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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
25/03/2021 |
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Actualizado : |
25/03/2021 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
SCARIOTTO, S.; DINI, M.; RASEIRA, M.C.B.; SANTOS, J. |
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Afiliación : |
S. SCARIOTTO, Empresa Brasileira de Pesquisa Agropecua?ria (Embrapa), Embrapa Clima Temperado, Pelotas-RS, Brazil;; MAXIMILIANO ANTONIO DINI VIÑOLY, Programa de Po?s-Graduaça?o em Agronomia (PPGA), Universidade Federal de Pelotas (UFPel), Pelotas-RS, Brazil;; M.C.B. RASEIRA, Empresa Brasileira de Pesquisa Agropecua?ria (Embrapa), Embrapa Clima Temperado, Pelotas-RS, Brazil;; J. SANTOS, Departamento de Biologia, Universidade Federal do Maranha?o, Sa?o Lu??s-MA, Brazil. |
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Título : |
Estimates of genetic parameters for brown rot resistance in Prunus persica. [Conference paper]. |
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Fecha de publicación : |
2021 |
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Fuente / Imprenta : |
Acta Horticulturae, February 2021, N°1304, p. 289-298. DOI: https://doi.org/10.17660/ActaHortic.2021.1304.40 |
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ISBN : |
978-94-62613-02-7 |
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ISSN : |
0567-7572 (print); 2406-6168 (electronic) |
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DOI : |
10.17660/ActaHortic.2021.1304.40 |
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Idioma : |
Inglés |
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Notas : |
Article history: Published 26 February 2021. Acta Horticulturae (ISHS) 1304: Proceedings of the IX International Peach Symposium, Bucharest, Romania.
Convener: F. Stanica. Editors: F. Stanica, T. DeJong. |
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Contenido : |
ABSTRACT.
Brown rot can cause dramatic economic impacts on the commercial production of several Prunus species, including peach, nectarine, plum, apricot, cherry and almond, in most producer countries. In Brazil, this disease is caused by Monilinia fructicola. The objective of this work was to estimate the genetic parameters for brown rot resistance in 144 accessions from the germplasm bank of Embrapa Clima Temperado. Fruits of each accession were harvested at the firm ripe stage. Prior to inoculation, twnety fruits per genotype were disinfected by immersion in a 70% v/v alcohol solution, followed by a three minute immersion in a 0.5% v/v NaClO solution, and rinsing in distilled water. The inoculation was made by deposition of a 10 μL drop with 2.5×104 spores mL‑1 of M. fructicola, on the fruit surface that was wounded with a microsyringe. Inoculated fruits were incubated at 25±1°C and 75% relative humidity for 72 h. The samples were evaluated for disease severity (average diameter of lesions and sporulation). Broad sense heritability estimates for lesion and sporulation diameter was 50 and 13%, respectively. The coefficient of variance was 46% for sporulation diameter and 20% for lesion diameter. The studied population exhibited genetic variability and therefore potential for breeding. 'Bolinha', 'Marfim', 'Jade', 'Dourado II' and selections SB9, Cascata 1281, Cascata 967, SB25, Conserva 1904, Conserva 1844 and Cascata 657 showed higher resistance than the other accessions.
@ International Society for Horticultural Science. MenosABSTRACT.
Brown rot can cause dramatic economic impacts on the commercial production of several Prunus species, including peach, nectarine, plum, apricot, cherry and almond, in most producer countries. In Brazil, this disease is caused by Monilinia fructicola. The objective of this work was to estimate the genetic parameters for brown rot resistance in 144 accessions from the germplasm bank of Embrapa Clima Temperado. Fruits of each accession were harvested at the firm ripe stage. Prior to inoculation, twnety fruits per genotype were disinfected by immersion in a 70% v/v alcohol solution, followed by a three minute immersion in a 0.5% v/v NaClO solution, and rinsing in distilled water. The inoculation was made by deposition of a 10 μL drop with 2.5×104 spores mL‑1 of M. fructicola, on the fruit surface that was wounded with a microsyringe. Inoculated fruits were incubated at 25±1°C and 75% relative humidity for 72 h. The samples were evaluated for disease severity (average diameter of lesions and sporulation). Broad sense heritability estimates for lesion and sporulation diameter was 50 and 13%, respectively. The coefficient of variance was 46% for sporulation diameter and 20% for lesion diameter. The studied population exhibited genetic variability and therefore potential for breeding. 'Bolinha', 'Marfim', 'Jade', 'Dourado II' and selections SB9, Cascata 1281, Cascata 967, SB25, Conserva 1904, Conserva 1844 and Cascata 657 showed higher resistance than the other ... Presentar Todo |
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Palabras claves : |
Genetic resistance; Genetic variability; Heritability; Mixed models; Monilinia fructicola. |
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Thesagro : |
PRUNUS PERSICA. |
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Asunto categoría : |
F30 Genética vegetal y fitomejoramiento |
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Marc : |
LEADER 02662naa a2200277 a 4500
001 1061866
005 2021-03-25
008 2021 bl uuuu u00u1 u #d
020 $a978-94-62613-02-7
022 $a0567-7572 (print); 2406-6168 (electronic)
024 7 $a10.17660/ActaHortic.2021.1304.40$2DOI
100 1 $aSCARIOTTO, S.
