Regional water resources regulation method to improve grain production based on irrigation contribution rate

Abstract

The present invention discloses a regional water resource regulation method for improving grain production based on irrigation contribution rate, which comprises S1 obtaining regional statistical data and site observation data of a study area; S2 calculating the site-scale irrigation contribution rates of all sites in the study area and the regional-scale irrigation contribution rate in the study area; S3 using the regional-scale irrigation contribution rate to verify the average value of all site-scale irrigation contribution rates, entering S5 when the verification is passed, and entering S4 when the verification is not passed; S4 adjusts the regional statistical data and / or the site observation data, and returns to step S2; S5 uses all site-scale irrigation contribution rates to perform spatial interpolation to obtain the site-scale irrigation contribution rates of multiple unknown points; S6 uses the maximization of grain production in the study area as an objective function, and the site-scale irrigation contribution rate in the study area as a water demand constraint, solves the objective function, and obtains the optimal water distribution in the study area.

Description

Technical Field

[0001] The invention relates to agricultural water resource regulation technology, and in particular to a regional water resource regulation method for increasing grain output based on irrigation contribution rate. Background Art

[0002] Food production is the foundation of agricultural development, and irrigation is the lifeblood of food production. Although irrigation has a very obvious effect on agricultural production and income increase, agricultural production is the result of the joint action of various production factors such as water, land and other natural resources, seeds, fertilizers, labor, machinery and other inputs, as well as farming technology, management level and other production factors. These factors both promote and restrict each other in food production, and different input factors contribute differently to food production. Studying the contribution rate of irrigation to food production is to study the incremental benefits of irrigation for food production in addition to other agricultural input factors.

[0003] The "Economic Evaluation Specifications for Water Conservancy Construction Projects (SL72-2013)" gives an irrigation benefit sharing coefficient range of 0.2-0.6. However, due to the large range, it is difficult to determine the value for complex situations such as different regions, different crops, and different irrigation forms in actual applications, resulting in inaccurate selected sharing coefficients, which in turn affects the rational formulation of regional irrigation volumes and ultimately affects grain production.

[0004] At present, there are many studies on single influencing factors of grain yield, but the calculation results are uncertain and there is numerical instability, which requires further research in theory and method. In addition, domestic and foreign scholars widely use principal component analysis (Yue, 2022) and grey correlation method (Xu Tiantian et al., 2020) to analyze the contribution between grain yield and influencing factors, but the data indicators and methods are partly subjective, and the results obtained for data requiring quantitative analysis are less accurate, which makes the irrigation amount determined based on the data inaccurate, thus affecting the final grain yield. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the regional water resource regulation method for improving grain production based on irrigation contribution rate provided by the present invention solves the problem that the irrigation benefit sharing coefficient in the prior art is inaccurately determined, resulting in inaccurate determination of irrigation area water allocation.

[0006] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0007] A method for regulating regional water resources based on irrigation contribution rate to increase grain production is provided, comprising the steps of:

[0008] S1. Obtain regional statistical data and site observation data of the study area stored in the model database;

[0009] S2. Calculate the site-scale irrigation contribution rate of all sites in the study area and the regional-scale irrigation contribution rate in the study area based on regional statistical data and site observation data;

[0010] S3, using the regional scale irrigation contribution rate to verify the average value of all site scale irrigation contribution rates, and if the verification is passed, proceed to step S5, and if the verification is not passed, proceed to step S4;

[0011] S4, adjusting the regional statistical data and / or the site observation data, and then returning to step S2;

[0012] S5. Use the weighted summation method of all site-scale irrigation contribution rates to perform spatial interpolation to obtain the site-scale irrigation contribution rates of multiple unknown points in the area between sites in the study area;

[0013] S6. Taking the maximization of grain yield in the study area as the objective function and the contribution rate of site-scale irrigation in the study area as the water demand constraint, the objective function is solved to obtain the optimal water allocation in the study area.

[0014] Furthermore, the calculation method of the site-scale irrigation contribution rate includes:

[0015] S211. Based on regional statistical data and site observation data, multiple linear regression is used to obtain the water sensitivity index of each crop in different growth cycles;

[0016] S212. Calculate the effective rainfall in each growth period of the crop based on the actual rainfall in the current year:

[0017]

[0018] Among them, P eki is the effective rainfall of the kth crop in the i-th growth period; P kij is the effective precipitation of the k-th crop in the j-th rainfall in the i-th growth period; N is the number of precipitations of the k-th crop in the i-th growth period; P kij0 is the actual precipitation of the k-th crop in the j-th rainfall during the i-th growth period;

[0019] S213, according to the effective rainfall P eki , calculate the rain-fed yield of crops under rain-fed conditions:

[0020]

[0021] in, is the rain-fed yield of the kth crop; ki is the water sensitivity index of the kth crop in the i-th growth period; ET mkiY is the evapotranspiration of the kth crop in the i-th growth period under full irrigation conditions; mk is the yield of the kth crop under sufficient water supply; n is the number of growth periods of crop k;

[0022] S214. Calculate the site-scale irrigation contribution rate for each site in the study area based on the rain-fed yield of crops under rain-fed conditions:

[0023]

[0024] Among them, ρ 0 is the site-scale irrigation contribution rate at the site location; A k is the sowing area of ​​the kth crop; A is the total sowing area of ​​all crops; Y k0 is the actual output of the kth crop in the current year.

[0025] The beneficial effects of the above technical scheme are as follows: for the point scale, the crop water production function model for calculating rain-fed yield in this scheme is constructed using experimental data from irrigation experimental stations. These data are all from historical data of the study area. Compared with other methods, the calculated site-scale irrigation contribution rate is more in line with the corresponding area and is more objective and authentic.

[0026] Furthermore, step S211 further includes:

[0027] Based on the drought irrigation test data of crop k at different growth stages obtained under the same agricultural production level in the study area, one test with relatively sufficient water supply is selected from several test results each year, and its yield is used as the yield Y under sufficient water supply conditions. mk , the water consumption of each stage is ET mki ;

[0028] Construct the relationship between relative yield and relative water consumption of crop k:

[0029]

[0030] in, is the relative output; Y Mk is the yield of crop k under deficit irrigation conditions; ET ki is the evapotranspiration of crop k in the i-th growth period under deficit irrigation conditions;

[0031] Take the logarithm of the relationship between relative yield and relative water consumption:

[0032]

[0033] Obtain precipitation data at the site location, consider the effects of plant interception, field infiltration, and evapotranspiration, and use CropWat software to convert actual precipitation into effective precipitation for crop utilization;

[0034] The effective precipitation during the growing period is taken as ET ki , the rain-fed crop yield is taken as Y Mk , using multiple linear regression, we can get the water sensitivity index λ of crop k in different growth stages. ki .

[0035] Furthermore, the calculation method of the regional scale irrigation contribution rate includes:

[0036] S221. Based on the input factors that affect grain output, use multiple linear regression analysis to find the output elasticity of irrigation water consumption on grain output;

[0037] S222. Based on the output elasticity of irrigation water consumption to grain output, calculate the allocation coefficient of irrigation to grain production:

[0038]

[0039] Among them, ε6 is the allocation coefficient of irrigation to grain production; β6 is the output elasticity of irrigation water consumption to grain output; Δx6 is the difference between the maximum and minimum values ​​of lnX6; X6 is the irrigation water consumption; Δy is the difference between the maximum and minimum values ​​of ln Y; Y is the grain output; is the multi-year average of lnX6; is the multi-year average value of ln Y;

[0040] S223. Calculate the regional scale irrigation contribution rate ρ of the study area based on the allocation coefficient ε6 1 :

[0041] ρ 1 =ε6×(1+γ)

[0042] Among them, γ is the grain production increase rate.

[0043] The beneficial effects of the above technical scheme are as follows: for the regional scale, this scheme calculates the irrigation sharing benefit through the improved CD production function model. When other factors are fixed, the only variable is changed, and the benefit sharing coefficient of the input factor can also be calculated. Compared with other models that can only calculate a single value, this is more comprehensive and convenient.

[0044] Furthermore, step S221 further includes:

[0045] Establish a production economic model for evaluating irrigation contribution rate at the regional scale:

[0046]

[0047] Among them, α is the influencing factor of other factors; X1, X2, X3, X4, X5 and X6 are the number of primary industry practitioners, seed usage, fertilizer conversion, pesticide application, total mechanical power and irrigation water consumption respectively; β1, β2, β3, β4, β5 and β6 are the output elasticity of X1, X2, X3, X4, X5 and X6 to output respectively; X o is the input of the oth production factor; β o For X o Output elasticity to output; Y is food output;

[0048] Taking the logarithm of the production economics model:

[0049] According to the grain output and production factor input X o , perform multiple linear regression and find the output elasticity β6 of irrigation water consumption to grain output.

