Transcript Document

Global change information needs
for decision makers
dealing with food security
Walter E. Baethgen
Maxx Dilley
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International Research Institute for Climate Prediction (IRI)
The Earth Institute
Columbia University
Linking
Science
to
Society
Global change information needs for decision makers dealing with food security
Decision Makers (including Policy makers):
Extremely Heterogeneous Community (like “Users”)
Global / International  ... Country  ... Village
Different Decision Makers require different Information
(demanded information is also extremely heterogeneous)
Linking
Science
to
Society
Global change information needs for decision makers dealing with food security
Example: Climate Change Information
Typically: Food security maps for 2050’s- 2080’s
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Linking
Science
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Society
Multiple cropping zones 1961-90
Season Length 1961-90
Multiple cropping zones 2080
Season Length 2080’s
Rainfed cereals: CC Impacts 2080’s
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Science
to
Society
Global change information needs for decision makers dealing with food security
Food Security Maps at Global Level:
•Excellent for COP negotiators (UNFCCC)
•Excellent for increasing general awareness
•Useful for UN-type organizations (FAO, UNDP, WB, IFPRI)
At Country Level:
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•Place Climate Change as a “Problem of the Future”
•Beyond the agenda of Decision / Policy Makers (2080’s)
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Global change information needs for decision makers dealing with food security
At Country Level:
Most commonly, Global information is not easily applicable
1. Degree of Uncertainty
2. Full agenda with immediate-term issues (vs 2050’s)
requiring immediate action.
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Challenge:
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Overcome the “Incompatibility” of Time Frames
Introduce Global Change Issues in Development Agenda
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Science
to
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Global change information needs for decision makers dealing with food security
Overcoming the “Incompatibility” of Time Frames
1. Climate Change is happening now (vs 2050’s, 2080’s)
2. Climate change is affecting and will continue
to affect societies through increased
Climate Variability often including more frequent
and more damaging Extreme Events
(droughts, floods, etc.)
Linking
Science
to
Society
Premise
One of the most effective ways 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
Overcomes time frame Incompatibility:
Actions are needed within Policy Makers term
Results of actions can be verified also within the PM term
Global change information needs for decision makers dealing with food security
Examples of
Information that can assist Decision Makers at
Country (or smaller) scale
Decision Support tools tailored for different Policy Makers
but focused on Climate Variability
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and
its impacts (on food security and other)
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Science
to
Society
A few common features of Decision Support Systems
with shown success:
