Effective breeding index for prolonging breeding period and milk production period of Holstein cattle

Abstract

The invention discloses an effective breeding index for improving Holstein cattle breeding and milk production age limit, and relates to the technical field of breeding, and the effective breeding index comprises the following steps: obtaining a target data set of a breeding object; determining at least one performance index of the breeding object, and taking the total milk yield character of each month age as a breeding index; and evaluating the association between the total milk yield character of each month age and each performance index to determine the month age optimal value of the breeding index. The breeding method has the beneficial effects that the Holstein cattle which are excellent in reproductive performance, milk production performance and milk production age limit and are greatly improved compared with the prior art can be further bred according to the optimal month age of the Holstein cattle total milk production character, and economic benefits of Holstein cattle and enterprises corresponding to other breeding objects are improved.

Description

Technical Field

[0001] The invention relates to the field of breeding technology, in particular to an effective breeding index that can be used to improve the reproduction and milk production years of Holstein cattle. Background Art

[0002] Genomic selection refers to a breeding method that estimates the breeding value of an individual genome-wide range through a large amount of marker information in the genome and selects it. It is an important breeding technology means to win the battle of selecting breeding objects, especially the dairy cattle breeding industry.

[0003] However, due to the low heritability of reproductive performance and difficulty in measuring lifetime milk production of Holstein cattle, especially Holstein cows among dairy cows, in the existing technology, it is difficult to advance the breeding of Holstein cattle in terms of reproductive performance, milk production performance and milk production years; and because the reproductive performance and milk production performance of Holstein cattle are genetically negatively correlated, the existing technology increases the milk production of Holstein cattle while reducing its reproductive performance. At present, there is no effective breeding indicator that can be used to comprehensively improve the reproductive performance, milk production performance and milk production years of Holstein cattle. Summary of the invention

[0004] In order to overcome the problems existing in the related art, the present disclosure provides a breeding method. The technical solution is as follows:

[0005] According to a first aspect of an embodiment of the present disclosure, a breeding method is provided, comprising:

[0006] Obtain target data set for breeding objects;

[0007] Determine at least one performance index of the breeding object, and use the total milk production trait at each month of age as the breeding index;

[0008] The correlation between the total milk production trait at each month age and each performance index is evaluated to determine the optimal value of the month age for the breeding index.

[0009] The technical scheme provided by the embodiment of the present disclosure may include the following beneficial effects: obtaining a target data set of a breeding object, determining at least one performance index of the breeding object, and using the total milk production trait of each month as a breeding index, evaluating the correlation between the total milk production trait of each month and each performance index, so as to determine the optimal value of the breeding index in terms of month age. By actively obtaining a target data set of Holstein cattle within a preset time period and a preset target quantity range, including the daily milk production record and trait information of Holstein cattle, the best breeding index for improving the performance of Holstein cattle in terms of reproductive performance, milk production performance and milk production years is obtained by proofreading and evaluating the total milk production trait of each month age; after calculating the genetic correlation and phenotypic correlation between the total milk production trait of Holstein cattle at each month age and each performance index, the optimal month age for obtaining the total milk production trait of Holstein cattle is evaluated, so that Holstein cattle with excellent reproductive performance, milk production performance and milk production years and greatly improved compared with the prior art can be further bred according to the optimal month age of the total milk production trait of Holstein cattle, so as to achieve the improvement of the economic benefits of Holstein cattle and other breeding objects corresponding enterprises.

[0010] In one embodiment, the breeding method further comprises:

[0011] The optimal value of the specific age of the breeding indicator is 60 months.

[0012] In one embodiment, the evaluating the correlation between the total milk production trait at each month of age and each performance indicator comprises:

[0013] Obtain the genetic parameters of total milk production traits and performance indicators of breeding objects at each month of age;

[0014] Obtain the phenotypic correlation between total milk production traits and performance indicators of breeding objects at each month of age;

[0015] Obtain the phenotypic selection differences of total milk production traits at each month of age on performance indicators in specific breeding groups of the selected breeding objects.

[0016] In one embodiment, the performance indicator includes: lifetime milk production.

[0017] In one embodiment, the performance indicators also include: milk production years;

[0018] The evaluation of the correlation between the total milk production trait at each month age and each performance index also includes:

[0019] Obtain the phenotypic selection differences of total milk production traits at each month of age for the breeding objects on milk production years, and determine the proportion of selectable breeding objects.

[0020] In one embodiment, the performance indicators further include: milk component properties;

[0021] The genetic parameters of the total milk production traits and performance indicators of the breeding objects at each month of age are obtained, including:

[0022] Obtain the genetic parameters of total milk production traits at each month of age and lifetime milk production and milk composition traits of the breeding objects.

[0023] In one embodiment, the performance indicators further include: reproductive traits;

[0024] The evaluation of the association between the total milk production trait at each month of age and each performance index includes:

[0025] Obtain the genetic parameters of total milk production at each month of age, lifetime milk production, reproductive traits, milk production years, and milk composition traits of the breeding objects;

[0026] Obtain the phenotypic correlation between the total milk production trait of the breeding object at each month of age and the lifetime milk production, reproductive traits and milk production years;

[0027] To obtain the phenotypic selection differences of total milk production at each month of age on lifetime milk production, reproductive traits, and milk production years in a specific breeding group of selected breeding targets;

[0028] Obtain the phenotypic selection differences of total milk production traits at each month of age for the breeding objects on milk production years, and determine the proportion of selectable breeding objects.

[0029] In one embodiment,

[0030] The genetic parameters of the total milk production trait at each month of the breeding object and the lifetime milk production, reproductive traits, milk production years, and milk composition traits are calculated according to the variance and / or covariance components of the total milk production trait at each month of the breeding object, including the following steps:

[0031] Obtain the additive genetic variance of total milk production traits of breeding objects at each month of age Permanent environmental variance Residual variance and the additive genetic covariance Cov(a1, a2);

[0032] Calculate the phenotypic variance of total milk production traits of breeding objects at each month of age The expression is:

[0033]

[0034] Among them, the phenotypic variance It is the sum of the phenotypic variation of the total milk production trait of the breeding object at each month of age in the population; additive genetic variance Indicates the part of phenotypic variation caused by genetic factors of breeding objects at each age; permanent environmental variance Represents the phenotypic variation caused by the permanent environmental effects of the breeding objects at each month of age; residual variance It represents the part of phenotypic variation caused by random errors and other non-genetic and non-permanent environmental factors;

[0035] Calculate the heritability h of the total milk production trait of the breeding object at each month of age 2 , the expression is:

[0036]

[0037] Among them, heritability h 2 The phenotypic variation of the total milk production trait of the breeding object at each month of age is represented by the additive genetic variance The size of the ratio caused;

[0038] Calculate the heritability h of the total milk production trait of the breeding object at each month of age 2 Repeatability of the level e , the expression is:

[0039]

[0040] Among them, the repeatability r e h represents the heritability of the total milk production trait of the breeding object at each month of age 2 Correlation between at least one set of measurements taken at different times or under different conditions;

[0041] Calculate the genetic correlation of total milk production at each month of breeding objects and lifetime milk production, reproductive traits, milk production years, and milk composition traits a , the expression is:

[0042]

[0043] Among them, genetic correlation a It represents the correlation between the total milk production traits at each month of age and the lifetime milk production, reproductive traits, and milk production years of the breeding objects at the genetic level; the additive genetic covariance Cov(a1, a2) represents the degree of association between the genetic variations caused by common genetic factors between two groups of different traits of the breeding objects.

[0044] In one embodiment,

[0045] Calculating the phenotypic correlation between the total milk production trait of the breeding object at each month of age and the lifetime milk production, reproductive traits, and milk production years according to the variance and / or covariance components of the total milk production trait of the breeding object at each month of age includes the following steps:

[0046] Obtain the total milk production traits of the breeding objects at each month of age, lifetime milk production, reproductive traits, and phenotypic covariance Cov(p1, p2) and phenotypic variance of the milk production years

[0047] Calculate the phenotypic correlation r between the total milk production trait at each month of breeding objects and the lifetime milk production, reproductive traits, and milk production years p , the expression is:

[0048]

[0049] Among them, phenotype correlation r p It represents the correlation between the total milk production traits of the breeding objects at each month of age and the lifetime milk production, reproductive traits, and milk production years at the phenotypic level; the phenotypic covariance Cov(p1, p2) represents the degree of association between the phenotypic variations of at least two groups of traits of the breeding objects.

[0050] In one embodiment,

[0051] The standard error SE of the heritability of the total milk production trait at each month of age, lifetime milk production, reproductive traits, and milk production years of the breeding objects was calculated using the expression:

[0052]

[0053] in, represents additive genetic variance The variance of Represents phenotypic variance The variance of represents additive genetic variance and phenotypic variance The covariance between .

[0054] In one embodiment, the method of obtaining the phenotypic selection differences of the total milk production trait at each month of the specific breeding group for the lifetime milk production, reproductive traits, and milk production years includes the following steps:

[0055] Obtain the average ranking of daily milk production of the breeding objects at each month of age in the target data set, and set the high-yield group and low-yield group of the total milk production trait at each month of the breeding objects;

[0056] The differences in lifetime milk production, reproductive traits, and milk production years between high-yield groups and low-yield groups are compared, and the quality of lifetime milk production, reproductive traits, and milk production years is determined based on the size of the differences.

[0057] In one embodiment, obtaining the phenotypic selection difference of the total milk production trait of the breeding objects at each month of age for the milk production years and determining the proportion of the breeding objects that can be selected comprises the following steps:

[0058] Obtain the distribution rate and survival rate of breeding objects at each month age, and determine the proportion of selectable breeding objects based on the distribution rate and survival rate.

[0059] In one embodiment, after obtaining the target data set of the breeding object, the method further includes:

[0060] Classify and summarize the filtered target data set.

[0061] In one embodiment, the categorizing and summarizing the filtered target data set includes the following steps:

[0062] The target data set is selected using the trait information and daily milk production records of the breeding objects as control factors, wherein the trait information of the breeding objects includes calving season, age at first calving, parity, and year of birth;

[0063] Import the screened target data set into R language software for classification and summary;

[0064] Calculate and analyze the variance and / or covariance of total milk production traits at each month of age based on the classified and summarized target data set;

[0065] The expression of the calculation analysis model is:

[0066] y=Xb+Za+Wc+e

[0067] Where y is the phenotypic value vector of total milk production trait at each age; X, Z and W are the association matrices of fixed effects, additive genetic effects and permanent environmental effects, respectively; b is the fixed effect vector, including calving season, age at first calving, parity and birth year; a is the random additive genetic effect vector; c is the permanent environmental effect vector; e is the residual vector;

[0068] Different variances and / or covariances are split into values ​​from the phenotypic value vector of the total milk production trait at each age.

[0069] In one embodiment, after determining the optimal value of the specific month age of the breeding indicator, the method further includes:

[0070] To evaluate the factors affecting the total milk production trait at a specific age;

[0071] Determine breeding indicators.

[0072] In one embodiment, the factors affecting the total milk production trait when evaluating a specific age as the optimal value include:

[0073] Multifactorial ANOVA was performed on the total milk production trait when the specific age was the optimal value;

[0074] The general linear model was used to analyze the effects of parity, season, lactation stage and birth year on total milk production at optimal values ​​at a specific age.

[0075] The general linear model expression is:

[0076] Y=u+N+M1+M2+C+T+H+e

[0077] Among them, Y is the total milk production when the specific month age is the optimal value; u is the overall mean; N is the fixed effect of parity at the end of the specific month age optimal value; M1 is the fixed effect of calving season; M2 is the fixed effect of season at the end of the specific month age optimal value; C is the fixed effect of lactation stage at the end of the specific month age optimal value; T is the fixed effect of birth year; H is the fixed effect of first calving age; e is the random residual;

[0078] The different significance levels of total milk production traits at a specific age as the optimal value in different parities, seasons, lactation stages and birth years were multiple compared, and the level of milk production traits at a specific age as the optimal value was determined based on the level of significance.

[0079] According to a second aspect of the embodiments of the present disclosure, a method for genomic selection of Holstein cattle is provided, comprising:

[0080] The total milk production trait of Holstein cattle from the first milking to a specific age at each month is used as a breeding indicator for Holstein cattle.

