Prediction model for lodging resistance of highland barley, establishment method and prediction method thereof

Abstract

The invention discloses a prediction model for lodging resistance of highland barley, an establishment method thereof and a prediction method, and relates to the technical field of crop breeding, the establishment method of the prediction model is scientific and reasonable, and main characters influencing the lodging resistance are determined through systematic screening and presented through a simple regression equation. In the application process of the prediction model, the prediction model has the characteristics of convenience in data acquisition, simplified calculation process, better consistency between a calculation result and an actual value, accuracy, high efficiency and scientificity, and has better practical value for highland barley resource evaluation, crop breeding, gene analysis and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of crop breeding, and in particular, to a prediction model for the lodging resistance of highland barley, a method for establishing the same, and a prediction method. Background Art

[0002] Highland barley is the largest crop in the Tibetan area and is of great significance to the food security and regional economic development in the Tibetan area. However, lodging is the main limiting factor for its yield increase. Due to its soft and thin stems and shallow roots, combined with the harsh growth environment, it is prone to lodging. After lodging, the grains and stems are severely damaged, resulting in a reduction in grain quality and yield. Therefore, it is very necessary to predict and identify the lodging resistance of highland barley, which is an important way for the selection and breeding of lodging-resistant highland barley germplasm resources and is beneficial to food security and social stability in the Tibetan area.

[0003] At present, the traditional method for lodging phenotype identification mainly focuses on counting the ratio of the lodging area where the plant tilts to less than 30° from the ground. However, due to the different lodging characteristics and degrees of highland barley, it is impossible to accurately and effectively detect the field population lodging rate of the plants, and it is impossible to directly view the field population lodging rate when the amount of seeds is small, which greatly hinders the evaluation and development of germplasm resources. Therefore, it is necessary to develop an accurate, efficient, and scientific method for predicting the lodging resistance of highland barley at the single-plant level. Summary of the Invention

[0004] The purpose of the present invention is to provide a prediction model for the lodging resistance of highland barley and a method for establishing the same. The model is scientific and reasonable, easy to use, and can accurately and efficiently predict the lodging resistance of highland barley.

[0005] Another purpose of the present invention is to provide a prediction method for the lodging resistance of highland barley, which is simple and convenient to operate, and the prediction result is accurate, efficient, and scientific, and has better practical value.

[0006] The embodiments of the present invention are implemented as follows:

[0007] A method for establishing a prediction model for the lodging resistance of highland barley, which includes:

[0008] S11. Select several highland barleys from different sources for planting;

[0009] S12. Test multiple trait indexes of highland barley at each growth stage, and count the field population lodging rate of highland barley at the mature stage;

[0010] S13. Perform statistical analysis on the data of the multiple trait indexes and the field population lodging rate obtained by the test, and perform standardization processing on the data;

[0011] S14. Perform correlation analysis on the standardized data, and screen out the trait indicators with the absolute value of the correlation coefficient greater than 0.5 and p less than 0.05 with the lodging rate of the field population as the main indicators;

[0012] S15. Perform principal component analysis on the selected main indicators, select the first n principal components with the cumulative contribution rate reaching 80% - 90%, and calculate the comprehensive score value, that is, the F value, according to the contribution rate of each of the n principal components;

[0013] S16. Use the F value as the dependent variable and the main indicators as the independent variables, and perform dimensionality reduction through linear stepwise regression analysis to obtain multiple regression equations; screen the regression equations with the determination coefficient greater than 0.95, that is, the prediction model for the lodging resistance ability of highland barley.

[0014] A prediction model for the lodging resistance ability of highland barley, which is obtained by the above method for establishing the prediction model for the lodging resistance ability of highland barley.

[0015] A prediction method for the lodging resistance ability of highland barley, which uses the above prediction model, including:

[0016] S21. Plant the highland barley to be measured as a prediction sample;

[0017] S22. At the heading stage of highland barley, randomly sample the prediction sample and measure the trait indicators corresponding to the prediction model;

[0018] S23. Standardize the data of the trait indicators;

[0019] S24. Substitute the standardized data into the prediction model, calculate the prediction score value of the prediction sample, compare the prediction score value with the preset threshold, and determine the prediction sample with the prediction score value greater than or equal to the preset threshold as lodging-resistant highland barley, and determine the prediction sample with the prediction score value less than the preset threshold as non-lodging-resistant highland barley.

