The State of Food Security Analysis: A FEWS NET...

The State of Food Security Analysis:

A FEWS NET Perspective

Bruce Isaacson

FEWS NET

MORE THAN JUST DATACharting the Road to Zero Hunger

08 March 2017The Hague, Netherlands

__________________________________________FAMINE EARLY WARNING SYSTEMS NETWORK 1

Outline

• Introduction to FEWS NET

• Analytical Approach

• Fit for Purpose??


Introduction to FEWS NET


• To prevent famine and

mitigate risks of food

insecurity by providing

decision makers with

information that is

accurate, credible,

timely, and actionable.

• To strengthen the

ability of FEWS NET

countries and regional

organizations to

provide timely early

warning and

vulnerability analysis.

The purpose of FEWS NET


• A USAID-funded project, started in 1985

• Field activities implemented by private sector contractor

• Independent, not a voice for the US Government

• Tracks food security conditions in 70 countries

• Reports monthly on 35 countries, many special reports

• Flexible country coverage

• 200 fulltime staff plus 100 field monitors in 25 countries

• Annual budget of approximately $25m

FEWS NET Fact Sheet


FEWS NET Country Coverage

2016Presence …….. 25

RM3 Report ..... 10

RM2 Watch ….. 10

RM1 Prep .…… 25

70 Countries

35 Reports}

2015Presence ……. 20

Remote Monitoring ..... 15

35


Our network

• National governments

• Regional technical organizations

(CILSS, SADC, COMESA, IGAD)

• WFP, FAO, UNICEF, UNHCR,

other international agencies

Implementing Team

Partners

USG Science Partners Private Sector

• Non-governmental organizations

• Price/Market Information Systems

• Meteorological, statistical offices

• IPC working groups


FEWS NET Analytical Approach


A Brief History of FEWS NET Analysis

FEWS FEWS 2 FEWS 3 FEWS NET FEWS NET 2 FEWS NET 3 FEWS NET 7

1985 2000 2011 2017?1990 1995 2005

Initial focus on needs assessment

Heavy reliance on satellite imagery

What is good food security analysis?

The theory of famine (Amartya Sen)

FS dimensions, focus on production

Food Security vulnerability framework




1985 2000 2011 2017?1990 1995 2005

Started work on accessibility dimension

Market/price monitoring

Developed and introduced a five point

vulnerability scale: same colors as IPC




1985 2000 2011 2017?1990 1995 2005

Not much technical advancement

Geographic expansion

Data managers




1985 2000 2011 2017?1990 1995 2005

Move from USAID AFR to FFP

Greater focus on the “network”

Expansion out of Africa (AF, LAC)

Convergence of indicators

Introduced LH-based framework




1985 2000 2011 2017?1990 1995 2005

From classifying vulnerability to

classifying food insecurity

Adopted and joined IPC

Emphasis on early warning

Scenario development (SADC)




1985 2000 2011 2017?1990 1995 2005

Introduced remote monitoring

Network development

Focus on assumption building for better

scenario development

Primary data collection

FEWS NET Data Warehouse (FDW)




1985 2000 2011 2017?1990 1995 2005

Multiple contract awards

Global, geographically flexible

Focus remains on early warning

Food security dynamics

Data as a product

Innovation


Livelihoods-based early

warning analysis

Livelihoods-based

analysis

Knowledge Base

Scenario Development

Comparable

classification

Efficient Monitoring

Effective

decision support

FEWS NETAnalytical Approach


1. Build and use

knowledge bases to

understand the context

Livelihoods-based

analysis


Comparable

classification

Knowledge Base


Effective

decision support



2. Monitor key risk factors

Remote sensing

Rainfall Forecasts

Market/Price monitoringField Assessments

Nutrition Surveys/

Surveillance

Conflict

Livelihoods-based

analysis

Knowledge Base


Comparable

classification


Effective

decision support



STEP 1: Set scenario parameters

STEP 2: Describe and classify current food security

STEP 3: Develop key assumptions

STEP 4: Describe impacts on HH income sources

STEP 5: Describe impacts on HH food sources

STEP 6: Describe and classify projected HH food security

STEP 7: Describe and classify projected area food security

STEP 8: Identify events that could change the scenario

Livelihoods-based

analysis

Knowledge Base


Comparable

classification


Effective

decision support

3. Use scenarios to estimate

projected food security



4. Use a common language for

describing food insecurity

West Africa Food Security Outcomes,

May 2015

Source: FEWS NET

Livelihoods-based

analysis

Knowledge Base


Comparable

classification


Effective

decision support



Livelihoods-based

analysis

Knowledge Base


Comparable

classification


Effective

decision support

5. Communicate analysis

to decision-makers



FEWS NET Products

•Food Security

Outlook

•Food Security

Outlook Update

•Food Security

Alert

•Special Report

•Food Assistance

Outlook Brief

• Global Weather

Hazards

• Seasonal Monitor

• Monthly Seasonal

Forecast Review

• Knowledge Base

Products: rainfall

estimation;

drought metrics;

vegetation and

crop conditions;

surface water

monitoring

• Analysis tools

• Market Fundamentals

Report

• Price Watch

• Price Bulletin

• Regional Supply and

Market Outlook

• Cross border trade

monitoring systems

• Market monitoring

bulletin

• Production and trade

flow mapping

• Livelihood Zone

Map

• Livelihood Zone

description

• Livelihood

Baseline

• Household

Economy

Approach (HEA)

Outcome analysis

• SMART Surveys

• Nutrition Causal

Analysis

• Nutrition Context

Document

Agro-Climatology

Markets and Trade

LivelihoodsIntegrated Analysis

Nutrition


Fit for Purpose?


Fit for Purpose??

FEWS NET’s forward looking analysis

currently meets the needs of our client.

But does it meet the needs or add value

for other users?

How does expert judgment fit in?

Greater emphasis on consensus?

Is there a better way to build consensus

amongst partners?



“ … There is almost certainly some value in the idea that

different members of a group can learn from one another’s

expertise. But this introduces the possibility of groupthink

and herding. Some members of a group may be more

influential because of their charisma or status and not

necessarily because they have the better idea.

Empirical studies of consensus-driven predictions have

found mixed results, in contrast to a process wherein

individual members of a group submit independent

forecasts and those are averaged or aggregated together,

which can almost always be counted on to improve

predictive accuracy.”


1. Everyone’s data is shared in real time and available to everyone. (e.g., FEWS NET’s CHIRPS data)

2. Different partners run independent analyses. Common language and analytical concepts, standards and protocols should be followed but no constraints on methods

3. The consensus process is centered around presentations and discussions of these different analyses rather than getting a group of people together and starting from scratch.

A better approach to consensus building?


WWW.FEWS.NET


Questions?

Bruce IsaacsonChief of Party

FEWS NET

[email protected]

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The State of Food Security Analysis: A FEWS NET...

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