Planting variety screening and promoting method based on diagonal test system

Abstract

The invention relates to the technical field of corn breeding, and discloses a planting variety screening and promoting method based on a diagonal test system, which comprises the following steps: step 1, collecting the number of identified varieties, grouping the varieties, and determining intra-group control and diagonal control; 2, determining the boundary of a rectangular plot according to the distribution of the test plot, and determining the number of rows and columns in the plot; 3, diagonal contrast design is carried out on the segmented land parcels, and planting coordinates of diagonal varieties are determined; 4, seed metering is conducted according to the variety partition groups, the diagonal contrast positions are fixed, and the varieties and the contrast in the groups are arranged randomly. According to the diagonal design method provided by the invention, the test variety is corrected through the diagonal variety and the intra-group contrast, so that the test error is greatly reduced. Through the diagonal test design platform, the variety list can be uploaded, the diagonal test design planting scheme can be automatically generated, and the operability is high.

Description

Technical Field

[0001] The present invention relates to the technical field of corn breeding, and in particular to a planting variety screening and promotion method based on a diagonal test system. Background Art

[0002] Primary tests for corn breeding identification are characterized by a large number of tested varieties, a large planting area, and a small amount of seeds, which lead to large field errors in primary tests, low test precision, and poor seed selection accuracy.

[0003] The primary experimental design methods currently commonly used are intercropping experimental design and augmented experimental design. The following introduces these two experimental design methods. Intercropping experimental design is to group the test varieties, add a control for every 10 or 20 varieties, and plant them in the field in sequence. Based on the yield measurement data after harvest, the variety ranking and the percentage increase in yield compared with the control are calculated by group. Augmented experimental design is to group the test varieties, add a control for every 10 or 20 varieties, and randomly insert the control within the group. Plant them in the field according to the seeding order. Based on the yield measurement data after harvest, the variety ranking and the percentage increase in yield compared with the control are calculated by group.

[0004] The inter-group comparison experimental design and augmented experimental design use the form of grouping and control to correct environmental errors of varieties within the group. The varieties between groups can be ranked and screened according to the yield increase percentage of the control. Among them, the augmented experimental design adopts the form of random control, which can control system errors and is better than the inter-group comparison method.

[0005] The diagonal control test design uses diagonal controls. First, diagonal controls are set up on the plot according to a certain proportion, and then field planting is carried out according to different groups. The yield measurement data after harvest can be corrected by using statistical models based on the position of the diagonal controls and the row and column information of the variety, thereby reducing field errors and improving the accuracy of variety breeding value assessment. Summary of the Invention

[0006] In order to solve the technical problems raised in the background technology, the present invention provides a planting variety screening and promotion method based on a diagonal test system.

[0007] The present invention is implemented by the following technical solution: a method for screening and upgrading planting varieties based on a diagonal test system, comprising the following steps:

[0008] Step 1: Collect the number of identified varieties, group the varieties, and determine the intra-group control and diagonal control;

[0009] Step 2: Determine the boundaries of the rectangular plots and the number of rows and columns in the plots according to the distribution of the test plots;

[0010] Step 3: Perform diagonal comparison design on the divided plots to determine the planting coordinates of the diagonal varieties;

[0011] Step 4: Sowing seeds according to the variety grouping, where the diagonal control position is fixed and the varieties and controls within the group are randomly arranged;

[0012] Step 5: Draw a field planting map, print the diagonal test design plan, prepare seeds and sow them according to the planting plan, perform field management, use a plot harvester to measure yield at harvest time, record yield and moisture content, and calculate standard moisture yield per mu data;

[0013] Step 6: Use the diagonal design data analysis model to correct the variety data;

[0014] Step 7: Rank the varieties based on the corrected data, calculate the average ranking of multiple locations, and promote the varieties based on the average ranking.

[0015] Preferably, in step 1, 45 varieties are grouped into one block, and 3 intra-group control varieties and 4 diagonal control groups are added to each block, for a total of 52 planting varieties.

[0016] Preferably, the 52 varieties are divided into four rows, each row has one diagonal control group, and the three intra-group control varieties in each block are randomly distributed.

