Planting structure optimization and adjustment method based on total nitrogen pollution load reduction

Abstract

The invention discloses a planting structure optimization and adjustment method based on total nitrogen pollution load reduction, and relates to the technical field of agricultural non-point source pollution control, and the method comprises the following steps: investigating and collecting basic data of a research area, and preprocessing the basic data; constructing an optimized and adjusted target function; based on agricultural product supply guarantee, water resources and cultivated land resources, constraint conditions related to the planting industry are established; performing model standardization on the target function and the constraint condition to obtain a planting structure optimization adjustment model; according to the planting structure optimization adjustment model, an initial simplex table is compiled; and performing iterative solution according to the initial simplex table to obtain an optimal planting structure. The method solves the problems of nitrogen element loss caused by limitation of the existing technical bureau in a single crop planting process, less research on direct establishment of a response relationship between a planting structure and river water quality, and insufficient overall consideration of market factors and water and soil resource factors which influence agricultural production.

Description

Technical Field

[0001] The invention relates to the technical field of agricultural non-point source pollution prevention and control, and in particular to a planting structure optimization adjustment method based on total nitrogen pollution load reduction. Background Art

[0002] At present, the water environment problems caused by non-point source pollution in my country need to be solved urgently. In particular, the total nitrogen in some important rivers entering lakes (reservoirs) and rivers entering the sea has become the primary pollutant in lakes and coastal waters. The total nitrogen load caused by non-point source pollution is increasing and has become one of the main contradictions restricting the continuous improvement of my country's water ecological environment. Non-point source pollution in the planting industry refers to the surplus nitrogen in the soil in agricultural production activities, especially in the planting industry, due to the irrational use of nitrogen-containing fertilizers, which is driven by rainfall and topography. It enters the environmental water body through surface runoff, underground runoff and other channels, posing a threat to the ecological environment.

[0003] Different crops have large differences in their absorption and utilization of soil nitrogen, which is affected by many factors such as crop type, variety, growth period, soil conditions, and nitrogen fertilizer form. The purpose of adjusting the planting industry structure is to reduce agricultural non-point source pollution while ensuring the safety of agricultural product supply, that is, to achieve the lowest agricultural non-point source pollution load in the basin without affecting agricultural development and regional agricultural product supply.

[0004] The current relevant research has the following main deficiencies: first, it is limited to the loss of nitrogen caused by the planting process of a single crop; second, there are few studies that directly establish the response relationship between planting structure and river water quality; third, there is insufficient overall consideration of market factors and water and soil resources factors that affect agricultural production. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for optimizing and adjusting the planting structure based on reducing the total nitrogen pollution load, which solves the problems that the prior art is limited to the loss of nitrogen caused by the planting process of a single crop, there are few studies on directly establishing the response relationship between the planting structure and the river water quality, and there is insufficient overall consideration of the market factors and water and soil resources factors that affect agricultural production.

[0006] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is: a method for optimizing and adjusting the planting structure based on the reduction of total nitrogen pollution load, comprising the following steps:

[0007] S1: Investigate and collect basic data of the study area and pre-process the basic data;

[0008] S2: Based on the pre-processed basic data, the objective function of optimization adjustment is constructed with the goal of minimizing the total nitrogen pollution load;

[0009] S3: Establish constraints related to the planting industry based on agricultural product supply security, water resources and arable land resources;

[0010] S4: Standardize the objective function and constraint conditions to obtain the planting structure optimization adjustment model;

[0011] S5: Optimize and adjust the model according to the planting structure and compile the initial simplex table;

[0012] S6: Perform iterative solution based on the initial simplex table to obtain the optimal planting structure and complete the optimization adjustment of the planting structure based on the reduction of total nitrogen pollution load.

[0013] Furthermore, the basic data in S1 include the planting area of ​​grain crops, the planting area of ​​oil crops, the planting area of ​​cash crops, the total sown area, the fertilizer application per unit area of ​​different crops, the organic fertilizer application per unit area of ​​different crops, the irrigation water per unit area of ​​different crops, the available irrigation water, the number of people supplied with agricultural products and the annual per capita consumption of agricultural products in the region;

[0014] The food crops include wheat and corn;

[0015] The oil crops include rapeseed and soybean;

[0016] The economic crops include leafy vegetables and melons and tomatoes.

