A method for improving egg production of laying hens
Patent Information
- Application Number
- CN202310009306.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2023-01-04
- Publication Date
- 2026-10-16
- Estimated Expiration
- 2043-01-04
AI Technical Summary
[0006] The beneficial effects of this invention are as follows: This invention provides a method for breeding laying hens to increase egg production. By using animal models in conjunction with data from the candidate population itself and data from previous generations, the weight of data from the mid-to-late stages of egg production is increased, and more progress can be made in egg production when breeding laying hens.
Smart Images
Figure SMS_4 
Figure SMS_5
Abstract
Description
Technical Field
[0001] This invention relates to the field of genetic breeding, specifically to a method for breeding laying hens to increase egg production. Background Technology
[0002] For over 30 consecutive years, my country has maintained the world's largest number of laying hens and the highest egg production. Egg production is the most important trait for high-producing laying hens, and cumulative egg production is often used as a metric. The heritability of egg production is estimated at 0.1-0.2, classifying it as a low-heritability trait, making selective breeding challenging. To shorten generation intervals, current selection for egg production in laying hens is generally based on the candidate flock's early-stage egg production and the parents' total egg production over the entire breeding period. When the size of the core breeding flock and the production process are fixed, the only ways to increase egg production are to increase the culling weight for egg production and improve the accuracy of egg production selection. Summary of the Invention
[0003] To address the problems existing in the above-mentioned technologies, the present invention provides a method for breeding laying hens to increase egg production, wherein the laying hens are Rocky Reds; The egg-laying hen breeding method includes the following steps. S1 constructs a candidate population with pedigree data and previous generations' egg production data; S2 divides the egg-laying cycle of laying hens into: n A time period; S3 calculates the number of eggs laid by each female individual in the candidate population and by female individuals in each previous generation for each time period; S4 inputs the egg production count of female individuals in the candidate population for each time period, the egg production count of each female individual in previous generations, and pedigree data into the animal model BLUP to calculate the breeding value of the candidate population for each time period. Each individual in the candidate population receives... n Each breeding value, namely ENs1 to ENs n ; S5 assigns weights to the breeding values for each time period, i.e., W1 to W... n total n Each time period has a weight, and the sum of the weights corresponding to the breeding values in each time period is 100%. Calculate the egg production index of each individual in the candidate population, wherein the egg production index ENX = (ENs1 × W1) + (ENs2 × W2) + ... + (ENs... n ×W n ); Among them, ENs1 to ENs n The weighting ratio gradually increases; S6 sorts the individuals in the candidate population by sex according to the egg production index ENX, and eliminates the last-place individuals to select the winning population.
[0004] Among them, the breeding values were normalized before calculating the egg production index ENX; When calculating the breeding value, the age at first spawning was used as a covariate for the animal model BLUP.
[0005] in, n =3, W1 is 10%, W2 is 30%, W3 is 60%.
[0006] The beneficial effects of this invention are as follows: This invention provides a method for breeding laying hens to increase egg production. By using animal models in conjunction with data from the candidate population itself and data from previous generations, the weight of data from the mid-to-late stages of egg production is increased, and more progress can be made in egg production when breeding laying hens. Detailed Implementation
[0007] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.
[0008] This invention provides a method for screening laying hens, the purpose of which is to select a group with higher egg production by screening candidate groups, thereby increasing economic benefits.
[0009] The screening method specifically includes the following steps: the laying hens are Loch Ness Reds. S1 constructs a candidate population with pedigree data and previous generations' egg production data; S2 divides the egg-laying cycle of laying hens into: n A time period; S3 calculates the number of eggs laid by each female individual in the candidate population and by female individuals in each previous generation for each time period; S4 inputs the egg production count of female individuals in the candidate population for each time period, the egg production count of each female individual in previous generations, and pedigree data into the animal model BLUP to calculate the breeding value of the candidate population for each time period. Each individual in the candidate population receives... n Each breeding value, namely ENs1 to ENs n ; S5 assigns weights to the breeding values for each time period, i.e., W1 to W... n total n Each time period has a weight, and the sum of the weights corresponding to the breeding values in each time period is 100%. Calculate the egg production index of each individual in the candidate population, wherein the egg production index ENX = (ENs1 × W1) + (ENs2 × W2) + ... + (ENs... n ×Wn ); Among them, ENs1 to ENs n The weighting ratio gradually increases; S6 sorts each individual in the candidate population according to the egg production index ENX, and eliminates the last-place individuals to select the winning population.
[0010] Among them, the breeding values were normalized before calculating the egg production index ENX; The normalization method is Min-Max Normalization: ; In the above formula, For individuals i Breeding values after normalization For individuals i Breeding values without normalization, max( x ) represents the maximum breeding value among all individual breeding values before normalization, min( x () represents the smallest breeding value among all individual breeding values before normalization.
[0011] Assuming the candidate population has 800 hens, the maximum ENS1 value is 140, the minimum value is 10, and a hen's ENS1 egg production count is 100, then the standardized ENS1 is (100-10) / (140-10) = 0.69. Furthermore, when calculating the breeding value, the age at first spawning was used as a covariate for the BLUP animal model in the first time period.
