Generalized additive mixed model on the analysis of total transport losses of market-weight pigs1
Abstract Transportation losses of market-weight pigs are an animal welfare concern, and result in direct economic impact for producers and abattoirs. Such losses are related to multiple factors including pig genetics, human handling, management, and weather conditions. Understanding the factors asso...
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Veröffentlicht in: | Journal of animal science 2019-04, Vol.97 (5), p.2025-2034 |
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creator | Passafaro, Tiago L Van de Stroet, Denise Bello, Nora M Williams, Noel H Rosa, Guilherme J M |
description | Abstract
Transportation losses of market-weight pigs are an animal welfare concern, and result in direct economic impact for producers and abattoirs. Such losses are related to multiple factors including pig genetics, human handling, management, and weather conditions. Understanding the factors associated with total transport losses (TTL) is important to the swine industry because it can aid decision-making, and help in the development of transportation strategies to minimize the risk of losses. Hence, the objective of this study was to investigate factors associated with TTL on market-weight pigs in typically field conditions for Midwestern United States using a generalized additive mixed model (GAMM). The final quasi-binomial GAMM included the fixed (main and interactions) effects of abattoir of destination, type of driver, average market weight, distance traveled, wind speed, precipitation, and temperature-humidity index (THI), as well as the random effects of truck companies and the combination of site of origin and period of the year. Results indicate significant associations between TTL and the main effect of all explanatory variables (P < 0.05), except for wind speed and precipitation. Interactions of average market weight × abattoir, and wind speed × precipitation were also significant. A complex nonlinear relationship between TTL and model covariates were observed for distance traveled, THI, and interaction terms. This study showed that TTL of market-weight pigs are caused by a complex system involving multiple interacting factors, which can be potentially managed to mitigate the risk of losses. In addition, the GAMM showed to be a simple and flexible approach to model TTL because it can capture nonlinear relationships, handle non-normal data, and can potentially accommodate data structure. |
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Transportation losses of market-weight pigs are an animal welfare concern, and result in direct economic impact for producers and abattoirs. Such losses are related to multiple factors including pig genetics, human handling, management, and weather conditions. Understanding the factors associated with total transport losses (TTL) is important to the swine industry because it can aid decision-making, and help in the development of transportation strategies to minimize the risk of losses. Hence, the objective of this study was to investigate factors associated with TTL on market-weight pigs in typically field conditions for Midwestern United States using a generalized additive mixed model (GAMM). The final quasi-binomial GAMM included the fixed (main and interactions) effects of abattoir of destination, type of driver, average market weight, distance traveled, wind speed, precipitation, and temperature-humidity index (THI), as well as the random effects of truck companies and the combination of site of origin and period of the year. Results indicate significant associations between TTL and the main effect of all explanatory variables (P < 0.05), except for wind speed and precipitation. Interactions of average market weight × abattoir, and wind speed × precipitation were also significant. A complex nonlinear relationship between TTL and model covariates were observed for distance traveled, THI, and interaction terms. This study showed that TTL of market-weight pigs are caused by a complex system involving multiple interacting factors, which can be potentially managed to mitigate the risk of losses. In addition, the GAMM showed to be a simple and flexible approach to model TTL because it can capture nonlinear relationships, handle non-normal data, and can potentially accommodate data structure.