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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; INIA Treinta y Tres. |
Fecha : |
12/11/2015 |
Actualizado : |
09/10/2019 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Autor : |
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. |
Afiliación : |
MARIA VIRGINIA PRAVIA NIN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration. |
Fecha de publicación : |
2015 |
Fuente / Imprenta : |
Agricultural and Forest Meteorology, 2015, v.214-215, p. 483-493. |
ISSN : |
0168-1923 |
DOI : |
10.1016/j.agrformet.2015.09.013 |
Idioma : |
Inglés |
Notas : |
Article history: Received 6 March 2015 / Received in revised form 29 July 2015 / Accepted 20 September 2015 / Available online 1 October 2015. |
Contenido : |
ABSTRACT.
Ensembles of process-based crop models are increasingly used to simulate crop growth for scenariosof temperature and/or precipitation changes corresponding to different projections of atmospheric CO2concentrations. This approach generates large datasets with thousands of simulated crop yield data. Suchdatasets potentially provide new information but it is difficult to summarize them in a useful way due totheir structural complexities. An associated issue is that it is not straightforward to compare crops and tointerpolate the results to alternative climate scenarios not initially included in the simulation protocols.Here we demonstrate that statistical models based on random-coefficient regressions are able to emulateensembles of process-based crop models. An important advantage of the proposed statistical models isthat they can interpolate between temperature levels and between CO2concentration levels, and canthus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without re-running the original complex crop models. Our approach is illustrated with three yield datasets simulatedby 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to thesedatasets, and are then used to analyze the variability of the yield response to [CO2] and temperature.Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effectof a temperature increase of +2◦C in the considered sites. Compared to wheat, required levels of [CO2]increase are much higher for maize, and intermediate for rice. For all crops, uncertainties in simulatingclimate change impacts increase more with temperature than with elevated [CO2].
© 2015 Elsevier B.V. All rights reserved. MenosABSTRACT.
Ensembles of process-based crop models are increasingly used to simulate crop growth for scenariosof temperature and/or precipitation changes corresponding to different projections of atmospheric CO2concentrations. This approach generates large datasets with thousands of simulated crop yield data. Suchdatasets potentially provide new information but it is difficult to summarize them in a useful way due totheir structural complexities. An associated issue is that it is not straightforward to compare crops and tointerpolate the results to alternative climate scenarios not initially included in the simulation protocols.Here we demonstrate that statistical models based on random-coefficient regressions are able to emulateensembles of process-based crop models. An important advantage of the proposed statistical models isthat they can interpolate between temperature levels and between CO2concentration levels, and canthus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without re-running the original complex crop models. Our approach is illustrated with three yield datasets simulatedby 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to thesedatasets, and are then used to analyze the variability of the yield response to [CO2] and temperature.Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effectof a temperature increase of +2◦C in... Presentar Todo |
Palabras claves : |
Climate change; CROP MODEL; Emulator; MAIZE; Meta-model; MODELIZACIÓN DE LOS CULTIVOS; RICE; Statistical model; WHEAT; Yield. |
Thesagro : |
ARROZ; CAMBIO CLIMÁTICO; MAÍZ; MODELOS ESTADISTICOS; TRIGO. |
Asunto categoría : |
A50 Investigación agraria |
Marc : |
