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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
09/09/2014 |
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Actualizado : |
23/10/2019 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
LOURENCO, D.A.L.; MISZTAL, I.; TSURUTA, S.; AGUILAR, I.; EZRA, E.; RON, M.; SHIRAK, A.; WELLER, J.I. |
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Afiliación : |
IGNACIO AGUILAR GARCIA, Instituto Nacional de Investigación Agropecuaria (INIA), Uruguay. |
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Título : |
Methods for genomic evaluation of a relatively small genotyped dairy population and effect of genotyped cow information in multiparity analyses. |
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Fecha de publicación : |
2014 |
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Fuente / Imprenta : |
Journal of Dairy Science, 2014, v.97, no.3, p.1742-1752. OPEN ACCESS. |
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ISSN : |
0022-0302 |
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DOI : |
10.3168/jds.2013-6916 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received September 10, 2013. / Accepted December 6, 2013. |
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Contenido : |
ABSTRACTS.
Methods for genomic prediction were evaluated for an Israeli Holstein dairy population of 713,686 cows and 1,305 progeny-tested bulls with genotypes. Inclusion of genotypes of 343 elite cows in an evaluation method that considers pedigree, phenotypes, and genotypes simultaneously was also evaluated. Two data sets were available: a complete data set with production records from 1985 through 2011, and a reduced data set with records after 2006 deleted. For each production trait, a multitrait animal model was used to compute traditional genetic evaluations for parities 1 through 3 as separate traits. Evaluations were calculated for the reduced and complete data sets. The evaluations from the reduced data set were used to calculate parent average for validation bulls, which was the benchmark for comparing gain in predictive ability from genomics. Genomic predictions for bulls in 2006 were calculated using a Bayesian regression method (BayesC), genomic BLUP (GBLUP), single-step GBLUP (ssGBLUP), and weighted ssGBLUP (WssGBLUP). Predictions using BayesC and GBLUP were calculated either with or without an index that included parent average. Genomic predictions that included elite cow genotypes were calculated using ssGBLUP and WssGBLUP. Predictive ability was assessed by coefficients of determination (R2) and regressions of predictions of 135 validation bulls with no daughters in 2006 on deregressed evaluations of those bulls in 2011. A reduction in R2 and regression coefficients was observed from parities 1 through 3. Fat and protein yields had the lowest R2 for all the methods. On average, R2 was lowest for parent averages, followed by GBLUP, BayesC, ssGBLUP, and WssGBLUP. For some traits, R2 for direct genomic values from BayesC and GBLUP were lower than those for parent averages. Genomic estimated breeding values using ssGBLUP were the least biased, and this method appears to be a suitable tool for genomic evaluation of a small genotyped population, as it automatically accounts for parental index, allows for inclusion of female genomic information without preadjustments in evaluations, and uses the same model as in traditional evaluations. Weighted ssGBLUP has the potential for higher evaluation accuracy.
© 2014 American Dairy Science Association. MenosABSTRACTS.
Methods for genomic prediction were evaluated for an Israeli Holstein dairy population of 713,686 cows and 1,305 progeny-tested bulls with genotypes. Inclusion of genotypes of 343 elite cows in an evaluation method that considers pedigree, phenotypes, and genotypes simultaneously was also evaluated. Two data sets were available: a complete data set with production records from 1985 through 2011, and a reduced data set with records after 2006 deleted. For each production trait, a multitrait animal model was used to compute traditional genetic evaluations for parities 1 through 3 as separate traits. Evaluations were calculated for the reduced and complete data sets. The evaluations from the reduced data set were used to calculate parent average for validation bulls, which was the benchmark for comparing gain in predictive ability from genomics. Genomic predictions for bulls in 2006 were calculated using a Bayesian regression method (BayesC), genomic BLUP (GBLUP), single-step GBLUP (ssGBLUP), and weighted ssGBLUP (WssGBLUP). Predictions using BayesC and GBLUP were calculated either with or without an index that included parent average. Genomic predictions that included elite cow genotypes were calculated using ssGBLUP and WssGBLUP. Predictive ability was assessed by coefficients of determination (R2) and regressions of predictions of 135 validation bulls with no daughters in 2006 on deregressed evaluations of those bulls in 2011. A reduction in R2 and regression coe... Presentar Todo |
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Thesagro : |
MEJORAMIENTO GENÉTICO ANIMAL; MODELOS MATEMÁTICOS; SELECCIÓN DE GENOTIPOS; SELECCIÓN GENÓMICA. |
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Asunto categoría : |
L10 Genética y mejoramiento animal |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/3043/1/Aguilar-I.-2014-Jr.Dairy-Sci.-v.973-p.1742-1752.pdf
https://www.journalofdairyscience.org/article/S0022-0302(14)00052-6/pdf
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Marc : |
LEADER 03270naa a2200289 a 4500
001 1050061
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008 2014 bl uuuu u00u1 u #d
022 $a0022-0302
024 7 $a10.3168/jds.2013-6916$2DOI
100 1 $aLOURENCO, D.A.L.