[0020] The beneficial effects of the embodiments of the present invention are:

[0021] The embodiments of the present invention provide a prediction model for the lodging resistance ability of highland barley, its establishment method and prediction method. The establishment method of this prediction model is scientific and reasonable. The main traits affecting the lodging resistance ability are determined through systematic screening and presented in a simple regression equation. In the process of using this prediction model, it has the advantages of convenient data collection, simplified calculation process, good consistency between the calculation result and the actual value, and is accurate, efficient and scientific, and has better practical value for highland barley resource evaluation, crop breeding and gene analysis, etc. Description of the Drawings

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0023] Figure 1 It is a trend chart of the Y value and the field population lodging rate of 13 highland barley materials provided in Embodiment 1 of the present invention;

[0024] Figure 2 It is a trend chart of the Y' value and the field population lodging rate of 5 lodging-resistant highland barley materials provided in Embodiment 1 of the present invention;

[0025] Figure 3 It is a trend chart of the Y value and the field population lodging rate of 6 highland barley materials provided in Embodiment 2 of the present invention;

[0026] Figure 4 It is a trend chart of the Y' value and the field population lodging rate of 3 lodging-resistant highland barley materials provided in Embodiment 2 of the present invention;

[0027] Figure 5 It is a trend chart of the Y value and the field population lodging rate of 6 highland barley materials provided in Embodiment 3 of the present invention;

[0028] Figure 6 It is a trend chart of the Y' value and the field population lodging rate of 3 lodging-resistant highland barley materials provided in Embodiment 3 of the present invention. Detailed implementation manners

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. For those not specified in the embodiments, they are carried out according to conventional conditions or conditions recommended by the manufacturer. For reagents or instruments without indicating the manufacturer, they are all conventional products that can be obtained through commercial purchase.

[0030] The following specifically describes a prediction model for the lodging resistance ability of highland barley in the embodiments of the present invention, its establishment method, and prediction method.

[0031] The present invention provides a method for establishing a prediction model for the lodging resistance ability of highland barley, which includes:

[0032] S11. Select several highland barley from different sources for planting.

[0033] S12. Test multiple trait indexes of highland barley at each growth stage, and count the field population lodging rate of highland barley at maturity;

[0034] S13. Statistically analyze the data of multiple trait indicators and the lodging rate of the field population obtained from the test, and perform standardization processing on the data;

[0035] S14. Conduct a correlation analysis on the standardized data, and screen out the trait indicators with an absolute value of the correlation coefficient greater than 0.5 and p less than 0.05 with the lodging rate of the field population as the main indicators;

[0036] S15. Conduct a principal component analysis on the selected main indicators, select the first n principal components with a cumulative contribution rate reaching 80% - 90%, and calculate the comprehensive score value, that is, the F value, according to the contribution rate of each of the n principal components;

[0037] S16. Use the F value as the dependent variable and the main indicators as the independent variables, and perform dimensionality reduction processing through linear stepwise regression analysis to obtain multiple regression equations; screen out the regression equations with a determination coefficient greater than 0.95, that is, the prediction model for the lodging resistance ability of hulless barley.

[0038] Furthermore, in step S11, the number of sources of hulless barley is not less than 10. In order to make the obtained prediction model more universal, there should be a certain difference in the lodging rate of the field population among the selected hulless barley from different sources. When planting, the hulless barley from different sources is planted in different areas for subsequent statistics of the trait indicators of the hulless barley from each source. Each source of hulless barley is planted in at least 3 - 6 areas, and the areas are randomly arranged among different sources.

[0039] In step S12, the trait indicators include but are not limited to at least one of the field pushing force, the breaking force of the first internode, the breaking force of the second internode, the breaking force of the third internode, the length of the basal internode, the length of the first internode, the length of the second internode, the length of the third internode, the length of the fourth internode, the length of the fifth internode, the length of the sixth internode, the thickness of the basal internode, the thickness of the first internode, the thickness of the second internode, the thickness of the third internode, the thickness of the fourth internode, the thickness of the fifth internode, the thickness of the sixth internode, the sum of the lengths of the first and second internodes, and the proportion of the lengths of the first and second internodes to the plant height during the heading stage of hulless barley. When conducting the trait indicators, all or part of the above - mentioned trait indicators can be selected according to needs, and some unlisted trait indicators can also be appropriately added. For example, only the trait indicators during the heading stage are listed above, and the corresponding indicators during the filling stage and the maturity stage can also be added. Generally, for the accuracy of the results, the number of selected trait indicators should not be less than 10.

