0. Indaba Agricultural Policy Research Institute
Nutritional Effects of Agricultural Diversification and Commercialization on Children in Zambia
Presentation at the Zambia Science Conference
20th October, 2016.
Lusaka
Rhoda Mofya-Mukuka & Christian H. Kuhlgatz
Indaba Agricultural Policy Research Institute

1. Presentation Outline Introduction Data Sources Study Area
Impact Assessment Framework
Results
Conclusions and Policy Implications

2. Introduction
Malnutrition and nutrition related problems remain high in Africa
Zambia has one of the highest rates of malnutrition in the world
Stunting rates: 40 % (reduced from 46.7% in 2007)
Underweight: 15% (reduced from 25.1% in 1992)
Wasting: 6% (increased from 3.1% in 1996)
Zambia is rated “critical” or “very high” for stunting, “medium” for wasting and “medium” for underweight (WHO,2014).

3. Child Nutrition Status: Achieving the SDG Target 2025?
Indicator
1990
2001/2
2007
2014
2025
Percentage of underweight children 25 23 15 5
Percentage of stunted children 40 53 45 10
Percentage of wasted children
5.1 6 -
Source: UNDP, 2003, DHS various years

4. The vulnerable …..
Most vulnerable to malnutrition are the children from rural households
depend entirely on seasonal agricultural production and income
survive on diets that are deficiency in proteins and other important nutrients
Other factors- Mother’s education, young mothers
Photo: Care and Hope for all

5. Why agricultural diversification and Commercialization?
Agricultural diversification and commercialization provide alternative strategies for the rural households to improve diets (Hendrick & Msaki 2009; Khandker & Mahmud 2012)
Diversification makes available diverse food items for consumption
Commercialization increases income and the household’s ability to purchase a diverse range of food items.
High stunting rates in Zambia relates to protein and energy deficiency

6. 2. Study area: Zambia’s Eastern province

7. Research questions
Does a diversified farm production system significantly affect the nutritional status of children?
Does participation in agricultural markets improve the nutritional status of the rural smallholder households?

8. 3. Data and Sources
Socioeconomic, agricultural and anthropometric data
2012 Rural Agricultural Livelihood Survey (RALS) 2010/2011 farming season (IAPRI/CSO/MAL, 2012)
2012 Anthropometric data used to calculate stunting wasting and underweight in children (USAID FTF, 2013)
1120 children (under five years) from different households in five districts in Eastern province.

9. Nutrition outcome variables
Haz (Stunting)
height-for-age z-score
-1.86
1.69
Waz (Underweight)
Weight-for-age z-score
-0.86
1.18
Whz (Wasting)
Weight-for-height z-score
0.26
1.51

10. Impact Assessment Framework
Generalized Propensity-Score (GPS)
Method to identify causal relationships
 If all other variables are fixed, how does a change in treatment affect the outcome?
approach estimates the heterogeneous effects of different intensity levels
Treatment 1: Diversification Treatment 2: Commercialization
Outcome: Nutritional status of children

11. Measuring Treatment Variables
Commercialization (COM),
index derived from the share of agricultural sales in household’s total value of agricultural production.
Diversification (DIV)
Simpson Index over production of 8 major food groups; starchy foods, legumes-nuts-seeds, starchy vegetables, non-starchy vegetables, starchy fruits, non-starchy fruits, dairy, and eggs.
Protein Diversification index
Calorie Diversification index

12. Food Groups Food Group Agricultural Produce Starchy Foods
Maize, Sorghum, Rice, Millet,
Legumes-nuts and Seeds
Sunflower, Groundnuts, Soybeans, Mixed beans, Bambara nuts, Cowpeas
Starchy Vegetables
Green maize, Sweet potatoes, Irish potatoes, Cassava
Non-Starchy Vegetables
Cabbage, Carrots, Rape, Spinach, Tomato, Onion, Okra, Egg plants, Pumpkin, Chilies, Chomolia, Lettuce, Green beans, Impwa, Pumpkin leaves, leaves, leaves, Beans/Cowpea leaves, Chinese Cabbage, Bondwe
Starch Fruits
Bananas, Avocado
Non-Starchy Fruits
Oranges, Pineapples, Guavas, Pawpaw, Water melon, Mangos, Tangerine, Lemons, Grape Fruits, Sugarcane, Sweet Sorghum
Dairy
Milk
Eggs

