Method for improving, regulating and controlling ecological barrier function of farmland in plain area

Abstract

The invention discloses a method for improving, regulating and controlling functions of a farmland ecological barrier in a plain area, relates to a related algorithm based on a multi-dimensional coupling model, and aims to solve the technical problems of system element splitting, single ecological function, poor space adaptability and the like in the conventional farmland ecological barrier construction. Different land utilization type areas are taken as decision variables, and underground water footprint reduction, farmland carbon reserve increase, economic benefit improvement, crop water resource utilization efficiency improvement and pollutant emission reduction are taken as objective functions; the method comprises the following steps of: constructing a water volume, water quality, economy, efficiency, carbon, grain and ecology multi-dimensional coupled optimal configuration model by taking available water volume and land utilization type occupied area as constraint conditions, carrying out gridding land utilization distribution through a space optimization algorithm, and applying an optimal configuration scheme to a space to finish multi-dimensional element coupled water and soil resource optimal configuration. The method can be used in agricultural planning field.

Description

Technical Field

[0001] The present invention relates to a calculation method for the optimal allocation scheme of regional agricultural water and soil resources, and belongs to the field of agricultural resource management. Background Art

[0002] The model is a multi-functional spatial optimization tool. It is directly applied in the agricultural field to optimize the crop planting structure to increase agricultural production and economic benefits, while reducing the pressure on water resources and the environment. In the field of environmental protection, the model is used for the planning and management of ecological protection areas to protect and restore the ecosystem by optimizing the crop planting structure and the spatial layout of land use. In the field of urban planning, it ensures the sustainability of land use and balances the needs of urban development and environmental protection. In the field of water resources management, the model is used for watershed management and irrigation system design to optimize water resources allocation, improve irrigation efficiency, and reduce water pollution.

[0003] Chinese Patent "A Method for Spatial Optimization Allocation of Land Resources Applied to Land Use Planning Compilation" with the application number CN201610721593.X comprehensively considers the direct goal and spatial goal of land use, and realizes the spatial optimization allocation of land resources based on the spatial multi-objective genetic algorithm (GA). However, the defect of this method is that it mainly relies on the genetic algorithm. When dealing with more complex multi-dimensional coupling problems, such as multi-objective optimization of water volume, water quality, efficiency, carbon, food, and ecology, the adaptability and flexibility of the algorithm may be insufficient, and it cannot fully consider the complex interaction relationships between these goals. Under the condition of rigid water resource constraints, how to comprehensively consider multiple goals to achieve the optimal allocation of water and soil resources remains a frontier issue to be solved. Summary of the Invention

[0004] The present invention aims to solve the technical problems of poor adaptability and poor flexibility existing in the existing land resource optimization allocation method when dealing with multi-dimensional coupling problems, and provides a method for enhancing and regulating the function of the farmland ecological barrier in the plain area. The present invention constructs a "spatial optimization allocation model of water and soil resources with multi-dimensional coupling of water volume, water quality, economy, efficiency, carbon, food, and ecology", incorporates multi-objectives such as water volume, water quality, efficiency, carbon cycle, ecological connectivity, and food production into a unified framework, and combines the genetic algorithm NSGAⅢ and the developed spatial layout optimization algorithm to achieve the collaborative optimization of multi-dimensional goals.

[0005] The method for enhancing and regulating the function of the farmland ecological barrier in the plain area of the present invention is carried out according to the following steps:

[0006] Step 1: Analysis of the supply and demand of agricultural water and soil resources: Based on the planting data, water volume data, and meteorological data of the main crops in the area to be optimized, the FAO improved Penman-Monteith formula is used to calculate the reference crop evapotranspiration ET0, and combined with the dynamic correction method of crop coefficients, a simulation model of the actual water requirement during the growth period is constructed; then, using Landsat 8 satellite image data, land-atmosphere correction and land surface reflectance correction are carried out to obtain the land use data of this area;

[0007] Step 2: Construct a multi-objective collaborative optimization model, which consists of three parts: decision variables, objective functions, and constraint conditions; among them, the decision variables are the areas of different land use types; there are five objective functions, namely: ① Reduction of groundwater footprint; ② Increase in farmland carbon storage; ③ Improvement of economic benefits; ④ Increase in crop water use efficiency; ⑤ Reduction of pollutant emissions; with the available water volume and the occupied area of land use types as constraint conditions, a multi-objective optimal allocation model of regional agricultural water and soil resources is constructed; under different hydrological years, the model is solved using the optimization algorithm for 24 different key scenarios respectively, and an optimal allocation plan of agricultural water and soil resources based on the coupling of "water volume, water quality, economy, efficiency, carbon, food, and ecology" is obtained;

[0008] Step 3: Spatial allocation of land resources: Through the spatial optimization algorithm, grid-based land use allocation is carried out. Taking the grid numbers and spatial distribution of each land use type as decision variables, with ecological connectivity, conversion costs, and the matching degree between the target land use as optimization objectives, and with economic costs and the total land use area remaining unchanged as constraint conditions, the solution is obtained through the optimization algorithm, and the optimal allocation plan of agricultural water and soil resources obtained in Step 2 is applied to space to complete the improvement and regulation of the farmland ecological barrier function in the plain area.

[0009] Furthermore, the specific process of the agricultural water and soil resources supply and demand analysis described in Step 1 is as follows:

[0010] (1) Collection of meteorological data: Collect meteorological information including precipitation, temperature, wind speed, relative humidity, and sunshine hours;

[0011] (2) Collection of land use type and crop production data. Using Landsat 8 satellite image data, land-atmosphere correction and land surface reflectance correction are carried out to obtain the land use data of this area, and crop yield, planting cost, selling price, water fee, carbon absorption rate, moisture content, and economic coefficient indicators are obtained; among them, the planting cost includes labor, fertilizer, pesticide, and seed costs; the water fee involves surface water and groundwater subsidies;

[0012] (3) Calculation of the net irrigation water requirement for the entire growth period of crops: Based on the crop planting and meteorological data in the area to be optimized, the Penman formula recommended by the Food and Agriculture Organization of the United Nations is used to calculate the potential evapotranspiration of each crop at each growth stage, and the actual evapotranspiration is obtained by combining the crop coefficient; subsequently, the effective rainfall at each growth stage is estimated using the method recommended by the Soil Conservation Service of the United States Department of Agriculture; by subtracting the effective rainfall from the actual evapotranspiration, the net irrigation water requirement at each stage is obtained, and the results of each stage are accumulated to finally determine the net irrigation water requirement for the entire growth period of each crop;

