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
val
ue
uti
lity
ne
t in
co
me
man
ag
emen
t
fore
cas
ts
we
ath
er
en
viro
nm
en
t
clim
ato
log
y
*
*
1 *
|1
1 *
|1
( | ; , )
( | ; , )
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
Meeting the Salience Challenge:Predicting Agricultural Impacts
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:Predicting Agricultural Impacts
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
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
Enhancing Understanding:A Workshop-Based Process
• Relate measurements to farmers’ experience
• Convert series to relative frequency, then probability
• Explanation & repetition
?
Enhancing Understanding:A Workshop-Based Process
• Relate measurements to farmers’ experience
• Convert series to relative frequency, then probability
• 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
Enhancing Understanding:A Workshop-Based Process
• Relate measurements to farmers’ experience
• Convert series to relative frequency, then probability
• Explanation & repetition
• Compare with e.g., El Niño years to convey forecast as a shifted distribution
• Explore management implications
Enhancing Understanding:A Workshop-Based Process
• Relate measurements to farmers’ experience
• Convert series to relative frequency, then probability
• Explanation & repetition
• Compare with e.g., El Niño years to convey forecast as a shifted distribution
• 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?
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