Method for breeding livestock and poultry by using novel comprehensive index

Abstract

The invention relates to a method for breeding livestock and poultry by utilizing a novel comprehensive index, which comprises the following steps of: firstly, converting a traditional performance index into a uniform scale score through linear equal proportion, then fusing scores of different characters into a performance index, fusing indexes such as production, disease purification and management into a decision factor, and finally, selecting the decision factor as a basis for selecting the comprehensive index. And finally fusing the performance index and the decision factor into a novel selection index. According to the method, a novel individual comprehensive selection index is constructed, and multiple factors such as performance indexes and production management of candidate individuals are integrated, so that the problem of relatively low efficiency of a traditional selection method is solved.

Description

Technical Field

[0001] The present invention relates to animal husbandry, and particularly to a method for selecting livestock and poultry using a new comprehensive index. Background Art

[0002] In the commercial breeding practice of livestock and poultry, in order to meet the needs of customers in the entire industrial chain, it is often necessary to select multiple traits simultaneously. When conducting actual selection operations, the method commonly used at present is to select traits sequentially one by one, and various practical factors also need to be considered comprehensively. For example, to select 5 traits within a candidate population, first select according to the first trait, and then select the second trait based on the selection of the first trait, and so on until all traits are selected. On this basis, factors such as production, disease purification, and management also need to be considered comprehensively. For example, if the individual identity identification is lost, there are some disease-positive individuals or individuals that do not conform to the breed or strain characteristics in the selected population, secondary or even tertiary selection needs to be carried out on the basis of performance-based selection. This selection method is cumbersome and complex, requiring a selection process of up to dozens of steps. In the case of selecting multiple breeds or strains, it is very time-consuming and laborious, and the entire process is not easy to be standardized and automated, resulting in low selection efficiency. Summary of the Invention

[0003] To solve the problems existing in the above technologies, the present invention provides a method for selecting livestock and poultry using a new comprehensive index, which is characterized by including the following steps:

[0004] S1 Construct a candidate population, set multiple traits to be selected, the weight w of each trait, the number N of individuals to be retained, and the original value h of the traits to be selected;

[0005] S2 Calculate the conversion score X of each individual for each trait, and set the minimum value a and the maximum value b of the converted scores of the candidate population. When the trait is a unidirectional selection trait,

[0006]

[0007] where min and max are respectively the minimum value and the maximum value of the original value h of this trait in the candidate population, and h is the original data corresponding to this trait;

[0008] S3 Calculate the breeding index I of each individual;

[0009]

[0010] where w j is the selection weight of the jth trait, and S j is the sign of each trait in the summation process. When the unidirectional selection trait is positive selection, Sj = 1 when the unidirectional selection trait is a negative selection, S j = -1; X ij is the conversion score of the j-th trait of the i-th individual;

[0011] S4 ranks each individual in the candidate population according to the breeding index I, and leaves the top N individuals as the selected population.

[0012] Furthermore, it also includes the steps:

[0013] S2-1 When the trait is a stable selection trait, the original data h of the trait is subjected to normal standardization to obtain the standardized data h' = (h - μ) / σ of the trait data;

[0014] where μ is the mean of the trait data h in the candidate population, and σ is the standard deviation of the trait data h in the candidate population;

[0015] S2-2 Calculate the probability density pd = dnorm h' of each stable selection trait, where dnorm is the probability density function;

[0016] S2-3 Calculate the conversion score X of each stable selection trait;

[0017]

[0018] where min and max are the minimum and maximum values of the probability density pd of the trait in the candidate population respectively;

[0019] In step S3, when the trait is a stable selection trait, S j = 1.

[0020] Furthermore, in step S4, individuals with defects are removed before sorting.

[0021] Furthermore, the defects include that the individual characteristics do not meet the breeding objectives of the variety or strain, the disease purification status is positive, or there are records that do not meet the expectations in production management.

