A method for crop precision irrigation water use decision-making based on global uncertainty analysis

Abstract

The present invention provides a method for making a precise irrigation water decision for crops based on global uncertainty analysis, comprising the following steps: S1, obtaining weather station data during the crop growth period, calculating the crop reference evaporation and transpiration ET0 by combining the Priestley-Taylor formula with the weather data, and calculating the crop water requirement according to the crop coefficient method; S2, obtaining the crop coefficients k of different crops at different growth periods. c value, determine the k value of different crops c Value parameter range; S3, according to the irrigation method, irrigation technology, and irrigation scale of the crop planting area, obtain the crop irrigation efficiency I, including the field irrigation efficiency I a , transmission efficiency I c , irrigation management factor I f , and determine the range of values ​​of the three parameters; S4, use variance analysis to analyze ET0, k c and I for global sensitivity analysis; S5, making accurate irrigation decisions on crop irrigation water according to the uncertainty analysis results. The present invention is scientifically and rationally designed, simple to operate, and can be actually applied to agricultural production areas, providing an effective reference for realizing accurate irrigation of local farmland.

Description

Technical Field

[0001] The present invention belongs to the technical field of agricultural information-based precision irrigation, and specifically relates to a method for making decisions on crop precision irrigation water based on global uncertainty analysis. Background Art

[0002] Water resources are an important factor limiting agricultural productivity and sustainability, which largely depend on timely and adequate water supply. In the context of water scarcity, agricultural irrigation water is threatened, affecting crop production and human food security, and inaccurate estimation of irrigation water consumption will cause serious waste of water resources and increase water scarcity.

[0003] With the rapid development of informatization, large-scale, real-time, and precise irrigation of farmland has become a development trend. Existing irrigation decisions usually include the following: 1. Relying on experience, by observing several irrigation factors, and using weather forecast data to determine by experience, which has great uncertainty and may cause crop water shortage and yield reduction, or excessive irrigation and waste of water resources; 2. Determine soil moisture content, collect soil moisture by laying soil sensors in the field, and achieve precise irrigation based on the collected soil moisture data, but for large areas of farmland, laying too few sensors will result in incomplete data collection and inaccurate irrigation decisions, and laying too many sensors will greatly increase the cost of irrigation, which is not conducive to promotion; 3. Calculate crop water demand through the ET0 formula, but the water demand of different crops, different regions, and different growth periods varies greatly, the uncertainty of the parameters in the formula is large, and the calculation accuracy is low. If it is applied alone to large-scale irrigation of farmland, it will cause a waste of water resources. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a crop precision irrigation water decision method based on global uncertainty analysis in view of the deficiencies of the above-mentioned prior art. The variance analysis method is used to perform uncertainty analysis on each parameter in the crop irrigation water model to obtain a crop precision irrigation water decision method. The present invention combines historical irrigation data, integrates the ET0 calculation method and crop coefficient method proposed by Priestley-Taylor, establishes a crop irrigation water model and performs global uncertainty analysis on the parameters in the model, which can be actually applied to agricultural production areas and provide an effective reference basis for local farmland to achieve precision irrigation.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: a method for crop precision irrigation water decision-making based on global uncertainty analysis, characterized in that it includes the following steps:

[0006] S1. Obtain weather station data during the crop growth period, calculate crop reference evapotranspiration ET0 using Priestley-Taylor formula combined with weather data, and calculate crop water requirement based on crop coefficient method;

[0007] S2. Obtain the crop coefficient k of different crops at different growth stages c value, determine the k value of different crops c Value parameter range;

[0008] S3. Obtain the irrigation efficiency I of crops, including field irrigation efficiency I, according to the irrigation method, irrigation technology and irrigation scale of the crop planting area. a , transmission efficiency I c , irrigation management factor I f , and determine the value ranges of the three parameters;

[0009] S4. Use variance analysis to analyze ET0 and k c and I for global sensitivity analysis;

[0010] S5. Make accurate irrigation water decisions for crops based on uncertainty analysis results.