[0081] In one embodiment, the Holstein cattle genome selection method further comprises:

[0082] The optimal specific age of the Holstein cattle is 60 months old.

[0083] According to a second aspect of an embodiment of the present disclosure, there is provided a breeding device, comprising:

[0084] A data acquisition module is used to obtain a target data set of breeding objects;

[0085] An indicator determination module, used to determine at least one performance indicator of the breeding object, and use the total milk production trait at each month from the first milk production to a specific month age as a breeding indicator; and

[0086] The correlation evaluation module evaluates the correlation between the total milk production trait at each month from the first milk production to a specific month age and each performance index to determine the optimal value of the breeding index at a specific month age.

[0087] In one embodiment, the breeding device further comprises:

[0088] The data classification module is used to classify and summarize the filtered target data set.

[0089] In one embodiment, the breeding device further comprises:

[0090] Factor evaluation module, used to evaluate the influencing factors of total milk production trait when a specific age is the optimal value;

[0091] Verification and confirmation module, used to determine breeding indicators.

[0092] According to a third aspect of an embodiment of the present disclosure, there is provided an electronic device, including:

[0093] A memory, a processor and a computer program stored on the memory;

[0094] The processor executes the computer program to implement the steps of the first method in the first aspect.

[0095] According to a fourth aspect of an embodiment of the present disclosure, there is provided a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in the first item of the first aspect are implemented.

[0096] According to a fifth aspect of an embodiment of the present disclosure, a computer program product is provided, including:

[0097] Computer programs;

[0098] When the computer program is executed by a processor, the steps of the first method described in the first aspect are implemented.

[0099] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0100] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0101] Figure 1 The present invention is a flow chart of a breeding method according to an exemplary embodiment.

[0102] Figure 2 The figure is a phenotypic correlation diagram of the total milk production trait at each month of age and the lifetime milk production of the Holstein cattle in the age range of 40-70 months of the present invention.

[0103] In the figure: DETAILED DESCRIPTION

[0104] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0105] Example 1

[0106] Figure 1 is a flow chart of a breeding method according to an exemplary embodiment. Figure 1 As shown, the method includes the following steps S101-S103:

[0107] In S101, a target data set of breeding objects is obtained.

[0108] In S102, at least one performance index of the breeding object is determined, and the total milk production trait at each month of age is used as the breeding index.

[0109] In S103, the correlation between the total milk production trait at each month age and each performance index is evaluated to determine the optimal value of the breeding index at each month age.

[0110] The breeding objects in the present invention may include various mammals that can produce milk;

[0111] Among them, since the reproductive performance and milk production performance of Holstein cattle are genetically negatively correlated, there is a greater need for an effective breeding index that can comprehensively improve the reproductive performance, milk production performance and milk production years of Holstein cattle, as well as a breeding implementation method corresponding to the breeding index. Therefore, the breeding object of the present invention is preferably Holstein cattle.

[0112] The target data set in the present invention may include any information data of Holstein cattle;

[0113] Among them, since an effective breeding index that can comprehensively improve the reproductive performance, milk production performance and milk production years of Holstein cattle is needed, the effectiveness of the target data set needs to be reflected in the comprehensive correlation of time span and category comprehensiveness at the same time, that is, it is necessary to ensure that the data obtained under the guidance of the selected category can respectively and comprehensively reflect the reproductive performance, milk production performance and milk production years of Holstein cattle, and the obtained target data set can form a corresponding time curve according to different classification standards;

[0114] Therefore, the target data set for Holstein cattle includes the daily milk production record of each Holstein cattle and the trait information corresponding to each Holstein cattle obtained on the basis of a preset predetermined time period, a preset predetermined target quantity and a preset distribution law of each month age;

[0115] Among them, the trait information includes the calving season, age at first birth, parity, and year of birth of Holstein cattle; the selected preset time period must not only meet the cumulative amount of daily records in terms of span, but also meet the requirement that the trait information occurring within this preset time period must also have a certain amount of data; the total number of Holstein cattle to be prepared must consider the balanced distribution of Holstein cattle of each age, and meet the established mortality rate of Holstein cattle of each age within this preset time period.

[0116] The performance indicators in the present invention may include the reproductive performance, milk production performance and milk production years of Holstein cattle;

[0117] Among them, milk production performance includes daily milk production, total milk production at current month age, lifetime milk production, milk composition traits, and somatic cell count; milk composition traits include milk fat rate, milk protein rate and lactose rate, which represent economic performance indicators by reflecting the nutritional content of milk; somatic cell count represents economic performance indicators by reflecting the health status and quality of milk; since it is necessary to breed Holstein cows that can comprehensively reflect and improve the reproductive performance, milk production performance and milk production years of Holstein cows, lifetime milk production is the top priority in milk production performance; lifetime milk production can take into account the milk production performance of Holstein cows in different growth stages and different lactation periods to comprehensively reflect the comprehensive milk production capacity of a Holstein cow throughout its production career; since in the existing technology, the somatic cell count of Holstein cow milk has basically reached compliance, therefore, milk composition traits representing economic performance indicators bear the brunt of economic indicator evaluation.

[0118] The total milk production trait at each month of age in the present invention represents the total milk production of a Holstein cow from the first milking to the current month of age. The target data set can represent the real-time update of the total milk production trait at the current month of age of each Holstein cow within a preset time period and the corresponding relationship between each performance indicator. Among all the observable traits of Holstein cows, the total milk production trait at each month of age is reflected and associated with each performance indicator. Therefore, the effective comprehensive breeding indicator for improving the reproductive performance, milk production performance and milk production years of Holstein cows is preferably the total milk production trait at each month of age.

[0119] The correlation between the total milk production trait at each month of age and each performance index in the present invention includes genetic correlation and phenotypic correlation. Genetic correlation is used to measure whether two traits are related in genetics, and phenotypic correlation is used to measure whether two traits are related in external performance. Traits refer to at least one characteristic of Holstein cattle that can be observed.

[0120] Among them, the higher the genetic correlation and phenotypic correlation are, the better the performance index used for comparison of the Holstein cattle of the current month age. Only when the total milk production traits of Holstein cattle at each month age are compared with each performance index for genetic correlation and phenotypic correlation respectively, the total output at a specific month age when the genetic correlation and phenotypic correlation are the highest can be obtained, and the optimal month age for the total milk production trait of Holstein cattle at each month age can be obtained. The optimal month age for the total milk production trait of Holstein cattle at each month age can be used to improve the reproductive performance, milk production performance and milk production years of Holstein cattle.

[0121] In the present invention, when determining the comprehensive breeding index of Holstein cattle,

[0122] Priority is given to obtaining the daily milk production record and corresponding trait information of each Holstein cow within a preset time period and a preset target quantity;

[0123] Find the Holstein cow with the best performance index according to the daily milk production record and corresponding trait information of each Holstein cow;

[0124] The selected Holstein cows were classified and summarized for comparison based on their age in months and total milk production from the first milking to the current age. This revealed that the total milk production trait at each month of age was closely related to each performance index, and the total milk production trait at each month of age was determined as a comprehensive breeding index for improving each performance index of Holstein cows.

[0125] Further obtain the genetic correlation and phenotypic correlation between the total milk production traits and performance indicators of Holstein cattle at each month of age;

[0126] After evaluation, it was determined that the total production traits of Holstein cattle had excellent performance indicators at a specific age. The total milk production traits at a specific age can not only be used to comprehensively and effectively improve the reproductive performance, milk production performance and milk production years of Holstein cattle, but can also be used to comprehensively improve the reproductive performance, milk production performance and milk production years of other types of milk-producing mammals, which will help greatly improve the economic benefits of the corresponding enterprises of the breeding objects.

[0127] In one implementable manner of the present invention, the daily milk production of each Holstein cow can be recorded by an automatic milking system, and the specific trait information of each Holstein cow can be recorded by a veterinarian and a breeder; the daily milk production records and the corresponding trait information data can be statistically analyzed by statistical software to obtain the Holstein cow with the best performance in each performance indicator, and the genetic potential of each Holstein cow can be predicted by a genetic evaluation model, such as BLUP (Best Linear Unbiased Prediction); the degree of correlation between the total milk production trait at each age and each performance indicator can be determined by correlation analysis, and the influence of one trait on another trait can be predicted by regression analysis; genetic correlation and phenotypic correlation can be estimated by a genetic evaluation model, and the genetic correlation and phenotypic correlation can be calculated by statistical software; based on the analysis results of genetic correlation and phenotypic correlation, the influence of the total milk production trait on reproductive performance, milk production performance and milk production years at a specific age is determined, and the optimal age for the total milk production trait is selected with the assistance of a decision support system.

[0128] The disclosed embodiment actively obtains a target data set of Holstein cattle within a preset time period and a preset target quantity range, including daily milk production records and trait information of Holstein cattle, so as to calibrate and evaluate and obtain the best breeding index for improving the reproductive performance, milk production performance and milk production years of Holstein cattle, which is the total milk production trait at each month of age; after calculating the genetic correlation and phenotypic correlation between the total milk production trait at each month of Holstein cattle and each performance index, the best month of the total milk production trait of Holstein cattle is evaluated and obtained, so that Holstein cattle with excellent reproductive performance, milk production performance and milk production years that are greatly improved compared with the prior art can be further bred according to the best month of the total milk production trait of Holstein cattle, thereby achieving improved economic benefits for enterprises corresponding to Holstein cattle and other types of milk-producing mammals.

[0129] Example 2

[0130] This embodiment is the second embodiment, which is different from the embodiment 1 in that: Step S101 in the embodiment 1 obtains the target data set of the breeding object, which may include the following sub-steps A1-A4:

[0131] In A1, the preset time period of the target data set source of the breeding object and the total number of breeding objects prepared are determined respectively;

[0132] In A2, the target data set for the breeding object is determined to include the daily milk production record of the breeding object and the trait information corresponding to the breeding object;

[0133] In A3, starting from the start date of the preset time period, the daily milk production records of the breeding objects are obtained one by one according to the total number of breeding objects prepared;

[0134] In A4, a collection of trait information generated each time by the breeding objects within a preset time period is obtained one by one according to the prepared total number of breeding objects.

[0135] In the present invention, in order to ensure that the trait analysis and evaluation results obtained based on the target data set are sufficiently accurate and reliable, when actually obtaining the target data set, by giving priority to determining the preset time period for the cumulative acquisition of the Holstein cow target data set and the total number of Holstein cows to be prepared, a good basis is provided for the continuous reliability of the daily milk production record of each Holstein cow and the corresponding trait information included in the specific target data set. Starting from the starting day of the preset time period, each Holstein cow is tracked daily, and the daily milk production of each Holstein cow and the trait information generated each time are recorded in detail to avoid information errors and omissions.

[0136] In one possible implementation of the present invention, it is the same as Example 1.

[0137] The technical solution provided by the embodiments of the present invention may include the following beneficial effects: based on the pre-setting of established factors, it can ensure that the acquired target data set can simultaneously present different curve distributions according to the various performance indicators and reference factors of Holstein cattle or other types of milk-producing mammals, which helps to comprehensively improve the comprehensive correlation, accuracy and effectiveness of the breeding indicators for the reproductive performance, milk production performance and milk production years of Holstein cattle.

[0138] Example 3

[0139] This embodiment is the third embodiment, which is different from the embodiment 1 in that: step S102 in the embodiment 1 determines at least one performance index of the breeding object, and uses the total milk production trait at each month of age as the breeding index, which may include the following sub-steps B1-B3:

[0140] In B1, the screened target data set is classified and summarized;

[0141] In B2, the traits that can simultaneously reflect the performance indicators of the breeding object are obtained based on the classified and summarized target data set, and the breeding indicator is determined to be the total milk production trait at each month of age;

[0142] In B3, the priority of the performance indicators of the breeding objects is determined according to the priority of the traits affecting the total milk production at each month of age.

[0143] In the present invention, the performance indicators of the total milk production trait at each month age are arranged according to the priority of influence, firstly the lifetime milk production, then the milk component trait, and finally the reproductive trait and the milk production years.

