New management grading for pig farms: management grading system using pig carcass weight, back fat thickness and k-means algorithm
Objective This study categorized farm management levels to improve the productivity and uniformity of pork from pigs shipped from farms. Methods A total of 48,298 pigs were grouped (A, B, C, D group) using the k-means algorithm, carcass weight and backfat thickness. The results of the grouping were...
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| Format: | Article |
| Language: | English |
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Asian-Australasian Association of Animal Production Societies
2025-02-01
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| Series: | Animal Bioscience |
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| Online Access: | http://www.animbiosci.org/upload/pdf/ab-24-0350.pdf |
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| author | Youngho Lim Jaeyoung Kim Gwantae Kim Jungseok Choi |
| author_facet | Youngho Lim Jaeyoung Kim Gwantae Kim Jungseok Choi |
| author_sort | Youngho Lim |
| collection | DOAJ |
| description | Objective This study categorized farm management levels to improve the productivity and uniformity of pork from pigs shipped from farms. Methods A total of 48,298 pigs were grouped (A, B, C, D group) using the k-means algorithm, carcass weight and backfat thickness. The results of the grouping were used to classify Farm Management Grades (A, B, C, D grade). Results The proportion of primal cuts in pigs, according to the new classification method, increased from group A to group D for shoulder blade, shoulder picnic, and ham, but decreased for loin and belly. In the regression analysis of the five primal cuts (shoulder blade, shoulder picnic, loin, belly, and ham) production (kg) for each group, all regression equations showed low errors (MAE<0.7), indicating that the model can predict the production of primal cuts by group. As the Farm Management Grade decreased, the proportion of pigs in the group with large differences from the mean of carcass weight and backfat thickness of the whole pig increased. Conclusion The results of this study confirmed the differences in primal cut traits by pig grouping and created a method to classify farms who ship non-uniform pigs. This is expected to provide indicators for improvement and supplementation to farms that ship uneven pigs, helping to enhance the production of standardized pigs at the farm level. |
| format | Article |
| id | doaj-art-b7579d4ab33742768e2b2223bf907aab |
| institution | Kabale University |
| issn | 2765-0189 2765-0235 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Asian-Australasian Association of Animal Production Societies |
| record_format | Article |
| series | Animal Bioscience |
| spelling | doaj-art-b7579d4ab33742768e2b2223bf907aab2025-01-03T04:14:23ZengAsian-Australasian Association of Animal Production SocietiesAnimal Bioscience2765-01892765-02352025-02-0138237138010.5713/ab.24.035025326New management grading for pig farms: management grading system using pig carcass weight, back fat thickness and k-means algorithmYoungho Lim0Jaeyoung Kim1Gwantae Kim2Jungseok Choi3 Department of Animal Science, Chungbuk National University, Cheongju 28644, Korea Department of Animal Science, Chungbuk National University, Cheongju 28644, Korea Department of Animal Science, Chungbuk National University, Cheongju 28644, Korea Department of Animal Science, Chungbuk National University, Cheongju 28644, KoreaObjective This study categorized farm management levels to improve the productivity and uniformity of pork from pigs shipped from farms. Methods A total of 48,298 pigs were grouped (A, B, C, D group) using the k-means algorithm, carcass weight and backfat thickness. The results of the grouping were used to classify Farm Management Grades (A, B, C, D grade). Results The proportion of primal cuts in pigs, according to the new classification method, increased from group A to group D for shoulder blade, shoulder picnic, and ham, but decreased for loin and belly. In the regression analysis of the five primal cuts (shoulder blade, shoulder picnic, loin, belly, and ham) production (kg) for each group, all regression equations showed low errors (MAE<0.7), indicating that the model can predict the production of primal cuts by group. As the Farm Management Grade decreased, the proportion of pigs in the group with large differences from the mean of carcass weight and backfat thickness of the whole pig increased. Conclusion The results of this study confirmed the differences in primal cut traits by pig grouping and created a method to classify farms who ship non-uniform pigs. This is expected to provide indicators for improvement and supplementation to farms that ship uneven pigs, helping to enhance the production of standardized pigs at the farm level.http://www.animbiosci.org/upload/pdf/ab-24-0350.pdfk-meanslandrace×yorkshire×duroc (lyd) pigmanagement gradepig graderegression analysisvcs2000 |
| spellingShingle | Youngho Lim Jaeyoung Kim Gwantae Kim Jungseok Choi New management grading for pig farms: management grading system using pig carcass weight, back fat thickness and k-means algorithm Animal Bioscience k-means landrace×yorkshire×duroc (lyd) pig management grade pig grade regression analysis vcs2000 |
| title | New management grading for pig farms: management grading system using pig carcass weight, back fat thickness and k-means algorithm |
| title_full | New management grading for pig farms: management grading system using pig carcass weight, back fat thickness and k-means algorithm |
| title_fullStr | New management grading for pig farms: management grading system using pig carcass weight, back fat thickness and k-means algorithm |
| title_full_unstemmed | New management grading for pig farms: management grading system using pig carcass weight, back fat thickness and k-means algorithm |
| title_short | New management grading for pig farms: management grading system using pig carcass weight, back fat thickness and k-means algorithm |
| title_sort | new management grading for pig farms management grading system using pig carcass weight back fat thickness and k means algorithm |
| topic | k-means landrace×yorkshire×duroc (lyd) pig management grade pig grade regression analysis vcs2000 |
| url | http://www.animbiosci.org/upload/pdf/ab-24-0350.pdf |
| work_keys_str_mv | AT youngholim newmanagementgradingforpigfarmsmanagementgradingsystemusingpigcarcassweightbackfatthicknessandkmeansalgorithm AT jaeyoungkim newmanagementgradingforpigfarmsmanagementgradingsystemusingpigcarcassweightbackfatthicknessandkmeansalgorithm AT gwantaekim newmanagementgradingforpigfarmsmanagementgradingsystemusingpigcarcassweightbackfatthicknessandkmeansalgorithm AT jungseokchoi newmanagementgradingforpigfarmsmanagementgradingsystemusingpigcarcassweightbackfatthicknessandkmeansalgorithm |