Crop irrigation quota determination method, device, equipment and medium

Abstract

The application discloses a crop irrigation quota determination method and relates to the field of water resource management.The method comprises the following steps: obtaining basic data; the basic data comprises meteorological data, soil physicochemical property data and crop characteristic data; calculating a leaf area index curve based on the meteorological data, the soil physicochemical property data and the crop characteristic data; wherein the leaf area index curve is used for representing the relationship between a crop growth period cumulative heat unit fraction and a leaf area index; the crop growth period cumulative heat unit fraction is related to temperature; calculating a dynamic crop coefficient based on the meteorological data and the leaf area index curve; calculating soil water content based on the dynamic crop coefficient, the meteorological data and the soil physicochemical property data; determining irrigation water quantity of each time based on the principle of ensuring that the soil water content is in a preset range, accumulating the irrigation water quantity of each time and determining a crop irrigation quota.

Description

Technical Field

[0001] The present invention relates to the field of water resource management, and in particular to a method, device, equipment and medium for determining crop irrigation quotas. Background Art

[0002] Agricultural irrigation water consumption in my country accounts for more than 60% of the country's total water consumption. Taking agricultural irrigation water quotas as the starting point, formulating accurate irrigation water quotas that meet the actual water needs of local crops is of great significance for improving agricultural water use efficiency, promoting efficient use of regional water resources, and alleviating water use conflicts in water-scarce areas.

[0003] Commonly used methods for setting agricultural irrigation water quotas include empirical methods, typical quota methods, sample statistics methods, and experimental measurement methods, all of which have certain shortcomings. The empirical method generally collects large amounts of data and combines it with empirical experience to produce quota estimates. While simple and easy to implement, it is subject to subjective factors and can be biased and uninformed. The typical quota method analyzes representative regions and compares them with existing water quotas across regions to ultimately determine a quota that meets local realities. This method is simple to implement and produces rapid results, but its drawbacks include high subjectivity and significant influence from the selected comparison samples. The sample statistics method generally determines irrigation quotas for major crops based on the province and hydrological year, collecting statistical and monitoring data on irrigation area and irrigation water consumption. Due to extensive agricultural water management in many regions of my country, low irrigation metering rates, and inaccurate metering results, the reliability of the data samples is significantly affected, leading to significant uncertainty in the quota results.

[0004] Agricultural irrigation water use is influenced by a variety of natural factors, including geography, climate, soil characteristics, and water resources. Irrigation water quotas developed using empirical, typical, and sample statistical methods fail to account for the combined effects of hydrometeorological variations, field moisture conditions, and crop growth. Consequently, an experimental measurement method has been proposed for calculating irrigation water quotas. This method primarily utilizes crop water requirement parameter verification and test data analysis at irrigation experimental stations. It calculates and analyzes crop water requirements throughout their growth period based on crop reference evapotranspiration and crop coefficients. The irrigation water quota is then calculated based on the difference between effective precipitation and water requirements.

[0005] While this method takes into account the growth characteristics of specific crops, it uses a static crop coefficient, meaning a fixed coefficient value is applied to each growth stage. In reality, the crop coefficient is not only related to crop growth characteristics but also influenced by natural factors such as air humidity and temperature, changing with changes in the crop canopy. Using a static crop coefficient can lead to errors in the simulated crop water requirements. Furthermore, this method ignores the impact of soil moisture stress on actual evapotranspiration, resulting in inaccurate simulation of soil hydrological processes, which in turn affects the determination of quotas.

[0006] Therefore, the current agricultural crop irrigation quotas do not meet actual needs. Summary of the Invention

[0007] In view of the above shortcomings, the purpose of the present invention is to provide a method, device, equipment and medium for determining crop irrigation quotas.

[0008] The present invention provides a method for determining a crop irrigation quota, comprising:

[0009] Obtaining basic data; the basic data includes meteorological data, soil physical and chemical property data, and crop characteristic data;

[0010] Calculating a leaf area index curve based on meteorological data, soil physical and chemical property data, and crop characteristic data; wherein the leaf area index curve is used to characterize the relationship between the cumulative heat unit fraction during the crop growth period and the leaf area index; the cumulative heat unit fraction during the crop growth period is related to temperature;

[0011] Calculating a dynamic crop coefficient based on the meteorological data and the leaf area index curve;

[0012] Calculating soil moisture based on the dynamic crop coefficient, meteorological data, and soil physical and chemical property data;

[0013] Based on the principle of ensuring that the soil moisture content is within the preset range, the amount of irrigation water for each irrigation is determined, and the crop irrigation quota is determined by accumulating the amount of irrigation water for each irrigation.

[0014] In some embodiments, obtaining basic information includes:

[0015] The meteorological data include: average number of days between rainfalls, potential evapotranspiration, precipitation, average wind speed at a height of 2m, average temperature, maximum temperature, minimum temperature, relative humidity, radiation and wind speed of the day;

[0016] The soil physical and chemical property data include: wilting coefficient, field wilting point moisture content, field water holding capacity, sand content, clay content, critical threshold value for soil water stress, recommended value, water replenishment of upper soil layer; water replenishment of lower soil layer; field water holding capacity of upper soil layer; wilting point moisture content of upper soil layer;

[0017] The crop characteristic data includes: the date of the critical water requirement period of the crop, the maximum and minimum temperatures suitable for crop growth, the total amount of potential heat units during the crop growth period, the recommended values of the crop coefficient during the early, middle and late growth stages, the measured data of the leaf area index, the maximum leaf area index and the maximum growth height, the leaf area ratio, the heat unit fraction, the cumulative heat unit fraction at the time of reaching the maximum leaf area index, the cumulative heat unit fraction at the time of starting to decline at the time of reaching the maximum leaf area index.

