Seasonal to Inter-Annual Climate Forecasts and their Applications in Agriculture James Hansen International Workshop on Addressing the Livelihood Crisis

Seasonal to Inter-Annual

Climate Forecasts and their

Applications in Agriculture

James Hansen

International Workshop on Addressing the Livelihood Crisis of Farmers: Weather and Climate Services

Belo Horizonte, Brazil, 13 July 2010

Introduction

• Basis for seasonal, interannual prediction

• Relevance for farmer livelihoods

• Underexploited opportunity or underappreciated constraints?

El Niño

neutral

La Niña

Overview

• Value of seasonal forecasts for agriculture

• Challenges to achieving potential value

• Enhancing salience

• Enhancing understanding

• Enhancing legitimacy

• Re-invigorating seasonal forecasts for agriculture

EVIDENCE OF VALUE

The Cost of Climate Risk:Ex-post Impacts of Climate Shocks

• Loss of life, assets, infrastructure

• Persistent impacts of coping responses:

– Reduce consumption

– Overexploit resources

– Liquidate productive assets

– Default on loans

– Withdraw children

– from school

– Abandonment CRISIS

HARDSHIP

Climatic outcome (e.g. production, income)

Pro

ba

bili

ty d

en

sit

y

The Cost of Climate Risk:Ex-Ante Cost of Moving Target

•Katumani, Kenya

•Simulated maize yields:

•Observed weather

•11 N fertilizer rates

•4 planting densities

•Enterprise budget

•Optimal management

•Fixed

•By year

Hansen, Mishra, Rao, Indeje, Ngugi. 2009. Agric. Syst. 101:80-90.

The Cost of Climate Risk:Ex-Ante Cost of Moving Target

Plants

(m-2)Fertilizer (kg N ha-1)

Yield

(Mg ha-1)

N effic.

(kg grain kg-1 N)

Net

income

(KSH ha-1)

Perfect information

(Optimized by year) 3.9 56 3.10 55.4 22,919

Climatology

(Optimized for all years) 3.5 50 2.34 46.8 13,586

Difference 0.4 6 0.76 8.6 9,333

Percent difference 10.3% 10.7% 24.5% 15.5% 40.7%

The Cost of Climate Risk: Ex-Ante Cost of Risk Aversion

• Risk aversion effect

– Low-risk crops, varieties

– Under-use of inputs

– Shift household labor

– Non-productive precautionary assets

– Poor adoption of

– innovation

– Also affects markets

• Cost of uncertainty is large, inequitable

Climatic outcome (e.g. production, income)

Pro

ba

bili

ty d

en

sit

y

CRISIS

HARDSHIP

FORFE

ITED

OPPORTUNITY

Model-Based Ex-Ante Valuation

Expected outcome of best response to new information minus

expected outcome of best response to prior information:

* *E{ ( ( | ; , ))} E{ ( ( | ; , ))}FV U F Ux e x e

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ue

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*

*

1 *

|1

1 *

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( | ; , )

( | ; , )

i

n

F T i i T Fi

n

T i Ti

V n P y F C

n P y C

x

x

x e

x e

Model-Based Ex-Ante Valuation

• Reviewed 58 estimates from 33 papers

• Most focused on rainfed agronomic crops

• Highest values estimated for horticultural crops

0 5 10 15 20 25

agronomic crops

horticultural crops

livestock

No. publications

0.1 1 10 100 1000

agronomic crops

horticultural crops

livestock

Median value (US$/ha)

Meza, Hansen, Osgood. 2008. J. Appli. Meteorol. Climatol. 47:1269-1286.

Empirical Evidence of Demand and Value

• Burkina Faso (Roncoli et al. 2009. Climatic Change 92:433-460)

– Most workshop participants (91%) and non-participants (78%) changed management in response to forecast

– Participants disseminated to 2/3 of non-participants

• Zimbabwe (Patt, Suarez, Gwata, 2005. PNAS 102: 12623-12628)

– Of the 75% who received forecasts, 57% changed management resulting in yield increases

– Workshop participants 5 X more likely to respond

• Successes within reported failures

• Evidence of latent demand

Challenges to Achieving Potential Value

• Do poor smallholder farmers lack the capacity to change management in response?