[0144] In the present invention, when determining effective breeding indicators that can comprehensively improve the reproductive performance, milk production performance and milk production years of Holstein cattle, the initially acquired target data set is further screened first, and the messy / redundant data that is not related to the breeding indicators but is also recorded by the equipment is eliminated, duplicate data is eliminated, and abnormal / damaged data is repaired. Then, the screened scattered data is further classified with reference to different classification standards and summarized in their respective columns. At the same time, corresponding visualization chart results are generated for data sets of different categories, and the total milk production at each month of age is determined after further comparison and analysis of the numerous visualization chart results. The total milk production trait not only encompasses various performance indicators in terms of performance, but also can fully express the changing rules of various performance indicators. It is clear that the total milk production trait at each month's age can be an effective breeding indicator that can comprehensively improve the reproductive performance, milk production performance and milk production years of Holstein cattle; further based on the results of many visualized charts, we can preliminarily know the priority of each performance indicator in affecting the performance of the total milk production trait at each month's age, which is helpful to further evaluate the optimal age for obtaining the total milk production trait of Holstein cattle according to the impact priority, so as to comprehensively improve the reproductive performance, milk production performance and milk production years of Holstein cattle through the total milk production trait at the optimal month's age.

[0145] In one possible implementation of the present invention, the correlation between each data indicator in the target data set and the reproductive traits, milk production traits and milk production years can be calculated by statistical methods (such as Pearson correlation coefficient, Spearman rank correlation coefficient), and data with low correlation or statistically insignificant data can be eliminated; duplicate data records can be identified and deleted by programming or database query statements (such as the DISTINCT keyword in SQL or the pandas.DataFrame.drop_duplicates() method in Python); outliers or damaged data can be identified and repaired by setting a reasonable data range, using statistical methods (such as mean and median filling) or machine learning algorithms (such as outlier detection algorithms); programming tools (such as Python) can be used to identify and repair outliers or damaged data. on, R) or database query functions can be used to group data into different categories according to classification criteria; the classified data can be summarized into respective columns through programming languages ​​(such as Python's pandas library) or data processing software (such as Excel, SPSS), and visualization tools (such as Python's Matplotlib, Seaborn, Plotly, or R's ggplot2) can be used to generate charts, such as line charts, bar charts, scatter plots, box plots, etc., to intuitively show the changing trends of various performance indicators over time (such as age in months); different charts can be visually compared to observe the performance of each performance indicator at different ages and its changing patterns, and statistical tests (such as ANOVA, t-test) can be combined to further verify whether the observed trends are statistically significant.

[0146] The technical solution provided by the embodiments of the present invention may include the following beneficial effects: through data screening, deduplication and cleaning, messy / redundant data, duplicate data and abnormal / damaged data that are not directly relevant to the breeding objectives are eliminated, which significantly improves the accuracy and reliability of the data set, which lays a solid foundation for subsequent analysis and reduces analysis deviations caused by data quality issues; through classification and summary, the differences and connections between different categories of data can be seen more clearly, which helps to discover potential breeding key points; by comparing and analyzing different charts, decision makers can quickly identify the performance indicators and optimal age that have the greatest impact on the breeding objectives, thereby speeding up the decision-making process and improving breeding efficiency.

[0147] Example 4

[0148] This embodiment is the fourth embodiment, which is different from the embodiment 1 in that: step S103 in the embodiment 1 evaluates the correlation between the total milk production trait at each month age and each performance index to determine the optimal value of the breeding index at the month age, which may include the following sub-steps C1-C3:

[0149] In C1, the target data set was screened using the trait information of the breeding object and the daily milk production records as control factors;

[0150] In C2, the screened target data set is imported into R language software for classification and summary;

[0151] In C3, the variance and / or covariance of the total milk production trait at each month of age is calculated and analyzed based on the classified and summarized target data set;

[0152] In C4, different variances and / or covariances are split into values ​​from the phenotypic value vector of the total milk production trait at each age.

[0153] The present invention aims at classifying and summarizing the target data set of Holstein cattle. It is not only necessary to clean and transform the target data sets from different devices and systems and then merge them, but also to further classify and compare the target data sets through at least one statistical algorithm according to the manifestation of various performance indicators and various intuitive reference factors, and it is also necessary to present the final comparison results in charts. Therefore, the complex processing and analysis process of the target data set of Holstein cattle can be fully supported by R language software, and the compatibility is extremely strong.

[0154] The variance and / or covariance of the total milk production trait at each month of the present invention is the basis for calculating the genetic correlation and phenotypic correlation between the total milk production trait at each month of Holstein cattle and each performance index. The covariance reflects the variation relationship between the total milk production trait at each month of Holstein cattle and each performance index, and the variance reflects the degree of variation between the total milk production trait at each month of Holstein cattle and each performance index. The calculation of the genetic correlation and phenotypic correlation of Holstein cattle requires the application of the corresponding covariance and variance.

[0155] The variance and / or covariance of the total milk production trait at each month of age is extracted from the phenotypic value vector of the total milk production trait at each month of age of Holstein cattle according to the proportion evaluation of each performance index of Holstein cattle.

[0156] The phenotypic value vector of the total milk production trait at each month of age of the present invention represents the sum of the total milk production data of all Holstein cows that reach each month of age value at each month of age, which includes all observable and measurable trait information of Holstein cows, and specifically may include reproductive traits, milk production traits, milk production years, various traits representing economic indicators, and morphological and behavioral traits of Holstein cows.

[0157] In the present invention, the total variance and the total covariance are preferentially calculated according to the classified and summarized target data set, and the variance and covariance at the genetic level are further calculated; the variance and covariance at the environmental level are obtained by subtracting the total variance, the total covariance and the variance and covariance at the genetic level; the additive genetic variance and covariance of each Holstein cattle are estimated, and the residual variance and covariance, that is, the non-additive genetic variance and covariance and the variance and covariance at the environmental level are obtained by subtracting the additive genetic variance and covariance from the total variance and covariance; and the variance and covariance of each performance indicator are calculated and extracted from the total variance and the total covariance according to the proportion evaluation of each performance indicator.

[0158] In one possible implementation of the present invention, the expression for calculating and analyzing the variance and / or covariance model of the total milk production trait at each month of age can be:

[0159] y=Xb+Za+Wc+e

[0160] Where y is the phenotypic value vector of total milk production trait at each age; X, Z and W are the association matrices of fixed effects, additive genetic effects and permanent environmental effects, respectively; b is the fixed effect vector, including calving season, age at first calving, parity and birth year; a is the random additive genetic effect vector; c is the permanent environmental effect vector; e is the residual vector;

[0161] Among them, the total variance and covariance can be calculated through functions in statistical software (such as R language, SAS, STATA, etc.), the variance and covariance at the genetic level can be calculated by estimating the genetic value of each individual through a genetic evaluation model (such as BLUP), and the additive genetic variance and covariance can be calculated by estimating the additive genetic value of each individual through a genetic evaluation model.

[0162] The technical solution provided by the embodiments of the present invention may include the following beneficial effects: by using R language software to classify, summarize and statistically analyze data, a large amount of data can be processed and analyzed more quickly, thereby improving breeding efficiency; by calculating the variance and covariance of the total milk production trait at each month of age, and splitting these variances and covariances, the genetic potential of Holstein cattle can be more accurately evaluated; by calculating and extracting the variance and covariance of each performance indicator, the genetic correlation and phenotypic correlation between key indicators such as the reproductive performance, milk production performance and milk production years of Holstein cattle and the total milk production trait at each month of age can be comprehensively analyzed.

[0163] Example 5

[0164] This embodiment is the fifth embodiment, which is different from the embodiments 1 and 4 in that: step S103 in the embodiment 1 evaluates the correlation between the total milk production trait at each month age and each performance index to determine the optimal value of the breeding index at the month age, which may include the following sub-steps D1-D3:

[0165] In D1, the genetic parameters of total milk production traits and lifetime milk production of the breeding objects at each month of age were obtained;

[0166] In D2, the phenotypic correlation between the total milk production trait of the breeding object at each month of age and the lifetime milk production was obtained;

[0167] In D3, the phenotypic selection differences of total milk production traits at each month of age on lifetime milk production in a specific breeding group of the breeding object are obtained.

[0168] The genetic parameters of the present invention are calculated based on the analysis of the target data set (phenotypic data) of Holstein cattle, and include heritability and genetic correlation. Heritability is the proportion of the phenotypic variation of a trait caused by genetic factors. A higher heritability means that the trait is easier to be improved through genetic selection. Genetic correlation measures the degree of association between two traits at the genetic level. A positive genetic correlation means that the two traits tend to change together genetically, so the production efficiency can be improved by improving the two traits at the same time.

[0169] Among them, by obtaining the heritability of the total milk production trait and lifetime milk production of Holstein cattle at each month's age and comparing them, the month's age with higher heritability of the total milk production trait and lifetime milk production was obtained, and the genetic correlation of the total milk production trait and lifetime milk production at each month's age was calculated. After comparison, the month's age when the genetic correlation between the total milk production trait and lifetime milk production was positive and high was obtained.

[0170] The phenotypic correlation of the present invention measures the degree of association between two traits at the phenotypic level, that is, whether their changing trends in the real world are consistent. A positive phenotypic correlation means that the two traits tend to change together genetically.

[0171] Among them, the phenotypic correlation between the total milk production trait and lifetime milk production at each month's age was calculated, and after comparison, the month's age at which the phenotypic correlation between the total milk production trait and lifetime milk production was positive and high was obtained.

[0172] Among them, by obtaining the phenotypic selection differences in total milk production traits and lifetime milk production at each month of age in a specific breeding group of Holstein cattle and comparing them, the month age of the breeding group with the largest phenotypic selection differences was obtained.

[0173] In one possible implementation of the present invention, the additive genetic variance of the total milk production trait and lifetime milk production of the breeding object at each month of age is obtained. Permanent environmental variance Residual variance and the additive genetic covariance Cov(a1, a2);

[0174] Calculate the phenotypic variance of total milk production traits and lifetime milk production of breeding objects at each month of age The expression is:

[0175]

[0176] Among them, the phenotypic variance It is the sum of the phenotypic variation of the total milk production traits of the breeding objects at each month of age and the lifetime milk production in the population; additive genetic variance Indicates the part of phenotypic variation caused by genetic factors of breeding objects at each age; permanent environmental variance Represents the phenotypic variation caused by the permanent environmental effects of the breeding objects at each month of age; residual variance It represents the part of phenotypic variation caused by random errors and other non-genetic and non-permanent environmental factors;

[0177] Calculate the heritability h of total milk production traits and lifetime milk production of breeding objects at each month of age 2 , the expression is:

[0178]

[0179] Among them, heritability h 2 The additive genetic variance represents the phenotypic variation of total milk production at each month of breeding objects and lifetime milk production. The size of the ratio caused;

[0180] Calculate the heritability h of total milk production traits and lifetime milk production of breeding objects at each month of age 2 Repeatability of the level e , the expression is:

[0181]

[0182] Among them, the repeatability r eh represents the heritability of total milk production at each month of the breeding object and lifetime milk production 2 Correlation between at least one set of measurements taken at different times or under different conditions;

[0183] Calculate the genetic correlation r of total milk production traits and lifetime milk production of breeding objects at each month of age a , the expression is:

[0184]

[0185] Among them, genetic correlation a It indicates the correlation between the total milk production trait of each month of breeding object and the lifetime milk production at the genetic level; the additive genetic covariance Cov(a1, a2) indicates the degree of association between the genetic variation caused by common genetic factors between two groups of different traits of the breeding object;

[0186] Calculating the phenotypic correlation between the total milk production trait at each month of the breeding object and the lifetime milk production trait according to the variance and / or covariance components of the total milk production trait at each month of the breeding object comprises the following steps:

[0187] Obtain the phenotypic covariance Cov(p1, p2) and phenotypic variance of the total milk production trait and lifetime milk production of the breeding objects at each month of age

[0188] Calculate the phenotypic correlation r between total milk production traits at each month of breeding objects and lifetime milk production p , the expression is:

[0189]

[0190] Among them, phenotype correlation r p It represents the correlation between the total milk production traits at each month of age and the lifetime milk production of the breeding object at the phenotypic level; the phenotypic covariance Cov(p1, p2) represents the degree of association between the phenotypic variations of at least two groups of traits of the breeding object.

