Trans-department agricultural carbon neutralization potential fine accounting and optimization method based on water-energy-carbon cooperation

Abstract

The invention provides a cross-department agricultural carbon neutralization potential refined accounting and optimization method based on water-energy-carbon cooperation, and the method comprises the steps: determining a research region of typical food crops, and obtaining the multi-source related data of main food crops in the research region; water-energy-carbon correlation modeling is carried out based on multi-source correlation data; water footprints, direct / indirect energy use and carbon emission of grain crop planting are calculated based on the multi-source correlation data, and the productivity of a net ecosystem is introduced to evaluate the carbon neutralization capability; performing multi-policy scenario simulation based on the modeled water-energy-carbon correlation model; based on Pearson correlation analysis, the influence among water footprint, energy use, net ecosystem productivity, carbon emission and net carbon emission is evaluated, and an optimization scheme is provided. According to the method, the optimal path is quantified through water-energy-carbon correlation modeling and multi-scenario simulation, accurate decision is made, and the problem of collaboration and tradeoff of cross-department policies and measures is solved.

Description

Technical Field

[0001] This invention relates to the technical field of agricultural resource management and carbon neutrality assessment, and in particular to a method for refined accounting and optimization of agricultural carbon neutrality potential. Background Technology

[0002] To address climate change, an increasing number of countries are implementing carbon neutrality plans. While the overall carbon neutrality goal is clear, specific practical measures for achieving this goal in the agricultural sector still require further exploration and validation. Refined accounting of carbon sources / sinks in crop farming is a crucial indicator for determining whether agricultural carbon neutrality has been achieved, and also a key indicator for assessing whether agricultural management measures are reasonable and effective in reducing emissions and increasing carbon sinks. Crop farming carbon emissions and carbon absorption involve many aspects, such as water and energy use. With increasing food demand, constructing a scientific, multi-sectoral accounting methodology for crop farming carbon sources / sinks and formulating emission reduction and carbon sink-increasing agricultural management measures based on this methodology is crucial for China to achieve its carbon neutrality goals.

[0003] Currently, existing technologies have the following shortcomings: First, most existing methods assess the impact of a single agricultural management practice on carbon sources / sinks. Agricultural management practices involve multiple sectors, and it is necessary to consider the synergistic and trade-off effects between different agricultural management practices to avoid exacerbating one problem while solving another. For example, water-saving irrigation may increase energy consumption, as shown in the literature (Zhao, X., Ma, X., et al., 2022. Challenges toward carbon neutrality in China: Strategies and countermeasures. Resources, Conservation & Recycling 176, 105959.). Second, estimating the carbon source / sink in crop farming is the primary task in formulating relevant agricultural management measures. Current research mostly focuses on carbon sources, neglecting carbon sinks, resulting in incomplete and unsystematic carbon source / sink calculations. This hinders the development of scientifically effective agricultural management measures to achieve carbon neutrality, as illustrated in the literature (Fan, X., Zhang, W., et al., 2020. Land–water–energy nexus in agricultural management for greenhouse gasmitigation. Applied Energy 265, 114796.). Third, existing research has not fully integrated nitrogen fertilizer reduction and straw return to the field goals, leading to insufficient implementation of agricultural management measures, as illustrated in the literature (Wu, H., MacDonald, GK, et al., 2021. The influence of crop and chemical fertilizer combinations on greenhouse gas emissions: A partial life-cycle assessment of fertilizer production and use in China. Resources, Conservation & Recycling 168, 105303.). Given the above problems, there is an urgent need to propose a cross-sectoral refined carbon source / sink accounting method to scientifically and reasonably assess the emission reduction effects of different agricultural management measures under cross-sectoral scenarios. Summary of the Invention

[0004] To address the technical problem that current methods for calculating carbon sources / sinks in planting agriculture are incomplete and unsystematic, failing to resolve the coordination and trade-offs between cross-sectoral scenarios and measures, this invention proposes a refined calculation and optimization method for cross-sectoral agricultural carbon neutrality potential based on water-energy-carbon synergy. This method integrates water, energy, carbon, and scenarios, quantifies the optimal path through multi-scenario simulation, and makes precise decisions to solve the problem of coordination and trade-offs between cross-sectoral scenarios and measures.

[0005] To achieve the above objectives, the technical solution of the present invention is implemented as follows:

[0006] A refined accounting and optimization method for cross-sectoral agricultural carbon neutrality potential based on water-energy-carbon synergy, characterized by the following steps:

[0007] Step 1: Determine the study area for typical grain crops and obtain multi-source correlation data for the main grain crops in the study area;

[0008] Step 2: Water-Energy-Carbon Correlation Modeling Based on Multi-Source Correlation Data: Based on multi-source correlation data, calculate the water footprint, direct / indirect energy use and carbon emissions of food crop cultivation, and introduce net ecosystem productivity to assess carbon neutrality capacity;

[0009] Step 3: Conduct multi-scenario simulations based on the water-energy-carbon correlation model;

[0010] Step 4: Based on Pearson correlation analysis, assess the impacts between water footprint, energy use, net ecosystem productivity, carbon emissions, and net carbon emissions, and propose optimization schemes.

[0011] Furthermore, the multi-source related data includes yield data of major grain crops in the study area, fertilizer input, diesel consumption for mechanical operations, pesticide usage during crop production, meteorological data, and agricultural-related scenario files.

