CN110622911A - Selection method capable of simultaneously improving pig feed efficiency and growth speed - Google Patents
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- 238000010187 selection method Methods 0.000 title claims abstract description 9
- 238000009395 breeding Methods 0.000 claims abstract description 54
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- 230000037396 body weight Effects 0.000 claims abstract description 42
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- 230000037406 food intake Effects 0.000 claims description 8
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- 235000012631 food intake Nutrition 0.000 claims description 5
- 238000010171 animal model Methods 0.000 claims description 4
- 238000003556 assay Methods 0.000 claims description 3
- 235000021052 average daily weight gain Nutrition 0.000 claims description 3
- 238000001514 detection method Methods 0.000 claims description 3
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- 208000012868 Overgrowth Diseases 0.000 claims 2
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- 238000004364 calculation method Methods 0.000 abstract description 3
- 238000006243 chemical reaction Methods 0.000 description 4
- 235000019750 Crude protein Nutrition 0.000 description 3
- 244000144980 herd Species 0.000 description 3
- 241001465754 Metazoa Species 0.000 description 2
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- 241000255969 Pieris brassicae Species 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 239000006027 corn-soybean meal Substances 0.000 description 1
- QTCANKDTWWSCMR-UHFFFAOYSA-N costic aldehyde Natural products C1CCC(=C)C2CC(C(=C)C=O)CCC21C QTCANKDTWWSCMR-UHFFFAOYSA-N 0.000 description 1
- 235000019621 digestibility Nutrition 0.000 description 1
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Abstract
The invention relates to a selection method capable of simultaneously improving the efficiency and the growth speed of pig feed, which is characterized by comprising the following steps: the selection method for improving the pig feed efficiency and the growth speed simultaneously by utilizing the properties of the super-growth body weight comprises the steps of basic conditions of selection, measurement index and data processing, estimation of breeding value, calculation of comprehensive selection index, selection and matching of backup breeding pigs and verification. Simple and practical, has good selection effect, can be used for phenotype selection of simultaneous determination groups, can also compare and select different determination groups according to heritability and genetic correlation, and can be used for directly selecting and improving the feed efficiency and growth speed of swinery.
Description
Technical Field
The invention relates to a selection method capable of simultaneously improving the efficiency and the growth speed of pig feed, belonging to the field of genetic breeding of breeding pigs.
Background
With the energy crisis becoming more serious, the feed efficiency of pigs is always a problem of special attention of breeding industry and pig breeding enterprises. The traditional method for evaluating the Feed efficiency of pigs is represented by Feed Conversion Rate (FCR), namely Feed-meat ratio (Feed/Gain) or meat-meat ratio (Gain/Feed), and although FCR can evaluate the Feed efficiency of different groups more intuitively, the traditional method has defects in breeding selection. Residual Feed Intake (RFI) is also a trait that assesses the feed efficiency of animals, and is the difference between the actual feed intake of an animal and the estimated feed intake for growth and maintenance needs. The feed efficiency of the pigs can be effectively improved by reducing the RFI of the pigs, but the RFI has the defects that the RFI is independently selected, the feed intake and the growth speed of the pigs are reduced, the feed efficiency is improved, and the marketing day age is prolonged.
Disclosure of Invention
The invention aims to provide a selection method capable of simultaneously improving the efficiency and the growth speed of pig feed, which is a simple and practical technical method with good selection effect for improving the efficiency and the growth speed of pig feed, and can directly select and improve the feed efficiency and the growth speed of swinery. The method adopts a simple and practical selection character, namely, the Excess Weight Gain (EWG), wherein the excess weight gain refers to the difference value between the actual weight gain and the predicted weight gain (actual feed intake/group FCR), the predicted weight gain can be obtained by the ratio of the feed intake of a pig individual to the group FCR, and can be used for directly selecting and improving the growth rate and the feed efficiency of a pig group.
In order to achieve the purpose, the invention is realized by the following technical scheme: a selection method capable of simultaneously improving the efficiency and the growth speed of pig feed is characterized by comprising the following specific steps:
firstly, establishing a selected basic condition;
(1) the measurement of the pigsty and the measurement column should be carried out according to the GB/T17824.1.
