Association Between Bio-fortification and Child Nutrition Among Smallholder Households in Uganda

Association Between Bio-fortification and Child Nutrition Among Smallholder Households in Uganda
Association Between Bio-fortification and Child Nutrition
Among Smallholder Households in Uganda
Bernard Bashaasha1*, Racheal Namulondo2, Rosemary Isoto Emegu3, Patrick Webb4,7, Shibani
Ghosh5,7 and Edgar Agaba6
1,3Department of Agribusiness and Natural Resource Economics, School of Agricultural Sciences, College of Agricultural
and Environmental Sciences; Makerere University P.O. Box 7062 Kampala, Uganda
2Department of Economics, Busitema University, Busitema, Uganda
4,5,6USAID Feed the Future Innovation Lab for Nutrition at Tufts University, Boston, Massachusetts, USA
7Gerald J. and Dorothy R. Friedman School of Nutrition Science and Policy at Tufts University, Boston, Massachusetts,
USA
We explored the empirical relationship between bio-fortification and child nutrition in Uganda.
The research expanded the traditional approach used to address child nutrition by including in
the model a categorical dependent variable for a household growing bio-fortified crop varieties.
We used three waves of panel data from the Feed. The Future Innovation Lab for Nutrition,
collected from 6 districts in Uganda. We performed univariate analysis and also estimated a panel
logistic regression model to study the association between child stunting and production of bio-
fortified crop varieties among smallholder farmers in Uganda. The results confirmed a very strong
association between production of bio-fortified crop varieties and child stunting among children
aged 0-59 months of age. The strength of the relationship, however was insensitive to the number
of bio-fortified crop varieties grown by a particular household. Other important covariates of child
stunting were male gender, 24-59 months age bracket, child birth weight, dietary diversity,
education of caregiver, antenatal care, household size, access to improved water and household
livestock score. These results can be used to vindicate current government policy of promoting
production of bio-fortified crop varieties and underscore the need for intensified efforts to
promote bio-fortification as a complementary means of addressing long-term child malnutrition
in Uganda.
Keywords: Bio-fortification, child stunting, panel logistic regression, Uganda
INTRODUCTION
Hunger and malnutrition remain important global problems
that have recently been exacerbated by climate change
and environmental degradation. Although important
achievements in food and nutrition security have been
recorded over the last few decades, undernutrition
remains high, especially in Africa and Asia (Godfrey et al.,
2010, Dube et al., 2012 and IFPRI 2014). Worldwide, more
than 790 million people are still classified as chronically
hungry, meaning that they lack access to adequate
calories (FAO, IFAD and WFP, 2015). Meanwhile an
estimated 2 billion people suffer from micronutrient
malnutrition, mostly due to low intake of vitamins and
minerals such as Iron and Zinc (IFPRI, 2014). Globally
undernutrition is estimated to be responsible for the death
of 45 percent of all children (Black et al., 2013).
Over the last few decades, Uganda has experienced
impressive economic growth and a general decline in
poverty levels. Economic growth has averaged 4.5 percent
*Corresponding Author: Bernard Bashaasha,
Department of Agribusiness and Natural Resource
Economics, School of Agricultural Sciences, College of
Agricultural and Environmental Sciences; Makerere
University P.O. Box 7062 Kampala, Uganda. E-mail:
bashaasha@caes.mak.ac.ug.
Co-Author Email: 2
renamulondo@gmail.com;
3
Email: emegur@gmail.com;
4Email: Patrick.Webb@tufts.edu;
5Email: Shibani.Ghosh@tufts.edu
6Email: Edgar.Agaba@tufts.edu;
Research Article
Vol. 6(2), pp. 752-759, May, 2020. © www.premierpublishers.org, ISSN: 2167-0477
Journal of Agricultural Economics and Rural Development

Bashaasha et al. 753
per year over the period 2012/13-2016/17 (MFPED, 2017).
