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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
29/01/2024 |
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Actualizado : |
29/01/2024 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
DINI, M.; FRANZON, R.C.; RASEIRA, M.C.B; UENO, B.; MARCHI, P.M.; VIZZOTTO, M. |
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Afiliación : |
MAXIMILIANO ANTONIO DINI VIÑOLY, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Universidade Federal de Pelotas, Faculdade de Agronomia Eliseu Maciel, Programa de Pós-Graduação em Agronomia, Rio Grande do Sul, Pelotas, Brazil; RODRIGO CEZAR FRANZON, Empresa Brasileira de Pesquisa Agropecuária, Embrapa Clima Temperado, Rio Grande do Sul, Pelotas, Brazil; MARIA DO CARMO BASSOLS RASEIRA, Empresa Brasileira de Pesquisa Agropecuária, Embrapa Clima Temperado, Rio Grande do Sul, Pelotas, Brazil; BERNARDO UENO, Empresa Brasileira de Pesquisa Agropecuária, Embrapa Clima Temperado, Rio Grande do Sul, Pelotas, Brazil; PRISCILA MONALISA MARCHI, Universidade Federal de Pelotas, Faculdade de Agronomia Eliseu Maciel, Programa de Pós-Graduação em Agronomia, Rio Grande do Sul, Pelotas, Brazil; Faculdade Santo Ângelo (FASA), Agronomia, Rio Grande do Sul, Santo Ângelo, Brazil; MARCIA VIZZOTTO, Empresa Brasileira de Pesquisa Agropecuária, Embrapa Clima Temperado, Rio Grande do Sul, Pelotas, Brazil. |
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Título : |
Blossom blight resistance in peach: phenotyping and antioxidants content in petals. |
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Fecha de publicación : |
2023 |
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Fuente / Imprenta : |
Brazilian Archives of Biology and Technology, 2023, Volume 66, e23220730. https://doi.org/10.1590/1678-4324-2023220730 -- OPEN ACCESS. |
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ISSN : |
1516-8913 |
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DOI : |
10.1590/1678-4324-2023220730 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 15 September 2022, Accepted 31 May 2023, Publication in this collection 30 October 2023, Date of issue 2023. -- Document type: Article Gold Open Access. -- Correspondence: Dini, M.; Universidade Federal de Pelotas, Faculdade de Agronomia Eliseu Maciel, Programa de Pós-Graduação em Agronomia, Rio Grande do Sul, Pelotas, Brazil; email:mdini@inia.org.uy -- Funding: This research was funded by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brazil (CAPES), through the first author's doctoral scholarship. -- Editor in Chief: Paulo Vitor Farago. -- Associate Editor: Jane Manfron Budel.-- License: Creative Commons Attribution (CC BY NC) license (https://creativecommons.org/licenses/by-nc/4.0/). |
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Contenido : |
ABSTRACT.- Brown rot and blossom blight caused by fungi of the genus Monilinia are the most important peach diseases. The increased concern with the environment and the health of workers and consumers, as well as the emergence of fungus isolates resistant to the main fungicide molecules favor control strategies such as genetic resistance. The objective of this study was to adjust a phenotyping protocol for evaluation of resistance/susceptibility to blossom blight in peach, as well as to quantify the antioxidant compounds present in the petals of these flowers and their correlation with the disease incidence and severity. The experiment was arranged in a randomized complete block split-split plot design, the plot being four concentrations of Monilinia fructicola conidia; the subplot two phenological flower stage; and the sub-subplot four peach genotypes. The quantification of antioxidant compounds and their correlation with susceptibility to blossom blight was performed in the four genotypes analyzed. Phenotyping was more efficient when concentrations between 400 and 4,000 conidia mL-1 were used, regardless of phenological flower stage. The phenolic compounds, anthocyanins and antioxidant activity are positively correlated among them, and negatively correlated with the blossom blight incidence and severity. In order to estimate the blossom blight susceptibility, it is recommended to use flowers at the pink or bloom stage, inoculum equivalent to 20-200 conidia per flower, and perform the evaluation at 96 hours after inoculation. This study suggests that more intense pink flowers have a higher content of antioxidant compounds and less blossom blight susceptibility. © 2023 by the authors. MenosABSTRACT.