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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
20/05/2022 |
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Actualizado : |
20/05/2022 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
SESSIM, A. G.; CANOZZI, M.E.A.; PEREIRA, G. R.; CASTILHO, E. M.; BARCELLOS, J. O. J. |
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Afiliación : |
AMIR GIL SESSIM, Federal University of Rio Grande do Sul - UFRGS, Faculty of Agronomy, Department of Animal Science, RS, Porto Alegre, Brazil; MARÍA EUGENIA ANDRIGHETTO CANOZZI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GABRIEL RIBAS PEREIRA, Federal University of Rio Grande do Sul - UFRGS, Faculty of Agronomy, Department of Animal Science, RS, Porto Alegre, Brazil; EDUARDO MADEIRA CASTILHO, Federal University of Pelotas, UFPEL, Faculty of Agronomy, Department of Animal Science, RS, Pelotas, Brazil; JÚLIO OTÁVIO JARDIM BARCELLOS, Federal University of Rio Grande do Sul - UFRGS, Faculty of Agronomy, Department of Animal Science, RS, Porto Alegre, Brazil. |
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Título : |
Financial performance and opportunistic commercialization of beef production systems in southern Brazil. |
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Fecha de publicación : |
2022 |
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Fuente / Imprenta : |
Revista Mexicana De Ciencias Pecuarias, 2022, Volume 13, Issue 1, Pages 127 - 144. OPEN ACCESS. doi: https://doi.org/10.22319/rmcp.v13i1.5888 |
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ISSN : |
2007-1124; e-ISSN: 2448-6698 |
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DOI : |
10.22319/RMCP.V13I1.5888 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 3 December 2020; Accepted 07 June 2021; Published January 2022.
Publisher: INIFAP-CENID Parasitologia Veterinaria. Corresponding author: Sessim, A.G.; Federal University of Rio Grande do Sul - UFRGS, Faculty of Agronomy, Department of Animal Science, RS, Porto Alegre, Brazil; email:amirsessim@hotmail.com -- This study was supported by the Brazilian Council of Scientific and Technological Development (Project CNPq No. 133454/2014-2) and the Coordination for the Improvement of Higher Education Personnel/CAPES, Brazil (Project CAPES/PNPD No. 2842/2010). Licencia: licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional. |
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Contenido : |
ABSTRACT.- This study compares the technical and financial performance of different beef cattle production systems and assesses the opportunistic commercialization practiced in these systems. It was evaluated data from four production units located in southern Brazil: cow-calf in native pastures (CCNP; 1,155 ha; 1,529 animals); cow-calf with agriculture (CCA; 1,008 ha; 1,313 animals); rearing-fattening (RFU; 360 ha; 435 animals); and fattening (FU; 205 ha; 168 animals) as well as an integrated system simulating the physical and economic parameters of the four units (IAS; 2,728 ha; 3,445 animals). The four independent units were considered as opportunistic commercialization and IAS as non-opportunistic. The highest yield was obtained for RFU (297 kg/ha), followed by IAS (114 kg/ha), FU (98 kg/ha), CCNP (87 kg/ha), and CCA (83 kg/ha). The CCNP was the most economically efficient, considering the gross margin per kilogram (GM/kg) (US$ 0.93). The GM/kg value of IAS (US$ 0.74) was 37 % higher compared to the sum of the average of the four units (US$ 0.54), and IAS had the lowest total production costs per kg (22.5 %). It was concluded that each independent unit could increase GM/kg (37 %) and GM/ha (3.8 %) and use calves in a rearing-fattening unit for further sale. Hence, the integration of beef production systems increases the gross margin of firms, presenting a profitable business advantage to rural entrepreneurs through non-opportunistic commercialization.
© 2022 INIFAP-CENID Parasitologia Veterinaria. All rights reserved. MenosABSTRACT.- This study compares the technical and financial performance of different beef cattle production systems and assesses the opportunistic commercialization practiced in these systems. It was evaluated data from four production units located in southern Brazil: cow-calf in native pastures (CCNP; 1,155 ha; 1,529 animals); cow-calf with agriculture (CCA; 1,008 ha; 1,313 animals); rearing-fattening (RFU; 360 ha; 435 animals); and fattening (FU; 205 ha; 168 animals) as well as an integrated system simulating the physical and economic parameters of the four units (IAS; 2,728 ha; 3,445 animals). The four independent units were considered as opportunistic commercialization and IAS as non-opportunistic. The highest yield was obtained for RFU (297 kg/ha), followed by IAS (114 kg/ha), FU (98 kg/ha), CCNP (87 kg/ha), and CCA (83 kg/ha). The CCNP was the most economically efficient, considering the gross margin per kilogram (GM/kg) (US$ 0.93). The GM/kg value of IAS (US$ 0.74) was 37 % higher compared to the sum of the average of the four units (US$ 0.54), and IAS had the lowest total production costs per kg (22.5 %). It was concluded that each independent unit could increase GM/kg (37 %) and GM/ha (3.8 %) and use calves in a rearing-fattening unit for further sale. Hence, the integration of beef production systems increases the gross margin of firms, presenting a profitable business advantage to rural entrepreneurs through non-opportunistic commercialization.
