A model-based method for screening drought-resistant varieties of food crops

Abstract

This invention discloses a model-based method for screening drought-resistant varieties of grain crops, comprising the following steps: S1, inputting measured data corresponding to different varieties of grain crops into the model, adjusting model parameters to adapt to field measurements, and verifying the model's simulation effect; S2, fixing irrigation time in the model, arranging the total irrigation amount in an arithmetic sequence, and outputting the grain crop yield and income under different irrigation amounts; S3, inputting meteorological data from different hydrological years into the model, and outputting the grain crop yield and income under different hydrological and meteorological conditions. This invention employs the above-mentioned model-based method for screening drought-resistant varieties of grain crops, avoiding long-term field experiments and large-scale index measurements, saving manpower, material resources, and financial resources, innovating the method for screening drought-resistant varieties of grain crops, and shortening the screening time.

Description

Technical Field

[0001] This invention relates to the field of grain crop variety screening technology, and in particular to a model-based method for screening drought-resistant grain crop varieties. Background Technology

[0002] In recent years, rapid population growth and urbanization have continuously increased the demand for water resources, forcing some countries to face water shortages. Freshwater resources are crucial for human survival and make a significant contribution to achieving the development goals of various sectors in different national plans. For many years, the demand for urban, agricultural, and industrial water has been steadily increasing. However, unfortunately, the amount of water resources available to any country remains limited even with sustained economic development.

[0003] Limited water resources are a major factor restricting the production of field crops. Agriculture in arid regions is particularly constrained by water shortages. Efficient use of irrigation water and selection of drought-resistant, water-saving, and high-yield varieties are crucial for the sustainable development of agriculture and the economy.

[0004] Arid regions cover approximately 45% of the Earth's land surface, and dryland agricultural systems may constitute the world's largest biological communities, indispensable for food production. Currently, there is an urgent need for a simple, rapid, and effective drought-resistant food crop variety adapted to arid regions. This is of great significance for increasing national grain output, reducing dependence on imported grain, and enhancing national food security. However, the selection of superior varieties of different crops mainly relies on long-term field experiments and extensive index measurements. While this method can achieve the desired results, it consumes a large amount of human, material, and financial resources and cannot be completed in a short period. If uncontrollable factors cause experimental failure, the time required to achieve the intended goal will be greatly increased. In other words, the long time required and the large amount of human and financial resources consumed by traditional experimental methods necessitate finding a new, simple, rapid, and effective method to achieve the goal of selecting superior varieties. Summary of the Invention

[0005] The purpose of this invention is to provide a model-based method for screening drought-resistant varieties of grain crops, which avoids years of field experiments and extensive index measurements, saves manpower, material resources and financial resources, innovates the method for screening drought-resistant varieties of grain crops, and shortens the screening time.

[0006] To achieve the above objectives, this invention provides a model-based method for screening drought-resistant varieties of grain crops, comprising the following steps:

[0007] S1. Input the measured data corresponding to different varieties of grain crops into the model, adjust the model parameters to adapt to the measured values ​​in the field, and verify the simulation effect of the model.

[0008] S2. In the model, the irrigation time is fixed, the total irrigation amount is arranged in an arithmetic sequence, and the model outputs the crop yield and income under different irrigation amounts.

[0009] S3. In the model, by inputting meteorological data from different hydrological years in the local area, the model outputs the yield and income of grain crops under different hydrological and meteorological conditions.

[0010] S4. Conduct a comprehensive analysis of the data from steps S2 and S3.

[0011] Preferably, in step S1, the data includes yield, meteorological conditions, farmland management, and soil moisture content.

[0012] Preferably, in step S4, the analysis method is as follows:

[0013] Introducing a comprehensive index that takes into account both output and economic factors—Comprehensive Index II:

[0014]

[0015]

[0016] II = 0.5 × AF + 0.5 × VCR

[0017] AF and VCR represent agronomic and economic factors, respectively. After standardizing these two components, a comprehensive index II is calculated with weights of 0.5 and 0.5, respectively. In the formula, WUE is water use efficiency; Y is yield (kg / ha); YP is the market price of grain (yuan / kg); W is irrigation volume (mm); and WP is water cost (yuan / m³). 3 F represents nitrogen fertilizer usage (kg); FP represents nitrogen fertilizer price (yuan / kg); E represents electricity consumption (kW / h); EP represents electricity price (yuan / (kW / h)).

[0018] Preferably, by comparing and analyzing the comprehensive index of different varieties of grain crops under different hydrological and meteorological conditions and irrigation amounts, the drought resistance of grain crops is compared, and then the best drought-resistant, water-saving and high-yielding grain crop varieties suitable for growth in the study area are selected.

[0019] Preferably, the model includes, but is not limited to, the soil-crop system model WHCNS.

[0020] Preferably, the food crops include, but are not limited to, soybeans.