[0013] Furthermore, the specific form of the multi-objective optimization model described in step two is as follows:

[0014] (1) The decision variables are the areas of various land use types, including the area of rice planting, the area of corn planting, the area of forest land, the area of grassland, the area of wetland, and the area of saline-alkali land;

[0015] (2) Objective functions:

[0016] ①. Minimize the groundwater water footprint:

[0017] To ensure the sustainable use of groundwater, an objective function with the minimum groundwater water footprint is established; the design of this objective function aims to minimize the extraction of groundwater on the premise of meeting water demand. This helps to promote the more efficient use of water resources in the agricultural system, reduce the impact of over-pumping on the groundwater system, and improve water use efficiency. The formula is as follows:

[0018]

[0019] Q S = Q PR + Q CR + Q T

[0020] Q P = Q QE + Q TR + Q RE

[0021] Among them, the rainfall infiltration recharge Q PR refers to the amount of precipitation that infiltrates into the soil and infiltrates to recharge groundwater under the action of gravity; rainfall infiltration recharge is one of the main recharge sources in the study area and is determined by the rainfall infiltration coefficient method. The formula is as follows:

[0022] Q PR = a × F × P

[0023] The lateral runoff recharge Q CR is calculated using Darcy's formula. The formula is as follows:

[0024] Q CR= K×I×B×M×ΔT×sinθ

[0025] Irrigation infiltration recharge Q T Determined by the irrigation infiltration coefficient method, and the calculation formula is

[0026] Q T = Q g ×β

[0027] Phreatic evaporation Q QE The calculation formula is:

[0028] Q QE = C×F×E m

[0029] E m = E 20 ×C e

[0030] Lateral runoff discharge Q TR Calculated according to Darcy's formula:

[0031] Q TR = K×I×B×M×ΔT×sinθ

[0032] Artificial extraction Q RE , which is the actual extraction of groundwater, including industrial and agricultural water use, rural human and livestock water use, and domestic water use, and is calculated as follows:

[0033]

[0034] ②. Maximum carbon sequestration:

[0035] Increase the ability of the farmland ecosystem to absorb and store carbon to promote carbon fixation and mitigate the adverse effects on the climate. The formula is as follows:

[0036] maxF2 = E - E′

[0037] The total CO2 absorption equivalent E consists of the carbon absorption amounts Cm of forest land, grassland, and wetland and the carbon absorption E of cultivated land m ;

[0038] The carbon absorption amount C of forest land, grassland, and wetland m The calculation method is as follows:

[0039] C m = S m ×a m

[0040] The carbon absorption E of cultivated land m The calculation method is as follows:

[0041]

[0042] E i = A i × e i

[0043] The calculation formula for the total CO2 emission equivalent E' is as follows:

[0044] Among them, E CO2 represents the CO2 equivalent emitted by the farmland ecosystem, that is, the first layer of carbon emissions, which refers to the carbon emissions generated by the input of agricultural production materials in agricultural production, the direct carbon emissions of agricultural production, and the carbon emissions of crops themselves. Agricultural production materials include chemical fertilizers, pesticides, and agricultural films. The carbon footprint formula of E CO2 is as follows:

[0045]

[0046] E CH4 represents the conversion of CH4 emissions into CO2 equivalent, that is, the second layer of carbon emissions, which refers to the methane (CH4) emissions in paddy fields. The formula is as follows:

[0047]

[0048] E N2O represents the conversion of total N2O into CO2 equivalent, that is, the third layer of carbon footprint, which is the N2O emissions caused by straw incorporation and chemical fertilizer application. The calculation formula:

[0049]

[0050] ③. Maximum agricultural economic benefits:

[0051] The increase in agricultural economic benefits is of great significance for promoting social harmony and stability. Through reasonable optimization and decision-making, agricultural economic benefits can be more effectively improved, thus realizing a sustainable and high-benefit agricultural production system. The formula is as follows:

[0052]

[0053] Combined with the crop coefficients K cmid(Tab) , K cend(Tab) provided by FAO-56 data during the mid-growing and maturity stages of crops and the crop coefficient correction formula, the corresponding crop coefficients during the mid-growing and maturity stages of rice and maize are corrected;

[0054] K cmid = K cmid(Tab) + [0.04(u2 - 2) - 0.004(RH min - 45)](h / 3) 0.3

[0055]

[0056] ④. Maximum crop water use efficiency:

[0057] Given the high dependence of agriculture on water resources, optimizing crop water use efficiency has become a key factor in addressing food security, economic development, and environmental sustainability. Through scientific and reasonable water resource management strategies, agricultural production can achieve greater yields with less water input, thus improving economic efficiency. The formula is:

[0058]

[0059] Calculate the soil evaporation ES according to the Belanger-Langemeier formula i ,

[0060]

[0061] n represents the total number of time periods;

[0062] ⑤. Minimum agricultural pollutant emissions:

[0063]

[0064] Among them: The amount of chemical fertilizer used is determined by the "Limited Standard for Chemical Fertilizer Application in Corn" and the "Limited Standard for Chemical Fertilizer Application in Rice", and the actual emissions are determined by local measured data;

[0065] Calculate the mean and maximum values of individual pollution indices for multiple pollution indices, obtain the Nemerow pollution index of the water body, and determine the water quality grade of the water body according to the evaluation grade classification table. The calculation formula is as follows:

[0066]

[0067] P 内 is the Nemerow pollution index method, P imax is the maximum value in the individual pollution indices, is the average value of the individual pollution indices;

[0068] (3) Constraint conditions:

[0069] ①. Surface water volume constraint:

[0070] The surface water consumption cannot exceed the available surface water volume in the region;

[0071]

[0072] ②. Groundwater volume constraint:

[0073] The groundwater consumption cannot exceed the exploitable groundwater volume in the region;

[0074]

[0075] ③ Total water resources constraint:

[0076] The surface water consumption and groundwater consumption cannot exceed the total available water volume in the region.

[0077]

[0078] ④ Food security constraint:

[0079] To ensure food security, the optimized total grain output cannot be lower than the specified grain demand;

[0080]

[0081] ⑤ Total crop area constraint:

[0082] Limited by the regional cultivated land area, the sum of the areas occupied by each crop cannot exceed the total cultivated land area;

[0083]

[0084] ⑥ Non - negative constraint:

[0085] Considering the actual situation, the values of surface water consumption, groundwater consumption, and crop planting area cannot be negative;

[0086] WS i ≥0

[0087] WG i ≥0

[0088] A i ≥0.

