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Registro Completo |
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Biblioteca(s): |
Embrapa Caprinos e Ovinos. |
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Data corrente: |
17/05/2010 |
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Data da última atualização: |
23/09/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
COSTA, R. G.; DAL MONTE, H. L. B.; PIMENTA FILHO, E. C.; HOLANDA JUNIOR, E. V.; CRUZ, G. R. B. da; MENEZES, M. P. C. |
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Afiliação: |
Roberto Germano Costa, Universidade Federal da Paraíba; Helio Luiz Beretta Dal Monte, UFPB; Edigard Cavalcanti Pimenta Filho, UFPB; EVANDRO VASCONCELOS HOLANDA JUNIOR, CNPC; George Rodrigo Beltrão da Cruz, UFPB; Marcos Paulo Carrera Menezes, UFPB. |
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Título: |
Typology and characterization of goat milk production systems in the Cariris Paraibanos. |
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Ano de publicação: |
2010 |
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Fonte/Imprenta: |
Revista Brasileira de Zootecnia, v. 39, n. 3, p. 656-666, 2010. |
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Idioma: |
Inglês |
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Conteúdo: |
This work was aimed to identify the typology and the characteristics of the goat milk production systems. Seventy rural properties were researched. Multivariate data analysis with cluster formation techinique was used, and also the identification of five different groups of production systems. The production systems were classified through technical and economical efficiency, ranking groups 1 and 2 as high technological level, groups 3 and 4 as medium level, and the group 5 as low level. The high technological level systems used practices of more rational handling with larger technological employment becoming better specialized. These systems present the largest revenues of milk and unitary net profit. Medium technological production systems adopt technological recommendations and look for alternatives of handling practices. Production system 1 adopts intensive sanitary practices for the farmyard facilities and stables, but this does not happen in the production system 2. Vermifuge is applied three times a year in system 1 while in the system 2 it is applied twice a year. The producers of the systems 1 and 2 obtain more financial resources through official credit lines, and the producers of the group 2 are more punctual on the pay off deadlines. |
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Palavras-Chave: |
Análise de agrupamento; Caprino leiteiro; Cluster; Handling; Production system; Tipologia; Typology. |
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Thesagro: |
Leite de cabra; Sistema de produção. |
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Thesaurus Nal: |
Goat milk. |
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Categoria do assunto: |
E Economia e Indústria Agrícola |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/27048/1/API-Typology-and-characterization-of-goat-milk-production-systems-in-the.pdf
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Marc: |
LEADER 02143naa a2200301 a 4500
001 1853145
005 2019-09-23
008 2010 bl uuuu u00u1 u #d
100 1 $aCOSTA, R. G.
245 $aTypology and characterization of goat milk production systems in the Cariris Paraibanos.$h[electronic resource]
260 $c2010
520 $aThis work was aimed to identify the typology and the characteristics of the goat milk production systems. Seventy rural properties were researched. Multivariate data analysis with cluster formation techinique was used, and also the identification of five different groups of production systems. The production systems were classified through technical and economical efficiency, ranking groups 1 and 2 as high technological level, groups 3 and 4 as medium level, and the group 5 as low level. The high technological level systems used practices of more rational handling with larger technological employment becoming better specialized. These systems present the largest revenues of milk and unitary net profit. Medium technological production systems adopt technological recommendations and look for alternatives of handling practices. Production system 1 adopts intensive sanitary practices for the farmyard facilities and stables, but this does not happen in the production system 2. Vermifuge is applied three times a year in system 1 while in the system 2 it is applied twice a year. The producers of the systems 1 and 2 obtain more financial resources through official credit lines, and the producers of the group 2 are more punctual on the pay off deadlines.
650 $aGoat milk
650 $aLeite de cabra
650 $aSistema de produção
653 $aAnálise de agrupamento
653 $aCaprino leiteiro
653 $aCluster
653 $aHandling
653 $aProduction system
653 $aTipologia
653 $aTypology
700 1 $aDAL MONTE, H. L. B.
700 1 $aPIMENTA FILHO, E. C.
700 1 $aHOLANDA JUNIOR, E. V.
700 1 $aCRUZ, G. R. B. da
700 1 $aMENEZES, M. P. C.
773 $tRevista Brasileira de Zootecnia$gv. 39, n. 3, p. 656-666, 2010.
