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: j.m.gathenya@reading.ac.uk j.m.gathenya@jkuat.ac.ke mgathenya@gmail.com