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 | Acesso ao texto completo restrito à biblioteca da Embrapa Pecuária Sul. Para informações adicionais entre em contato com cppsul.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Pecuária Sul. |
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Data corrente: |
02/03/2020 |
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Data da última atualização: |
02/03/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
YOKOO, M. J. I.; SENO, L. de O.; OLIVEIRA, L. C.; COSTA, P. U. N. da; SILVA, G. M. da; SUÑÉ, R. W.; CARDOSO, F. F. |
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Afiliação: |
MARCOS JUN ITI YOKOO, CPPSUL; Leonardo de Oliveira Seno, UFGD; Luiza Corrêa Oliveira, UNIPAMPA; Pedro U. N. da Costa, EMATER; GUSTAVO MARTINS DA SILVA, CPPSUL; RENATA WOLF SUNE MARTINS DA SILVA, CPPSUL; FERNANDO FLORES CARDOSO, CPPSUL. |
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Título: |
Economic selection index in small rural dairy farms. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Journal of Dairy Research, v. 86, n. 1, p. 25-33, Feb. 2019. |
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Idioma: |
Inglês |
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Conteúdo: |
This study aimed to calculate economic values (EVs) and economic selection indices for milk production systems in small rural properties. The traits 305-d milk yield in kg (MY), fat (FP) and protein (PP) percentage, daily fat (FY) and protein (PY) yield, cow live weight in kg (LW), calving interval (CI), and logarithm of daily somatic cell count (SCC) in milk were considered the goals and selection criteria. The production systems were identified from 29 commercial properties based on the inventory of revenues and costs and of zootechnical field data. Later, bioeconomic models were developed to calculate the productive performance, revenues, and costs concerning milk production to estimate EVs, which were calculated as the difference in annual profit with dairy production resulting from a change in one unit of the trait while keeping the others constant and dividing the value by the number of cows. After the EVs were known, ten economic selection indices were estimated for each system so they could be compared by modifying the selection criteria and calculating the relative importance of each selection criteria, the accuracy of the economic selection index, and response expected to the selection in USD, among other parameters. One of the systems detected was called less intensive (LS) and was characterized by having ten cows in lactation that produced 13·5 l/d and consumed 1·8 kg of concentrate/d. The second system detected was called more intensive (IS) and had 22 cows in lactation that produced 17·5 l/d and consumed 3·4 kg of concentrate/ d. Monthly profits per cows in lactation of USD 2·60 and USD 68·77 were recorded for LS and IS, respectively. The EVs of the traits MY, FP, and PP were all positive, while for the other traits they were all negative in all situations. The best economic selection indices were those featuring selection criteria MY, LW, and CI, while the trait LW had the greatest importance in both systems. These results indicate that animal frame must be controlled in order to maximize the system?s profit. MenosThis study aimed to calculate economic values (EVs) and economic selection indices for milk production systems in small rural properties. The traits 305-d milk yield in kg (MY), fat (FP) and protein (PP) percentage, daily fat (FY) and protein (PY) yield, cow live weight in kg (LW), calving interval (CI), and logarithm of daily somatic cell count (SCC) in milk were considered the goals and selection criteria. The production systems were identified from 29 commercial properties based on the inventory of revenues and costs and of zootechnical field data. Later, bioeconomic models were developed to calculate the productive performance, revenues, and costs concerning milk production to estimate EVs, which were calculated as the difference in annual profit with dairy production resulting from a change in one unit of the trait while keeping the others constant and dividing the value by the number of cows. After the EVs were known, ten economic selection indices were estimated for each system so they could be compared by modifying the selection criteria and calculating the relative importance of each selection criteria, the accuracy of the economic selection index, and response expected to the selection in USD, among other parameters. One of the systems detected was called less intensive (LS) and was characterized by having ten cows in lactation that produced 13·5 l/d and consumed 1·8 kg of concentrate/d. The second system detected was called more intensive (IS) and had 22 cows in l... Mostrar Tudo |
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Thesagro: |
Agricultura Familiar; Leite; Seleção; Sistema de Produção. |
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Categoria do assunto: |
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Marc: |
LEADER 02740naa a2200241 a 4500
001 2120756
005 2020-03-02
008 2019 bl uuuu u00u1 u #d
100 1 $aYOKOO, M. J. I.
