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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha : |
21/06/2023 |
Actualizado : |
21/06/2023 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Autor : |
CARRA, B.; RODRIGUEZ, P.; CABRERA, D.; FALERO, M.; DINI, M.; FRANCESCATTO, P. |
Afiliación : |
BRUNO CARRA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; PABLO ANDRES RODRIGUEZ BRUNO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CARLOS DANILO CABRERA BOLOGNA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARCELO FABIAN FALERO DELGADO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MAXIMILIANO ANTONIO DINI VIÑOLY, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; P. FRANCESCATTO, Global Technical Development Specialist, PhD., Valent BioSciences Corporation, 870 Technology Way, Libertyville, IL, 60048, USA. |
Título : |
Plant growth regulators to increase fruit set in two different 'Williams' pear orchards in Uruguay: with and without pollinizer. [Conference paper]. |
Fecha de publicación : |
2023 |
Fuente / Imprenta : |
Acta Horticulturae. 2023, 1366, pp.17-26. https://doi.org/10.17660/ActaHortic.2023.1366.2 |
ISSN : |
0567-7572 (print); 2406-6168 (electronic) |
DOI : |
10.17660/ActaHortic.2023.1366.2 |
Idioma : |
Inglés |
Notas : |
Article history: Published 30 April 2023. -- Correspondence: Bruno Carra, email: bcarra@inia.org.uy -- In: Acta Horticulturae (ISHS) 1366: XXXI International Horticultural Congress (IHC2022): International Symposium on Innovative Perennial Crops Management. Editors: S. Serra, P.-E. Lauri. -- Place: Angers, France. |
Contenido : |
ABSTRACT.- Pear production in Uruguay covers 628 ha being 'Williams' the most planted cultivar. Orchards in Uruguay are managed without the use of pollinizers (parthenocarpy). Pear production in recent years has not been stable, there are many factors that could be influencing, including climatic conditions, low fruit set, among others. The aim of this study was to evaluate the use of plant growth regulators to increase fruit set in 'Williams' pears in orchards with and without pollinizers. The study was performed during the 2021/2022 growing seasons using two different 'Williams' pears orchards, the first one five-year-old orchard grafted on 'Adams' with 'Packham's Triumph' as pollinizer, and the second one, a three-year-old orchard grafted on 'OH×F40' without pollinizer. Treatments consisted of an untreated control, and different aminoethoxyvinylglycine (AVG) and 6-benzyladenine + gibberellins 4+7 (6-BA + GA4+7) rates sprayed at full bloom and 7 days after full bloom. Productive and quality parameters were assessed. Fruit set was affected differently in the two orchards (with and without pollinizer), where in the orchard with pollinizers, AVG and 6-BA + GA4+7 increased fruit set, number of fruit and yield of 'Williams' pears. A negative effect of 6-BA + GA4+7 sprays compared to untreated trees and AVG-treated trees was fruit misshapenness, where the combination of these active ingredients showed a higher level of misshapen fruit. In the orchard without pollinizers, the best results were observed with 6-BA + GA4+7 sprays, where AVG did not show significant differences between treated trees and control trees. Collectively, our results showed both plant growth regulators may represent an efficient tool to increase fruit set and yields of 'Williams' pears, however, AVG showed increase in fruit set only in the orchard with pollinizer and 6-BA + GA4+7 in both orchards. 6-BA + GA4+7 treated trees had an increase in the misshapen fruit level when compared to untreated control trees and AVG-treated trees and could reduce the fruit commercial value. © 2023 International Society for Horticultural Science. All rights reserved. MenosABSTRACT.- Pear production in Uruguay covers 628 ha being 'Williams' the most planted cultivar. Orchards in Uruguay are managed without the use of pollinizers (parthenocarpy). Pear production in recent years has not been stable, there are many factors that could be influencing, including climatic conditions, low fruit set, among others. The aim of this study was to evaluate the use of plant growth regulators to increase fruit set in 'Williams' pears in orchards with and without pollinizers. The study was performed during the 2021/2022 growing seasons using two different 'Williams' pears orchards, the first one five-year-old orchard grafted on 'Adams' with 'Packham's Triumph' as pollinizer, and the second one, a three-year-old