Bioclimatic zoning for dairy cows in Brazil by statistical modeling
BACKGROUND Climate conditions affect animal welfare directly, influencing milk production. The Midwest region is the largest cattle‐producing region in Brazil. The objective of this study was to elaborate on bioclimatic zoning for dairy cattle in the Midwest region of Brazil. Air temperature (Ta, °C...
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| Veröffentlicht in: | Journal of the science of food and agriculture 2022-07, Vol.102 (9), p.3847-3857 |
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| container_title | Journal of the science of food and agriculture |
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| creator | Aparecido, Lucas Eduardo de Oliveira Lorençone, João Antonio Lorençone, Pedro Antonio Torsoni, Guilherme Botega Moraes, José Reinaldo da Silva Cabral Meneses, Kamila Cunha |
| description | BACKGROUND
Climate conditions affect animal welfare directly, influencing milk production. The Midwest region is the largest cattle‐producing region in Brazil. The objective of this study was to elaborate on bioclimatic zoning for dairy cattle in the Midwest region of Brazil. Air temperature (Ta, °C) and relative humidity (%, RH) data from a 30‐year historical series (1989–2019) collected by the National Aeronautics and Space Administration/Prediction of Worldwide Energy Resources (NASA/POWER) platform were used. The Temperature and Humidity Index (THI) was determined for the hottest and coldest months. Milk production losses due to climate factors in the Midwest of Brazil for two daily production levels, 10 kg Milk (PL10) and 25 kg Milk (PL25), were estimated.
RESULTS
The Midwest presented three THI classifications throughout the year: ‘normal’, ‘alert’, and ‘critical alert’. The entire Midwest region was classified as ‘normal’ (THI |
| doi_str_mv | 10.1002/jsfa.11734 |
| format | Article |
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Climate conditions affect animal welfare directly, influencing milk production. The Midwest region is the largest cattle‐producing region in Brazil. The objective of this study was to elaborate on bioclimatic zoning for dairy cattle in the Midwest region of Brazil. Air temperature (Ta, °C) and relative humidity (%, RH) data from a 30‐year historical series (1989–2019) collected by the National Aeronautics and Space Administration/Prediction of Worldwide Energy Resources (NASA/POWER) platform were used. The Temperature and Humidity Index (THI) was determined for the hottest and coldest months. Milk production losses due to climate factors in the Midwest of Brazil for two daily production levels, 10 kg Milk (PL10) and 25 kg Milk (PL25), were estimated.
RESULTS
The Midwest presented three THI classifications throughout the year: ‘normal’, ‘alert’, and ‘critical alert’. The entire Midwest region was classified as ‘normal’ (THI < 70) between autumn and winter. The decrease in milk production (DMP) during the autumn and winter presented no loss for both production levels (PL10 and PL25).
CONCLUSION
On the other hand, a 1 to 2 kg reduction in milk production was observed for cows with a PL25 production level between spring and summer in the southern Midwest region, while cows with a PL10 production level showed no reduction in milk production. Only the cities of Sinop and Cuiabá did not present a ‘critical alert’ during spring/summer for the risk of heat stress. © 2021 Society of Chemical Industry.</description><identifier>ISSN: 0022-5142</identifier><identifier>EISSN: 1097-0010</identifier><identifier>DOI: 10.1002/jsfa.11734</identifier><identifier>PMID: 34932219</identifier><language>eng</language><publisher>Chichester, UK: John Wiley & Sons, Ltd</publisher><subject>Aeronautics ; Air temperature ; Animal husbandry ; Animal welfare ; Autumn ; Bioclimatology ; Biometeorology ; Cattle ; climate modeling ; Climate models ; Climatic conditions ; Dairy cattle ; Energy resources ; Energy sources ; Heat stress ; Heat tolerance ; Humidity ; Mathematical models ; Milk ; Milk production ; Reduction ; Relative humidity ; Spring ; Spring (season) ; Statistical models ; Summer ; thermal stress ; Winter ; Zoning</subject><ispartof>Journal of the science of food and agriculture, 2022-07, Vol.102 (9), p.3847-3857</ispartof><rights>2021 Society of Chemical Industry.</rights><rights>Copyright © 2022 Society of Chemical Industry</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2874-cdf91c898ec226f5e0e89105f167e29a3ce4a568b01f520fec08043b9f1377e63</citedby><cites>FETCH-LOGICAL-c2874-cdf91c898ec226f5e0e89105f167e29a3ce4a568b01f520fec08043b9f1377e63</cites><orcidid>0000-0002-4561-6760</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fjsfa.11734$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjsfa.11734$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34932219$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Aparecido, Lucas Eduardo de Oliveira</creatorcontrib><creatorcontrib>Lorençone, João Antonio</creatorcontrib><creatorcontrib>Lorençone, Pedro Antonio</creatorcontrib><creatorcontrib>Torsoni, Guilherme Botega</creatorcontrib><creatorcontrib>Moraes, José Reinaldo da Silva Cabral</creatorcontrib><creatorcontrib>Meneses, Kamila Cunha</creatorcontrib><title>Bioclimatic zoning for dairy cows in Brazil by statistical modeling</title><title>Journal of the science of food and agriculture</title><addtitle>J Sci Food Agric</addtitle><description>BACKGROUND
Climate conditions affect animal welfare directly, influencing milk production. The Midwest region is the largest cattle‐producing region in Brazil. The objective of this study was to elaborate on bioclimatic zoning for dairy cattle in the Midwest region of Brazil. Air temperature (Ta, °C) and relative humidity (%, RH) data from a 30‐year historical series (1989–2019) collected by the National Aeronautics and Space Administration/Prediction of Worldwide Energy Resources (NASA/POWER) platform were used. The Temperature and Humidity Index (THI) was determined for the hottest and coldest months. Milk production losses due to climate factors in the Midwest of Brazil for two daily production levels, 10 kg Milk (PL10) and 25 kg Milk (PL25), were estimated.