[0012] Example 1
[0013] 1. Construct a candidate population with pedigree data and previous generations' egg production data, consisting of 125 roosters and 773 hens. This candidate population is a purebred population obtained through continuous self-breeding in previous generations. In this embodiment, previous generations refer to the female individuals in the parent and grandparent groups of the candidate population. 2. The laying cycle of laying hens is divided into three time periods. The laying cycle of laying hens is generally from the start of laying to 90 weeks of age. In this embodiment, time period 1 is from the start of laying to 44 weeks of age, time period 2 is from 45 weeks of age to 66 weeks of age, and time period 3 is from 67 weeks of age to 90 weeks of age. In this embodiment, in order to accelerate generational progress, screening is carried out at 45 weeks of age, after which offspring can be bred.
[0014] 3. Calculate the number of eggs laid by each female individual in the candidate population and by female individuals in each previous generation for the three time periods mentioned above. Since male individuals in the candidate population do not lay eggs, they are not included in the calculation.
[0015] 4. Input the number of eggs laid by female individuals in the candidate population corresponding to each time period, the number of eggs laid by each female individual in previous generations (grandparent and parent populations), and pedigree data into the animal model BLUP to calculate the breeding value corresponding to each time period of the candidate population. Each individual in the candidate population obtains 3 breeding values, namely ENs1 to ENs3. Since time period 1 is from the start of egg production to 44 weeks of age, as the early egg production number, in order to correct the influence of different individuals' start of egg production time on the estimation of breeding value, the age at the start of egg production is used as a covariate in the animal model BLUP when calculating the breeding value.
[0016] It should be noted that, due to the availability of pedigree data, even if the male individuals in the candidate population do not have a number of eggs laid, inputting the above data into the male individuals in the animal model will still yield three corresponding breeding values. Furthermore, since the candidate population in this embodiment begins to be screened and eliminated at 45 weeks of age, the female individuals in the candidate population do not have egg production data for time periods 2 and 3. In the next step when using the animal model, the data of the female individuals in the candidate population input will only be the egg production data for time period 1.
[0017] Therefore, in this embodiment, the data required for calculating the individual sport index in the candidate population is as follows: ENs1: Egg production of female individuals in time period 1, egg production of parent and grandparent populations in time period 1, pedigree file; ENs2: Egg production numbers for parent and grandparent populations in time period 2, pedigree file.
[0018] ENs3: Egg production numbers for parent and grandparent populations in time period 3, pedigree file.
[0019] 5. Assign weights to the breeding values for each time period, i.e., three weights from W1 to W3, with the sum of the weights for each time period being 100%; In this embodiment, W1 is 10%, W2 is 30%, and W3 is 60%. Calculate the egg production index ENX for each individual in the candidate population, wherein the egg production index ENX = (ENs1 × W1) + (ENs2 × W2) + (ENs3 × W3); wherein the breeding values are normalized before calculating the egg production index ENX. It should be noted that the weighting of time period 1 is set to be relatively small, while the weighting of later time periods is set to be relatively large, because through experiments, the inventors found that after several generations of selective breeding, the early egg production of laying hens has reached its limit. However, if we pay attention to the egg production in the middle and later stages of egg production and use this as the basis for selective breeding, we will achieve more progress in egg production. Specific data will be provided later.
[0020] 6. Individuals in the candidate population were sorted by sex from highest to lowest according to their egg production index (ENX). The lowest-ranked individuals were eliminated to obtain the selected experimental population. The egg production (ENX) of the selected experimental population during time period 1-3 was then calculated. S1 EN S2 EN S3 The average number of eggs laid from the start of laying to 90 weeks of age was used as experimental data. In this example, the culling rate was 30%.
[0021] The selected experimental group is simply called the selected group. The name "selected experimental group" is used to distinguish it from the selected group 1 and selected group 2 mentioned later.
[0022] Comparative Example As a weighting reference, we set the weighting weight of time period 1 and time period 2 to 60% respectively, that is, to focus on the early and middle stages of egg production. The detailed weights are as follows: According to weight 1: W1 is 60%, W2 is 30%, and W3 is 10%; Weighted average 2: W1 = 10%, W2 = 60%, W3 = 30%; Steps 5 and 6 were repeated for the candidate populations according to control weight 1 and control weight 2, respectively, to obtain selected population 1 and selected population 2, which served as control 1 and control 2, respectively. The egg production (EN) of the two selected experimental populations in time period 1-3 was calculated. S1 EN S2 EN S3 ), the average cumulative number of eggs laid from the start of laying to 90 weeks of age (EN90).
[0023] Based on the cumulative egg production and pedigree data of previous generations of hens from the start of egg production to 90 weeks of age in the candidate population, the BLUP model was used to estimate the breeding value EN90 of the cumulative egg production at 90 weeks of age in the candidate population. The populations were then sorted from highest to lowest, and the bottom 30% of individuals were culled to obtain selected population 3, which served as control group 3. The egg production (EN90) of the selected experimental population during time periods 1-3 was calculated. S1 EN S2 EN S3 ), the average cumulative number of eggs laid from the start of laying to 90 weeks of age (EN90).