Understanding the past effects
(linking CV, crop yields, responses, etc)
Strong component: MONITORING (measuring) the present
Adequate and understandable FORECASTS
Risk Assessment / Risk Management Approach
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Science
to
Society
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Understanding the Baseline:
Measuring food security
Slides courtesy T. Boudreau, Food Economy Group/FEWS)
% annual food requirements
Households become food insecure
when they cannot meet 100% of
food requirements
Climate
Variability
High lan d pastoral: Goa ts
Food Economy Zones
(baseline)
Nug al Valley-lo wla nd pa sto ral: Shee p, ca mel
CALU ULA
#
<
KAND ALA
Golis-G ub an pa sto ral: Go ats, camel
ZEYL AC
BO SS AS O
#
DJIBOUTI
#
LAS QORA Y
Y
#
#
BARGA AL
#
LUGHA YE
CE ERIGAABO
#
Y
#
BERBERA
XA AFU UN
SANAG
#
BAKI
<
AWDAL
ISKUSHU BAN
#
BARI
BO ROMAGALBEED
SH EIKH
#
CEEL AF WEYN
#
Y
#
#
#
GEBILEY
<
#
BURCO
BAND AR WAN AAG
#
Y
#
GA RAD AG
BAND ER B EYLA
Y
#
HARGE YSA
#
QA RDH O
#
<
XU DUN
#
BALL I GU BAD LE
(B alleh Khad ar )
Ag ro -pa sto ral: Sorghu m, cattle
TOGDHEER
#
CAYNA BO
#
TALEEX
#
SOOL
Dan Gor ayo
<
OW DWEYNE
<
#
LAS CAANOO D
Y
#
Togd her: Ag ro-pa storal
GARO OWE
Y
#
BUUH O OD LE
#
NUGAL
Hau d & So ol pa storal: Ca mel, sh oats
#
EYL
#
BURT INL E
Ka ka ar p astora l:
Sh eep & go ats
JA RIB AN
#
GOLD OGOB
Fish ing
<
ET
HI
O
PI
A
#
GALKACYO
Y
#
MUDUG
CABU DWA AQ
#
Ca daado
<
#
DHUS A-MAREE B
Ba y-Bakool hig h po te ntial sorg hum ;
Cattle & ca mel
Y
#
Gu ri Ceel
#
#
<
BE LET-WEY NE
#
Ceel B ar de
CEEL BU UR
Y
#
#
BAKOOL
#
HIRAN
<
Y
#
<
Ju ba, pu mp irrigate d
co mmercia l fa rmin g:
Toba cco , on ions,
maize
BULO-BU RTO
TAYEGLOW
#
#
#
ADAN YAB AL
GARBAHARE Y
#
Y
#
#
BAYDHABA
JA LAL AQSI
CEEL DH EER
Ag ro -pa sto ral:
Cow pe a, sh oats, came l, cattle
M. SHABELLE
Y
#
QA NSAX D HEER E
GEDO
#
Galcad
#
#
WAJID
#
#
Ad dun pastoral:
Mixe d Shoa ts, ca mel
XA RAR DHEERE
Gal Hareeri
XUDUR
#
LUUQ
BELET XA WO
#
#
Rab D huu re
#
<
YEED
#
HOBYO
GALGADUD
Ag ro -pa sto ral: C amel, cattle & sorghu m
DOLOW
Pa sto ral: Shee p
<
Hiran rive rin e:
So rg hum, maize, ca ttle
#
BURH AKA BA
#
#
CEEL WA Q
WANL EW EYN
Y
#
MAHA DAY WEYN E
JOWHAR
#
CADA LE
#
<
BAAR -DHEERE
#
#
#
BAY
AFGOOYE
QO RYOLEY
SA KO W
#
#
Y
#
Ñ
MOGADISHU
MERKA
GOLW EYN
SO BL AAL E
Y BUAALE
#
#
<
#
AFMA DO W
BRAW E
L. Sh abe lle ra infed & flood irrigated : Maize & ca ttle
L. SHABELLE
Coa sta l p asto ra l:G oats & cattle
JILIB
#
Sh abe lle riverine : Irriga te d maize
<
<
M. JUBA
KENYA
#
KURT UNW AAREY
#
BALC AD
#
WARSHEI KH
#
<
Pa sto ral:
Cam el &
sh oats
DIN SOR
<
#
LOWER
JUBA
#
Ju ba D heshe k: Maize, se same
JA MAA ME
KISMAYO
<
Y
#
Low er Jub a: Maize & cattle
SOMALIA
<
FO OD ECON OMY GR OUPS / AR EA S (D raft)
BADH ADH E
#
N
Pa sto ral:
Cattle & sh oats
Y
#
#
0
50
100
150
Cap ital
Reg ion al ca pital
District tow n
Reg ion al Bo un dary
Ma jor roa d
District Bo und ary
River
co astlin e
200
In te rn ationa l b ou nda ry
250
300 Kilom eters
Prop erty o f FSAU -FAO .
P.O. Box 123 0 Vilage Ma rket (N airobi),
Tel 745 734 /829 7/129 9/65 09,
Fax: 7 405 98
E-mail: fsa uin fo @fsau .o r.ke.
Ja nua ry, 20 01
FOOD SECURITY ASSESSMENT UNIT
FSA U is m ana ge d b y t he FAO , fu nde d b y
EC an d su ppo rted by US A ID-S om alia
and W FP-S om alia
FSA U p artn ers are W FP-S om alia , FE W S -S om alia ,FA O ,
UNIC EF, S CFUK a nd UND P-S om alia.
