This study carried out data analyses and relevant researches by using tested pedigree material of Korea Animal Improvement Association, and dividing it into Theme 1,2,3.
In brief, results of this research are same as follows,
Theme 1 looked into overall program such as propelling purpose, ...
This study carried out data analyses and relevant researches by using tested pedigree material of Korea Animal Improvement Association, and dividing it into Theme 1,2,3.
In brief, results of this research are same as follows,
Theme 1 looked into overall program such as propelling purpose, methods, status, and assessment of genetic ability etc. on swine genetic improvement network program having been promoted from 2008.
Swine genetic improvement network program aims at setting up assessment of genetic ability in national unit, and improving, exporting Korean breeding pigs fit to domestic situations through selection, exchange, evaluation. This program is being propelled based on relevant laws such as Article 3 of Livestock Industry Act (Devising livestock development policy), Article 5 (Setting improvement goals), Article 6 (Livestock registration), Article 7 (Livestock test), Article 21 (Company authentication on processing excellent semen), and Article 47 (The use of funds), and is generalized by Ministry of Agriculture, Food and Rural Affairs (Livestock Management Department). Main agency is National Institute of Animal Science, and program enforcement is being propelled with responsibility by Korea Animal Improvement Association.
Focus of propelling this program was to set up improvement basement through genetic connections between participating (cooperative) swine farms by commonly selecting and utilizing swine, and it made overseas genetic resources (swine or frozen semen) imported and shared. Sharing genetic resources were operated by designating Nuclei AI Center, and the program was propelled by considering smooth supply and equity of each region after dividing maternal line and paternal line, and then letting designation possible.
In propelling performance of this program, 124 swine with 774 sows were selected in case of paternal line based on 2015, and semen sharing of 1.105 pigs was executed. In case of maternal line, total 3,308 sows of Yorkshire species participated in this program, and genetic sharing of 1,097 swine was made by using overseas frozen semens, and also gene sharing was carried out by using 407 overseas frozen semens after 795 sows participated in case of in Landrace species. From 2017, selecting excellent swine will be possible through genetic ability evaluation on the herd to which gene was shared.
90kg reaching age on phenotype value and annual improvement amount of back fat thickness according to test end year from 2008 when genetic ability evaluation of paternal line was begun in swine genetic improvement network program appeared –1.036, 0.08 respectively, and those of breeding value showed each –1.057 , 0.004. Besides, when comparing offspring ability of joint selection side with farm selection pigs, end weight was 1.039 and inbreeding coefficient was –0.019 in its selection difference. And when looking in ability comparison results between phenotype value and breeding value on main traits, it was come out with –1.064, -1.508 respectively, so the herd of having network of paternal line was displayed excellently.
Theme 2 assumed effects according to participation in breed, sex, parity, test year, test season, test classification, and swine genetic improvement network program which affected influences to daily gain, 90kg reaching age, back fat thickness, eye muscle area, and lean percent which were test traits of swine by using datum on tested 235,596 pigs of farms by Korea Animal Improvement Association from 2010 to 2015.
As a result of verifying significance on each factor in all investigated traits, significance in daily gain regarding swine genetic improvement network program was not admitted, but all effects on breed, sex, parity, test year, test season, test classifications accepted high significance from investigated whole traits (p<0.01). In total average and standard deviation by trait, daily gain was turned up to each 622.83±69.66g, 606.72±68.64g, 662.18±67.07 from Landrace, Yorkshire, Duroc by breed, and 148.86±12.63 days, 150.23±13.13 days, 140.14±11.41 days were shown in case of 90kg reaching age. In case of back fat thickness, 13.46±2.63㎜, 29.60±4.22㎠ and 14.24±2.62㎜, 12.95±2.46㎜, respectively, and each 57.05±8.12%, 57.38±6.41%, 50.48±19.64% were investigated in case of lean percent. Breed, sex, parity, test classifications, division of program participation, effects of environmental factors showed significant differences (p<0.05).
Assumed regression coefficient on effects of included end age by covariance analysis in this research, 0.498g daily gain, 2,891 days of 90kg reaching age, -0.224㎜ back fat thickness, -0.037㎠ eye muscle area, 0.047% lean percent were increased whenever end age was increased 1 day to test end age.
