Update on dairy value chain development in Tanzania
Update on dairy value chain development in Tanzania Amos Omore (ILRI) CGIAR Research Program on Livestock and Fish Planning Meeting ILRI Nairobi 28 September 2011
Outline• Value chain overview - highlights updated from proposal;• Previous engagements;• Status of existing engagement and key strategic partners;• Overview of outcome pathways;• Synthesis of existing and planned activities/ resources from sub-components (iterative);• Analysis of gaps;• Priorities for resource mobilization
Good growth and market opportunity• Many people (43m; 37% poor) and cattle (19m): 3rd largest cattle pop in Africa after Eth and Sudan• Improved cattle only 0.6m (3%) but growing fast (6%)• Milk supply about 1.6b litres/yr (56% consumed on farm)• Milk consumption rising sharply: from 28 to 39 litres per capita over the last decade (Country-stats)• Nascent formal milk sector (approx 80,000l/d; <5% of marketed milk from local production)• Growth patterns could follow Kenya’s (longer history of investment but similar prod – consumption systems)• Private sector growth/collective action has been unable to fill gap after withdrawal of public support
Projections in milk supply & demand to 2020 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 ProductionDairying in EA is the most important ag sector commodity for GDP gains(ASARECA/IFPRI report)
Where are the cattle? Cattle pop. Density/km2 Lindi 0.1 M twara 1.37 Ruvuma 1.48 M orogoro 1.61 Kigoma 3.5 P wani 3.99 <5 Rukwa 6 >5-10 Tanga 11.54 M beya 14.01 Dar es Salaam 14.72 Dodoma 19.55 Iringa 21.13 Kagera 23.52 Tabora 23.86 >10-25 Singida 36.69 Arusha 41.75 Kilimanjaro 45.34 >25-50 M ara 65.72 Shinyanga 75.19 >50-100 M wanza 111.62 >100
Where are the improved dairy cattle? Improved dairy cattle /100km 2 Lindi 2 Rukwa 2 Kigoma 2 Singida 2 Tabora 2 Mtwara 5 Morogoro 7 Dodoma 11 Shinyanga 22 Ruvuma 24 Manyara 30 Iringa 31 Pwani 33 Mwanza 40 Mara 45 Kagera 60 Mbeya 68 Tanga 103 Arusha 158 Zanzibar 203 Dar es Salaam 591 Kilimanjaro 1036
Where are the milk processing units? 32 29 28 27 26 25 15 14 13 UGANDA 11 30 31 RWANDA KENYA 12 32 MARA 10 6 KAGERA 9 24 33 16 19 34 35 36 BURUNDI MWANZA ARUSHA 17 20 37 38 SHINYANGA KILIMANJARO 8 39 MANYARA 18 40 7 23 21 KIGOMA MANYARA Pemba 41 22 L a 46 TANGA 5 I NDIAN I NDIAN I NDIAN TABORA k e SINGIDA25 Musoma Dairy 40,000 4 T a n26 Utegi Plant (Ex TDL ) (Hakifanyikazi) 45,000 DODOMA27 Makilagi SSDU 1,500 Unguja Processor name Installed28 Baraki Sisters 3,000 capacity29 Mara Milk 15,000 49 g g g a (litres/day) OCEAN OCEAN OCEAN30 Mwanza Mini Dairy 3,000 D.R.C n31 Kagera Milk (KADEFA) 3,000 1 Azam Dairy 3,00032 Kyaka Milk Plant 1,000 DAR ES SALAAM 2 Tommy Dairy (Hakifanyikazi) 15,000 y iii33 Del Food 1,000 3 Tan Dairies34 Bukoba Market Milk Bar RUKWA 15,000 500 k MOROGORO PWANI 4 Tanga Fresh Ltd 40,00035 Bukoba Milk Bar - Soko Kuu 500 5 Ammy Brothers Ltd 2,000 1 a36 Mutungi Milk Bar 80037 Salari Milk Bar38 Kashai Milk Bar 800 800 45 6 Brookside (T) Ltd (Hakifanyikazi) 7 International Dairy Products 45,000 5,00039 Kikulula Milk Processing Plant40 Kayanga Milk Processing Plant 1,000 1,000 MBEYA 2 8 Mountain Green Dairy 9 Arusha Dairy Company 1,500 5,00041 MUVIWANYA 1,000 IRINGA 10 Kijimo Dairy Cooperative 1,00042 SUA43 Shambani Graduates44 New Tabora Dairies 3,000 4000 16,000 47 46 3 11 Longido (Engiteng) 12 LITI Tengeru 500 50045 ASAS Dairy 12,000 13 Terrat (Engiteng) 50046 CEFA Njombe Milk Factory 10,000 14 Orkesumet (Engiteng) 50047 Mbeya Maziwa 1,00048 Vwawa Dairy Cooperative Society49 Gondi Foods 900 600 ZAMBIA 48 LINDI 42 15 Naberera (Engiteng) 16 Nronga Women 17 West Kilimamnjaro 1,000 3,500 1,000 La 43 18 Mboreni Women 1,000 k k k k ke 19 Marukeni 1,000 Key 20 Nguni Women N y as a 1,000 y as y as y as y as 21 Kalali Women 1,000 22 Same (Engiteng) 500 Less than 5000 litres/day RUVUMA MTWARA 23 Fukeni Mini Dairies 3,000 24 Kondiki Small Scale Dairy 1,200 5000-30,000 litres/day More than 40,000 litres/day MOZAMBIQUE Milk processing installation 1995-2000. (Total approx. 315,000 l/day)