[0050] Furthermore, the method of using regional scale irrigation contribution rate to verify the average value of all site scale irrigation contribution rates includes:

[0051] S31. Establish initial hypothesis based on the basic principles of T distribution and hypothesis testing Determine the inspection level; is the average irrigation contribution rate of all site scales; ρ 1 is the irrigation contribution rate at the regional scale;

[0052] S32. Calculate the statistic based on the regional scale irrigation contribution rate and the average of all site scale irrigation contribution rates:

[0053]

[0054] Among them, t is the statistic; s is the standard deviation of irrigation contribution rate at the site scale, and v is the total number of sites in the study area;

[0055] S33. Determine whether the absolute value of the statistic is greater than the critical value. If so, the initial hypothesis is established and passes the verification. Otherwise, the initial hypothesis is not established and fails the verification.

[0056] The beneficial effects of the above technical scheme are: the single-sample T test calculation method used in this scheme is relatively simple, easy to understand and implement, suitable for continuous data, and has high test efficiency when the sample size is moderate. By calculating the T statistic and degrees of freedom, an accurate P value can be obtained, providing a strong basis for hypothesis testing.

[0057] Furthermore, the method for adjusting the regional statistical data and / or the site observation data includes:

[0058] When the test data is not excluded, the rationality of the irrigation test sample group in the model database is checked to exclude abnormal experimental data in the irrigation test data, and then the process returns to step S2;

[0059] When the abnormal test data is eliminated and still cannot pass the verification, the input factors affecting grain production are optimized: the input factors with the smallest output elasticity are removed, and then return to step S2.

[0060] Furthermore, step S5 further includes:

[0061] Calculate a set of optimal coefficients δ that minimize the covariance between the estimated values ​​and the true values ​​of the unknown measurement points at the station and the station area h :

[0062]

[0063] Among them, δ h is the weight coefficient of the hth station in the study area; v is the total number of stations in the study area; f[(x h ,y h ),(x j ,y j ) is ρ 0 (x h ,y h ) and ρ 0 (x j ,y j ), ρ 0 (x h ,y h ) is a known site (x h ,y h ) irrigation contribution rate, (x h ,y h ) is the location coordinate of the hth station; ρ 0 (x j ,y j ) is the site (x j ,y j ) is the irrigation contribution rate at the point; μ is the mathematical expectation of the attribute value of the known point; for and ρ 0 (x j ,y j )’s covariance function; is the estimated value of the irrigation contribution rate between the station and any point (x, y) within the station area;

[0064] According to the optimal coefficient δ h And the known irrigation contribution rate of the site, calculate the estimated value of the irrigation contribution rate of the site and any point (x, y) within the site area:

[0065]

[0066] The beneficial effect of the above technical solution is that the spatial interpolation method adopted in this solution estimates an optimal, unbiased, and minimum variance interpolation for the unknown point by minimizing the sum of squares of the prediction errors. Compared with other simple interpolation methods, this method takes into account the spatial correlation between sample points, so the result is more accurate.

[0067] Furthermore, the objective function is expressed as:

[0068]

[0069] Among them, max is the maximum value; F is the total grain output; X p1 , X p2 , X p3 , X p4 , X p5 are the number of primary industry practitioners, seed usage, fertilizer conversion, pesticide application, and total mechanical power input in the pth sub-area; X p6 is the water allocation of the pth sub-area; β1, β2, β3, β4, β5 and β6 are the output elasticities of the number of primary industry practitioners, seed usage, fertilizer conversion, pesticide application, total mechanical power and irrigation water consumption to grain output; α is the influencing factor of other factors; P is the number of sub-areas controlled by bucket mouth in the study area;

[0070] The constraints of the objective function include water demand constraint, water source constraint and non-negative constraint. The water demand constraint is:

[0071]

[0072] in, is the water demand of the pth partition; ρ p is the irrigation contribution rate of the pth subarea; is the estimated value of the irrigation contribution rate between the station and any point (x, y) within the station area; S p is the area of ​​the pth partition;

