Method for evaluating influence of drought stress process and magnitude on agricultural yield

Abstract

The invention belongs to the technical field of agricultural production simulation, and discloses a method for evaluating the influence of drought stress process and magnitude on agricultural yield, which comprises the following steps of: constructing a standard scene and a plurality of drought stress scenes in a crop growth period; simulating the standard yield of the crops in the standard scene and the water shortage yield of the crops in each drought stress scene by using an agricultural production model; determining a water shortage rate and a yield reduction rate in each drought stress scene according to the standard yield and the water shortage yield; and constructing a response relation curve between the drought stress scene and the water shortage rate and the yield reduction rate so as to evaluate the influence of the drought stress process and magnitude on the agricultural yield. According to the method, the advanced agricultural production model is constructed and applied, so that the range and the number of statistical samples are effectively expanded, and the accuracy of evaluating the drought stress response mechanism of the agricultural system is improved. The method can quantify the response relationship between the regional agricultural yield and the drought stress process and magnitude, thereby revealing the loss characteristic of the agricultural yield under drought stress.

Description

Technical Field

[0001] The invention discloses an evaluation method for the influence of drought stress process and magnitude on agricultural output, and belongs to the technical field of agricultural production simulation. Background Art

[0002] As global warming continues to intensify, the problem of water shortage has become increasingly prominent, and drought events have occurred frequently and have a wide range of impacts. As one of the most common natural disasters in the world, drought poses a serious threat to human society, economy, natural environment and agricultural production. In China, agriculture, as a pillar industry of the national economy, accounts for more than 60% of the country's total water consumption, but compared with life, industry and other sectors, the guaranteed rate of agricultural water is relatively low. This situation makes it difficult for agriculture to obtain sufficient irrigation water when facing drought conditions, which in turn leads to the stunting of crop growth, or even a significant reduction in production or even a total crop failure, which seriously threatens the country's food security.

[0003] However, with existing technologies, it is difficult to collect a large amount of data on yield reduction and water shortage rates under agricultural drought stress, or there are cases where statistical data is missing. This makes it impossible for researchers to fully and accurately understand the specific impact of drought on agricultural production. Due to the lack and incompleteness of data, it has become extremely difficult to study the response relationship between agricultural yield and drought stress. Lacking sufficient data support, researchers find it difficult to establish effective models to simulate and predict the impact of drought on agricultural production, accurately identify the critical period of water demand for crops, and develop targeted irrigation strategies and disaster reduction measures, thereby effectively improving the drought resistance and sustainability of agricultural production. Summary of the invention

[0004] The core purpose of the present invention is to provide a scientific method for evaluating the impact of drought stress processes and magnitudes on agricultural yields, aiming to solve the problem of insufficient qualitative and quantitative analysis of the relationship between agricultural yields and drought stress processes and magnitudes in current research. In order to more effectively optimize water resource allocation, ensure that precious water resources are used rationally and efficiently, and reduce the losses caused by drought disasters to agricultural production, the present invention is committed to developing a calculation method that can analyze the relationship between agricultural yields and drought stress processes and magnitudes, as well as a method for determining the critical period of crop water demand. To achieve this goal, the specific evaluation scheme proposed by the present invention is as follows:

[0005] A method for evaluating the impact of drought stress process and magnitude on agricultural yields comprises the following steps:

[0006] Step 1: construct a standard scenario and multiple drought stress scenarios during the crop growth period, and use an agricultural production model to simulate the standard yield of the crop under the standard scenario and the water shortage yield under each drought stress scenario;

[0007] Step 2: determining the water shortage rate and yield reduction rate under each drought stress scenario according to the standard yield and the water shortage yield;

[0008] Step 3: Based on the water shortage rate and yield reduction rate, a response relationship curve between the drought stress scenario and the water shortage rate and yield reduction rate is constructed to evaluate the impact of the drought stress process and magnitude on agricultural yield.

[0009] Preferably, after step 3, the method further includes: determining a critical period of water demand during the growth period of the crop, wherein the critical period of water demand is used to evaluate the impact of drought stress on agricultural yield at various growth stages of the crop.