Farmer groups are struggling! Performance of milk collection at Nnronga w omen dairy co-operative Society, Hai Kilimanjaro and CHAWAMU-Muheza Tanga (1994-2007) 750000 700000 650000 600000Volume of Milk (Litres) 550000 500000 450000 400000 Nnronga 350000 CHAWAMU-Muheza 300000 250000 200000 150000 100000 50000 0 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Year 2007
Low milk processing capacity utilization Region Average Average No. of Total Installed plant capacity per capacityS/N Plants capacity* plant Milk processed/day utilisation 1 Dar es Salaam 3 33,000 11,000 8000 24.2 2 Tanga 2 42,000 21,000 30500 72.6 3 Arusha 6 58,000 9,667 6500 11.2 4 Manyara 3 2,000 667 1100 55.0 5 Kilimanjaro 9 13,200 1,467 4580 34.7 6 Mara 5 104,500 20,900 39100 37.4 7 Mwanza 1 3,000 3,000 1000 33.3 8 Kagera 11 11,400 1,036 3450 30.3 9 Morogoro 2 7,000 3,500 950 13.6 10 Tabora 1 16,000 16,000 200 1.3 11 Iringa 2 22,000 11,000 8700 39.5 12 Mbeya 2 1,900 950 1100 57.9 13 Dodoma 1 600 600 200 33.3 Total 48 314,600 6,554 105380 33.5Note Plant capacity defined on single 8 hr shift. Source: Tanzania Dairy Board/ MLDF Records
Researchable supply constraints• Large productivity gaps by genotype (Mwacharo et al.,)• Breed choices/breeding services delivery: European, Mpwapwa, Sahiwal, Gir?• Feed: seasonality & quality• Health: e.g., important but unclear epid picture: benefits from ITM use /other AH techns• Interventions to grow formal milk market sector given low processing capacity utilisation (<25%) and reduce imports of processed milk• The role of farmer groups in facilitating access to input supply and milk marketing is very small• Main challenge for R&D: Value chain upgrading /expansion options that can be taken up by dev partners
Value Chain Outcomes (proposal list)Components Value chain outcomesInputs & Increased private sector /farmer group participation in inputs and servicesServices provision. Improved feed quality and increased quantity of feed (forage and concentrates) Increased access to affordable animal health careProduction Reduced seasonality in milk supply Increased milk off-take from existing herds in extensive areas Increased feed options available New more adaptable breeds introduced and accessible (e.g .,Gir cattle) Reduced yield gap for cows with under-exploited genetic potential Reduced disease risk and mortality, especially ECFTransport & Increased volume and proportion of processed milkProcessing Increased number of small-scale milk traders selling more milk Reduced transport and transaction costsMarketing Increased number of farmer groups engaged in milk marketing Reduced transactions costs Participating milk businesses enjoying price premiums from improved milk quality Higher milk volumes sold to more and profitable outlets More women participating in larger milk businesses and farmer organisations
Activities & partnerships• Past – 1998: SDC funded dairy sector appraisal (with SUA & Min of Livestock) – 2000: DFID funded research to improve informal milk markets while addressing milk-borne health risks (with SUA, ILRI RR# 19)• Ongoing/approved – ASARECA-PAAP funded pilot to scale out BDS schemes in Mwanza and Arusha /integrate informal into formal VC (with TDB) – ASARECA-LFP – Milk Marketing and value addition (quality and safety) – BMGF funded livestock data collection methods project – MiLKIT (recent scoping visit; stakeholder meeting with CRP3.7 soon) – CIAT recently initiated steps to build expertise on tropical forage research• Proposed (with probable ILRI involvement) – EADD 2, Irish Aid (with SUA), EPINAV(Norway, SUA), EAAPP (WB/IDA loan with ASARECA convening role)• Other key players/potential partners – TAMPA/TAMPRODA, ACT, LoL, HPI, MLDF Res Centres (e.g., Mpwapwa), FAO/UNDP ag value chains projects, AGRA’s “breadbaskets” partnerships
