최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기한국농공학회논문집 = Journal of the Korean Society of Agricultural Engineers, v.63 no.6, 2021년, pp.77 - 88
김다인 (Research Institute of Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University) , 이인복 (Research Institute of Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University) , 여욱현 (Research Institute of Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University) , 이상연 (Research Institute of Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University) , 박세준 (Research Institute of Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University) , 크리스티나 (Research Institute of Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University) , 김준규 (Research Institute of Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University) , 최영배 (Research Institute of Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University) , 조정화 (Research Institu) , 정효혁 , 강솔뫼
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 di...
Korean Duck Association, 2019. A report of research on the improvement of duck breeding facilities I. Duck Village 195: 24-35 (in Korean).
Korean Duck Association, 2019. A report of research on the improvement of duck breeding facilities II. Duck Village 196: 20-40 (in Korean).
Korean Duck Association, 2019. A report of research on the improvement of duck breeding facilities III Duck Village 197: 30-51 (in Korean).
Statistics Korea, 2020. Results of the farm and fishery household economy survey in 2020.
Statistics Korea, 2020. 2020 Livestock production cost survey.
Bang, H. T., H. C. Choi, H. S. Chae, J. C. Na, H. G. Kang, M. J. Kim, D. W. Kim, S. B. Park, S. H. Jung, and O. S. Seo, 2010. A study on the productivity and environmental change according to duck house litter. Korean Society of Poultry Science 27: 132-134 (in Korean).
Bang, H. T., D. W. Kim, H. B. Jong, J. C. Na, H. K. Kang, M. J. Kim, M. M. H. Mushtaq, H. C. Choi, S. B. Lee, M. Kang, and J. H. Kim, 2013. Effect of various forms of floor system on performance of meat-type duck and environments of duck house. Korean Journal Society of Poultry Science 40(3): 253-262 (in Korean). doi:10.5536/ KJPS.2013.40.3.253.
Yeo, U. H., Y. S. Cho, K. S. Kwon, T. H. Ha, S. J. Park, R. W. Kim, S. Y. Lee, S. N. Lee, I. B. Lee, and I. H. Seo, 2015. Analysis of ventilation efficiency for the summer about a design plan of standard duck house using CFD. Korean Journal Society of Poultry Science 57(5): 51-60 (in Korean). doi:10.5389/KSAE.2015.57.5.051.
Kim, Y. H., 2017. Analysis of ventilation efficiency of standard duck house using computational fluid dynamics. Seoul National University: 1-91 (in Korean).
Hong, E. C., B. S. Kang, W. K. Kang, J. J. Jeon, H. S. Kim, J. S. Son, and C. H. Kim, 2019. Effect of different stocking densities in plastic wired-floor house on performance and uniformity of Korean native commercial ducks. Korean Journal Society of Poultry Science 46(4): 215-221 (in Korean). doi:10.5336/KJPS.2019.46.4.215.
Lee, S. Y., I. B. Lee, R. W. Kim, U. H. Yeo, C. Decano, J. G, Kim, Y. B. Choi, Y. M. Park, and H. H. Jeong, 2019. Assessment of evaporation rates from litter of duck house, Journal of the Korean Society of Agricultural Engineers 61(5): 101-108 (in Korean). doi:10.5389/KSAE.2019.61.5.101.
Lee, S. Y., I. B. Lee, R. W. Kim, U. H. Yeo, J. G. Kim, and K. S. Kwon, 2020. Dynamic energy modeling for analysis of the thermal and hygroscopic environment in a mechanically ventilated duck house, Biosystems Engineering, 200: 431-449. doi:10.1016/j.biosystemseng.2020.10.015
Kwon, K. S., J. S. Woo, J. H. Noh, S. I. Oh, J. B. Kim, J. K. Kim, K. Y. Yang, D. H. Jang, and S. M. Choi, 2021. Development and field-evaluation of automatic spreader for bedding materials in duck houses. Journal of the Korean Society of Agricultural Engineers 63(1): 37-48 (in Korean). doi:10.5389/KSAE.2021.63.1.037.
Bernhart, M., and O. O. Fasina, 2009. Moisture effect on the storage, handling and flow properties of poultry litter. Waste Management 29: 1392-1398. doi:10.1016/j.wasman.2008.09.005.
Brye, K. R., N. A. Slaton, R. J. Norman, and M. C. Savin, 2005. Short-term effects of poultry litter form and rate on soil bulk density and water content. Communications in Soil Science and Plant Analysis 35(15 & 16): 2311-2325. doi:10.1081/LCSS-200030655.
Choi, I. H., and Nahm K. H., 2004. Effects of applying two different chemical additives to the litter on broiler performance and the carbon dioxide gas production in poultry houses. Korean Society of Poultry Science 31(3): 171-176 (in Korean).