[0073] The water source constraints are:

[0074]

[0075] Among them, Q p The total available water volume from water storage projects, external water diversion channels, groundwater and other water supplies; The amount of water supplied to the water storage project; The amount of water supplied to external water diversion channels; Amount of water supplied to groundwater; is other water supply; c1, c2, c3, c4 are water supply coefficients of water storage projects, external water diversion channels, groundwater and other water supplies; X pr is the water allocation of the rth water supply method in the pth zone; is the water supply of the rth water supply method in the pth zone;

[0076] The non-negative constraint is: pr ≥0,

[0077] The beneficial effects of the present invention are as follows: this scheme calculates the irrigation contribution rate at the site scale through the historical data of the study area stored in the model database. The data used in the calculation process are all from the study area, and no subjective judgment factors are introduced in the calculation process, which can ensure that the obtained irrigation contribution rate is more suitable for the study area that needs water resource regulation; then, based on the irrigation contribution rate and the bucket water distribution amount coupled together, the water distribution amount under the maximum output can be obtained, which promotes the efficient use of water resources and realizes the maximization of regional grain output value. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 Flow chart of the regional water resources regulation method for increasing grain production based on irrigation contribution rate. DETAILED DESCRIPTION

[0079] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0080] refer to Figure 1 , Figure 1 A regional water resource regulation method based on irrigation contribution rate to improve grain production is shown; Figure 1 As shown, the method S includes steps S1 to S6.

[0081] In step S1, the regional statistical data and site observation data of the study area stored in the model database are obtained; the model database contains two modules: regional statistical data and site observation data. The regional statistical data includes multi-year data on the number of primary industry labor force X1, seed cost X2, pesticide usage X3, fertilizer application X4, total mechanical power X5, irrigation water consumption X6 and other consumables in different provinces, prefecture-level cities and counties, as well as planting structure and area A iThe data comes from publicly published industry statistical materials, including: China Statistical Yearbook, Water Conservancy Statistical Yearbook, National Agricultural Product Cost and Benefit Data Compilation, National Bureau of Statistics official website, etc.

[0082] The observation data of the stations include the number n and time of different crop growth periods in different regions of the country, the yield Y under insufficient irrigation conditions, Mk , ET at each stage under insufficient irrigation conditions ki , Yield Y under sufficient water supply conditions mk , ET at each stage under sufficient water supply conditions mki , the successive precipitation information P at the station location kij0 The data comes from the irrigation test stations in the irrigation test station network, the meteorological stations and rainfall stations in the national meteorological station network, and are regularly supplemented and updated to form a model database storage system based on network disk software and supported by statistical data, test data, and downloaded data. When conducting regional water resources regulation in the future, the data in the model database can be directly read for calculation.

[0083] The model database constructed by this solution can realize data sharing, thus avoiding users from creating application files individually, reducing a large amount of duplicate data, reducing data redundancy, maintaining data consistency, and using the database to centrally control and manage data. The data model can be used to represent the organization of various data and the relationship between data, ensuring data security and reliability.

[0084] In step S2, the site-scale irrigation contribution rate of all sites in the study area and the regional-scale irrigation contribution rate in the study area are calculated based on the regional statistical data and the site observation data;

[0085] In one embodiment of the present invention, the method for calculating the site-scale irrigation contribution rate includes:

[0086] S211. Based on regional statistical data and site observation data, multiple linear regression is used to obtain the water sensitivity index of each crop in different growth cycles;

[0087] Step S211 further includes:

[0088] Based on the drought irrigation test data of crop k at different growth stages obtained under the same agricultural production level in the study area, one test with relatively sufficient water supply is selected from several test results each year, and its yield is used as the yield Y under sufficient water supply conditions. mk , the water consumption of each stage is ET mki ;

[0089] Construct the relationship between relative yield and relative water consumption of crop k:

[0090]

[0091] in, is the relative output; Y Mk is the yield of crop k under deficit irrigation conditions; ET ki is the evapotranspiration of crop k in the i-th growth period under deficit irrigation conditions;

[0092] Take the logarithm of the relationship between relative yield and relative water consumption:

[0093]

[0094] Obtain precipitation data at the site location, consider the effects of plant interception, field infiltration, and evapotranspiration, and use CropWat software to convert actual precipitation into effective precipitation for crop utilization;

[0095] The effective precipitation during the growing period is taken as ET ki , the rain-fed crop yield is taken as Y Mk , using multiple linear regression, we can get the water sensitivity index λ of crop k in different growth stages. ki .

