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Exploring patterns in child nutrition and livestock ownership in East Africa

Internal presentation for the Policy Institution and Livelihood team at the International Livestock Research Institute.

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Exploring patterns in child nutrition and livestock ownership in East Africa

  1. 1. Exploring patterns in child nutrition and livestock ownership in East Africa Catherine Pfeifer PIL seminar Nairobi, 22 March 2018
  2. 2. Overview 1. Introduction & Objective 2. Methods to identify drivers 1. Milk and egg consumption 2. Children diet diversity score 3. Data 1. DHS data 2. Geographical data 1. Climatic conditions 2. Season map 4. Results
  3. 3. Introduction Animal source food : β€’ Source of protein and critical micronutrients For children in developing countries, modest amount of ASF improves β€’ anthropometric indices β€’ cognitive function and reduces β€’ morbidity and mortality So what is the role of the livestock sector in contributing to improved child nutrition?
  4. 4. Introduction : Pathways linking agriculture to nutrition (World Bank 2007) 1. Increased consumption from increased food production 2. Increased income from the sale of agricultural commodities 3. Increased empowerment of women as agents instrumental to improved household food security and health and nutrition outcomes 4. Reductions in real food prices associated with increased food supply 5. Agricultural growth, leading to increased national income and macroeconomic growth and to poverty reduction and improved nutrition outcomes
  5. 5. Objective : to understand drivers of child nutrition in relation to livestock Children are getting more milk, eggs and have a more diverse diet when : Hypotheses of the pathways : 1 & 2. Livestock ownership β€’ Directly (milk and eggs) β€’ Indirectly (diet diversity score) 3. Increased decision making power of women 4. Market access – decreases transport cost β€’ Decrease real prices for consumers β€’ Increases opportunities for sellers 5. Wealth Other hypothesis assessed: β€’ Favorable climatic conditions β€’ Timing of the interview
  6. 6. Overview 1. Introduction & Objectives 2. Methods to identify drivers 1. Milk and egg consumption 2. Children diet diversity score 3. Data 1. DHS data 2. Geographical data 1. Climatic conditions 2. Season map 4. Results and conclusion
  7. 7. Methods Direct link : probit estimation π‘šπ‘–π‘™π‘˜ = 𝑓 πΆπ‘Žπ‘‘π‘‘π‘™π‘’, π‘‘π‘šπ‘Žπ‘˜π‘–π‘›π‘”, π‘Žπ‘π‘π‘’π‘ π‘ , π‘€π‘’π‘Žπ‘™π‘‘β„Ž, π‘π‘™π‘–π‘šπ‘Žπ‘‘π‘’, π‘ π‘’π‘Žπ‘ π‘œπ‘›, π‘π‘œπ‘›π‘‘π‘Ÿπ‘œπ‘™ 𝑒𝑔𝑔 = 𝑓 πΆβ„Žπ‘–π‘π‘˜π‘’π‘›, π‘‘π‘šπ‘Žπ‘˜π‘–π‘›π‘”, π‘Žπ‘π‘π‘’π‘ π‘ , π‘€π‘’π‘Žπ‘™π‘‘β„Ž, π‘π‘™π‘–π‘šπ‘Žπ‘‘π‘’, π‘ π‘’π‘Žπ‘ π‘œπ‘›, π‘π‘œπ‘›π‘‘π‘Ÿπ‘œπ‘™ Indirect link : negative binomial estimation π‘‘π‘‘π‘–π‘£π‘’π‘Ÿ = 𝑓 π‘‡πΏπ‘ˆ, π‘‘π‘šπ‘Žπ‘˜π‘–π‘›π‘”, π‘Žπ‘π‘π‘’π‘ π‘ , π‘€π‘’π‘Žπ‘™π‘‘β„Ž, π‘π‘™π‘–π‘šπ‘Žπ‘‘π‘’, π‘ π‘’π‘Žπ‘ π‘œπ‘›, π‘π‘œπ‘›π‘‘π‘Ÿπ‘œπ‘™
  8. 8. Overview 1. Introduction & Objectives 2. Method to identify drivers 1. Milk and egg consumption 2. Children diet diversity score 3. Data 1. DHS data 2. Geographical data 1. Climatic conditions 2. Season map 4. Results and conclusion
  9. 9. Data 1 : Demographic Health Survey (DHS) Description Variable level 24h diet recall for children between 1-5 Milk consumption (0/1) Egg consumption (0/1) Child diet diversity score(1-7) Ind. Livestock ownership Cattle, chicken, goat, sheep, pigs (0/1 or number) Hous. Decision making Who takes decision in the household Ind. Wealth Wealth index (1=poorest, 5= richest) Hous. Control Age of the mother & HHH Education of the mother & HHH breastfeeding Ind. Control Agricultural land ownership Hous. Geographic GPS coordinates Cluster Dataset : Ethiopia 2016, Kenya 2014, Uganda 2011, Tanzania 2016 total of 14,167 non-missing observations