© 2022 INIFAP-CENI... Presentar Todo |
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Palabras claves : |
Animal production; Economy; Gross revenue; Integration; PLATAFORMA SALUD ANINMAL; Production cost. |
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Asunto categoría : |
L01 Ganadería |
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URL : |
https://cienciaspecuarias.inifap.gob.mx/index.php/Pecuarias/article/download/5888/4707
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Marc : |
LEADER 03152naa a2200277 a 4500
001 1063153
005 2022-05-20
008 2022 bl uuuu u00u1 u #d
022 $a2007-1124; e-ISSN: 2448-6698
024 7 $a10.22319/RMCP.V13I1.5888$2DOI
100 1 $aSESSIM, A. G.
245 $aFinancial performance and opportunistic commercialization of beef production systems in southern Brazil.$h[electronic resource]
260 $c2022
500 $aArticle history: Received 3 December 2020; Accepted 07 June 2021; Published January 2022. Publisher: INIFAP-CENID Parasitologia Veterinaria. Corresponding author: Sessim, A.G.; Federal University of Rio Grande do Sul - UFRGS, Faculty of Agronomy, Department of Animal Science, RS, Porto Alegre, Brazil; email:amirsessim@hotmail.com -- This study was supported by the Brazilian Council of Scientific and Technological Development (Project CNPq No. 133454/2014-2) and the Coordination for the Improvement of Higher Education Personnel/CAPES, Brazil (Project CAPES/PNPD No. 2842/2010). Licencia: licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional.
520 $aABSTRACT.- This study compares the technical and financial performance of different beef cattle production systems and assesses the opportunistic commercialization practiced in these systems. It was evaluated data from four production units located in southern Brazil: cow-calf in native pastures (CCNP; 1,155 ha; 1,529 animals); cow-calf with agriculture (CCA; 1,008 ha; 1,313 animals); rearing-fattening (RFU; 360 ha; 435 animals); and fattening (FU; 205 ha; 168 animals) as well as an integrated system simulating the physical and economic parameters of the four units (IAS; 2,728 ha; 3,445 animals). The four independent units were considered as opportunistic commercialization and IAS as non-opportunistic. The highest yield was obtained for RFU (297 kg/ha), followed by IAS (114 kg/ha), FU (98 kg/ha), CCNP (87 kg/ha), and CCA (83 kg/ha). The CCNP was the most economically efficient, considering the gross margin per kilogram (GM/kg) (US$ 0.93). The GM/kg value of IAS (US$ 0.74) was 37 % higher compared to the sum of the average of the four units (US$ 0.54), and IAS had the lowest total production costs per kg (22.5 %). It was concluded that each independent unit could increase GM/kg (37 %) and GM/ha (3.8 %) and use calves in a rearing-fattening unit for further sale. Hence, the integration of beef production systems increases the gross margin of firms, presenting a profitable business advantage to rural entrepreneurs through non-opportunistic commercialization. © 2022 INIFAP-CENID Parasitologia Veterinaria. All rights reserved.
653 $aAnimal production
653 $aEconomy
653 $aGross revenue
653 $aIntegration
653 $aPLATAFORMA SALUD ANINMAL
653 $aProduction cost
700 1 $aCANOZZI, M.E.A.
700 1 $aPEREIRA, G. R.
700 1 $aCASTILHO, E. M.
700 1 $aBARCELLOS, J. O. J.
