Update on dairy value chain development in Tanzania

Update on dairy value chain development in Tanzania

Amos Omore (ILRI)

Planning Meeting

ILRI Nairobi

28 September 2011

CGIAR Research Program on Livestock and Fish

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

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 20201,000,000,000

1,500,000,000

2,000,000,000

2,500,000,000

3,000,000,000

3% GDP Growth 2% GDP Growth Milk ProductionYear

Million Lts Milk/ Yr

Dairying in EA is the most important ag sector commodity for GDP gains (ASARECA/IFPRI report)

Where are the cattle?

　 Lindi 0.1　 Mtwara 1.37　 Ruvuma 1.48　 Morogoro 1.61　 Kigoma 3.5　 Pwani 3.99 <5　 Rukwa 6 >5-10　 Tanga 11.54　 Mbeya 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　 Mara 65.72　 Shinyanga 75.19 >50-100　 Mwanza 111.62 >100

Cattle pop. Density/km2

　 Lindi 0.1　 Mtwara 1.37　 Ruvuma 1.48　 Morogoro 1.61　 Kigoma 3.5　 Pwani 3.99 <5　 Rukwa 6 >5-10　 Tanga 11.54　 Mbeya 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　 Mara 65.72　 Shinyanga 75.19 >50-100　 Mwanza 111.62 >100

Cattle pop. Density/km2

Where are the improved dairy cattle?

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 158Zanzibar 203 Dar es Salaam 591 Kilimanjaro 1036

Improved dairy cattle /100km2

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 158Zanzibar 203 Dar es Salaam 591 Kilimanjaro 1036

Improved dairy cattle /100km2

Where are the milk processing units?

Milk processing installation 1995-2000. (Total approx. 315,000 l/day)

Processor name Installed capacity (litres/day)

1 Azam Dairy 3,0002 Tommy Dairy (Hakifanyikazi) 15,0003 Tan Dairies 15,0004 Tanga Fresh Ltd 40,0005 Ammy Brothers Ltd 2,0006 Brookside (T) Ltd (Hakifanyikazi) 45,0007 International Dairy Products 5,0008 Mountain Green Dairy 1,5009 Arusha Dairy Company 5,000

10 Kijimo Dairy Cooperative 1,00011 Longido (Engiteng) 50012 LITI Tengeru 50013 Terrat (Engiteng) 50014 Orkesumet (Engiteng) 50015 Naberera (Engiteng) 1,00016 Nronga Women 3,50017 West Kilimamnjaro 1,00018 Mboreni Women 1,00019 Marukeni 1,00020 Ng'uni Women 1,00021 Kalali Women 1,00022 Same (Engiteng) 50023 Fukeni Mini Dairies 3,00024 Kondiki Small Scale Dairy 1,200

25 Musoma Dairy 40,00026 Utegi Plant (Ex TDL ) (Hakifanyikazi) 45,00027 Makilagi SSDU 1,50028 Baraki Sisters 3,00029 Mara Milk 15,00030 Mwanza Mini Dairy 3,00031 Kagera Milk (KADEFA) 3,00032 Kyaka Milk Plant 1,00033 Del Food 1,00034 Bukoba Market Milk Bar 50035 Bukoba Milk Bar - Soko Kuu 50036 Mutungi Milk Bar 80037 Salari Milk Bar 80038 Kashai Milk Bar 80039 Kikulula Milk Processing Plant 1,00040 Kayanga Milk Processing Plant 1,00041 MUVIWANYA 1,00042 SUA 3,00043 Shambani Graduates 400044 New Tabora Dairies 16,00045 ASAS Dairy 12,00046 CEFA Njombe Milk Factory 10,00047 Mbeya Maziwa 1,00048 Vwawa Dairy Cooperative Society 90049 Gondi Foods 600

Less than 5000 litres/day

5000-30,000 litres/day

More than 40,000 litres/day

Key

26

12

16

6

7

8

12

9

4

5

10

3

11131415

17

18

19

20

2122

23

24

42

45

25272829

3031

32

3233

34 35 3637 38 39

4041

43

4647

48

49

46

D.R.C

Pemba

Unguja

LINDI

RUKWA

TABORA

IRINGAMBEYA

RUVUMA

SINGIDA

MOROGORO

KIGOMA

PWANI

ARUSHA

DODOMA

SHINYANGA

TANGA

MARA

KAGERA

MANYARA

MTWARA

MWANZA

MANYARA

KILIMANJARO

DAR ES SALAAM

La

ke

Ta

ng

an

yi

ka

La k

eN

ya

sa

I N D

I A N

O C

E A

N

KENYA

UGANDA

RWANDA

BURUNDI

ZAMBIA

MOZAMBIQUE

Milk processing installation 1995-2000. (Total approx. 315,000 l/day)

Processor name Installed capacity (litres/day)

1 Azam Dairy 3,0002 Tommy Dairy (Hakifanyikazi) 15,0003 Tan Dairies 15,0004 Tanga Fresh Ltd 40,0005 Ammy Brothers Ltd 2,0006 Brookside (T) Ltd (Hakifanyikazi) 45,0007 International Dairy Products 5,0008 Mountain Green Dairy 1,5009 Arusha Dairy Company 5,000