  10. 10. Data 2 : geographic data overview Travelling time to cities (Weiss et al 2018) Fourier decomposed NDVI and temperature from modis (Wint et al) Season map (12) Elevation (CGIAR-CSI) - - - - - - - - - - - -
  11. 11. Data 2 : Fourier decomposition of NDVI and temperature Simplified way to capture climatic seasonality : amplitude and phase of 3 harmonic/frequencies (A0,A1,A2,A3,P1,P2,P3)
  12. 12. Data 2 : season map Automatic clustering of monthly precipitation (k-mean) οƒž Each month is classified into β€œdry”, β€œmedium”, β€œwet” οƒž Match with the month of the interview
  13. 13. Data 2 : assessing the season map - Season map vs Rhomis data
  14. 14. Overview 1. Introduction & Objectives 2. Method to identify drivers 1. Milk and egg consumption 2. Children diet diversity score 3. Data 1. DHS data 2. Geographical data 1. Climatic conditions 2. Season map 4. Results and discussion
  15. 15. Result 1 : milk consumption Variable Influence Household in Marsabit Household in Kiambu parameter mfx parameter mfx Cattle + no 0.096 no 0.085 Goat + no 0.083 no 0.073 Women decision making 0 joint - Joint - Wealth - 1 -0.005 1 -0.006 Age + 28 0.002 28 0.003 Education + 5 0.016 5 0.02 breastfeeding - no -0.11 no -0.15 Land 0 yes -0.022 yes -0.03 Travel time + 680 0.0002 18 0.0003 Wet season interview - dry - dry - Elevation (in km) + 769 0.00009 1637 0.0001 NDVI A1, A2,P3 /Temp A0,A2 - Marsabit Kiambu NDVI A3, P2/ Temp A1, A3, P2 + Marsabit Kiambu R- squared 0.12 prob 0.61 0.35
  16. 16. Result 2 : egg consumption Variable Influence Household in Marsabit Household in Kiambu parameter mfx parameter mfx Chicken + no 0.004 no 0.025 Women decision making 0 joint - joint - Wealth + 1 0.002 1 0.013 Age 0 28 0.00009 28 0.0005 Education + 5 0.0008 5 0.004 breastfeeding - no -0.008 no -0.04 Land 0 yes 0.001 yes 0.0067 Travel time - 680 0.000035 18 0.00019 Wet season interview - dry dry Elevation (in km) + 769 0.000003 1637 0.00001 NDVI A1, A2,P3 /Temp A2 - Marsabit Kiambu NDVI A3 / Temp A3, P3 + Marsabit Kiambu R-squared 0.10 prob 0.011 0.092
  17. 17. Result 3 : diet diversity score Variable Model 1 Model 2 Mfx Marsabit Mfx Kiambu Cattle, Goat, Sheep (number) 0 0.009 0.012 Chicken (number) + TLU + 0.009 0.012 Women decision making 0 0 Wealth + + 0.12 0.15 Age + + 0.025 0.03 Education + + 0.082 0.10 breastfeeding - - -1.1 -1.38 Land + + 0.11 0.13 Travel time - - -0.002 -0.003 Travel time squared - - 0.000003 0.000004 Wet season interview , Elevation (in km) 0 0 NDVI A2, P1 / Temp A0, P1 - - NDVI A0, P1, P3 / Temp A2, P3 + + R-squared 0.056 0.056 Score= 1.01 1.25
  18. 18. Conclusion 1. Cattle/Chicken ownership increases child consumption of milk/eggs 2. Livestock intensity (TLU) increases diversity score 3. Market access and wealth : reduces milk consumption, increases egg consumption & diet diversity 4. Women’s decision power has no influence 5. Season of the interview matters for milk and egg consumption but not diet diversity score
  19. 19. This presentation is licensed for use under the Creative Commons Attribution 4.0 International Licence. better lives through livestock ilri.org ILRI thanks all donors and organizations who globally supported its work through their contributions to the CGIAR system

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