[0012] Furthermore, before proceeding to step S2, the rainfall data in the meteorological data is preprocessed: the preprocessed rainfall data of the study area is obtained using ArcGIS software, and the effective rainfall is calculated based on the preprocessed rainfall data of the study area. Potential evapotranspiration was calculated based on meteorological data using an improved Penman-Monteith algorithm. .

[0013] Furthermore, the method for calculating the effective rainfall is as follows:

[0014]

[0015] in, Represents effective rainfall. This represents the total rainfall.

[0016] Furthermore, the method for calculating the water footprint of grain crop cultivation based on multi-source correlation data is as follows:

[0017] Step 2.1: Based on the yield data of major grain crops, calculate the green water footprint and blue water footprint according to the effective rainfall and potential evapotranspiration respectively; calculate the gray water footprint based on the nitrogen fertilizer input in the fertilizer input.

[0018] The method for calculating direct / indirect energy use is as follows:

[0019] Step 2.2: Calculate the energy consumed by groundwater extraction for irrigation based on the Blue Water footprint, and take the energy consumed by groundwater extraction for irrigation and the amount of diesel energy used as direct energy use; calculate the indirect energy use based on the amount of fertilizer used, the amount of pesticides used, and the energy consumed in laying drip irrigation pipelines.

[0020] The method for calculating carbon emissions is as follows:

[0021] Step 2.3: Calculate the direct... Emissions, indirect Emissions, direct Emissions, indirect Emissions and rice-related factors during production Emissions, and converted into Expressed in equivalent form, this is used as the total carbon emissions after accounting. .

[0022] Furthermore, the method for calculating the green water footprint is as follows:

[0023]

[0024] Among them, crop evapotranspiration from precipitation Take the potential evapotranspiration and effective rainfall The smaller value in, WF green Representing the green water footprint, Area is the planting area of ​​a single crop, Y is the total yield of a single crop, and B is the unit conversion factor;

[0025] The method for calculating the blue water footprint is as follows:

[0026]

[0027] Among them, crop evapotranspiration from irrigation water Take 0 and ET c -P e The larger value in Representing the Blue Water Footprint, Area is the planting area of ​​a single crop.

[0028] The method for calculating the grey water footprint is as follows:

[0029]

[0030] in, This represents the total leaching fraction, where AC is the total amount of nitrogen fertilizer applied per unit area. and These are the maximum acceptable concentration and the natural concentration of nitrogen fertilizer, respectively.

[0031] Furthermore, the energy consumed in extracting groundwater for irrigation... The method is as follows:

[0032]

[0033] in, This represents the total blue water footprint across all crop production processes. For irrigation water ratio, To be The energy required to raise water by 1 meter, where 3.6 represents the energy conversion coefficient. This refers to the height of the groundwater level that needs to be raised, measured in meters. Eff. is the efficiency of the water pump used to extract groundwater. The mass of the water being lifted is expressed in kilograms.

[0034] The method for calculating indirect energy use based on fertilizer usage, pesticide usage, and energy consumed in laying drip irrigation pipes is as follows: The fertilizer usage, pesticide usage, and energy consumed in laying drip irrigation pipes are converted into energy equivalents using an energy conversion formula. These converted energy equivalents are then used as indirect energy. The energy conversion formula is:

[0035]

[0036] in, Indicates the converted energy. This represents the planting area of ​​the i-th main crop, where n is the total number of main crops. This represents the energy conversion coefficient for crop production, where k represents the type of energy.

[0037] Furthermore, the aforementioned direct Emissions, indirect Emissions, direct Emissions, indirect Emissions and rice-related factors during production The method for calculating emissions is as follows:

[0038] (1): Calculate directly Emissions: During crop production, Discharges originate from direct emissions from groundwater irrigation and indirect emissions from other agricultural activities; assuming the pumps used for groundwater irrigation are electrically powered, direct discharges... Emissions The calculation method is as follows:

[0039]

[0040] in, This indicates the carbon emission factor resulting from electricity consumption;

[0041] (2): Calculation of indirect Emissions: Calculate indirect carbon emissions related to diesel fuel, fertilizers, pesticides, and the laying of polyethylene pipelines during crop production:

[0042]

[0043] in, Represents crop yield, i represents crop type, ef j Indicates carbon emission factors from different sources;

[0044] (3): Calculate directly Emissions:

[0045]

[0046] in, This indicates the amount of nitrogen fertilizer used. It is the direct nitrogen emission factor, coefficient From Transform into The conversion factor, coefficient 265 is The 20-year global warming potential will be used to... Transform into equivalent;

[0047] (4): Calculation of indirect Emissions:

[0048]

[0049] in, It is produced by the volatilization of nitrogen from ammonia and nitrogen oxides. Emission factors It refers to the nitrogen fertilizer volatilization ratio. It is produced by nitrate leaching or runoff. Emission factors It is the proportion of nitrogen fertilizer lost due to leaching or runoff;

[0050] (5): Calculate the impact of rice on crop production. Emissions:

[0051]

[0052] in, This indicates the area planted with rice. This indicates the methane emission factor of rice.

[0053] Furthermore, the method for calculating the net ecosystem productivity is as follows:

[0054]

[0055] Where NE represents net carbon emissions, net ecosystem productivity (NEP) represents the carbon source / sink of arable land, and the coefficient 12 / 44 is derived from... The conversion factor is used to determine carbon content, measured in g C. The smaller the net carbon emission value, the greater the carbon neutrality potential.

[0056] Furthermore, the multiple scenarios include various combinations of five scenarios: nitrogen fertilizer reduction by 30%, improved efficiency of nitrogen fertilizer production technology, switching from irrigation to drip irrigation, switching from irrigation to sprinkler irrigation, and increasing the straw return rate to 50%.

