| |
|
|
 | Acceso al texto completo restringido a Biblioteca INIA Las Brujas. Por información adicional contacte bibliolb@inia.org.uy. |
|
Registro completo
|
|
Biblioteca (s) : |
INIA Las Brujas. |
|
Fecha : |
06/12/2016 |
|
Actualizado : |
29/10/2019 |
|
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
|
Autor : |
PAZ MARTY, A.; CASTILLO, A.; ZOPPOLO, R. |
|
Afiliación : |
A. PAZ MARTY, GranaSur (Tinfol S.A.); ALICIA MARIA CASTILLO SALLE, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ROBERTO JOSE ZOPPOLO GOLDSCHMIDT, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
|
Título : |
Pomegranate: A growing alternative for fruit production in Uruguay. |
|
Fecha de publicación : |
2015 |
|
Fuente / Imprenta : |
Acta Horticulturae, 2015, no. 1089, p. 351-355. |
|
Serie : |
(Acta Horticulturae; 1089) |
|
ISBN : |
9789462610835 |
|
ISSN : |
0567-7572 |
|
DOI : |
10.17660/ActaHortic.2015.1089.46 |
|
Idioma : |
Inglés |
|
Notas : |
In: Acta Horticulturae (ISHS) 1089: III International Symposium on Pomegranate and Minor Mediterranean Fruits. Editors: E. Wilkins, Dong Wang, Zhaohe Yuan. Publication date: July 2015. |
|
Contenido : |
ABSTRACT.
The pomegranate (Punica granatum) was presumably introduced in Uruguay by Spanish immigrants during the XVIII century. The type of pomegranate introduced was ?Mollar?, with yellow rind, pink and sweet arils with medium hard seeds being used mainly for self-consumption. The need of new alternatives in fruit growing has promoted new developments with this crop. Since 2008 in a joint project between the private sector and the National Agricultural Research Institute (INIA), with the support of the National Agency for Research and Innovation (ANII), more than 50 cultivars of diverse origins were introduced and the first commercial plantations were implanted with ?Wonderful? plants. Most of the varietal introduction was made from the USDA National Clonal Germplasm Repository, Wolfskill, USA, of selected cultivars by Dr. Gregory Levin. These cultivars were introduced in the form of cuttings and multiplied in vitro after adjustment of protocols. With these plants, assessment blocks were installed and some of the cultivars were selected for commercial plantations. The results of in vitro multiplication were not equal for all cultivars, having some that were easily propagated while others had fairly low propagation rates. The first commercial
plantations of ?Wonderful? were introduced in the spring of 2009 and planted in a tree spacing of 4 by 2 m (1250 plants/ha); harvesting of the first fruit took place in the fall of 2011.
@2015 ISHS |
|
Palabras claves : |
IN VITRO PROPAGATION; USDA NATIONAL CLONAL GERMPLASM REPOSITORY. |
|
Thesagro : |
GRANADA (FRUTA); PROPAGACION VEGETATIVA; PUNICA GRANATUM. |
|
Asunto categoría : |
F30 Genética vegetal y fitomejoramiento |
|
Marc : |
LEADER 02434naa a2200265 a 4500
001 1056220
005 2019-10-29
008 2015 bl uuuu u00u1 u #d
020 $a9789462610835
022 $a0567-7572
024 7 $a10.17660/ActaHortic.2015.1089.46$2DOI
100 1 $aPAZ MARTY, A.
245 $aPomegranate$bA growing alternative for fruit production in Uruguay.$h[electronic resource]
260 $c2015
490 $a(Acta Horticulturae; 1089)
500 $aIn: Acta Horticulturae (ISHS) 1089: III International Symposium on Pomegranate and Minor Mediterranean Fruits. Editors: E. Wilkins, Dong Wang, Zhaohe Yuan. Publication date: July 2015.
