Improving evidence for better policy making in Ethiopia’s livestock sector
- 1. Photo Credit Goes Here IMPROVING EVIDENCE FOR BETTER POLICY MAKING IN ETHIOPIA’S LIVESTOCK SECTOR Webinar 24 June 2020
- 2. Photo Credit Goes Here ETHIOPIA’S EVOLVING LIVESTOCK SECTOR Fantu Bachewe 24 June 2010 Improving evidence for better policy making in Ethiopia’s livestock sector
- 3. INTRODUCTION • Agriculture accounted for 29% of GDP growth during 2004-2014 • Crop accounted for 24% and livestock for 5% of the growth o Importance of livestock in agriculture declined in all but two years Figure 1 — Share in GDP and average contribution to GDP growth, 2004/5-2015/16 0 20 40 60 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Share in GDP (%) Agriculture Crop Livestock Industry Services Crop 24% Livestock 5% Other primary 2% Industry 16% Services 53% Average contribution to GDP growth
- 4. INTRODUCTION…CONTD. Livestock number • Livestock means of ASF production & are themselves end-products • Cattle (58 million in 2015) largest & on average grew at 3.8% during 2004-2015 o Number of sheep (29 mill) grew at 4.6% and others at 5.6% or higher • Livestock deaths significant and adversely affect productivity o Over 3 million cattle, sheep, & goats lost to death annually since 2005 • Deaths as % of stock declined, e.g. cattle 16% Supply and demand side factors indicate considerable potential in livestock production 1. Home to large stocks of many livestock types: means of ASF prod’n & end-products • Cattle largest & grew at 3.8% during 2004-2015; sheep (4.6%), & others at ≥5.6% • About 4 heads of cattle & poultry; No variation in size of ownership • Real value of livestock 11.2 billion USD (720 USD/farm) in 2015, o Grew by 58.4% (4.3% per year); similar to growth in TLU (56.4% or 4.2% per year) 2. Livestock/ASF produced by large number of farmers • About 92% farm HHs owned at least one livestock in 2015 • Proportion of owners remained about same but number increased by 51% 3. Consumption spending on ASF as well as livestock exports grew • Further increase in spending expected with growth in income
- 5. PRODUCTION AND PRODUCTIVITY Live animal marketing • Number sold for off-farm slaughter, net commercial off-take (NCOT), doubled in cattle, grew at 56% in sheep and at 131% in goats • NCOT rate (NCOT/average stock) grew in all animals except poultry but growth slow Figure 2 — Net commercial off-take number (millions) and rate (%), 2005-2014 0 2 4 6 8 10 12 Cattle Sheep Goat Poultry Cattle Sheep Goat Poultry NCOT (number) NCOT rate (%) 2005 2010 2014
- 6. PRODUCTION AND PRODUCTIVITY…CONTD. Animal-sourced food (ASF) production and productivity • ASF output grew considerably o Egg output grew by 93% and milk output by 41% during 2005 – 2015 • Productivity (output per animal) stagnated • Milk yields in Uganda, Kenya, & Malawi were 27%, 125%, & 239% higher than Ethiopia in 2014 Figure 3 — Annual milk and egg production and productivity, 2005-2015 0 2 4 6 8 10 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Output Eggs (million dozens) Milk (billion liters) 0 1 2 3 4 5 2005 2010 2015 Productivity Milk (100 lts/cow/year) Egg (dozens/hen/year)
- 7. PRODUCTION AND PRODUCTIVITY…CONTD. Real value of livestock output per TLU and per farmer • Real livestock output in 2015 was 73% higher than in 2004 • Real output per TLU & per farmer was 11% & 15% higher in 2015 than 2004 Growth accounting analyses of the livestock subsector • Labor and livestock most important, accounted for three-quarters of output growth • Modern inputs contributed only 11% to livestock output growth 700 800 900 1,000 1,100 1,200 2,000 2,500 3,000 3,500 4,000 4,500 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Output/TLU Output/holder Value of output/farmer Value of output/TLU Labor 49% Grazing land 9% Livestock 25% Improved feed 9% RTS & rural roads 5% Other factors 2% ∆ TFP 1% Figure 4 — Real value of output per farmer and per TLU, 2004-2015 Figure 5 — Average contribution as % of livestock output growth (2004 – 2014)
- 8. INPUT USE AND INTENSIFICATION Livestock feed • Green fodder (grazing) important but share of users declining o Consistent with decline in grazing area per farmer observed during the period • Lower share use improved feed but share doubled o Purchased, particularly Improved feed, increasing in importance Livestock extension • Number of dairy, meat, & poultry extension users increased by 28%, 11%, & 37% • Proportion of extension users stagnant & considerably lower in livestock Figure 6 — Number of farmers using livestock extension services, 2005 and 2014 0 40,000 80,000 120,000 160,000 Dairy Meat Poultry 2005 2014 0 10 20 30 40 50 2005 2014 Crop extension (%) Livestock extension (%)
