Building capacity to assess the impact of climate change/variability and develop adaptive responses for the mixed crop/livestock production systems in.

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Presentation transcript:

Building capacity to assess the impact of climate change/variability and develop adaptive responses for the mixed crop/livestock production systems in the Argentinean, Brazilian and Uruguayan Pampas Principal Scientists Principal Scientists Graciela Magrin, INTA, ArgentinaGraciela Magrin, INTA, Argentina María I. Travasso, INTA, ArgentinaMaría I. Travasso, INTA, Argentina Osvaldo Canziani, ArgentinaOsvaldo Canziani, Argentina Gilberto Cunha, BrazilGilberto Cunha, Brazil Mauricio Fernandes, BrazilMauricio Fernandes, Brazil Agustin Gimenez, GRAS- INIA, UruguayAgustin Gimenez, GRAS- INIA, Uruguay Walter E. Baethgen, IFDC, UruguayWalter E. Baethgen, IFDC, Uruguay Holger Meinke, APSRU, DPI, AustraliaHolger Meinke, APSRU, DPI, Australia

Project Premise prepared and adapt to possible climate change scenarios better cope with current climate variability One of the most effective manners for assisting agricultural stakeholders to be prepared and adapt to possible climate change scenarios, is by helping them to better cope with current climate variability

CLIMATE VARIABILITY in the Pampas Region ENSO and other sources

CLIMATE and VARIABILITY Example: Climatology in La Estanzuela, Uruguay Mean Rainfall in EELE ( ) MONTH

Monthly Rainfall in EELE MONTH Example: Observed Monthly Rainfall CLIMATE and VARIABILITY

Example: Monthly rainfall in 12 years (La Estanzuela) Monthly Rainfall in EELE MONTH MEAN None of the years shows monthly rainfall similar to the long-term values Still, planning is based on long-term values (Probability  0) CLIMATE and VARIABILITY

Currently planning for conditions that will not exist (Probability = 0) Can we plan for conditions with Probability > 0 ? Improve Planning and Decision Making RESEARCH PROJECTS INIA – INTA - IFDC

Oct-NovNov-DecDec-JanJan-FebFeb-MarMar-Apr Chance of having precipitations higher (blue) or lower (red) than normal during "El Niño" and “La Niña” years. "El Niño" “La Niña”

Differences in three-monthly Precipitation (mm) and Maximum Temperature (ºC) During “EL NIÑO” and “LA NIÑA” years.

Probability of having high yields (blue) or low yields (red) during El Niño and La Niña years.

December 1997 January 1998February 1998 November 1997October 1997 OND 1997 SST

December 1999 January 2000February 2000 November 1999October 1999 OND 1999 SST

December 1998 January 1999February 1999 November 1998October 1998 OND 1998 SST

ENSO-related Forecasts are Poor in January and February Pantanal: 150,000 km 2 of Wetlands

Sources of Interannual Climate Variability other than ENSO Correlation Between Rainfall in November in the Pantanal And Rainfall in Jan-Feb in SE South America r = 0.6 – 0.8

South Atlantic SST impacts on summer precipitation and crops yield Soybean Yield SST-SA (May) and Precipitation (January + February)

CV = 128% 9 years in 30: result (  0) 60% of Total Income in 6 years Gross Margins for Rainfed Maize (1960 – 2001) CERES Model

Gross Margins for Rainfed vs Irrigated Maize (1960 – 2001) CERES Model