• Will climate forecasts that could be wrong expose farmers to unacceptable risk?

• Can farmers understand and deal with the complexities of probabilistic forecasts?

• Communication challenges:

– Salience

The Salience Challenge: Farmers’ Forecast Information Needs

• Local spatial scale

• Temporal scale – “Weather-within-climate”

• Agricultural impacts and management implications

• Transparent presentation of forecast accuracy

The Salience Challenge:Representative Forecast Products

Challenges to Achieving Potential Value

• Do poor smallholder farmers lack the capacity to change management in response?

• Will climate forecasts that could be wrong expose farmers to unacceptable risk?

• Can farmers understand and deal with the complexities of probabilistic forecasts?

• Communication challenges:

– Salience

– Legitimacy

The Legitimacy Challenge: Illustrated by the RCOFs

• The RCOF purpose, design, process

• Credibility, legitimacy, salience

• Illustrative of broader challenge

climate community,

COFs

“users”

applications

“…a hub for activation

and coordination of

regional climate

forecasting and

applications activities

into informal networks”

SALIENCE

Meeting the Salience Challenge:Downscaling in Space

Correlation of observed (85 stations) vs. predicted rainfall in Ceará, NE Brazil, as a function of spatial scale. Gong, Barnston, Ward, 2003. J. Climate 16:3059-71.

Co

rre

latio

n

Scale

Meeting the Salience Challenge:“Weather Within Climate”

• Seasonal total = frequency × mean intensity

• Frequency more spatially coherent, predictable

• Dry, wet spell length distributions

• Timing of season onset, length

Meeting the Salience Challenge:Predicting Agricultural Impacts

0

1000

2000

3000

4000

0 2 4 6 8

Rainfall (mm/d)

Yie

ld (

kg

/ha

)

0

1000

2000

3000

4000

25 27 29 31 33

Max Temp Average (C)

Yie

ld (k

g/h

a)

y = 0.9204x + 202.61

R2 = 0.81

0

500

1000

1500

2000

2500

3000

3500

4000

0 1000 2000 3000 4000

Simulated Yield (kg/ha)O

bser

ved

Yiel

d (k

g/ha

)

Observed soybean yields (GA yield trials) vs. seasonal rainfall, temperature, simulated yields


1982 Queensland, Australia wheat yield forecast. Hansen et al., 2004. Agric. For. Meteorol. 127:77-92

climatology only + GCM forecast

Forecast date

Gra

in y

ield

(M

g h

a-1)

Traditional sorghum, Dori, Burkina Faso. Mishra et al., 2008. Agric. For. Meteorol. 148:1798-1814.

Correlations of Jun-Sep rainfall, and observed, de-trended wheat yields with May GCM output, prior to planting, Qld., Australia. Hansen et al., 2004. Agric. For. Meteorol. 127:77-92

200 0 200 400 km

Correlation< 0.34 (n.s.)0.34 - 0.450.45 - 0.500.50 - 0.550.55 - 0.600.60 - 0.65 > 0.65

Rain

Yield

• Improves accuracy = reduces uncertainty

• Benefit greatest early in growing season

• Before planting, forecasts potentially more accurate for yield than for seasonal rainfall

• Have developed & evaluated a suite of methods

Ris

k an

alys

is

Inp

ut

sup

ply

m

anag

emen

t

Far

mer

ad

viso

ries Food security

early warning, planning

Trade planning, strategic imports

Insu

ran

ce

eval

uat

ion

, p

ayo

ut

Insu

ran

ce

con

trac

t d

esig

n Time of year

Un

cert

ain

ty (

e.g

., R

MS

EP

)

sea

son

alfo

reca

st

pla

nti

ng

mar

keti

ng

har

ves

t

anth

esi

s

growing season

EVENT

APPLICATION


Meeting the Salience Challenge:A Minimum Information Package for Farmers?