Theme 3 applied breed, parity, sex, test classifications which were confirmed as environmental factors affecting influences to test traits in Theme 2 by using datum on farm-tested 235,596 pigs by Korea Animal Improvement Association from 2010 to 2015 together with farm test records of 128,433 pigs at participated swine farms in swine genetic improvement network program and 106,410 pigs of non-participant farms to a model formula, and assumed genetic parameter and accuracy of breeding value by applying polymorphic entity model after setting up farms and test end year, test classifications to contemporary group. In addition, a research on genetic improvement trends between participation swine farms and non-participant ones in swine genetic improvement network program was executed by using estimated breed value.
If reviewing assumed heritability, each 0.43, 0.45, 0.53, 0.25, 0.43 in daily gain, reaching age, back fat thickness, eye muscle area, lean percent were presumed, and 1651, 48.55, 2.59, 6.60, 3.40 appeared in genetic distribution, and also 0.77, 0.77, 0.78, 0.69, 0.78 were displayed respectively in parity distribution.
Genetic corelation coefficient which was estimated to was estimated the highest from genetic correlation coefficient that was estimated to –0.99 of daily gain and 90kg reaching age when checking analytical results on gene correlation and phenotype correlation, so they had high negative (-) correlation, and daily gain and 90kg reaching age showed the highest correlation with –0.98 in phenotype correlation. And, genetic correlation with daily gain amount had comparatively high negative (-) correlation with –0.38 in case of eye muscle area, and it had negative correlation with 90kg reaching age with 0.37. The lean percent had low correlation with daily gain amount and 90kg reaching age, but showed –0.86 high negative correlation with back fat thickness, and 0.43 positive(+) correlation with eye muscle area.
If looking at heritability by each breed of swine having low blood line connectivity between breeds, heritability of Landrace species of participating group in swine genetic network program, 0.43, 0.42, 0.51, 0.20, 0.49 were estimated in daily gain amount, 90kg reaching age, back fat thickness, eye muscle area and lean percents respectively, and 0.39, 0.39, 0.42, 0.25, 0.46 were presumed in case of Yorkshire heritability, and also 0.42, 0.43, 0.36, 0.22, 0.39 in Duroc heritability.
If synthesizing genetic correlation between economic traits on 3 breeds regarding swine genetic network program, high positive correlation of 0.43~0.47 was displayed in genetic correlation between eye muscle area and lean percent, and –0.99 genetic correlation between daily gain amount and 90kg reaching age was turned up, and also high negative correlation of –0.88~-0.92 was displayed in genetic correlation between back fat thickness and lean percent. Phenotype correlation showed high positive correlation of 0.25~0.32 between phenotype correlation between eye muscle area and lean percent, and had high negative correlation of –0.98 in phenotype correlation between daily gain amount and 90kg reaching age, and also high negative correlation of –0.35~-0.40 in phenotype correlation between back fat thickness and lean percent.
From non-participant group in swine genetic network program, heritability of Landrace species was estimated to each 0.49, 0.51, 0.48, 0.23, 0.45 in daily gain amount, 90kg reaching age, back fat thickness, eye muscle area, lean percent, and heritability of Yorkshire to 0.43, 0.41, 0.43, 0.22, 0.43 in daily gain amount, 90kg reaching age, back fat thickness, eye muscle area, lean percent, and heritability to each 0.45, 0.48, 0.42, 0.19, 0.44 in daily gain amount, 90kg reaching age, back fat thickness, eye muscle area, lean percent.
If synthesizing genetic correlation between economic traits in non-participant group, it had high positive correlation of 0.44~0.47 in genetic correlation between eye muscle area and lean percent, and had high negative correlation of –0.90~-0.91 in genetic correlation between back fat thickness and lean percent. Phenotype correlation had high positive correlation of 0.40~0.42 in phenotype correlation between eye muscle area and lean percent, and showed high negative correlation of –0.97~-0.98 in phenotype correlation between back fat thickness and lean correlation, and also had high negative correlation of –0.34~-0.46 in phenotype correlation between back fat thickness and lean percent.
