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Registros recuperados : 51 | |
41. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | PEREIRA, A.L.; PIMIENTA, A.; AGUIRRE, P.; FERRISO, M.; PRAVIA, V. White clover seed production response to irrigation. ln: International Grassland Congress, 22, 2013, Orange New South Wales, Australia Michalk, D.L.; Millar, G.D.; Badgery, W.B.; Broadfoot, K.M.; eds. Proceedings of the 22 International Grassland Congress : revitalising grasslands to sustain our communities. Orange New South Wales, (Australia): SCIRO Publishing, 2013 Formato electrónicoBiblioteca(s): INIA Treinta y Tres. |
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43. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | BALDASSINI, P.; BAETHGEN, W.; CAMBA SANS, G.; QUINCKE, A.; PRAVIA, V.; TERRA, J.A.; MACEDO, F.; PIÑEIRO, G.; PARUELO, J. Carbon stocks and potential sequestration of Uruguayan soils. A road map to a comprehensive characterization of temporal and spatial changes to assess Carbon footprint. Original research. Frontiers in Sustainable Food Systems. 2023, Volume 7. https://doi.org/10.3389/fsufs.2023.1045734 Article history: Received 16 Sep 2022; Accepted 25 May 2023; Published 20 July 2023. -- Correspondence: Dr. Pablo Baldassini, Instituto Nacional de Investigación Agropecuaria, INIA La Estanzuela, Colonia, Uruguay. -- Edited by: Bruno José...Biblioteca(s): INIA Las Brujas. |
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44. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | TERRA, J.A.; MOLINA, F.; DEAMBROSI, E.; PRAVIA, V.; ROEL, A.; CASALES, L.; FERREIRA, D.; FERREIRA, R. Densidad de siembra y nitrógeno en EP144 a escala de chacra. Manejo de suelos y nutrición vegetal. ln: INIA TREINTA Y TRES. Arroz. Resultados Experimentales 2006-2007. Treinta y Tres (Uruguay): INIA, 2007. Cap. 7., p. 1-8. (INIA Serie Actividades de Difusión; 502)Biblioteca(s): INIA Treinta y Tres. |
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45. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | TERRA, J.A.; CANTOU, G.; DEAMBROSI, E.; MOLINA, F.; PRAVIA, V.; ROEL, A.; PEREIRA, M.; SARTORI, J.; STIRLING, N. Impacto de la intensidad de laboreo en los rendimientos de arroz de la UPAG, durante tres zafras (2006/07, 2007/08 y 2008/2009). In: DEAMBROSI, E.; MONTOSSI, F.; SARAVIA, H.; BLANCO, P.H.; AYALA, W. (Eds.). 10 años de la Unidad de Producción Arroz-Ganadería. Montevideo (Uruguay): INIA, 2009. p. 167-188 (INIA Serie Técnica; 180)Biblioteca(s): INIA Tacuarembó; INIA Treinta y Tres. |
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46. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | VISITA DE CAMPO Y REUNIÓN TÉCNICA, 2 DE OCTUBRE 2012, TACUAREMBÓ, URUGUAY; BERMÚDEZ, R.; PRAVIA, V.; CUADRO, R.; GIORELLO, D.; QUINCKE, A.; MORÓN, A. Fertilización fosfatada en pasturas. Montevideo, UY: INIA, 2012. 20 p. Martes 02 de octubre, INIA Tacuarembó.Biblioteca(s): INIA Tacuarembó. |
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47. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | AYALA, W.; MONTOSSI, F.; BARRIOS, E.; BERMÚDEZ, R.; CUADRO, R.; LUZARDO, S.; SILVEIRA, C.; PÉREZ GOMAR, E.; PRAVIA, V.; ROVIRA, P.J.; VELAZCO, J.I. Summer forage feeding alternatives: opportunities and chalenges for pastoral systems in Uruguay. ln: Proceedings of the International Workshop to honor Prof. John Hodgson. An overview of research on pastoral - based systems in the southern part of South America: Tandil (Argentina):Universidad Nacional del Centro de la Provincia de Buenos Aires, 2010. p. 81-93Biblioteca(s): INIA Treinta y Tres. |