[0096] S212. Calculate the effective rainfall in each growth period of the crop based on the actual rainfall in the current year:

[0097]

[0098] Among them, P eki is the effective rainfall of the kth crop in the i-th growth period; P kij is the effective precipitation of the k-th crop in the j-th rainfall in the i-th growth period; N is the number of precipitations of the k-th crop in the i-th growth period; P kij0 is the actual precipitation of the k-th crop in the j-th rainfall during the i-th growth period;

[0099] S213, according to the effective rainfall P eki , calculate the rain-fed yield of crops under rain-fed conditions:

[0100]

[0101] in, is the rain-fed yield of the kth crop; ki is the water sensitivity index of the kth crop in the i-th growth period; ET mki Y is the evapotranspiration of the kth crop in the i-th growth period under full irrigation conditions; mk is the yield of the kth crop under sufficient water supply; n is the number of growth periods of crop k;

[0102] S214. Calculate the site-scale irrigation contribution rate for each site in the study area based on the rain-fed yield of crops under rain-fed conditions:

[0103]

[0104] Among them, ρ 0 is the site-scale irrigation contribution rate at the site location; A k is the sowing area of ​​the kth crop; A is the total sowing area of ​​all crops; Y k0 is the actual output of the kth crop in the current year.

[0105] In one embodiment of the present invention, the method for calculating the regional scale irrigation contribution rate includes:

[0106] S221. Based on the input factors that affect grain output, use multiple linear regression analysis to find the output elasticity of irrigation water consumption on grain output;

[0107] Step S221 further includes:

[0108] Establish a production economic model for evaluating irrigation contribution rate at the regional scale:

[0109]

[0110] Among them, α is the influencing factor of other factors; X1, X2, X3, X4, X5 and X6 are the number of primary industry practitioners, seed usage, fertilizer conversion, pesticide application, total mechanical power and irrigation water consumption respectively; β1, β2, β3, β4, β5 and β6 are the output elasticity of X1, X2, X3, X4, X5 and X6 to output respectively; X o is the input of the oth production factor; β o For X o Output elasticity to output; Y is food output;

[0111] Taking the logarithm of the production economics model:

[0112] According to the grain output and production factor input X o , perform multiple linear regression and find the output elasticity β6 of irrigation water consumption to grain output.

[0113] S222. Based on the output elasticity of irrigation water consumption to grain output, calculate the allocation coefficient of irrigation to grain production:

[0114]

[0115] Among them, ε6 is the allocation coefficient of irrigation to grain production; β6 is the output elasticity of irrigation water consumption to grain output; Δx6 is the difference between the maximum and minimum values ​​of lnX6; X6 is the irrigation water consumption; Δy is the difference between the maximum and minimum values ​​of ln Y; Y is the grain output; is the multi-year average of lnX6; is the multi-year average of ln Y;

[0116] S223. Calculate the regional scale irrigation contribution rate ρ of the study area based on the allocation coefficient ε6 1 :

[0117] ρ 1 =ε6×(1+γ)

[0118] Among them, γ is the grain production increase rate.

[0119] In step S3, the regional scale irrigation contribution rate is used to verify the average value of all site scale irrigation contribution rates, and if the verification is passed, the process proceeds to step S5, and if the verification is not passed, the process proceeds to step S4;

[0120] In one embodiment of the present invention, the method of verifying the average value of all site-scale irrigation contribution rates using the regional-scale irrigation contribution rate includes:

[0121] S31. Based on the basic principles of T distribution and hypothesis testing, establish the initial hypothesis H0: Determine the inspection level; is the average irrigation contribution rate of all site scales; ρ 1 is the irrigation contribution rate at the regional scale;

[0122] S32. Calculate the statistic based on the regional scale irrigation contribution rate and the average of all site scale irrigation contribution rates:

[0123]

[0124] Among them, t is the statistic; s is the standard deviation of irrigation contribution rate at the site scale, and v is the total number of sites in the study area;

[0125] S33. Determine whether the absolute value of the statistic is greater than the critical value. If so, the initial hypothesis is established and passes the verification. Otherwise, the initial hypothesis is not established and fails the verification.

