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| Registros recuperados : 7 | |
| 4. |  | MENDOZA, A.; CAJARVILLE, C.; BRAMBILLASCA, S.; ROSAS, F.; IRIARTE, E.; REPETTO, J.L. Evaluación in vitro de la actividad del líquido ruminal de vacas alimentadas con forraje fresco y ración totalmente mezclada. In: JORNADAS URUGUAYAS DE BUIATRÍA, 41, 2013, Paysandú, Uruguay. Posters. Paysandú, UY: Centro Médico Veterinario de Paysandú, SUB, SMVU. 2013. p.148-149.| Biblioteca(s): INIA La Estanzuela. |
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| 6. | | BAUZÁ, R.; BRAMBILLASCA, S.; FERNÁNDEZ-GARCÍA, M.; BARRETO, R.; SILVA, D.; BRATSCHI, C.; CAJARVILLE, C.; REPETTO, J.; TEJERO, B.; BOLLAZZI, I..; CARDOZO, A.; ROSAS, F. Estudio de alimentos alternativos al maíz y la harina de soja en dietas para cerdos en engorde. Montevideo (UY): INIA, 2018. 89 p. (Serie FPTA-INIA; 64) Proyecto Fpta-326. "Aporte nutritivo para cerdos de: sorgo bajo tanino, sorgo extrusado, sorgo ensilado como grano húmedo, expeller de canola y arveja". Período de ejecución: Junio 2014 ? Agosto 2017.| Biblioteca(s): INIA Las Brujas; INIA Treinta y Tres. |
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| 7. | | RIVAS, N.; GORGOGLIONE, A.; CARRIQUIRY, M.; CAL, A.; TISCORNIA, G.; GARCIA, C.; ROSAS, F.; SARACHO, A.; HASTINGS, F.; FRABASILE, F.; SILVEIRA, L. Plataforma agroambiental para la toma de decisión en agricultura irrigada sostenible: una construcción interdisciplinaria e interinstitucional. [En memoria de la Dra. Gabriela Eguren, pilar fundamental en la construcción de esta iniciativa]. Sustentabilidad. Revista INIA Uruguay, Setiembre 2023, no.74 p.57-61. (Revista INIA; 74).| Biblioteca(s): INIA Las Brujas. |
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| Registros recuperados : 7 | |
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 | Acceso al texto completo restringido a Biblioteca INIA La Estanzuela. Por información adicional contacte bib_le@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha actual : |
12/12/2017 |
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Actualizado : |
12/12/2017 |
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Tipo de producción científica : |
Trabajos en Congresos/Conferencias |
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Autor : |
LADO, B.; BATTENFIELD, S.; SILVA, P.; QUINCKE, M.; GUZMAN, C.; SINGH, R.P.; DREISIGACKER, S.; PEÑA, J.; FRITZ, A.; POLAND, J.; GUTIERREZ, L. |
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Afiliación : |
BETTINA LADO, Facultad de Agronomía, Universidad de la República, Montevideo, Uruguay.; SARAH BATTENFIELD, AgriPro Wheat, Syngenta, 11783 Ascher Rd. Junction City, KS, 66441, USA.; MARIA PAULA SILVA VILLELLA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARTIN CONRADO QUINCKE WALDEN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CARLOS GUZMAN, CIMMYT, El Batan, Mexico, Mexico.; RAVI P. SINGH, CIMMYT, El Batan, Mexico, Mexico.; SUSANNE DREISIGACKER, CIMMYT, El Batan, Mexico, Mexico.; JAVIER PEÑA, CIMMYT, El Batan, Mexico, Mexico.; ALLAN FRITZ, Wheat Genetics Resource Center, Department of Plant Pathology, 1712 Claflin Rd., Kansas State University, Manhattan, KS 66506, USA; .; JESSE POLAND, Wheat Genetics Resource Center, Department of Plant Pathology, 1712 Claflin Rd., Kansas State University, Manhattan, KS 66506, USA.; LUCIA GUTIERREZ, Department of Agronomy, University of Wisconsin, 1575 Linden Dr., Madison, WI 53706, USA. |
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Título : |
Comparing strategies to select crosses using genomic prediction in two wheat breeding programs. |
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Fecha de publicación : |
2017 |
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Fuente / Imprenta : |
In: International Wheat Genetics Symposium, 12, Tulln, Austria; April 23-28, 2017; BOKU: University of Natural Resources and Life Sciences, Vienna, Austria. |
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Páginas : |
p.88-90. |
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Idioma : |
Español |
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Contenido : |
Key message: Evaluation of crosses prediction methods with and without accounting for progeny variance. Mid-parent values was a much larger factor determining genetic gain than increasing the progeny variance of a cross.