[0017] Furthermore, the objective function f1 optimized and adjusted in S2 is:

[0018]

[0019] Among them, a i is the total nitrogen output coefficient of food crops, oil crops and cash crops, A i is the planting area of ​​grain crops, oil crops and cash crops, and i is the crop type.

[0020] Furthermore, the constraints related to the planting industry in S3 include agricultural product supply guarantee constraints, water resource constraints and arable land resource constraints.

[0021] Furthermore, the agricultural product supply guarantee constraints are:

[0022]

[0023] Among them, c i is the annual yield per unit area of ​​grain crops, oil crops and cash crops, l i and h i They are the lower and upper limits of the annual per capita demand for agricultural products under the condition of ensuring the stable supply of various agricultural products, and P is the population supplied by the agricultural area;

[0024] The water resource constraints are:

[0025]

[0026] Among them, w i is the irrigation water requirement per unit area of ​​grain crops, oil crops and cash crops, Q is the total annual planned irrigation water in the study area;

[0027] The farmland resource constraints are:

[0028]

[0029] Among them, d i is the multiple cropping index of grain crops, oil crops and cash crops, and A is the total annual sown area in the study area.

[0030] Furthermore, the planting structure optimization adjustment model in S4 is:

[0031] minf1=a1A1+a2A2+a3A3

[0032]

[0033] Among them, A4, A5, A6 and A7 are all slack variables.

[0034] Furthermore, the iterative solution in S6 is performed according to the initial simplex table to obtain the optimal planting structure, including the following sub-steps:

[0035] S6-1: Iterate according to the initial simplex table and calculate each test number σ;

[0036] S6-2: Determine whether each test number σ satisfies σ≤0. If yes, obtain the optimal planting structure, otherwise proceed to step S6-3;

[0037] S6-3: Select the variable with the largest test number σ as the base variable, replace it with the base variable with the smallest ratio θ of the input variables, continue iterating and return to step S6-2.

[0038] Furthermore, the calculation formula of the test number σ in S6-1 is:

[0039]

[0040] in, is the coefficient matrix of non-basic variables, is the coefficient matrix of the basic variables, B -1 N is the non-basic variable coefficient matrix after the basic variable coefficient matrix is ​​converted to the unit matrix.

[0041] Furthermore, the calculation formula for the variable ratio θ in S6-3 is:

[0042]

[0043] Among them, b is a constant term, is the coefficient matrix of the replaced variables.

[0044] The beneficial effects of the present invention are:

[0045] 1. The present invention generalizes the planting industry into three categories: food crops, oil crops, and cash crops. The calculation parameters required for each crop type are determined by the main crops in the statistical yearbook or field research in the study area. Food crops are mainly wheat and corn, oil crops are mainly rapeseed and soybeans, and cash crops are mainly common leafy vegetables and melons and tomatoes. After determining the study area, select the average value of 2-3 representative specific crops in the area to determine the parameters.

[0046] 2. At present, most of the research on the adjustment of planting structure adopts the mechanism model to simulate the migration process of fertilizers in runoff and soil during the planting process, which requires the calibration of a large number of model parameters. The present invention directly constructs the output of total nitrogen pollutants of different crop types as the objective function, and the parameter calibration is relatively simple.

[0047] 3. The present invention takes agricultural product supply security, water resource constraints, and arable land resource constraints as rigid constraints for adjusting the planting structure, and achieves pollution reduction while ensuring the security of agricultural product supply.

[0048] 4. The main advantages of the present invention using the simplex method to solve the problem of optimizing and adjusting the planting structure are: the iterative process of the simplex method is intuitive and easy to understand, and each iteration is carried out in the optimization direction of the objective function, which makes the algorithm easy to program and implement; if the data of the optimization problem changes, the simplex method can be easily adjusted to adapt to these changes, and has good portability. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 The present invention is a flow chart of a method for optimizing and adjusting the planting structure based on the reduction of total nitrogen pollution load. DETAILED DESCRIPTION

[0050] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0051] like Figure 1 As shown, a method for optimizing and adjusting the planting structure based on reducing the total nitrogen pollution load comprises the following steps:

[0052] S1: Investigate and collect basic data of the study area and pre-process the basic data;

[0053] S2: Based on the pre-processed basic data, the objective function of optimization adjustment is constructed with the goal of minimizing the total nitrogen pollution load;

[0054] S3: Establish constraints related to the planting industry based on agricultural product supply security, water resources and arable land resources;

[0055] S4: Standardize the objective function and constraint conditions to obtain the planting structure optimization adjustment model;

[0056] S5: Optimize and adjust the model according to the planting structure and compile the initial simplex table;

[0057] S6: Perform iterative solution based on the initial simplex table to obtain the optimal planting structure and complete the optimization adjustment of the planting structure based on the reduction of total nitrogen pollution load.

