Maziwa Zaidi updates

MilkiT Coordination Meeting

22 Jan 2014, Morogoro, Tanzania

Amos Omore

What is ‘Maziwa Zaidi’? • Coined to domesticate CGIAR Livestock and Fish (LaF)

Program in Tanzania• It’s a dream for pro-poor transformation of the

Tanzania dairy VC over the next decade++ that is not ‘yet’ fully funded

• The impact pathway for Tanzania dairy VC defines how to get there

• Seeks to enrol/get buy-in by non-LaF dairy R&D partners for greater synergy

What is ‘Maziwa Zaidi’? • Individual projects help us to achieve ‘Maziwa Zaidi’

but they are not singly• Other rationle:

– Reduce confusion among stakeholders regarding which projects they are collaborating with,

– Encourage synergy among collaborating projects, – Rally value chain research and development partners towards

a shared purpose.

What is ‘Maziwa Zaidi’? ‘Maziwa Zaidi’ projects so far:

Name Donor PI Theme ObjectiveMilkIT IFAD B Mass Feeds Feeds

innovations/IPsMoreMilkiT Irish Aid A Omore/L.K VCD HubsSFFF2 (ACIAR?) BMZ/GIZ D Grace A4HN Food safety

(nutrition?)Cow Killer BMZ S Alonso /D.

GraceAnimal Health/FS

Disease prev survey

CGP IDRC A Galie / (A. Omore)

Gender/M&E Food security

What is ‘Maziwa Zaidi’? ‘Maziwa Zaidi’ projects proposals/pipeline

Research & piloting partnerships (e.g., MoreMilkiT, MilkiT; SFFF2; new proposals)Maz

iwa

Zaid

i pla

nnin

g

Place of LaF R4D in ‘Maziwa Zaidi’

More milk, income, assets and better health & Nutrition thro’a) access to quality inputs and services b) access to reliable, well-coordinated, marketing arrangementc) access to quality, safe and nutritious products at affordable prices

Scaling out development partnerships (e.g., EADD2? + +)

Time 10 years

Maziwa Zaidi Strategic Research

Scaling out development partnerships (e.g., EADD2)

Highlights of progress of Flagship Project (More MilkiT) and other integrated projects

Objectives (derived from Irish Aid Country Strategy Paper for Tanzania and ASDS)

Goal: • Inclusive growth and reduced poverty and

vulnerability among dairy-dependent livelihoods in relevant rural areas in Tanzania

Outcome: • Rural poor are more income secure through enhanced

access to demand-led dairy market business services and viable organisational options, and low-income consumers have better access to affordable milk.

More Milk in Tanzania Project

Contributing Objectives over 5 yrs1. Develop scalable value chains approaches with improved

organization and institutions serving resource-poor male and female smallholder dairy households

2. Generate and communicate evidence on business and organizational options for increasing participation of resource-poor male and female households in dairy value chains

3. Inform policy on appropriate role for pro-poor smallholder-based informal sector value chains in dairy sector development

More Milk in Tanzania Project

Addressing 4 inter-related problems that face resource-poor milk producers

1. Dominant direct sales of small volumes by smallholder producers that preclude economies of scale

2. Credit facilities for basic inputs and services or working capital are lacking. This discourages investment to improve productivity

3. Lack of appropriate organizational models for pre-commercial producers (complex cooperative models and technology-driven solutions have largely failed)

4. Seasonality of rainfall and related effects are strong (with MilkIT)

Milk marketing outlets (NBS, 2003)

Milk Buyer%

Neighbours 86.1

Local market 5.5

Secondary market 0.5

Processors 1.4

Large scale farms 0.2

Trader at farm 4.5

Other 1.7

TOTAL 100.0

More Milk in Tanzania Project

Addressing 4 inter-related problems that face resource-poor milk producers

1. Dominant direct sales of small volumes by smallholder producers that preclude economies of scale

2. Credit facilities for basic inputs and services or working capital are lacking. This discourages investment to improve productivity

3. Lack of appropriate organizational models for pre-commercial producers (complex cooperative models and technology-driven solutions have largely failed)

4. Seasonality of rainfall and related effects are strong (with MilkIT)

More Milk in Tanzania Project

Women participate more in milk related tasks

Addressing 4 inter-related problems that face resource-poor milk producers

More Milk in Tanzania Project

Milk processing in Tanzania has been declining since 1990

1. Dominant direct sales of small volumes by smallholder producers that preclude economies of scale

2. Credit facilities for basic inputs and services or working capital are lacking. This discourages investment to improve productivity

3. Lack of appropriate organizational models for pre-commercial producers (complex cooperative models and technology-driven solutions have largely failed)

4. Seasonality of rainfall and related effects are strong (with MilkIT)

Addressing 4 inter-related problems that face resource-poor milk producers

1. Dominant direct sales of small volumes by smallholder producers that preclude economies of scale

2. Credit facilities for basic inputs and services or working capital are lacking. This discourages investment to improve productivity

3. Lack of appropriate organizational models for pre-commercial producers (complex cooperative models and technology-driven solutions have largely failed)

