A method for screening corn varieties suitable for soybean-corn 4:2 and 6:4 strip interplanting

Abstract

The application discloses a corn variety screening method suitable for soybean-corn 4:2 and 6:4 strip composite planting. Firstly, newly approved or large-area popularized corn varieties are selected, and different growth environment conditions of edge 1 row, edge 2 row and edge 3 row in each variety plot are created through high-density, non-protection row and relatively wide plot spacing. The agronomic characters, dry matter, yield and constituent factors of the edge 1 row, edge 2 row and edge 3 row are analyzed by using the principal component analysis, fuzzy membership function method, stepwise regression analysis and grey correlation degree analysis method, the key indicators reflecting the edge row advantage of corn are selected and identified, and the edge row advantage of the tested corn varieties is evaluated, so that the corn varieties with strong and weak edge row advantages are screened out, and the corn varieties are recommended as suitable for soybean-corn 4:2 and 6:4 strip composite planting. The screening method is suitable for providing reference for corn varieties of different strip type modes of soybean-corn composite planting and improving the strip composite planting benefit.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of agricultural production, and particularly relates to a method for screening a maize variety suitable for soybean-maize 4:2 and 6:4 strip interplanting. BACKGROUND

[0002] In recent years, although the planting area of soybean in China has been increasing, the demand for soybean has been large, and the safety risk coefficient caused by a large amount of imports has been increasing. In the case of ensuring the planting area of maize, strip interplanting of soybean and maize can increase the planting area of soybean and improve the self-sufficiency rate of soybean strips. In the strip interplanting mode of soybean and maize, 2-4 rows of maize are more suitable for playing the edge row advantage. In the strip interplanting mode of 2 rows of maize, each row of maize is an edge row, and the edge row advantage can be maximally played. In the interplanting mode of 4 rows of maize, the middle two rows of maize show a weaker edge row advantage compared with the edge rows.

[0003] The marginal effect is an important factor affecting the yield in the process of strip interplanting of soybean and maize. Different varieties of maize have great differences in the marginal effect due to different genetic characteristics and adaptability to ecological environment conditions. Different edge rows also show differences between individual plants and groups, which are reflected in the yield traits, commodity quality traits, agronomic traits and many other aspects of single plant grains.

[0004] At present, more researches have been made on the photosynthetic efficiency, nitrogen and phosphorus nutrient utilization, yield components and soil microorganisms of crops in strip interplanting of soybean and maize, reasonable explanations have been proposed for the growth law of crops in strip interplanting of soybean and maize, but the researches on the marginal effect in strip interplanting of soybean and maize mostly focus on the total of two crops, and there are few explorations on the size of the marginal effect of different varieties of maize in strip interplanting of soybean and maize. The row ratio of 4:2 and 6:4 of soybean and maize is the most commonly used strip interplanting mode of soybean and maize in production. By evaluating the marginal effect of different varieties of maize, the maize variety suitable for planting in the interplanting mode of 4:2 and 6:4 can be screened out, the advantages of strip interplanting can be fully played, one season of soybean can be harvested under the condition of ensuring the yield of maize, and the planting benefit per unit area can be improved. The method has important application value for guiding the popularization of strip interplanting technology of soybean and maize. SUMMARY

[0005] In view of the problems in the prior art, the present application provides a corn variety screening method suitable for soybean-corn 4:2 and 6:4 strip interplanting, which creates different growth environment conditions of edge 1 row, edge 2 row and edge 3 row in each variety plot through high density, no protective row and wide plot spacing. The indexes of each agronomic character, dry matter, yield and constituent factors of edge 1 row, edge 2 row and edge 3 row are determined, and the key indexes reflecting the edge row advantage of corn are analyzed and selected by using principal component analysis, fuzzy membership function method, stepwise regression analysis and grey correlation degree analysis, and the edge row advantage of the tested corn varieties is evaluated, so that the corn varieties with strong and weak edge row advantage are screened out, and the corn varieties with strong and weak edge row advantage are recommended for soybean-corn 4:2 and 6:4 strip interplanting, respectively

[0006] To solve the above technical problems, the present application adopts the following technical scheme:

[0007] A corn variety screening method suitable for soybean-corn 4:2 and 6:4 strip interplanting, comprising the following steps:

