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 | Acceso al texto completo restringido a Biblioteca INIA Las Brujas. Por información adicional contacte bibliolb@inia.org.uy. |
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
24/10/2014 |
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Actualizado : |
15/10/2019 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
COZZOLINO, D.; MORON, D. |
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Afiliación : |
DANIEL COZZOLINO, Australian Wine Research Institute, Adelaide, Australia; DAVID ALEJANDRO MORON YACOEL, Instituto Nacional de Investigación Agropecuaria (INIA), Uruguay. |
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Título : |
Influence of soil particle size on the measurement of sodium by near-infrared reflectance spectroscopy. |
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Fecha de publicación : |
2010 |
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Fuente / Imprenta : |
Communications in Soil Science and Plant Analysis, 2010, v. 41, no.19, p. 2330-2339. |
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ISSN : |
0010-3624 |
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DOI : |
10.1080/00103624.2010.508097 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 10 March 2009 / Accepted 12 January 2010. |
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Contenido : |
ABSTRACT.
This study evaluates the effect of soil particle size (SPS) on the measurement of exchangeable sodium (Na) (EXC-Na) by near-infrared reflectance (NIR) spectroscopy. Three hundred thirty-two (n = 332) top soil samples (0-10 cm) were taken from different locations across Uruguay, analyzed by EXC-Na using emission spectrometry, and scanned in reflectance using a NIR spectrophotometer (1100-2500 nm). Partial least squares (PLS) and principal component regression (PCR) models between reference chemical data and NIR data were developed using cross validation (leaving one out). The coefficient of determination in calibration (R2) and the root mean square of the standard error of cross validation (RMSECV) for EXC-Na concentration were 0.44 (RMSECV: 0.12 mg kg-1) for soil with small particle size (SPS-0.053) and 0.77 (RMSECV: 0.09 mg kg-1) for soils with particle sizes greater than 0.212 mm (SPS-0.212), using the NIR region after second derivative as mathematical transformation. The R2 and RMSECV for EXC-Na concentration using PCR were 0.54 (RMSECV: 0.07 mg kg-1) and 0.80 (RMSECV: 0.03 mg kg-1) for SPS-0.053 and SPS-0.212 samples, respectively.
© Taylor & Francis Group, LLC. |
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Thesagro : |
ESPECTROSCOPÍA DEL INFRARROJO CERCANO; NEAR INFRARED REFLECTANCE SPECTROSCOPY; SUELOS AGRÍCOLAS; URUGUAY. |
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Asunto categoría : |
P30 Ciencia del suelo y manejo del suelo |
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Marc : |
LEADER 01989naa a2200217 a 4500
001 1051303
005 2019-10-15
008 2010 bl uuuu u00u1 u #d
022 $a0010-3624
024 7 $a10.1080/00103624.2010.508097$2DOI
100 1 $aCOZZOLINO, D.
245 $aInfluence of soil particle size on the measurement of sodium by near-infrared reflectance spectroscopy.$h[electronic resource]
260 $c2010
500 $aArticle history: Received 10 March 2009 / Accepted 12 January 2010.
520 $aABSTRACT. This study evaluates the effect of soil particle size (SPS) on the measurement of exchangeable sodium (Na) (EXC-Na) by near-infrared reflectance (NIR) spectroscopy. Three hundred thirty-two (n = 332) top soil samples (0-10 cm) were taken from different locations across Uruguay, analyzed by EXC-Na using emission spectrometry, and scanned in reflectance using a NIR spectrophotometer (1100-2500 nm). Partial least squares (PLS) and principal component regression (PCR) models between reference chemical data and NIR data were developed using cross validation (leaving one out). The coefficient of determination in calibration (R2) and the root mean square of the standard error of cross validation (RMSECV) for EXC-Na concentration were 0.44 (RMSECV: 0.12 mg kg-1) for soil with small particle size (SPS-0.053) and 0.77 (RMSECV: 0.09 mg kg-1) for soils with particle sizes greater than 0.212 mm (SPS-0.212), using the NIR region after second derivative as mathematical transformation. The R2 and RMSECV for EXC-Na concentration using PCR were 0.54 (RMSECV: 0.07 mg kg-1) and 0.80 (RMSECV: 0.03 mg kg-1) for SPS-0.053 and SPS-0.212 samples, respectively. © Taylor & Francis Group, LLC.
650 $aESPECTROSCOPÍA DEL INFRARROJO CERCANO
650 $aNEAR INFRARED REFLECTANCE SPECTROSCOPY
650 $aSUELOS AGRÍCOLAS
650 $aURUGUAY
700 1 $aMORON, D.
773 $tCommunications in Soil Science and Plant Analysis, 2010$gv. 41, no.19, p. 2330-2339.
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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 : |
15/11/2015 |
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Actualizado : |
09/10/2019 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
B - 2 |
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Autor : |
FRAGOMENI, B.O.; MISZTAL, I.; LOURENCO, D.L.; AGUILAR, I.; OKIMOTO, R.; MUIR, W.M. |
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Afiliación : |
IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Changes in variance explained by top SNP windows over generations for three traits in broiler chicken |
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Fecha de publicación : |
2014 |
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Fuente / Imprenta : |
Frontiers in Genetics, 2014, v.5, no.Oct., Article number 332. OPEN ACCESS. |
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ISSN : |
1664-8021 |
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DOI : |
10.3389/fgene.2014.00332 |
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Idioma : |
Inglés |
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Notas : |
Article history: Published 01 October 2014. |
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Contenido : |
ABSTRACT.