RESULTS
The Midwest presented three THI classifications throughout the year: ‘normal’, ‘alert’, and ‘critical alert’. The entire Midwest region was classified as ‘normal’ (THI < 70) between autumn and winter. The decrease in milk production (DMP) during the autumn and winter presented no loss for both production levels (PL10 and PL25).
CONCLUSION
On the other hand, a 1 to 2 kg reduction in milk production was observed for cows with a PL25 production level between spring and summer in the southern Midwest region, while cows with a PL10 production level showed no reduction in milk production. Only the cities of Sinop and Cuiabá did not present a ‘critical alert’ during spring/summer for the risk of heat stress. © 2021 Society of Chemical Industry.</description><subject>Aeronautics</subject><subject>Air temperature</subject><subject>Animal husbandry</subject><subject>Animal welfare</subject><subject>Autumn</subject><subject>Bioclimatology</subject><subject>Biometeorology</subject><subject>Cattle</subject><subject>climate modeling</subject><subject>Climate models</subject><subject>Climatic conditions</subject><subject>Dairy cattle</subject><subject>Energy resources</subject><subject>Energy sources</subject><subject>Heat stress</subject><subject>Heat tolerance</subject><subject>Humidity</subject><subject>Mathematical models</subject><subject>Milk</subject><subject>Milk production</subject><subject>Reduction</subject><subject>Relative humidity</subject><subject>Spring</subject><subject>Spring (season)</subject><subject>Statistical models</subject><subject>Summer</subject><subject>thermal stress</subject><subject>Winter</subject><subject>Zoning</subject><issn>0022-5142</issn><issn>1097-0010</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp90E1LwzAcx_EgipvTiy9AAl5E6Pwn6VOO23A-MPCgnkuaJpLRNTNZGd2rN7PTgwdPOeSTH-GL0CWBMQGgd0uvxZiQjMVHaEiAZxEAgWM0DJc0SkhMB-jM-yUAcJ6mp2jAYs4oJXyIZlNjZW1WYmMk3tnGNB9YW4crYVyHpd16bBo8dWJnalx22G-C9AGLGq9sperw4BydaFF7dXE4R-h9fv82e4wWLw9Ps8kikjTP4khWmhOZ81xJSlOdKFA5J5BokmaKcsGkikWS5iUQnVDQSkIOMSu5JizLVMpG6KbfXTv72Sq_KVbGS1XXolG29QVNCWWcQ5wFev2HLm3rmvC7oLKEMgY0Duq2V9JZ753SxdqFFK4rCBT7tMU-bfGdNuCrw2RbrlT1S39aBkB6sDW16v6ZKp5f55N-9AugF4H1</recordid><startdate>202207</startdate><enddate>202207</enddate><creator>Aparecido, Lucas Eduardo de Oliveira</creator><creator>Lorençone, João Antonio</creator><creator>Lorençone, Pedro Antonio</creator><creator>Torsoni, Guilherme Botega</creator><creator>Moraes, José Reinaldo da Silva Cabral</creator><creator>Meneses, Kamila Cunha</creator><general>John Wiley & Sons, Ltd</general><general>John Wiley and Sons, Limited</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QL</scope><scope>7QQ</scope><scope>7QR</scope><scope>7SC</scope><scope>7SE</scope><scope>7SN</scope><scope>7SP</scope><scope>7SR</scope><scope>7ST</scope><scope>7T5</scope><scope>7T7</scope><scope>7TA</scope><scope>7TB</scope><scope>7TM</scope><scope>7U5</scope><scope>7U9</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>H94</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M7N</scope><scope>P64</scope><scope>SOI</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-4561-6760</orcidid></search><sort><creationdate>202207</creationdate><title>Bioclimatic zoning for dairy cows in Brazil by statistical modeling</title><author>Aparecido, Lucas Eduardo de Oliveira ; Lorençone, João Antonio ; Lorençone, Pedro Antonio ; Torsoni, Guilherme Botega ; Moraes, José Reinaldo da Silva Cabral ; Meneses, Kamila Cunha</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2874-cdf91c898ec226f5e0e89105f167e29a3ce4a568b01f520fec08043b9f1377e63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Aeronautics</topic><topic>Air temperature</topic><topic>Animal husbandry</topic><topic>Animal welfare</topic><topic>Autumn</topic><topic>Bioclimatology</topic><topic>Biometeorology</topic><topic>Cattle</topic><topic>climate modeling</topic><topic>Climate models</topic><topic>Climatic conditions</topic><topic>Dairy cattle</topic><topic>Energy resources</topic><topic>Energy sources</topic><topic>Heat stress</topic><topic>Heat tolerance</topic><topic>Humidity</topic><topic>Mathematical models</topic><topic>Milk</topic><topic>Milk