[0008] (1) selecting newly approved or widely promoted corn varieties in production, and creating completely different growth environment conditions of edge 1 row, edge 2 row and edge 3 row in each variety plot through high density, no protective row and wide plot spacing;

[0009] (2) according to the lodging situation of the varieties after high density, eliminating the lodging varieties and retaining the lodging-resistant varieties, and determining the analysis indexes of edge 1 row, edge 2 row and edge 3 row of the lodging-resistant varieties;

[0010] (3) using correlation analysis and multivariate analysis methods to select the key indexes reflecting the edge row advantage of corn;

[0011] (4) according to the results of cluster analysis, stepwise regression, correlation analysis and grey correlation degree analysis, statistically analyzing the differences of the key indexes of edge row advantage between edge 1 row and edge 2 row of each variety, judging the edge row advantage performance characteristics of each corn variety, and screening out the corn varieties with strong and weak edge row advantage;

[0012] (5) recommending the corn varieties with high yield and strong edge row advantage for soybean-corn 4:2 strip interplanting, and recommending the corn varieties with high yield and weak edge row advantage for soybean-corn 6:4 strip interplanting.

[0013] Further, the planting conditions in (1) are as follows: the density is set to 8000 plants per mu, 6 rows, equal row spacing of 60 cm, row length of 10 m, plot spacing of 1.5 m, and 3 repetitions.

[0014] Further, the analysis indexes in (2) are edge row agronomic characters, dry matter accumulation, grain yield and constituent factors.

[0015] Furthermore, the agronomic traits include plant height, stem diameter, ear height, leaf area index, leaf angle of the three-leaf clover, specific leaf weight of the ear leaf, canopy light transmittance, chlorophyll a of the three-leaf clover, and chlorophyll b of the three-leaf clover.

[0016] Furthermore, the multivariate analysis methods described in (3) include principal component analysis, fuzzy membership function method, stepwise regression analysis, and grey relational analysis.

[0017] Furthermore, in the principal component analysis, the marginal effect index of each indicator trait is: marginal effect index = trait value of side 1 row / trait value of side 2 row; principal components are extracted based on the criterion that the eigenvalue is greater than 1.

[0018] Furthermore, in the fuzzy membership function method, the membership function value U(X) j )=(X j -X min ) / (X max -X min ); j = 1, 2, 3, ..., n; where X j Let X represent the j-th comprehensive index. min and X max These represent the minimum and maximum scores of each trait index on each principal component, respectively; weights W j P represents the weight of the j-th principal component; j This represents the eigenvalues ​​corresponding to the extracted principal components.

[0019] Furthermore, in (4), cluster statistics are performed using the D value. The D value represents the comprehensive evaluation value of the edge advantage of each maize variety obtained from the comprehensive index evaluation.

[0020] Furthermore, in (4) the grey relational formula

[0021] Grey relational coefficient Lo j (k)=(Δmin+ρΔmax) / (Δo j (k)+ρΔmax)

[0022] In the formula, Δo j (k) represents the absolute difference between the two sequences at time k; Δmin and Δmax are the minimum and maximum absolute differences among all the sequences being compared, respectively, with Δmin = 0 and resolution coefficient ρ = 0.5.

[0023] Grey relational degree

[0024] Furthermore, the key indicators mentioned in (3) are chlorophyll b in the lower leaf, dry matter at maturity, dry matter at silking stage, ear length, ear diameter, number of grains per row, number of grains per ear, and yield.

[0025] The beneficial effects of the present application are: 1. The different soil types in different ecological zones of Xuchang City and Zhoukou City are fully utilized, and different growth environment conditions of edge 1 row, edge 2 row and edge 3 row in each variety plot are created, and the edge row advantages of the varieties are comprehensively evaluated by analyzing the differences of agronomic traits, dry matter and yield and constituent factors of different edge rows in different regions.

[0026] 2. The differences of each trait of corn varieties in different edge rows are obvious, and there is a large difference in the evaluation results of each single index in the evaluation of corn edge row advantage. Using multivariate statistical method to analyze the edge row advantage can accurately and truly classify and evaluate the edge row advantage of corn varieties, and provide theoretical and technical support for the selection of corn varieties in soybean-corn strip interplanting.