- Brown rot and blossom blight caused by fungi of the genus Monilinia are the most important peach diseases. The increased concern with the environment and the health of workers and consumers, as well as the emergence of fungus isolates resistant to the main fungicide molecules favor control strategies such as genetic resistance. The objective of this study was to adjust a phenotyping protocol for evaluation of resistance/susceptibility to blossom blight in peach, as well as to quantify the antioxidant compounds present in the petals of these flowers and their correlation with the disease incidence and severity. The experiment was arranged in a randomized complete block split-split plot design, the plot being four concentrations of Monilinia fructicola conidia; the subplot two phenological flower stage; and the sub-subplot four peach genotypes. The quantification of antioxidant compounds and their correlation with susceptibility to blossom blight was performed in the four genotypes analyzed. Phenotyping was more efficient when concentrations between 400 and 4,000 conidia mL-1 were used, regardless of phenological flower stage. The phenolic compounds, anthocyanins and antioxidant activity are positively correlated among them, and negatively correlated with the blossom blight incidence and severity. In order to estimate the blossom blight susceptibility, it is recommended to use flowers at the pink or bloom stage, inoculum equivalent to 20-200 conidia per flower, and ... Presentar Todo |
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Palabras claves : |
Anthocyanins; Antioxidant activity; Monilinia fructicola (Winter) Honey; Phenolic compounds; Prunus persica (L.) Batsch; SISTEMA VEGETAL INTENSIVO - INIA. |
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Asunto categoría : |
F01 Cultivo |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/17478/1/Dini-eta-2023-BABT-1678-4324-2023220730.pdf
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Marc : |
LEADER 03401naa a2200289 a 4500
001 1064442
005 2024-01-29
008 2023 bl uuuu u00u1 u #d
022 $a1516-8913
024 7 $a10.1590/1678-4324-2023220730$2DOI
100 1 $aDINI, M.
245 $aBlossom blight resistance in peach$bphenotyping and antioxidants content in petals.$h[electronic resource]
260 $c2023
500 $aArticle history: Received 15 September 2022, Accepted 31 May 2023, Publication in this collection 30 October 2023, Date of issue 2023. -- Document type: Article Gold Open Access. -- Correspondence: Dini, M.; Universidade Federal de Pelotas, Faculdade de Agronomia Eliseu Maciel, Programa de Pós-Graduação em Agronomia, Rio Grande do Sul, Pelotas, Brazil; email:mdini@inia.org.uy -- Funding: This research was funded by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brazil (CAPES), through the first author's doctoral scholarship. -- Editor in Chief: Paulo Vitor Farago. -- Associate Editor: Jane Manfron Budel.-- License: Creative Commons Attribution (CC BY NC) license (https://creativecommons.org/licenses/by-nc/4.0/).
520 $aABSTRACT.- Brown rot and blossom blight caused by fungi of the genus Monilinia are the most important peach diseases. The increased concern with the environment and the health of workers and consumers, as well as the emergence of fungus isolates resistant to the main fungicide molecules favor control strategies such as genetic resistance. The objective of this study was to adjust a phenotyping protocol for evaluation of resistance/susceptibility to blossom blight in peach, as well as to quantify the antioxidant compounds present in the petals of these flowers and their correlation with the disease incidence and severity. The experiment was arranged in a randomized complete block split-split plot design, the plot being four concentrations of Monilinia fructicola conidia; the subplot two phenological flower stage; and the sub-subplot four peach genotypes. The quantification of antioxidant compounds and their correlation with susceptibility to blossom blight was performed in the four genotypes analyzed. Phenotyping was more efficient when concentrations between 400 and 4,000 conidia mL-1 were used, regardless of phenological flower stage. The phenolic compounds, anthocyanins and antioxidant activity are positively correlated among them, and negatively correlated with the blossom blight incidence and severity. In order to estimate the blossom blight susceptibility, it is recommended to use flowers at the pink or bloom stage, inoculum equivalent to 20-200 conidia per flower, and perform the evaluation at 96 hours after inoculation. This study suggests that more intense pink flowers have a higher content of antioxidant compounds and less blossom blight susceptibility. © 2023 by the authors.
