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Multivariate analysis for data mining to characterize poultry house environment in winter
The processing and analysis of massive high-dimensional datasets are important issues in precision livestock farming (PLF). This study explored the use of multivariate analysis tools to analyze environmental data from multiple sensors located throughout a broiler house. An experiment was conducted t...
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Published in: | Poultry science 2024-05, Vol.103 (5), p.103633-103633, Article 103633 |
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description | The processing and analysis of massive high-dimensional datasets are important issues in precision livestock farming (PLF). This study explored the use of multivariate analysis tools to analyze environmental data from multiple sensors located throughout a broiler house. An experiment was conducted to collect a comprehensive set of environmental data including particulate matter (TSP, PM10, and PM2.5), ammonia, carbon dioxide, air temperature, relative humidity, and in-cage and aisle wind speeds from 60 locations in a typical commercial broiler house. The dataset was divided into 3 growth phases (wk 1–3, 4–6, and 7–9). Spearman's correlation analysis and principal component analysis (PCA) were used to investigate the latent associations between environmental variables resulting in the identification of variables that played important roles in indoor air quality. Three cluster analysis methods; k-means, k-medoids, and fuzzy c-means cluster analysis (FCM), were used to group the measured parameters based on their environmental impact in the broiler house. In general, the Spearman and PCA results showed that the in-cage wind speed, aisle wind speed, and relative humidity played critical roles in indoor air quality distribution during broiler rearing. All 3 clustering methods were found to be suitable for grouping data, with FCM outperforming the other 2. Using data clustering, the broiler house spaces were divided into 3, 2, and 2 subspaces (clusters) for wk 1 to 3, 4 to 6, and 7 to 9, respectively. The subspace in the center of the house had a poorer air quality than other subspaces. |
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This study explored the use of multivariate analysis tools to analyze environmental data from multiple sensors located throughout a broiler house. An experiment was conducted to collect a comprehensive set of environmental data including particulate matter (TSP, PM10, and PM2.5), ammonia, carbon dioxide, air temperature, relative humidity, and in-cage and aisle wind speeds from 60 locations in a typical commercial broiler house. The dataset was divided into 3 growth phases (wk 1–3, 4–6, and 7–9). Spearman's correlation analysis and principal component analysis (PCA) were used to investigate the latent associations between environmental variables resulting in the identification of variables that played important roles in indoor air quality. Three cluster analysis methods; k-means, k-medoids, and fuzzy c-means cluster analysis (FCM), were used to group the measured parameters based on their environmental impact in the broiler house. In general, the Spearman and PCA results showed that the in-cage wind speed, aisle wind speed, and relative humidity played critical roles in indoor air quality distribution during broiler rearing. All 3 clustering methods were found to be suitable for grouping data, with FCM outperforming the other 2. Using data clustering, the broiler house spaces were divided into 3, 2, and 2 subspaces (clusters) for wk 1 to 3, 4 to 6, and 7 to 9, respectively. The subspace in the center of the house had a poorer air quality than other subspaces.</description><identifier>ISSN: 0032-5791</identifier><identifier>EISSN: 1525-3171</identifier><identifier>DOI: 10.1016/j.psj.2024.103633</identifier><identifier>PMID: 38552343</identifier><language>eng</language><publisher>England: Elsevier Inc</publisher><subject>Air Pollution, Indoor - analysis ; air quality ; Animal Husbandry - methods ; Animals ; broiler house ; Chickens - physiology ; Cluster Analysis ; Data Mining ; Environmental Monitoring - methods ; Housing, Animal ; MANAGEMENT AND PRODUCTION ; microclimate ; Multivariate Analysis ; Seasons</subject><ispartof>Poultry science, 2024-05, Vol.103 (5), p.103633-103633, Article 103633</ispartof><rights>2024 The Authors</rights><rights>Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.