Single - step genomic prediction of Eucalyptus dunnii using different identity by descent and identity by state relation ship matrices
- Autores
- Jurcic, Esteban Javier; Villalba, Pamela Victoria; Pathauer, Pablo Santiago; Palazzini, Dino; Oberschelp, Gustavo Pedro Javier; Harrand, Leonel; Garcia, Martin Nahuel; Aguirre, Natalia Cristina; Acuña, Cintia Vanesa; Martinez, Maria Carolina; Rivas, Juan Gabriel; Cisneros, Esteban F.; Lopez, Juan Adolfo; Marcucci Poltri, Susana Noemi; Munilla, Sebastian; Cappa, Eduardo Pablo
- Año de publicación
- 2021
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Eucalyptus L’Hér. (Myrtaceae) is the most valuable and globally planted forest tree genus. Fast growth, adaptability to a broad diversity of tropical and subtropical regions, combined with versatile wood properties for energy, solid products, pulp, and paper have warranted their outstanding position in current world forestry (de Lima et al. 2019). Eucalyptus dunnii Maiden (hereafter E. dunnii) has become increasingly used in commercial afforestation due to its combined good performance for growth, stem straightness, and frost tolerance, together with suitable wood density and pulp yield. In a broad sense, genomic selection (GS) is a family of statistical methods developed for predicting the breeding values of nonphenotyped individuals with the assistance of a large number of molecular markers widespread distributed throughout the genome (Meuwissen et al. 2001). These methods exploit cosegregation between markers and quantitative trait loci (QTL) in linkage disequilibrium (LD). In forest trees, GS is of particular benefit due to the extended breeding cycles caused by delayed reproductive maturity and the need for early selection of traits that express late in life (Mphahlele et al. 2020). In this context, GS has a potentially substantial impact on the rate of genetic gain by increasing the intensity and accuracy of selection and, particularly, by shortening the generational interval (Grattapaglia et al. 2018). The genomic best linear unbiased prediction (GBLUP) is one of the most commonly GS methods. It is basically a variant of the standard BLUP method (hereafter ABLUP, cf. Henderson 1984), where the pedigree-based numerator relationship matrix (Amatrix) is replaced by a genomic relationship matrix (G-matrix, e.g., Habier et al. 2013). Many empirical studies with forest tree species have shown that GBLUP is a very promising approach for tree breeding (e.g., Mphahlele et al. 2020; Resende et al. 2017; Lenz et al. 2019). However, to our knowledge, only two of them have directly investigated the efficiency of genomic prediction using only genotyped trees in E. dunnii through GBLUP (Naidoo et al. 2018; Jones et al. 2019).
Fil: Jurcic, Esteban J. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Villalba, Pamela Victoria. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Pathauer, Pablo Santiago. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina
Fil: Palazzini, Dino A. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Oberschelp, Gustavo Pedro Javier. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Concordia; Argentina
Fil: Harrand, Leonel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Concordia; Argentina
Fil: Garcia, Martin Nahuel. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Aguirre, Natalia Cristina. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Acuña, Cintia Vanesa. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Martinez, Maria Carolina. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Rivas, Juan Gabriel. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Cisneros, Esteban F. Universidad Nacional de Santiago del Estero. Facultad de Ciencias Forestales; Argentina
Fil: Lopez, Juan Adolfo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bella Vista; Argentina
Fil: Marcucci Poltri, Susana Noemi. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Munilla, Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina - Fuente
- Heredity 127 (2) : 176-189 (2021)
- Materia
-
Eucalyptus
Genética
Genetics
Eucalyptus dunnii
genomic prediction
predicción genómica
ssGBLUP - Nivel de accesibilidad
- acceso restringido
- Condiciones de uso
- Repositorio
.jpg)
- Institución
- Instituto Nacional de Tecnología Agropecuaria
- OAI Identificador
- oai:localhost:20.500.12123/10732
Ver los metadatos del registro completo
| id |
INTADig_8d84d360f7d724147fa381aebcb32a15 |
|---|---|
| oai_identifier_str |
oai:localhost:20.500.12123/10732 |
| network_acronym_str |
INTADig |
| repository_id_str |
l |
| network_name_str |
INTA Digital (INTA) |
| spelling |
