SlideShare a Scribd company logo
1 of 21
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 Production

Dairying 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




                                                                                                                                SINGIDA
25 Musoma Dairy                                40,000
                                                                                                                                                                                                                              4
                                                  T a n




26 Utegi Plant (Ex TDL ) (Hakifanyikazi)       45,000                                                                                                DODOMA
27 Makilagi SSDU                                1,500                                                                                                                                                  Unguja                      Processor name                        Installed
28 Baraki Sisters                               3,000                                                                                                                                                                                                                   capacity
29 Mara Milk                                   15,000
                                                                                                                                               49
                                                        g
                                                        g
                                                        g




                                                             a                                                                                                                                                                                                          (litres/day)




                                                                                                                                                                                                                    OCEAN
                                                                                                                                                                                                                    OCEAN
                                                                                                                                                                                                                    OCEAN
30 Mwanza Mini Dairy                            3,000
                                     D.R.C                       n
31 Kagera Milk (KADEFA)                         3,000                                                                                                                                                                             1 Azam Dairy                                      3,000
32 Kyaka Milk Plant                             1,000                                                                                                                                   DAR ES SALAAM                             2 Tommy Dairy (Hakifanyikazi)                    15,000
                                                                 y iii




33 Del Food                                     1,000                                                                                                                                                                             3 Tan Dairies
34 Bukoba Market Milk Bar                                                            RUKWA                                                                                                                                                                                         15,000
                                                  500
                                                                         k




                                                                                                                                                                       MOROGORO               PWANI                               4 Tanga Fresh Ltd                                40,000
35 Bukoba Milk Bar - Soko Kuu                     500
                                                                                                                                                                                                                                  5 Ammy Brothers Ltd                               2,000
                                                                                                                                                                                                                    1
                                                                             a




36 Mutungi Milk Bar                               800
37 Salari Milk Bar
38 Kashai Milk Bar
                                                  800
                                                  800                                                                                          45                                                                                 6 Brookside (T) Ltd (Hakifanyikazi)
                                                                                                                                                                                                                                  7 International Dairy Products
                                                                                                                                                                                                                                                                                   45,000
                                                                                                                                                                                                                                                                                    5,000
39 Kikulula Milk Processing Plant
40 Kayanga Milk Processing Plant
                                                1,000
                                                1,000
                                                                                                                     MBEYA
                                                                                                                                                                                                                2               8 Mountain Green Dairy
                                                                                                                                                                                                                                9 Arusha Dairy Company
                                                                                                                                                                                                                                                                                    1,500
                                                                                                                                                                                                                                                                                    5,000
41 MUVIWANYA                                    1,000                                                                                           IRINGA                                                                         10 Kijimo Dairy Cooperative                          1,000
42 SUA
43 Shambani Graduates
44 New Tabora Dairies
                                                3,000
                                                 4000
                                               16,000
                                                                                               47                                                                 46                                            3
                                                                                                                                                                                                                               11 Longido (Engiteng)
                                                                                                                                                                                                                               12 LITI Tengeru
                                                                                                                                                                                                                                                                                      500
                                                                                                                                                                                                                                                                                      500
45 ASAS Dairy                                  12,000                                                                                                                                                                          13 Terrat (Engiteng)                                   500
46 CEFA Njombe Milk Factory                    10,000                                                                                                                                                                          14 Orkesumet (Engiteng)                                500
47 Mbeya Maziwa                                 1,000
48 Vwawa Dairy Cooperative Society
49 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 Ng'uni 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
                          600000
Volume 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                 capacity
S/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       33.5
Note 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 outcomes
Inputs &      Increased private sector /farmer group participation in inputs and services
Services      provision.
              Improved feed quality and increased quantity of feed (forage and concentrates)
              Increased access to affordable animal health care
Production    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 ECF
Transport &   Increased volume and proportion of processed milk
Processing    Increased number of small-scale milk traders selling more milk
              Reduced transport and transaction costs
Marketing     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 levels