The percentage of people living below the poverty line
declined from 24.5 percent in 2009/10 to 19.7 percent in
2012/13 but increased again to 27 percent in 2016/17
(UBOS, 2016/17). There has been an improvement in life
expectancy at birth from 51.5 years in 2009/10 to 54.5
years in 2011/12 (NPA, 2015). However, the country
continues to lag behind as regards the reduction of child
mortality and malnutrition. According to the 2016 Uganda
Demographic and Health Survey (UDHS) (UBOS and ICF
2018), infant mortality in Uganda stands at 54/1000 live
birth falling short of the Health Sector Strategic and
Investment Plan (HSSIP) of 41 death per 1000 live birth.
Meanwhile, 3.6 percent of children under five years of age
are wasted (underweight) and 29 percent have chronic
malnutrition or stunting which has been linked with long
term irreversible consequences affecting both the child,
community and the nation (UBOS and ICF 2018). Over the
last 15 years, there has been a substantial reduction in the
prevalence of child stunting, with national prevalence
dropping from 45% in 2000 to 29% in 2016 but further
improvement is needed to meet the World Health
Assembly (WHA) target of reducing the absolute number
of stunted children by 40%, by 2025. Applying national
population projections to prevalence data shows that there
were 1.87 million stunted children under five years of age
in 2016. Moreover, there is substantial economic disparity,
with the prevalence of stunting in each of the poorest three
wealth quintiles nearly double that of the richest quintile.
Micronutrient malnutrition is also high among children
under 5 years of age with nearly five out of every ten (53%)
children being anemic (UBOS, 2016). According to MAAIF
(2015), food insecurity and malnutrition are common in
Uganda’s rural areas. This essentially means that recent
public policies that have spurred economic growth are
lacking in terms of addressing household and child
malnutrition yet child nutrition is important for any country’s
long-term economic and human development.
Although several factors contribute to child nutritional
outcomes, supportive agri-food policies (or agricultural
value chain policies) that are implemented in a timely and
effective manner can go a long way in impacting child
nutritional outcomes. According to World Health
Organisation (WHO, 2006) child nutritional outcomes can
be measured along several dimensions underpinned by
anthropometric indicators including: Length/height for age
(Stunting), weight for age (Wasting), weight for length,
weight for height and body mass index (BMI)-for age. In a
recent review, Haddad (2013) highlights several important
pathways that link improvements in agriculture to
improvements in nutrition. These include higher
agricultural incomes; lower food prices; more nutritious on-
farm production and consumption and synergies between
agriculture and nutrition arising from women’s
empowerment. In Uganda, key policies intended to
synergize agriculture for nutrition include first and foremost
the Second National Development Plan (NDPII) (2015)
that has ending hunger, achieving food security and
improved nutrition as key policy goals; The Uganda Food
and Nutrition Policy (2003) with the policy goal of ensuring
food security and adequate nutrition for all the people of
Uganda; The National Agricultural Research Policy (2003)
that prioritizes research for food security; The Agriculture
Sector Strategic Plan (ASSP) (2015) that among other
things, highlights food and nutrition security. Under cross
cutting priorities the ASSP cites one of the key
interventions to improve food and nutrition security as
promotion of appropriate technologies.
OBJECTIVE
The standard empirical approach to explaining observed
variation in child nutrition outcomes is to use multiple
regression analysis to measure correlations between
anthropometric indicators and the underlying household,
child and maternal characteristics (Shively and
Sununtnasuk, 2015). In the present research we augment
this type of analysis by incorporating information about
agricultural technology with a focus on production of bio-
fortified food crops. Bio-fortification, an agricultural
technology that can increase the micronutrient content of
staples, may confer large benefits to poor rural children
who have limited access to expensive high-quality foods.
In Uganda, three bio-fortified crops are currently being
promoted to enhance the micronutrirnt intake of
households. They include orange fleshed sweet potata
(OFSP), quality protein maize (QPM), and high iron beans.
