Rain-fed agricultural area drought index construction and application method, equipment and medium

Abstract

The invention discloses a rain-fed agricultural area drought index construction and application method, equipment and a medium. The rain-fed agricultural area drought index construction and application method comprises the following steps: S1, determining drought influence coefficients of crops in different growth stages; s2, constructing an APSIM model, and carrying out parameter calibration and model verification; s3, the APSIM model dynamically simulates the phenological period, the yield and soil water in the growing season of the crops in the sufficient irrigation and rain culture mode; s4, calculating the crop yield reduction rate; s5, constructing a drought stress index relational expression including the cumulative drought stress degree, the cumulative drought stress index and the standardized cumulative drought stress index; s6, based on the dynamic simulation result of the APSIM model, the crop yield reduction rate and the accumulated drought stress index are calculated in a fitting mode. According to the method, the soil humidity data acquisition problem is considered, the sensitivity difference of different growth stages to drought is also considered, and accurate quantitative evaluation of the drought stress response in the whole crop growth process can be further realized.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural drought assessment, and particularly relates to a method, device, and storage medium for constructing and applying a drought index in a rainfed agricultural area based on the APSIM model. Background Art

[0002] In modern agricultural science, the construction of drought indices is crucial for assessing agricultural drought. Drought indices are usually calculated based on parameters such as regional precipitation, crop evapotranspiration, and soil moisture content to monitor and evaluate agricultural drought conditions. For example, the applicability of the standardized soil moisture index in agricultural drought monitoring has been analyzed, or a drought prediction model has been constructed using regression analysis methods based on the water sensitivity coefficients at four growth stages. However, most of them rely on traditional meteorological data and simple statistical analysis methods.

[0003] Conventional operations often focus on using historical meteorological data and soil moisture information to establish drought indices for predicting and evaluating drought events. However, this method has significant technical limitations:

[0004] (1) It is difficult to obtain layered soil moisture data, which limits the accuracy and reliability of drought indices;

[0005] (2) Most studies tend to use regression analysis or time series analysis methods to construct drought indices, but these methods usually ignore the cumulative impact of drought on crops;

[0006] (3) During the crop growth process, many studies only focus on the drought response at a single growth stage and fail to fully consider the continuous impact of drought on crop development and yield throughout the entire growth cycle.

[0007] In addition, the improvement measures taken in the prior art to solve the above problems generally focus more on capturing the specific impact of drought on crops, but still fail to completely solve the problem of the difference in the impact of drought on different growth stages and are still insufficient when facing complex agricultural ecosystems.

[0008] Therefore, this application specifically proposes a method for constructing and applying a drought index in a rainfed agricultural area to solve the above technical problems. Summary of the Invention

[0009] The main object of the present invention is to provide a method for constructing and applying a drought index in a rain-fed agricultural area, so as to solve the technical problems of difficult acquisition of soil moisture data, non-consideration of the drought sensitivity in different growth stages, and the response of crop growth process to drought persistence in the background technology, and to be able to more accurately evaluate the drought degree of crops in different growth stages, improve the regional drought risk management ability, and provide a more comprehensive and detailed drought assessment framework, which not only takes into account the difficulty of obtaining soil moisture data, but also considers the sensitivity differences of different growth stages to drought, and is convenient for further realizing the accurate quantitative assessment of the drought stress response of the whole process of crop growth.

[0010] The present invention adopts the following technical solutions to solve the above technical problems:

[0011] A method for constructing and applying a drought index in a rain-fed agricultural area, comprising:

[0012] S1. Based on the historical experimental data of crop drought stress response, determine the drought impact coefficients of different growth stages of crops;

[0013] S2. Construct an APSIM model, collect crop data, meteorological data, and soil data in the historical experimental data of the research area, and calibrate the parameters and verify the APSIM model;

[0014] S3. Collect and input the historical meteorological data of a specified time period to drive the calibrated and verified APSIM model, dynamically simulate the phenological period, yield, and soil water during the growing season of crops under fully irrigated and rain-fed modes through the APSIM model, and take their changes as output variables;

