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Emerging opportunities to deliver relevant information and services
to smallholder farmers at scale
John M. Gathenya Jomo Kenyatta University of Agriculture & Technology Strengthening Regional Capacity for Climate Services in Africa Pre Event at the 5th Conference on Climate Change and Development in Africa (CCDA-V), Elephant Hills Resort, Victoria Falls, Zimbabwe, 27th October, 2015
Scope
• Why weather and climate information ?
• Weather and Climate Information and Services needed by smallholder Farmers
• Structured engagement of producers, intermediaries and users of weather and climate information
• Role of CCAFS, Regional and National Meteorological Agencies and Partners
• What is needed going forward
Theory of change
Weather & Climate; Agriculture,& Livestock data
Analysis and synthesis of data to create Information; Communication
Integration of Scientific and Indigenous Knowledge in Planning
Decision support tools & frameworks at all levels
Food Security and Livelihoods
Climate Information Value Chain
Source: Netherlands Cooperation on Water and Climate Service www.waterandclimateservices.org
Producer? Intermediary? User?
Weather and Climate Information - What is needed?
• Spatial resolution (of historical, monitored and predicted information) relevant for planning of smallholder agriculture
• Participatory approaches for communicating climate information and helping smallholder farmers incorporate it into their planning
• Efficient and effective communication that reaches communities equitably and at scale
• New ways of presenting and communicating weather and climate information
• Support systems that help communities act on climate information to improve their livelihoods
Spatially Complete Weather and climate information that meets smallholder needs
Historical / Monitored
Data
Seasonal Climate Forecast
Short Term Forecasts & Alerts
Projections of future climate
19xx to 2015 3 months 2030 2050 2100
1month1 day
Long Before the Season
Historical Climate Data
sans sequence seches (10 jours dans 21)
gfedcb
Premiere date pour le semi
gfedcb
2010
2000
1990
1980
1970
1960
1950
1940
1930
13 Jul
28 Jun
13 Jun
29 May
14 May
29 Apr
Seasonal Forecasts from http://rava.qsens.net/themes/climate_template/seasonal-forecasts/
During the Season
Short-term Forecast & Warnings
Just Before the Season
Seasonal Forecast & revisit
plans
Participatory Planning
Shortly After the Season
Review weather, production, forecasts &
process
PICSA – Structured approach of engaging smallholder farmers
Credit Peter Dorward
Participatory Integrated Climate Services for Agriculture (PICSA)
• Developed by CCAFS and partners
• Participatory approaches for communicating climate information and helping smallholder farmers incorporate it into their planning.
• Training Manual for intermediaries has been developed => scale up.
• PICSA depends on products from analysed historical climate information; this information is not routinely available.
Participatory Integrated Climate Services for Agriculture (PICSA)
• PICSA has been piloted successfully in Africa
• Success in scaling up PICSA depends on
– Capacity of NMS to produce relevant climate information products,
– capacity of intermediaries to communicate this information with farmers
• Partners are needed to test and use these training approaches
TOT TOF FOF
PICSA + Pilot areas
PICSA+ pilot areas
• Tanzania in late 2013 and is now being scaled up under GFCS, with the goal of reaching farmers in 10 districts across Tanzania and Malawi by 2016.
• Kenya: Nyando
Improving spatial resolution of weather monitoring
TAHMO
Satellite Rainfall
WMO Plastic rain gauge
Trans-African Hydro-Meteorological Observatory (TAHMO) - a joint initiative of Oregon State University and Delft University of Technology
MAPROOMS
Met Station
From National to Downscaled SCF
CPT FactFit
Using probability graphs to represent a seasonal forecast
• E El Niño years in a probability graph
?
Communicating climate information
Frequency: Expresses variability with numbers. For example, in four out of the past ten years I was not able to produce enough maize to feed my family until the next harvest.
Uncertainty: Deals with what will happen in the future. Because the climate has been variable in the past, I am uncertain about what the weather will be like in next growing season.
Probability: Expresses uncertainty with numbers. For example, there are two chances in five that I will not produce enough maize to feed my family until the next harvest.
Forecast (or Prediction): New information that changes probabilities about the future. A forecast reduces uncertainty, but doesn’t eliminate it completely. We will show how to use probability distributions to describe past variability and express a seasonal forecast.
Variability: Deals with what happened in the past. For example, rainfall in 2012 was different from rainfall in 2011, which was different from rainfall in 2010.
Communication
• Leverage on growth in ICT, Mobile phone and radio coverage in Africa
• Train intermediaries to use different channels
• 2 way communication
• Partnering with private sector
Partnerships & Support Systems
• Market information and access (inputs, outputs)
• Agronomic information from NARIs
• Extension services
Conclusion
• We need weather and climate information and services provided at a scale relevant for decision making by smallholder farmers
• Data collected from multiple sources; processed and communicated by the NMS in partnership with other organizations
• We need new ways of presenting and communicating weather and climate information
Thanks
Contact information: Prof. John M. Gathenya Jomo Kenyatta University of Agriculture &Technology School of Agricultural and Biosystems Engineering Email: [email protected] [email protected] [email protected]