245 $aEconomic selection index in small rural dairy farms.$h[electronic resource]
260 $c2019
520 $aThis study aimed to calculate economic values (EVs) and economic selection indices for milk production systems in small rural properties. The traits 305-d milk yield in kg (MY), fat (FP) and protein (PP) percentage, daily fat (FY) and protein (PY) yield, cow live weight in kg (LW), calving interval (CI), and logarithm of daily somatic cell count (SCC) in milk were considered the goals and selection criteria. The production systems were identified from 29 commercial properties based on the inventory of revenues and costs and of zootechnical field data. Later, bioeconomic models were developed to calculate the productive performance, revenues, and costs concerning milk production to estimate EVs, which were calculated as the difference in annual profit with dairy production resulting from a change in one unit of the trait while keeping the others constant and dividing the value by the number of cows. After the EVs were known, ten economic selection indices were estimated for each system so they could be compared by modifying the selection criteria and calculating the relative importance of each selection criteria, the accuracy of the economic selection index, and response expected to the selection in USD, among other parameters. One of the systems detected was called less intensive (LS) and was characterized by having ten cows in lactation that produced 13·5 l/d and consumed 1·8 kg of concentrate/d. The second system detected was called more intensive (IS) and had 22 cows in lactation that produced 17·5 l/d and consumed 3·4 kg of concentrate/ d. Monthly profits per cows in lactation of USD 2·60 and USD 68·77 were recorded for LS and IS, respectively. The EVs of the traits MY, FP, and PP were all positive, while for the other traits they were all negative in all situations. The best economic selection indices were those featuring selection criteria MY, LW, and CI, while the trait LW had the greatest importance in both systems. These results indicate that animal frame must be controlled in order to maximize the system?s profit.
650 $aAgricultura Familiar
650 $aLeite
650 $aSeleção
650 $aSistema de Produção
700 1 $aSENO, L. de O.
700 1 $aOLIVEIRA, L. C.
700 1 $aCOSTA, P. U. N. da
700 1 $aSILVA, G. M. da
700 1 $aSUÑÉ, R. W.
700 1 $aCARDOSO, F. F.
773 $tJournal of Dairy Research$gv. 86, n. 1, p. 25-33, Feb. 2019.
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| 1. |  | ZUFFA, S.; SCHMID, R.; BAUERMEISTER, A.; GOMES, P. W. P.; CARABALLO-RODRIGUEZ, A. M.; EL ABIEAD, Y.; ARON, A. T.; GENTRY, E. C.; ZEMLIN, J.; MEEHAN, M. J.; AVALON, N. E.; CICHEWICZ, R. H.; BUZUN, E.; CARRILLO TERRAZAS, M.; HSU, C. Y.; OLES, R.; AYALA, A. V.; ZHAO, J.; CHU, H.; KUIJPERS, M. C. M.; JACKREL, S. L.; TUGIZIMANA, F.; NEPHALI, L. P.; DUBERY, I. A.; MADALA, N. E.; MOREIRA, E. A.; COSTA-LOTUFO, L. V.; LOPES, N. P.; REZENDE-TEIXEIRA, P.; JIMENEZ, P. C.; RIMAL, B.; PATTERSON, A. D.; TRAXLER, M. F.; PESSOTTI, R. de C.; ALVARADO-VILLALOBOS, D.; TAMAYO-CASTILLO, G.; CHAVERRI, P.; ESCUDERO-LEYVA, E.; QUIROS-GUERRERO, L. M.; BORY, A. J.; JOUBERT, J.; RUTZ, A.; WOLFENDER, J. L.; ALLARD, P. M.; SICHERT, A.; PONTRELLI, S.; PULLMAN, B. S.; BANDEIRA, N.; GERWICK, W. H.; GINDRO, K.; MASSANA-CODINA, J.; WAGNER, B. C.; FORCHHAMMER, K.; PETRAS, D.; AIOSA, N.; GARG, N.; LIEBEKE, M.; BOURCEAU, P.; KANG, K. B.; GADHAVI, H.; CARVALHO, L. P. S. de; SANTOS, M. S. dos; PÉREZ-LORENTE, A. I.; MOLINA-SANTIAGO, C.; ROMERO, D.; FRANKE, R.; BRÖNSTRUP, M.; PONCE DE LEÓN, A. V.; POPE, P. B.; LA ROSA, S. L.; LA BARBERA, G.; ROAGER, H. M.; LAURSEN, M. F.; HAMMERLE, F.; SIEWERT, B.; PEINTNER, U.; LICONA-CASSANI, C.; RODRIGUEZ-ORDUÑA, L.; RAMPLER, E.; HILDEBRAND, F.; KOELLENSPERGER, G.; SCHOENY, H.; HOHENWALLNER, K.; PANZENBOECK, L.; GREGOR, R.; O'NEILL, E. C.; ROXBOROUGH, E. T.; ODOI, J.; BALE, N. J.; DING, S.; DAMSTÉ, J. S. S.; GUAN, X. L.; CUI, J. J.; JU, K. S.; SILVA, D. B.; SILVA, F. M. R.; SILVA, G. F. da; KOOLEN, H. H. F.; GRUNDMANN, C.; CLEMENT, J. A.; MOHIMANI, H.; BRODERS, K.; McPHAIL, K. L.; OBER-SINGLETON, S. E.; RATH, C. M.; McDONALD, D.; KNIGHT, R.; WANG, M.; DORRESTEIN, P. C. MicrobeMASST: a taxonomically informed mass spectrometry search tool for microbial metabolomics data. Nature Microbiology, v. 9, p. 336-345, Feb. 2024.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
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