[0015] S4. Calculate the crop yield reduction rate according to the crop yields obtained by dynamic simulation under fully irrigated and rain-fed modes within a specified time period;

[0016] S5. Based on the drought impact coefficients and combined with the available soil water data, successively construct the drought stress index relationships including the cumulative drought stress degree, cumulative drought stress index, and standardized cumulative drought stress index;

[0017] S6. Based on the crop yields and the results of soil water during the growing season dynamically simulated by the APSIM model, substitute them into the drought stress index relationship for calculation, and fit and calculate the relationship between the crop yield reduction rate and the cumulative drought stress index, so as to evaluate the performance of the constructed drought index for monitoring the drought impact on crops.

[0018] Preferably, the calculation formula for determining the drought impact coefficient in step S1 is:

[0019]

[0020] Among them, k = 1, 2, 3, 4 represent the emergence - jointing stage, jointing - flowering stage, flowering - filling stage, and filling - maturity stage respectively, and b k is the sensitivity coefficient of drought response of crops in different growth stages in historical experimental data.

[0021] Preferably, in the S2 step, the data of the study area includes historical meteorological data, crop cultivation management data, soil data, and layered soil moisture content data, where:

[0022] The meteorological data includes the daily maximum temperature, minimum temperature, rainfall, and sunshine hours;

[0023] The cultivation management data includes the main phenological periods of crops (jointing stage, initial flowering stage, flowering stage, filling stage, maturity stage), yield, irrigation time, irrigation water volume, fertilization time, fertilization amount, and sowing density;

[0024] The soil data includes the wilting coefficient, field water holding capacity, saturated water content, bulk density, pH, and organic matter content of layered soil.

[0025] Preferably, in the S4 step, the crop yield reduction rate is defined as the ratio of the yield difference between full irrigation and rainfed conditions in the current year to the crop yield under full irrigation conditions, and the formula for calculating the crop yield reduction rate is:

[0026]

[0027] Among them, Y reduce is the crop yield reduction rate in the specified time period, Yield f is the simulated yield under full irrigation, Yield r is the simulated yield in the rainfed mode.

[0028] Preferably, in the S5 step, the available soil water is the soil water content between the field water holding capacity and the wilting coefficient, and there is:

[0029] θ p = θ f - θ w

[0030] Among them, θ p is the available soil water, θ f is the field water holding capacity, θ w is the wilting coefficient.

[0031] Preferably, the construction process of the cumulative drought stress degree in the S5 step includes:

[0032] Combining the deficit degree of available soil water and the cumulative impact of drought during the crop growth and development process, establish a formula for calculating the cumulative drought stress degree, which is:

[0033]

[0034] Among them, k = 1, 2, 3, 4 represent the emergence - jointing stage, jointing - flowering stage, flowering - filling stage, and filling - maturity stage, and t k is the growth days corresponding to the growth stage k, and DS i,j is the drought stress degree on the i - th day of the j - th year's crop growth stage, and θ s is the lower limit of the suitable soil moisture content, taking 75% of the field capacity.

[0035] Preferably, the calculation formula of the cumulative drought stress index in the step S5 is:

[0036] NADSI j = α1ADSI j,1 + α2ADSI j,2 + α3ADSI j,3 + α4ADSI j,4

[0037] Among them, NADSI j is the cumulative drought stress index within the j - th year's crop growth season, and α1, α2, α3, α4 respectively represent the drought impact coefficients in the emergence - jointing stage, jointing - flowering stage, flowering - filling stage, and filling - maturity stage; ADSI j,1 , ADSI j,2 , ADSI j,3 , ADSI j,4 are respectively the cumulative drought stress degrees in the emergence - jointing stage, jointing - flowering stage, flowering - filling stage, and filling - maturity stage of the j - th year's crop.

[0038] Preferably, the calculation formula of the standardized cumulative drought stress index in the step S5 is:

[0039]

[0040] j Among them, SNADSI is the calculated value of the standardized cumulative drought stress index in the j - th year,

[0041] On the other hand, the present invention also discloses a computer - readable storage medium storing a computer program, and when the computer program is executed by a processor, the processor is made to execute the steps of the above - mentioned method.

[0042] On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0043] It can be seen from the above technical solution that the present invention provides a method for constructing and applying a drought index in rain-fed agricultural areas. Compared with the prior art, the present invention has the following advantages:

[0044] 1. The present invention can quantify the sensitivity of each growth stage to drought by setting the influence coefficient of drought stress at different growth stages in multi-year field experiments, further ensure the reliability of the index construction process and crop model simulation, and thus accurately evaluate the impact of drought on crop yield.

[0045] 2. The present invention can dynamically simulate the impact of drought on different crops in different regions by setting the changing responses of meteorological conditions, soil characteristics and management measures in the calibrated and verified crop model, which facilitates the adjustment and verification of crop model parameters and ensures the reliability of the index construction process and crop model simulation.

[0046] 3. The present invention uses a daily-scale step size to simulate the physiological and biochemical parameters and structural parameters in the process of crop growth and development, which can achieve continuous response of different growth stages and yields of crops to crop growth environmental conditions over many years. At the same time, it is also widely used in the simulation of different crops at a regional or even global scale, which is convenient for further evaluating the impact of drought on crop production, improving the breadth of drought assessment, and is easy to promote and apply.

[0047] 4. The present invention can accurately describe the response mechanism of crop drought process by introducing drought stress influence coefficients at different growth stages, so as to accurately describe the impact of drought on crop production. Compared with the traditional cumulative drought stress degree ADS I, the cumulative drought stress index NADS I proposed in the present invention significantly improves the correlation coefficient between yield and yield reduction rate, as well as the determination coefficient of the linear regression expression, indicating that the newly constructed drought index is more accurate and effective in assessing the negative impact of drought on wheat production.

[0048] 5. The present invention not only takes into account the difficulty in obtaining soil moisture data, but also takes into account the differences in sensitivity to drought at different growth stages, thereby facilitating further accurate quantitative assessment of drought stress response throughout the entire crop growth process.

[0049] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become easy to understand through the following description. Of course, it is not necessary to achieve all of the advantages described above simultaneously for any product implementing the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings of the specification, which form a part of this application, are used to provide a further understanding of the present invention. The schematic embodiments and descriptions thereof of the present invention are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0051] Figure 1 is a flowchart of the method for constructing a drought index based on field experiments and crop models according to the present invention;

[0052] Figure 2 is a comparison diagram of the correlation between the cumulative drought stress degree and the multi-year crop yield according to the present invention;

[0053] Figure 3 is a comparison diagram of the correlation between the cumulative drought stress index and the multi-year crop yield according to the present invention;

[0054] Figure 4 is a schematic diagram of the regression analysis of the multi-year crop yield reduction rate and the constructed drought index according to the present invention;

[0055] Figure 5 is a fitting schematic diagram of the multi-year wheat yield reduction rate and the standardized cumulative drought stress index according to the present invention. Detailed implementation manners

[0056] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention.

[0057] In the embodiments, refer in detail to Figures 1 to 5 .

[0058] The method for constructing and applying a drought index in a rain-fed agricultural area proposed in the embodiments of the present invention is implemented based on the APSIM model. It not only takes into account the difficulty in obtaining soil moisture data but also considers the differences in drought sensitivity at different growth stages, facilitating the further accurate quantitative assessment of the drought stress response throughout the entire growth process of crops. Further, in this embodiment, wheat crops are taken as an example. As Figure 1 shown, the method of this application specifically includes the following steps:

[0059] Step S1. Based on a five-year wheat field drought experiment (wheat field experiment), establish a mathematical relationship between the dry matter weight of wheat and the degree of drought stress under drought conditions at different growth stages, quantify the impact of drought at different growth stages on wheat production, and determine the drought impact coefficient α of different growth stages of wheat, based on the experimental data of drought stress response of wheat in three seasons or more in the previous two years. k (k = 1, 2, 3, 4).

[0060] Among them, the wheat drought treatment experiment is a field experiment carried out under different growth stages and different drought intensities. Different growth stages include the emergence-jointing stage, jointing-flowering stage, flowering-filling stage, and filling-maturity stage of wheat; the drought intensity of wheat is controlled by setting different irrigation lower limits, mainly including 55% DUL (mild drought) and 35% DUL (severe drought).