- 9. INPUT USE AND INTENSIFICATION…CONTD. Veterinary services • Share of vaccinated cattle, sheep, and goats more than doubled • Share afflicted with diseases grew slightly in cattle & sheep and declined in goats (excluding 2008 it declined in all 3 species) • Sick cattle treated grew by 63% and by over 114% in sheep & goats Figure 7 – Share of vaccinated and treated livestock, 2004-2015 0 20 40 60 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Vaccinated (% out of stock) Cattle Sheep Goats 0 20 40 60 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Share of treated out of afflicted (%) Cattle Sheep Goats
- 10. INPUT USE AND INTENSIFICATION…CONTD. Artificial insemination • Artificial insemination (AI) increasingly being adopted in the country • Calves born using AI 11 times higher in 2016 (230,000) than in 2005, but still low Dairy processing and livestock breed composition • Number of dairy processors increased by 260% & processed milk output by 93% • Share of cross-bred cows and poultry low but grew considerably Figure 8 — Number of milk processing plants and processed milk output, 2007-2015 Figure 9 — Share of cross-bred cows and poultry (%), 2004-2014 0 2 4 6 8 10 0 10 20 30 40 2007 2008 2009 2010 2011 2012 2013 2014 2015 Monthlyprocessedmilk output Dairyprocessors Dairy processors (number) Monthly processed milk (million liters) 0 1 2 3 4 5 2004 2010 2014 Cross-bred cows (%) Cross-bred poultry (%)
- 11. ASSOCIATES OF MODERN INPUT USE Results of econometric analyses • Period marked with rapid growth in % using vaccination, improved feeds & breeds, • Intensification in use of such inputs need to drive future livestock output growth • Study investigates factors associated with adoption of 3 inputs for cattle & shoats Results indicate that: • Education and household size positively associated with adoption, • Adoption of all inputs positively associated with receiving extension advice • Distance to urban centers negatively associated with adoption of inputs, • Larger herd size positively associated with adoption of inputs, • Adoption positively influenced by past levels of adoption in the woreda- neighborhood effect and ease of access
- 12. CONCLUSIONS • Rapid growth in number of livestock and livestock owners o Livestock contributed little to GDP and importance declined • Demand and supply factors imply considerable potential for livestock output growth • Number sold for meat production increased considerably but share still low • Milk and egg output grew considerably but productivity is stagnant • Labor & livestock accounted for most of output growth • Improved feed and purchased feed growing in importance but share still low • Cross-bred cows & poultry growing in importance • Share using livestock extension low and stagnant • Vaccination & treatment increased considerably; share diseased overall declined • Death rates declined slightly o However, higher than twice proportion sold for meat are lost to deaths,
- 14. Photo Credit Goes Here Transformation of Ethiopia’s livestock sector: The role of income and prices Bart Minten, IFPRI May 24th, 2020
- 15. Quick transformation in Ethiopia • Rapid poverty reduction • Rapid decline in stunting • However, stunting levels still very high • Monotonous diets an important issue 0 20 40 60 80 2000 2005 2011 2016 % Stunting Poverty head count
- 16. Signs of increasing diversification in the food basket • Share of cereals in expenditures on the decline 0 10 20 30 40 50 60 70 80 2000 2005 2011 2016 Shareinfoodexpenditures(%) Cereals Non-cereals
- 17. 0 2 4 6 8 10 12 14 2000 2006 2011 2016Shareinfoodexpenditures(%) Animal source foods Fruits & vegetables Increasing expenditures on high-value and nutritious crops 1/ Animal source foods (dairy most important) 2/ Fruits and vegetables
- 18. However, part of increased expenditures to pay for increased prices of ASF -60 -40 -20 0 20 40 60 80 Grains, roots and tubers Legumes and nuts Dairy products Eggs Flesh foods and others/small animal… Vitamin A dark green leafy vegetables Other Vitamin A rich vegetables and fruits Other fruits and vegetables Oils and fats Sugar and honey % Price changes (2018 compared to 2015, %)
- 19. Richer people spend significantly more on ASF than poorer people 0 2 4 6 8 10 12 14 16 18 20 Poorest q2 q3 q4 Richest %foodexpenditures
- 20. • Demand estimations based on national household surveys (HICES) • Estimates of price and income elasticities, i.e. if prices/change change with x%, consumption changes with y% What is the impact of changes in prices and incomes on consumption?
