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| Registros recuperados : 17 | |
| 11. |  | VALE, M. M.; MOURA, D. J. de; NÄÄS, I. de A.; OLIVEIRA, S. R. de M.; RODRIGUES, L. H. A. Data mining to estimate broiler mortality when exposed to heat wave. Scientia Agricola, Piracicaba, v. 65, n. 3, p. 223-229, May/June 2008.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 14. | | CORDEIRO, A. F. da S.; NÄÄS, I. de A.; OLIVEIRA, S. R. de M.; VIOLARO, F.; ALMEIDA, A. C. M. de Almeida. Efficiency of distinct data mining algorithms for classifying stress level in piglets from their vocalization. Engenharia Agrícola, Jaboticabal, v. 32, n. 2, p. 208-216, Mar./Apr. 2012.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 15. | | ROMANINI, C. E. B.; OLIVEIRA, S. R. de M.; RODRIGUES, L. H. A.; GIGLI, A. C. de S.; BARACHO, M. dos S.; NÄÄS, I. de A. Análise de processo de incubação de ovos de matrizes pesadas usando mineração de dados. In: CONFERÊNCIA APINCO' DE CIÊNCIA E TECNOLOGIA AVÍCOLA, 2008, Santos. Trabalhos expostos... Santos: Facta, 2008. Não paginado. Suplemento 10 Rev. Bras. de Ciência Avícola, 2008.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 16. | | BRANCO, T.; MOURA, D. J. de; NÄÄS, I. de A.; LIMA, N. D. da S.; KLEIN, D. R.; OLIVEIRA, S. R. de M. The sequential behavior pattern analysis of broiler chickens exposed to heat stress. AgriEngineering, v. 3, n. 3, p. 447-457, Sept. 2021.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 17. | | BARRETO, B.; FELIX, G. A. F; PAZ, I. C. de L. A.; PIOVEZAN, U.; NÄÄS, I. de A.; GARCIA, R. G.; MIRANDA, G. A.; MOI, M. Uso do método AHP para avaliar o comportamento alimentar das capivaras (Hydrochoerus hydrochaeris linnaeus, 1766) em áreas agrícolas. In: CONGRESSO BRASILEIRO DE ZOOTECNIA, 22., 2012, Cuiabá. A importância da zootecnia para a segurança alimentar. Cuiaba: UFMT, 2012.| Biblioteca(s): Embrapa Pantanal. |
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| Registros recuperados : 17 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
|
Data corrente: |
17/12/2008 |
|
Data da última atualização: |
11/04/2017 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Circulação/Nível: |
Nacional - A |
|
Autoria: |
VALE, M. M.; MOURA, D. J. de; NÄÄS, I. de A.; OLIVEIRA, S. R. de M.; RODRIGUES, L. H. A. |
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Afiliação: |
MARCOS MARTINEZ VALE, FEAGRI/UNICAMP; DANIELLA JORGE DE MOURA, FEAGRI/UNICAMP; IRENILZA DE ALENCAR NÄÄS, FEAGRI/UNICAMP; STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; LUIZ HENRIQUE ANTUNES RODRIGUES, FEAGRI/UNICAMP. |
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Título: |
Data mining to estimate broiler mortality when exposed to heat wave. |
|
Ano de publicação: |
2008 |
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Fonte/Imprenta: |
Scientia Agricola, Piracicaba, v. 65, n. 3, p. 223-229, May/June 2008. |
|
DOI: |
http://dx.doi.org/10.1590/S0103-90162008000300001 |
|
Idioma: |
Inglês |
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Conteúdo: |
Heat waves usually result in losses of animal production since they are exposed to thermal stress inducing an increase in mortality and consequent economical losses. Animal science and meteorological databases from the last years contain enough data in the poultry production business to allow the modeling of mortality losses due to heat wave incidence. This research analyzes a database of broiler production associated to climatic data, using data mining techniques such as attribute selection and data classification (decision tree) to model the impact of heat wave incidence on broiler mortality. The temperature and humidity index (THI) was used for screening environmental data. The data mining techniques allowed the development of three comprehensible models for estimating specifically high mortality during broiler production. Two models yielded a classification accuracy of 89.3% by using Principal Component Analysis (PCA) and Wrapper feature selection approaches. Both models obtained a class precision of 0.83 for classifying high mortality. When the feature selection was made by the domain experts, the model accuracy reached 85.7%, while the class precision of high mortality was 0.76. Meteorological data and the calculated THI from meteorological stations were helpful to select the range of harmful environmental conditions for broilers 29 and 42 days old. The data mining techniques were useful for building animal production models. |
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Palavras-Chave: |
Agropecuária; Dados ambientais; Data mining; ITU; Mineração de dados. |
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Thesagro: |
Frango de Corte. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/158853/1/AP-datamining-Valeetal-2008.pdf
|
|
Marc: |
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024 7 $ahttp://dx.doi.org/10.1590/S0103-90162008000300001$2DOI
100 1 $aVALE, M. M.
245 $aData mining to estimate broiler mortality when exposed to heat wave.$h[electronic resource]
260 $c2008
520 $aHeat waves usually result in losses of animal production since they are exposed to thermal stress inducing an increase in mortality and consequent economical losses. Animal science and meteorological databases from the last years contain enough data in the poultry production business to allow the modeling of mortality losses due to heat wave incidence. This research analyzes a database of broiler production associated to climatic data, using data mining techniques such as attribute selection and data classification (decision tree) to model the impact of heat wave incidence on broiler mortality. The temperature and humidity index (THI) was used for screening environmental data. The data mining techniques allowed the development of three comprehensible models for estimating specifically high mortality during broiler production. Two models yielded a classification accuracy of 89.3% by using Principal Component Analysis (PCA) and Wrapper feature selection approaches. Both models obtained a class precision of 0.83 for classifying high mortality. When the feature selection was made by the domain experts, the model accuracy reached 85.7%, while the class precision of high mortality was 0.76. Meteorological data and the calculated THI from meteorological stations were helpful to select the range of harmful environmental conditions for broilers 29 and 42 days old. The data mining techniques were useful for building animal production models.
650 $aFrango de Corte
653 $aAgropecuária
653 $aDados ambientais
653 $aData mining
653 $aITU
653 $aMineração de dados
700 1 $aMOURA, D. J. de
700 1 $aNÄÄS, I. de A.
700 1 $aOLIVEIRA, S. R. de M.
700 1 $aRODRIGUES, L. H. A.
773 $tScientia Agricola, Piracicaba$gv. 65, n. 3, p. 223-229, May/June 2008.
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