[0126] In step S4, the regional statistical data and / or the site observation data are adjusted, and then the process returns to step S2;

[0127] During implementation, the method for adjusting regional statistical data and / or site observation data preferably includes:

[0128] When the test data is not excluded, the rationality of the irrigation test sample group in the model database is checked to exclude abnormal experimental data in the irrigation test data, and then the process returns to step S2;

[0129] When the abnormal test data is eliminated and still cannot pass the verification, the input factors affecting grain production are optimized: the input factors with the smallest output elasticity are removed, and then return to step S2.

[0130] In step S5, spatial interpolation is performed by weighted summing of all site-scale irrigation contribution rates to obtain site-scale irrigation contribution rates of multiple unknown points in the area between sites in the study area;

[0131] During implementation, the preferred step S5 of this solution further includes:

[0132] Calculate a set of optimal coefficients δ that minimize the covariance between the estimated values ​​and the true values ​​of the unknown measurement points at the station and the station area h :

[0133]

[0134] Among them, δ h is the weight coefficient of the hth station in the study area; v is the total number of stations in the study area; f[(x h ,y h ),(x j ,y j ) is ρ 0 (x h ,y h ) and ρ 0 (x j ,y j ), ρ 0 (x h ,y h ) is a known site (x h ,y h ) irrigation contribution rate, (x h ,y h ) is the location coordinate of the hth station; ρ 0 (x j ,y j ) is the site (x j ,y j ) is the irrigation contribution rate at the point; μ is the mathematical expectation of the attribute value of the known point; for and ρ 0 (x j ,y j )’s covariance function; is the estimated value of the irrigation contribution rate between the station and any point (x, y) within the station area;

[0135] According to the optimal coefficient δ h And the known irrigation contribution rate of the site, calculate the estimated value of the irrigation contribution rate of the site and any point (x, y) within the site area:

[0136]

[0137] In step S6, the objective function is taken as the maximization of grain yield in the study area, and the contribution rate of site-scale irrigation in the study area is taken as the water demand constraint. The objective function is solved to obtain the optimal water allocation in the study area.

[0138] In one embodiment of the present invention, the expression of the objective function is:

[0139]

[0140] Among them, max is the maximum value; F is the total grain output; X p1 , X p2 , X p3 , X p4 , X p5 are the number of primary industry practitioners, seed usage, fertilizer conversion, pesticide application, and total mechanical power input in the pth sub-area; X p6 is the water allocation of the pth sub-area; β1, β2, β3, β4, β5 and β6 are the output elasticities of the number of primary industry practitioners, seed usage, fertilizer conversion, pesticide application, total mechanical power and irrigation water consumption to grain output; α is the influencing factor of other factors; P is the number of sub-areas controlled by bucket mouth in the study area;

[0141] The constraints of the objective function include water demand constraint, water source constraint and non-negative constraint. The water demand constraint is:

[0142]

[0143] in, is the water demand of the pth partition; ρ p is the irrigation contribution rate of the pth subarea; is the estimated value of the irrigation contribution rate between the station and any point (x, y) within the station area; S p is the area of ​​the pth partition;

[0144] The water source constraints are:

[0145]

[0146] Among them, Q p The total available water volume from water storage projects, external water diversion channels, groundwater and other water supplies; The amount of water supplied to the water storage project; The amount of water supplied to external water diversion channels; Amount of water supplied to groundwater; is other water supply; c1, c2, c3, c4 are water supply coefficients of water storage projects, external water diversion channels, groundwater and other water supplies; X pr is the water allocation of the rth water supply method in the pth zone; is the water supply of the rth water supply method in the pth partition.

[0147] The non-negative constraint is: pr ≥0,

[0148] In summary, this scheme verifies the site-scale irrigation contribution rate through the regional-scale irrigation contribution rate, and obtains a relatively accurate site-scale irrigation contribution rate by adjusting the regional statistical data and / or site observation data. On this basis, it guides the bucket control unit to rationally allocate water resources, thereby ensuring the accuracy of water distribution, and providing scientific decision-making basis and support for the "National Modern Irrigation Development Plan" and the rational allocation of agricultural water resources.