(2) The detection equipment comprises a pig free ingestion recording system, a living body backfat determinator, an electronic scale, a platform scale and the like, and the data determination equipment can be subjected to metrological verification to meet the specified precision requirement and is managed and used by a specially-assigned person;
(3) there are professional assay technicians;
(4) the feed meets the nutritional requirements of various pigs, and the determination environment is basically consistent;
(5) the complete record of the measured file is needed, and the system environment factors influencing the character performance are marked up: year, season, location, operator, etc.;
(6) the tested pigs are healthy, have uniform body and weight, are clearly numbered, have more than three generations of pedigree records, and accord with the breed characteristics;
(7) the pig farm production management technology is executed according to the GB/T17824.2;
(8) the pig farm environment management is executed according to the GB/T17824.3;
secondly, measuring production performance indexes;
(1) daily gain: the initial weight is 27 kg-35 kg, the final weight is 90 kg-115 kg, and the average daily weight gain (g/d) in the measuring period is measured;
(2) backfat thickness: measuring the back fat thickness (mm) of the living body at the point P2 when the weight of the pig reaches 90 kg-115 kg;
(3) food intake: recording feed intake (kg) per pig during the measurement period;
(4) weight gain in the determination period: weight gain (kg) = end weight (kg) measurement — start weight (kg) measurement;
thirdly, sorting the measured data;
in the actual measurement, the weight selection of the test sample starts within the range of 27-35 kg, the measurement is finished when the weight reaches 90-110 kg, when the daily gain is calculated, the weight of the test sample of an individual is corrected to 30kg, the weight correction to 100kg is finished, and then the corrected daily gain is calculated; similarly, synchronously correcting the day age of 30kg of body weight and the day age of 100kg of body weight, and converting the correction of the living back fat thickness into the 100kg of body weight and the living back fat thickness;
fourthly, calculating the weight of the super-increased body;
(1) predicting the weight gain: predicting the absolute value of the weight gain (kg) = actual food intake (kg) ÷ population FCR;
(2) super-increasing body weight: excess growth body weight (kg) = actual growth body weight (kg) -predicted growth body weight (kg);
fifthly, estimating a breeding value by using an animal model BLUP method;
(1) super-growth weight breeding value estimation model EBVEWG= ADG-Group-Pen-Litter-b 1 (ADFI) -b 2 (BF) -e, and the Estimated Breeding Value (EBV) of the excess growth body weight was calculated according to the modelEWG) Wherein:
ADG-correcting average daily gain, and correcting the daily gain to 30-100 kg stage daily gain as a covariate;
group-measuring batch effect, fixing effect;
pen-the effect of the column, the anchoring effect;
litter-the effects of nests, random effects;
ADFI-average daily food intake;
BF-correcting backfat thickness, correcting backfat to 100kg, as covariate;
EBVEWGsuper-gain weight estimation breeding value;
e-random error;
(2) the backfat thickness breeding value estimation model is EBVBF= BFA-Group-Offwt-e, calculating the Estimated Breeding Value (EBV) of the backfat thickness according to the modelBF) Wherein:
EBVBF-backfat thickness estimation breeding value;
BFA-correcting backfat thickness, correcting backfat to 100kg body weight;
group-batch effect, fixed effect;
offwt — end of body weight measurement as covariate;
e is a random error;
sixthly, calculating a comprehensive selection index;
calculating a comprehensive selection index of Z-EBV ═0.8EBVEWG－0.2EBVBFIn order to improve the feed efficiency and the growth speed of the pigs on the premise of not changing the back fat thickness (BF), the selection weight of the comprehensive breeding value selection index to EWG and BF is 0.8 and 0.2; (Note that backfat thickness is a negative choice, and therefore a minus sign)
Seventhly, selecting, reserving, matching and detecting the backup breeding pigs;
sorting according to the Z value, selecting the first 10 percent of reserve boars as the boars to be detected and sows with the genetic relationship less than 5 percent for breeding, making breeding records and farrowing records, and detecting the reserved boars after normal farrowing of a batch of sows;
sorting according to the Z value, selecting the first 60 percent of replacement gilts as boars with the genetic relationship less than 5 percent of the sows to be detected, making mating records and farrowing records, and detecting the replacement gilts as breeding sows after normal farrowing.