[0022] Furthermore, the unidirectional selection traits include 6-week-old body weight, 6-week-old feed-to-meat ratio, breast muscle rate, abdominal fat rate, and number of eggs laid at 45 weeks of age.

[0023] Furthermore, the stable selection traits include body weight, egg weight, body size, shank length, eggshell color, and eggshell strength.

[0024] The beneficial effects of the present invention are as follows: First, the traditional performance indicators are linearly and proportionally converted into a unified scale score, then the scores of different traits are fused into a performance index, the indicators such as production, disease purification and management are fused into a decision factor, and finally the performance index and the decision factor are fused into a new selection index.

[0025] The present invention constructs a new individual comprehensive selection index, integrating performance indicators of candidate individuals and various factors such as production management, to solve the problem of low efficiency of traditional selection methods. Detailed implementation manners

[0026] The following combines the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Many specific details are set forth in the following description to facilitate a thorough understanding of the present invention, but the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0027] Embodiment 1

[0028] S1 Construct a candidate population. The candidate population of roosters of the Cornish strain of white - feather broilers in the 20th generation is 3,600, and 712 (N = 712) individuals need to be selected as the selected population and transferred to the production performance measurement chicken house.

[0029] The traits in this embodiment are all unidirectional selection traits, including body weight at 6 weeks of age, feed - to - meat ratio at 6 weeks of age, breast muscle rate, abdominal fat rate, and egg production at 45 weeks of age. These five traits are represented by BW6, FCR6, BMR6, AFR6, and EN45 respectively, and the original values h of these traits are obtained. The selection weight w and selection direction of each trait are shown in Table 1 - 1, and the sum of the weights of each trait is 1.

[0030] Table 1 - 1 Selection data, selection weights and selection directions of each trait

[0031]

[0032] S2 Calculate the conversion score X of each individual for each trait, and set the minimum value a = 0 and the maximum value b = 100 of the converted score of the candidate population. When the trait is a unidirectional selection trait,

[0033]

[0034] where min and max are the minimum and maximum values of the original value h of this trait in the candidate population respectively, and h is the original data corresponding to this trait;

[0035] S3 Calculate the breeding index I of each individual.

[0036]

[0037] where w j is the selection weight of the j-th trait, and S j is the sign of each trait in the summation process. When the unidirectional selection trait is positive selection, S j = 1, and when the unidirectional selection trait is negative selection, S j = -1; X ij is the conversion score of the j-th trait of the i-th individual. Positive selection is to select individuals with larger trait values, and negative selection is the opposite;

[0038] S4 sorts each individual in the candidate population according to the breeding index I, and keeps the top 712 individuals as the selected population.

[0039] Before sorting, individuals with defects are excluded, and the defects are used as a judgment factor for an individual to determine whether it is kept for breeding.

[0040] The defects include that the individual characteristics do not meet the breeding objectives of the variety or strain, the disease purification status is positive, or there are records that do not meet expectations in production management.

[0041] Here, the variety (strain) characteristics include but are not limited to body shape, skin color, toe type, toe number, behavior, and other body appearance related to the breeding objectives of the variety (strain); the disease purification status includes whether the disease detection status of the individual concerned in breeding is positive or negative, etc.; production management includes but is not limited to information related to the individual's identity information and breeding value, such as whether the individual's identity identification is lost, whether the breeding male has breeding ability, and so on.

[0042] Comparative Example 1

[0043] Select the same candidate population as in the example, which is 3,600. The number of individuals to be excluded G = 3,600 - N;

[0044] The trait selection and weights are the same as in Example 1;

[0045] The following steps are used for screening:

[0046] S1 sorts the candidate population according to the original BW6 value and eliminates the last G * 25% individuals;

[0047] S2 sorts the remaining individuals after S1 screening according to the original FCR6 value and eliminates the last remaining individual quantity * 40% individuals;

[0048] S3 sorts the remaining individuals after S2 screening according to the original BMR6 value and eliminates the last remaining individual quantity * 15% individuals;

[0049] S4 sorts the remaining individuals after the screening in S3 according to the original value of AFR6, and eliminates the last *15% of the remaining individuals;

[0050] S5 sorts the remaining individuals after the screening in S4 according to the original value of EN45, and eliminates the last *5% of the remaining individuals;

[0051] Finally, the selected population is obtained.