[0011] Preferably, the specific operation mode of S1 is to obtain precipitation, wind speed, humidity and temperature as weather station data, and the specific calculation method of crop water requirement is:

[0012] S101. Calculate ET0:

[0013] where Δ is the slope of the change in water vapor pressure with temperature, A is the net radiation minus the soil heat flux, and γ is the psychrometric constant. represents the Priestley-Taylor constant;

[0014] S102. Crop water requirement

[0015] Among them, P is the rainfall actually used by crops (mm), ET c represents the actual evaporation of crops (mm), I represents the irrigation efficiency, i.e., the ratio of the irrigation water actually used by crops to the total actual water use;

[0016] S103,

[0017] Among them, P m represents monthly rainfall;

[0018] S104, ET c =k c ET0 Formula (4)

[0019] Among them, Kc Indicates the crop coefficient, ET0 crop reference evapotranspiration;

[0020] S105. Calculate irrigation efficiency I: I = I a I c I f Formula (5)

[0021] Among them, I a represents the field irrigation efficiency, I c represents the transmission efficiency, I f represents the irrigation management factor;

[0022] S106. Substituting equation (4) and equation (5) into equation (2), we obtain

[0023]

[0024] Preferably, the specific operation mode of S4 is:

[0025] S401, the first-order effect S of the variance ratio transmitted by multiple model inputs corresponding to multiple crops i It is expressed as:

[0026]

[0027]

[0028]

[0029] T i =S i +S ij +...+S ij...k Formula (10).

[0030] Where y is a scalar, V y Represents the total variance of the model, V i Represents x i The model variance caused by input changes alone, V ij Represents x i By x j The coupling variance of the contribution, V ij...k Represents x i By connecting with x j , …, x k The coupling variance contributed by the interaction, S i is x i The first-order effect on y, S ij is x i The second-order sensitivity index, S ij...k is x i The multi-order sensitivity index, T i Represents xi The overall sensitivity index.

[0031] Compared with the prior art, the present invention has the following advantages:

[0032] 1. The key to the global uncertainty analysis of the present invention is the calculation of the uncertainty index based on the variance, which is to perform uncertainty analysis on all parameters in the model, allow the uncertainty of the parameters in the model, and describe the parameters with probability distribution rather than point distribution. The ET0 calculation method proposed by Priestley-Taylor is used in combination with the crop coefficient method to calculate the crop irrigation water. A global uncertainty analysis is performed on all parameters in the crop irrigation water formula to obtain an accurate crop irrigation water decision method for crops.

[0033] 2. The present invention uses the site meteorological station data to calculate the crop irrigation water, determines the value range of each parameter, and uses the EFAST method to perform uncertainty analysis on the parameters in the crop irrigation water, so as to achieve accurate irrigation water decision-making for crops, which can greatly save water resources and provide a reference basis for local agricultural irrigation water.

[0034] The present invention is further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is the overall flow chart of the present invention.

[0036] Figure 2 It is a schematic diagram of sensitivity analysis of wheat in the present invention.

[0037] Figure 3 It is a schematic diagram of sensitivity analysis of corn in the present invention. DETAILED DESCRIPTION

[0038] This example conducts a global uncertainty analysis on the irrigation water for winter wheat and summer corn in Yangling, Shaanxi, and makes accurate irrigation decisions for crops based on the water balance equation. The meteorological data during the crop growth period and the crop coefficients and irrigation efficiencies of different crops at different growth stages are selected, and the variance analysis method is used to conduct a global uncertainty analysis on the parameters, so as to make accurate irrigation water decisions for different crops at different growth stages. Figure 1 As shown, this embodiment includes the following steps:

[0039] S1. Obtain the meteorological station data during the crop growth period at Yangling Station, calculate the crop reference evapotranspiration ET0 using the Priestley-Taylor formula combined with the meteorological data, and calculate the crop water requirement according to the crop coefficient method;

[0040] S2. Obtain the crop coefficient k of different crops at different growth stages cvalue, determine the k value of different crops c The value parameter range is shown in Table 1;

[0041] S3. Obtain the irrigation efficiency I of crops, including field irrigation efficiency I, according to the irrigation method, irrigation technology and irrigation scale of the crop planting area. a , transmission efficiency I c , irrigation management factor I f , and determine the value ranges of the three parameters, as shown in Table 1;

[0042] Table 1 Parameter value range

[0043]

[0044]

[0045] S4. Use variance analysis to analyze ET0 and k c and I for global sensitivity analysis, such as Figure 2-Figure 3 As shown;

[0046] S5. Make accurate irrigation water decisions for crops based on uncertainty analysis results. Tables 2 and 3 show the irrigation water for wheat and corn under different parameter combinations.