[0089] Parameters and units in the formulas in Table 1

[0090]

[0091]

[0092]

[0093] Furthermore, the algorithm for solving the model in step two is the third - generation non - dominated sorting genetic algorithm (NSGA - Ⅲ). The solution set of this algorithm is more evenly distributed and more representative, and finally a coordinated optimization plan for the coupling of water and soil resources is obtained.

[0094] Furthermore, in step three, grid-based land use allocation is carried out through a spatial optimization algorithm. The grid numbers and spatial distributions of various land use types are used as decision variables, ecological connectivity, conversion costs, and the matching degree between the target land use are used as optimization objectives, and economic costs and the total land use area remaining unchanged are used as constraints. The solution is obtained through an optimization algorithm, as follows:

[0095] (1) The decision variables are the grid numbers and spatial distributions of various land use types, including rice, corn, forest land, grassland, wetland, and saline-alkali land;

[0096] (2) The objective function is as follows:

[0097] ① Maximize the ecological connectivity index (ECI):

[0098] The ecological connectivity index (ECI) is an indicator used to evaluate the connectivity between land use types. This index takes into account the adjacency and type similarity between land units. The formula is:

[0099]

[0100] where N is the set of all pixels, w ij is the weight between pixels i and j. If they have the same land use type and are adjacent, then w ij is the weight of this adjacent land use type; otherwise, it is 0. The weight values are as follows:

[0101] Table 2 Ecological connectivity weight values

[0102] Cultivated land Forest land Grassland Wetland Construction land Saline-alkali land 1 3 2 4 0.5 0.2

[0103] δ(i,j) is the adjacency function, and the values are as follows:

[0104] Table 3 Adjacency function values

[0105]

[0106]

[0107] ② Minimize the land use conversion cost (LUC):

[0108]

[0109] where L ij is the cost required to convert the grid cell located in the i-th row and j-th column, and x ij is the decision variable; if the land use type is the same before and after optimization, then x ij = 1; if the land use types are different before and after optimization, then xij = 0;

[0110] ③ Minimize the difference between land use and the target:

[0111]

[0112] where p k is the actual proportion of optimized land use, p * k is the proportion of the target land use type, and K is the set of land use types;

[0113] (3) The constraint conditions are as follows:

[0114] ① Crop planting area constraint:

[0115]

[0116] In the formula, A is the total amount of land resources in the study area; A i ′ is the planting area of crop i in the current year;

[0117] ② Total land use area constraint:

[0118]

[0119] In the formula, A 林 is the area of forest land resources in the region; A 草 is the area of grassland resources in the region; A 湿 is the area of wetland resources in the region; A 盐碱 is the area of wetland resources in the region in the current year;

[0120] ③ Non - negative constraint:

[0121] A i ≥ 0.

[0122] Compared with the calculation methods of existing agricultural water and soil resources optimization allocation schemes, the present invention has the following advantages and beneficial effects:

[0123] (1) Incorporate water resources, economic benefits, carbon sequestration benefits, and ecological benefits into the same optimization framework, and construct a "water volume, water quality, economy, efficiency, carbon, food, and ecology" coupling model. This model breaks through the limitations of single - objective optimization and innovatively incorporates ecological benefits into the multi - objective optimization model, achieving an effective balance between ecology and economic development within the region.

[0124] (2) Adopt the third - generation non - dominated sorting genetic algorithm (NSGA - Ⅲ), which realizes the uniformity and representativeness of the solution set distribution in multi - objective trade - off solving, providing a more efficient and accurate solution for complex multi - objective optimization problems. Description of the Drawings

[0125] Figure 1 It is the analysis diagram of the economic benefit optimization results of each hydrological year under different scenarios in Example 1;

[0126] Figure 2 It is the analysis diagram of the crop water resource efficiency optimization results of each hydrological year under different scenarios in Example 1;

[0127] Figure 3 It is the analysis diagram of the carbon sequestration optimization results of each hydrological year under different scenarios in Example 1;

[0128] Figure 4 It is the analysis diagram of the NDI pollution index optimization results of each hydrological year under different scenarios in Example 1;

[0129] Figure 5 It is the schematic diagram of the Da'an Irrigation Area in Example 1; a is the functional area division diagram, and b is the main irrigation channel diagram;

[0130] Figure 6 It is the spatial configuration result diagram of the land use conversion in the Da'an Irrigation Area under various optimization strategies in a dry year;

[0131] Figure 7 It is the spatial configuration result diagram of the land use conversion in the Da'an Irrigation Area under various optimization strategies in a normal year;

[0132] Figure 8 It is the spatial configuration result diagram of the land use conversion in the Da'an Irrigation Area under various optimization strategies in a wet year;

[0133] Figure 9 It is the spatial configuration result diagram of the land use conversion in the Da'an Irrigation Area under various optimization strategies in a historical extreme rainfall year. Detailed implementation method

[0134] The beneficial effects of the present invention are verified by the following examples.

[0135] Example 1: In this example, the Da'an Irrigation Area in western Jilin is taken as the research area to improve and regulate the farmland ecological barrier function in the plain area. This method is carried out according to the following steps:

[0136] Step 1: Analysis of the supply and demand of agricultural water and soil resources, specifically as follows:

[0137] (1) Collection of meteorological data: Collect meteorological information including precipitation, temperature, wind speed, relative humidity, and sunshine hours;

[0138] (2) Collection of land use type and crop production data. Using Landsat 8 satellite image data, perform land-atmosphere correction and land surface reflectance correction to obtain land use data for the region, and acquire crop yield, planting cost, selling price, water fee, carbon absorption rate, water content rate, and economic coefficient indicators; among them, the planting cost includes labor, fertilizer, pesticide, and seed costs; the water fee involves surface water and groundwater subsidies;

[0139] (3) Calculation of the net irrigation water requirement during the entire growth period of crops: Based on the crop planting and meteorological data of the area to be optimized, use the Penman formula recommended by the Food and Agriculture Organization of the United Nations to calculate the potential evapotranspiration of each crop at each growth stage, and combine with the crop coefficient to obtain the actual evapotranspiration; subsequently, use the method recommended by the Soil Conservation Service of the United States Department of Agriculture to estimate the effective rainfall at each growth stage; by subtracting the effective rainfall from the actual evapotranspiration, obtain the net irrigation water requirement at each stage, and accumulate the results of each stage to finally determine the net irrigation water requirement of each crop during the entire growth period;

