints;

[0032] The crop production capacity constraints include:

[0033] A i(min) ≤A i ≤A i(max)

[0034] In the formula, A i represents the sowing area of ​​crop i, A i(min) A represents the lower limit of the expected planting area of ​​crop i, i(max) represents the upper limit of the expected planting area of ​​crop i;

[0035] The groundwater irrigation ratio constraints include:

[0036] R gw(min) ≤R gw ≤R gw(max)

[0037] In the formula, R gw represents the proportion of groundwater irrigation, R gw(min) Represents the lower limit of groundwater irrigation ratio, R gw(max) It indicates the upper limit of groundwater irrigation ratio;

[0038] The straw burning area constraints include:

[0039] A straw(min) ≤A straw ≤A straw(max)

[0040] In the formula, A straw represents the straw burning area, A straw(min) Indicates the lower limit of straw burning area, A straw(max) Indicates the upper limit of the straw burning area;

[0041] The carbon emission constraints include:

[0042] E agri(min) ≤E agri ≤E agri(max)

[0043] In the formula, E agri represents the carbon emission data of the target area, E agri(min) Indicates the lower limit of carbon emissions in the target area, E agri(max) Indicates the upper limit of carbon emissions in the target area.

[0044] As an improvement of the above scheme, the step of inputting the desired agricultural strategy of the target area into the agricultural water-grain-energy-carbon association model to obtain feasible information of the desired agricultural strategy includes:

[0045] Input the expected sowing type, expected sowing area range, expected groundwater irrigation ratio range, and expected straw burning area range corresponding to the expected agricultural strategy into the agricultural water-grain-energy-carbon association model to obtain the corresponding carbon emission data results;

[0046] It is determined whether the carbon emission data result meets a preset expected carbon emission range. If so, the expected agricultural strategy is determined to be a feasible strategy.

[0047] Another embodiment of the present invention provides an agricultural water-grain-energy-carbon correlation strategy analysis platform, including:

[0048] The water-grain link construction module is used to obtain the meteorological data, water volume data and crop data of the target area and build a water-grain link module including blue water footprint and green water footprint;

[0049] A water-energy nexus building module, used to build a water-energy nexus module including total irrigation energy consumption according to the water volume data and the blue water footprint;

[0050] A grain-energy nexus building module, used for obtaining the energy footprint of the target area according to the crop data and the total irrigation energy consumption, and building a grain-energy nexus module;

[0051] An energy-carbon bond building module, used to calculate the carbon emission data of the target area based on the energy footprint, and build an energy-carbon bond module;

[0052] A correlation model building module, used for coupling the water-grain linkage module, the water-energy linkage module, the grain-energy linkage module and the energy-carbon linkage module to obtain an agricultural water-grain-energy-carbon correlation model;

[0053] The association strategy analysis module is used to input the expected agricultural strategy of the target area into the agricultural water-grain-energy-carbon association model to obtain feasible information of the expected agricultural strategy.

[0054] Another embodiment of the present invention provides an agricultural water-grain-energy-carbon correlation strategy analysis platform, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, the agricultural water-grain-energy-carbon correlation strategy analysis method described in the above-mentioned embodiment of the invention is implemented.

[0055] Another embodiment of the present invention provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the agricultural water-grain-energy-carbon correlation strategy analysis method as described in the above-mentioned embodiment of the invention.

[0056] Compared with the prior art, in an embodiment of the present invention, a comprehensive analysis method for the coupling of agricultural water-grain-energy-carbon linkages is established. According to data such as meteorology, water volume, and crops, a nonlinear feedback relationship between the interaction of two factors such as water-grain, grain-energy, water-energy, energy-carbon, and energy-carbon is constructed, and each linkage module is further modeled to form an agricultural water-grain-energy-carbon linkage model with multiple factors interwoven. After the expected agricultural strategy is input into the agricultural water-grain-energy-carbon linkage model, the carbon emission results corresponding to agricultural strategies within different expected ranges can be analyzed and obtained, and it can be automatically determined whether the carbon emission results can meet the carbon emission expectations in the expected agricultural strategy, and the feasibility of different agricultural strategies can be obtained, thereby providing decision-making reference information for the coordinated optimization management of agricultural water resources, energy, grain production, and carbon emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a flow chart of an agricultural water-grain-energy-carbon correlation strategy analysis method provided by one embodiment of the present invention;

[0058] Figure 2 This is a schematic diagram of the structure of an agricultural water-grain-energy-carbon correlation strategy analysis platform provided by an embodiment of the present invention;

[0059] Figure 3 It is a structural schematic diagram of an agricultural water-grain-energy-carbon correlation strategy analysis platform provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] See also Figure 1 , is a flow chart of an agricultural water-grain-energy-carbon association strategy analysis method provided by an embodiment of the present invention, including steps S101 to S106:

[0062] S101, obtaining meteorological data, water volume data and crop data of the target area, and constructing a water-grain link module including blue water footprint and green water footprint;

[0063] Specifically, meteorological data, water volume data and crop data can be collected through site observation data and related information in the target area; meteorological data may include water vapor pressure, temperature, wind speed, etc.; water volume data may include supply and demand / water data, irrigation data, etc.; crop data may include crop growth period, crop yield data, etc.

[0064] S102, constructing a water-energy link module including total irrigation energy consumption according to the water volume data and the blue water footprint;

[0065] Specifically, the blue water footprint is the quantified irrigation water withdrawal data, which refers to the amount of water extracted directly from surface water bodies (such as rivers, lakes and reservoirs) or groundwater; and the total irrigation energy consumption is also composed of groundwater irrigation energy consumption and the surface water irrigation energy consumption.

