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New management grading for pig farms: management grading system using pig carcass weight, back fat thickness and k-means algorithm |
Youngho Lim1 
, Jaeyoung Kim1
, Gwantae Kim1
, Jungseok Choi1,*
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Department of Animal Science, Chungbuk National University, Cheongju 28644, Korea |
Correspondence:
Jungseok Choi, Tel: +82-43-261-2551, Email: Jchoi@chungbuk.ac.kr
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Received: 24 May 2024 • Revised: 3 July 2024 • Accepted: 26 July 2024 |
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| Abstract |
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. |
| Keywords:
K-means; Landrace×Yorkshire×Duroc (LYD) Pig; Management Grade; Pig Grade; Regression Analysis; VCS2000 |
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