13. Outcome and Treatment Variables
Description
Mean
Std. Dev.
Treatment variables
Calorie Simpson Index
index "=1-sum of squared calorie shares of the produce.
0.26
0.19
Protein Simpson Index
index "=1-sum of squared crop protein shares of the produce.
0.28
0.18
Commercialization
household commercialization index
0.50
0.27

14. 5. Results i. Treatment with Calorie Diversification (CDIV)
a Underweight b. Wasting
C. Stunting

15. ii. Treatment with Protein Diversification (PDIV)
a Underweight b. Wasting
C. Stunting

16. iii. Treatment with Commercialization (COM)
a Underweight b. Wasting
C. Stunting

17. Conclusion
Impact of Agricultural diversification varies according to intensity and to nutrition challenge
Low diversification results in lower impact on wasting and underweight and positive effect increases with increase in intensity
Increasing levels of commercialization have reducing effect on wasting and underweight (short term nutrition out comes)
Higher levels of commercialization has a significant but negative effect on improving the short-term malnutrition status of children
High and lower levels of commercialization gives high results on stunting

18. Recommendations
Consider intensity of diversification and commercialization when developing interventions
Enhance adequate and diverse protein and calorie sources
Provide households with the opportunity to sell their agricultural products on the market to meet their other income demands.

19. Thankyou!

20. Example of Simpson Index of Diversification
Crops 1ha
Maize
Cassava
Groundnuts
Sweet-potaoes
Rape
Cabbage
Tomato
Total
Share
0.5
0.1
0.2
0.07
0.03 1
Share*2
0.25
0.01
0.04
0.0049
0.0009
0.3158
Simpson Index
0.6842
Sweetpotaoes
0.8
0.64
0.68
0.32

21. Indicator Severity of Malnutrition by Prevalence Ranges (%)
Low
Medium
High
Very high
Underweight
<10
10-19
20-29
>=30
Stunting
<20
30-39
>=40
Wasting
< 5
5-9
10-14
>=15

22. Anthropometric data Index Nutritional challenges measured
Score (<- 2SD)
Score (<- 3SD)
Weight-for-height
Acute malnutrition (wasting)
Wasted
Severe Stunting
Height-for-age
Chronic malnutrition (Stunting)
Stunted
Weight-for-age
Any protein-energy malnutrition (Underweight)
Underweight
Severe Underweight

23. Child Nutrition Status in the five districts analyzed
Underweight
Stunting
Wasting
Mean
-.8633
.2551
Percentiles 10 20
-.6500 25
-.4875 30
-.3000 40
-.0200 50
-.8850
.2600 60
-.6400
.5000 70
-.3300
.7990 75
-.1700
-.9825
.9500 80
.0100
-.7620
1.1400 90
.4890
-.1200
1.6030
USAID FTF, 2013

24. Household Characteristics controlled for
Variable
Description
Mean
FHHdefacto
=1 if de facto female-headed HH
0.12
Noformaled
=1 if HH head has no formal education
0.18
grade1_4
=1 if HH head completed lower primary (grades 1 to 4)
grade5_7
=1 if HH head completed upper primary (grades 5 to 7)
0.34
agehead
Age of the HH head
40.48
ftesum
Full-time equivalent HH members
6.19
shareAgeun~5
Share of household members aged below 5
0.20
shareAge5_14
Share of household members aged 5 to 14
0.30
shareAbove60
Share of household members aged 60
0.04
deathinfam~y
=1 if the household experienced death of a member within the reference perion
0.05
landholdsz12
Total land holding size less rented in and borrowed in
3.58
landother
sum of land borrowed in and rented in
0.16
Landtitled
land with title deeds
0.28
deflstock
Value of livestock (real ZMK, 2007/08=100)
Kwacha-dollar rate was $1 = ZMK5012 (June 2012).

25. Household Characteristics controlled for
Variable
Description
Mean
defvalequip
Value of farm equipment (ZMK/10,000; 2007/08=100)
43.07
fisphh
=1 if HH acquired FISP fertilizer
0.47
remit_c
Cash remittances received
remit_m
Value of maize received
remit_o
Value of other commodities received
bomai
Km from the homestead to the nearest boma
31.20
feedroadi
Km from the homestead to the nearest feeder road
1.81
agrodealeri
Km from the homestead to the nearest agro-dealer
24.99
clinic_max
distance to the nearest clinic
6.49
district2
dist==Katete
0.22
district3
dist==Lundazi
0.25
district4
dist==Nyimba
0.10
district5
dist==Petauke
0.19