[0140] Step two: Construct a multi-objective collaborative optimization model, specifically as follows:

[0141] (1) The decision variables are the areas of various land use types, including the area of rice planting, the area of corn planting, the area of forest land, the area of grassland, the area of wetland, and the area of saline-alkali land;

[0142] (2) Objective function:

[0143] ①. Minimize the groundwater water footprint:

[0144] To ensure the sustainable use of groundwater, establish an objective function with the minimum groundwater water footprint; the design of this objective function aims to minimize the extraction of groundwater while meeting the water demand. This helps to promote the more efficient use of water resources in the agricultural system, reduce the impact of over-pumping on the groundwater system, and improve water resource utilization efficiency. The formula is as follows:

[0145]

[0146] Q S =Q PR +Q CR +Q T

[0147] Q P =Q QE +Q TR +Q RE

[0148] Among them, the rainfall infiltration recharge amount Q PRIt refers to the amount of precipitation that infiltrates into the soil and recharges groundwater under the action of gravity; precipitation infiltration recharge is one of the main recharge sources in the study area and is determined by the precipitation infiltration coefficient method. The formula is as follows:

[0149] Q PR = a × F × P

[0150] Lateral runoff recharge amount Q CR It is calculated by Darcy's formula. The formula is as follows:

[0151] Q CR = K × I × B × M × ΔT × sinθ

[0152] Irrigation infiltration recharge amount Q T It is determined by the irrigation infiltration coefficient method. The calculation formula is

[0153]

[0154] Phreatic evaporation amount Q QE : Since the phreatic water level in the study area is relatively shallow and evaporation is very strong, phreatic evaporation has become the main discharge path of groundwater in this area. The calculation formula for the phreatic evaporation amount Q QE is as follows:

[0155] Q QE = C × F × E m

[0156] E m = E 20 × C e

[0157] Lateral runoff discharge amount Q TR It is calculated according to Darcy's formula:

[0158] Q TR = K × I × B × M × ΔT × sinθ

[0159] Artificial extraction amount Q RE , which is the actual extraction amount of groundwater, including industrial and agricultural water use, rural human and livestock water use, and domestic water use. It is calculated as follows:

[0160]

[0161] ②. Maximum carbon sequestration:

[0162] Increase the ability of the farmland ecosystem to absorb and store carbon to promote carbon fixation and mitigate the adverse effects on the climate. The formula is as follows:

[0163] maxF2 = E - E′

[0164] The total CO2 absorption equivalent E consists of the carbon absorption amounts Cm of forest land, grassland, and wetland and the carbon absorption E of cultivated landm Composition;

[0165] For forest land, grassland, and wetland, the calculation method of the carbon absorption amount Cm is as follows:

[0166] C m = S m × a m

[0167] The calculation method of the carbon absorption E of cultivated land is as follows: m For the calculation method of the carbon absorption E of cultivated land is as follows:

[0168]

[0169] E i = A i × e i

[0170] The calculation formula for the total CO2 emission equivalent E' is:

[0171] Among them, E CO2 represents the CO2 equivalent emitted by the farmland ecosystem, that is, the first layer of carbon emissions. It refers to the carbon emissions generated by the input of agricultural production materials in agricultural production, the direct carbon emissions of agricultural production, and the carbon emissions of crops themselves. Agricultural production materials include chemical fertilizers, pesticides, and agricultural films. The carbon footprint formula of E CO2 is as follows:

[0172]

[0173] E CH4 represents the conversion of CH4 emissions into CO2 equivalent, that is, the second layer of carbon emissions. It refers to the methane (CH4) emissions from paddy fields. The formula is as follows:

[0174]

[0175] E N2O represents the conversion of total N2O into CO2 equivalent, that is, the third layer of carbon footprint. It is the N2O emissions caused by straw turning and returning to the field and chemical fertilizer application. The calculation formula:

[0176]

[0177] ③. The maximum agricultural economic benefit:

[0178] The increase in agricultural economic benefits is of great significance for promoting social harmony and stability. Through reasonable optimization and decision-making, the agricultural economic benefits can be more effectively improved, thus realizing a sustainable and high-benefit agricultural production system. The formula is as follows:

[0179]

[0180] Combined with the crop coefficients K at the mid - growth stage and maturity stage provided by FAO - 56 data cmid(Tab) and K cend(Tab) and the crop coefficient correction formula, the corresponding crop coefficients at the mid - growth stage and maturity stage of rice and maize are corrected and calculated, as shown in Table 4;

[0181] K cmid = K cmid(Tab) +[0.04(u2 - 2)-0.004(RH min -45)](h / 3) 0.3

[0182]

[0183] Table 4 Crop coefficients at each growth stage of modified crops

[0184]

[0185]

[0186]

[0187] According to the industry water use quota standard, the water use efficiency coefficients of surface water and groundwater for paddy fields are 0.65 and 0.80 respectively, and those for dry fields are 0.65 and 0.85 respectively, which are already higher than the national average level, but there is still great potential for water conservation. Through water conservation measures, the water use efficiency coefficients of surface water and groundwater for paddy fields can reach 0.65 and 0.90 respectively; those for dry fields are 0.75 and 0.95 respectively.

[0188] ④. Maximum crop water resource utilization efficiency:

[0189] Considering the high dependence of agriculture on water resources, optimizing the crop water resource utilization efficiency has become a key factor in solving food security, economic development, and environmental sustainability. Through scientific and reasonable water resource management strategies, agricultural production can achieve greater yields with less water resource input, thereby improving economic efficiency. The formula is:

[0190]

[0191] Calculate the soil evaporation ES according to the Belanger - Langemeier formula i ,

[0192]

[0193] n represents the total number of time periods;

[0194] ⑤. Minimum agricultural pollutant emissions:

[0195]

[0196] Among them: the amount of chemical fertilizer used is determined by the "Limited Standards for Chemical Fertilizer Application in Corn" and the "Limited Standards for Chemical Fertilizer Application in Rice", and the actual emissions are determined by the local measured data;

[0197] Through the single pollution indices of multiple pollution indicators, calculate the mean and maximum values of the single pollution indicators, obtain the Nemerow pollution index of the water body, and determine the water quality level of the water body according to the evaluation grade division table. The calculation formula is as follows:

[0198]

[0199] P 内 is the Nemerow pollution index method, and P imax is the maximum value in the single pollution index; is the average value of the single pollution index;

[0200] (3) Constraint conditions:

[0201] ① Surface water volume constraint:

[0202] The surface water consumption cannot exceed the available surface water volume in this area;

[0203]

[0204] ② Groundwater volume constraint:

[0205] The groundwater consumption cannot exceed the exploitable groundwater volume in this area;

[0206]

[0207] ③ Total water resources constraint:

[0208] The surface water consumption and groundwater consumption cannot exceed the total available water volume in this area.

