Presented by Baltazary, C., Kinuthia, E., Baltenweck, I. and Omondi, I. (ILRI) at the 6th Tanzania Dairy Development Forum Meeting, Njombe, Tanzania, 29 May 2016

1. East Africa Dairy Development in Tanzania—Cost of milkproduction
Baltazary, C., Kinuthia, E., Baltenweck, I. and Omondi, I. (ILRI)
6th Tanzania Dairy Development Forum Meeting, Njombe, Tanzania, 29 May 2016

2. Presentation Outline
1. EADD Overview
2. Introduction
3. Methodology
4. Variables for computation of cost of milk production
5. Findings/results
6. Conclusion and recommendations

3. Milk Production in Tz

4. SOUTHERN HIGHLANDSSOUTHERN HIGHLANDS
MILK SHEDMILK SHED
• Total cattle = 1.35 mil (6%)
– Dairy herd = 86,982 (12%)
– Local herd = 1.26 mil (6%)
• Opportunity for breed
improvement
• Annual milk production =
538 mil L/year (27%)
• Households keeping
livestock = 295,547 (13%)
– Critical mass
EADD Project Area

5. Introduction cont’d
• Since the inception EADD II , there is inadequate
information regarding costs of milk production and
profitability of smallholder dairy enterprise in the project
sites
Survey Objectives
i. Assessing the cost of milk production and profitability of
dairy enterprise in the study areas
ii. Identifying cost components EADD II should target in
order to enhance profitability of the dairy farms in EADD
II project sites

6. Methodology
• From the project’s baseline sample, a random sample of 20
households per hub was drawn with additional of 10
households as replacement to cater for non-response
• A total of 217 households keeping cattle (irrespective of the
breed and scale of production) were surveyed in three
ClustersClusters POs/Hubs
Mbeya Kyimo (Faraja), Vwawa (Mviwambo), Ilembo (ISAIMA)
and Isange (Busokelo)
Njombe Igima (Mshikamano), Kichiwa (WAWAHANJO) and
Uwemba (Lukamo)
Iringa Igowole (MUDCO), Ifunda (Iringa) and Mtitu (Dabaga)

7. Variables forcomputation of Cost of Production
Revenues included in
calculations
Costs included in calculations
Scenario
1
1. Milk sales
2. Milk consumed by
households
3. Milk given to calves
and laborers
4. Sale of animals
5. Manure sales
Variable costs
Fixed costs
Milk given to calves and
laborers
Milk spoilage (defined as the
volume of milk rejected by the
buyer)
Scenario
2
1. Milk sales
2. Milk consumed by
households
3. Milk given to calves
and laborers
Variable costs
Fixed costs
Milk given to calves and
laborers
Milk spoilage

8. Analytical Procedure

9. Cattle production systems and Breeds
Cluster Prod system DomBreed Av milkprod
(L/d/HH)
Mbeya Intensive Improved
(88%)
9.2*
Njombe Extensive Zebu (58%) 2.3*
Iringa Extensive Zebu (65%) 2.3*
* The average milk production volumes are figures from the
annual survey conducted between Nov – Dec 2015

10. Results/findings
Profit perliter(Tsh/L) from milk, cattle and manure
revenues combined across Clusters
Cluster Av Pr/L
Tsh
TR/L
(MaR+CR+MR)
TVC/L TFC/L Total Prod
cost/L
Profit/L
Mbeya 598 702 146 18 164 538
Njombe 657 747 390 52 442 305
Iringa 996 822 634 72 706 116
Overall
(SH)
750 757 390 47 437 320
MR= Milk Re ve nue , CR= Cattle sale s re ve nue , MaR= Manure sale s re ve nue and SH
= So uthe rn Hig hlands

11. Profit perlitre from milkrevenue only
(Tsh)
Cluster MilkRev/L Total Cost/L Profit/L
Mbeya 571 164 407
Njombe 654 442 212
Iringa 772 706 66
Overall (SH) 666 437 229
• There is a significant drop in average profit per litre
across all clusters when revenue was calculated from milk
sales only.
• This shows the contribution of other dairy components
(manure and sales of live animals) on the profitability of

12. Comparison of revenue, costs and
profits by systems of production
• Farmers in intensive system generated significantly higher
milk profit than those in extensive system (P>0.1)
• Farmers practicing extensive system were incurring
significantly higher cost per litre on milk given out, milk
consumed, variable costs, total costs and fixed costs
(P>0.01) than the farmers practicing intensive system of
production.
• Contrary to expectations the cost of hired labor, animal
health and extension costs per litre were higher in extensive
system than in intensive system.

13. Distribution of Variable Costs by
Clusters

14. BreakEven Volumes (BEV)

16. • Mbeya cluster had the highest (4.8 litres) break even volume
of milk production per household per day as a result of low
average milk price per litre
• Similarly farmers under the intensive system have to
produce higher (4.5 litres) volume per day compared to their
counterparts who practice extensive system (2.6 litres) to
cover for the low average price per litre that farmers in
intensive system receive.

17. Conclusion
• Generally farmers in SH generate on average a profit of 320
Tsh per litre
• Hired labour, purchased feeds and animal health were the
major cost drivers of milk production in both the extensive
and intensive systems.
• In addition, milk spoilage contributed to revenue loss across
the three clusters.
• Farmers in intensive system have to produce more milk for
them to generate revenue enough to cover their production
costs.

18. Recommendations
• In order to increase production efficiency, EADD II will
continue facilitating farmers in the adoption of good animal
husbandry practices to reduce the major cost drivers.
• To realize bigger impact, EADD II is calling upon
development partners to invest on improvement of dairy
farming through facilitating market access, access to
improved breeds and linkages of POs with service providers
(financial institutions)