[0010] Preferably, determining the critical period of water demand during the crop growth period specifically includes:

[0011] Determine the total water shortage rate during the crop growth period and the stage water shortage rate at each growth stage under each drought stress scenario;

[0012] Determining the yield reduction rate of the crop under each drought stress scenario according to the standard yield and the water-deficient yield under each drought stress scenario;

[0013] The influence of the water shortage rate in each growth stage on the yield reduction rate under the drought stress scenario of the growth stage is analyzed, and the growth stage with the greatest influence on the yield reduction rate is recorded as the critical water demand period of the crop.

[0014] Preferably, analyzing the influence of the water shortage rate at each growth stage on the yield reduction rate under the drought stress scenario corresponding to the growth stage specifically includes:

[0015] The water shortage rate at each growth stage was used as the control variable, and variance analysis was used to determine the impact of the water shortage rate at each growth stage on the yield reduction rate under the drought stress scenario at that growth stage.

[0016] Preferably, analyzing the influence of the water shortage rate at each growth stage on the yield reduction rate under the drought stress scenario corresponding to the growth stage also includes:

[0017] According to the interaction between the water shortage rates at various growth stages, the influence of the interaction on the crop yield reduction rate is determined.

[0018] Preferably, before step 1, the method further includes: calibrating the sensitivity parameters of the agricultural production model and verifying whether the sensitivity parameters are qualified;

[0019] If not, recalibrate the sensitivity parameters until the sensitivity parameters are verified to be qualified.

[0020] Preferably, the sensitivity parameters of the agricultural production model are calibrated, specifically including:

[0021] Determine the parameters of the agricultural production model, and record the selected parameters that meet the preset requirements as sensitivity parameters;

[0022] With the goal of minimizing the difference between the actual statistical yield of crops within the calibration period and the yield simulated by the agricultural production model, the particle swarm intelligent optimization algorithm is used to calibrate the sensitivity parameters.

[0023] Preferably, multiple drought stress scenarios are constructed, specifically including:

[0024] Various drought stress scenarios are constructed based on the water supply coefficients at various growth stages during the crop growth period.

[0025] Preferably, constructing a response relationship curve between the drought stress scenario and the water shortage rate and yield reduction rate specifically includes:

[0026] With the total water shortage rate during the crop growth period as the horizontal axis and the yield reduction rate as the vertical axis, the data points of the water shortage rate and yield reduction rate under each drought stress scenario are marked;

[0027] The data points were fitted using a Logistic curve to obtain a response relationship curve between the drought stress scenario and the water shortage rate and yield reduction rate.

[0028] Preferably, the agricultural production model is an AquaCrop-OSPy model, an EPIC crop growth model or an APSIM model.

[0029] Beneficial effects: (1) When it is difficult to obtain a large amount of statistical data on yield reduction and water shortage under agricultural drought stress, or when statistical data is missing, the present invention can be used to construct and apply an agricultural production model to simulate the changes in agricultural yield in a region under different drought conditions. This method can not only effectively increase the number of statistical samples, but also more accurately evaluate the response mechanism of the agricultural system to drought stress.

[0030] (2) According to the present invention, by quantifying the response relationship between regional agricultural yield and different drought stress processes and scenarios, it is possible to deeply reveal the loss characteristics of agricultural yield under drought stress, determine the critical period of water demand for crop production, and provide a solid scientific basis for formulating regional drought resistance and disaster reduction strategies. This method can not only conduct a preliminary assessment of the impact of drought on agricultural yield, but also provide comprehensive services and support for regional disaster prevention and reduction work. This will help improve the drought resistance and sustainability of agricultural production, ensure national food security, and promote the stable development of social economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a flow chart of agricultural yield simulation under different drought stress scenarios in an embodiment of the present invention;

[0032] Figure 2The parameter sensitivity analysis results of winter wheat and summer corn in the embodiments of the present invention;

[0033] Figure 3 Schematic diagram of the response relationship between winter wheat and summer corn yields to different drought stress processes and magnitudes in an embodiment of the present invention, wherein (a) represents winter wheat and (b) represents summer corn;

[0034] Figure 4 It is a schematic diagram of the Logistic curve function of the present invention. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solution and advantages of the present invention more clear, the present invention is further described in detail below in conjunction with specific implementation methods. It should be understood that the specific implementation methods described here are only used to explain the present invention and do not limit the scope of protection of the present invention.