[0191] The standard error SE of heritability of total milk production at each month of age and lifetime milk production of the breeding object is calculated as follows:

[0192]

[0193] in, represents additive genetic variance The variance of Represents phenotypic variance The variance of represents additive genetic variance and phenotypic variance The covariance between .

[0194] The method of obtaining the phenotypic selection difference of the total milk production trait of each month of the breeding object specific breeding group on the lifetime milk production comprises the following steps:

[0195] Obtain the average ranking of daily milk production of the breeding objects at each month of age in the target data set, and set the high-yield group and low-yield group of the total milk production trait of the breeding objects at each month of age;

[0196] The difference in lifetime milk production between high-yield groups and low-yield groups is compared, and the quality of lifetime milk production is determined based on the size of the difference.

[0197] In the present invention, Figure 2 The phenotypic correlation diagram of the total milk production of Holstein cows at 40-70 months of age and their lifetime milk production is shown in the figure. As can be seen from the figure, the total milk production of Holstein cows at 40-70 months of age is positively correlated with the corresponding lifetime milk production, which is similar to the research results of Liu Xianxia (Correlation analysis of milk production of Holstein cows at different parities and early prediction of lifetime milk production_Liu Xianxia, ​​2003).

[0198]

[0199] Table 1 shows the heritability of the total milk production trait at each age in the 40-70 month age range of Holstein cattle and the genetic correlation with lifetime milk production; it can be seen from the table that the heritability of the total milk production trait at each age in the 40-70 month age range is generally high, and the genetic correlation between the total milk production trait at each age and lifetime milk production is also high;

[0200] Among them, although the heritability of the total milk production traits at each month of age in the 40-45 month age range is relatively higher than that in the 45-70 month age range, the genetic correlation between the total milk production traits at each month of age in the 40-45 month age range and lifetime milk production is relatively lower than that in the 45-70 month age range. Figure 2 It can be seen that the phenotypic correlation between the total milk production traits at each age and lifetime milk production in the 40-45 month age range is significantly lower than that in the 45-70 month age range;

[0201]

[0202] Table 3 shows the differences in phenotypic selection of total milk production traits at each age of Holstein cattle between 40 and 70 months of age on lifetime milk production; Group a: the top 10% of the herds were selected by sorting the herds by milk production; Group b: the bottom 10% of the herds were selected by sorting the herds by milk production.

[0203] As can be seen from the table, the daily mean values ​​of total milk production traits at each month of high-yield group a and low-yield group b in the 40-70 month age range are very different, and the difference is generally more than 12, indicating that the phenotypic selection differences of total milk production traits at each month of age in the 40-70 month age range on lifetime milk production are generally very large;

[0204] Among them, although the difference in the daily mean of total milk production traits at each month of age between the high-yield group a and the low-yield group b in the 40-45 month age range is as high as 13, it shows that the phenotypic selection difference of total milk production traits at each month of age in the 40-45 month age range on lifetime milk production is significantly higher than the phenotypic selection difference of total milk production traits at each month of age in the 45-70 month age range and lifetime milk production; however, combined with Figure 2 As shown in Table 1, not only the phenotypic correlation between total milk production traits at each month of age and lifetime milk production in the 40-45 month age range is significantly lower than that in the 45-70 month age range, but also the genetic correlation between total milk production traits at each month of age and lifetime milk production in the 40-45 month age range is relatively lower than that in the 45-70 month age range.

[0205] If the total milk production trait at each month of 40-45 months of age is determined as a breeding indicator, it will be impossible to simultaneously improve milk production performance, led by lifetime milk production, and reproductive performance, because the total milk production trait at each month of 40-45 months of age has an impact on lifetime milk production. Figure 2 The results shown in Table 1 are contrary to those in Table 3, which means that the milk production performance, headed by lifetime milk production, cannot be guaranteed to be comprehensively improved; secondly, the age of each month in the 40-45 month age range is relatively lower than that of each month in the 45-70 month age range; therefore, the total milk production trait of each month in the 40-45 month age range cannot be determined as an effective breeding indicator for comprehensively improving the reproductive performance, milk production performance and milk production years of Holstein cattle;

[0206] Further combined with Table 3, it can be seen that the difference in the daily mean of total milk production traits at 60 months of age between the high-yield group a and the low-yield group b is the highest in the 45-70 month age range, indicating that the phenotypic selection difference of total milk production traits at each month of age on lifetime milk production at 60 months of age is the highest in the 45-70 month age range; 60 months of age is equivalent to a watershed, and the difference in the daily mean of total milk production traits at each month of age between the high-yield group a and the low-yield group b in the 45-60 month age range is on a downward trend, indicating that the phenotypic selection difference of total milk production traits at each month of age on lifetime milk production at 45-60 months of age and the total milk production traits at each month of age in the 60-70 month age range are The phenotypic selection difference of the total milk production trait at 60 months of age on lifetime milk production is more or less lower than the phenotypic selection difference of the total milk production trait at 60 months of age on lifetime milk production, so the phenotypic selection difference of the total milk production trait at 60 months of age on lifetime milk production is at its peak in the 45-70 month age range; although the difference of 12.86 in the daily mean of the total milk production trait at 60 months of age between the high-yield group a and the low-yield group b is not as high as 13, it is also the closest to 13 in the 45-70 month age range, so when the total milk production trait at 60 months of age is used as a breeding indicator, there is a lot of room for selection variation and selection, and after careful breeding, there is a lot of room for improvement;

[0207] Combination Figure 2 It can be seen that in the 45-70 month age range, the phenotypic correlation coefficient between total milk production at 60 months of age and lifetime milk production is as high as 0.9, and the phenotypic correlation between total milk production at 60 months of age and lifetime milk production is significantly higher than that between 40-60 months of age; therefore, when the trait of total milk production at 60 months of age is used as a breeding indicator, the improvement of the milk production performance and reproductive performance of Holstein cattle is stable and certain;

[0208] Although the phenotypic correlation between total milk production and lifetime milk production in the 60-70 month age range continues to rise on the basis of 60 months of age, combined with Table 1, it can be seen that the heritability of total milk production traits at each month of the 60-70 month age range is relatively higher than that at 60 months of age, and the genetic correlation between total milk production traits at each month of age and lifetime milk production is also relatively higher than that at 60 months of age. However, combined with Table 3, it can be seen that the difference in the daily mean of total milk production traits at each month of the high-yield group a and the low-yield group b in the 60-70 month age range is on a downward trend, that is, the phenotypic selection difference of total milk production traits at each month of the 60-70 month age range on lifetime milk production is on a downward trend. Therefore, the total milk production traits at each month of the 60-70 month age range as breeding indicators cannot positively determine the improvement of breeding effects.

[0209] From the perspective of milk production years, firstly, there is a positive correlation between milk production years and lifetime milk production. The lifetime milk production represented by the total milk production trait of 60 months of age is the highest and most certain, and the milk production years are also the highest and most certain accordingly; secondly, the time period of 60 months of age is significantly higher than that of Holstein cattle in the 45-60 month age range. Although the time period of 60-70 month age range is also higher than that of 60 months of age, the breeding time and space dimensions provided by Holstein cattle in the 60-70 month age range are not as good as those of Holstein cattle at 60 months of age; the further breeding space of Holstein cattle at 60 months of age can continue to rise to 70 months of age, but the breeding space of Holstein cattle at 60-70 months of age cannot be determined;

[0210] It can be seen from this that the total milk production trait at 60 months of age can fully represent the lifetime milk production of Holstein cattle and has the best trait performance. The high-yield group of Holstein cattle bred by the total milk production trait at 60 months of age has the highest lifetime milk production, the corresponding longest milk production years, the shortest calving interval, and the best reproductive performance represented by the calving interval.

[0211] The technical solution provided by the embodiments of the present invention may include the following beneficial effects: by acquiring and comprehensively comparing corresponding data on the heritability of the total milk production trait at each month's age of Holstein cattle, the genetic correlation, performance correlation and phenotypic selection differences between the total milk production trait at each month's age and the performance indicator of lifetime milk production of Holstein cattle, it is finally determined that 60 months of age is the best when the total milk production trait at each month's age is used as a breeding indicator, that is, the total milk production trait at 60 months of age is the best and effective breeding indicator for comprehensively improving the reproductive performance, milk production performance and milk production years of Holstein cattle, which can not only effectively ensure the good effect of the comprehensive improvement of the reproductive performance, milk production performance and milk production years of Holstein cattle, but also effectively ensure the certainty of the comprehensive improvement of the reproductive performance, milk production performance and milk production years of Holstein cattle.

[0212] Example 6

[0213] This embodiment is the sixth embodiment, which is different from Embodiment 1, Embodiment 4 and Embodiment 5 in that: Step S103 in Embodiment 1 evaluates the correlation between the total milk production trait at each month age and each performance index to determine the optimal value of the breeding index at the month age, which may include the following sub-steps E1-E4:

[0214] In E1, the genetic parameters of total milk production at each month of age, lifetime milk production and milk composition traits of the breeding objects are obtained;

[0215] In E2, the phenotypic correlation between the total milk production trait of the breeding object at each month of age and the lifetime milk production was obtained;

[0216] In E3, the phenotypic selection differences of total milk production traits at each month of age on lifetime milk production, reproductive traits, and milk production years of a specific breeding group of the breeding target were obtained;

[0217] In E4, the phenotypic selection differences of the total milk production trait at each month of age for the breeding objects on the milk production years are obtained, and the proportion of selectable breeding objects is determined.

[0218] The genetic parameters of the present invention are calculated based on the analysis of the target data set (phenotypic data) of Holstein cattle, and include heritability and genetic correlation. Heritability is the proportion of the phenotypic variation of a trait caused by genetic factors. A higher heritability means that the trait is easier to be improved through genetic selection. Genetic correlation measures the degree of association between two traits at the genetic level. A positive genetic correlation means that the two traits tend to change together genetically, so the production efficiency can be improved by improving the two traits at the same time.

[0219] Among them, by obtaining the heritability of total milk production, lifetime milk production and milk composition traits of Holstein cattle at each age and comparing them, the age with higher heritability of total milk production, lifetime milk production and milk composition traits was obtained, and the genetic correlation between total milk production traits and lifetime milk production and milk composition traits at each age was calculated. After comparison, the age with a positive and higher genetic correlation between total milk production traits and lifetime milk production and milk composition traits was obtained.

[0220] The phenotypic correlation of the present invention measures the degree of association between two or more traits at the phenotypic level, that is, whether their changing trends in the real world are consistent. A positive phenotypic correlation means that the two traits tend to change together genetically.

[0221] Among them, the phenotypic correlation between the total milk production trait and lifetime milk production at each month's age was calculated, and after comparison, the month's age at which the phenotypic correlation between the total milk production trait and lifetime milk production was positive and high was obtained.

[0222] Among them, by obtaining and comparing the phenotypic selection differences of total milk production traits and lifetime milk production, reproductive traits and milk production years of specific herds of Holstein cattle at each month of age, the age of the herd with larger phenotypic selection differences is obtained; and in the herds with larger phenotypic selection differences, it is necessary to ensure that there is a certain proportion of selectable breeding objects, that is, the herd with this age of birth can currently have a certain proportion of effective breeding objects, and when the herd has been observed after a period of time, it can still have a certain proportion of effective breeding objects, so as to avoid the herds with larger phenotypic selection differences that do not have actually operable effective breeding objects.

[0223] In one possible implementation of the present invention, except for the following contents and specific performance indicators, it is the same as Example 5;

[0224] The method of obtaining the phenotypic selection difference of the total milk production trait of the breeding objects at each month of age on the milk production years and determining the proportion of the breeding objects that can be selected comprises the following steps:

[0225] Obtain the distribution rate and survival rate of breeding objects at each month age, and determine the proportion of selectable breeding objects based on the distribution rate and survival rate.

[0226] In the present invention, Figure 2 A phenotypic correlation diagram showing the total milk production trait at each age and lifetime milk production of Holstein cows in the age range of 40-70 months.

[0227] Depend on Figure 2 It can be seen that the total milk production of Holstein cows at each age of 40-70 months is positively correlated with the corresponding lifetime milk production, which is similar to the research results of scholar Liu Xianxia (Correlation analysis of milk production of Holstein cows at each parity and early prediction of lifetime milk production_Liu Xianxia, ​​2003).