- 21. • Price elasticities (QUAIDS model): - Beef: -0.73 - Mutton and goat: -0.12 - Dairy products: - 0.45 - Other meat and animal products: - 1.46 • High impacts of price changes. Noted price increases in last decade ->25% reduction in beef (ceteris paribus)… Prices matter enormously for ASF
- 22. • Income elasticities (QUAIDS model): - Beef: 0.465 - Mutton and goat: 2.702 - Dairy products: 0.511 - Other meat and animal products: 0.479 Income changes important drivers for change of the livestock sector
- 23. Especially commercial ASF markets are expected to quickly grow 0 5 10 15 2011 2030 Billion2011Birr rural urban 0 5 10 15 2011 2030 Billion2011Birr Beef Mutton Dairy Other
- 24. • Ethiopia successful in cereal sector • Less so for nutritious crops, and more in particular in livestock sector • More attention needed: 1. Want to keep prices low - nutritional benefits 2. Massive demand changes are coming, esp. leading to large changes in commercial demand 3. Issue of inclusiveness
- 26. Photo Credit Goes Here Trade and Value-Chain Assessment of Live Animal Exports from Ethiopia Tadesse Kuma, Endale Gebre, and Ashagrie Gibtan, PSI June 24, 2020 Virtual Seminar Improving evidence for better policy making in Ethiopia’s livestock sector Co-Organized by IFPRI, the Livestock Systems Innovation Lab (LSIL), the Ethiopian Institute of Agricultural Research (EIAR) and Policy Studies Institute (PSI)
- 27. 1. INTRODUCTION Livestock sector has an important place in Ethiopian economy (12-16 percent of total GDP; 30 percent of agricultural GDP) In the lowlands areas (with pastoral management), livestock is the principal source of subsistence Export earning from live animal and animal products contributes substantial share of national export earnings; however, its earnings declined in the recent years: 400 million USD in 2011 to 252 million in 2018 (36 percent drop) Similarly, live animal exports declined from 207 million in 2011; 148 million in 2015; 45 million in 2018/19 (a 17% drop per year). Purpose analysis is to understand: 1/ Reasons for this drop; 2/ Implications on smallholder income
- 28. 1. INTRODUCTION Trends of live animal and animal products export earnings (mill USD) 0.0 50.0 100.0 150.0 200.0 250.0 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Value(inMilliionUSD) Leather and Leather Products Live Animals Meat & Meat Products Source: Ethiopia Revenue and Customs Authority, 2018
- 29. 2. DATA AND METHOD Data sources: • Secondary data on production, price (producer, retail), access costs, trade volume and value, livestock population, animal feed, etc. from CSA, ERCA, NBE, Regional ECRA, and International sources. • Primary data gathered through consultative meeting with Key Informants • Field visits to quarantine centers Method of analysis: • Both descriptive and simple statistical tools • The MAFAP/FAO methodology
- 30. 3. KEY CHALLENGES OF LIVE CATTLE EXPORT IN ETHIOPIA (a) Lack of accredited quarantine center for Ethiopia - The efforts GoE to put in place standard accredited quarantine centers; Since early 2000, the GoE made efforts to establish 5 export quarantine centers (Mile, Jijiga, Metema, Humera, and Almehal); 230 million Birr investment However, none of them are operational that relay on Djibuti QC (a) Governance of quarantine in Djibouti (Live animals kept before being exported to Middle East (re-certified Djibouti origin). • Quarantine by the Government of Djibouti in hands of one person (Mr. Abu Yasir), who seemingly manages at his will • Excessive cost of quarantine services: USD 2/shots and USD 7- 8/cattle/per night plus cost of pre-quarantine health services • Lack of transparency: rejection of live cattle from Ethiopia without detection of sufficient defect or disease;
- 31. 3. KEY CHALLENGES LIVE CATTLE EXPORT FROM ETHIOPIA (c) Excessive and costly legal requirements for live animal exporting which is also highly bureaucratic Export license Hiring a transitory company for custom clearance Currency regulation requirements (letter of credit (LC) or Cash Against Document (CAD), or deposit an advance payment in a bank) Health certificate from a quarantine service station (7- 21 days of quarantine during which the exporter is responsible for all feed and watering expenses).
- 32. (d) Booming informal trade Informal cross-border trade practiced in the eastern, western, southern and northwestern borderlands of Ethiopia (5 routs); 75-80% of total export Trade via Ethio-Somaliland, Djibouti, and Kenya account for lion’s share Reasons: Excessive costs and inefficiencies of the export requirements Invisible hands of politicians – although it is difficult to prove, ICB live animal trade backed by invisible/untouchable/powerful politicians High foreign exchange demand Lack of access to market infrastructure in the close proximity for border communities Price difference (higher price at the border markets) Historical economic and social attachments of border community coordination failure of different tiers of government instituions 3. KEY CHALLENGES LIVE CATTLE EXPORT FROM ETHIOPIA
- 33. 4. RESULTS Access costs of feedlot operators (%) Trend of average feed price (Birr/Kg) Cost of Handling 1% Feed cost at felots (75 days) 59% Other cost at feedlot (for 75 days) 4% Transport cost from FG to PoC 8% Taxes and fees 4% Capital cost 7% Overhead cost 4% Trader margin 13% 0.98 1.06 1.6 2.49 2.41 3.64 4.83 6.06 7.4 7.98 2003 2004 2006 2008 2009 2010 2015 2017 2019 2020 Value(Birr//Kg)
- 34. Access costs of exporter (% share) Average composition of Market Development Gap for live animal export trade (2005-2019) (%) Handling costs, 22 Transport cost from PoC to Djibouti , 12 Taxes and levies, 30 Trader margin, 12 Capital cost, 18 Overhead costs & Miscellane ous , 6 35% 65% Inefficiencies from border to point of competition Inefficiencies from the point of competition to farm gate 4. RESULTS Future market development in the sector needs substantive reduction of excessive transaction/access costs which account for current market inefficiencies
