webinar: manual launch for participatory integrative climate information services for agriculture...

Participatory Integrated Climate

Services for Agriculture

PICSA

Peter Dorward

[email protected]

Acknowledgements

• University of Reading• CCAFS• Rockefeller Foundation• Nuffield Foundation• National Meteorological

Services• Government extension

services• GFCS

• WFP• NGOs especially Oxfam,

ADRA Ghana, Practical Action

• IFAD• AIMS• ICRISAT• ICRAF• and many others!

Structure of the launch event

• An overview of PICSA• The role of meteorological data and national

Met. Services in PICSA• Preparing for PICSA• Short video of work in Ghana

Participatory Integrated Climate

Services for Agriculture

PICSA

• Zimbabwe

• Tanzania

• Kenya

• Malawi

• Ghana

• Lesotho

• Zambia

• Mali

• Rwanda

• Zimbabwe

• Tanzania

• Kenya

• Malawi

• Ghana

Long Before the Season

Historical Climate Data

sans sequence seches (10 jours dans 21)

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Premiere date pour le semi

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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-termForecasts & Warnings

WARNING

Just Before the Season

Seasonal Forecast & Revise

Plans

Participatory Planning

Shortly After the Season

Review weather, production, forecasts &

processCrop + Livestock

Options

Farmers

• Challenges• Opportunities

Climate Information

• Historical Records• Forecasts

Participatory Decision

Making Tools

Options

• Crops• Livestock• Livelihoods

‘The Farmer Decides’ ‘Options by Context’

PICSA

Further principles / aims of PICSA

Sustainability

Scalability




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During the Season


WARNING



Plans





Options




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13 Jun

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Crop + Livestock Options

Step A – What does the farmer do?

Dodoma: Annual Total rainfall

Year2010200520001995199019851980197519701965196019551950194519401935

Ann

ual r

ainf

all (

mm

)

1100

1000

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400

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Steps B & C – Historical climate information

ANALYZED HISTORICAL CLIMATIC DATA

SEASONAL RAINFALL TOTALS -YENDI

Steps B & C– Historical climate information

Provides essential information farmers don’t have access to - for making decisions• Seasonal totals• Dates of start of rains• Dates of end of season• Season length• Occurrence of dry spells• etc• ‘What is the variability here?

MORE ANALYSIS

Start of Rains Length of the Seasons


• Explore with farmers whether there are any trends to be seen in the graphs

• If there are differences between perceptions and the graphs then consider why

Dodoma: Annual Total rainfall

Year2010200520001995199019851980197519701965196019551950194519401935

Ann

ual r

ainf

all (

mm

)

1100

1000

900

800

700

600

500

400

300


TEMPERATURE ANALYSIS


Provides essential information farmers don’t have access to - for making decisions• Seasonal totals• Dates of start of rains• Dates of end of season• Season length• Occurrence of dry spells etc• What is the variability here? • Risks e.g. ‘1 year out of 3 can expect rainfall

of more than 500mm’.

ANALYZED HISTORICAL CLIMATIC DATA CALCULATING CROP RISKS

SEASONAL RAINFALL TOTALS -YENDI

Calculating the risks of growing different crops

Example of a crop table (not real values)

Crop Variety Days to maturity

Crop water requirement

Chance of sufficient rainfall if season starts on x (Early)

Chance of sufficient rainfall if season starts on x (Middle)

Chance of sufficient rainfall if season starts on x (Late)

Maize Local 120 480 5/10 4/10 2/10

Maize Pioneer xxx

100 350 7/10 5/10 4/10

Sorghum Seed Co xxx

110 300 5/10 7/10 6/10

Step D – What are the farmers options

• Crop options

• Livestock options

• Livelihood options

Step D – What are the farmers options

Steps E to G – the farmer compares and decides which options to try

• Options by context

• Compare different options using participatory budgets

• Farmers make individual decisions




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Plans



Steps H & I: The seasonal forecast

• Understanding and interpreting the seasonal forecast

• Leaving plans unchanged or adjusting them

Explaining the seasonal rainfall




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During the Season


WARNING



Plans



Steps J & K: Short term forecasts and warnings

• Understanding and interpreting short-term forecasts and warnings – what do SMS texts mean – local languages & signs

• Fitting in and building on existing initiatives• Farmers adjusting plans or reacting to and

using new information for management




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During the Season


WARNING



Plans





Options

Step L: Learn and improve

• Support throughout the process

• Monitoring and evaluation

• Review and improve

Components of PICSA

Farmers


Climate Information



Making Tools

Options



Thank you

Peter Dorward

[email protected]

The role of meteorological data and

National Met. Services in PICSA

Roger Stern,

Statistical Services Centre (SSC), Reading

([email protected])

Contents

• What’s different about PICSA?• The role of the Met Service• The future?

