Uganda SES Baseline Report 05-28-09

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Evaluating the Impact of Introducing Beta-Carotene-Rich

Orange-Fleshed Sweet Potato (OFSP) Varieties in Uganda:

A Report on the 2007 Baseline Survey

DRAFT

May 12, 2009

Prepared by

Mary Arimond1

Alan de Brauw1

Patrick Eozenou2

Daniel O. Gilligan1

Cornelia Loechl3

J.V. Meenakshi2

1International Food Policy Research Institute2HarvestPlus3International Potato Center (CIP)


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ACKNOWLEDGEMENTS

We would like to thank the OFSP project implementation team in Uganda for helpful

guidance and support during the collection of the baseline survey data. In particular, weare grateful to Martin Wamaniala for his patience and support as we planned the survey

activities and coordinated site selection with him and his team. We also thank Harriet

Nsubuga, Sylvia Magezi, Sam Namanda, and Charles Musoke from the PRAPACE team.We also thank Anna-Marie Ball of HarvestPlus for logistical support during the data

collection.

We thank everyone who participated in the enumeration of the surveys, especiallyGeoffrey Kiguli, Fieldwork Manager of the SES survey, and Abdelrahman Lubowa,

Fieldwork Manager of the Nutrition Survey. Jaspher Okello played a critical role as

Finance and Logistics Manager. As always, we are grateful for his assistance. JamesKakande managed all aspects of data entry, from design of the data entry program,

management of the data entry teams, creation of code books, variable labeling and

preliminary data cleaning. We thank him for providing a very clean baseline data set in a

timely manner. We also thank everyone who took part in the enumeration and in the dataentry.

The design of the impact evaluation strategy and the survey instruments was a

collaborative effort between IFPRI, HarvestPlus and CIP. The extent of collaboration isevident in the list of coauthors of this report. We would also like to acknowledge the

helpful advice of others at these institutions, including Howdy Bouis, Director of

HarvestPlus, Christine Hotz, HarvestPlus Nutrition Coordinator, Jan Low of CIP, andMarie Ruel of IFPRI. We also received helpful suggestions on the questionnaire design

from Andrew Westby and Claire Coote of NRI.

Finally, we would like to thank all of the survey respondents who endured repeatvisits from the survey teams and several long hours of questions about their lives to

contribute to this research.


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CONTENTS

ACKNOWLEDGEMENTS ................................................................................................. iACRONYMS ..................................................................................................................... iv1. Introduction ..................................................................................................................... 12. The Orange-Fleshed Sweet Potato REU Interventions in Uganda ................................. 23. Evaluation Design ........................................................................................................... 4

3.1 The Experimental Design ..................................................................................... 43.2 The Implications of Technology Diffusion and Trade Spillovers for ImpactMeasurement ............................................................................................................... 6

4. Sample Design ................................................................................................................ 84.1 Site Selection ........................................................................................................ 84.2 Reference Groups.................................................................................................. 94.3 Sample Size ......................................................................................................... 10

5. Baseline Survey ............................................................................................................ 155.1 Survey Instruments and Topics ........................................................................... 155.2 Enumeration Team and Training ........................................................................ 185.3 Fieldwork ............................................................................................................ 195.4 Data Entry and Cleaning ..................................................................................... 21

6. Baseline Characteristics and Suitability of Sample to OFSP Intervention ................... 226.1 Adoption and Area under Orange-Fleshed Sweet Potato ................................... 226.2 Prominence of Crops that are Close Substitutes to OFSP in Production ............ 236.3 Consumption Patterns ......................................................................................... 246.4 Nutrition Knowledge .......................................................................................... 266.5 Farming Practices................................................................................................ 27

7. Test of Effectiveness of Randomization ....................................................................... 29References ......................................................................................................................... 34


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TABLES

Table 1. Sample Size Parameters for Serum Retinol, Uganda DHS 2000-1 ................... 12Table 2: Sample Design for the Uganda OFSP Evaluation Study .................................... 14Table 3: Uganda Baseline Socioeconomic Survey Questionnaire Table of Contents ...... 16Table 4: Experience Growing OFSP................................................................................. 23Table 5: Crop Mix and Area Devoted to Substitute Crops ............................................... 24Table 6: Share of Households Consuming Selected Staples in the Past 7 Days............... 25Table 7: Sweet Potato Consumption Habits ..................................................................... 26 Table 8: Mothers Nutritional Knowledge about Vitamin A ............................................ 27Table 9: Farming Practices ............................................................................................... 28Table 10: Baseline Household and Farm Characteristics by Treatment Group, 2007 ...... 32Table 11: Household Consumption per Adult Equivalent by Treatment Group, 2007 .... 33


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ACRONYMS

BMI Body mass index

CIP International Potato Center

FADEP Farming for Food and Development

HAZ Height-for-age z-score

IFPRI International Food Policy Research Institute

NAADS National Agricultural Advisory Service

NGO Nongovernmental organization

NRI Natural Resources Institute

OFSP Orange-fleshed sweet potato

PRAPACE Regional Potato and Sweet Potato Improvement Network in Eastern and

Central Africa (French acronym)

RAE Retinol activity equivalent

REU Reaching End Users

VAD Vitamin-A deficiency

VEDCO Volunteer Efforts for Development Concerns

WAZ Weight-for-age z-score

WFSP White-fleshed sweet potato

WHZ Weight-for-height Z-score

YFSP Yellow-fleshed sweet potato


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1. Introduction

Micronutrient malnutrition continues to be a major health crisis in developing countries.

It is responsible for a significant share of infant mortality (Bryce et al., 2003) and

hampers human capital development (Alderman, Hoddinott, Kinsey, 2006).

Biofortification seeks to reduce micronutrient deficiencies by substituting staple foodcrops that are low in nutrients with nutrient-dense varieties of the same or similar crops

(Bouis, 2002). HarvestPlus is conducting a biofortification initiative to support the

introduction of several new varieties of staple food crops that are a rich source of a key

nutrient, such as iron, vitamin A, or zinc.

The first crop supported for distribution by HarvestPlus is beta-carotene-rich

orange-fleshed sweet potato (OFSP). Although OFSP varieties have long been available

in some areas, many existing varieties provide limited health benefits because they are

low in beta-carotene and have not been widely adopted. HarvestPlus is supporting the

introduction of the latest improved varieties in new areas in Uganda and Mozambique.These varieties are being introduced into areas in both countries in which white or yellow

fleshed sweet potato is either the staple crop (Uganda) or an important secondary source

of starch (Mozambique). This first HarvestPlus initiative, referred to as the Reaching

End Users (REU) project, has the objective of reducing vitamin A deficiency (VAD) by

conducting coordinated agricultural extension, marketing and nutrition training to

encourage the production and consumption of OFSP in these areas of Uganda and

Mozambique. VAD is a major health concern in many low-income populations with

persistent high mortality rates, including Uganda (Ezzati et al., 2002; UBOS and ORC

Macro, 2001). It is an important cause of morbidity, impaired night vision and, in more

severe manifestations, of blindness and increased mortality in young children (Villamorand Fawzi 2000; Beaton, Martorell and Aronson 1993, Fawzi et al. 1993; West, 2002).

Vitamin A deficiency disorders also affect adult women by increasing morbidity and

mortality during pregnancy (Christian et al., 2000; West et al., 1999).

HarvestPlus has contracted the International Food Policy Research Institute

(IFPRI), in conjunction with the International Potato Center (CIP) to conduct an

evaluation of the impact and cost effectiveness of the REU project. The purpose of the

evaluation is to develop rigorous quantitative estimates of the impact of alternative

dissemination strategies on key outcomes along the pathway to improving vitamin A

status for vulnerable groups, including OFSP adoption, farm production and profitability,nutrition knowledge, dietary intakes of OFSP and vitamin A, and biochemical measures

of vitamin A status.