[0066] S103, obtaining the energy footprint of the target area according to the crop data and the total irrigation energy consumption, and constructing a grain-energy linkage module;

[0067] Specifically, by calculating the direct or indirect energy consumption generated by the use of agricultural machinery, agricultural films, pesticides, and fertilizer application in the crop production process, and combining it with the total irrigation energy consumption, the grain-energy nexus can be obtained.

[0068] S104, calculating the carbon emission data of the target area based on the energy footprint, and constructing an energy-carbon linkage module;

[0069] Specifically, by calculating the direct or indirect nitrous oxide (N2O) emissions from farmland, the methane (CH4) emissions from rice fields, and the carbon sinks generated by plant photosynthesis and straw return to the fields, we can obtain comprehensive carbon emission data and build a link module for the grain-carbon system.

[0070] S105, coupling the water-grain linkage module, the water-energy linkage module, the grain-energy linkage module and the energy-carbon linkage module to obtain an agricultural water-grain-energy-carbon association model;

[0071] Specifically, using irrigation water and food production energy consumption as bridges and access points, the water demand for food crop irrigation (water-food) is calculated, the energy required for crop irrigation pumping and water delivery (water-energy), and the greenhouse gas emissions generated by these energy consumption (energy-carbon). The direct and indirect energy footprints generated by the use of agricultural machinery, agricultural films, pesticides, and fertilizers in the food production process are further included (food-energy), and the direct / indirect N2O emissions from farmland, CH4 emissions from rice fields, and emission processes such as carbon fixation due to plant photosynthesis and carbon sinks from returning straw to the field are considered (food-carbon). The aforementioned water-food, water-energy, food-energy, energy-carbon, and food-carbon links are coupled to ultimately achieve parameterization and modeling of the agricultural water-food-energy-carbon relationship, and obtain a multi-dimensional interwoven strategy analysis model.

[0072] S106: Input the expected agricultural strategy of the target area into the agricultural water-grain-energy-carbon association model to obtain feasible information of the expected agricultural strategy.

[0073] In a specific implementation method, the expansion of the sown area in a region will often lead to an increase in the carbon emission data of the region. Therefore, the planting area cannot be expanded indefinitely in order to increase the total crop yield; similarly, the planting area cannot be reduced indefinitely in order to reduce carbon emission data, or other agricultural strategies cannot be adjusted indefinitely in order to meet a certain indicator.

[0074] It can be seen that the expected agricultural strategy needs to comprehensively consider the carbon emission indicators, crop yield indicators and other data of the target area; in this embodiment, the expected agricultural strategy includes but is not limited to the expected sowing type, expected sowing area range, expected groundwater irrigation ratio range, expected straw burning area range, etc. in the target area in the future; after the expected sowing type, expected sowing area range, expected groundwater irrigation ratio range, and expected straw burning area range are input into the agricultural water-grain-energy-carbon association model, the carbon emission data results corresponding to the strategy can be calculated. If the carbon emission data results cannot meet the expected carbon emission range, the strategy is judged to be infeasible; if so, it is judged to be feasible.

[0075] In summary, the agricultural water-grain-energy-carbon correlation model provided in this embodiment can quickly and accurately screen out feasible agricultural strategy results based on the agricultural water-grain-energy-carbon correlation relationship, and provide a reference for decision-making on the coordinated optimization management of agricultural water resources, energy, food production and carbon emissions.

[0076] In this embodiment, preferably, the step of acquiring the meteorological data, water volume data and crop data of the target area and constructing a water-grain linkage module including blue water footprint and green water footprint includes:

[0077] Based on the crop surface net radiation, soil heat flux, average temperature, wind speed and water vapor pressure data in the meteorological data, the reference crop evapotranspiration is obtained using the Penman-Monteith equation;

[0078] Calculating actual evapotranspiration of crops based on the water stress coefficient and crop coefficient in the crop data;

[0079] Based on the effective rainfall in the water volume data, the blue water evapotranspiration and the green water evapotranspiration are calculated, and the blue water footprint and the green water footprint are obtained.

[0080] Specifically, the method for calculating the reference crop evapotranspiration using the Penman-Monteith equation is as follows:

[0081]

[0082] Where ET0 represents the reference crop evapotranspiration (mm); R n represents the net radiation on the crop surface (MJ·m -2 ·d -1 ); G represents soil heat flux (MJ·m -2 ·d -1 ); T is the average temperature (℃); U2 is the wind speed at 2m above the ground (m·s -1 );e s Indicates saturated water vapor pressure (kPa); e a represents the actual water vapor pressure (kPa); δ represents the slope of the saturated water vapor pressure-temperature curve (kPa·℃ -1 ); γ represents the thermometer constant (kPa·℃ -1 ).

[0083] Specifically, based on the water stress coefficient and crop coefficient in the crop data, the method for calculating the actual evapotranspiration of the crop is as follows:

[0084] ET a =K s ×K c ×ET0

[0085] In the formula, ET a Indicates the actual evapotranspiration of crops (mm); K s represents the water stress coefficient; K c The crop coefficient expressing the evapotranspiration relative to a reference crop.

[0086] Specifically, based on the effective rainfall in the water volume data, the method for calculating the blue water evapotranspiration and the green water evapotranspiration is as follows:

[0087] P eff =σP

[0088] Where P eff and P are the effective rainfall and total rainfall during the crop growth period (mm), respectively; σ is the effective utilization coefficient of rainfall.