[0057] The beneficial effects of this invention are as follows: This invention constructs a refined accounting and optimization method for cross-sectoral agricultural carbon neutrality potential based on water-energy-carbon synergy, with the following characteristics: First, this invention integrates water, energy, carbon, and policy scenarios for the first time, linking irrigation management by the water conservancy sector with energy consumption accounting by the energy sector and the achievement of carbon neutrality, solving the problem of coordination and trade-offs between cross-sectoral policy scenarios and measures. Considering different agricultural management scenarios, it quantifies the optimal path through multi-scenario simulation, enabling precise decision-making and providing a scientific basis for the government. Second, this invention directly responds to goals such as nitrogen fertilizer reduction and straw return to the field, with highly operable measures. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 This is a flowchart of the present invention;

[0060] Figure 2 This is a map showing the study area and crop yield distribution of this invention;

[0061] Figure 3It is a spatial distribution map of water footprints;

[0062] Figure 4 It is a footprint of green water, blue water, and gray water;

[0063] Figure 5 It is a spatial distribution map of energy use;

[0064] Figure 6 It refers to the amount of direct energy use and indirect energy use;

[0065] Figure 7 This is a spatial distribution map of net ecosystem productivity (NEP);

[0066] Figure 8 It is a map of carbon emissions, net ecosystem productivity (NEP), and net total carbon emissions;

[0067] Figure 9 It is a spatial distribution map of carbon emissions;

[0068] Figure 10 These are the direct and indirect carbon emissions from different crops;

[0069] Figure 11 The percentage of carbon emissions from different crop sources;

[0070] Figure 12 These are different percentages of carbon emissions;

[0071] Figure 13 This is a spatial distribution map of net carbon emissions;

[0072] Figure 14 The correlation between water footprint, energy use, carbon emissions, NEP, and net carbon emissions;

[0073] Figure 15 It compares the changes in water, energy, and carbon under different scenarios.

[0074] The map of China involved in the image has the map approval number GS(2023)2767 and the coordinate system is GCS_WGS_1984. Detailed Implementation

[0075] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0076] A refined accounting and optimization method for cross-sectoral agricultural carbon neutrality potential based on water-energy-carbon synergy, such as Figure 1 As shown, the steps are as follows:

[0077] Step 1: Determine the study area for typical grain crops and obtain multi-source correlation data for the main grain crops in the study area.

[0078] The multi-source related data includes yield data of major grain crops in the study area, fertilizer input, diesel fuel consumption for mechanical operations, pesticide usage during crop production, meteorological data, and agricultural-related scenario files.

[0079] In this embodiment, the yield data of wheat, rice, and corn, fertilizer input, diesel fuel consumption for machinery operations, and pesticide usage during crop production in the multi-source related data are taken from the 2018 China Agricultural Statistical Yearbook, such as... Figure 2 As shown. Meteorological data comes from the National Meteorological Science Data Center. Land use data comes from the European Space Agency's Climate Change Initiative, used to determine crop planting area and spatial distribution, and is combined with subsequent calculations of water footprint, energy use, net ecosystem productivity, carbon emissions, and net carbon data to generate spatial distribution maps using ArcGIS software. Net ecosystem productivity (NEP) data comes from the National Ecological Science Data Center. Data used to calculate carbon emissions comes from the China Life Cycle Assessment Basic Database (CLCD) and the China Product Carbon Footprint Factor Database (CPCD), from which relevant energy conversion coefficients and greenhouse gas emission factors are selected (Tables 1 and 2) to ensure the scientific reliability of this invention.

[0080] Meteorological data preprocessing: First, taking rainfall data as an example, the rainfall data in text format needs to be imported into Excel. Meteorological stations are then selected according to the chosen study area. The meteorological station data covered by the selected study area is saved as a new file. The saved meteorological station data is then added to ArcGIS software and opened. Interpolation is performed to obtain rainfall data that meets the subsequent calculation conditions.

[0081] Calculate the effective rainfall based on the preprocessed rainfall data of the study area:

[0082]

[0083] in, Represents effective rainfall. This represents the total rainfall.

[0084] Potential evapotranspiration, i.e. crop water requirements, was calculated using meteorological data from the study area and an improved Penman-Monteith algorithm.

[0085]

[0086] in, (mm / day) represents potential evapotranspiration. ( (This refers to the net radiation emitted by the crop surface.) ( ( ) represents soil heat flux. (°C) represents the daily average temperature at a height of 2m. (m / s) represents the wind speed at a height of 2m. (kPa) is the saturated vapor pressure. (kPa) represents the actual water vapor pressure. (kPa) represents the saturated water vapor pressure difference. The slope of the saturated water vapor pressure curve. ( () is the hygrometer constant.

[0087] Step 2: Water-Energy-Carbon Correlation Modeling Based on Multi-Source Correlation Data: Based on multi-source correlation data, calculate the water footprint, direct / indirect energy use and carbon emissions of food crop cultivation, and introduce net ecosystem productivity (NEP) to assess carbon neutrality capacity.

[0088] Step 2.1: Calculate the water footprint of grain crop cultivation based on preprocessed multi-source correlation data:

[0089] First, based on yield data and land use data of major food crops, the green water footprint is calculated according to effective rainfall and potential evapotranspiration. ) and Blue Water Footprints ( ).

