보고서 정보
주관연구기관 |
국립축산과학원 National Institute of Animal Science |
보고서유형 | 최종보고서 |
발행국가 | 대한민국 |
언어 |
한국어
|
발행년월 | 2016-02 |
과제시작연도 |
2015 |
주관부처 |
농촌진흥청 Rural Development Administration(RDA) |
등록번호 |
TRKO201600003112 |
과제고유번호 |
1395039921 |
사업명 |
친환경안전농축산물생산기술 |
DB 구축일자 |
2016-06-25
|
DOI |
https://doi.org/10.23000/TRKO201600003112 |
초록
▼
Ⅳ. 연구개발결과
< 제1세부과제 >
○ 돼지 및 가금(닭)의 육종 피라미드 구조 분석
- 국내외 돼지 및 가금 개량 피라미드 구조 분석 및 바람직한 구조(안) 도출
○ 돼지 및 가금의 생산 단계별 생산성(지표) 추이 분석
- 국내 종돈 및 종계(산란계 및 육계) 생산성 조사
- 국내외 돼지 및 가금 생산성(개량효과) 변화 비교 분석
○ 돼지 개량추세 모니터링 구축 및 예측 모델링 개발
- 양돈농가 모니터링을 위한 농가선정 기준 설정(안) 및 자료 수집 체계 제시
< 제1협동과제 ><
Ⅳ. 연구개발결과
< 제1세부과제 >
○ 돼지 및 가금(닭)의 육종 피라미드 구조 분석
- 국내외 돼지 및 가금 개량 피라미드 구조 분석 및 바람직한 구조(안) 도출
○ 돼지 및 가금의 생산 단계별 생산성(지표) 추이 분석
- 국내 종돈 및 종계(산란계 및 육계) 생산성 조사
- 국내외 돼지 및 가금 생산성(개량효과) 변화 비교 분석
○ 돼지 개량추세 모니터링 구축 및 예측 모델링 개발
- 양돈농가 모니터링을 위한 농가선정 기준 설정(안) 및 자료 수집 체계 제시
< 제1협동과제 >
○ 돼지 및 가금 개량 관련 기관 및 농가의 수집 체계 분석
- 수집 가능 자료 및 D/B화 및 모니터링 방안
○ 농장 및 기관 자료의 수집 빈도 및 체계의 설정
- 돼지와 가금 개량 자료의 수집 빈도 및 체계 설정
○ 돼지 도축 성적 활용을 위한 환산계수
- 돼지의 도체중 대비 부분육 중량 및 절식체중, 냉도체중 대비 비율 분석 등
< 제2협동과제 >
○ 돼지 및 가금분야의 기록관리 방법과 항목에 대한 조사 및 자료 수집
- 돼지분야는 10가지 기초항목에 대하여 전산자료 수집
- 가금분야는 11가지 기초항목에 대한 수기자료 수집
○ 돼지 및 가금분야에서 개량수준을 분석하기 위한 D/B 구축
- 돼지 및 가금분야의 D/B 구축 및 프로그램 개발
○ 돼지 및 가금분야에서 개량수준을 알 수 있는 분석
- 돼지(산자수, 등지방, PSY, MSY 등), 가금(육성율, 일당증체, 산란율, 부화율 등)
< 제3협동과제 >
○ 종돈의 최말단 (돼지 비육농장)의 현황분석
○ 유통단계 (도축장) 자료의 분석
○ 종돈개량체계 개선(소비자 요구를 맞추기 위한 종돈개량)을 위한 분석
- 체측치(體測値)를 이용 선호부위 생산 제고를 위한 종돈개량
- 체측치들과 육질과의 관계규명
○ 국내 현실에 적합하고 활용 가능한 종돈장 선발실험
- 종돈의 도입을 억제하는 폐쇄 축군의 선발 실험
- 폐쇄 축군에서 근친도와 종돈의 개량을 동시 성취하기 위한 선발
○ 모니터링 서비스 시스템의 설계
- 종돈정보 모니터링 파일럿 시스템 구축을 통한 설계
Abstract
▼
The Livestock Industry Act, Article 5 states that the government shall set up the goals for livestock improvement every 10 years. In line with this, the National Improvement System of Korea developed a plan in improving the Hanwoo and dairy cattle. However, swine and poultry improvement were carried
The Livestock Industry Act, Article 5 states that the government shall set up the goals for livestock improvement every 10 years. In line with this, the National Improvement System of Korea developed a plan in improving the Hanwoo and dairy cattle. However, swine and poultry improvement were carried out by private companies alone. In order to establish the goal of improving livestock, it is necessary to collect field data, analyze, predict and set up a plan for improvement. In relevant with this, the study conducted between GGP and GP farms showed that there is difference between GGP and GP in total number of born per litter but there is no significant difference in number of born alive. The pig industry is the highest sector in the domestic livestock industry. The data were collected and gathered by private companies, Korea Animal Improvement Association (KAIA), Kora Pork Producers Association(KPPA) and other organizations. In the case of poultry, private organizations such as Korea Poultry Association (KPA), Korea Duck Association (KDA) and National Institute of Animal Science (NIAS) collect and save the data. Therefore, the aim of this research project is developing a system for assessing the skill level and genetic performance to livestock on the farm. Looking at the main result for achieving the above object are follows :
According to the results of analysis improved the pyramid structure of the breeding pigs were calculated by considering the numbers of pigs and genetic performance of each country. In case of Korea it appeared to be necessary to secure the approximately 8,000 heads in the GGP breeding farm for production of 15 million finishing pigs. Most of the chicken and the form of dissemination and proliferation imported from GPS(Grand Parent Stock) step, the ability of breeders showed a similar trends with foreign countries. Looking at the pig and chicken production stage showed a difference in the genetic performance of farm and year of production, for litter size, especially MSY(Marketed-pigs per Sow per Year) in sow productivity compared to foreign appeared to relatively low. When selecting a population in order to collect data relating to the pig farm it was judged more than 15% of the farm that appropriate to calculate the representative value when sampled. For pigs and chickens improvement goals based on the information presented above it suggests a framework for monitoring the step by step improvement trend.