</rights><rights>2024 The Authors 2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c470t-8160a0442b2127883a532452a2b9b7c92cc5ba5e1d324e15347d5dfaa2a03fa63</cites><orcidid>0000-0001-6219-4718</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0032579124002128$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>230,314,780,784,885,3549,27924,27925,45780</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38552343$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Mingyang</creatorcontrib><creatorcontrib>Zhou, Zilin</creatorcontrib><creatorcontrib>Zhang, Qiang</creatorcontrib><creatorcontrib>Zhang, Jie</creatorcontrib><creatorcontrib>Suo, Yunpeng</creatorcontrib><creatorcontrib>Liu, Junze</creatorcontrib><creatorcontrib>Shen, Dan</creatorcontrib><creatorcontrib>Luo, Lu</creatorcontrib><creatorcontrib>Li, Yansen</creatorcontrib><creatorcontrib>Li, Chunmei</creatorcontrib><title>Multivariate analysis for data mining to characterize poultry house environment in winter</title><title>Poultry science</title><addtitle>Poult Sci</addtitle><description>The processing and analysis of massive high-dimensional datasets are important issues in precision livestock farming (PLF). This study explored the use of multivariate analysis tools to analyze environmental data from multiple sensors located throughout a broiler house. An experiment was conducted to collect a comprehensive set of environmental data including particulate matter (TSP, PM10, and PM2.5), ammonia, carbon dioxide, air temperature, relative humidity, and in-cage and aisle wind speeds from 60 locations in a typical commercial broiler house. The dataset was divided into 3 growth phases (wk 1–3, 4–6, and 7–9). Spearman's correlation analysis and principal component analysis (PCA) were used to investigate the latent associations between environmental variables resulting in the identification of variables that played important roles in indoor air quality. Three cluster analysis methods; k-means, k-medoids, and fuzzy c-means cluster analysis (FCM), were used to group the measured parameters based on their environmental impact in the broiler house. In general, the Spearman and PCA results showed that the in-cage wind speed, aisle wind speed, and relative humidity played critical roles in indoor air quality distribution during broiler rearing. All 3 clustering methods were found to be suitable for grouping data, with FCM outperforming the other 2. Using data clustering, the broiler house spaces were divided into 3, 2, and 2 subspaces (clusters) for wk 1 to 3, 4 to 6, and 7 to 9, respectively. The subspace in the center of the house had a poorer air quality than other subspaces.</description><subject>Air Pollution, Indoor - analysis</subject><subject>air quality</subject><subject>Animal Husbandry - methods</subject><subject>Animals</subject><subject>broiler house</subject><subject>Chickens - physiology</subject><subject>Cluster Analysis</subject><subject>Data Mining</subject><subject>Environmental Monitoring - methods</subject><subject>Housing, Animal</subject><subject>MANAGEMENT AND PRODUCTION</subject><subject>microclimate</subject><subject>Multivariate Analysis</subject><subject>Seasons</subject><issn>0032-5791</issn><issn>1525-3171</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNp9kcuOEzEQRVsIxISBD2CDvGTTwc9-iAVCIx4jDWIDC1ZW2V2duNWxg-1kFL4ehx5GzIaV5fKtU657q-olo2tGWfNmWu_TtOaUy3IXjRCPqhVTXNWCtexxtaJU8Fq1PbuonqU0UcpZ07RPqwvRKcWFFKvqx5fDnN0RooOMBDzMp-QSGUMkA2QgO-ed35AciN1CBJsxul9I9qG0xRPZhkNCgv7oYvA79Jk4T26dL7Ln1ZMR5oQv7s7L6vvHD9-uPtc3Xz9dX72_qa1saa471lCgUnLDGW-7ToASXCoO3PSmtT23VhlQyIZSRqaEbAc1jAAcqBihEZfV9cIdAkx6H90O4kkHcPpPIcSNhpidnVEboXrR9bzHXkhpGgPGmo6qjg9yMIYX1ruFtT-YHQ62LBRhfgB9-OLdVm_CUTNGKWW0LYTXd4QYfh4wZb1zyeI8g8filRaUc9WKvjlL2SK1MaQUcbyfw6g-56vLOmnS53z1km_pefXvB-87_gZaBG8XARbLjw6jTtahtzi4iDYXT9x_8L8BNdW3UA</recordid><startdate>20240501</startdate><enddate>20240501</enddate><creator>Li, Mingyang</creator><creator>Zhou, Zilin</creator><creator>Zhang, Qiang</creator><creator>Zhang, Jie</creator><creator>Suo, Yunpeng</creator><creator>Liu, Junze</creator><creator>Shen, Dan</creator><creator>Luo, Lu</creator><creator>Li, Yansen</creator><creator>Li, Chunmei</creator><general>Elsevier Inc</general><general>Elsevier</general><scope>6I.