Wadud, S., A. Michaelsen, E. Gallagher, G. Parcsi, O. Zemb, R. Stuetz, and M. Manefield, 2012. Bacterial and fungal community composition over time in chicken litter with high or low moisture content. British Poultry Science 53(5): 561-569. doi:10.1080/00071668.2012.723802.
Dunlop, M. W., P. J. Blackall, and R. M. Stuetz, 2015. Water addition, evaporation and water holding capacity of poultry litter. Science of the Total Environment 538: 979-985. doi:10.1016/j.scitotenv.2015.08.092.
Dunlop, M. W., J. McAuley, P. J. Blackall, and R. M. Stuetz, 2016. Water activity of poultry litter: relationship to moisture content during a grow-out. Journal of Environmental Management 172: 201-206. doi:10.1016/j.jenvman.2016.02.036.
Kim, S. H., S. Y. Park, S. J. Lee, and K. S. Ryu, 2003. Effect of feeding lactobacillus reuteri to broiler on growing performance, intestinal microflora and environmental factor. Korean Society of Poultry Science 30(1): 17-28 (in Korean).
Miles, D. M., D. E. Rowe, and T. C. Cathcart, 2011. Litter ammonia generation: moisture content and organic versus inorganic bedding materials. Poultry Science 90: 1162-1169. doi:10.3382/ps.2010-01113.
Lee, W. S., S. M. Park, T. W. Ban, S. H. Kim, J. Y. Ryu, and K. Y. Sung, 2018. Health monitoring of livestock using neck sensor based on machine learning. Journal of the Korea Institute of Information and Communication Engineering 22(11): 1421-1427 (in Korean). doi:10.6109/jkiice.2018. 22.11.1421.
Lee, W. S., J. Y. Ryu, T. W. Ban, S. H. Kim, S. K. Kang, Y. H. Ham, and H. J. Lee, 2018. Estimation of body core temperature of cow using neck sensor based on machine learning. Journal of the Korea Institute of Information and Communication Engineering 22(12): 1611-1617 (in Korean). doi:106109/ jkiice.2018.22.12.1611.
Gorczyca, M. T., H. F. M. Milan, A. S. C. Maia, and K. G. Gerbremedhin, 2018. Machine learning algorithms to predict core, skin and hair-coat temperatures of piglets. Computers and Electronics in Agriculture 151: 286-294. doi:10.1016/j.compag.2018.06.028.
Gorczyca, M. T., 2019. Machine learning applications for monitoring heat stress in livestock. Faculty of the Graduate School of Cornell University: 1-64.
ASTM, Standard test methods for laboratory determination of water (moisture) content of soil and rock by mass (Designation: D2216-10).
Hong, S. E., T. J. Park, J. I. Bang, and H. J. Kim, 2020. A study on the prediction model for tomato production and growth using ConvLSTM. The Journal of Korean Institute of Information Technology 18(1): 1-10 (in Korean). doi:10.14801/jkiit.2020.18.1.1.
Alhnaity, B., S. Pearson, G. Leontidis, and S. Kollias, 2020. Using deep learning to predict plant growth and yield in greenhouse environments. Acta Hortic 1296: 425-432. doi:10.17660/ActaHortic.2020.1296.55.
Cho, Y. H., Y. D. Seo, D. J. Park, and J. C. Jeong, 2016. Study on the activation functions for efficient learning in DNN. The Institute of Electronics and Information Engineers: 800-803 (in Korean).
Efron B., and R. J. Tibshirani, 1998. An Introduction to the bootstrap. Monographs on statistics and applied probability 57. London: Chapman & Hall/CHC.
Akinoglu, B. G., 1991. A review of sunshine-based models used to estimate monthly average global solar radiation. Renewable Energy 1(3): 479-497.
Bakay, M. S., and U. Agbulut, 2021. Electricity production based forecasting of greenhouse gas emissions in turkey with deep learning, Support Vector Machine and Artificial Neural Network algorithms. Journal of Cleaner Production 285: 1-18.
Gouda, S. G., Z. Hussein, S. Luo, and Q. X. Yuan, 2019. Model selection for accurate daily global solar radiation prediction in China. Journal of Cleaner Production 221: 132-144. doi:10.1016/j.jclepro.2019.02.211.
Bakirci, K. 2009. Correlations for estimation of daily global solar radiation with hours of bright sunshine in Turkey. Energy 34: 485-501. doi:10.1016/j.energy.2009.02.005.
Wang, H. K., L. Li, W. Yong, M. Fanjia, H. H. Wang, and N. A. Sigrimis, 2018. Recurrent Neural Network model for prediction of microclimate in solar greenhouse. IFAC PapersOnLine 51(17): 790-795. doi : 10.1016/j.ifacol.2018.08.099.
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
Free Access. 출판사/학술단체 등이 허락한 무료 공개 사이트를 통해 자유로운 이용이 가능한 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.