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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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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha actual : |
30/05/2023 |
Actualizado : |
30/05/2023 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
Internacional - -- |
Autor : |
CAZZULI, F.; SÁNCHEZ, J.; HIRIGOYEN, A.; ROVIRA, P.J.; BERETTA, V.; SIMEONE, A.; JAURENA, M.; DURANTE, M.; SAVIAN, J.V.; POPPI, D.; MONTOSSI, F.; LAGOMARSINO, X.; LUZARDO, S.; BRITO, G.; VELAZCO, J.I.; BREMM, C.; LATTANZI, F. |
Afiliación : |
FIORELLA CARLA CAZZULI ALBA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JAVIER SÁNCHEZ, Department of Health Management, Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, PEI, Canada; ANDRES EDUARDO HIRIGOYEN DOMINGUEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; PABLO JUAN ROVIRA SANZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; VIRGINIA BERETTA, Animal Science Department, Universidad de la República, Paysandú, Uruguay; ÁLVARO SIMEONE, Animal Science Department, Universidad de la República, Paysandú, Uruguay; MARTIN ALEJANDRO JAURENA BARRIOS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARTÍN DURANTE, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Estación Experimental Agropecuaria Concepción del Uruguay, Concepción del Uruguay, Argentina; JEAN VICTOR SAVIAN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; DENNIS POPPI, School of Agriculture and Food Sciences, The University of Queensland, Gatton, Australia; FABIO MARCELO MONTOSSI PORCHILE, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; XIMENA MARIA LAGOMARSINO LARRIERA, FCA-UDE, Facultad de Ciencias Agrarias, Universidad de la Empresa, Montevideo, Uruguay; SANTIAGO FELIPE LUZARDO VILLAR, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GUSTAVO WALTER BRITO DIAZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JOSÉ IGNACIO VELAZCO DE LOS REYES, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CAROLINA BREMM, Grazing Ecology Research Group, Federal University of Rio Grande do Sul, Porto Alegre, Brazil; FERNANDO A. LATTANZI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Supplement feed efficiency of growing beef cattle grazing native Campos grasslands during winter: a collated analysis. |
Fecha de publicación : |
2023 |
Fuente / Imprenta : |
Translational Animal Science. 2023, Volume 7, Issue 1, txad028. https://doi.org/10.1093/tas/txad028 -- OPEN ACCESS |
ISSN : |
2573-2102 (online) |
DOI : |
10.1093/tas/txad028 |
Idioma : |
Inglés |
Notas : |
Article history: Received 03 October 2022; Accepted 09 March 2023; Published 10 March 2023; Corrected and typeset 01 April 2023. -- Corresponding author: fcazzuli@inia.org.uy -- Issue Section: Forage Based Livestock Systems. -- License: This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), -- Supplementary data: txad028_suppl_Supplementary_Data - docx file |
Contenido : |
Supplementing growing cattle grazing native subtropical Campos grasslands during winter improves the low, even negative, average daily weight gain (ADG) typical of extensive animal production systems in Uruguay. Nonetheless, to render the practice profitable, it is crucial to control supplement feed efficiency (SFE), that is, the difference in ADG between supplemented and control animals (ADGchng) per unit of supplement dry matter (DM) intake. Little has been studied specifically on how SFE varies in these systems. The objective of this study was to quantify the magnitude and variation in SFE of growing beef cattle grazing stockpiled native Campos grasslands during winter and assess putative associations with herbage, animals, supplements, and climatic variables. We compiled data from supplementation trials carried out in Uruguay between 1993 and 2018, each evaluating between one and six supplementation treatments. © The Author(s) 2023. Published by Oxford University Press on behalf of the American Society of Animal Science. |
Palabras claves : |
Concentrate supplementation; Growing cattle; Native grasslands; Nutritive value; Protein; Supplement feed efficiency. |
Asunto categoría : |
L02 Alimentación animal |
URL : |
https://academic.oup.com/tas/article-pdf/7/1/txad028/49725363/txad028.pdf
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Marc : |
LEADER 02746naa a2200421 a 4500 001 1064170 005 2023-05-30 008 2023 bl uuuu u00u1 u #d 022 $a2573-2102 (online) 024 7 $a10.1093/tas/txad028$2DOI 100 1 $aCAZZULI, F. 245 $aSupplement feed efficiency of growing beef cattle grazing native Campos grasslands during winter$ba collated analysis.$h[electronic resource] 260 $c2023 500 $aArticle history: Received 03 October 2022; Accepted 09 March 2023; Published 10 March 2023; Corrected and typeset 01 April 2023. -- Corresponding author: fcazzuli@inia.org.uy -- Issue Section: Forage Based Livestock Systems. -- License: This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), -- Supplementary data: txad028_suppl_Supplementary_Data - docx file 520 $aSupplementing growing cattle grazing native subtropical Campos grasslands during winter improves the low, even negative, average daily weight gain (ADG) typical of extensive animal production systems in Uruguay. Nonetheless, to render the practice profitable, it is crucial to control supplement feed efficiency (SFE), that is, the difference in ADG between supplemented and control animals (ADGchng) per unit of supplement dry matter (DM) intake. Little has been studied specifically on how SFE varies in these systems. The objective of this study was to quantify the magnitude and variation in SFE of growing beef cattle grazing stockpiled native Campos grasslands during winter and assess putative associations with herbage, animals, supplements, and climatic variables. We compiled data from supplementation trials carried out in Uruguay between 1993 and 2018, each evaluating between one and six supplementation treatments. © The Author(s) 2023. Published by Oxford University Press on behalf of the American Society of Animal Science. 653 $aConcentrate supplementation 653 $aGrowing cattle 653 $aNative grasslands 653 $aNutritive value 653 $aProtein 653 $aSupplement feed efficiency 700 1 $aSÁNCHEZ, J. 700 1 $aHIRIGOYEN, A. 700 1 $aROVIRA, P.J. 700 1 $aBERETTA, V. 700 1 $aSIMEONE, A. 700 1 $aJAURENA, M. 700 1 $aDURANTE, M. 700 1 $aSAVIAN, J.V. 700 1 $aPOPPI, D. 700 1 $aMONTOSSI, F. 700 1 $aLAGOMARSINO, X. 700 1 $aLUZARDO, S. 700 1 $aBRITO, G. 700 1 $aVELAZCO, J.I. 700 1 $aBREMM, C. 700 1 $aLATTANZI, F. 773 $tTranslational Animal Science. 2023, Volume 7, Issue 1, txad028. https://doi.org/10.1093/tas/txad028 -- OPEN ACCESS
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