[0089] If the actual evapotranspiration of crops on that day is ET a Less than effective rainfall P eff , then the green water evapotranspiration is equal to the actual evapotranspiration ET a ; If the actual evapotranspiration of crops on that day is ET a Greater than effective rainfall P eff , then the green water evapotranspiration is equal to the effective rainfall P eff ;

[0090] If the actual evapotranspiration of crops on that day is ET a Less than effective rainfall P eff , then the blue water evapotranspiration is recorded as 0; if the actual evapotranspiration of crops on that day is ET a Greater than effective rainfall P eff , then the blue water evapotranspiration is equal to the actual evapotranspiration ET a and effective rainfall P eff The difference.

[0091] Specifically, the blue water footprint and the green water footprint are obtained by the following formula:

[0092]

[0093]

[0094] In the formula, ET blue and E.T. green are the evapotranspiration of blue water and green water respectively (mm); WF blue , W.F. green represent blue water footprint and green water footprint respectively; A represents the total crop planting area (km 2 ); Y represents the total crop yield (kg); B represents the unit conversion constant (B = 1000); where A = (A1 + A2 + ... + A i-1 +A i ), A i represents the planting area of ​​the i-th crop.

[0095] In this embodiment, preferably, the water-energy link module including total irrigation energy consumption is constructed according to the water volume data and the blue water footprint, including:

[0096] Obtaining a groundwater irrigation ratio and a surface water irrigation ratio based on the water volume data;

[0097] Calculate groundwater irrigation energy consumption according to the groundwater irrigation ratio and the blue water footprint, and calculate surface water irrigation energy consumption according to the surface water irrigation ratio and the blue water footprint;

[0098] The total irrigation energy consumption is obtained according to the sum of the groundwater irrigation energy consumption and the surface water irrigation energy consumption.

[0099] Specifically, the calculation formula for total irrigation energy consumption is as follows:

[0100] DE irri =DE gw +DE sw =GIR×R gw ×EC gw ×3.6+GIR×(1-R gw )×EC sw

[0101] Where DE irri Total irrigation energy consumption (MJha -1 yr -1 );DE gw represents groundwater irrigation energy consumption (MJha -1 yr -1 );DE sw represents the energy consumption of surface water irrigation (MJha -1 yr -1 ); GIR represents the total amount of water required for crop irrigation (m 3 ha -1 yr -1 );R gw Indicates the proportion of groundwater irrigation (%); EC gw Indicates that 1m 3 The energy required to lift water 1 meter; 3.6 represents the average lower calorific value of electrical energy (MJkWh -1 );EC sw Indicates the energy intensity of a unit water diversion project. Parameter EC gw The calculation method is as follows:

[0102] EC gw =(9.8×Lift×Mass) / (3.6×10 6 ×Efficiency

[0103] Where Lift is the height of groundwater lifting (m); Mass is the water mass (kg); Efficirncy is the water resource efficiency in the water extraction process (excluding head loss, etc., %).

[0104] In this example, the total amount of water required for crop irrigation GIR is taken from the blue water footprint WF blue .

[0105] In this embodiment, preferably, the energy footprint of the target area is obtained according to the crop data and the total irrigation energy consumption, and a grain-energy link module is constructed, including:

[0106] Calculating energy consumption during the use of agricultural machinery based on the crop data;

[0107] The indirect energy consumption in the production of seeds, pesticides, agricultural films and fertilizers was calculated using the Leontief inverse matrix model;

[0108] The energy footprint of the target area is obtained according to the sum of the energy consumption during the use of the agricultural machinery, the indirect energy consumption and the total irrigation energy consumption.

[0109] Specifically, different types of agricultural machinery (such as tractors, harvesters, seeders, etc.) have different energy consumption rates, operating times, operating areas, and operating efficiencies. Therefore, the energy consumption of agricultural machinery during use is calculated based on the crop data, including:

[0110]

[0111] Where DE mach Represents the total energy consumption during the use of agricultural machinery; Con j A represents the energy consumption rate of the jth type of agricultural machinery under standard working conditions; i represents the sowing area of ​​the i-th crop; EF j Represents the operating efficiency of the jth type of agricultural machinery.

[0112] Specifically, the energy footprint is calculated as follows:

[0113] DE=DE mach +DE irri +DE indr

[0114] Where DE represents energy footprint; DE mach Indicates the energy consumption during the use of agricultural machinery; DE irri Indicates the energy consumption during irrigation; DE indr Represents indirect energy consumption.

[0115] In a specific implementation, a combination of bottom-up and top-down methods can be used to calculate the energy footprint of agricultural water-grain-energy-carbon, where the bottom-up method is used to estimate direct energy consumption related to the use of agricultural machinery and irrigation, and the top-down method based on input-output analysis is used to estimate indirect energy consumption.

[0116] In a specific implementation method, the indirect energy consumption generated in the production process of seeds, pesticides, agricultural films, fertilizers, etc. is calculated through an environmentally extended input-output model with the Leontief inverse matrix as the core; illustratively, based on the homogeneity assumption of the input-output table, the indirect energy consumption in the grain production process in the main grain producing areas of North China can be expressed as the product of the indirect energy utilization intensity of the agricultural sector and the economic output of crop production; among them, the indirect energy utilization intensity of the upstream department in producing each unit of economic output of the department is expressed by the difference between the direct energy use intensity and the total energy use intensity, and the total energy use intensity is expressed as the product of the direct energy use intensity vector and the Leontief inverse matrix.

