A waterlogging prediction, crop yield reduction calculation method and system based on a crop model

Abstract

The present invention relates to the technical field of calculating waterlogging reduction in agricultural crops, and discloses a waterlogging prediction, crop yield reduction calculation method and system based on a crop model, which includes obtaining meteorological data, farming data and soil data; calculating soil-water characteristic data according to the soil data; wherein the soil-water characteristic data includes saturated water content, wilting point, field water holding capacity and saturated hydraulic conductivity; calculating the number of days of farmland waterlogging according to the soil-water characteristic data; calculating the root mortality rate of crops according to the number of days of farmland waterlogging; and obtaining the percentage of crop yield reduction according to the meteorological data, farming data, soil-water characteristic data and root mortality rate of crops. The present invention can accurately measure the number of days of natural waterlogging and whether artificial waterlogging is needed, calculate the percentage of yield reduction caused by flood disasters to crop production, so as to assist in agricultural decision-making and management.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural crop waterlogging reduction calculation, and particularly relates to a waterlogging prediction, crop yield reduction calculation method and system based on a crop model. Background Art

[0002] Predicting the impact of flood disasters on crop production is an important part of agricultural risk management. It helps to build a more scientific and reasonable agricultural management system, helps farmers and farms make preparations for flood control and drainage in advance, and conduct scientific yield reduction calculations afterwards.

[0003] Existing technologies usually obtain the percentage of yield reduction caused by flood disasters to crop production through satellite remote sensing. However, satellite remote sensing can only monitor the growth status of crops in real time and cannot directly predict future crop yield reduction. Moreover, optical remote sensing is greatly restricted by weather conditions. Clouds, fog, etc. will interfere with data collection. At the same time, the crop canopy will also affect the judgment of the remote sensing satellite on the water layer of farmland, reducing the availability and continuity of data. The existing method through satellite remote sensing is even more unable to make decisions and management in advance to cope with flood disasters, resulting in lag.

[0004] Therefore, there is an urgent need for a waterlogging prediction, crop yield reduction calculation method and system based on a crop model, which can accurately measure the natural waterlogging drainage days and whether artificial waterlogging drainage is needed, calculate the percentage of yield reduction caused by flood disasters to crop production, so as to assist in agricultural decision-making and management. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention provides a waterlogging prediction, crop yield reduction calculation method and system based on a crop model, which can accurately measure the natural waterlogging drainage days and whether artificial waterlogging drainage is needed, calculate the percentage of yield reduction caused by flood disasters to crop production, so as to assist in agricultural decision-making and management.

[0006] The present invention provides a waterlogging prediction, crop yield reduction calculation method based on a crop model, including the following steps:

[0007] S1. Obtain meteorological data, agricultural operation data and soil data;

[0008] S2. Calculate soil-water characteristic data according to the soil data; wherein, the soil-water characteristic data includes saturated water content, wilting point, field capacity and saturated hydraulic conductivity;

[0009] S3. Calculate the farmland waterlogging drainage days according to the soil-water characteristic data;

[0010] S4. Calculate the crop root mortality according to the farmland waterlogging drainage days;

[0011] S5. Obtain the percentage of crop yield reduction based on meteorological data, agricultural data, soil and water characteristic data, and crop root mortality rate.

[0012] Further, in S1, the meteorological data includes temperature, wind speed, water surface evaporation rate, precipitation, runoff, and solar irradiance.

[0013] The agricultural data includes farmland area, planted variety, sowing date, sowing quantity, irrigation date, irrigation quantity, fertilization date, fertilization quantity, and fertilization type.

[0014] The soil data includes the percentage content of sand, loam, clay, soil porosity, soil bulk density, and organic matter content data in the soil.

[0015] Further, in S2, calculating the soil and water characteristic data based on the soil data includes:

[0016] S21. Divide the soil into n layers according to the soil census data.

[0017] S22. Calculate the initial value of the saturated water content of each layer of soil based on the soil data.

