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Feeding innovation – Update on the feed innovation toolkit and where we are with FEAST and Techfit

  1. Feeding innovation – Update on the feed innovation toolkit and where we are with FEAST and Techfit Alan Duncan Feeding Innovation - Stocktaking workshop on a Feed Innovation Toolkit for Livestock in the tropics Dak Lak, Vietnam, 22-24 September 2014
  2. Feed interventions often don’t work – why? Silos
  3. Scientists work alone in their plots
  4. The extra NPN in the straw will provide better microbial growth which will improve digestibility as well as intake…. Extensionists and farmers speak apart
  5. All that extra work for a handful of straw. What if that chemical kills my cow? Extensionists and farmers speak apart The extra NPN in the straw will provide better microbial growth which will improve digestibility as well as intake….
  6. Extensionists and farmers speak apart All that extra work for a handful of straw. What if that chemical kills my cow? The extra NPN in the straw will provide better microbial growth which will improve digestibility as well as intake….
  7. Land Labour Knowledge Cash Inputs Animals need a lot of support
  8. Feed interventions often do not work – why? •Failure to place feed in broader livelihood context •Lack of farmer design and ownership •Neglect of how interventions fit the context: land, labour, cash, knowledge etc FEAST Techfit
  9. FEAST The problem
  10. Feed assessment •Conventionally focuses on: –The feeds –Their nutritive value –Ways of improving nutritive value •FEAST broadens assessment: –Is livestock an important livelihood strategy? –How important are feed problems relative to other problems? –What about labour, input availability, credit, seasonality, markets for products etc.?
  11. How does FEAST work? •Overview of farming system and livestock feed aspect •Milk marketing, veterinary services •Major problems for livestock production 1. Farmer centred diagnosis •Quantitative information on crop- livestock production, feed availability, feeding rations •Qualitative information - perception on feed quality 2. Individual farmer survey •Enter data in FEAST template •Based on result develop ideas for intervention 3. Data analysis and developing interventions
  12. Farmer centred diagnosis •General description of farming system e.g. –farm labour availability –annual rainfall pattern –types of animals raised by households •General description of livestock production e.g. –types of animals raised –purpose of raising these animals (e.g. draught, income, fattening, calf production) –general animal husbandry (including; management, veterinary services and reproduction). –ease of access to credit and inputs •Problem identification and potential solutions
  13. Quantitative questionnaire •Quantitative information on livestock production e.g. –Animals – livestock inventory –Crops - yields and areas to derive crop residue availability –Cultivated forages – yields and areas –Collected fodder: proportion of diet –Purchased feed –Grazing: proportion of diet –Contributors to household income –Production. •Milk production •Sale of livestock –Seasonality. •Feed supply: overall seasonal availability •What is fed in different months?
  14. Sample output 32% 22% 20% 14% 6% 6% Contribution of livelihood activities to household income (as a percentage) AgricultureLivestockRemmitanceLabourOthersBusiness
  15. More sample output Crop residues5% Cultivated fodder25% Grazing30% Naturally occurring and collected33% Purchased7% DM content of total diet
  16. Final output •Feast report with some ideas for key problems and solutions •Better links and understanding between farmers, research and development staff
