Improving Applications in Agriculture of ENSO-Based Seasonal Rainfall Forecasts Considering Atlantic Ocean Surface Temperatures

  • G. O. Magrin
  • M. I. Travasso
  • W. E. Baethgen
  • R. T. Boca


Climate uncertainties, derived from annual climatic variability, often lead to conservative crop management strategies that sacrifice some productivity to reduce the risk of losses in bad years. The availability of ENSO-based climate forecasts has led many to believe that such forecasts may benefit decision-making in agriculture. The forecasting capability may allow the mitigation of negative effects of ENSO-related climate variability as well as taking advantage of favorable conditions (Stern and Easterling 1999).


Maize Yield Climate Forecast South Atlantic Convergence Zone South Atlantic Ocean ENSO Phase 
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  1. Barros V, Castañeda ME, Doyle M (1996) Variabilidad ineranual de la precipitación: señles del ENSO y del gradiente meridional hemisférico de temperatura, en Impacto de las variaciones climáticas en el desarrollo regional un análisis interdisciplinario. VII Congreso Latinoamericano e Ibérico de Meteorología, pp 321–322Google Scholar
  2. Barros V, González M, Liebmann B, Camilloni I (2000) Influence of the South Atlantic convergence zone and South Atlantic sea surface temperature on interannual summer rainfall variability in southeastern South America. Theor Appl Climatol 67:123–133CrossRefGoogle Scholar
  3. Berri G J, Bertossa GI (2004) The influence of the tropical and subtropical Atlantic and Pacific Oceans on precipitation variability over southern central South America on seasonal time scales. Int J Climatol 24:415–435CrossRefGoogle Scholar
  4. Cleveland WS, Devlin SJ, Grosse E (1988) Regression by local fitting. Methods, properties and computational algorithms. J Econ 37:87–114Google Scholar
  5. Diaz AF, Studzinski C, Mechoso CR (1998) Relationships between precipitation anomalies in Uruguay and southern Brazil and sea surface temperature in the Pacific and Atlantic Oceans. J Climate 11:251–271CrossRefGoogle Scholar
  6. Ferreira RA, Podestá GP, Messina CD, Letson D, Dardanelli J, Guevara E, Meira S (2001) A linked-modeling framework to estimate maize production risk associated with ENSO-related climate variability in Argentina. Agr Forest Meteorol 107:177–192CrossRefGoogle Scholar
  7. Jones JW, Hansen JW, Royce FS, Messina CD (2000) Potential benefits of climate forecasting to agriculture. Agr Ecosyst Environ 82:169–184CrossRefGoogle Scholar
  8. Magrin GO, Travasso MI (2001) Economic value of ENSO-based climatic forecasts in the agricultural sector of Argentina. Proceedings of the 2nd International Symposium “Modelling Cropping Systems”, European Society of Agronomy (ESA), Florence, Italy, pp 139–140Google Scholar
  9. Magrin GO, Grondona MO, Travasso MI, Boullón DR, Rodriguez CD, Messina CD (1998) Impacto del fenómeno “ENSO” sobre la producción de cultivos en la región Pampeana Argentina. INTA, Instituto de Clima y Agua, CIRN, Castelar, BsAs, Argentina (Boletín Técnico)Google Scholar
  10. Magrin GO, Travasso MI, Jones JW, Rodriguez GR, Boullón DR (2000) Using climate forecasts in agriculture: a pilot application in Argentina. In: Bowen WT, White JW (eds) Proceedings of the Third International Symposium on Systems Approaches for Agricultural Development (SAADIII), November 1999, Lima, Peru (CD-ROM)Google Scholar
  11. Messina CD, Hansen JW, Hall AJ (1999) Land allocation conditioned on El Niño-Southern Oscillation phases in the Pampas of Argentina. Agr Syst 60:197–212CrossRefGoogle Scholar
  12. Podestá G P, Letson D, Messina CD, Royce FS, Ferreyra RA, Jones JW, Hansen JW, Llovet I, Grondona MO, O’Brien JJ (2002) Use of ENSO-related climate information in agricultural decision making in Argentina. Agr Syst 74:371–392CrossRefGoogle Scholar
  13. Stern PC, Easterling WE (Eds) (1999) Making climate forecasts matter. National Academy Press, Washington, DC, USAGoogle Scholar
  14. Travasso MI, Magrin GO (2001) Testing crop models at the field level in Argentina. Proceedings of the 2nd International Symposium “Modelling Cropping Systems”, European Society of Agronomy (ESA), Florence, Italy, pp 89–90Google Scholar
  15. Travasso MI, Magrin GO, Rodríguez GR (2003a) Relations between sea surface temperature and crop yields in Argentina. Int J Climatol 23:1655–1662CrossRefGoogle Scholar
  16. Travasso MI, Magrin GO, Rodríguez GR (2003b) Crops yield and climatic variability related to ENSO and South Atlantic sea surface temperature in Argentina. Preprint volume of the Seventh International Conference on Southern Hemisphere Meteorology and Oceanography (7ICSHMO), Wellington, New Zealand, pp 74–75Google Scholar
  17. Tsuji GY, Uehara G, Balas S (1994) Decision support system for agrotechnology transfer (DSSAT), version 3. University of Hawaii, Honolulu, HawaiiGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • G. O. Magrin
    • 1
  • M. I. Travasso
    • 1
  • W. E. Baethgen
    • 2
  • R. T. Boca
    • 1
  1. 1.Instituto Nacional de Tecnología AgropecuariaInstituto de Clima y AguaCastelarArgentina
  2. 2.International Research Institute for Climate and Society (IRI)PalisadesUSA

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