As a result of confirming change trends of breeding value by year regarding test traits according to breed and participation in swine genetic network improvement program through estimated breeding value by individual, a fact could be confirmed that they made a little different aspects according to breed and participation in swine genetic improvement network. First of all, if checking change trends according to breed differences, increasing trends of Yorkshire appeared the largest because genetic improvement amount of Landrace, Yorkshire, Duroc species showed increasing trends like about 1.01g, 6.65g, 4.31g. in relation with genetic improvement amount of daily gain amount. In 90kg reaching age, trends of each –0.19 day, -1.17 days, -0.64 day were displayed from Landrace, Yorkshire, Duroc species, and the largest decrease were made from Duroc species. Regarding back fat thickness, trends of 0.01㎜, -0.22㎜, -0.09㎜ were displayed from Landrace, Yorkshire, Duroc species, and the largest decrease was come out from Yorkshire. Eye muscle area showed trends of each -0.05㎜, -0.02㎜, -0.001㎜ from Landrace, Yorkshire, Duroc species, and great changes by year were not made, and also there was not most changes in Duroc especially. The lean percent showed trends of each -0.02%, -0.19%, -0.06% from Landrace, Yorkshire, Duroc species.
Improvement trends of test traits by year according to participation in swine genetic promotion network program showed increasing tents of about 7.16g and 2.77g every year from participation group and non-participant one in relation with genetic improvement amount of daily gain amount, and increasing trends of participation group turned up to be larger than that of non-participation group. Each of it showed trends of –1.23 day and –0.50 day, and reaching age was come out to be largely decreased in participation group. In relation with back fat thickness, trends of each –0.18㎜ and -0.11㎜ were displayed from participation group and non-participant one. Regarding eye muscle area, trends of each –0.04㎜ and 0.02㎜ showed from participation group and non-participant one, and differences between participation group and non-participant one were not large in case of eye muscle area, and area of non-participant group was analyzed to be a little increased. In the lean percent, trends of each 0.09% and 0.15% were displayed from participation group and non-participant one.
In case of Landrace, each 4.73g, 0.83 day, 0.24㎜, 0.15㎠, 0.25% were displayed in daily gain amount, 90kg reaching age, back fat thickness, lean percent in swine genetic improvement network program. In contrast, genetic improvement amount by household in the lean percent was come out to be 0.99 day, 0.22㎜, 0.16㎠, 0.23%. In case of Yorkshire, genetic improvement amount by household in daily gain amount, 90kg reaching age, back fat thickness, eye muscle area, lean percent showed each 4.48g, 0.82 day, 0.22㎜, 0.19㎠, 0.25%. from participation group in swine genetic improvement network program. However, genetic improvement amount by household in daily gain amount, 90kg reaching age, back fat thickness, eye muscle, lean percent from non-participant group were each 4.59g, 0.82 day, 0.20㎜, 0.17㎠, 0.22%. In case of Duroc, genetic improvement amount by household in daily gain amount, 90kg reaching age, back fat thickness, eye muscle area, lean percent showed each 5.27g, 0.83 day, 0.16㎜, 0.16㎠, 0.20%i in participation group in swine genetic improvement network program. In contrast, genetic improvement amount by household in daily gain amount, 90kg reaching age, back fat thickness, eye muscle area, lean percent was come out to be each 9.69g, 0.86 day, 0.15㎜, 0.13㎠, 0.19%. There are cases of showing a little different trends with genetic change ones, and such reason is derived from different selecting methods, selecting weight, selecting strength by each farm. It is thought to be derived from some reasons such like this research did an assumption with same value for theoretical unity, and variance than non-participant group is small through improvement during a ling time while the number of swine farms is small and participation group is composed by centering on large-scale swine farms.
If looking into above results, a fact could be confirmed that trends of each trait improvement are displayed differently according to participation classifications in swine genetic improvement network program. In case of daily gain amount and back fat thickness, improvement amount by breeding value was turned up to be increased largely in participation swine farms than non-participant ones. On the other hand, in case of other traits such as eye muscle area and lean percent, changes of test records from non-participant swine farms appeared larger. It seems that effects of business system from participation swine farms in swine genetic improvement network program compared to non-participant ones, and distribution value of participation swine farms was smaller than non-participant ones, even though growth trait was estimated a little high in non-participant swine farms at all breeds. As participation classifications were decided based on steady participation farms in swine genetic improvement network program for over 3 years, it is thought that time is required yet to make visual performance of constructing business on swine genetic improvement network program. Specially, in case of maternal line, gene sharing on imported frozen semen is under process and proper model on genetic evaluation is being progressed yet, so a lot of time for selecting excellent material-line swine would be required.
Therefore, propelling business positively by aggressively progressing gene exchanges between swine farms through active participation of a lot of excellent swine farms in order to prepare production basement of Korean swine through swine genetic improvement network program seem to be considered very importantly, and continuous analyses and researches are encouraged to be progressed afterwards so as to make improvement business through swine genetic improvement network program proceeded smoothly, and thus the results could be utilized as guidelines on business enforcement.