[0018] The calculation formula is as follows:

[0019] Sm n 1 = -0.251sand + 0.195clay + 0.011som + 0.006(sand×som) - 0.027(clay×som) + 0.452(sand×clay) + 0.299;

[0020] Where, clay represents the percentage content of clay, sand represents the percentage content of sand, som represents the organic matter content, and Sm n 1 represents the initial value of the first saturated water content of the nth layer of soil;

[0021] Sm n 1'=(Sm n 1)+[1.283×(Sm n 1)² - 0.374×(Sm n 1) - 0.015];

[0022] Where, Sm n 1' represents the corrected value of the first saturated water content of the nth layer of soil;

[0023] Sm n 2 = 0.287sand + 0.034clay + 0.022som - 0.018(sand×som) - 0.027(clay×som) - 0.584(sand×clay) + 0.078;

[0024] Among them, Sm n 2 represents the initial value of the second saturated water content of the nth layer of soil;

[0025] Sm n 2' = (Sm n 2) + (0.636×(Sm n 2) - 0.107);

[0026] Among them, Sm n 2' represents the corrected value of the second saturated water content of the nth layer of soil;

[0027] Sm n = Sm n 1' + Sm n 2' - 0.097sand + 0.043;

[0028] Among them, Sm n represents the initial value of the saturated water content of the nth layer of soil;

[0029] S23. Calculate the soil compaction correction coefficient according to the soil compaction degree and soil compaction ratio of each layer of soil;

[0030] The calculation formula is as follows:

[0031] dfi n = c1v×cw1 + c2v×cw2;

[0032] Among them, dfi n represents the initial soil density of the nth layer of soil, c1v represents the compaction degree value of the soil with the first compaction degree in the nth layer of soil, c2v represents the compaction degree value of the soil with the second compaction degree in the nth layer of soil, cw1 represents the proportion of the soil with the first compaction degree in the nth layer of soil, and cw2 represents the proportion of the soil with the second compaction degree in the nth layer of soil;

[0033] Df n = dfi n ×[0.9 + (Day - agro_date + T rain ×10)0.1×(1 / 365)];

[0034] Among them, Df n represents the soil compaction correction coefficient of the nth layer of soil, Day represents the current date, T rain represents the number of rainfall times, and agro_date represents the seeding date, that is, the last disturbance time of the soil;

[0035] S24. Calculate the corrected value of the saturated water content of each layer of soil according to the soil compaction correction coefficient and the initial value of the saturated water content;

[0036] The calculation formula is as follows:

[0037] Sm n 0 = 1 - ((1 - Sm n ) × Df n );

[0038] Where, Sm n 0 represents the corrected saturated water content of the nth layer of soil;

[0039] S25. Calculate the field water holding capacity and saturated hydraulic conductivity of each layer of soil according to the initial value and corrected value of the saturated water content of each layer of soil;

[0040] The calculation formula is as follows:

[0041] Sm n f = Sm n 1' - 0.2(Sm n - Sm n 0);

[0042] Where, Sm n f represents the field water holding capacity of the nth layer of soil;

[0043] K n 0 = 1930(Sm n 0 - Sm n 2') (3-BL) ;

[0044] Where, K n 0 represents the saturated hydraulic conductivity of the nth layer of soil, and BL represents the slope of the soil water tension logarithmic curve;

[0045] S26. Calculate the wilting point of each layer of soil according to the soil data;

[0046] The calculation formula is as follows:

[0047] ;

[0048] Where, θ r represents the wilting point, and Ø represents the soil porosity.

[0049] Furthermore, in S21, the soil is divided into n layers according to the depth, and n is 8; dividing the soil into 8 layers according to the depth includes:

[0050] L 1 = 4.5 cm, L 2 = 9.1 cm, L 3 = 16.6 cm, L 4 = 28.9 cm, L 5 = 49.3 cm, L6 = 82.9 cm, L 7 = 138.3 cm, and L 8 = 229.6 cm.

[0051] Further, in S3, calculating the farmland drainage days according to the soil and water characteristic data includes:

[0052] S31. Calculate the water retention in the farmland based on precipitation and runoff. In case of continuous or intermittent precipitation for multiple days, take (C - Sm 1 0×L 1 -…-Sm n 0×L n ) being equal to zero as one precipitation cycle;

[0053] When (Sm 1 0×L 1 -…-Sm n 0×L n ) > 0, the stored water C needs to be added with the cumulative precipitation;

[0054] The drainage days need to be added with the previous natural days, that is, calculate starting from the first precipitation time until the surface water is drained dry.