The data collected from farms and other organizations are different in criteria and definition. For better improvement plans, it is necessary to standardize these data. The data in pig include animal information and traits such as growth performance, reproductive and meat quality traits. In terms of poultry, livability, performance traits, feed efficiency and egg quality. From these data, the database was built to easy enable the goals of animal improvement by getting the huge data easily. The traits that were included in swine database are ‘average daily gain’, ‘backfat thickness’, ‘age at 90kg’, ‘feed efficiency’,‘reproductive traits’, and ‘appearance rate of 1st grade meat’. The data were from KAIA, KPPA, Farm facilities modernization project, Korea Institute for Animal Products Quality Evaluation (KAPE) and some private companies. In poultry improvement, the data were from related companies, farm facilities modernization project, organizations such as KPA and KDA.The poultry database traits included were ‘livability, ‘performance traits’, ‘feed efficiency’, ‘egg quality traits and other important traits. From these databases, improvement in pig and poultry are possible and feasible. In addition, we developed conversion coefficient from the weight before and after slaughter for meat quality improvement. In the analysis of 1,709 heads, the weight before slaughter is 133.4% of after slaughter.
The main purpose of this research is to compile a database and develop a web program for managing the improvement goal of swine and poultry husbandry. To set up the improvement goal which is possible to be monitored and analyzed, the research about methodology and item of record keeping on swine and poultry husbandry has been progressed. Data was collected from Total 48,100 farms(47,400 swine farms and 700 poultry farms), and reviewed for this research. As a result of this research, Database for managing the improvement goal of swine and poultry husbandry is compiled with selected target items, which are 10 item for swine and 11 item for poultry. Base on this database, the web program to be able to analyze an improvement level of swine and poultry. The outcome from this research make the monitoring and analysis of data related on the improvement more accurate and effective now than in the past. It has a significance to suggest a possible approach of the development of improvement analysis index and comparison of records in national level.
This part of the study was carried out to improve swine selection system. This report presents the estimates of heritabilities of body measurement traits and carcass traits, and genetic and phenotypic correlations of those traits for crossbred pigs in Korea. Body and ultrasound (A mode: Piglog 105) measurements in 221 pigs including body weight, length, height and width, three back fat thickness at the points of 4th, 14th rib and chine bone, eye muscle area and lean meat percent were collected at the ages of 70, 145 and 180 days and then slaughtered to measure carcass weight, back fat, belly, collar butt, spare rib, picnic shoulder, hind leg, loin, tenderloin, lean meat yield and intramuscular rough fat content in loin. Genetic analysis was done using a multi-trait animal model. Heritabilties of the body measurements were ranged from 0.331 to 0.559 and three measurements of back fat thickness were also high as range varying from 0.402 to 0.475 for the ages of 145 and 180 days. However, eye muscle area was moderate (0.296) at the age of 180 days. Heritabilities of retail cut yields were also high as ranged from 0.387 to 0.474 and of IMF content in loin was 0.499. Heritabilities of the cut percent traits were ranged from 0.249 to 0.488. Important positive genetic and phenotypic correlations were noted for all carcass yield traits (0.298 to 0.875 and 0.432 to 0.922, respectively). IMF showed low negative genetic correlations with carcass yield traits, such as carcass weight, picnic shoulder, hind leg, loin, tenderloin and lean meat yield whereas low positive genetic correlations with back fat, belly, collar butt and spare rib. Loin, tenderloin and lean meat percent showed negative genetic correlations with carcass weight, back fat thickness, collar butt, spare rib and picnic shoulder percent. The four body measurements at the ages of 70, 145 and 180 days had positive genetic correlations with belly, shoulder butt, spare rib, picnic shoulder and hind leg percent, but negative genetic correlations were shown with loin and tenderloin percent except body measurements at 70 days. The results suggest that carcass yield are negatively correlated with intramuscular fat content, which is a major factor deciding pork quality and the yield of loin and tenderloin are not increased as much as increase in body size. However, the proportions of belly and collar butt are increased with the body size. In conclusion, selection strategy should be designed according to the preference on composition of carcass in each country.