This study carried out data analyses and relevant researches by using tested pedigree material of Korea Animal Improvement Association, and dividing it into Theme 1,2,3.
In brief, results of this research are same as follows,
Theme 1 looked into overall program such as propelling purpose, methods, status, and assessment of genetic ability etc. on swine genetic improvement network program having been promoted from 2008.
Swine genetic improvement network program aims at setting up assessment of genetic ability in national unit, and improving, exporting Korean breeding pigs fit to domestic situations through selection, exchange, evaluation. This program is being propelled based on relevant laws such as Article 3 of Livestock Industry Act (Devising livestock development policy), Article 5 (Setting improvement goals), Article 6 (Livestock registration), Article 7 (Livestock test), Article 21 (Company authentication on processing excellent semen), and Article 47 (The use of funds), and is generalized by Ministry of Agriculture, Food and Rural Affairs (Livestock Management Department). Main agency is National Institute of Animal Science, and program enforcement is being propelled with responsibility by Korea Animal Improvement Association.
Focus of propelling this program was to set up improvement basement through genetic connections between participating (cooperative) swine farms by commonly selecting and utilizing swine, and it made overseas genetic resources (swine or frozen semen) imported and shared. Sharing genetic resources were operated by designating Nuclei AI Center, and the program was propelled by considering smooth supply and equity of each region after dividing maternal line and paternal line, and then letting designation possible.
In propelling performance of this program, 124 swine with 774 sows were selected in case of paternal line based on 2015, and semen sharing of 1.105 pigs was executed. In case of maternal line, total 3,308 sows of Yorkshire species participated in this program, and genetic sharing of 1,097 swine was made by using overseas frozen semens, and also gene sharing was carried out by using 407 overseas frozen semens after 795 sows participated in case of in Landrace species. From 2017, selecting excellent swine will be possible through genetic ability evaluation on the herd to which gene was shared.
90kg reaching age on phenotype value and annual improvement amount of back fat thickness according to test end year from 2008 when genetic ability evaluation of paternal line was begun in swine genetic improvement network program appeared –1.036, 0.08 respectively, and those of breeding value showed each –1.057 , 0.004. Besides, when comparing offspring ability of joint selection side with farm selection pigs, end weight was 1.039 and inbreeding coefficient was –0.019 in its selection difference. And when looking in ability comparison results between phenotype value and breeding value on main traits, it was come out with –1.064, -1.508 respectively, so the herd of having network of paternal line was displayed excellently.
Theme 2 assumed effects according to participation in breed, sex, parity, test year, test season, test classification, and swine genetic improvement network program which affected influences to daily gain, 90kg reaching age, back fat thickness, eye muscle area, and lean percent which were test traits of swine by using datum on tested 235,596 pigs of farms by Korea Animal Improvement Association from 2010 to 2015.
As a result of verifying significance on each factor in all investigated traits, significance in daily gain regarding swine genetic improvement network program was not admitted, but all effects on breed, sex, parity, test year, test season, test classifications accepted high significance from investigated whole traits (p<0.01). In total average and standard deviation by trait, daily gain was turned up to each 622.83±69.66g, 606.72±68.64g, 662.18±67.07 from Landrace, Yorkshire, Duroc by breed, and 148.86±12.63 days, 150.23±13.13 days, 140.14±11.41 days were shown in case of 90kg reaching age. In case of back fat thickness, 13.46±2.63㎜, 29.60±4.22㎠ and 14.24±2.62㎜, 12.95±2.46㎜, respectively, and each 57.05±8.12%, 57.38±6.41%, 50.48±19.64% were investigated in case of lean percent. Breed, sex, parity, test classifications, division of program participation, effects of environmental factors showed significant differences (p<0.05).
Assumed regression coefficient on effects of included end age by covariance analysis in this research, 0.498g daily gain, 2,891 days of 90kg reaching age, -0.224㎜ back fat thickness, -0.037㎠ eye muscle area, 0.047% lean percent were increased whenever end age was increased 1 day to test end age.
