Climate Applications and Agriculture: CGIAR Efforts, Capacities and Partner Opportunities

Statistical downscaling of GCM rainfall prediction – observed rainfall in two regions

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 kg N ha kg N ha -1 Dry (71)1052 (90)1206 (117) Normal to wet (182)2286 (243)2822 (323) All (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 DecisionsPeriod 2 DecisionsPeriod 3 Decisions Pre-plantingPlantingWeeding and intercropping Fertilizer-PhosPlant Millet – Early or LateFertilize-Nitrogen Buy/Sell LivestockFertilizer-Phosphorus/NitrogenTransplant rice Plant rice nurseryBuy/Sell Livestock Plant Cowpea-Density Transplant riceWage 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

Figure 1: Development Paths of Agricultural Systems in Semi-Arid Areas A. Subsistent Pastoralism and Agropastoralism (low input) B. Semi-subsistent Extensive Integrated (low external inputs) C. Semi-commercial Intensive Integrated (high external inputs) D. Commercial Intensive Specialized (high external inputs Population Pressure Access to Markets Rainfall limiting to intensification Rainfall conducive to intensification E. Commercial Extensive 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 necessarily because : 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 2002 Drought Index (%) Difference with average (%)

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 Academy for the Semi-Arid Tropics

Village ICT Hub at Addakal, South India Located in a highly drought-prone area; covers 37 hamlets, 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 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

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!