CN110892879A

CN110892879A - Binary hybridization cultivation method and training method and equipment

Info

Publication number: CN110892879A
Application number: CN201911239213.9A
Authority: CN
Inventors: 刘宝祥
Original assignee: Individual
Current assignee: Individual
Priority date: 2019-12-05
Filing date: 2019-12-05
Publication date: 2020-03-20

Abstract

A binary hybridization cultivation method, a training method and equipment. The binary hybridization cultivation method comprises the following steps: first breeding a first progeny comprising a binary cross of a first object and a second object, wherein said first object is located by a first functional gene and said second object is located by a second functional gene; and breeding the target object by using the first filial generation.

Description

Binary hybridization cultivation method and training method and equipment

Technical Field

The embodiment of the disclosure relates to a binary hybridization cultivation method, a training method and equipment.

Background

In recent years, with the improvement of living standard, people have more and more demands on beef, and the quality and the taste of the beef also have more and more attention. Not only is the beef required to have juicy and soft mouthfeel and special fragrance, but also the high-end part of the beef is required to have bright marbling on the cross section, and the high-end quality beef with the quality and mouthfeel is mainly imported and is expensive at present.

In the prior art, in order to obtain high-quality beef, a new variety is obtained by mainly utilizing binary hybridization (binary hybridization is also called simple hybridization, namely two varieties or strains are hybridized to obtain a first generation hybrid) between an exotic cow (male parent) and a local cow (female parent), but the fertility rate of a cow hybridized with the exotic cow is low, so that population propagation is influenced, the physique of an offspring is small, the rough feeding resistance is poor, and the feeding cost is high.

BRIEF SUMMARY OF THE PRESENT DISCLOSURE

The disclosed embodiment provides a binary hybridization cultivation method, a training method and equipment, the binary hybridization cultivation method can cultivate a new species with strong population propagation capacity, good growth performance, high meat yield, tender and juicy meat, and obvious marbling on the meat at the high-end part, the new species has strong rough feeding resistance, and the feeding cost is economical and practical.

According to an aspect of the present disclosure, at least one embodiment provides a binary hybridization breeding method, including: first breeding a first progeny comprising a binary cross of a first object and a second object, wherein said first object is located by a first functional gene and said second object is located by a second functional gene; and breeding the target object by using the first filial generation.

According to another aspect of the present disclosure, at least one embodiment also provides a method of training a first neural network model, or a method of training a second neural network model.

According to another aspect of the present disclosure, at least one embodiment also provides a training apparatus comprising: a processor adapted to implement instructions; and a memory adapted to store a plurality of instructions adapted to be loaded by the processor and to perform the above method of training the first neural network model, or training the second neural network model.

According to another aspect of the present disclosure, at least one embodiment also provides a computer-readable non-transitory storage medium storing computer program instructions which, when executed by a computer, perform the above-described method of training a first neural network model or training a second neural network model.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments of the present invention will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

In the drawings:

FIG. 1 is a flow chart of a method of binary hybridization breeding according to an embodiment of the present disclosure;

FIG. 2 is a process diagram of a convolutional neural network according to an embodiment of the present disclosure;

fig. 3 is a schematic diagram of a training apparatus according to an embodiment of the present disclosure.

Detailed Description

In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

In order to obtain high-end quality beef, a new variety is obtained by mainly utilizing binary hybridization (binary hybridization is also called simple hybridization, namely, two varieties or strains are hybridized to obtain a first generation hybrid) of an exotic cow (male parent) and a local cow (female parent). The exotic cattle can be German cattle, Simmental cattle, Limuzan, Pimont, Haford or Red Angel. The local cattle is local cattle. Although the new variety obtained by binary or multiple hybridization of the foreign cattle and the local cattle has good growth tendency and meat production performance, the grade of the meat at the high-grade part has a certain difference compared with the imported high-grade meat, which is mainly reflected in fat precipitation between muscles, in addition, the new variety has lower reproduction rate, and the feeding cost of the offspring in the fattening period is high.

At least one embodiment of the present disclosure provides a binary cross breeding method, as shown in fig. 1, which is a flowchart of the binary cross breeding method, and at least solves the problems of low fertilization rate of cows crossed with exotic cows, small size of offspring, and poor rough feeding resistance. The binary hybridization cultivation method comprises the following steps:

s101, first breeding a first filial generation which is subjected to binary crossing of a first object and a second object, wherein the first object is positioned by a first functional gene, and the second object is positioned by a second functional gene;

and S102, breeding the target object by using the first filial generation.

