L i n k i n g S c i e n c e t o S o c i e t y cover box Global change information needs for decision makers dealing with food security Walter E. Baethgen

L i n k i n g S c i e n c e t o S o c i e t yL i n k i n g S c i e n c e t o S o c i e t y

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Global change information needs for decision makers

dealing with food security

Walter E. Baethgen Maxx Dilley

International Research Institute for Climate Prediction (IRI) The Earth InstituteColumbia University


Decision Makers (including Policy makers): Extremely Heterogeneous Community (like “Users”)

Different Decision Makers require different Information(demanded information is also extremely heterogeneous)

Global / International ... Country ... Village

Global change information needs for decision makers dealing with food security


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Example: Climate Change Information

Typically: Food security maps for 2050’s- 2080’s



Season Length 1961-90

Season Length 2080’s

Multiple cropping zones 1961-90

Multiple cropping zones 2080

Rainfed cereals: CC Impacts 2080’s


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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:

•Place Climate Change as a “Problem of the Future”

•Beyond the agenda of Decision / Policy Makers (2080’s)


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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.

Challenge:

Overcome the “Incompatibility” of Time Frames

Introduce Global Change Issues in Development Agenda



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.)

PremisePremise

One of the most effective ways for assisting agricultural stakeholders to be prepared and prepared and

adapt to possible adapt to possible Climate ChangeClimate Change scenariosscenarios,

is by helping them to better cope with current better cope with current

Climate VariabilityClimate Variability

Overcomes time frame Incompatibility:

Actions are needed within Policy Makers term

Results of actions can be verified also within the PM term


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Examples ofInformation that can assist Decision Makers at

Country (or smaller) scale

Decision Support tools tailored for different Policy Makers

but focused on Climate Variability

and

its impacts (on food security and other)


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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

Understanding the Baseline:

Measuring food security

Slides courtesy T. Boudreau, Food Economy Group/FEWS)

Households become food insecure when they cannot meet

100% of food requirements

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IN CO-ORPERATION WITH UNDP- SOMALIA

FSAU is managed by the FAO, funded by EC and supported by USAID-Somalia and W FP-Somalia

FSAU partners are W FP-Somalia, FEWS-Somalia,FAO,UNICEF, SCFUK and UNDP-Somalia.

FOOD SECURITY ASSESSMENT UNIT

Property of FSAU-FAO.P.O. Box 1230 Vilage Market (Nairobi),Tel 745734/8297/1299/6509,Fax: 740598E-mail: [email protected].

January, 2001

FOOD ECONOMY GROUPS / AREAS (Draft)

Addun pastoral:Mixed Shoats, camel

Agro-pastoral: Camel, cattle & sorghum

Agro-pastoral:Cowpea, shoats, camel, cattle

Agro-pastoral: Sorghum, cattle

Bay-Bakool high potential sorghum;Cattle & camel

Fishing<

Golis-Guban pastoral: Goats, camel

Haud & Sool pastoral: Camel, shoats

Highland pastoral: Goats

Hiran riverine:Sorghum, maize, cattle

Juba Dheshek: Maize, sesame

Juba, pump irrigated commercial farming:Tobacco, onions,maize

Kakaar pastoral:Sheep & goats

Lower Juba: Maize & cattle

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Nugal Valley-lowland pastoral: Sheep, camel

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Pastoral:Camel &shoats

Coastal pastoral:Goats & cattle

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Pastoral: Sheep

Shabelle riverine: Irrigated maize

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Food Economy Zones(baseline)

SCENARIO ANALYSIS SUMMARY 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 Expandability Baseline + Expandability

Current problem

Final picture

Green crops 17 0 17 100% 17

Maize 35 13 48 25% 12

Milk 5 0 5 0% 0

Labour exchange 4 4 8 100% 8

Purchase: beans 4 See below

Purchase: maize 35 See below 48

Gifts 0 4 4 100% 4

Total 89%

Deficit 11%

Table 2: Income (cash)

Baseline Expandability Baseline + Expandability

Current problem

Final picture

Livestock sales 12000 0 12000 0% 0

Milk sales 7500 0 7500 0% 0

Maize sales 825 -825 0 25% 0

Labour migration 3600 3600 7200 100% 7200

Firewood sales 6240 6240 12480 100% 12480

Total 30165 19680

Table 3: Expenditure (cash)

Baseline Current problem

Final picture

Minimum non-staple 8700 100% 8700

Staple food 5250 10980

Other 16215

Total 30165 19680

Table 4: Staple purchase Cash available Price/kg Kg purchased % kcals

Maize 10980 20 549 1150 = 48%

Baseline and Method for Running Scenarios:

Simple Spreadsheet…

Food Economy : Western Rumphi & Mzimba Spreadsheet prepared by The Food Economy Group, 2003