This report has three objectives. First, it introduces the interventions and the

evaluation methodology and describes the baseline evaluation survey conducted from

July-September 2007 in Uganda. Second, the report summarizes evidence from the

baseline data about the appropriateness of the areas selected for conducting the REU


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intervention. Third, the report tests the effectiveness of the random assignment of farmer

groups into intervention models by comparing mean outcomes and control variables

across intervention groups. The baseline survey consisted of three parts: a demographic

and socioeconomic survey; a nutrition survey that included a detailed 24-hour dietary

recall module designed to measure dietary intakes of food energy, vitamin A and other

major nutrients for young children and women of child-bearing age; and a biochemical

component to measure serum retinol, serum carotenoids and ferritin levels in blood

samples of young children and their mothers. This report summarizes data from the

socioeconomic survey and selected modules from the nutrition survey, including

anthropometry and food frequency.

The report is organized as follows. In Section 2, we describe the REU

interventions in Uganda. Section 3 presents the evaluation methodology. In Section 4,

we describe the evaluation sample in Uganda and present results of statistical power

calculations conducted to determine the sample size and design. In Section 5, we

describe the baseline data collection, including the survey instruments, the selection ofsample sites, and issues encountered when conducting the fieldwork. Section 6 presents

summary statistics of the survey sites to develop an understanding of the suitability of the

sample sites for the sweet potato interventions. In Section 7, we examine the

effectiveness of the randomized treatment design by presenting tests of equality of the

distribution of key outcome and control variables across intervention groups.

2. The Orange-Fleshed Sweet Potato REU Interventions in Uganda

Uganda should provide an excellent context for testing the biofortificationstrategy of improving vitamin A status by encouraging adoption of OFSP. First, Vitamin

A deficiency disorders are a serious public health concern in Uganda. In the last

Ugandan Demographic and Health Survey of 2000-2001, it was found that about 30% of

children under five and 22% of women of child bearing age had moderate to severe

vitamin A deficiency (UBOS and ORC Macro, 2001). Second, white and yellow fleshed

sweet potato are staple food crops comprising a large share of food energy intake in many

areas of Uganda.

The efficacy of orange-fleshed sweet potato to improve vitamin A status has

previously been demonstrated (Jalal et al., 1998; van Jaarsveld et al., 2005). However,this and other efficacy studies are carried out in highly controlled settings, and so may not

provide accurate estimates of the impact of an OFSP intervention in the field. Another

field study similar to the one conducted here involved a multi-faceted intervention to

introduce OFSP in Mozambique called Toward Sustainable Nutrition Interventions


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(TSNI) (See Low et al., 2007).1

The Uganda study described here contributes to the

evidence from the TSNI study by measuring the impact of introducing OFSP crops

together with nutrition messages about the health benefits of consuming dietary sources

of vitamin A in another setting. Another purpose of this large scale intervention project

in Uganda and its partner study in Mozambique is to study the cost effectiveness of

modalities for scaling up the introduction of OFSP to improve vitamin A status.

In order to learn about the cost effectiveness of modalities for introducing OFSP,

the project will introduce and compare two intervention models. Both models include

three main components:

i. an agricultural extension component focused on OFSP vine distributionand trainings on how to grow the crop and on its agronomic properties,

ii. a nutrition education or demand creation component that providesinformation about food sources of vitamin A, their preparation, and thebenefits of vitamin A consumption, and

iii. a smaller market development component designed to provide limitedmarket infrastructure and advertising in order to encourage the

development of consumer markets for sweet potato roots.

The two models being studied differ primarily in timing and intensity of activities,

which then has cost implications. In the first year of the intervention, the two models are

identical in agricultural extension and nutrition education activities. Differences between

the two models occur in the second year. In Model 1, the high intensity of extension

visits and nutrition messages from year 1 are continued in year 2. In Model 2, theactivities in agriculture and nutrition are scaled back substantially in the second year to

provide cost savings and a basis for a cost-effectiveness comparison with Model 1. The

intensity of treatments was kept the same in year one because this initial high level of

activity was considered necessary for the crop to be adopted and accepted.

The mechanisms through which the introduction of OFSP would affect VAD

prevalence are fairly complex. Farmers must first learn about and decide to grow the new

OFSP varieties, initially through interaction with promoters linked to the agricultural

extension program. Other members of the community may later gain access to OFSP, by

purchasing vines or receiving them as gifts from other households, or by consumingOFSP obtained through purchase or as gifts. Once the OFSP roots are available from

fields or markets, households must decide how much OFSP to consume, who will

1 The TSNI study was a precursor to the larger scale partner study to this study now being conducted inMozambique. The TSNI study found that OFSP intake, total vitamin A intake, and serum retinol

concentrations in children under five years of age were significantly higher in intervention communities

than in comparison communities not exposed to the intervention.


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consume it, and in what form. The nutrition promotion activities should affect these

behaviors and increase demand for OFSP and other sources of vitamin A. The nutrition

trainings also teach households how to store and prepare the crop to maintain high levels

of beta-carotene in consumption.

Households in farmers groups in Model 1 and Model 2 attend training meetingsand receive periodic visits by promoters during the course of the intervention. The

promoters are farmer group members who are chosen to receive training about the

agricultural and nutrition components of the intervention. The agriculture and agronomy

trainings are provided by project agricultural extensionists. The promoters nutrition

trainings were designed by a separate team of demand creation specialists from the

project. Project extensionists then helped to implement these promoter trainings on

health and nutrition. Promoters used the lessons from these trainings to conduct their

own trainings of farmer group members in their own farmer groups and farmer groups

from surrounding communities. Farmer group members in the intervention sites may

also take part in community theater designed to increase awareness of the benefits ofvitamin A consumption, and are exposed to media messages via radio and billboard

advertising on how to grow OFSP and the benefits of consuming OFSP and other sources

of vitamin A.

The OFSP interventions were implemented by the Regional Potato and Sweet

Potato Improvement Network in Eastern and Central Africa (PRAPACE) under the

direction of HarvestPlus, and in collaboration with implementing partners Volunteer

Efforts for Development Concerns (VEDCO) and Farming for Food and Development

(FADEP). VEDCO and FADEP are nongovernmental organizations (NGOs) with

experience in providing agricultural extension services for the Ugandan government andaid agencies in the intervention districts and elsewhere in Uganda. VEDCO was

contracted to serve as the implementing partner in Kamuli and Mukono district. FADEP

was the implementing partner in Bukedea district. VEDCO and FADEP provided the

agricultural extensionists who trained promoters from the communities to provide advice

to beneficiary households on sweet potato farming practices and on the health and

nutrition messages relating to vitamin A and OFSP consumption. The Director of the

REU implementation project in Uganda for PRAPACE is Martin Wamaniala. Anna-

Marie Ball of HarvestPlus coordinated activities of the REU related to the OFSP projects.

3. Evaluation Design

3.1 The Experimental Design

This study uses a randomized-controlled (experimental), prospective evaluation design.

A prospective study requires collecting data both before the program begins and after it

has been ongoing, so that changes in outcome variables and controls can be observed


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the comparable change in the outcome for the control group. This is referred to as a

difference-in-differences (DID) impact estimate. The DID impact estimator has the

attraction of removing any unobserved time-invariant differences between the treatment

and control groups. It also controls for any baseline differences in mean outcome

variables due to sampling error.

3.2 The Implications of Technology Diffusion and Trade Spillovers for Impact

Measurement

An important detail that distinguishes this evaluation exercise from many common

evaluations of poverty programs and other narrowly targeted interventions is that the

program is intended to grow in an uncontrolled fashion as information about the

technology spreads and as vines and harvested roots from the improved crop become

more available through trade. Moreover, the radio messages funded by the program

about the health benefits of vitamin A consumption are likely to be heard by members ofcontrol farmer groups. This means that the benefits of the program spread over time, and

the initial investment in farm extension, demand creation and marketing has intentional

spillover effects on other households.

This spread of the program raises two types of challenges for the evaluation. One

is that the benefits of OFSP may spread to the control farmer groups, which are near the

treatment communities in order to control for selection effects, in a virtuous form of what

is referred to as contamination of the control group. This positive externality is

intentional to the program, but makes impact estimation more difficult. Any

improvements in outcomes, such as vitamin A status, in control farmer groups that are adirect result of the program create a downward bias in estimated impacts. This bias has a

doubling effect, first because the benefits to the control group should be removed from

the impact estimates to get a measure of the true direct effect of the program on targeted

primary beneficiaries. Second, these benefits to the control groups also represent positive

impacts of the program and so should be added to the estimated treatment effect. It is

difficult, if not impossible, to remove these two forms of bias from any potential

contamination of the control groups because it is difficult to attribute these control group

benefits to the interventions.