[0018] In some embodiments, the determining the leaf area index curve based on the meteorological data, the soil physical and chemical property data and the crop characteristic data comprises:

[0019] The leaf area index curve equation is constructed, and the expression is as follows:

[0020]

[0021] wherein, LAI i represents the leaf area index on the i-th day during the crop growth period; LAI M represents the maximum leaf area index; a chu,i represents the cumulative heat unit fraction on the i-th day during the crop growth period; a chum represents the cumulative heat unit fraction at the time of reaching the maximum leaf area index; a chud represents the cumulative heat unit fraction at the time of starting to decline at the time of reaching the maximum leaf area index; a Lm,i represents the maximum leaf area index fraction on the i-th day during the crop growth period;

[0022] a chu,i The calculation formula is as follows:

[0023]

[0024] wherein, T j represents the average temperature on the j-th day during the crop growth period, ℃; T max represents the maximum temperature suitable for crop growth; T min represents the minimum temperature suitable for crop growth, ℃; PT represents the total amount of potential heat units during the crop growth period, ℃;

[0025] a Lm,i The calculation formula is as follows:

[0026]

[0027] wherein, r1 represents the first shape coefficient; r2 represents the second shape coefficient, and the expression is as follows:

[0028]

[0029] Wherein, LAI1 and LAI2 represent the leaf area ratios corresponding to the first inflection point and the second inflection point on the optimal leaf area index curve, respectively; a chu1 represents the thermal unit fraction corresponding to the first inflection point on the optimal leaf area index curve; a chu2 They represent the thermal unit fraction corresponding to the second inflection point on the optimal leaf area index curve;

[0030] Determine LAI1, LAI2, and a in the leaf area index curve equation chu1 、a chu2 、a chum and a chud , to fit the leaf area index curve equation and obtain the leaf area index curve.

[0031] In some embodiments, the calculating the dynamic crop coefficient based on the meteorological data and the leaf area index curve includes:

[0032] According to the maximum growth height of the crop, the daily growth height of the crop is calculated. The expression is as follows:

[0033]

[0034] Among them, h M Indicates the maximum growth height of the crop, h i Indicates the crop growth height on day i.

[0035] The crop coefficients in the early, middle, and late stages of crop growth are calculated based on the recommended values ​​of the crop coefficients in the early, middle, and late stages of crop growth and the wind speed and relative humidity data of the day. The crop coefficient calculation formula for the early stage of crop growth is as follows:

[0036]

[0037] Among them, t w represents the average number of days between rainfalls; RW represents the amount of water evaporated during the period of atmospheric evaporation control; TW i Indicates the total amount of water evaporated after a rainfall; ET 0,i represents the potential evapotranspiration on day i. RW and TW i The expression is as follows:

[0038]

[0039] Among them, w Fc is the field water holding capacity of the soil; w WP is the field wilting point moisture content of the soil; r S is the sand content in the soil; r C Clay content in the soil.

[0040] The crop coefficient calculation formula of the middle and later growth stages of the crop is as follows:

[0041]

[0042] Wherein, v i represents the average wind speed at the height of 2m on the i day; K cm represents the recommended value of the crop coefficient of the middle growth stage of the crop; K cd represents the recommended value of the crop coefficient of the later growth stage of the crop; RH i represents the relative humidity data of the i day.

[0043] The equal proportion scaling method is adopted to construct a function relationship between the dynamic crop coefficient and the leaf area index, and to calculate the daily dynamic crop coefficient.

[0044]

[0045] Wherein, K c,i represents the crop coefficient of the i day.

[0046] In some embodiments, the soil water content is calculated based on the dynamic crop coefficient, meteorological data and soil physical and chemical property data, including:

[0047] A double-layer soil water balance model is constructed, and the expression is as follows:

[0048] P i +I i =ET i +L i -R i +RO i

[0049] Wherein, P i represents the rainfall of the i day; I i represents the irrigation amount of the i day; ET i represents the soil evapotranspiration of the i day; RO i represents the field yield flow of the i day; L i represents the soil water loss of the i day; R i represents the soil water supplement of the i day.

[0050] ET i The expression is as follows:

[0051] ET i =K s,i ·K c,i ·ET 0,i

[0052] Wherein, ET 0,i represents the potential evapotranspiration of the i day; K s,irepresents the soil moisture stress coefficient on day i, and the expression is as follows:

[0053]

[0054] Among them, S i represents the soil moisture content on day i; p i It represents the ratio of soil water consumed by the root layer to the total available soil water before the occurrence of water stress on day i. The expression is as follows:

[0055] p i =p tab +0.04·(5.0-K c,i ET 0,i )

[0056] Among them, p tab Indicates recommended value.

[0057] L i The expression is as follows:

[0058]

[0059] L i =Ls i +Lu i

[0060] Among them, Ls i represents the water loss of the upper soil layer on day i; Lu i represents the water loss of the lower soil layer on day i; Ss i represents the soil moisture content of the upper soil layer on day i; Su i represents the soil moisture content R of the lower soil layer on day i i and RO i The expression is as follows:

[0061] R i =min[P i +I i -K c,i ET 0,i ,w FC -w WP -(Ss i +Su i )]

[0062] Rs i =min[ws FC -ws WP ,R i ]

[0063] Ru i =R i -Rs i

[0064] RO i =P i +I i -ET i -[w FC -w WP -(Ss i +Su i )]

[0065] wherein, Rs i represents the water supplement of the upper layer soil on the i day; Ru i represents the water supplement of the lower layer soil on the i day; ws FC represents the field water capacity of the upper layer soil on the i day; and wsWPrepresents the wilting point water content of the upper layer soil on the i day; wherein, different levels of years include: low water level year, medium water level year and high water level year; and the irrigation amount is set to 0;