[0058] The basic data in S1 include the planting area of ​​grain crops, the planting area of ​​oil crops, the planting area of ​​cash crops, the total sown area, the fertilizer application per unit area of ​​different crops, the organic fertilizer application per unit area of ​​different crops, the irrigation water volume per unit area of ​​different crops, the available irrigation water, the number of people supplied with agricultural products and the annual per capita consumption of agricultural products in the region;

[0059] The food crops include wheat and corn;

[0060] The oil crops include rapeseed and soybean;

[0061] The economic crops include leafy vegetables and melons and tomatoes.

[0062] In this embodiment, after determining the study area, the average values ​​of 2-3 representative specific crops in the area are selected to determine the parameters. The scale of the planting industry includes data such as the planting area of ​​different crops and the amount of fertilizer applied, which are obtained from the local statistical yearbook. For areas lacking statistical data, such as typical small watersheds, township / village administrative units, data can be obtained through on-site surveys, household visits, etc.

[0063] The objective function f1 optimized and adjusted in S2 is:

[0064]

[0065] Among them, a i is the total nitrogen output coefficient of food crops, oil crops and cash crops, kg·hm -2 ·a -1 , A i is the planting area of ​​grain crops, oil crops and cash crops, hm 2 , i is the crop type.

[0066] The constraints related to the planting industry in S3 include agricultural product supply guarantee constraints, water resource constraints and arable land resource constraints.

[0067] The agricultural product supply guarantee constraints are:

[0068]

[0069] Among them, c i is the annual yield per unit area of ​​grain crops, oil crops and cash crops, kg / hm 2 , l i and h i They are the lower and upper limits of the annual per capita demand for agricultural products under the condition of ensuring the stable supply of various agricultural products, and P is the population supplied by the agricultural area;

[0070] The water resource constraints are:

[0071]

[0072] Among them, w i is the irrigation water requirement per unit area of ​​grain crops, oil crops and cash crops, m 3 / hm 2 , Q is the total annual planned irrigation water volume in the study area, m 3 ;

[0073] The farmland resource constraints are:

[0074]

[0075] Among them, d i is the multiple cropping index of grain crops, oil crops and cash crops, A is the total annual sown area in the study area, hm 2 .

[0076] The planting structure optimization adjustment model in S4 is:

[0077] minf1=a1A1+a2A2+a3A3

[0078]

[0079] Among them, A4, A5, A6 and A7 are all slack variables.

[0080] In one embodiment of the present invention, in step S5, an initial simplex table is compiled according to the constructed planting structure optimization adjustment model, as shown in the following table.

[0081] Table 1 Simplex table

[0082]

[0083] In S6, iterative solution is performed according to the initial simplex table to obtain the optimal planting structure, including the following sub-steps:

[0084] S6-1: Iterate according to the initial simplex table and calculate each test number σ;

[0085] S6-2: Determine whether each test number σ satisfies σ≤0. If yes, obtain the optimal planting structure, otherwise proceed to step S6-3;

[0086] S6-3: Select the variable with the largest test number σ as the base variable, replace it with the base variable with the smallest ratio θ of the input variables, continue iterating and return to step S6-2.

[0087] The final optimal solution vector is: Optimal solution = [A1, A2, A3], that is, the planting areas of grain crops, oil crops, and cash crops after optimization are A1, A2, and A3 respectively.

[0088] The calculation formula of the test number σ in S6-1 is:

[0089]

[0090] in, is the coefficient matrix of non-basic variables, is the coefficient matrix of the basic variables, B -1 N is the non-basic variable coefficient matrix after the basic variable coefficient matrix is ​​converted to the unit matrix.

[0091] The calculation formula of the variable ratio θ in S6-3 is:

[0092]

[0093] Among them, b is a constant term, is the coefficient matrix of the replaced variables.