[0040] Among the above - mentioned indicators, the field pushing force can be detected by a portable lodging resistance tester, the breaking force of the stem can be detected by a stem strength tester, and the thickness, plant height, length, etc. can be measured by a tape measure or a vernier caliper.

[0041] In addition, it is also necessary to count the field population lodging rate during the mature period of hulless barley. The field population lodging rate is calculated based on the ratio of lodging plants to the total number of plants in each area (unit: %). The lodging level is divided into five grades: when there is no lodging in the plot plants, it is grade 1; when 0 < the ratio of lodging plants ≤ 25%, it is grade 2 (light lodging); when 25% < the ratio of lodging plants ≤ 50%, it is grade 3 (medium lodging); when 50% < the ratio of lodging plants ≤ 75%, it is grade 4 (severe lodging); when the ratio of lodging plants ≥ 75%, it is grade 5 (extremely severe lodging). The field population lodging rate can be used to verify the accuracy of the subsequent prediction model.

[0042] In step S12, when testing the trait indicators, a random sampling form is adopted, and the number of samples selected for each source of hulless barley is 10 - 20 plants. Random sampling can reduce the sampling quantity and workload.

[0043] In step S13, the Z-score standardization method is adopted for data standardization, that is, each data point is subtracted by the mean and then divided by the standard deviation.

[0044] In step S14, the number of main indicators should not be too many. To simplify the calculation process, the number of main indicators is preferably 5 - 10. When the number of selected main indicators is too large, it can be appropriately reduced according to the importance.

[0045] In step S16, if there are multiple regression equations that meet the conditions in the screening results, according to the actual operation situation, those main indicators that are easier to collect can be selected, and the number of retained main indicators is preferably 2 - 4, and it is best to cover more analysis dimensions. For example, if there are multiple indicators related to thickness, one of them can be retained, and the best one can be selected from other dimension indicators (length, field pushing force, breaking force, etc.) for replacement.

[0046] The embodiment of the present invention also provides a prediction model for the lodging resistance ability of hulless barley, which is obtained by the above method for establishing a prediction model for the lodging resistance ability of hulless barley.

[0047] Furthermore, the prediction model is

[0048] Y = 0.748*Z1 + 0.48*Z2 + 1.065*Z3,

[0049] In the formula, Y is the predicted score value, Z1 is the field pushing force during the heading period of hulless barley, Z2 is the breaking force of the second section during the heading period of hulless barley, Z3 is the thickness of the third section during the heading period of hulless barley, and Z1, Z2, and Z3 are all values after standardization processing. This model is the optimal result after optimization. The three main indicators it needs to measure can all be collected during the heading period of hulless barley, and lodging-resistant hulless barley can be screened out in advance during the heading period. At the same time, the three main indicators cover three dimensions of field pushing force, thickness, and breaking force respectively, and the measurement is relatively simple and convenient.

[0050] An embodiment of the present invention also provides a method for predicting the lodging resistance of highland barley, which uses the above prediction model and includes:

[0051] S21. Plant the highland barley to be measured as a prediction sample.

[0052] S22. At the heading stage of highland barley, randomly sample the prediction sample and measure the trait indexes corresponding to the prediction model.

[0053] S23. Standardize the data of the trait indexes.

[0054] S24. Substitute the standardized data into the prediction model, calculate the prediction score value of the prediction sample, compare the prediction score value with a preset threshold. If the prediction score value is greater than or equal to the preset threshold, the prediction sample is determined to be lodging-resistant highland barley; if the prediction score value is less than the preset threshold, the prediction sample is determined to be non-lodging-resistant highland barley.

[0055] Furthermore, the prediction model is mainly used to screen out lodging-resistant highland barley. For different prediction models, there will be differences in their preset thresholds, and the threshold is determined by the types of test materials and the population size, and is adjusted by the tester according to the material situation of his own. For the optimal prediction model screened out by the present invention, that is, Y = 0.748*Z1 + 0.48*Z2 + 1.065*Z3, the preset threshold is -0.2, that is, if the prediction score value is greater than or equal to -0.2, the prediction sample is determined to be lodging-resistant highland barley; if the prediction score value is less than -0.2, the prediction sample is determined to be non-lodging-resistant highland barley. For other prediction models obtained by using the establishment method of the present invention, there may also be cases where the preset threshold is not -0.2. In actual operation, adjust the size of the preset threshold according to the situation. In addition, when using the optimal prediction model, the preset threshold can also be adjusted according to one's own accuracy requirements. If more lodging-resistant highland barley needs to be screened out, the preset threshold can be appropriately reduced, such as set to -0.3, -0.5, etc. In addition, when conducting a rough screening, 0 can also be directly used as the preset threshold, so that the judgment can be made only through the positive and negative signs of the prediction score value.