[0117] In this embodiment, preferably, the carbon emission data of the target area is calculated based on the energy footprint to construct an energy-carbon nexus module, including:

[0118] According to the crop data and the energy footprint, calculate the sum of CH4 emissions from rice fields, N2O emissions from agricultural land, CO2 emissions from direct energy use, CO2 emissions from indirect energy use, and CH4 and N2O emissions from straw burning, and use this as emission-side data;

[0119] According to the crop data, the photosynthetic carbon absorption of the crops and the increase in the soil organic carbon pool storage caused by returning the straw to the field are obtained, and the sum of the photosynthetic carbon absorption of the crops and the increase in the soil organic carbon pool storage caused by returning the straw to the field is used as the absorption side data;

[0120] The carbon emission data of the target area is obtained according to the difference between the emission side data and the absorption side data.

[0121] Specifically, the carbon footprint of the agricultural water-grain-energy-carbon system E agri The calculation method is as follows:

[0122] E agri =E rice +E land +E dire +E indi +E straw -Abs phot -Abs straw

[0123] In the formula, E rice 、E land 、E dire 、E indi 、E straw They represent CH4 emissions from rice fields, N2O emissions from agricultural land, and CO2 emissions from direct energy use (including total irrigation energy consumption E irri and emissions from agricultural machinery energy consumption mach), CO2 emissions from indirect energy use, CH4 and N2O emissions from straw burning; Abs phot and Abs straw They respectively represent the photosynthetic carbon absorption of crops and the increase in soil organic carbon storage caused by returning straw to the fields.

[0124] CH4 emissions from rice fields rice The calculation method is as follows:

[0125] E rice =A rice ×EF CH4 ×28

[0126] Where, EF CH4 = Rice CH4 emission factor (kgCH4ha -1 yr -1 );A rice represents the rice planting area (ha); 28 is the global warming potential (GWP) of methane.

[0127] Direct and indirect N2O emissions from agricultural land land The calculation method is as follows:

[0128]

[0129] Where N app(i) represents the nitrogen application rate of crop i (kgNha -1 yr -1 );EF ND The emission factor of N2O directly generated by fertilization and straw return to the field (kgN2O-Nkg -1 N input); EF AD Indicates that due to NH3 and NO x Indirect N2O emissions from atmospheric deposition due to volatilization (kgN2O-Nkg -1 N); Frac GAS Indicates the fraction of NH3-N emissions from fertilizer application (ignoring NOx emissions); EF NL Indicates indirect N2O emissions caused by leaching runoff (kgN2O-Nkg -1 N); Fra CL A represents the fraction of nitrogen leached into runoff from fertilizer application; i represents the sown area of ​​crop i (ha).

[0130] Specifically, this embodiment combines activity data (direct energy consumption) and emission factors (CO2 emissions per unit of energy consumption) to estimate CO2 emissions from direct energy use using a bottom-up approach. CO2 emissions from direct energy use are divided into two parts: CO2 emissions from irrigation and CO2 emissions from direct energy consumption by agricultural machinery. irre ,kgCO2eq) is calculated as follows:

[0131]

[0132] Where r (r = 1, 2, ...) is the type of energy used in irrigation; R ene(r) Indicates the proportion of the rth energy in the total energy consumption for irrigation (mainly electricity and diesel); EF ene_GHG(r) is the GHG emission factor of energy consumption (kgCO2eqkWh -1 ); the GHG conversion factors for electricity and diesel are 0.94773 and 0.32021 kgCO2eqkWh respectively -1 .

[0133] For CO2 emissions from direct energy consumption by agricultural machinery (E mach ), using energy consumption (TJha -1 ), carbon content (tCTJ -1 ), oxidation efficiency and CO2-C to CO2 conversion coefficient. Since agricultural machinery mainly uses diesel, its carbon content and oxidation efficiency are 20.2tCTJ -1 and 96% calculated.

[0134] CO2 emissions from indirect energy consumption E indi The calculation method is the same as that for indirect energy consumption, and indirect CO2 emissions are calculated from top to bottom through the environmentally extended Leontief input-output analysis.

[0135] In addition, CH4 and N2O emissions from straw burning straw The calculation method is as follows:

[0136] E straw =A straw ×M B ×C f ×G ef

[0137] In the formula, A straw represents the straw burning area (ha); M B Indicates the mass of fuel that can be burned (tha -1 );C f Indicates the combustion factor; G efExpresses emission factor (g / kg dry matter burned).

[0138] For carbon sinks, it mainly includes the photosynthetic carbon absorption of crops (Abs phot ) and the increase in soil organic carbon storage caused by returning straw to the field (Abs straw ), which is calculated as follows

[0139] Abs=Abs phot +Abs straw

[0140] Abs phot =∑Y i ×C i / Eco i

[0141] Abs straw =S×(e×Yn×β×ω+g)

[0142] In the formula, Abs represents the absorption side data, that is, the carbon sink (kgCO2); i represents the crop type; Y i represents the yield of the i-th crop (kg); C i Represents the carbon absorption coefficient of crops; Eco i represents the economic coefficient of crops; S represents the planting area (ha); Yn represents the yield per unit area (kgha -1 ); e and g represent the empirical coefficients in the carbon sequestration model; β represents the ratio of straw to grain; ω represents the rate of straw return to the field.