IrishAid: 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 outcomes
Sub-      Pathway                                        Outcome
component
2.1 Sectoral   CRP3.7 works with partners to             Consensus achieved among
& Policy       conduct analyses and generate             national and regional policymakers
Analysis       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     chains
2.2 Value      CRP3.7 works with R&D partners to         Improved and increased public &
Chain          conduct field studies to identify         private sector interventions being
Assessment     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 produce
2.3 Value      CRP3.7 works with partners to identify Enhanced pro-poor value chain
Chain          and test the principles and methods    performance and more equitable
Innovation     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                        2014
Outcomes    R&D alliance                       1. capacity to use tools      Evidence base
                                               2. Stakeholders aware         influencing decisions
Research    1. Scoping study to characterize 1. Inventory and evidence base 1. Best-bet intervention
Outputs     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 established
an R&D alliance to transform Dairy VC in Tanzania
Priority Research outputs for 2012                        Implications for Tanzania
                                                          /potential delivery mechanisms
1. Scoping study to characterize dairy VC and identify    •Build of rich knowledge available
stakeholders and potential partners                       •Initial scoping in early 2012 –
2. Basic toolkit for VC assessment compiled for testing   linked to MiLKIT start-up
3. Analytical framework for assessing VC performance      •EADD CE
established                                               •Irish Aid?
4. Rapid assessment of VC to inform design of in-         •ASARECA-PAAP BDS networks
depth assessment, and to identify preliminary priority    study
constraints and best-bet upgrading strategies to test     •ASARECA milk marketing and
(incl. specific components on environmental impacts,      value addition study (with
food safety risk assessment and gender analysis)          demand data)
5. Candidate intervention strategies for piloting         •Livestock Data Innovation



Linkages with sub-components (AH, Feed, Genetics          Joint assessments of constraints /
etc)                                                      Candidate technologies: ITM, ????
2.2 Value Chain Assessment

               Implications for Tanzania in 2012
Priority Organisational, Capacity Development and
Communication Activities
Activity                                             Implications for
                                                     Tanzania/comments
1. Restructure team to match CRP needs, with         1. Irish Aid would be happier to
shared vision and assignments for subject/VC focus   fund a country based office
2. Identify gaps for priority recruitment or         2.Lesson from EADD is that in-
partnership                                          country presence is critical
3. Identify strategy and mechanisms for working
links internally with other CRP3.7 components, and
externally with CRP2, CRP4
4. Develop a communication strategy targeted to
stakeholders and partners in each target VC
2.2 Value Chain Assessment


           Implications for Tanzania in 2012
Priority Resource Mobilisation Activities
Activity                                           Implications for
                                                   Tanzania/comments
1. Individual or multiple-country projects to       1. VC assessment beyond
identify and test best-bet upgrading strategies for    MiLKIT? Gap could be filled
each target VC (perhaps more manageable if done        through EADD CE/EADD 2
separately at farm and market levels)               2. Targeted trialling of ITM?
2. Project to design and test analytical framework  3. Research to adapt
for 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 for
prioritizing animal health and public health (with
CRP4.3) priorities for pro-poor VC development
Create linkages with sub-components (AH, Feed,     Joint assessments of constraints
Genetics, CRP2, 4 etc)
2.2 Value Chain Assessment

                Implications for Tanzania in 2013
Outcome 1. Partners have capacity /tools for VC assessment
Outcome 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-poor
VC 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)

More Related Content

More from ILRI

Preventing preventable diseases: a 12-slide primer on foodborne disease
Preventing preventable diseases: a 12-slide primer on foodborne diseasePreventing preventable diseases: a 12-slide primer on foodborne disease
Preventing preventable diseases: a 12-slide primer on foodborne diseaseILRI
 
Preventing a post-antibiotic era: a 12-slide primer on antimicrobial resistance
Preventing a post-antibiotic era: a 12-slide primer on antimicrobial resistancePreventing a post-antibiotic era: a 12-slide primer on antimicrobial resistance
Preventing a post-antibiotic era: a 12-slide primer on antimicrobial resistanceILRI
 
Food safety research in low- and middle-income countries
Food safety research in low- and middle-income countriesFood safety research in low- and middle-income countries
Food safety research in low- and middle-income countriesILRI
 
Food safety research LMIC
Food safety research LMICFood safety research LMIC
Food safety research LMICILRI
 
The application of One Health: Observations from eastern and southern Africa
The application of One Health: Observations from eastern and southern AfricaThe application of One Health: Observations from eastern and southern Africa
The application of One Health: Observations from eastern and southern AfricaILRI
 
One Health in action: Perspectives from 10 years in the field
One Health in action: Perspectives from 10 years in the fieldOne Health in action: Perspectives from 10 years in the field
One Health in action: Perspectives from 10 years in the fieldILRI
 
Reservoirs of pathogenic Leptospira species in Uganda
Reservoirs of pathogenic Leptospira species in UgandaReservoirs of pathogenic Leptospira species in Uganda
Reservoirs of pathogenic Leptospira species in UgandaILRI
 