We expect this potential source of bias to be relatively low because the project

implementation team is being careful to keep their activities (other than radio marketing)

away from control farmer groups.3

Also, the diffusion of technology through sharing of

3 Members of the implementation team reported one explicit case of a project-related spillover to the

control group. One of the agricultural promoters recruited by the project to provide vines and technicaltraining to farmers in her farmer group, provided a relatively large quantity of vines to one control farmer

group in Mukono district, citing that it seemed unfair not to provide them with vines. Other much smaller

spillovers to two other control farmer groups were also noted by project staff after one year of


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vines among neighbors and traders or the direct sales of OFSP roots in areas with control

farmer groups is likely to be limited during the two-year period of implementation.

The second challenge is to measure the impact of the interventions on households

other than those directly targeted for the initial intervention, or the primary

beneficiaries. This group includes neighboring households to the primary beneficiarieswho may receive OFSP vines or roots through gifts, exchange or local trade, as well as

households living in other communities reached through effective marketing,

unsponsored NGO activity, or exchanges through social networks that contribute to the

diffusion of the technology and OFSP products to other communities. Because much of

this diffusion of program impact to other households comes at little or no direct cost to

the project, including the benefits of this diffusion in the impact estimates can sharply

improve estimates of program cost-effectiveness.

The main sample and data collection for the evaluation were designed to capture

the impact of the programs on primary beneficiaries, and to capture spillovers of benefits

to neighboring households in the same communities. The main evaluation sample

includes five additional households randomly selected from the home village of each

farmer group leader, who were not members of the farmer group at the baseline. This

will allow the evaluation to measure local spillover effects.

Capturing spillover effects beyond the communities originally targeted for the

intervention requires assessing the scale of potential diffusion of vine material and

marketed OFSP to other communities. One direct form of spillover of this nature is easy

to measure. After the selection of the first intervention farmer groups, the

implementation team continued to add new farmer groups to the project and to conduct

the same implementation models in those groups. Extrapolating the benefits and costs of

the program in the first implementation groups to these other groups added later is fairly

straightforward. More difficult will be to estimate the effect of spillovers to other

communities. The evaluation team will investigate the potential scale of these spillovers

through discussions with the implementation team and others knowledgeable about

farming in these areas to determine whether a limited amount of additional data

collection in other communities is necessary to obtain more reliable estimates of benefits

in those communities. If these spillover effects are relatively large, omitting them would

underestimate the impacts of the programs and would lead to unfairly pessimistic

estimates of cost-effectiveness.

implementation. As a result, visits to a sub-sample of leaders of control farmer groups were organized after

one year of implementation to determine whether this phenomenon was widespread. At the time of

completion of this report, these visits had not yet taken place.


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4. Sample Design

4.1 Site Selection

The OFSP interventions were initially planned in three districts: Bukedea, Kamuli, and

Nakasongola. In each district, two or three sub-counties were selected for the

intervention. The selected areas of these three districts were chosen because sweet potato

(white-fleshed) is a staple of the diet in these areas, agroclimatic conditions are suitable

for growing OFSP, there has been no significant OFSP interventions in these locations in

the past few years, and market and transportation infrastructure is sufficient to suggest

that the minimum conditions exist for encouraging sustainable OFSP adoption. After the

evaluation team selected sub-counties in each district, drew household samples, and

beginning baseline survey interviews for the evaluation, the intended implementing

partner organization in Nakasongola district, Save the Children, was unable to finalize its

commitment to the project in time to begin implementation on schedule. No other

implementing partner organization was available in Nakasongola. As a result, surveydata collection in Nakasongola was stopped. The implementation team determined that

Mukono district represented a suitable alternative district for the intervention, based on

the criteria listed above. Mukono district was also selected because it lies along Jinja

Road, so it has inexpensive access to major urban markets in Kampala. Once Mukono

was added to the implementation plans, the evaluation team selected sample sub-counties

from the list of implementation sites and drew a new household sample for Mukono

district.

The unit of intervention for this project is the farmer group. Lists of farmer

groups were obtained for each sub-county from Harriet Nsubuga and other staff at

PRAPACE. Because farmer group formation and membership is somewhat fluid, these

lists included existing active farmer groups and recent farmer groups that operated in

these areas, sometimes in response to other agricultural crop promotion programs. From

these farmer group lists, farmer groups were randomly selected for the impact evaluation,

stratified by district. Within each district stratum, select farmer groups were randomly

assigned to one of the three study groups: Model 1, Model 2 and control. Survey teams

then visited the head of each selected farmer group to determine that the group met the

minimum requirements for the study: a willingness to be introduced to OFSP varieties, no

recent participation in OFSP vine distribution (in the past 2 years), and the necessary

number of farmer group members with children in the target age range for theintervention. In some cases, willing farmer groups did not have sufficient members with

children in the target age range, so farmer group leaders were asked to recruit new

members with children in the target age range. These lists of farmer group members

served as the sampling list from which the household samples were drawn at random.

Because of shifting farmer group membership, households on these lists were interpreted

as likely farmer group members at the time of the interventions.


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4.2 Reference Groups

The primary targeted reference groups for the intervention include children under age six

and women of child-bearing age, the two groups most vulnerable to vitamin A

deficiencies. To accommodate these target groups, household sampling lists wererestricted to farmer group members with children under age six. For the evaluation,

children are sub-divided into two age cohorts: those aged 6-35 months and those 36-71

months. Households selected for the sample had to have at least one child in at least one

of these age cohorts.

The 36-71 month age range is selected to accommodate measuring changes in

biochemical indicators in vitamin A status. The 36-71 month olds are selected in this

case because widespread vitamin A supplementation of children under 5 years of age

during Child Health Days in Uganda will make it difficult to detect any impact of the

intake of OFSP or other dietary sources of vitamin A on the vitamin A status of childrenreceiving vitamin A supplements throughout the evaluation study period.

4The older age

cohort was designed so that these children will age out of the vitamin A supplementation

during the study period, making it easier to detect changes in their vitamin A status

resulting from dietary sources. After considering the scheduling of child health days, the

age range of this older cohort was refined further, to 42-66 months. By focusing on

children aged 3.5-5.5 years, it will be possible to identify impacts on all outcomes,

including vitamin A status (measured by biochemical indicators), two years after the start

of the intervention, when these children are aged 66-90 months (i.e., 5.5-7.7 years). If the

second survey round is conducted at that time (July 2009), no child in this cohort should

have been targeted for vitamin A supplements during Child Health Days within six

months prior to data collection.

A smaller sample of 6-35 month olds is included in the sample to capture impacts

of the REU interventions on dietary intake of OFSP and vitamin A for this high priority

age cohort. The mother of one of the children selected to serve as the reference child for

either cohort during the data collection serves as the source of data on dietary intakes and

health status of women of child-bearing age.

4Children in this age range are outside of the prenatal period and the 0-24 month age range considered

optimal for micronutrient nutrition interventions, but the younger children are targeted for regular vitamin

A supplementation. Instead, we measure the impact of the interventions on the younger age group through

assessments of dietary intake of food rich in provitamin A.


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4.3 Sample Size

Calculations were made to determine the necessary sample size needed to be able to

identify plausible impacts on the primary outcomes for the evaluation study: dietary

intakes of vitamin A and serum retinol concentrations in blood samples. Preliminary

investigation demonstrated that serum retinol is the limiting outcome in terms of samplesize. Identifying impacts on serum retinol requires substantially larger samples than

identifying impacts on dietary intakes, so the first stage of analysis considered the sample

required to identify changes in serum retinol. We estimated the minimum sample size

needed to identify plausible changes in serum retinol levels in children as a result of the

orange-flesh sweet potato (OFSP) interventions in Uganda.

We used two data sources to inform the calculations on minimum sample size, the

Mozambique TSNI study sample (rounds 1 and 4), and the 2000/01 Uganda DHS.5

The

TSNI study data are extremely useful for this exercise because they provide estimates of

impacts from a similar OFSP intervention, together with the parameters needed to

estimate sample size, though in a different population. We used the Uganda DHS data to

modify the needed parameters to be appropriate to the Ugandan population.