[0066] When the current year is a low water level year, the daily soil water content Si of the low water level year is calculated based on the precipitation data of the low water level year through the constructed double-layer soil water balance model; when the current year is a medium water level year, the daily soil water content Si of the medium water level year is calculated based on the precipitation data of the medium water level year through the constructed double-layer soil water balance model; when the current year is a high water level year, the daily soil water content Si of the high water level year is calculated based on the precipitation data of the high water level year through the constructed double-layer soil water balance model; and the specific formula is as follows:

[0067] S i =Ss i +Su i

[0068] Ss i =Ss i-1 +Rs i -Ls i

[0069] Su i =Su i-1 +Ru i -Lu i

[0070] In some embodiments, the determination of the irrigation water amount of each irrigation, the accumulation of the irrigation water amount of each irrigation, and the determination of the crop irrigation quota based on the principle of ensuring that the soil water content is in a preset range, comprises:

[0071] From the crop planting day, on each irrigation triggering day, it is judged whether it is in the critical water requirement period of the crop;

[0072] If it is not in the critical water requirement period of the crop, and the soil water content result S iWhen the plant approaches the preset wilting point, irrigation is started and the irrigation amount for the day is determined. j for:

[0073] I j =w FC -w WP

[0074] If it is the critical water demand period of crops and the soil moisture content S i When the soil moisture stress point reaches or falls below the preset point, irrigation is started and the irrigation amount for the day is determined. i for:

[0075] I i =p i ·(w FC -w WP )

[0076] The net irrigation quota for the crop in different normal years is determined by accumulating the irrigation water volume over the past years using the following formula:

[0077]

[0078] Among them, W represents the net irrigation quota of the crop; K represents the total number of irrigations during the entire growth period of the crop.

[0079] The present invention also provides a crop irrigation quota determination device, comprising:

[0080] An acquisition module is used to acquire basic data; the basic data includes meteorological data, soil physical and chemical property data, and crop characteristic data;

[0081] A first calculation module is configured to calculate a leaf area index curve based on meteorological data, soil physical and chemical property data, and crop characteristic data; wherein the leaf area index curve is configured to characterize the relationship between the cumulative heat unit fraction during the crop growth period and the leaf area index; and wherein the cumulative heat unit fraction during the crop growth period is related to temperature;

[0082] a second calculation module, configured to calculate a dynamic crop coefficient based on the meteorological data and the leaf area index curve;

[0083] a third calculation module, configured to calculate soil moisture content based on the dynamic crop coefficient, meteorological data, and soil physical and chemical property data;

[0084] The control module is used to determine the irrigation water volume for each irrigation based on the principle of ensuring that the soil moisture content is within a preset range, accumulate the irrigation water volume for each irrigation, and determine the crop irrigation quota.

[0085] The present invention further provides an electronic device, comprising:

[0086] A processor, and a memory for storing a program executable by the processor;

[0087] The processor is used to implement the above-mentioned method for determining the crop irrigation quota by running the program in the memory.

[0088] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the processor is enabled to execute the above-mentioned method for determining crop irrigation quotas.

[0089] The crop irrigation quota determination device provided in the present application first obtains basic data; the basic data includes meteorological data, soil physical and chemical property data, and crop characteristic data; a leaf area index curve is calculated based on the meteorological data, soil physical and chemical property data, and crop characteristic data; wherein the leaf area index curve is used to characterize the relationship between the cumulative heat unit fraction during the crop growth period and the leaf area index; the cumulative heat unit fraction during the crop growth period is related to temperature; and a dynamic crop coefficient is calculated based on the meteorological data and the leaf area index curve.

[0090] Soil moisture is calculated based on the dynamic crop coefficient, meteorological data, and soil physical and chemical property data. Irrigation water amounts are determined based on the principle of ensuring that the soil moisture content remains within a preset range. The irrigation water amounts are accumulated to determine the crop irrigation quota. This allows crop irrigation quotas to be determined based on meteorological data, soil physical and chemical property data, and crop characteristic data, making them more consistent with actual agricultural crop irrigation needs.

[0091] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0093] Figure 1 is a schematic diagram of steps of a method for determining a crop irrigation quota according to an exemplary embodiment;

[0094] Figure 2 is a simulation result of the dynamic crop coefficient of sugarcane from 2005 to 2007 according to an exemplary embodiment;

[0095] Figure 3 1 is a simulation result of soil moisture content and irrigation amount in 50%, 75% and 90% level years according to an exemplary embodiment;

[0096] Figure 4is a structural schematic diagram of a device for determining crop irrigation quotas according to an exemplary embodiment;

[0097] Figure 5 It is a schematic structural diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0098] Exemplary embodiments will be described in detail herein, examples of which are illustrated in the accompanying drawings. In the following description, when referring to the drawings, like numbers in different figures represent like or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present invention, as detailed in the appended claims.

[0099] Figure 1 is a schematic diagram of the steps of a method for determining a crop irrigation quota according to an exemplary embodiment. Figure 1 , providing methods for determining crop irrigation quotas, including:

[0100] Step 110, obtaining basic data; the basic data includes meteorological data, soil physical and chemical property data, and crop characteristic data;

[0101] Specifically, step 110 is primarily about collecting and analyzing basic data, including meteorological data, soil physical and chemical properties, and crop characteristics. This data is used to plot a P-III frequency curve for annual precipitation and identify different level years.

[0102] Step 120, calculating a leaf area index curve based on meteorological data, soil physical and chemical property data, and crop characteristic data; wherein the leaf area index curve is used to represent the relationship between the cumulative heat unit fraction during the crop growth period and the leaf area index; the cumulative heat unit fraction during the crop growth period is related to temperature;

[0103] Specifically, crop leaf area index curve fitting includes: constructing a crop leaf area index curve equation, determining crop growth parameters in the equation based on daily temperature data and measured crop leaf area index data, and fitting the leaf area index curve.

