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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela; INIA Las Brujas. |
Fecha : |
14/11/2015 |
Actualizado : |
05/11/2019 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Autor : |
DAMIÁN, J.P.; BERACOCHEA, F.; HÖTZEL, M.J.; BANCHERO, G.; UNGERFELD, R. |
Afiliación : |
GEORGGET ELIZABETH BANCHERO HUNZIKER, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Reproductive and sexual behaviour development of dam or artificially reared male lambs. |
Fecha de publicación : |
2015 |
Fuente / Imprenta : |
Physiology and Behavior, 2015, v.147, p. 47-53. |
ISSN : |
0031-9384 |
DOI : |
10.1016/j.physbeh.2015.04.004 |
Idioma : |
Inglés |
Notas : |
Article history:Received date: 30 December 2014/Revised date: 27 March 2015/Accepted date: 2 April 2015. |
Contenido : |
ABSTRACT.
The objective of this study was to determine if artificially reared male lambs differ from those reared by their mothers in their reproductive development and sexual behaviour during the first breeding season and in their serum testosterone to a GnRH challenge at the end of the first breeding season. Lambs were assigned to two experimental groups: 1) artificially reared lambs, separated from their dams 24-36. h after birth (Week 0) and fed sheep milk until 10. weeks of age (group AR, n = 14); and 2) lambs reared by their dams until 10. weeks of age (group DR, n = 13). Reproductive parameters and sexual behaviour were recorded from Weeks 9 to 39. The GnRH challenge was performed on Week 40. Body weight, scrotal circumference, gonado-somatic index, testosterone concentration and sperm parameters were unaffected by group, but increased with age (P < 0.0001). Lambs reared by their mothers had greater values of gonado-somatic index on Weeks 9, 16 and 19 (P < 0.05), and tended to reach puberty earlier than AR (22.9 ± 0.7 vs. 25.1 ± 1.1. weeks, respectively, P = 0.087). Lambs reared by their mothers presented more lateral approaches and mount attempts than AR (P < 0.05), and DR lambs presented more mounts on Weeks 32 and 39 than AR (P < 0.05). Blood testosterone concentrations 3.5 and 4. h after the GnRH challenge were higher in AR than in DR lambs (P < 0.05). In conclusion mother rearing promoted sexual behaviour and reproductive performance of male lambs.
© 2015. |
Palabras claves : |
COMPORTAMIENTO SEXUAL; CONCENTRACIÓN DE TESTOSTERONA; CORDEROS; CRIANZA ARTIFICIAL DE CORDEROS; CRIANZA NATURAL DE CORDEROS; DESEMPEÑO REPRODUCTIVO EN CORDEROS; GnRH; HABILIDAD MATERNA EN LA OVEJA; HORMONA LIBERADORA DE GONADOTROPINA; ÍNDICE GONADOSOMÁTICO; LACTANCIA MATERNA EN OVINOS; MOTILIDAD DE LOS ESPERMATOZOIDES; PUBERTAD PRECOZ EN CORDEROS; VÍNCULO MADRE-CRÍA. |
Thesagro : |
COMPORTAMIENTO REPRODUCTIVO; CORDEROS; OVINOS; REPRODUCTIVIDAD. |
Asunto categoría : |
L52 Fisiología Animal- Crecimiento y desarrollo L53 Fisiología Animal - Reproducción |
Marc : |