IN CO -ORPERATIO N WITH UNDP- SO MALIA
Baseline and Method for Running Scenarios:
SCENARIO ANALYSIS SUMMARY
Simple Spreadsheet…
Livelihood Zone
Lowland Meru, Kenya
Wealth Group
Middle
Baseline year/type
‘Normal’
HH size
6
Current year/type
2nd year of drought
% of community HHs
50%
Table 1: Food
Baseline
Green crops
Exploring possible
responses
Expandability
17
0
Baseline +
Expandability
17
35 & Mzimba
13
48
Food Maize
Economy : Western Rumphi
MilkACCESS
5
0
5
BASELINE
PROBLEM
SPECIFICATION
Labour
exchange
Sources
of Food
: Poor HHs
Baseline
Purchase: beans
Access
41%
4%
3%
3%
1%
Purchase: maize
maize
g/nuts Gifts
pulses
s.potato
pumpkin
Total
Deficit
purch/exch.
38%
ganyu Table 2: Income
13%
(cash)
Livestock sales
Milk sales
Maize sales
Labour migration
Firewood sales
deficit
total
103%
4
4
8
Current
problem
100%
Final picture
17
25% prepared by12
Spreadsheet
The Food Economy Group, 20
0%
0
RESPONSE
100%
8
Expand 4
Max. See
Problem
Con.prob Max.curr
Curr.
belowFood Intake
-ability Access
%norm kcals/day
%norm Access Access
35
See below
48
41%
50% baseline:
50%
21%
21%
0
4
4
100%
42%
1%
5%
50%
2100
50%
2%
1%
4%
15% for analysis:
15%
0%
0%
1%
4%
100%
2100
100%
3%
3%
1%
100%
100%
1%
1%
0%
100%
100%
0%
0%
89%
0%
100%
100%
0%
0%
11%
0%
100%
100%
0%
0%
124%
100%
100%
71%
64%
Baseline +
Current
3%
16%
60%
60%
10%
9%
Baseline
Expandability
Final picture
Expandability
problem
0%
100%
100%
0%
0%
12000 0%
0
12000
0 0%
100%
100%0%
0%
100%
100%0%
0%
7500 0%
0
7500
0 0%
0%
100%
100%
0%
0%
825
-825
0
25%
0
0%
100%
100%
0%
0%
3600 0%
3600
7200
100% 0%
7200
100%
100%
0%
6240 0%
6240
12480
100% 0% 12480
100%
100%
0%
0%
195%
108%
adj.fact =
0.86
Income : Poor HHs
Baseline
Access
Cash
Total
g/nut sales
500
pulse sales
700
Table 3: Expenditure
(cash)
Minimum non-staple
Expand
-ability
Max.
Access
0
30165
-500
0
-700
0
Baseline
8700
Problem Comm.
Staple
Con.prob Max.curr
%norm Price
Price
%norm Access
50%
100%
118%
50%
0
50%
100%
118%
50%
250
15%
100%
118%
15%
105
Current
problem
100%
Curr.
Access
196800
250
105
Final picture
8700
Climate Change/Variability
impacts on food security
Assess Past Impacts
Develop good Monitoring
Improve Forecasts / Scenarios
Explore/Propose Responses
Forecasting food security variables
from climate models, Oct-Dec season
(climate prediction research by M. Indeje, IRI)
The following slides show "hindcast" and
forecast skill between observed and predicted
rainfall values for October-December for highskill areas in the Greater Horn of Africa
(Prediction skill for March-May or June-September is lower)
Statistically corrected ECHAM4 GCM
Oct-Dec precipitation to a station
Corr_coef. = 0.8
OBSERVATION
Model -MOS CORRECTED
Correlation between statistically
corrected climate model output and
observed rainfall, Oct-Dec
Still one step is needed:
Results are expressed in “terms” that Decision Makers
do not use (e.g., Rainfall)
Need to “Translate” information to the same
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terms that Decision Makers use
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(crop yields, pasture availability, water in reservoirs, etc.)
Linking
Science
to
Society
NDVI forecast skill, Oct-Dec
Correlation between:
1. GCM precipitation
for OctoberDecember (runs
from September*)
2. December NDVI
values.