[0061] Therefore, based on the field experiment data of different drought degrees at different growth stages of wheat in two years, taking the wheat drought stress degree as the independent variable and the wheat dry matter growth rate as the dependent variable, establish a regression relationship to quantify the sensitivity of wheat production to different drought intensities at different growth stages, and define the coefficient in the regression formula as the sensitivity coefficient b. k , calculate the drought stress impact coefficient α of each growth stage according to the sensitivity of wheat to drought at different growth stages. k , there is:

[0062]

[0063] Among them, k = 1, 2, 3, 4 represent the emergence-jointing stage, jointing-flowering stage, flowering-filling stage, and filling-maturity stage respectively; b k is the sensitivity coefficient of drought response at different growth stages.

[0064] At this time, the drought sensitivity coefficient b k and the drought impact coefficient α k The calculation results are shown in the following table:

[0065] Table of drought impact coefficients at different growth stages of wheat:

[0066]

[0067] At this time, by setting the drought stress impact coefficients at different growth stages in multi-year field experiments, the drought sensitivity of each growth stage can be quantified, further ensuring the reliability of the index construction process and crop model simulation, so as to accurately evaluate the impact of drought on crop yield.

[0068] Specifically, the simulation of the wheat growth process based on the APSIM model not only includes phenological period and yield prediction, but also covers the analysis of the dynamic changes of soil moisture, which enables the method of the present invention application to further construct a drought index on the basis of considering the degree of effective water deficit and the cumulative impact of drought stress, and also improve the accuracy and applicability of crop drought monitoring.

[0069] Step S2. Collect basic data such as historical meteorological data, wheat cultivation management data, soil data, and multi-year measured layered soil moisture content data in the wheat crop research area. Based on the field experiment data of the last three years, calibrate the parameters of the APSIM model and verify the model through genetic algorithm or trial and error method. Mainly according to the root mean square error (RMSE) and the coefficient of determination (R 2 ) to reflect the simulation performance of the crop model on the growth status and yield of wheat in Huaibei area.

[0070] At this time, the output variables simulated by the model include the jointing stage, booting stage, flowering stage, filling stage, maturity stage of wheat in three years, wheat yield, and the change of soil moisture content in the root soil layer during the growth season.

[0071] Among them, the meteorological data includes daily maximum temperature, minimum temperature, rainfall, sunshine hours, etc.; the cultivation management data includes the main phenological periods of wheat (jointing stage, initial flowering stage, flowering stage, filling stage, maturity stage), yield, irrigation time, irrigation water volume, fertilization time, fertilization amount, and sowing density, etc.; the soil data includes the wilting coefficient, field water holding capacity, saturated water content, bulk density, pH, organic matter content, etc. of the layered soil.

[0072] Step S3. Collect and input multi-year historical meteorological data, drive the calibrated and verified APSIM model, and dynamically simulate the phenological periods, long-time series soil water and yield changes of wheat crops in multiple years under fully irrigated and rain-fed modes respectively, and use them as output variables. The output variables mainly include the phenological periods of wheat in multiple years, rain-fed yield, soil moisture content, etc.

[0073] At this time, the historical meteorological data collected is the daily meteorological data of the specified year, mainly including daily maximum temperature, minimum temperature, rainfall, sunshine hours, etc. Therefore, after converting the sunshine hours into solar radiation through the Angstrom equation, by inputting the daily historical meteorological data of the experimental station for multiple years, the calibrated and verified APSIM model can be driven.

[0074] By setting the change responses of meteorological conditions, soil characteristics, and management measures in a calibrated and verified crop model, the drought impacts on different crops in different regions can be dynamically simulated, facilitating the adjustment and verification of crop model parameters, ensuring the reliability of the index construction process and crop model simulation. This method not only improves the comprehensiveness of drought assessment but also is easy to promote and apply. For example, during the calibration and verification process, the later field trial data are used for the adjustment and verification of crop model parameters, ensuring the reliability of the index construction process and crop model simulation.