[0055] S32. Calculate the time required for each layer of soil to reach saturated water absorption based on the saturated water content and saturated hydraulic conductivity of each layer of soil;

[0056] The calculation formula is as follows:

[0057] T n =(Sm n 0 - S n 0)×L n / min(D 1 ,…, D n );

[0058] Among them, T n represents the time required for the nth layer of soil to reach saturated water absorption, S n 0 represents the current water content of the nth layer of soil, L n represents the thickness of the nth layer of soil, D 1 represents the saturated hydraulic conductivity of the first layer of soil, D n represents the saturated hydraulic conductivity of the nth layer of soil;

[0059] S33. Calculate the seepage retention layer based on the saturated hydraulic conductivity of each layer of soil;

[0060] When D n >D n+1 , then mark the nth layer of soil corresponding to D n as the seepage retention layer;

[0061] S34. Calculate the drainage days of each retention layer;

[0062] The calculation formula is as follows:

[0063] P n =(T 1 +…+T n )+{(C - Sm 1 0×L 1 -…-Sm n 0×L n ) / [min(D 1 ,…,D n ) + E×(1 - lai) + ET]};

[0064] Among them, P n represents the drainage days of the nth retention layer, C represents the water storage in the farmland, E represents the water surface evaporation rate, lai represents the leaf area index and the maximum value of lai is 1, and ET represents the canopy evaporation rate;

[0065] S35. Calculate the farmland drainage days according to the drainage days of each retention layer.

[0066] Furthermore, in S4, calculating the crop root mortality rate according to the farmland drainage days includes:

[0067] S41. Determine the crop flood resistance index; among them, the flood resistance index i takes values from 1 to 9, 1 means the crop is not flood - tolerant, and 9 means the crop is flood - tolerant;

[0068] S42. Calculate the crop root mortality rate according to the farmland drainage days;

[0069] The calculation formula is as follows:

[0070] ;

[0071] Among them, RD represents the crop root mortality rate, t represents the tth day after the disaster occurs, and m represents the total number of days after the disaster occurs.

[0072] Furthermore, in S5, obtaining the crop yield reduction percentage according to the meteorological data, farming data, soil and water characteristic data, and crop root mortality rate includes:

[0073] S51. Input the meteorological data, farming data, soil and water characteristic data, and crop root mortality rate into the wofost model to obtain the actual crop yield;

[0074] S52. Set the crop root mortality rate to 0, and then input the meteorological data, farming data, soil and water characteristic data, and crop root mortality rate into the wofost model to obtain the comparative crop yield;

[0075] S53. Calculate the crop yield reduction percentage based on the actual crop yield and the comparative crop yield.

[0076] The present invention also provides a waterlogging prediction and crop yield reduction calculation system based on a crop model, which is used to execute the waterlogging prediction and crop yield reduction calculation method described in any one of the above. The system includes the following modules:

[0077] A data acquisition module, which is used to acquire meteorological data, farming data, and soil data;

[0078] A water and soil characteristic calculation module, which is connected to the data acquisition module and is used to calculate water and soil characteristic data according to the soil data; wherein, the water and soil characteristic data includes saturated water content, wilting point, field capacity, and saturated hydraulic conductivity;

[0079] A waterlogging days calculation module, which is connected to the water and soil characteristic calculation module and is used to calculate the farmland waterlogging days according to the water and soil characteristic data;

[0080] A mortality calculation module, which is connected to the waterlogging days calculation module and is used to calculate the crop root mortality according to the farmland waterlogging days;

[0081] An output module, which is connected to the data acquisition module, the water and soil characteristic calculation module, and the mortality calculation module, and is used to obtain the crop yield reduction percentage according to the meteorological data, farming data, water and soil characteristic data, and crop root mortality.

[0082] The embodiments of the present invention have the following technical effects:

[0083] By integrating meteorological data, farming data, and soil data, the present invention ensures the comprehensiveness and accuracy of the input information. The soil is divided into multiple layers by depth for detailed analysis, taking into account the influence of soil characteristics at different layers on water retention and drainage capacity, improving the authenticity of the simulation. It not only pays attention to the visible flood inundation on the surface, but also delves into the health level of crop roots. By calculating the root mortality, it more accurately reflects the actual damage of floods to crops, can accurately calculate the natural waterlogging days, and judge whether artificial intervention is needed, providing clear operation guidelines for farmers and managers; this solution can accurately calculate the natural waterlogging days and whether artificial waterlogging is needed, calculate the crop yield reduction percentage caused by flood disasters, so as to assist in agricultural decision-making and management. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required to be used in the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0085] Figure 1 is a flowchart of a waterlogging prediction and crop yield reduction calculation method based on a crop model provided by an embodiment of the present invention;

[0086] Figure 2 is a schematic structural diagram of a waterlogging prediction and crop yield reduction calculation system based on a crop model provided by an embodiment of the present invention. Detailed implementation manners

[0087] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope protected by the present invention.

