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 | Acceso al texto completo restringido a Biblioteca INIA La Estanzuela. Por información adicional contacte bib_le@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha : |
19/09/2014 |
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Actualizado : |
14/03/2018 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
GUIGOU, M.; LAREO, C.; PÉREZ, L. V.; LLUBERAS, M.E.; VÁZQUEZ, D.; FERRARI, M.D. |
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Afiliación : |
MAIRAN GUIGOU, Universidad de la República (UdelaR)/ Facultad de Ingeniería; CLAUDIA LAREO, Universidad de la República (UdelaR)/ Facultad de Ingeniería; LEETICIA VERÓNICA PÉREZ, Universidad de la República (UdelaR)/ Facultad de Ingeniería; MARÍA ELENA LLUBERAS, Universidad de la República (UdelaR)/ Facultad de Ingeniería; DANIEL VÁZQUEZ PEYRONEL, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARIO DANIEL FERRARI, Universidad de la República (UdelaR)/ Facultad de Ingeniería. |
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Título : |
Bioethanol production from sweet sorghum: Evaluation of post-harvest treatments on sugar extraction and fermentation. |
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Fecha de publicación : |
2011 |
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Fuente / Imprenta : |
Biomass and Bioenergy, 2011, v. 35, n.7, p. 3058-3062. |
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DOI : |
10.1016/j.biombioe.2011.04.028 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 14 September 2010, Revised 1 April 2011, Accepted 15 April 2011, Available online 17 May 2011. |
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Contenido : |
ABSTRACT.
Three experimental sweet sorghum varieties (M81, Topper and Theis) and three post-harvest conditions were evaluated for ethanol production: juices extracted by milling were obtained from the whole plant, plant without panicle, and stalk (plant without panicle and leaves), respectively. A linear relationship was found between the total fermentable sugar concentrations and Brix degrees of the juices, which can predict the potential ethanol yield by field analytical tests. The juice extractability presented different behavior among the sweet sorghum varieties with respect to the treatments studied. However such treatments did not affect the level of sugar concentration of the juices obtained and the fermentation efficiency. Topper and Theis showed the best performance in terms of ethanol concentration, fermentation efficiency and ethanol yield. The variety used and its post-harvest treatment should be appropriately selected in order to improve the ethanol production from sweet sorghum.
Copyright © 2011 Elsevier Ltd. All rights reserved. |
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Palabras claves : |
AGROENERGÍAS; ALCOHOLIC FERMENTATION; ETANOL; ETHANOL; SACCHAROMYCES CEREVISIAE; SORGO; SWEET SORGHUM. |
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Thesagro : |
CULTIVOS. |
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Asunto categoría : |
-- |
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Marc : |
LEADER 02046naa a2200301 a 4500
001 1050410
005 2018-03-14
008 2011 bl uuuu u00u1 u #d
024 7 $a10.1016/j.biombioe.2011.04.028$2DOI
100 1 $aGUIGOU, M.
245 $aBioethanol production from sweet sorghum$bEvaluation of post-harvest treatments on sugar extraction and fermentation.$h[electronic resource]
260 $c2011
500 $aArticle history: Received 14 September 2010, Revised 1 April 2011, Accepted 15 April 2011, Available online 17 May 2011.
520 $aABSTRACT. Three experimental sweet sorghum varieties (M81, Topper and Theis) and three post-harvest conditions were evaluated for ethanol production: juices extracted by milling were obtained from the whole plant, plant without panicle, and stalk (plant without panicle and leaves), respectively. A linear relationship was found between the total fermentable sugar concentrations and Brix degrees of the juices, which can predict the potential ethanol yield by field analytical tests. The juice extractability presented different behavior among the sweet sorghum varieties with respect to the treatments studied. However such treatments did not affect the level of sugar concentration of the juices obtained and the fermentation efficiency. Topper and Theis showed the best performance in terms of ethanol concentration, fermentation efficiency and ethanol yield. The variety used and its post-harvest treatment should be appropriately selected in order to improve the ethanol production from sweet sorghum. Copyright © 2011 Elsevier Ltd. All rights reserved.
650 $aCULTIVOS
653 $aAGROENERGÍAS
653 $aALCOHOLIC FERMENTATION
653 $aETANOL
653 $aETHANOL
653 $aSACCHAROMYCES CEREVISIAE
653 $aSORGO
653 $aSWEET SORGHUM
700 1 $aLAREO, C.
700 1 $aPÉREZ, L. V.
700 1 $aLLUBERAS, M.E.
700 1 $aVÁZQUEZ, D.
700 1 $aFERRARI, M.D.
773 $tBiomass and Bioenergy, 2011$gv. 35, n.7, p. 3058-3062.
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Registro original : |
INIA La Estanzuela (LE) |
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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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