[0104] Step 130, calculating a dynamic crop coefficient based on the meteorological data and the leaf area index curve;

[0105] In practical applications, the dynamic crop coefficient is calculated based on daily wind speed and relative humidity data, and based on the recommended crop coefficient values ​​for the early, middle, and late stages of crop growth, a functional relationship between the dynamic crop coefficient and the leaf area index is constructed to calculate the daily crop coefficient.

[0106] Step 140, calculating soil moisture based on the dynamic crop coefficient, meteorological data, and soil physical and chemical property data;

[0107] Specifically, this step uses soil water balance process simulation, uses dynamic crop coefficients and soil stress coefficients to calculate daily crop evapotranspiration, constructs a two-layer soil hydrological model, and performs water balance calculations based on precipitation, potential evapotranspiration, and basic soil hydrological parameters to obtain daily soil moisture content.

[0108] Step 150, based on the principle of ensuring that the soil moisture content is within a preset range, determine the irrigation water volume for each irrigation, accumulate the irrigation water volume for each irrigation, and determine the crop irrigation quota.

[0109] Specifically, irrigation regulation during the growth period refers to the irrigation initiation threshold when soil moisture content decreases to the soil moisture stress point during the crop's critical water-demand period; during non-critical water-demand periods, the irrigation initiation threshold is when soil moisture content decreases to the wilting point. The upper limit for a single irrigation is the irrigation volume required to reach field water holding capacity. By accumulating irrigation volumes from previous irrigation periods, crop irrigation quotas for different yearly periods are determined. This allows crop irrigation quotas to be determined based on meteorological data, soil physical and chemical properties, and crop characteristics, ensuring that agricultural crop irrigation quotas more closely align with actual needs.

[0110] In some embodiments, the meteorological data includes: average number of days between rainfalls, potential evapotranspiration, precipitation, average wind speed at a height of 2 meters, average temperature, maximum temperature, minimum temperature, relative humidity data, radiation, and wind speed of the day;

[0111] The soil physical and chemical property data include: wilting coefficient, field wilting point moisture content, field water holding capacity, sand content, clay content, critical threshold value for soil water stress, recommended value, water replenishment of upper soil layer; water replenishment of lower soil layer; field water holding capacity of upper soil layer; wilting point moisture content of upper soil layer;

[0112] The crop characteristic data include: the date of the critical water requirement period of the crop, the maximum and minimum temperatures suitable for crop growth, the total potential heat unit during the crop growth period, the water replenishment amount of the upper soil in the early and middle growth stages and the recommended crop coefficient in the late growth stage, the measured data of the leaf area index, the maximum leaf area index and the maximum growth height value, the leaf area ratio, the heat unit fraction, the cumulative heat unit fraction when the maximum leaf area index is reached, and the cumulative heat unit fraction when the maximum leaf area index begins to decay.

[0113] Specifically, step 120, calculating the leaf area index curve based on meteorological data, soil physical and chemical property data, and crop characteristic data, includes:

[0114] Construct the leaf area index curve equation, the expression is as follows:

[0115]

[0116] Among them, LAI i Represents the leaf area index on the i-th day during the crop growth period; LAI M Indicates the maximum leaf area index during the crop growth period; a chu,i represents the cumulative heat unit fraction on the i-th day of the crop growth period; a chum 、a chud represents the cumulative heat unit fraction when the maximum leaf area index is reached and the leaf area index begins to decay, respectively; a Lm,i It represents the maximum leaf area index score on the i-th day of the crop growth period.

[0117] a chu,i The calculation formula is as follows:

[0118]

[0119] Among them, T j represents the average temperature on the jth day during the crop growth period, ℃; T max and T min They represent the highest and lowest temperatures suitable for crop growth, respectively, ℃; PT represents the total amount of potential heat units during the crop growth period, ℃.

[0120] a Lm,i The calculation formula is as follows:

[0121]

[0122] Where r1 represents the first shape coefficient; r2 represents the second shape coefficient, and the expressions are as follows:

[0123]

[0124]

[0125] Wherein, LAI1 and LAI2 represent the leaf area ratios corresponding to the first inflection point and the second inflection point on the optimal leaf area index curve, respectively; a chu1 represents the thermal unit fraction corresponding to the first inflection point on the optimal leaf area index curve; a chu2 They represent the thermal unit fraction corresponding to the second inflection point on the optimal leaf area index curve;

[0126] Determine LAI1, LAI2, and a in the leaf area index curve equation chu1 、a chu2 、a chum and a chud, to fit the leaf area index curve equation and obtain the leaf area index curve. That is: according to the daily temperature data and the measured leaf area index data of the crop, the crop growth parameters in the leaf area index curve equation are determined, and the leaf area index curve is fitted. The crop growth parameters include the leaf area ratio (LAI1 and LAI2) and the accumulated temperature ratio (a) corresponding to the first inflection point and the second inflection point on the optimal leaf area index curve. chu1 and a chu2 ), the cumulative heat unit fraction when the maximum leaf area index is reached and the leaf area index begins to decay (a chum and a chud ).

[0127] Specifically, step 130, calculating a dynamic crop coefficient based on the meteorological data and the leaf area index curve, includes:

[0128] According to the maximum growth height of the crop, the daily growth height of the crop is calculated. The expression is as follows:

[0129]

[0130] Among them, h M Indicates the maximum growth height of the crop, h i Indicates the crop growth height on day i.

[0131] The crop coefficients in the early, middle, and late stages of crop growth are calculated based on the recommended values ​​of the crop coefficients in the early, middle, and late stages of crop growth and the wind speed and relative humidity data of the day. The crop coefficient calculation formula for the early stage of crop growth is as follows:

[0132]

[0133] Among them, t w represents the average number of days between rainfalls; RW represents the amount of water evaporated during the period of atmospheric evaporation control; TW i Indicates the total amount of water evaporated after a rainfall; ET 0,i represents the potential evapotranspiration on day i. RW and TW i The expression is as follows:

[0134]

[0135] Among them, w Fc is the field water holding capacity of the soil; w WP is the field wilting point moisture content of the soil; r S is the sand content in the soil; r C Clay content in the soil.

