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

1. Exploring patterns in child nutrition and
livestock ownership in East Africa
Catherine Pfeifer
PIL seminar
Nairobi, 22 March 2018

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. Introduction
Animal source food :
• Source of protein and critical micronutrients
For children in developing countries, modest
amount of ASF improves
• anthropometric indices
• cognitive function
and reduces
• morbidity and mortality
So what is the role of the livestock sector in
contributing to improved child nutrition?

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. 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
• Directly (milk and eggs)
• Indirectly (diet diversity score)
3. Increased decision making power of women
4. Market access – decreases transport cost
• Decrease real prices for consumers
• Increases opportunities for sellers
5. Wealth
Other hypothesis assessed:
• Favorable climatic conditions
• Timing of the interview

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. Methods
Direct link : probit estimation
𝑚𝑖𝑙𝑘 = 𝑓
𝐶𝑎𝑡𝑡𝑙𝑒, 𝑑𝑚𝑎𝑘𝑖𝑛𝑔, 𝑎𝑐𝑐𝑒𝑠𝑠, 𝑤𝑒𝑎𝑙𝑡ℎ,
𝑐𝑙𝑖𝑚𝑎𝑡𝑒, 𝑠𝑒𝑎𝑠𝑜𝑛, 𝑐𝑜𝑛𝑡𝑟𝑜𝑙
𝑒𝑔𝑔 = 𝑓
𝐶ℎ𝑖𝑐𝑘𝑒𝑛, 𝑑𝑚𝑎𝑘𝑖𝑛𝑔, 𝑎𝑐𝑐𝑒𝑠𝑠, 𝑤𝑒𝑎𝑙𝑡ℎ,
𝑐𝑙𝑖𝑚𝑎𝑡𝑒, 𝑠𝑒𝑎𝑠𝑜𝑛, 𝑐𝑜𝑛𝑡𝑟𝑜𝑙
Indirect link : negative binomial estimation
𝑑𝑑𝑖𝑣𝑒𝑟 = 𝑓
𝑇𝐿𝑈, 𝑑𝑚𝑎𝑘𝑖𝑛𝑔, 𝑎𝑐𝑐𝑒𝑠𝑠, 𝑤𝑒𝑎𝑙𝑡ℎ,
𝑐𝑙𝑖𝑚𝑎𝑡𝑒, 𝑠𝑒𝑎𝑠𝑜𝑛, 𝑐𝑜𝑛𝑡𝑟𝑜𝑙

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. 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. 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. 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. Data 2 : season map
Automatic clustering of monthly precipitation
(k-mean)
 Each month is classified into “dry”, “medium”,
“wet”
 Match with the month of the interview

13. Data 2 : assessing the season map
- Season map vs Rhomis data

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. 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. 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. 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. 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. This presentation is licensed for use under the Creative Commons Attribution 4.0 International Licence.
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