245 $aEstimates of genetic parameters for brown rot resistance in Prunus persica. [Conference paper].$h[electronic resource]
260 $c2021
500 $aArticle history: Published 26 February 2021. Acta Horticulturae (ISHS) 1304: Proceedings of the IX International Peach Symposium, Bucharest, Romania. Convener: F. Stanica. Editors: F. Stanica, T. DeJong.
520 $aABSTRACT. Brown rot can cause dramatic economic impacts on the commercial production of several Prunus species, including peach, nectarine, plum, apricot, cherry and almond, in most producer countries. In Brazil, this disease is caused by Monilinia fructicola. The objective of this work was to estimate the genetic parameters for brown rot resistance in 144 accessions from the germplasm bank of Embrapa Clima Temperado. Fruits of each accession were harvested at the firm ripe stage. Prior to inoculation, twnety fruits per genotype were disinfected by immersion in a 70% v/v alcohol solution, followed by a three minute immersion in a 0.5% v/v NaClO solution, and rinsing in distilled water. The inoculation was made by deposition of a 10 μL drop with 2.5×104 spores mL‑1 of M. fructicola, on the fruit surface that was wounded with a microsyringe. Inoculated fruits were incubated at 25±1°C and 75% relative humidity for 72 h. The samples were evaluated for disease severity (average diameter of lesions and sporulation). Broad sense heritability estimates for lesion and sporulation diameter was 50 and 13%, respectively. The coefficient of variance was 46% for sporulation diameter and 20% for lesion diameter. The studied population exhibited genetic variability and therefore potential for breeding. 'Bolinha', 'Marfim', 'Jade', 'Dourado II' and selections SB9, Cascata 1281, Cascata 967, SB25, Conserva 1904, Conserva 1844 and Cascata 657 showed higher resistance than the other accessions. @ International Society for Horticultural Science.
650 $aPRUNUS PERSICA
653 $aGenetic resistance
653 $aGenetic variability
653 $aHeritability
653 $aMixed models
653 $aMonilinia fructicola
700 1 $aDINI, M.
700 1 $aRASEIRA, M.C.B.
700 1 $aSANTOS, J.
773 $tActa Horticulturae, February 2021, N°1304, p. 289-298. DOI: https://doi.org/10.17660/ActaHortic.2021.1304.40
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INIA Las Brujas (LB) |
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| Acceso al texto completo restringido a Biblioteca INIA Treinta y Tres. Por información adicional contacte bibliott@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA Treinta y Tres. |
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Fecha actual : |
28/03/2016 |
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Actualizado : |
24/09/2018 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
A - 1 |
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Autor : |
BASSU, S.; BRISSON, N.; DURAND, J.L.; BOOTE, K.; LIZASO, J.; JONES, J.W.; ROSENZWEIG, C.; RUANE, A.C.; ADAM, M.; BARON, C.; BASSO, B.; BIERNATH, C.; BOOGAARD, H.; CONIJN, S.; CORBEELS, M.L; DERYNG, D.; SANTIS, G. DE; GAYLER, S.; GRASSINI, P.; HATFIELD, J.; HOEK, S.; IZAURRALDE, C.; JONGSCHAAP, R.; KEMANIAN, A.R.; KERSEBAUM, C.KIM, S-H.; KUMAR, N.; MAKOWSKI, D.; MÜLLER, C.; NENDEL, C.; PRIESACK, E.; PRAVIA, V.; SAU, F.; SHCHERBAK, I.; TAO, F.; TEXEIRA, E.; TIMLIN, D.; WAHA, K. |
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Afiliación : |
MARIA VIRGINIA PRAVIA NIN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Department of Plant Science, The Pennsylvania State University, USA. |
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Título : |
How do various maize crop models vary in their responses to climate change factors? |
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Fecha de publicación : |
2014 |
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Fuente / Imprenta : |
Global Change Biology, 2014, v.20(7), p. 2301-2320. |
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DOI : |
10.1111/gcb.12520 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 7 June 2013 and accepted 2 December 2013, published 2014. |
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Contenido : |
Abstract:
Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania).