[0036] The present invention proposes a method for calculating and analyzing the relationship between agricultural yield and drought stress process and magnitude response, aiming to quantify the relationship between the two, analyze the characteristics of agricultural yield loss, and provide scientific guidance for the formulation of drought resistance and disaster reduction measures in the region. The method specifically comprises the following steps:

[0037] A method for assessing the impact of drought stress on agricultural yields, such as Figure 1 As shown, the process is as follows:

[0038] Before entering the evaluation process, the following pre-processing steps are required:

[0039] Preprocessing step: Select a suitable agricultural production model, calibrate the sensitivity parameters of the agricultural production model, and verify whether it is qualified. The specific approach is to first determine the parameter range of the agricultural production model, and select the parameters that meet the preset requirements as sensitivity parameters. Then, with the goal of minimizing the difference between the actual statistical yield of crops during the calibration period and the model simulated yield, the particle swarm optimization algorithm (PSO) is used to optimize and adjust these sensitivity parameters. If the verification result shows that the sensitivity parameter is unqualified, it needs to be re-calibrated until all sensitivity parameters pass the verification.

[0040] Among them, the selection of agricultural production model can be carried out according to actual needs, such as AquaCrop-OSPy model, EPIC crop growth model or APSIM model, etc., and those skilled in the art can decide according to specific circumstances. In this embodiment, AquaCrop-OSPy model is selected as agricultural production model, and Jinghuiqu irrigation area is used as research area, and summer corn and winter wheat are used as research objects.

[0041] In this embodiment, calibrating the model parameters specifically includes, first, calculating the field water holding capacity, saturated water content, saturated hydraulic conductivity and wilting water content parameters in the AquaCrop-OSPy model.

[0042] Specifically, the meteorological element data (rainfall, temperature, potential evapotranspiration, etc.), soil type and soil texture of the Jinghuiqu irrigation area are collected; as well as the statistical yield of summer corn and winter wheat, the main crops in the study area.

[0043] SPAW software was used to calculate the field water holding capacity, saturated water content, saturated hydraulic conductivity and wilting water content in the Jinghuiqu irrigation area.

[0044] Secondly, the AquaCrop-OSPy model parameters related to the crop types and growth characteristics in the Jinghuiqu irrigation area were screened.

[0045] Collection of crop reference manuals from the Food and Agriculture Organization of the United Nations (FAO).

[0046] Parameters related to the main crops (summer corn and winter wheat) and growth characteristics in the Jinghuiqu irrigation area were selected from the crop reference manual, as shown in Table 1.

[0047] Table 1 Model parameters

[0048]

[0049] Again, sensitivity analysis was performed on the screened AquaCrop-OSPy model parameters, and parameters with higher sensitivity were selected.

[0050] Specifically, according to the OAT sensitivity analysis method, the size of one parameter is changed each time, while the sizes of other parameters remain unchanged, and the agricultural output is calculated by inputting it into the AquaCrop-OSPy model. Figure 2 Shown are the parameter sensitivity analysis results of winter wheat and summer corn in this example.

[0051] Calculate the relative sensitivity of each parameter , the formula is as follows:

[0052] Δ k = | [ f ( d + Δ d ) − f ( d ) ] / f ( d ) Δ d / d |

[0053] Where:

[0054] Indicates the relative sensitivity of the parameter;

[0055] Represents parameters;

[0056] Representation parameters The amount of change;

[0057] and Respectively represent parameters Target value before and after the change.

[0058] According to the relative sensitivity calculation formula, parameters with relative sensitivity greater than 0 are screened out as sensitivity parameters.

[0059] Then, the sensitivity parameters were calibrated with the goal of minimizing the difference between the actual statistical yields of winter wheat and summer corn and the simulated yields of their respective AquaCrop-OSPy models during the calibration period.

