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Registro completo
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Biblioteca (s) : |
INIA Tacuarembó. |
Fecha : |
21/02/2014 |
Actualizado : |
10/02/2020 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Autor : |
CRUZ, P.; LEZANA, L.; DURANTE, M.; JAURENA, M.; FIGARI, M.; DE OLIVEIRA, L.B.; THEAU, J.-P.; MASSA, E.; VIEGAS, J.; DE QUADROS, F.L.F. |
Afiliación : |
PABLO CRUZ, AGIR, Université de Toulouse, INRA, INPT, INP-EI PURPAN, Castanet-Tolosan, France; LUCRECIA LEZANA, EEA INTA Paraná, Entre Rios, Argentina; MARTÍN DURANTE, EEA INTA Concepción Del Uruguay, Argentina.; MARTIN ALEJANDRO JAURENA BARRIOS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MERCEDES FIGARI, MGAP (Ministerio Ganadería Agricultura y Pesca).; LEANDRO BITTENCOURT DE OLIVERA, Universidade Federal de Santa Maria, Santa Maria, Brazil.; JEAN-PIERRE THEAU; ERNESTO MASSA; JULIO VIEGAS, Universidade Federal de Santa Maria, Santa Maria, Brazil.; FERNANDO L. FERREIRA DE QUADROS, Universidade Federal de Santa Maria, Santa Maria, Brazil. |
Título : |
A functional classification of 63 common poaceae in the "campos" grasslands of South America. [Una clasificación funcional de 63 Poáceas comunes de los pastizales naturales de Sudamérica]. |
Fecha de publicación : |
2019 |
Fuente / Imprenta : |
Ecologia Austral, August 2019, Volume 29, Issue 2, Pages 239-248. OPEN ACCESS. |
DOI : |
10.25260/EA.19.29.2.0.727 |
Idioma : |
Inglés |
Notas : |
Article history: Recibido: 23 de Marzo de 2018 // Aceptado: 26 de Febrero de 2019. |
Contenido : |
ABSTRACT. The natural grasslands that form part of the ?Campos? of South America contain a large number of species at the paddock level (high α diversity), but little differentiation among paddocks (low β diversity). Thus, forage resources at the farm level have slightly different seasonal growth peaks, in part due to the low frequency of fertilization, the lack of differential grazing management and mowing of these plant communities. To stimulate diversification of these forage resources, it is possible to take advantage of differential responses of each type of plant community to changes in their use. Characterizing species by functional traits allows to orient the use of forage resources according their functional composition, thus increasing the diversity of vegetation types, which favors differentiation of growth peaks among paddocks. After an initial division between C3 and C4 species, 63 Poaceae species were classified into eight groups or plant functional types (PFT) according to their preferred degree of soil fertility and use intensity. Based on the leaf dry matter content (LDMC) measured in experiments in Brazil, Uruguay and Argentina, we distinguished four PFT, two for C3 species and two for C4 species, with LDMC less than or equal to 300 mg/g. The species of these four PFT are adapted to fertile environments and intensive defoliation. Fertilizing and using more intensively vegetation dominated by species with these low LDMC can diversify the use value of paddocks, thus facilitating use of forage resources at the farm level. RESUMEN. Una clasificación funcional de 63 Poáceas comunes de los pastizales naturales de Sudamérica. Los pastizales naturales que forman parte de los Campos de América del Sur se caracterizan por tener un gran número de especies a nivel de potrero (alta diversidad α) y una diferenciación entre potreros relativamente baja (baja diversidad β). Esto significa que, a nivel de predio, se dispone de un conjunto de recursos forrajeros con picos de crecimiento estacionales poco diferenciados. En parte, esto es consecuencia del uso escaso de prácticas diferenciales de fertilización, de manejo del pastoreo y de corte sobre dichas comunidades vegetales. Para incentivar la diversificación de los recursos forrajeros (aumentar la diversidad β) se puede aprovechar la respuesta diferencial de los pastizales frente a cambios en su uso. Caracterizar las especies a través de sus rasgos funcionales permite orientar el uso del recurso al aplicar el tratamiento adecuado a su composición, aumentar la diversidad de tipos de vegetación y favorecer, de esta manera, el escalonamiento de la producción entre distintos potreros. Luego de una primera división entre especies C3 y C4, se clasificaron 63 Poáceas en 8 grupos o tipos funcionales de plantas (PFT) según su preferencia por sitios más o menos fértiles e intensidades
