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

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

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).

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

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

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

2. Study area: Zambia’s Eastern province

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?  

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.

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

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

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

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

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

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

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

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

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

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.

Thankyou!

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

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

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

Child Nutrition Status in the five districts analyzed Underweight Stunting Wasting Mean -.8633 -1.8614 .2551 Percentiles 10 -2.2200 -3.6890 -1.1560 20 -1.7400 -2.9980 -.6500 25 -1.5400 -2.7375 -.4875 30 -1.3900 -2.5500 -.3000 40 -1.1260 -.0200 50 -.8850 -1.8650 .2600 60 -.6400 -1.5600 .5000 70 -.3300 -1.1730 .7990 75 -.1700 -.9825 .9500 80 .0100 -.7620 1.1400 90 .4890 -.1200 1.6030 USAID FTF, 2013

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) 2781176.00 Kwacha-dollar rate was $1 = ZMK5012 (June 2012).

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 139725.90 remit_m Value of maize received 7527.23 remit_o Value of other commodities received 15975.00 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