• Downscaled to local station

• Convey uncertainty in probabilistic terms

• Historic variability context

• …paired with historic model performance

• “Weather within climate”

• Packaged with training, group interaction

0

200

400

600

800

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

Year

Oct

-Dec

rain

(mm

)

what would have been forecast what actually happened

0%

25%

50%

75%

100%

0 200 400 600 800 1000

October-December rainfall, mm

Chan

ce o

f at le

ast t

his

muc

h ra

in

Historic

Predicted

0%

25%

50%

75%

100%

0 20 40 60

October-December rain days

Chan

ce o

f at le

ast t

his

man

y

Historic

Predicted

Downscaled Oct-Dec rainfall total & frequency forecast, Katumani, Kenya, presented to farmers Aug 2004.

UNDERSTANDING

Enhancing Understanding:A Workshop-Based Process

• Relate measurements to farmers’ experience



• Convert series to relative frequency, then probability

Oct-Dec rainfall (mm)

Year

s wi

th a

t lea

st th

is m

uch

rain




• Explanation & repetition

?





• Compare with e.g., El Niño years to convey forecast as a shifted distribution

0%

20%

40%

60%

80%

100%

Ch

an

ce

of

at

lea

st

this

mu

ch

ra

in

0 200 400 600 800October-December rain, mm

El Nino years






• Explore management implications






• Explore management implications

• Exploit co-learning in a group process

• Accelerated experience through decision games

• Build on indigenous indicators, culturally-relevant analogies of decisions under uncertainty

LEGITIMACY

Improving Institutional Support

• Mainstream climate information services into agricultural development strategy.

• Foster capacity for agriculture to use and effectively demand relevant climate information.

• Give agriculture greater ownership and effective voice in climate information products and services.

• Target & coordinate an expanded set of applications.

• Realign and resource NMS as providers of services for development, participants in development process.

• Treat meteorological data as a free public good and a resource for sustainable development.

REINVIGORATING SEASONAL

FORECASTS FOR AGRICULTURE

WCC3 and GFCS

• Strengthen the production, availability, delivery and application of science-based climate prediction and services

– Advance understanding and management of climate risks and opportunities

– Improve climate information

– Meet climate-related information needs of users

– Promote effective routine use of climate information

ClimDev-Africa

• Joint program of AU, AfDB, UN-ECA

• Overcome lack of climate information, analysis, options for decision-makers at all levels

– Institutional capacity to generate, disseminate useful information (beginning with RCCs)

– Capacity of end-users to mainstream climate into development

– Implement adaptation and mitigation programs that incorporate climate-related information

• Response to gap analysis

CCAFS

• Co-proposed by CGIAR & ESSP

• Overcome threats to food security, livelihoods, environment posed by a changing climate:

– Close critical knowledge gaps

– Develop & evaluate adaptation options

– Enable stakeholders to monitor, assess, adjust

Research Themes

• Diagnosing vulnerability and analyzing opportunities

• Unlocking the potential of macro-level policies

• Linking knowledge to action

• Adaptation pathways based on managing current climate risk

• Adaptation pathways under progressive climate change

• Poverty alleviation through climate mitigation

Theme 4: ...Managing Current Climate Risk

• Rural climate services

• Seasonal climate prediction

• Livelihood diversification

• Financial risk transfer

• CRM through food delivery, trade, crisis response

Most effective design, delivery mechanism for rural

climate products, services for local-scale risk

management? Institutional arrangements, policy

interventions needed?

How and when can seasonal prediction support adoption

of innovation, better proactive coping strategies, market opportunities linked

to climate variations?

Options for diversification at field, farm, market scales to reduce food insecurity and

livelihood risk? Optimal portfolio for given context?

How to target and implement to reduce vulnerability to

climate shocks and alleviate climate risk-related rural livelihood constraints?

Marcus Prior, WFP

Options for managing climate impacts through climate-informed grain

reserves, trade, distribution, food crisis response; and how to best implement?

Documents

Seasonal to Inter-Annual Climate Forecasts and their Applications in Agriculture James Hansen International Workshop on Addressing the Livelihood Crisis