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48. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | SAVIAN, J.V.; QUIÑONES, A.; PRAVIA, V.; CARDOZO, G.; GUIDO, A.; DEVINCENZI, T.; JAURENA, M.; ROVIRA, P.J.; LEONI, C.; CIGANDA, V.; DE BARBIERI, I.; AYALA, W.; CIAPPESONI, G.; LATTANZI, F. Un nuevo experimento de largo plazo en INIA Treinta y Tres con foco en la sostenibilidad del campo natural. Pasturas. Revista INIA Uruguay, Diciembre 2021, no.67, p.29-31. (Revista INIA; 67).Biblioteca(s): INIA Las Brujas. |
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49. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | VILLARINO, S.H.; PINTO, P.; DELLA CHIESA, T.; JOBBÁGY, E.G.; STUDDERT, G.A.; BAZZONI, B.; CONTI, G.; RUFINO, M.; ÁLVAREZ, R.; BODDEY , R.; BAYER, C.; DE F CARVALHO, P.C.; FERNÁNDEZ , R.J.; LATTANZI, F.; OESTERHELD , M.; OYHANTÇABAL, W.; PARUELO, J.; PRAVIA, V.; PIÑEIRO, G. The role of South American grazing lands in mitigating greenhouse gas emissions. A reply to: "Reassessing the role of grazing lands in carbon-balance estimations: Meta-analysis and review", by Viglizzo et al., (2019). (Letter). Science of the Total Environment, 20 October 2020, Volume 740, Article number 140108. Doi: https://doi-org/10.1016/j.scitotenv.2020.140108Biblioteca(s): INIA La Estanzuela; INIA Treinta y Tres. |
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50. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | 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. How do various maize crop models vary in their responses to climate change factors? Global Change Biology, 2014, v.20(7), p. 2301-2320. Article history: Received 7 June 2013 and accepted 2 December 2013, published 2014.Biblioteca(s): INIA Treinta y Tres. |
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51. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | MARCAIDA, M.; ASSENG, S.; EWERT, F.; BASSU, S.; DURAND, J.L.; LI, T.; MARTRE, P.; ADAM, M.; AGGARWAL, P.K.; ANGULO, C.; BARON, C.; BASSO, B.; BERTUZZI, P.; BIERNATH, C.; BOOGAARD, H.; BOOTE, K.J.; BOUMAN, B.; BREGAGLIO, S.; BRISSON, N.; BUIS, S.; CAMMARANO, D.; CHALLINOR, A.J.; CONFALONIERI, R.; CONIJN, J.G.; CORBEELS, M.; DERYNG, D.; DE SANCTIS, G.; DOLTRA, J.; FUMOTO, T.; GAYDON, D.; GAYLER, S.; GOLDBERG, R.; GRANT, R.F.; GRASSINI, P.; HATFIELD, J.L.; HASEGAWA, T.; HENG, L.; HOEK, S.; HOOKER, J.; HUNT, L.A.; INGWERSEN, J.; IZAURRALDE, R.C.; JONGSCHAAP, R.E.E.; JONES, J.W.; KEMANIAN, R.A.; KERSEBAUM, K.C.; KIM, S.-H.; LIZASO, J.; MÜLLER, C.; NAKAGAWA, H.; NARESH KUMAR, S.; NENDEL, C.; O'LEARY, G.J.; OLESEN, J.E.; ORIOL, P.; OSBORNE, T.M.; PALOSUO, T.; PRAVIA, V.; PRIESACK, E.; RIPOCHE, D.; ROSENZWEIG, C.; RUANE, A.C.; RUGET, F.; SAU, F.; SEMENOV, M.A.; SHCHERBAK, I.; SINGH, B.; SINGH, U.; SOO, H.K.; STEDUTO, P.; STÖCKLE, C.; STRATONOVITCH, P.; STRECK, T.; SUPIT, I.; TANG, L.; TAO, F.; TEIXEIRA, E.I.; THORBURN, P.; TIMLIN, D.; TRAVASSO, M.; RÖTTER, R.P.; WAHA, K.; WALLACH, D.; WHITE, J.W.; WILKENS, P.; WILLIAMS, J.R.; WOLF, J.; YIN, X.; YOSHIDA, H.; ZHANG, Z.; ZHU, Y. A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration. Agricultural and Forest Meteorology, 2015, v.214-215, p. 483-493. Article history: Received 6 March 2015 / Received in revised form 29 July 2015 / Accepted 20 September 2015 / Available online 1 October 2015.Biblioteca(s): INIA Las Brujas; INIA Treinta y Tres. |
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![](/consulta/web/img/deny.png) | Acceso al texto completo restringido a Biblioteca INIA Treinta y Tres. Por información adicional contacte bibliott@inia.org.uy. |
Registro completo
|
Biblioteca (s) : |
INIA Treinta y Tres. |
Fecha actual : |
28/03/2016 |
Actualizado : |
24/09/2018 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
A - 1 |
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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