Minyoo ya mbwa
Minyoo ya mbwaMinyoo ya mbwa
Minyoo ya mbwaILRI
 
Parasites in dogs
Parasites in dogsParasites in dogs
Parasites in dogsILRI
 
Assessing meat microbiological safety and associated handling practices in bu...
Assessing meat microbiological safety and associated handling practices in bu...Assessing meat microbiological safety and associated handling practices in bu...
Assessing meat microbiological safety and associated handling practices in bu...ILRI
 
Ecological factors associated with abundance and distribution of mosquito vec...
Ecological factors associated with abundance and distribution of mosquito vec...Ecological factors associated with abundance and distribution of mosquito vec...
Ecological factors associated with abundance and distribution of mosquito vec...ILRI
 
Livestock in the agrifood systems transformation
Livestock in the agrifood systems transformationLivestock in the agrifood systems transformation
Livestock in the agrifood systems transformationILRI
 
Development of a fluorescent RBL reporter system for diagnosis of porcine cys...
Development of a fluorescent RBL reporter system for diagnosis of porcine cys...Development of a fluorescent RBL reporter system for diagnosis of porcine cys...
Development of a fluorescent RBL reporter system for diagnosis of porcine cys...ILRI
 
Practices and drivers of antibiotic use in Kenyan smallholder dairy farms
Practices and drivers of antibiotic use in Kenyan smallholder dairy farmsPractices and drivers of antibiotic use in Kenyan smallholder dairy farms
Practices and drivers of antibiotic use in Kenyan smallholder dairy farmsILRI
 
A gentle push towards improved hygiene and food safety through ‘nudge’ interv...
A gentle push towards improved hygiene and food safety through ‘nudge’ interv...A gentle push towards improved hygiene and food safety through ‘nudge’ interv...
A gentle push towards improved hygiene and food safety through ‘nudge’ interv...ILRI
 
Evaluation of livestock vaccinations in response to humanitarian crises: Proc...
Evaluation of livestock vaccinations in response to humanitarian crises: Proc...Evaluation of livestock vaccinations in response to humanitarian crises: Proc...
Evaluation of livestock vaccinations in response to humanitarian crises: Proc...ILRI
 
Evaluation of livestock emergencies: Case study selection
Evaluation of livestock emergencies: Case study selectionEvaluation of livestock emergencies: Case study selection
Evaluation of livestock emergencies: Case study selectionILRI
 
Surveillance of climate-sensitive zoonotic diseases: Leptospirosis at livesto...
Surveillance of climate-sensitive zoonotic diseases: Leptospirosis at livesto...Surveillance of climate-sensitive zoonotic diseases: Leptospirosis at livesto...
Surveillance of climate-sensitive zoonotic diseases: Leptospirosis at livesto...ILRI
 
Stem cell and genetic engineering technologies for conservation and sustainab...
Stem cell and genetic engineering technologies for conservation and sustainab...Stem cell and genetic engineering technologies for conservation and sustainab...
Stem cell and genetic engineering technologies for conservation and sustainab...ILRI
 
What is One Health and why is it important?
What is One Health and why is it important?What is One Health and why is it important?
What is One Health and why is it important?ILRI
 

More from ILRI (20)

Preventing preventable diseases: a 12-slide primer on foodborne disease
Preventing preventable diseases: a 12-slide primer on foodborne diseasePreventing preventable diseases: a 12-slide primer on foodborne disease
Preventing preventable diseases: a 12-slide primer on foodborne disease
 
Preventing a post-antibiotic era: a 12-slide primer on antimicrobial resistance
Preventing a post-antibiotic era: a 12-slide primer on antimicrobial resistancePreventing a post-antibiotic era: a 12-slide primer on antimicrobial resistance
Preventing a post-antibiotic era: a 12-slide primer on antimicrobial resistance
 
Food safety research in low- and middle-income countries
Food safety research in low- and middle-income countriesFood safety research in low- and middle-income countries
Food safety research in low- and middle-income countries
 
Food safety research LMIC
Food safety research LMICFood safety research LMIC
Food safety research LMIC
 
The application of One Health: Observations from eastern and southern Africa
The application of One Health: Observations from eastern and southern AfricaThe application of One Health: Observations from eastern and southern Africa
The application of One Health: Observations from eastern and southern Africa
 
One Health in action: Perspectives from 10 years in the field
One Health in action: Perspectives from 10 years in the fieldOne Health in action: Perspectives from 10 years in the field
One Health in action: Perspectives from 10 years in the field
 