[0027] 3. Create completely different growth environment conditions of edge 1 row, edge 2 row and edge 3 row in each variety plot, analyze the differences of each trait of corn varieties in different edge rows, screen out corn varieties with strong edge row advantage and weak edge row advantage, and recommend them as suitable for soybean-corn 4:2 and 6:4 strip interplanting, respectively, to provide reference for screening corn varieties suitable for different strip interplanting modes of soybean-corn interplanting, and improve the benefits of strip interplanting. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 For yield marginal effect index and each index marginal effect index correlation analysis (different colors represent the strength of correlation, the closer to red (positive) or blue (negative), the higher the correlation).

[0029] Figure 2 For clustering analysis of corn variety edge row advantage in Xuchang area.

[0030] Figure 3 For clustering analysis of corn variety edge row advantage in Zhoukou area. DETAILED DESCRIPTION

[0031] The present application will be further described below in conjunction with specific examples. It should be understood that the following examples are only used to illustrate the present application and not to limit the scope of the present application, and those skilled in the art can make some non-essential improvements and adjustments according to the content of the above-mentioned application.

[0032] Example 1

[0033] This example selects 23 corn varieties that have been widely promoted in China and newly approved in recent years as test materials (see Table 1), and is planted in Henan Province in 2022 in Xuchang City, Jianan District, Chencao Township, Shizhuang Village (114°01'E, 34°08'N), with soil type being moist soil. The density is set to 8000 plants per mu, with 6 rows of area, equal row spacing of 60 cm, row length of 10 m, and interval of 1.5 m between plots, with 3 repetitions. The agronomic traits at silking stage, dry matter accumulation at silking stage and maturity stage, grain yield at maturity stage and its constituent factors are determined and analyzed. Through correlation analysis of agronomic traits, dry matter, yield and its constituent factors of the first, second and third rows of the plot, principal component analysis, fuzzy membership function method, cluster analysis, stepwise regression analysis and grey correlation degree analysis, etc. Multivariate analysis method, the edge row advantage of corn varieties is evaluated and classified, and the identification index of corn edge row advantage is determined, and the corn varieties with strong and weak edge row advantage are screened.

[0034] Table 1 Test variety situation

[0035]

[0036] In the soybean and corn belt composite planting in Huanghuaihai area, corn is suitable for planting 2-4 rows, which can better play the edge row advantage. In order to screen out strong and weak edge row advantage varieties and edge row advantage identification index, the marginal effect index of edge 1 row and edge 2 row index value is calculated and analyzed as follows.

[0037] The relevant indexes are calculated as follows:

[0038] (1) Marginal effect index of each index trait: Marginal effect index = edge 1 row trait value / edge 2 row trait value;

[0039] (2) Principal component analysis: extract principal components according to the criterion that the eigenvalue is greater than 1;

[0040] (3) Membership function value U(X j ) = (X j -X min ) / (X max -X min ); j = 1, 2, 3, …, n; wherein X j represents the jth comprehensive index, X min and X max represent the minimum and maximum values of the score value of each trait index on each principal component, respectively;

[0041] (4) Weight W j represents the weight of the jth principal component; P j represents the eigenvalue corresponding to the extracted principal component;

[0042] (5) D value represents the edge row advantage comprehensive evaluation value of each corn variety by comprehensive index evaluation; cluster statistics is carried out by D value.

[0043] (6) Grey correlation degree formula

[0044] Grey correlation coefficient Lo j (k) = (Δmin + ρΔmax) / (Δo j (k) + ρΔmax)

[0045] In the formula, Δo j (k) is the absolute difference of two sequences at time k; Δmin and Δmax are the minimum value and the maximum value of the absolute difference of all comparison sequences at each time, Δmin = 0, and the resolution coefficient ρ = 0.5.

[0046] Grey correlation degree

[0047] 1. Change of main traits of corn varieties and evaluation of edge row advantage of corn varieties

[0048] 1.1 Difference analysis of relevant traits of corn varieties in different edge rows

[0049] The difference analysis of relevant traits of corn varieties in different edge rows is shown in Table 2. Compared with edge 1 row, the stem diameter, dry matter at maturity, dry matter at silking, light transmittance at ear node layer, chlorophyll a of ear node leaf, chlorophyll a of leaf under ear, chlorophyll b of leaf under ear, ear length, ear diameter, row kernel number, ear kernel number and yield of edge 2 row and edge 3 row significantly decreased, and the bare tip length significantly increased, and the differences of other indexes between different edge rows were not significant.