[0090] Green Water Footprints ( The crop evapotranspiration is the amount of rainfall utilized in crop production, taking the smaller of the effective rainfall and potential evapotranspiration. The green water footprint is calculated by combining yield data of major grain crops and land use data. ):

[0091]

[0092] in, Take the potential evapotranspiration and effective rainfall The smaller value in Represents the potential evapotranspiration of a single crop. Representing the green water footprint, Area is the planting area of ​​a single crop (in hectares (ha)), Y is the total yield of a single crop (kg), and B is the unit conversion factor, which is 1000 here.

[0093] The North China Plain is mostly irrigated by groundwater, therefore, in this embodiment, the blue water footprint ( The amount of groundwater used in crop production (i.e., irrigation water) is calculated as follows:

[0094]

[0095] Among them, crop evapotranspiration from irrigation water Take 0 and The larger value in Representing the Blue Water Footprint, Area is the planting area of ​​a single crop (ha), Y is the total yield of a single crop (kg), and B is the unit conversion factor, which is 1000 here.

[0096] Furthermore, the grey water footprint was calculated based on nitrogen fertilizer input. ).

[0097] Grey water footprint ( This represents the amount of water required to dilute pollutants to meet water quality standards. The extensive use of nitrogen fertilizers has led to significant pollution; therefore, the focus is on diluting nitrogen pollutants to assess the greywater footprint. The greywater footprint is calculated as follows:

[0098]

[0099] in, =0.25 represents the total leaching fraction, and AC (kg / ha) is the total amount of nitrogen fertilizer applied per unit area. (g / L) and (g / L) represent the maximum acceptable concentration and the natural concentration of nitrogen fertilizer, respectively.

[0100] Step 2.2: Specifically, the method for calculating direct / indirect energy use is as follows: calculate the energy consumed by extracting groundwater for irrigation based on the Blue Water footprint, and take the energy consumed by extracting groundwater for irrigation and the amount of diesel energy used as direct energy use; calculate indirect energy use based on the amount of fertilizer used, the amount of pesticides used, and the energy consumed for laying drip irrigation pipelines.

[0101] Carbon emissions from crop production come from energy use, fertilizer use, and rice cultivation. Energy use is the primary cause of carbon emissions. Emissions and fertilizer use are the main causes Emissions, rice cultivation is the main cause Emissions. Therefore, the first step is to calculate the energy used in crop production, which can be categorized into direct energy use and indirect energy use based on the type of energy.

[0102] The consumption of diesel fuel in crop production and the energy consumed in extracting groundwater for irrigation are classified as direct energy use. Diesel fuel consumption data can be obtained from the China Statistical Yearbook and converted to energy equivalent using an energy conversion formula. The energy consumption for extracting groundwater for irrigation is calculated using the following formula:

[0103]

[0104] in, This represents the total blue water footprint in the production process of the three crops ( (The blue water footprint of corn + the blue water footprint of rice + the blue water footprint of wheat = the total blue water footprint). The irrigation water ratio is 66% here. To be The energy required to raise the groundwater level by 1 meter (MJ), 3.6 (MJ / kWh) represents the energy conversion coefficient, Raise (m) is the height the groundwater needs to be raised in meters (m), and Eff. is the efficiency of the pump used to extract the groundwater. The mass of the water being lifted is expressed in kilograms (kg).

[0105] Indirect energy use includes energy consumed in fertilizer production, pesticide production, and the laying of drip irrigation pipes (polyethylene pipes). The amount of indirect energy used in crop production is converted to energy equivalent using the energy conversion formula given in Table 1:

[0106]

[0107] Table 1 Energy conversion coefficients for crop production

[0108]

[0109] in, Let represent the planting area (ha) of the i-th main crop, and n be the total number of main crops (3 in this embodiment). This represents the energy conversion coefficient for crop production, where k represents the type of energy. This indicates megajoules per unit.

[0110] Total carbon emissions during crop production include Energy use mainly leads to Emissions and fertilizer use are the main causes Emissions, rice cultivation is the main cause Emissions. Among them, Emissions are generated from direct energy use and indirect energy use, respectively. Emissions. Emissions are divided into direct emissions from the use of nitrogen fertilizers. Emissions, and indirect emissions from ammonia volatilization and atmospheric deposition of nitrogen oxides produced by leaching or runoff. Emissions. Indicates the production of rice Emissions. For ease of comparison, all emissions are expressed in terms of... It is expressed in terms of equivalent and carbon content.

[0111] Step 2.3: Specifically, the method for calculating carbon emissions is as follows:

[0112] (1): Calculate directly Emissions: During crop production, Discharges originate from groundwater irrigation (direct discharge) and other agricultural activities (indirect discharge). It is assumed that the pumps used for groundwater irrigation are electrically powered, directly... Emissions ( The calculation method is as follows:

[0113]

[0114] Among them, EF elec_carbon (0.95 kg CO2 / kwh) represents the carbon emission factor caused by electricity consumption.

[0115] (2): Calculation of indirect Emissions: Indirect carbon emissions related to diesel fuel, fertilizers, pesticides, and the laying of polyethylene pipelines during crop production are calculated using the following formula based on the emission factors given in Table 2:

[0116]

[0117] Table 2 Carbon emission factors in crop production

[0118]

[0119] in, Represents crop yield, i represents crop type, ef j This indicates carbon emission factors from different sources.

[0120] (3): Calculate directly Emissions:

[0121]

[0122] in, Indicates crop yield. This indicates the amount of nitrogen fertilizer used. It is the direct nitrogen emission factor, coefficient From Transform into The conversion factor, coefficient 265 is The potential for global warming over the next 20 years.