10 Kijimo Dairy Cooperative 1,00011 Longido (Engiteng) 50012 LITI Tengeru 50013 Terrat (Engiteng) 50014 Orkesumet (Engiteng) 50015 Naberera (Engiteng) 1,00016 Nronga Women 3,50017 West Kilimamnjaro 1,00018 Mboreni Women 1,00019 Marukeni 1,00020 Ng'uni Women 1,00021 Kalali Women 1,00022 Same (Engiteng) 50023 Fukeni Mini Dairies 3,00024 Kondiki Small Scale Dairy 1,200

25 Musoma Dairy 40,00026 Utegi Plant (Ex TDL ) (Hakifanyikazi) 45,00027 Makilagi SSDU 1,50028 Baraki Sisters 3,00029 Mara Milk 15,00030 Mwanza Mini Dairy 3,00031 Kagera Milk (KADEFA) 3,00032 Kyaka Milk Plant 1,00033 Del Food 1,00034 Bukoba Market Milk Bar 50035 Bukoba Milk Bar - Soko Kuu 50036 Mutungi Milk Bar 80037 Salari Milk Bar 80038 Kashai Milk Bar 80039 Kikulula Milk Processing Plant 1,00040 Kayanga Milk Processing Plant 1,00041 MUVIWANYA 1,00042 SUA 3,00043 Shambani Graduates 400044 New Tabora Dairies 16,00045 ASAS Dairy 12,00046 CEFA Njombe Milk Factory 10,00047 Mbeya Maziwa 1,00048 Vwawa Dairy Cooperative Society 90049 Gondi Foods 600




Key

26

12

16

6

7

8

12

9

4

5

10

3

11131415

17

18

19

20

2122

23

24

42

45

25272829

3031

32

3233

34 35 3637 38 39

4041

43

4647

48

49

46




KeyLess than 5000 litres/day



Less than 5000 litres/dayLess than 5000 litres/day

5000-30,000 litres/day5000-30,000 litres/day

More than 40,000 litres/dayMore than 40,000 litres/day

Key

26

12

16

6

7

8

12

9

4

5

10

3

11131415

17

18

19

20

2122

23

24

42

45

25272829

3031

32

3233

34 35 3637 38 39

4041

43

4647

48

49

46

26

12

16

6

7

8

12

9

4

5

10

3

11131415

17

18

19

20

2122

23

24

42

45

25272829

3031

32

3233

34 35 3637 38 39

4041

43

4647

48

49

46

D.R.C

Pemba

Unguja

LINDI

RUKWA

TABORA

IRINGAMBEYA

RUVUMA

SINGIDA

MOROGORO

KIGOMA

PWANI

ARUSHA

DODOMA

SHINYANGA

TANGA

MARA

KAGERA

MANYARA

MTWARA

MWANZA

MANYARA

KILIMANJARO

DAR ES SALAAM

La

ke

Ta

ng

an

yi

ka

La k

eN

ya

sa

I N D

I A N

O C

E A

N

KENYA

UGANDA

RWANDA

BURUNDI

ZAMBIA

MOZAMBIQUE

D.R.C

Pemba

Unguja

LINDI

RUKWA

TABORA

IRINGAMBEYA

RUVUMA

SINGIDA

MOROGORO

KIGOMA

PWANI

ARUSHA

DODOMA

SHINYANGA

TANGA

MARA

KAGERA

MANYARA

MTWARA

MWANZA

MANYARA

KILIMANJARO

DAR ES SALAAM

La

ke

Ta

ng

an

yi

ka

La k

eL

a kL

a ke

Ny

as

ya

sy

as

a

I N D

I A N

O C

E A

N

KENYA

UGANDA

RWANDA

BURUNDI

ZAMBIA

MOZAMBIQUE

Farmer groups are struggling!

Performance of milk collection at Nnronga women dairy co-operative Society, Hai Kilimanjaro and CHAWAMU-Muheza Tanga (1994-2007)

050000

100000150000200000250000300000350000400000450000500000550000600000650000700000750000

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

Year

Volu

me

of M

ilk (L

itres

)

Nnronga

CHAWAMU-Muheza

Low milk processing capacity utilization

S/N

Region

No. of Plants

Total Installed plant capacity*

Average capacity per plant Milk processed/day

Average capacity 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 outcomesInputs & Services

Increased private sector /farmer group participation in inputs and 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 & Processing

Increased volume and proportion of processed milk 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 proposalsMiLKIT (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 outcomesSub-component

Pathway Outcome

2.1 Sectoral & Policy Analysis

CRP3.7 works with partners to conduct analyses and generate evidence and engage with policymakers and stakeholders to understand the whether and how the target value chain should be ‘enabled

Consensus achieved among national and regional policymakers regarding pro-poor policies and investment strategies to support development of the 9 target value chains

2.2 Value Chain Assessment

CRP3.7 works with R&D partners to conduct field studies to identify opportunities, test best-bet strategies and generate evidence to inform and stimulate development interventions for pro-poor upgrading of the target value chains

Improved and increased public & private sector interventions being applied by development actors to support women and resource-poor value chain actors and consumers, with lower ecological footprint per unit produce

2.3 Value Chain Innovation

CRP3.7 works with partners to identify and test the principles and methods that permit research to promote and replicate effective and sustained pro-poor change in value chains

Enhanced pro-poor value chain performance and more equitable distribution of benefits.