de uso más o menos altas. Sobre la base de los valores de contenido en materia seca foliar (LDMC) obtenidos en varios experimentos en Brasil, Uruguay y Argentina se separaron cuatro PFT, dos de especies en C3 y otros dos de especies en C4, que presentan valores inferiores o muy cercanos a 300 mg/g. Las especies de estos cuatro PFT están adaptadas a ambientes fértiles y defoliadas con gran intensidad, a diferencia de los cuatro PFT restantes. Fertilizar y usar de forma más intensa la vegetación donde dominan las especies con esas bajas LDMC permitirá diversificar el valor de uso de los potreros, lo que facilita el aprovechamiento de los recursos forrajeros a nivel de predio. MenosABSTRACT. The natural grasslands that form part of the ?Campos? of South America contain a large number of species at the paddock level (high α diversity), but little differentiation among paddocks (low β diversity). Thus, forage resources at the farm level have slightly different seasonal growth peaks, in part due to the low frequency of fertilization, the lack of differential grazing management and mowing of these plant communities. To stimulate diversification of these forage resources, it is possible to take advantage of differential responses of each type of plant community to changes in their use. Characterizing species by functional traits allows to orient the use of forage resources according their functional composition, thus increasing the diversity of vegetation types, which favors differentiation of growth peaks among paddocks. After an initial division between C3 and C4 species, 63 Poaceae species were classified into eight groups or plant functional types (PFT) according to their preferred degree of soil fertility and use intensity. Based on the leaf dry matter content (LDMC) measured in experiments in Brazil, Uruguay and Argentina, we distinguished four PFT, two for C3 species and two for C4 species, with LDMC less than or equal to 300 mg/g. The species of these four PFT are adapted to fertile environments and intensive defoliation. Fertilizing and using more intensively vegetation dominated by species with these low LDMC can diversify the use value ... Presentar Todo |
Palabras claves : |
DIVERSIDAD; DIVERSITY; MANAGEMENT; MANEJO. |
Thesagro : |
CAMPO NATURAL. |
Asunto categoría : |
A50 Investigación agraria |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/13459/1/JAURENA-2019.pdf
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Marc : |
LEADER 04662naa a2200313 a 4500 001 1027645 005 2020-02-10 008 2019 bl uuuu u00u1 u #d 024 7 $a10.25260/EA.19.29.2.0.727$2DOI 100 1 $aCRUZ, P. 245 $aA functional classification of 63 common poaceae in the "campos" grasslands of South America. [Una clasificación funcional de 63 Poáceas comunes de los pastizales naturales de Sudamérica].$h[electronic resource] 260 $c2019 500 $aArticle history: Recibido: 23 de Marzo de 2018 // Aceptado: 26 de Febrero de 2019. 520 $aABSTRACT. The natural grasslands that form part of the ?Campos? of South America contain a large number of species at the paddock level (high α diversity), but little differentiation among paddocks (low β diversity). Thus, forage resources at the farm level have slightly different seasonal growth peaks, in part due to the low frequency of fertilization, the lack of differential grazing management and mowing of these plant communities. To stimulate diversification of these forage resources, it is possible to take advantage of differential responses of each type of plant community to changes in their use. Characterizing species by functional traits allows to orient the use of forage resources according their functional composition, thus increasing the diversity of vegetation types, which favors differentiation of growth peaks among paddocks. After an initial division between C3 and C4 species, 63 Poaceae species were classified into eight groups or plant functional types (PFT) according to their preferred degree of soil fertility and use intensity. Based on the leaf dry matter content (LDMC) measured in experiments in Brazil, Uruguay and Argentina, we distinguished four PFT, two for C3 species and two for C4 species, with LDMC less than or equal to 300 mg/g. The species of these four PFT are adapted to fertile environments and intensive defoliation. Fertilizing and using more intensively vegetation dominated by species with these low LDMC can diversify the use value of paddocks, thus facilitating use of forage resources at the farm level. RESUMEN. Una clasificación funcional de 63 Poáceas comunes de los pastizales naturales de Sudamérica. Los pastizales naturales que forman parte de los Campos de América del Sur se caracterizan por tener un gran número de especies a nivel de potrero (alta diversidad α) y una diferenciación entre potreros relativamente baja (baja diversidad β). Esto significa que, a nivel de predio, se dispone de un conjunto de recursos forrajeros con picos de crecimiento estacionales poco diferenciados. En parte, esto es consecuencia del uso escaso de prácticas diferenciales de fertilización, de manejo del pastoreo y de corte sobre dichas comunidades vegetales. Para incentivar la