  17. www.ilri.org/feast
  18. Techfit The problem
  19. What is your main problem Extensionist talks to farmers
  20. What is your main problem Feed Farmer responds
  21. What feed technologies have you got? Extensionist approaches scientist
  22. What feed technologies have you got? Planted forage Urea treated straw Bypass protein Scientist offers what he has
  23. What feed technologies have you got? Planted forage Urea treated straw Bypass protein OK, let’s try those Extensionist takes what’s offered
  24. A solution
  25. Techfit •A discussion support tool for prioritizing feed technologies
  26. Key context attributes Land Labour Credit Input Knowledge Key technology attributes Land Labour Credit Input Knowledge The core concept
  27. Key context attributes Land Labour Credit Input Knowledge Key technology attributes Land Labour Credit Input Knowledge x = Score Matching context to technology
  28. Technology filter Scope for improve ment of attribute s Context relevanc e (score 1- 6; low-high)) Impact potential (score 1- 6; low-high) Total score (context X impact) Requ Score 1-3 (1 for more; 3 for less) Avail Score 1-3 (1 for less; 3 for more) Requ Score 1-3 (1 for more; 3 for less) Avail Score 1-3 (1 for less; 3 for more) Requ Score 1-3 (1 for high; 3 for low) Avail Score 1-3 (1 for less; 3 for more) Requ Score 1-3 (1 for high; 3 for low) Avail Score 1-3 (1 for less; 3 for more) Requ Score 1-3 (1 for high; 3 for low) Avail Score 1-3 (1 for less; 3 for more) Score 1-5 (1 for less and 5 for more) Urea treatment of straw 2 3 6 3 2 2 2 2 0 Supplement with UMMB 2 5 10 3 3 3 2 1 1 1 1 3 1 2 22 By-pass protein feed 1 3 3 3 3 1 1 3 0 Feed conservation (surplus) (HAY) 4 3 12 3 3 2 2 3 3 3 3 3 3 1 41 etc etc III. TECHNOLOGY FILTER (Technology options to address quantity, quality, seasonality issues) Pre-select the obvious (5-6) based on context relevance and impact potential Score the pre-selected technologies based on the requirement, availability and scope for improvement of five technology attributes Attribute 1: Land Attribute 2: Labour Attribute 3: Cash /credit Attribute 4: Input delivery Attribute 5: Knowledge /skill Total Score
  29. Technology filter Scope for improve ment of attribute s Context relevanc e (score 1- 6; low-high)) Impact potential (score 1- 6; low-high) Total score (context X impact) Requ Score 1-3 (1 for more; 3 for less) Avail Score 1-3 (1 for less; 3 for more) Requ Score 1-3 (1 for more; 3 for less) Avail Score 1-3 (1 for less; 3 for more) Requ Score 1-3 (1 for high; 3 for low) Avail Score 1-3 (1 for less; 3 for more) Requ Score 1-3 (1 for high; 3 for low) Avail Score 1-3 (1 for less; 3 for more) Requ Score 1-3 (1 for high; 3 for low) Avail Score 1-3 (1 for less; 3 for more) Score 1-5 (1 for less and 5 for more) Urea treatment of straw 2 3 6 3 2 2 2 2 0 Supplement with UMMB 2 5 10 3 3 3 2 1 1 1 1 3 1 2 22 By-pass protein feed 1 3 3 3 3 1 1 3 0 Feed conservation (surplus) (HAY) 4 3 12 3 3 2 2 3 3 3 3 3 3 1 41 etc etc III. TECHNOLOGY FILTER (Technology options to address quantity, quality, seasonality issues) Pre-select the obvious (5-6) based on context relevance and impact potential Score the pre-selected technologies based on the requirement, availability and scope for improvement of five technology attributes Attribute 1: Land Attribute 2: Labour Attribute 3: Cash /credit Attribute 4: Input delivery Attribute 5: Knowledge /skill Total Score