[0060] Specifically, based on the rainfall data of the Jinghuiqu irrigation area, consecutive years with annual rainfall changes are selected as the rate period.

[0061] The particle swarm intelligent optimization algorithm is used to calibrate the selected sensitivity parameters with the goal of minimizing the difference between the actual statistical yield of crops within the calibration period and the simulated yield of the AquaCrop-OSPy model.

[0062] The statistical yield of crops during the verification period and the yield simulated by the AquaCrop-OSPy model are used to calculate the evaluation indicators to determine whether the model calibration is qualified.

[0063] Finally, several years were randomly selected as validation periods.

[0064] Specifically, the calibration results of the AquaCrop-OSPy model parameters were input into the model to simulate the agricultural output during the validation period.

[0065] Specifically, the root mean square error (RMSE) and the standard root mean square error (NRMSE) between the crop yield and the simulated yield during the validation period are calculated to determine whether the simulation calibration is qualified. The calculation formulas for the root mean square error (RMSE) and the standard root mean square error (NRMSE) are as follows:

[0066]

[0067]

[0068] Where;

[0069] indicates the total number of years used for validation;

[0070] Represents the year sequence used for validation, ;

[0071] Indicates Simulated values ​​of annual agricultural production;

[0072] Indicates Actual statistical values ​​of annual agricultural output;

[0073] express The average of the actual statistical values ​​of agricultural output during the year.

[0074] The smaller the RMSE value, the smaller the error. NRMSE < 10% indicates an excellent simulation effect; 10% ≤ NRMSE < 20% indicates a good simulation effect; 20% ≤ NRMSE < 30% indicates a fair simulation effect; NRMSE ≥ 30% indicates a poor simulation effect.

[0075] After a series of preprocessing and verification steps, the calibrated and qualified AquaCrop-OSPy model is obtained. Next, this model will be used for formal evaluation. The following are the formal evaluation steps:

[0076] Step 1: Construct a standard scenario during the crop growth period and multiple drought stress scenarios. Then, use a calibrated and qualified agricultural production model to simulate the standard yield of crops under the standard scenario and the water-deficient yield under each drought stress scenario; specifically, the standard yield of crops is simulated under the standard scenario, and the water-deficient yield of crops is simulated under each drought stress scenario.

[0077] Furthermore, a standard scenario is constructed during the crop growth period, and a variety of drought stress scenarios are constructed based on the water supply coefficients of each growth stage during the crop growth period; the agricultural production model is used to simulate the standard yield of crops under the standard scenario and the water-deficient yield under each drought stress scenario; it should be clarified that the standard scenario represents the growth environment of crops without drought stress, including the irrigation water volume and irrigation process.

[0078] Subsequently, various drought stress scenarios are constructed based on the various growth stages of the crop growth period and the reference water supply coefficient. These scenarios represent the irrigation process and irrigation water volume of crops under drought stress of different degrees and durations. In this embodiment, the various growth stages of the crop growth period are statistically analyzed by the number of months in the crop growth period ( )get.

[0079] In this embodiment, the meteorological data of the Jinghuiqu irrigation area is used to calculate the multi-year average daily meteorological data and deduct the daily rainfall to form the meteorological data input of the AquaCrop-OSPy model. The net irrigation mode in the AquaCrop-OSPy model is selected, that is, irrigation when there is water shortage, so as to simulate the yield of crops without drought stress and the monthly water supply process (monthly irrigation amount). In this embodiment, this is defined as the standard scenario and the standard yield under the standard scenario.

[0080] In this example, in order to construct a variety of drought stress scenarios, the monthly water supply coefficients are scaled in the interval [0,1] and combined into 10,000 different schemes. These schemes are randomly generated in a 10,000×n matrix in the interval [0,1] by the Latin hypercube sampling method, and each row of the matrix represents a water supply coefficient scaling combination scheme.

[0081] According to these water supply coefficient combination schemes, the monthly irrigation volume was scaled accordingly, thus obtaining a variety of agricultural irrigation regimes with different water supply processes and water shortage rates, that is, a variety of different drought stress scenarios representing different drought stress processes and magnitudes.

