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| 1. |  | ROVIRA, P.J.; CARVALHO, P.C.F.; TERRA, J.A.; LATTANZI, F.; PIZZIO, R.; AYALA, W. Sustainable intensification in crop-livestock systems. In: MUREITHI, J.G.; NYANGITO, M.M.; WAMUONGO, J.W.; NJOKA, J.; NYAMBATI; E.M.; MIANO,D.; MBUKU, S.; OKOTI, M.; MARITIM. F., ed. Sustainable use of grassland and rangeland resources for improved livelihoods. Proceedings. Nairobi, (Kenia): KALRO, 2022. p. 650-654| Biblioteca(s): INIA Treinta y Tres. |
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| 2. | | KUHN, J.; ROSA, F.Q.; BASSO, C.; NUNES, L.R.; SCOTTA, R.; BECKER, R.; BORGES, A.; BOLZAN, A.S.; JAURENA, M.; BREMM, C.; CARVALHO, P.C.F. Como se comporta o NDVI em diferentes ofertas de forragem? [Resumen]. In: CONGRESO ASOCIACIÓN URUGUAYA DE PRODUCCIÓN ANIMAL (6º, Marzo, 2018, Tacuarembó, Uruguay). Resúmenes. Tacuarembó: AUPA, 2018. p. 77| Biblioteca(s): INIA Tacuarembó. |
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| 3. | | Aguinaga, A.J.Q.; Frizzo, A.; Nabinger, C.; Carvalho, P.C.F.; Aguinaga, A.A.Q.; Guma, J.M.C.R.; Cauduro, G.F. Efeito da manipulação estacional da oferta de forragem no bioma campos sulinos sobre a produção primária e secundária ln: Reunión del Grupo Técnico Regional del Cono Sur en Mejoramiento y Utilización de los Recursos Forrajeros del Area Tropical y Subtropical, Grupo Campos, 20., 2004, Salto, UY Saldanha, S.; Bemhaja, M.; Moliterno, E.; Olmos, F.; Uriarte, G., ed. Sustentabilidad, desarrollo y conservación de los ecosistemas : memorias. Salto: UdelaR. Regional Norte, 2004. p. 313-315| Biblioteca(s): INIA La Estanzuela. |
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| 4. | | AMARAL, G.A. DO; AZEVEDO, E. B. DE; SAVIAN, J.V.; BREMM, C.; DAVID, D.B. DE; GONDA, H.L.; BINDELLE, J.; CARVALHO, P.C.F. Purified and enriched lignin as a marker to estimate faecal output of sheep fed temperate and tropical grasses. Livestock Science, 2022, Volume 263, Article number 105011. doi: https://doi.org/10.1016/j.livsci.2022.105011 7 p. History article: Received 9 December 2021; Received in revised form 20 April 2022; Accepted 28 June 2022; Available online1 July 2022.
Corresponding author: eduardo.azevedo@ufsm.br (E.B. Azevedo).| Biblioteca(s): INIA Treinta y Tres. |
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| 5. | | FARIAS, G.D.; BREMM, C.; SAVIAN, J.V.; SOUZA FILHO, W. DE; LIMA, L.C. DE; NUNES, P.A.D.A.; ALVES, L.A.; SACIDO, M.; MONTOSSI, F.; TIECHER, T.; CARVALHO, P.C.F. Opportunities and challenges for the integration of sheep and crops in the Rio de la Plata region of South America. Small Ruminant Research, 2022, v. 215, no. 106776, 10 p. doi: https://doi.org/10.1016/j.smallrumres.2022.106776 History article: Received date: 18 September 202; Revised date: 7 July 2022; Accepted date: 8 July 2022.
Corresponding authors: gustavo.dfarias@hotmail.com (G. D. Farias), paulocfc@ufrgs.br (P. C. de Faccio Carvalho)| Biblioteca(s): INIA Treinta y Tres. |
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| 6. | | JAURENA, M.; DURANTE, M.; DEVINCENZI, T.; SAVIAN, J.V.; BENDERSKY, D.; MOOJEN, F.G.; PEREIRA, M.; SOCA, P.; QUADROS, F.L.F.; PIZZIO, R.; NABINGER, C.; CARVALHO, P.C.F.; LATTANZI, F. Native Grasslands at the Core: A New Paradigm of Intensification for the Campos of Southern South America to Increase Economic and Environmental Sustainability Front. Sustain. Food Syst., 05 March 2021, OPEN ACCESS. DOI: https://doi.org/10.3389/fsufs.2021.547834 Article history: Received: 31 March 2020, Accepted: 07 January 2021, Published: 05 March 2021. ACKNOWLEDGMENTS: The authors gratefully acknowledge Fiorella Cazulli for the
English corrections.Correspondence: Martín Jaurena...| Biblioteca(s): INIA Tacuarembó. |
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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
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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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