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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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Biblioteca (s) : |
INIA Treinta y Tres. |
Fecha : |
28/03/2016 |
Actualizado : |
24/09/2018 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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. |
Afiliación : |
MARIA VIRGINIA PRAVIA NIN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Department of Plant Science, The Pennsylvania State University, USA. |
Título : |
How do various maize crop models vary in their responses to climate change factors? |
Fecha de publicación : |
2014 |
Fuente / Imprenta : |
Global Change Biology, 2014, v.20(7), p. 2301-2320. |
DOI : |
10.1111/gcb.12520 |
Idioma : |
Inglés |
Notas : |
Article history: Received 7 June 2013 and accepted 2 December 2013, published 2014. |
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 |
Palabras claves : |
AGMIP; CARBON DIOXIDE; CLIMATE; CO2; GRAIN YIELD; MAIZE; MODEL INTERCOMPARISON; MODELIZACIÓN DE CULTIVOS; SIMULATION MODELS; TEMPERATURE. |
Thesagro : |
CLIMA; DIOXIDO DE CARBONO; INCERTIDUMBRE; MAÍZ; MODELOS DE SIMULACIÓN; TEMPERATURA. |
Asunto categoría : |
U10 Métodos matemáticos y estadísticos |
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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INIA Treinta y Tres (TT) |
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| Acceso al texto completo restringido a Biblioteca INIA Las Brujas. Por información adicional contacte bibliolb@inia.org.uy. |
Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha actual : |
22/11/2016 |
Actualizado : |
22/11/2016 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
A - 1 |
Autor : |
COZZOLINO, D.; FASSIO, A.; RESTAINO, E.; FERNANDEZ, E.; LA MANNA, A. |
Afiliación : |
DANIEL COZZOLINO GÓMEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ALBERTO SANTIAGO FASSIO ARAUJO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ERNESTO ANGEL RESTAINO GALUP, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ENRIQUE GENARO FERNANDEZ RODRIGUEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ALEJANDRO FRANCISCO LA MANNA ALONSO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Verification of silage type using near-infrared spectroscopy combined with multivariate analysis. |
Fecha de publicación : |
2008 |
Fuente / Imprenta : |
Journal of Agricultural and Food Chemistry, 2008, v. 56, no.1, p.79-83. |
DOI : |
10.1021/jf072566d |
Idioma : |
Inglés |
Notas : |
Received 28 August 2007 // Date accepted 8 November 2007 // Published online 27 November 2007 // Published in print 1 January 2008. |
Contenido : |
ABSTRACT.
The ability to authenticate the feed given to animals has become a major challenge in animal production, where the diet fed to the animal is one of the most important production factors affecting the composition of milk and meat from cattle, sheep, and goats. Hence, there is currently an increased consumer demand for information on herbivore production factors and particularly the animal diet. The aim of this study was to evaluate the reliability and accuracy of near-infrared (NIR) reflectance spectroscopy as a tool to verify and authenticate the type of silage used as fed for ruminants. Grain silage (GrS, n ) 94), grass and legume silage (GLegS, n ) 121), and sunflower silage (SunS, n ) 50) samples were collected from commercial farms and analyzed in the visible and NIR regions (400-2500 nm) in a monochromator instrument in reflectance. Principal component analysis (PCA),
partial least-squares discriminant analysis (PLS1-DA), and linear discriminant analysis (LDA) models ere used as methods to verify the different silage types. The classification models based on the NIR data correctly classified more than 90% of the silage samples according to their type. The results from this study showed that NIR spectra combined with multivariate analysis could be used as a tool to objectively authenticate silage samples used as a feed for ruminants.
© 2008 American Chemical Society |
Palabras claves : |
IDENTIFICATION; LINEAR DISCRIMINANT ANALYSIS; NEAR-INFRARED SPECTROSCOPY; PARTIAL LEAST-SQUARES DISCRIMINANT ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; SILAGE. |
Asunto categoría : |
-- |
Marc : |
LEADER 02371naa a2200265 a 4500 001 1056114 005 2016-11-22 008 2008 bl uuuu u00u1 u #d 024 7 $a10.1021/jf072566d$2DOI 100 1 $aCOZZOLINO, D. 245 $aVerification of silage type using near-infrared spectroscopy combined with multivariate analysis.$h[electronic resource] 260 $c2008 500 $aReceived 28 August 2007 // Date accepted 8 November 2007 // Published online 27 November 2007 // Published in print 1 January 2008. 520 $aABSTRACT. The ability to authenticate the feed given to animals has become a major challenge in animal production, where the diet fed to the animal is one of the most important production factors affecting the composition of milk and meat from cattle, sheep, and goats. Hence, there is currently an increased consumer demand for information on herbivore production factors and particularly the animal diet. The aim of this study was to evaluate the reliability and accuracy of near-infrared (NIR) reflectance spectroscopy as a tool to verify and authenticate the type of silage used as fed for ruminants. Grain silage (GrS, n ) 94), grass and legume silage (GLegS, n ) 121), and sunflower silage (SunS, n ) 50) samples were collected from commercial farms and analyzed in the visible and NIR regions (400-2500 nm) in a monochromator instrument in reflectance. Principal component analysis (PCA), partial least-squares discriminant analysis (PLS1-DA), and linear discriminant analysis (LDA) models ere used as methods to verify the different silage types. The classification models based on the NIR data correctly classified more than 90% of the silage samples according to their type. The results from this study showed that NIR spectra combined with multivariate analysis could be used as a tool to objectively authenticate silage samples used as a feed for ruminants. © 2008 American Chemical Society 653 $aIDENTIFICATION 653 $aLINEAR DISCRIMINANT ANALYSIS 653 $aNEAR-INFRARED SPECTROSCOPY 653 $aPARTIAL LEAST-SQUARES DISCRIMINANT ANALYSIS 653 $aPRINCIPAL COMPONENT ANALYSIS 653 $aSILAGE 700 1 $aFASSIO, A. 700 1 $aRESTAINO, E. 700 1 $aFERNANDEZ, E. 700 1 $aLA MANNA, A. 773 $tJournal of Agricultural and Food Chemistry, 2008$gv. 56, no.1, p.79-83.
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