- 35. Producer, wholesale and export (f.o.b.) prices, 2005 to 2019• Wholesales price is 28% higher compared to fob price throughout 2012- 2019, yet cattle is exported • Under normal condition, farmgate prices should be lower than export (FOB) price but here it was more or less equal to FOB price 2017-2019 • Efforts to access exchange rate accounts for these abnormalities 0 5000 10000 15000 20000 25000 30000 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Birr/headoflivecattle Export price Producer price Wholesale price Export price (parallel ER) 4. RESULTS
- 36. Wholesale price vs. observed and adjusted reference FOB price at wholesale market (Birr/head) Farm gate price vs. observed and adjusted reference FOB price at farmgate (Birr/head) 0 5 000 10 000 15 000 20 000 25 000 30 000 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Birr/headofcattle Wholesale price Observed FOB price Adjusted FOB price 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Price:Birr/headoflivecattle Farmgate price Observed FOB Price Adjusted Fob Price • Live cattle exporters make substantial losses Farmers receive disincentives 2005- 2011 4. RESULTS
- 37. 5. CONCLUDING REMARKS • Key findings – booming informal trade in the recent years (mainly since 2015) – High domestic demand for meat together with skyrocketing access costs made livestock export trade uncompetitive (feed, quarantine services, inefficient government system) – Unmet demand for foreign exchange - exporters offer generous prices to wholesalers; high prices passed on to producers – Exporters incur substantial loss, compensated through importing goods with low import parity price and high domestic prices (similar to what happened for coffee export);
- 38. Following policy measures required: Improve institutional arrangement for live animal export trade Strengthening quarantine services – meet international standard and accredited Re-visiting causes of current foreign exchange misalignment Without prejudice of historical ties of cross border communities, strengthening enforcement capacity of existing laws and regulation to reduce current informal trade Improving access, availability and quality of animal feed has an important implication for future development of the sector 5. CONCLUDING REMARKS
- 40. Photo Credit Goes Here Alternative national development scenarios and their implications for the livestock system in Ethiopia Emerta A Aragie (with James Thurlow, Seneshaw Tamiru and Ermias Engida) June 24, 2020 Virtual Seminar
- 41. PROBLEM • IFPRI research shows that demand for ASF has grown rapidly. • Income growth and urbanization is driving this change. • The LMP lays out an ambitious and sizable investment plan based on very high projected demand growth for 2015-2020 period • But income growth slowed down considerably (from 10% to IMFs latest forecast of 6.5% ) • COVID and other risks could push this down even further over the next five years • This means the LMP is no longer appropriate for the new economic trajectory. >35% annual increase in meat consumption >10% annual increase in meat production >10% annual increase in milk consumption >12% annual increase in milk production
- 42. SOLUTION • For national planning and identifying investment needs, it is crucial to reassess projections and policy priorities. • We developed a more sophisticated integration of advanced economic modeling with traditional herd dynamics. • This tool allows us to quantify in a more consistent way • the linkages between economic growth and agri-food system transformation, and • develop trajectory of the livestock system. Fig.: Integrated modeling framework with information flows and outcome indicators Herd Dynamics Model (HDM) Economy wide model (CGE) Microsim ulation module (MSM) Herd sizes, feed needs, etc. Consumer demand Changes in GDP and employment, etc. Changes in poverty and dietary diversity Incomes and prices
- 43. HERD DYNAMICS MODEL The HDM: • Feature: tracks herd sizes disaggregated by age, sex and breed. • Inputs: utilizes baseline stock, indicative birth, death, offtake and intake rates • Outputs: system consistent births, deaths and offtakes, meat, milk production. • Nature of the data • Currently focusing on cattle • Ethiopia’s Agric. Sample Surveys (ASS). • The data runs from 2003 – 2017 • Survey reports cattle by five major age groups by sex • We build the cattle database for 5 agro- ecological zones. Fig.: Schematic representation of herd dynamics- e.g.: cattle Figure: Schematic representation of herd dynamics Survive Survive Survive Survive Survive SurviveFemale Birth Male Birth Young male Young female Immature male Immature female Adult male Adult female Offtake Male deaths Female deaths Life expectancy Milk Manure Manure Hides and skins Livestock capital
- 44. LINKING TO ECONOMIC MODEL • Data transformation and demographic variables computation • 13 single-year age class cattle database – from 5 class • Transform the data to create smoothed, demographically consistent life-table. • Linking the demographic information to the gams based HDM • Once internally consistent demographic parameters are computed, export to the HDM. • Smoothed baseline data – for 2016/17 • Fertility rate, death rate, offtake rate, etc • Interacting the HDM and the core CGE model –> bi-directional • From the HDM – feed demand, livestock capital trend for the meat and milk activity • From the CGE model – activity prices (meat and milk offtake rates), input availability (live-weight, etc.)