</description><identifier>ISSN: 0021-8812</identifier><identifier>EISSN: 1525-3163</identifier><identifier>DOI: 10.1093/jas/skz087</identifier><identifier>PMID: 30873547</identifier><language>eng</language><publisher>US: Oxford University Press</publisher><subject>Abattoirs ; Animal Health and Well Being ; Animal Husbandry - economics ; Animal Welfare ; Animals ; Body Weight ; Humidity ; Midwestern United States ; Models, Statistical ; Motor Vehicles ; Swine - growth & development ; Swine - physiology ; Temperature ; Time Factors ; Transportation ; Weather</subject><ispartof>Journal of animal science, 2019-04, Vol.97 (5), p.2025-2034</ispartof><rights>The Author(s) 2019. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. 2019</rights><rights>The Author(s) 2019. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2537-8fd4a670e2665887bd4d0a7e0536e686653ec988c06ed4734f4001f6e5da85963</citedby><cites>FETCH-LOGICAL-c2537-8fd4a670e2665887bd4d0a7e0536e686653ec988c06ed4734f4001f6e5da85963</cites><orcidid>0000-0001-5393-4446</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6488317/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6488317/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,314,723,776,780,881,1578,27903,27904,53770,53772</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30873547$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Passafaro, Tiago L</creatorcontrib><creatorcontrib>Van de Stroet, Denise</creatorcontrib><creatorcontrib>Bello, Nora M</creatorcontrib><creatorcontrib>Williams, Noel H</creatorcontrib><creatorcontrib>Rosa, Guilherme J M</creatorcontrib><title>Generalized additive mixed model on the analysis of total transport losses of market-weight pigs1</title><title>Journal of animal science</title><addtitle>J Anim Sci</addtitle><description>Abstract
Transportation losses of market-weight pigs are an animal welfare concern, and result in direct economic impact for producers and abattoirs. Such losses are related to multiple factors including pig genetics, human handling, management, and weather conditions. Understanding the factors associated with total transport losses (TTL) is important to the swine industry because it can aid decision-making, and help in the development of transportation strategies to minimize the risk of losses. Hence, the objective of this study was to investigate factors associated with TTL on market-weight pigs in typically field conditions for Midwestern United States using a generalized additive mixed model (GAMM). The final quasi-binomial GAMM included the fixed (main and interactions) effects of abattoir of destination, type of driver, average market weight, distance traveled, wind speed, precipitation, and temperature-humidity index (THI), as well as the random effects of truck companies and the combination of site of origin and period of the year. Results indicate significant associations between TTL and the main effect of all explanatory variables (P < 0.05), except for wind speed and precipitation. Interactions of average market weight × abattoir, and wind speed × precipitation were also significant. A complex nonlinear relationship between TTL and model covariates were observed for distance traveled, THI, and interaction terms. This study showed that TTL of market-weight pigs are caused by a complex system involving multiple interacting factors, which can be potentially managed to mitigate the risk of losses. In addition, the GAMM showed to be a simple and flexible approach to model TTL because it can capture nonlinear relationships, handle non-normal data, and can potentially accommodate data structure.</description><subject>Abattoirs</subject><subject>Animal Health and Well Being</subject><subject>Animal Husbandry - economics</subject><subject>Animal Welfare</subject><subject>Animals</subject><subject>Body Weight</subject><subject>Humidity</subject><subject>Midwestern United States</subject><subject>Models, Statistical</subject><subject>Motor Vehicles</subject><subject>Swine - growth & development</subject><subject>Swine - physiology</subject><subject>Temperature</subject><subject>Time Factors</subject><subject>Transportation</subject><subject>Weather</subject><issn>0021-8812</issn><issn>1525-3163</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kU1LxDAQhoMo7rp68QdILoII1aRp0vQiiPgFC170HGIz3Y2bNrXJ-vXrja6KXjyFyTy88868CO1SckRJxY4fdDgOizciyzU0pjznGaOCraMxITnNpKT5CG2F8EAIzXnFN9GIJZbxohwjfQkdDNrZNzBYG2OjfQLc2pdUtt6Aw77DcQ5Yd9q9Bhuwb3D0UTscB92F3g8ROx8CfHZaPSwgZs9gZ_OIezsLdBttNNoF2Pl6J-ju4vz27Cqb3lxen51OszrnrMxkYwotSgK5EFzK8t4UhugSCGcChEyfDOpKypoIMEXJiqZI-zQCuNGSV4JN0MlKt1_et2Bq6JJBp_rBJlOvymur_nY6O1cz_6REISWjZRI4-BIY_OMSQlStDTU4pzvwy6ByWiUsXbBI6OEKrYe0-gDNzxhK1EcmKmWiVpkkeO-3sR_0O4QE7K8Av-z_E3oHfIuW8Q</recordid><startdate>20190429</startdate><enddate>20190429</enddate><creator>Passafaro, Tiago L</creator><creator>Van de Stroet, Denise</creator><creator>Bello, Nora