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| Registros recuperados : 18 | |
| 5. |  | LEONI, C.; CONDE, P.; BRUZZONE, J.; MARTINEZ, C.; VILLAMIL, J.J.; BIANCHI, D.; ASHFIELD, R. Ante una nueva zafra olivícola, ¿cómo nos preparamos para manejar la aceituna jabonosa?. Revista INIA Uruguay, 2016, no.46, p. 34-37. (Revista INIA; 46)| Biblioteca(s): INIA Las Brujas; INIA Tacuarembó. |
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| 6. | | IBÁÑEZ, F.; FERRARI, V.; RODRÍGUEZ, G.; VICENTE, E.; MARTINEZ, C.; ALVAREZ, A. Cebollas del Programa de Mejoramiento Genético de Hortalizas de INIA como fuente de quercetina para una dieta saludable. [Resumen] In: INIA (Instituto Nacional de Investigación Agropecuaria); Programa Nacional Producción Hortícola. Resúmenes. Jornada Mejoramiento Genético de Hortalizas: Ciencia y Tecnología para la producción y el consumidor, 2019, Salto, Uruguay. Trabajos de investigación relacionados al proyecto. Salto (UY): INIA, 2019. p. 12-13.| Biblioteca(s): INIA Las Brujas. |
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| 8. | | LEONI, C.; BRUZZONE, J.; VILLAMIL, J.J.; MARTINEZ, C.; MONTELONGO, M.J.; BENTANCUR, O.; CONDE, P. Percentage of anthracnose (Colletotrichum acutatum s.s.) acceptable in olives for the production of extra virgin olive oil. Crop Protection, June 2018, v.108: 47-43. Article history: Received 15 February 2017; Revised 27 September 2017; Accepted 8 February 2018; Available online 22 February 2018.
Partial results were presented at 8th International Olive Symposium, Split, Croatia, 10?14 October 2016.| Biblioteca(s): INIA Las Brujas. |
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| 14. | | VILLAMIL, J.J.; MARTINELLI, L.; BRUZZONE, J.; ASHFIELD, R.; BIANCHI, D.; MARTINEZ, C.; ZOPPOLO, R.; VILLAMIL, J.M.; CONDE, P. Variedades destacadas para el sur de Uruguay. In: INIA (Instituto Nacional de Investigación Agropecuaria); Programa Nacional Producción Frutícola. Resultados experimentales en olivo. Jornada de Divulgación. Canelones (Uruguay): INIA, 2015. p. 12-16 (Serie Actividades de Difusión ; 754).| Biblioteca(s): INIA Las Brujas. |
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| 15. | | CONDE, P.; VILLAMIL, J.J.; MARTINELLI, L.; BRUZZONE, J.; ASHFIELD, R.; BIANCHI, D.; MARTINEZ, C.; DAVILA, J.; ZOPPOLO, R.; VILLAMIL, J.M. Comportamiento agronómico de seis variedades de olivo en el sur y norte de Uruguay. In: INIA (Instituto Nacional de Investigación Agropecuaria); Programa Nacional Producción Frutícola. Resultados experimentales en olivo. Jornada de Divulgación. Canelones (Uruguay): INIA, 2015. p. 8-11 (Serie Actividades de Difusión ; 754).| Biblioteca(s): INIA Las Brujas. |
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| 16. | | FERRARI, V.; IBÁÑEZ, F.; LADO, J.; ALVAREZ, A.; MARTINEZ, C.; MOLTINI, A.; GIMÉNEZ, G.; GONZÁLEZ-ARCOS, M.; RODRÍGUEZ, G.; VICENTE, E. Compuestos bioactivos y capacidad antioxidante en boniatos, frutillas y tomates del Programa de Mejoramiento Genético de INIA. [Resumen] In: INIA (Instituto Nacional de Investigación Agropecuaria); Programa Nacional Producción Hortícola. Resúmenes. Jornada Mejoramiento Genético de Hortalizas: Ciencia y Tecnología para la producción y el consumidor, 2019, Salto, Uruguay. Trabajos de investigación relacionados al proyecto. Salto (UY): INIA, 2019. p. 14-15.| Biblioteca(s): INIA Las Brujas. |
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| 17. | | CONDE, P.; VILLAMIL, J.J.; FREDES, A.; BRUZZONE, J.; MARTINEZ, C.; MONTELONGO, M.J.; ELLIS, A.C.; GÁMBARO, A.; LEONI, C. Determinación del umbral máximo tolerable de fruta infectada por colletotrichum Spp. para la obtención de aceite de oliva extra virgen. In: INIA Las Brujas; Programa Nacional Producción Frutícola. Resultados experimentales en olivos. Canelones (UY): INIA, 2013. p. 31-37 (INIA Serie Actividades de Difusión; 721)| Biblioteca(s): INIA Las Brujas. |
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| 18. | | MARTÍNEZ, C.P.; TORRES, E.A.; CHATEL, M.; MOSQUERA, G.; DUITAMA, J.; ISHITANI, M.; SELVARAJ, M.; DEDICOVA, B.; TOHME, J.; GRENIER, C.; LORIEUX, M.; CRUZ, M.; BERRÍO, L.; CORREDOR, E.; ZORRILLA DE SAN MARTÍN, G.; BRESEGHELLO, F.; PEIXOTO, O.; COLOMBARI FILHO, J.M.; CASTRO, A. PEREIRA DE; LOPES, S.I. GINDRI; BARBOSA, M.; FUNCK, G.R. DALTROZZO; BLANCO, P.H.; PÉREZ DE VIDA, F.; MOLINA, F.; ROSAS, J.E.; MARTÍNEZ, S.; BONNECARRERE, V.; CARRACELAS, G.; MARIN, A.; CORREA-VICTORIA, F.; CAMARGO, I.; BRUZZONE, C.B . Rice breeding in Latin America. Plant Breeding Reviews, 2014 v.38, p. 187-277., 2014| Biblioteca(s): INIA Tacuarembó; INIA Treinta y Tres. |
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| Registros recuperados : 18 | |
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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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