- 45. FOUR FUTURE SCENARIOS • Baseline – using a core CGE model for Ethiopia - 8 livestock products. • 6.5% GDP growth with current pace of urbanization (+5 %-points by 2030) • Four future economic-demographic scenarios • Rate of economic growth (4.0% vs. 9%) • Rate of urbanization (no change in pop share by 2030 vs. +5%-points by 2030) • Hypothetical growth and urbanization scenario to provide a risk-based approach to the prospects of the livestock sector. • Results reported as deviations from baseline trajectory.
- 46. RESULTS | LIVESTOCK IN THE ECONOMY • Demand for ASF heavily depends on economic-demographic changes • Income growth main driver of overall livestock system expansion • Urbanization shifts the composition of livestock system towards proc. ASF Fig.: Percentage points deviation from baseline growth Fig.: Percentage points deviation from baseline change in livestock product consumption 2.7% 2.1% 2.0% 5.5% 2.4% -2.8% -2.2% -2.2% -6.5% -2.6% Total GDP Cattle Milk Meat processing Dairy processing Fast growth & fast urbanization Fast growth & slow urbanization Slow growth & fast urbanization Slow growth & slow urbanization 1.9% 1.8% 2.0% 1.2% 2.5% 1.6% -1.2% 0.8% -0.6% -1.9% 1.5% -1.0% Rural Urban National
- 47. RESULTS | HERD DYNAMICS • Consistent results within the HDM framework • High economic growth is linked to high meat and milk offtake levels • High urbanization leads to higher meat offtake rate, but not to a clear increase in milk offtake. Fig.: Percentage points deviation from baseline change in meat and milk offtake 1.2% 0.8% -0.6% -0.7% Fast growth & fast urbanization Fast growth & slow urbanization Slow growth & fast urbanization Slow growth & slow urbanization Meat offtake 1.1% 1.0% -1.1% -1.1% Milk offtake
- 48. CONCLUSION • Demand for ASF heavily depends on economic-demographic changes • Income growth main driver of overall livestock system expansion • Urbanization shifts the composition of the livestock system towards processed ASF product • Livestock development plans should reflect economic reality & potential risks • Previous LMP may have overestimated economic growth & hence resource needs (but not priorities) • Integrating economywide & livestock systems models can strengthen basis for planning • Modeling component of the IFPRI project continues until August • Identifying future development scenarios for Ethiopia’s livestock system • Evaluating drought damages and recovery scenarios
- 49. Thank you! For further questions and comments: [email protected]
- 51. Photo Credit Goes Here Transformation in the dairy sector Seneshaw Tamru (IGC) June 24, 2020
- 52. INTRODUCTION • Transformation in Ethiopia • Drivers: GDP growth Urbanization Infrastructure • Dairy sector? Policy Lack of good data
- 53. DATA • Primary data: survey fielded in January – February 2018 • Focus: major producing areas supplying Addis • Stratified by distance to Addis: • Addis Ababa: 97 farms; • Suburban: 256 farms; • Rural (North and West Shewa): 602 farms
- 54. DATA • Look at : • 1/ Remote vs non-remote farms • 2/ Small vs large dairy farms
- 56. Upstream - Yields - Access to services - Adoption of modern inputs
- 57. Yield 200400600800 1000 0 50 100 150 Travel costs (birr/person) at time of survey 10 years earlier 0 200040006000 yield(l/year/cow) 1-2 cows 3-24 cows >24 cows at time of survey 10 years earlier
- 58. Services - Extension: 0 .2.4.6.8 1 0 50 100 150 Travel costs (birr/person) at time of survey 10 years earlier 0 .2.4.6.8 1 ShareDAs(mostly)available 1-2 cows 3-24 cows >24 cows at time of survey 10 years earlier
- 59. Adoption - Cross bred cows 0 .1.2.3 0 50 100 150 Travel costs (birr/person) at time of survey 10 years earlier .2.4.6.8 1 sharecross-bredcows 1-2 cows 3-24 cows >24 cows at time of survey 10 years earlier
- 60. Feed -Purchased Wheat Bran 0 .1.2.3.4.5 0 50 100 150 Travel costs (birr/person) at time of survey 10 years earlier .2.4.6.8 1 Shareusingpurchasedwheatbran 1-2 cows 3-24 cows >24 cows at time of survey 10 years earlier
- 61. Health -Distance to nearest pharmacy 406080 100120140 0 50 100 150 Travel costs (birr/person) at time of survey 10 years earlier 20406080 100 Walkingtime(minutes)togetmedicine 1-2 cows 3-24 cows >24 cows at time of survey 10 years earlier
- 62. Conclusion • Large changes in the dairy sector in Ethiopia • Implications: (1) Positive dynamics, but from a low base - more effort needed to further stimulate dynamics (2) Increasing importance of purchased feed (3) The role of market access (4) Inclusiveness of remote and small dairy farms
- 63. THANK YOU DISCUSSION PAPER (ESSP WP 129): “TRANSFORMING AGRI-FOOD SYSTEMS IN ETHIOPIA: EVIDENCE FROM THE DAIRY SECTOR” BART M. (IFPRI), YETIMWORK H. (PSI), SENESHAW T.(KULEUVEN) AND AGAJIE TESFAYE (EIAR)
- 65. Photo Credit Goes Here Fasting, food and farmers: The case of dairy Eline d’Haene, University of Gent Senne Vandevelde, EU Bart Minten, IFPRI May 24th, 2020
- 66. Introduction The role of religion in shaping food choices, nutrition, and food value chains not well understood. Ethiopia: 1. Large group of Orthodox Christians 2. Multiple Orthodox fasting periods - abstinence from animal-source foods 3. Low overall consumption of milk in the country, especially by children, and underdevelopment of the dairy sector
- 67. Research questions 1. What is the effective number of fasting days adhered to by Orthodox Christians? 2. How do milk producing households and markets cope with these cycles of religious fasting? 3. What is the impact of fasting on children’s milk consumption?