[0136] The calculation formulas for the crop coefficient in the middle and late stages of crop growth are as follows:

[0137]

[0138] Among them, v i represents the average wind speed at a height of 2m on the i-th day; K cm Indicates the recommended value of crop coefficient in the middle of crop growth; K cd Indicates the recommended value of crop coefficient in the late growth period of crops; RH i Represents the relative humidity data for day i.

[0139] The proportional expansion method was used to construct the functional relationship between the dynamic crop coefficient and the leaf area index, and the daily dynamic crop coefficient was calculated.

[0140]

[0141] Among them, K c,i represents the crop coefficient on day i.

[0142] Specifically, step 140, calculating soil moisture based on the dynamic crop coefficient, meteorological data, and soil physical and chemical property data, includes:

[0143] Taking into account the soil hydrological processes such as rainfall, irrigation, evaporation, runoff, soil water loss and water replenishment, a two-layer soil water balance model is constructed, which is expressed as follows:

[0144] P i +I i =ET i +L i -R i +RO i

[0145] Among them, P i represents the rainfall on day i; I i represents the irrigation amount on day i; ET i represents the soil evapotranspiration on day i; RO i represents the field flow rate on day i; L i represents the soil water loss on day i; R i represents the soil water replenishment on day i.

[0146] ET i The expression is as follows:

[0147] ET i =K s,i ·K c,i ET 0,i

[0148] Among them, ET 0,i represents the potential evapotranspiration on day i; K s,irepresents the soil moisture stress coefficient on day i, and the expression is as follows:

[0149]

[0150] Among them, S i represents the soil moisture content on day i; p i It represents the ratio of soil water consumed by the root layer to the total available soil water before the occurrence of water stress on day i. The expression is as follows:

[0151] p i =p tab +0.04·(5.0-K c,i ET 0,i )

[0152] Among them, p tab Indicates recommended value.

[0153] L i The expression is as follows:

[0154]

[0155] L i =Ls i +Lu i

[0156] Among them, Ls i represents the water loss of the upper soil layer on day i; Lu i represents the water loss of the lower soil layer on day i; Ss i represents the soil moisture content of the upper soil layer on day i; Su i represents the soil moisture content ET of the lower soil layer on day i i The expression is as follows:

[0157]

[0158] R i and RO i The expression is as follows:

[0159] R i =min[P i +I i -K c,i ET 0,i ,w FC -w WP -(Ss i +Su i )]

[0160] Rs i =min[ws FC -ws WP ,R i]

[0161] Ru i =R i -Rs i

[0162] RO i =P i +I i -ET i -[w FC -w WP -(Ss i +Su i )]

[0163] Among them, Rs i represents the amount of water replenished in the upper soil layer on day i; Ru i represents the amount of water replenished in the lower soil layer on day i; ws FC represents the field water holding capacity of the upper soil on the i-th day; wsWP represents the wilting point moisture content of the upper soil on the i-th day; different level years include: low water level year, medium water level year and high water level year; the irrigation amount is set to 0; specifically, the low water level year is the 50% level year; the medium water level year is the 75% level year; and the high water level year is the 90% level year.

[0164] When the current year is classified as a low water level year, the daily soil moisture content Si of the low water level year is calculated based on the precipitation data of the low water level year through the constructed double-layer soil water balance model; when the current year is classified as a medium water level year, the daily soil moisture content Si of the medium water level year is calculated based on the precipitation data of the medium water level year through the constructed double-layer soil water balance model; when the current year is classified as a high water level year, the daily soil moisture content Si of the high water level year is calculated based on the precipitation data of the high water level year through the constructed double-layer soil water balance model; the specific formula is as follows:

[0165] S i =Ss i +Su i

[0166] Ss i =Ss i-1 +Rs i -Ls i

[0167] Su i =Su i-1 +Ru i -Lu i

[0168] In the above scheme, the irrigation amount is set to 0, and the precipitation data of different level years (50%, 75%, and 90%) are input. The daily soil moisture content Si of different level years is calculated through the constructed two-layer soil water balance model. Among them, the precipitation data of the 50% level year is the low water level year; the precipitation data of the 75% level year is the medium water level year; and the precipitation data of the 90% level year is the high water level year.

[0169] In some embodiments, step 150, based on the principle of ensuring that the soil moisture content is within a preset range, determines the irrigation water amount for each irrigation, accumulates the irrigation water amount for each irrigation, and determines the crop irrigation quota, including:

[0170] From the date of crop planting, on each irrigation trigger day, determine whether it is the critical water demand period of the crop;

[0171] If it is not the critical water demand period for crops, and the soil moisture result on that day is S i When the plant approaches the preset wilting point, irrigation is started and the irrigation amount for the day is determined. j for:

[0172] I j =w FC -w WP

[0173] If it is the critical water demand period of crops and the soil moisture content S i When the soil moisture stress point reaches or falls below the preset point, irrigation is started and the irrigation amount for the day is determined. i for:

[0174] I i =p i ·(w FC -w WP )

[0175] It should be noted that the solution provided in this application is executed on every irrigation trigger day.

[0176] The irrigation water volume of all previous times is accumulated and the irrigation quota for the crop in different level years (i.e., in the 50%, 75% and 90% level years) is determined using the following formula:

[0177]

[0178] Among them, W represents the net irrigation quota of the crop; K represents the total number of irrigations during the entire growth period of the crop.

[0179] This embodiment is described with reference to specific application examples:

[0180] 1. Overview of the study area: The formulation of sugarcane irrigation quotas in Yulin City, Guangxi is used as an example.