[0209]

[0210] ④ Food security constraint:

[0211] To ensure food security, the optimized total grain output cannot be lower than the specified grain demand;

[0212]

[0213] ⑤ Total crop area constraint:

[0214] Limited by the regional cultivated land area, the sum of the areas occupied by each crop cannot exceed the total cultivated land area;

[0215]

[0216] ⑥ Non - negative constraint:

[0217] Considering the actual situation, the values of surface water consumption, groundwater consumption, and crop planting area cannot be negative;

[0218] WS i ≥0

[0219] WG i ≥0

[0220] A i ≥0.

[0221] Parameter values in the formula of Table 5

[0222]

[0223]

[0224] Under different hydrological years, the third - generation non - dominated sorting genetic algorithm (NSGA - Ⅲ) is used to solve the model for 24 different key scenarios respectively, and an optimized allocation scheme of agricultural water and soil resources based on the coupling of "water volume, water quality, economy, efficiency, carbon, food, and ecology" is obtained. The optimization results are as follows:

[0225] ㈠ Optimized allocation results of agricultural crop planting structure for each hydrological year under different scenarios:

[0226]

[0227] ㈡ Optimized allocation results of groundwater agricultural irrigation water consumption for each hydrological year under different scenarios

[0228]

[0229]

[0230] ㈢ Analysis of economic benefit optimization results for each hydrological year under different scenarios is as Figure 1 shown. Among them, Pre is before optimization, S1 focuses on groundwater footprint, S2 focuses on economic benefit, S3 focuses on ecological benefit, S4 focuses on crop water use efficiency, S5 focuses on carbon sequestration, and S6 focuses on pollutant emissions.

[0231] ㈣ Analysis of crop water use efficiency optimization results for each hydrological year under different scenarios is as Figure 2 shown. Among them, Pre is before optimization, S1 focuses on groundwater footprint, S2 focuses on economic benefit, S3 focuses on ecological benefit, S4 focuses on crop water use efficiency, S5 focuses on carbon sequestration, and S6 focuses on pollutant emissions.

[0232] (5) Analysis chart of the optimized carbon sequestration amount in each hydrological year under different scenarios is as Figure 3 shown, where Pre is before optimization, S1 focuses on groundwater footprint, S2 focuses on economic benefits, S3 focuses on ecological benefits, S4 focuses on crop water use efficiency, S5 focuses on carbon sequestration amount, and S6 focuses on pollutant emissions.

[0233] (6) Analysis chart of the optimized NDI pollution index in each hydrological year under different scenarios is as Figure 4 shown, where Pre is before optimization, S1 focuses on groundwater footprint, S2 focuses on economic benefits, S3 focuses on ecological benefits, S4 focuses on crop water use efficiency, S5 focuses on carbon sequestration amount, and S6 focuses on pollutant emissions.

[0234] Step 3: Spatial allocation of land resources: Through the spatial optimization algorithm, grid-based land use allocation is carried out. Taking the grid quantity and spatial distribution of each land use type as decision variables, taking ecological connectivity, conversion cost, and the matching degree between the target land use as optimization objectives, and taking economic cost and the total land use area remaining unchanged as constraints, as follows:

[0235] (1) The decision variables are the grid quantity and spatial distribution of each land use type, including paddy rice, corn, forest land, grassland, wetland, and saline-alkali land;

[0236] (2) The objective function is as follows:

[0237] ① Maximize the ecological connectivity index (ECI)

[0238] The ecological connectivity index (ECI) is an indicator used to evaluate the connectivity between land use types. This index considers the adjacency and type similarity between land units:

[0239]

[0240] where N is the set of all pixels, w ij is the weight between pixels i and j. If they have the same land use type and are adjacent, then w ij is the weight of this adjacent land use type, otherwise it is 0. The weight values are as follows:

[0241] Table 6 Weight values of ecological connectivity

[0242] Cultivated land Forest land Grassland Wetland Construction land Saline-alkali land 1 3 2 4 0.5 0.2

[0243] δ(i,j) is the adjacency function, and the values are as follows

[0244] Table 7 Values of the adjacency function

[0245]

[0246]

[0247] ② Minimize the land use conversion cost (LUC):

[0248]

[0249] where L ij is the cost required to convert the grid cell located in the i-th row and j-th column, and x ij is a decision variable. If the land use types before and after optimization are the same, then x ij = 1; if the land use types before and after optimization are different, then x ij = 0;

[0250] ③ Minimize the difference between the land use and the target

[0251]

[0252] where p k is the actual proportion of the land use after optimization, p * k is the proportion of the target land use type, and K is the set of land use types;

[0253] (3) The constraint conditions are as follows:

[0254] ① Crop planting area constraint:

[0255]

[0256] In the formula, A is the total land resources in the study area; A i ′ is the planting area of crop i in the current year;

[0257] ② Total land use area constraint:

[0258]

[0259] In the formula, A 林 is the forest land resource area in the region; A 草 is the grassland resource area in the region; A 湿 is the wetland resource area in the region; A 盐碱 is the wetland resource area in the region in the current year;

[0260] ③ Non-negativity constraint:

[0261] A i ≥ 0;

[0262] Solve through an optimization algorithm, apply the optimized allocation plan of agricultural water and soil resources obtained in Step 2 to space, and complete the improvement and regulation of the farmland ecological barrier function.