520 $aABSTRACT. The pomegranate (Punica granatum) was presumably introduced in Uruguay by Spanish immigrants during the XVIII century. The type of pomegranate introduced was ?Mollar?, with yellow rind, pink and sweet arils with medium hard seeds being used mainly for self-consumption. The need of new alternatives in fruit growing has promoted new developments with this crop. Since 2008 in a joint project between the private sector and the National Agricultural Research Institute (INIA), with the support of the National Agency for Research and Innovation (ANII), more than 50 cultivars of diverse origins were introduced and the first commercial plantations were implanted with ?Wonderful? plants. Most of the varietal introduction was made from the USDA National Clonal Germplasm Repository, Wolfskill, USA, of selected cultivars by Dr. Gregory Levin. These cultivars were introduced in the form of cuttings and multiplied in vitro after adjustment of protocols. With these plants, assessment blocks were installed and some of the cultivars were selected for commercial plantations. The results of in vitro multiplication were not equal for all cultivars, having some that were easily propagated while others had fairly low propagation rates. The first commercial plantations of ?Wonderful? were introduced in the spring of 2009 and planted in a tree spacing of 4 by 2 m (1250 plants/ha); harvesting of the first fruit took place in the fall of 2011. @2015 ISHS
650 $aGRANADA (FRUTA)
650 $aPROPAGACION VEGETATIVA
650 $aPUNICA GRANATUM
653 $aIN VITRO PROPAGATION
653 $aUSDA NATIONAL CLONAL GERMPLASM REPOSITORY
700 1 $aCASTILLO, A.
700 1 $aZOPPOLO, R.
773 $tActa Horticulturae, 2015, no. 1089, p. 351-355.
Descargar
Esconder MarcPresentar Marc Completo |
|
Registro original : |
INIA Las Brujas (LB) |
|
|
Biblioteca
|
Identificación
|
Origen
|
Tipo / Formato
|
Clasificación
|
Cutter
|
Registro
|
Volumen
|
Estado
|
Volver
|
|
|
|
Registro completo
|
|
Biblioteca (s) : |
INIA Las Brujas. |
|
Fecha actual : |
14/09/2023 |
|
Actualizado : |
14/09/2023 |
|
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
|
Circulación / Nivel : |
Internacional - -- |
|
Autor : |
REBOLLO, I.; AGUILAR, I.; PÉREZ DE VIDA, F.; MOLINA, F.; GUTIÉRREZ, L.; ROSAS, J.E. |
|
Afiliación : |
MARÍA INÉS REBOLLO PANUNCIO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Department of Statistics, University de la República, College of Agriculture, Garzón 780, Montevideo, Montevideo, Uruguay; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FERNANDO BLAS PEREZ DE VIDA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FEDERICO MOLINA CASELLA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LUCÍA GUTIÉRREZEPARTMENT OF STATISTICS, UNIVERSITY DE LA REPÚBLICA, COLLEGE OF AGRICULTURE, GARZÓN 780, MONTEVIDEO, MONTEVIDEO, URUGUAY DEPARTMENT OF AGRONOMY, UNIVERSITY OF WISCONSIN–MADISON, 1575 LINDEN DRIVE, MADISON, WI, UNITED STATES, Department of Statistics, University de la República, College of Agriculture, Montevideo, Uruguay; Department of Agronomy, University of Wisconsin-Madison, 1575 Linden Drive, Madison, WI, United States; JUAN EDUARDO ROSAS CAISSIOLS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Department of Statistics, University de la República, College of Agriculture, Garzón 780, Montevideo, Montevideo, Uruguay. |
|
Título : |
Genotype by environment interaction characterization and its modeling with random regression to climatic variables in two rice breeding populations. |
|
Complemento del título : |
Original article. |
|
Fecha de publicación : |
2023 |
|
Fuente / Imprenta : |
Crop Science. 2023, Volume 63, Issue 4, Pages 2220-2240. https://doi.org/10.1002/csc2.21029 -- OPEN ACCESS. |
|
ISSN : |
0011-183X (print); 1435-0653 (electronic). |
|
DOI : |
10.1002/csc2.21029 |
|
Idioma : |
Inglés |
|
Notas : |