[0088] The present invention proposes a waterlogging prediction and crop yield reduction calculation method based on a crop model, Figure 1 is a flowchart of a waterlogging prediction and crop yield reduction calculation method based on a crop model provided by an embodiment of the present invention. Refer to Figure 1 , and specifically includes:

[0089] S1. Obtain meteorological data, agricultural data and soil data.

[0090] In some embodiments, the meteorological data can be obtained through a meteorological center;

[0091] The soil data can be obtained through the "Dataset of Simulated Soil Characteristics over the Land Surface of China".

[0092] In some embodiments, the meteorological data includes temperature, wind speed, water surface evaporation rate, precipitation, runoff and solar irradiance, etc.;

[0093] The agricultural data includes farmland area, planted variety, sowing date, sowing amount, irrigation date, irrigation amount, fertilization date, fertilization amount and fertilization type, etc.;

[0094] Among them, the farmland area: the farmland area demarcated by the user;

[0095] The planted variety: the variety that the user needs to calculate;

[0096] The sowing date: the sowing date corresponding to the growing season that the user needs to calculate;

[0097] The sowing amount: the sowing amount corresponding to the growing season that the user needs to calculate;

[0098] The irrigation date: the date of each irrigation in the growing season that the user needs to calculate;

[0099] Irrigation amount: The water layer thickness of each irrigation during the growing season that the user needs to calculate;

[0100] Fertilization date: The date of each fertilization during the growing season that the user needs to calculate;

[0101] Fertilization amount: The fertilization amount during the growing season that the user needs to calculate;

[0102] Soil data includes the percentage content of sandy soil, loam soil, clay soil, soil porosity, soil bulk density, and organic matter content data in the soil, etc.; among them, the soil data refers to the data of the soil at the location of the farmland delineated by the user.

[0103] S2. Calculate the soil-water characteristic data according to the soil data.

[0104] Specifically include:

[0105] S21. Divide the soil into n layers according to the soil census data by depth.

[0106] In some embodiments, the soil can be divided into n layers by depth, where n = 8; dividing the soil into 8 layers by depth includes:

[0107] L1 = 4.5cm, L2 = 9.1cm, L3 = 16.6cm, L4 = 28.9cm, L5 = 49.3cm, L6 = 82.9cm, L7 = 138.3cm, and L8 = 229.6cm.

[0108] S22. Calculate the initial value of the saturated water content of each layer of soil according to the soil data;

[0109] The calculation formula is as follows:

[0110] Sm n 1=-0.251sand + 0.195clay + 0.011som + 0.006(sand×som)-0.027(clay×som)+0.452(sand×clay)+0.299;

[0111] where clay represents the percentage content of clay soil, sand represents the percentage content of sandy soil, som represents the organic matter content, and Sm n 1 represents the initial value of the first saturated water content of the nth layer of soil;

[0112] Sm n 1'=(Sm n 1)+[1.283×(Sm n 1)² - 0.374×(Sm n 1)-0.015];

[0113] where Smn 1' represents the first saturated water content correction value of the nth layer of soil;

[0114] Sm n 2 = 0.287sand + 0.034clay + 0.022som - 0.018(sand × som) - 0.027(clay × som) - 0.584(sand × clay) + 0.078;

[0115] where, Sm n 2 represents the initial value of the second saturated water content of the nth layer of soil;

[0116] Sm n 2' = (Sm n 2) + (0.636 × (Sm n 2) - 0.107);

[0117] where, Sm n 2' represents the corrected value of the second saturated water content of the nth layer of soil;

[0118] Sm n = Sm n 1' + Sm n 2' - 0.097sand + 0.043;

[0119] where, Sm n represents the initial value of the saturated water content of the nth layer of soil;

[0120] S23. Calculate the soil compaction correction coefficient according to the soil compaction and soil compaction ratio of each layer of soil;

[0121] Table 1 Soil Compaction Assignment Table

[0122]

[0123] Table 2 Soil Compaction Correspondence Table

[0124]

[0125] Refer to Table 1 and Table 2, the calculation formula is as follows:

[0126] dfi n = c1v × cw1 + c2v × cw2;

