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Participatory Integrated Climate
Services for Agriculture
PICSA
Peter Dorward
p.t.dorward@reading.ac.uk
Acknowledgements
• University of Reading
• CCAFS
• Rockefeller Foundation
• Nuffield Foundation
• National Meteorological
...
Structure of the launch event
• An overview of PICSA
• The role of meteorological data and national
Met. Services in PICSA...
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
g...
Farmers
• Challenges
• Opportunities
Climate
Information
• Historical Records
• Forecasts
Participatory
Decision
Making To...
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
g...
Long Before
the Season
Historical
Climate Data
sans sequence seches (10 jours dans 21)
gfedcb
Premiere date pour le semi
g...
Step A – What does the farmer do?
Step A – What does the farmer do?
Dodoma: Annual Total rainfall
Year
2010200520001995199019851980197519701965196019551950194519401935
1100
1000
900
800
700
...
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 decis...
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
•...
Dodoma: Annual Total rainfall
Year
2010200520001995199019851980197519701965196019551950194519401935
1100
1000
900
800
700
...
TEMPERATURE ANALYSIS
Steps B & C– Historical climate
information
Provides essential information farmers don’t
have access to - for making decis...
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
rainfa...
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...
Long Before
the Season
Historical
Climate Data
sans sequence seches (10 jours dans 21)
gfedcb
Premiere date pour le semi
g...
Long Before
the Season
Historical
Climate Data
sans sequence seches (10 jours dans 21)
gfedcb
Premiere date pour le semi
g...
Steps H & I: The seasonal forecast
• Understanding and interpreting the seasonal
forecast
• Leaving plans unchanged or adj...
Explaining the seasonal rainfall
Long Before
the Season
Historical
Climate Data
sans sequence seches (10 jours dans 21)
gfedcb
Premiere date pour le semi
g...
Steps J & K: Short term forecasts and warnings
• Understanding and interpreting short-term
forecasts and warnings – what d...
Long Before
the Season
Historical
Climate Data
sans sequence seches (10 jours dans 21)
gfedcb
Premiere date pour le semi
g...
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
Participator...
Thank you
Peter Dorward
p.t.dorward@reading.ac.uk
The role of meteorological data and
National Met. Services in PICSA
Roger Stern,
Statistical Services Centre (SSC), Readin...
Contents
• What’s different about PICSA?
• The role of the Met Service
• The future?
Long Before
the Season
Historical
Climate Data
sans sequence seches (10 jours dans 21)
gfedcb
Premiere date pour le semi
g...
Seasonal Forecasts from http://rava.qsens.net/themes/climate_template/seasonal-forecasts/
During the
Season
Short-term
For...
Seasonal Forecasts from http://rava.qsens.net/themes/climate_template/seasonal-forecasts/
During the
Season
Short-term
For...
Long Before
the Season
Historical
Climate Data
sans sequence seches (10 jours dans 21)
gfedcb
Premiere date pour le semi
g...
Components of PICSA
Farmers
• Challenges
• Opportunities
Climate
Information
• Historical Records
• Forecasts
Participator...
Components of PICSA
Farmers
• Challenges
• Opportunities
Climate
Information
• Historical Records
• Forecasts
Participator...
Climate information projects and the NMS
• Try to ignore the NMS?
• Or
• Just ask for (historical) data and forecasts?
• O...
ICRISAT/ILRI project for ASARECA
• Project from 2006 to 2009
• Involved each NMS right
from the start
• Not always easy!
•...
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 ...
Long Before
the Season
Historical
Climate Data
sans sequence seches (10 jours dans 21)
gfedcb
Premiere date pour le semi
g...
SEASONAL FORECAST
A
KEY
Above Normal
Normal
Below Normal
25
40
35
Akus
e
Takorad
i
Tema
Abetifi
Ada
Akim
Oda
Axim
Bole
Ho
...
Possible improvements with NMS work
• Data management and analysis
– CLIMSOFT, CLIDATA
– Data “rescue” – WMO
– Usually cus...
Improving the network
• One issue with possible scaling out
– Lack of data from a close station
• Possible solution
– Merg...
The manual
LONG BEFORE THE SEASON
And before and during the season
Thank you
r.d.stern@reading.ac.uk
Preparing for PICSA
& Conclusions
ACTIVITIES
FOR PICSA
Scoping &
Engagement
Planning with
Key Service
Providers
Analysis of
Historical
Climate
Information
I...
Components of PICSA
Farmers
• Challenges
• Opportunities
Climate
Information
• Historical Records
• Forecasts
Participator...
Some conclusions
• Farmers value and are using the climate
information
• Not just climate as a cause of problems and
oppor...
Some conclusions – final thoughts
• How to scale up and achieve sustainability
• The importance of complimentary services
...
Thank you
Peter Dorward
p.t.dorward@reading.ac.uk
Webinar: Manual launch for Participatory Integrative Climate Information Services for Agriculture (PICSA)
Webinar: Manual launch for Participatory Integrative Climate Information Services for Agriculture (PICSA)
Webinar: Manual launch for Participatory Integrative Climate Information Services for Agriculture (PICSA)
Webinar: Manual launch for Participatory Integrative Climate Information Services for Agriculture (PICSA)
Webinar: Manual launch for Participatory Integrative Climate Information Services for Agriculture (PICSA)
Webinar: Manual launch for Participatory Integrative Climate Information Services for Agriculture (PICSA)
Webinar: Manual launch for Participatory Integrative Climate Information Services for Agriculture (PICSA)
Webinar: Manual launch for Participatory Integrative Climate Information Services for Agriculture (PICSA)
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Webinar: Manual launch for Participatory Integrative Climate Information Services for Agriculture (PICSA)