  30. Technology filter Scope for improve ment of attribute s Context relevanc e (score 1- 6; low-high)) Impact potential (score 1- 6; low-high) Total score (context X impact) Requ Score 1-3 (1 for more; 3 for less) Avail Score 1-3 (1 for less; 3 for more) Requ Score 1-3 (1 for more; 3 for less) Avail Score 1-3 (1 for less; 3 for more) Requ Score 1-3 (1 for high; 3 for low) Avail Score 1-3 (1 for less; 3 for more) Requ Score 1-3 (1 for high; 3 for low) Avail Score 1-3 (1 for less; 3 for more) Requ Score 1-3 (1 for high; 3 for low) Avail Score 1-3 (1 for less; 3 for more) Score 1-5 (1 for less and 5 for more) Urea treatment of straw 2 3 6 3 2 2 2 2 0 Supplement with UMMB 2 5 10 3 3 3 2 1 1 1 1 3 1 2 22 By-pass protein feed 1 3 3 3 3 1 1 3 0 Feed conservation (surplus) (HAY) 4 3 12 3 3 2 2 3 3 3 3 3 3 1 41 etc etc III. TECHNOLOGY FILTER (Technology options to address quantity, quality, seasonality issues) Pre-select the obvious (5-6) based on context relevance and impact potential Score the pre-selected technologies based on the requirement, availability and scope for improvement of five technology attributes Attribute 1: Land Attribute 2: Labour Attribute 3: Cash /credit Attribute 4: Input delivery Attribute 5: Knowledge /skill Total Score
  31. Technology filter Scope for improve ment of attribute s Context relevanc e (score 1- 6; low-high)) Impact potential (score 1- 6; low-high) Total score (context X impact) Requ Score 1-3 (1 for more; 3 for less) Avail Score 1-3 (1 for less; 3 for more) Requ Score 1-3 (1 for more; 3 for less) Avail Score 1-3 (1 for less; 3 for more) Requ Score 1-3 (1 for high; 3 for low) Avail Score 1-3 (1 for less; 3 for more) Requ Score 1-3 (1 for high; 3 for low) Avail Score 1-3 (1 for less; 3 for more) Requ Score 1-3 (1 for high; 3 for low) Avail Score 1-3 (1 for less; 3 for more) Score 1-5 (1 for less and 5 for more) Urea treatment of straw 2 3 6 3 2 2 2 2 0 Supplement with UMMB 2 5 10 3 3 3 2 1 1 1 1 3 1 2 22 By-pass protein feed 1 3 3 3 3 1 1 3 0 Feed conservation (surplus) (HAY) 4 3 12 3 3 2 2 3 3 3 3 3 3 1 41 etc etc III. TECHNOLOGY FILTER (Technology options to address quantity, quality, seasonality issues) Pre-select the obvious (5-6) based on context relevance and impact potential Score the pre-selected technologies based on the requirement, availability and scope for improvement of five technology attributes Attribute 1: Land Attribute 2: Labour Attribute 3: Cash /credit Attribute 4: Input delivery Attribute 5: Knowledge /skill Total Score
  32. Current Techfit filters •Core Feed issue (Quantity/Quality/Seasonality) •Core commodity (Cattle fattening, dairy etc) •Farming system (pastoral, agro-pastoral, mixed, landless) •Core context attributes (land, labour, cash, inputs, knowledge, water) •Impact (only interventions are scored, not context)
  33. Cost-benefit assessment •What does the technology cost? –Inputs, labour, land etc? •What does the technology deliver? –Enhanced milk yield, improved reproductive performance, better growth etc •Does it make sense?
  34. Final output •Ideas for some promising feed interventions that might work •Better understanding of why the usual suspects often don’t work.
  35. FEAST evolution
  36. Originally developed at cross-country workshop in Hyderabad, 2009
  37. Historical refinements •Luke York – worked on original Excel sheet •Ephraim Getahun/Addis Mulugeta – produced macro-driven version •Arindam Samaddar – developed problems and solutions approach •Ben Lukuyu – extensive field testing and training •Peter Ballantyne – popularizing and shepherding the evolution
  38. New initiatives •FEAST Aggregator –Allows individual FEAST excel sheets to be imported –Aggregated data from many FEAST assessments can be downloaded –Will provide global database of FEAST data
  39. New initiatives •FEAST Learning Materials –Training presentations –Video clips –Instructions for hosting FEAST training –Enhanced manuals
  40. Participants in the ILRI-ICRISAT FEAST and Techfit training workshop 27-30 Nov 2013 Also trainings in Addis, Botswana …..