The animal model BLUP method is used for calculating a mathematical model established by the super-growth body weight estimated breeding value, and the parthenocarpic BLUP method is used for estimating the breeding value EBV of the super-growth body weight of each pigEWGMeanwhile, the genetic correlation between EWG and ADG and BF and the estimated heritability of EWG, ADG and BF are obtained.
The invention has the advantages of simplicity, practicability and good selection effect, can be used for phenotype selection of simultaneous determination groups, and can also compare and select different determination groups according to heritability and genetic correlation;
TABLE 1 genetic correlation between EWG heritability and traits
The EWG heritability is 0.46, belongs to a high heritability character, has high genetic correlation (0.79) with daily gain, so that the breeding of the swinery by utilizing the property of super-increased weight can obtain larger selection reaction, and the growth speed and the feed efficiency of the swinery can be directly improved by selection.
Detailed Description
The invention is further described with reference to the following examples: a selection method capable of simultaneously improving the efficiency and the growth speed of pig feed is characterized by comprising the following specific steps:
1. establishing basic conditions for breeding
(1) The measurement of the pigsty and the measurement column should be carried out according to the GB/T17824.1.
(2) The detection equipment comprises a pig free ingestion recording system, a living body backfat determinator, an electronic scale, a platform scale and the like, and the data determination equipment can be subjected to metrological verification to meet the specified precision requirement and is managed and used by a specially-assigned person;
(3) there are professional assay technicians;
(4) the feed meets the nutritional requirements of various pigs, and the determination environment is basically consistent;
(5) the complete record of the measured file is needed, and the system environment factors influencing the character performance are marked up: year, season, location, operator, etc.;
(6) the tested pigs are healthy, have uniform body and weight, are clearly numbered, have more than three generations of pedigree records, and accord with the breed characteristics;
(7) the pig farm production management technology is executed according to the GB/T17824.2;
(8) the pig farm environment management is executed according to the GB/T17824.3;
2. determination of production Performance indicators
(1) Determination of daily gain
Measuring when the individual weight reaches 27-35 kg, finishing measuring when the weight reaches 90-110 kg, and simultaneously recording the weighing date and the weighing amount;
(2) measurement of feed intake
Recording the feed consumption of each pig during the measurement;
(3) measurement of backfat thickness
And (5) measuring the back fat thickness of the living body when the body weight reaches 90-110 kg and the measurement is finished. Measuring the average value of two points of backfat thicknesses of the left sides of the 3 rd to 4 th costal vertebrae and the 4 th reciprocal sacral vertebra and 5cm away from the back center line by adopting an A ultrasonic method;
3. collating measured data
In the actual measurement, the weight selection of the test sample starts within the range of 27-35 kg, the measurement is finished when the weight reaches 90-110 kg, when the daily gain is calculated, the weight of the test sample of an individual is corrected to 30kg, the weight correction to 100kg is finished, and then the corrected daily gain is calculated; similarly, the age of day up to 30kg body weight and the age of day up to 100kg body weight were also corrected in synchronization. The calculation formula is as follows:
the average daily gain (g) = (end weight kg-test weight kg) ÷ (measurement period days d) × 1000g daily gain correction formula is as follows (refer to "third pig breeding" in chinese pig breeding adults, 2001):
corrected daily gain (g) = (70 kg × 1000 g/kg) ÷ (up to 100kg corrected age d-up to 30kg corrected age d);
the correction formula for day ages of 30kg in the formula is as follows:
30kg of day age (d) = measured test day age d + [ (30 kg-measured test body weight kg) × b ]
The b values are respectively: duroc =1.536, long white =1.565, large white = 1.550;
the correction formula for day ages of 100kg in the formula is as follows:
correction of day-to-day age measurement- [ (actual body weight-100)/CF ]
The CF calculation formula is as follows:
CF = (measured body weight kg ÷ measured age in days) x 1.826040 (boar)
CF = (measured body weight kg/measured day of age d) × 1.714615 (sow)