[0056] In addition, several samples to be measured can be planted separately, and the prediction model can be used to screen out lodging-resistant highland barley through the prediction score values of different samples to be measured, and rank the lodging resistance of multiple lodging-resistant highland barley.

[0057] During the ranking process, the ranking of lodging-resistant highland barley is corrected by the plant height at the maturity stage of highland barley, and the correction equation is as follows:

[0058] Y’ = 0.83*Z4

[0059] Among them, Y’ is the corrected score value, Z4 is the plant height at maturity, and Z4 is the value after standardization; the lower the corrected score value, the stronger the lodging resistance of the sample to be tested.

[0060] The features and performance of the present invention will be further described in detail below in conjunction with the embodiments.

[0061] Example 1

[0062] This example provides a method for establishing a prediction model for the lodging resistance of highland barley, which includes:

[0063] S11. Thirteen highland barley germplasms with different genotypes and geographical origins were used as research materials, including 4 from Sichuan, 1 from Tibet, 6 from Qinghai, and 2 from other regions (Table 1).

[0064] Table 1. Tested highland barley germplasms

[0065]

[0066] In 2022, at the Bamai Agricultural Experiment Station of the Ganzi Prefecture Academy of Agricultural Sciences, three replicated plots were planted for each of the above 13 materials, and a total of 39 plots were randomly arranged to construct a prediction and evaluation system for the lodging resistance of highland barley. Artificial broadcasting was carried out in the same row spacing method, with a row spacing of 25 cm. The experiment was designed in a randomized block design, and the area of each plot was 3 m 2 .

[0067] S12. During the planting period, the agronomic trait indicators of the tested materials were investigated and measured in the field. Ten individual plants with strong growth, conforming to the population characteristics and with little difference were randomly selected for trait measurement.

[0068] The measured traits include: the lodging rate of the field population at maturity (X1 MS ), the pushing force of the field at the heading stage (X2 HS ), the breaking force of the first stem section at the heading stage (X3 HS ), the breaking force of the second stem section at the heading stage (X4 HS ), the breaking force of the third stem section at the heading stage (X5 HS ), the length of the basal stem section at the heading stage (X6 HS ), the length of the first stem section at the heading stage (X7 HS ), the length of the second stem section at the heading stage (X8 HS ), the length of the third stem section at the heading stage (X9 HS ), the length of the fourth stem section at the heading stage (X10 HS ), the length of the fifth stem section at the heading stage (X11 HS ), the length of the sixth stem section at the heading stage (X12 HS ), the thickness of the basal stem section at the heading stage (X13HS ) The thickness of the first internode of the culm at the heading stage (X14 HS ) The thickness of the second internode of the culm at the heading stage (X15 HS ) The thickness of the third internode of the culm at the heading stage (X16 HS ) The thickness of the fourth internode of the culm at the heading stage (X17 HS ) The thickness of the fifth internode of the culm at the heading stage (X18 HS ) The thickness of the sixth internode of the culm at the heading stage (X19 HS ) The length of the first and second internodes of the culm at the heading stage (X20 HS ) The ratio of the length of the first and second internodes of the culm to the plant height at the heading stage (X21 HS ).

[0069] Among them, the culm breaking force (unit: N) was measured using a YYD-1 type culm strength tester (Zhejiang Top Cloud-Agri Technology Co., Ltd.); the field pushing force (unit: kPa) was measured using a YYD-1A portable lodging resistance tester (Zhejiang Top Cloud-Agri Technology Co., Ltd.); the length was measured with a tape measure (unit: cm), and the thickness was measured with a vernier caliper (unit: mm).

[0070] Meanwhile, at the mature stage of hulless barley, the field population lodging rate of the tested materials was investigated, and the field population lodging rate (unit: %) was statistically analyzed based on the ratio of lodging plants to the plants in the plot. The lodging grades were divided into five levels: no lodging of the plants in the plot was level 1; 0 < lodging plant ratio ≤ 25% was level 2 (light lodging); 25% < lodging plant ratio ≤ 50% was level 3 (medium lodging); 50% < lodging plant ratio ≤ 75% was level 4 (severe lodging); lodging plant ratio ≥ 75% was level 5 (extremely severe lodging).