[0075] Step S4. Simulate the dynamic changes in the long-term sequence of wheat yields under fully irrigated conditions, and take it as the fully irrigated yield. Then, based on the wheat yields over the years under fully irrigated and rainfed modes, calculate the annual wheat yield reduction rate. The crop yield reduction rate is defined as the ratio of the yield difference between the fully irrigated and rainfed conditions in the current year to the wheat yield under fully irrigated conditions, as follows:

[0076]

[0077] Where, Y reduce is the annual wheat yield reduction rate, Yield f and Yield r are the simulated yields under fully irrigated and rainfed modes, respectively.

[0078] Step S5. Based on the degree of deficit of soil available water and the stage drought impact coefficient, through the drought stress response, successively construct the drought stress index relationships including the cumulative drought stress degree, cumulative drought stress index, and standardized cumulative drought stress index. The soil available water, which is also the soil water, specifically refers to the soil water that can be utilized by crop roots (the amount of water in the soil that can be absorbed and utilized by crops), that is, the soil water content between the field capacity and the wilting coefficient, generally quantified as the difference between the field capacity and the wilting coefficient, as follows:

[0079] θ p = θ f - θ w

[0080] Where, θ p is the available soil water, θ f is the field capacity, θ w is the wilting coefficient;

[0081] For crops, when the water content is around the field capacity, crop production is better. When it is around the wilting coefficient, it indicates that the crops are severely drought-stressed and even wilted;

[0082] Furthermore, the construction process of the drought stress index relationship is specifically as follows:

[0083] Step S51. Considering the degree of deficit of effective soil water and the cumulative impact of drought during wheat growth and development, an ADSI is established. j ,have:

[0084]

[0085]

[0086] Among them, DS i,j is the drought stress degree of the crop in the jth year and the i-th day of the crop growth stage, j and i represent the i-th day of the crop growth stage in the jth year, respectively; θ f is the field water capacity; θ s As the lower limit of soil moisture content, 0.75θ is taken based on many years of experiments. f θ w is the wilting coefficient; k = 1, 2, 3, 4 represents the seedling-jointing stage, jointing-flowering stage, flowering-grain filling stage, grain filling-maturity stage, t k is the number of growth days corresponding to growth stage k.

[0087] Step S52: Since the impact of drought on wheat production varies at different growth stages, the drought impact coefficient α at different growth stages calculated in (1) is i (i=1,2,3,4) and cumulative drought stress index ADSI j , construct the cumulative drought stress index NADSI j ,have:

[0088] NADSI j =α1ADSI j,1 +α2ADSI j,2 +α3ADSI j,3 +α4ADSI j,4

[0089] Among them, NADSI j is the cumulative drought stress index in the crop growing season of the jth year, ADSI j,1 is the cumulative drought stress degree of the crop from seedling to jointing stage in year j, ADSI j,2 is the cumulative drought stress degree of the crop from jointing to flowering stage in year j, ADSI j,3 is the cumulative drought stress degree of the crop during the flowering-grain filling stage in year j, ADSI j,4 is the cumulative drought stress degree of the crop in the filling-maturity stage in the jth year, α1, α2, α3, and α4 represent the drought impact coefficients in the seedling-jointing stage, jointing-flowering stage, flowering-filling stage, and filling-maturity stage, respectively.

[0090] Step S53. Considering the probabilities of occurrence of different drought levels over the years and the applicability of drought assessment, to facilitate the classification of drought levels, a standardized cumulative drought stress index NADSI is constructed j , there is:

[0091]

[0092] where SNADSI j is the calculated value of the standardized cumulative drought stress index in the jth year, is the mean value of the multi-year cumulative drought stress index, and σ is the standard deviation of the multi-year cumulative drought stress index.

