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More Milk in Tanzania: Entry points for extending the frontiers of dairy value chains in Tanzania through hubs

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Presented by A. Omore, L. Kurwijila and S. Nandonde at the Tanzania Society of Animal Production (TSAP) Conference, Arusha, 23-26 October 2012 …

Presented by A. Omore, L. Kurwijila and S. Nandonde at the Tanzania Society of Animal Production (TSAP) Conference, Arusha, 23-26 October 2012



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  • 1. More Milk in Tanzania Entry points for extending the frontiers of dairy value chains in Tanzania through hubs Presentation at TSAP Conference A. Omore, L. Kurwijila and S. Nandonde 35th Tanzania Society of Animal Production (TSAP) Conference, Arusha, 23-26 October 2012
  • 2. BACKGROUND: Projections indicate demand to 2020 outstrips supply 3,000 2,500 2,000 Million Lts Milk/ Yr 1,500 1,000 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Year 3% GDP Growth 2% GDP Growth Milk Production Dairying in EA is the most important ag sector commodity for GDP gains in the medium term (ASARECA/IFPRI report)
  • 3. Milk yield per Lactation (Kg) BACKGROUND: Large Yield Gaps 6000 5000 x3 4000 x3 3000 x3 2000 y2 1000 0 y1 x3 x2 x2 x1 x1 Indigenous Crossbred Exotic Indigenous Mixed rain fed temperate/highlan d Crossbreds Mixed rain fed humid/sub-humid Synthetics Exotics Large-scale commercial ranches Xi = Yield gaps due to “animal husbandry practices” : 33 - 76 % Yi = Gap in productivity due to “genotype”: 18 - 74% Source: Mwacharo et al., 2009
  • 4. CONTEXT CGIAR Research Program on: More milk, meat and fish by and for the poor (LaF) is one of 15 CRPs in a re-organized CGIAR system Goal: To sustainably increase the productivity of small-scale livestock and fish systems to increase the availability and affordability of animal-source foods for poor consumers and, in doing so, reduce poverty through greater participation by the poor along the whole value chains for animal-source foods.
  • 5. Approach: Solution-driven R4D to achieve impact Addressing the whole value chain to transform the sector over a decade R4D integrated to transform selected value chains In targeted commodities and countries. Inputs & Services Production Processing Marketing Consumers Major intervention with development partners Value chain development team + research partners Strategic LaF Cross-cutting Platforms • Technology Generation • Market Innovation • Targeting & Impact INTERVENTIONS TO SCALE OUT REGIONALLY GLOBAL RESEARCH PUBLIC GOODS
  • 6. LaF: Prepare intervention Working toward interventions for impact at scale Time Performance Target: double production in x poor households Scaling out Development Partners $90m Knowledge Partners $10m LaF: Strategic Research $10m 10 years
  • 7. Delivering Livestock + Fish Programme Structure: Three integrated Components 3 Targeting: Foresight, prioritization, gender, impact 2 Value chain development 1 Technology development: − Genetics − Feeds − Health Consumers Commodity X in Country Y Cross-cutting: M&E, communications, capacity building
  • 8. 9 Target Value Chains SHEEP & GOATS AQUACULTURE PIGS DAIRY Website for news updates on subscription: http://livestockfish.cgiar.org/
  • 9. LaF Catalyst Role NARS ARIs Investors Research Private Sector NGOs LaF CG partners Ministry Investors Development Website for news updates on subscription: http://livestockfish.cgiar.org/
  • 10. Links to other CGIAR Programs (CRPs)
  • 11. Current and anticipated CGIAR linked dairy VC projects in Tanzania in 2012 1. 2. 3. 4. 5. 6. MilkIT: Promoting feed innovations ILRI/CIAT/TALIRI/SUA; IFAD MoreMilkIT: Adapting dairy market hubs: ILRI/SUA/TDB; Irish Aid Safe Food Fair Food (SFFF2); - ILRI/SUA; BMZ Livestock Data Innovation (LDIP) – MLDF/FAO/ILRI; BMGF/WB funded Integrated Crops and Goat project (CGP) – SUA/UA/ILRI; IDRC EADD – HPI/TNS/ILRI/ICRAF/ABS; BMGF 7. Equitable access to animal source foods - ILRI/WFC/SUA - AUSAid
  • 12. Main challenge for solution driven research for development : • How can research be more responsive? • How can research deliver value beyond knowledge? • How can research serve development in real time?