orchard grafted on 'OH×F40' without pollinizer. Treatments consisted of an untreated control, and different aminoethoxyvinylglycine (AVG) and 6-benzyladenine + gibberellins 4+7 (6-BA + GA4+7) rates sprayed at full bloom and 7 days after full bloom. Productive and quality parameters were assessed. Fruit set was affected differently in the two orchards (with and without pollinizer), where in the orchard with pollinizers, AVG and 6-BA + GA4+7 increased fruit set, number of fruit and yield of 'Williams' pears. A negative effect of 6-BA + GA4+7 sprays compared to untreated trees and AVG-treated trees was fruit misshapenness, where the combination of these active ingredients showed a higher level of misshapen fruit. In the orchard without pollinizers, the bes... Presentar Todo |
Palabras claves : |
6-benzyladenine; Aminoethoxyvinilglycine; Fruit quality; Fruitlet drop; Gibberellins; Pyrus communis L; SISTEMA VEGETAL INTENSIVO - INIA; Yield. |
Asunto categoría : |
F30 Genética vegetal y fitomejoramiento |
Marc : |
LEADER 03487naa a2200313 a 4500 001 1064203 005 2023-06-21 008 2023 bl uuuu u00u1 u #d 022 $a0567-7572 (print); 2406-6168 (electronic) 024 7 $a10.17660/ActaHortic.2023.1366.2$2DOI 100 1 $aCARRA, B. 245 $aPlant growth regulators to increase fruit set in two different 'Williams' pear orchards in Uruguay$bwith and without pollinizer. [Conference paper].$h[electronic resource] 260 $c2023 500 $aArticle history: Published 30 April 2023. -- Correspondence: Bruno Carra, email: bcarra@inia.org.uy -- In: Acta Horticulturae (ISHS) 1366: XXXI International Horticultural Congress (IHC2022): International Symposium on Innovative Perennial Crops Management. Editors: S. Serra, P.-E. Lauri. -- Place: Angers, France. 520 $aABSTRACT.- Pear production in Uruguay covers 628 ha being 'Williams' the most planted cultivar. Orchards in Uruguay are managed without the use of pollinizers (parthenocarpy). Pear production in recent years has not been stable, there are many factors that could be influencing, including climatic conditions, low fruit set, among others. The aim of this study was to evaluate the use of plant growth regulators to increase fruit set in 'Williams' pears in orchards with and without pollinizers. The study was performed during the 2021/2022 growing seasons using two different 'Williams' pears orchards, the first one five-year-old orchard grafted on 'Adams' with 'Packham's Triumph' as pollinizer, and the second one, a three-year-old orchard grafted on 'OH×F40' without pollinizer. Treatments consisted of an untreated control, and different aminoethoxyvinylglycine (AVG) and 6-benzyladenine + gibberellins 4+7 (6-BA + GA4+7) rates sprayed at full bloom and 7 days after full bloom. Productive and quality parameters were assessed. Fruit set was affected differently in the two orchards (with and without pollinizer), where in the orchard with pollinizers, AVG and 6-BA + GA4+7 increased fruit set, number of fruit and yield of 'Williams' pears. A negative effect of 6-BA + GA4+7 sprays compared to untreated trees and AVG-treated trees was fruit misshapenness, where the combination of these active ingredients showed a higher level of misshapen fruit. In the orchard without pollinizers, the best results were observed with 6-BA + GA4+7 sprays, where AVG did not show significant differences between treated trees and control trees. Collectively, our results showed both plant growth regulators may represent an efficient tool to increase fruit set and yields of 'Williams' pears, however, AVG showed increase in fruit set only in the orchard with pollinizer and 6-BA + GA4+7 in both orchards. 6-BA + GA4+7 treated trees had an increase in the misshapen fruit level when compared to untreated control trees and AVG-treated trees and could reduce the fruit commercial value. © 2023 International Society for Horticultural Science. All rights reserved. 653 $a6-benzyladenine 653 $aAminoethoxyvinilglycine 653 $aFruit quality 653 $aFruitlet drop 653 $aGibberellins 653 $aPyrus communis L 653 $aSISTEMA VEGETAL INTENSIVO - INIA 653 $aYield 700 1 $aRODRIGUEZ, P. 700 1 $aCABRERA, D. 700 1 $aFALERO, M. 700 1 $aDINI, M. 700 1 $aFRANCESCATTO, P. 773 $tActa Horticulturae. 2023, 1366, pp.17-26. https://doi.org/10.17660/ActaHortic.2023.1366.2
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INIA Las Brujas (LB) |
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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.
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