LEADER 05363naa a2201417 a 4500 001 1053856 005 2019-10-09 008 2015 bl uuuu u00u1 u #d 022 $a0168-1923 024 7 $a10.1016/j.agrformet.2015.09.013$2DOI 100 1 $aMARCAIDA, M. 245 $aA statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration. 260 $c2015 500 $aArticle history: Received 6 March 2015 / Received in revised form 29 July 2015 / Accepted 20 September 2015 / Available online 1 October 2015. 520 $aABSTRACT. Ensembles of process-based crop models are increasingly used to simulate crop growth for scenariosof temperature and/or precipitation changes corresponding to different projections of atmospheric CO2concentrations. This approach generates large datasets with thousands of simulated crop yield data. Suchdatasets potentially provide new information but it is difficult to summarize them in a useful way due totheir structural complexities. An associated issue is that it is not straightforward to compare crops and tointerpolate the results to alternative climate scenarios not initially included in the simulation protocols.Here we demonstrate that statistical models based on random-coefficient regressions are able to emulateensembles of process-based crop models. An important advantage of the proposed statistical models isthat they can interpolate between temperature levels and between CO2concentration levels, and canthus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without re-running the original complex crop models. Our approach is illustrated with three yield datasets simulatedby 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to thesedatasets, and are then used to analyze the variability of the yield response to [CO2] and temperature.Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effectof a temperature increase of +2◦C in the considered sites. Compared to wheat, required levels of [CO2]increase are much higher for maize, and intermediate for rice. For all crops, uncertainties in simulatingclimate change impacts increase more with temperature than with elevated [CO2]. © 2015 Elsevier B.V. All rights reserved. 650 $aARROZ 650 $aCAMBIO CLIMÁTICO 650 $aMAÍZ 650 $aMODELOS ESTADISTICOS 650 $aTRIGO 653 $aClimate change 653 $aCROP MODEL 653 $aEmulator 653 $aMAIZE 653 $aMeta-model 653 $aMODELIZACIÓN DE LOS CULTIVOS 653 $aRICE 653 $aStatistical model 653 $aWHEAT 653 $aYield 700 1 $aASSENG, S. 700 1 $aEWERT, F. 700 1 $aBASSU, S. 700 1 $aDURAND, J.L. 700 1 $aLI, T. 700 1 $aMARTRE, P. 700 1 $aADAM, M. 700 1 $aAGGARWAL, P.K. 700 1 $aANGULO, C. 700 1 $aBARON, C. 700 1 $aBASSO, B. 700 1 $aBERTUZZI, P. 700 1 $aBIERNATH, C. 700 1 $aBOOGAARD, H. 700 1 $aBOOTE, K.J. 700 1 $aBOUMAN, B. 700 1 $aBREGAGLIO, S. 700 1 $aBRISSON, N. 700 1 $aBUIS, S. 700 1 $aCAMMARANO, D. 700 1 $aCHALLINOR, A.J. 700 1 $aCONFALONIERI, R. 700 1 $aCONIJN, J.G. 700 1 $aCORBEELS, M. 700 1 $aDERYNG, D. 700 1 $aDE SANCTIS, G. 700 1 $aDOLTRA, J. 700 1 $aFUMOTO, T. 700 1 $aGAYDON, D. 700 1 $aGAYLER, S. 700 1 $aGOLDBERG, R. 700 1 $aGRANT, R.F. 700 1 $aGRASSINI, P. 700 1 $aHATFIELD, J.L. 700 1 $aHASEGAWA, T. 700 1 $aHENG, L. 700 1 $aHOEK, S. 700 1 $aHOOKER, J. 700 1 $aHUNT, L.A. 700 1 $aINGWERSEN, J. 700 1 $aIZAURRALDE, R.C. 700 1 $aJONGSCHAAP, R.E.E. 700 1 $aJONES, J.W. 700 1 $aKEMANIAN, R.A. 700 1 $aKERSEBAUM, K.C. 700 1 $aKIM, S.-H. 700 1 $aLIZASO, J. 700 1 $aMÜLLER, C. 700 1 $aNAKAGAWA, H. 700 1 $aNARESH KUMAR, S. 700 1 $aNENDEL, C. 700 1 $aO'LEARY, G.J. 700 1 $aOLESEN, J.E. 700 1 $aORIOL, P. 700 1 $aOSBORNE, T.M. 700 1 $aPALOSUO, T. 700 1 $aPRAVIA, V. 700 1 $aPRIESACK, E. 700 1 $aRIPOCHE, D. 700 1 $aROSENZWEIG, C. 700 1 $aRUANE, A.C. 700 1 $aRUGET, F. 700 1 $aSAU, F. 700 1 $aSEMENOV, M.A. 700 1 $aSHCHERBAK, I. 700 1 $aSINGH, B. 700 1 $aSINGH, U. 700 1 $aSOO, H.K. 700 1 $aSTEDUTO, P. 700 1 $aSTÖCKLE, C. 700 1 $aSTRATONOVITCH, P. 700 1 $aSTRECK, T. 700 1 $aSUPIT, I. 700 1 $aTANG, L. 700 1 $aTAO, F. 700 1 $aTEIXEIRA, E.I. 700 1 $aTHORBURN, P. 700 1 $aTIMLIN, D. 700 1 $aTRAVASSO, M. 700 1 $aRÖTTER, R.P. 700 1 $aWAHA, K. 700 1 $aWALLACH, D. 700 1 $aWHITE, J.W. 700 1 $aWILKENS, P. 700 1 $aWILLIAMS, J.R. 700 1 $aWOLF, J. 700 1 $aYIN, X. 700 1 $aYOSHIDA, H. 700 1 $aZHANG, Z. 700 1 $aZHU, Y. 773 $tAgricultural and Forest Meteorology, 2015$gv.214-215, p. 483-493.