[0143] In this embodiment, preferably, the coupling of the water-grain linkage module, the water-energy linkage module, the grain-energy linkage module and the energy-carbon linkage module to obtain an agricultural water-grain-energy-carbon association model includes:

[0144] Constructing constraint models of the water-grain linkage module, the water-energy linkage module, the grain-energy linkage module and the energy-carbon linkage module to obtain the agricultural water-grain-energy-carbon association model;

[0145] The agricultural water-grain-energy-carbon association model includes: crop production capacity constraints, groundwater irrigation ratio constraints, straw burning area constraints and carbon emission constraints;

[0146] The crop production capacity constraints include:

[0147] A i(min) ≤A i ≤A i(max)

[0148] In the formula, A i represents the sowing area of ​​crop i, A i(min)A represents the lower limit of the expected planting area of ​​crop i, i(max) represents the upper limit of the expected planting area of ​​crop i;

[0149] The groundwater irrigation ratio constraints include:

[0150] R gw(min) ≤R gw ≤R gw(max)

[0151] In the formula, R gw represents the proportion of groundwater irrigation, R gw(min) Represents the lower limit of groundwater irrigation ratio, R gw(max) It indicates the upper limit of groundwater irrigation ratio;

[0152] The straw burning area constraints include:

[0153] A straw(min) ≤A straw ≤A straw(max)

[0154] In the formula, A straw represents the straw burning area, A straw(min) Indicates the lower limit of straw burning area, A straw(max) Indicates the upper limit of the straw burning area;

[0155] The carbon emission constraints include:

[0156] E agri(min) ≤E agri ≤E agri(max)

[0157] In the formula, E agri represents the carbon emission data of the target area, E agri(min) Indicates the lower limit of carbon emissions in the target area, E agri(max) Indicates the upper limit of carbon emissions in the target area.

[0158] Specifically, the constraints provided in this embodiment are only exemplary constraints. In a specific implementation, constraints on the proportion of straw return to the field, the type of agricultural machinery and the operation time can also be constructed, and the corresponding expected data can be used as part of the expected agricultural strategy, so as to take into account agricultural decisions in this regard, so that the final feasible decision is more in line with the agricultural characteristics of the target area.

[0159] In this embodiment, preferably, the step of inputting the desired agricultural strategy of the target area into the agricultural water-grain-energy-carbon association model to obtain feasible information of the desired agricultural strategy includes:

[0160] Input the expected sowing type, expected sowing area range, expected groundwater irrigation ratio range, and expected straw burning area range corresponding to the expected agricultural strategy into the agricultural water-grain-energy-carbon association model to obtain the corresponding carbon emission data results;

[0161] It is determined whether the carbon emission data result meets a preset expected carbon emission range. If so, the expected agricultural strategy is determined to be a feasible strategy.

[0162] Specifically, when the sowing type, sowing area, groundwater irrigation ratio, and straw burning area change, the corresponding carbon emission data of the area will often change accordingly.

[0163] Specifically, in the agricultural water-grain-energy-carbon association model in this embodiment, each parameter can be assigned a category, and the assignable categories include decision-making parameters and non-decision-making parameters; for example, the total crop planting area A, the groundwater irrigation ratio R gw Parameters that can be adjusted by human agricultural decisions are decision-making parameters, while parameters that do not change with human agricultural decisions, such as evapotranspiration of blue water and green water, GHG conversion coefficients of electricity and diesel, are non-decision-making parameters.

[0164] After assigning the parameters to the categories of decisionable parameters and non-decisive parameters, the agricultural water-grain-energy-carbon association model receives the expected parameters and corresponding numerical ranges corresponding to the decisionable parameters in the expected agricultural strategy, calculates the corresponding carbon emission data results, and outputs the feasibility of the expected agricultural strategy based on the carbon emission data results and the expected carbon emission range; when the expected agricultural strategy is not feasible, the agricultural water-grain-energy-carbon association model performs reverse calculations and determines the influence coefficient and correlation of each decisionable parameter on the carbon emission data results through a global sensitivity analysis method, that is, the size of the influence, and whether it is positively correlated or negatively correlated; after obtaining the influence coefficient and correlation of the decisionable parameters, the influencing parameters of the decisionable parameters are sorted, and combined with the correlation, the optimization suggestions for the expected agricultural strategy are output, such as increasing a certain decisionable parameter or reducing several decisionable parameters.

[0165] Through the above optimization suggestion output steps, the optimization direction of agricultural strategy after coordinating water-grain-energy-carbon elements can be provided, the intelligent analysis of the model can be realized, and the optimization efficiency of agricultural strategy can be improved.

[0166] In summary, in an embodiment of the present invention, a comprehensive analysis method for the coupling of agricultural water-grain-energy-carbon nexus is established, and based on meteorological, water volume, crop and other data, a nonlinear feedback relationship of the interaction of dual factors such as water-grain, grain-energy, water-energy, energy-carbon, and energy-carbon is constructed, and each link module is further modeled to form an agricultural water-grain-energy-carbon correlation model with multiple factors interwoven; after the expected agricultural strategy is input into the agricultural water-grain-energy-carbon correlation model, the carbon emission results corresponding to agricultural strategies with different expected ranges can be analyzed, and it can be automatically judged whether the carbon emission results can meet the carbon emission expectations in the expected agricultural strategy, and the feasibility of different agricultural strategies can be obtained, thereby providing decision-making reference information for the coordinated optimization management of agricultural water resources, energy, grain production and carbon emissions.