[0050] Table 2 Difference of main traits of corn varieties in different edge rows

[0051]

[0052]

[0053] 1.2 Marginal effect index and correlation analysis of single index of each corn variety

[0054] As shown in Table 3, Table 4, the change rule of 28 indexes of different edge rows of each corn variety is quite different. Compared with edge 1 row, the indexes of edge 2 row such as ear height (X3), bulk density (X22), bare tip length (X24), ear row number (X25) and the like are increased, the marginal effect index is less than 1, and the indexes of other indexes except plant height (X1) are decreased, and the marginal effect index is greater than 1. In the marginal effect index of each single index, the marginal effect indexes of yield (X28) and dry matter at silking stage (X7) are the largest, and are 1.324 and 1.323 respectively; the marginal effect indexes of bare tip length (X24) and ear height (X3) are the smallest, and are 0.885 and 0.980 respectively. Therefore, there is a certain one-sidedness in using the marginal effect index of a single trait to evaluate the edge row advantage of different corn varieties. In order to more accurately analyze the edge row advantage of each variety, the correlation of the marginal effect indexes of 23 varieties is analyzed. Figure 1

[0055] Except that there is no significant correlation between the marginal effect indexes of ear height (X3), leaf area index (X4), specific leaf weight (X5), ear leaf angle (X9), leaf angle below ear (X10), top layer transmittance (X11), ear transmittance (X12), bottom layer transmittance (X13), chlorophyll a of ear leaf (X16), bulk density (X22), hundred-grain weight (X23) and bare tip length (X24) and other indexes, there is a significant correlation between the marginal effect indexes of the remaining each single index, which indicates that there is information overlap between each single index. Since there is a great difference in the evaluation result of the single index in the corn edge row advantage, it is indicated that the corn edge row advantage is a complex comprehensive trait, and it is difficult to accurately and directly evaluate the corn edge row advantage by using each single index. Therefore, in order to make up for the deficiency of the single index in evaluating the edge row advantage, the edge row advantage is analyzed by using a multivariate statistical method.

[0056] Table 3 Marginal effect index of agronomic traits, dry matter accumulation and canopy transmittance

[0057]

[0058] X1: plant height; X2: stem diameter; X3: ear height; X4: leaf area index; X5: specific leaf weight; X6: dry matter at mature stage; X7: dry matter at silking stage; X8: leaf angle above ear; X9: ear leaf angle; X10: leaf angle below ear; X11: top layer transmittance; X12: ear transmittance; X13: bottom layer transmittance; and the same below.

[0059] Table 4 Marginal effect index of three-leaf chlorophyll, yield and constituent factors

[0060] ​

[0061] X14: leaf chlorophyll a above the ear; X15: leaf chlorophyll b above the ear; X16: leaf chlorophyll a at the ear; X17: leaf chlorophyll b at the ear; X18: leaf chlorophyll a below the ear; X19: leaf chlorophyll b below the ear; X20: ear length; X21: ear diameter; X22: bulk density; X23: 100-grain weight; X24: length of bare tip; X25: number of rows of the ear; X26: number of grains per row; X27: number of grains per ear; X28: yield. The same below.

[0062] 1.3 Principal component analysis of marginal effect index of each single index of corn varieties

[0063] After eliminating 14 indexes of ear height (X3), leaf area index (X4), specific leaf weight (X5), leaf angle at the ear (X9), leaf angle below the ear (X10), top layer transmittance (X11), ear position transmittance (X12), bottom layer transmittance (X13), leaf chlorophyll a above the ear (X14), leaf chlorophyll b above the ear (X15), leaf chlorophyll a at the ear (X16), bulk density (X22), 100-grain weight (X23) and length of bare tip (X24), the principal components were extracted based on the marginal effect index of 14 single indexes of 23 corn varieties according to the criterion of eigenvalue greater than 1 (Table 5), and the results showed that the cumulative contribution rate of the first four comprehensive indexes was 81.940%, which had great information representation. Thus, the original 14 single indexes were converted into four new independent comprehensive indexes, which could be used to summarize and analyze the edge row advantage of different corn varieties.