</scope><scope>AAFTH</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-6219-4718</orcidid></search><sort><creationdate>20240501</creationdate><title>Multivariate analysis for data mining to characterize poultry house environment in winter</title><author>Li, Mingyang ; Zhou, Zilin ; Zhang, Qiang ; Zhang, Jie ; Suo, Yunpeng ; Liu, Junze ; Shen, Dan ; Luo, Lu ; Li, Yansen ; Li, Chunmei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c470t-8160a0442b2127883a532452a2b9b7c92cc5ba5e1d324e15347d5dfaa2a03fa63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Air Pollution, Indoor - analysis</topic><topic>air quality</topic><topic>Animal Husbandry - methods</topic><topic>Animals</topic><topic>broiler house</topic><topic>Chickens - physiology</topic><topic>Cluster Analysis</topic><topic>Data Mining</topic><topic>Environmental Monitoring - methods</topic><topic>Housing, Animal</topic><topic>MANAGEMENT AND PRODUCTION</topic><topic>microclimate</topic><topic>Multivariate Analysis</topic><topic>Seasons</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Mingyang</creatorcontrib><creatorcontrib>Zhou, Zilin</creatorcontrib><creatorcontrib>Zhang, Qiang</creatorcontrib><creatorcontrib>Zhang, Jie</creatorcontrib><creatorcontrib>Suo, Yunpeng</creatorcontrib><creatorcontrib>Liu, Junze</creatorcontrib><creatorcontrib>Shen, Dan</creatorcontrib><creatorcontrib>Luo, Lu</creatorcontrib><creatorcontrib>Li, Yansen</creatorcontrib><creatorcontrib>Li, Chunmei</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Poultry science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Mingyang</au><au>Zhou, Zilin</au><au>Zhang, Qiang</au><au>Zhang, Jie</au><au>Suo, Yunpeng</au><au>Liu, Junze</au><au>Shen, Dan</au><au>Luo, Lu</au><au>Li, Yansen</au><au>Li, Chunmei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multivariate analysis for data mining to characterize poultry house environment in winter</atitle><jtitle>Poultry science</jtitle><addtitle>Poult Sci</addtitle><date>2024-05-01</date><risdate>2024</risdate><volume>103</volume><issue>5</issue><spage>103633</spage><epage>103633</epage><pages>103633-103633</pages><artnum>103633</artnum><issn>0032-5791</issn><eissn>1525-3171</eissn><abstract>The processing and analysis of massive high-dimensional datasets are important issues in precision livestock farming (PLF). This study explored the use of multivariate analysis tools to analyze environmental data from multiple sensors located throughout a broiler house. An experiment was conducted to collect a comprehensive set of environmental data including particulate matter (TSP, PM10, and PM2.5), ammonia, carbon dioxide, air temperature, relative humidity, and in-cage and aisle wind speeds from 60 locations in a typical commercial broiler house. The dataset was divided into 3 growth phases (wk 1–3, 4–6, and 7–9). Spearman's correlation analysis and principal component analysis (PCA) were used to investigate the latent associations between environmental variables resulting in the identification of variables that played important roles in indoor air quality. Three cluster analysis methods; k-means, k-medoids, and fuzzy c-means cluster analysis (FCM), were used to group the measured parameters based on their environmental impact in the broiler house. In general, the Spearman and PCA results showed that the in-cage wind speed, aisle wind speed, and relative humidity played critical roles in indoor air quality distribution during broiler rearing. All 3 clustering methods were found to be suitable for grouping data, with FCM outperforming the other 2. Using data clustering, the broiler house spaces were divided into 3, 2, and 2 subspaces (clusters) for wk 1 to 3, 4 to 6, and 7 to 9, respectively. The subspace in the center of the house had a poorer air quality than other subspaces.</abstract><cop>England</cop><pub>Elsevier Inc</pub><pmid>38552343</pmid><doi>10.1016/j.psj.2024.103633</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0001-6219-4718</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Air Pollution, Indoor - analysis air quality Animal Husbandry - methods Animals broiler house Chickens - physiology Cluster Analysis Data Mining Environmental Monitoring - methods Housing, Animal MANAGEMENT AND PRODUCTION microclimate Multivariate Analysis Seasons |
title | Multivariate analysis for data mining to characterize poultry house environment in winter |
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