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| 1. |  | BASTIN, J. F.; RUTISHAUSER, E.; KELLNER, J. R.; SAATCHI, S.; PÉLISSIER, R.; HÉRAULT, B.; SLIK, F.; BOGAERT, J.; DE CANNIÈRE, C.; MARSHALL, A. R.; POULSEN, J.; ALVAREZ-LOYAYZA, P.; ANDRADE, A.; ANGBONGA-BASIA, A.; ARAUJO-MURAKAMI, A.; ARROYO, L.; AYYAPPAN, N.; AZEVEDO, C. P. de; BANKI, O.; BARBIER, N.; BARROSO, J. G.; BEECKMAN, H.; BITARIHO, R.; BOECKX, P.; BOEHNING-GAESE, K.; BRANDÃO, H.; BREARLEY, F. Q.; HOCKEMBA, M. B. N.; BRIENEN, R.; CAMARGO, J. L. C.; CAMPOS-ARCEIZ, A.; CASSART, B.; CHAVE, J.; CHAZDON, R.; CHUYONG, G.; CLARK, D. B.; CLARK, C. J.; CONDIT, R.; CORONADO, E. N. H.; DAVIDAR, P.; HAULLEVILLE, T. de; DESCROIX, L.; DOUCET, J-L.; DOURDAIN, A.; DROISSART, V.; DUNCAN, T.; ESPEJO. J. S.; ESPINOSA, S.; FARWIG, N.; FAYOLLE, A.; FELDPAUSCH, T. R.; FERRAZ, A.; FLETCHER, C.; GAJAPERSAD, K.; GILLET, J-F.; AMARAL, I. L. do; GONMADJE, C.; GROGAN, J.; HARRIS, D.; HERZOG, S. K.; HOMEIER, J.; HUBAU, W.; HUBBELL, S. P.; HUFKENS, K.; HURTADO, J.; KAMDEM, N. G.; KEARSLEY, E.; KENFACK, D.; KESSLER, M.; LABRIÈRE, N.; LAUMONIER, Y.; LAURANCE, S.; LAURANCE, W. F.; LEWIS, S. L.; LIBALAH, M. B.; LIGOT, G.; LLOYD, J.; LOVEJOY, T. E.; MALHI, Y.; MARIMON, B. S.; JUNIOR, B. H. M.; MARTIN, E. H.; MATIUS, P.; MEYER, V.; BAUTISTA, C. M.; MONTEAGUDO-MENDOZA, A.; MTUI, A.; NEILL, D.; GUTIERREZ, G. A. P.; PARDO, G.; PARREN, M.; PARTHASARATHY, N.; PHILLIPS, O. L.; PITMAN, N. C. A.; PLOTON, P.; PONETTE, Q.; RAMESH, B. R.; RAZAFIMAHAIMODISON, J-C.; RÉJOU-MÉCHAIN, M.; ROLIM, S. G.; SALTOS, H. R.; ROSSI, L. M. B.; SPIRONELLO, W. R.; ROVERO, F.; SANER, P.; SASAKI, D.; SCHULZE, M.; SILVEIRA, M.; SINGH, J.; SIST, P.; SONKE, B.; SOTO, J. D.; SOUZA, C. R. de; STROPP, J.; SULLIVAN, M. J. P.; SWANEPOEL, B.; STEEGE, H. ter.; TERBORGH, J.; TEXIER, N.; TOMA, T.; VALENCIA, R.; VALENZUELA, L.; FERREIRA, L. V.; VALVERDE, F. C.; ANDEL, T. R. van.; VASQUE, R.; VERBEECK, H.; VIVEK, P.; VLEMINCKX, J.; VOS, V. A.; WAGNER, F. H.; WARSUDI, P. P.; WORTEL, V.; ZAGT, R. J.; ZEBAZE, D. Pan-tropical prediction of forest structure from the largest trees. Global Ecology and Biogeography, v. 27, n. 11, p. 1366-1383, Nov. 2018.| Biblioteca(s): Embrapa Amazônia Ocidental; Embrapa Florestas. |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
25/09/2020 |
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Data da última atualização: |
11/01/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
BRANCO, T.; MOURA, D. J.; NÄÄS, I. A.; OLIVEIRA, S. R. de M. |
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Afiliação: |
TATIANE BRANCO, Feagri/Unicamp; DANIELLA J. MOURA, Feagri/Unicamp; IRENILZA ALENCAR NÄÄS, Feagri/Unicamp; STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA. |
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Título: |
Detection of broiler heat stress by using the generalised sequential pattern algorithm. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Biosystems Engineering, v. 199, p. 121-126, Nov. 2020. |
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DOI: |
https://doi.org/10.1016/j.biosystemseng.2019.10.012 |
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Idioma: |
Inglês |
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Notas: |
Na publicação: Stanley R. M. Oliveira. |
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Conteúdo: |