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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 : |
21/07/2017 |
Actualizado : |
21/07/2017 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
Internacional - -- |
Autor : |
VICENTE-SERRANO, S.M.; BIDEGAIN, M.; TOMAS-BURGUERA, M.; DOMÍNGUEZ-CASTRO, F.; EL KENAWY, A.; MCVICAR, T.R.; AZORIN-MOLINA, C.; LÓPEZ-MORENO, J.I.; NIETO, R.; GIMENO, L.; GIMÉNEZ, A. |
Afiliación : |
SERGIO M. VICENTE-SERRANO, Consejo Superior de Investigaciones Científicas (IPE–CSIC), Instituto Pirenaico de Ecología, Zaragoza, Spain; MARIO BIDEGAIN, Instituto Uruguayo de Meteorología, Montevideo, Uruguay; MIQUEL TOMAS-BURGUERA, Department of Soil and Water, Estación Experimental de Aula Dei (EEAD-CSIC), Zaragoza, Spain; FERNANDO DOMÍNGUEZ-CASTRO, Consejo Superior de Investigaciones Científicas (IPE–CSIC), Instituto Pirenaico de Ecología, Zaragoza, Spain; AHMED EL KENAWY, Consejo Superior de Investigaciones Científicas (IPE–CSIC), Instituto Pirenaico de Ecología, Zaragoza, Spain; Department of Geography, Mansoura University, Egypt; TIM R. MCVIAR, CSIRO Land and Water, Canberra, ACT, Australia; CESAR AZORIN-MOLINA, Regional Climate Group, Department of Earth Sciences, University of Gothenburg, Sweden; JUAN I. LÓPEZ-MORENO, Consejo Superior de Investigaciones Científicas (IPE–CSIC), Instituto Pirenaico de Ecología, Zaragoza, Spain; RAQUEL NIETO, Environmental Physics Laboratory, Universidade de Vigo, Ourense, Spain; LUIS GIMENO, Environmental Physics Laboratory, Universidade de Vigo, Ourense, Spain; AGUSTIN EDUARDO GIMÉNEZ FUREST, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
A comparison of temporal variability of observed and model-based pan evaporation over Uruguay (1973?2014). |
Fecha de publicación : |
2017 |
Fuente / Imprenta : |
International Journal of Climatology, 2017 |
DOI : |
10.1002/joc.5179 |
Idioma : |
Inglés |
Notas : |
Article history: Received 16 March 2017; Revised 8 May 2017; Accepted 23 May 2017 |
Contenido : |
ABSTRACT.
This study analyses variability and trends of atmospheric evaporative demand (AED) across Uruguay in the past four decades. Changes were assessed using pan evaporation measurements from 10 meteorological stations and compared to PenPan model calculations, which is a physically based model that employs meteorological data as input. Results demonstrate a high agreement between the observed AED and those estimated from the PenPan model. Both observations and model estimations agree on a high interannual variability in AED, though being statistically insignificant (p>0.05) at seasonal and annual scales. Given that AED shows high sensitivity to changes in relative humidity and sunshine duration, as a surrogate of solar radiation, the lack of significant trends in the AED observations and estimations over Uruguay can be linked to the insignificant trend found for these climate variables for the period from 1973 to 2014. This is the first study that reports Pan evaporation trends for this part of the world, helping to infill gaps for mid-latitude Southern Hemisphere areas, which are
poorly represented in Pan evaporation trends.
© 2017 Royal Meteorological Society |
Palabras claves : |
CLIMATE CHANGE; EVAPORATION; PENMAN-MONTEITH; REFERENCE EVAPOTRANSPIRATION; SOUTH AMERICA; URUGUAY. |
Thesagro : |
CAMBIO CLIMÁTICO; CLIMA; EVAPOTRANSPIRACION; MEDIO AMBIENTE. |
Asunto categoría : |
P40 Meteorología y climatología |
Marc : |
LEADER 02352naa a2200385 a 4500 001 1057393 005 2017-07-21 008 2017 bl uuuu u00u1 u #d 024 7 $a10.1002/joc.5179$2DOI 100 1 $aVICENTE-SERRANO, S.M. 245 $aA comparison of temporal variability of observed and model-based pan evaporation over Uruguay (1973?2014).$h[electronic resource] 260 $c2017 500 $aArticle history: Received 16 March 2017; Revised 8 May 2017; Accepted 23 May 2017 520 $aABSTRACT. This study analyses variability and trends of atmospheric evaporative demand (AED) across Uruguay in the past four decades. Changes were assessed using pan evaporation measurements from 10 meteorological stations and compared to PenPan model calculations, which is a physically based model that employs meteorological data as input. Results demonstrate a high agreement between the observed AED and those estimated from the PenPan model. Both observations and model estimations agree on a high interannual variability in AED, though being statistically insignificant (p>0.05) at seasonal and annual scales. Given that AED shows high sensitivity to changes in relative humidity and sunshine duration, as a surrogate of solar radiation, the lack of significant trends in the AED observations and estimations over Uruguay can be linked to the insignificant trend found for these climate variables for the period from 1973 to 2014. This is the first study that reports Pan evaporation trends for this part of the world, helping to infill gaps for mid-latitude Southern Hemisphere areas, which are poorly represented in Pan evaporation trends. © 2017 Royal Meteorological Society 650 $aCAMBIO CLIMÁTICO 650 $aCLIMA 650 $aEVAPOTRANSPIRACION 650 $aMEDIO AMBIENTE 653 $aCLIMATE CHANGE 653 $aEVAPORATION 653 $aPENMAN-MONTEITH 653 $aREFERENCE EVAPOTRANSPIRATION 653 $aSOUTH AMERICA 653 $aURUGUAY 700 1 $aBIDEGAIN, M. 700 1 $aTOMAS-BURGUERA, M. 700 1 $aDOMÍNGUEZ-CASTRO, F. 700 1 $aEL KENAWY, A. 700 1 $aMCVICAR, T.R. 700 1 $aAZORIN-MOLINA, C. 700 1 $aLÓPEZ-MORENO, J.I. 700 1 $aNIETO, R. 700 1 $aGIMENO, L. 700 1 $aGIMÉNEZ, A. 773 $tInternational Journal of Climatology, 2017
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