[0021] Therefore, the present invention adopts the above-mentioned model-based method for screening drought-resistant varieties of grain crops, which avoids long-term field experiments and large-scale index measurements, saves manpower, material resources and financial resources, innovates the method for screening drought-resistant varieties of grain crops, improves the accuracy of screening drought-resistant varieties of grain crops, and shortens the screening time.

[0022] This invention analyzes the planting areas of different drought-resistant grain varieties by introducing hydrological and meteorological data from different regions and years, which can provide important guidance for the distribution of grain crop planting structure; by introducing a comprehensive index, a complete application system is formed; and by using numerical simulation methods, the yield and income of different grain crops under different hydrological and meteorological conditions and irrigation amounts are compared and analyzed in a more intuitive way.

[0023] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0024] Figure 1 This is the input / output page for the WHCNS crop model;

[0025] Figure 2 These are measured and simulated values ​​of soybean soil moisture under different irrigation systems in 2017, 2018, and 2020.

[0026] Figure 3 These are measured and simulated values ​​of soybean yield under different irrigation systems in 2017, 2018, and 2020.

[0027] Figure 4 This shows the changes in various indicators of soybeans with varying irrigation amounts at four irrigation frequencies in 2017, 2018, and 2020. Detailed Implementation

[0028] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0029] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0030] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention, and no reference numerals in the claims should be construed as limiting the scope of the claims.

[0031] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be appropriately combined to form other embodiments that can be understood by those skilled in the art. These other embodiments are also covered within the scope of protection of this invention.

[0032] It should also be understood that the specific embodiments described above are only used to explain the present invention, and the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

[0033] All terms used in this disclosure (including technical or scientific terms) have the same meaning as understood by one of ordinary skill in the art to which this disclosure pertains, unless otherwise specifically defined. It should also be understood that terms defined in general dictionaries should be interpreted as having meanings consistent with their meanings in the context of the relevant art, and not as being interpreted with idealized or highly formalized meanings, unless expressly defined herein.

[0034] Techniques, methods, and apparatus known to those skilled in the art may not be discussed in detail, but where appropriate, they should be considered part of the specification.

[0035] All prior art documents cited in this specification are incorporated herein by reference in their entirety and are therefore part of the disclosure of this invention.

[0036] Example 1

[0037] Taking the soil-crop system model WHCNS (Soil Water Heat Carbon Nitrogen Simulator) as an example, soybean experimental data was used to calibrate the model so that the simulated values ​​of soil moisture and yield matched the measured values.

[0038] Using the irrigation scenario setting function of the calibrated model, a comprehensive index—Comprehensive Index II—is introduced to analyze the yield, water use efficiency, and index II of different soybean varieties under different hydrological years and irrigation regimes. This allows for a comparative analysis of the advantages and disadvantages of planting different soybean varieties in Northwest China. Consequently, the growth changes of different soybean varieties under multiple scenario variables can be realized in a relatively short time, quickly determining the optimal drought-resistant soybean variety.

[0039] I. Model Calibration

[0040] Model calibration requires field experimental data as support. Calibration can be achieved using data from a single soybean crop (one year), requiring only four types of indicators: yield, weather conditions, farmland management, and soil moisture content. Input the field experimental data from one soybean crop into the model, as shown in Tables 1 and 2 below, adjust the model parameters to adapt to the field measured values, and verify the model's simulation effect.

[0041] Figure 1 The input and output pages of the WHCNS crop model are displayed, including the soil module, crop module, meteorological module, field management module, and organic carbon and nitrogen module.

[0042] Table 1. Experimental treatments and irrigation conditions for soybeans.

[0043]

[0044] In Table 1, I 350-9 The total irrigation amount is 350 mm, and the total number of irrigations is 9. The data in the same column are interpreted in the same way. 34(45) indicates that the single irrigation amount on the date marked with a horizontal line in Irrigation Date is 45 mm, and the single irrigation amount on other dates is 34 mm. The data in the same column are interpreted in the same way.

[0045] Table 2. Experimental and simulation results of soybean varieties in different years.

[0046]

[0047] Table 2 compares the measured and simulated yields of different soybean varieties LH1 (Longhuang No. 1) and LH3 (Longhuang No. 3) under different irrigation systems in 2017, 2018, and 2020, and calculates the simulation effect evaluation index. In the table, S represents the simulated value, and M represents the measured value.