Claims

1. A method for regulating regional water resources based on irrigation contribution rate to increase grain production, characterized in that: Includes steps: S1. Obtain regional statistical data and site observation data of the study area stored in the model database; S2. Calculate the site-scale irrigation contribution rate of all sites in the study area and the regional-scale irrigation contribution rate in the study area based on regional statistical data and site observation data; S3, using the regional scale irrigation contribution rate to verify the average value of all site scale irrigation contribution rates, and if the verification is passed, proceed to step S5, and if the verification is not passed, proceed to step S4; S4, adjusting the regional statistical data and / or the site observation data, and then returning to step S2; S5. Use the weighted summation method of all site-scale irrigation contribution rates to perform spatial interpolation to obtain the site-scale irrigation contribution rates of multiple unknown points in the area between sites in the study area; S6. Taking the maximization of grain yield in the study area as the objective function and the contribution rate of irrigation at the site scale in the study area as the water demand constraint, the objective function is solved to obtain the optimal water allocation in the study area; The calculation method of the site-scale irrigation contribution rate includes: S211. Based on regional statistical data and site observation data, multiple linear regression is used to obtain the water sensitivity index of each crop in different growth cycles; S212. Calculate the effective rainfall in each growth period of the crop based on the actual rainfall in the current year: ， in, is the effective rainfall of the kth crop in the i-th growth period; is the effective precipitation of the k-th crop in the j-th precipitation in the i-th growth period; N is the number of precipitations of the k-th crop in the i-th growth period; is the actual precipitation of the k-th crop in the j-th rainfall during the i-th growth period; S213, based on effective rainfall , calculate the rain-fed yield of crops under rain-fed conditions: in, is the rain-fed yield of the kth crop; is the water sensitivity index of the kth crop in the i-th growth period; ET mki Y is the evapotranspiration of the kth crop in the i-th growth period under full irrigation conditions; mk is the yield of the kth crop under sufficient water supply; n is the number of growth periods of crop k; S214. Calculate the site-scale irrigation contribution rate for each site in the study area based on the rain-fed yield of crops under rain-fed conditions: in, is the site-scale irrigation contribution rate at the site location; A k is the sowing area of ​​the kth crop; A is the total sowing area of ​​all crops; Y k0 is the actual yield of the kth crop in the current year; The calculation method of the regional scale irrigation contribution rate includes: S221. Based on the input factors that affect grain output, use multiple linear regression analysis to find the output elasticity of irrigation water consumption on grain output; S222. Based on the output elasticity of irrigation water consumption to grain output, calculate the allocation coefficient of irrigation to grain production: Among them, ε6 is the allocation coefficient of irrigation to grain production; is the output elasticity of irrigation water use to grain output; for The difference between the maximum and minimum values ​​of; X6 is the irrigation water consumption; for The difference between the maximum and minimum values ​​of; Y is the grain output; for The multi-year average of for The multi-year average of S223, according to the apportionment coefficient , calculate the regional scale irrigation contribution rate of the study area : in, For the increase in food production rate.

2. The method for regulating regional water resources based on irrigation contribution rate to increase grain production according to claim 1, characterized in that: Step S211 further includes: Based on the drought irrigation test data of crop k at different growth stages obtained under the same agricultural production level in the study area, one test with relatively sufficient water supply is selected from several test results each year, and its yield is used as the yield Y under sufficient water supply conditions. mk , the water consumption of each stage is ET mki ; Construct the relationship between relative yield and relative water consumption of crop k: in, is the relative output; Y Mk is the yield of crop k under deficit irrigation conditions; ET ki is the evapotranspiration of crop k in the i-th growth period under deficit irrigation conditions; Take the logarithm of the relationship between relative yield and relative water consumption: ； Obtain precipitation data at the site location, consider the effects of plant interception, field infiltration, and evapotranspiration, and use Crop Wat software to convert actual precipitation into effective precipitation for crop utilization; The effective precipitation during the growing period is taken as , the rain-fed crop yield is taken as Y Mk , using multiple linear regression, we can get the water sensitivity index λ of crop k in different growth stages. ki .