Single - step genomic prediction of Eucalyptus dunnii using different identity by descent and identity by state relation ship matricesJurcic, Esteban JavierVillalba, Pamela VictoriaPathauer, Pablo SantiagoPalazzini, DinoOberschelp, Gustavo Pedro JavierHarrand, LeonelGarcia, Martin NahuelAguirre, Natalia CristinaAcuña, Cintia VanesaMartinez, Maria CarolinaRivas, Juan GabrielCisneros, Esteban F.Lopez, Juan AdolfoMarcucci Poltri, Susana NoemiMunilla, SebastianCappa, Eduardo PabloEucalyptusGenéticaGeneticsEucalyptus dunniigenomic predictionpredicción genómicassGBLUPEucalyptus L’Hér. (Myrtaceae) is the most valuable and globally planted forest tree genus. Fast growth, adaptability to a broad diversity of tropical and subtropical regions, combined with versatile wood properties for energy, solid products, pulp, and paper have warranted their outstanding position in current world forestry (de Lima et al. 2019). Eucalyptus dunnii Maiden (hereafter E. dunnii) has become increasingly used in commercial afforestation due to its combined good performance for growth, stem straightness, and frost tolerance, together with suitable wood density and pulp yield. In a broad sense, genomic selection (GS) is a family of statistical methods developed for predicting the breeding values of nonphenotyped individuals with the assistance of a large number of molecular markers widespread distributed throughout the genome (Meuwissen et al. 2001). These methods exploit cosegregation between markers and quantitative trait loci (QTL) in linkage disequilibrium (LD). In forest trees, GS is of particular benefit due to the extended breeding cycles caused by delayed reproductive maturity and the need for early selection of traits that express late in life (Mphahlele et al. 2020). In this context, GS has a potentially substantial impact on the rate of genetic gain by increasing the intensity and accuracy of selection and, particularly, by shortening the generational interval (Grattapaglia et al. 2018). The genomic best linear unbiased prediction (GBLUP) is one of the most commonly GS methods. It is basically a variant of the standard BLUP method (hereafter ABLUP, cf. Henderson 1984), where the pedigree-based numerator relationship matrix (Amatrix) is replaced by a genomic relationship matrix (G-matrix, e.g., Habier et al. 2013). Many empirical studies with forest tree species have shown that GBLUP is a very promising approach for tree breeding (e.g., Mphahlele et al. 2020; Resende et al. 2017; Lenz et al. 2019). However, to our knowledge, only two of them have directly investigated the efficiency of genomic prediction using only genotyped trees in E. dunnii through GBLUP (Naidoo et al. 2018; Jones et al. 2019).Fil: Jurcic, Esteban J. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Villalba, Pamela Victoria. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Pathauer, Pablo Santiago. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; ArgentinaFil: Palazzini, Dino A. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Oberschelp, Gustavo Pedro Javier. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Concordia; ArgentinaFil: Harrand, Leonel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Concordia; ArgentinaFil: Garcia, Martin Nahuel. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Aguirre, Natalia Cristina. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Acuña, Cintia Vanesa. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Martinez, Maria Carolina. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Rivas, Juan Gabriel. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Cisneros, Esteban F. Universidad Nacional de Santiago del Estero. Facultad de Ciencias Forestales; ArgentinaFil: Lopez, Juan Adolfo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bella Vista; ArgentinaFil: Marcucci Poltri, Susana Noemi. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Munilla, Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaNature2021-11-10T11:31:46Z2021-11-10T11:31:46Z2021-06-18info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/10732https://www.nature.com/articles/s41437-021-00450-91365-25400018-067Xhttps://doi.org/10.1038/s41437-021-00450-9Heredity 127 (2) : 176-189 (2021)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repograntAgreement/INTA/PNFOR-1104062/AR./Mejoramiento genético de especies forestales introducidas para usos de alto valor.info:eu-repograntAgreement/INTA/PNFOR-1104064/AR./Aplicación de herramientas moleculares para el uso y la conservación de la diversidad genética forestal.info:eu-repo/semantics/restrictedAccess2025-10-23T11:17:47Zoai:localhost:20.500.12123/10732instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-10-23 11:17:48.033INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse |
| dc.title.none.fl_str_mv |
Single - step genomic prediction of Eucalyptus dunnii using different identity by descent and identity by state relation ship matrices |
| title |
Single - step genomic prediction of Eucalyptus dunnii using different identity by descent and identity by state relation ship matrices |
| spellingShingle |