We assumed a clustered, randomized evaluation design with treatments

administered at the cluster level and data collection before and after initiation of the

treatments. With this design, impact estimates can be measured using the preferred

approach of taking difference-in-differences or double difference. This is the change in

the outcome,y, in the treatment group, T, minus the change in the outcome in the

randomized control (or alternate treatment) group, C, or CCTT yyyy 1212 , wheresubscripts index time periods. The purpose of the sample size estimates is to determine

the minimum number of clusters,g, and households per cluster, m, needed in each

treatment group of the evaluation sample to be able to identify a plausible impact or

effect size, , with reasonable confidence. Formally, we seek a large enough sample to

have an 80% probability, the power of the test ( 8.0 ), of identifying as statistically

significant at the 90% level ( 0.1) a minimum effect size of. If the impact is at least

as large as , we have considerable confidence we will be able to detect it.

According to Murray (1998), the relationship between the sample size and the

minimum effect size that can be detected in a clustered randomized trial can be estimated

as

(1)

mg

ttm gy2

2

2 112

,

5 Thanks to Jan Low for providing the TSNI data and to Mary Arimond for guidance in the use of the data

to measure impacts on serum retinol status of children.


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where 2y is the variance of outcomey and g is the intracluster correlation coefficient

within clusters,g. Also, 2t and t are the t-values for a two-tailed test of statistical

significance and for power, , respectively. As shown in (1), the needed sample size

depends on the variance of the outcome variable and the intracluster correlation. We

obtained estimates of these parameters from the TSNI and Uganda DHS data.

We used data from the TSNI study and the results later published in Low et al.,

2007, to provide estimates of a plausible minimum effect size, , and for estimates of 2y

and g . Based on these parameters, it is possible to determine the optimal number of

clusters,g, and cluster size, m. The TSNI data includes serum retinol measures before

and after the introduction of OFSP for children under age 5 years by the fourth round of

data collection. Our analysis of those data show that, controlling for infection (CRP), the

overall intervention lead to a difference in the change in serum retinol levels betweenchildren in the treatment and comparison groups of 0.075 mol/L on average.

6Because

the TSNI intervention was more intensive than the planned REU interventions in Uganda

and took place among a population with lower mean serum retinol levels than in Uganda,

we would not expect to find as large an impact of the OFSP REU intervention in Uganda.

Moreover, the widespread provision of vitamin A supplements over the past 2 years in

Uganda would suggest that plausible impacts of OFSP may be even smaller.

For 2y , we need the variance of the change in serum retinol over time because

treatment effects will be estimated as difference-in-differences. The variance of serum

retinol in round 4 of the TSNI data was 0.07 and the variance of the difference in serum

retinol between rounds 1 and 4 was 0.09. Estimates of intracluster correlation were

around 0.1. Based on these parameter estimates, we would need a sample of 38 clusters

per treatment group and 25 households per cluster, or 2850 households, to ensure

sufficient power to identify the TSNI impact of 0.075 mol/L. This sample is larger than

feasible and would still only identify a very large impact on change in serum retinol.

Next, we sought to improve these estimates using the 2000/01 Uganda DHS.

The 2000/01 Uganda DHS data includes serum retinol for children under age 5.

The variance of serum retinol levels was 0.2 when accounting for stratification. The

estimates from the TSNI data suggest that the correlation between serum retinol levelsover time is low, so that the variance of the difference in serum retinol suggested by the

Uganda DHS data is very high, possibly as high as 0.3. This variance is higher than in

the TSNI sample because the TSNI sample is relatively more homogenous, while the

6 Our analysis of the TSNI data was preliminary and was used only to inform sample size estimates for the

Uganda study. The definitive impact estimates are those published in Low et al., 2007. Nonetheless, our

estimates were similar to those in that published paper.


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Uganda DHS sample is nationally representative. In fact, the distribution of serum

retinol in Uganda is bunched around 1.05 mol/L, with a fat tail reaching towards 3

mol/L. This suggests that the necessary sample size for collecting blood samples in

Uganda would be well above 3000 households, which was more than the budget would

allow.

As a result, we made two important changes to the analysis of serum retinol.

First, we decided to measure the impact on change in serum retinol from Model 1 only,

which would require collecting blood samples in households in Model 1 and the control

group, but not in Model 2. Second, we decided to focus on children or women of child

bearing age who are more vulnerable to vitamin A deficiency, and therefore are more

likely to respond to the intervention. We considered a revised design of the sample to

identify impacts on children with inadequate vitamin A status (serum retinol levels below

1.05 mol/L) at baseline, a level that indicates vulnerability to VAD (Gillespie et al.,

2004). Roughly half of the Uganda DHS sample had serum retinol levels below this

threshold in 2000/01. This implies that we would need to collect blood samples on twiceas many children as needed for the analysis to find enough children with serum retinol

levels below 1.05 mol/L. Also, children may make for a more realistic sample, because

the variance and intracluster correlation of serum retinol levels in children in the Uganda

DHS, conditional on being below 1.05, are lower than those among women (Table 1).

Table 1. Sample Size Parameters for Serum Retinol, Uganda DHS 2000-1

Sample Size Sample below

1.05 mol/L

Variance below

1.05 mol/L

Intracluster

correlation

Women, 15-49 403 206 0.046 0.242

Children, 0-59 months 313 163 0.035 0.043

Notes: Sample is restricted to women and children residing in rural areas of the Eastern and Central regions

of Uganda. Source: UBOS and ORC Macro, 2001.

Based on these parameter estimates, our power calculations determined that a

sample of 864 children in 72 farmer groups (432 children from 36 farmer groups in each

treatment/control group) is necessary to identify an increase in mean serum retinol levels

between Model 1 and the control group of 0.044 mol/L at the 95 percent level,

conditioning on an initial serum retinol level of less than 1.05 mol/L. This wasconsidered a plausible minimum effect size for this study.

Based on this estimated sample size of children age 3.5-5.5 years old, we

designed the rest of the sample, as shown in Table 2. First, the number of households in

Model 1 and the control group sites was increased from 12 to 14 to allow for some

attrition during the study. For dietary intake measurement, we determined that six


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children age 3.5-5.5 years from households in the 36 farmer groups each in Model 1 and

the control group would be needed to identify an impact on the change in vitamin A

intake (measured in g of retinol activity equivalent (RAE) per day) of 228.4. For cost

reasons, 12 farmer groups would be selected from Model 2. With 12 households per

farmer group in Model 2, it would be possible to identify an impact on the change in

vitamin A intake for children age 3.5-5.5 of 345.5 g RAE/day.7 Next, a second

reference child age 6-35 months would be selected in a subsample of these households.

We determined that sampling 3 children age 6-35 months in each Model 1 and control

group farmer group and 6 children age 6-35 months in each Model 2 farmer group would

be enough to identify impacts of at least 277.3 g RAE/day and 408.3 g RAE/day,

respectively.

Finally, another five households that are not members of the sample farmer

groups were added to the sample from each village that was the primary home of the

sample farmer groups in order to measure spillover effects of the program in terms of

diffusion of the OFSP vine technology. This yields a sample of 1572 householdsparticipating in at least some component of the data collection. In some farmer groups,

additional interviews were conducted as additional insurance against attrition. The final

baseline sample on which the complete SES questionnaire was administered was 1596

households.

7 For comparison, the estimated impact of the intervention on dietary intake of vitamin A for children under

age 5 years from the TSNI study was 893 g RAE/day.


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Table 2: Sample Design for the Uganda OFSP Evaluation Study

Serum Retinol & SES

& Anthropometry

Dietary Intake and

other nutrition

modules

Dietary Intake and

other nutrition

modules SE

Farmer (36-71 mos & mother) (36-71 mos & mother) (6-35 mos & mother) (hou

Groups HH/FG HH HH/FG HH HH/FG HH HH/FG

Model 1 36 14 504 6 216 3 108 5

Model 2 12 0 0 12 144 6 72 5

Control 36 14 504 6 216 3 108 5

Subttl 1008 576 288

SES interviews 1572

Blood samples (child & mother) 2016

Child dietary intake measures 864

Mother dietary intake measures 576

Notes: SES refers to the socioeconomic survey component of the household questionnaire. Details of the contents of the

are described in Section 5.


