This document outlines the results of a survey on the cost of milk production in East Africa. 128 farmers were surveyed across Kenya, Rwanda and Uganda. The objectives were to calculate the cost per liter of milk production and identify cost components to target for increased profitability. Key findings included that intensive systems in Rwanda had higher costs than extensive systems. Extensive systems in Uganda had higher revenues from cattle sales. Mortality was a major cost component. Small-scale farmers generally had higher profits than medium-scale farmers.

1. Cost of Milk Production in EADD
Hubs in East Africa
Baltenweck I, Kinuthia E, Lukuyu B
Menjo D, Atyang S and Kamanzi E
Presentation at the EADD Regional Office, 07 May 2012

2. Outline
Background
Survey objectives
Survey design
Analytical procedure
Results
Conclusion and Recommendation

3. Background
 In East African region, millions of smallholder farmers live in poverty in spite of the
potential to earn well-above subsistence income of $2 a day.
 In this predominantly agricultural region of Africa, keeping cattle and selling milk are
common, though not always profitable, household activities. Challenges include
poor breeds, inadequate feeding, poor animal health etc.
Country Kenya Rwanda Uganda
Cattle population 000' 18,000 1,500 12,000
Milk production 000' 4,400,000 97,981 1,190,000
Per capita consumption (ltr) 100 13 55
Dairy contribution to GDP 8% 6% 3%

4. Survey objectives
 Calculate cost of producing a litre of milk in the three
countries and make comparison according to scale of
operation and production system
 Comparison of costs and returns
 Identify cost components that EADD should target to
enhance profitability

5. Survey design
 Six hubs were selected in each country, 3 representing intensive (mainly stall
feeding) production system and 3 representing extensive system (mainly grazing) in
Rwanda and Uganda. Kenya had 3 hubs representing extensive and 3 representing
semi-extensive system
 Sampling plan was to survey a total of 7 small scale farmers and 3 medium scale
farmers (a total of 10) per hub; however, the actual sample size and distribution
were different for some hubs and countries
 60 farmers were interviewed in Rwanda and Uganda and 48 in Kenya (128 farmers
in total)
Rwanda and Uganda sample distribution Kenya sample distribution
Production Systems Intensive Extensive Production Systems Extensive Semi-Extensive
Uganda Rwanda Uganda Rwanda
Hubs per system 3 3
Hubs per system 3 3 3 3
Small-scale farmers 4 12
Small-scale farmers 20 21 19 21
Medium- scale farmer 4 9 17 9 Medium- scale farmer 18 14
Total sample size 24 30 36 30 Total sample size 22 26

6. Survey design (cont’)
Definition of farmers
Cows owned
Country System Small-scale Medium
Kenya Extensive 1 to 3 >3
Semi extensive 1 to 3 >3
Rwanda Intesive 1 to 3 >3
Extensive 1 to 10 >10
Uganda Intesive 1 to 3 >3
Extensive 1 to 15 >15

7. Questionnaire
 Structured survey questionnaires were used to collect data using 3 month recall
questions
Data collected include;
 Farmer’s personal information
 Cattle inventory
 Production systems and scale of operational
 Milk production and utilization
 Cattle management
 Cattle prices at various hubs was also collected using a separate questionnaire
filled at hub level

8. Analytical procedure
Profits were calculated using revenue from milk and cattle sales combined
(option1) and revenue from milk sales only (Option2)
Revenues included in
Costs included in
calculations calculations
Option 1 1. Milk sales Variable Costs
2. Milk consumed by household Fixed costs
3. Milk given to calves and Milk given to calves and
labourers labourers
4. Sale of animal Milk spoilage
Mortality
Option 2 1. Milk sales Variable Costs
2. Milk consumed by household Fixed costs
3. Milk given to calves and Milk given to calves and
labourers labourers
Milk spoilage
Mortality
Profit = Total revenue - Total cost
Cost of Milk given to labourers and calves is also include as a revenue because it is a product of the farm