[0127] where, dfi nIt represents the initial soil density of the nth layer of soil. c1v represents the compactness value of the soil with the highest compactness in the nth layer of soil, c2v represents the compactness value of the soil with the second highest compactness in the nth layer of soil, cw1 represents the proportion of the soil with the highest compactness in the nth layer of soil, and cw2 represents the proportion of the soil with the second highest compactness in the nth layer of soil;

[0128] Df n =dfi n ×[0.9 + (Day - agro_date + T rain ×10) 0.1×(1 / 365)];

[0129] Among them, Df n represents the soil compactness correction coefficient of the nth layer of soil. Day represents the current date, T rain represents the number of rainfall events, and agro_date represents the seeding date, that is, the last time the soil was disturbed;

[0130] S24. Calculate the saturated water content correction value of each layer of soil according to the soil compactness correction coefficient and the initial value of the saturated water content;

[0131] The calculation formula is as follows:

[0132] Sm n 0 = 1 - ((1 - Sm n ) × Df n );

[0133] Among them, Sm n 0 represents the saturated water content correction value of the nth layer of soil;

[0134] S25. Calculate the field capacity and saturated hydraulic conductivity of each layer of soil according to the initial value of the saturated water content and the saturated water content correction value of each layer of soil;

[0135] The calculation formula is as follows:

[0136] Sm n f = Sm n 1' - 0.2(Sm n - Sm n 0);

[0137] Among them, Sm n f represents the field capacity of the nth layer of soil;

[0138] K n 0 = 1930(Sm n 0 - Sm n 2') (3-BL) ;

[0139] Among them, K n0 represents the saturated hydraulic conductivity of the nth layer of soil, and BL represents the slope of the logarithmic curve of soil water tension;

[0140] S26. Calculate the wilting point of each layer of soil based on the soil data;

[0141] The calculation formula is as follows:

[0142] ;

[0143] Among them, θ r represents the wilting point, and Ø represents the soil porosity;

[0144] The saturated water content refers to the water content when all soil pores are filled with water under natural conditions;

[0145] The field capacity is the relatively stable soil water content that the soil profile can maintain after sufficient irrigation or precipitation on land with deep groundwater and good drainage, allowing water to infiltrate fully and preventing water evaporation for a certain period of time;

[0146] The saturated hydraulic conductivity refers to the ability of water to pass through the soil when the soil is in a completely saturated state, that is, all pores are filled with water, expressed in cm / day.

[0147] S3. Calculate the farmland waterlogging drainage days based on the soil and water characteristic data.

[0148] Specifically, it includes:

[0149] S31. Calculate the farmland retained water volume based on the precipitation and runoff.

[0150] In some embodiments, the farmland retained water volume C = precipitation - runoff.

[0151] S32. Calculate the time required for each layer of soil to absorb water saturated based on the saturated water content and saturated hydraulic conductivity of each layer of soil.

[0152] The calculation formula is as follows:

[0153] T n =(Sm n 0 - S n 0) × L n / min(D 1 , …, D n );

[0154] Among them, T n represents the time required for the nth layer of soil to absorb water saturated, S n 0 represents the current water content of the nth layer of soil, L n represents the thickness of the nth layer of soil, D 1 represents the saturated hydraulic conductivity of the first layer of soil, D nDenote the saturated hydraulic conductivity of the nth layer of soil. The water content of each soil layer is obtained by multiplying the saturated water content of the soil in each layer by the soil layer thickness. Dividing the water content of the soil layer by the minimum saturated hydraulic conductivity above that soil layer gives the time required for each soil layer to saturate and absorb water.

[0155] S33. Calculate the waterlogging retention layer based on the saturated hydraulic conductivity of each soil layer.

[0156] In some embodiments, when D n >D n+1 , waterlogging retention will occur. Denote the corresponding nth layer of soil as the waterlogging retention layer. n

[0157] S34. Calculate the drainage days for each retention layer.

[0158] The calculation formula is as follows:

[0159] P n =(T 1 +…+T n )+{(C - Sm 1 0×L 1 -…-Sm n 0×L n ) / [min(D 1 ,…,D n )+E×(1 - lai)+ET]};

[0160] Condition for establishment: (C - Sm 1 0×L 1 -…-Sm n 0×L n )>0;

[0161] Among them, P n represents the drainage days of the nth retention layer, C represents the water storage in the farmland, E represents the water surface evaporation rate, lai represents the leaf area index and the maximum value of lai is 1, and ET represents the canopy evaporation rate.