Article history: Received 21 November 2022, Accepted 10 May 2023, Published online 16 June 2023. -- Correspondence: Rosas, J.E.; INIA, Estación Experimental Treinta y Tres, Road 8 km 281, Treinta y Tres, Uruguay; email:jrosas@inia.org.uy -- FUNDING: Funding for this project was provided by Instituto Nacional de Investigación Agropecuaria (Projects AZ35, AZ13, and fellowship to I. R.), Agencia Nacional de Investigación Agropecuaria (grant MOV_CA_2019_1_156241), Comisión Sectorial de Investigación Científica, Universidad de la República (grant Iniciación a la Investgación 2019 No. 8), Comité Académico de Posgrado (fellowship to I. R.), and the Agriculture and Food Research Initiative Competitive Grant 2022-68013-36439 (WheatCAP) from the USDA National Institute of Food and Agriculture. -- LICENSE: This is an open access article under the terms of theCreative Commons Attribution-NonCommercial (http://creativecommons.org/licenses/by-nc/4.0/ ) |
|
Contenido : |
ABSTRACT.- Genotype by environment interaction (GEI) is one of the main challenges in plant breeding. A complete characterization of it is necessary to decide on proper breeding strategies. Random regression models (RRMs) allow a genotype-specific response to each regressor factor. RRMs that include selected environmental variables represent a promising approach to deal with GEI in genomic prediction. They enable to predict for both tested and untested environments, but their utility in a plant breeding scenario remains to be shown. We used phenotypic, climatic, pedigree, and genomic data from two public subtropical rice (Oryza sativa L.) breeding programs; one manages the indica population and the other manages the japonica population. First, we characterized GEI for grain yield (GY) with a set of tools: variance component estimation, mega-environment (ME) definition, and correlation between locations, sowing periods, and MEs. Then, we identified the most influential climatic variables related to GY and its GEI and used them in RRMs for single-step genomic prediction. Finally, we evaluated the predictive ability of these models for GY prediction in tested and untested years and environments using the complete dataset and within each ME. Our results suggest large GEI in both populations while larger in indica than in japonica. In indica, early sowing periods showed crossover (i.e., rank-change) GEI with other sowing periods. Climatic variables related to temperature, radiation, wind, and precipitation affecting GY were identified and differed in each population. RRMs with selected climatic covariates improved the predictive ability in both tested and untested years and environments. Prediction using the complete dataset performed better than predicting within each ME. © 2023 The Authors. Crop Science © 2023 Crop Science Society of America. MenosABSTRACT.- Genotype by environment interaction (GEI) is one of the main challenges in plant breeding. A complete characterization of it is necessary to decide on proper breeding strategies. Random regression models (RRMs) allow a genotype-specific response to each regressor factor. RRMs that include selected environmental variables represent a promising approach to deal with GEI in genomic prediction. They enable to predict for both tested and untested environments, but their utility in a plant breeding scenario remains to be shown. We used phenotypic, climatic, pedigree, and genomic data from two public subtropical rice (Oryza sativa L.) breeding programs; one manages the indica population and the other manages the japonica population. First, we characterized GEI for grain yield (GY) with a set of tools: variance component estimation, mega-environment (ME) definition, and correlation between locations, sowing periods, and MEs. Then, we identified the most influential climatic variables related to GY and its GEI and used them in RRMs for single-step genomic prediction. Finally, we evaluated the predictive ability of these models for GY prediction in tested and untested years and environments using the complete dataset and within each ME. Our results suggest large GEI in both populations while larger in indica than in japonica. In indica, early sowing periods showed crossover (i.e., rank-change) GEI with other sowing periods. Climatic variables related to temperature, radiati... Presentar Todo |
|
Palabras claves : |
Genotype by environment interaction (GEI); Random regression models (RRMs); Rice (Oryza sativa L.). |
|
Asunto categoría : |
-- |
|
URL : |
https://acsess.onlinelibrary.wiley.com/doi/epdf/10.1002/csc2.21029
|
|
Marc : |
LEADER 03749naa a2200253 a 4500
001 1064311
005 2023-09-14
008 2023 bl uuuu u00u1 u #d
022 $a0011-183X (print); 1435-0653 (electronic).