[0064] As shown in Table 5, the contribution rate of principal component 1 was 39.863%, which basically reflected the information of four indexes of ear diameter (X21), number of grains per row (X26), number of grains per ear (X27) and yield (X28); the contribution rate of principal component 2 was 24.571%, which basically reflected the information of four indexes of leaf chlorophyll b at the ear (X17), leaf chlorophyll a below the ear (X18), leaf chlorophyll b below the ear (X19) and number of rows of the ear (X25); the contribution rate of principal component 3 was 9.987%, which basically reflected the information of two indexes of stem diameter (X2) and leaf angle above the ear (X8); and the contribution rate of principal component 4 was 7.520%, which basically reflected the information of four indexes of plant height (X1), dry matter at the mature stage (X6), dry matter at the silking stage (X7) and ear length (X20).

[0065] Table 5 Characteristic vector and contribution rate of principal component of each trait

[0066]

[0067] * indicates the maximum absolute value of a certain index in each factor; PI1: principal component 1; PI2: principal component 2; PI3: principal component 3; PI4: principal component 4.

[0068] 1.4 Establishment of a comprehensive evaluation method for the edge row advantage of maize

[0069] 1.4.1 Membership Function Analysis

[0070] The membership function values ​​of each comprehensive index for each tested material were calculated according to the formula (Table 6). For the same comprehensive index CI1, the largest membership function value U(X1) is for Zhongyuan 211, which is 1.000, indicating that this variety exhibits the strongest edge advantage under this comprehensive index. On the other hand, the smallest U(X1) value is for Zhengdan 2098, which is 0.000, indicating that this variety exhibits the weakest edge advantage under this comprehensive index.

[0071] 1.4.2 Weight Determination

[0072] Based on the contribution rate of each comprehensive indicator, the weights of the four principal component comprehensive indicators are calculated to be 0.486, 0.300, 0.122, and 0.092, respectively (Table 6).

[0073] 1.4.3 Comprehensive Evaluation

[0074] The comprehensive evaluation value (D) of the edge advantage is calculated based on the index weights and membership functions. The larger the D value, the stronger the edge advantage, and vice versa. The edge advantage of different varieties is then classified according to the size of the D value (Table 6). As shown in Table 6, MC121 has the largest D value of 0.662, indicating that it has the strongest edge advantage; Zhengdan 2098 has the smallest D value of 0.122, indicating that this variety has the weakest edge advantage.

[0075] Table 6. Comprehensive index values, weights, and U(X) of each tested material. j D-value and overall evaluation

[0076]

[0077] CI: Comprehensive index value; U(X): Membership function value; D value: Comprehensive evaluation value; VP value: Predicted value calculated based on the stepwise regression equation. 1.5 Screening of indicators for identifying side dominance.

[0078] 1.5.1 Stepwise Regression Analysis

[0079] To screen and evaluate the identification indicators for the edge row advantage of maize, stepwise regression analysis was conducted using the marginal effect indices of 14 traits as independent variables and the comprehensive evaluation D-value as the dependent variable. The resulting equation was Y = -1.265 + 0.368X. 26 +0.191X 19 +0.772X 21 +0.06X 17+0.047X6+0.11X2(R2=0.995, F=520.551, P<0.001). According to the equation, the row number of kernels (X26), chlorophyll b content of leaf under ear (X19), ear diameter (X21), chlorophyll b content of leaf under ear (X17), dry matter at maturity (X6), stem diameter (X2) can be used as the identification index of maize row superiority. The predicted value (VP) of maize row superiority is significantly correlated with the comprehensive evaluation D value (r=0.967) by using the regression equation to predict maize row superiority, and the accuracy is better.

[0080] 1.5.2 Correlation analysis between marginal effect index of single index and comprehensive evaluation D value

[0081] The correlation analysis between marginal effect index of single index and comprehensive evaluation D value showed that D value was significantly correlated with marginal effect index of dry matter at maturity (X6), dry matter at silking stage (X7), chlorophyll b content of leaf under ear (X19), ear length (X20), ear diameter (X21), row number of kernels (X26), ear number of kernels (X27) and yield (X28), and the above 8 indexes can be used for maize row superiority evaluation (Table 7).