Reservoirs of pathogenic Leptospira species in Uganda
Reservoirs of pathogenic Leptospira species in UgandaReservoirs of pathogenic Leptospira species in Uganda
Reservoirs of pathogenic Leptospira species in Uganda
 
Minyoo ya mbwa
Minyoo ya mbwaMinyoo ya mbwa
Minyoo ya mbwa
 
Parasites in dogs
Parasites in dogsParasites in dogs
Parasites in dogs
 
Assessing meat microbiological safety and associated handling practices in bu...
Assessing meat microbiological safety and associated handling practices in bu...Assessing meat microbiological safety and associated handling practices in bu...
Assessing meat microbiological safety and associated handling practices in bu...
 
Ecological factors associated with abundance and distribution of mosquito vec...
Ecological factors associated with abundance and distribution of mosquito vec...Ecological factors associated with abundance and distribution of mosquito vec...
Ecological factors associated with abundance and distribution of mosquito vec...
 
Livestock in the agrifood systems transformation
Livestock in the agrifood systems transformationLivestock in the agrifood systems transformation
Livestock in the agrifood systems transformation
 
Development of a fluorescent RBL reporter system for diagnosis of porcine cys...
Development of a fluorescent RBL reporter system for diagnosis of porcine cys...Development of a fluorescent RBL reporter system for diagnosis of porcine cys...
Development of a fluorescent RBL reporter system for diagnosis of porcine cys...
 
Practices and drivers of antibiotic use in Kenyan smallholder dairy farms
Practices and drivers of antibiotic use in Kenyan smallholder dairy farmsPractices and drivers of antibiotic use in Kenyan smallholder dairy farms
Practices and drivers of antibiotic use in Kenyan smallholder dairy farms
 
A gentle push towards improved hygiene and food safety through ‘nudge’ interv...
A gentle push towards improved hygiene and food safety through ‘nudge’ interv...A gentle push towards improved hygiene and food safety through ‘nudge’ interv...
A gentle push towards improved hygiene and food safety through ‘nudge’ interv...
 
Evaluation of livestock vaccinations in response to humanitarian crises: Proc...
Evaluation of livestock vaccinations in response to humanitarian crises: Proc...Evaluation of livestock vaccinations in response to humanitarian crises: Proc...
Evaluation of livestock vaccinations in response to humanitarian crises: Proc...
 
Evaluation of livestock emergencies: Case study selection
Evaluation of livestock emergencies: Case study selectionEvaluation of livestock emergencies: Case study selection
Evaluation of livestock emergencies: Case study selection
 
Surveillance of climate-sensitive zoonotic diseases: Leptospirosis at livesto...
Surveillance of climate-sensitive zoonotic diseases: Leptospirosis at livesto...Surveillance of climate-sensitive zoonotic diseases: Leptospirosis at livesto...
Surveillance of climate-sensitive zoonotic diseases: Leptospirosis at livesto...
 
Stem cell and genetic engineering technologies for conservation and sustainab...
Stem cell and genetic engineering technologies for conservation and sustainab...Stem cell and genetic engineering technologies for conservation and sustainab...
Stem cell and genetic engineering technologies for conservation and sustainab...
 
What is One Health and why is it important?
What is One Health and why is it important?What is One Health and why is it important?
What is One Health and why is it important?
 

Recently uploaded

Key Features Of Token Development (1).pptx
Key  Features Of Token  Development (1).pptxKey  Features Of Token  Development (1).pptx
Key Features Of Token Development (1).pptxLBM Solutions
 
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...HostedbyConfluent
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024Rafal Los
 
IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsIAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsEnterprise Knowledge
 
Understanding the Laravel MVC Architecture
Understanding the Laravel MVC ArchitectureUnderstanding the Laravel MVC Architecture
Understanding the Laravel MVC ArchitecturePixlogix Infotech
 
Pigging Solutions Piggable Sweeping Elbows
Pigging Solutions Piggable Sweeping ElbowsPigging Solutions Piggable Sweeping Elbows
Pigging Solutions Piggable Sweeping ElbowsPigging Solutions
 
How to Remove Document Management Hurdles with X-Docs?
How to Remove Document Management Hurdles with X-Docs?How to Remove Document Management Hurdles with X-Docs?
How to Remove Document Management Hurdles with X-Docs?XfilesPro
 
Pigging Solutions in Pet Food Manufacturing
Pigging Solutions in Pet Food ManufacturingPigging Solutions in Pet Food Manufacturing
Pigging Solutions in Pet Food ManufacturingPigging Solutions
 