[0094] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the invention.

Claims

1. A method for optimizing and adjusting planting structure based on reducing total nitrogen pollution load, characterized in that: The following steps are involved: S1: Investigate and collect basic data of the study area and pre-process the basic data; S2: Based on the pre-processed basic data, the objective function of optimization adjustment is constructed with the goal of minimizing the total nitrogen pollution load; S3: Establish constraints related to the planting industry based on agricultural product supply security, water resources and arable land resources; S4: Standardize the objective function and constraint conditions to obtain the planting structure optimization adjustment model; S5: Optimize and adjust the model according to the planting structure and compile the initial simplex table; S6: Perform iterative solution based on the initial simplex table to obtain the optimal planting structure and complete the optimization adjustment of the planting structure based on the reduction of total nitrogen pollution load.

2. The method for optimizing and adjusting the planting structure based on reducing the total nitrogen pollution load according to claim 1, characterized in that: The basic data in S1 include the planting area of ​​grain crops, the planting area of ​​oil crops, the planting area of ​​cash crops, the total sown area, the fertilizer application per unit area of ​​different crops, the organic fertilizer application per unit area of ​​different crops, the irrigation water volume per unit area of ​​different crops, the available irrigation water, the number of people supplied with agricultural products and the annual per capita consumption of agricultural products in the region; The food crops include wheat and corn; The oil crops include rapeseed and soybean; The economic crops include leafy vegetables and melons and tomatoes.

3. The method for optimizing and adjusting the planting structure based on reducing the total nitrogen pollution load according to claim 1, characterized in that: The objective function f1 optimized and adjusted in S2 is: Among them, a i is the total nitrogen output coefficient of food crops, oil crops and cash crops, A i is the planting area of ​​grain crops, oil crops and cash crops, and i is the crop type.

4. The method for optimizing and adjusting the planting structure based on reducing the total nitrogen pollution load according to claim 3, characterized in that: The constraints related to the planting industry in S3 include agricultural product supply guarantee constraints, water resource constraints and arable land resource constraints.

5. The method for optimizing and adjusting the planting structure based on reducing the total nitrogen pollution load according to claim 4, characterized in that: The agricultural product supply guarantee constraints are: Among them, c i is the annual yield per unit area of ​​grain crops, oil crops and cash crops, l i and h i They are the lower and upper limits of the annual per capita demand for agricultural products under the condition of ensuring the stable supply of various agricultural products, and P is the population supplied by the agricultural area; The water resource constraints are: Among them, w i is the irrigation water requirement per unit area of ​​grain crops, oil crops and cash crops, Q is the total annual planned irrigation water in the study area; The farmland resource constraints are: Among them, d i is the multiple cropping index of grain crops, oil crops and cash crops, and A is the total annual sown area in the study area.

6. The method for optimizing and adjusting the planting structure based on reducing the total nitrogen pollution load according to claim 5, characterized in that: The planting structure optimization adjustment model in S4 is: minf1=a1A1+a2A2+a3A3 Among them, A4, A5, A6 and A7 are all slack variables.

7. The method for optimizing and adjusting the planting structure based on reducing the total nitrogen pollution load according to claim 1, characterized in that: In S6, iterative solution is performed according to the initial simplex table to obtain the optimal planting structure, including the following sub-steps: S6-1: Iterate according to the initial simplex table and calculate each test number σ; S6-2: Determine whether each test number σ satisfies σ≤0. If yes, obtain the optimal planting structure, otherwise proceed to step S6-3; S6-3: Select the variable with the largest test number σ as the base variable, replace it with the base variable with the smallest ratio θ of the input variables, continue iterating and return to step S6-2.

8. The method for optimizing and adjusting the planting structure based on reducing the total nitrogen pollution load according to claim 7, characterized in that: The calculation formula of the test number σ in S6-1 is: in, is the coefficient matrix of non-basic variables, is the coefficient matrix of the basic variables, B -1 N is the non-basic variable coefficient matrix after the basic variable coefficient matrix is ​​converted to the unit matrix.

9. The method for optimizing and adjusting the planting structure based on reducing the total nitrogen pollution load according to claim 7, characterized in that: The calculation formula of the variable ratio θ in S6-3 is: Among them, b is a constant term, is the coefficient matrix of the replaced variables.

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