  • 13. Entry points for More Milk in Tanzania Project: Key considerations • Strong focus on pro-poor marginalised precommercial men and women and ‘growing’ inputs and outputs markets that serve them • Explore new organizational models to achieve economies of scale • Aim is to provide proof-of-concept on how marginalised groups can also be targeted successfully • Generate evidence for influencing policy Following are highlights of dairy VC R4D engagement and findings on entry points since Jan 2012
  • 14. More Milk in Tanzania Project Objectives (derived from ASDS and Irish Aid Country Strategy Paper for Tanzania) Goal: Inclusive growth and reduced poverty and vulnerability among dairy-dependent livelihoods in relevant rural areas in Tanzania Outcome: Rural poor are more income secure through enhanced access to demand-led dairy market business services and viable organisational options Contributing objectives: – Develop scalable value chains approaches – Generate and communicate evidence on business and organizational options – Inform policy on appropriate role for pro-poor smallholderbased value chains
  • 15. More Milk in Tanzania Project Addressing 5 inter-related problems that face resource-poor milk producers 1. Dominant direct milk sales of small volumes. This precludes economies of scale  ↑costs 2. Credit facilities are lacking. This contributes to low access to basic inputs and services or working capital to purchase them 3. Lack of appropriate organizational models for pre-commercial producers. These are required to facilitate collective action 4. Seasonality of rainfall and related effects are strong Milk marketing outlets Milk Buyer (NBS, 2003) % Neighbours 86.1 Local market 5.5 Secondary market 0.5 Processors 1.4 Large scale farms 0.2 Trader at farm 4.5 Other 1.7 TOTAL 100.0
  • 16. More Milk in Tanzania Project Addressing 5 inter-related problems that face resource-poor milk producers 1. Dominant direct milk sales of small volumes. This precludes economies of scale  ↑costs 2. Credit facilities are lacking. This contributes to low access to basic inputs and services or working capital to purchase them 3. Lack of appropriate organizational models for pre-commercial producers. These are required to facilitate collective action 4. Seasonality of rainfall and related effects are strong Women participate more where there’s no collective milk bulking and marketing
  • 17. More Milk in Tanzania Project Addressing 5 inter-related problems that face resource-poor milk producers 1. Dominant direct milk sales of small volumes. This precludes economies of scale  ↑costs 2. Credit facilities are lacking. This contributes to low access to basic inputs and services or working capital to purchase them 3. Lack of appropriate organizational models for pre-commercial producers. These are required to facilitate collective action 4. Seasonality of rainfall and related effects are strong
  • 18. More Milk in Tanzania Project Addressing 5 inter-related problems that face resource-poor milk producers 1. Dominant direct milk sales of small volumes. This precludes economies of scale  ↑costs 2. Credit facilities are lacking. This contributes to low access to basic inputs and services or working capital to purchase them 3. Lack of appropriate organizational models for pre-commercial producers. These are required to facilitate collective action 4. Seasonality of rainfall and related effects are strong Milk processing in Tanzania has been declining since 1990
  • 19. More Milk in Tanzania Project Addressing 5 inter-related problems that face resource-poor milk producers 1. Dominant direct milk sales of small volumes. This precludes economies of scale  ↑costs 2. Credit facilities are lacking. This contributes to low access to basic inputs and services or working capital to purchase them 3. Lack of appropriate organizational models for pre-commercial producers. These are required to facilitate collective action 4. Seasonality of rainfall and related effects are strong
  • 20. More Milk in Tanzania Project Huge seasonal fluctuation in milk supply from traditional herd Volume of milk (litres/month) Milk collection by a small scale processor from traditional herd in Morogoro, 2009 13000 12500 12000 11500 11000 10500 10000 9500 9000 8500 8000 7500 7000 6500 6000 5500 5000 4500 4000 3500 3000 2500 2000 1500 1000 500 0 Jan Feb Mar Apr May Jun Jul Month Average/month Aug Total supply Sep Oct Nov Dec
  • 21. More Milk in Tanzania Project Huge seasonality in milk supply from the indigenous vs. improved dairy cattle 02/02/2014 NAI/EGM/Mara Region/Kurwijila 21
  • 22. More Milk in Tanzania Project Less rainfall variation in highlands 30 25 20 15 Lowland/extensive 10 Highland/semiintensive 5 0
  • 23. More Milk in Tanzania Project Less seasonal milk yield fluctuation with better feeding 16,000.00 14,000.00 12,000.00 10,000.00 8,000.00 6,000.00 4,000.00 2,000.00 - Source: SUA dairy research farm) 2009 2010 Average
