1. Rapid livestock feed assessment tools to support intervention strategies: FEAST and Techfit Alan Duncan and Ben Lukuyu

2. Mixed systems  Interactions between crops and livestock  Crop residues are substantial component of livestock diets  Feeding of livestock needs to take account of arable realities: competition for land, free grazing in off season etc.

3. Challenges to improved feed supply  Food security  Land scarcity/tenure  Markets for livestock products  Free grazing  Traction a sink for feed

4. The way ahead  Things are changing –Dwindling grazing resources forcing other feed sources to be considered –Urbanization leading to increased demand for livestock products –Improving infrastructure –Are we about to see things moving?

5. 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

6. FEAST The problem

7. 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.?

8. How does FEAST work? Overview of farming system and livestock feed aspect Milk marketing, veterinary services Major problems for livestock production 1. PRA Exercise 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

9. PRA  General description of farming system –range of farm sizes, –farm labour availability –annual rainfall pattern –irrigation availability –types of animals raised by households.  General description of livestock production –the types of animals raised (% of households raising these animals and average herd/flock sizes) –the purpose of raising these animals (e.g. draught, income, fattening, calf production) –the general animal husbandry (including; management, veterinary services and reproduction). –Ease of access to credit –How available are necessary inputs – plastic, urea, concentrates etc  Problem identification and potential solutions

10. Quantitative questionnaire  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?

12. Sample output

13. More sample output

14. Final output  Feast report with some ideas for key problems and solutions  Better links and understanding between farmers, research and development staff

15. www.ilri.org/feast

16. Techfit The problem

17. What is your main problem Feed

18. What feed technologies have you got? Planted forage Urea treated straw Bypass protein OK, let’s try those

19. A solution

20. Techfit  A discussion support tool for prioritizing feed technologies

21. Key context attributes Land Labour Credit Input Knowledge Key technology attributes Land Labour Credit Input Knowledge The core concept

22. Key context attributes Land Labour Credit Input Knowledge Key technology attributes Land Labour Credit Input Knowledge x= Score Matching context to technology

23. Technology filter Technology listPre-filter Main filter Score

24. 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?

25. Final output  Ideas for some promising feed interventions that might work  Better understanding of why the usual suspects often don’t work.