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

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

CLIMATE VARIABILITY CLIMATE VARIABILITY in the in the

Pampas RegionPampas Region

ENSO and other sourcesENSO and other sources

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

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

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

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

-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

-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.

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.

December 1997 January 1998 February 1998

November 1997October 1997

OND 1997 SST

December 1999 January 2000 February 2000

November 1999October 1999

OND 1999 SST

December 1998 January 1999 February 1999

November 1998October 1998

OND 1998 SST

ENSO-related Forecasts are Poor in January and February

Pantanal:

150,000 km2 of Wetlands

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

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)

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

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