  • 24. More Milk in Tanzania Project Farmer groups are struggling in most places except in Tanga 750000 700000 650000 600000 550000 500000 450000 400000 350000 300000 250000 200000 150000 100000 50000 0 Nnronga Year 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 CHAWAMU-Muheza 1994 Volume of Milk (Litres) Performance of milk collection at Nnronga w omen dairy co-operative Society, Hai Kilimanjaro and CHAWAMU-Muheza Tanga (1994-2007)
  • 25. Entry Points: More Milk in Tanzania Project Spatially overlays was the initial step…. + Prod systems: arid to humid/temperate = Map A: Mixed prod sys + ↑pop Persons/sq km (25) + = R-U R-R Map A Market access (0.5& 5hrs) Maps B & C : Mixed prod sys + ↑pop + ↓ & ↑ market access: = R-R & R-U
  • 26. R-R Maps B : Mixed prod sys + ↑pop + ↓ market access: = R-R
  • 27. R-U Maps C : Mixed prod sys + ↑pop + ↑ market access: = R-U
  • 28. More Milk in Tanzania Project Identified entry points in the field Maps + stakeholder consultations Region District Market access Cattle classification population* % improved Dominant production dairy breeds system Kilosa R-to-R 215,100 1 Extensive/Agropastoral (zebu) Mvomero R-to-U 187,350 5 Extensive/Agropastoral (zebu) with significant semiintensive & intensive (improved) Handeni R-to-R 126,780 1 Extensive/Agropastoral & Extensive/Sedentary (all zebu) Lushoto R-to-U 119,492 24 Extensive/Sedentary (zebu) with significant semi-intensive & intensive (improved) Morogoro Tanga Detailed value chain assessments have been conducted at these sites
  • 29. More Milk in Tanzania Project Morogoro: Kilosa and Mvomero
  • 30. More Milk in Tanzania Project Tanga: Lushoto and Handeni
  • 31. More Milk in Tanzania Project Which Hub Model might be appropriate? Hubs are localized groups of smallholder producers with common interests in accessing inputs (feed, breeding, animal health) and services (training, credit, insurance), as a means to achieve a critical mass of supply Diversified Profit-Max Model for CPs Some EADD Hub Models being tested - - Collection Center Chilling Plant Processing Plant Sales to Processor - - - - Sales to individuals and vendors Diversified profit max through: higher prices for milk sold locally lower costs (transport, chilling) overall for milk handled
  • 32. More Milk in Tanzania Project Outcome Mapping identified the following hubs for piloting in the Tanzania context Dairy Market Hubs (DMHs) with emphasis on improving access to inputs and services through business development services (BDS) and check-off arrangements: a) DMHs revolving around chilling plants or accessing them (if under-utilized) through transport arrangements that provide both outputs marketing and inputs and services through check-offs; b) DMHs revolving around check-offs for inputs and services provided through milk traders; and c) DMHs revolving around check-offs for inputs and services provided through cattle traders.
  • 33. Illustration of a hub for provision of inputs and services on credit without collective bulking and marketing Producers BASIC Dairy Market Hub for Provision of Inputs and Services on Check-off Traders Milk Cattle $$ Payment agreement Inputs & Service Providers Targeting 50 villages with 8000 cattle keepers across 4 districts
  • 34. Design of integrated R4D to extend the frontiers of dairy value chains to achieve wider impact in Tanzania Addressing the whole value chain with downstream emphasis Consumers Intervention with development partner Value chain development team + research partners Strategic Cross-cutting Platforms • Technology Generation (Feed, genetics, health • Market Innovation • Targeting & Impact (includes gender) Cross-cutting: M&E, communications, capacity building INTERVENTIONS TO SCALE OUT NATIONALLY Baseline for M&E planned for Dec 2012
  • 35. Integrating other actors into the Tanzania dairy value chain R4D SUA /TALIRI Irish institutions Other Investors (e.g., IFAD BMZ) Research Private Sector Dev Partner NGO ILRI / Other CG partners MLDF TDB, other NGOs (DDF) Investors (e.g., BMGF) Development
  • 36. Organogram of DDF and stakeholder linkages Dairy Development Forum Secretariat: Tanzania Dairy Board Advisory Committee Govt Membership Academic & res institutions organisations Dairy development organisations Stakeholder organisations Civil society Private sector
  • 37. More Milk in Tanzania Project Key messages on entry points • Validity of the need to focus attention on ‘growing’ the existing informal system of milk production (with zebu cattle) and marketing to extend the frontiers of commercial dairying • New organizational models to achieve economies of scale for access to inputs and services required to unleash incentives for raised productivity to levels that will justify bulking • This is riskier than classical approaches but more inclusive and promises wider impact on marginalised • Policy support for pro-poor shift needed