The daily age of up to 100kg body weight was measured, along with the measurement of the 100kg body weight in vivo backfat thickness. Scanning and measuring the thickness of skin and subcutaneous fat between the 3 rd to 4 th ribs from the last to the back of the body and 5cm away from the back center line by adopting an A-type ultrasonic measuring instrument, namely backfat thickness, unit: mm; after measurement, the measurement was converted into the 100kg body weight in vivo backfat thickness according to the following calibration formula:
corrected backfat thickness = measured backfat thickness × CF
Wherein: CF = a/{ a + [ B × (lining weight-100) ] }
A and B are given in Table 2;
TABLE 2 correction of backfat thickness A and B values
4. Calculating the weight of the superincreased body
(1) Predicting the weight gain: predicting the absolute value of the weight gain (kg) = actual food intake (kg) ÷ population FCR;
(2) super-increasing body weight: excess growth body weight (kg) = actual growth body weight (kg) -predicted growth body weight (kg);
5. method for estimating breeding value by using animal model BLUP method
The breeding value EBV of the super-growth body weight of each boar is estimated by a program for estimating the breeding value by a parthenocarpic BLUP method in MTDFREML softwareEWGAnd estimated breeding value EBV of backfat thicknessBF；
Super-growth weight breeding value estimation model EBVEWG= ADG-Group-Pen-Litter-b 1 (ADFI) -b 2 (BF) -e, and the Estimated Breeding Value (EBV) of the excess growth body weight was calculated according to the modelEWG）；
The backfat thickness breeding value estimation model is EBVBF= BFA-Group-Offwt-e, calculating the Estimated Breeding Value (EBV) of the backfat thickness according to the modelBF）；
5. Calculating the comprehensive selection index as Z-EBV ═ 0.8EBVEWG－0.2EBVBFIn order to improve the feed efficiency and the growth rate of the pigs on the premise of not changing the back fat thickness (BF), the selection weights of 0.8 and 0.2 are applied to the EWG and the BF by the comprehensive breeding value selection index. (Note that backfat thickness is a negative choice, and therefore a minus sign)
6. Selection, matching and verification of reserve boars
Sorting according to the Z value, selecting the first 10 percent of reserve boars as the boars to be detected and sows with the genetic relationship less than 5 percent for breeding, making breeding records and farrowing records, and detecting the reserved boars after normal farrowing of a batch of sows;
sorting according to the Z value, selecting the first 60 percent of replacement gilts as boars with the genetic relationship less than 5 percent of the sows to be detected, making mating records and farrowing records, and detecting the replacement gilts as breeding sows after normal farrowing.
Example 1
In the junge No. 1 group of the original pig farm of Jilin university, 60 piglets are randomly selected and divided into 2 batches (with limited measuring equipment), 30 piglets are selected in each batch, alternate measurement is carried out on a limiting column, and the alternate measurement is carried out every 2 weeks. The measurement is started from about 90 days old of the young boar, the weekly feed intake and weight gain of each boar are recorded during the measurement, the measurement is finished when the weight reaches about 110kg, and the back fat thickness is measured by using a pig carcass lean rate ultrasonic determinator piglog105 supplied by SFK in Denmark when the measurement is finished. Of these, 3 boars were eliminated for health reasons during the test period. Feeding corn-soybean meal type daily feed with Digestion Energy (DE) of 13.6MJ/Kg and Crude Protein (CP) of 16.6%. Mathematical model of Average Daily Gain (ADG) in pigs: ADG = Group + Pen + Litter + b1 (ADFI) + b2 (BF) + EBVEWG+ e. Wherein the lot (Group) is a fixed factor, the field (Pen) and the Litter (Litter) are random factors. And (4) correcting the average daily gain to 30kg of the initial measurement, and correcting the average daily gain and the backfat thickness of 100kg of the final weight to 100kg of the backfat thickness of the boar. Breeding value EBV of pig with super-increased body weightEWGIs the residual of the multivariate regression equation for Average Daily Gain (ADG) of pigs with respect to Average Daily Feed Intake (ADFI) and backfat thickness (BF). The breeding value EBV of the super-growth body weight of each boar is estimated by a program for estimating the breeding value by a parthenocarpic BLUP method in MTDFREML softwareEWGAnd simultaneously obtaining the genetic correlation between the EWG and the ADG and BF and the estimated heritability of the ADG and the BF. And obtaining the Feed Conversion Rate (FCR) of each boar according to the ratio of feed intake to weight gain.