[0082] 1.5.3 Grey correlation degree analysis

[0083] The grey correlation degree analysis showed that the correlation degree between ear number of kernels (X27) and comprehensive evaluation D value was the largest, which was 0.909, and the correlation degree between chlorophyll b content of leaf under ear (X19) and comprehensive evaluation D value was the smallest, which was 0.528 (Table 8). The indexes closely related to comprehensive evaluation D value (greater than 0.75) were row number of kernels (X26), ear number of kernels (X27), yield (X28), dry matter at maturity (X6), dry matter at silking stage (X7), ear length (X20) and ear diameter (X21).

[0084] In order to enhance the scientificity and persuasiveness of identification index, the common indexes obtained by any two methods were used as the identification index of maize row superiority, i.e. dry matter at maturity (X6), dry matter at silking stage (X7), ear length (X20), ear diameter (X21), row number of kernels (X26), ear number of kernels (X27), yield (X28) and chlorophyll b content of leaf under ear (X19).

[0085] Table 7 Correlation between marginal effect index of single index and comprehensive evaluation D value

[0086]

[0087] Table 8 Grey correlation degree between marginal effect index of single index and comprehensive evaluation D value

[0088]

[0089] 1.6 The classification of the varieties in Xuchang area according to the D value

[0090] The D value was analyzed by cluster analysis using the square Euclidean distance method Figure 2 When the genetic distance was 8, 23 corn varieties could be divided into 3 categories. The first category included Zhongyuan 211, MC121, Denghai 111, Denghai 618, LA505, Denghai 3206, Yudan 132, and Denghai 605, which had the strongest edge row advantage. The second category included MJ1907, Yudan 983, Yudan 9966, MC4520, Denghai 1875, Fuer 5152, Zhongdi 6, Denghai 533, Liangyu 99, Denghai 187, Zhengdan 958, Haoyu 16, Dongdan 913, and GL732, which had a medium edge row advantage. The third category included Zhengdan 2098, which had the weakest edge row advantage.

[0091] 1.7 Comparison of characteristics between varieties in different edge row advantage categories

[0092] According to the results of cluster analysis, stepwise regression, correlation analysis, and grey correlation degree analysis, the performance characteristics of each identification index between corn varieties in different edge row advantage categories were compared. From the varieties with strong edge row advantage to the varieties with weak edge row advantage, the decrease in the decrease range of ear length, ear thickness, row grain number, ear grain number, yield, chlorophyll b content of the leaf under the ear, dry matter at the silking stage, and dry matter at the mature stage from the edge 1 row to the edge 2 row showed a decreasing trend. For the varieties with strong edge row advantage, ear length, ear thickness, row grain number, ear grain number, yield, chlorophyll b content of the leaf under the ear, dry matter at the silking stage, and dry matter at the mature stage showed a decreasing trend from the edge 1 row to the edge 2 row. For the varieties with weak edge row advantage, ear length, ear thickness, row grain number, ear grain number, yield, and chlorophyll b content of the leaf under the ear increased from the edge 1 row to the edge 2 row, while dry matter at the silking stage and dry matter at the mature stage showed a decreasing trend, with a smaller decrease range than the varieties with strong edge row advantage.

[0093] In summary, in Xuchang area, the corn varieties with strong edge row advantage and yield in the edge 1 row greater than 600 kg / mu were suitable for the 4:2 mode of soybean-corn strip interplanting, including Zhongyuan 211, MC121, Denghai 111, Denghai 618, LA505, Denghai 3206, Yudan 132, and Denghai 605. The corn variety with weak edge row advantage and average yield in the edge 1 row and the edge 2 row greater than 600 kg / mu was suitable for the 6:4 mode of soybean-corn strip interplanting, which was Zhengdan 2098. Chlorophyll b content of the leaf under the ear, dry matter at the mature stage, dry matter at the silking stage, ear length, ear thickness, row grain number, ear grain number, and yield were the identification indexes of corn edge row advantage.