- 68. Milk production and output data are recorded using a one- month recall period Consumption data: one-week and one-day recall period The timing of the data collection good to evaluate influence fasting Data and methods
- 69. Fasting participation Fasting reduces annual dairy consumption at national level by 12%
- 70. Impact fasting on dairy sector Prices fall during fasting – good for consumption of those that do not fast. Production goes down during (long) fasting periods Price swings larger for storable dairy commodities compared to liquid milk
- 71. Adaptation strategies To cope with fasting, dairy farmers adopt different strategies: They reduce milk output at the farm by aligning number of cows in milk with fasting periods (esp. remote) They increasingly channel surplus milk to children (esp. non-remote) They expand home processing activities
- 72. Milk Butter Prices (1) Lent; (2) Easter; (3) Easter-May; (4) Jun-Advent; (5) Advent; (6) Christmas; (7) Christmas-current
- 73. Dairy consumption Reduction of consumption of dairy products at the household level Generally, higher milk consumption by children during fasting Differential effect of remoteness. In accessible areas, consumption of children: - Non-fasting: 2 l/week - Fasting: 3 l/week
- 74. Implications Fasting matters but availability and affordability important impediments to increased dairy consumption in Ethiopia further investments in the dairy sector needed A small number of children participates in fasts awareness creation is needed To smoothen effects of demand swings, further efforts are needed towards enhancing processing practices (UHT and powdered milk) improving transportation/chilling facilties to assure better market integration
- 76. Photo Credit Goes Here Food safety, modernization and food prices: Evidence from milk in Ethiopia Yetimwork Habte, ESSP/PSI June 24, 2020 Addis Ababa
- 77. Photo Credit Goes Here Introduction • Assuring food safety is a challenge in many developing countries. • Typically a problem with milk value chains (perishability, adulteration) • Modern processing and marketing mechanisms to solve this issues • However, there is lack of evidence on how these modern arrangements perform in early stages of roll-out. • We study this issue in the case of rural-urban milk value chain in Ethiopia focusing on answering the following three questions: 1) To what extent modern processing companies linked to improved hygienic practices? 2) Does improved practices reflected in prices? 3) What is the role of modern retail?
- 78. Photo Credit Goes Here Data • Survey fielded in January - February 2018 • 955 dairy producers (97 Addis Ababa, 256 Sub-urban and 602 Rural areas (North and West Shewa zones) Modern channel: Milk producers normally sells to agents or traders of processing companies. Traditional channel: producers selling to traditional traders or directly to consumers, institutions, or service sector • 208 outlets in Addis Ababa that sells liquid milk (28 modern mostly supermarket, 113 Minimarket and regular shop 67 Dairy shops)
- 79. Photo Credit Goes Here Hygienic practices, testing and milk handling 0 20 40 60 80 100 Udder and teats are cleaned Stored milk kept in a fridge Morning and evening milk are put together Different use of milk when cows are sick Hygienic practices (%) Modern channel Traditional channel 0 20 40 60 80 100 quality is checked through lactometer quality is checked through alcohol test buyer uses stainless steel/aluminum buckets/cans Testing and milk handling (%) Modern channel Traditional channel
- 80. Photo Credit Goes Here Prices at the producer level Density function of prices modern versus traditional value chain • Prices paid to modern channel suppliers are overall lower than in traditional ones • However, prices similar when controlling for other factors (e.g. suppliers located further from city)
- 81. Photo Credit Goes Here Modern channels rely more on non-price mechanism to bind producers to buyers 0 10 20 30 40 50 60 70 advice on improved milk production advice on hygienic milk handling advice on health risks inflicted by adulteration keeps buying milk during fasting periods provides medecine/veterinary services/AI supplies milk storage cans supplies feed provides credit/loans Service delivery by the most important buyer (%) Modern channel Traditional
- 82. Photo Credit Goes Here Prices at the retail level – Pasteurized milk Density function of prices pasteurized versus raw • No large differences between pasteurized and raw milk • The price difference vanishes once we control for type of retail outlet, fasting period dummy & location
- 83. Photo Credit Goes Here Prices at the retail level – Modern retail Price density function modern versus traditional retail • Not much difference of price for modern compared to traditional outlets. • In regression, modern retailers sell milk at a price that is 3% lower than in traditional shops.