[0181] II. Basic data input:

[0182] Meteorological observation data, soil type data, evapotranspiration, sugarcane growth monitoring data and planting management data required for model construction.

[0183] Meteorological observation data: daily precipitation, air temperature, wind speed, solar radiation, relative humidity from 1990 to 2013 at three stations in Lingshan, Yulin and Qinzhou; daily precipitation data from 1990 to 2013 at two rainfall stations in Pubei and Bobai, and P-III frequency curve method for annual precipitation data to arrange frequency to obtain 50%, 75% and 90% level years;

[0184] Soil type data provided by soil science database, including wilting coefficient, field water capacity, etc.

[0185] Crop growth characteristic data: including Heping irrigation area sugarcane leaf area index test data from 2005 to 2007, suitable growth maximum and minimum temperature, potential heat unit total amount of growth period, recommended value of crop coefficient in early, middle and late growth stages, measured data of leaf area index, maximum leaf area index and maximum growth height, used for leaf area index curve fitting;

[0186] III. Parameter calibration and leaf area index simulation:

[0187] The correlation coefficient R 2 and Nash-Sutcliffe efficiency coefficient E ns are selected to evaluate the simulation adaptability. Generally, R 2 greater than 0.6 and E ns greater than 0.6 are considered to be satisfactory simulation accuracy.

[0188] The main calibrated sugarcane growth parameters are the leaf area ratio corresponding to the first and second inflection points of the optimal leaf area index curve (LAI1 and LAI2) and the heat accumulation ratio (a chu1 and a chu2 ), the fraction of cumulative heat units when reaching maximum leaf area index and leaf area index begins to decay (a chum and a chud ), etc. All are corrected in daily steps. The main parameter calibration results of sugarcane are shown in Table 1.

[0189] parameter Parameter adjustment <![CDATA[a chu1 ]]> 0.16 <![CDATA[a chu2 ]]> 0.42 LAI1 0.1 <![CDATA[LAI2]]> 0.9 <![CDATA[a chum ]]> 0.45 <![CDATA[a chud ]]> 0.73

[0190] Table 1

[0191] Referring to Figure 2 , it can be seen that the fitting degree of the simulation value and the measured value is good. The leaf area index calibration and verification results are shown in Table 2. The correlation coefficient R 2and Nash efficiency coefficient E ns Almost all of them are above 0.90, reaching the required value.

[0192]

[0193] Table 2

[0194] 4. Soil hydrological process simulation and irrigation amount determination:

[0195] According to the determined crop growth parameters, the daily crop coefficient is calculated, and then the soil hydrological process is calculated. The soil moisture simulation results and irrigation amount of the 50%, 75% and 90% level years are shown in Figure 3 As shown in the figure, the net irrigation volume of sugarcane in the 50%, 75% and 90% levels is 172.6mm, 227.7mm and 277.2mm, or 115.1m 3 / mu, 151.8m 3 / mu and 184.8m3 / mu.

[0196] Based on the same inventive concept, the present application also provides a crop irrigation quota determination device, referring to Figure 4 , a crop irrigation quota determination device, comprising:

[0197] An acquisition module 41 is used to acquire basic data; the basic data includes meteorological data, soil physical and chemical property data, and crop characteristic data;

[0198] A first calculation module 42 is configured to calculate a leaf area index curve based on meteorological data, soil physical and chemical property data, and crop characteristic data; wherein the leaf area index curve is configured to represent the relationship between the cumulative heat unit fraction during the crop growth period and the leaf area index; and wherein the cumulative heat unit fraction during the crop growth period is related to temperature;

[0199] A second calculation module 43 is configured to calculate a dynamic crop coefficient based on the meteorological data and the leaf area index curve;

[0200] a third calculation module 44 for calculating soil moisture content based on the dynamic crop coefficient, meteorological data, and soil physical and chemical property data;

[0201] The control module 45 is used to determine the irrigation water volume for each irrigation based on the principle of ensuring that the soil moisture content is within a preset range, accumulate the irrigation water volume for each irrigation, and determine the crop irrigation quota.

[0202] It should be noted that the specific functions and execution process of the crop irrigation quota determination device can refer to the description in the above method embodiment.

[0203] Below, reference Figure 5 To describe the electronic device according to the embodiment of the present application. Figure 5 The figure shows a block diagram of an electronic device according to an embodiment of the present application.

[0204] like Figure 5 As shown, electronic device 500 includes one or more processors 510 and memory 520 .

[0205] The processor 510 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 500 to perform desired functions.

[0206] The memory 520 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), a hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 510 may execute the program instructions to implement the crop irrigation quota determination method of the various embodiments of the present application described above and / or other desired functions. The computer-readable storage medium may also store various contents, such as category correspondences.

[0207] In one example, the electronic device 500 may further include an input device 530 and an output device 540 , and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0208] In addition, the input device 530 may also include, for example, a keyboard, a mouse, an interface, etc. The output device 540 may output various information to the outside, including analysis results, etc. The output device 540 may include, for example, a display, a speaker, a printer, a communication network and its connected remote output device, etc.

[0209] Of course, to simplify, Figure 5 Only some of the components in the electronic device related to the present application are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device may further include any other appropriate components according to specific application scenarios.

[0210] Exemplary computer program products and computer-readable storage media

[0211] In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the crop irrigation quota determination method according to various embodiments of the present application described in the above-mentioned "Exemplary Method" section of this specification.