While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data forcalibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly 0.5 Mg ha1 per °C. Doubling [CO2] from 360 to 720 lmol mol1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among models. Model responses to temperature and [CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information. MenosAbstract:
Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania).
While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data forcalibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly 0.5 Mg ha1 per °C. Doubling [CO2] from 360 to 720 lmol mol1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2]... Presentar Todo |
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Palabras claves : |
AGMIP; CARBON DIOXIDE; CLIMATE; CO2; GRAIN YIELD; MAIZE; MODEL INTERCOMPARISON; MODELIZACIÓN DE CULTIVOS; SIMULATION MODELS; TEMPERATURE. |
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Thesagro : |
CLIMA; DIOXIDO DE CARBONO; INCERTIDUMBRE; MAÍZ; MODELOS DE SIMULACIÓN; TEMPERATURA. |
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Asunto categoría : |
U10 Métodos matemáticos y estadísticos |
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Marc : |
LEADER 03684naa a2200769 a 4500
001 1054517
005 2018-09-24
008 2014 bl uuuu u00u1 u #d
024 7 $a10.1111/gcb.12520$2DOI
100 1 $aBASSU, S.
245 $aHow do various maize crop models vary in their responses to climate change factors?$h[electronic resource]
260 $c2014
500 $aArticle history: Received 7 June 2013 and accepted 2 December 2013, published 2014.
520 $aAbstract: Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data forcalibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly 0.5 Mg ha1 per °C. Doubling [CO2] from 360 to 720 lmol mol1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among models. Model responses to temperature and [CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.
650 $aCLIMA
650 $aDIOXIDO DE CARBONO
650 $aINCERTIDUMBRE
650 $aMAÍZ
650 $aMODELOS DE SIMULACIÓN
650 $aTEMPERATURA
653 $aAGMIP
653 $aCARBON DIOXIDE
653 $aCLIMATE
653 $aCO2
653 $aGRAIN YIELD
653 $aMAIZE
653 $aMODEL INTERCOMPARISON
653 $aMODELIZACIÓN DE CULTIVOS
653 $aSIMULATION MODELS
653 $aTEMPERATURE
700 1 $aBRISSON, N.
700 1 $aDURAND, J.L.
700 1 $aBOOTE, K.
700 1 $aLIZASO, J.
700 1 $aJONES, J.W.
700 1 $aROSENZWEIG, C.
700 1 $aRUANE, A.C.
700 1 $aADAM, M.
700 1 $aBARON, C.
700 1 $aBASSO, B.
700 1 $aBIERNATH, C.
700 1 $aBOOGAARD, H.
700 1 $aCONIJN, S.
700 1 $aCORBEELS, M.L
700 1 $aDERYNG, D.
700 1 $aSANTIS, G. DE
700 1 $aGAYLER, S.
700 1 $aGRASSINI, P.
700 1 $aHATFIELD, J.
700 1 $aHOEK, S.
700 1 $aIZAURRALDE, C.
700 1 $aJONGSCHAAP, R.
700 1 $aKEMANIAN, A.R.
700 1 $aKERSEBAUM, C.KIM, S-H.
700 1 $aKUMAR, N.
700 1 $aMAKOWSKI, D.
700 1 $aMÜLLER, C.
700 1 $aNENDEL, C.
700 1 $aPRIESACK, E.
700 1 $aPRAVIA, V.
700 1 $aSAU, F.
700 1 $aSHCHERBAK, I.
700 1 $aTAO, F.
700 1 $aTEXEIRA, E.
700 1 $aTIMLIN, D.
700 1 $aWAHA, K.
773 $tGlobal Change Biology, 2014$gv.20(7), p. 2301-2320.
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