Human Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsHuman Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsMark Billinghurst
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationSafe Software
 
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreternaman860154
 
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...shyamraj55
 
SIEMENS: RAPUNZEL – A Tale About Knowledge Graph
SIEMENS: RAPUNZEL – A Tale About Knowledge GraphSIEMENS: RAPUNZEL – A Tale About Knowledge Graph
SIEMENS: RAPUNZEL – A Tale About Knowledge GraphNeo4j
 
Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)Allon Mureinik
 
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 3652toLead Limited
 
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsKotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticscarlostorres15106
 
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024BookNet Canada
 
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | DelhiFULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhisoniya singh
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)Gabriella Davis
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonetsnaman860154
 

Recently uploaded (20)

Key Features Of Token Development (1).pptx
Key  Features Of Token  Development (1).pptxKey  Features Of Token  Development (1).pptx
Key Features Of Token Development (1).pptx
 
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
 
IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsIAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI Solutions
 
Understanding the Laravel MVC Architecture
Understanding the Laravel MVC ArchitectureUnderstanding the Laravel MVC Architecture
Understanding the Laravel MVC Architecture
 
Pigging Solutions Piggable Sweeping Elbows
Pigging Solutions Piggable Sweeping ElbowsPigging Solutions Piggable Sweeping Elbows
Pigging Solutions Piggable Sweeping Elbows
 
How to Remove Document Management Hurdles with X-Docs?
How to Remove Document Management Hurdles with X-Docs?How to Remove Document Management Hurdles with X-Docs?
How to Remove Document Management Hurdles with X-Docs?
 
Pigging Solutions in Pet Food Manufacturing
Pigging Solutions in Pet Food ManufacturingPigging Solutions in Pet Food Manufacturing
Pigging Solutions in Pet Food Manufacturing
 
Human Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsHuman Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR Systems
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
 
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreter
 
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
 
SIEMENS: RAPUNZEL – A Tale About Knowledge Graph
SIEMENS: RAPUNZEL – A Tale About Knowledge GraphSIEMENS: RAPUNZEL – A Tale About Knowledge Graph
SIEMENS: RAPUNZEL – A Tale About Knowledge Graph
 
Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)
 
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
 
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsKotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
 
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
 
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | DelhiFULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
 