[0071] S13. Statistical analysis was carried out on the data of multiple trait indicators and the field population lodging rate obtained from the tests, and the analysis results are shown in Table 2.

[0072] Table 2. Statistics of the field population lodging rate and trait indicators of 13 hulless barley materials

[0073]

[0074]

[0075] As can be seen from Table 2, among the 21 traits, the coefficient of variation of 9 traits exceeded 40%, indicating that the genetic variation of the tested materials was relatively rich. Among all the trait indicators, the coefficient of variation of the field population lodging rate was the largest, at 77%, indicating that the interspecific differences in the field population lodging rate of the tested materials were obvious and the genetic variation was the richest. Thirteen phenotypic traits showed a distribution to the right of the mean value, and 9 phenotypic traits showed a distribution to the left of the mean value, and the distance from the mean value was relatively close, showing differences among different genotypes of hulless barley in the same trait.

[0076] S14. Standardize the data in Table 2 and perform a correlation analysis on the standardized data. The analysis results are shown in Table 3.

[0077] Table 3. Correlation analysis between the lodging rate of the field population and various trait indicators

[0078]

[0079]

[0080] Note: *: p < 0.05; **: P < 0.01.

[0081] It can be seen from Table 3 that 8 agronomic trait indicators, namely the field pushing force at the heading stage, the breaking force of the first internode of the stem, the breaking force of the second internode of the stem, the thickness of the second internode, the thickness of the third internode, the thickness of the fourth internode, the thickness of the fifth internode, and the thickness of the sixth internode, are significantly negatively correlated with the lodging rate of the field population (p < 0.05), and the highest correlation is with the thickness of the third internode of the stem. This result indicates that the stem thickness, stem strength, and root grasping ability are important factors affecting the lodging rate of the field population. Therefore, the above 8 trait indicators are selected as the main indicators for subsequent analysis.

[0082] S15. Perform a principal component analysis on the selected main indicators. In the KMO test (Kaiser - Meyer - Olkin), the KMO value is 0.794, and the principal component analysis method can be used to calculate the weights. The eigenvalues corresponding to the first 2 principal components are 5.890 and 1.252 > 1, and their cumulative contribution rate is 89.285%, indicating that the 2 principal components basically reflect the index information of the 8 lodging - related traits, and the principal component analysis results have high representativeness. Among these two principal components, the first principal component is mainly the stem thickness factor, and the higher - loading ones are the thickness of the third internode and the thickness of the fourth internode of the stem; the second principal component is mainly the root grasping ability and stem strength factor, and the highest - loading ones are the field pushing force and the breaking force of the second internode of the stem.

[0083] Table 4. Total variance explained and factor component matrix

[0084]

[0085]

[0086] Calculate the comprehensive score value, that is, the F value, according to the contribution rate of each of the 2 principal components; F = Component 1 * (73.630 / 89.285)+Component 2 * (15.654 / 89.285).

[0087] Perform a correlation analysis on the F value and each main indicator. The results are shown in Table 5.

[0088] Table 5. Correlation analysis between F value (comprehensive score) and various traits

[0089]

[0090] Note: **: P < 0.01.

[0091] As can be seen from Table 5, the F value is extremely significantly correlated (P < 0.01) with the lodging rate of the field population and the other 8 lodging-related traits. Among them, the F value is extremely significantly negatively correlated (P < 0.01) with the lodging rate of the field population, which can be used to construct a regression equation in the follow-up.

[0092] S16. Using the F value as the dependent variable and the main indicators as the independent variables, dimensionality reduction is carried out through linear stepwise regression analysis to obtain multiple regression equations; screening the regression equations with a determination coefficient greater than 0.95, that is, the prediction model for the lodging resistance ability of highland barley. By combining factors such as the ease of collection and multi-dimensional situation of the main indicators, a regression equation containing three main indicators of the field pushing force, the breaking force of the second stem section, and the thickness of the third stem section is finally selected. Specifically:

[0093] Y = 0.748 * Z1 + 0.48 * Z2 + 1.065 * Z3,

[0094] In the formula, Y is the predicted score value, Z1 is the field pushing force at the heading stage of highland barley, Z2 is the breaking force of the second section at the heading stage of highland barley, Z3 is the thickness of the third section at the heading stage of highland barley, and Z1, Z2, and Z3 are all values after standardization. The correlation coefficient r is 0.995, and the determination coefficient R2 is 0.990, indicating that the three independent variables in the regression equation can determine 99% of the total variation of the F value.