773 $tRevista Mexicana De Ciencias Pecuarias, 2022, Volume 13, Issue 1, Pages 127 - 144. OPEN ACCESS. doi: https://doi.org/10.22319/rmcp.v13i1.5888
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INIA Las Brujas (LB) |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
14/09/2023 |
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Actualizado : |
14/09/2023 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
Internacional - -- |
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Autor : |
REBOLLO, I.; AGUILAR, I.; PÉREZ DE VIDA, F.; MOLINA, F.; GUTIÉRREZ, L.; ROSAS, J.E. |
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Afiliación : |
MARÍA INÉS REBOLLO PANUNCIO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Department of Statistics, University de la República, College of Agriculture, Garzón 780, Montevideo, Montevideo, Uruguay; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FERNANDO BLAS PEREZ DE VIDA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FEDERICO MOLINA CASELLA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LUCÍA GUTIÉRREZEPARTMENT OF STATISTICS, UNIVERSITY DE LA REPÚBLICA, COLLEGE OF AGRICULTURE, GARZÓN 780, MONTEVIDEO, MONTEVIDEO, URUGUAY DEPARTMENT OF AGRONOMY, UNIVERSITY OF WISCONSIN–MADISON, 1575 LINDEN DRIVE, MADISON, WI, UNITED STATES, Department of Statistics, University de la República, College of Agriculture, Montevideo, Uruguay; Department of Agronomy, University of Wisconsin-Madison, 1575 Linden Drive, Madison, WI, United States; JUAN EDUARDO ROSAS CAISSIOLS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Department of Statistics, University de la República, College of Agriculture, Garzón 780, Montevideo, Montevideo, Uruguay. |
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Título : |
Genotype by environment interaction characterization and its modeling with random regression to climatic variables in two rice breeding populations. |
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Complemento del título : |
Original article. |
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Fecha de publicación : |
2023 |
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Fuente / Imprenta : |
Crop Science. 2023, Volume 63, Issue 4, Pages 2220-2240. https://doi.org/10.1002/csc2.21029 -- OPEN ACCESS. |
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ISSN : |
0011-183X (print); 1435-0653 (electronic). |
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DOI : |
10.1002/csc2.21029 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 21 November 2022, Accepted 10 May 2023, Published online 16 June 2023. -- Correspondence: Rosas, J.E.; INIA, Estación Experimental Treinta y Tres, Road 8 km 281, Treinta y Tres, Uruguay; email:jrosas@inia.org.uy -- FUNDING: Funding for this project was provided by Instituto Nacional de Investigación Agropecuaria (Projects AZ35, AZ13, and fellowship to I. R.), Agencia Nacional de Investigación Agropecuaria (grant MOV_CA_2019_1_156241), Comisión Sectorial de Investigación Científica, Universidad de la República (grant Iniciación a la Investgación 2019 No. 8), Comité Académico de Posgrado (fellowship to I. R.), and the Agriculture and Food Research Initiative Competitive Grant 2022-68013-36439 (WheatCAP) from the USDA National Institute of Food and Agriculture. -- LICENSE: This is an open access article under the terms of theCreative Commons Attribution-NonCommercial (http://creativecommons.org/licenses/by-nc/4.0/ ) |
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Contenido : |
ABSTRACT.- Genotype by environment interaction (GEI) is one of the main challenges in plant breeding. A complete characterization of it is necessary to decide on proper breeding strategies. Random regression models (RRMs) allow a genotype-specific response to each regressor factor. RRMs that include selected environmental variables represent a promising approach to deal with GEI in genomic prediction. They enable to predict for both tested and untested environments, but their utility in a plant breeding scenario remains to be shown. We used phenotypic, climatic, pedigree, and genomic data from two public subtropical rice (Oryza sativa L.) breeding programs; one manages the indica population and the other manages the japonica population. First, we characterized GEI for grain yield (GY) with a set of tools: variance component estimation, mega-environment (ME) definition, and correlation between locations, sowing periods, and MEs. Then, we identified the most influential climatic variables related to GY and its GEI and used them in RRMs for single-step genomic prediction. Finally, we evaluated the predictive ability of these models for GY prediction in tested and untested years and environments using the complete dataset and within each ME. Our results suggest large GEI in both populations while larger in indica than in japonica. In indica, early sowing periods showed crossover (i.e., rank-change) GEI with other sowing periods. Climatic variables related to temperature, radiation, wind, and precipitation affecting GY were identified and differed in each population. RRMs with selected climatic covariates improved the predictive ability in both tested and untested years and environments. Prediction using the complete dataset performed better than predicting within each ME. © 2023 The Authors. Crop Science © 2023 Crop Science Society of America. MenosABSTRACT.- Genotype by environment interaction (GEI) is one of the main challenges in plant breeding. A complete characterization of it is necessary to decide on proper breeding strategies. Random regression models (RRMs) allow a genotype-specific response to each regressor factor. RRMs that include selected environmental variables represent a promising approach to deal with GEI in genomic prediction. They enable to predict for both tested and untested environments, but their utility in a plant breeding scenario remains to be shown. We used phenotypic, climatic, pedigree, and genomic data from two public subtropical rice (Oryza sativa L.) breeding programs; one manages the indica population and the other manages the japonica population. First, we characterized GEI for grain yield (GY) with a set of tools: variance component estimation, mega-environment (ME) definition, and correlation between locations, sowing periods, and MEs. Then, we identified the most influential climatic variables related to GY and its GEI and used them in RRMs for single-step genomic prediction. Finally, we evaluated the predictive ability of these models for GY prediction in tested and untested years and environments using the complete dataset and within each ME. Our results suggest large GEI in both populations while larger in indica than in japonica. In indica, early sowing periods showed crossover (i.e., rank-change) GEI with other sowing periods. Climatic variables related to temperature, radiati... Presentar Todo |
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Palabras claves : |
Genotype by environment interaction (GEI); Random regression models (RRMs); Rice (Oryza sativa L.). |
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Asunto categoría : |
-- |
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URL : |
https://acsess.onlinelibrary.wiley.com/doi/epdf/10.1002/csc2.21029
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Marc : |
LEADER 03749naa a2200253 a 4500
001 1064311
005 2023-09-14
008 2023 bl uuuu u00u1 u #d
022 $a0011-183X (print); 1435-0653 (electronic).