- 84. Photo Credit Goes Here Conclusions • Adoption levels of hygienic practices and practices leading to safer milk by dairy producers in Ethiopia low • No major differences between traditional and modern milk value chains; • Modern processing companies do more formal milk testing; no price premiums paid by them at the producer level for the adoption of improved practices. • At urban retail level, no prices difference for pasteurized and raw milk. • Modern retail sells pasteurized milk at lower prices ceteris paribus, possibly driven by shorter value chain. • Modern value chains to better reward hygiene and food safety in these settings are therefore called for.
- 86. Photo Credit Goes Here The quest for safe food: The COVID-19 crisis and dairy value chains in Ethiopia Agajie Tesfaye (EIAR), Yetimwork Habte (ESSP-PSI), and Bart Minten (ESSP-IFPRI) Presenter: Agajie Tesfaye Date: 24 June 20 Venue: Webinar
- 87. INTRODUCTION • COVID-19 pandemic started in Ethiopia since mid-March 2020 • A rapid qualitative appraisal was made on the dairy value chain supplying the city of Addis Ababa • Purpose was to understand the effects that COVID-19 might have on the dairy sector • Interviews to nearly 100 key informants/Value chain actors: • Telephone interviews with dairy farmers and institutes, milk collectors, processing companies • Face-to-face interviews to supermarkets markets, retails shops, commercial service centers, consumers
- 88. COVID-19 EFFECTS ON INPUTS IN DAIRY SECTOR Effects on dairy feeds • Large scale concentrate feed processing companies had stocks of raw materials – few impacts (no noticeable changes on supplies) • Some of the dairy farms attempted to keep stocks of feeds at the beginning of the pandemic because of uncertainty • Most of the dairy farms in the city of Addis Ababa faced no supply shortages and price changes on the feed market • Instead, suburban dairy farms faced short lived price rise (about 30 – 40%) for wheat bran and nugcake because of restricted transportation access • Overall, no noticeable effects on feed supply, demand and prices on dairy feeds because of COVID-19
- 89. Effects on dairy labor • Most of the medium and small-scale dairy farms depend on family labor • Almost no labor lay-offs reported on commercial dairy farms • Dairy labor not yet affected noticeably Effect on veterinary medicines • International supply chains interrupted • Prices of vet medicines increased by 20% COVID-19 EFFECTS ON INPUTS IN DAIRY SECTOR
- 90. COVID-19 EFFECTS ON MILK PRODUCTION • More than 90% of the dairy inputs not disrupted by the pandemic • Overall milk demand not yet noticeably affected • Farm-gate whole-milk prices not noticeably affected by COVID-19 • Because of these, the supply side of milk has not been noticeably affected by COVID-19 in the short run • Instead, fasting season affected milk demand, supplies and prices temporarily
- 91. COVID-19 EFFECTS ON MIDSTREAM VALUE CHAINS Effect on large and medium-scale milk suppliers • Milk collectors collect the milk from suburb dairy farms (commercial and family dairy) and supply to the city of Addis Ababa (5,000 – 20,000 liters/day per collector) • These milk collectors supply whole-milk directly for milk processors • Temporary disruption of milk demand at the beginning because of mis- information by mass-media (not to drink/eat animal source foods) • Corrections made later to consume safest/pasteurized products, but raw milk still not advised. • Prices remained unaffected • COVID-19 pandemic has not much affected the milk collectors in the short run
- 92. Effect on small-scale milk collectors and distributors • Small-scale collectors aggregate <5000 liters of milk/day per collector from urban and peri-urban dairy farms • Supply raw-milk to households, coffee houses, pastries, café’s & restaurants • House-to-house supplies was affected: fear that milk distributors will spread the virus as they are in contact with many clients, travel in public transport • Most of the consumers deserted commercial service enters because of stay-at-home and physical distancing guidelines: • Whole-milk demand dropped in the range of 50 – 70% • Overall, whole-milk supply to households and commercial service centers adversely affected by COVID-19 pandemic
- 93. Effect on milk processing sector • They process milk and produce safest products (pasteurized milk and yogurt) • At the beginning of the pandemic, temporary disruption of demand because of mass-media mis-information • Corrections made (panel discussions held on safety of pasteurized products) • Prices were not affected and demands for pasteurized milk and yogurt returned to normal cycle • COVID-19 little impact on milk processing sector • Instead, fasting season did affect the demand, supplies and prices
- 94. COVID-19 EFFECTS DOWNSTREAM IN VALUE CHAINS Effect on shift in demand • The demand for unsafe products declined: • raw milk • local yogurt prepared from raw milk (local yogurt houses adversely affected) • The demand for safest products highly increased: * Pasteurized milk and yogurt * Powdered milk • So, there is a shift in demand: Unsafe products *Raw milk *local yogurt Safest products • Pasteurized milk • Pasteurized yogurt • Powdered milk Price rise • Pasteurized milk (7%) • Powder milk (20%)
