Linking weather generators and crop models for assessment of climate forecast outcomes

Somkiat Apipattanavis, Federico Bert, Guillermo Podestá, Balaji Rajagopalan

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


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).

Original languageEnglish (US)
Pages (from-to)166-174
Number of pages9
JournalAgricultural and Forest Meteorology
Issue number2
StatePublished - Feb 15 2010


  • Argentina
  • Climate impacts
  • Maize
  • Risk assessment
  • Seasonal forecasting
  • Statistical downscaling

ASJC Scopus subject areas

  • Forestry
  • Global and Planetary Change
  • Agronomy and Crop Science
  • Atmospheric Science


Dive into the research topics of 'Linking weather generators and crop models for assessment of climate forecast outcomes'. Together they form a unique fingerprint.

Cite this