024 7 $a10.1002/csc2.21029$2DOI
100 1 $aREBOLLO, I.
245 $aGenotype by environment interaction characterization and its modeling with random regression to climatic variables in two rice breeding populations.$h[electronic resource]
260 $c2023
500 $aArticle history: Received 21 November 2022, Accepted 10 May 2023, Published online 16 June 2023. -- Correspondence: Rosas, J.E.; INIA, Estación Experimental Treinta y Tres, Road 8 km 281, Treinta y Tres, Uruguay; email:jrosas@inia.org.uy -- FUNDING: Funding for this project was provided by Instituto Nacional de Investigación Agropecuaria (Projects AZ35, AZ13, and fellowship to I. R.), Agencia Nacional de Investigación Agropecuaria (grant MOV_CA_2019_1_156241), Comisión Sectorial de Investigación Científica, Universidad de la República (grant Iniciación a la Investgación 2019 No. 8), Comité Académico de Posgrado (fellowship to I. R.), and the Agriculture and Food Research Initiative Competitive Grant 2022-68013-36439 (WheatCAP) from the USDA National Institute of Food and Agriculture. -- LICENSE: This is an open access article under the terms of theCreative Commons Attribution-NonCommercial (http://creativecommons.org/licenses/by-nc/4.0/ )
520 $aABSTRACT.- Genotype by environment interaction (GEI) is one of the main challenges in plant breeding. A complete characterization of it is necessary to decide on proper breeding strategies. Random regression models (RRMs) allow a genotype-specific response to each regressor factor. RRMs that include selected environmental variables represent a promising approach to deal with GEI in genomic prediction. They enable to predict for both tested and untested environments, but their utility in a plant breeding scenario remains to be shown. We used phenotypic, climatic, pedigree, and genomic data from two public subtropical rice (Oryza sativa L.) breeding programs; one manages the indica population and the other manages the japonica population. First, we characterized GEI for grain yield (GY) with a set of tools: variance component estimation, mega-environment (ME) definition, and correlation between locations, sowing periods, and MEs. Then, we identified the most influential climatic variables related to GY and its GEI and used them in RRMs for single-step genomic prediction. Finally, we evaluated the predictive ability of these models for GY prediction in tested and untested years and environments using the complete dataset and within each ME. Our results suggest large GEI in both populations while larger in indica than in japonica. In indica, early sowing periods showed crossover (i.e., rank-change) GEI with other sowing periods. Climatic variables related to temperature, radiation, wind, and precipitation affecting GY were identified and differed in each population. RRMs with selected climatic covariates improved the predictive ability in both tested and untested years and environments. Prediction using the complete dataset performed better than predicting within each ME. © 2023 The Authors. Crop Science © 2023 Crop Science Society of America.
653 $aGenotype by environment interaction (GEI)
653 $aRandom regression models (RRMs)
653 $aRice (Oryza sativa L.)
700 1 $aAGUILAR, I.
700 1 $aPÉREZ DE VIDA, F.
700 1 $aMOLINA, F.
700 1 $aGUTIÉRREZ, L.
700 1 $aROSAS, J.E.
773 $tCrop Science. 2023, Volume 63, Issue 4, Pages 2220-2240. https://doi.org/10.1002/csc2.21029 -- OPEN ACCESS.
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