9. Data analysis
Milk yield estimation
 Estimate of total milk production in the last 3
months preceding the survey was conducted
 Regression analysis was done using milk
Daily milk production in litres
production against specific time (Time) of lactation
B
C for every lactating cow
 The area under the curve (ABCD )was estimated to
get milk yield
D A
0 Time
Days in milk
 This was done for the various breeds and
aggregated for every farmer to get total volume

10. Costs
Cattle mortalities
 Calculated as value of the herd (obtained from hubs’ market price for different
animal types) multiplied by 8.5%, 1.8% & 7.4% which are mortality rates for Kenya,
Rwanda and Uganda
 This was calculated from baseline survey data and apportioned for three months
period.
Depreciation of machines and buildings
 Calculated on annual basis and apportioned for three months period
Maintenance of buildings
 Calculated on annual basis and apportioned for three months period

11. Revenues
Milk revenue
 Calculated as total value of milk consumed at home, milk sales through various
channels, milk given to labourers and to calves
 Milk consumed at home and milk given to labourers and to calves was valued at
respective hub’s price.
Cattle Revenue
 Calculated as total revenue of cattle sold in the last three months

12. Analytical procedure cont’
 Partial budget analysis was done to assess how costs and profits are varying
among small-scale & medium-scale farmers under different production systems in
the respective countries
 Descriptive statistics were mainly used to quantify means
 Significant differences between groups were tested, and comparisons within
countries were done using t-tests
 Local currency values were converted to the United states dollar (USD) using
prevailing exchange rates at time of survey.
 Currency exchange rates ($1=Kshs 89.4 = RwFrc 577.7 = Ushs 2600)

13. Comparison of cost, profit and
total revenue
0.8
 All hubs in Kenya made profits
0.7
when total revenue was
considered
0.6
 In Rwanda, Kigabiro and Muhazi
0.5
made losses due to high
0.4
production cost which was mainly
driven by purchased feed and
US$ per litre
0.3 Profit hired labour in the two hubs
Cost
0.2 Total Revenue
 In Uganda, Bbale and Kiboga also
0.1 made losses while the rest
registered profits and cost was
0 mainly driven by mortalities
Sot
Metkei
Kabiyet
Bbale
Tindiret
Ggulama
Tanykina
Muhazi
Matimba
Bubusi
Kiboga
Mudacos
Rwabiharamba
Kigabiro
Buikwe
Kinyogoga
Gahengeri
Sirikwa
-0.1
 There were more cattle sales in
-0.2 Ugandan hubs than Rwanda and
Extensive Semi IntensiveExtensiveIntensive Extensive
Kenya, and this greatly contributed
-0.3 Kenya Rwanda Uganda
to the overall dairy profitability

14. Comparison of cost, profit and
milk revenue
0.8
 All hubs in three countries
0.6 experienced reduction in profits
when cattle sales were excluded
0.4
 In Kenya all hubs registered profits
0.2
US$ per litre
Profit
0 Cost
 In Uganda, hubs under extensive
production system incurred higher
Sot
Kabiyet
Bbale
Ggulama
Tindiret
Metkei
Gahengeri
Tanykina
Muhazi
Matimba
Bubusi
Kiboga
Sirikwa
Mudacos
Rwabiharamba
Kigabiro
Buikwe
Kinyogoga
Milk revenue
losses than those practicing
-0.2
intensive due to significant
contribution of cattle sales to dairy
Extensive Semi Intensive Extensive Intensive Extensive
-0.4
Kenya Rwanda Uganda
profitability
-0.6
-0.8