Single - step genomic prediction of Eucalyptus dunnii using different identity by descent and identity by state relation ship matrices Jurcic, Esteban Javier Eucalyptus Genética Genetics Eucalyptus dunnii genomic prediction predicción genómica ssGBLUP |
| title_short |
Single - step genomic prediction of Eucalyptus dunnii using different identity by descent and identity by state relation ship matrices |
| title_full |
Single - step genomic prediction of Eucalyptus dunnii using different identity by descent and identity by state relation ship matrices |
| title_fullStr |
Single - step genomic prediction of Eucalyptus dunnii using different identity by descent and identity by state relation ship matrices |
| title_full_unstemmed |
Single - step genomic prediction of Eucalyptus dunnii using different identity by descent and identity by state relation ship matrices |
| title_sort |
Single - step genomic prediction of Eucalyptus dunnii using different identity by descent and identity by state relation ship matrices |
| dc.creator.none.fl_str_mv |
Jurcic, Esteban Javier Villalba, Pamela Victoria Pathauer, Pablo Santiago Palazzini, Dino Oberschelp, Gustavo Pedro Javier Harrand, Leonel Garcia, Martin Nahuel Aguirre, Natalia Cristina Acuña, Cintia Vanesa Martinez, Maria Carolina Rivas, Juan Gabriel Cisneros, Esteban F. Lopez, Juan Adolfo Marcucci Poltri, Susana Noemi Munilla, Sebastian Cappa, Eduardo Pablo |
| author |
Jurcic, Esteban Javier |
| author_facet |
Jurcic, Esteban Javier Villalba, Pamela Victoria Pathauer, Pablo Santiago Palazzini, Dino Oberschelp, Gustavo Pedro Javier Harrand, Leonel Garcia, Martin Nahuel Aguirre, Natalia Cristina Acuña, Cintia Vanesa Martinez, Maria Carolina Rivas, Juan Gabriel Cisneros, Esteban F. Lopez, Juan Adolfo Marcucci Poltri, Susana Noemi Munilla, Sebastian Cappa, Eduardo Pablo |
| author_role |
author |
| author2 |
Villalba, Pamela Victoria Pathauer, Pablo Santiago Palazzini, Dino Oberschelp, Gustavo Pedro Javier Harrand, Leonel Garcia, Martin Nahuel Aguirre, Natalia Cristina Acuña, Cintia Vanesa Martinez, Maria Carolina Rivas, Juan Gabriel Cisneros, Esteban F. Lopez, Juan Adolfo Marcucci Poltri, Susana Noemi Munilla, Sebastian Cappa, Eduardo Pablo |
| author2_role |
author author author author author author author author author author author author author author author |
| dc.subject.none.fl_str_mv |
Eucalyptus Genética Genetics Eucalyptus dunnii genomic prediction predicción genómica ssGBLUP |
| topic |
Eucalyptus Genética Genetics Eucalyptus dunnii genomic prediction predicción genómica ssGBLUP |
| dc.description.none.fl_txt_mv |
Eucalyptus L’Hér. (Myrtaceae) is the most valuable and globally planted forest tree genus. Fast growth, adaptability to a broad diversity of tropical and subtropical regions, combined with versatile wood properties for energy, solid products, pulp, and paper have warranted their outstanding position in current world forestry (de Lima et al. 2019). Eucalyptus dunnii Maiden (hereafter E. dunnii) has become increasingly used in commercial afforestation due to its combined good performance for growth, stem straightness, and frost tolerance, together with suitable wood density and pulp yield. In a broad sense, genomic selection (GS) is a family of statistical methods developed for predicting the breeding values of nonphenotyped individuals with the assistance of a large number of molecular markers widespread distributed throughout the genome (Meuwissen et al. 2001). These methods exploit cosegregation between markers and quantitative trait loci (QTL) in linkage disequilibrium (LD). In forest trees, GS is of particular benefit due to the extended breeding cycles caused by delayed reproductive maturity and the need for early selection of traits that express late in life (Mphahlele et al. 2020). In this context, GS has a potentially substantial impact on the rate of genetic gain by increasing the intensity and accuracy of selection and, particularly, by shortening the generational interval (Grattapaglia et al. 2018). The genomic best linear unbiased prediction (GBLUP) is one of the most commonly GS methods. It is basically a variant of the standard BLUP method (hereafter ABLUP, cf. Henderson 1984), where the pedigree-based numerator relationship matrix (Amatrix) is replaced by a genomic relationship matrix (G-matrix, e.g., Habier et al. 2013). Many empirical studies with forest tree species have shown that GBLUP is a very promising approach for tree breeding (e.g., Mphahlele et al. 2020; Resende et al. 2017; Lenz et al. 2019). However, to our knowledge, only two of them have directly investigated the efficiency of genomic prediction using only genotyped trees in E. dunnii through GBLUP (Naidoo et al. 2018; Jones et al. 2019). Fil: Jurcic, Esteban J. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Villalba, Pamela Victoria. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Pathauer, Pablo Santiago. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina Fil: Palazzini, Dino A. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Oberschelp, Gustavo Pedro Javier. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Concordia; Argentina Fil: Harrand, Leonel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Concordia; Argentina Fil: Garcia, Martin Nahuel. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Aguirre, Natalia Cristina. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Acuña, Cintia Vanesa. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Martinez, Maria Carolina. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Rivas, Juan Gabriel. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Cisneros, Esteban F. Universidad Nacional de Santiago del Estero. Facultad de Ciencias Forestales; Argentina Fil: Lopez, Juan Adolfo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bella Vista; Argentina Fil: Marcucci Poltri, Susana Noemi. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Munilla, Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina |
| description |
Eucalyptus L’Hér. (Myrtaceae) is the most valuable and globally planted forest tree genus. Fast growth, adaptability to a broad diversity of tropical and subtropical regions, combined with versatile wood properties for energy, solid products, pulp, and paper have warranted their outstanding position in current world forestry (de Lima et al. 2019). Eucalyptus dunnii Maiden (hereafter E. dunnii) has become increasingly used in commercial afforestation due to its combined good performance for growth, stem straightness, and frost tolerance, together with suitable wood density and pulp yield. In a broad sense, genomic selection (GS) is a family of statistical methods developed for predicting the breeding values of nonphenotyped individuals with the assistance of a large number of molecular markers widespread distributed throughout the genome (Meuwissen et al. 2001). These methods exploit cosegregation between markers and quantitative trait loci (QTL) in linkage disequilibrium (LD). In forest trees, GS is of particular benefit due to the extended breeding cycles caused by delayed reproductive maturity and the need for early selection of traits that express late in life (Mphahlele et al. 2020). In this context, GS has a potentially substantial impact on the rate of genetic gain by increasing the intensity and accuracy of selection and, particularly, by shortening the generational interval (Grattapaglia et al. 2018). The genomic best linear unbiased prediction (GBLUP) is one of the most commonly GS methods. It is basically a variant of the standard BLUP method (hereafter ABLUP, cf. Henderson 1984), where the pedigree-based numerator relationship matrix (Amatrix) is replaced by a genomic relationship matrix (G-matrix, e.g., Habier et al. 2013). Many empirical studies with forest tree species have shown that GBLUP is a very promising approach for tree breeding (e.g., Mphahlele et al. 2020; Resende et al. 2017; Lenz et al. 2019). However, to our knowledge, only two of them have directly investigated the efficiency of genomic prediction using only genotyped trees in E. dunnii through GBLUP (Naidoo et al. 2018; Jones et al. 2019). |
| publishDate |
2021 |
| dc.date.none.fl_str_mv |
2021-11-10T11:31:46Z 2021-11-10T11:31:46Z 2021-06-18 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
| format |
article |
| status_str |
publishedVersion |
| dc.identifier.none.fl_str_mv |
http://hdl.handle.net/20.500.12123/10732 https://www.nature.com/articles/s41437-021-00450-9 1365-2540 0018-067X https://doi.org/10.1038/s41437-021-00450-9 |
| url |
http://hdl.handle.net/20.500.12123/10732 https://www.nature.com/articles/s41437-021-00450-9 https://doi.org/10.1038/s41437-021-00450-9 |
| identifier_str_mv |
1365-2540 0018-067X |
| dc.language.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.none.fl_str_mv |
info:eu-repograntAgreement/INTA/PNFOR-1104062/AR./Mejoramiento genético de especies forestales introducidas para usos de alto valor. info:eu-repograntAgreement/INTA/PNFOR-1104064/AR./Aplicación de herramientas moleculares para el uso y la conservación de la diversidad genética forestal. |
| dc.rights.none.fl_str_mv |
info:eu-repo/semantics/restrictedAccess |
| eu_rights_str_mv |
restrictedAccess |
| dc.format.none.fl_str_mv |
application/pdf |
| dc.publisher.none.fl_str_mv |
Nature |
| publisher.none.fl_str_mv |
Nature |
| dc.source.none.fl_str_mv |
Heredity 127 (2) : 176-189 (2021) reponame:INTA Digital (INTA) instname:Instituto Nacional de Tecnología Agropecuaria |
| reponame_str |
INTA Digital (INTA) |
| collection |
INTA Digital (INTA) |
| instname_str |
Instituto Nacional de Tecnología Agropecuaria |
| repository.name.fl_str_mv |
INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria |
| repository.mail.fl_str_mv |
tripaldi.nicolas@inta.gob.ar |
| _version_ |
1846787551501746176 |
| score |
12.982451 |