Can citizen science accelerate climate adaptation by poor farming households?
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Presentation at CSA Conference 2015 in Montpellier by Jacob van Etten Read more about the conference: http://ccafs.cgiar.org/3rd-global-science-conference-%E2%80%9Cclimate-smart-agriculture-2015%E2%80%9D#.
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Can citizen science accelerate climate adaptation by poor farming households?
- 1. Can citizen science accelerate climate adaptation by poor farming households? Jacob van Etten et al. (see next slide) Bioversity International Montpellier March 16-18, 2015
- 2. Jacob van Etten Jeffrey Alwang Elizabeth Arnaud Eskender Beza Luis Calderer Rhiannon Crichton Anton Eitzinger Kees van Duijvendijk Carlo Fadda Basazen Fantahun Jeske van de Gevel Elisabetta Gotor Dejene Kassahun Mengistu SS Kaushik Yosef G. Kidane Prem Mathur Leida Mercado Sarika Mittra Anne Marie Moeller Ashis Mondal M. Enrico Pฤ Susan Richter Juan Carlos Rosas R.K. Singh Juan Carlos Rosas I.S Solanki Jonathan Steinke Inge Van Den Bergh Karl Zimmerer Authors
- 3. Affiliations
- 4. CSA 2015 โ CSA 2050 Problem
- 5. CSA 2015 โ CSA 2050 CSA is location-specific Problem
- 6. CSA 2015 โ CSA 2050 CSA is location-specific ๏ช Constant, massive testing is required Problem
- 7. CSA 2015 โ CSA 2050 CSA is location-specific ๏ช Constant, massive testing is required ๏ช Can science be accelerated? Problem
- 8. Solution
- 9. Citizen science (also known as crowd science, crowdsourced science, civic science or networked science) is scientific research conducted, in whole or in part, by amateur or nonprofessional scientists. Wikipedia Solution
- 10. Crowdsourcing is the process of obtaining needed services, ideas, or content by soliciting contributions from a large group of people, and especially from an online community, rather than from traditional employees or suppliers. Wikipedia Solution
- 15. Solution
- 16. Implementation
- 17. Implementation
- 18. Implementation
- 20. Implementation
- 21. Implementation
- 22. Implementation
- 23. Implementation
- 24. Implementation
- 25. Implementation
- 26. Results Victoria Macuzalito Chepe Cedrรณn Campechano Amadeus 77 Don Kike Overall performance Bradley-Terry model results Worse Better
- 27. Results
- 28. Results โWhy do you participate in crowdsourcing?โ
- 29. 0 5 10 15 20 25 30 35 40 45 50 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 Pilot, 2011-12 Wheat, 2012- 13 Rice, 2013 Wheat, 2013- 14 Rice, 2014 Wheat, 2014- 15 Varieties Farmers Upscaling process in India Number of varieties Number of farmers Results
- 30. 0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00 IGKVR-2 IGKVR-1 MALVIADHAN2302 MAHAMAYA SWARNASUB-1 MTU-7029 MALVIASUGANDH-43 NDR-97 DANTESHWARI IR-36 GOTRAVIDHAN INDRABARANIDHAN RANJENDRAMANSURI IMPROVEDSHAMBAMANSURI RAJSHREE PUSASUGANDH-5 NDR-359 SUGANDH-4 PUSA-44 PUSABASMATI-1509 IR-64 MTU-1010 PURNIMA RAJENDRASWETA BPT-5204 PNR-381 PRABHAT SARYU-52 HUR-105 ANNADA Farmer27p31 FarmerArize6444 Farmerjk FarmerPioneer71 FarmerDhaanyaseeds Yield of 31 varieties of rice (orange) and 5 locally grown varieties (blue) (q/ha) Results
- 31. Conclusions 1. Local learning fostered, accelerated 2. Reliable, generalizable results 1. Reduced costs comparing to other participatory approaches 2. Simple field experiments make upscaling possible
- 32. Nextsteps 1. User-friendly information platform to be launched mid-2015 โ GxE analysis 2. Input retail: suitable business models 3. Building capacity for implementation 4. Observe other variables and evaluate more CSA technologies