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Presentation from online launch of the Participatory Climate Information Services for Agriculture Field Manual. Learn more: https://ccafs.cgiar.org/online-launch-participatory-climate-information-services-agriculture-manual

Published in: Education

Webinar: Manual launch for Participatory Integrative Climate Information Services for Agriculture (PICSA)

  1. 1. Participatory Integrated Climate Services for Agriculture PICSA Peter Dorward p.t.dorward@reading.ac.uk
  2. 2. 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!
  3. 3. 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
  4. 4. Participatory Integrated Climate Services for Agriculture PICSA
  5. 5. • Zimbabwe • Tanzania • Kenya • Malawi • Ghana
  6. 6. • Lesotho • Zambia • Mali • Rwanda • Zimbabwe • Tanzania • Kenya • Malawi • Ghana
  7. 7. 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 Forecasts & Warnings Just Before the Season Seasonal Forecast & Revise Plans Participatory Planning Shortly After the Season Review weather, production, forecasts & process Crop + Livestock Options
  8. 8. Farmers • Challenges • Opportunities Climate Information • Historical Records • Forecasts Participatory Decision Making Tools Options • Crops • Livestock • Livelihoods ‘The Farmer Decides’ ‘Options by Context’ PICSA
  9. 9. Further principles / aims of PICSA Sustainability Scalability
  10. 10. 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 Forecasts & Warnings Just Before the Season Seasonal Forecast & Revise Plans Participatory Planning Shortly After the Season Review weather, production, forecasts & process Crop + Livestock Options
  11. 11. 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
  12. 12. Step A – What does the farmer do?
  13. 13. Step A – What does the farmer do?
  14. 14. Dodoma: Annual Total rainfall Year 2010200520001995199019851980197519701965196019551950194519401935 1100 1000 900 800 700 600 500 400 300 Steps B & C – Historical climate information
  15. 15. Steps B & C – Historical climate information
  16. 16. ANALYZED HISTORICAL CLIMATIC DATA SEASONAL RAINFALL TOTALS -YENDI
  17. 17. 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?
  18. 18. MORE ANALYSIS Start of Rains Length of the Seasons
  19. 19. 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
  20. 20. Dodoma: Annual Total rainfall Year 2010200520001995199019851980197519701965196019551950194519401935 1100 1000 900 800 700 600 500 400 300 Steps B & C – Historical climate information
  21. 21. TEMPERATURE ANALYSIS
  22. 22. 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’.
  23. 23. ANALYZED HISTORICAL CLIMATIC DATA CALCULATING CROP RISKS SEASONAL RAINFALL TOTALS -YENDI
  24. 24. Calculating the risks of growing different crops
  25. 25. 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
  26. 26. Step D – What are the farmers options • Crop options • Livestock options • Livelihood options
  27. 27. Step D – What are the farmers options
  28. 28. Step D – What are the farmers options
  29. 29. Step D – What are the farmers options
  30. 30. 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
  31. 31. 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
  32. 32. 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
  33. 33. Steps H & I: The seasonal forecast • Understanding and interpreting the seasonal forecast • Leaving plans unchanged or adjusting them
  34. 34. Explaining the seasonal rainfall