Objectives of new proposals MiLKIT (IFAD): Enhancing livelihoods through feed innovation and value chain development approaches (1.0m 3yrs) • Institutional strengthening: To strengthen use of VC and innovation approaches among dairy stakeholders to improve feeding strategies. • Productivity enhancement: To dev options for improved feeding • Knowledge sharing: To strengthen knowledge sharing mechanisms on feed development strategies at local, regional and international levelsIrishAid: Getting milk to market: res. on dairy hubs in Tanzania (0.5m/yr)• Generate and communicate evidence on business and organisational options for milk marketing in marginalised Tanzanian dairy households, and for associated service providers• Identify mechanisms for establishing micro and small enterprises, or appropriate links to the commercial sector for provision of inputs and services• Develop scalable dairy value chain interventions from pilots (TM bubs)
Impact pathways and outcomesSub- Pathway Outcomecomponent2.1 Sectoral CRP3.7 works with partners to Consensus achieved among& Policy conduct analyses and generate national and regional policymakersAnalysis evidence and engage with regarding pro-poor policies and policymakers and stakeholders to investment strategies to support understand the whether and how the development of the 9 target value target value chain should be ‘enabled chains2.2 Value CRP3.7 works with R&D partners to Improved and increased public &Chain conduct field studies to identify private sector interventions beingAssessment opportunities, test best-bet strategies applied by development actors to and generate evidence to inform and support women and resource- stimulate development interventions poor value chain actors and for pro-poor upgrading of the target consumers, with lower ecological value chains footprint per unit produce2.3 Value CRP3.7 works with partners to identify Enhanced pro-poor value chainChain and test the principles and methods performance and more equitableInnovation that permit research to promote and distribution of benefits. replicate effective and sustained pro- poor change in value chains
2.2 Value Chain Assessment Proposed Priority Outcomes & Outputs 2012 2013 2014Outcomes R&D alliance 1. capacity to use tools Evidence base 2. Stakeholders aware influencing decisionsResearch 1. Scoping study to characterize 1. Inventory and evidence base 1. Best-bet interventionOutputs target VC and identify (literature review) for key strategy formulated and stakeholders and potential constraints and proposed tested, ready for piloting partners solutions compiled 2. Basic toolkit for VC assessment 2. Quantitative assessment of compiled for testing VC performance 3. Analytical framework for 3. Technical and economic assessing VC performance assessments of key VC established components to target for 4. Rapid assessment of target VC upgrading (e.g. farm-level: to inform design of in-depth husbandry, feeds, breeds, assessment, and to identify health, environmental issues; preliminary priority constraints market-level: institutional and best-bet upgrading strategies environment, food safety, to test (including specific demand characteristics; components on environmental overall: policies, organizational impacts, food safety risk strategies assessment and gender analysis)
2.2 Value Chain Assessment Implications for Tanzania in 2012 (Focus on 2.2)Outcome: CRP3.7, local and international partners have establishedan R&D alliance to transform Dairy VC in TanzaniaPriority Research outputs for 2012 Implications for Tanzania /potential delivery mechanisms1. Scoping study to characterize dairy VC and identify •Build of rich knowledge availablestakeholders and potential partners •Initial scoping in early 2012 –2. Basic toolkit for VC assessment compiled for testing linked to MiLKIT start-up3. Analytical framework for assessing VC performance •EADD CEestablished •Irish Aid?4. Rapid assessment of VC to inform design of in- •ASARECA-PAAP BDS networksdepth assessment, and to identify preliminary priority studyconstraints and best-bet upgrading strategies to test •ASARECA milk marketing and(incl. specific components on environmental impacts, value addition study (withfood safety risk assessment and gender analysis) demand data)5. Candidate intervention strategies for piloting •Livestock Data InnovationLinkages with sub-components (AH, Feed, Genetics Joint assessments of constraints /etc) Candidate technologies: ITM, ????