[0093] Step S6. Output the daily-scale soil moisture content and rainfed yield during the growth process of wheat crops through the multi-year dynamic simulation results of the APSIM model, and calculate the long-term cumulative drought stress degree ADSI j , cumulative drought stress index NADSI h , standardized cumulative drought stress index SNADSI h ;

[0094] Finally, perform correlation analysis and statistical regression on the wheat yields and yield reduction rates in the historical period with ADSI and NADSI, compare the correlation coefficient r and the determination coefficient R 2 , and fit the relationship between the wheat crop yield reduction rate and the cumulative drought stress index for evaluating the performance of the constructed drought index in monitoring the impact of crop drought.

[0095] At this time, by adopting a daily-scale time step to simulate the physiological and biochemical parameters and structural parameters during the growth and development process of crops, the calibrated and verified crop model can realize the continuous response of crop different growth stages and yields to crop growth environmental conditions over the years. At the same time, it is also widely used in the simulation of different crops at the regional and even global scales, which is convenient for further evaluating the impact of drought on crop production, improving the universality of drought assessment, and being easy to promote and apply. Therefore, this method is particularly suitable for the long-term monitoring and early warning system of regional agricultural drought, providing a scientific basis and technical support, helping to improve the regional drought disaster risk management ability, and ensuring food security and ecological security.

[0096] In summary, by introducing the drought stress impact coefficients at different growth stages, the response mechanism of crops to drought stress can be accurately described, so as to accurately describe the impact of drought on crop production. Compared with the traditional accumulated drought stress degree ADSI, the cumulative drought stress index NADSI proposed by this method takes into account the effective water deficit degree and the cumulative impact of drought stress, improves the accuracy and applicability of crop drought monitoring, and significantly increases the correlation coefficient between yield and yield reduction rate, as well as the determination coefficient of the linear regression expression. This indicates that the newly constructed drought index is more accurate and effective in evaluating the negative impact of drought on wheat production. Therefore, the present invention provides an important technical support for the prevention and response to drought disasters, which is conducive to promoting the sustainable development of agriculture.

[0097] In a specific implementation process, referring to Figure 2 and Figure 3 , the correlations between wheat yields over the years and the above two drought indices were analyzed. Among them, the correlation coefficient between wheat yield and the accumulated drought stress degree is 0.685, and the correlation coefficient between wheat yield and the cumulative drought stress index NADSI j is 0.804; the results show that constructing a drought index (cumulative drought stress index) considering the impact coefficients of drought stress at each growth stage can more accurately reflect the changes in wheat yield.

[0098] Referring to Figure 4 , the regression relationships between the wheat yield reduction rates over the years and the accumulated drought stress degree ADSI j and the cumulative drought stress index NADSI j were further analyzed, and the fitting results are shown in the following table:

[0099] Table of fitting results of wheat yield and yield reduction rate with the constructed cumulative drought stress index:

[0100]

[0101]

[0102] Specifically, compared with the accumulated drought stress degree ADSI j , the cumulative drought stress index NADSI j constructed considering the drought impact coefficients at each growth stage has a determination coefficient R 2 increased from 0.520 to 0.684.

[0103] Referring to Figure 5 , specifically, considering the probabilities of occurrence of different drought levels over the years to strengthen the applicability of drought assessment, the cumulative drought stress index is further improved to a standardized cumulative drought stress index.

[0104] The wheat yields over the years and the standardized cumulative drought stress index SNADSIj Significant correlation, determination coefficient R of the fitting equation 2 The wheat yield reduction rate over the years is also related to the standardized cumulative drought stress index SNADSI j Significant correlation, determination coefficient R of the fitting equation 2 The standardized cumulative drought stress index follows a normal distribution (μ=0, σ=1). According to the drought level classification, the separation between each drought level is 1σ, 1.5σ, and 2.0σ of the sequence data, which can be divided into light drought, moderate drought, severe drought, and extreme drought.

