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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
26/02/2020 |
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Actualizado : |
26/02/2020 |
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Tipo de producción científica : |
Abstracts/Resúmenes |
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Autor : |
ANTELO, L.; AREOSA, P.; BENTANCOUR, S.; BERETTA, A.; BOLLAZI, I.; BRATSCHI, C.; CARDINALE, A.; CORREA, P.; DEVITTA, F.; GALVÁN, P.; GONZÁLEZ, J.; GONZÁLEZ, T.; GONZÁLEZ, M.; HUERGA, M.; MACIEL, L.; MESA, I.; MORALES, S.; MUGURUZA, A.; PEREIRA, V.; SALVARREY, J.; VALLEJO, A.; ZANOTTA, G. |
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Afiliación : |
LUCÍA ANTELO, Bachiller de Facultad de Agronomía (UdelaR); PABLO AREOSA, Departamento de Ciencias Sociales (FAgro. UdelaR); SILVIA BENTANCOUR, Bachiller de Facultad de Agronomía (UdelaR); ANDRES NICOLAS BERETTA BLANCO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; IVANNA BOLLAZI, Ciclo Introducción a la Realidad Agropecuaria (FAgro. UdelaR); CECILIA BRATSCHI, Ciclo Introducción a la Realidad Agropecuaria (FAgro. UdelaR); ALFREDO CARDINALE, Bachiller de Facultad de Agronomía (UdelaR); PASTORA CORREA, Departamento de Ciencias Sociales (FAgro. UdelaR); FRANCO DEVITTA, Bachiller de Facultad de Agronomía (UdelaR); PABLO GALVÁN, Bachiller de Facultad de Agronomía (UdelaR); JULIO GONZÁLEZ, Departamento de Producción Vegetal (FAgro. UdelaR); TACUABÉ GONZÁLEZ, Ciclo Introducción a la Realidad Agropecuaria (FAgro. UdelaR); MATÍAS GONZÁLEZ, Bachiller de Facultad de Agronomía (UdelaR); MARÍA HUERGA, Departamento de Producción Forestal (FAgro. UdelaR); LUCÍA MACIEL, Bachiller de Facultad de Agronomía (UdelaR); INÉS MESA, Bachiller de Facultad de Agronomía (UdelaR); SEBASTIÁN MORALES, Bachiller de Facultad de Agronomía (UdelaR); ADRIÁN MUGURUZA, Ciclo Introducción a la Realidad Agropecuaria (FAgro. UdelaR); VICTORIA PEREIRA, Bachiller de Facultad de Agronomía (UdelaR); JULIA SALVARREY, Departamento de Producción Vegetal (FAgro. UdelaR); ADRIANA VALLEJO, Departamento de Producción Animal (FAgro. UdelaR); GABRIELA ZANOTTA, Departamento de Producción Animal (FAgro. UdelaR). |
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Título : |
Aproximación a la situación del relevo generacional en comunidades rurales de diez zonas agroeconómicas del Uruguay. [Resumen]. |
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Fecha de publicación : |
2016 |
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Fuente / Imprenta : |
In: Libro de resúmenes de las TERCERAS JORNADAS INTERDISCIPLINARIAS EN BIODIVERSIDAD Y ECOLOGIA. "Desafíos socio-ambientales para el Uruguay del futuro" 28 de Noviembre a 2 de Diciembre 2016, Centro Universitario Regional del Este Rocha, Uruguay. p.110. |
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Idioma : |
Español |
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Contenido : |
Se reflexionó en torno a la problemática del relevo generacional en la sustentabilidad de las explotacions agropecuarias, en especial en la agricultura familiar. Se identificaron distintas realidades (intra e interzonales), el relevo generacional es una temática compleja, que se ve afectada por innumerables factores. |
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Palabras claves : |
Familiar rurales; Relevo generacional. |
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Asunto categoría : |
A50 Investigación agraria |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/14201/1/Aproximacion-a-la-situacion-del-nuevo-generacional-2016.-Resumen-III-JIBE.pdf
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Marc : |
LEADER 01600nam a2200385 a 4500
001 1060833
005 2020-02-26
008 2016 bl uuuu u01u1 u #d
100 1 $aANTELO, L.