[0167] See also Figure 2 , is a schematic diagram of the structure of an agricultural water-grain-energy-carbon association strategy analysis platform provided by an embodiment of the present invention, including:

[0168] A water-grain link construction module 201 is used to obtain meteorological data, water volume data and crop data of the target area, and to construct a water-grain link module including blue water footprint and green water footprint;

[0169] A water-energy nexus building module 202, for building a water-energy nexus module including total irrigation energy consumption according to the water volume data and the blue water footprint;

[0170] A grain-energy linkage construction module 203 is used to obtain the energy footprint of the target area according to the crop data and the total irrigation energy consumption, and to construct a grain-energy linkage module;

[0171] An energy-carbon bond building module 204 is used to calculate the carbon emission data of the target area based on the energy footprint and build an energy-carbon bond module;

[0172] The association model building module 205 is used to couple the water-grain linkage module, the water-energy linkage module, the grain-energy linkage module and the energy-carbon linkage module to obtain an agricultural water-grain-energy-carbon association model;

[0173] The correlation strategy analysis module 206 is used to input the expected agricultural strategy of the target area into the agricultural water-grain-energy-carbon correlation model to obtain feasible information of the expected agricultural strategy.

[0174] Furthermore, the acquisition of meteorological data, water volume data and crop data of the target area and the construction of a water-grain linkage module including blue water footprint and green water footprint include:

[0175] Based on the crop surface net radiation, soil heat flux, average temperature, wind speed and water vapor pressure data in the meteorological data, the reference crop evapotranspiration is obtained using the Penman-Monteith equation;

[0176] Calculating actual evapotranspiration of crops based on the water stress coefficient and crop coefficient in the crop data;

[0177] Based on the effective rainfall in the water volume data, the blue water evapotranspiration and the green water evapotranspiration are calculated, and the blue water footprint and the green water footprint are obtained.

[0178] Furthermore, the water-energy nexus module including total irrigation energy consumption is constructed based on the water volume data and the blue water footprint, including:

[0179] Obtaining a groundwater irrigation ratio and a surface water irrigation ratio based on the water volume data;

[0180] Calculate groundwater irrigation energy consumption according to the groundwater irrigation ratio and the blue water footprint, and calculate surface water irrigation energy consumption according to the surface water irrigation ratio and the blue water footprint;

[0181] The total irrigation energy consumption is obtained according to the sum of the groundwater irrigation energy consumption and the surface water irrigation energy consumption.

[0182] Furthermore, the energy footprint of the target area is obtained according to the crop data and the total irrigation energy consumption, and a grain-energy link module is constructed, including:

[0183] Calculating energy consumption during the use of agricultural machinery based on the crop data;

[0184] The indirect energy consumption in the production of seeds, pesticides, agricultural films and fertilizers was calculated using the Leontief inverse matrix model;

[0185] The energy footprint of the target area is obtained according to the sum of the energy consumption during the use of the agricultural machinery, the indirect energy consumption and the total irrigation energy consumption.

[0186] Furthermore, the carbon emission data of the target area is calculated based on the energy footprint to construct an energy-carbon linkage module, including:

[0187] According to the crop data and the energy footprint, calculate the sum of CH4 emissions from rice fields, N2O emissions from agricultural land, CO2 emissions from direct energy use, CO2 emissions from indirect energy use, and CH4 and N2O emissions from straw burning, and use this as emission-side data;

[0188] According to the crop data, the photosynthetic carbon absorption of the crops and the increase in the soil organic carbon pool storage caused by returning the straw to the field are obtained, and the sum of the photosynthetic carbon absorption of the crops and the increase in the soil organic carbon pool storage caused by returning the straw to the field is used as the absorption side data;

[0189] The carbon emission data of the target area is obtained according to the difference between the emission side data and the absorption side data.

[0190] Furthermore, the coupling of the water-grain linkage module, the water-energy linkage module, the grain-energy linkage module and the energy-carbon linkage module to obtain an agricultural water-grain-energy-carbon association model includes:

[0191] Constructing constraint models of the water-grain linkage module, the water-energy linkage module, the grain-energy linkage module and the energy-carbon linkage module to obtain the agricultural water-grain-energy-carbon association model;

[0192] The agricultural water-grain-energy-carbon association model includes: crop production capacity constraints, groundwater irrigation ratio constraints, straw burning area constraints and carbon emission constraints;

[0193] The crop production capacity constraints include:

[0194] A i(min) ≤A i ≤A i(max)

[0195] In the formula, A i represents the sowing area of ​​crop i, A i(min) A represents the lower limit of the expected planting area of ​​crop i, i(max) represents the upper limit of the expected planting area of ​​crop i;

[0196] The groundwater irrigation ratio constraints include:

[0197] R gw(min) ≤R gw ≤R gw(max)

[0198] In the formula, R gw represents the proportion of groundwater irrigation, R gw(min) Represents the lower limit of groundwater irrigation ratio, R gw(max) It indicates the upper limit of groundwater irrigation ratio;

[0199] The straw burning area constraints include:

[0200] A straw(min) ≤A straw ≤A straw(max)

[0201] In the formula, A straw represents the straw burning area, Astraw(min) Indicates the lower limit of straw burning area, A straw(max) Indicates the upper limit of the straw burning area;

[0202] The carbon emission constraints include:

[0203] E agri(min) ≤E agri ≤E agri(max)

[0204] In the formula, E agri represents the carbon emission data of the target area, E agri(min) Indicates the lower limit of carbon emissions in the target area, E agri(max) Indicates the upper limit of carbon emissions in the target area.