[0228] Table 1 shows the heritability of total milk production traits at each age of Holstein cattle in the age range of 40-70 months and the genetic correlation with lifetime milk production. The specific content of Table 1 is shown in Example 5.

[0229] As shown in Table 1, the heritability of the total milk production trait at each age in the 40-70 month age range of Holstein cattle is generally high, and the genetic correlation between the total milk production trait at each age in the 40-70 month age range of Holstein cattle and the lifetime milk production is also high;

[0230] Among them, although the heritability of the total milk production trait at each month of Holstein cattle in the 40-45 month age range is relatively higher than that of the total milk production trait at each month of Holstein cattle in the 45-70 month age range, the genetic correlation between the total milk production trait at each month of Holstein cattle in the 40-45 month age range and lifetime milk production is relatively lower than the genetic correlation between the total milk production trait at each month of Holstein cattle in the 45-70 month age range and lifetime milk production.

[0231] Combination Figure 2 It can be seen that the phenotypic correlation between the total milk production traits at each month of age and lifetime milk production in Holstein cattle in the 40-45 month age range is significantly lower than the phenotypic correlation between the total milk production traits at each month of age and lifetime milk production in Holstein cattle in the 45-70 month age range.

[0232] Table 3 shows the phenotypic selection differences of total milk production traits at each age of Holstein cattle between 40 and 70 months of age on lifetime milk production and reproductive traits; see Example 5 for the specific content of Table 3.

[0233] Among them, the phenotypic selection difference of lifetime milk production is specifically manifested as the difference in the daily mean value of total milk production traits at each month of age between the high-yield group A and the low-yield group B of Holstein cows in the age range of 40-70 months, where the daily mean value = lifetime milk production / milk-producing day; group A: the top 10% of the herds were selected after the herds were ranked by milk production; group B: the bottom 10% of the herds were selected after the herds were ranked by milk production;

[0234] The phenotypic selection differences in reproductive traits are specifically manifested in the differences in the calving interval of total milk production at each age between the high-yielding group a and the low-yielding group b of Holstein cattle in the age range of 40-70 months.

[0235] As shown in Table 3, the daily mean values ​​of total milk production at each age between the high-yield group a and the low-yield group b of Holstein cattle in the 40-70 month age range are very different, and the difference is generally more than 12, indicating that the phenotypic selection of total milk production at each age on lifetime milk production between the high-yield group a and the low-yield group b of Holstein cattle in the 40-70 month age range is generally very different;

[0236] The differences in calving intervals of total milk production at each month between the high-yield group a and the low-yield group b of Holstein cattle in the 40-70 month age range vary, ranging from 7 to 15, indicating that the differences in phenotypic selection of total milk production at each month between the high-yield group a and the low-yield group b of Holstein cattle in the 40-70 month age range vary.

[0237] Among them, although the difference in the daily mean of total milk production at each month of age between the high-yield group a and the low-yield group b of Holstein cattle in the 40-45 month age range is as high as more than 13, it shows that the difference in phenotypic selection of total milk production at each month of age between the high-yield group a and the low-yield group b of Holstein cattle in the 40-45 month age range on lifetime milk production is significantly higher than the difference in phenotypic selection of total milk production at each month of age between the high-yield group a and the low-yield group b of Holstein cattle in the 45-70 month age range on lifetime milk production;

[0238] However, the maximum difference in calving interval for total milk production at each month between the high-yield group a and the low-yield group b of Holstein cattle in the age range of 40-55 months was only 9, indicating that the difference in phenotypic selection for total milk production at each month between the high-yield group a and the low-yield group b of Holstein cattle in the age range of 40-45 months for this part of reproductive traits was significantly lower than the difference in phenotypic selection for total milk production at each month between the high-yield group a and the low-yield group b of Holstein cattle in the age range of 55-70 months.

[0239]

[0240]

[0241] Table 4 shows the differences in phenotypic selection of total milk production at each age of Holstein cattle between 40 and 70 months of age on reproductive traits; the reproductive traits are specifically manifested as the incidence of reproductive disorders, including calving difficulty, re-examination of fetal anomaly, abortion and prohibition of mating; Group a: the cows were ranked by milk production and the top 10% of the herds were selected; Group b: the cows were ranked by milk production and the bottom 10% of the herds were selected.

[0242] As shown in Table 4, the calving difficulty rate, re-inspection infertility rate, abortion rate and ban rate of the total milk production trait at each month of the high-yield group a of Holstein cattle in the 40-70 month age range were significantly lower than those of the low-yield group b of Holstein cattle in the 40-70 month age range; this indicates that the phenotypic selection of the total milk production trait at each month of the high-yield group a of Holstein cattle in the 40-70 month age range for reproductive traits is significantly better than the phenotypic selection of the total milk production trait at each month of the low-yield group b of Holstein cattle in the 40-70 month age range for reproductive traits;

[0243] Among them, the difference in calving difficulty rate of total milk production traits at 50 months of age between high-yield group a and low-yield group b of Holstein cattle was the highest among the differences in calving difficulty rate of total milk production traits at each month of age between high-yield group a and low-yield group b of Holstein cattle in the age range of 40-70 months, and the difference in calving difficulty rate of total milk production traits at 60 months of age between high-yield group a and low-yield group b of Holstein cattle was the lowest among the differences in calving difficulty rate of total milk production traits at each month of age between high-yield group a and low-yield group b of Holstein cattle in the age range of 40-70 months.

[0244] The difference in the retest abortion rate of total milk production traits at 60 months of age between the high-yield group a and the low-yield group b of Holstein cattle was the highest among the differences in the retest abortion rate of total milk production traits at each month of age between the high-yield group a and the low-yield group b of Holstein cattle in the 40-70 month age range. The difference in the retest abortion rate of total milk production traits at 70 months of age between the high-yield group a and the low-yield group b of Holstein cattle was the lowest among the differences in the retest abortion rate of total milk production traits at each month of age between the high-yield group a and the low-yield group b of Holstein cattle in the 40-70 month age range.

[0245] The difference in abortion rate of total milk production traits at 50 months of age between high-yield group a and low-yield group b of Holstein cattle was the highest among the differences in abortion rate of total milk production traits at each month of age between high-yield group a and low-yield group b in the 40-70 month range. The difference in abortion rate of total milk production traits at 70 months of age between high-yield group a and low-yield group b of Holstein cattle was the lowest among the differences in abortion rate of total milk production traits at each month of age between high-yield group a and low-yield group b in the 40-70 month range.

[0246] The difference in the ban on mating rate of total milk production traits at 70 months of age between the high-yield group a and the low-yield group b of Holstein cattle was the highest among the differences in the ban on mating rates of total milk production traits at each age between the high-yield group a and the low-yield group b in the 40-70 month age range. The difference in the ban on mating rate of total milk production traits at 50 months of age between the high-yield group a and the low-yield group b of Holstein cattle was the lowest among the differences in the ban on mating rates of total milk production traits at each age between the high-yield group a and the low-yield group b in the 40-70 month age range.

[0247]

[0248] Table 6 shows the differences in phenotypic selection of total milk production traits at different ages for Holstein cows between 40 and 70 months of age on milk production years; Group a: the cows were ranked by milk production and the top 10% of the cows were selected; Group b: the cows were ranked by milk production and the bottom 10% of the cows were selected.

[0249] As shown in Table 6, the phenotypic selection of total milk production traits at each month between the high-yield group a and the low-yield group b of Holstein cattle in the 40-70 month age range on the milk production years is positively correlated with the age; the phenotypic selection difference of total milk production traits at each month between the high-yield group a and the low-yield group b of Holstein cattle in the 40-70 month age range on the milk production years is negatively correlated with the age;

[0250] Among them, the phenotypic selection of the total milk production trait at each month of the high-yield group a of Holstein cattle in the 40-70 month age range for the milk production years was significantly better than that of the low-yield group b of Holstein cattle in the 40-70 month age range for the milk production years;

[0251] The difference in phenotypic selection of total milk production trait at 40 months of age on milk production years between high-yield group a and low-yield group b of Holstein cattle was significantly higher than the difference in phenotypic selection of total milk production trait at each month of age on milk production years between high-yield group a and low-yield group b of Holstein cattle in the 40-70 month age range. This indicates that the phenotypic selection of total milk production trait at 40 months of age on milk production years between high-yield group a and low-yield group b of Holstein cattle was significantly lower than the phenotypic selection of total milk production trait at each month of age on milk production years between high-yield group a and low-yield group b of Holstein cattle in the 40-70 month age range.

[0252]

[0253]

[0254] Table 5 shows the total milk production traits of Holstein cattle in the age range of 40-70 months at each month, and the phenotypic selection differences in milk production years at each month are obtained based on the group survival rate and coverage rate at each month.

[0255] As shown in Table 5, both the survival rate and coverage rate are negatively correlated with the age in months;

[0256] Among them, the survival rate of Holstein cattle in the 40-60 month age range decreases evenly and regularly, indicating that Holstein cattle gradually become difficult to survive with age; even though the survival rate of Holstein cattle before 60 months of age is higher than that of Holstein cattle at 60 months of age, it is difficult for Holstein cattle before 60 months of age to survive to 60 months of age, so the total milk production trait before 60 months of age is significantly lower than the total milk production trait at 60 months of age. The 60-month age represents the highest milk production period in the 40-60 month age range;

[0257] The survival rate of Holstein cattle after 60 months of age drops significantly. 60 months of age represents the age when Holstein cattle are most likely to survive in the 60-70 month range.

[0258] Although the coverage rate of Holstein cattle aged 40 to 70 months decreases regularly, the coverage rate of Holstein cattle aged 60 months is significantly higher than that of Holstein cattle after 60 months of age. Therefore, the selectable ratio of 60-month-old Holstein cattle as breeding objects is determinable, and both the current selection and the selection during the test period after the selection are deterministic breeding object ratios.

[0259] Further integration Figure 2 As can be seen from Table 1, not only is the phenotypic correlation between the total milk production traits at each month of age in the Holstein cattle in the 40-45 month age range and the lifetime milk production significantly lower than the phenotypic correlation between the total milk production traits at each month of age in the Holstein cattle in the 45-70 month age range and the lifetime milk production; but also the genetic correlation between the total milk production traits at each month of age in the Holstein cattle in the 40-45 month age range and the lifetime milk production is relatively lower than the genetic correlation between the total milk production traits at each month of age in the Holstein cattle in the 45-70 month age range and the lifetime milk production.

[0260] Further combined with Table 3, it can be seen that the phenotypic selection difference of the total milk production trait at each month between the high-yield group a and the low-yield group b of Holstein cattle in the 40-45 month age range for lifetime milk production is significantly higher than the phenotypic selection difference of the total milk production trait at each month between the high-yield group a and the low-yield group b of Holstein cattle in the 45-70 month age range for lifetime milk production; the phenotypic selection difference of the total milk production trait at each month between the high-yield group a and the low-yield group b of Holstein cattle in the 40-45 month age range for this part of reproductive traits is significantly lower than the phenotypic selection difference of the total milk production trait at each month between the high-yield group a and the low-yield group b of Holstein cattle in the 55-70 month age range for this part of reproductive traits;

[0261] This indicates that the difference in phenotypic selection for total milk production at each month between the high-yield group a and the low-yield group b of Holstein cattle in the 40-45 month age range on lifetime milk production is opposite to the difference in phenotypic selection for total milk production at each month between the high-yield group a and the low-yield group b of Holstein cattle in the 40-45 month age range on this part of the reproductive traits.

[0262] Further combined with Table 4, it can be seen that the total milk production trait at each age between the high-yield group a and the low-yield group b of Holstein cattle in the 40-45 month age range is between the highest and lowest incidences of reproductive disorders, indicating that the total milk production trait at each age between the high-yield group a and the low-yield group b of Holstein cattle in the 40-45 month age range does not have a comprehensive and outstanding effect on the phenotypic selection differences for this part of the reproductive traits.

[0263] Further combined with Table 6, it can be seen that the phenotypic selection of the total milk production trait at each month for the milk production years between the high-yield group a and the low-yield group b of Holstein cattle in the age range of 40-45 months is significantly lower than the phenotypic selection of the total milk production trait at each month for the milk production years between the high-yield group a and the low-yield group b of Holstein cattle in the age range of 45-70 months.