245 $aAproximación a la situación del relevo generacional en comunidades rurales de diez zonas agroeconómicas del Uruguay. [Resumen].$h[electronic resource]
260 $aIn: Libro de resúmenes de las TERCERAS JORNADAS INTERDISCIPLINARIAS EN BIODIVERSIDAD Y ECOLOGIA. "Desafíos socio-ambientales para el Uruguay del futuro" 28 de Noviembre a 2 de Diciembre 2016, Centro Universitario Regional del Este Rocha, Uruguay. p.110.$c2016
520 $aSe reflexionó en torno a la problemática del relevo generacional en la sustentabilidad de las explotacions agropecuarias, en especial en la agricultura familiar. Se identificaron distintas realidades (intra e interzonales), el relevo generacional es una temática compleja, que se ve afectada por innumerables factores.
653 $aFamiliar rurales
653 $aRelevo generacional
700 1 $aAREOSA, P.
700 1 $aBENTANCOUR, S.
700 1 $aBERETTA, A.
700 1 $aBOLLAZI, I.
700 1 $aBRATSCHI, C.
700 1 $aCARDINALE, A.
700 1 $aCORREA, P.
700 1 $aDEVITTA, F.
700 1 $aGALVÁN, P.
700 1 $aGONZÁLEZ, J.
700 1 $aGONZÁLEZ, T.
700 1 $aGONZÁLEZ, M.
700 1 $aHUERGA, M.
700 1 $aMACIEL, L.
700 1 $aMESA, I.
700 1 $aMORALES, S.
700 1 $aMUGURUZA, A.
700 1 $aPEREIRA, V.
700 1 $aSALVARREY, J.
700 1 $aVALLEJO, A.
700 1 $aZANOTTA, G.
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Registro original : |
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 : |
11/12/2018 |
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Actualizado : |
06/02/2019 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
A - 1 |
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Autor : |
MOTA, R. R.; LOPES, P. S.; TEMPELMAN, R. J.; SILVA, F. F.; AGUILAR, I.; GOMES, C. C. G.; CARDOSO, F. F. |
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Afiliación : |
R. R. MOTA, Animal Science Department, Federal University of Viçosa, Brazil; P. S. LOPES, Animal Science Department, Federal University of Viçosa, Viçosa, Brazil; R. J. TEMPELMAN, Animal Science Department, Michigan State University, United States; F. F. SILVA, Animal Science Department, Federal University of Viçosa, Brazil; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; C. C. G. GOMES, Embrapa South Livestock, Brazil; F. F. CARDOSO, eAnimal Science Department, Federal University of Pelotas, Brazil. |
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Título : |
Genome-enabled prediction for tick resistance in Hereford and Braford beef cattle via reaction norm models. |
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Fecha de publicación : |
2016 |
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Fuente / Imprenta : |
Journal of Animal Science, May 2016, Volume 94, Issue 5, Pages 1834 - 1843. |
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ISSN : |
0021-8812 |
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DOI : |
10.2527/jas.2015-0194 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received December 11, 2015. // Accepted March 10, 2016. |
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Contenido : |
ABSTRACT.
Very few studies have been conducted to infer genotype × environment interaction (G×E) based in genomic prediction models using SNP markers. Therefore, our main objective was to compare a conventional genomic-based single-step model (HBLUP) with its reaction norm model extension (genomic 1-step linear reaction norm model [HLRNM]) to provide EBV for tick resistance as well as to compare predictive performance of these models with counterpart models that ignore SNP marker information, that is, a linear animal model (ABLUP) and its reaction norm extension (1-step linear reaction norm model [ALRNM]). Phenotypes included 10,673 tick counts on 4,363 Hereford and Braford animals, of which 3,591 were genotyped. Using the deviance information criterion for model choice, ABLUP and HBLUP seemed to be poorer fitting in comparison with their respective genomic model extensions. The HLRNM estimated lower average and reaction norm genetic variability compared with the ALRNM, whereas ABLUP and HBLUP seemed to be poorer fitting in comparison with their respective genomic reaction norm model extensions. Heritability and repeatability estimates varied along the environmental gradient (EG) and the genetic correlations were remarkably low between high and low EG, indicating the presence of G×E for tick resistance in these populations. Based on 5-fold K-means partitioning, mean cross-validation estimates with their respective SE of predictive accuracy were 0.66 (SE 0.02), 0.67 (SE 0.02), 0.67 (SE 0.02), and 0.66 (SE 0.02) for ABLUP, HBLUP, HLRNM, and ALRNM, respectively. For 5-fold random partitioning, HLRNM (0.71 ± 0.01) was statistically different from ABLUP (0.67 ± 0.01). However, no statistical significance was reported when considering HBLUP (0.70 ± 0.01) and ALRNM (0.70 ± 0.01). Our results suggest that SNP marker information does not lead to higher prediction accuracies in reaction norm models. Furthermore, these accuracies decreased as the tick infestation level increased and as the relationship between animals in training and validation data sets decreased.