653 $aAnthocyanins
653 $aAntioxidant activity
653 $aMonilinia fructicola (Winter) Honey
653 $aPhenolic compounds
653 $aPrunus persica (L.) Batsch
653 $aSISTEMA VEGETAL INTENSIVO - INIA
700 1 $aFRANZON, R.C.
700 1 $aRASEIRA, M.C.B
700 1 $aUENO, B.
700 1 $aMARCHI, P.M.
700 1 $aVIZZOTTO, M.
773 $tBrazilian Archives of Biology and Technology, 2023, Volume 66, e23220730. https://doi.org/10.1590/1678-4324-2023220730 -- OPEN ACCESS.
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Registro original : |
INIA Las Brujas (LB) |
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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha actual : |
10/08/2020 |
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Actualizado : |
05/09/2022 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
Internacional - -- |
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Autor : |
BHATTA, M.; GUTIERREZ, L.; CAMMAROTA, L.; CARDOZO, F.; GERMAN, S.; GÓMEZ-GUERRERO, B.; PARDO, M.F.; LANARO, V.; SAYAS, M.; CASTRO, A.J. |
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Afiliación : |
MADHAV BHATTA, Department of Agronomy, University of Wisconsin-Madison, 1575 Linden Dr., WI, 53706, USA.; LUCIA GUTIERREZ, Agronomy, University of Wisconsin-Madison, 1575 Linden Dr., WI, 53706, USA.; LORENA CAMMAROTA, Department of plant production, Facultad de Agronomía, Universidad de la República, Ruta 3, Km363, Paysandú 60000, Uruguay./Maltería Uruguay S.A. Ruta 55, Km26, Ombúes de Lavalle, Uruguay.; FERNANDA CARDOZO, Maltería Uruguay S.A. Ruta 55, Km26, Ombúes de Lavalle, Uruguay.; SILVIA ELISA GERMAN FAEDO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; BLANCA GÓMEZ-GUERRERO, LATU Foundation, Av Italia 6201, Montevideo 11500, Uruguay.; MARÍA FERNANDA PARDO, Maltería Oriental S.A., Camino Abrevadero 5525, Montevideo 12400, Uruguay.; VALERIA LANARO, LATU Foundation, Av Italia 6201, Montevideo 11500, Uruguay.; MERCEDES SAYAS, Maltería Oriental S.A., Camino Abrevadero 5525, Montevideo 12400, Uruguay.; ARIEL J. CASTRO, Ariel J. Castro ?Department of plant production, Facultad de Agronomía, Universidad de la República, Ruta 3, Km363, Paysandú 60000, Uruguay,. |
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Título : |
Multi-trait genomic prediction model increased the predictive ability for agronomic and malting quality traits in barley (Hordeum vulgare L.). |
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Fecha de publicación : |
2020 |
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Fuente / Imprenta : |
G3: Genes, Genomes, Genetics, March 1, 2020 vol. 10 no. 3 1113-1124. Open Acces. Doi: https://doi.org/10.1534/g3.119.400968 |
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DOI : |
10.1534/g3.119.400968 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received July 26, 2019/Accepted January 22, 2020/Published online March 5, 2020. This work was funded in part by the following grants from ANII (FSA-1-2013-12977), CSIC (CSIC_I+D_ 1131 and CSIC_Movilidad_ 1131). The work was also funded by the Cereals Breeding and Quantitative Genetics group at the University of Wisconsin - Madison. We would like to acknowledge Dr. Juan Diaz at INIA, who developed the double haploid population and also contributed to the planning of the study. Malteria Oriental S.A. (MOSA) contributed with the experiments in their experimental areas and with some of the lab work. Malteria Uruguay S.A. (MUSA) contributed to the experiments in their experimental areas. We would also like to acknowledge: USDA-ARS small grains genotyping lab at Fargo, North Dakota for genotyping service; the Center for High Throughput Computing (CHTC) service at the University of Wisconsin-Madison for providing the high-performance computing resources; and Dr. Bettina Lado for sharing the R scripts. We would like to thank two anonymous reviewers and editors who provided constructive suggestions to this manuscript. |
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Contenido : |
Abstract:
Plant breeders regularly evaluate multiple traits across multiple environments, which opens an avenue for using multiple traits in genomic prediction models. We assessed the potential of multi-trait (MT) genomic prediction model through evaluating several strategies of incorporating multiple traits (eight agronomic and malting quality traits) into the prediction models with two cross-validation schemes (CV1, predicting new lines with genotypic information only and CV2, predicting partially phenotyped lines using both genotypic and phenotypic information from correlated traits) in barley. The predictive ability was similar for single (ST-CV1) and multi-trait (MT-CV1) models to predict new lines. However, the predictive ability for agronomic traits was considerably increased when partially phenotyped lines (MT-CV2) were used. The predictive ability for grain yield using the MT-CV2 model with other agronomic traits resulted in 57% and 61% higher predictive ability than ST-CV1 and MT-CV1 models, respectively. Therefore, complex traits such as grain yield are better predicted when correlated traits are used. Similarly, a considerable increase in the predictive ability of malting quality traits was observed when correlated traits were used. The predictive ability for grain protein content using the MT-CV2 model with both agronomic and malting traits resulted in a 76% higher predictive ability than ST-CV1 and MT-CV1 models. Additionally, the higher predictive ability for new environments was obtained for all traits using the MT-CV2 model compared to the MT-CV1 model. This study showed the potential of improving the genomic prediction of complex traits by incorporating the information from multiple traits (cost-friendly and easy to measure traits) collected throughout breeding programs which could assist in speeding up breeding cycles. MenosAbstract:
Plant breeders regularly evaluate multiple traits across multiple environments, which opens an avenue for using multiple traits in genomic prediction models. We assessed the potential of multi-trait (MT) genomic prediction model through evaluating several strategies of incorporating multiple traits (eight agronomic and malting quality traits) into the prediction models with two cross-validation schemes (CV1, predicting new lines with genotypic information only and CV2, predicting partially phenotyped lines using both genotypic and phenotypic information from correlated traits) in barley. The predictive ability was similar for single (ST-CV1) and multi-trait (MT-CV1) models to predict new lines. However, the predictive ability for agronomic traits was considerably increased when partially phenotyped lines (MT-CV2) were used. The predictive ability for grain yield using the MT-CV2 model with other agronomic traits resulted in 57% and 61% higher predictive ability than ST-CV1 and MT-CV1 models, respectively. Therefore, complex traits such as grain yield are better predicted when correlated traits are used. Similarly, a considerable increase in the predictive ability of malting quality traits was observed when correlated traits were used. The predictive ability for grain protein content using the MT-CV2 model with both agronomic and malting traits resulted in a 76% higher predictive ability than ST-CV1 and MT-CV1 models. Additionally, the higher predictive ability for ... Presentar Todo |
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Palabras claves : |
GENOMIC PREDICTION; GENPRED; GRAIN QUALITY; GRAIN YIELD; MALTING QUALITY; MULTI-ENVIRONMENT; MULTI-TRAIT; SHARED DATA RESOURCES. |
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Asunto categoría : |
-- |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/16688/1/G3-Bethesda-2020.pdf
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7056970/pdf/1113.pdf
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Marc : |
LEADER 04092naa a2200349 a 4500
001 1061265
005 2022-09-05
008 2020 bl uuuu u00u1 u #d
024 7 $a10.1534/g3.119.400968$2DOI
100 1 $aBHATTA, M.