[0017] Preferably, the plots in step 2 are divided into square plots with 52 rows and 13 columns, and the size of each square plot is 5m×0.6m, in which 20 seedlings are planted.

[0018] Preferably, in step 5, a plot seeder and a plot harvester are used to perform sowing and yield measurement respectively.

[0019] Preferably, the diagonal design data analysis model is constructed as follows:

[0020] Step 6.1, collect yield measurement data, including test variety, block group, control variety, diagonal comparison, row and column information, equivalent per mu yield, water content, plant height, ear height, etc., and organize the data;

[0021] Step 6.2: Use a mixed linear model with first-order autoregressive spatial analysis of row and column information, with diagonal control as a fixed factor and variety as a random factor, to calculate the best linear unbiased prediction of variety.

[0022] The hybrid model structure is as follows:

[0023] y=Xb+Zu+e

[0024] Where Xb is a fixed factor, Zu is a random factor, and e is the residual;

[0025] The residuals use first-order autoregression;

[0026] The residual formula is as follows:

[0027]

[0028] The first-order autoregressive structure of row and column information is as follows:

[0029]

[0030] Step 6.3: After correction in step 6.2, each trait of the variety in each block group obtains a correction value. Using the corrected data, the mixed linear model is again used, with the block group and the control within the group as fixed factors and the variety as a random factor, to calculate the best linear unbiased prediction (BLUP) value of the variety as the final correction value.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] 1. The diagonal design method proposed in this paper significantly reduces experimental errors by correcting test varieties through diagonal varieties and intra-group controls. The diagonal test design platform allows users to upload a variety list and automatically generate a diagonal test planting plan, making it highly operational.

[0033] 2. Based on the data structure and planting characteristics of the diagonal experiment, a diagonal experiment design analysis model was developed. It can combine diagonal control, row and column information, intra-group control and other information, and use the first-order autoregressive spatial analysis method to perform data correction on the test varieties and intra-group controls. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a flow chart of the planting variety screening and promotion method based on the diagonal test system of the present invention;

[0035] Figure 2 Schematic diagram of the plot division in the diagonal test method proposed for this scheme;

[0036] Figure 3 This is a schematic diagram of the distribution of diagonal comparisons after the diagonal design of the plot is performed in the present invention;

[0037] Figure 4 Schematic diagram of planting in four blocks of the present invention;

[0038] Figure 5 This is a statistical graph of data collected after planting using the method proposed by the present invention;

[0039] Figure 6 This is the plot yield distribution map before correction using the diagonal design data analysis model proposed by the present invention;

[0040] Figure 7 This is the plot yield distribution diagram after correction using the diagonal design data analysis model proposed in this invention. DETAILED DESCRIPTION

[0041] The present invention will be further described below in conjunction with the accompanying drawings and specific implementation methods. It should be noted that, under the premise of no conflict, the various embodiments or technical features described below can be arbitrarily combined to form new embodiments.

[0042] Example:

[0043] Please combine Figure 1-Figure 7 This proposal proposes a method for screening and upgrading planting varieties based on a diagonal test system, which includes:

[0044] Step 1: Based on the number of varieties tested in the primary corn trial, divide the blocks into 45 varieties, add 3 intra-group controls, and if the last block contains less than 45 varieties, add filler varieties to form a block of 45 varieties. For example, if there are 2,000 varieties to be tested, add 25 filler varieties and divide the blocks into 45 blocks, with 45 varieties in each block, for a total of 45 blocks;

[0045] Among them: the test variety is a newly combined hybrid variety, the control variety is a variety with a relatively large promotion area in the current breeding area, and the filler variety is a variety with relatively good stability, which is also used for planting protection rows;

[0046] Step 2: For the planting site, select a rectangular plot and divide it into rows and columns. Perform a diagonal control design on the divided plots. The diagonal control setting standard is 1 diagonal control for every 12 varieties, with a ratio of 7.69%. This ratio is the most cost-effective ratio after field testing at different ratios, so we use this ratio for the diagonal test design.