[0162] The data of the water surface evaporation rate comes from meteorological data;

[0163] The canopy evaporation rate is the intermediate data calculated by the wofost model and is obtained by the wofost model. Since the calculation of flood disasters is not a one-time calculation but a daily calculation, the current canopy evaporation data can be replaced by the canopy evaporation data under non-flood conditions. The canopy evaporation rate is the historical data of the previous day or the latest historical data obtained from the database.

[0164] The leaf area index is obtained by calculation of the wofost model or remote sensing inversion, and the leaf area index is the historical data of the previous day or the latest historical data obtained from the database. ​

[0165] S35. Calculate the farmland drainage days according to the drainage days of each retention layer.

[0166] When (C - Sm 1 0×L 1 -…-Sm n 0×L n ) > 0, the maximum value max(P n ) of the drainage days corresponding to all retention layers is the farmland drainage days.

[0167] S4. Calculate the crop root mortality rate according to the farmland drainage days.

[0168] Specifically, it includes:

[0169] S41. Determine the crop flood resistance index.

[0170] In some embodiments, the flood resistance index i takes values from 1 to 9, where 1 means the crop is not flood - tolerant and 9 means the crop is flood - tolerant.

[0171] S42. Calculate the crop root mortality rate according to the farmland drainage days;

[0172] The calculation formula is as follows:

[0173] ;

[0174] where RD represents the crop root mortality rate, t represents the t - th day after the disaster, m represents the total number of days after the disaster, and m is less than or equal to the farmland drainage days max(P n ).

[0175] Exemplarily, for example: Chinese cabbage is not flood - tolerant, define the flood resistance index as 1, and calculate the crop root mortality rate after the disaster:

[0176] On the first day: RD = 9%;

[0177] On the second day: RD = 9% + 18% = 27%;

[0178] On the third day: RD = 9% + 18% + 27% = 54%;

[0179] On the fourth day: RD = 9% + 18% + 27% + 36% = 90%;

[0180] On the fifth day: RD = 9% + 18% + 27% + 36% + 45% = 100%; when the crop root mortality rate is greater than 100%, the final result is equal to 100%.

[0181] S5. Obtain the crop yield reduction percentage according to the meteorological data, farming data, soil and water characteristic data, and crop root mortality rate.

[0182] Specifically, it includes:

[0183] S51. Input meteorological data, agricultural data, soil and water characteristic data, and crop root mortality rate into the WOFOST model to obtain the actual crop yield.

[0184] S52. Set the crop root mortality rate to 0, and then input meteorological data, agricultural data, soil and water characteristic data, and crop root mortality rate into the WOFOST model to obtain the comparative crop yield.

[0185] S53. Calculate the crop yield reduction percentage based on the actual crop yield and the comparative crop yield.

[0186] In some embodiments, the crop yield reduction percentage = 1 - (actual crop yield / comparative crop yield).

[0187] By integrating meteorological data, agricultural data, and soil data, the present invention ensures the comprehensiveness and accuracy of the input information. The soil is divided into multiple layers by depth for detailed analysis, taking into account the influence of soil characteristics at different layers on water retention and drainage capabilities, improving the authenticity of the simulation. It not only focuses on the visible flood inundation on the surface but also delves into the health level of crop roots. By calculating the root mortality rate, it can more accurately reflect the actual damage of floods to crops, accurately measure the natural drainage days, and determine whether artificial intervention is needed, providing clear operation guidelines for farmers and managers. This solution can accurately measure the natural drainage days and whether artificial drainage is required, calculate the crop yield reduction percentage caused by flood disasters, and thus assist in agricultural decision-making and management.

[0188] Figure 2 It is a schematic structural diagram of a waterlogging prediction and crop yield reduction calculation system based on a crop model provided by an embodiment of the present invention. This system is used to execute a waterlogging prediction and crop yield reduction calculation method based on a crop model described in the above embodiment, as Figure 2 shown. The system includes the following modules:

[0189] A data acquisition module, used to acquire meteorological data, agricultural data, and soil data;

[0190] A soil and water characteristic calculation module, connected to the data acquisition module, used to calculate soil and water characteristic data according to the soil data; wherein, the soil and water characteristic data includes saturated water content, wilting point, field water holding capacity, and saturated hydraulic conductivity;

[0191] A waterlogging days calculation module, connected to the soil and water characteristic calculation module, used to calculate the farmland waterlogging days according to the soil and water characteristic data;

[0192] A mortality rate calculation module, connected to the waterlogging days calculation module, used to calculate the crop root mortality rate according to the farmland waterlogging days;

[0193] An output module, connected to the data acquisition module, the soil and water characteristic calculation module, and the mortality calculation module, is configured to obtain the percentage of crop yield reduction based on meteorological data, farming data, soil and water characteristic data, and the mortality rate of crop roots.