[0123] (4): Calculation of indirect Emissions:

[0124]

[0125] in, Represents crop yield. It is produced by the volatilization of nitrogen from ammonia and nitrogen oxides. Emission factors ( ), It is the nitrogen fertilizer volatilization ratio ( =0.1), It is produced by nitrate leaching or runoff. Emission factors, ( ), This represents the proportion of nitrogen fertilizer lost due to leaching or runoff; the coefficient 44 / 28 is from... Transform into The conversion factor, and 265 is The 20-year global warming potential will be used to... Transform into equivalent.

[0126] (5): Calculate the impact of rice on crop production. Emissions:

[0127]

[0128] in, This indicates the area planted with rice. The methane emission factor of rice is... .

[0129] (6): Calculate the total carbon emissions during crop production:

[0130]

[0131] in, and These are generated from direct energy use and indirect energy use, respectively. Emissions It is the direct result of using nitrogen fertilizer Emissions It is an indirect result of atmospheric deposition caused by ammonia volatilization and nitrogen oxides produced by leaching or runoff. Emissions Indicates the production of rice Emissions. All units are... This refers to carbon dioxide equivalent. For ease of calculating total carbon emissions, all emissions are expressed in this way. Equivalent representation.

[0132] Step 2.4: Introduce Net Ecosystem Productivity (NEP) to assess carbon neutrality capacity.

[0133] Specifically, net ecosystem productivity (NEP) refers to the difference between the amount of carbon fixed through photosynthesis and the amount of carbon lost through ecosystem respiration. It reflects the total carbon source / sink accumulation within the ecosystem and is calculated using the following formula to determine the carbon neutrality potential of crop farming:

[0134]

[0135] Where NE represents net carbon emissions, net ecosystem productivity (NEP) represents the carbon source / sink of arable land, and the coefficient 12 / 44 is derived from... The conversion factor for equivalent carbon content, expressed in g C. The smaller the net carbon emissions value, the greater the carbon neutrality potential.

[0136] Step 3: Conduct multi-scenario simulations based on the water-energy-carbon correlation model.

[0137] In this embodiment, 21 agricultural management scenarios were designed (such as a 30% reduction in nitrogen fertilizer use, improved efficiency in nitrogen fertilizer production technology, upgraded irrigation methods, and increased straw return utilization rate to 50%). These scenarios simulated changes in water, energy, and carbon, and their synergistic impacts on water, energy, and carbon were assessed. The combinations of different agricultural management scenarios are shown in Table 3.

[0138] The North China Plain (32°–40°N, 114°–121°E) is China's second largest and most densely populated plain. The North China Plain has predominantly temperate monsoon and subtropical monsoon climates. Its flat terrain and fertile soil provide excellent conditions for agricultural production. According to data from China's National Bureau of Statistics in 2018, the arable land area of ​​the North China Plain was… The North China Plain, covering 23.5% of the country's arable land, is home to three main crops: wheat, rice, and corn. These crops account for 97% of the arable land in the North China Plain. Irrigated agriculture in the North China Plain has undergone large-scale expansion, significantly impacting carbon sources and sinks. Therefore, exploring the potential for carbon neutrality in the North China Plain's crop farming through cross-sectoral agricultural management practices is of great significance.

[0139] Existing research clearly indicates that nitrogen fertilizer use should be limited and straw return to the field should be implemented. Regarding irrigation, it is recommended to accelerate the construction of water-saving projects in large-scale irrigation areas. In the North China Plain, nitrogen fertilizer dominates fertilizer use, and existing research shows that reducing nitrogen fertilizer use by 30% will not reduce crop yields. Therefore, this invention attempts to simulate the impact of reducing nitrogen fertilizer use by 30% on water footprint, energy use, and carbon source / sink. Furthermore, by improving nitrogen fertilizer production efficiency to achieve energy conservation and emission reduction, this scenario is defined as the "nitrogen fertilizer production technology efficiency enhancement" scenario. After achieving technology efficiency enhancement, the volatilization of nitrogen from ammonia and nitrogen oxides... Emission factors are Become This can reduce greenhouse gas emissions caused by nitrogen fertilizer. Returning straw to the field refers to the process of mechanically crushing, deep plowing, or covering the remaining stems and leaves of crops after harvest and directly returning them to the farmland soil as organic material for resource recycling. Rational utilization of straw can reduce the use of chemical fertilizers, thereby reducing environmental pollution and greenhouse gas emissions. China's comprehensive utilization rate of straw is 82%, and the straw return rate is 47.2%. Regarding the irrigation upgrade scenario simulation, this invention upgrades the irrigation methods to drip irrigation and sprinkler irrigation. Previous studies estimated that drip irrigation and sprinkler irrigation achieve water-saving rates of 60%-85% and 50%-75%, respectively. Based on this, this invention selects the lower values ​​of 60% and 50% for drip irrigation and sprinkler irrigation, respectively. The baseline scenario uses crop yield data, fertilizer input, diesel fuel consumption for mechanical operations, and pesticide usage during crop production from the 2018 China Agricultural Statistical Yearbook.

[0140] Table 3 Different combinations of agricultural management scenarios

[0141]

[0142] Step 3.1: The first step in rationally utilizing crop straw is to calculate crop straw yield. This invention defines crop straw yield as the quantity of post-harvest dried residue, including stems, stubble, and leaves. The total straw yield in the North China Plain is estimated by multiplying the crop yield (CY) of each crop by the field residue index (FR):

[0143]

[0144] Where i represents the crop type and j represents the provinces included in the North China Plain.

[0145] The field residue index (FR) for the three crops is shown in Table 4 below.