These bio-fortified crops can benefit nutrition through the
own-consumption pathway when they are directly
consumed by the children of producer farmers, through the
income pathway when they are sold or through the food
price pathway when they increase the availability of
micronutrient- rich foods in the market place. Despite the
celebrated nutritional importance of bio-fortified foods, the
possible causal linkages between the production of bio-
fortified crops and child nutritional outcomes has not been
adequately investigated. The present study is an attempt
to empirically establish the link between bio-fortification as
a policy instrument to achieve food security and adequate
nutrition for all the people of Uganda and child stunting in
particular. The research is guided by the following two
specific objectives:
a) To empirically estimate the relationship between
household level production of bio-fortified food crops
and child stunting in Uganda
b) To generate empirical evidence to inform and
influence the design and targeting of agricultural and
nutrition policies to reduce child stunting in Uganda.
METHODOLOGY
SAMPLE SELECTION
As part of USAID Feed the Future (FtF) Innovation
Laboratory for Nutrition, a cross-sectional Randomized

J. Agric. Econ. Rural Devel. 754
Control Trial (RCT) study design was implemented in six
districts selected in Western and Northern Uganda in three
time periods 2012, 2014 and 2016 constituting three
waves of panel data. A pre-tested questionnaire was used
to collect data from selected households in each district.
Eligible households were chosen on the basis of having at
least one of the following; one child aged less than 24
months and a woman of child-bearing age of 18-49 years.
Household participants who declined consent to
participate in the study were excluded. The target survey
respondents were primary mothers (or primary caregivers)
of the randomly selected child aged 0 –23 months, or
women of child-bearing age.
A self-weighting pre-determined sample of 600
households to represent the study district was selected. A
random sample of 50% of the parishes in each district was
randomly pre-selected. Each district has on average
between 35 –50 parishes, thus between 17 –25 parishes
were randomly pre-selected. The estimated number of
households in each of the pre-selected parishes was
obtained from Uganda Bureau of Statistics (UBOS, 2002)
population and housing census data. The number of
households selected in each parish was determined
proportional to estimated number of households in that
parish. The number of selected households in the parish
were equally divided among the villages in the parish. On
average there were 5 –8 villages in each parish. In each
village in the selected parish, the research team worked
with village local council (LC) leaders to construct
household sampling frames. The assigned village sample
size was randomly selected from the village sampling
frames. With the help of village guides, the study Research
Assistants (RA) proceeded to locate the sampled
households and administered the study tool(s). If a
sampled household was found to have more than one child
aged less than 24 months, all children of eligible age were
enumerated on a numbered list, then one child was
randomly selected using a random number list that had
been prepared in advance. If a household had no child
aged less than 24 months, one woman of childbearing age
(18-45 years) was randomly selected to respond to the
questionnaire. If a household had no child aged less than
24 months, and no woman of childbearing age, the head
of household served as the respondent.
Data was directly captured electronically by means of an
electronic questionnaire loaded on a PDA, administered
face-to-face to mothers/caregivers and/or household
heads in their home settings. The questionnaire was
administered in local language of the respondents.
Household identification information included the
demographics of respondents, health and sanitation,
including anemia and malaria parasite assessment in
children 6 months or older, and women of child bearing
age, nutritional status, gender issues, general household
information on: socio-economic status, agriculture and
income generation, food security and hunger, diet-food
consumption patterns.
Analytical Model
As earlier noted, our objective is to understand the link
between bio-fortification of food crops as a policy
instrument to achieve food security and adequate nutrition,
and child stunting in Uganda. We specifically focus on
height for age indicator or stunting, HAZ (zit) as the
dependent variable. We then use panel logistic regression
analysis to assess which variables are most likely
correlated with the probability of child stunting. The height
for age HAZ (𝑍𝑖𝑡) Z-score of child (i) at time (t) measures
the dispersion of the child health indicator as standard
deviations around a reference population and is calculated
as in equation (1) below:
𝑍𝑖𝑡 =
𝑥 𝑖𝑡−𝑥̅
𝛿 𝑥
(1)
Where 𝑥𝑖𝑡 is the individual observation, and
𝑥̅ and 𝜎𝑥 are the median and the standard deviation of the
reference population.