diversificación de los recursos forrajeros (aumentar la diversidad β) se puede aprovechar la respuesta diferencial de los pastizales frente a cambios en su uso. Caracterizar las especies a través de sus rasgos funcionales permite orientar el uso del recurso al aplicar el tratamiento adecuado a su composición, aumentar la diversidad de tipos de vegetación y favorecer, de esta manera, el escalonamiento de la producción entre distintos potreros. Luego de una primera división entre especies C3 y C4, se clasificaron 63 Poáceas en 8 grupos o tipos funcionales de plantas (PFT) según su preferencia por sitios más o menos fértiles e intensidades de uso más o menos altas. Sobre la base de los valores de contenido en materia seca foliar (LDMC) obtenidos en varios experimentos en Brasil, Uruguay y Argentina se separaron cuatro PFT, dos de especies en C3 y otros dos de especies en C4, que presentan valores inferiores o muy cercanos a 300 mg/g. Las especies de estos cuatro PFT están adaptadas a ambientes fértiles y defoliadas con gran intensidad, a diferencia de los cuatro PFT restantes. Fertilizar y usar de forma más intensa la vegetación donde dominan las especies con esas bajas LDMC permitirá diversificar el valor de uso de los potreros, lo que facilita el aprovechamiento de los recursos forrajeros a nivel de predio. 650 $aCAMPO NATURAL 653 $aDIVERSIDAD 653 $aDIVERSITY 653 $aMANAGEMENT 653 $aMANEJO 700 1 $aLEZANA, L. 700 1 $aDURANTE, M. 700 1 $aJAURENA, M. 700 1 $aFIGARI, M. 700 1 $aDE OLIVEIRA, L.B. 700 1 $aTHEAU, J.-P. 700 1 $aMASSA, E. 700 1 $aVIEGAS, J. 700 1 $aDE QUADROS, F.L.F. 773 $tEcologia Austral, August 2019, Volume 29, Issue 2, Pages 239-248. OPEN ACCESS.
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INIA Tacuarembó (TBO) |
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Biblioteca (s) : |
INIA La Estanzuela. |
Fecha actual : |
21/02/2014 |
Actualizado : |
05/12/2018 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
A - 1 |
Autor : |
BRANDARIZ , S.; GONZÁLEZ RAYMÚNDEZ, A.; LADO, B.; MALOSETTI, M.; FRANCO GARCIA, A.; QUINCKE, M.; VON ZITZEWITZ, J.; CASTRO, M.; MATUS,I.; DEL POZO, A.; CASTRO, A.J.; GUTIÉRREZ, L. |
Afiliación : |
SOFÍA P. BRANDARIZ, Universidad de la República (UdelaR); Facultad de Agronomía, Uruguay.; AGUSTÍN GONZÁLEZ REYMÚNDEZ; BETTINA LADO; MARCOS MALOSETTI; ANTONIO AUGUSTO FRANCO GARCIA; MARTIN CONRADO QUINCKE WALDEN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JARISLAV RAMON VON ZITZEWITZ VON SALVIATI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARINA CASTRO DERENYI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; IVÁN MATUS; ALEJANDRO DEL POZO; ARIEL J. CASTRO; LUCÍA GUTIÉRREZ. |
Título : |
Ascertainment bias from imputation methods evaluation in wheat. |
Fecha de publicación : |
2016 |
Fuente / Imprenta : |
BMC Genomics, 2016, v. 17, p.773. |
DOI : |
10.1186/s12864-016-3120-5 |
Idioma : |
Inglés |
Notas : |
OPEN ACCESS. Article history: Received 2016 Feb 24 // Accepted 2016 Sep 23. |
Contenido : |
Abstract
BACKGROUND:
Whole-genome genotyping techniques like Genotyping-by-sequencing (GBS) are being used for genetic studies such as Genome-Wide Association (GWAS) and Genomewide Selection (GS), where different strategies for imputation have been developed. Nevertheless, imputation error may lead to poor performance (i.e. smaller power or higher false positive rate) when complete data is not required as it is for GWAS, and each marker is taken at a time. The aim of this study was to compare the performance of GWAS analysis for Quantitative Trait Loci (QTL) of major and minor effect using different imputation methods when no reference panel is available in a wheat GBS panel.
RESULTS:
In this study, we compared the power and false positive rate of dissecting quantitative traits for imputed and not-imputed marker score matrices in: (1) a complete molecular marker barley panel array, and (2) a GBS wheat panel with missing data. We found that there is an ascertainment bias in imputation method comparisons. Simulating over a complete matrix and creating missing data at random proved that imputation methods have a poorer performance. Furthermore, we found that when QTL were simulated with imputed data, the imputation methods performed better than the not-imputed ones. On the other hand, when QTL were simulated with not-imputed data, the not-imputed method and one of the imputation methods performed better for dissecting quantitative traits. Moreover, larger differences between imputation methods were detected for QTL of major effect than QTL of minor effect. We also compared the different marker score matrices for GWAS analysis in a real wheat phenotype dataset, and we found minimal differences indicating that imputation did not improve the GWAS performance when a reference panel was not available.