M</creator><creator>Williams, Noel H</creator><creator>Rosa, Guilherme J M</creator><general>Oxford University Press</general><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><orcidid>https://orcid.org/0000-0001-5393-4446</orcidid></search><sort><creationdate>20190429</creationdate><title>Generalized additive mixed model on the analysis of total transport losses of market-weight pigs1</title><author>Passafaro, Tiago L ; Van de Stroet, Denise ; Bello, Nora M ; Williams, Noel H ; Rosa, Guilherme J M</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2537-8fd4a670e2665887bd4d0a7e0536e686653ec988c06ed4734f4001f6e5da85963</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Abattoirs</topic><topic>Animal Health and Well Being</topic><topic>Animal Husbandry - economics</topic><topic>Animal Welfare</topic><topic>Animals</topic><topic>Body Weight</topic><topic>Humidity</topic><topic>Midwestern United States</topic><topic>Models, Statistical</topic><topic>Motor Vehicles</topic><topic>Swine - growth & development</topic><topic>Swine - physiology</topic><topic>Temperature</topic><topic>Time Factors</topic><topic>Transportation</topic><topic>Weather</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Passafaro, Tiago L</creatorcontrib><creatorcontrib>Van de Stroet, Denise</creatorcontrib><creatorcontrib>Bello, Nora M</creatorcontrib><creatorcontrib>Williams, Noel H</creatorcontrib><creatorcontrib>Rosa, Guilherme J M</creatorcontrib><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><jtitle>Journal of animal science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Passafaro, Tiago L</au><au>Van de Stroet, Denise</au><au>Bello, Nora M</au><au>Williams, Noel H</au><au>Rosa, Guilherme J M</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Generalized additive mixed model on the analysis of total transport losses of market-weight pigs1</atitle><jtitle>Journal of animal science</jtitle><addtitle>J Anim Sci</addtitle><date>2019-04-29</date><risdate>2019</risdate><volume>97</volume><issue>5</issue><spage>2025</spage><epage>2034</epage><pages>2025-2034</pages><issn>0021-8812</issn><eissn>1525-3163</eissn><abstract>Abstract
Transportation losses of market-weight pigs are an animal welfare concern, and result in direct economic impact for producers and abattoirs. Such losses are related to multiple factors including pig genetics, human handling, management, and weather conditions. Understanding the factors associated with total transport losses (TTL) is important to the swine industry because it can aid decision-making, and help in the development of transportation strategies to minimize the risk of losses. Hence, the objective of this study was to investigate factors associated with TTL on market-weight pigs in typically field conditions for Midwestern United States using a generalized additive mixed model (GAMM). The final quasi-binomial GAMM included the fixed (main and interactions) effects of abattoir of destination, type of driver, average market weight, distance traveled, wind speed, precipitation, and temperature-humidity index (THI), as well as the random effects of truck companies and the combination of site of origin and period of the year. Results indicate significant associations between TTL and the main effect of all explanatory variables (P < 0.05), except for wind speed and precipitation. Interactions of average market weight × abattoir, and wind speed × precipitation were also significant. A complex nonlinear relationship between TTL and model covariates were observed for distance traveled, THI, and interaction terms. This study showed that TTL of market-weight pigs are caused by a complex system involving multiple interacting factors, which can be potentially managed to mitigate the risk of losses. In addition, the GAMM showed to be a simple and flexible approach to model TTL because it can capture nonlinear relationships, handle non-normal data, and can potentially accommodate data structure.</abstract><cop>US</cop><pub>Oxford University Press</pub><pmid>30873547</pmid><doi>10.1093/jas/skz087</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0001-5393-4446</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Abattoirs Animal Health and Well Being Animal Husbandry - economics Animal Welfare Animals Body Weight Humidity Midwestern United States Models, Statistical Motor Vehicles Swine - growth & development Swine - physiology Temperature Time Factors Transportation Weather |
title | Generalized additive mixed model on the analysis of total transport losses of market-weight pigs1 |
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