Tanzania dairy value chain development update

  • 1. 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
  • 2. 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
  • 3. 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
  • 4. 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 Production Dairying in EA is the most important ag sector commodity for GDP gains (ASARECA/IFPRI report)
  • 5. 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
  • 6. 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
  • 7. 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 SINGIDA 25 Musoma Dairy 40,000 4 T a n 26 Utegi Plant (Ex TDL ) (Hakifanyikazi) 45,000 DODOMA 27 Makilagi SSDU 1,500 Unguja Processor name Installed 28 Baraki Sisters 3,000 capacity 29 Mara Milk 15,000 49 g g g a (litres/day) OCEAN OCEAN OCEAN 30 Mwanza Mini Dairy 3,000 D.R.C n 31 Kagera Milk (KADEFA) 3,000 1 Azam Dairy 3,000 32 Kyaka Milk Plant 1,000 DAR ES SALAAM 2 Tommy Dairy (Hakifanyikazi) 15,000 y iii 33 Del Food 1,000 3 Tan Dairies 34 Bukoba Market Milk Bar RUKWA 15,000 500 k MOROGORO PWANI 4 Tanga Fresh Ltd 40,000 35 Bukoba Milk Bar - Soko Kuu 500 5 Ammy Brothers Ltd 2,000 1 a 36 Mutungi Milk Bar 800 37 Salari Milk Bar 38 Kashai Milk Bar 800 800 45 6 Brookside (T) Ltd (Hakifanyikazi) 7 International Dairy Products 45,000 5,000 39 Kikulula Milk Processing Plant 40 Kayanga Milk Processing Plant 1,000 1,000 MBEYA 2 8 Mountain Green Dairy 9 Arusha Dairy Company 1,500 5,000 41 MUVIWANYA 1,000 IRINGA 10 Kijimo Dairy Cooperative 1,000 42 SUA 43 Shambani Graduates 44 New Tabora Dairies 3,000 4000 16,000 47 46 3 11 Longido (Engiteng) 12 LITI Tengeru 500 500 45 ASAS Dairy 12,000 13 Terrat (Engiteng) 500 46 CEFA Njombe Milk Factory 10,000 14 Orkesumet (Engiteng) 500 47 Mbeya Maziwa 1,000 48 Vwawa Dairy Cooperative Society 49 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 Ng'uni 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)
  • 8. 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 600000 Volume 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
  • 9. Low milk processing capacity utilization Region Average Average No. of Total Installed plant capacity per capacity S/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 33.5 Note Plant capacity defined on single 8 hr shift. Source: Tanzania Dairy Board/ MLDF Records
  • 10. 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
  • 11. Value Chain Outcomes (proposal list) Components Value chain outcomes Inputs & Increased private sector /farmer group participation in inputs and services Services provision. Improved feed quality and increased quantity of feed (forage and concentrates) Increased access to affordable animal health care Production 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 ECF Transport & Increased volume and proportion of processed milk Processing Increased number of small-scale milk traders selling more milk Reduced transport and transaction costs Marketing 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
  • 12. 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
  • 13. 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 levels IrishAid: 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)
  • 14. Impact pathways and outcomes Sub- Pathway Outcome component 2.1 Sectoral CRP3.7 works with partners to Consensus achieved among & Policy conduct analyses and generate national and regional policymakers Analysis 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 chains 2.2 Value CRP3.7 works with R&D partners to Improved and increased public & Chain conduct field studies to identify private sector interventions being Assessment 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 produce 2.3 Value CRP3.7 works with partners to identify Enhanced pro-poor value chain Chain and test the principles and methods performance and more equitable Innovation that permit research to promote and distribution of benefits. replicate effective and sustained pro- poor change in value chains
  • 15. 2.2 Value Chain Assessment Proposed Priority Outcomes & Outputs 2012 2013 2014 Outcomes R&D alliance 1. capacity to use tools Evidence base 2. Stakeholders aware influencing decisions Research 1. Scoping study to characterize 1. Inventory and evidence base 1. Best-bet intervention Outputs 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)
  • 16. 2.2 Value Chain Assessment Implications for Tanzania in 2012 (Focus on 2.2) Outcome: CRP3.7, local and international partners have established an R&D alliance to transform Dairy VC in Tanzania Priority Research outputs for 2012 Implications for Tanzania /potential delivery mechanisms 1. Scoping study to characterize dairy VC and identify •Build of rich knowledge available stakeholders and potential partners •Initial scoping in early 2012 – 2. Basic toolkit for VC assessment compiled for testing linked to MiLKIT start-up 3. Analytical framework for assessing VC performance •EADD CE established •Irish Aid? 4. Rapid assessment of VC to inform design of in- •ASARECA-PAAP BDS networks depth assessment, and to identify preliminary priority study constraints and best-bet upgrading strategies to test •ASARECA milk marketing and (incl. specific components on environmental impacts, value addition study (with food safety risk assessment and gender analysis) demand data) 5. Candidate intervention strategies for piloting •Livestock Data Innovation Linkages with sub-components (AH, Feed, Genetics Joint assessments of constraints / etc) Candidate technologies: ITM, ????
  • 17. 2.2 Value Chain Assessment Implications for Tanzania in 2012 Priority Organisational, Capacity Development and Communication Activities Activity Implications for Tanzania/comments 1. Restructure team to match CRP needs, with 1. Irish Aid would be happier to shared vision and assignments for subject/VC focus fund a country based office 2. Identify gaps for priority recruitment or 2.Lesson from EADD is that in- partnership country presence is critical 3. Identify strategy and mechanisms for working links internally with other CRP3.7 components, and externally with CRP2, CRP4 4. Develop a communication strategy targeted to stakeholders and partners in each target VC
  • 18. 2.2 Value Chain Assessment Implications for Tanzania in 2012 Priority Resource Mobilisation Activities Activity Implications for Tanzania/comments 1. Individual or multiple-country projects to 1. VC assessment beyond identify and test best-bet upgrading strategies for MiLKIT? Gap could be filled each target VC (perhaps more manageable if done through EADD CE/EADD 2 separately at farm and market levels) 2. Targeted trialling of ITM? 2. Project to design and test analytical framework 3. Research to adapt for 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 for prioritizing animal health and public health (with CRP4.3) priorities for pro-poor VC development Create linkages with sub-components (AH, Feed, Joint assessments of constraints Genetics, CRP2, 4 etc)
  • 19. 2.2 Value Chain Assessment Implications for Tanzania in 2013 Outcome 1. Partners have capacity /tools for VC assessment Outcome 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??
  • 20. 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-poor VC 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??
  • 21. 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)