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Biblioteca (s) : |
INIA Las Brujas. |
Fecha : |
21/02/2014 |
Actualizado : |
28/07/2023 |
Tipo de producción científica : |
Capítulo en Libro Técnico-Científico |
Autor : |
DE LA FUENTE, L.; BAJSA, N.; QUAGLIOTTO, L.; FABIANO, E.; PÉREZ, C.; ALTIER, N.; ARIAS, A. |
Afiliación : |
L. DE LA FUENTE, Departamento de Bioquímica, IIBCE, Montevideo Facultad de Ciencias, Montevideo; N. BAJSA, Departamento de Bioquímica, IIBCE, Montevideo; L. QUAGLIOTTO, Departamento de Bioquímica, IIBCE, Montevideo; E. FABIANO, Departamento de Bioquímica, IIBCE, Montevideo; C. PÉREZ, Facultad de Agronomía, Paysandú; NORA ADRIANA ALTIER MANZINI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; A. ARIAS, Departamento de Bioquímica, IIBCE, Montevideo. |
Título : |
Interactions among Rhizobia and biocontrol agents in forage legumes. |
Complemento del título : |
Proceedings of the 12 th International Congress on Nitrogen Fixation, Foz do Iguaçu, Paraná, Brazil, September 12?17, 1999. |
Fecha de publicación : |
2002 |
Fuente / Imprenta : |
In: Pedrosa, F.O., Hungria, M., Yates, G., Newton, W.E. (eds). Nitrogen Fixation: From Molecules to Crop Productivity. Current Plant Science and Biotechnology in Agriculture, vol 38. Springer, Dordrecht. p. 552. https://doi.org/10.1007/0-306-47615-0_311 |
DOI : |
10.1007/0-306-47615-0_311 |
Idioma : |
Inglés |
Notas : |
This work was partially supported by PEDECIBA, The European Union, and CSIC. |
Contenido : |
Forage legumes play an important role in the agriculture of Uruguay as a major source of high quality feed for livestock. Their nitrogen fixation capability in symbiotic association with rhizobia confers them a beneficial carryover effect in crop rotation. Nevertheless, important losses in pasture
production are due to soilborne fungal pathogens (Altier 1996). The utilization of rhizospheric fluorescent pseudomonads as biocontrol agents is a feasible alternative to solve this problem. © 2002 Kluwer Academic Publishers. |
Palabras claves : |
Biocontrol Agent; Forage Legume; Rhizoctonia Solani. |
Asunto categoría : |
F01 Cultivo |
Marc : |
LEADER 01486nam a2200241 a 4500 001 1010795 005 2023-07-28 008 2002 bl uuuu u01u1 u #d 024 7 $a10.1007/0-306-47615-0_311$2DOI 100 1 $aDE LA FUENTE, L. 245 $aInteractions among Rhizobia and biocontrol agents in forage legumes.$h[electronic resource] 260 $aIn: Pedrosa, F.O., Hungria, M., Yates, G., Newton, W.E. (eds). Nitrogen Fixation: From Molecules to Crop Productivity. Current Plant Science and Biotechnology in Agriculture, vol 38. Springer, Dordrecht. p. 552. https://doi.org/10.1007/0-306-47615-0_311$c1007 500 $aThis work was partially supported by PEDECIBA, The European Union, and CSIC. 520 $aForage legumes play an important role in the agriculture of Uruguay as a major source of high quality feed for livestock. Their nitrogen fixation capability in symbiotic association with rhizobia confers them a beneficial carryover effect in crop rotation. Nevertheless, important losses in pasture production are due to soilborne fungal pathogens (Altier 1996). The utilization of rhizospheric fluorescent pseudomonads as biocontrol agents is a feasible alternative to solve this problem. © 2002 Kluwer Academic Publishers. 653 $aBiocontrol Agent 653 $aForage Legume 653 $aRhizoctonia Solani 700 1 $aBAJSA, N. 700 1 $aQUAGLIOTTO, L. 700 1 $aFABIANO, E. 700 1 $aPÉREZ, C. 700 1 $aALTIER, N. 700 1 $aARIAS, A.
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Biblioteca (s) : |
INIA Las Brujas; INIA Treinta y Tres. |
Fecha actual : |
12/11/2015 |
Actualizado : |
09/10/2019 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
Internacional - -- |
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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