Here, the first subject is a female parent, the female parent is a male parent and the bovine female parent, and the second subject is a male parent. Specifically, the first object is pure blood Japanese black hair and female cattle, the weight of an adult cow is about 620 kg, the weight of a bull is about 950 kg, and a calf is fattened by 27 months, so that the weight reaches over 700 kg, and the average daily gain is over 1.2 kg. Japan and cattle are the best quality beef cattle recognized in the world today, and the meat has obvious marbling, also called as snowflake meat. Since the meat of Japan and cattle is juicy and tender, the saturated fatty acid content in the muscle fat is very low, the flavor is unique, the meat value is extremely high, the meat is regarded as 'national treasure' in Japan, and the meat is extremely expensive in the Western Europe market. Japan and cattle are very precious high-quality beef cattle variety resources in Japan.

In an alternative embodiment, the male parent is the heford male parent. The Haifute cattle, which is produced in Haifute county in England south of England, is the oldest early-maturing small and medium-sized beef cattle variety in the world, and is a special beef cattle variety in America, namely horn-free Hafu cattle, after years of improvement and breeding in America, the Hafu cattle has the advantages of wide body, developed chest, full muscles of the whole body, short head, wide forehead, short and thick neck, developed neck sag and front and back regions, straight and wide back and waist, expanded ribs, straight and short limbs, cylindrical trunk and typical cuboid shape of beef cattle. The hair of the quilt is reddish brown except for white at the head, neck, abdomen, lower parts of limbs and tail end, and the skin is orange red. The calves are born heavy, the male is 34 kg, and the female is 32 kg; the weight of the body reaches 400 kilograms when the body is 12 months old, and the average daily gain is more than 1 kilogram. The weight of the adult is 1100 jin for male cattle at 1000-. When slaughtering is carried out 400 days after birth, the slaughtering rate is 60-65%, and the net meat rate reaches 57%. The meat is tender, the taste is delicious, the deposited fat among muscle fibers is rich, and the meat is in a marble shape. The Haifute cattle has the characteristics of strong physique, coarse feeding resistance, suitability for grazing, high meat yield and the like.

In another alternative embodiment, the male parent is angus. Angses have good performance in meat and are considered to be one of the typical species of specialized beef cattle in the world. It is also called black horn-free black cattle because it is an important feature of black fur and horn-free. The cattle has low trunk, is firm, has small and square head, wide forehead, deep trunk, cylindrical shape, short and straight limbs, wide front and back steps, full muscle and typical body type of modern beef cattle. The carcass quality is high, the meat yield is high, the slaughter rate is generally 60-65%, and the muscle marbling is good. The average live weight of an Angus bull adult bull is 700-900 kg, the average cow is 500-600 kg, the average birth weight of a calf is 25-32 kg, the average adult height bull and cow are 130.8 cm and 118.9 cm respectively, the daily weight gain in lactation period is 900-1000 g, and the average daily weight gain in fattening period (within 1.5 years) is 0.7-0.9 kg.

In step S101, a first offspring is first bred which is a binary cross of a first object and a second object, wherein the first object is located by a first functional gene and the second object is located by a second functional gene. The first functional gene locates a first economic trait, and the first economic trait at least comprises a meat quality trait and a propagation trait; the second functional gene maps a second economic trait, which includes at least a growth trait and a reproductive trait. Optionally, the first economic trait may further comprise a growth trait and an appearance trait, and the second economic trait may further comprise a meat quality trait and an appearance trait.

In addition, with reference to molecular breeding techniques, the economic traits of animals (such as the above-mentioned economic traits, milk production, carcass yield, and intramuscular fat deposition) belong to the category of quantitative traits, and are controlled by multiple alleles and show co-dominance. First breeding a first progeny that is binary crossed by a first object and a second object comprises: and a first breeding step of stably inheriting a first functional gene and a first filial generation of a second functional gene, wherein if the filial generation of the first object and the second object in the binary hybridization reaches a third economic trait, the filial generation is the first filial generation stably inheriting the first functional gene and the second functional gene, and the third economic trait at least comprises a meat quality trait, a reproductive trait and a growth trait. For example, if the hybrid progeny has a growth trait of 16 months and a weight of 600 kg, an appearance characteristic trait of white top of head and whole body black, a meat quality trait of high-end meat with obvious marbling, and a reproductive trait of a third economic trait in which the fertilization rate reaches a preset threshold, the hybrid progeny is the first progeny of the first breeding. The specific breeding standard is as follows:

growth performance: the weight of the patient reaches 600 kg in 16 months;

the net meat rate: 40-42%, or more than 40%;

appearance characteristics: white flower on the top of the head (or a larger white flower on the forehead of the head), glossy and uniform hair, black whole body;

the meat at the high end has obvious marbling;

the meat quality of the high-end part is tender and succulent, and reaches the production standard of western cuisine.