[0205] Furthermore, the inputting the expected agricultural strategy of the target area into the agricultural water-grain-energy-carbon association model to obtain feasible information of the expected agricultural strategy includes:

[0206] Input the expected sowing type, expected sowing area range, expected groundwater irrigation ratio range, and expected straw burning area range corresponding to the expected agricultural strategy into the agricultural water-grain-energy-carbon association model to obtain the corresponding carbon emission data results;

[0207] It is determined whether the carbon emission data result meets a preset expected carbon emission range. If so, the expected agricultural strategy is determined to be a feasible strategy.

[0208] In summary, in an embodiment of the present invention, a comprehensive analysis method for the coupling of agricultural water-grain-energy-carbon nexus is established, and based on meteorological, water volume, crop and other data, a nonlinear feedback relationship of the interaction of dual factors such as water-grain, grain-energy, water-energy, energy-carbon, and energy-carbon is constructed, and each link module is further modeled to form an agricultural water-grain-energy-carbon correlation model with multiple factors interwoven; after the expected agricultural strategy is input into the agricultural water-grain-energy-carbon correlation model, the carbon emission results corresponding to agricultural strategies with different expected ranges can be analyzed, and it can be automatically judged whether the carbon emission results can meet the carbon emission expectations in the expected agricultural strategy, and the feasibility of different agricultural strategies can be obtained, thereby providing decision-making reference information for the coordinated optimization management of agricultural water resources, energy, grain production and carbon emissions.

[0209] See also Figure 3, is a schematic diagram of an agricultural water-grain-energy-carbon correlation strategy analysis platform provided by an embodiment of the present invention. The agricultural water-grain-energy-carbon correlation strategy analysis platform of this embodiment includes: a processor 1, a memory 2, and a computer program stored in the memory 2 and executable on the processor, such as an agricultural water-grain-energy-carbon correlation strategy analysis program. When the processor 1 executes the computer program, the steps in each of the above-mentioned agricultural water-grain-energy-carbon correlation strategy analysis method embodiments are implemented. Alternatively, when the processor 1 executes the computer program, the functions of each module / unit in the above-mentioned device embodiments are implemented.

[0210] Exemplarily, the computer program can be divided into one or more modules / units, one or more modules / units are stored in a memory and executed by a processor to complete the present invention. One or more modules / units can be a series of computer program instruction segments that can complete specific functions, and the instruction segments are used to describe the execution process of the computer program in the agricultural water-grain-energy-carbon association strategy analysis platform.

[0211] The agricultural water-grain-energy-carbon correlation strategy analysis platform may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the schematic diagram is merely an example of the agricultural water-grain-energy-carbon correlation strategy analysis platform and does not constitute a limitation on the agricultural water-grain-energy-carbon correlation strategy analysis platform. The platform may include more or fewer components than shown in the diagram, or may combine certain components, or different components. For example, the agricultural water-grain-energy-carbon correlation strategy analysis platform may also include input and output devices, network access devices, CAN buses, etc.

[0212] An embodiment of the present invention provides a computer-readable storage medium, characterized in that the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the agricultural water-grain-energy-carbon correlation strategy analysis method such as the first embodiment of the present invention.

[0213] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the agricultural water-grain-energy-carbon correlation strategy analysis platform, and uses various interfaces and lines to connect various parts of the entire agricultural water-grain-energy-carbon correlation strategy analysis platform.

[0214] The memory can be used to store computer programs and / or modules. The processor realizes various functions of the agricultural water-grain-energy-carbon association strategy analysis platform by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0215] Among them, if the module / unit integrated in the agricultural water-grain-energy-carbon association strategy analysis platform is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can be executed by the processor. The steps of the above-mentioned various method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media does not include electrical carrier signals and telecommunications signals.

[0216] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0217] The above are preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for analyzing agricultural water-grain-energy-carbon linkage strategies, characterized in that: include: Obtain meteorological data, water volume data and crop data of the target area, and build a water-grain link module including blue water footprint and green water footprint; constructing a water-energy nexus module including total irrigation energy consumption based on the water volume data and the blue water footprint; Obtaining the energy footprint of the target area according to the crop data and the total irrigation energy consumption, and constructing a food-energy linkage module; Calculate the carbon emission data of the target area based on the energy footprint, and build an energy-carbon linkage module; The water-grain linkage module, the water-energy linkage module, the grain-energy linkage module and the energy-carbon linkage module are coupled to obtain an agricultural water-grain-energy-carbon linkage model; The desired agricultural strategy of the target area is input into the agricultural water-grain-energy-carbon association model to obtain feasible information of the desired agricultural strategy.

2. The agricultural water-grain-energy-carbon linkage strategy analysis method according to claim 1, characterized in that: The method of obtaining the meteorological data, water volume data and crop data of the target area and constructing a water-grain link module including blue water footprint and green water footprint includes: Based on the crop surface net radiation, soil heat flux, average temperature, wind speed and water vapor pressure data in the meteorological data, the reference crop evapotranspiration is obtained using the Penman-Monteith equation; Calculating actual evapotranspiration of crops based on the water stress coefficient and crop coefficient in the crop data; Based on the effective rainfall in the water volume data, the blue water evapotranspiration and the green water evapotranspiration are calculated, and the blue water footprint and the green water footprint are obtained.