[0146] Table 4. Field Residue Index (FR) of Three Crops in Various Provinces of the North China Plain

[0147]

[0148] Step 3.2: In this invention, straw nutrient resources (SNR) are defined as the available nitrogen (represented by N) and phosphorus (represented by ...) in straw residues. (represented) and potassium (in) The net content (indicated by the crop name) is calculated. This invention calculates the nutrient resources of straw from three crops to obtain the total nutrient resource value of straw in the North China Plain:

[0149]

[0150] Where i represents the crop type. They are N, and The nutrient content coefficients of straw are shown in Table 5. In the formula, coefficients 2.29 and 1.2 represent the nutrient content coefficients of straw. Convert to phosphorus content and Conversion factor for potassium (K).

[0151] Table 5 Nutrient content coefficients of straw from three crops in the North China Plain ( , and )

[0152]

[0153] Based on the above formula and nutrient content coefficient, the straw yield and nutrient resource content of the three crops in various provinces of the North China Plain are calculated and shown in Tables 6 and 7 below.

[0154] Table 6. Straw Yield (SY) in Different Provinces of the North China Plain

[0155]

[0156] Table 7 Nutrient content of straw from three crops in the North China Plain

[0157]

[0158] The rational utilization of crop straw requires calculating the crop straw yield and the actual nutrient resources contained in the straw based on the field residue index and nutrient content coefficient of different crops. Only then can the different fertilizer inputs involved in the straw return scenario be calculated, along with the corresponding water footprint, energy use, and carbon emissions. Ultimately, the water footprint, energy use, and carbon emissions under 21 scenarios can be obtained, such as... Figure 15 As shown.

[0159] Step 4: Based on the simulation results, use Pearson correlation analysis to assess the relationship between water footprint, energy use and carbon neutrality, and propose cross-sectoral optimization schemes.

[0160] This invention uses Pearson correlation analysis to assess the relationships between total water footprint, total energy use and total carbon emissions, carbon source / sink (NEP), and net carbon. The Pearson correlation coefficient between any two variables X and Y is defined as the product of their covariance and standard deviation. Here, the standard deviation calculation serves to normalize the various factors. The correlation coefficient calculation formula is as follows:

[0161]

[0162] Where, x i and y i These are sample observations of any two variables from total water footprint, total energy use and total carbon emissions, carbon source / sink (NEP), and net carbon, respectively. The value ranges from -1 to 1; the larger the value, the stronger the variable. and The higher the linear correlation, the greater the mutual influence between the two variables. Taking the correlation between energy use and net carbon as an example, assuming both... A positive value, especially a large one, indicates that energy use has a positive impact on net carbon. Energy use needs to be considered as a variable in the process of achieving carbon neutrality to achieve emission reduction. When = 1, the two variables are perfectly positively correlated. When = 0, there is no obvious linear correlation between the two variables.

[0163] This invention aims to achieve carbon neutrality in agriculture. Therefore, when determining the optimal scenario, the scenario with the greatest reduction in carbon emissions is selected as the optimal scenario obtained by this invention.

[0164] Experimental Analysis: Taking the baseline scenario as an example, this embodiment comprehensively assesses the potential for agriculture to achieve carbon neutrality by simultaneously considering water footprint, energy use, and carbon source / sink. The data used in this embodiment includes crop yields, fertilizer inputs, diesel fuel consumption for mechanized operations, and pesticide usage during crop production, as reported in the China Agricultural Statistical Yearbook for provinces and cities included in the North China Plain. Figure 2 As shown.

[0165] The net carbon emissions calculated in this embodiment are: ( Figure 7 and Figure 8 For arable land NEP, its value is per year. This can offset 16.9% of the carbon emissions from crop production in the North China Plain. Figure 8 Based on the spatial distribution characteristics of carbon emissions, ArcGIS classifies carbon emission areas into three categories: high carbon emission (…). ), medium carbon emissions ( Low carbon emissions 40.7% of the North China Plain is a high-carbon emission area, located in the central and southeastern parts of the North China Plain. Figure 9 The total carbon emissions from the production of the three crops are... ( Figure 10 ). Emissions are categorized into those generated from direct energy use and those generated from indirect energy use. Emissions. Emissions are divided into direct emissions from the use of nitrogen fertilizers. Emissions, and indirect emissions from ammonia volatilization and atmospheric deposition of nitrogen oxides produced by leaching or runoff. Emissions. Indicates the production of rice Emissions. Carbon emissions from different sources vary considerably among the three crops. Figure 11 Specifically, The highest emissions (accounting for 68% of total carbon emissions) are from [country name missing]. Figure 11 The main source of N2O emissions is the application of nitrogen fertilizer (accounting for 56.6% of total N2O emissions). Figure 12 ).for Direct emissions from irrigation and indirect emissions from diesel, fertilizer, and pesticide use accounted for 29.1% and 70.9% of total emissions, respectively. Figure 12 ). The emissions come entirely from rice (accounting for 28.7% of total direct carbon emissions). Figure 12 Of these three crops, wheat has the highest carbon emissions, reaching [amount missing]. ( Figure 11 In terms of the spatial distribution of net carbon emissions, Beijing, Tianjin, and Hebei are the three provincial-level administrative regions with the lowest net carbon emissions. Figure 13 The central and southern parts of the North China Plain are net carbon emitters (accounting for 88.9%), while the northern and southwestern parts of the North China Plain are net carbon absorbers. Figure 13 ).