BASELINE ACCESS PROBLEM SPECIFICATION RESPONSE

Sources of Food : Poor HHsBaseline Expand Max. Problem Food Intake Con.prob Max.curr Curr.Access -ability Access %norm kcals/day %norm Access Access

maize 41% 41% 50% baseline: 50% 21% 21%g/nuts 4% 1% 5% 50% 2100 50% 2% 2%pulses 3% 1% 4% 15% for analysis: 15% 0% 0%s.potato 3% 1% 4% 100% 2100 100% 3% 3%pumpkin 1% 1% 100% 100% 1% 1%

0% 100% 100% 0% 0%0% 100% 100% 0% 0%0% 100% 100% 0% 0%

purch/exch. 38% 124% 100% 100% 71% 64%ganyu 13% 3% 16% 60% 60% 10% 9%

0% 100% 100% 0% 0%0% 100% 100% 0% 0%0% 100% 100% 0% 0%0% 100% 100% 0% 0%0% 100% 100% 0% 0%0% 100% 100% 0% 0%0% 100% 100% 0% 0%

deficit 0%total 103% 195% 108%

adj.fact = 0.86

Income : Poor HHsBaseline Expand Max. Problem Comm. Staple Con.prob Max.curr Curr.

Cash Access -ability Access %norm Price Price %norm Access Access0 50% 100% 118% 50% 0 0

g/nut sales 500 -500 0 50% 100% 118% 50% 250 250pulse sales 700 -700 0 15% 100% 118% 15% 105 105

Exploring possibleresponses

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 high-

skill areas in the Greater Horn of Africa

(Prediction skill for March-May or June-September is lower)

Corr_coef. = 0.8

Model -MOS CORRECTED

OBSERVATION

Statistically corrected ECHAM4 GCMOct-Dec precipitation to a station

Correlation between statistically corrected climate model output and observed rainfall, Oct-Dec


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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

terms that Decision Makers use

(crop yields, pasture availability, water in reservoirs, etc.)

NDVI forecast skill, Oct-Dec

Correlation between:

1. GCM precipitation for October-December (runs from September*)

2. December NDVI values.

(Eastern Kenya r=0.74)

(*) persisted-SST and 850mb

zonal wind forecasts

COF11 – Forecast Crop Conditions at End of Season

Actual Crop Conditions at End of Season

Slide Courtesy G. Galu

Predicting end-of-season crop conditions using the Water

Requirements Satisfaction Index

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 livelihoods-based food security analysis

Involving the Decision Makers:

•Developing Trust

•Affecting / Changing Decisions

•Assisting policies


December 1999 January 2000 February 2000

November 1999October 1999

Example in Uruguay

Decision Support SystemProvided this Information to MAF and to NationalEmergency System(Evolution of the Drought)

IMPORTANCE of MONITORING

19 January 23 March

Volume Changes in Water Reservoirs during the 1999/2000 drought

(prepared for the National Emergency System)

Example in Northern Uruguay

Remote Sensing

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”.





The results of your work during the recent drought were

useful for making both, operational and political decisions.





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.

Involving the Decision Makers (2):

Move from “Supply” Approach

To

“Demand Driven” Approach

RegionalOutlookMeetings

IRI

NOAA

ECMWF

Others

Nat. ClimateRes. Ctrs.

IFDCINIA

NASAUn.Fla.QSLD

Tech. Reps.

Agri-Business

MAF Planning Policies

NGOs

Gov.Organiz.

Growers

Local Outlook

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utlo

ok

Needs (Variables, Timing, Tools)

Tools(IDSS)

ENSO and “Global” Climate

Forecasts

RegionalOutlook

MediaInternet

IAI

Met. Service

Workshops(Quarterly)

Pilot Project IFDC/INIA/NASA: Climate Forecast Applications in Agriculture

“TWG”

InsuranceCredit

Nat. ClimateRes. Ctrs.

IFDCINIA

NASAUn.Fla.QSLD

Tech. Reps.

Agri-Business

MAF Planning Policies

NGOs

Gov.Organiz.

Growers

Local Outlook

Needs (Variables, Timing, Tools)

Tools(IDSS)

Workshops(Quarterly)

“TWG”

InsuranceCredit“Hands-on” Training (Education) for Users

(CC, CV, probabilities, role of FCSTs, risks)

Demand for Researchers (info and tools)

DS Tools:

Risk AssessmentRiskManagement

“Users”

Who are the “clients”?

MinistriesAgro, Health,Water

InsuranceCredit

NGOsAdvisers

DS Tools:


“Users”

InsuranceCredit

NGOsAdvisers

(Pilot Projects: Keep on track)

“Users”

MinistriesAgro, Health,Water

DS Tools:



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

Develop Decision Support Systems (Risk Assessment/Risk Managementapproach) that assist:

•Understanding the past•Monitoring the present•Forecasting the future (probabilitistic scenarios)

Involve Decision Makers from the start (Demand-driven approach)


Thank you!

Walter E. Baethgen Maxx Dilley

International Research Institute for Climate Prediction

The Earth Institute, Columbia University

Documents

L i n k i n g S c i e n c e t o S o c i e t y cover box Global change information needs for decision makers dealing with food security Walter E. Baethgen