- 95. Effect on shift in mode of consumption Consumption in commercial service centers • Pastries • Coffee houses • Café’s & restaurants Consumption at home Deserting of commercial service centers Stay-at-home and physical distancing A shift in mode of consumption
- 96. CONCLUSION • The dairy sector has proven resilient in the face of the pandemic in the short run • However, COVID-19 might start negatively impacting the dairy sector as the pandemic persists: • Feed supplies will decline & prices will rise • Imports of feed ingredients, vet. medicines will decline • Imports of powdered milk will be affected • Purchasing power of consumers will decline & demand for milk products will start dropping • Following decline in demand, milk supply will also be reduced • Therefore, corrective measures need to be planned at the outset to minimize the impact
- 98. Photo Credit Goes Here Trade, value chain technology, and prices for dairy in East Africa Liz Ignowski (KU Leuven) June 24, 2020
- 99. DAIRY SECTOR TRANSFORMATION -20 -10 0 10 20 30 40 50 60 2005 2010 2015 NetExportValue(MillionUSD) Ethiopia Uganda 0 1000 2000 3000 4000 5000 2005 2007 2009 2011 2013 2015 1000Tonnes Production Ethiopia Production Uganda Consumption Ethiopia Consumption Uganda Sources: Production data: FAOstat; Consumption data: Minten et al. (2018); Van Campenhout et al. (2019); Trade data: Comtrade Production and Consumption Net Exports
- 100. DATA Ethiopia • Collected in January/February2018 around Addis 870 Farmers (317 sell fresh milk) 50 Traders 254 Retailers Uganda • Collected in September 2018 in the central milk shed north of Kampala 1,264 Farmers 440 Traders 55 MCCs
- 101. TECHNOLOGY AND THE VALUE CHAIN
- 102. FRESH MILK PRICES
- 103. PRICES AND DISTANCE
- 104. CROSSBRED COWS Adoption of Crossbred Cows Price of Crossbred Cows
- 105. CONCLUSION • Between country price differences trade position • Within country price differences value chain structure and use of modern technology • Shorter value chains not necessarily more efficient than longer value chains • Use of technology improves milk quality and reduces the impact of farmer's remoteness on prices
- 107. Photo Credit Goes Here Leading the way – FDI and dairy value chain upgrading in Uganda Bjorn Van Campenhout Wednesday, June 24, 2020
- 108. BACKGROUND DVC in Uganda have transformed dramatically over the last few decades • Increase in local consumption and exports • Increase in production and processing capacity • Facilitated by policy reforms • => vibrant sub-sector with dairy becoming the third most important export commodity Describe endogenous technological and institutional innovations that both: • Enabled VC up-grading • Resulted from modernizing value chains Observational data collected at different nodes – compare modern value chains to traditional ones.
- 110. STUDY AREA • Uganda is organized in milk sheds • Two types of value chains: – Southwestern milk shed = modern export oriented VC – Central milk shed = traditional local market oriented VC • Data collected on 4 actors – Producers -1600 – Traders - 700 – Milk collection centers - 100 – Processors - 2
- 111. PROCESSOR LEVEL
- 112. MILK COLLECTION CENTERS • 60 MCCs in Central – 160 MCCs in Kiruhura alone! • Average distance to MCC is 10 km in Central versus 5.4 km in SW • Offer a range of mid-stream services • Medicines and vaccinations (acaricides!) • Milk cans (sanitation) • Credit/loans • training
- 113. MILK HANDLING AND SANITATION
- 114. PRODUCTION Other innovations at this level include: • Paddocking • Dams for water • Cooperative membership • Access to credit/financing for investment • Participate in trainings
- 115. CONCLUSION • MCCs are central, providing many of the support services that enable other actors to produce enough milk and maintain milk sanitation levels necessary for an export sector to emerge. • But MCCs cannot do it alone: – Ticks become resistant to acaricides and there are signs of disadoption – R&D is beyond capacity of MCCs – Artificial insemination too complex to organize – Feed sector needed to increase yields of cross-bred cows • Lack of support service sector also at processor level – Knowledge on processing is scarce – experts need to be flown in – Lack of industrial base – packaging material needs to be imported from Kenya
- 116. MORE INFORMATION Van Campenhout, Bjorn; Minten, Bart; and Swinnen, Johan. 2019. Domestic versus export-led agricultural transformation: Evidence from Uganda’s dairy value chain. IFPRI Discussion Paper 1883. Washington, DC: International Food Policy Research Institute (IFPRI). https://www.ifpri.org/publication/domestic-versus-export- led-agricultural-transformation-evidence-ugandas-dairy- value
- 118. IFPRI-ESSP: http://essp.ifpri.info/ EIAR: http://www.eiar.gov.et/ PSI: https://psi.gov.et/ IMPROVING EVIDENCE FOR BETTER POLICY MAKING IN ETHIOPIA’S LIVESTOCK SECTOR Virtual Seminar | June 24, 2020