CONCLUSIONS:
Poorer performance was found in GWAS analysis when an imputed marker score matrix was used, no reference panel is available, in a wheat GBS panel. MenosAbstract
BACKGROUND:
Whole-genome genotyping techniques like Genotyping-by-sequencing (GBS) are being used for genetic studies such as Genome-Wide Association (GWAS) and Genomewide Selection (GS), where different strategies for imputation have been developed. Nevertheless, imputation error may lead to poor performance (i.e. smaller power or higher false positive rate) when complete data is not required as it is for GWAS, and each marker is taken at a time. The aim of this study was to compare the performance of GWAS analysis for Quantitative Trait Loci (QTL) of major and minor effect using different imputation methods when no reference panel is available in a wheat GBS panel.
RESULTS:
In this study, we compared the power and false positive rate of dissecting quantitative traits for imputed and not-imputed marker score matrices in: (1) a complete molecular marker barley panel array, and (2) a GBS wheat panel with missing data. We found that there is an ascertainment bias in imputation method comparisons. Simulating over a complete matrix and creating missing data at random proved that imputation methods have a poorer performance. Furthermore, we found that when QTL were simulated with imputed data, the imputation methods performed better than the not-imputed ones. On the other hand, when QTL were simulated with not-imputed data, the not-imputed method and one of the imputation methods performed better for dissecting quantitative traits. Moreover, larger differences between ... Presentar Todo |
Palabras claves : |
FALSE POSITIVE; FALSO POSITIVO; GBS; GWAS; POWER; QTLs. |
Thesagro : |
MEJORAMIENTO DE TRIGO. |
Asunto categoría : |
F30 Genética vegetal y fitomejoramiento |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/12122/1/s12864-016-3120-5.pdf
https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-016-3120-5
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Marc : |
LEADER 02972nam a2200349 a 4500 001 1047336 005 2018-12-05 008 2016 bl uuuu u0uu1 u #d 024 7 $a10.1186/s12864-016-3120-5$2DOI 100 1 $aBRANDARIZ , S. 245 $aAscertainment bias from imputation methods evaluation in wheat.$h[electronic resource] 260 $aBMC Genomics, 2016, v. 17, p.773.$c2016 500 $aOPEN ACCESS. Article history: Received 2016 Feb 24 // Accepted 2016 Sep 23. 520 $aAbstract BACKGROUND: Whole-genome genotyping techniques like Genotyping-by-sequencing (GBS) are being used for genetic studies such as Genome-Wide Association (GWAS) and Genomewide Selection (GS), where different strategies for imputation have been developed. Nevertheless, imputation error may lead to poor performance (i.e. smaller power or higher false positive rate) when complete data is not required as it is for GWAS, and each marker is taken at a time. The aim of this study was to compare the performance of GWAS analysis for Quantitative Trait Loci (QTL) of major and minor effect using different imputation methods when no reference panel is available in a wheat GBS panel. RESULTS: In this study, we compared the power and false positive rate of dissecting quantitative traits for imputed and not-imputed marker score matrices in: (1) a complete molecular marker barley panel array, and (2) a GBS wheat panel with missing data. We found that there is an ascertainment bias in imputation method comparisons. Simulating over a complete matrix and creating missing data at random proved that imputation methods have a poorer performance. Furthermore, we found that when QTL were simulated with imputed data, the imputation methods performed better than the not-imputed ones. On the other hand, when QTL were simulated with not-imputed data, the not-imputed method and one of the imputation methods performed better for dissecting quantitative traits. Moreover, larger differences between imputation methods were detected for QTL of major effect than QTL of minor effect. We also compared the different marker score matrices for GWAS analysis in a real wheat phenotype dataset, and we found minimal differences indicating that imputation did not improve the GWAS performance when a reference panel was not available. CONCLUSIONS: Poorer performance was found in GWAS analysis when an imputed marker score matrix was used, no reference panel is available, in a wheat GBS panel. 650 $aMEJORAMIENTO DE TRIGO 653 $aFALSE POSITIVE 653 $aFALSO POSITIVO 653 $aGBS 653 $aGWAS 653 $aPOWER 653 $aQTLs 700 1 $aGONZÁLEZ RAYMÚNDEZ, A. 700 1 $aLADO, B. 700 1 $aMALOSETTI, M. 700 1 $aFRANCO GARCIA, A. 700 1 $aQUINCKE, M. 700 1 $aVON ZITZEWITZ, J. 700 1 $aCASTRO, M. 700 1 $aMATUS,I. 700 1 $aDEL POZO, A. 700 1 $aCASTRO, A.J. 700 1 $aGUTIÉRREZ, L.
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