3. The agricultural water-grain-energy-carbon linkage strategy analysis method according to claim 1, characterized in that: The water-energy nexus module including total irrigation energy consumption is constructed according to the water volume data and the blue water footprint, comprising: Obtaining a groundwater irrigation ratio and a surface water irrigation ratio based on the water volume data; Calculate groundwater irrigation energy consumption according to the groundwater irrigation ratio and the blue water footprint, and calculate surface water irrigation energy consumption according to the surface water irrigation ratio and the blue water footprint; The total irrigation energy consumption is obtained according to the sum of the groundwater irrigation energy consumption and the surface water irrigation energy consumption.

4. The agricultural water-grain-energy-carbon linkage strategy analysis method according to claim 1, characterized in that: The energy footprint of the target area is obtained according to the crop data and the total irrigation energy consumption, and a grain-energy link module is constructed, including: Calculating energy consumption during the use of agricultural machinery based on the crop data; The indirect energy consumption in the production of seeds, pesticides, agricultural films and fertilizers was calculated using the Leontief inverse matrix model; The energy footprint of the target area is obtained according to the sum of the energy consumption during the use of the agricultural machinery, the indirect energy consumption and the total irrigation energy consumption.

5. The agricultural water-grain-energy-carbon linkage strategy analysis method according to claim 1, characterized in that: The step of calculating the carbon emission data of the target area based on the energy footprint and constructing an energy-carbon linkage module includes: According to the crop data and the energy footprint, calculate the sum of CH4 emissions from rice fields, N2O emissions from agricultural land, CO2 emissions from direct energy use, CO2 emissions from indirect energy use, and CH4 and N2O emissions from straw burning, and use this as emission-side data; According to the crop data, the photosynthetic carbon absorption of the crops and the increase in the soil organic carbon pool storage caused by returning the straw to the field are obtained, and the sum of the photosynthetic carbon absorption of the crops and the increase in the soil organic carbon pool storage caused by returning the straw to the field is used as the absorption side data; The carbon emission data of the target area is obtained according to the difference between the emission side data and the absorption side data.

6. The agricultural water-grain-energy-carbon linkage strategy analysis method according to claim 1, characterized in that: The coupling of the water-grain linkage module, the water-energy linkage module, the grain-energy linkage module and the energy-carbon linkage module to obtain an agricultural water-grain-energy-carbon association model includes: Constructing constraint models of the water-grain linkage module, the water-energy linkage module, the grain-energy linkage module and the energy-carbon linkage module to obtain the agricultural water-grain-energy-carbon association model; The agricultural water-grain-energy-carbon association model includes: crop production capacity constraints, groundwater irrigation ratio constraints, straw burning area constraints and carbon emission constraints; The crop production capacity constraints include: A i(min) ≤A i ≤A i(max) In the formula, A i represents the sowing area of ​​crop i, A i(min) A represents the lower limit of the expected planting area of ​​crop i, i(max) represents the upper limit of the expected planting area of ​​crop i; The groundwater irrigation ratio constraints include: R gw(min) ≤R gw ≤R gw(max) In the formula, R gw represents the proportion of groundwater irrigation, R gw(min) Represents the lower limit of groundwater irrigation ratio, R gw(max) Indicates the upper limit of groundwater irrigation ratio; The straw burning area constraints include: A straw(min) ≤A straw ≤A straw(max) In the formula, A straw represents the straw burning area, A straw(min) Indicates the lower limit of straw burning area, A straw(max) Indicates the upper limit of the straw burning area; The carbon emission constraints include: AND agri(min) ≤E agri ≤E agri(max) In the formula, E agri represents the carbon emission data of the target area, E agri(min) Indicates the lower limit of carbon emissions in the target area, E agri(max) Indicates the upper limit of carbon emissions in the target area.

7. The agricultural water-grain-energy-carbon linkage strategy analysis method according to claim 6, characterized in that: The step of inputting the desired agricultural strategy of the target area into the agricultural water-grain-energy-carbon association model to obtain feasible information of the desired agricultural strategy includes: Input the expected sowing type, expected sowing area range, expected groundwater irrigation ratio range, and expected straw burning area range corresponding to the expected agricultural strategy into the agricultural water-grain-energy-carbon association model to obtain the corresponding carbon emission data results; It is determined whether the carbon emission data result meets a preset expected carbon emission range. If so, the expected agricultural strategy is determined to be a feasible strategy.

8. An agricultural water-grain-energy-carbon correlation strategy analysis platform, characterized in that: include: The water-grain link construction module is used to obtain the meteorological data, water volume data and crop data of the target area and build a water-grain link module including blue water footprint and green water footprint; A water-energy nexus building module, used to build a water-energy nexus module including total irrigation energy consumption according to the water volume data and the blue water footprint; A grain-energy nexus building module, used for obtaining the energy footprint of the target area according to the crop data and the total irrigation energy consumption, and building a grain-energy nexus module; An energy-carbon bond building module, used to calculate the carbon emission data of the target area based on the energy footprint, and build an energy-carbon bond module; A correlation model building module, used for coupling the water-grain linkage module, the water-energy linkage module, the grain-energy linkage module and the energy-carbon linkage module to obtain an agricultural water-grain-energy-carbon correlation model; The association strategy analysis module is used to input the expected agricultural strategy of the target area into the agricultural water-grain-energy-carbon association model to obtain feasible information of the expected agricultural strategy.

9. An agricultural water-grain-energy-carbon correlation strategy analysis platform, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements an agricultural water-grain-energy-carbon correlation strategy analysis method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the agricultural water-grain-energy-carbon correlation strategy analysis method as described in any one of claims 1 to 7.

Images