LEADER 02907naa a2200421 a 4500 001 1053881 005 2019-11-05 008 2015 bl uuuu u00u1 u #d 022 $a0031-9384 024 7 $a10.1016/j.physbeh.2015.04.004$2DOI 100 1 $aDAMIÁN, J.P. 245 $aReproductive and sexual behaviour development of dam or artificially reared male lambs.$h[electronic resource] 260 $c2015 500 $aArticle history:Received date: 30 December 2014/Revised date: 27 March 2015/Accepted date: 2 April 2015. 520 $aABSTRACT. The objective of this study was to determine if artificially reared male lambs differ from those reared by their mothers in their reproductive development and sexual behaviour during the first breeding season and in their serum testosterone to a GnRH challenge at the end of the first breeding season. Lambs were assigned to two experimental groups: 1) artificially reared lambs, separated from their dams 24-36. h after birth (Week 0) and fed sheep milk until 10. weeks of age (group AR, n = 14); and 2) lambs reared by their dams until 10. weeks of age (group DR, n = 13). Reproductive parameters and sexual behaviour were recorded from Weeks 9 to 39. The GnRH challenge was performed on Week 40. Body weight, scrotal circumference, gonado-somatic index, testosterone concentration and sperm parameters were unaffected by group, but increased with age (P < 0.0001). Lambs reared by their mothers had greater values of gonado-somatic index on Weeks 9, 16 and 19 (P < 0.05), and tended to reach puberty earlier than AR (22.9 ± 0.7 vs. 25.1 ± 1.1. weeks, respectively, P = 0.087). Lambs reared by their mothers presented more lateral approaches and mount attempts than AR (P < 0.05), and DR lambs presented more mounts on Weeks 32 and 39 than AR (P < 0.05). Blood testosterone concentrations 3.5 and 4. h after the GnRH challenge were higher in AR than in DR lambs (P < 0.05). In conclusion mother rearing promoted sexual behaviour and reproductive performance of male lambs. © 2015. 650 $aCOMPORTAMIENTO REPRODUCTIVO 650 $aCORDEROS 650 $aOVINOS 650 $aREPRODUCTIVIDAD 653 $aCOMPORTAMIENTO SEXUAL 653 $aCONCENTRACIÓN DE TESTOSTERONA 653 $aCORDEROS 653 $aCRIANZA ARTIFICIAL DE CORDEROS 653 $aCRIANZA NATURAL DE CORDEROS 653 $aDESEMPEÑO REPRODUCTIVO EN CORDEROS 653 $aGnRH 653 $aHABILIDAD MATERNA EN LA OVEJA 653 $aHORMONA LIBERADORA DE GONADOTROPINA 653 $aÍNDICE GONADOSOMÁTICO 653 $aLACTANCIA MATERNA EN OVINOS 653 $aMOTILIDAD DE LOS ESPERMATOZOIDES 653 $aPUBERTAD PRECOZ EN CORDEROS 653 $aVÍNCULO MADRE-CRÍA 700 1 $aBERACOCHEA, F. 700 1 $aHÖTZEL, M.J. 700 1 $aBANCHERO, G. 700 1 $aUNGERFELD, R. 773 $tPhysiology and Behavior, 2015$gv.147, p. 47-53.
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha actual : |
14/09/2023 |
Actualizado : |
14/09/2023 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
Internacional - -- |
Autor : |
REBOLLO, I.; AGUILAR, I.; PÉREZ DE VIDA, F.; MOLINA, F.; GUTIÉRREZ, L.; ROSAS, J.E. |
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. |
Título : |
Genotype by environment interaction characterization and its modeling with random regression to climatic variables in two rice breeding populations. |
Complemento del título : |
Original article. |
Fecha de publicación : |
2023 |
Fuente / Imprenta : |
Crop Science. 2023, Volume 63, Issue 4, Pages 2220-2240. https://doi.org/10.1002/csc2.21029 -- OPEN ACCESS. |
ISSN : |
0011-183X (print); 1435-0653 (electronic). |
DOI : |
10.1002/csc2.21029 |
Idioma : |
Inglés |
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/ ) |
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 |
Palabras claves : |
Genotype by environment interaction (GEI); Random regression models (RRMs); Rice (Oryza sativa L.). |
Asunto categoría : |
-- |
URL : |
https://acsess.onlinelibrary.wiley.com/doi/epdf/10.1002/csc2.21029
|
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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