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5. Baseline Survey

5.1 Survey Instruments and Topics

The survey instruments included a two-part household questionnaire, a community and

price questionnaire, and a farmer group questionnaire. The household questionnaire

included a socioeconomic survey (SES) questionnaire and a separate nutrition

questionnaire. These two questionnaires were administered during separate interviews on

different days. In addition, data collection forms were designed for the biochemical

component.

The SES questionnaire was designed to collect information on household

demographic characteristics, agricultural production, nutrition knowledge, risk

preferences, and measures and determinants of household welfare, including

consumption expenditure. Table 3 reproduces the contents page from the SES

questionnaire. Several aspects of the questionnaire deserve mention. The sections on

demographics characteristics gave additional attention to the details of characteristics ofchildren under age 5 years because these children are the primary subjects of the dietary

assessment, which provides information on vitamin A intakes of young children, a

primary outcome of the evaluation. The agricultural production module is designed to

capture all crops grown and area under each crop, but devotes more attention to carefully

measuring timing and quantity of production of various types of sweet potato and closely

competing crops like cassava. Information on area under sweet potato production is

gathered through recall and using a GPS while walking the perimeter of sweet potato

plots. The questionnaire also focuses on the role of gender in intrahousehold decision-

making, capturing the individual ID and gender of the individual with primary control

over farming decisions of each plot, as well as the nutrition knowledge of the mother and

father of the reference child (gathered in separate interviews). The final module on risk

preferences is also administered separately to the mother and father of the reference child

in a sub-sample of households.

The nutrition questionnaire contains five modules: anthropometry, food

frequency, 24-hour dietary recall, child feeding practices and child health.

Anthropometry data (height or length, weight, and age) were collected on all reference

children age 3-5 years or 6-35 months, and on the mothers of those children if they were

household members. The food frequency module collected recall information on the

number of days over the past 7 days that 35 foods were consumed by each reference child

(in both age groups). The food list focused on vitamin A sources, fat sources (which

assist vitamin A absorption) and some primary staples.


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This report does not include an analysis of the dietary recall data, child feeding

practices or child health. For completeness, we will briefly describe these modules, but

will not summarize the data in later sections. The dietary recall module was developed

by Cornelia Loechl, Mary Arimond and Christine Hotz to measure prevalence of

individual adequacy of vitamin A intakes, based on the method developed by Gibson and

Ferguson (1999) for measuring prevalence of adequacy of iron and zinc intakes. The

multiple-pass 24-hr recall procedure was followed and had been modified to enhance the

visual impression of the foods consumed, reduce the number of memory lapses, and

improve the estimation of portion sizes consumed by asking mothers to: (a) serve food to

their reference child and themselves from the individual bowls and plates to be supplied

by the enumerators instead of from the common pot; (b) record all foods eaten by the

reference child and themselves on a picture chart supplied by the investigators; and (c) to

estimate the quantities of main staple food items consumed using salted replicas.

The dietary recall module collected detailed information from the mother or

primary caregiver on all foods consumed by the 3-5 year old reference child and,separately, by the mother herself, over the 24-hour period beginning in the morning on

the day before the interview. The same module was enumerated for both reference

children in households that also contained a reference child in the 6-35 month age range.

Food intakes were collected for individual foods and for mixed dishes. For dishes

consumed by multiple household members, the volume consumed by the reference

subject (child or mother) was ascertained.8

Household recipes were collected by the

enumerators for dishes that were not defined as standard.

The child feeding practices module measures a variety of behaviors for feeding

young children in the household. This module is useful to help explain behavioraldeterminants of dietary intakes. The child health module gathers information on maternal

fertility, and, for reference children, child immunizations and child morbidity (diarrhea

and respiratory infections and related symptoms of each).

The survey forms of the biochemical component included forms to be used during

the blood sample collection to record the ID of the blood sample, the corresponding

HHID and individual ID of the child or mother, the time of the day when the blood

sample was drawn, the time of the last meal, the hemoglobin level and information on the

outcome (blood successfully drawn, refusal etc.). Other forms were designed to track

processing of the blood samples (separation of serum) in the laboratory (time ofprocessing and number of cryotubes obtained for each blood sample ID) and to track

storage and shipping of the samples. This report does not include an analysis of the

biochemical data.

8 See Arimond et al. (2008) for a more detailed explanation of the dietary recall method used in the Uganda

Baseline Survey.


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5.2 Enumeration Team and Training

All of the fieldwork was supervised by Cornelia Loechl of CIP. She also took primary

responsibility for management of the enumeration of the nutrition survey and the blood

sample collection. The daily conduct of the socioeconomic survey data collection was

supervised by Geoffrey Kiguli. The training of socioeconomic enumerators wasconducted jointly by Dan Gilligan of IFPRI, J.V. Meenakshi of HarvestPlus and Geoffrey

Kiguli. Cornelia was closely assisted in the management of the nutrition training and

data collection by Abdelrahman Lubowa.

The socioeconomic survey and nutrition survey were conducted by separate

teams. The SES enumeration was organized around three teams, initially one for each

district. Each SES team consisted of four enumerators and two fieldwork supervisors.

There were three nutrition survey enumeration teams, including six enumerators and two

supervisors per district.

The supervisors were responsible for locating sample households and assuring thetimely collection of quality data. Supervisors also reviewed questionnaires for errors,

attached comment flags to the questionnaires for the enumerators review, and worked

with enumerators to agree to a final set of edits of the questionnaires before they could be

submitted for data entry.

Enumerators were selected for the teams based on education level, prior survey

enumeration experience, a test of arithmetic skills and proficiency in at least one of the

relevant local languages. The primary languages spoken in the three districts in the

sample include Luganda, Ateso and Lusoga. Luganda is the primary language of

Mukono district and Teso language dominates in Bukedea. Both Luganda and Lusogaare common in Kamuli. In all, nearly 52 percent of interviews were conducted in

Luganda, 32 percent were conducted in Ateso, and 16 percent were done in Lusoga.

Also, seven interviews were conducted in English and four in Lugwere.

One difficulty encountered in the fieldwork is that Lusoga was more common in

our sample than expected. In the SES team, we had few Lusoga speakers because we did

not find many qualified Lusoga speakers during enumerator recruitment. We planned to

have some of the better qualified Luganda-speaking enumerators conduct interviews with

Lusoga-speaking respondents because the languages are similar, but this proved to be less

effective than expected. As a result, the Lusoga-speaking enumerators had to conduct a

larger number of interviews, which slowed the pace of data collection in Kamuli.

All enumerators were proficient in English, and the questionnaires were in

English as well. Part of the training for both surveys included a consultation among the

field teams to decide on accurate and uniform wording of questions in the local language.

Training of the SES team was conducted from June 2029. The training covered

principals of good enumeration and how to obtain informed consent for the interview. It


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also involved a detailed review of the meaning of each question in the socioeconomic

questionnaire. Extensive role playing was conducted as well as tests of enumerator

knowledge. Pilot tests of the questionnaire were conducted on June 25 and June 27.

Retraining was conducted after each pilot test.

A 2-day pre-training was conducted with the supervisors of the nutrition teams sothat they would be able to assist during the training of enumerators. The nutrition survey

training was conducted from July 23 August 11. It also covered principals of good

enumeration and how to obtain informed consent for the interview. The different

questionnaires were reviewed and practiced in role plays. Intensive training on aspects of

the 24 hour recall method included: how to obtain lists of foods consumed, how to collect

detailed information for each food and how to estimate the amount consumed of each

food/dish. Practical exercises and role playing were conducted. Pilot tests of the

questionnaires were conducted in the three different districts during two days (August 2nd

and 3rd

), followed by a revision day. A sub-group of enumerators was trained and

standardized in anthropometrical measurements during 6 days.

The teams of phlebotomists consisted of 2 lab technicians per district. The

training for the blood sample collection was conducted on August 16-23 by our

collaborators Dr. James Tumwine and Dr. Grace Ndeezi from the Department of

Paediatrics and Child Health, Makerere University Medical School with assistance from

Cornelia Loechl of CIP and Christine Hotz of HarvestPlus. A pilot test was done on

August 21 in Mukono.