Theme 3 applied breed, parity, sex, test classifications which were confirmed as environmental factors affecting influences to test traits in Theme 2 by using datum on farm-tested 235,596 pigs by Korea Animal Improvement Association from 2010 to 2015 together with farm test records of 128,433 pigs at participated swine farms in swine genetic improvement network program and 106,410 pigs of non-participant farms to a model formula, and assumed genetic parameter and accuracy of breeding value by applying polymorphic entity model after setting up farms and test end year, test classifications to contemporary group. In addition, a research on genetic improvement trends between participation swine farms and non-participant ones in swine genetic improvement network program was executed by using estimated breed value.
If reviewing assumed heritability, each 0.43, 0.45, 0.53, 0.25, 0.43 in daily gain, reaching age, back fat thickness, eye muscle area, lean percent were presumed, and 1651, 48.55, 2.59, 6.60, 3.40 appeared in genetic distribution, and also 0.77, 0.77, 0.78, 0.69, 0.78 were displayed respectively in parity distribution.
Genetic corelation coefficient which was estimated to was estimated the highest from genetic correlation coefficient that was estimated to –0.99 of daily gain and 90kg reaching age when checking analytical results on gene correlation and phenotype correlation, so they had high negative (-) correlation, and daily gain and 90kg reaching age showed the highest correlation with –0.98 in phenotype correlation. And, genetic correlation with daily gain amount had comparatively high negative (-) correlation with –0.38 in case of eye muscle area, and it had negative correlation with 90kg reaching age with 0.37. The lean percent had low correlation with daily gain amount and 90kg reaching age, but showed –0.86 high negative correlation with back fat thickness, and 0.43 positive(+) correlation with eye muscle area.
If looking at heritability by each breed of swine having low blood line connectivity between breeds, heritability of Landrace species of participating group in swine genetic network program, 0.43, 0.42, 0.51, 0.20, 0.49 were estimated in daily gain amount, 90kg reaching age, back fat thickness, eye muscle area and lean percents respectively, and 0.39, 0.39, 0.42, 0.25, 0.46 were presumed in case of Yorkshire heritability, and also 0.42, 0.43, 0.36, 0.22, 0.39 in Duroc heritability.
If synthesizing genetic correlation between economic traits on 3 breeds regarding swine genetic network program, high positive correlation of 0.43~0.47 was displayed in genetic correlation between eye muscle area and lean percent, and –0.99 genetic correlation between daily gain amount and 90kg reaching age was turned up, and also high negative correlation of –0.88~-0.92 was displayed in genetic correlation between back fat thickness and lean percent. Phenotype correlation showed high positive correlation of 0.25~0.32 between phenotype correlation between eye muscle area and lean percent, and had high negative correlation of –0.98 in phenotype correlation between daily gain amount and 90kg reaching age, and also high negative correlation of –0.35~-0.40 in phenotype correlation between back fat thickness and lean percent.
From non-participant group in swine genetic network program, heritability of Landrace species was estimated to each 0.49, 0.51, 0.48, 0.23, 0.45 in daily gain amount, 90kg reaching age, back fat thickness, eye muscle area, lean percent, and heritability of Yorkshire to 0.43, 0.41, 0.43, 0.22, 0.43 in daily gain amount, 90kg reaching age, back fat thickness, eye muscle area, lean percent, and heritability to each 0.45, 0.48, 0.42, 0.19, 0.44 in daily gain amount, 90kg reaching age, back fat thickness, eye muscle area, lean percent.
If synthesizing genetic correlation between economic traits in non-participant group, it had high positive correlation of 0.44~0.47 in genetic correlation between eye muscle area and lean percent, and had high negative correlation of –0.90~-0.91 in genetic correlation between back fat thickness and lean percent. Phenotype correlation had high positive correlation of 0.40~0.42 in phenotype correlation between eye muscle area and lean percent, and showed high negative correlation of –0.97~-0.98 in phenotype correlation between back fat thickness and lean correlation, and also had high negative correlation of –0.34~-0.46 in phenotype correlation between back fat thickness and lean percent.
As a result of confirming change trends of breeding value by year regarding test traits according to breed and participation in swine genetic network improvement program through estimated breeding value by individual, a fact could be confirmed that they made a little different aspects according to breed and participation in swine genetic improvement network. First of all, if checking change trends according to breed differences, increasing trends of Yorkshire appeared the largest because genetic improvement amount of Landrace, Yorkshire, Duroc species showed increasing trends like about 1.01g, 6.65g, 4.31g. in relation with genetic improvement amount of daily gain amount. In 90kg reaching age, trends of each –0.19 day, -1.17 days, -0.64 day were displayed from Landrace, Yorkshire, Duroc species, and the largest decrease were made from Duroc species. Regarding back fat thickness, trends of 0.01㎜, -0.22㎜, -0.09㎜ were displayed from Landrace, Yorkshire, Duroc species, and the largest decrease was come out from Yorkshire. Eye muscle area showed trends of each -0.05㎜, -0.02㎜, -0.001㎜ from Landrace, Yorkshire, Duroc species, and great changes by year were not made, and also there was not most changes in Duroc especially. The lean percent showed trends of each -0.02%, -0.19%, -0.06% from Landrace, Yorkshire, Duroc species.