© 2016 American Society of Animal Science. All rights reserved. MenosABSTRACT.
Very few studies have been conducted to infer genotype × environment interaction (G×E) based in genomic prediction models using SNP markers. Therefore, our main objective was to compare a conventional genomic-based single-step model (HBLUP) with its reaction norm model extension (genomic 1-step linear reaction norm model [HLRNM]) to provide EBV for tick resistance as well as to compare predictive performance of these models with counterpart models that ignore SNP marker information, that is, a linear animal model (ABLUP) and its reaction norm extension (1-step linear reaction norm model [ALRNM]). Phenotypes included 10,673 tick counts on 4,363 Hereford and Braford animals, of which 3,591 were genotyped. Using the deviance information criterion for model choice, ABLUP and HBLUP seemed to be poorer fitting in comparison with their respective genomic model extensions. The HLRNM estimated lower average and reaction norm genetic variability compared with the ALRNM, whereas ABLUP and HBLUP seemed to be poorer fitting in comparison with their respective genomic reaction norm model extensions. Heritability and repeatability estimates varied along the environmental gradient (EG) and the genetic correlations were remarkably low between high and low EG, indicating the presence of G×E for tick resistance in these populations. Based on 5-fold K-means partitioning, mean cross-validation estimates with their respective SE of predictive accuracy were 0.66 (SE 0.02), 0.67 (SE 0.02)... Presentar Todo |
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Palabras claves : |
ACCURACY; CROSS-VALIDATION; GENETIC CORRELATION; HERITABILITY. |
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Asunto categoría : |
-- |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/12162/1/mota2016.pdf
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Marc : |
LEADER 03053naa a2200277 a 4500
001 1059370
005 2019-02-06
008 2016 bl uuuu u00u1 u #d
022 $a0021-8812
024 7 $a10.2527/jas.2015-0194$2DOI
100 1 $aMOTA, R. R.
245 $aGenome-enabled prediction for tick resistance in Hereford and Braford beef cattle via reaction norm models.$h[electronic resource]
260 $c2016
500 $aArticle history: Received December 11, 2015. // Accepted March 10, 2016.
520 $aABSTRACT. Very few studies have been conducted to infer genotype × environment interaction (G×E) based in genomic prediction models using SNP markers. Therefore, our main objective was to compare a conventional genomic-based single-step model (HBLUP) with its reaction norm model extension (genomic 1-step linear reaction norm model [HLRNM]) to provide EBV for tick resistance as well as to compare predictive performance of these models with counterpart models that ignore SNP marker information, that is, a linear animal model (ABLUP) and its reaction norm extension (1-step linear reaction norm model [ALRNM]). Phenotypes included 10,673 tick counts on 4,363 Hereford and Braford animals, of which 3,591 were genotyped. Using the deviance information criterion for model choice, ABLUP and HBLUP seemed to be poorer fitting in comparison with their respective genomic model extensions. The HLRNM estimated lower average and reaction norm genetic variability compared with the ALRNM, whereas ABLUP and HBLUP seemed to be poorer fitting in comparison with their respective genomic reaction norm model extensions. Heritability and repeatability estimates varied along the environmental gradient (EG) and the genetic correlations were remarkably low between high and low EG, indicating the presence of G×E for tick resistance in these populations. Based on 5-fold K-means partitioning, mean cross-validation estimates with their respective SE of predictive accuracy were 0.66 (SE 0.02), 0.67 (SE 0.02), 0.67 (SE 0.02), and 0.66 (SE 0.02) for ABLUP, HBLUP, HLRNM, and ALRNM, respectively. For 5-fold random partitioning, HLRNM (0.71 ± 0.01) was statistically different from ABLUP (0.67 ± 0.01). However, no statistical significance was reported when considering HBLUP (0.70 ± 0.01) and ALRNM (0.70 ± 0.01). Our results suggest that SNP marker information does not lead to higher prediction accuracies in reaction norm models. Furthermore, these accuracies decreased as the tick infestation level increased and as the relationship between animals in training and validation data sets decreased. © 2016 American Society of Animal Science. All rights reserved.
653 $aACCURACY
653 $aCROSS-VALIDATION
653 $aGENETIC CORRELATION
653 $aHERITABILITY
700 1 $aLOPES, P. S.
700 1 $aTEMPELMAN, R. J.
700 1 $aSILVA, F. F.
700 1 $aAGUILAR, I.
700 1 $aGOMES, C. C. G.
700 1 $aCARDOSO, F. F.
773 $tJournal of Animal Science, May 2016, Volume 94, Issue 5, Pages 1834 - 1843.
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