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Registro original: |
Embrapa Caprinos e Ovinos (CNPC) |
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Biblioteca |
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Origem |
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Classificação |
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Registro |
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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital; Embrapa Algodão; Embrapa Amazônia Oriental; Embrapa Arroz e Feijão; Embrapa Milho e Sorgo; Embrapa Semiárido; Embrapa Soja; Embrapa Trigo; Embrapa Uva e Vinho. |
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Data corrente: |
06/08/2018 |
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Data da última atualização: |
03/10/2018 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
BARBEDO, J. G. A.; KOENIGKAN, L. V.; HALFELD-VIEIRA, B. de A.; COSTA, R. V. da; NECHET, K. de L.; GODOY, C. V.; LOBO JUNIOR, M.; PATRÍCIO, F. R. A.; TALAMINI, V.; CHITARRA, L. G.; OLIVEIRA, S. A. S. de; ISHIDA, A. K. N.; FERNANDES, J. M. C.; SANTOS, T. T.; CAVALCANTI, F. R.; TERAO, D.; ANGELOTTI, F. |
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Afiliação: |
JAYME GARCIA ARNAL BARBEDO, CNPTIA; LUCIANO VIEIRA KOENIGKAN, CNPTIA; BERNARDO DE ALMEIDA HALFELD VIEIRA, CNPMA; RODRIGO VERAS DA COSTA, CNPMS; KATIA DE LIMA NECHET, CNPMA; CLAUDIA VIEIRA GODOY, CNPSO; MURILLO LOBO JUNIOR, CNPAF; F. R. A. PATRÍCIO, Instituto Biológico, Campinas, SP; VIVIANE TALAMINI, CPATC; LUIZ GONZAGA CHITARRA, CNPA; SAULO ALVES SANTOS DE OLIVEIRA, CNPMF; ALESSANDRA KEIKO NAKASONE ISHIDA, CPATU; JOSE MAURICIO CUNHA FERNANDES, CNPT; THIAGO TEIXEIRA SANTOS, CNPTIA; FABIO ROSSI CAVALCANTI, CNPUV; DANIEL TERAO, CNPMA; FRANCISLENE ANGELOTTI, CPATSA. |
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Título: |
Annotated plant pathology databases for image-based detection and recognition of diseases. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
IEEE Latin America Transactions, v. 16, n. 6, p. 1749-1757, June 2018. |
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Idioma: |
Inglês
Português |
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Notas: |
Na publicação: B. A. Halfeld-Vieira, R. V. Costa, K. L. Nechet, S. A. S. Oliveira. |
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Conteúdo: |
Over the last few years, considerable effort has been spent by Embrapa in the construction of a plant disease database representative enough for the development of effective methods for automatic plant disease detection and recognition. In October of 2016, this database, called PDDB, had 2326 images of 171 diseases and other disorders affecting 21 plant species. PDDB size, although considerable, is not enough to allow the use of powerful techniques such as deep learning. In order to increase its size, each image was subdivided according to certain criteria, increasing the number of images to 46,513. Both the original (PDDB) and subdivided (XDB) databases are now being made freely available for academic research purposes, thus supporting new studies and contributing to speed up the advances in the area. Both collections are expected to grow continuously in order to expand their reach. PDDB and XDB can be accessed in the link https://www.digipathosrep.cnptia.embrapa.br/. Keywords— plant pathology, database, deep learning, image processing. |
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Palavras-Chave: |
Aprendizagem profunda; Banco de dados; Deep learning; Imagem em processamento; Patologia vegetal; Processamento de imagem. |
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Thesagro: |
Doença de Planta. |
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Thesaurus NAL: |
Databases; Image analysis; Plant diseases and disorders; Plant pathology. |
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Categoria do assunto: |
--
H Saúde e Patologia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/182246/1/16TLA6-27GarciaArnalBarbedo.pdf
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/181156/1/16TLA6-27GarciaArnalBarbedo.pdf
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/198847/1/ID44389-2018v16n6p1749IEEELatinAmericaTransaction.pdf
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Marc: |
LEADER 02472naa a2200457 a 4500
001 2094883
005 2018-10-03
008 2018 bl uuuu u00u1 u #d
100 1 $aBARBEDO, J. G. A.
245 $aAnnotated plant pathology databases for image-based detection and recognition of diseases.$h[electronic resource]
260 $c2018
500 $aNa publicação: B. A. Halfeld-Vieira, R. V. Costa, K. L. Nechet, S. A. S. Oliveira.
520 $aOver the last few years, considerable effort has been spent by Embrapa in the construction of a plant disease database representative enough for the development of effective methods for automatic plant disease detection and recognition. In October of 2016, this database, called PDDB, had 2326 images of 171 diseases and other disorders affecting 21 plant species. PDDB size, although considerable, is not enough to allow the use of powerful techniques such as deep learning. In order to increase its size, each image was subdivided according to certain criteria, increasing the number of images to 46,513. Both the original (PDDB) and subdivided (XDB) databases are now being made freely available for academic research purposes, thus supporting new studies and contributing to speed up the advances in the area. Both collections are expected to grow continuously in order to expand their reach. PDDB and XDB can be accessed in the link https://www.digipathosrep.cnptia.embrapa.br/. Keywords— plant pathology, database, deep learning, image processing.
650 $aDatabases
650 $aImage analysis
650 $aPlant diseases and disorders
650 $aPlant pathology
650 $aDoença de Planta
653 $aAprendizagem profunda
653 $aBanco de dados
653 $aDeep learning
653 $aImagem em processamento
653 $aPatologia vegetal
653 $aProcessamento de imagem
700 1 $aKOENIGKAN, L. V.
700 1 $aHALFELD-VIEIRA, B. de A.
700 1 $aCOSTA, R. V. da
700 1 $aNECHET, K. de L.
700 1 $aGODOY, C. V.
700 1 $aLOBO JUNIOR, M.
700 1 $aPATRÍCIO, F. R. A.
700 1 $aTALAMINI, V.
700 1 $aCHITARRA, L. G.
700 1 $aOLIVEIRA, S. A. S. de
700 1 $aISHIDA, A. K. N.
700 1 $aFERNANDES, J. M. C.
700 1 $aSANTOS, T. T.
700 1 $aCAVALCANTI, F. R.
700 1 $aTERAO, D.
700 1 $aANGELOTTI, F.
773 $tIEEE Latin America Transactions$gv. 16, n. 6, p. 1749-1757, June 2018.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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