Tilting the odds in maize yields: How climate information can help manage risks

Bulletin of the American Meteorological Society

Volumn 90, Issue 2, 2009, Pages 179-183

  Baethgen, Walter E ^a   Carriquiry, M ^b   Ropelewsk, C ^a,c  

^a Earth Institute   (United States)

^b IOWA STATE UNIVERSITY   (United States)

^c NOAA EARTH SYSTEM RESEARCH LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLIMATE INFORMATIONS; CLIMATE SCIENTISTS; CROP MODELS; CROP YIELDS; DRY SPELLS; GROWING SEASONS; MAIZE YIELDS; PLANTING DATES; RISK MANAGEMENT STRATEGIES; SMALL VARIATIONS; SOUTHERN OSCILLATIONS; URUGUAY;

ATMOSPHERIC PRESSURE; CROPS; DROUGHT; LANTHANUM; NICKEL COMPOUNDS; RISK ANALYSIS; RISK MANAGEMENT; SENSITIVITY ANALYSIS;

RISK PERCEPTION;

EID: 64949110907     PISSN: 00030007     EISSN: None     Source Type: Journal    
DOI: 10.1175/2008BAMS2429.1     Document Type: Article

References (14)

1. Aceituno, P., 1988: On the functioning of the Southern Oscillation in the South American sector. Part I: Surface climate. Mon. Wea. Rev., 116, 505-524.
2. Baethgen, W. E., 1998: El Niño and La Niña impacts in Southeastern South America. Proc. Review of the Causes and Consequences of Cold Events: A La Niña Summit, NCAR.
3. Baethgen, W. E., and G. O. Magrin, 1995: Assessing the impacts of climate change on winter crop production in Uruguay and Argentina using crop simulation models. Climate Change and Agriculture: Analysis of Potential International Impacts, C. Rosenzweig et al., Eds., 207-228.
4. Baethgen, W. E., and A. Giménez, 2002: Seasonal climate forecasts and the agricultural sector of Uruguay. ENSO and Society - Examples of ENSO-Society Interactions: Agricultural Sector of Uruguay. International Research Institute of Climate Prediction.
5. Casella, G., and R. L. Berger, 2002: Statistical Inference. 2d ed., Wadsworth/Thomson Learning, 660 pp.
6. Greene, W. H., 2000: Econometric Analysis. 4th ed., Prentice Hall, 1,004 pp.
7. Grimm, A. M., V. R. Barros, and M. E., Doyle, 2000: Climate variability in southern South America associated with the El Niño and La Niña events. J. Climate, 13, 35-58.
8. Jones, C. A., and J. R. Kiniry, 1986: CERES-Maize: A Simulation Model of Maize Growth and Development. Texas A&M University Press, 194 pp.
9. Magrin, G., M. Travasso, W. E. Baethgen, and R. Boca, 2007: Improving applications in agriculture of ENSO-based seasonal rainfall forecasts considering Atlantic Ocean surface temperatures. Climate Prediction and Agriculture Advances and Challenges, M. Sivakumar and J. Hansen, Eds., Springer, 249-257.
10. Pisciottano, G., A. Diaz, G. Cazes, and C. R. Mechoso, 1994: El Niño/Southern Oscillation impact on rainfall in Uruguay. J. Climate, 7, 1286-1302.
11. Ropelewski, C. F., and M. S. Halpert, 1987: Global and regional scale precipitation patterns associated with the El Niño/Southern Oscillation. Mon. Wea. Rev., 115, 1606-1626.
12. Ropelewski, C. F., and M. S. Halpert, 1989: Precipitation patterns associated with the high index phase of the Southern Oscillation. J. Climate, 2, 268-284.
13. Ropelewski, C. F., and M. S. Halpert, 1996: Quantifying Southern Oscillation-precipitation relationships. J. Climate, 9, 1043-1059.
14. Tsuji, G. Y., G. Uehara, and S. Balas, 1994: Decision Support System for Agrotechnology Transfer (DSSAT) Version 3. University of Hawaii.