[0094] Table 9 Performance characteristics of each edge row advantage category in the cluster analysis results

[0095]

[0096] Example 2

[0097] The present example selects 23 corn varieties which are widely promoted in China and newly approved in recent years as test materials (see Table 10), and is planted in Zhouzhuang Village, Shangshui County, Zhoukou City, Henan Province in 2022 (114°51'E, 33°31'N), the soil type is sandy ginger black soil, the density is set to 8000 plants per mu, 6 rows of area, equal row spacing 60 cm, row length 10 m, interval between plots 1.5 m, 3 times repetition. Through the analysis of the yield and its constituent factors of the plants in the edge 1, edge 2 and edge 3 rows, the edge row advantage of corn varieties is evaluated and classified, and corn varieties with strong and weak edge row advantage are screened.

[0098] Table 10 Test variety situation

[0099]

[0100] In the soybean-corn belt composite planting in the Huanghuaihai region, corn is suitable for planting 2-4 rows, which can better play the edge row advantage. In order to screen out strong and weak edge row advantage varieties and edge row advantage identification index, the marginal effect index of edge 1 row and edge 2 row index value is calculated and analyzed as follows. The marginal effect index of each index trait: Marginal effect index = edge 1 row trait value / edge 2 row trait value;

[0101] 2, Change of main traits of corn varieties and evaluation of edge row advantage of corn varieties

[0102] 2.1 Difference analysis of related traits of corn varieties in different edge rows

[0103] The difference analysis of each trait of corn varieties in different edge rows is shown in Table 11. Compared with edge 1 row, the ear length, row kernel number, ear kernel number and yield of edge 2 row and edge 3 row are significantly reduced, and the difference between different edge rows is not significant.

[0104] Table 11 Difference of main traits of corn varieties in different edge rows

[0105]

[0106] 2.2 Marginal effect index of each variety index of corn

[0107] The marginal effect index of each variety index of corn is shown in Table 12. There are great differences in the change rule of each single index of corn varieties in different edge rows. Compared with edge 1 row, each single index of edge 2 row is reduced, and the marginal effect index is greater than 1. Among the marginal effect index of each single index, the marginal effect index of yield and bare tip length is the largest, which is 1.198 and 1.225 respectively, and the marginal effect index of bulk density and ear thickness is the smallest, which is 1.004 and 1.014 respectively.

[0108] Table 12 Marginal effect index of each variety index of corn

[0109]

[0110]

[0111] 2.3 Classification of varieties according to edge row advantage

[0112] The edge row advantage identification indexes of yield, ear length, ear thickness, row grain number and ear grain number were used as evaluation indexes, and the marginal effect indexes of the five indexes were used for edge row advantage cluster analysis. Figure 3 At a distance of 15, 23 corn varieties can be divided into three categories. Yudan 1881, LA505, MY73, Denghai 1875, Dongdan 913, Denghai 3206, Fur 5152 and Zhengdan 958 are the first category with the strongest edge row advantage; MJ1907, Denghai 528, Denghai 605, Denghai 1717, Denghai 710, Yudan 983, GL732, Yudan 9953, MC4520, Liangyu 99 and Zhongyuan 211, Denghai 618 are the second category with medium edge row advantage; Zhengdan 2098, Haoyu 16 and Zhongdi 6 are the third category with the weakest edge row advantage.

[0113] 2.4 Comparison of characteristics between varieties in different edge row advantage categories

[0114] From the varieties with strong edge row advantage to the varieties with weak edge row advantage, the decrease of ear length, ear thickness, row grain number, ear grain number and yield from edge 1 row to edge 2 row showed a decreasing trend. For the varieties with strong edge row advantage, ear length, ear thickness, row grain number, ear grain number, yield all showed a downward trend from edge 1 row to edge 2 row, and the decrease was greater than that of the varieties with weak edge row advantage and medium edge row advantage. For the varieties with weak edge row advantage, ear thickness, row grain number and ear grain number showed an increasing trend from edge 1 row to edge 2 row, and ear length and yield showed a downward trend, and the decrease was smaller than that of the varieties with strong edge row advantage.

