building capacity to assess the impact of climate change/variability and develop adaptive responses...
TRANSCRIPT
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Building capacity to assess the impact of Building capacity to assess the impact of climate change/variability and climate change/variability and
develop adaptive responses for the develop adaptive responses for the mixed crop/livestock production systems mixed crop/livestock production systems
in the Argentinean , Brazilian andin the Argentinean , Brazilian andUruguayan PampasUruguayan 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
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Project Premise
One of the most effective manners
for assisting agricultural
stakeholders to be prepared and prepared and
adapt to possible climate change adapt to possible climate change
scenariosscenarios, is by helping them to
better cope with current climate better cope with current climate
variabilityvariability
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CLIMATE VARIABILITY CLIMATE VARIABILITY in the in the
Pampas RegionPampas Region
ENSO and other sourcesENSO and other sources
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CLIMATE and VARIABILITYExample: Climatology in La Estanzuela, Uruguay
LLUVIAS EN LA ESTANZUELA: PROMEDIO (1915 - 1998)
MESES
1 2 3 4 5 6 7 8 9 10 11 12
LL
UV
IA (
mm
)
0
50
100
150
200
250
300
Mean Rainfall in EELE (1915-2000)
MONTH
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LLUVIAS EN LA ESTANZUELA: 1915 - 1998 Y PROMEDIO
MESES
1 2 3 4 5 6 7 8 9 10 11 12
LL
UV
IA (
mm
)
0
50
100
150
200
250
300
Monthly Rainfall in EELE
MONTH
Example: Observed Monthly Rainfall
CLIMATE and VARIABILITY
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LLUVIAS EN LA ESTANZUELA: VARIABILIDAD ANUAL
MESES
1 2 3 4 5 6 7 8 9 10 11 12
LL
UV
IA (
mm
)
0
50
100
150
200
250
300
1935 1936 1955 1956 1962 1963 1975 1976 1988 1989 1990 1997 PROM
Example: Monthly rainfall in 12 years (La Estanzuela)
Monthly Rainfall in EELE
MONTH
MEAN
None of the years shows monthly rainfallsimilar to the long-term values
Still, planning is based on long-term values(Probability 0)
CLIMATE and VARIABILITY
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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
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-6 6 -64 -62 -60 -58
-41-40-39-38-37-36-35-34-33-32-31-30-29-28
-6 6 -64 -62 -60 -58
-41-40-39-38-37-36-35-34-33-32-31-30-29-28
-6 6 -6 4 -6 2 -6 0 -5 8
-4 1-4 0-3 9-3 8-3 7-3 6-3 5-3 4-3 3-3 2-3 1-3 0-2 9-2 8
-66 -64 -62 -60 -58
-41-40-39-38-37-36-35-34-33-32-31-30-29-28
-6 6 -64 -62 -60 -58
-41-40-39-38-37-36-35-34-33-32-31-30-29-28
-6 6 -64 -62 -60 -58
-41-40-39-38-37-36-35-34-33-32-31-30-29-28
Oct-NovOct-Nov Nov-DecNov-Dec Dec-JanDec-Jan Jan-FebJan-Feb Feb-MarFeb-Mar Mar-AprMar-Apr 0.00
1.40
1.60
1.80
2.00
2.20
2.40
2.60
2.80
Chance of having precipitations Chance of having precipitations higher higher (blue) (blue) or or lower lower (red) than normal during(red) than normal during
"El Niño""El Niño" and and “La Niña”“La Niña” years. years.
-66 -64 -62 -60 -58
-41-40-39-38-37-36-35-34-33-32-31-30-29-28
-6 6 -64 -62 -60 -58
-41-40-39-38-37-36-35-34-33-32-31-30-29-28
-6 6 -6 4 -6 2 -6 0 -5 8
-4 1-4 0-3 9-3 8-3 7-3 6-3 5-3 4-3 3-3 2-3 1-3 0-2 9-2 8
-6 6 -64 -62 -60 -58
-41-40-39-38-37-36-35-34-33-32-31-30-29-28
-6 6 -64 -62 -60 -58
-41-40-39-38-37-36-35-34-33-32-31-30-29-28
-6 6 -64 -62 -60 -58
-41-40-39-38-37-36-35-34-33-32-31-30-29-28
"El Niño""El Niño"
““La Niña”La Niña”0.00
1.40
1.60
1.80
2.00
2.20
2.40
2.60
2.80
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-60
-40
-20
0
20
40
60
80
100
J -A-S O-N-D J -F-M A-M-J
Chan
ges
in P
reci
pit
atio
n (
mm
) NiñoNiña
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
J -A-S O-N-D J -F-M A-M-JChanges
in T
.Maxim
um
(ºC
) NiñoNiña
Differences in three-monthly Differences in three-monthly Precipitation Precipitation (mm) (mm) and and MaximumMaximum TemperatureTemperature (ºC) (ºC)
During “EL NIÑO” and “LA NIÑA” years.During “EL NIÑO” and “LA NIÑA” years.
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Probability of having Probability of having high yieldshigh yields (blue) (blue) or or low yieldslow yields (red) during (red) during El NiñoEl Niño and and La NiñaLa Niña years. years.
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December 1997 January 1998 February 1998
November 1997October 1997
OND 1997 SST
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December 1999 January 2000 February 2000
November 1999October 1999
OND 1999 SST
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December 1998 January 1999 February 1999
November 1998October 1998
OND 1998 SST
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ENSO-related Forecasts are Poor in January and February
Pantanal:
150,000 km2 of Wetlands
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Sources of Interannual Climate Variability other than ENSO
Correlation BetweenRainfall in Novemberin the Pantanal
And
Rainfall in Jan-Febin SE South America
r = 0.6 – 0.8
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0
10
20
30
40
50
60
70
80
90
100
0 100 200 300 400 500
Precipitation (January + February)
Cum
ula
tive
pro
babi
lity
Low SST (lower quartile)High SST (upper quartile)
South Atlantic SSTSouth Atlantic SST impacts on impacts on summer precipitation and crops yieldsummer precipitation and crops yield
SoybeanYield
SST-SA (May) and Precipitation (January + SST-SA (May) and Precipitation (January + February) February)
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Márgenes Brutos (US $/ha) para Riego de Maíz en Secano(Ciclo Corto, Siembra de Setiembre, 1968 - 1999)
Modelo CERES-Maize
1965 1970 1975 1980 1985 1990 1995 2000
Má
rgen
Bru
to (
US
$ /
ha)
-400
-200
0
200
400
600
800
CV = 128%9 years in 30: result ( 0)60% of Total Income in 6 years
Gross Margins for Rainfed Maize (1960 – 2001) CERES Model
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Márgenes Brutos (US $/ha) para Riego de Maíz Regado y en Secano(Ciclo Corto, Siembra de Setiembre, 1968 - 1999)
Modelo CERES-Maize
1965 1970 1975 1980 1985 1990 1995 2000
Má
rgen
Bru
to (
US
$ /
ha)
-400
-200
0
200
400
600
800
IrrigadoSecano
Gross Margins for Rainfed vs Irrigated Maize (1960 – 2001) CERES Model