In wheat breeding programs, a critical decision is to determine crosses that have high probability to deliver progenies with higher genetics gains (Zhong & Jannink 2007, Bernardo 2014). We present an application of genomic models for predicting parental cross combinations for grain yield, grain protein, and loaf volume across two wheat-breeding programs, INIA-Uruguay and CIMMYT. We evaluated three methods for selecting the ?best? crosses based on (1) mid-parents, (2) top 10% of the progeny within a cross, and (3) maximizing mean and variance within progeny using thresholds. The last two methods were evaluated with the predicted variances obtained through progeny simulation using the PopVar (Mohammadi et al. 2015, Tiede et al. 2015) package in R software. The first two methods showed 82% of crosses in common for yield, 55% for loaf volume and 53% for grain protein, even though only the second method accounts for the variance of the progeny (Figure 1). While the expected variance of the progeny is important to increase chances of finding superior individuals from transgressive segregation, we observed that the mid-parent values of the crosses selected was a much larger factor determining genetic gain than increasing the progeny variance of a cross (Figure 2). Overall, the genomic resources and the statistical models are now available to plant breeders to predict both the performance of breeding lines per se as well as the value of progeny from any potential crosses, but further understanding on optimizing the cross combinations is needed. MenosKey message: Evaluation of crosses prediction methods with and without accounting for progeny variance. Mid-parent values was a much larger factor determining genetic gain than increasing the progeny variance of a cross.
In wheat breeding programs, a critical decision is to determine crosses that have high probability to deliver progenies with higher genetics gains (Zhong & Jannink 2007, Bernardo 2014). We present an application of genomic models for predicting parental cross combinations for grain yield, grain protein, and loaf volume across two wheat-breeding programs, INIA-Uruguay and CIMMYT. We evaluated three methods for selecting the ?best? crosses based on (1) mid-parents, (2) top 10% of the progeny within a cross, and (3) maximizing mean and variance within progeny using thresholds. The last two methods were evaluated with the predicted variances obtained through progeny simulation using the PopVar (Mohammadi et al. 2015, Tiede et al. 2015) package in R software. The first two methods showed 82% of crosses in common for yield, 55% for loaf volume and 53% for grain protein, even though only the second method accounts for the variance of the progeny (Figure 1). While the expected variance of the progeny is important to increase chances of finding superior individuals from transgressive segregation, we observed that the mid-parent values of the crosses selected was a much larger factor determining genetic gain than increasing the progeny variance of a cross (Figure 2)... Presentar Todo |
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Palabras claves : |
WHEAT BREEDING PROGRAMS; WHEAT QUALITY. |
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Thesagro : |
MEJORAMIENTO GENETICO DE PLANTAS; TRIGO. |
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Asunto categoría : |
F30 Genética vegetal y fitomejoramiento |
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Marc : |
LEADER 02689nam a2200289 a 4500
001 1057873
005 2017-12-12
008 2017 bl uuuu u01u1 u #d
100 1 $aLADO, B.
245 $aComparing strategies to select crosses using genomic prediction in two wheat breeding programs.
260 $aIn: International Wheat Genetics Symposium, 12, Tulln, Austria; April 23-28, 2017; BOKU: University of Natural Resources and Life Sciences, Vienna, Austria.$c2017
300 $ap.88-90.
520 $aKey message: Evaluation of crosses prediction methods with and without accounting for progeny variance. Mid-parent values was a much larger factor determining genetic gain than increasing the progeny variance of a cross. In wheat breeding programs, a critical decision is to determine crosses that have high probability to deliver progenies with higher genetics gains (Zhong & Jannink 2007, Bernardo 2014). We present an application of genomic models for predicting parental cross combinations for grain yield, grain protein, and loaf volume across two wheat-breeding programs, INIA-Uruguay and CIMMYT. We evaluated three methods for selecting the ?best? crosses based on (1) mid-parents, (2) top 10% of the progeny within a cross, and (3) maximizing mean and variance within progeny using thresholds. The last two methods were evaluated with the predicted variances obtained through progeny simulation using the PopVar (Mohammadi et al. 2015, Tiede et al. 2015) package in R software. The first two methods showed 82% of crosses in common for yield, 55% for loaf volume and 53% for grain protein, even though only the second method accounts for the variance of the progeny (Figure 1). While the expected variance of the progeny is important to increase chances of finding superior individuals from transgressive segregation, we observed that the mid-parent values of the crosses selected was a much larger factor determining genetic gain than increasing the progeny variance of a cross (Figure 2). Overall, the genomic resources and the statistical models are now available to plant breeders to predict both the performance of breeding lines per se as well as the value of progeny from any potential crosses, but further understanding on optimizing the cross combinations is needed.
650 $aMEJORAMIENTO GENETICO DE PLANTAS
650 $aTRIGO
653 $aWHEAT BREEDING PROGRAMS
653 $aWHEAT QUALITY
700 1 $aBATTENFIELD, S.
700 1 $aSILVA, P.
700 1 $aQUINCKE, M.
700 1 $aGUZMAN, C.
700 1 $aSINGH, R.P.
700 1 $aDREISIGACKER, S.
700 1 $aPEÑA, J.
700 1 $aFRITZ, A.
700 1 $aPOLAND, J.
700 1 $aGUTIERREZ, L.
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