The purpose of this study was to determine if the set of genomic regions inferred as accounting for the majority of genetic variation in quantitative traits remain stable over multiple generations of selection. The data set contained phenotypes for five generations of broiler chicken for body weight, breast meat, and leg score. The population consisted of 294,632 animals over five generations and also included genotypes of 41,036 single nucleotide polymorphism (SNP) for 4,866 animals, after quality control. The SNP effects were calculated by a GWAS type analysis using single step genomic BLUP approach for generations 1-3, 2-4, 3-5, and 1-5. Variances were calculated for windows of 20 SNP. The top ten windows for each trait that explained the largest fraction of the genetic variance across generations were examined. Across generations, the top 10 windows explained more than 0.5% but less than 1% of the total variance. Also, the pattern of the windows was not consistent across generations. The windows that explained the greatest variance changed greatly among the combinations of generations, with a few exceptions. In many cases, a window identified as top for one combination, explained less than 0.1% for the other combinations. We conclude that identification of top SNP windows for a population may have little predictive power for genetic selection in the following generations for the traits here evaluated.
© 2014 Fragomeni, Misztal, Lourenco, Aguilar, Okimoto and Muir. MenosABSTRACT.
The purpose of this study was to determine if the set of genomic regions inferred as accounting for the majority of genetic variation in quantitative traits remain stable over multiple generations of selection. The data set contained phenotypes for five generations of broiler chicken for body weight, breast meat, and leg score. The population consisted of 294,632 animals over five generations and also included genotypes of 41,036 single nucleotide polymorphism (SNP) for 4,866 animals, after quality control. The SNP effects were calculated by a GWAS type analysis using single step genomic BLUP approach for generations 1-3, 2-4, 3-5, and 1-5. Variances were calculated for windows of 20 SNP. The top ten windows for each trait that explained the largest fraction of the genetic variance across generations were examined. Across generations, the top 10 windows explained more than 0.5% but less than 1% of the total variance. Also, the pattern of the windows was not consistent across generations. The windows that explained the greatest variance changed greatly among the combinations of generations, with a few exceptions. In many cases, a window identified as top for one combination, explained less than 0.1% for the other combinations. We conclude that identification of top SNP windows for a population may have little predictive power for genetic selection in the following generations for the traits here evaluated.
© 2014 Fragomeni, Misztal, Lourenco, Aguilar, Okimoto and... Presentar Todo |
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Palabras claves : |
Gene identification; Genome-wide association study; Genomic selection; QTL; SsGBLUP. |
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Thesagro : |
MEJORAMIENTO GENETICO ANIMAL; POLLO DE ENGORDE. |
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Asunto categoría : |
L10 Genética y mejoramiento animal |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/5195/1/Aguilar-I.-2014.-Frontiers-in-Genetics.pdf
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Marc : |
LEADER 02443naa a2200301 a 4500
001 1053891
005 2019-10-09
008 2014 bl uuuu u00u1 u #d
022 $a1664-8021
024 7 $a10.3389/fgene.2014.00332$2DOI
100 1 $aFRAGOMENI, B.O.
245 $aChanges in variance explained by top SNP windows over generations for three traits in broiler chicken$h[electronic resource]
260 $c2014
500 $aArticle history: Published 01 October 2014.
520 $aABSTRACT. The purpose of this study was to determine if the set of genomic regions inferred as accounting for the majority of genetic variation in quantitative traits remain stable over multiple generations of selection. The data set contained phenotypes for five generations of broiler chicken for body weight, breast meat, and leg score. The population consisted of 294,632 animals over five generations and also included genotypes of 41,036 single nucleotide polymorphism (SNP) for 4,866 animals, after quality control. The SNP effects were calculated by a GWAS type analysis using single step genomic BLUP approach for generations 1-3, 2-4, 3-5, and 1-5. Variances were calculated for windows of 20 SNP. The top ten windows for each trait that explained the largest fraction of the genetic variance across generations were examined. Across generations, the top 10 windows explained more than 0.5% but less than 1% of the total variance. Also, the pattern of the windows was not consistent across generations. The windows that explained the greatest variance changed greatly among the combinations of generations, with a few exceptions. In many cases, a window identified as top for one combination, explained less than 0.1% for the other combinations. We conclude that identification of top SNP windows for a population may have little predictive power for genetic selection in the following generations for the traits here evaluated. © 2014 Fragomeni, Misztal, Lourenco, Aguilar, Okimoto and Muir.
650 $aMEJORAMIENTO GENETICO ANIMAL
650 $aPOLLO DE ENGORDE
653 $aGene identification
653 $aGenome-wide association study
653 $aGenomic selection
653 $aQTL
653 $aSsGBLUP
700 1 $aMISZTAL, I.
700 1 $aLOURENCO, D.L.
700 1 $aAGUILAR, I.
700 1 $aOKIMOTO, R.
700 1 $aMUIR, W.M.
773 $tFrontiers in Genetics, 2014$gv.5, no.Oct., Article number 332. OPEN ACCESS.
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