[0082] The irrigation schedules of the 10,000 different drought stress scenarios generated above were input into the calibrated AquaCrop-OSPy model to simulate the crop yields under different drought stress scenarios, that is, the water-deficient yields under various drought stress scenarios.

[0083] This embodiment uses the AquaCrop-OSPy model of summer corn and winter wheat respectively to simulate the yield of summer corn and winter wheat in the Jinghuiqu irrigation area.

[0084] Step 2: determining the water shortage rate and yield reduction rate under each drought stress scenario according to the standard yield and the water shortage yield;

[0085] Specifically, in this embodiment, after obtaining the standard scenario and the standard yield under the standard scenario, as well as the water shortage yield under various drought stress scenarios, the water shortage rate and yield reduction rate are calculated, and the water shortage rate of different scenarios is calculated. and reduction rate It should be noted that, in this embodiment, the above water shortage rate represents the total water shortage rate under each drought stress scenario.

[0086]

[0087]

[0088] Where:

[0089] Indicates water shortage rate, %;

[0090] represents the irrigation water volume without drought stress, that is, the irrigation volume under the standard scenario;

[0091] It represents the irrigation water volume under drought stress, that is, the irrigation volume under drought stress scenario;

[0092] Represents the production reduction rate, %;

[0093] It represents the yield without drought stress, i.e. standard yield;

[0094] It indicates the yield under drought stress, that is, water deficit yield.

[0095] Step 3: Based on the water shortage rate and yield reduction rate, a response relationship curve between the drought stress scenario and the water shortage rate and yield reduction rate is constructed to evaluate the impact of the drought stress process and magnitude on agricultural yield.

[0096] Furthermore, a response relationship curve between the drought stress scenario and the water shortage rate and the yield reduction rate is constructed, specifically including: taking the total water shortage rate during the crop growth period as the horizontal coordinate and the yield reduction rate as the vertical coordinate, marking the data points of the water shortage rate and the yield reduction rate under each drought stress; using a Logistic curve to fit the data points to obtain the response relationship curve between the drought stress scenario and the water shortage rate and the yield reduction rate.

[0097] Specifically, in this embodiment, Figure 3 As shown in the figure, the water shortage rate and yield reduction rate data points under each drought stress scenario are marked on the coordinate graph, with the total water shortage rate during the crop growth period as the horizontal axis and the yield reduction rate as the vertical axis. These data points reflect the changes in crop yield under different drought stress scenarios. The Logistic curve is used to fit these data points to obtain the response relationship curve between the drought stress scenario and the water shortage rate and yield reduction rate. The Logistic curve can well describe this nonlinear relationship because it has an S-shaped characteristic and can reflect the change process from the initial stage to the rapid development stage and then to the extreme stage.

[0098] In addition, if Figure 3 As shown, this embodiment also determines the upper envelope and lower envelope of the response relationship curve. The upper envelope represents the maximum loss that agricultural output may suffer under a given drought stress scenario; while the lower envelope represents the minimum loss that agricultural output may suffer under the same conditions. These two envelopes provide upper and lower limits for evaluating the impact of drought stress.

[0099] like Figure 4As shown in the figure, the S-shaped response relationship curve can be divided into three stages according to its characteristic points A, B and C: the initial stage, the rapid development stage and the stage tending to the extreme value. Through in-depth analysis of these characteristic points, important information about the impact of drought stress on agricultural yield can be obtained. Specifically, point B with the largest slope reveals the stage with the fastest increase in yield reduction rate, which is a period that requires special attention. Points A and C, where the concavity of the curve changes, respectively mark the critical state where drought losses begin to increase significantly and collapse damage may occur. These characteristic points not only deepen the understanding of the impact mechanism of drought stress, but also provide strong data support and theoretical basis for the formulation of scientific and reasonable drought resistance strategies.

[0100] In this embodiment, the fitted Logistic curve and its characteristic points are shown in the following diagram: Figure 3 As shown in the figure, the response relationship between drought stress scenario and water shortage rate and yield reduction rate is more intuitively demonstrated.