[0263] According to the ecological protection areas designated by the government, the Da'an Irrigation Area is divided into the plain grassland protection and the farming-pastoral transition area in the lower reaches of the Tao'er River, the ecological restoration of the Tongyu sandy land and the farming-pastoral forestry buffer zone, and the wetland protection and flood control core area of the Nenjiang River, as Figure 5 shown in a of Figure 5 b of

[0264] The spatial optimization results of GridLandOpt in Step 3 are as follows:

[0265] In a dry year, the spatial allocation results of land use conversion in the Da'an Irrigation Area under various optimization strategies are as Figure 6 shown, (a) focuses on the groundwater footprint; (b) focuses on economic benefits; (c) focuses on the water use efficiency of crops; (d) focuses on the carbon sequestration amount; (e) focuses on pollutant emissions. From Figure 6 it can be seen that in the scenario focusing on economic benefits, the conversion of dry farmland to paddy fields is significant, reaching 1,488 grid units, mainly concentrated in the northern core area and the transition area. In the scenario focusing on ecological benefits, dry land is mainly converted into forests and grasslands, with 145 and 772 grid units respectively, especially in the ecological protection areas, such as the western and southern edges of the irrigation area. In the scenario focusing on the groundwater footprint, there is a partial conversion of paddy fields to wetlands in the low-lying areas of the Nenjiang River Basin, involving 227 grid cells. In the scenario focusing on the water use efficiency of crops, the conversion rate of dry land to grassland is relatively low, only 574 grid units.

[0266] During a normal year, the spatial allocation results of land use conversion in the Da'an Irrigation Area under various optimization strategies are as Figure 7 shown, (a) focuses on the groundwater footprint; (b) focuses on economic benefits; (c) focuses on the water use efficiency of crops; (d) focuses on the carbon sequestration amount; (e) focuses on pollutant emissions. From Figure 7It can be seen that in the scenario focusing on economic benefits, the conversion of dry land to paddy fields occurs the most, involving 1,449 grid units. At the same time, the transformation from saline-alkali land to dry land is also significant, involving a total of 1,103 grid units, mainly occurring in the transition zone. In the scenario focusing on ecological benefits, the focus is on converting dry land into forests and grasslands, with 145 and 772 grid units converted respectively. In the scenario focusing on groundwater footprint, the number of paddy fields converted to wetlands is relatively small, only 103 grid units. In the scenario focusing on crop water use efficiency, the conversion of dry land to paddy fields is also relatively significant, involving 1,322 grid units, mainly concentrated in the core area.

[0267] In the wet year, the spatial allocation results of land use conversion in the Da'an Irrigation Area under various optimization strategies are as Figure 8 shown, (a) focusing on groundwater footprint; (b) focusing on economic benefits; (c) focusing on crop water use efficiency; (d) focusing on carbon sequestration; (e) focusing on pollutant emissions. From Figure 8 it can be seen that in the scenario focusing on crop water use efficiency, the conversion rate of dry land to paddy fields is the highest, reaching 1,489 grid units. The conversion of paddy fields to wetlands has decreased significantly, with only 25 grid units. In the scenario focusing on groundwater footprint, the conversion of dry land to paddy fields involves 461 grid units, indicating that while ensuring water resource sustainability, the advantages of resources in wet years are actively utilized. In addition, in wet years, the conversion of paddy fields to wetlands is still relatively high, involving 228 grid units, which is conducive to improving the natural purification and flood regulation capabilities of water bodies.

[0268] In the historical extreme rainfall year, the spatial allocation results of land use conversion in the Da'an Irrigation Area under various optimization strategies are as Figure 9 shown, where (a) focuses on groundwater footprint; (b) focuses on economic benefits; (c) focuses on crop water use efficiency; (d) focuses on carbon sequestration; (e) focuses on pollutant emissions. From Figure 9It can be seen that the conversion from dryland to paddy fields is particularly frequent, especially under the economic-oriented optimization strategy. This spatial distribution shows a concentration and dispersion towards the suburbs and around transportation hubs, which is highly correlated with the regional development strategy and infrastructure integrity. Specifically, this conversion mainly occurred in the northern core area and the transition zone, involving 1429 grid cells. The northern core area and the transition zone have become hotspots for conversion due to their good water resources and proximity to the market. In the scenario emphasizing economic benefits, the transformation of saline-alkali land into dryland is particularly prominent. The conversion activities are mainly concentrated in the eastern and southern parts of the transition zone, involving 1322 grid units. These areas were chosen because they have high potential for soil improvement and are close to major irrigation channels, which helps in the management of saline-alkali land and the connectivity of arable land. This effort not only improved the quality of the original saline-alkali land but also increased the agricultural value and economic benefits of the land. In addition, the conversion of dryland into forest and grassland is also significant in the strategy emphasizing ecological benefits, especially in ecological reserve areas and biodiversity hotspots. Specifically, the conversion of dryland to forest occurred in the western and southern marginal areas of the irrigation area, involving 145 grid units, while the conversion of dryland to grassland was concentrated in the southern region, involving 772 grid units. This aims to enhance the ecological functions of the region, such as improving carbon sequestration capacity, protecting soil and water, and enhancing biodiversity. It also helps to form an ecological buffer zone and reduce environmental degradation. Finally, in the optimization strategy focusing on groundwater protection, the conversion of paddy fields to wetlands is also significant, especially in the low-lying areas of the Nenjiang River Basin, involving 227 grid cells. These transformations aim to improve flood regulation capacity, restore aquatic ecosystems, enhance the flood prevention ability and ecological quality of the region, and ensure the sustainable use of water resources and ecological security.

Claims

1. A method for enhancing and regulating the ecological barrier function of farmland in plain areas, characterized in that, The method is carried out according to the following steps: Step 1: Analysis of the supply and demand of agricultural water and soil resources: Based on the planting data, water volume data, and meteorological data of the main crops in the area to be optimized, the FAO improved Penman-Monteith formula is used to calculate the reference crop evapotranspiration ET0, and combined with the dynamic correction method of crop coefficients, a simulation model of the actual water requirement during the growth period is constructed; then, using Landsat 8 satellite image data, land-atmosphere correction and land surface reflectance correction are carried out to obtain the land use data of the area. Step 2: Construct a multi-objective collaborative optimization model, which consists of three parts: decision variables, objective functions, and constraint conditions. Among them, the decision variables are the areas of different land use types. There are five objective functions, namely: ① Reduction of groundwater footprint; ② Increase in farmland carbon storage; ③ Improvement of economic benefits; ④ Improvement of crop water use efficiency; ⑤ Reduction of pollutant emissions; with the available water volume and the occupied area of land use types as constraint conditions, a multi-objective optimization allocation model of regional agricultural water and soil resources is constructed; under different hydrological years, the model is solved using optimization algorithms for different key scenarios to obtain an optimized allocation plan for agricultural water and soil resources based on the coupling of "water volume, water quality, economy, efficiency, carbon, food, and ecology". Step 3: Spatial allocation of land resources: Through the spatial optimization algorithm, grid-based land use allocation is carried out. With the number of grids and spatial distribution of each land use type as decision variables, ecological connectivity, conversion cost, and the matching degree between the target land use as optimization objectives, and with economic cost and the total land use area remaining unchanged as constraint conditions, the solution is obtained through the optimization algorithm, and the optimized allocation plan for agricultural water and soil resources obtained in Step 2 is applied to space to complete the improvement and regulation of the farmland ecological barrier function in the plain area.