Improvement trends of test traits by year according to participation in swine genetic promotion network program showed increasing tents of about 7.16g and 2.77g every year from participation group and non-participant one in relation with genetic improvement amount of daily gain amount, and increasing trends of participation group turned up to be larger than that of non-participation group. Each of it showed trends of –1.23 day and –0.50 day, and reaching age was come out to be largely decreased in participation group. In relation with back fat thickness, trends of each –0.18㎜ and -0.11㎜ were displayed from participation group and non-participant one. Regarding eye muscle area, trends of each –0.04㎜ and 0.02㎜ showed from participation group and non-participant one, and differences between participation group and non-participant one were not large in case of eye muscle area, and area of non-participant group was analyzed to be a little increased. In the lean percent, trends of each 0.09% and 0.15% were displayed from participation group and non-participant one.
In case of Landrace, each 4.73g, 0.83 day, 0.24㎜, 0.15㎠, 0.25% were displayed in daily gain amount, 90kg reaching age, back fat thickness, lean percent in swine genetic improvement network program. In contrast, genetic improvement amount by household in the lean percent was come out to be 0.99 day, 0.22㎜, 0.16㎠, 0.23%. In case of Yorkshire, genetic improvement amount by household in daily gain amount, 90kg reaching age, back fat thickness, eye muscle area, lean percent showed each 4.48g, 0.82 day, 0.22㎜, 0.19㎠, 0.25%. from participation group in swine genetic improvement network program. However, genetic improvement amount by household in daily gain amount, 90kg reaching age, back fat thickness, eye muscle, lean percent from non-participant group were each 4.59g, 0.82 day, 0.20㎜, 0.17㎠, 0.22%. In case of Duroc, genetic improvement amount by household in daily gain amount, 90kg reaching age, back fat thickness, eye muscle area, lean percent showed each 5.27g, 0.83 day, 0.16㎜, 0.16㎠, 0.20%i in participation group in swine genetic improvement network program. In contrast, genetic improvement amount by household in daily gain amount, 90kg reaching age, back fat thickness, eye muscle area, lean percent was come out to be each 9.69g, 0.86 day, 0.15㎜, 0.13㎠, 0.19%. There are cases of showing a little different trends with genetic change ones, and such reason is derived from different selecting methods, selecting weight, selecting strength by each farm. It is thought to be derived from some reasons such like this research did an assumption with same value for theoretical unity, and variance than non-participant group is small through improvement during a ling time while the number of swine farms is small and participation group is composed by centering on large-scale swine farms.
If looking into above results, a fact could be confirmed that trends of each trait improvement are displayed differently according to participation classifications in swine genetic improvement network program. In case of daily gain amount and back fat thickness, improvement amount by breeding value was turned up to be increased largely in participation swine farms than non-participant ones. On the other hand, in case of other traits such as eye muscle area and lean percent, changes of test records from non-participant swine farms appeared larger. It seems that effects of business system from participation swine farms in swine genetic improvement network program compared to non-participant ones, and distribution value of participation swine farms was smaller than non-participant ones, even though growth trait was estimated a little high in non-participant swine farms at all breeds. As participation classifications were decided based on steady participation farms in swine genetic improvement network program for over 3 years, it is thought that time is required yet to make visual performance of constructing business on swine genetic improvement network program. Specially, in case of maternal line, gene sharing on imported frozen semen is under process and proper model on genetic evaluation is being progressed yet, so a lot of time for selecting excellent material-line swine would be required.
Therefore, propelling business positively by aggressively progressing gene exchanges between swine farms through active participation of a lot of excellent swine farms in order to prepare production basement of Korean swine through swine genetic improvement network program seem to be considered very importantly, and continuous analyses and researches are encouraged to be progressed afterwards so as to make improvement business through swine genetic improvement network program proceeded smoothly, and thus the results could be utilized as guidelines on business enforcement.
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