4. Seasonality of rainfall and related effects are strong (with MilkIT)

More Milk in Tanzania Project

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

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

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

Less than 5000 litres/day

5000-30,000 litres/day

More than 40,000 litres/day

KeyLess than 5000 litres/day

5000-30,000 litres/day

More than 40,000 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

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

I N D I A N O C E A N

KENYA

UGANDA

RWANDA

BURUNDI

ZAMBIA

MOZAMBIQUE

Addressing 4 inter-related problems that face resource-poor milk producers

1. Dominant direct sales of small volumes by smallholder producers that preclude economies of scale

2. Credit facilities for basic inputs and services or working capital are lacking. This discourages investment to improve productivity

3. Lack of appropriate organizational models for pre-commercial producers (complex cooperative models and technology-driven solutions have largely failed)

4. Seasonality of rainfall and related effects are strong (with MilkIT)

More Milk in Tanzania Project

Farmer groups are struggling in most places except in Tanga

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

More Milk in Tanzania Project

Key messages on identified entry points

• Validity of the need to focus attention on ‘growing’ the existing informal system of milk production (with zebu cattle) and marketing to extend the frontiers of commercial dairying

• Organizational models to achieve economies of scale for access to inputs and services required to unleash incentives for raised productivity to levels that will justify bulking

• This is riskier than classical approaches but more inclusive in ensuring wider impact on marginalised

• Policy support for pro-poor shift needed

Identified field sites

Dairy Market Hubs (DMHs) with emphasis on improving access to inputs and services through business development services (BDS) and check-off arrangements:

a) DMHs revolving around chilling plants or accessing them (if under-utilized) through transport arrangements that provide both outputs marketing and inputs and services through check-offs; b) DMHs revolving around check-offs for inputs and services provided through milk traders; and c) DMHs revolving around check-offs for inputs and services provided through cattle traders.

Hubs for piloting in the Tanzania context

Producers In

puts, $$

Inputs & services

$$

Payment agreement

Milk, C

attle

Check-off agreement

Inputs & Service Providers(BDS)

Traders Milk

Cattle

BASIC Dairy Market Hubfor Provision of Inputs and

Services on Check-off

Illustration of a hub for provision of inputs and services on credit without collective bulking and marketing

Milk Trader

Training Service Providers

(BDS)

Regulatory Authority

Certific

ation/Lice

nsing

Training & certification of

competence

Accreditation & monitoring

Reporting

Cess f

ee

Training guides

BDS linkages in milk quality assurance in informal markets (with TDB)

Hygieniccans

Fee

(Trialled in Kenya, Uganda and Tanzania (Arusha & Mwanza)

It is evolving and continuing to catalyze policy dialogue for a pro-poor transformation of the dairy value chain…

DDF update:

MoreMilkiT update:

• R&D partnerships formed for piloting have started to mobilize value chain actors for piloting of interventions

Range of partnerships :1. Strategic Research Partnerships

• SUA• TALIRI

Reinforced by CGIAR (ILRI/CIAT) and ARIs partnerships

2. Development Partnerships• Servicing the system: Heifer and SNV• From the system: TDB, FAIDA MaLi

3. Mechanisms for strengthening relationships

• DDF• Local platforms

MoreMilkiT update:

DMH category Criteria for becoming a DMH

a): Collective bulking and sale of milk by members of a farmers group

Farmers group i) is registered at district level

ii) has at least 1 link with a milk trader/ buyer and at least 1 link with an input & services provider

iii) members are able to access inputs & services on check off system

b) and c): Individual members of a farmers group sell milk and/or cattle directly to traders

Farmers group

i) is registered at district level ii) members are able to access inputs & services on check off

system

Criteria for becoming a dairy market hub defined

Hub for provision of inputs and services on credit without collective bulking and marketing

Impact pathway and MLE developed

• Monitoring, learning and evaluation (MLE) framework) developed

• Several targeted research activities and ex-ante assessment of interventions initiated, some through students

Baseline (benchmark) results available

• Most findings re-affirm VCA findings, with figures

• It’s mostly about feed, less so other constraints!

1. Dominant direct sales of small volumes by smallholder producers that preclude economies of scale

2. Credit facilities for basic inputs and services or working capital are lacking. This discourages investment to improve productivity

3. Lack of appropriate organizational models for pre-commercial producers (complex cooperative models and technology-driven solutions have largely failed)

4. Seasonality of rainfall and related effects are strong (with MilkIT ++)

MoreMilkiT: Main Successes and challenges

Successes • Entry points for piloting of interventions identified, the project

is now ready for take-off• Early success in preparing for impact in the dairy value chain in

Tanzania in the long-term through DDF and ‘Maziwa Zaidi’ value chain transformation agenda

Challenges• Lengthy bureaucracies in reaching agreements with partners• Delays in recruiting additional staff • Innovating for inclusive upgrading of dairy value chains is

riskier but has more potential for wider impact

CGIAR is a global partnership that unites organizations engaged in research for a food secure future. The CGIAR Research Program on Livestock and Fish aims to increase the productivity of small-scale livestock and fish systems in sustainable ways, making meat, milk and fish more available and affordable across the developing world.

CGIAR Research Program on Livestock and Fish

livestockfish.cgiar.org

In support of: