Climate Applications and
Agriculture: CGIAR Efforts,
Capacities and Partner
Opportunities
Statistical downscaling of GCM rainfall
prediction – observed rainfall in two regions
JASO-Nandyala (1970-03)
1200
observed
1000
predicted
Rainfall (mm)
800
600
400
200
0
1968
1973
1978
1983
1988
1993
1998
2003
Year (0.66)
JJASO-ATP (1970-2003)
Rainfall (mm)
900
800
observed
700
predicted
600
500
400
300
200
100
0
1968
1973
1978
1983
1988
Year (r=0.4675)
1993
1998
2003
Results of farmers’ participatory cropping
decisions based on climate prediction
 Anantapur region
 Crop management decisions were based on climate, and
revolved around peanut sole or intercrop systems
 Rainfall prediction failed in JAS months with low rainfall
 Kurnool region
 Crop management decisions based on climate prediction by 1/3
of the farmers
 Rest based decisions on crop rotation and commodity market
prices
 Farmers achieved higher productivity with intercrop systems
(>50%) than either sequential double cropping or post rainy
season sole crop, due to terminal stress.
Potential benefits from forecast
based farming in Kenya
Type of season
Farmer
practice
Forecast based farming with 35,000 plants
ha-1 and
30 kg N ha-1
40 kg N ha-1
1052 (90)
1206 (117)
(71)
60 kg N ha-1
Dry
555
951
Normal to wet
666
1879 (182)
2286 (243)
2822 (323)
All
613
1467 (139)
1747 (185)
2151 (251)
Gap in potential and achievable yields with
forecast based farming in normal to above
normal seasons – Katumani, Kenya
Predicting Global Warming Effects
Global maize production
could fall 10%, especially
harming developing countries
and the poor, according to
CIAT and ILRI scientists
Period 1 Decisions
Pre-planting
Period 2 Decisions
Planting
Period 3 Decisions
Weeding and intercropping
Fertilizer-Phos
Buy/Sell Livestock
Plant rice nursery
Transplant rice
Plant Millet – Early or Late
Fertilize-Nitrogen
Fertilizer-Phosphorus/Nitrogen Transplant rice
Buy/Sell Livestock
Plant Cowpea-Density
Wage Labor – Buy or Sell
Weed millet/rice
Effects of Various Technologies and a Subsidy on Adoption of
Fertilizer on a Representative Farm in the Sahelo-Sudanian
Zone in Niger
Policy or
program
1. Current practices
Fertlizer use
(ha)
Rainfed crop
income (US$)
% change
crop income
N/A
486
-
0
631
20
3. Phosphorus only
2.1
685
41
4. Long-cycle cultivars*
1.5
651
34
5. Input subsidy (10%)
1.2
657
35
2. Improved Short-cycle
cultivars
* Combine with both N and P fertilizers.
Exchange rate: 273 FCFA/US$ (IMF, 1990).
Source: adapted from Sanders et al. (1996).
Figure 1:
Development Paths of Agricultural Systems in Semi-Arid Areas
Rainfall
Rainfall limiting
to intensification
Rainfall conducive
to intensification
E. Commercial
Extensive Specialized
(low input)
cow-calf operations
C. Semi-commercial
Intensive
Integrated
(high external
inputs)
B. Semi-subsistent
Extensive Integrated
(low external
inputs)
A. Subsistent
Pastoralism and
Agropastoralism
(low input)
Population Pressure
D. Commercial
Intensive
Specialized
(high external
inputs
Climate: what is different about West Africa?


There are no such things as climate ‘normals’ in sudano-sahelian West Africa
“What is ‘normal’ to the Sahel is not some […] rainfall total […] but variability of the rainfall
supply in space and from year-to-year and from decade-to-decade” (Hulme, 2001)
Climate: what is different about West Africa?
Sahel: higher
variations on
decadal time
steps (low
frequency)
High variability in both cases but…
does this mean relatively more
risk for an annual crop /
farmer in SEA?
not necessarily
because :
Predictability is higher
in SEA (both yearly and
in the long term)
SEA: higher
variations on
yearly time
steps (high
frequency)
Risk = uncertainty x
vulnerability
(reproduced from IPCC, 2001)
CG Generation Challenge
Program:
Participatory Biotechnology
Stress
microarray
Drought
The DDPA
Game
Plan
DESERTIFICATION,
DROUGHT,
POVERTY, and
AGRICULTURE
(DDPA)
Research
Consortium
Spatial distribution of drought vulnerability in West Asia
New Tools to Assess and
Monitor Drought and
Desertification
Southern Africa, March 2002
Drought Index (%)
Difference with average 1999-2001 (%)
Improving Knowledge Flows:
Community Engagement and New
Information Technology
Learning to Learn from Farmers
Fakara, Niger
Farmer Perceptions of Drought
A DDPA-Sponsored study in Burkina Faso by the Univ. of Wageningen
What matters to
farmers: how drought
affects their food
security and
livelihoods
Conclusion: help
farmers make
better use of limited
rainfall
VASAT -- Virtual Academy
for the Semi-Arid Tropics
Village ICT Hub at Addakal, South
India
•
•
•
•
•
Located in a highly drought-prone area; covers 37
hamlets, 45 000 population (app)
All-women micro-credit federation owns the hub
premises; 4500 members
Internet connectivity available; small group of women
trained in IT and info-mediation on agri/drought matters
PRA for info needs conducted and updated; regular
feedback received
Now acts as informal extension access point
New program on Drought Preparedness in
Maharashtra
NASHIK
AHMEDNAGAR
PUNE
30,000 rural youth receiving a 4-hr
module on drought literacy for
monitoring activities
Content from VASAT adopted by
Maharashtra Knowledge
Corporation Ltd. And Pune Univ.
VASAT
Virtual Academy
for the Semi-Arid Tropics
(Reaching the Un-reached)
A community-based distance learning
coalition for SSA WITH THE DMP
Desert Margins
Program
Community Radio Hub in Kahe, Niger
• Uses WorldSpace digital satellite
radio technology to receive info
from the Web
• Hosts community radio station
covering 50 sq km area
• Functional since September 2004
DMP Website www.dmpafrica.net
CGIAR’s assets to institutionalize and further
operationalize climate applications
•
•
•
•
Major repository of dynamic knowledge on GxE (genotype x
environment) interactions can be activated to target farmer-friendly
biotech interventions for improved management of climate variability and
change (CIMMYT, CIP, ICRISAT, IITA, IRRI…)
Existing poverty mapping expertise can be expanded to address climate
risk management following the [risk = uncertainty x vulnerability]
paradigm, e.g. to determine priority focus regions for applications of
climate forecasting (CIAT, IFPRI, ILRI…)
Strong capacity building and ICT/KM capacity can be mobilized to help
solve communication bottlenecks linked to user understanding of the
abstract, probabilistic nature of forecasts (VASAT, …)
Combination of highly decentralized, network structure and
international mandate can help tailor options for local climate
management while ensuring standardized, science-based methodologies
that allow for regional and global assessments of climate management
impacts
Future CG Contributions
• Combining indigenous and science-generated knowledge
• Advancing knowledge on GxE [genotype x environment]
interactions
• Building climate science & monitoring capacity
• Using ICT4D to communicate climate information to
farmers
• Combining bio-economic modeling and advanced
computing power to improve use and impact of adaptive
recommendations
• Combining poverty and climate variability mapping
[risk = uncertainty x vulnerability]
• CG very good at networking
Drought!
Not Just ‘Their’ Problem
Thank You!