245 $aMulti-trait genomic prediction model increased the predictive ability for agronomic and malting quality traits in barley (Hordeum vulgare L.).$h[electronic resource]
260 $c2020
500 $aArticle history: Received July 26, 2019/Accepted January 22, 2020/Published online March 5, 2020. This work was funded in part by the following grants from ANII (FSA-1-2013-12977), CSIC (CSIC_I+D_ 1131 and CSIC_Movilidad_ 1131). The work was also funded by the Cereals Breeding and Quantitative Genetics group at the University of Wisconsin - Madison. We would like to acknowledge Dr. Juan Diaz at INIA, who developed the double haploid population and also contributed to the planning of the study. Malteria Oriental S.A. (MOSA) contributed with the experiments in their experimental areas and with some of the lab work. Malteria Uruguay S.A. (MUSA) contributed to the experiments in their experimental areas. We would also like to acknowledge: USDA-ARS small grains genotyping lab at Fargo, North Dakota for genotyping service; the Center for High Throughput Computing (CHTC) service at the University of Wisconsin-Madison for providing the high-performance computing resources; and Dr. Bettina Lado for sharing the R scripts. We would like to thank two anonymous reviewers and editors who provided constructive suggestions to this manuscript.
520 $aAbstract: Plant breeders regularly evaluate multiple traits across multiple environments, which opens an avenue for using multiple traits in genomic prediction models. We assessed the potential of multi-trait (MT) genomic prediction model through evaluating several strategies of incorporating multiple traits (eight agronomic and malting quality traits) into the prediction models with two cross-validation schemes (CV1, predicting new lines with genotypic information only and CV2, predicting partially phenotyped lines using both genotypic and phenotypic information from correlated traits) in barley. The predictive ability was similar for single (ST-CV1) and multi-trait (MT-CV1) models to predict new lines. However, the predictive ability for agronomic traits was considerably increased when partially phenotyped lines (MT-CV2) were used. The predictive ability for grain yield using the MT-CV2 model with other agronomic traits resulted in 57% and 61% higher predictive ability than ST-CV1 and MT-CV1 models, respectively. Therefore, complex traits such as grain yield are better predicted when correlated traits are used. Similarly, a considerable increase in the predictive ability of malting quality traits was observed when correlated traits were used. The predictive ability for grain protein content using the MT-CV2 model with both agronomic and malting traits resulted in a 76% higher predictive ability than ST-CV1 and MT-CV1 models. Additionally, the higher predictive ability for new environments was obtained for all traits using the MT-CV2 model compared to the MT-CV1 model. This study showed the potential of improving the genomic prediction of complex traits by incorporating the information from multiple traits (cost-friendly and easy to measure traits) collected throughout breeding programs which could assist in speeding up breeding cycles.
653 $aGENOMIC PREDICTION
653 $aGENPRED
653 $aGRAIN QUALITY
653 $aGRAIN YIELD
653 $aMALTING QUALITY
653 $aMULTI-ENVIRONMENT
653 $aMULTI-TRAIT
653 $aSHARED DATA RESOURCES
700 1 $aGUTIERREZ, L.
700 1 $aCAMMAROTA, L.
700 1 $aCARDOZO, F.
700 1 $aGERMAN, S.
700 1 $aGÓMEZ-GUERRERO, B.
700 1 $aPARDO, M.F.
700 1 $aLANARO, V.
700 1 $aSAYAS, M.
700 1 $aCASTRO, A.J.
773 $tG3: Genes, Genomes, Genetics, March 1, 2020 vol. 10 no. 3 1113-1124. Open Acces. Doi: https://doi.org/10.1534/g3.119.400968
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