(Eastern Kenya r=0.74)
(*) persisted-SST and 850mb
zonal wind forecasts
Predicting end-of-season crop
conditions using the Water
Requirements Satisfaction Index
COF11 – Forecast Crop Conditions at
End of Season
Actual Crop Conditions at
End of Season
Slide Courtesy G. Galu
Translating Climate Information into
Food Security Information
Regional food security outlooks based
on climate forecast-derived projections
of crop yields, livestock condition and
other food security-related variables,
and use as input into a livelihoodsbased food security analysis
Involving the Decision Makers:
•Developing Trust
•Affecting / Changing Decisions
•Assisting policies
Linking
Science
to
Society
IMPORTANCE of MONITORING
Example in Uruguay
Decision Support System
Provided this Information
to MAF and to National
Emergency System
(Evolution of the Drought)
December 1999
October 1999
November 1999
January 2000
February 2000
Remote Sensing
Volume Changes in Water Reservoirs
during the 1999/2000 drought
(prepared for the National Emergency System)
Example in Northern Uruguay
19 January
23 March
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 were
useful 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”.
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 were
useful for making both, operational and political decisions.
The results
of your work during the recent drought were
From the operational standpoint, your work allowed us to
concentrate our efforts in the regions highlighted as being
usefulthefor
both,
operational
political decisions.
onesmaking
with the worst
and longest
water deficit.and
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”.
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 were
useful 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
work
provided
us with
objective
farmer
in the country
was asking
for aidinformation
. We received no to defend
your
complaints in this
In the in
samealine,
your workin
alsowhich every
our prioritization
ofrespect.
regions,
moment
to mitigate pressures since we provided the press and
governor,allowed
politician
and farmer in the country was asking for aid.
the general public with transparent, technically sound and
precise information”.
Involving the Decision Makers (2):
Move from “Supply” Approach
To
“Demand Driven” Approach
Pilot Project IFDC/INIA/NASA: Climate Forecast Applications in Agriculture
Workshops
(Quarterly)
Regional
Outlook
Meetings
Regional
Outlook
“TWG”
Nat. Climate
Res. Ctrs.
Local Outlook
IAI
Agri-Business
Local
Outlook
ENSO and
“Global” Climate
Forecasts
Needs
(Variables, Timing, Tools)
Tools
(IDSS)
IFDC
INIA
ECMWF
MAF
Planning
Policies
NGOs
IRI
NOAA
Tech. Reps.
Gov.Organiz.
NASA
Un.Fla.
QSLD
Growers
Met. Service
Others
Media
Internet
Insurance
Credit
Workshops
(Quarterly)
“TWG”
Nat. Climate
Res. Ctrs.
Local Outlook
Tech. Reps.
Agri-Business
Needs
(Variables, Timing, Tools)
Tools
(IDSS)
IFDC
INIA
NASA
Un.Fla.
QSLD
“Hands-on” Training (Education) for Users
(CC, CV, probabilities, role of FCSTs, risks)
Demand for Researchers (info and tools)
MAF
Planning
Policies
NGOs
Gov.Organiz.
Growers
Insurance
Credit
Who are the “clients”?
“Users”
DS Tools:
Risk
Assessment
Risk
Management
Ministries
Agro, Health,
Water
DS Tools:
Risk
Assessment
Risk
Management
Insurance
Credit
NGOs
Advisers
“Users”
(Pilot Projects: Keep on track)
Ministries
Agro, Health,
Water
DS Tools:
Risk
Assessment
Risk
Management
Insurance
Credit
NGOs
Advisers
“Users”
Final Comments
Introduce Climate Change in current agendas overcoming
time frame incompatibilities:
•CC is a current problem
•CV approach
Translate Climate information to the terms that Decision Makers
use to make decisions
Involve Decision Makers from the start (Demand-driven approach)
Develop Decision Support Systems (Risk Assessment/Risk Management
approach) that assist:
•Understanding the past
•Monitoring the present
•Forecasting the future (probabilitistic scenarios)
Linking
Science
to
Society
Thank you!
Walter E. Baethgen
Maxx Dilley
International Research Institute for Climate Prediction
The Earth Institute, Columbia University
Linking
Science
to
Society