Evaluation of different chilling scenarios for improving and increasing the milk supply. Summary of study findings. Read the study: https://cgspace.cgiar.org/bitstream/handle/10568/106246/WP288.pdf

1. CCAFS Case: Milk Chain Ethiopia
2019, Bert Dijkink, Erik Esveld, Martijntje Vollebrecht, Jan Broeze
Wageningen Food & Biobased Research
This work is implemented as part of the Consultative Group on International Agricultural Research (CGIAR) Research
Program on Climate Change, Agriculture and Food Security (CCAFS), which is carried out with support from CGIAR
Fund Donors and through bilateral funding agreements.
Evaluation of different chilling scenarios for improving and
increasing the milk supply

2. Milk Chain Ethiopia: Introduction CCAFS
2
 The milk chain in Ethiopia is studied as one of the four cases
in this CAFFS project.
 The goal of the CCAFS project is to the evaluated different
interventions in the food chain on the effect on food security
and Green House Gas (GHG) emissions.
 Other cases:
● Cassava processing: Mobile factory in Mozambique to improve yields
compared to traditional processing and avoid spoilage by long
transport times to centralized factories.
● Hermetic Bags for the storage of Maize: In field results of post
harvest and quality losses and the economical perspective for
farmers in different sub Sahara countries.
● Tomatoes: Reduction of post harvest losses by changes in transport.

3. Milk Supply Chain Ethiopia: Introduction
3
 Milk factories buying Traditional milk from small farm holders for
their production. As this milk is uncooled until it arrives at the
factory only the morning milk can be bought as evening milk will be
spoiled before the collection.
 Chilling the milk before collection can help to increase the supply of
the milk (evening milk) and reduce the amount that is rejected at
factory
 In this case the effect of different chilling scenarios are studied by
simulating the spoilage of the milk in the supply chain.

4. Milk Chain Ethiopia: Zagol Factory
4
 The current largest challenge for the milk factory in Solulta is to obtain
enough milk for their production.
● 50% of the milk comes from two larger farms
● The rest of the milk comes from small farmers which is difficult to
collect, small volumes and irregular supply:
● Average milk production per farmer is 12 litres
● Most of the milk is for own use >80%
● Only the morning milk can be used
● Increase the supply during fasting periods (total 180 days/year)
Processing of milk in the
Zagol factory (Solulta)

5. Milk Chain Ethiopia: Introduction
5
 Current Milk collecting system
Farmers gathered on roadside to
offer their milk
Testing milk for quality
and freshness
pH test : >6.4  Reject
Determine volumes and
preparing for transport

6. Milk Chain Ethiopia: Introduction
6
 To increase the supply from small farms holders different kind of
intervention are made : introduction cold chains, improve hygiene
and feeding.
 In this case the effect introduction of different chilling chains on
milk spoilage of a milk factory in Solulta is studied
● A model is presented to simulated the rejection % of the milk
during collection of the milk
● The estimated of the amount of rejected milk is calculated for
different cooling storage scenario’s
● The effect of cooling scenario's on the sourcing distance is
calculated

7. Milk Chain Ethiopia: Model
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 The model used consist of four elements:
● Change of milk temperature based on container size and surrounding.
● Temperature dependent growth of the lactic acid producing bacteria
● Formation of lactic acid with biomass increase and maintenance.
● Buffer relation between amount of lactic acid and resulting milk pH.
 The model uses:
● Grow rates reference from Fresco culture DVS 1010. A mixture of
Lactic acid production bacteria.
● Production of lactic acid calculated with the model of Piret.
● Relation of Lactic acid and the pH of milk from own titration curve

8. Milk Chain Ethiopia: Chilling scenario’s
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No Cooling
Cooling
@Farm
Cooling
@Centre
Cooling
@Centre 2x

9. Milk Chain Ethiopia: model simulations
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 current situation morning milk
• With an initial contamination of
log 4 cfu/ml the milk arrives
fresh at the factory

10. Milk Chain Ethiopia: model simulations
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 current situation morning milk
• with an initial contamination of
log 6 cfu/ml the milk will be
rejected at the factory gate

11. Milk Chain Ethiopia: model simulations
11
 current situation evening milk
• with an initial contamination of
log 4 cfu/ml the milk will be
rejected at the collection point

12. Milk Chain Ethiopia:
12
 Estimation of rejected milk with model and survey data of raw milk in
Ethiopia (Tegegne 2017) and Normandy (Mallet 2012)
 The Tegegne model estimates a rejection rate of 8%, which is in good
comparison with the 7% of the actually measured rejection rate.
 With the Normandy quality, no rejection is expected.

13. Milk Chain Ethiopia: Cooling@Farm
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* Plastic cans in freezer
(cooling rate 0.23 1/hr)
** Metal cans in Simgas chiller
(cooling rate 0.6 1/hr)
rejection of milk samples %
Scenario
8:00
collection road
10:00
factory gate
A Current evening 96% 97%
A Current morning 0% 8%
B Cooling Farm evening plastic* 21% 26%
B Cooling Farm evening metal** 0% 3%
B Cooling Farm morning Same as A morning

14. Milk Chain Ethiopia: Cooling@Centre
14

15. Milk Chain Ethiopia: Chilling Senario’s
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 Expected reduction of milk rejection for the different cooling interventions

16. Milk Chain Ethiopia: long distance transport
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 Possibilities to increase supply area
• Chilling centres but more preferable cooling trucks can be used to
increase the transport times efficiently
Open Truck Cooling truck Chilled collection point

17. Milk Chain Ethiopia: Evaluation
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 On farm cooling with metal cans is effective to chill evening milk
Challenge :
● power availability of electric power
● high investment costs
● relevancy of milk for the farmer’s family nutrition
 Cooling centres at collection point limits the risk of rejection.
This option is useful only for evening milk, or for late open truck
collection.
Challenges:
● farmers are not willing to walk at night to the chilling centres
● when a tank filling would be rejected at factory that would
have a major effect
 Cooling truck: flexible, increases sourcing area of morning milk

18. Milk Chain Ethiopia: Conclusions
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 Simulation tool is useful for ranking and communication of interventions
 The study shows which cooling options could support increasing supply of
milk with low rejection rates. However, it is not obvious that any of the
studied interventions will increase the milk supply for the milk factory as this
will also depends on:
● The willingness of the farmer to sell more milk
● Investment needed for a chiller
● The willingness of the farmer to walk the chilling centre twice a day
 For the factory the best options to increase their volume is by increasing
the supply area for the morning milk. Simulations shows that collection with
a cooling truck probably is the best choice to do this.