[0105] Therefore, the present invention combines the results of many years of field tests, and on the basis of determining the influence coefficient of drought on wheat production at different growth stages, based on the dynamic simulation of the wheat growth process (phenological period), yield and soil water by the APSIM model, constructs a cumulative drought stress index and a standardized cumulative drought stress index, and analyzes the drought assessment ability of the drought index constructed by the present invention through correlation coefficient and regression analysis method. The results show that compared with the cumulative drought stress degree ADSI, the correlation coefficient between yield and the cumulative drought stress index NADSI is increased from 0.685 to 0.804, and the correlation coefficient between wheat yield reduction rate and the cumulative drought stress index NADSI is increased from 0.721 to 0.827; compared with the cumulative drought stress degree ADSI, the determination coefficient of the linear regression expression of yield and cumulative drought stress index NADSI is increased from 0.469 to 0.646, and the determination coefficient of the linear regression expression of wheat yield reduction rate and cumulative drought stress index NADSI is increased from 0.520 to 0.684.

[0106] On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0107] On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0108] In another embodiment provided in the present application, a computer program product comprising instructions is also provided, which, when executed on a computer, enables the computer to execute any of the methods for constructing and applying drought indexes for rain-fed agricultural areas in the above-mentioned embodiments.

[0109] It is understandable that the system provided by the embodiment of the present invention corresponds to the method provided by the embodiment of the present invention, and the explanation, examples and beneficial effects of the relevant contents can refer to the corresponding parts in the above method.

[0110] An embodiment of the present application also provides an electronic device, including a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete communication with each other through the communication bus.

[0111] The memory is used to store a computer program.

[0112] When the processor is used to execute the program stored in the memory, it realizes the above-mentioned method for constructing and applying the drought index in the rain-fed agricultural area.

[0113] The communication bus mentioned in the above electronic device can be a peripheral component interconnect standard bus or an extended industry standard architecture bus, etc. This communication bus can be divided into an address bus, a data bus, a control bus, etc.

[0114] The communication interface is used for communication between the above electronic device and other devices.

[0115] The memory may include a random access memory, and may also include a non-volatile memory, such as at least one disk memory. Optionally, the memory may also be at least one storage device located far from the aforementioned processor.

[0116] The above-mentioned processor may be a general-purpose processor, including a central processing unit, a network processor, etc.; it may also be a digital signal processor, an application-specific integrated circuit, a field programmable gate array, or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0117] It should also be noted that the electronic device further includes a terminal device, which can also be referred to as a terminal, a user equipment, a mobile station, a mobile terminal, etc. The terminal device can be a mobile phone, a smart TV, a wearable device, a tablet computer, a computer with wireless transceiver function, a virtual reality terminal device, an augmented reality terminal device, a wireless terminal in industrial control, a wireless terminal in unmanned driving, a wireless terminal in remote surgery, a wireless terminal in smart grid, a wireless terminal in transportation safety, a wireless terminal in smart city, a wireless terminal in smart home, and so on. The specific technologies and specific device forms adopted by the terminal device in the embodiments of the present application are not limited.

[0118] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from a website, computer, server, or data center to another website, computer, server, or data center by wire (such as coaxial cable, optical fiber, digital subscriber line) or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that the computer can access, or a data storage device such as a server or data center that includes one or more integrated available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid-state drive), etc.

[0119] The foregoing is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

[0120] In addition, it should be noted that if there are directional indications (such as up, down, left, right, front, back...) involved in the embodiments of the present invention, the directional indications are only used to explain the relative positional relationship and movement conditions between components in a specific posture. If the specific posture changes, the directional indications will also change accordingly.

[0121] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" can explicitly or implicitly include at least one such feature. In addition, the meaning of "and / or" appearing throughout the text includes three parallel scenarios. Taking "A and / or B" as an example, it includes scenario A, scenario B, or the scenario where A and B are satisfied simultaneously. In addition, the technical solutions between various embodiments can be combined with each other, but it must be based on the ability of those of ordinary skill in the art to implement. When the combination of technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the protection scope required by the present invention.

Claims

1. A method for constructing and applying a drought index in a rainfed agricultural area, characterized in that, Including: S1. Based on historical experimental data, determine the drought impact coefficients at different growth stages of crops; S2. Construct an APSIM model, collect data in the study area, and calibrate the parameters and validate the APSIM model; S3. Input historical meteorological data for a specified time period, and dynamically simulate the phenological periods, yields of crops, and soil water during the growing season under full irrigation and rainfed modes through the APSIM model; S4. Calculate the crop yield reduction rate according to the crop yields in the specified time period under full irrigation and rainfed modes; S5. Based on the drought impact coefficients and combined with available soil water data, construct relationships of drought stress indices including cumulative drought stress degree, cumulative drought stress index, and standardized cumulative drought stress index; S6. Based on the dynamic simulation results of the APSIM model, fit and calculate the crop yield reduction rate and the cumulative drought stress index.