[0095] This embodiment also provides a method for predicting the lodging resistance ability of highland barley, which uses the prediction model provided in Embodiment 1, including:

[0096] S21. The data of the three indicators of the field pushing force, the breaking force of the second stem section, and the thickness of the third stem section of the 13 highland barley materials collected in Embodiment 1 at the heading stage are standardized, and the processed results are shown in Table 6.

[0097] Table 6. Standardization of data of each main trait

[0098]

[0099]

[0100] S22. Substitute the data in Table 6 into the regression equation Y = 0.748 * Z1 + 0.48 * Z2 + 1.065 * Z3 to calculate the Y value, and count the lodging rate of the field population of the 13 highland barley materials at the maturity stage. The calculation and statistical results are shown in Table 7 and Figure 1As shown

[0101] Table 7. Y values of each tested hulless barley, actual field population lodging rate and lodging grade

[0102]

[0103] From Table 7 and Figure 1 it can be seen that the predicted scoring values of the five hulless barley sources, namely Kunlun 14, Kangqing 9, Zangqing 3000, Zhongnuo 8, and Kunlun 16, are all greater than -0.2, and they are determined as lodging-resistant hulless barley; while the predicted scoring values of the remaining eight hulless barley sources are all less than -0.2, and they are determined as non-lodging-resistant hulless barley. By comparing the field population lodging rate, it can be seen that the field population lodging rates of the five hulless barley sources determined as lodging-resistant hulless barley are basically 0, and only Kangqing 9 has a field population lodging rate of 0.3. In addition, although Kangqing 9 has a field population lodging rate of 0.3, its predicted scoring value is higher than that of Zangqing 3000, Zhongnuo 8, and Kunlun 16, indicating that the predicted scoring value is only suitable for screening out lodging-resistant hulless barley, but the lodging resistance ability of the screened lodging-resistant hulless barley cannot be directly compared by the size of the predicted scoring value.

[0104] Furthermore, considering that the main indicators in this prediction model are all related data at the heading stage, and do not include the plant height and ear weight at maturity that reflect the pressure borne by the plant, this may be the reason for the data difference. Therefore, for the 3 lodging-resistant materials with relatively low field population lodging rates in Table 7, namely Kangqing 9, Kunlun 14, and Zangqing 3000, they were replanted in the Bamei experimental field. Each hulless barley material was planted at 2 densities, with 3 replicated plots, and 18 plots were randomly arranged. Corresponding indicators were collected at the heading stage and maturity stage respectively, and the collection results are shown in Table 8.

[0105] Table 8. Data index of various traits of lodging-resistant hulless barley population

[0106]

[0107]

[0108] It can be seen from Table 8 that the differences in various trait indicators among species are small, the data is relatively stable, while the coefficient of variation of the field population lodging rate is the largest among all indicators, and the difference in field pushing force among various traits is the smallest. Further correlation analysis was carried out, and the results are shown in Table 9.

[0109] Table 9. Correlation analysis between field population lodging rate and various agronomic traits

[0110]

[0111] Note: *: P < 0.05.

[0112] As can be seen from Table 9, the lodging rate of the field population was positively correlated with the plant height at maturity (p < 0.05), and other traits were not correlated with the lodging rate of the field population (p > 0.05), indicating that the plant height at maturity can be used to rank the lodging resistance of the selected lodging-resistant hulless barley. Therefore, a linear regression equation was constructed using the lodging rate of the field population and plant height:

[0113] Y’ = 0.83 * Z4

[0114] where Z4 is the plant height of hulless barley at maturity, and its value is a normalized value. The correlation coefficient r of the model is 0.83, and the determination coefficient R 2 is 0.688, indicating that the plant height independent variable in the regression equation can determine 68.8% of the total variation of the lodging rate of the field population. And the Y’ value is positively correlated with the lodging rate of the field population. The larger the Y’ value, the larger the lodging rate of the field population, and the worse the lodging resistance of hulless barley.

[0115] After substituting the plant height data at maturity into the regression equation, the Y’ value was calculated, and the lodging resistance of the lodging-resistant hulless barley was ranked based on the Y’ value. The results are shown in Table 10 and Figure 2 as follows.