[0166] In crop production in the North China Plain, water footprint, energy use, net carbon emissions, net ecosystem productivity (NEP), and carbon emissions show significant spatial differences. Figure 3 , Figure 5 , Figure 7 , Figure 9 and Figure 13 The high water footprint areas in the northwestern (northern Hebei Province) and northeastern (central Shandong Province) parts of the North China Plain face both high energy use and high carbon emissions. However, due to high net ecosystem productivity, the net carbon emissions in these areas are relatively low. The total water footprint generated by the production of the three crops in the North China Plain is [missing information]. Total energy consumption is ( Figure 4 and Figure 6 Based on distribution characteristics, this embodiment classifies water footprint and energy use into three categories in ArcGIS: high (water footprint) Energy use ),middle( ),Low( Most of the central and northern North China Plain (44.4%) has a low water footprint, but only 16.7% of the area has low energy use. Specifically, Anhui Province has the highest water footprint (…). Shandong, Henan, and Hebei are the top three provinces in terms of energy consumption.

[0167] There was no significant linear correlation between water footprint and energy use, carbon emissions, net ecosystem productivity (NEP), and net carbon emissions. Figure 14 The relationships between water footprint and net ecosystem productivity (correlation coefficient r = 0.20) and between water footprint and net carbon emissions (correlation coefficient r = 0.03) are positively correlated, while the relationships between water footprint and energy use (correlation coefficient r = -0.17) and between water footprint and carbon emissions (correlation coefficient r = -0.18) are negatively correlated. Similarly, there are negative correlations between energy use and net ecosystem productivity (correlation coefficient r = -0.09) and between carbon emissions and net ecosystem productivity (correlation coefficient r = -0.05). However, there are significant positive correlations between energy use and carbon emissions, as well as between energy use and net carbon emissions. The correlation between energy use and carbon emissions is the most significant (correlation coefficient r = 0.74). Both net ecosystem productivity and carbon emissions show significant positive correlations with net carbon emissions. The correlation coefficient between net ecosystem productivity and net carbon emissions (r = 0.73) is higher than that between carbon emissions and net carbon emissions (r = 0.65).

[0168] Simulation results for 21 scenarios show that, under comprehensive optimization of agricultural management measures (optimal scenario S21 and S22) in the North China Plain, the optimal agricultural management measures can be achieved. Figure 15 This can reduce the water footprint by 4.9%, energy use by 28.5%, and carbon emissions by 39.2%. Figure 15 This embodiment estimates the total straw yield in the North China Plain as follows: (Table 6), where the nutrient resource quantity for scenario S5 is: (Table 7). The two scenarios with the lowest carbon emissions include S17 (reducing nitrogen fertilizer application by 30%, improving nitrogen fertilizer production technology, and increasing straw return rate to 50%) and S21 (upgrading to sprinkler irrigation based on S17). The seven scenarios resulting in the lowest water footprint (reduction of 5.9%) are scenarios S3, S7, S10, S13, S15, S18, and S20, which include upgrading to drip irrigation alone and adding other agricultural management measures to S3. Figure 15 The three scenarios with the lowest energy consumption include S9, S17, and S21, which reduce energy consumption by 28.5%, 27.6%, and 25.6%, respectively. Figure 15 It is worth noting that updating only one agricultural management practice, namely upgrading to drip irrigation, would minimize the water footprint; however, energy use and carbon emissions would increase by up to 6.8% and 12.9%, respectively. Figure 15 It is worth noting that the scenario with the lowest water footprint would weaken the effects of energy use and carbon emission reductions, which is detrimental to carbon neutrality.

[0169] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A refined accounting and optimization method for cross-sectoral agricultural carbon neutrality potential based on water-energy-carbon synergy, characterized in that, Including the following steps: Step 1: Determine the study area for typical grain crops and obtain multi-source correlation data for the main grain crops in the study area; Step 2: Water-Energy-Carbon Correlation Modeling Based on Multi-Source Correlation Data: Based on multi-source correlation data, calculate the water footprint, direct / indirect energy use and carbon emissions of food crop cultivation, and introduce net ecosystem productivity to assess carbon neutrality capacity; Step 3: Conduct multi-scenario simulations based on the water-energy-carbon correlation model; Step 4: Based on Pearson correlation analysis, assess the impacts between water footprint, energy use, net ecosystem productivity, carbon emissions, and net carbon emissions, and propose optimization schemes.

2. The method for refined accounting and optimization of cross-sectoral agricultural carbon neutrality potential based on water-energy-carbon synergy as described in claim 1, characterized in that, The multi-source related data includes yield data of major grain crops in the study area, fertilizer input, diesel fuel consumption for mechanical operations, pesticide usage during crop production, meteorological data, and agricultural-related scenario files.

3. The method for refined accounting and optimization of cross-sectoral agricultural carbon neutrality potential based on water-energy-carbon synergy as described in claim 2, characterized in that, Before proceeding to step S2, the rainfall data in the meteorological data is preprocessed: the preprocessed rainfall data of the study area is obtained using ArcGIS software, and the effective rainfall is calculated based on the preprocessed rainfall data of the study area. Potential evapotranspiration was calculated based on meteorological data using an improved Penman-Monteith algorithm. .

4. The method for refined accounting and optimization of cross-sectoral agricultural carbon neutrality potential based on water-energy-carbon synergy as described in claim 3, characterized in that, The method for calculating the effective rainfall is as follows: ； Among them, P e Represents effective rainfall. This represents the total rainfall.

