webinar: manual launch for participatory integrative climate information services for agriculture...
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TRANSCRIPT
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)
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-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
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-termForecasts & Warnings
WARNING
Just Before the Season
Seasonal Forecast & Revise
Plans
Participatory Planning
Shortly After the Season
Review weather, production, forecasts &
processCrop + Livestock
Options
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/
Participatory Planning
Crop + Livestock Options
Step A – What does the farmer do?
Step A – What does the farmer do?
Dodoma: Annual Total rainfall
Year2010200520001995199019851980197519701965196019551950194519401935
Ann
ual r
ainf
all (
mm
)
1100
1000
900
800
700
600
500
400
300
Steps B & C – Historical climate information
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
Steps B & C– Historical climate information
• 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
Steps B & C – Historical climate information
TEMPERATURE ANALYSIS
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? • 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
Step D – What are the farmers 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
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/
Participatory Planning
Crop + Livestock Options
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/
Just Before the Season
Seasonal Forecast & Revise
Plans
Participatory Planning
Crop + Livestock Options
Steps H & I: The seasonal forecast
• Understanding and interpreting the seasonal forecast
• Leaving plans unchanged or adjusting them
Explaining the seasonal rainfall
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-termForecasts & Warnings
WARNING
Just Before the Season
Seasonal Forecast & Revise
Plans
Participatory Planning
Crop + Livestock Options
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
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-termForecasts & Warnings
WARNING
Just Before the Season
Seasonal Forecast & Revise
Plans
Participatory Planning
Shortly After the Season
Review weather, production, forecasts &
processCrop + Livestock
Options
Step L: Learn and improve
• Support throughout the process
• Monitoring and evaluation
• Review and improve
Components of PICSA
Farmers
• Challenges• Opportunities
Climate Information
• Historical Records• Forecasts
Participatory Decision
Making Tools
Options
• Crops• Livestock• Livelihoods
‘The Farmer Decides’ ‘Options by Context’
The role of meteorological data and
National Met. Services in PICSA
Roger Stern,
Statistical Services Centre (SSC), Reading
Contents
• What’s different about PICSA?• The role of the Met Service• The future?
Long Before the SeasonHistorical
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-termForecasts & Warnings
WARNING
Just Before the Season
Seasonal Forecast & Revise
Plans
Participatory Planning
Shortly After the Season
Review weather, production, forecasts &
processCrop, Livestock +
Livelihood Options
PICSA
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
Shortly After the Season
Review weather, production, forecasts &
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!
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
Shortly After the Season
Review weather, production, forecasts &
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?
Long Before the SeasonHistorical
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/
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.
Participatory Planning
Livelihoods and livestock options, not
just crops
Crop, Livestock + Livelihood Options
PICSA – what’s different?
The participatory approaches
Just Before the Season
During the Season
Shortly After the Season
By the Met Service
Components of PICSA
Farmers
• Challenges• Opportunities
Climate Information
• Historical Records• Forecasts
Participatory Decision
Making Tools
Options
• Crops• Livestock• Livelihoods
‘The Farmer Decides’ ‘Options by Context’
Components of PICSA
Farmers
• Challenges• Opportunities
Climate Information
• Historical Records• Forecasts
Participatory Decision
Making Tools
Options
• Crops• Livestock• Livelihoods
‘The Farmer Decides’ ‘Options by Context’
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
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?
Long Before the SeasonHistorical
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/
Just Before the Season
Seasonal Forecast & Revise
Plans
Participatory Planning
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
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
• Challenges• Opportunities
Climate Information
• Historical Records• Forecasts
Participatory Decision
Making Tools
Options
• Crops• Livestock• Livelihoods
‘The Farmer Decides’ ‘Options by Context’
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