[0116] Table 10. Y’ values of each tested hulless barley, actual field population lodging rate and lodging grade

[0117]

[0118] It can be seen that Kangqing 9 has a larger Y’ value, showing a larger lodging rate of the field population. The trend of the corrected lodging rate of the lodging-resistant hulless barley field population is consistent with the Y’ ranking.

[0119] Example 2

[0120] This example provides a method for predicting the lodging resistance of hulless barley, which uses the prediction model provided in Example 1, including:

[0121] S21. Conduct a planting experiment in Luhuo County, and select six hulless barley materials, namely Zangqing 3000, Kunlun 14, Kangqing 9, Menyuan Lianglan, 13 - 1036, and Beiqing 6, for testing. The results of the variance analysis are shown in Table 11.

[0122] Table 11. Data indexes of various traits of hulless barley materials at the test site in Luhuo

[0123]

[0124] As can be seen from Table 11, the coefficient of variation of the lodging rate of the field population is the largest, at 70%, and the genetic variation is the richest. In addition, there are obvious differences in the field pushing force and the second breaking force among species; the difference in plant height at maturity among species is the smallest, with a coefficient of variation of 6%.

[0125] S22. Standardize the relevant data of six highland barley materials and substitute them into the regression equation Y = 0.748*Z1 + 0.48*Z2 + 1.065*Z3 to calculate the Y value. The statistical results are shown in Table 11 and Figure 3 as follows.

[0126] Table 12. Y values of each tested highland barley and the actual field population lodging rate and lodging grade

[0127]

[0128] Among them, the top three Y values are Kangqing 9 (2.79), Zangqing 3000 (1.65), and Kunlun 14 (1.34) in sequence, which are determined as lodging-resistant highland barley.

[0129] S23. Substitute the mature plant height data of lodging-resistant highland barley Kangqing 9, Zangqing 3000, and Kunlun 14 into the correction equation Y’ = 0.83*Z4. The statistical results are shown in Table 13 and Figure 4 as follows.

[0130] Table 13. Y’ values of each tested highland barley and the actual field population lodging rate and lodging grade

[0131]

[0132] It can be seen that Kangqing 9 has a larger Y’ value, showing a larger field population lodging rate. The lodging rate of the corrected lodging-resistant highland barley in the field is consistent with the trend of Y’ ranking.

[0133] Example 3

[0134] This example provides a method for predicting the lodging resistance ability of highland barley, which adopts the prediction model provided in Example 1, including:

[0135] S21. Conduct a planting experiment in Bamei Town, and select six highland barley materials, namely Zangqing 3000, Kunlun 14, Kangqing 9, Menyuan Lianglan, 13-1036, and Beiqing 6, for testing. The results of variance analysis are shown in Table 14.

[0136] Table 14. Data indexes of various traits of highland barley materials at the test point Bamei

[0137]

[0138] It can be seen from Table 14 that the coefficient of variation of the field population lodging rate is the largest, which is 62%, and the genetic variation is rich; the mature plant height and the thickness of the third stem section at the heading stage are relatively larger than the values in 2023, indicating that different regions and planting years have a certain impact on the plant phenotype.

[0139] S22. Standardize the relevant data of six highland barley materials and substitute them into the regression equation Y = 0.748*Z1 + 0.48*Z2 + 1.065*Z3 to calculate the Y value. The statistical results are shown in Table 15 and Figure 5 as follows.

[0140] Table 15. Y values of each tested highland barley and the actual field population lodging rate and lodging grade

[0141]

[0142] Among them, the top three Y values are Zangqing 3000 (2.40), Kangqing 9 (2.32), and Kunlun 14 (1.11) in sequence, which are determined as lodging-resistant highland barley.

[0143] S23. Substitute the plant height data at the mature stage of lodging-resistant highland barley Kangqing 9, Zangqing 3000, and Kunlun 14 into the correction equation Y' = 0.83*Z4. The statistical results are shown in Table 16 and Figure 6 as follows.

[0144] Table 16. Y' values of each tested highland barley and the actual field population lodging rate and lodging grade

[0145]

[0146] It can be seen that Kangqing 9 has a larger Y' value, showing a larger field population lodging rate. The lodging rate of the corrected lodging-resistant highland barley in the field is consistent with the trend of Y' ranking.

[0147] To sum up, the embodiment of the present invention provides a prediction model for the lodging resistance of highland barley, its establishment method, and prediction method. The establishment method of this prediction model is scientific and reasonable. The main traits affecting the lodging resistance are determined through systematic screening and presented in a simple regression equation. In the process of using this prediction model, it is convenient to collect data, the calculation process is simplified, the calculation result is in good agreement with the actual value, and it has the characteristics of accuracy, efficiency, and science, and has better practical value for highland barley resource evaluation, crop breeding, gene analysis, etc.