[0047] like Figure 2 As shown, there is a plot of land with 52 rows and 13 columns, divided according to the planting standards. Each small square represents one variety. Each square is 0.6m wide and 5m long, and 20 plants are planted.

[0048] Step 3: Design diagonal lines for the plots. There are two diagonal controls, x_diagck1 and x_diagck2. After adding the diagonal controls, Figure 3 As shown;

[0049] Step 4: Arrange the plots according to the variety groups based on the plot information, with 4 rows and 13 columns as a group. Randomly arrange the different groups, varieties within the groups, and controls, and keep the diagonal controls fixed.

[0050] Figure 4 This is a planting diagram. The red lines represent diagonal controls. Based on the ratio of one diagonal control per row, each of the 12 varieties has a diagonal control. Varieties and controls are randomly arranged within the block. Every four rows form a block, with rows 1-4 forming Block 1, rows 5-8 forming Block 2, and so on.

[0051] The number of varieties in each block group is 13*4=52, including 3 intra-group controls and 4 diagonal controls, and the number of tested varieties is 45.

[0052] Figure 4 The red blocks in the figure represent the diagonal control, the black blocks represent the three controls ck1, ck2, and ck3 within the group, and every four rows form a block group, represented by different colors;

[0053] Step 5: Draw a field planting map, generate a field planting plan, prepare and arrange seeds according to the plan, use a plot seeder for sowing, manage the field, use a plot harvester for yield measurement at harvest time, record yield and moisture content, and calculate standard moisture per mu yield data.

[0054] Step 6: Use the diagonal design data analysis model to correct the variety data. The model is constructed as follows:

[0055] Step 6.1: Collect yield data, including test varieties, block groups, control varieties, diagonal comparison, row and column information, equivalent per-acre yield, water content, plant height, ear height, etc., and organize the data. The organized data format is as follows: Figure 5 As shown;

[0056] Step 6.2: Use a mixed linear model with first-order autoregressive spatial analysis of row and column information, diagonal control as a fixed factor, and variety as a random factor to calculate the best linear unbiased prediction (BLUP) of variety.

[0057] The hybrid model structure is as follows:

[0058] y=Xb+Zu+e

[0059] Where Xb is a fixed factor, Zu is a random factor, and e is the residual.

[0060] In the analysis model, diagonal control was used as a fixed factor, variety as a random factor, and the residuals used first-order autoregression.

[0061] The residual formula is as follows:

[0062]

[0063] The first-order autoregressive structure of row and column information is as follows:

[0064]

[0065] The core code model is as follows:

[0066] mod_a1=asreml(y1~ID:is_diag_ck,random=~geno2,residual=~aexp(row,col),data=re3,workspace="8Gb",pworkspace="8Gb",na.action=na.method(x="include",y="include")).

[0067] In the above code, ID is the variety ID, and id_diag_ck indicates whether the diagonal control is present (1 if yes, 0 if no). Therefore, ID: is_diag_ck only includes diagonal controls. geno2 is the block variety, which serves as a random factor. The residuals are first-order autoregressive, and the row-column autoregressive model is defined using the aexp function.

[0068] Step 6.3: After correction for diagonal controls and row and column information, each trait of each variety in each block group has a correction value. Using these data, once again use the mixed linear model, with block group and within-group controls as fixed factors and variety as a random factor, to calculate the best linear unbiased prediction (BLUP) value of the variety as the final correction value.

[0069] y=Xb+Zu+e

[0070] Where Xb is a fixed factor, Zu is a random factor, and e is the residual.

[0071] In the analysis model, the block and within-block control were used as fixed factors, and the variety was used as a random factor to run the model.

[0072] The core code is as follows:

[0073] mod_a2=asreml(y1~Block+ID:is_Block_ck,random=~geno3,data=re4,workspace="8Gb",pworkspace="8Gb",na.action=na.method(x="include",y="include"))

[0074] In the above code, Block is the block group, ID is the variety number, and id_Block_ck indicates whether it is an intra-group control (1 if yes, 0 if no). Therefore, ID: is_Block_ck only includes intra-group controls. geno3 is the variety within the block group, which serves as a random factor.

