Genome-wide association study identifying genetic variants associated with carcass backfat thickness, lean percentage and fat percentage in a four-way crossbred pig population using SLAF-seq technology. [PDF]
BMC Genomics, 2022 Wang H +8 more
europepmc +1 more source
Comparative effect of crumbled or mashed feed form on growth performance, nutrient digestibility, backfat thickness, and carcass quality of growing-finishing pigs [PDF]
, 2023 Sharif Uddin Khan, In Ho Kim
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Integrative Analysis Between Genome-Wide Association Study and Expression Quantitative Trait Loci Reveals Bovine Muscle Gene Expression Regulatory Polymorphisms Associated With Intramuscular Fat and Backfat Thickness. [PDF]
Front Genet, 2022 Silva-Vignato B +10 more
europepmc +1 more source
High-level dietary crude protein decreased backfat thickness and increased carcass yield score in finishing Hanwoo beef cattle (Bos taurus coreanae). [PDF]
J Anim Sci Technol, 2021 Jeon S +5 more
europepmc +1 more source
Comparative Analysis of Machine Learning Models to Predict Backfat Thickness in Hanwoo Cattle [PDF]
Taeyong Yun +3 more
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, 2000 Backfat thickness, daily gain, and five exterior traits were measured in 36 814 Large White and Landrace gilts from nucleus and multiplier herds. Functional stayability, defined as survived (1) or not (0), from first to second (STAY12) and from first to ...
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Journal of animal science, 2015 This research was supported in the United States by the College of Agriculture and Life Sciences, the Ensminger program, State of Iowa, and Hatch funding and in Spain by the MINECO-funded projects AGL2009-09779 and AGL2012-33529. Comments made by the editor and 2 reviewers are greatly appreciated.
Kim, Eui-Soo +5 more
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The effect of backfat thickness determined in vivo in breeding gilts on their reproductive performance and longevity [PDF]
, 2023 J. Więcek +6 more
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Non-Contact Measurement of Pregnant Sows’ Backfat Thickness Based on a Hybrid CNN-ViT Model [PDF]
, 2023 Xuan Li +5 more
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Hanwoo Carcass Dataset for Backfat Thickness Modeling and Machine Learning Benchmarking
Taeyong Yun, Dawoon Jeong, Woongsup Lee
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