2. The method for enhancing and regulating the function of a farmland ecological barrier in a plain area according to claim 1, characterized in that, The specific process of the supply and demand analysis of agricultural water and soil resources described in Step 1 is as follows: (1) Collection of meteorological data: Collect meteorological information including precipitation, temperature, wind speed, relative humidity, and sunshine hours. (2) Collection of land use type and crop production data. Using Landsat 8 satellite image data, land-atmosphere correction and land surface reflectance correction are carried out to obtain the land use data of the area, and obtain crop yield, planting cost, selling price, water fee, carbon absorption rate, water content, and economic coefficient indicators; among them, the planting cost includes labor, fertilizer, pesticide, and seed costs; the water fee involves surface water and groundwater subsidies. (3) Calculation of the net irrigation water requirement during the entire growth period of the crop: Based on the crop planting and meteorological data in the area to be optimized, the Penman formula recommended by the United Nations Food and Agriculture Organization is used to calculate the potential evapotranspiration of each crop at each growth stage, and combined with the crop coefficient to obtain the actual evapotranspiration; subsequently, the method recommended by the Soil Conservation Service of the United States Department of Agriculture is used to estimate the effective rainfall at each growth stage; by subtracting the effective rainfall from the actual evapotranspiration, the net irrigation water requirement at each stage is obtained, and the results of each stage are accumulated to finally determine the net irrigation water requirement during the entire growth period of each crop.

3. A method for enhancing and regulating the functions of a farmland ecological barrier in a plain area, according to claim 1 or 2, characterized in that The specific multi-objective optimization model described in Step 2 is as follows: (1) The decision variables are the areas of various land use types, including the area of rice cultivation, the area of corn cultivation, the area of forest land, the area of grassland, the area of wetland, and the area of saline-alkali land; (2) Objective function: ① Minimize the groundwater water footprint: The formula is as follows: Q S = Q PR + Q CR + Q T Q P = Q QE + Q TR + Q RE Among them, the rainfall infiltration recharge Q PR is determined by the precipitation infiltration coefficient method, and the formula is as follows: Q PR = a × F × P Lateral runoff recharge Q CR Calculated using Darcy's formula, the formula is as follows: Q CR = K × I × B × M × ΔT × sinθ Irrigation infiltration recharge volume Q T Determined by the irrigation infiltration coefficient method, the calculation formula is: Q T = Q g ×β The phreatic evaporation Q QE is calculated by the formula: Q QE = C×F×E m , where E m = E 20 ×C e Lateral runoff discharge Q TR Calculated according to Darcy's formula: Q TR = K × I × B × M × ΔT × sinθ Artificial extraction volume Q RE It is calculated according to the following formula: ② Maximize the carbon sequestration amount: The formula is as follows: maxF2 = E - E′ The total CO2 absorption equivalent E consists of the carbon absorption amounts Cm of forest land, grassland, and wetland and the carbon absorption E of cultivated land. m It is composed of... Carbon sequestration C in forest land, grassland and wetland m The calculation method is as follows: C m = S m × a m Cultivated land carbon absorption E m The calculation method is as follows: The calculation formula for the total CO2 emission equivalent E' is as follows: Among them, E CO2 represents the CO2 equivalent emitted by the farmland ecosystem, and the formula is as follows: E CH4 Indicates the conversion of CH4 emissions to CO2 equivalent, with the formula as follows: E N2O Indicates the total N2O converted to CO2 equivalent, calculation formula: ③ Maximize the agricultural economic benefit: The formula is as follows: Combined with the crop coefficient K at the mid-growing and maturity stages provided by FAO-56 data cmid(Tab) K cend(Tab) and the crop coefficient correction formula, the corresponding crop coefficients at the mid-growing and maturity stages of rice and maize are corrected and calculated; K cmid = K cmid(Tab) + [0.04(u2 - 2) - 0.004(RH min - 45)](h / 3) 0.3 ④ Maximize the crop water use efficiency: The formula is as follows: Calculate the soil evaporation ES according to the Bell-Langmuir formula i , n represents the total number of time periods; ⑤ Minimize the agricultural pollutant emissions: The formula is as follows: Among them: The amount of chemical fertilizer used is determined by the "Limit Standards for Chemical Fertilizer Application in Corn" and the "Limit Standards for Chemical Fertilizer Application in Rice", and the actual emissions are determined by the local measured data; Through the single pollution indices of multiple pollution indicators, calculate the mean and maximum values of the single pollution indicators, obtain the Nemerow pollution index of the water body, and determine the water quality grade of the water body according to the evaluation grade division table. The calculation formula is as follows: P 内 is the Nemerow pollution index method, P imax is the maximum value among the single pollution indices, P im is the average value of the single pollution indices; (3) Constraints: ①. Surface water volume constraint: ② Groundwater volume constraint: ③ Total water resources constraint: ④Food security constraints: ⑤ Total crop area constraint: ⑥Non - negative constraint: WS i ≥ 0, WG i ≥ 0, A i ≥ 0. The parameters and their units in the formula are as follows: Q S Groundwater recharge, m 3 ; Q E Groundwater discharge, m 3 ; Q PR Precipitation infiltration recharge, m 3 ; Q CR Lateral runoff recharge volume, m 3 ; Q T Irrigation infiltration recharge volume, m 3 ; α precipitation infiltration recharge coefficient, m 3 ; F calculation area, km 2 ; P Precipitation in the calculation period, mm; K Permeability coefficient of the aquifer near the section, m / a; I Hydraulic gradient perpendicular to the section; B Width of the cross-section of the balanced area, km; M Thickness of the aquifer, m; T Calculation time, d; θ Angle between the groundwater flow direction and the cross-section; Q g Agricultural irrigation water consumption, m 3 ; β Irrigation infiltration recharge coefficient; Q QE Evaporation by submergence, m 3 ; C Phreatic evaporation coefficient, E m Φm Evaporation of evaporating dish, mm; E 20 Evaporation of Φ20 evaporating dish, mm; C e Conversion factor for calculating Em from E20 Q RE Artificial extraction volume, m 3 ; Q ij represents the groundwater extraction volume of the i-th category in the j-th time period, m 3 ; E Total CO2 absorption equivalent, kg; E' Total CO2 emission equivalent, kg; C m Carbon sequestration of forest, grassland and wetland, kg S m Area of forest, grassland and wetland, hm 2 ; a m Carbon sequestration coefficient of the m-th land type, kg / m 2 ; CEF Conversion coefficient for carbon conversion to CO2; E i Carbon uptake of crop i, kg; Y i Crop yield per hectare, kg / hm 2 ; g i Root-shoot ratio coefficient of the i-th type of crop; Water content of crop i i Water content of crop i; H i Economic coefficient of crop i; A i Area of crop i, hm 2 ; Carbon absorption rate of crop i, kg / hm 2 ; E CO2 CO2 equivalent emissions from the farmland ecosystem, kg; a, b, c, d, f, h, j, k Carbon emission coefficients; G g Application rate of fertilizer of type G, kg; G p Pesticide application rate, kg; G m Usage amount of agricultural film, kg; G i Effective irrigated area of agriculture, hm 2 ; A e Total sown area of crops, hm 2 ; W e Total power of agricultural machinery, kw; G s Diesel consumption of agricultural machinery, kg; S e Plowed area, hm 2 ; E CH4 CH4 emissions converted to CO2 equivalent, kg; GWP CH4 Conversion factor for converting CH4 emissions to CO2 A 水稻 Rice sown area, hm 2 ; EF 水稻 CH4 emission factor, kg / hm 2 ; E N2O Total N2O converted to CO2 equivalent, kg; It is N2O The converted N2O emission from straw incorporation into the soil to CO2 equivalent, kg; E” N2O Direct emissions of N2O converted to CO2 equivalent, kg; GWP N2O Conversion coefficient for the conversion of N2O to CO2; EF N2O Conversion coefficient of N to N2O; J Straw return rate, %; b i Nitrogen content of straw of crop i, %; r i Dry weight ratio of the economic product part of crop i; N 总 Total input of nitrogen element in chemical fertilizer, kg; l' N2O direct emission coefficient; P i Market price of crop i in the current year, yuan / kg; P w Water supply cost per unit area, yuan / hm 2 ; IC Irrigation water use coefficient in the study area, %; L i Planting cost of crop i, yuan / hm 2 ; M i Subsidy amount, yuan / hm 2 ; ET c Actual crop water requirement, mm; ET0 Reference crop water requirement, mm; P e Effective precipitation, mm; Δ water vapor pressure curve slope, kPa·°C -1 ; u2 Wind speed at 2m height, m / s; R n Net radiation on the crop surface, MJ·m -2 ·d -1 ; G Soil heat flux, MJ·m -2 ·d -1 ; γ Psychrometer thermometer constant, kPa·°C -1 ; T Air temperature at 2m height, °C; e s Saturation water vapor pressure, kPa; e a Actual water vapor pressure, kPa; K cmid Modified mid-season crop coefficient K cend Modified maturity crop coefficient RH min Minimum relative humidity, %; h Average height of the crop at each stage, m; P Actual precipitation, mm; x i Area of each land type, hm 2 ; a i Economic benefit coefficient of each land use type, yuan / hm 2 ; WS i Surface water consumption of each city, mm; Total available surface water volume in each SW city, m 3 ; WG i Groundwater water consumption of each city, mm; Total available groundwater volume in each city of GW, m 3 ; FDP Per capita food demand, kg / person; TPR Population, person; Y i Crop yield in the i-th time period, kg; I i Irrigation water volume in the i-th time period, m 3 ; R i Precipitation within the i-th time period, m 3 ; T i Crop transpiration in the i-th time period, m 3 ; ET i Crop evapotranspiration in the i-th time period, mm; ES i Soil evaporation in the i-th time period, mm; A T Crop area, m 2 ; A S Soil area, m 2 ; K l Soil evaporation coefficient; N ai Application rate of the i-th chemical fertilizer, kg; λ ai Emission coefficient of the i-th type of chemical fertilizer E a Actual pollutant emissions.