[0148] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for establishing a prediction model for the lodging resistance of highland barley, characterized in that, Including: S11. Select several hulless barleys from different sources for planting; S12. Test multiple trait indexes at each growth stage of the hulless barley, and count the field population lodging rate of the hulless barley at the mature stage; S13. Conduct statistical analysis on the data of the multiple trait indexes and the field population lodging rate obtained from the test, and perform standardization processing on the data; S14. Conduct correlation analysis on the data after standardization processing, and screen out the trait indexes with the absolute value of the correlation coefficient greater than 0.5 and p less than 0.05 with the field population lodging rate as the main indexes; S15. Conduct principal component analysis on the selected main indexes, select the first n principal components with the cumulative contribution rate reaching 80% - 90%, and calculate the comprehensive score value, that is, the F value, according to the contribution rate of each of the n principal components; S16. Using the F value as the dependent variable and the main indexes as the independent variables, perform dimensionality reduction processing through linear stepwise regression analysis to obtain multiple regression equations; screen out the regression equations with the determination coefficient greater than 0.95, that is, the prediction model for the lodging resistance ability of the hulless barley.

2. The establishment method according to claim 1, wherein In the step S11, the number of sources of the hulless barley is not less than 10.

3. The establishment method according to claim 1, wherein In the step S12, the trait indexes include at least one of the field pushing force, the breaking force of the first internode, the breaking force of the second internode, the breaking force of the third internode, the length of the basal internode, the length of the first internode, the length of the second internode, the length of the third internode, the length of the fourth internode, the length of the fifth internode, the length of the sixth internode, the thickness of the basal internode, the thickness of the first internode, the thickness of the second internode, the thickness of the third internode, the thickness of the fourth internode, the thickness of the fifth internode, the thickness of the sixth internode, the sum of the lengths of the first and second internodes, and the proportion of the lengths of the first and second internodes to the plant height of the hulless barley at the heading stage.

4. The establishment method according to claim 3, characterized in that In the step S12, the number of the selected trait indexes is not less than 10.

5. The establishment method according to claim 1, wherein In the step S12, when testing the trait indexes, a random sampling form is adopted, and the number of samples selected for each source of hulless barley is 10 - 20 plants.

6. A prediction model for the lodging resistance of highland barley, characterized in that, Obtained by the method for establishing the prediction model for the lodging resistance ability of the hulless barley according to any one of claims 1 - 5.

7. The prediction model according to claim 6, wherein The prediction model is Y = 0.748*Z1 + 0.48*Z2 + 1.065*Z3, wherein, Y is the prediction score value, Z1 is the field pushing force of the hulless barley at the heading stage, Z2 is the breaking force of the second internode of the hulless barley at the heading stage, Z3 is the thickness of the third internode of the hulless barley at the heading stage, and Z1, Z2, and Z3 are all values after standardization processing.

8. A method for predicting the lodging resistance of highland barley, characterized in that, Adopting the prediction model according to claim 6 or 7, including: S21. Plant the hulless barley to be measured as a prediction sample; S22. At the heading stage of the hulless barley, conduct random sampling on the prediction sample, and measure the trait indexes corresponding to the prediction model; S23. Conduct standardization processing on the data of the trait indexes; S24. Substitute the data after standardization processing into the prediction model, calculate the prediction score value of the prediction sample, compare the prediction score value with a preset threshold, and determine the prediction sample with the prediction score value greater than or equal to the preset threshold as a lodging-resistant hulless barley, and determine the prediction sample with the prediction score value less than the preset threshold as a non-lodging-resistant hulless barley.

9. The prediction method according to claim 8, wherein The preset threshold is -0.

2.

10. The prediction method according to claim 9, characterized in that Plant several samples to be tested respectively, and select lodging-resistant hulless barley through the predicted score values of different samples to be tested. When sorting multiple samples to be tested determined as lodging-resistant hulless barley, correct the sorting of the lodging-resistant hulless barley through the plant height at the mature stage of the hulless barley. The correction equation is as follows: Y’=0.83*Z4 Where Y’ is the corrected score value, Z4 is the plant height at the mature stage of the plant, and Z4 is the value after standardization; the lower the corrected score value, the stronger the lodging resistance of the sample to be tested.

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