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
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Webinar: Manual launch for Participatory Integrative Climate Information Services for Agriculture (PICSA)
- 1. Participatory Integrated Climate Services for Agriculture PICSA Peter Dorward p.t.dorward@reading.ac.uk
- 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. 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. Participatory Integrated Climate Services for Agriculture PICSA
- 5. • Zimbabwe • Tanzania • Kenya • Malawi • Ghana
- 6. • Lesotho • Zambia • Mali • Rwanda • Zimbabwe • Tanzania • Kenya • Malawi • Ghana
- 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. Farmers • Challenges • Opportunities Climate Information • Historical Records • Forecasts Participatory Decision Making Tools Options • Crops • Livestock • Livelihoods ‘The Farmer Decides’ ‘Options by Context’ PICSA
- 9. Further principles / aims of PICSA Sustainability Scalability
- 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. 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
- 14. Step A – What does the farmer do?
- 15. Step A – What does the farmer do?
- 16. Dodoma: Annual Total rainfall Year 2010200520001995199019851980197519701965196019551950194519401935 1100 1000 900 800 700 600 500 400 300 Steps B & C – Historical climate information
- 18. Steps B & C – Historical climate information
- 19. ANALYZED HISTORICAL CLIMATIC DATA SEASONAL RAINFALL TOTALS -YENDI
- 20. 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?
- 21. MORE ANALYSIS Start of Rains Length of the Seasons
- 22. 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
- 23. Dodoma: Annual Total rainfall Year 2010200520001995199019851980197519701965196019551950194519401935 1100 1000 900 800 700 600 500 400 300 Steps B & C – Historical climate information
- 25. 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’.
- 26. ANALYZED HISTORICAL CLIMATIC DATA CALCULATING CROP RISKS SEASONAL RAINFALL TOTALS -YENDI
- 27. Calculating the risks of growing different crops
- 28. 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
- 29. Step D – What are the farmers options • Crop options • Livestock options • Livelihood options
- 30. Step D – What are the farmers options
- 31. Step D – What are the farmers options
- 32. Step D – What are the farmers options
- 33. 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
- 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/ Participatory Planning Crop + Livestock Options
- 36. 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
- 37. Steps H & I: The seasonal forecast • Understanding and interpreting the seasonal forecast • Leaving plans unchanged or adjusting them
- 38. Explaining the seasonal rainfall
- 39. 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
- 40. 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
- 41. 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
- 42. Step L: Learn and improve • Support throughout the process • Monitoring and evaluation • Review and improve
- 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’
- 49. The role of meteorological data and National Met. Services in PICSA Roger Stern, Statistical Services Centre (SSC), Reading (r.d.stern@reading.ac.uk)
- 50. Contents • What’s different about PICSA? • The role of the Met Service • The future?
- 51. 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
- 52. 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!
- 53. 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?
- 54. 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
- 55. 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’
- 56. 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’
- 57. 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
- 58. 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
- 60. Annual rainfall totals – Dodoma - Tanzania
- 61. CALCULATING RISKS WITH FARMERS
- 62. CALCULATING RISKS WITH FARMERS
- 63. Number of rain days - Dodoma
- 64. Longest dry spell (Jan to March)
- 65. Start and end of rains - Dodoma
- 66. Season length, days - Dodoma
- 68. 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?
- 69. 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
- 70. 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
- 71. 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?
- 72. 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!
- 73. The manual LONG BEFORE THE SEASON
- 74. And before and during the season
- 76. Preparing for PICSA & Conclusions
- 77. 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
- 78. 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’
- 79. 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
- 80. 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
Notas do Editor
- Add CCAFS Logo
- Add CCAFS Logo
- 2011, Developed in Zimbabwe, Piloted and improved in Tanzania and Kenya, Gone to greater scale in Tanzania, Ghana and Malawi Continually adapting & improving
- So what is PICSA – providing information and services to smallholders, ahead of and during the season, mainly by extension and NGO field staff, complimented by radio and SMS
- Farmers have many difficulties- one of these is climate
- Manual. A-G are the steps that field staff do with groups of farmers (say 4 meetings) before the season
- Farmer meetings, literate and non / semi literate
- What are the farmer’s main resources and activities What aspects of climate and weather affect the balance of the livelihoods that farmers’ use What key decisions that farmers make are influenced by the weather – eg in crops, and how can we help How is this different for different farmers, i.e. gender, wealth and farming systems
- As above – but helps identify what key decisions that farmers make are influenced by the weather - and when – eg in crops and cop management Current situation, effects of weather and climate, what kinds of information and activities may be useful
- Standard set of graphs – farmers interpret relatively easily – vertical axis is amount and horizontal always is years
- Emphasise these uses
- Emphasise these uses
- Stick a ruler over the top – and count ….
- Many egs – rainfall starts on date x, chances of no dry spells before seedlings emerged, chances of season length of x days... FARMERS worked out probabilities (see graph)
- For field staff to use in field – with farmers. After field staff had 1 week training course….
- And sustainable and scalable – Overall people see to like it – farmers, NGOs and extension staff Continues to be work in progress – learning, improving – particularly around scaling out new areas in Qs time