[0101] Logistic curve Figure 4 As shown, the function expression of the Logistic curve is as follows:

[0102]

[0103] Where:

[0104] Represents the production reduction rate, %;

[0105] Represents the total water shortage rate during the crop growth cycle, %;

[0106] , , They are undetermined parameters, which are determined by data fitting and jointly determine the shape, position, inflection point and other characteristics of the curve;

[0107] It usually represents the maximum possible value of the yield reduction rate (i.e. the yield reduction rate corresponding to the upper envelope), reflecting the maximum loss that agricultural output may suffer under extreme drought stress;

[0108] It is a parameter related to the position of the midpoint of the curve, affecting the position of the curve on the water shortage axis;

[0109] It is related to the steepness of the curve and affects the change in the slope of the curve.

[0110] Furthermore, after step 3, the method further includes: determining a critical period of water demand during the growth period of the crop, wherein the critical period of water demand is used to evaluate the impact of drought stress on agricultural yield at various growth stages of the crop.

[0111] Furthermore, determining the critical period of water demand during the crop growth period specifically includes:

[0112] The total water shortage rate of the crop during the growth period and the stage water shortage rate of each growth stage under each drought stress scenario are determined; the yield reduction rate of the crop under each drought stress scenario is determined according to the standard yield and the water shortage yield under each drought stress scenario; the influence of the stage water shortage rate of each growth stage on the yield reduction rate under the drought stress scenario corresponding to the growth stage is analyzed, and the growth stage with the greatest influence on the yield reduction rate is recorded as the critical water requirement period of the crop.

[0113] Furthermore, the influence of the stage water shortage rate of each growth stage on the yield reduction rate under the drought stress scenario corresponding to that growth stage is analyzed, specifically including: taking the water shortage rate of each growth stage as the control variable, and using the variance analysis method to determine the influence of the stage water shortage rate of each growth stage on the yield reduction rate under the drought stress scenario corresponding to that growth stage.

[0114] Furthermore, analyzing the influence of the water shortage rate in each growth stage on the yield reduction rate under the drought stress scenario corresponding to the growth stage also includes: determining the influence of the interaction on the crop yield reduction rate based on the interaction between the water shortage rates in each growth stage.

[0115] Specifically, in this embodiment, the monthly water shortage rate, the total water shortage rate and the production reduction rate of different scenarios are calculated.

[0116]

[0117]

[0118]

[0119] In the formula, , indicating that the crops are in the Monthly water shortage rate, %;

[0120] and Represents the crops in Monthly irrigation water volume when not under drought stress and under drought stress;

[0121] It represents the total water shortage rate during the crop growth period, %;

[0122] and They represent the total irrigation water volume when the crops are not under drought stress and when they are under drought stress;

[0123] Represents the production reduction rate, %;

[0124] and Represent the crop yield when not under drought stress and when under drought stress, respectively.

[0125] Taking the water shortage rate of each month as the control variable, the variance analysis method is used to clarify the impact of the water shortage rate of each month on crop yield, as well as the degree of influence of the interaction of the water shortage rate of each month, and the month with the greatest impact is taken as the critical period of crop water demand. Analysis of variance (ANOVA) is often used to evaluate differences between and within groups. It is a statistical method used to compare the mean differences of two or more groups. It is usually used in experimental design to determine whether different treatments or conditions have a significant effect on a certain outcome variable. When the variance ratio (P) of the independent variable is less than 0.05, it indicates that the independent variable has a significant effect on the dependent variable, and the larger the sum of squares, the greater the degree of influence.

[0126] By constructing and applying advanced agricultural production models, the present invention not only effectively expands the scope and quantity of statistical samples, but also greatly improves the accuracy of evaluating the response mechanism of agricultural systems to drought stress. This method can quantify the response relationship between regional agricultural output and various drought stress processes and specific scenarios, thereby deeply revealing the loss characteristics of agricultural output under drought stress and accurately determining the critical period of water demand for crops. It not only provides a solid and scientific basis for the formulation of regional drought resistance and disaster reduction strategies, preliminarily assesses the impact of drought on agricultural output, but also brings comprehensive services and support to regional disaster prevention and mitigation work. Ultimately, this will greatly enhance the drought resistance and sustainability of agricultural production, build a solid line of defense for national food security, and promote social and economic stability and development.