[0048] II. Using Models for Scenario Analysis

[0049] Under the condition that the model calibration effect is qualified, the model's scenario setting function is used: on the one hand, by fixing the irrigation time, the total irrigation amount is arranged in an arithmetic sequence, and the model outputs the soybean yield and income under different irrigation amounts; on the other hand, by inputting local meteorological data of different hydrological years into the model, the model outputs the soybean yield and income under different hydrological and meteorological conditions. A comprehensive index that considers both yield and economic factors is introduced—Comprehensive Index II:

[0050]

[0051]

[0052] II = 0.5 × AF + 0.5 × VCR

[0053] AF and VCR represent agronomic and economic factors, respectively. After standardizing these two components, a comprehensive index II is calculated with weights of 0.5 and 0.5, respectively. In the formula, WUE is water use efficiency; Y is yield (kg / ha); YP is the market price of grain (yuan / kg); W is irrigation volume (mm); and WP is water cost (yuan / m³). 3 F represents nitrogen fertilizer usage (kg); FP represents nitrogen fertilizer price (yuan / kg); E represents electricity consumption (kW / h); EP represents electricity price (yuan / (kW / h)).

[0054] By comparing and analyzing the comprehensive index of different soybean varieties under different hydrological and meteorological conditions and irrigation amounts, the drought resistance of soybeans is compared, and then the best drought-resistant, water-saving, and high-yielding soybean varieties suitable for growth in the study area are selected.

[0055] like Figures 2-3 This study compares the measured values ​​and simulated values ​​of various indicators of soybean, a grain crop, under different irrigation systems in 2017, 2018, and 2020 with those from the WHCNS crop model. The evaluation indicators include the coefficient of determination (R²). 2 The consistency index (IA), Nash coefficient (NSE), and standard root mean square (nRMSE) are also considered. Additionally, LH1 and LH3 represent the soybean varieties Longhuang No. 1 and Longhuang No. 3, respectively.

[0056] Figure 2 A comparison of measured and simulated soil moisture values ​​under different irrigation regimes in 2017, 2018, and 2020 showed that the simulated and measured values ​​were in good agreement, indicating a good simulation effect. Figure 3 A scatter plot comparison of measured and simulated yields of different soybean varieties LH1 (Longhuang No. 1) and LH3 (Longhuang No. 3) under different irrigation regimes in 2017, 2018, and 2020 shows that the WHCNS model can simulate the yield of different soybean varieties under different irrigation regimes quite well. Figure 4This section presents scenario analysis results for 2017, 2018, and 2020, based on actual meteorological data and the variation of actual irrigation time with irrigation volume. The four irrigation schemes for 2017, 2018, and 2020 correspond to the irrigation dates in Table 1 for the same year. S1, S2, S3, and S4 represent the irrigation dates for 2017, 2018, the six irrigation dates in 2020, and the five irrigation dates in 2020, respectively. Figure 4 A comparison of the yield, water use efficiency (WUE), and composite index (II) of two soybean varieties shows that yield and water use efficiency increase and eventually stabilize with increasing irrigation volume, while composite index II first increases and then decreases with increasing irrigation volume.

[0057] In practical applications, experimental and meteorological data are used to calibrate the parameters of various models to ensure that their simulation effects meet the requirements. In turn, multiple models work together to achieve the goal of selecting grain crop varieties and improve their accuracy.

[0058] Therefore, the present invention adopts the above-mentioned model-based method for screening drought-resistant varieties of grain crops, which avoids long-term field experiments and large-scale index measurements, saves manpower, material resources and financial resources, innovates the method for screening drought-resistant varieties of grain crops, and shortens the screening time for drought-resistant varieties of grain crops.

[0059] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A model-based method for screening drought-resistant varieties of grain crops, characterized in that, The steps are as follows: S1. Input the measured data corresponding to different varieties of grain crops into the model, adjust the model parameters to adapt to the measured values ​​in the field, and verify the simulation effect of the model; the model is the soil-crop system model WHCNS. S2. In the model, the irrigation time is fixed, the total irrigation amount is arranged in an arithmetic sequence, and the output of the model's yield and income under different irrigation amounts is shown. S3. In the model, by inputting meteorological data from different local hydrological years, the model outputs yield and revenue under different hydrological and meteorological conditions. S4. Analyze the data from steps S2 and S3 comprehensively, and introduce an indicator that comprehensively considers both output and economic factors—Comprehensive Index II: ； ； ； AF and VCR are agronomic and economic factors, respectively. After standardizing the two components, a comprehensive index II is calculated with weights of 0.5 and 0.5, respectively. In the formula, WUE is water use efficiency. Y represents the maximum water use efficiency; Y represents the yield. YP is the maximum yield; W is the market price of grain; WP is the irrigation water volume; F is the water cost; FP is the nitrogen fertilizer usage; E is the nitrogen fertilizer price; E is the electricity consumption; EP is the electricity price. By comparing and analyzing the comprehensive index of different varieties of grain crops under different hydrological and meteorological conditions and irrigation amounts, the drought resistance of grain crops is compared, and then the best drought-resistant, water-saving, and high-yielding varieties suitable for growth in the study area are selected.

2. The model-based method for screening drought-resistant varieties of grain crops according to claim 1, characterized in that: In step S1, the data includes yield, weather conditions, farmland management, and soil moisture content.

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