building capacity to assess the impact of climate change/variability and
DESCRIPTION
Building capacity to assess the impact of climate change/variability and develop adapt ation responses for the mixed crop/livestock production systems in the Argentinean , Brazilian and Uruguayan Pampas. Principal Scientists Graciela Magrin, INTA, Argentina - PowerPoint PPT PresentationTRANSCRIPT
Building capacity to assess the impact of climate change/variability and
develop adaptation responses for the mixed crop/livestock production systems
in the Argentinean , Brazilian andUruguayan Pampas
Principal Scientists
• Graciela Magrin, INTA, Argentina• María I. Travasso, INTA, Argentina• Osvaldo Canziani, Argentina
• Gilberto Cunha, Brazil• Mauricio Fernandes, Brazil
• Agustin Gimenez, GRAS- INIA, Uruguay• Walter E. Baethgen, IFDC, Uruguay
• Holger Meinke, APSRU, DPI, Australia
Establishing
Applied Systems Analysis
Networks
for
Building Regional Adaptation Capacity
(AIACC LA 27)
OBJECTIVE:
Incorporate Climate Information forImproving Planning / Decision Making
Agriculture / Natural Resources
Planning Agencies (Public, International)
Emergency Systems
Credit / Insurance Programs
Farmers (commercial, subsistence)
Applied Systems Analysis
WHY?
Planning, Decision Making are Complex Processes
•Many Variables
•Many Interactions
•Different Priorities
Improving Planning / Decision Making
Is Climate the main source of Variability?
Consider other sources of variability
• Product Prices
• Production Cost
• Other Factors
0.00
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0.40
0.60
0.80
1.00
1.20
Jul-83 Mar-86 Dec-88 Sep-91 Jun-94 Mar-97 Dec-99
US
$/kg
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vill
o G
ord
o
Price Trend for Finished Steer (1983-1999)(INAC, Uruguay)
US
$/k
g o
f Fin
ish
ed
Ste
er
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0.20
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0.80
1.00
1.20
Jul-83 Mar-86 Dec-88 Sep-91 Jun-94 Mar-97 Dec-99
US
$/kg
No
vill
o G
ord
o
Price Trend for Finished Steer (1983-1999)(INAC, Uruguay)
US
$/k
g o
f Fin
ish
ed
Ste
er
0.00
0.20
0.40
0.60
0.80
1.00
1.20
Jul-83 Nov-84 Mar-86 Aug-87 Dec-88 May-90
US
$/kg
No
vill
o G
ord
o
Price Trend for Finished Steer (1983-1999)(INAC, Uruguay)
US
$/k
g o
f Fin
ish
ed
Ste
er
(100% Interannual Variability)
Improving Planning / Decision Making
Is Climate the main source of Variability?
Consider other sources of variability
•Product Prices
•Production Cost
•TECHNOLOGY ?
MAIZ (1960 - 2000): DESVIOS DE RENDIMIENTOS
1960 1965 1970 1975 1980 1985 1990 1995 2000
DE
SV
IOS
D
E
RE
ND
IMIE
NT
OS
(%
)
-60
-50
-40
-30
-20
-10
0
10
20
30
40
50
60
Maize
Yield (detrended) variability(1960 – 2000)
Detrended considering technology changesTherefore: Mostly Climate Variability
ARROZ (1960 - 2000): DESVIOS DE RENDIMIENTOS
1960 1965 1970 1975 1980 1985 1990 1995 2000D
ES
VIO
S
DE
R
EN
DIM
IEN
TO
S (
%)
-60
-50
-40
-30
-20
-10
0
10
20
30
40
50
60
Rice
Why lower variability?100% rice is irrigatedTECHNOLOGY
Improving Planning / Decision Making
Consider many sources of variability
+ Complex Interactions
+ Environmental Impacts
+ Socio-economic Impacts
NEED TO INTEGRATE DATA AND TOOLS
APPLIED SYSTEMS ANALYSIS
INFORMATION
Some Difficulties for Disseminating
Often Information is Available (Especially Latin America)(even “in excess”)
NOT USED EFFECTIVELY
But:
No PriorizationNo ProcessingNo Analysis
Tools for Processing and Anlyzing Information
•Simulation Models
•Expert Systems
•Risk analysis
•Remote Sensing (Satellites)
•Geographic Information Systems (GIS)
•Global Positioning Systems(GPS)
But: Use is not generalized
Answer: IDSS Approach
Use Modern Tools for:
Acquiring, Processing and Analyzing Information
and generate results in simple formats,
Understandable and therefore USABLE
by stakeholders acting in the Agricultural Sector
(e.g., map of Rio de la Plata with red and green areas)
Information andDECISIONSUPPORTSYSTEMS
1990’s Established and IDSS working group in SESA
NASA, USA
NOAA, USA
EPA, USA
Australia
Spain
European Commission
ColumbiaUniversity
Brazil
Argentina
Uruguay
Latin America
Since 1990’s
IDSS Approach
SIMULATIONMODELS
REMOTESENSING
CLIMATECHANGE/VAR
GIS
Monitoring, risk analyses, environmental impact, projections
Impact Studies:Example of Climate Change
Technology Impact• New Alternatives (not only wheat)• Management (fertilizers, cultivars, irrigation)
• Variation of Prices and Cost
NOVEMBER JANUARYDECEMBER
FEBRUARY MARCH
REMOTE SENSING: MONITORING
La Niña 1999 / 2000
Very high
Very low
Low
High
Ing. Juan Notaro, Uruguayan Minister of Agriculture in 1999/2000
(Letter to our INIA-IFDC-NASA Project)
"(...) The results of your work during the recent drought wereuseful for making both, operational and political decisions. From the operational standpoint, your work allowed us to concentrate our efforts in the regions highlighted as being the ones with the worst and longest water deficit. We prioritized those identified regions for concentrating the use of our resources, both financial aid and machines for dams, water reservoirs, etc.