Fertilization rate: greater than a preset threshold.

In step S102, the target object is bred using the first child. Wherein, the breeding the target object by using the first filial generation comprises the following steps: and secondly, breeding a second filial generation bred by the first filial generation, and breeding the target object by utilizing the second filial generation. Here, the second breeding stably inherits the first functional gene and the second progeny of the second functional gene, wherein if the bred progeny bred by the first progeny reaches a third economic trait, the bred progeny is the second progeny stably inheriting the first functional gene and the second functional gene, and the third economic trait includes at least a meat quality trait, a reproductive trait, and a growth trait.

For example, the second progeny possesses the following advantageous properties: the weight of the chicken is 600 kilograms in 16 months, the marble patterns of meat at the high end part are obvious, the meat is tender and succulent, the manufacturing standard of western cuisine is met, the whole black head is white, and the coarse feeding resistance is strong.

Optionally, the second breeding of the second progeny stably inheriting the first functional gene and the second functional gene comprises: and (5) performing fixed cross breeding on the first filial generation to obtain a second filial generation.

Optionally, the second breeding of the second progeny stably inheriting the first functional gene and the second functional gene comprises: and backcrossing the first filial generation with the first object to obtain a second filial generation.

The method utilizes the high-quality meat quality characteristics of the cattle and the excellent growth characteristics of Haeford cattle or Angus, takes the cattle as a female parent and the Haeford cattle or Angus as a male parent to perform hybridization to obtain hybrid filial generation, and performs breeding on the hybrid filial generation through the breeding standard to obtain first filial generation. And the first filial generation is subjected to transverse crossing fixation or backcross with a cow female parent to establish a new strain core group, namely a second filial generation (the embryo transplantation breeding technology can also be utilized for rapid propagation), and the progeny of the second filial generation has good production performance and high-quality meat quality and completely meets the standard and the characteristics of high-end beef cattle. In addition, the offspring population of the second filial generation has strong propagation capacity and coarse feeding resistance, and the feeding cost is economical and practical.

It should be noted that the breeding direction of breeding the first filial generation and the second filial generation can be reversed through market application, demand standard and economic value.

In the prior art, the first subject and the second subject are mainly determined by observing dominant traits of cattle. In the present disclosure, further comprising: determining a first object based on the trained first neural network model, wherein the first neural network model determines a first object located by the first functional gene based on the first economic trait data; determining a second object based on the trained second neural network model, wherein the second neural network model determines the second object located by the second functional gene based on the second economic trait data.

The neural network model may be any suitable type of neural network model and may be pre-trained with a large amount of data, adjusting parameters, before being used to determine the first object, or the second object. Next, the neural network will be described by taking a convolutional neural network as an example. Convolutional Neural Networks (CNN) are locally connected networks. After acquiring the first economic trait data or the second economic trait data of the cattle to be selected, the convolutional neural network sequentially passes through a plurality of processing processes (such as each hierarchy in fig. 2) and then outputs a type identifier, wherein the type identifier can be positioned by a first functional gene or a second functional gene. The processing procedure of each level may include: convolution (convolution) and downsampling (down-sampling). Features of a given first economic trait data or second economic trait data may be extracted by performing a convolution with the first economic trait data or the second economic trait data for one convolution kernel, and different convolution kernels may extract different features. In general terms, the calculation method of the convolutional layer can be performed according to the following formula:

where σ represents an activation function; imgMat represents an economic trait matrix; w represents a convolution kernel; "

"denotes a convolution operation; b represents an offset value. The processing procedure of each level may further include a normalization process (e.g., LCN) and the like as necessary.

The embodiment of the disclosure can perform convolution operation on the first economic trait data or the second economic trait data of the cattle to be selected through a convolution neural network, for example, to determine a first object or a second object, wherein the determined first object is strongly positioned by the first functional gene and has excellent quality and meat quality characteristics; the determined second object is strongly positioned by the second functional gene and has excellent growth characteristics.

It should be noted that the steps illustrated in the flowchart of fig. 2 may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in an order different than presented herein. Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present disclosure may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method of the embodiments of the present disclosure.