The sequence pattern mining method aims to identify frequent sequences that exceed a user-specified support threshold. The present study uses the same approach based on sequential standards to estimate the heat stress of broilers from a resulting behavioural pattern. Experimental data were recorded in a climate chamber where the behaviour of broilers was recorded under thermoneutral (comfort) conditions, set as standard, and when exposed to thermal stress (cold and heat). The Generalised Sequential Patterns (GSP) algorithm was used to evaluate the heat stress of broilers in the third and fourth week of growth. The results indicated that the mining of pattern sequences is a useful and straightforward technique to estimate the welfare of broiler chickens, allowing the identification of temporal relations between thermal stress and the consequent behaviour of the broiler. Temperature 8 oC below the standard thermoneutral conditions showed that the broiler remained lying down most of the time, walking only to the drinker and feeder trough. Broilers exposed to temperatures 8 oC above the standard thermoneutral conditions () tend to decrease locomotor activities, showing lower welfare status. |
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Palavras-Chave: |
Behavioural pattern; Bem estar animal; Broiler; Data mining algorithm; Detection of sequential frequency; Mineração de dados; Padrão de comportamento; Stress térmico. |
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Thesagro: |
Frango. |
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Thesaurus NAL: |
Animal welfare; Heat stress. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02190naa a2200313 a 4500
001 2125113
005 2021-01-11
008 2020 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.biosystemseng.2019.10.012$2DOI
100 1 $aBRANCO, T.
245 $aDetection of broiler heat stress by using the generalised sequential pattern algorithm.$h[electronic resource]
260 $c2020
500 $aNa publicação: Stanley R. M. Oliveira.
520 $aThe sequence pattern mining method aims to identify frequent sequences that exceed a user-specified support threshold. The present study uses the same approach based on sequential standards to estimate the heat stress of broilers from a resulting behavioural pattern. Experimental data were recorded in a climate chamber where the behaviour of broilers was recorded under thermoneutral (comfort) conditions, set as standard, and when exposed to thermal stress (cold and heat). The Generalised Sequential Patterns (GSP) algorithm was used to evaluate the heat stress of broilers in the third and fourth week of growth. The results indicated that the mining of pattern sequences is a useful and straightforward technique to estimate the welfare of broiler chickens, allowing the identification of temporal relations between thermal stress and the consequent behaviour of the broiler. Temperature 8 oC below the standard thermoneutral conditions showed that the broiler remained lying down most of the time, walking only to the drinker and feeder trough. Broilers exposed to temperatures 8 oC above the standard thermoneutral conditions () tend to decrease locomotor activities, showing lower welfare status.
650 $aAnimal welfare
650 $aHeat stress
650 $aFrango
653 $aBehavioural pattern
653 $aBem estar animal
653 $aBroiler
653 $aData mining algorithm
653 $aDetection of sequential frequency
653 $aMineração de dados
653 $aPadrão de comportamento
653 $aStress térmico
700 1 $aMOURA, D. J.
700 1 $aNÄÄS, I. A.
700 1 $aOLIVEIRA, S. R. de M.
773 $tBiosystems Engineering$gv. 199, p. 121-126, Nov. 2020.
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