2. The method for constructing and applying the drought index in the rain-fed agricultural area according to claim 1, wherein, The calculation formula for determining the drought impact coefficient in step S1 is: Among them, k = 1, 2, 3, 4 represent the emergence-jointing stage, jointing-flowering stage, flowering-filling stage, and filling-maturity stage respectively, and b k is the sensitivity coefficient of drought response of crops at different growth stages in historical experimental data.

3. The method for constructing and applying the drought index in the rain-fed agricultural area according to claim 1, characterized in that The data in the study area in step S2 includes historical meteorological data, crop cultivation management data, soil data, and layered soil moisture content data, where: The meteorological data includes daily maximum temperature, minimum temperature, rainfall, and sunshine hours; The cultivation management data includes the main phenological periods, yields, irrigation times, irrigation water volumes, fertilization times, fertilization amounts, and sowing densities of crops; The soil data includes the wilting coefficient, field capacity, saturated water content, bulk density, pH, and organic matter content of layered soil.

4. The method for constructing and applying the drought index in the rain-fed agricultural area according to claim 1, wherein The calculation formula for the crop yield reduction rate in step S4 is: Among them, Y reduce is the crop yield reduction rate during the specified time period, Yield f is the simulated yield under full irrigation, Yield r is the simulated yield under rainfed mode.

5. The method for constructing and applying the drought index in the rain-fed agricultural area according to claim 1, wherein The available soil water in step S5 is the soil water content between the field capacity and the wilting coefficient, and there is: θ p = θ f - θ w where θ p is the available soil water, θ f is the field capacity, and θ w is the wilting coefficient.

6. The method for constructing and applying the drought index in the rain-fed agricultural area according to claim 5, characterized in that, The construction process of the cumulative drought stress degree in step S5 includes: Combining the deficit degree of available soil water and the cumulative impact of drought during the crop growth and development process, establish a calculation formula for the cumulative drought stress degree, which is: Among them, k = 1, 2, 3, 4 represent the emergence-jointing stage, jointing-flowering stage, flowering-filling stage, and filling-maturity stage, and t k is the number of growing days corresponding to the growth stage k, and DS i,j is the drought stress degree on the i-th day of the j-th year's crop growth stage, and θ s is the lower limit of the suitable soil moisture content.

7. The method for constructing and applying the drought index in the rain-fed agricultural area according to claim 6, wherein The calculation formula for the cumulative drought stress index in step S5 is: NADSI j = α1ADSI j,1 + α2ADSI j,2 + α3ADSI j,3 + α4ADSI j,4 Among them, NADSI j is the cumulative drought stress index during the crop growth season in the j-th year, and α1, α2, α3, and α4 represent the drought impact coefficients in the emergence-jointing stage, jointing-flowering stage, flowering-filling stage, and filling-maturity stage respectively; ADSI j,1 , ADSI j,2 , ADSI j,3 , and ADSI j,4 are the cumulative drought stress degrees in the emergence-jointing stage, jointing-flowering stage, flowering-filling stage, and filling-maturity stage of the crop in the j-th year respectively.

8. The method for constructing and applying the drought index in the rain-fed agricultural area according to claim 7, characterized in that, The calculation formula for the standardized cumulative drought stress index in step S5 is: Among them, SNADSI j is the calculated value of the standardized cumulative drought stress index in the jth year, is the mean of the historical cumulative drought stress index, and σ is the standard deviation of the historical cumulative drought stress index.

9. A computer-readable storage medium, characterized in that, There is a computer program stored, and when the computer program is executed by a processor, the processor is caused to execute the steps of the method according to any one of claims 1 to 8.

10. A computer device, characterized in that, Including a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor is caused to execute the steps of the method according to any one of claims 1 to 8.

Images