2.2 Value Chain Assessment Implications for Tanzania in 2012Priority Organisational, Capacity Development andCommunication ActivitiesActivity Implications for Tanzania/comments1. Restructure team to match CRP needs, with 1. Irish Aid would be happier toshared vision and assignments for subject/VC focus fund a country based office2. Identify gaps for priority recruitment or 2.Lesson from EADD is that in-partnership country presence is critical3. Identify strategy and mechanisms for workinglinks internally with other CRP3.7 components, andexternally with CRP2, CRP44. Develop a communication strategy targeted tostakeholders and partners in each target VC
2.2 Value Chain Assessment Implications for Tanzania in 2012Priority Resource Mobilisation ActivitiesActivity Implications for Tanzania/comments1. Individual or multiple-country projects to 1. VC assessment beyondidentify and test best-bet upgrading strategies for MiLKIT? Gap could be filledeach target VC (perhaps more manageable if done through EADD CE/EADD 2separately at farm and market levels) 2. Targeted trialling of ITM?2. Project to design and test analytical framework 3. Research to adaptfor assessing and monitoring VC performance Traditional milk hubs(both as basis for M&E and as analytical tool) (without chilling plants)(under CRP2??)3. Field studies to develop assessment methods forprioritizing animal health and public health (withCRP4.3) priorities for pro-poor VC developmentCreate linkages with sub-components (AH, Feed, Joint assessments of constraintsGenetics, CRP2, 4 etc)
2.2 Value Chain Assessment Implications for Tanzania in 2013Outcome 1. Partners have capacity /tools for VC assessmentOutcome 2. Stakeholders are increasingly aware of potential,constraints and initial options for pro-poor development of target VC Priority Research outputs for 2013 Implications for Tanzania /potential delivery mechanisms 1. Inventory and evidence base (literature review) MiLKIT - mainly feed/farm-level in for key constraints and proposed solutions target locations compiled 2. Quantitative assessment of VC performance EADD2 3. Technical and economic assessments of key VC components to target for upgrading (e.g. farm- Irish Aid? level: husbandry, feeds, breeds, health, environmental issues; market-level: institutional EAAPP has adopted ILRI developed environment, food safety, demand characteristics; tools for baseline surveys; may overall: policies, organizational strategies seek ILRI’s collaboration in targeted studies Linkages with sub-components (AH, Feed, Joint assessments of constraints / Genetics) Candidate techns: ITM, others??
2.2 Value Chain Assessment Implications for Tanzania in 2014 (optional for now)Outcome 1. Evidence base in each target VC for best-bet pro-poorVC dev interventions is influencing dev investment decisions Priority Research outputs for 2014 Implications for Tanzania /potential delivery mechanisms 1. Best-bet intervention strategy (better refined) MiLKIT formulated and tested, ready for piloting EADD2 Irish Aid? New project development? Linkages with sub-components (AH, Feed, Joint assessments of constraints / Genetics) Candidate techns??
2.2 Value Chain Assessment 2012 Priorities for Organisational, Capacity Development and Communication Activities Create a country VC team to match CRP needs (CIAT already has an office (Selian Agric Res Inst in Arusha); ILRI office? Initiate engagements under MiLKIT, Pursue new proposals under EADD SUA led projects, EAAPP, Others Identify gaps for priority recruitment or partnerships across sub- component and other CRP’s Develop a communication strategy targeted to stakeholders and partners (with 2.3)