At least one embodiment of the present disclosure also provides a training device, which may have multiple implementation manners, for example, the training device may be implemented by a single computer, may be implemented by multiple computers, may be implemented by being deployed in a cloud, or the like, or may be implemented by a combination of these manners. As shown in fig. 3, the training device comprises a processor 301 and a memory 302, the memory 302 being configured to store computer program instructions adapted to be loaded by the processor and to perform the above-described method of training a first neural network model, or training a second neural network model. The processor 301 may be any suitable processor, such as a central processing unit, a microprocessor, an embedded processor, and the like, and may adopt an architecture such as X86, ARM, and the like; the memory 302 may be a variety of suitable storage devices including, but not limited to, magnetic storage devices, semiconductor storage devices, optical storage devices, etc., and may be arranged as a single storage device, an array of storage devices, or a distributed storage device, which are not limited by embodiments of the present disclosure.

At least one embodiment of the present disclosure also provides a computer-readable non-transitory storage medium storing computer program instructions that, when executed by a computer, perform the above-described method of training a first neural network model, or training a second neural network model.

It should be noted that, for the sake of simplicity, the above-mentioned embodiments of the training device and the storage medium are all described as a series of acts or a combination of modules, but those skilled in the art should understand that the present disclosure is not limited by the described sequence of acts or the connection of modules, because some steps may be performed in other sequences or simultaneously and some modules may be performed in other connection manners according to the present disclosure.

Those skilled in the art should also appreciate that the embodiments described in this specification are all one embodiment, and the above-described embodiment numbers are merely for description, and the acts and modules involved are not necessarily essential to the disclosure. In the above embodiments of the present disclosure, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

The above is merely an embodiment of the present disclosure, and it should be noted that, for those skilled in the art, several modifications and decorations can be made without departing from the principle of the present disclosure, and these modifications and decorations should also be regarded as the protection scope of the present disclosure.

Claims

1. A binary hybridization breeding method comprising:

first breeding a first progeny comprising a binary cross of a first object and a second object, wherein said first object is located by a first functional gene and said second object is located by a second functional gene;

and breeding the target object by using the first filial generation.

2. The method of claim 1, wherein the first functional gene maps a first economic trait comprising at least a meat quality trait and a reproductive trait; the second functional gene maps a second economic trait, which includes at least a growth trait and a reproductive trait.

3. The method of claim 2, wherein breeding a target object using the first child comprises:

and secondly, breeding a second filial generation bred by the first filial generation, and breeding the target object by utilizing the second filial generation.

4. The method of claim 3, wherein second breeding second progeny that are bred by the first progeny comprises:

and a second breeding step of stably inheriting a first functional gene and a second filial generation of a second functional gene, wherein if the filial generation bred by the first filial generation reaches a third economic trait, the filial generation bred by the first filial generation is the second filial generation stably inheriting the first functional gene and the second functional gene, and the third economic trait at least comprises a meat quality trait, a propagation trait and a growth trait.

5. The method of claim 4, wherein the second breeding of the second progeny that stably inherit the first functional gene and the second functional gene comprises:

and carrying out fixed cross breeding on the first filial generation to obtain a second filial generation.

6. The method of claim 4, wherein the second breeding of the second progeny that stably inherit the first functional gene and the second functional gene comprises:

and backcrossing the first filial generation with the first object to obtain a second filial generation.

7. The method of claim 2, wherein first selecting the first progeny that is bidden by the first object and the second object comprises:

and a first breeding step of stably inheriting a first filial generation of the first functional gene and a second functional gene, wherein if the filial generation of the first object and the second object in the binary hybridization reaches a third economic trait, the filial generation is the first filial generation stably inheriting the first functional gene and the second functional gene, and the third economic trait at least comprises a meat quality trait, a propagation trait and a growth trait.

8. The method of claim 7, wherein the third economic trait further comprises an appearance trait, and the first breeding the first progeny stably inherited with the first functional gene and the second functional gene comprises:

and if the growth character of the hybrid filial generation reaches 16 months, the weight reaches 600 kg, the appearance characteristic character is white flower at the top of the head, the body is black, the meat quality character is that the meat at the high end has obvious marbling, and the reproductive character is a third economic character of which the fertilization rate reaches a preset threshold value, the hybrid is the first filial generation of the first breeding.

9. The method of claim 2, wherein the method further comprises:

determining a first subject based on the trained first neural network model, wherein the first neural network model determines a first subject located by a first functional gene based on first economic trait data;

determining a second object based on the trained second neural network model, wherein the second neural network model determines a second object located by a second functional gene based on second economic trait data.

10. The method according to claims 1-9, wherein the first subject is a female parent, the female parent is a Japanese female parent, the second subject is a male parent, the male parent is a Hefote male parent or an Angus male parent.