[0127] The above are only several embodiments of the present invention and are not intended to limit the present invention in any form. Although the present invention is disclosed as above in the form of a preferred embodiment, it is not intended to limit the present invention. Any technician familiar with the profession, without departing from the scope of the technical solution of the present invention, using the above disclosed technical content to make slight changes or modifications are equivalent to equivalent implementation cases and fall within the scope of the technical solution.

Claims

1. A method for evaluating the impact of drought stress process and magnitude on agricultural yield, characterized in that: The following steps are involved: Step 1: construct a standard scenario and multiple drought stress scenarios during the crop growth period, and use an agricultural production model to simulate the standard yield of the crop under the standard scenario and the water shortage yield under each drought stress scenario; Step 2: determining the water shortage rate and yield reduction rate under each drought stress scenario according to the standard yield and the water shortage yield; Step 3: Based on the water shortage rate and yield reduction rate, a response relationship curve between the drought stress scenario and the water shortage rate and yield reduction rate is constructed to evaluate the impact of the drought stress process and magnitude on agricultural yield.

2. The evaluation method according to claim 1, characterized in that: The step 3 also includes: determining the critical period of water demand during the crop growth period, wherein the critical period of water demand is used to evaluate the impact of drought stress on agricultural yield at various growth stages of the crop.

3. The evaluation method according to claim 2, characterized in that: Determine the critical water demand period during the crop growth period, including: Determine the total water shortage rate during the crop growth period and the stage water shortage rate at each growth stage under each drought stress scenario; Determining the yield reduction rate of the crop under each drought stress scenario according to the standard yield and the water-deficient yield under each drought stress scenario; The influence of the water shortage rate in each growth stage on the yield reduction rate under the drought stress scenario of the growth stage is analyzed, and the growth stage with the greatest influence on the yield reduction rate is recorded as the critical water demand period of the crop.

4. The evaluation method according to claim 3, characterized in that: Analyze the influence of water shortage rate at each growth stage on the yield reduction rate under drought stress scenario at that growth stage, including: The water shortage rate at each growth stage was used as the control variable, and variance analysis was used to determine the impact of the water shortage rate at each growth stage on the yield reduction rate under the drought stress scenario at that growth stage.

5. The evaluation method according to claim 3, characterized in that: Analyze the influence of water shortage rate at each growth stage on the yield reduction rate under drought stress scenario at that growth stage, including: According to the interaction between the water shortage rates at various growth stages, the influence of the interaction on the crop yield reduction rate is determined.

6. The evaluation method according to claim 1, characterized in that: Before step 1, the method further includes: calibrating the sensitivity parameters of the agricultural production model and verifying whether the sensitivity parameters are qualified; If not, recalibrate the sensitivity parameters until the sensitivity parameters are verified to be qualified.

7. The evaluation method according to claim 6, characterized in that: The sensitivity parameters of the agricultural production model are calibrated, including: Determine the parameters of the agricultural production model, and record the selected parameters that meet the preset requirements as sensitivity parameters; With the goal of minimizing the difference between the actual statistical yield of crops within the calibration period and the yield simulated by the agricultural production model, the particle swarm intelligent optimization algorithm is used to calibrate the sensitivity parameters.

8. The evaluation method according to claim 1, characterized in that: Construct a variety of drought stress scenarios, including: Various drought stress scenarios are constructed based on the water supply coefficients at various growth stages during the crop growth period.

9. The evaluation method according to claim 1, characterized in that: Construct the response relationship curve between drought stress scenario and water shortage rate and yield reduction rate, including: With the total water shortage rate during the crop growth period as the horizontal axis and the yield reduction rate as the vertical axis, the data points of the water shortage rate and yield reduction rate under each drought stress scenario are marked; The data points were fitted using a Logistic curve to obtain a response relationship curve between the drought stress scenario and the water shortage rate and yield reduction rate.

10. The evaluation method according to claim 1, characterized in that: The agricultural production model is an AquaCrop-OSPy model, an EPIC crop growth model or an APSIM model.

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