  35. 35. 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 Forecasts & Warnings Just Before the Season Seasonal Forecast & Revise Plans Participatory Planning Crop + Livestock Options
  36. 36. 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
  37. 37. 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 Forecasts & Warnings Just Before the Season Seasonal Forecast & Revise Plans Participatory Planning Shortly After the Season Review weather, production, forecasts & process Crop + Livestock Options
  38. 38. Step L: Learn and improve • Support throughout the process • Monitoring and evaluation • Review and improve
  39. 39. 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’
  40. 40. Thank you Peter Dorward p.t.dorward@reading.ac.uk
  41. 41. The role of meteorological data and National Met. Services in PICSA Roger Stern, Statistical Services Centre (SSC), Reading (r.d.stern@reading.ac.uk)
  42. 42. Contents • What’s different about PICSA? • The role of the Met Service • The future?
  43. 43. 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 Forecasts & Warnings Just Before the Season Seasonal Forecast & Revise Plans Participatory Planning Shortly After the Season Review weather, production, forecasts & process Crop, Livestock + Livelihood Options PICSA
  44. 44. Seasonal Forecasts from http://rava.qsens.net/themes/climate_template/seasonal-forecasts/ During the Season Short-term Forecasts & Warnings 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!
  45. 45. Seasonal Forecasts from http://rava.qsens.net/themes/climate_template/seasonal-forecasts/ During the Season Short-term Forecasts & Warnings 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?
  46. 46. 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/ Emphasise Options by Context – O by C As opposed to fixed “recommendations” 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
  47. 47. 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’
  48. 48. 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’
  49. 49. 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
  50. 50. 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
  51. 51. TEMPERATURE ANALYSIS
  52. 52. Annual rainfall totals – Dodoma - Tanzania
  53. 53. CALCULATING RISKS WITH FARMERS
  54. 54. CALCULATING RISKS WITH FARMERS
  55. 55. Number of rain days - Dodoma
  56. 56. Longest dry spell (Jan to March)
  57. 57. Start and end of rains - Dodoma
  58. 58. Season length, days - Dodoma
  59. 59. Conditional season lengths!
  60. 60. 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?
  61. 61. 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 + 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
  62. 62. SEASONAL FORECAST A KEY Above Normal Normal Below Normal 25 40 35 Akus e Takorad i Tema Abetifi Ada Akim Oda Axim Bole Ho Kete- Krachi Koforidua Navrongo Saltpon d Sefwi Bekwai Wa Wenchi Yendi Accra Sunyan i Tamal e 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
  63. 63. 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?
  64. 64. 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!
  65. 65. The manual LONG BEFORE THE SEASON
  66. 66. And before and during the season
  67. 67. Thank you r.d.stern@reading.ac.uk
  68. 68. Preparing for PICSA & Conclusions
  69. 69. 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
  70. 70. 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’
  71. 71. 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
  72. 72. 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
  73. 73. Thank you Peter Dorward p.t.dorward@reading.ac.uk

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