[0264] Further combined with Table 5, it can be seen that although the survival rate and coverage rate of Holstein cattle in the 40-45 month age range are significantly higher than those of Holstein cattle in the 40-45 month age range, the survival rate and coverage rate of Holstein cattle in the 40-45 month age range show an overall downward trend, indicating that Holstein cattle in the 40-45 month age range are not only difficult to survive to the 45-70 month age range, but also the proportion of Holstein cattle in the 40-45 month age range that can be selected for breeding is gradually decreasing; This indicates that the phenotypic selection differences in the total milk production traits of Holstein cattle at each age of 40-45 months for milk production years cannot ensure a definite and selectable proportion of breeding objects.

[0265] If the total milk production trait of Holstein cows in the 40-45 month age range is determined as a breeding indicator, it is impossible to comprehensively, effectively and reliably improve the milk production performance of Holstein cows, especially the lifetime milk production, because the total milk production trait of Holstein cows in the 40-45 month age range has an impact on the lifetime milk production. Figure 2This is contrary to the low results shown in Table 1 and the high results shown in Table 3; secondly, it is impossible to comprehensively, effectively and definitively improve the milk production performance of Holstein cattle, led by lifetime milk production, and the reproductive performance represented by calving interval, because in the results shown in Table 3, the high phenotypic selection difference of the total milk production trait at each age between the high-yielding group a and the low-yielding group b of Holstein cattle in the 40-45 month age range for lifetime milk production is opposite to the low phenotypic selection difference of the total milk production trait at each age between the high-yielding group a and the low-yielding group b of Holstein cattle in the 40-45 month age range for reproductive traits involving calving interval; thirdly, it is impossible to comprehensively, effectively and definitively improve the overall reproductive performance of Holstein cattle. , because in the results shown in Table 4, the total milk production trait at each month between the high-yield group a and the low-yield group b of Holstein cattle in the 40-45 month age range does not have a comprehensive and prominent effect on the phenotypic selection difference of this part of the reproductive traits; finally, it is impossible to comprehensively, effectively and definitely improve the milk production years of Holstein cattle, because in the results shown in Tables 6 and 5, the phenotypic selection of the total milk production trait at each month between the high-yield group a and the low-yield group b of Holstein cattle in the 40-45 month age range is not only significantly lower, but also under the guidance of this phenotypic selection, it is difficult to determine the proportion of selectable breeding objects, and even cannot meet the minimum actual needs;

[0266] Therefore, the total milk production trait at each age between 40 and 45 months cannot be identified as an effective breeding indicator for comprehensively improving the reproductive performance, milk production performance and milk production years of Holstein cattle.

[0267] Further combined with Table 3, it can be seen that the difference in the daily mean of total milk production traits at 60 months of age between the high-yield group a and the low-yield group b of Holstein cattle is the highest among the differences in the daily mean of total milk production traits at each month of age between the high-yield group a and the low-yield group b of Holstein cattle in the age range of 45-70 months, indicating that the difference in phenotypic selection of total milk production traits at 60 months of age between the high-yield group a and the low-yield group b of Holstein cattle on lifetime milk production is the highest among the differences in phenotypic selection of total milk production traits at each month of age between the high-yield group a and the low-yield group b of Holstein cattle in the age range of 45-70 months.

[0268] With 60 months of age as a watershed, the differences in the daily averages of the total milk production traits at each month of age between the high-yield group a and the low-yield group b of Holstein cattle in the age range of 45-60 months are all on a downward trend, indicating that the differences in phenotypic selection for the total milk production traits at each month of age between the high-yield group a and the low-yield group b of Holstein cattle in the age range of 45-60 months and the differences in phenotypic selection for the total milk production traits at each month of age between the high-yield group a and the low-yield group b of Holstein cattle in the age range of 60-70 months are more or less declining relative to the differences in phenotypic selection for the total milk production traits at 60 months of age. Therefore, the differences in phenotypic selection for the total milk production traits at 60 months of age for the lifetime milk production of Holstein cattle are at their peak in the differences in phenotypic selection for the total milk production traits at each month of age between the high-yield group a and the low-yield group b of Holstein cattle in the age range of 45-70 months.

[0269] In addition, although the difference of 12.86 in the daily average of total milk production at 60 months of age between the high-yield group a and the low-yield group b of Holstein cattle did not reach 13, it was also the closest to 13 in the phenotypic selection difference of total milk production at each age on lifetime milk production between the high-yield group a and the low-yield group b of Holstein cattle in the age range of 45-70 months.

[0270] Further combined with Table 3, it can be seen that in the 40-70 month age range, the calving interval of 15 for the total milk production trait at 55 months of age between the high-yield group a and the low-yield group b of Holstein cattle is the highest, followed by the calving interval of 13 for the total milk production trait at 70 months of age between the high-yield group a and the low-yield group b of Holstein cattle and the calving interval of 12 for the total milk production trait at 60 months of age between the high-yield group a and the low-yield group b of Holstein cattle; this shows that although the calving interval of the total milk production trait at 60 months of age between the high-yield group a and the low-yield group b of Holstein cattle is not the lowest, it is relatively low, and the corresponding reproductive performance is also relatively high.

[0271] Further combined with Table 4, it can be seen that the calving difficulty rate, re-examination infertility rate, abortion rate and prohibition rate of the high-yield group a of Holstein cattle in the 40-70 month age range are significantly lower than those of the low-yield group b of Holstein cattle in the 40-70 month age range;

[0272] Among them, the difference in calving difficulty rate of total milk production traits at 60 months of age between high-yield group a and low-yield group b of Holstein cattle is the lowest among the differences in calving difficulty rate of total milk production traits at each month between high-yield group a and low-yield group b of Holstein cattle in the 40-70 month age range, indicating that the corresponding reproductive performance is more stable; the difference in re-examination non-fertility rate of total milk production traits at 60 months of age between high-yield group a and low-yield group b of Holstein cattle is the highest among the differences in re-examination non-fertility rate of total milk production traits at each month between high-yield group a and low-yield group b of Holstein cattle in the 40-70 month age range, indicating that the corresponding reproductive performance is more unstable; The difference in abortion rate of total milk production at 60 months of age was relatively low among the differences in abortion rate of total milk production at each age between the high-yield group a and the low-yield group b in the 40-70 month age range. The difference in ban on mating rate of total milk production at 60 months of age between the high-yield group a and the low-yield group b in the 40-70 month age range was relatively high among the differences in ban on mating rate of total milk production at each age between the high-yield group a and the low-yield group b in the 40-70 month age range. The two can be neutralized in reproductive traits; the calving difficulty rate and the re-examination fetal absence rate can also be neutralized in reproductive traits; the effect of total milk production at 60 months of age between the high-yield group a and the low-yield group b in Holstein cattle on reproductive performance is relatively stable overall.

[0273] Further integration Figure 2 It can be seen that in the age range of 45-70 months, the phenotypic correlation coefficient between the total milk production trait at 60 months of age and the lifetime milk production of Holstein cattle is as high as 0.9. The phenotypic correlation between the total milk production trait at 60 months of age and the lifetime milk production of Holstein cattle is significantly higher than the phenotypic correlation between the total milk production trait at each age in the age range of 40-60 months and the lifetime milk production of Holstein cattle.

[0274] Further integration Figure 2 As shown in Table 1, although the phenotypic correlation between the total milk production traits at each age and lifetime milk production in the Holstein cattle between 60 and 70 months of age continues to rise on the basis of 60 months of age; the heritability of the total milk production traits at each age in the Holstein cattle between 60 and 70 months of age is also relatively higher than the heritability of the total milk production traits at 60 months of age in the Holstein cattle, and the genetic correlation between the total milk production traits at each age and lifetime milk production in the Holstein cattle between 60 and 70 months of age is also relatively higher than the genetic correlation between the total milk production traits at 60 months of age in the Holstein cattle and lifetime milk production;

[0275] Further combined with Table 3, it can be seen that the difference in the daily average of the total milk production trait at each age between the high-yield group a and the low-yield group b of Holstein cattle in the 60-70 month age range is on a downward trend, that is, the difference in the phenotypic selection of the total milk production trait at each age for lifetime milk production in Holstein cattle in the 60-70 month age range is on a downward trend. Therefore, the total milk production trait at each age in Holstein cattle in the 60-70 month age range as a breeding indicator cannot be positively determined to improve the breeding effect of milk production performance.

[0276] Further combined with Table 6, it can be seen that the phenotypic selection and phenotypic selection differences of the total milk production trait at each month of the age range of 60-70 months of Holstein cattle on the milk production years are better than the phenotypic selection and phenotypic selection differences of the total milk production trait at 60 months of age of Holstein cattle on the milk production years;

[0277] Further combined with Table 5, it can be seen that the survival rate of Holstein cattle after 60 months of age decreases significantly. 60 months of age represents the age at which Holstein cattle in the 60-70 month age range are most likely to survive. The coverage rate of 60-month-old Holstein cattle is also significantly higher than the coverage rate of Holstein cattle after 60 months of age. Therefore, the selectable ratio of 60-month-old Holstein cattle as breeding objects is determinable and better than the selectable ratio of Holstein cattle in the 60-70 month age range. Both the current selection and the selection during the test period after the selection are deterministic breeding object ratios.

[0278]

[0279] Table 2 shows the heritability and genetic correlation of total milk production and milk composition traits in Holstein cows at 60 months of age.

[0280] Combined with Table 2, it can be seen that the milk fat rate of 60-month-old Holstein cows reaches 0.21, and the milk protein rate of 60-month-old Holstein cows reaches 0.20, which is similar to the research results of milk composition traits obtained by Lu Xubin after evaluating dairy cows by parity (Lu Xubin. Genetic parameter estimation, molecular function mining verification and genome prediction of milk production traits of Chinese Holstein cows in Jiangsu Province [D]. Yangzhou University, 2023); the somatic cell genetic score of 60-month-old Holstein cows reaches 0.25, which is significantly higher than the somatic cell genetic score of 0.11 in the research results of Zhang Shixi (Zhang Shixi. Study on genetic evaluation of milk production traits of Holstein cows in Shandong Province based on random regression measurement day model [D]. Shandong Agricultural University, 2024);

[0281] There have been many reports on the evaluation of genetic parameters of milk production traits of Chinese Holstein dairy cows at home and abroad. Among them, the research of Meng Yuan found that the heritability of milk fat rate and milk protein rate of Chinese Holstein dairy cows in Shuozhou, Shanxi Province was 0.13 and 0.23 respectively, which was significantly lower than the research results of the present invention and Lu Xubin on milk composition traits.

[0282] This indicates that the genetic score of the 60-month-old Holstein cows of the present invention for milk component traits is higher.

[0283] Therefore, the total milk production trait at 60 months of age can fully represent the lifetime milk production of Holstein cattle and its trait performance is the best. When the total milk production trait at 60 months of age is used as a comprehensive breeding indicator for Holstein cattle, there is a lot of selection variation and optional space. After careful breeding, there can be a good room for improvement.

[0284] The technical solution provided by the embodiments of the present invention may include the following beneficial effects: by acquiring and comprehensively comparing corresponding data from the heritability of the total milk production trait and milk component traits at each month of age of Holstein cattle, the genetic correlation between the total milk production trait at each month of Holstein cattle and the lifetime milk production and milk component traits, and the phenotypic correlation and phenotypic selection differences between the total milk production trait at each month of Holstein cattle and the lifetime milk production, reproductive traits and milk production years, it is finally determined that 60 months of age is the best when the total milk production trait at each month of age is used as a breeding indicator, that is, the total milk production trait at 60 months of age is the best and effective breeding indicator for comprehensively improving the reproductive performance, milk production performance and milk production years of Holstein cattle; the high-yield group of Holstein cattle selected by the breeding indicator of the total milk production trait at 60 months of age has the highest lifetime milk production and milk production years, the shortest calving interval, the best reproductive performance represented by the calving interval, a low incidence of reproductive disorders, and relatively stable reproductive performance represented by the incidence of reproductive disorders.