245 $aMethods for genomic evaluation of a relatively small genotyped dairy population and effect of genotyped cow information in multiparity analyses.$h[electronic resource]
260 $c2014
500 $aArticle history: Received September 10, 2013. / Accepted December 6, 2013.
520 $aABSTRACTS. Methods for genomic prediction were evaluated for an Israeli Holstein dairy population of 713,686 cows and 1,305 progeny-tested bulls with genotypes. Inclusion of genotypes of 343 elite cows in an evaluation method that considers pedigree, phenotypes, and genotypes simultaneously was also evaluated. Two data sets were available: a complete data set with production records from 1985 through 2011, and a reduced data set with records after 2006 deleted. For each production trait, a multitrait animal model was used to compute traditional genetic evaluations for parities 1 through 3 as separate traits. Evaluations were calculated for the reduced and complete data sets. The evaluations from the reduced data set were used to calculate parent average for validation bulls, which was the benchmark for comparing gain in predictive ability from genomics. Genomic predictions for bulls in 2006 were calculated using a Bayesian regression method (BayesC), genomic BLUP (GBLUP), single-step GBLUP (ssGBLUP), and weighted ssGBLUP (WssGBLUP). Predictions using BayesC and GBLUP were calculated either with or without an index that included parent average. Genomic predictions that included elite cow genotypes were calculated using ssGBLUP and WssGBLUP. Predictive ability was assessed by coefficients of determination (R2) and regressions of predictions of 135 validation bulls with no daughters in 2006 on deregressed evaluations of those bulls in 2011. A reduction in R2 and regression coefficients was observed from parities 1 through 3. Fat and protein yields had the lowest R2 for all the methods. On average, R2 was lowest for parent averages, followed by GBLUP, BayesC, ssGBLUP, and WssGBLUP. For some traits, R2 for direct genomic values from BayesC and GBLUP were lower than those for parent averages. Genomic estimated breeding values using ssGBLUP were the least biased, and this method appears to be a suitable tool for genomic evaluation of a small genotyped population, as it automatically accounts for parental index, allows for inclusion of female genomic information without preadjustments in evaluations, and uses the same model as in traditional evaluations. Weighted ssGBLUP has the potential for higher evaluation accuracy. © 2014 American Dairy Science Association.
650 $aMEJORAMIENTO GENÉTICO ANIMAL
650 $aMODELOS MATEMÁTICOS
650 $aSELECCIÓN DE GENOTIPOS
650 $aSELECCIÓN GENÓMICA
700 1 $aMISZTAL, I.
700 1 $aTSURUTA, S.
700 1 $aAGUILAR, I.
700 1 $aEZRA, E.
700 1 $aRON, M.
700 1 $aSHIRAK, A.
700 1 $aWELLER, J.I.
773 $tJournal of Dairy Science, 2014$gv.97, no.3, p.1742-1752. OPEN ACCESS.
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Registro original : |
INIA Las Brujas (LB) |
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 | Acceso al texto completo restringido a Biblioteca INIA Treinta y Tres. Por información adicional contacte bibliott@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA Treinta y Tres. |
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Fecha actual : |
21/02/2014 |
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Actualizado : |
11/10/2019 |
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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 : |
TERRA, J.A.; SHAW, J.; REEVES, D. W.; RAPER, R.L.; VAN SANTEN, E.; SCHWAB, E.B.; MASK, P.L. |
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Afiliación : |
JOSÉ ALFREDO TERRA FERNÁNDEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Soil management and landscape variability affects field-scale cotton productivity. |
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Fecha de publicación : |
2006 |
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Fuente / Imprenta : |
Soil Science Society of America Journal, 2006, v.70 (1), p. 98-107. |
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ISSN : |
0361-5995 |
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DOI : |
10.2136/sssaj2005.0179 |
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Idioma : |
Inglés |
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Notas : |
Artilce history: Received 8 June 2005 // Published Jan. 2006. |
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Contenido : |
A better understanding of interactions between soil management and landscape variability and their effects on cotton (Gossypium hirsutum L.) productivity is needed for precision management. We assessed management practices and landscape variability effects on seed cotton yield in a 9-ha, Alabama field (Typic and Aquic Paleudults) during 2001–2003. We hypothesize that landscapes have major effects on cotton productivity, but these effects vary based on management and climate. Treatments were established in replicated strips traversing the landscape in a corn (Zea mays L.)–cotton rotation.