5.3 FieldworkBefore the data collection started a series of mobilization activities were conducted to

inform about the upcoming survey and prepare for the arrival of the different teams. This

included meetings of Cornelia Loechl with district health authorities, information

meetings at sub-county level by a member of the survey team and meetings with the

Farmers Groups that were selected for the survey to inform of the study and its activities.

The aims of the project, its expected benefits, and the nature of its evaluation were

described in detail, including the requested participation by households, women, and

children. Ethical approval was obtained from the Uganda National Council for Science

and Technology (UNCST), the Medical School of Makerere University and IFPRI.

Data collection was conducted from July 2 October 5, 2007. SES data

collection started July 2 and was completed on September 4, 2007.

Nutrition data collection began August 16 and was completed on October 5. On

the first day in one community, the Farmers Group leader facilitated a group gathering

of mothers and the selected children. The nutrition team trained/sensitized the mothers

regarding the 24-hour recall in an information session, and weighed and measured


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children and their mothers. During the information session, the purpose of the study, and

the methods involved were fully explained to the mothers. Mothers were given a plastic

plate and a bowl for themselves and a plastic plate and bowl for their child(ren) and asked

to use them for serving and eating their food and that of their participating child(ren) on

the next day to help them visualize more easily what and how much of each food item

they or their child ate on that day. Mothers were also given copies of picture charts at

this time with a pencil and asked to identify each of the foods in the pictures to make sure

they can identify them correctly. Then, they were instructed on how to mark on the

childs chart and their own chart each food that was eaten on the next day. Emphasis was

given to stressing to the mothers the importance of following theirusualeating pattern

and that of their child on the intake recording day. Finally, mothers were shown how the

amount of each food eaten will be estimated by using different methods.

For the anthropometrical measurements, mothers and children were weighed

using electronic scales (SECA mother and child scales) precise to 0.1 kg, with a tare

function. The scales were a loan from the Ugandan Bureau of Statistics (UBOS).HarvestPlus purchased length/height boards from Shorr Productions (portable child and

adult height measuring devices) that were used for determining length/height of children

and mothers.

In general, the nutrition teams did anthropometry and information sessions in 2

villages on 2 consecutive days. Then, 24-hour recalls along with the questionnaires on

food frequency, child feeding practices and morbidity were done on the following 2

consecutive days. On recall days each enumerator completed about 2 interviews. On

average, the interviewer spent between 3-4 hours in one household. 24-hour recalls were

done on all days of the week in order to capture possible variation of the diets on marketdays or weekends.

The enumerator reviewed each questionnaire before leaving the household where

it was administered. At the end of each day of fieldwork, the supervisors reviewed each

questionnaire for accuracy, logical patterns, and legible writing. Enumerators were asked

to return to survey households in cases where missing data or other problems were

observed. The nutrition teams were regularly supervised by Mr. Lubowa and C. Loechl.

Refresher trainings were conducted at the beginning of the data collection about twice in

each team (each one day long) to address needs observed during the field visits.

Repeat dietary recalls were conducted in a sub-sample of about 30 participants perstratum on a non-consecutive day to obtain information on within-subject variation of

nutrient intakes.

Blood sample collection began August 26 and was completed on October 5. The

nutrition teams requested mothers and their children to attend special clinics to be held in

a school or the house of the Farmers Group leader on a particular day. At these clinics,

the experienced and trained phlebotomists obtained four ml blood samples from each


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mother and the reference child (i.e., 36-71 months of age) following standard, sterile

procedures. The venous blood was drawn from the antecubital vein in the morning hours.

A topical anesthetic (xylocaine gel) was applied to the site of the veni-puncture of both

the mother and child 30-45 minutes prior to the blood draw to minimize discomfort.

Trace-element free evacuated containers without anticoagulant were used for the blood

collection. At the time of the blood draw, hemoglobin concentration of whole blood was

determined using a portable hemoglobinometer (Hemocue AB, Sweden). The mother was

immediately informed of the results and any mother or child with hemoglobin less than

90 g/L was referred to their local health facility for treatment and provided with the cost

for return transportation.

The vacutainers were wrapped immediately in aluminum foil and placed in a dark

cooler under refrigerated temperatures after collection. The samples were transported to a

district level facility (eg., district hospital in Kamuli and Mbale and health center 4 in

Mukono) where the lab technicians then centrifuged the blood at 2000-3000 g for 10-15

minutes to separate the serum. The serum was aliquoted to 3 separate 500 L cryotubes,labeled, and deposited into a tank charged with liquid nitrogen. Each week, the liquid

nitrogen tanks were transported to Kampala for storage in a -20 C freezer until the

transfer to the facility where analysis was done. The tanks were recharged with liquid

nitrogen before returning to the field sites.

Drs. Tumwine and Ndeezi conducted regular supervision visits to assess the

quality of the blood sample collection and the serum separation. If necessary, they

provided refresher training sessions.

Serum retinol and serum carotenoid concentrations were determined by High

Pressure Liquid Chromatography (HPLC). Serum -1-glycoprotein, C-reactive protein

and ferritin was determined by Enzyme Linked Immunosorbent Assays (ELISA) using

commercial kits and following manufacturers instructions. The analysis was conducted at

the University of Makerere Department of Biochemistry.

5.4 Data Entry and Cleaning

James Kakande managed all aspects of the data entry. This included designing the CSPro

data entry programs, managing the data entry teams, creating code books, labeling

variables and preliminary data cleaning. The data entry programs included a number ofrestrictions on variables at the time of entry, including restrictions on variable type

(binary, continuous, alphabetical), range limits, forced skip patterns and some cross

variable restrictions for consistency of responses.

All of the data were entered by five data entrants working at the CIP office in

Kampala. Supervisors from the enumeration teams conducted a final review of


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questionnaires before data entry to assure completeness, if possible, and legibility of the

written questionnaire responses.

James Kakande conducted a preliminary data cleaning of all variables after the

data entry. When inconsistencies or unlikely values were found, he referred to the

questionnaires to confirm that outliers were not due to data entry errors and corrected thedata accordingly.

6. Baseline Characteristics and Suitability of Sample to OFSP Intervention

In this section, we present household and farm characteristics that demonstrate the

suitability of the sample sites in these three districts to the OFSP REU interventions. As

with biofortification in general, the effectiveness of the OFSP REU interventions depends

on the following baseline characteristics: (i) low baseline adoption of the biofortified crop

(OFSP), (ii) a high share of crop area devoted to crops that are close substitutes in

production, and (iii) a high share of baseline food consumption of substitute crops. These

characteristics serve as necessary conditions for the OFSP REU interventions to be

effective. We examine the status of these characteristics in the baseline sample. We also

summarize the level of knowledge about the health benefits of vitamin A among female

respondents, and summarize past experience with agricultural extension and previous

sweet potato farming practices.

6.1 Adoption and Area under Orange-Fleshed Sweet Potato

The agriculture module of the baseline survey gathered information on all crops grownduring the second agricultural season of 2006 and the first season of 2007. Table 4

shows that very little OFSP was grown by respondent households during these two

seasons. Less than one percent of respondents grew OFSP during either season, and only

0.1 percent of area planted was devoted to OFSP in the second season of 2006. There

was virtually no OFSP grown by sample households in the first season of 2007.

A series of retrospective questions asked whether respondents had previous

experience growing OFSP. As shown in Table 4, roughly six percent of households had

ever grown OFSP, with this share somewhat higher in Kamuli and Mukono than in

Bukedea. Among those households that had ever grown OFSP, two thirds had grown thecrop since 2004. This suggests only limited familiarity with the growing the crop, and a

great deal of room for new adoption during introduction of the crop in 2007.


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Table 4: Experience Growing OFSP

Variable Kamuli Bukedea Mukono All

(percent)

Grew OFSP in 2006-07, % 0.8 1.1 0.4 0.8

Share of area planted in OFSP, 2nd season 2006 0.1 0.2 0.1 0.1

Ever grew OFSP 7.0 2.3 8.1 5.8

If ever grew OFSP, started after 2004 75.7 50.0 62.8 66.3

Notes: Sample size is 1592 households. Four households that were interviewed for the Socioeconomic

Survey had incomplete responses in the agricultural module.

