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Effects of milk cooling: A case study on milk supply chain for a factory in Ethiopia


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Evaluation of different chilling scenarios for improving and increasing the milk supply. Summary of study findings.
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Effects of milk cooling: A case study on milk supply chain for a factory in Ethiopia

  1. 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. 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. 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. 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. 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. 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. 7. Milk Chain Ethiopia: Model 7  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. 8. Milk Chain Ethiopia: Chilling scenario’s 8 No Cooling Cooling @Farm Cooling @Centre Cooling @Centre 2x
  9. 9. Milk Chain Ethiopia: model simulations 9  current situation morning milk • With an initial contamination of log 4 cfu/ml the milk arrives fresh at the factory
  10. 10. Milk Chain Ethiopia: model simulations 10  current situation morning milk • with an initial contamination of log 6 cfu/ml the milk will be rejected at the factory gate
  11. 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. 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. 13. Milk Chain Ethiopia: Cooling@Farm 13 * 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. 14. Milk Chain Ethiopia: Cooling@Centre 14
  15. 15. Milk Chain Ethiopia: Chilling Senario’s 15  Expected reduction of milk rejection for the different cooling interventions
  16. 16. Milk Chain Ethiopia: long distance transport 16  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. 17. Milk Chain Ethiopia: Evaluation 17  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. 18. Milk Chain Ethiopia: Conclusions 18  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.