15. Comparison between production
systems (within countries)
Kenya Rwanda Uganda
US$ Extensive Semi-extensive Sign Intensive Extensive Sign Intensive Extensive Sign
Total Milk revenue 0.27 0.28 0.31 0.3 0.25 0.24 ***
Cattle revenue 0.12 0.04 * 0.05 0.08 0.08 0.33 **
Total Revenue 0.4 0.32 0.35 0.38 0.33 0.57 *
Total Cost 0.16 0.12 0.31 0.13 *** 0.21 0.73 **
Milk Profit only 0.12 0.17 -0.01 0.17 *** 0.04 -0.21 ***
Total Profit 0.24 0.21 0.04 0.25 *** 0.12 0.13
*** ** * significant at 1%, 5% and 10% respectively
 Extensive system farmers in Kenya made higher revenue from cattle sales than those
practicing semi extensive system of production
 Intensive system farmers in Rwanda incurred higher production cost and consequently made
lower profits than those practicing extensive system of production
 Intensive system farmers in Uganda made higher revenue from milk sales while extensive
ones made higher revenue from cattle sales and overall revenue
 Extensive system farmers from Uganda were incurring higher production cost than intensive
production farmers due to mortalities. Thus intensive system farmers made higher profits when
revenue was calculated from milk sales only

16. Comparison between scale
of operation (total revenue)
0.6
 Small scale farmers in all three
countries made profits when revenue
0.5 was calculated from both milk and
cattle sales
0.4
 Only medium scale farmers in
US$ per litre
0.3
Uganda incurred losses and this was
Profit
Cost
as a result of high mortality cost
0.2
Total Revenue
0.1  Medium scale farmers in Uganda
incurred losses due to mortalities
0
Medium-scale
Medium-scale
Smallscale
Medium
Small-scale
Small-scale
-0.1
Kenya Rwanda Uganda

17. Comparison between scale of
operation (milk revenue)
0.6
 Profits declined significantly in all
countries when revenue from cattle
0.4
sales were excluded
0.2
 Uganda recorded the highest decline
in profitability indicating significance
of cattle sales
US$ per litre
Profit
0 Cost
 Only Medium scale farms in Uganda
Smallscale
Medium
Small-scale
Medium-scale
Small-scale
Medium-scale
Milk Revenue
incurred losses when revenue from
-0.2
cattle sales was excluded
Kenya Rwanda Uganda
 Small-scale farmers in Kenya made
higher profits from milk revenue
-0.4
compared to Rwanda and Uganda
-0.6

18. Comparison between scale of
operation (within countries)
Kenya Rwanda Uganda
US$ Small scale Medium Sign Small scale Medium Sign Small scale Medium Sign
Milk revenue 0.29 0.27 ** 0.3 0.3 0.21 0.17 **
Cattle revenue 0.12 0.04 * 0.03 0.18 ** 0.17 0.35 *
Total Revenue 0.4 0.31 ** 0.33 0.48 * 0.38 0.52
Total Cost 0.13 0.16 0.24 0.19 0.19 0.52 **
Milk Profit only 0.15 0.11 0.06 0.11 0.03 -0.35 ***
Total Profit 0.22 0.15 ** 0.09 0.3 ** 0.2 -0.002 *
*** ** * significant at 1%, 5% and 10% respectively
 Small scale farmers in Kenya made higher revenue from milk and cattle sales then medium scale
farmers and hence higher profits
 Medium scale farmers in Rwanda made higher revenues from cattle sales than small scale farmers
and thus higher total profit
 Small scale farmers in Uganda made higher revenue from milk sales while medium scale farmers
made higher revenue from cattle sales.
 Total production cost was high among the medium scale farmers in Uganda and thus lower profits,
this was mainly driven by mortalities
 There was no difference in production cost among small and medium scales in Kenya and Rwanda

19. Cost distribution in Kenya
Small-scale Labour Medium scale
Feed  Important costs among
Animal health
8% Breeding 10%
smallholders and medium scale
22%
Extension 27% farmers include feeds, mortality
24%
Transport 24% and calf milk
15% Fixed cost
11%
8% Given out milk
7% 11%
Calf milk
5% 5% 4%
2%
Mortality
4%
5%
3% 1% 4%
 Mortalities, purchased feed and
animal health were the highest
cost components for farmers in
extensive system
Extensive Semi extensive
6%
19%
13%  Calf milk, purchased feeds and
29%
23% mortalities were the most
20%
7%
22% significant costs
13%
10%
7% 7%
2% 4% 5%
3% 4% 3% 1% 2%