6.2 Prominence of Crops that are Close Substitutes to OFSP in Production

White- and yellow-fleshed sweet potato (WYFSP) varieties are the closest substitutes to

OFSP in agronomic characteristics. It is expected that OFSP may also substitute for thewhite- and yellow-fleshed varieties in consumption. These sweet potato varieties may

also compete with cassava as a source of starchy staple foods. Table 5 presents evidence

on the adoption rates and share of area under production of WYFSP and cassava during

the two agricultural seasons in 2006-07 covered by the baseline survey. For comparison,

we also include the same information for maize, the most prominent staple grain in the

survey area.

WYFSP and cassava are both very popular crops in the intervention sites. On

average, 84 percent of households grew at least some WYFSP in 2006-07 and 88 percent

grew cassava. Nearly 82 percent of households grew maize during this period, so the

starchy roots and tubers were at least as popular as maize as a staple food crop. In terms

of planted area, cassava held nearly 30 percent of planted area in both seasons. Maize

covered 23-27 percent of planted area, and 16-18 percent of area was devoted to WYFSP.

These figures suggest ample opportunity for the introduction of OFSP if it is to substitute

for WYFSP or cassava in production. Those two crops together represent almost 50

percent of planted area in 2006-07. If OFSP is able to replace a meaningful share of

these crops in the fields, it is likely to be widely available for consumption.

There are some differences in the mix of these staple crops across the three

districts in the intervention. WYFSP is popular in Kamuli and Mukono, but less so in

Bukedea. In Bukedea, cassava was planted over 40 percent of the crop area in 2006-07,

and less than 10 percent of planted area was devoted to WYFSP. In Kamuli, maize

appears to be the primary staple, with cassava placing third behind WYFSP in terms of

planted area. The pattern is reversed in Mukon, where maize is the least common of the

three crops and cassava is planted over the greatest area.


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Table 5: Crop Mix and Area Devoted to Substitute Crops

Variable Kamuli Bukedea Mukono All(percent)

Grew white/yellow sweet potato in 2006-07 98.3 58.1 97.2 84.5

Grew cassava in 2006-07 72.6 96.8 94.5 88.0

Grew maize in 2006-07 94.1 86.8 64.2 81.7

Share of area planted, 2ndseason 2006, %

White/yellow sweet potato 22.1 7.1 23.5 17.6

Cassava 12.4 45.3 29.4 29.1

Maize 33.3 21.1 15.3 23.2

Share of area planted, 1stseason 2007, %

White/yellow sweet potato 20.2 4.6 23.2 16.0

Cassava 15.5 41.8 32.1 29.8

Maize 31.0 34.6 15.2 26.9

Notes: Sample size is 1592 households.

6.3 Consumption Patterns

Here we examine consumption patterns of foods for which OFSP is likely to substitute

and also summarize information from the survey about sweet potato consumption habits.

Using data from the food consumption expenditure module, we consider the

prominence of sweet potato, cassava and maize in consumption. The food consumption

expenditure module measures the value of food consumed from all sources over the past

seven days. The questionnaire does not distinguish between varieties of sweet potato, butincludes sweet potato consumed fresh or as dried chips. Similarly, information on

cassava consumption is collected separately for fresh cassava and cassava flour. Maize

consumption is measured separately for maize grain, cobs and flour. For each crop,

consumption in all forms was aggregated.

Table 6 presents the share of households consuming sweet potato, cassava and

maize in the past seven days for the entire sample and by district. The share of

households consuming any sweet potato in the past seven days is low, at less than five

percent. This likely reflects the timing of the interviews in July and early August. This

period is well after the first sweet potato harvest and before the second harvest, so theremay not have been much sweet potato available at the time of the interviews. Cassava

consumption was somewhat more common, with 11 percent of households having

consumed cassava in the past seven days. This period also lies outside the peak period of

cassava consumption. Maize was much more widely available at this time, with more

than one quarter of the sample, and half of the households in Mukono, having consumed

maize in the past week. Overall, these figures suggest limited potential for OFSP to enter


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the diet by substituting for WYFSP or cassava in consumption, though these results

provide limited information on this issue because of the timing of the baseline survey.

Table 6: Share of Households Consuming Selected Staples in the Past 7 Days


(percent)

White/yellow/orange sweet potato 1.3 8.3 4.5 4.7

Cassava 3.6 14.2 15.4 11.1

Maize 9.9 20.4 51.7 27.4

Notes: Sample size is 1585 households. Eleven households had incomplete food consumption data.

Because the survey was conducted outside the main periods of sweet potato

consumption, we included several questions about sweet potato consumption habits when

sweet potato roots are more widely available. Table 7 summarizes this information. The

use of markets as a source of sweet potato roots for consumption is relatively common.

Across the sample, roughly one quarter of respondents buy sweet potato when it is

available in the market, though this average hides considerable variability in this practice

by district. Half of the respondents in Bukedea buy sweet potato in the market, whereas

only five percent of respondents in Kamuli district ever buy sweet potato. Combining

this evidence with the information on sweet potato production and consumption from

Tables 6 and 7, we see that residents of Bukedea district do not typically grow sweet

potato, but they will buy it in the market when it is available and they are just as likely toconsume sweet potato outside the peak season for consumption.

Table 7 also reports the frequency of sweet potato consumption during periods

when sweet potato is abundant. Forty four percent of households report eating sweet

potato daily when it is widely available, and another 35 percent of households eat it at

least 3-5 times per week. Overall, nearly 80 percent of households consume sweet potato

regularly during periods of peak availability. The area of highest frequency of sweet

potato consumption is Kamuli district, with 66 percent of households eating sweet potato

daily and 93 percent eating sweet potato at least 3-5 days per week during peak periods.

This suggests a high level of own-cultivation of sweet potato in Kamuli because only fivepercent of households in the district report buying sweet potato in the market.

We also asked about methods of preparing sweet potato for consumption. Table 7

reports that it is very uncommon for households to consume sweet potato leaves, or for

children or adults to consume porridge made from sweet potato.


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Table 7: Sweet Potato Consumption Habits


(percent)

Do you ever buy sweet potato? 5.1 52.1 20.3 25.8

When sweet potato is abundant,

how often do you eat it?

daily 66.5 33.6 33.0 44.3

3-5 times per week 25.9 28.2 50.5 34.9

1-2 times per week 6.3 16.6 15.2 12.7

Do you or anyone in your household

eat sweet potato leaves? 0.0 5.3 0.6 2.0

make sweet potato porridge for children? 0.2 4.0 0.4 1.5

make sweet potato porridge for adults? 0.2 4.0 0.6 1.6

Notes: Sample size is 1595 households.

6.4 Nutrition Knowledge

In each household, the mother and father of the reference child age 3-5 years old were

interviewed separately about their nutrition knowledge of practices related to infant

feeding and child care and about health benefits and sources of vitamin A. Here we focus

on the mothers knowledge about vitamin A to establish baseline levels of knowledge for

women, who are the primary target for nutrition and demand creation trainings under the

REU interventions.As shown in Table 8, 85 percent of mothers in the sample had heard of vitamin A.

However, their knowledge about the health benefits of vitamin A consumption was not

specific. Seventy one percent of women knew vitamin A is good for your health, a

relatively low share given the phrasing of the question. Also, only 36 percent knew

vitamin A protects against disease and only 10 percent knew it improves vision. This

leaves a lot of room for the nutrition trainings to improve knowledge about the benefits of

vitamin A consumption. Some good news regarding the health care system is that more

than three quarters of the women said they had learned about vitamin A at a health center.


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Table 8: Mothers Nutritional Knowledge about Vitamin A


(percent)

Heard of vitamin A 83.3 91.8 80.1 85.0

How is it good for us?

good for health 71.2 73.5 67.7 70.9

protects against disease 32.4 42.2 32.3 35.9

good for vision 17.5 4.2 7.8 9.6

Learn about vitamin A at health center 75.6 74.3 80.7 76.8

Notes: Sample size is 1550 households. The mother of the reference child was not a household

member in 46 households.