As shown in Table 3, the initial weight average of the 57-head boar was 31kg, the final weight average was 106.5kg, the average daily feed intake during the measurement period was 2369g, the average daily weight gain was 908g, the average backfat thickness during the final measurement was 17mm, the feed conversion ratio (feed-meat ratio) during the measurement period of the whole group was 2.61, the apparent digestibility of crude protein was 0.8, and the standard deviation of the super-growth body weight breeding value was 34.7. Further analysis revealed that the genetic differences between the first 10 individuals with high EWG and the first 10 individuals with low EWG exceeded 3.5 standard deviations.
TABLE 3 mean and standard deviation of the respective traits
The heritability of each trait is related to the inheritance between traits, and as shown in table 4, the data on the diagonal line in the table is the heritability of the trait, and the other data is the inheritance between two traits.
TABLE 4 heritability of individual traits and genetic associations between traits
The heritability of EWG, FCR, ADG, BF in a herd of herd number 1 boars was 0.46, 0.33, 0.45, 0.14, respectively. It was demonstrated that EWG and FCR both belong to the intermediate heritability trait and ADG to the high heritability trait. The correlation coefficients between EWG and ADFI, ADG, BF are-0.01, 0.79, -0.17, respectively. Indicating that EWG is not essentially genetically related to daily feed intake, has a low correlation to BF, is highly genetically related to the presence of daily gain, and is also highly genetically related to the presence of FCR. Correlation coefficients between the FCR and ADFI, ADG and BF are respectively 0.55, -0.65 and-0.11, and the FCR and the ADG have high negative correlation and the BF has lower negative correlation. The correlation coefficients between ADG and ADFI and BF were 0.65 and 0.12, respectively. The correlation coefficient between ADFI and BF is 0.2. The correlation between backfat thickness and several production traits was low in the army group No. 1.
There is a large genetic difference in the weight gain EWG in a herd number 1 boar population, and therefore the EWG can be used as a selection trait to improve both feed efficiency and growth rate of pigs.
Claims (3)
1. A selection method capable of simultaneously improving the efficiency and the growth speed of pig feed is characterized by comprising the following specific steps:
firstly, establishing a selected basic condition;
(1) the measurement of the pigsty and the measurement column are carried out according to the GB/T17824.1;
(2) the detection equipment comprises a pig free ingestion recording system, a living body backfat determinator, an electronic scale and a platform scale, the data determination equipment is calibrated by metering to meet the specified precision requirement, and a specially-assigned person is responsible for management and use;
(3) there are professional assay technicians;
(4) the feed meets the nutritional requirements of various pigs, and the determination environment is basically consistent;
(5) the complete record of the measured file is needed, and the system environment factors influencing the character performance are marked up: year, season, location, operator;
(6) the tested pigs are healthy, have uniform body and weight, are clearly numbered, have more than three generations of pedigree records, and accord with the breed characteristics;
(7) the pig farm production management technology is executed according to the GB/T17824.2;
(8) the pig farm environment management is executed according to the GB/T17824.3;
secondly, measuring production performance indexes;
(1) daily gain: the initial weight is 27 kg-35 kg, the final weight is 90 kg-115 kg, and the average daily weight gain (g/d) in the measuring period is measured;
(2) backfat thickness: measuring the back fat thickness (mm) of the living body at the point P2 when the weight of the pig reaches 90 kg-115 kg;
(3) food intake: recording feed intake (kg) per pig during the measurement period;
(4) weight gain in the determination period: weight gain (kg) = end weight (kg) measurement — start weight (kg) measurement;
thirdly, sorting the measured data;
in the actual measurement, the weight selection of the test sample starts within the range of 27-35 kg, the measurement is finished when the weight reaches 90-110 kg, when the daily gain is calculated, the weight of the test sample of an individual is corrected to 30kg, the weight correction to 100kg is finished, and then the corrected daily gain is calculated; similarly, synchronously correcting the day age of 30kg of body weight and the day age of 100kg of body weight, and converting the correction of the living back fat thickness into the 100kg of body weight and the living back fat thickness;