024 7 $a10.1002/csc2.21029$2DOI
100 1 $aREBOLLO, I.
245 $aGenotype by environment interaction characterization and its modeling with random regression to climatic variables in two rice breeding populations.$h[electronic resource]
260 $c2023
500 $aArticle history: Received 21 November 2022, Accepted 10 May 2023, Published online 16 June 2023. -- Correspondence: Rosas, J.E.; INIA, Estación Experimental Treinta y Tres, Road 8 km 281, Treinta y Tres, Uruguay; email:jrosas@inia.org.uy -- FUNDING: Funding for this project was provided by Instituto Nacional de Investigación Agropecuaria (Projects AZ35, AZ13, and fellowship to I. R.), Agencia Nacional de Investigación Agropecuaria (grant MOV_CA_2019_1_156241), Comisión Sectorial de Investigación Científica, Universidad de la República (grant Iniciación a la Investgación 2019 No. 8), Comité Académico de Posgrado (fellowship to I. R.), and the Agriculture and Food Research Initiative Competitive Grant 2022-68013-36439 (WheatCAP) from the USDA National Institute of Food and Agriculture. -- LICENSE: This is an open access article under the terms of theCreative Commons Attribution-NonCommercial (http://creativecommons.org/licenses/by-nc/4.0/ )
520 $aABSTRACT.- Genotype by environment interaction (GEI) is one of the main challenges in plant breeding. A complete characterization of it is necessary to decide on proper breeding strategies. Random regression models (RRMs) allow a genotype-specific response to each regressor factor. RRMs that include selected environmental variables represent a promising approach to deal with GEI in genomic prediction. They enable to predict for both tested and untested environments, but their utility in a plant breeding scenario remains to be shown. We used phenotypic, climatic, pedigree, and genomic data from two public subtropical rice (Oryza sativa L.) breeding programs; one manages the indica population and the other manages the japonica population. First, we characterized GEI for grain yield (GY) with a set of tools: variance component estimation, mega-environment (ME) definition, and correlation between locations, sowing periods, and MEs. Then, we identified the most influential climatic variables related to GY and its GEI and used them in RRMs for single-step genomic prediction. Finally, we evaluated the predictive ability of these models for GY prediction in tested and untested years and environments using the complete dataset and within each ME. Our results suggest large GEI in both populations while larger in indica than in japonica. In indica, early sowing periods showed crossover (i.e., rank-change) GEI with other sowing periods. Climatic variables related to temperature, radiation, wind, and precipitation affecting GY were identified and differed in each population. RRMs with selected climatic covariates improved the predictive ability in both tested and untested years and environments. Prediction using the complete dataset performed better than predicting within each ME. © 2023 The Authors. Crop Science © 2023 Crop Science Society of America.
653 $aGenotype by environment interaction (GEI)
653 $aRandom regression models (RRMs)
653 $aRice (Oryza sativa L.)
700 1 $aAGUILAR, I.
700 1 $aPÉREZ DE VIDA, F.
700 1 $aMOLINA, F.
700 1 $aGUTIÉRREZ, L.
700 1 $aROSAS, J.E.
773 $tCrop Science. 2023, Volume 63, Issue 4, Pages 2220-2240. https://doi.org/10.1002/csc2.21029 -- OPEN ACCESS.
Descargar
Esconder MarcPresentar Marc Completo |
|
Registro original : |
INIA Las Brujas (LB) |
|
|
Biblioteca
|
Identificación
|
Origen
|
Tipo / Formato
|
Clasificación
|
Cutter
|
Registro
|
Volumen
|
Estado
|
Volver
|
| Expresión de búsqueda válido. Check! |
|
|
