Linking weather generators and crop models for assessment of climate forecast outcomes
Agricultural production responses to climate variability require salient information to support decisions. We coupled a new hybrid stochastic weather generator [combining parametric and nonparametric components] with a crop simulation model to assess yields and economic returns relevant to maize pro...
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| Otros Autores: | , , , |
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| Formato: | Artículo |
| Lenguaje: | Inglés |
| Materias: | |
| Acceso en línea: | http://ri.agro.uba.ar/files/intranet/articulo/2010Apipattanavis.pdf LINK AL EDITOR |
| Aporte de: | Registro referencial: Solicitar el recurso aquí |
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| 245 | 1 | 0 | |a Linking weather generators and crop models for assessment of climate forecast outcomes |
| 520 | |a Agricultural production responses to climate variability require salient information to support decisions. We coupled a new hybrid stochastic weather generator [combining parametric and nonparametric components] with a crop simulation model to assess yields and economic returns relevant to maize production in two contrasting regions [Pergamino and Pilar] of the Pampas of Argentina. The linked models were used to assess likely outcomes and production risks for seasonal forecasts of dry and wet climate. Forecasts involving even relatively small deviations from climatological probabilities of precipitation may have large impacts on agricultural outcomes. Furthermore, yield changes under alternative scenarios have a disproportionate effect on economic risks. Additionally, we show that regions receiving the same seasonal forecast may experience fairly different outcomes: a forecast of dry conditions did not change appreciably the expected distribution of economic margins in Pergamino [a climatically optimal location] but modified considerably economic expectations [and thus production risk] in Pilar [a more marginal location]. | ||
| 653 | 0 | |a ARGENTINA | |
| 653 | 0 | |a CLIMATE IMPACTS | |
| 653 | 0 | |a MAIZE | |
| 653 | 0 | |a RISK ASSESSMENT | |
| 653 | 0 | |a SEASONAL FORECASTING | |
| 653 | 0 | |a STATISTICAL DOWNSCALING | |
| 653 | 0 | |a AGRICULTURAL PRODUCTION | |
| 653 | 0 | |a CLIMATE EFFECT | |
| 653 | 0 | |a CROP YIELD | |
| 653 | 0 | |a DOWNSCALING | |
| 653 | 0 | |a REGIONAL CLIMATE | |
| 653 | 0 | |a RISK ASSESSMENT | |
| 653 | 0 | |a SIMULATION | |
| 653 | 0 | |a WEATHER FORECASTING | |
| 653 | 0 | |a BUENOS AIRES [ARGENTINA] | |
| 653 | 0 | |a CORDOBA [ARGENTINA] | |
| 653 | 0 | |a PERGAMINO | |
| 653 | 0 | |a PILAR | |
| 653 | 0 | |a ZEA MAYS | |
| 700 | 1 | |a Apipattanavis, Somkiat |9 70046 | |
| 700 | 1 | |9 12448 |a Bert, Federico E. | |
| 700 | 1 | |9 23487 |a Podestá, Guillermo P. | |
| 700 | 1 | |a Rajagopalan, Balaji |9 68362 | |
| 773 | |t Agricultural and Forest Meteorology |g Vol.150, no.2 (2010), p.166-174 | ||
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| 900 | |a ^tLinking weather generators and crop models for assessment of climate forecast outcomes | ||
| 900 | |a ^aApipattanavis^bS. | ||
| 900 | |a ^aBert^bF. | ||
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| 900 | |a ^aPodestá^bG. P. | ||
| 900 | |a ^aRajagopalan^bB. | ||
| 900 | |a ^aApipattanavis^bS.^tDepartment of Civil, Environmental and Architectural Engineering [CEAE], University of Colorado, Boulder, CO, United States | ||
| 900 | |a ^aBert^bF.^tSchool of Agronomy, University of Buenos Aires, Argentina | ||
| 900 | |a ^aPodestá^bG.^tRosenstiel School of Marine and Atmospheric Sciences [RSMAS/MPO], University of Miami, 4600 Rickenbacker Causeway, Miami, FL 33149-1098, United States | ||
| 900 | |a ^aRajagopalan^bB.^tCooperative Institute for Research in Environmental Sciences [CIRES], University of Colorado, Boulder, CO, United States | ||
| 900 | |a ^tAgricultural and Forest Meteorology^cAgric. For. Meterol. | ||
| 900 | |a en | ||
| 900 | |a 166 | ||
| 900 | |a ^i | ||
| 900 | |a Vol. 150, no. 2 | ||
| 900 | |a 174 | ||
| 900 | |a ARGENTINA | ||
| 900 | |a CLIMATE IMPACTS | ||
| 900 | |a MAIZE | ||
| 900 | |a RISK ASSESSMENT | ||
| 900 | |a SEASONAL FORECASTING | ||
| 900 | |a STATISTICAL DOWNSCALING | ||
| 900 | |a AGRICULTURAL PRODUCTION | ||
| 900 | |a CLIMATE EFFECT | ||
| 900 | |a CROP YIELD | ||
| 900 | |a DOWNSCALING | ||
| 900 | |a REGIONAL CLIMATE | ||
| 900 | |a RISK ASSESSMENT | ||
| 900 | |a SIMULATION | ||
| 900 | |a WEATHER FORECASTING | ||
| 900 | |a BUENOS AIRES [ARGENTINA] | ||
| 900 | |a CORDOBA [ARGENTINA] | ||
| 900 | |a PERGAMINO | ||
| 900 | |a PILAR | ||
| 900 | |a ZEA MAYS | ||
| 900 | |a Agricultural production responses to climate variability require salient information to support decisions. We coupled a new hybrid stochastic weather generator [combining parametric and nonparametric components] with a crop simulation model to assess yields and economic returns relevant to maize production in two contrasting regions [Pergamino and Pilar] of the Pampas of Argentina. The linked models were used to assess likely outcomes and production risks for seasonal forecasts of dry and wet climate. Forecasts involving even relatively small deviations from climatological probabilities of precipitation may have large impacts on agricultural outcomes. Furthermore, yield changes under alternative scenarios have a disproportionate effect on economic risks. Additionally, we show that regions receiving the same seasonal forecast may experience fairly different outcomes: a forecast of dry conditions did not change appreciably the expected distribution of economic margins in Pergamino [a climatically optimal location] but modified considerably economic expectations [and thus production risk] in Pilar [a more marginal location]. | ||
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