4. A method for enhancing and regulating the functions of a farmland ecological barrier in a plain area, according to claim 1 or 2, characterized in that The algorithm for solving the model described in step 2 is the third-generation non-dominated sorting genetic algorithm.

5. A method for enhancing and regulating the ecological barrier function of farmland in plain areas according to claim 1 or 2, characterized in that As described in step 3, through the spatial optimization algorithm for grid-based land use allocation, with the grid numbers and spatial distribution of various land use types as decision variables, and with ecological connectivity, conversion cost, and the matching degree between the target land use as optimization objectives, and with economic cost and total land use area unchanged as constraints, solve through the optimization algorithm as follows: (1) The decision variables are the grid numbers and spatial distribution of various land use types, including rice, corn, forest land, grassland, wetland, and saline-alkali land; (2) The objective function is as follows: ① Maximize the ecological connectivity index (ECI): The formula is: where N is the set of all pixels, and w ij is the weight between pixels i and j. If they have the same land use type and are adjacent, then w ij is the weight of this adjacent land use type; otherwise it is 0. The weight values are: 1 for cultivated land, 3 for forest land, 2 for grassland, 4 for wetland, 0.5 for built-up land, and 0.2 for saline-alkali land; δ(i,j) is the adjacency function, and its value is: no connectivity is 0, one connectivity is 0.25, two connectivity is 0.5, three connectivity is 0.75, four connectivity is 1; ② Minimize the land use conversion cost (LUC): The formula is as follows: Among them, L ij is the cost required to convert the grid cell located in the i-th row and j-th column, and x ij is a decision variable; if the land use types before and after optimization are the same, then x ij = 1; if the land use types before and after optimization are different, then x ij = 0; ③ Minimize the difference between the land use and the target: The formula is: Among them, p k is the actual proportion of optimized land use, and p * k is the proportion of the target land use type, and K is the set of land use types; (3) The constraints are as follows: ① Crop planting area constraint: where A is the total land resources in the study area; A i ′ is the planting area of crop i in the base year; ②Total land use area constraint: In the formula, A 林 is the area of forest land resources in the region; A 草 is the area of grassland resources in the region; A 湿 is the area of wetland resources in the region; A 盐碱 is the area of wetland resources in the region in the current year; ③ Non - negative constraint: A i ≥ 0.

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