Treatments included a conventional system with or without 10 Mg ha21 yr21 dairy manure (CTmanure or CT), and a conservation system with and without manure (NTmanure or NT). Conventional systems consisted of chisel plowing/disking 1 in-row subsoiling without cover crops. Conservation systems combined no surface tillage with in-row subsoiling and winter cover crops. A soil survey, topographic survey, and interpolated surfaces of soil electrical conductivity (EC), soil organic carbon (SOC), and surface soil texture were used to delineate five zones using fuzzy k-means clustering. Overall (2001–2003), conservation systems improved cotton yield compared with conventional systems (2710 vs. 2380 kg ha21 ); neither manure nor treatment 3 year interactions were significant. The conservation system was more
productive than the conventional system in 87% of the cluster 3 year combinations. Slope, EC, SOC, and clay content were correlated with yield in all treatments. Soil and terrain attributes explained 16 to 64% of yield variation, however, their significance fluctuated between years and treatments. In dry years, factor analyses suggested variables related with soil quality and field-scale water dynamics had greater impacts on CT yields than NT yields. Our results indicate that management zones developed using relatively static soil-landscape data are relatively more suitable for conservation systems, and these zones are affected by soil management. In addition, the impact of NT on yields is most apparent on degraded soils in dry years. MenosA better understanding of interactions between soil management and landscape variability and their effects on cotton (Gossypium hirsutum L.) productivity is needed for precision management. We assessed management practices and landscape variability effects on seed cotton yield in a 9-ha, Alabama field (Typic and Aquic Paleudults) during 2001–2003. We hypothesize that landscapes have major effects on cotton productivity, but these effects vary based on management and climate. Treatments were established in replicated strips traversing the landscape in a corn (Zea mays L.)–cotton rotation.
Treatments included a conventional system with or without 10 Mg ha21 yr21 dairy manure (CTmanure or CT), and a conservation system with and without manure (NTmanure or NT). Conventional systems consisted of chisel plowing/disking 1 in-row subsoiling without cover crops. Conservation systems combined no surface tillage with in-row subsoiling and winter cover crops. A soil survey, topographic survey, and interpolated surfaces of soil electrical conductivity (EC), soil organic carbon (SOC), and surface soil texture were used to delineate five zones using fuzzy k-means clustering. Overall (2001–2003), conservation systems improved cotton yield compared with conventional systems (2710 vs. 2380 kg ha21 ); neither manure nor treatment 3 year interactions were significant. The conservation system was more
productive than the conventional system in 87% of the cluster 3 year combinations. Slope, EC, S... Presentar Todo |
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Thesagro : |
ALGODON; SUELOS. |
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Asunto categoría : |
P36 Erosión conservación y recuperación del suelo |
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Marc : |
LEADER 02913naa a2200253 a 4500
001 1032797
005 2019-10-11
008 2006 bl uuuu u00u1 u #d
022 $a0361-5995
024 7 $a10.2136/sssaj2005.0179$2DOI
100 1 $aTERRA, J.A.
245 $aSoil management and landscape variability affects field-scale cotton productivity.$h[electronic resource]
260 $c2006
500 $aArtilce history: Received 8 June 2005 // Published Jan. 2006.
520 $aA better understanding of interactions between soil management and landscape variability and their effects on cotton (Gossypium hirsutum L.) productivity is needed for precision management. We assessed management practices and landscape variability effects on seed cotton yield in a 9-ha, Alabama field (Typic and Aquic Paleudults) during 2001–2003. We hypothesize that landscapes have major effects on cotton productivity, but these effects vary based on management and climate. Treatments were established in replicated strips traversing the landscape in a corn (Zea mays L.)–cotton rotation. Treatments included a conventional system with or without 10 Mg ha21 yr21 dairy manure (CTmanure or CT), and a conservation system with and without manure (NTmanure or NT). Conventional systems consisted of chisel plowing/disking 1 in-row subsoiling without cover crops. Conservation systems combined no surface tillage with in-row subsoiling and winter cover crops. A soil survey, topographic survey, and interpolated surfaces of soil electrical conductivity (EC), soil organic carbon (SOC), and surface soil texture were used to delineate five zones using fuzzy k-means clustering. Overall (2001–2003), conservation systems improved cotton yield compared with conventional systems (2710 vs. 2380 kg ha21 ); neither manure nor treatment 3 year interactions were significant. The conservation system was more productive than the conventional system in 87% of the cluster 3 year combinations. Slope, EC, SOC, and clay content were correlated with yield in all treatments. Soil and terrain attributes explained 16 to 64% of yield variation, however, their significance fluctuated between years and treatments. In dry years, factor analyses suggested variables related with soil quality and field-scale water dynamics had greater impacts on CT yields than NT yields. Our results indicate that management zones developed using relatively static soil-landscape data are relatively more suitable for conservation systems, and these zones are affected by soil management. In addition, the impact of NT on yields is most apparent on degraded soils in dry years.
650 $aALGODON
650 $aSUELOS
700 1 $aSHAW, J.
700 1 $aREEVES, D. W.
700 1 $aRAPER, R.L.
700 1 $aVAN SANTEN, E.
700 1 $aSCHWAB, E.B.
700 1 $aMASK, P.L.
773 $tSoil Science Society of America Journal, 2006$gv.70 (1), p. 98-107.
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