[0075] The yield distribution before correction is as follows Figure 6 As shown: The corrected output distribution is as follows Figure 7 As shown:

[0076] Step 7: Perform BLUP calculations on the equivalent yield per mu, water content, plant height, and ear height of the variety, and screen the varieties based on this data. Specifically, rank the varieties based on the corrected data, calculate the average ranking of multiple locations, and promote the varieties based on the average ranking.

[0077] In summary, this solution has the following advantages:

[0078] 1. The diagonal design method proposed in this paper significantly reduces experimental errors by correcting test varieties through diagonal varieties and intra-group controls. The diagonal test design platform allows users to upload a variety list and automatically generate a diagonal test planting plan, making it highly operational.

[0079] 2. Based on the data structure and planting characteristics of the diagonal experiment, a diagonal experiment design analysis model was developed. It can combine diagonal control, row and column information, intra-group control and other information, and use the first-order autoregressive spatial analysis method to perform data correction on the test varieties and intra-group controls.

[0080] The above embodiments are only preferred embodiments of the present invention and cannot be used to limit the scope of protection of the present invention. Any non-substantial changes and replacements made by technicians in this field on the basis of the present invention fall within the scope of protection required by the present invention.

Claims

1. A method for screening and upgrading plant varieties based on a diagonal test system, characterized in that: The steps include: Step 1: Collect the number of identified varieties, group the varieties, and determine the intra-group control and diagonal control; Step 2: Determine the boundaries of the rectangular plots and the number of rows and columns in the plots according to the distribution of the test plots; Step 3: Perform diagonal comparison design on the divided plots to determine the planting coordinates of the diagonal varieties; Step 4: Sowing seeds according to the variety grouping, where the diagonal control position is fixed and the varieties and controls within the group are randomly arranged; Step 5: Draw a field planting map, print the diagonal test design plan, prepare seeds and sow them according to the planting plan, perform field management, use a plot harvester to measure yield at harvest time, record yield and moisture content, and calculate standard moisture yield per mu data; Step 6: Use the diagonal design data analysis model to correct the variety data; Step 7: Rank the varieties based on the corrected data, calculate the average ranking of multiple locations, and promote the varieties based on the average ranking.

2. The method for screening and upgrading plant varieties based on the diagonal test system according to claim 1, characterized in that: In step 1, 45 varieties are grouped into a block, and 3 intra-group control varieties and 4 diagonal control groups are added to each block, for a total of 52 planting varieties.

3. The method for screening and upgrading plant varieties based on the diagonal test system according to claim 1, characterized in that: The 52 varieties were divided into four rows, with one diagonal control group in each row, and the three intra-group control varieties in each block were randomly distributed.

4. The method for screening and upgrading plant varieties based on the diagonal test system according to claim 1, characterized in that: The plots in step 2 are divided into 52 rows and 13 columns of square plots, each square plot has a size of 5m×0.6m, and 20 seedlings are planted therein.

5. The method for screening and upgrading plant varieties based on the diagonal test system according to claim 1, characterized in that: In step 5, a plot seeder and a plot harvester are used to respectively carry out sowing and yield measurement.

6. The method for screening and upgrading plant varieties based on the diagonal test system according to claim 1, characterized in that: The diagonal design data analysis model is constructed as follows: Step 6.

1. Collect yield measurement data, including test variety, block group, control variety, diagonal comparison, row and column information, equivalent per-acre yield, moisture content, plant height, ear height, etc., and organize the data; Step 6.2: Use a mixed linear model with first-order autoregressive spatial analysis of row and column information, with diagonal control as a fixed factor and variety as a random factor, to calculate the best linear unbiased prediction of variety. The hybrid model structure is as follows: y=Xb+Zu+e Where Xb is a fixed factor, Zu is a random factor, and e is the residual; The residuals use first-order autoregression; The residual formula is as follows: The first-order autoregressive structure of row and column information is as follows: Step 6.3: After correction in step 6.2, each trait of the variety in each block group obtains a correction value. Using the corrected data, the mixed linear model is again used, with the block group and the control within the group as fixed factors and the variety as a random factor, to calculate the best linear unbiased prediction (BLUP) value of the variety as the final correction value.

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