production</topic><topic>Reduction</topic><topic>Relative humidity</topic><topic>Spring</topic><topic>Spring (season)</topic><topic>Statistical models</topic><topic>Summer</topic><topic>thermal stress</topic><topic>Winter</topic><topic>Zoning</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Aparecido, Lucas Eduardo de Oliveira</creatorcontrib><creatorcontrib>Lorençone, João Antonio</creatorcontrib><creatorcontrib>Lorençone, Pedro Antonio</creatorcontrib><creatorcontrib>Torsoni, Guilherme Botega</creatorcontrib><creatorcontrib>Moraes, José Reinaldo da Silva Cabral</creatorcontrib><creatorcontrib>Meneses, Kamila Cunha</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Ceramic Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Ecology Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Environment Abstracts</collection><collection>Immunology Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environment Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of the science of food and agriculture</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Aparecido, Lucas Eduardo de Oliveira</au><au>Lorençone, João Antonio</au><au>Lorençone, Pedro Antonio</au><au>Torsoni, Guilherme Botega</au><au>Moraes, José Reinaldo da Silva Cabral</au><au>Meneses, Kamila Cunha</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Bioclimatic zoning for dairy cows in Brazil by statistical modeling</atitle><jtitle>Journal of the science of food and agriculture</jtitle><addtitle>J Sci Food Agric</addtitle><date>2022-07</date><risdate>2022</risdate><volume>102</volume><issue>9</issue><spage>3847</spage><epage>3857</epage><pages>3847-3857</pages><issn>0022-5142</issn><eissn>1097-0010</eissn><abstract>BACKGROUND
Climate conditions affect animal welfare directly, influencing milk production. The Midwest region is the largest cattle‐producing region in Brazil. The objective of this study was to elaborate on bioclimatic zoning for dairy cattle in the Midwest region of Brazil. Air temperature (Ta, °C) and relative humidity (%, RH) data from a 30‐year historical series (1989–2019) collected by the National Aeronautics and Space Administration/Prediction of Worldwide Energy Resources (NASA/POWER) platform were used. The Temperature and Humidity Index (THI) was determined for the hottest and coldest months. Milk production losses due to climate factors in the Midwest of Brazil for two daily production levels, 10 kg Milk (PL10) and 25 kg Milk (PL25), were estimated.
RESULTS
The Midwest presented three THI classifications throughout the year: ‘normal’, ‘alert’, and ‘critical alert’. The entire Midwest region was classified as ‘normal’ (THI < 70) between autumn and winter. The decrease in milk production (DMP) during the autumn and winter presented no loss for both production levels (PL10 and PL25).
CONCLUSION
On the other hand, a 1 to 2 kg reduction in milk production was observed for cows with a PL25 production level between spring and summer in the southern Midwest region, while cows with a PL10 production level showed no reduction in milk production. Only the cities of Sinop and Cuiabá did not present a ‘critical alert’ during spring/summer for the risk of heat stress. © 2021 Society of Chemical Industry.</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Ltd</pub><pmid>34932219</pmid><doi>10.1002/jsfa.11734</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-4561-6760</orcidid></addata></record> |
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| ispartof | Journal of the science of food and agriculture, 2022-07, Vol.102 (9), p.3847-3857 |
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| source | Wiley Online Library Journals Frontfile Complete |
| subjects | Aeronautics Air temperature Animal husbandry Animal welfare Autumn Bioclimatology Biometeorology Cattle climate modeling Climate models Climatic conditions Dairy cattle Energy resources Energy sources Heat stress Heat tolerance Humidity Mathematical models Milk Milk production Reduction Relative humidity Spring Spring (season) Statistical models Summer thermal stress Winter Zoning |
| title | Bioclimatic zoning for dairy cows in Brazil by statistical modeling |
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