fourthly, calculating the weight of the super-increased body;
(1) predicting the weight gain: predicting the absolute value of the weight gain (kg) = actual food intake (kg) ÷ population FCR;
(2) super-increasing body weight: excess growth body weight (kg) = actual growth body weight (kg) -predicted growth body weight (kg);
fifthly, estimating a breeding value by using an animal model BLUP method;
(1) super-growth weight breeding value estimation model EBVEWG= ADG-Group-Pen-Litter-b 1 (ADFI) -b 2 (BF) -e, and the Estimated Breeding Value (EBV) of the excess growth body weight was calculated according to the modelEWG) Wherein:
ADG-correcting average daily gain, and correcting the daily gain to 30-100 kg stage daily gain as a covariate;
group-measuring batch effect, fixing effect;
pen-the effect of the column, the anchoring effect;
litter-the effects of nests, random effects;
ADFI-average daily food intake;
BF-correcting backfat thickness, correcting backfat to 100kg, as covariate;
EBVEWGsuper-gain weight estimation breeding value;
e-random error;
(2) the backfat thickness breeding value estimation model is EBVBF= BFA-Group-Offwt-e, calculating the Estimated Breeding Value (EBV) of the backfat thickness according to the modelBF) Wherein:
EBVBF-backfat thickness estimation breeding value;
BFA-correcting backfat thickness, correcting backfat to 100kg body weight;
group-batch effect, fixed effect;
offwt — end of body weight measurement as covariate;
e is a random error;
sixthly, calculating a comprehensive selection index;
calculating the comprehensive selection index as Z-EBV ═ 0.8EBVEWG－0.2EBVBFTo achieve without changingThe feed efficiency and the growth speed of the pigs are improved on the premise of changing the back fat thickness (BF), so the selection weight of the comprehensive breeding value selection index to EWG and BF is 0.8 and 0.2;
seventhly, selecting, reserving, matching and detecting the backup breeding pigs;
sorting according to the Z value, selecting the first 10 percent of reserve boars as the boars to be detected and sows with the genetic relationship less than 5 percent for breeding, making breeding records and farrowing records, and detecting the reserved boars after normal farrowing of a batch of sows;
sorting according to the Z value, selecting the first 60 percent of replacement gilts as boars with the genetic relationship less than 5 percent of the sows to be detected, making mating records and farrowing records, and detecting the replacement gilts as breeding sows after normal farrowing.
2. The method of claim 1, wherein the Excess Weight Gain (EWG) is the difference between the actual Weight Gain and the predicted Weight Gain, the predicted Weight Gain being equal to the ratio of the actual feed intake of the individual to the population FCR, the actual feed intake per population FCR absolute.
3. The method of claim 1, wherein the BLUP method is used to calculate the over-growth body weight estimated breeding value and the mathematical model is used to estimate the over-growth body weight estimated breeding value EBV of each pig by the BLUP methodEWGMeanwhile, the genetic correlation between EWG and ADG and BF and the estimated heritability of EWG, ADG and BF are obtained.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112528180A (en) * | 2020-12-16 | 2021-03-19 | 河北农业大学 | Sheep feed intelligent analysis selection platform |
| CN113591287A (en) * | 2021-07-19 | 2021-11-02 | 中国科学院亚热带农业生态研究所 | Method for evaluating weight of yellow cattle in western Hunan province |
| CN113790757A (en) * | 2021-09-15 | 2021-12-14 | 山东农业大学 | Method for measuring long-hair rabbit productivity and application thereof |
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| CN113591287A (en) * | 2021-07-19 | 2021-11-02 | 中国科学院亚热带农业生态研究所 | Method for evaluating weight of yellow cattle in western Hunan province |
| CN113790757A (en) * | 2021-09-15 | 2021-12-14 | 山东农业大学 | Method for measuring long-hair rabbit productivity and application thereof |
| CN113790757B (en) * | 2021-09-15 | 2022-07-19 | 山东农业大学 | Method for measuring long-hair rabbit productivity and application thereof |
| US11672237B2 (en) | 2021-09-15 | 2023-06-13 | Shandong Agricultural University | Method for measuring productivity of angora rabbit and its application |
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