[0047] Table 2 Wheat irrigation water

[0048]

[0049]

[0050]

[0051] Table 3 Corn irrigation water

[0052]

[0053]

[0054]

[0055] In this embodiment, the specific operation mode of S1 is to obtain precipitation, wind speed, humidity and temperature as weather station data, and the specific calculation method of crop water requirement is:

[0056] S101. Calculate ET0:

[0057] where Δ is the slope of the change in water vapor pressure with temperature, A is the net radiation minus the soil heat flux, and γ is the psychrometric constant. represents the Priestley-Taylor constant;

[0058] S102. Water for crop irrigation

[0059] Among them, P is the rainfall actually used by crops (mm), ET c represents the actual evaporation of crops (mm), I represents the irrigation efficiency, i.e., the ratio of the irrigation water actually used by crops to the total actual water use;

[0060] S103,

[0061] Among them, P m represents monthly rainfall;

[0062] S104, ET c =k c ET0 formula (4),

[0063] Among them, K c Indicates the crop coefficient, ET0 crop reference evapotranspiration;

[0064] S105. Calculate irrigation efficiency I: I = I a I c I f Formula (5)

[0065] Among them, I a represents the field irrigation efficiency, I c represents the transmission efficiency, I f represents the irrigation management factor;

[0066] S106. Substituting equation (4) and equation (5) into equation (2), we obtain

[0067]

[0068] In this embodiment, the specific operation mode of S4 is:

[0069] S401, the first-order effect S of the variance ratio transmitted by multiple model inputs corresponding to multiple crops i It is expressed as:

[0070]

[0071]

[0072]

[0073] T i =S i +S ij +...+S ij...k Formula (10).

[0074] Where y is a scalar, V y Represents the total variance of the model, V i Represents x i The model variance caused by input changes alone, V ij Represents x i By x j The coupling variance of the contribution, V ij...k Represents x i By connecting with x j , …, x k The coupling variance contributed by the interaction, S i is x i The first-order effect on y, S ij is x i The second-order sensitivity index, S ij...k is x i The multi-order sensitivity index, T i Represents x i The overall sensitivity index.

[0075] There are 9 parameters in the crop irrigation water model. The variance analysis method is used to conduct a global sensitivity analysis of the crop irrigation water model. Figures 2 to 3 The results show that when the Priestley-Taylor (PT) formula is used to calculate irrigation water, effective rainfall is the most sensitive to irrigation water demand, followed by field irrigation efficiency. The net radiation minus soil heat flux also has a significant effect on irrigation water demand. The difference in the effect of parameters on irrigation water demand is obvious, but the difference between different crops is not obvious.

[0076] The above is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any simple modification, change and equivalent change made to the above embodiment according to the technical essence of the invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for crop precision irrigation water decision-making based on global uncertainty analysis, characterized in that: The following steps are involved: S1. Obtain weather station data during the crop growth period, calculate crop reference evapotranspiration ET0 using Priestley-Taylor formula combined with weather data, and calculate crop water requirement based on crop coefficient method; S2. Obtain the crop coefficient k of different crops at different growth stages c value, determine the k value of different crops c Value parameter range; S3. Obtain the irrigation efficiency of crops I, including field irrigation efficiency, according to the irrigation method, irrigation technology and irrigation scale of the crop planting area. , transmission efficiency , irrigation management factors , and determine the value ranges of the three parameters; S4. Use variance analysis to analyze ET0 and k c and I for global sensitivity analysis; S5. Make accurate irrigation water decisions for crops based on uncertainty analysis results; The specific operation mode of S1 is to obtain precipitation, wind speed, humidity and temperature as weather station data, and the specific calculation method of crop water requirement is: S101, Calculation : Formula (1) in, represents the slope of the change of water vapor pressure with temperature, represents the net radiation minus the soil heat flux, represents the psychrometer constant, represents the Priestley-Taylor constant; S102. Crop water requirement Formula (2) Among them, P is the rainfall actually used by crops (mm), Indicates the actual evaporation of crops in mm, It indicates irrigation efficiency, i.e. the ratio of the amount of irrigation water actually used by crops to the total amount of water actually used; S103, Formula (3) in, represents monthly rainfall; S104, Formula (4) in, represents the crop coefficient, Crop reference evapotranspiration; S105. Calculate irrigation efficiency Formula (5) in, represents the field irrigation efficiency, It represents the transmission efficiency. represents the irrigation management factor; S106. Substituting equation (4) and equation (5) into equation (2), we obtain Formula (6); The specific operation mode of S4 is as follows: S401. First-order effect of the variance ratio transferred from multiple model inputs corresponding to multiple crops It is expressed as: Formula (7) Formula (8) Formula (9) Formula (10) Where y is a scalar, represents the total variance of the model, express The model variance caused by input changes alone, express pass The coupled variance of the contribution of the action, express Through The coupled variance contributed by the interaction, yes right y The first-order effect of yes The second-order sensitivity index is yes The multi-order sensitivity index of express The overall sensitivity index.

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