For a selection experiment of typical swine seedstock farm, a herd of Berkshire pigs was established in 2003 and subjected to selection without introduction of any genetic resources until 2007. The complete pedigree, including 410 boars and 916 sows, as well as the records from 5,845 pigs and 822 litters were used to investigate the results obtained from the selections. The index of selection for breeding values included days to 90 kg (D90kg), backfat thickness (BF) and number of piglets born alive (NBA). The average inbreeding coefficients of pigs were found to be 0.023, 0.008, 0.013, 0.025, 0.026, and 0.005 from 2003 to 2007, respectively. The genetic gains per year were 12.1 g, –0.04 mm, –3.13 days, and 0.181 head for average daily gain (ADG), BF, D90kg, and NBA, respectively. Breeding values of ADG, BF and D90kg were not significantly correlated with inbreeding coefficients of individuals, except for NBA (–0.21). The response per additional 1% of inbreeding was 0.0278 head reduction in NBA. The annual increase of inbreeding was 0.23% and the annual decrease in NBA due to inbreeding was 0.0064 head. This magnitude could be disregarded when compared with the annual gain in NBA (0.181 head). These results suggest that inbreeding and inbreeding depression on ordinary farms can be controlled with a proper breeding scheme and that breeding programs are economical and safe relative to the risks associated with importation of pigs.
For better productivity in the meat processing and packing industry, a chain of the monitoring system, this report also presents the estimates of heritabilities of body measurement traits and carcass traits, and genetic and phenotypic correlations of those traits for crossbred pigs in Korea. Body and ultrasound (A mode: Piglog 105) measurements in 221 pigs including body weight, length, height and width, three back fat thickness at the points of 4th, 14th rib and chine bone, eye muscle area and lean meat percent were collected at the ages of 70, 145 and 180 days and then slaughtered to measure carcass weight, back fat, belly, collar butt, spare rib, picnic shoulder, hind leg, loin, tenderloin, lean meat yield and intramuscular rough fat content in loin. Genetic analysis was done using a multi-trait animal model. Heritabilties of the body measurements were ranged from 0.331 to 0.559 and three measurements of back fat thickness were also high as range varying from 0.402 to 0.475 for the ages of 145 and 180 days. However, eye muscle area was moderate (0.296) at the age of 180 days. Heritabilities of retail cut yields were also high as ranged from 0.387 to 0.474 and of IMF content in loin was 0.499. Heritabilities of the cut percent traits were ranged from 0.249 to 0.488. Important positive genetic and phenotypic correlations were noted for all carcass yield traits (0.298 to 0.875 and 0.432 to 0.922, respectively). IMF showed low negative genetic correlations with carcass yield traits, such as carcass weight, picnic shoulder, hind leg,loin, tenderloin and lean meat yield whereas low positive genetic correlations with back fat,belly, collar butt and spare rib. Loin, tenderloin and lean meat percent showed negative genetic correlations with carcass weight, back fat thickness, collar butt, spare rib and picnic shoulder percent. The four body measurements at the ages of 70, 145 and 180 days had positive genetic correlations with belly, shoulder butt, spare rib, picnic shoulder and hind leg percent, but negative genetic correlations were shown with loin and tenderloin percent except body measurements at 70 days. The results suggest that carcass yield are negatively correlated with intramuscular fat content, which is a major factor deciding pork quality and the yield of loin and tenderloin are not increased as much as increase in body size. However, the proportions of belly and collar butt are increased with the body size. In conclusion, selection strategy should be designed according to the preference on composition of carcass in each country.
This report also sketches the strategies and designs for monitoring system. The monitoring system for swine improvement should reflect every side of pig production. The system leads us to the assessment on the efficiency of pig production and the scope of the system includes not only nucleus, multiplying and commercial herds, but also packing and processing sectors. The data for the monitoring system should be collected from all of these areas in pig production with information network or affiliates, differently from random sampling for the national statistics. The analysis of data including seedstocks and the distribution of information including genetic trend of pigs should be carried out in the system. The schema of knowledge base database system could be employed in the system. The monitoring system in the final destination would unify the systems derived from various sources and provide any solution in swine industry including pig breeding.
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