5. The method for refined accounting and optimization of cross-sectoral agricultural carbon neutrality potential based on water-energy-carbon synergy according to any one of claims 2 to 4, characterized in that, The method for calculating the water footprint of grain crop planting based on multi-source correlation data is as follows: Step 2.1: Based on the yield data of major grain crops, calculate the green water footprint and blue water footprint according to the effective rainfall and potential evapotranspiration respectively; calculate the gray water footprint based on the nitrogen fertilizer input in the fertilizer input. The method for calculating direct / indirect energy use is as follows: Step 2.2: Calculate the energy consumed by groundwater extraction for irrigation based on the Blue Water footprint, and take the energy consumed by groundwater extraction for irrigation and the amount of diesel energy used as direct energy use; calculate the indirect energy use based on the amount of fertilizer used, the amount of pesticides used, and the energy consumed in laying drip irrigation pipelines. The method for calculating carbon emissions is as follows: Step 2.3: Calculate the direct... Emissions, indirect Emissions, direct Emissions, indirect Emissions and rice-related factors during production Emissions, and converted into Expressed in equivalent form, this is used as the total carbon emissions after accounting. .

6. The method for refined accounting and optimization of cross-sectoral agricultural carbon neutrality potential based on water-energy-carbon synergy as described in claim 5, characterized in that, The method for calculating the green water footprint is as follows: ； Among them, crop evapotranspiration from precipitation Take the potential evapotranspiration and effective rainfall The smaller value in Representing the green water footprint, Area is the planting area of ​​a single crop, Y is the total yield of a single crop, and B is the unit conversion factor; The method for calculating the blue water footprint is as follows: ； Among them, crop evapotranspiration from irrigation water Take 0 and The larger value in Representing the Blue Water Footprint, Area is the planting area of ​​a single crop. The method for calculating the grey water footprint is as follows: ； in, This represents the total leaching fraction, where AC is the total amount of nitrogen fertilizer applied per unit area. and These are the maximum acceptable concentration and the natural concentration of nitrogen fertilizer, respectively.

7. The method for refined accounting and optimization of cross-sectoral agricultural carbon neutrality potential based on water-energy-carbon synergy as described in claim 6, characterized in that, The energy consumed in extracting groundwater for irrigation The method is as follows: ； in, This represents the total blue water footprint across all crop production processes. For irrigation water ratio, To be The energy required to raise water by 1 meter, where 3.6 represents the energy conversion coefficient. This refers to the height of the groundwater level that needs to be raised, measured in meters. Eff. is the efficiency of the water pump used to extract groundwater. The mass of the water being lifted is expressed in kilograms. The method for calculating indirect energy use based on fertilizer usage, pesticide usage, and energy consumed in laying drip irrigation pipes is as follows: The fertilizer usage, pesticide usage, and energy consumed in laying drip irrigation pipes are converted into energy equivalents using an energy conversion formula. These converted energy equivalents are then used as indirect energy. The energy conversion formula is: ； in, Indicates the converted energy. This represents the planting area of ​​the i-th main crop, where n is the total number of main crops. This represents the energy conversion coefficient for crop production, where k represents the type of energy.

8. The method for refined accounting and optimization of cross-sectoral agricultural carbon neutrality potential based on water-energy-carbon synergy, as described in claim 6 or 7, is characterized in that... The direct Emissions, indirect Emissions, direct Emissions, indirect Emissions and rice-related factors during production The method for calculating emissions is as follows: (1): Calculate directly Emissions: During crop production, Discharges originate from direct emissions from groundwater irrigation and indirect emissions from other agricultural activities; assuming the pumps used for groundwater irrigation are electrically powered, direct... Emissions The calculation method is as follows: ； in, This indicates the carbon emission factor resulting from electricity consumption; (2): Calculation of indirect Emissions: Calculate indirect carbon emissions related to diesel fuel, fertilizers, pesticides, and the laying of polyethylene pipelines during crop production: ； in, Represents crop yield, i represents crop type, ef j Indicates carbon emission factors from different sources; (3): Calculate directly Emissions: ； in, This indicates the amount of nitrogen fertilizer used. It is the direct nitrogen emission factor, coefficient From Transform into The conversion factor, coefficient 265 is The 20-year global warming potential will be used to... Transform into equivalent; (4): Calculation of indirect Emissions: ； in, It is produced by the volatilization of nitrogen from ammonia and nitrogen oxides. Emission factors It refers to the nitrogen fertilizer volatilization ratio. It is produced by nitrate leaching or runoff. Emission factors It is the proportion of nitrogen fertilizer lost due to leaching or runoff; (5): Calculate the impact of rice on crop production. Emissions: ； in, This indicates the area sown with rice. This indicates the methane emission factor of rice.

9. The method for refined accounting and optimization of cross-sectoral agricultural carbon neutrality potential based on water-energy-carbon synergy as described in claim 5, characterized in that, The method for calculating net ecosystem productivity is as follows: ； Where NE represents net carbon emissions, net ecosystem productivity (NEP) represents the carbon source / sink of arable land, and the coefficient 12 / 44 is derived from... The conversion factor is used to determine carbon content, measured in gC. The smaller the net carbon emission value, the greater the carbon neutrality potential.

10. The method for refined accounting and optimization of cross-sectoral agricultural carbon neutrality potential based on water-energy-carbon synergy, as described in any one of claims 1-4, 6, 7, or 9, is characterized in that... The multiple scenarios include various combinations of five scenarios: nitrogen fertilizer reduction by 30%, improved efficiency in nitrogen fertilizer production technology, switching from drip irrigation to sprinkler irrigation, and increasing the straw return rate to 50%.

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