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)

0

200

400

600

800

1000

1200

1968 1973 1978 1983 1988 1993 1998 2003

Year (0.66)

Rai

nfa

ll (m

m)

observed

predicted

96

143130127

145

12010196

136

192

115

185

149164

5645

114

48

132

59

36

2

Jun Jul Aug Sep Oct Nov

Months

Rainfall prediction of ECHAM-4 downscaled to monthly, and observed rainfall for Nandyala during 2003

Monthly_prediction Actual-03-ndk Actual-03-ndl Actual-03-vor

JJASO-ATP (1970-2003)

0

100

200

300

400

500

600

700

800

900

1968 1973 1978 1983 1988 1993 1998 2003

Year (r=0.4675)

Rai

nfa

ll (m

m)

observed

predicted

58

40

7

54

73

65

85

48

129

22

71

96

109

150

54

00900

J un J ul Aug Sep Oct Nov Dec

Months

Rainfall prediction of ECHAM-4 downscaled to monthly, and observed rainfall for Anantapur during 2003

Monthly_prediction Observed-2003-SGML Observed-2003-ATMK

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 seasonFarmer practice

Forecast based farming with 35,000 plants ha-1 and

30 kg N ha-1 40 kg N ha-1 60 kg N ha-1

Dry 555 951 (71) 1052 (90) 1206 (117)

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 Period 2 Decisions Period 3 DecisionsPre-planting Planting Weeding and intercropping

Fertilizer-Phos Plant Millet – Early or Late Fertilize-NitrogenBuy/Sell Livestock Fertilizer-Phosphorus/Nitrogen Transplant ricePlant rice nursery Buy/Sell Livestock Plant Cowpea-DensityTransplant rice Wage Labor – Buy or Sell

Weed millet/rice 

Effects of Various Technologies and a Subsidy on Adoption of Effects of Various Technologies and a Subsidy on Adoption of Fertilizer on a Representative Farm in the Sahelo-Sudanian Fertilizer on a Representative Farm in the Sahelo-Sudanian

Zone in Niger Zone in Niger

Policy or Fertlizer use Rainfed crop % changeprogram (ha) income (US$) crop income

1. Current practices N/A 486 -

2. Improved Short-cycle 0 631 20 cultivars

3. Phosphorus only 2.1 685 41

4. Long-cycle cultivars* 1.5 651 34

5. Input subsidy (10%) 1.2 657 35

* 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

A. SubsistentPastoralism and

Agropastoralism (low input)

B. Semi-subsistentExtensive Integrated

(low external inputs)

C. Semi-commercial Intensive Integrated

(high external inputs)

D. Commercial IntensiveSpecialized

(high external inputs

Population Pressure

Acc

ess

to M

arke

ts

Rainfall limiting to intensification

Rainfall conducive to intensification

E. CommercialExtensive Specialized

(low input)cow-calf operations

Rainfall

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?

High variability in both cases but…

(reproduced from IPCC, 2001)

Sahel: higher variations on decadal time

steps (low frequency)

SEA: higher variations on yearly time steps (high frequency)

does this mean relatively more risk for an annual crop /

farmer in SEA?

not necessarilybecause :

Predictability is higher in SEA (both yearly and in the long term)

Risk = uncertainty x vulnerability

CG Generation Challenge Program:Participatory Biotechnology

Drought

Stress microarray

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 2002Drought 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

What matters to farmers: how drought affects their food security and livelihoods

A DDPA-Sponsored study in Burkina Faso by the Univ. of Wageningen

Conclusion: help farmers make better use of limited rainfall

VASAT -- Virtual Academyfor 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

PUNE

AHMEDNAGAR

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 (Africa)

ww w.vusat .org

(Interface of community radio and Internet through WorldSpace technology)

VASATVirtual 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!