[0212] The computer program product may be written in any combination of one or more programming languages ​​to implement the program code for performing the operations of the embodiments of the present application, including object-oriented programming languages ​​such as Java, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0213] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0214] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for determining crop irrigation quotas, characterized in that: The method comprises the following steps: Obtaining basic data; the basic data includes meteorological data, soil physical and chemical property data, and crop characteristic data; Calculating a leaf area index curve based on meteorological data, soil physical and chemical property data, and crop characteristic data; wherein the leaf area index curve is used to characterize the relationship between the cumulative heat unit fraction during the crop growth period and the leaf area index; the cumulative heat unit fraction during the crop growth period is related to temperature; Calculating a dynamic crop coefficient based on the meteorological data and the leaf area index curve; Calculating soil moisture content based on the dynamic crop coefficient, meteorological data, and soil physical and chemical property data; Based on the principle of ensuring that the soil moisture content is within the preset range, the irrigation water volume is determined, and the crop irrigation quota is determined by accumulating the irrigation water volume; The meteorological data include: average number of days between rainfalls, potential evapotranspiration, precipitation, average wind speed at a height of 2m, average temperature, maximum temperature, minimum temperature, relative humidity, radiation and wind speed of the day; The soil physical and chemical property data include: wilting coefficient, field wilting point moisture content, field water holding capacity, sand content, clay content, critical threshold value for soil water stress, recommended value, water replenishment of upper soil layer; water replenishment of lower soil layer; field water holding capacity of upper soil layer; wilting point moisture content of upper soil layer; The crop characteristic data include: the date of the critical water requirement period of the crop, the maximum and minimum temperatures suitable for crop growth, the total potential heat unit during the crop growth period, the water replenishment amount of the upper soil in the early and middle growth stages and the recommended crop coefficient in the late growth stage, the measured data of the leaf area index, the maximum leaf area index and the maximum growth height value, the leaf area ratio, the heat unit fraction, the cumulative heat unit fraction when the maximum leaf area index is reached, and the cumulative heat unit fraction when the maximum leaf area index begins to decay; The calculation of the leaf area index curve based on meteorological data, soil physical and chemical property data and crop characteristic data includes: Construct the leaf area index curve equation, the expression is as follows: in, LAI i Indicates the first i daily leaf area index; LAI M represents the maximum leaf area index; a chu,i Indicates the first stage of crop growth i the cumulative heat unit fraction of the day; a chum represents the fraction of cumulative heat units at which the maximum leaf area index is reached; a chud represents the fraction of cumulative heat units at which decay begins when the maximum leaf area index is reached; a Lm,i Indicates the first stage of crop growth i maximum leaf area index score on the day; a chu,i The calculation formula is as follows: in, T j Indicates the first j Average daily temperature, °C; T max Indicates the maximum temperature suitable for crop growth; T min Indicates the lowest temperature suitable for crop growth, ℃; PT It indicates the total amount of potential heat units during the crop growth period, ℃; a Lm,i The calculation formula is as follows: in, r 1 represents the first shape coefficient; r 2 represents the second shape coefficient, which is expressed as follows: in, LAI 1 and LAI 2 represents the leaf area ratios corresponding to the first and second inflection points on the optimal leaf area index curve; a chu1表示 The heat unit fraction corresponding to the first inflection point on the optimal leaf area index curve; a chu2 They represent the thermal unit fraction corresponding to the second inflection point on the optimal leaf area index curve; Determine the leaf area index curve equation LAI 1、 LAI 2. a chu1 、 a chu2 、 a chum and a chud , to fit the leaf area index curve equation and obtain the leaf area index curve.

2. The method for determining crop irrigation quota according to claim 1, characterized in that: The calculating of the dynamic crop coefficient based on the meteorological data and the leaf area index curve includes: According to the maximum growth height of the crop, the daily growth height of the crop is calculated. The expression is as follows: in, h M Indicates the maximum growth height of the crop, h i Indicates the i Daily crop growth height; The crop coefficients in the early, middle, and late stages of crop growth are calculated based on the recommended values ​​of the crop coefficients in the early, middle, and late stages of crop growth and the wind speed and relative humidity data of the day. The crop coefficient calculation formula for the early stage of crop growth is as follows: in, t w Indicates the average number of days between rainfall events; RW It indicates the amount of water evaporated during the stage controlled by atmospheric evaporation force; TW i Indicates the total amount of water evaporated after a rainfall; ET 0,i Indicates the i Daily potential evapotranspiration; RW and TW i The expression is as follows: in, w Fc is the field capacity of the soil; w WP is the field wilting point moisture content of the soil; r S The sand content in the soil ；r C is the clay content in the soil; The calculation formulas for the crop coefficient in the middle and late stages of crop growth are as follows: in, v i Indicates the i Daily average wind speed at 2m height; K cm Indicates the recommended value of crop coefficient in the middle of crop growth; K cd Indicates the recommended value of crop coefficient in the late growth period of crops; RH i Indicates the i Daily relative humidity data; The proportional expansion method is used to construct the functional relationship between the dynamic crop coefficient and the leaf area index, and the daily dynamic crop coefficient is calculated. in, K c,i Indicates the i The crop coefficient for the day.