20. Cost distribution in Rwanda
Labour
Small-scale Medium scale
5%
2% 2%
Feed
Animal health
 Significant costs among small
0% 0%
Breeding 6%
6%
and medium scale farmers
20% 0%
8% Extension
include feeds, transport and
Transport 7% 34%
Fixed cost
hired labour although animal
20% 14%
29%
Given out milk health was also high among
0%
0% 12%
Calf milk
1% 18% 14% medium scale farmers
Mortality
2% Spoliage
 Purchased feeds, hired labour
Intensive Extensive and transport were significant
2% 0% among farmers practicing
0% 5%
5% 5% 5% intensive system
8% 23% 0% 21%
7%
22% 11%  Purchased feeds, hired labour,
24%
27% 0% and animal health were highest
2%
20%
0% 11%
cost components among in the
2%
extensive system

21. Cost distribution in Uganda
Small-scale Labour Medium scale
2% Feed
0%
 Significant costs among small
Animal health
12% Breeding scale farmers include feeds,
11%
30%
Extension
9% mortalities and calf milk while
20% Transport
Fixed cost 0% among medium scale was
8%
0%
9%
Given out milk
63% 8% 1%
mortalities
17% Calf milk
Mortality
3%
1% Spoilage
3% 2% 1%
 Calf milk, purchased feeds,
hired labour mortalities and
Intensive Extensive animal health were significant
2% 1% among farmers practicing
9% intensive system
17% 18%
11%
8%
24%
18%
63%
0%
0%  Mortalities and purchased
7%
10%
0% feeds were the highest cost
1%
2% components among farmers
2% 2% 1% 4%
practicing in the extensive
system

22. Conclusion
 Uganda incurred the highest cost followed by Rwanda while Kenya had the least
cost of production.
 The most significant costs of production in Uganda included cattle mortality, hired
labour, calf milk and purchased feeds. In Rwanda, they included purchased feeds,
hired labour, animal health and transport costs; while in Kenya, the most important
cost components included cattle mortality, purchased feeds and calf milk
respectively.
 Interventions should be devised to address feeds cost in all countries, mortalities
and calf milk cost in Kenya and Uganda. Transport cost should also be addressed in
Rwanda
 Rwanda had the highest milk revenue ($0.32 in intensive hubs), while Uganda
trailed ($0.25), Kenya did not have intensive hubs included in the survey for
comparison

23. Plan for:
Round 2 of CoP survey
Productivity Monitoring survey

24. Rationale
 Cost of milk production data only available for 1 season
 Need to collect similar information for at least 1 different season to
estimate yearly costs and profitability
 EADD is currently not collecting any data at farm level on a regular basis
 The vision indicator of dairy income was measured at baseline, at mid
term, and will be collected at final evaluation
 More regular data collection are required to capture trends and
seasonal variation
 The cost of production data can also be used to track dairy income
 The data can also be used to differentiate 1. farmers selling milk to
hubs; 2. farmers selling milk elsewhere; 3. farmers using hub inputs
and services; 4. any combination of the above
 Changes in milk production not monitored, yet this is EADD key
variable of intervention
 Even though it’s late to start, ‘better late than never’
 Will inform design of M&E system for possible EADD2

25. Herrerro S (2012). Integrated
monitoring. A Practical Manual for
Organisations That Want to Achieve
Results

26. Points of discussion
 We collect Round 2 of CoP data in same sites and same farmers as Round
1 except Kenya where sampling got messed up
 We start cow productivity monitoring
 On the same farms as CoP
 AND for 10 additional farms in all the other hubs
 (this means we will start monitoring milk production on 10 farms in ALL
the hubs)
 Besides milk production, we also collect data on milk consumption and
sale (by outlet) and use of hub inputs and services
 See draft questionnaire (on milk production only)
 Pending issues
 Costs (shared between ILRI, Heifer RO and Heifer countries?)
 Do we include Rwanda?
 Are 10 farmers/ hub sufficient?