6.5 Farming Practices

The baseline survey measured past experience with agricultural advisory services,

particularly with regard to sweet potato farming practices, to measure familiarity with

extension services and willingness to modify practices in response to advice from various

sources. Seventy three percent of respondents had received farming advice from any

source. Also, 41 percent of respondents claimed that the best source for farming advice

was an agricultural extensionist, including those from the Uganda National Agricultural

Advisory Service (NAADS) or from the REU project implementing partners VEDCO or

FADEP. Overall, 55 percent of respondents indicated that they had changed farming

practices as a result of receiving advice.

Regarding sweet potato farming in particular, 20 percent of respondents have everreceived advice about growing sweet potatoes, and 44 percent believed that agricultural

extensionists from NAADS, VEDCO or FADEP were the best source of advice for sweet

potato farming. Among the 1353 households that grew some type of sweet potato in

2006-07, more than one half of respondents said they had ever changed sweet potato

varieties in response to advice they had received. This provides encouraging evidence

that farmers in this area are receptive to farming advice and may be willing to adopt new

sweet potato varieties or new farming techniques.

The baseline survey also captured information about sweet potato farming

practices for households that had grown sweet potato in 2006-07. A summary of thesepractices is provided in Table 9. Regarding sources of sweet potato vines, there is more

encouraging news for the REU intervention. Among farmers growing sweet potato, 83

percent used some of their own vines and 86 percent had received vines free, most likely

from a neighboring farmer or farmer group member. This suggests that vine retention

between seasons is not a significant problem in Uganda, so that farmers receiving new

OFSP varieties from the REU project should be able to maintain vines for planting in


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future seasons. Also, the practice of sharing vines is widespread, which should

encourage diffusion of the new OFSP varieties to farmers who are not direct beneficiaries

of the REU interventions.

Table 9: Farming Practices


(percent)

Experience with farming adviceEver get farming advice 72.2 72.6 74.5 73.1

Best source: extension, NAADS, VEDCO,

FADEP 48.2 35.5 39.0 40.9

Ever change farming practices due to advice 55.8 58.1 51.7 55.2

Ever received sweet potato advice 11.0 34.3 13.3 19.5

If so, from extension, NAADS, VEDCO, FADEP 51.7 44.2 40.9 44.8If ever grew sweet potato, ever change sweet

potato 47.2 44.4 60.1 51.9

variety due to advice

Sweet potato farming practices

Used own vines last year 95.0 52.4 89.8 83.3

Received vines free 94.9 83.2 86.9 86.7

Have you ever stored sweet potato 9.8 22.1 6.2 11.2

Have you ever dried sweet potato 21.8 61.9 1.7 23.2

Sliced (not grated) before drying 99.1 90.5 100.0 93.9

Dried in the sun 98.2 95.8 100.0 96.8

Correctly identified weevil infestation in picture 43.8 13.7 28.2 31.0of infected root

Notes: Sample size is 1596 households for variables referring to experience with farming advice.

Variables on sweet potato farming practices are restricted to the sample of 1353 households that

grew any sweet potato in 2006-07.

Regarding sweet potato storage practices, only 11 percent of sweet potato growers

had ever stored sweet potato. Apparently, the most common practice is to consume sweet

potato only when it is available fresh. The REU intervention hopes to encourage storage

of OFSP for future availability, provided that it can teach households about properstorage techniques for retaining the beta-carotene content in the stored roots. These

techniques include drying OFSP out of the sun because beta-carotene concentrations drop

sharply when OFSP is dried in the sun. Overall, 23 percent of sweet potato growers have

any experience drying sweet potato for storage or consumption as flour. This practice is

common in Bukedea, where 62 percent of sweet potato farmers have dried the crop, but is

not practiced at all in Mukono. Among those that had ever dried sweet potato, 93 percent


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had sliced the roots before drying, and nearly all had dried the crop in the sun. Proper

drying techniques of OFSP will be an important message for the training of farmers

during the interventions. Finally, only 31 percent of sweet potato farmers could correctly

identify a weevil infestation when shown a picture of a weevil infested root. Weevil

infestations are an important source of crop loss of sweet potato in Uganda. More

training is needed to help farmers identify this pest.

7. Test of Effectiveness of Randomization

In a randomized evaluation design, it is important to test the effectiveness of the

randomization by comparing baseline distributions of key outcome and control variables

between the treatment (and control) groups. If the random assignment of farmer groups

to intervention groups Model 1, Model 2 and control was effective, farmer groups andhouseholds should have similar distributions of characteristics across treatment groups.

That is the case because when treatment assignment is random, the treatment variables

cannot be correlated with household characteristics. In fact, in very large samples with

many intervention groups, there should be no differences in the distribution of variables

across treatment groups. However, in programs of moderate size, some differences in

characteristics between randomized treatment groups may persist because ofsampling

error. Sampling error occurs when an imbalance in the distribution of variables across

groups arises by chance. For example, if there are two communities with unusually large

farms, they could both end up being assigned to Model 1 by chance and we would find

larger average farm size in Model 1 than in Model 2 or the control group.9 As we

increase the number of intervention units (farmer groups) in the sample, these differences

tend to be smoothed out so that intervention groups have similar characteristics on

average. We expect that the baseline sample for this study is sufficiently large that there

will be relatively few differences in characteristics across intervention groups.

In this section, we present t-tests of the difference in mean outcome and control

variables across the three treatment groups in the evaluation design. We test for

significant differences in means across all pairwise comparisons of these groups because

the analysis of these data will often involve comparing only one intervention group

against another. For example, we will ask is OFSP adoption higher in Model 1 thanModel 2? Or, is the change in vitamin A status greater in Model 1 than in the control

group? These pairwise t-tests are slightly more conservative than a joint F test of the

equality of means across all three treatment groups. A joint test would also be a valid test

9 This is equivalent to tossing a coin ten times and obtaining eight heads. We expect the share of heads

to be 0.5, but it may be lower or higher in a small number of tosses. As the number of tosses increases, the

sampling error falls and the share of heads approaches 0.5.


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of the effectiveness of the randomization, but we present pairwise tests to be consistent

with the plan for the upcoming analysis. We test the null hypothesis of equality of mean

characteristics for many outcome and control variables at the five percent significance

level. It is worth keeping in mind that at this significance level, one out of every 20

hypothesis tests would be expected to reject the null hypothesis even in a random sample

with no sampling error.

Table 10 presents the means and tests for equality of means for variables related

to household demographics, farming and crop choice, farmer group participation and

farmer group characteristics. There are some differences across treatment groups. For

example, Model 2 households are significantly more likely to have a female household

head than Model 1 households. Mean household head education is slightly higher in the

control group than in Model 1, though this difference is only weakly significant. Very

few households in the sample grew any OFSP in the last year. This share was

significantly lower in Model 2 farmer groups than in Model 1 or the control farmer

groups, but the difference is tiny and the average across the sample is so small that this isunlikely to have much effect on the analysis after the follow-up survey.

Farmers in the control farmer groups had significantly higher tenure in their

groups than those in the Model 2 groups. Also, the female share of group members was

significantly higher in Model 2 groups than in Model 1. Some farmer groups had all

female members, which drives up the share and may be responsible for this imbalance

across treatment groups. Not surprisingly, Model 2 farmer groups were also significantly

more likely to have a female leader than farmer groups in Model 1 or the control. The

share of female farmer group members and the gender of the farmer group leader may

have important effects on OFSP adoption rates and on OFSP consumption as well, so wewill need to control for these baseline differences in the analysis. For control variables

like farmer group characteristics, this can be done by adding the variable to the model

that estimates differences in outcomes across treatment groups. For most outcome

variables, we will control for any differences in baseline mean outcomes by estimating

impacts using difference-in-difference (DID) estimators. The DID estimator is simply

the difference in the change in the mean outcome over time across treatment groups.

Estimating impacts in this way removes the effect of baseline differences in means; that

difference is subtracted out. If there is no significant difference in mean outcomes at

baseline, the DID estimator may still be preferred for reasons of statistical efficiency.

Also, even an insignificant difference in mean outcomes at baseline could contribute to a

significant difference in the change in mean outcomes, so DID estimators are generally

preferred to the single, post-treatment difference in means.

Table 11 presents means of measures of household consumption per adult

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Uganda SES Baseline Report 05-28-09