[0285] Embodiment 7

[0286] This embodiment is the seventh embodiment, which is different from Embodiment 1, Embodiment 4, Embodiment 5 and Embodiment 6 in that: Step S103 in Embodiment 1 evaluates the correlation between the total milk production trait at each month age and each performance index to determine the optimal value of the breeding index at the month age, which may include the following sub-steps F1-F4:

[0287] In F1, the genetic parameters of total milk production at each month of age and lifetime milk production, reproductive traits, milk production years, and milk composition traits of the breeding objects were obtained;

[0288] In F2, the total milk production trait of the selected breeding objects at each month of age was obtained to correlate with the phenotype of lifetime milk production, reproductive traits, and milk production years;

[0289] In F3, the phenotypic selection differences of total milk production at each month of age on lifetime milk production, reproductive traits, and milk production years of a specific breeding group of the breeding object were obtained;

[0290] In F4, the phenotypic selection differences of the total milk production trait of the breeding objects at each month of age on the milk production years were obtained, and the proportion of selectable breeding objects was determined.

[0291] In one possible implementation of the present invention, except for specific performance indicators, it is the same as Example 6.

[0292] The technical solution provided by the embodiment of the present invention may include beneficial effects that are the same as those of Example 6.

[0293] Example 8

[0294] This embodiment is the eighth embodiment, which is different from the above embodiments in that: Step S103 in Embodiment 1 evaluates the correlation between the total milk production trait at each month age and each performance index to determine the optimal value of the breeding index at the month age, which may include the following sub-steps G1-G4:

[0295] In G1, multifactorial ANOVA was performed on the total milk production trait when the specific age was the optimal value;

[0296] In G2, the effects of parity, season, lactation stage, and birth year on the total milk production trait at the optimal value at a specific age were analyzed;

[0297] In G3, multiple comparisons were made on the different significance levels of total milk production traits at optimal values ​​at specific months of age in different parities, seasons, lactation stages and birth years, and the level of milk production traits at optimal values ​​at specific months of age was determined based on the significance level.

[0298] The specific optimum age in months of the present invention is 60 months.

[0299] Existing scholars' research shows that the total milk production trait of Holstein cattle at the current month age is actually determined and influenced by multiple factors. Among them, the total milk production at the current month age is positively correlated with the current parity of Holstein cattle, the total milk production is highest in autumn and winter, especially in winter, at the end of the current month age, and the total milk production at the current month age is positively correlated with the number of lactation days at the end of the current month age until the middle of lactation.

[0300] In one possible implementation of the present invention, a general linear model is used to analyze the effects of parity, season, lactation stage and birth year on the total milk production trait when a specific age is the optimal value;

[0301] The general linear model expression is:

[0302] Y=u+N+M1+M2+C+T+H+e

[0303] Among them, Y is the total milk production when the specific age is the optimal value; u is the overall mean; N is the fixed effect of parity at the end of the specific optimal age; M1 is the fixed effect of calving season; M2 is the fixed effect of season at the end of the specific optimal age; C is the fixed effect of lactation stage at the end of the specific optimal age; T is the fixed effect of birth year; H is the fixed effect of age at first calving; e is the random residual.

[0304]

[0305] In the present invention, Table 7 shows the effect of the parity of Holstein cows at the end of 60 months of age on the total milk production at 60 months of age.

[0306] Combined with Table 7, it can be seen that the average daily output value of Holstein cattle at the end of 60 months of age increases with the increase of parity, indicating that the total milk production of Holstein cattle at the end of 60 months of age increases with the increase of parity.

[0307]

[0308] Table 8 shows the effect of the lactation stage of Holstein cows at the end of 60 months of age on the total milk production at 60 months of age.

[0309] Combined with Table 8, it can be seen that the average daily output value of Holstein cattle at the end of 60 months of age increases with the increase of lactation days until the middle of lactation and then decreases with the increase of lactation days. This shows that the total milk production of Holstein cattle at the end of 60 months of age increases with the increase of lactation days until the middle of lactation.

[0310]

[0311] Table 9 shows the effect of different first calving ages on total milk production at 60 months of age in Holstein cows.

[0312] Combined with Table 9, it can be seen that the average daily output value of Holstein cattle at the end of 60 months of age increases with the increase of the age of first calving, indicating that the total milk production of Holstein cattle at the end of 60 months of age increases with the increase of the age of first calving.

[0313]

[0314] Table 10 shows the effect of different birth years on milk production at 60 months of age in Holstein cows.

[0315] Combined with Table 10, it can be seen that the average daily output value of Holstein cattle at the end of 60 months of age increases with the increase of birth year, indicating that the total milk production of Holstein cattle at the end of 60 months of age increases with the increase of birth year.

[0316] The technical solution provided by the embodiments of the present invention may include the following beneficial effects: by conducting a multi-factor analysis based on the parity, lactation days and stage, age of first calving and year of birth of Holstein cattle at the end of 60 months of age, it is further determined that the total milk production trait at 60 months of age determined in the present invention as a comprehensive and effective breeding indicator for Holstein cattle is in line with the actual milk production law, that is, the breeding scope can be further refined on the basis of the total milk production trait at 60 months of age as a comprehensive and effective breeding indicator for Holstein cattle, ensuring that the comprehensive breeding improvement of Holstein cattle in milk production performance, reproductive performance and milk production years achieves better results.

[0317] Example 9

[0318] This embodiment is the ninth embodiment, which is different from embodiments 1 to 8 in that: a breeding method is provided, the method comprising the following steps S201-S217:

[0319] In S201, the preset time period of the breeding object target data set source and the total number of breeding objects prepared are determined respectively;

[0320] In S202, it is determined that the target data set of the breeding object includes the daily milk production record of the breeding object and the trait information corresponding to the breeding object;

[0321] In S203, starting from the start date of the preset time period, the daily milk production records of the breeding objects are obtained one by one according to the total number of breeding objects prepared;

[0322] In S204, a collection of trait information generated each time by the breeding objects within a preset time period is obtained one by one according to the prepared total number of breeding objects;

[0323] In S205, the target data set is selected using the trait information and daily milk production records of the breeding object as control factors;

[0324] In S206, the screened target data set is imported into R language software for classification and aggregation;

[0325] In S207, the traits that can simultaneously reflect the performance indicators of the breeding object are obtained according to the classified and summarized target data set, and the breeding indicator is determined to be the total milk production trait at each month of age;

[0326] In S208, the priority of the performance indicators of the breeding objects is determined according to the priority of the traits affecting the total milk production at each month of age;

[0327] In S209, the variance and / or covariance of the total milk production trait at each month age is calculated and analyzed based on the classified and summarized target data set;

[0328] In S210, different variances and / or covariances are split and valued from the phenotypic value vector of the total milk production trait at each month of age;

[0329] In S211, the genetic parameters of the total milk production traits and performance indicators of the breeding objects at each month of age are obtained;

[0330] In S212, the phenotypic correlation between the total milk production trait and the performance index of the breeding object at each month of age is obtained;

[0331] In S213, the phenotypic selection differences of the total milk production trait at each month age on the performance index of the specific breeding group of the breeding object are obtained;

[0332] In S214, the phenotypic selection difference of the total milk production trait of the breeding objects at each month of age on the milk production years is obtained, and the proportion of the breeding objects that can be selected is determined;

[0333] In S215, a multi-factor analysis of variance was performed on the total milk production trait when the specific age was the optimal value;

[0334] In S216, the effects of parity, season, lactation stage and birth year on the total milk production trait at a specific age as the optimal value were analyzed;

[0335] In S217, multiple comparisons are made of the different significance levels of the total milk production trait when the specific month age is the optimal value in different parities, different seasons, different lactation stages and different birth years, and the level of the milk production trait when the specific month age is the optimal value is determined according to the level of significance.

[0336] The present invention determines that the total milk production trait of 60 months of age is the best breeding indicator through comprehensive analysis of the heritability of the total milk production trait at each month of age of Holstein cattle, the genetic correlation with performance indicators, the performance correlation and the phenotypic selection difference. This indicator can deterministically and comprehensively improve the reproduction, milk production performance and milk production years of Holstein cattle. Among them, when the performance indicator is only lifetime milk production, the determination of the total milk production trait at the best month of age can be achieved.

[0337] Example 10

[0338] This embodiment is the tenth embodiment, which is different from embodiments 1 to 9 in that: a method for selecting a Holstein cattle genome is provided, comprising:

[0339] The total milk production of Holstein cattle at each month of age is used as a breeding index for Holstein cattle;

[0340] The optimum age of the Holstein cattle is 60 months.

[0341] The breeding method of the present invention can be applied to various types of milk-producing mammals, among which the effect obtained when Holstein cattle are selected as the breeding object is remarkable. The total milk production trait at 60 months of age determined in the present invention is the best and effective breeding indicator that can comprehensively improve the reproductive performance, milk production performance and milk production years of Holstein cattle.

[0342] Embodiment 11

[0343] This embodiment is the eleventh embodiment, which is different from embodiments 1 to 10 in that: a breeding device is provided, which can be implemented as part or all of an electronic device through software, hardware or a combination of both. The breeding device includes:

[0344] A data acquisition module 11 is used to acquire a target data set of a breeding object;

[0345] An indicator determination module 12 is used to determine at least one performance indicator of the breeding object, and use the total milk production trait at each month from the first milk production to a specific month age as a breeding indicator; and

[0346] The association evaluation module 13 evaluates the association between the total milk production trait at each month from the first milk production to a specific month of age and each performance index to determine the optimal value of the breeding index at a specific month of age.

[0347] Example 12

[0348] This embodiment is the twelfth embodiment, which is different from the embodiment 11 in that: the data acquisition module 11 in the embodiment 11 includes: a data planning submodule 111, a data element submodule 112, a data collection submodule 113, and a data integration submodule 114;

[0349] The data planning submodule 111 is used to respectively determine the preset time period of the breeding object target data set source and the total number of breeding objects to be prepared;

[0350] The data element submodule 112 is used to determine that the target data set of the breeding object includes the daily milk production record of the breeding object and the trait information corresponding to the breeding object;

[0351] The data collection submodule 113 is used to obtain the daily milk production records of the breeding objects one by one according to the total number of breeding objects prepared, starting from the starting day of the preset time period;

[0352] The data integration submodule 114 is used to obtain the collection of trait information generated each time by the breeding objects within a preset time period one by one according to the prepared total number of breeding objects.

[0353] Embodiment 13

[0354] This embodiment is the thirteenth embodiment, which is different from the embodiment 11 in that: the indicator determination module 12 in the embodiment 11 includes: a performance indicator summary submodule 121, a breeding indicator determination submodule 122, and a performance indicator ranking submodule 123;

[0355] The performance index summary submodule 121 is used to summarize all performance indexes related to the possibility of the breeding object, and determine a breeding index based on the characteristics of being able to simultaneously reflect multiple performance indexes of the breeding object;

[0356] The breeding index determination submodule 122 is used to determine the breeding index as the total milk production trait at each month of age;

[0357] The performance indicator ranking submodule 123 is used to determine the priority of the performance indicators of the breeding objects according to the priority of the traits affecting the total milk production at each month of age.

[0358] Embodiment 14

[0359] This embodiment is the fourteenth embodiment, which is different from the embodiment 11 in that: the association assessment module 13 in the embodiment 13 includes: an association factor determination submodule 131, a genetic correlation acquisition submodule 132, a first phenotype correlation acquisition submodule 133, a second phenotype correlation acquisition submodule 134, and an optimal month age determination submodule 135;

[0360] The correlation factor determination submodule 131 is used to use the genetic correlation and phenotypic correlation between the total milk production trait at each month of age and each performance index as correlation evaluation factors;

[0361] The genetic correlation acquisition submodule 132 is used to obtain the genetic correlation between the total milk production trait at each month of age and each performance index arranged by priority;

[0362] The first phenotypic correlation acquisition submodule 133 is used to obtain the theoretical phenotypic correlation between the total milk production trait at each month of age and each performance index arranged by priority;

[0363] The second phenotypic correlation acquisition submodule 134 is used to obtain the actual phenotypic correlation between the total milk production trait at each month of age and each performance indicator arranged by priority;

[0364] The optimal month age determination submodule 135 is used to determine the optimal month age for the total milk production trait of the breeding object after comprehensive comparison based on the genetic related data, theoretical phenotype related data and actual phenotype related data of the breeding object at each month age.

[0365] Embodiment 15

[0366] This embodiment is the fifteenth embodiment, which is different from the embodiment 11 in that: the breeding device in the embodiment 11 further includes:

[0367] The data classification module 14 is used to classify and summarize the filtered target data set.