3. The method for determining crop irrigation quota according to claim 2, characterized in that: The calculating of soil moisture content based on the dynamic crop coefficient, meteorological data and soil physical and chemical property data includes: Construct a double-layer soil water balance model, the expression is as follows: in, P i Indicates the i daily rainfall; I i Indicates the i Daily irrigation volume; ET i Indicates the i Soil evapotranspiration per day; RO i Indicates the i daily field flow rate; L i Indicates the i Daily soil water loss; R i Indicates the i Daily soil water replenishment; ET i The expression is as follows: in, ET 0,i Indicates the i Daily potential evapotranspiration; K s,i Indicates the i The soil moisture stress coefficient of the day is expressed as follows: in, S i Indicates the i daily soil moisture content; p i Indicates the i The ratio of soil water consumed by the root layer to the total available soil water before the occurrence of daily water stress can be expressed as follows: in, p tab Indicates recommended value; L i The expression is as follows: in, Ls i Indicates the i Daily water loss from the upper soil layer; Lu i Indicates the i Water loss from the subsoil layer; Ss i Indicates the i Soil moisture content of the upper soil layer on the day; Su i Indicates the i Soil moisture content of the subsoil layer ET i The expression is as follows: R i and RO i The expression is as follows: in, Rs i Indicates the i Daily water replenishment of the upper soil layer; Ru i Indicates the i The amount of water replenished in the lower soil layer; ws FC Indicates the i Field water capacity of the upper soil layer; ws WP represents the wilting point moisture content of the upper soil layer on day i; different level years include: low water level year, medium water level year and high water level year; the irrigation amount is set to 0; When the current year is a low water level year, the daily soil moisture content Si of the low water level year is calculated based on the precipitation data of the low water level year through the constructed double-layer soil water balance model; when the current year is a medium water level year, the daily soil moisture content Si of the medium water level year is calculated based on the precipitation data of the medium water level year through the constructed double-layer soil water balance model; when the current year is a high water level year, the daily soil moisture content Si of the high water level year is calculated based on the precipitation data of the high water level year through the constructed double-layer soil water balance model; the specific formula is as follows: 。 4. The method for determining crop irrigation quota according to claim 3, characterized in that: Based on the principle of ensuring that the soil moisture content is within the preset range, the irrigation water volume is determined, the irrigation water volume is accumulated, and the crop irrigation quota is determined, including: From the date of crop planting, on each irrigation trigger day, determine whether it is the critical water demand period of the crop; If it is not the critical water demand period for crops, and the soil moisture result on that day is S i When the plant approaches the preset wilting point, irrigation is started and the irrigation amount for the day is determined. for: If it is the critical water demand period for crops and the soil moisture content is S i When the soil moisture stress point reaches or falls below the preset point, irrigation is started and the irrigation amount for the day is determined. for: The net irrigation quota for the crop in different normal years is determined by accumulating the irrigation water volume over the past years using the following formula: in, W Indicates the net irrigation quota for the crop; K Indicates the total number of irrigations during the entire crop growth period.

5. A device for determining crop irrigation quotas, characterized in that: include: An acquisition module is used to acquire basic data; the basic data includes meteorological data, soil physical and chemical property data, and crop characteristic data; A first calculation module is configured to calculate a leaf area index curve based on meteorological data, soil physical and chemical property data, and crop characteristic data; wherein the leaf area index curve is configured to characterize the relationship between the cumulative heat unit fraction during the crop growth period and the leaf area index; and wherein the cumulative heat unit fraction during the crop growth period is related to temperature; a second calculation module, configured to calculate a dynamic crop coefficient based on the meteorological data and the leaf area index curve; a third calculation module, configured to calculate soil moisture content based on the dynamic crop coefficient, meteorological data, and soil physical and chemical property data; The control module is used to determine the irrigation water volume for each irrigation based on the principle of ensuring that the soil moisture content is within a preset range, accumulate the irrigation water volume for each irrigation, and determine the crop irrigation quota; The meteorological data include: average number of days between rainfalls, potential evapotranspiration, precipitation, average wind speed at a height of 2m, average temperature, maximum temperature, minimum temperature, relative humidity, radiation and wind speed of the day; The soil physical and chemical property data include: wilting coefficient, field wilting point moisture content, field water holding capacity, sand content, clay content, critical threshold value for soil water stress, recommended value, water replenishment of upper soil layer; water replenishment of lower soil layer; field water holding capacity of upper soil layer; wilting point moisture content of upper soil layer; The crop characteristic data include: the date of the critical water requirement period of the crop, the maximum and minimum temperatures suitable for crop growth, the total potential heat unit during the crop growth period, the water replenishment amount of the upper soil in the early and middle growth stages and the recommended crop coefficient in the late growth stage, the measured data of the leaf area index, the maximum leaf area index and the maximum growth height value, the leaf area ratio, the heat unit fraction, the cumulative heat unit fraction when the maximum leaf area index is reached, and the cumulative heat unit fraction when the maximum leaf area index begins to decay; The calculation of the leaf area index curve based on meteorological data, soil physical and chemical property data and crop characteristic data includes: Construct the leaf area index curve equation, the expression is as follows: in, LAI i Indicates the first i daily leaf area index; LAI M represents the maximum leaf area index; a chu,i Indicates the first stage of crop growth i the cumulative heat unit fraction of the day; a chum represents the fraction of cumulative heat units at which the maximum leaf area index is reached; a chud represents the fraction of cumulative heat units at which decay begins when the maximum leaf area index is reached; a Lm,i Indicates the first stage of crop growth i maximum leaf area index score on the day; a chu,i The calculation formula is as follows: in, T j Indicates the first j Average daily temperature, °C; T max Indicates the maximum temperature suitable for crop growth; T min Indicates the lowest temperature suitable for crop growth, ℃; PT It indicates the total amount of potential heat units during the crop growth period, ℃; a Lm,i The calculation formula is as follows: in, r 1 represents the first shape coefficient; r 2 represents the second shape coefficient, which is expressed as follows: in, LAI 1 and LAI 2 represents the leaf area ratios corresponding to the first and second inflection points on the optimal leaf area index curve; a chu1表示 The heat unit fraction corresponding to the first inflection point on the optimal leaf area index curve; a chu2 They represent the thermal unit fraction corresponding to the second inflection point on the optimal leaf area index curve; Determine the leaf area index curve equation LAI 1、 LAI 2. a chu1 、 a chu2 、 a chum and a chud , to fit the leaf area index curve equation and obtain the leaf area index curve.

6. An electronic device, characterized in that: include: A processor, and a memory for storing a program executable by the processor; The processor is configured to implement the crop irrigation quota determination method according to any one of claims 1 to 4 by running the program in the memory.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, causes the processor to execute the crop irrigation quota determination method according to any one of claims 1 to 4.

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