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Cost of milk production in EADD hubs in East Africa
 

Cost of milk production in EADD hubs in East Africa

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Presented by Baltenweck, I., Kinuthia, E., Lukuyu, B., Menjo, D., Atyang, S. and E. Kamanzi at the East Africa Dairy Development Regional Office, Nairobi, Kenya, 07 May 2012

Presented by Baltenweck, I., Kinuthia, E., Lukuyu, B., Menjo, D., Atyang, S. and E. Kamanzi at the East Africa Dairy Development Regional Office, Nairobi, Kenya, 07 May 2012

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    Cost of milk production in EADD hubs in East Africa Cost of milk production in EADD hubs in East Africa Presentation Transcript

    • 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
    • OutlineBackgroundSurvey objectivesSurvey designAnalytical procedureResultsConclusion and Recommendation
    • 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%
    • 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
    • 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 distributionProduction Systems Intensive Extensive Production Systems Extensive Semi-Extensive Uganda Rwanda Uganda Rwanda Hubs per system 3 3Hubs per system 3 3 3 3 Small-scale farmers 4 12Small-scale farmers 20 21 19 21Medium- scale farmer 4 9 17 9 Medium- scale farmer 18 14Total sample size 24 30 36 30 Total sample size 22 26
    • 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
    • Questionnaire Structured survey questionnaires were used to collect data using 3 month recall questionsData 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
    • Analytical procedureProfits 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 costCost of Milk given to labourers and calves is also include as a revenue because it is a product of the farm
    • 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
    • CostsCattle 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 periodMaintenance of buildings Calculated on annual basis and apportioned for three months period
    • RevenuesMilk 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
    • 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 ($1=Kshs 89.4 = RwFrc 577.7 = Ushs 2600)
    • Comparison of cost, profit and total revenue 0.8  All hubs in Kenya made profits 0.7 when total revenue was considered 0.6  In Rwanda, Kigabiro and Muhazi 0.5 made losses due to high 0.4 production cost which was mainly driven by purchased feed andUS$ 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
    • 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 0.2US$ 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
    • Comparison between productionsystems (within countries) Kenya Rwanda UgandaUS$ Extensive Semi-extensive Sign Intensive Extensive Sign Intensive Extensive SignTotal 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
    • Comparison between scaleof 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
    • Comparison between scale ofoperation (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 salesUS$ 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
    • Comparison between scale ofoperation (within countries) Kenya Rwanda UgandaUS$ Small scale Medium Sign Small scale Medium Sign Small scale Medium SignMilk 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 0.52Total 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
    • 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%
    • 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
    • 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
    • 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
    • Plan for: Round 2 of CoP surveyProductivity Monitoring survey
    • 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
    • Herrerro S (2012). Integratedmonitoring. A Practical Manual forOrganisations That Want to AchieveResults
    • 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?