[0194] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting waterlogging drainage and calculating crop yield reduction based on a crop model, characterized in that: The steps include: S1. Obtain meteorological data, agricultural data and soil data; S2. Calculate soil and water characteristic data based on soil data; wherein the soil and water characteristic data include saturated water content, wilting point, field holding capacity and saturated hydraulic conductivity; S3. Calculate the number of days for farmland drainage based on water and soil characteristic data; S4. Calculate crop root mortality based on the number of days the farmland is drained; Specifically include: S41, determining a crop flood resistance index; wherein the flood resistance index i ranges from 1 to 9, 1 being the crop not tolerant to floods, and 9 being the crop tolerant to floods; S42. Calculate the root mortality of crops based on the number of days of farmland drainage; The calculation formula is as follows: ； Among them, RD represents crop root mortality, t represents the tth day after the disaster, and m represents the total number of days after the disaster; S5. Obtain the percentage of crop yield reduction based on meteorological data, farming data, water and soil characteristic data and crop root mortality rate; Specifically include: S51, inputting meteorological data, farming data, water and soil characteristic data and crop root mortality rate into the wofost model to obtain the actual crop yield; S52, setting the crop root mortality rate to 0, and then inputting the meteorological data, farming data, water and soil characteristic data and the crop root mortality rate into the wofost model to obtain the crop comparative yield; S53. Calculate the crop yield reduction percentage based on the actual crop yield and the comparative crop yield.

2. The method for predicting waterlogging drainage and calculating crop yield reduction based on a crop model according to claim 1, characterized in that: In S1, the meteorological data include temperature, wind speed, water surface evaporation rate, precipitation, runoff and solar radiation; Agricultural data include farmland area, planting varieties, sowing date, sowing amount, irrigation date, irrigation amount, fertilization date, fertilization amount and fertilizer type; Soil data include the percentage of sand, loam, clay, soil porosity, soil bulk density and organic matter content in the soil.

3. The method for predicting waterlogging drainage and calculating crop yield reduction based on crop model according to claim 2, characterized in that: In S2, calculating the water and soil characteristic data according to the soil data includes: S21. Based on soil survey data, the soil is divided into n layers according to depth; S22. Calculate the initial value of the saturated water content of each layer of soil according to the soil data; The calculation formula is as follows: Sm n 1=-0.251sand+0.195clay+0.011som+0.006(sand×som)-0.027(clay×som)+0.452(sand×clay)+0.299; Among them, clay represents the percentage of clay, sand represents the percentage of sand, som represents the organic matter content, Sm n 1 represents the initial value of the first saturated water content of the nth layer of soil; Sm n 1'=(Sm n 1)+[1.283×(Sm n 1)²-0.374×(Sm n 1)-0.015]; Among them, Sm n 1' represents the first saturated moisture content correction value of the nth layer of soil; Sm n 2=0.287sand+0.034clay+0.022som-0.018(sand×som)-0.027(clay×som)-0.584(sand×clay)+0.078; Among them, Sm n 2 represents the initial value of the second saturated water content of the nth layer of soil; Sm n 2'=(Sm n 2)+(0.636×(Sm n 2)-0.107); Among them, Sm n 2' represents the second saturated moisture content correction value of the nth layer of soil; Sm n =Sm n 1'+Sm n 2'-0.097sand+0.043; Among them, Sm n represents the initial value of the saturated water content of the nth layer of soil; S23, calculating a soil compaction correction coefficient according to the soil compaction degree and soil compaction ratio of each soil layer; The calculation formula is as follows: dfi n =c1v×cw1+c2v×cw2; Among them, dfi n represents the initial density of the soil in the nth layer, c1v represents the compactness value of the soil with the highest compactness in the nth layer, c2v represents the compactness value of the soil with the second highest compactness in the nth layer, cw1 represents the proportion of the soil with the highest compactness in the nth layer, and cw2 represents the proportion of the soil with the second highest compactness in the nth layer; Df n =dfi n ×[0.9+(Day-agro_date+T rain ×10)0.1×(1 / 365)]; Among them, Df n represents the soil compactness correction coefficient of the nth layer of soil, Day represents the current date, T rain It indicates the number of rainfalls, and agro_date indicates the planting date; S24, calculating the saturated water content correction value of each layer of soil according to the soil compaction correction coefficient and the initial value of saturated water content; The calculation formula is as follows: Sm n 0=1-((1-Sm n )×Df n ); Among them, Sm n 0 represents the corrected value of the saturated moisture content of the nth layer of soil; S25, calculating the field water holding capacity and saturated hydraulic conductivity of each layer of soil according to the initial value of the saturated water content and the corrected value of the saturated water content of each layer of soil; The calculation formula is as follows: Sm n f=Sm n 1'-0.2(Sm n -Sm n 0); Among them, Sm n f represents the field water holding capacity of the nth layer of soil; K n 0=1930(Sm n 0-Sm n 2') (3-BL) ; Among them, K n 0 represents the saturated hydraulic conductivity of the nth layer of soil, BL represents the slope of the logarithm of soil water tension; S26. calculating the wilting point of each layer of soil according to the soil data; The calculation formula is as follows: ； Among them, θ r represents the wilting point and Ø represents the soil porosity.