  41. Ethiopia, 90Kenya, 34India, 29United States, 22France, 11Tanzania, 11Uganda, 11Nigeria, 9Bangladesh, 8Pakistan, 8Germany, 7Indonesia, 7Netherlands, 7Rwanda, 7United Kingdom, 7Zambia, 7Other, 155DOWNLOAD BY COUNTRY
  42. ExtensionNGOOtherResearchBlankDOWNLOADS BY ORGANIZATION TYPE
  43. 0102030405060 Feb-12Apr-12Jun-12Aug-12Oct-12Dec-12Feb-13Apr-13Jun-13Aug-13Oct-13Dec-13Feb-14Apr-14Jun-14Aug-14 Downloads by month
  44. Techfit evolution
  45. Steps •Two pager from Steve Staal •Dehra Dun workshop – Sept 2011 –Introduced general concept and developed preliminary tool •Addis Ababa workshop – Mar 2013 –Refined technology list •Addis Ababa workshop – May 2013 –Detailed scoring of technologies
  46. Dehra Dun workshop – Sept 2011 •Introduced general concept •Group work to come up with Context Attributes – 20 plus •Preliminary list of technologies •Some experimentation with scoring methods
  47. Addis Ababa workshop - Mar 2013
  48. Addis Ababa workshop - Mar 2013 •In groups we covered: –cost-benefit analysis approaches –the 'pre-filters‘ –the 'scope for impact' measure –the technology/interventions scoring –the 'context' scoring –approach/instrument –what to improve in the tool - design, functionality ... •Some significant developments were: –more filters –the tool helps to prioritize interventions, not just technologies –CBA needs to be piloted to discover how to really use it –the intervention list was extended and refined –TechFit and FEAST can be adapted to better fit each other
  49. Addis Ababa Techfit Development Meeting - May 2013
  50. Addis Ababa workshop - May 2013 •Areas worked on included: –Adoptability component –Finalising the intervention expert scoring –Initial template for intervention 'factsheets' (or decision-sheets) –Ideas to adapt FEAST to generate context scores for Techfit –CBA approach –TechFit Manual
  51. Actions from Addis Ababa - May 2013 Task Responsible Status Overall coordination, budgets etc Alan, Peter Thorne, Michael Blummel Ongoing Generate updated overall process/flow diagram Alan Done. May also need description of overall scope/boundaries Modify FEAST tool to generate 'context score of attributes and filters' for Techfit Alan, Ben, Jane, Brigitte Done by Ben in April 2014 Update FEAST materials online Done as part of Iddo's training material work Test 'new' FEAST with techfit Not yet finalized - Gregory Sikumba Create online FEAST aggregator Beta version online - Addis Mulugeta FEAST ‘user meeting’ Brigitte Partly this meeting Finalise an 'adoptability' protocol and process description/checklist Peter T, Brigitte, Biruk ???
  52. Task Responsible Status Devise/Finalise/Test a ‘rapid’ easy to use CBA approach/tool Isabelle, Padma Partly done - Padmakumar. Still needs some work Set up some CBA pilots (a few interventions, across species/filters, easy/not easy) Nicholas, Barbara, Isabelle, ... Not done Finalise the expert scoring of interventions Werner, Alan, Adugna, Harinder, others Completed - July 2013 Werner, Adugna others Capture reasoning, document scoring process, finalise all row and column descriptions Partly done - Werner Sensitivity analysis and Testing Eduardo, Jane Poole Some preliminary thoughts - Eduardo Develop a manual Padma, Adugna, Keith Not done … fact sheets Devise next generation tool design PeterT, Alan, Padma, Nils Partly done - Nils, Padma Techfit testing in different projects and locations Ethiopia, Tanzania, India - Padma, Jane, Gregory/Ben Actions from Addis Ababa - May 2013
  53. New initiatives •Techfit score sheets - Werner –A score sheet for each intervention giving basic details of what is involved –Draws on Techfit scores to help users to see where the intervention might work
  54. New initiatives •Techfit scoring – Nils –Finding a way of combining scores to come up with a more useful and realistic prioritized list
  55. New initiatives •Ration balancing methods –Various least-cost feed formulation programmes are around –Smallholder farmers are often too small scale to justify the costs of a tailored approach –link to FEAST and Techfit? –Use FEAST to cluster farmers to reduce cost per farmer and allow general recommendations
  56. New initiatives •FEAST and Techfit report inventory
  57. Links •http://techfit.wikispaces.com •http://feed-tool.wikispaces.com •www.ilri.org/feast •www.ilri.org/feastaggregator •http://fodderadoption.wordpress.com
  58. The presentation has a Creative Commons license. You are free to re-use or distribute this work, provided credit is given to ILRI. better lives through livestock ilri.org
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