[0052] Compare the performance of the selected populations in Example 1 and Comparative Example 1. The specific results are shown in Table 1-2.

[0053] Table 1-2 Comparison of the results of the traditional selection method, i.e., the selection methods in Comparative Example 1 and Example 1

[0054]

[0055] Example Two

[0056] S1 constructs a candidate population. The candidate population of roosters in the 20th generation of the White - feather broiler Cornish strain is 3,600, and 712 (N = 712) individuals need to be selected as the selected population and transferred to the production performance measurement chicken house;

[0057] The traits in this example are unidirectional selection traits and stable selection traits, including the phenotypic coefficient of variation of egg weight, feed - to - meat ratio at 6 weeks of age, breast muscle rate, abdominal fat rate, and number of eggs laid at 45 weeks of age. These five traits are represented by BW6, FCR6, BMR6, AFR6, and EN45 respectively, and the original values h of these traits are obtained. The selection weight w and selection direction of each trait are shown in Table 2-1, and the sum of the weights of each trait is 1;

[0058] Among them, traits such as body weight, egg weight, body size, shank length, eggshell color, eggshell strength, etc. can all be used as stable selection traits. In this example, it is egg weight.

[0059] Among them, the coefficient of variation of phenotypic value is used to measure the variation or uniformity of this phenotype. The calculation formula is coefficient of variation = mean / standard deviation, that is, first calculate the average value and standard deviation of all individuals in this phenotype, and then divide them.

[0060] Table 2-1 Breeding selection data, selection weights, and selection directions for each trait

[0061]

[0062] Note: The smaller the value of the feed - to - meat ratio at 6 weeks of age, the more in line with the selection direction

[0063] S2 Calculate the conversion score X for each individual for each trait, and set the minimum value a = 0 and the maximum value b = 100 of the converted scores of the candidate population. When the trait is a unidirectional selection trait,

[0064]

[0065] where min and max are respectively the minimum and maximum values of the original value h of this trait in the candidate population, and h is the original data corresponding to this trait;

[0066] S2-1 When the trait is a stable selection trait, perform normal standardization on the original data h of the trait to obtain the standardized data h'=(h - μ) / σ of this trait data;

[0067] where μ is the mean of the trait data h in the candidate population, and σ is the standard deviation of the trait data h in the candidate population;

[0068] S2-2 Calculate the probability density pd = dnorm h' of each stable selection trait, where dnorm is the probability density function;

[0069] S2-3 Calculate the conversion score X of each stable selection trait;

[0070]

[0071] where min and max are respectively the minimum and maximum values of the probability density pd of this trait in the candidate population;

[0072] S3 Calculate the breeding index I of each individual;

[0073]

[0074] where w j is the selection weight of the jth trait, and S j is the sign of each trait in the summation process. When the unidirectional selection trait is positive selection, S j = 1. When the unidirectional selection trait is negative selection, S j = -1. When the trait is a stable selection trait, S j = 1; X ij is the conversion score of the jth trait of the ith individual;

[0075] S4 Rank each individual in the candidate population according to the breeding index I, and leave the top 712 individuals as the selected population.

[0076] Eliminate individuals with defects before ranking.