Proposed Priority Outcomes & Outputs2012 2013 2014

Outcomes R&D alliance 1. capacity to use tools 2. Stakeholders aware

Evidence base influencing decisions

Research Outputs

1. Scoping study to characterize target VC and identify stakeholders and potential partners2. Basic toolkit for VC assessment compiled for testing3. Analytical framework for assessing VC performance established4. Rapid assessment of target VC to inform design of in-depth assessment, and to identify preliminary priority constraints and best-bet upgrading strategies to test (including specific components on environmental impacts, food safety risk assessment and gender analysis)

1. Inventory and evidence base (literature review) for key constraints and proposed solutions compiled2. Quantitative assessment of VC performance3. Technical and economic assessments of key VC components to target for upgrading (e.g. farm-level: husbandry, feeds, breeds, health, environmental issues; market-level: institutional environment, food safety, demand characteristics; overall: policies, organizational strategies

1. Best-bet intervention strategy formulated and tested, ready for piloting


Implications for Tanzania in 2012 (Focus on 2.2)

Priority Research outputs for 2012 Implications for Tanzania /potential delivery mechanisms

1. Scoping study to characterize dairy VC and identify stakeholders and potential partners2. Basic toolkit for VC assessment compiled for testing3. Analytical framework for assessing VC performance established4. Rapid assessment of VC to inform design of in-depth assessment, and to identify preliminary priority constraints and best-bet upgrading strategies to test (incl. specific components on environmental impacts, food safety risk assessment and gender analysis)5. Candidate intervention strategies for piloting

•Build of rich knowledge available•Initial scoping in early 2012 –linked to MiLKIT start-up•EADD CE•Irish Aid?•ASARECA-PAAP BDS networks study•ASARECA milk marketing and value addition study (with demand data)•Livestock Data Innovation

Linkages with sub-components (AH, Feed, Genetics etc)

Joint assessments of constraints / Candidate technologies: ITM, ????

Outcome: CRP3.7, local and international partners have established an R&D alliance to transform Dairy VC in Tanzania


Activity Implications for Tanzania/comments

1. Restructure team to match CRP needs, with shared vision and assignments for subject/VC focus2. Identify gaps for priority recruitment or partnership3. Identify strategy and mechanisms for working links internally with other CRP3.7 components, and externally with CRP2, CRP44. Develop a communication strategy targeted to stakeholders and partners in each target VC

1. Irish Aid would be happier to fund a country based office2.Lesson from EADD is that in-country presence is critical

Priority Organisational, Capacity Development and Communication Activities

Implications for Tanzania in 2012 2.2 Value Chain Assessment

Activity Implications for Tanzania/comments

1. Individual or multiple-country projects to identify and test best-bet upgrading strategies for each target VC (perhaps more manageable if done separately at farm and market levels)2. Project to design and test analytical framework for assessing and monitoring VC performance (both as basis for M&E and as analytical tool) (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

1. VC assessment beyond MiLKIT? Gap could be filled through EADD CE/EADD 2

2. Targeted trialling of ITM?3. Research to adapt

Traditional milk hubs (without chilling plants)

Create linkages with sub-components (AH, Feed, Genetics, CRP2, 4 etc)

Joint assessments of constraints

Priority Resource Mobilisation Activities

Implications for Tanzania in 2012


Implications for Tanzania in 2013


1. Inventory and evidence base (literature review) for key constraints and proposed solutions compiled2. Quantitative assessment of VC performance3. Technical and economic assessments of key VC components to target for upgrading (e.g. farm-level: husbandry, feeds, breeds, health, environmental issues; market-level: institutional environment, food safety, demand characteristics; overall: policies, organizational strategies

MiLKIT - mainly feed/farm-level in target locations

EADD2

Irish Aid?

EAAPP has adopted ILRI developed tools for baseline surveys; may seek ILRI’s collaboration in targeted studies

Linkages with sub-components (AH, Feed, Genetics)

Joint assessments of constraints / Candidate techns: ITM, others??

Outcome 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


Implications for Tanzania in 2014 (optional for now)


1. Best-bet intervention strategy (better refined) formulated and tested, ready for piloting

MiLKIT

EADD2

Irish Aid?

New project development?

Linkages with sub-components (AH, Feed, Genetics)

Joint assessments of constraints / Candidate techns??

Outcome 1. Evidence base in each target VC for best-bet pro-poor VC dev interventions is influencing dev investment decisions


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)

Update on dairy value chain development in Tanzania

Technology