[0368] Example 16

[0369] This embodiment is the sixteenth embodiment, which is different from the embodiment 15 in that: the data classification module 14 in the embodiment 17 includes: a data screening submodule 141, and a data preprocessing submodule 142;

[0370] The data screening submodule 141 is used to screen the target data set using the trait information and daily milk production records of the breeding object as control factors;

[0371] The data preprocessing submodule 142 is used to classify and aggregate the filtered target data sets.

[0372] Embodiment 17

[0373] This embodiment is the seventeenth embodiment, which is different from the embodiment 11 in that: the breeding device in the embodiment 11 further includes:

[0374] A factor evaluation module 15, for evaluating the influencing factors of the total milk production trait when a specific age is the optimal value;

[0375] The verification and confirmation module 16 is used to determine the breeding indicators.

[0376] Embodiment 18

[0377] This embodiment is the eighteenth embodiment, which is different from the embodiment 17 in that: the factor evaluation module 15 in the embodiment 17 includes: a multi-factor variance analysis submodule 151, and a multi-factor result analysis submodule 152;

[0378] The multi-factor variance analysis submodule 151 is used to perform multi-factor variance analysis on the total milk production trait when a specific month age is the optimal value;

[0379] The multi-factor result analysis submodule 152 is used to analyze the influence of parity, season, lactation stage and birth year on the total milk production trait when a specific age is the optimal value.

[0380] Embodiment 19

[0381] This embodiment is the nineteenth embodiment, which is different from the embodiment 11 in that: a breeding device is provided, comprising:

[0382] processor;

[0383] a memory for storing processor-executable instructions;

[0384] The processor is configured as follows:

[0385] Obtain target data set for breeding objects;

[0386] Determine at least one performance index of the breeding object, and use the total milk production trait at each month of age as the breeding index;

[0387] The correlation between the total milk production trait at each month age and each performance index is evaluated to determine the optimal value of the month age for the breeding index.

[0388] The above processor may also be configured as follows: the same as in Embodiment 9.

[0389] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0390] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0391] Embodiment 20

[0392] This embodiment is the twentieth embodiment, which is different from the embodiment 19 in that: a computer-readable storage medium is provided, including:

[0393] A non-temporary computer-readable storage medium. When the instructions in the storage medium are executed by the processor of a breeding device, the breeding device is enabled to perform the above-mentioned breeding method, and the specific steps included in the method are the same as those in Example 9.

[0394] It is important to note that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A breeding method, characterized in that: include, Obtain target data set for breeding objects; Determine at least one performance index of the breeding object, and use the total milk production trait at each month of age as the breeding index; The correlation between the total milk production trait at each month age and each performance index is evaluated to determine the optimal value of the month age for the breeding index.

2. The breeding method according to claim 1, characterized in that: The optimal age for the breeding index is 60 months.

3. The breeding method according to claim 1, characterized in that: The evaluation of the association between the total milk production trait at each month of age and each performance index includes: Obtain the genetic parameters of total milk production traits and performance indicators of breeding objects at each month of age; Obtain the phenotypic correlation between total milk production traits and performance indicators of breeding objects at each month of age; Obtain the phenotypic selection differences of total milk production traits at each month of age on performance indicators in specific breeding groups of the selected breeding objects.

4. The breeding method according to claim 3, characterized in that: The performance indicators include lifetime milk production.

5. The breeding method according to claim 3, characterized in that: The performance indicators also include milk production years; The evaluation of the association between the total milk production trait at each month age and each performance index also includes: Obtain the phenotypic selection differences of total milk production traits at each month of age for the breeding objects on milk production years, and determine the proportion of selectable breeding objects.

6. The breeding method according to claim 3, characterized in that: The performance indicators also include milk component traits; The genetic parameters of the total milk production traits and performance indicators of the breeding objects at each month of age are obtained, including: Obtain the genetic parameters of total milk production traits at each month of age and lifetime milk production and milk composition traits of the breeding objects.

7. The breeding method according to claim 3, characterized in that: The performance indicators also include reproductive traits; The evaluation of the association between the total milk production trait at each month of age and each performance index includes: Obtain the total milk production traits of the breeding objects at each month of age, lifetime milk production, reproductive traits, milk production years, and and genetic parameters of milk composition traits; Obtain the phenotypic correlation between the total milk production trait of the breeding object at each month of age and the lifetime milk production, reproductive traits and milk production years; To obtain the phenotypic selection differences of total milk production at each month of age on lifetime milk production, reproductive traits, and milk production years in a specific breeding group of selected breeding targets; Obtain the phenotypic selection differences of total milk production traits at each month of age for the breeding objects on milk production years, and determine the proportion of selectable breeding objects.

8. The breeding method according to any one of claims 3, 4, 6 and 7, characterized in that: The genetic parameters of the total milk production trait at each month of the breeding object and the lifetime milk production, reproductive traits, milk production years, and milk composition traits are calculated according to the variance and / or covariance components of the total milk production trait at each month of the breeding object, including the following steps: Obtain the additive genetic variance of total milk production traits of breeding objects at each month of age Permanent environmental variance Residual variance and additive genetic covariance Calculate the phenotypic variance of total milk production traits of breeding objects at each month of age The expression is: Among them, the phenotypic variance It is the sum of the phenotypic variation of the total milk production trait of the breeding object at each month of age in the population; additive genetic variance Indicates the phenotypic variation caused by genetic factors of the breeding objects at each age; permanent environmental variance Represents the phenotypic variation caused by the permanent environmental effects of the breeding objects at each month of age; residual variance It represents the part of phenotypic variation caused by random errors and other non-genetic and non-permanent environmental factors; Calculate the heritability h of the total milk production trait of the breeding object at each month of age 2 , the expression is: Among them, heritability h 2 The phenotypic variation of the total milk production trait of the breeding object at each month of age is represented by the additive genetic variance The size of the ratio caused; Calculate the heritability h of the total milk production trait of the breeding object at each month of age 2 Repeatability of the level e , the expression is: Among them, the repeatability r e h represents the heritability of the total milk production trait of the breeding object at each month of age 2 Correlation between at least one set of measurements taken at different times or under different conditions; Calculate the genetic correlation of total milk production at each month of breeding objects and lifetime milk production, reproductive traits, milk production years, and milk composition traits a , the expression is: Among them, genetic correlation a It represents the correlation between the total milk production traits at each month of age and the lifetime milk production, reproductive traits, and milk production years of the breeding objects at the genetic level; the additive genetic covariance Cov(a1, a2) represents the degree of association between the genetic variations caused by common genetic factors between two groups of different traits of the breeding objects.

9. The breeding method according to any one of claims 3, 4, 5 and 7, characterized in that: Calculating the phenotypic correlation between the total milk production trait of the breeding object at each month of age and the lifetime milk production, reproductive traits, and milk production years according to the variance and / or covariance components of the total milk production trait of the breeding object at each month of age includes the following steps: Obtain the total milk production traits of the breeding objects at each month of age, lifetime milk production, reproductive traits, and phenotypic covariance Cov(p1, p2) and phenotypic variance of the milk production years Calculate the phenotypic correlation r between the total milk production trait at each month of breeding objects and the lifetime milk production, reproductive traits, and milk production years p , the expression is: Among them, phenotype correlation r p It represents the correlation between the total milk production traits of the breeding objects at each month of age and the lifetime milk production, reproductive traits, and milk production years at the phenotypic level; the phenotypic covariance Cov(p1, p2) represents the degree of association between the phenotypic variations of at least two groups of traits of the breeding objects.

10. The breeding method according to claim 8, characterized in that: The standard error SE of the heritability of the total milk production trait at each month of age, lifetime milk production, reproductive traits, and milk production years of the breeding objects was calculated using the expression: in, represents additive genetic variance The variance of Represents phenotypic variance The variance of represents additive genetic variance and phenotypic variance The covariance between .

11. The breeding method according to any one of claims 3, 4, 5 and 7, characterized in that: The method of obtaining the phenotypic selection differences of the total milk production trait at each month of the specific breeding group for the lifetime milk production, reproductive traits, and milk production years includes the following steps: Obtain the average ranking of daily milk production of the breeding objects at each month of age in the target data set, and set the high-yield group and low-yield group of the total milk production trait of the breeding objects at each month of age; The differences in lifetime milk production, reproductive traits, and milk production years between high-yield groups and low-yield groups are compared, and the quality of lifetime milk production, reproductive traits, and milk production years is determined based on the size of the differences.

12. The breeding method according to claim 5 or 7, characterized in that: The method of obtaining the phenotypic selection difference of the total milk production trait of the breeding objects at each month of age on the milk production years and determining the proportion of the breeding objects that can be selected comprises the following steps: Obtain the distribution rate and survival rate of breeding objects at each month age, and determine the proportion of selectable breeding objects based on the distribution rate and survival rate.

13. The breeding method according to any one of claims 1 to 7, characterized in that: The step of obtaining the target data set of the breeding object further includes: Classify and summarize the filtered target data set.

14. The breeding method according to claim 13, characterized in that: The process of classifying and aggregating the filtered target data set includes the following steps: The target data set is selected using the trait information and daily milk production records of the breeding objects as control factors, wherein the trait information of the breeding objects includes calving season, age at first calving, parity, and year of birth; Import the screened target data set into R language software for classification and summary; Calculate and analyze the variance and / or covariance of total milk production traits at each month of age based on the classified and summarized target data set; The expression of the calculation analysis model is: y=Xb+Za+Wc+e Where y is the phenotypic value vector of total milk production trait at each age; X, Z and W are the association matrices of fixed effects, additive genetic effects and permanent environmental effects, respectively; b is the fixed effect vector, including calving season, age at first calving, parity and birth year; a is the random additive genetic effect vector; c is the permanent environmental effect vector; e is the residual vector; Different variances and / or covariances are split into values ​​from the phenotypic value vector of the total milk production trait at each age.

15. The breeding method according to any one of claims 1 to 7 and 14, characterized in that: The step of determining the optimal value of the specific month age of the breeding index also includes: To evaluate the factors affecting the total milk production trait at a specific age; Determine breeding indicators.

16. The breeding method according to claim 15, characterized in that: The factors affecting the total milk production trait when evaluating the optimal value at a specific age include: Multifactorial ANOVA was performed on the total milk production trait when the specific age was the optimal value; The general linear model was used to analyze the effects of parity, season, lactation stage and birth year on total milk production at optimal values ​​at a specific age. The general linear model expression is: Y=u+N+M1+M2+C+T+H+e Among them, Y is the total milk production when the specific month age is the optimal value; u is the overall mean; N is the fixed effect of parity at the end of the specific month age optimal value; M1 is the fixed effect of calving season; M2 is the fixed effect of season at the end of the specific month age optimal value; C is the fixed effect of lactation stage at the end of the specific month age optimal value; T is the fixed effect of birth year; H is the fixed effect of first calving age; e is the random residual; The different significance levels of total milk production traits at a specific age as the optimal value in different parities, seasons, lactation stages and birth years were multiple compared, and the level of milk production traits at a specific age as the optimal value was determined based on the level of significance.

17. A method for genomic selection of Holstein cattle, characterized in that: This includes using the total milk production trait of Holstein cattle at each month of age as a breeding indicator for Holstein cattle.

18. The Holstein cattle genomic selection method according to claim 17, characterized in that: The optimum age of the Holstein cattle is 60 months.

19. A breeding device, characterized in that: include, A data acquisition module is used to obtain a target data set of breeding objects; An indicator determination module is used to determine at least one performance indicator of the breeding object, and use the total milk production trait at each month of age as the breeding indicator; as well as, The correlation evaluation module evaluates the correlation between the total milk production trait at each month age and each performance index to determine the optimal value of the breeding index at the month age.

20. The breeding device according to claim 19, characterized in that: Also includes, The data classification module is used to classify and summarize the filtered target data set.

21. The breeding device according to claim 19 or 20, characterized in that: Also includes, Factor evaluation module, used to evaluate the influencing factors of total milk production when the age is the optimal value; Verification and confirmation module, used to determine breeding indicators.

22. An electronic device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method of claim 1.

23. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to claim 1 are implemented.

24. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to claim 1 are implemented.

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