3. The method for regulating regional water resources based on irrigation contribution rate to increase grain production according to claim 1, characterized in that: Step S221 further includes: Establish a production economic model for evaluating irrigation contribution rate at the regional scale: Among them, 𝛼 is the influencing factor of other factors; X1, X2, X3, X4 and X5 are the number of primary industry practitioners, seed usage, fertilizer conversion, pesticide application and total mechanical power respectively; β1, β2, β3, β4 and β5 are the output elasticity of X1, X2, X3, X4 and X5 to output respectively; X o is the input of the oth production factor; β o For X o output elasticity with respect to output; Taking the logarithm of the production economics model: ; According to the grain output and production factor input , perform multiple linear regression and find the output elasticity β6 of irrigation water consumption to grain output.

4. The method for regulating regional water resources based on irrigation contribution rate to increase grain production according to claim 1, characterized in that: Methods for verifying the average of all site-scale irrigation contribution rates using regional-scale irrigation contribution rates include: S31. Based on the basic principles of T distribution and hypothesis testing, establish the initial hypothesis H0: =ρ 1 , determine the inspection level; is the average irrigation contribution rate of all site scales; ρ 1 is the irrigation contribution rate at the regional scale; S32. Calculate the statistic based on the regional scale irrigation contribution rate and the average of all site scale irrigation contribution rates: Among them, t is the statistic; s is the standard deviation of irrigation contribution rate at the site scale; v is the total number of sites in the study area; S33. Determine whether the absolute value of the statistic is greater than the critical value. If so, the initial hypothesis is established and passes the verification. Otherwise, the initial hypothesis is not established and fails the verification.

5. The method for regulating regional water resources for increasing grain production based on irrigation contribution rate according to claim 1 or 4, characterized in that: Methods for adjusting regional statistics and / or site observations include: When the test data is not excluded, the rationality of the irrigation test sample group in the model database is checked to exclude abnormal experimental data in the irrigation test data, and then the process returns to step S2; When the abnormal test data is eliminated and still cannot pass the verification, the input factors affecting grain production are optimized: the input factors with the smallest output elasticity are removed, and then return to step S2.

6. The method for regulating regional water resources for increasing grain production based on irrigation contribution rate according to claim 1, characterized in that: Step S5 further comprises: Calculate a set of optimal coefficients δ that minimize the covariance between the estimated values ​​and the true values ​​of the unknown measurement points at the station and the station area h : Among them, δ h is the weight coefficient of the hth site in the study area; v is the total number of sites in the study area; for and The covariance function of For known sites The irrigation contribution rate at is the location coordinate of the hth site; For Site The irrigation contribution rate at the point; μ is the mathematical expectation of the attribute value of the known point; for and The covariance function of is the estimated value of the irrigation contribution rate between the station and any point (x, y) within the station area; According to the optimal coefficient δ h And the known irrigation contribution rate of the site, calculate the estimated value of the irrigation contribution rate of the site and any point (x, y) within the site area: 。 7. The method for regional water resource regulation based on irrigation contribution rate to increase grain production according to any one of claims 1 to 4 and 6, characterized in that: The expression of the objective function is: Among them, max is the maximum value; F is the total grain output; X p1 , X p2、Xp3 , X p4、Xp5 They are the number of primary industry practitioners, seed usage, fertilizer conversion, pesticide application, and total mechanical power input in the pth sub-zone; X p6 For the p Water allocation for each zone; β 1. β 2. β 3. β 4. β 5 and β 6 are the output elasticity of the number of primary industry practitioners, seed usage, fertilizer conversion, pesticide application, total mechanical power and irrigation water consumption to grain output; α is the influencing factor of other factors; P is the number of bucket-mouth controlled zones in the study area; The constraints of the objective function include water demand constraint, water source constraint and non-negative constraint. The water demand constraint is: ， in, is the water demand of the pth partition; is the irrigation contribution rate of the pth partition; is the estimated value of the irrigation contribution rate between the station and any point (x, y) within the station area; S p is the area of ​​the pth partition; The water source constraints are: ； Among them, Q p The total available water volume from water storage projects, external water diversion channels, groundwater and other water supplies; The amount of water supplied to the water storage project; The amount of water supplied to external water diversion channels; Amount of water supplied to groundwater; For other water supply; They are the water supply coefficients of water storage projects, external water diversion channels, groundwater and other water supplies; is the water allocation of the rth water supply method in the pth zone; is the water supply of the rth water supply method in the pth zone; The non-negative constraints are: , .

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