• Title/Summary/Keyword: Life Sciences

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Genetic, management, and nutritional factors affecting intramuscular fat deposition in beef cattle - A review

  • Park, Seung Ju;Beak, Seok-Hyeon;Jung, Da Jin Sol;Kim, Sang Yeob;Jeong, In Hyuk;Piao, Min Yu;Kang, Hyeok Joong;Fassah, Dilla Mareistia;Na, Sang Weon;Yoo, Seon Pil;Baik, Myunggi
    • Asian-Australasian Journal of Animal Sciences
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    • v.31 no.7
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    • pp.1043-1061
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    • 2018

  • Intramuscular fat (IMF) content in skeletal muscle including the longissimus dorsi muscle (LM), also known as marbling fat, is one of the most important factors determining beef quality in several countries including Korea, Japan, Australia, and the United States. Genetics and breed, management, and nutrition affect IMF deposition. Japanese Black cattle breed has the highest IMF content in the world, and Korean cattle (also called Hanwoo) the second highest. Here, we review results of research on genetic factors (breed and sex differences and heritability) that affect IMF deposition. Cattle management factors are also important for IMF deposition. Castration of bulls increases IMF deposition in most cattle breeds. The effects of several management factors, including weaning age, castration, slaughter weight and age, and environmental conditions on IMF deposition are also reviewed. Nutritional factors, including fat metabolism, digestion and absorption of feed, glucose/starch availability, and vitamin A, D, and C levels are important for IMF deposition. Manipulating IMF deposition through developmental programming via metabolic imprinting is a recently proposed nutritional method to change potential IMF deposition during the fetal and neonatal periods in rodents and domestic animals. Application of fetal nutritional programming to increase IMF deposition of progeny in later life is reviewed. The coordination of several factors affects IMF deposition. Thus, a combination of several strategies may be needed to manipulate IMF deposition, depending on the consumer's beef preference. In particular, stage-specific feeding programs with concentrate-based diets developed by Japan and Korea are described in this article.

  • https://doi.org/10.5713/ajas.18.0310 인용 PDF KSCI

Estimation of Duck House Litter Evaporation Rate Using Machine Learning (기계학습을 활용한 오리사 바닥재 수분 발생량 분석)

  • Kim, Dain;Lee, In-bok;Yeo, Uk-hyeon;Lee, Sang-yeon;Park, Sejun;Decano, Cristina;Kim, Jun-gyu;Choi, Young-bae;Cho, Jeong-hwa;Jeong, Hyo-hyeog;Kang, Solmoe
    • Journal of The Korean Society of Agricultural Engineers
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    • v.63 no.6
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    • pp.77-88
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    • 2021

  • Duck industry had a rapid growth in recent years. Nevertheless, researches to improve duck house environment are still not sufficient enough. Moisture generation of duck house litter is an important factor because it may cause severe illness and low productivity. However, the measuring process is difficult because it could be disturbed with animal excrements and other factors. Therefore, it has to be calculated according to the environmental data around the duck house litter. To cut through all these procedures, we built several machine learning regression model forecasting moisture generation of litter by measured environment data (air temperature, relative humidity, wind velocity and water contents). 5 models (Multi Linear Regression, k-Nearest Neighbors, Support Vector Regression, Random Forest and Deep Neural Network). have been selected for regression. By using R-Square, RMSE and MAE as evaluation metrics, the best accurate model was estimated according to the variables for each machine learning model. In addition, to address the small amount of data acquired through lab experiments, bootstrapping method, a technique utilized in statistics, was used. As a result, the most accurate model selected was Random Forest, with parameters of n-estimator 200 by bootstrapping the original data nine times.

  • https://doi.org/10.5389/KSAE.2021.63.6.077 인용 PDF KSCI HTML