(...) From the strictly political standpoint, your work provided us with objective information to defend our prioritization of regions, in a moment in which every governor, politician and farmer in the country was asking for aid. We received no complaints in this respect. In the same line, your work also allowed to mitigate pressures since we provided the press and the general public with transparent, technically sound and precise information”.
FeasibleModerately feasibleUnfeasible
Drainage
Gravel
Slope
RootingDepth
Fertility
GIS + Databases=
Agro-climaticZoning:Land Use Feasibility
Agro-Climatic Zoning WHEAT
•Soil•Climate•Terrain
FeasibleModerately feasibleUnfeasible
Climate Change will have different effect on areas withDifferent Feasibility (Risk)
(And: Feasibility is Dynamic (Technology)
System 1
Farm Level: Expected Income (US$/ha) for Diferent Systems30-year Mean Climate Change Scenario 1
System 2
System 3 System 4
AIACC LA 27 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-proof SystemsClimate-proof Systems””
Uruguay: Beef Production and Number of Calves
1988 1990 1992 1994 1996 1998 2000
Tota
l Beef
Pro
duct
ion (
million T
on)
400
500
600
700
800
900
1000
Ca
lves
(1,00
0 hea
ds)
1000
1200
1400
1600
1800
2000
2200
2400
Total ProdCalves
1999/2000??
1988/89
PreviousLa Nina
La Nina1999/2000
Uruguay: Beef Production and Number of Calves
1988 1990 1992 1994 1996 1998 2000
Tota
l Beef
Pro
duct
ion (
million T
on)
400
500
600
700
800
900
1000
Ca
lves
(1,00
0 hea
ds)
1000
1200
1400
1600
1800
2000
2200
2400
Total ProdCalves
1999/2000
Improved PasturesSupplementary feed
MORE RESILIENT SYSTEM
“CLIMATE PROOF”
RegionalOutlookMeetings
IRI
NOAA
ECM
Others
Nat. ClimateRes. Ctrs.
IFDCINIA
NASAUn.Fla.QSLD
Tech. Reps.
Agri-Business
MAF Planning Statistics
NGOs
Gov.Org.
Growers
Local Outlook
Loc
alO
utlo
ok
Needs (Variables, Moments, Tools)
Tools
ENSO and “Global” Climate
Forecasts
RegionalOutlook
MediaInternet
IAI
Met. Service
Workshops(Quarterly)
Uruguay: IFDC/INIA/NASA: Climate Forecast Applications in Agriculture
“TWG”
Establishing Applied Systems Analysis Networks
for Building Regional Adaptation Capacity
Next steps (2003):
Assist establishing IDSS Approach in other developing countries (Latin America and beyond)
Train “operators” (as opposed to MSc, Ph.D.)
How?
Establish an IDSS “Center” for South-South Cooperation
Objectives of the Proposed IDSS Center for South-South Cooperation
To take advantage and build upon the capacity developed by the IDSS work group and the existing technical and scientific cooperation agreements established with specialized institutes (NASA, NOAA, IRI, APSRU, JRC, EPA, US Universities) and apply the concept of South-South Cooperation to:
1. Collaborate with developing countries to establish applications of the IDSS approach (including climate variability and climate change) to improve agricultural planning and decision-making
2. Utilize the Center to train personnel from developing countries in the application of the IDSS approach under conditions and with resources (hardware, software) that are typical of developing countries.
Seed funds: IDB, UNDP, FAOMost Activities: Funded with Specific Projects
Walter E. BaethgenInternational Soil Fertility and Agricultural Development CenterIFDC Oficina Uruguay
NDVI en Anthesis
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Rendim
iento
(to
n/ha)
1.0
2.0
3.0
4.0
5.0
CERES - Wheat Calibration:Grain Yield
Observed (kg/ha)
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Sim
ula
ted
(k
g/h
a)
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8000
TURKEY MOROCCO SYRIA-1 SYRIA-2 BRAZIL ROMANIA INDIACHINA URUGUAYARGENTINA
Regional Crop Yield Forecasts: ANNUAL (Planning, FEWS, etc.)
2. NDVI at anthesis
1. Field Identification and area measurement
3. NDVI vs Yields
6. Surveys, groundtruthing, etc
5. Crop Simulation Models
4. Seasonal Climateforecasts
NDVI en Anthesis
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Rendim
iento
(to
n/ha)
1.0
2.0
3.0
4.0
5.0
CERES - Wheat Calibration:Grain Yield
Observed (kg/ha)
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Sim
ula
ted
(k
g/h
a)
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4000
5000
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7000
8000
TURKEY MOROCCO SYRIA-1 SYRIA-2 BRAZIL ROMANIA INDIACHINA URUGUAYARGENTINA
Regional Crop Yield Forecasts LONG TERM (Planning)
2. NDVI at anthesis
1. Field Identification and area measurement
3. NDVI vs Yields
5. Crop Simulation Models4. Climate Change
Scenarios
6. Consulting with PlanningAgencies