Long Before the SeasonHistorical

Climate Data


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13 Jul

28 Jun

13 Jun

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14 May

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During the Season


WARNING



Plans




processCrop, Livestock +

Livelihood Options

PICSA


During the Season


WARNING


Seasonal Forecast



process

Possible climate service projects

Remain in the NMS “comfort zone”.

And maybe add some automatic stations.

Better 10-day bulletin

Start with the NMS as a key partner!


During the Season


WARNING



Plans



process

Possible climate service projects

Emphasise the “demand side”

Start with the NMS as a key partner!

When do the rains start?

Are dry spells getting longer?

How long is the season?


Climate Data


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Emphasise Options by Context – O by C

As opposed to fixed “recommendations”

WARNING

Extensive use of the historical data

The daily data are needed for this.


Livelihoods and livestock options, not

just crops

Crop, Livestock + Livelihood Options

PICSA – what’s different?

The participatory approaches


During the Season


By the Met Service

Components of PICSA

Farmers


Climate Information



Making Tools

Options



Climate information projects and the NMS

• Try to ignore the NMS?• Or• Just ask for (historical) data and forecasts?• Or• Include the NMS as a key partner?

• PICSA includes the NMS– And does not ask for data!– We can provide capacity building

ICRISAT/ILRI project for ASARECA

• Project from 2006 to 2009• Involved each NMS right

from the start

• Not always easy!• Conclusion was:The strategy was sound. We need to try harder!

See also “Lessons Learned” Coe and Stern: Exp. Agriculture 2011

TEMPERATURE ANALYSIS

Annual rainfall totals – Dodoma - Tanzania

CALCULATING RISKS WITH FARMERS

Number of rain days - Dodoma

Longest dry spell (Jan to March)

Start and end of rains - Dodoma

Season length, days - Dodoma

Conditional season lengths!

Role of NMS

• Not asking for data– NMS staff do the analyses to produce the graphs– They also present the graphs at the workshops

• Success story – Ghana Met Service (Gmet)– The GMet staff worked closely with AIMS Ghana

graduates– See https://www.aims.edu.gh/ – Other AIMS centres may help with this formula?

https://www.aims.edu.gh/





Climate Data


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Plans


Crop, Livestock + Livelihood Options

PICSA

Now move to the second stage

This is the Seasonal Forecast

The NMS remains the key partner.

This forecast can modify the baseline risks for the activities previously specified by the farmers

SEASONAL FORECAST

A

KEY Above Normal Normal

Below Normal

25 40 35

Akuse

Takoradi

Tema

Abetifi

Ada Akim Oda

Axim

Bole

Ho

Kete-Krachi

Koforidua

Navrongo

Saltpond

Sefwi Bekwai

Wa

Wenchi

Yendi

Accra

Sunyani

Tamale

D 30 40 30

C 35 40 25

B 25 35 40

2015 Seasonal Forecast (GMET)• Presented like this in

most countries• We find it to have 3

limitations:– What – 3-months– Where – large area– How – terciles

• Good if the 3 are improved

Possible improvements with NMS work

• Data management and analysis– CLIMSOFT, CLIDATA– Data “rescue” – WMO– Usually custodians rather than analysts– Analysis shows issues with data

• Excellent goodwill to improve– Supported by WMO, UKMO and others

• Data in much better “shape than other areas– e.g. agricultural research data?

Improving the network

• One issue with possible scaling out – Lack of data from a close station

• Possible solution– Merge station data with satellite estimates– Satellite data are from about 1983– ENACTS at IRI and TAMSAT at Reading– They are working well together!

The manualLONG BEFORE THE SEASON

And before and during the season

Thank you

[email protected]

Preparing for PICSA

& Conclusions

ACTIVITIES FOR PICSA

Scoping & Engagement

Planning with Key Service Providers

Analysis of Historical Climate

Information

Identification of Crop, Livestock

& Livelihood Options

Adapting Training

Materials to Local Contexts

Training of Field Staff & Managers

Implementation by Field Staff, Radio & SMS

Monitoring & Evaluation

Reflection, Learning &

Opportunities

Preparatory Activities

Implementation

Components of PICSA

Farmers


Climate Information



Making Tools

Options



Some conclusions

• Farmers value and are using the climate information

• Not just climate as a cause of problems and opportunities

• Enabled to look at options that fit farmers situations

• Changes in behaviours – varieties, crops, livelihoods, use of tools

• Seems to fit well with extension and NGO activities and aims

Some conclusions – final thoughts

• How to scale up and achieve sustainability• The importance of complimentary services

and activities e.g. access to seed• Learning and adapting, and for local

situations• Further areas of research and development

Thank you

Peter Dorward

[email protected]

webinar: manual launch for participatory integrative climate information services for agriculture...

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