[0024] Results Explanation Table 1. Comparison of egg production performance of progeny selected by weighted index and those selected by traditional egg production number.
[0025] Note: The egg production performance of offspring refers to the number of eggs laid by daughters of the selected population based on pedigree information, and the same applies below.
[0026] From the perspective of egg production from the start of laying to 90 weeks of age, the experimental group's offspring had the best egg production performance, at 445.8 eggs, which was 5.9 eggs higher than the population average. Control groups 1 and 2 focused on selecting egg production from the start of laying to 44 weeks of age and from 45 to 66 weeks of age, respectively. The results showed that control group 1's offspring had the largest increase in egg production at 44 weeks of age compared to other selection methods, by 2.4 eggs; control group 2's offspring had the largest increase in egg production from 45 to 66 weeks of age compared to other selection methods, by 1.2 eggs. However, neither control group 1 nor control group 2 could increase egg production from 67 to 90 weeks of age. In the experimental group, egg production from 46 to 66 weeks of age and from 67 to 90 weeks of age were effectively improved. Therefore, setting the key selection stage for the egg production weighted index as 67 to 90 weeks of age is reasonable.
[0027] Control group 3 used the traditional egg production selection method, i.e., without segmentation. The selected experimental population's progeny had an egg production of 5.9 eggs at 90 weeks of age higher than the traditional method. Looking at the stage-specific egg production, the early-stage egg production (EN) of the selected experimental population's progeny... S1 Slightly higher than traditional selection methods, mid-term egg production (EN) S2 The number of eggs laid in the later stages was 1.9, higher than the traditional selection. S3 The number of eggs laid was 3.6 higher than with traditional selection, which is considerable for a trait with low heritability. This demonstrates that weighted index selection can effectively improve the accuracy of egg production selection and increase breeding efficiency in the mid-to-late stages of egg production.
[0028] This also proves that, as mentioned earlier, after years of selective breeding, the early egg production of laying hens has reached its limit; therefore, increasing the weighting of later stages can achieve greater progress in egg production. (See attached table) 125 candidate roosters were selected using the method described above, with a culling rate of 30%. The egg production performance of the offspring in different selected groups is shown in Table 2. The results show that the offspring of the experimental group performed best among the methods, indicating that the egg production weighted index is also effective in selecting rooster groups for egg production performance.
[0029] Table 2 Comparison of egg production performance of progeny selected by weighted index of roosters and those selected by traditional egg production number.
[0030] The concept of balanced breeding is constantly evolving. Egg-laying hen breeding aims not only to increase egg production but also to improve egg quality and feed efficiency. To meet the goals of balanced breeding, it is necessary to reduce the culling weight of egg production count and improve the accuracy of selection based on egg production count. Therefore, as discussed earlier in this application, using multi-stage weighted selection based on egg production count can reduce the overall weight of egg production count while maintaining traditional breeding progress, thereby allocating it to other target traits such as egg quality and feed efficiency.
[0031] Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.
Claims
1. A method for breeding laying hens to increase egg production, characterized in that, The laying hens are Rock Island Reds; The method for breeding laying hens includes the following steps: S1 constructs a candidate population with pedigree data and previous generations' egg production data; S2 divides the egg-laying cycle of laying hens into: n A time period; S3 calculates the number of eggs laid by each female individual in the candidate population and by female individuals in each previous generation for each time period; S4 inputs the egg production count of female individuals in the candidate population for each time period, the egg production count of each female individual in previous generations, and pedigree data into the animal model BLUP to calculate the breeding value of the candidate population for each time period. Each individual in the candidate population receives... n Each breeding value, namely ENs1 to ENs n ; S5 assigns weights to the breeding values for each time period, i.e., W1 to W... n total n Each time period has a weight, and the sum of the weights corresponding to the breeding values in each time period is 100%. Calculate the egg production index of each individual in the candidate population, wherein the egg production index ENX = (ENs1 × W1) + (ENs2 × W2) + ... + (ENs... n ×W n ); Among them, ENs1 to ENs n The weighting ratio gradually increases; S6 sorts the individuals in the candidate population by sex according to the egg production index ENX, and eliminates the last-place individuals to select the winning population. Among them, the breeding values were normalized before calculating the egg production index ENX; The normalization method is Min-Max Normalization: ; In the above formula, For individuals i Breeding values after normalization For individuals i Breeding values without normalization, max( x ) represents the maximum breeding value among all individual breeding values before normalization, min( x () represents the smallest breeding value among all individual breeding values before normalization.
2. The method for breeding laying hens to increase egg production according to claim 1, characterized in that, When calculating the breeding value, the age at first spawning was used as a covariate for the animal model BLUP.
3. The method for breeding laying hens to increase egg production according to claim 2, characterized in that, n =3, W1 is 10%, W2 is 30%, W3 is 60%.