[0115] Table 13 Performance characteristics of each edge row advantage category in the clustering result

[0116]

[0117] In summary, in Zhoukou region, the corn varieties with strong edge row advantage and edge 1 row yield greater than 600 kg / mu are suitable for soybean-corn strip interplanting 4:2 mode, including Yudan 1881, MY73, Denghai 1875, Dongdan 913 and Denghai 3206. The corn varieties with weak edge row advantage and average yield of edge 1 row and edge 2 row greater than 600 kg / mu are suitable for soybean-corn strip interplanting 6:4 mode, including Zhengdan 2098 and Haoyu 16. The chlorophyll b of lower leaf, dry matter at maturity, dry matter at spinning period, ear length, ear thickness, row grain number, ear grain number and yield are the identification indexes of corn edge row advantage.

[0118] The above shows and describes the basic principles and main features of the present application and the advantages of the present application. Those skilled in the art should understand that the present application is not limited to the above-mentioned embodiments, and the above-mentioned embodiments and descriptions in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for screening corn varieties suitable for soybean-corn 4:2 and 6:4 strip interplanting, characterized by It comprises the following steps: (1) selecting newly approved or widely promoted maize varieties, creating different growth environment conditions of edge 1 row, edge 2 row and edge 3 row in each variety plot through high density, no protection row, and wider plot spacing; (2) according to the lodging situation of the varieties after high density, eliminating the lodging varieties, retaining the lodging-resistant varieties, and measuring the analysis indexes of edge 1 row, edge 2 row and edge 3 row of the lodging-resistant varieties; (3) using correlation analysis and multivariate analysis method to select the key indexes that can reflect the edge row advantage of maize; (4) according to the results of cluster analysis, stepwise regression, correlation analysis and grey correlation degree analysis, statistically analyzing the differences of the key indexes of edge row advantage between edge 1 row and edge 2 row of each variety, judging the edge row advantage performance characteristics of each maize variety, and screening the maize varieties with strong and weak edge row advantage; (5) recommending the maize varieties with high yield and strong edge row advantage for suitable 4:2 strip planting of soybean and maize, and recommending the maize varieties with high yield and weak edge row advantage for suitable 6:4 strip planting of soybean and maize; (2) the analysis indexes in the description are edge row agronomic traits, dry matter accumulation, grain yield and its constituent factors; (3) the multivariate analysis method in the description includes principal component analysis, fuzzy membership function method, stepwise regression analysis and grey correlation degree analysis; The membership function value U(X j ) in the fuzzy membership function method is (X j -X min ) / (X max -X min ); j=1, 2, 3, …, n; where X j represents the jth comprehensive index, X min and X max respectively represent the minimum and maximum values of the score of each trait index on each principal component; the weight represents the weight of the jth principal component; represents the eigenvalue corresponding to the extracted principal component; The planting conditions in (1) are: the density is set to 8000 plants per mu, 6 rows of area, equal row spacing of 60 cm, row length of 10 m, plot spacing of 1.5 m, and 3 times of repetition; The agronomic traits include plant height, stem diameter, ear height, leaf area index, angle of three-leaf clamps, specific leaf weight of ear leaf, crown layer light transmittance, chlorophyll a of three-leaf clamps, and chlorophyll b of three-leaf clamps. The marginal effect index of each index trait in the principal component analysis is: marginal effect index = edge 1 row trait value / edge 2 row trait value; according to the criterion that the eigenvalue is greater than 1, the principal components are extracted.

2. The method for screening corn varieties suitable for soybean-corn 4:2 and 6:4 strip intercropping according to claim 1, characterized in that: The D value is used for cluster statistics in (4), D value represents the edge row advantage comprehensive evaluation value of each corn variety from the comprehensive index evaluation.

3. The method for screening corn varieties suitable for soybean-corn 4:2 and 6:4 strip intercropping according to claim 1, characterized in that: (4) The formula of the gray correlation degree The gray correlation degree coefficient Lo j (k) = (Δmin + pΔmax) / (Δoj(k) + pΔmax) In the formula, Δo j (k) is the absolute difference of the two sequences at time k; Δmin and Δmax are the minimum value and the maximum value of the absolute difference of all comparison sequences at each time, Δmin = 0, and the resolution coefficient p = 0.

5. Grey correlation degree γ .

4. The method for screening corn varieties suitable for soybean-corn 4:2 and 6:4 strip intercropping according to claim 1, characterized in that: The key indexes in (3) are chlorophyll b of ear leaf, dry matter at maturity, dry matter at silking stage, ear length, ear diameter, row grain number, ear grain number and yield.

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