4. The method for predicting waterlogging drainage and calculating crop yield reduction based on crop model according to claim 3, characterized in that: In S21, the soil is divided into n layers according to depth, where n is 8; and the soil is divided into 8 layers according to depth includes: L1=4.5cm, L2=9.1cm, L3=16.6cm, L4=28.9cm, L5=49.3cm, L6=82.9cm, L7=138.3cm, and L8=229.6cm.

5. The method for predicting waterlogging drainage and calculating crop yield reduction based on crop model according to claim 3, characterized in that: In S3, calculating the number of days for farmland drainage based on water and soil characteristic data includes: S31. Calculate the amount of water retained in farmland based on precipitation and runoff; S32, calculating the time required for saturated water absorption of each layer of soil according to the saturated water content correction value and saturated hydraulic conductivity of each layer of soil; The calculation formula is as follows: T n =(Sm n 0-S n 0)×L n / min(D1,…,D n ); Among them, T n It represents the time required for the nth layer of soil to absorb water to saturation, S n 0 represents the current moisture content of the nth layer of soil, L n represents the thickness of the nth soil layer, D1 represents the saturated hydraulic conductivity of the first soil layer, and D n represents the saturated hydraulic conductivity of the nth layer of soil; S33, calculating the water retention layer according to the saturated hydraulic conductivity of each layer of soil; When D n >D n+1 , then D n The corresponding nth layer of soil is recorded as the water retention layer; S34, calculating the number of days for drainage of each retention layer; The calculation formula is as follows: P n =(T1+…+T n )+{(C-Sm10-…-Sm n 0) / [min(D1,…,D n )+E×(1-lai)+ET]}; Among them, P n represents the number of days of drainage of the nth retention layer, C represents the amount of water retained in the farmland, E represents the evaporation rate of the water surface, lai represents the leaf area index and lai<1, and ET represents the canopy evaporation rate; S35. Calculate the drainage days for farmland based on the drainage days for each retention layer.

6. A system for waterlogging prediction and crop yield reduction calculation based on a crop model, used to implement a method for waterlogging prediction and crop yield reduction calculation based on a crop model as described in any one of claims 1 to 5, characterized in that: The system includes the following modules: Data acquisition module, used to obtain meteorological data, agricultural data and soil data; A water and soil characteristic calculation module, connected to the data acquisition module, for calculating water and soil characteristic data according to soil data; wherein the water and soil characteristic data include saturated water content, wilting point, field holding capacity and saturated hydraulic conductivity; A drainage days calculation module, connected to the water and soil characteristics calculation module, is used to calculate the farmland drainage days based on the water and soil characteristics data; A mortality rate calculation module, connected to the drainage days calculation module, is used to calculate the crop root mortality rate according to the farmland drainage days; The output module is connected to the data acquisition module, the water and soil characteristic calculation module and the mortality calculation module, and is used to obtain the crop yield reduction percentage based on meteorological data, farming data, water and soil characteristic data and crop root mortality rate.

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