[0077] For Comparative Example II

[0078] Select the same candidate population as in Example 2, which is 3,600. The number of individuals to be eliminated, G = 3,600 - N;

[0079] The trait selection and weights are the same as in Example 1;

[0080] The following steps are used for screening:

[0081] S1 Rank the candidate population according to the original BW6 values. In this comparative example, directly calculate the phenotypic value coefficient of variation of the egg weight of individuals in all candidate populations. The smaller this coefficient, the better, and eliminate the G * 25% individuals at the back;

[0082] S2 Rank the remaining individuals after S1 screening according to the original FCR6 values, and eliminate the remaining individual quantity * 40% individuals at the back;

[0083] S3 Rank the remaining individuals after S2 screening according to the original BMR6 values, and eliminate the remaining individual quantity * 15% individuals at the back;

[0084] S4 Rank the remaining individuals after S3 screening according to the original AFR6 values, and eliminate the remaining individual quantity * 15% individuals at the back;

[0085] S5 Rank the remaining individuals after S4 screening according to the original EN45 values, and eliminate the remaining individual quantity * 5% individuals at the back;

[0086] Finally, obtain the selected population.

[0087] Compare the performance of the selected populations in Example 2 and Comparative Example 2. The specific results are shown in Table 2-2.

[0088] Table 2-2 Comparison of the results of the traditional selection method, i.e., the selection methods of Comparative Example 2 and Example 2

[0089]

[0090] Note: Among them, in Example 2, egg weight is a stable selection trait. Then calculate the phenotypic value coefficient of variation of the egg weight of the selected population. The larger the coefficient of variation, the worse the selection effect, and the smaller the coefficient of variation, the better the selection effect.

[0091] It can be seen from Table 1-2 and Table 2-2 that using the new index constructed by the present invention for selection shows certain advantages in all traits, indicating that the new selection index has great breeding application value

[0092] Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

Claims

1. A method for livestock and poultry breeding using a new comprehensive index, characterized in that, The steps are as follows: S1 Construct a candidate population, set multiple traits to be selected, the weight w of each trait, the number N of individuals to be retained, and the original value h of the traits to be selected. S2 Calculate the conversion score X of each individual for each trait, and set the minimum value a and the maximum value b of the converted scores of the candidate population. When the trait is a unidirectional selection trait, where min and max are respectively the minimum and maximum values of the original value h of this trait in the candidate population, and h is the original data corresponding to this trait. S4 Calculate the breeding index I of each individual. where, w j is the selection weight of the j-th trait, S j is the sign of each trait in the summation process. When the unidirectional selection trait is positive selection, S j = 1; when the unidirectional selection trait is negative selection, S j = -1; X ij is the conversion score of the j-th trait of the i-th individual; S5 Sort each individual in the candidate population according to the breeding index I, and retain the top N individuals as the selected population.

2. The method for livestock and poultry breeding using a new comprehensive index according to claim 1, characterized in that, It further includes the step: S2-1 When the trait is a stable selection trait, perform normal standardization on the original data h of the trait to obtain the standardized data h'=(h - μ) / σ of this trait data; where μ is the mean value of the trait data h in the candidate population, and σ is the standard deviation of the trait data h in the candidate population; S2-2 Calculate the probability density pd = dnorm h' of each stable selection trait, where dnorm is the probability density function; S2-3 Calculate the conversion score X of each stable selection trait; where min and max are respectively the minimum and maximum values of the probability density pd of this trait in the candidate population; In step S3, when the trait is a stable selective trait, S j = 1.

3. A method for livestock and poultry breeding using a new comprehensive index according to claim 2, characterized in that, In step S5, individuals with defects are excluded before sorting.

4. A method for livestock and poultry breeding using a new comprehensive index according to claim 3, characterized in that, The defects include that the individual characteristics do not meet the breeding objectives of the variety or strain, the disease purification status is positive, or there are records that do not meet expectations in production management.

5. A method for livestock and poultry breeding using a new comprehensive index according to any one of claims 2-4, characterized in that, The unidirectional selection traits include body weight at 6 weeks of age, feed conversion ratio at 6 weeks of age, breast muscle rate, abdominal fat rate, and egg production at 45 weeks of age.

6. A method for livestock and poultry breeding using a new comprehensive index according to any one of claims 2-4, characterized in that, The stable selection traits include body weight, egg weight, body size, shank length, eggshell color, and eggshell strength.

Images