In the present analysis, the Z-scores were calculated using
WHO’s current Child Growth Standards reference
population median and standard deviation. Normal growth
patterns of well-nourished children under the age of five
years exhibit similar heights and weights despite
geographic, ethnic and cultural differences (Habicht et al.,
1974). Departures from the distribution of optimal growth
can be attributed to socio-economic and environmental
factors. For the present analysis we use height for age Z
scores (HAZ). These reflect the impact of health and/or
nutritional factors on growth and development during
gestation as well as exogenous factors that affect the child
after birth. Low values are associated with chronic food
insufficiency and an unhealthy physical environment. Low
HAZ is a strong indicator of long term nutritional
deficiencies and repeated illness (Puffer and Serano,
1973). A child is considered stunted if HAZ falls below -2.0
and severely stunted if HAZ falls below -3.0 (WHO, 2006).
Our sample of analysis consists of 3200 children per year
aged 0-24 months. This translates into approximately
9,600 observations over the three year panel allowing for
attrition that was on average 11 percent per year. The child
stunting regression model we estimate takes the form
specified in Equation (2).
𝑍𝑖𝑡 = 𝑋𝑖𝑡 𝛽 + 𝑈𝑖𝑡 (2)
Where Zit is height-for-age of child i at time t
Xit is a vector of control and explanatory variables,
𝑈𝑖𝑡 is an error term and 𝛽 the vector of parameters to be
estimated.
The panel logistic regression model assumes that the
dependent variable Sit takes values 1 and 0 as specified in
Equation (3).
𝑆𝑖𝑡 = {
1 𝑖𝑓 𝑍𝑖𝑡 < −2.0
0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
(3)
and is distributed according to the specification in Equation
(4)

𝑃(𝑆𝑖𝑡 = 1) =
𝑒 𝛼+𝛽𝑥 𝑖𝑡
1+𝑒 𝛼+𝛽𝑥 𝑖𝑡
(4)
In the model, Xi includes geographic, household, child,
caretaker, health, sanitation and agricultural variables.
Food bio-fortification variables including household
adoption /production of Quality Protein Maize (QPM),
Orange Fleshed Sweet Potato (OFS) and Iron Fortified
(red) beans are the main focus with the other variables
controlling for other factors known to influence child
stunting. According to Hilmer and Hilmer (2014), estimated
slope coefficients indicating the log-odds ratio lack a
simple intuitive economic meaning necessitating their
conversion into estimated marginal effects (dy/dx) that
should be interpreted. We therefore report average
marginal effects for each independent variable. For
continuous independent variables, we compute the
marginal effects for each observation and then average
these over the entire sample. For binary variables,
marginal effects are computed as the difference between
the predicted probability at 0 and the predicted probability
at 1. These individual differences are then averaged over
the sample. Table 1 presents the hypothesized
explanatory variables and a priori sign expectations. The
quantitative data from the household questionnaire were
analysed using STATA Econometrics program to generate
descriptive statistics and regression results.
RESULTS AND DISCUSSION
Descriptive Results
Table 1 presents descriptive results that show a household
population of seven, low caregiver educational levels with
a good antenatal care services of about 60 percent of the
caregivers. The household size reported is slightly higher
than the national household size of 5 persons reported by
UBOS (2018). Overall, only 19 percent of households grew
bio-fortified crop varieties and household annual
agricultural income was estimated at under USD200. This
result is consistent with UBOS (2018) that reports a
worsening of the income situation of crop/subsistence
farmers in Uganda. According to UBOS (2018), poverty
among crop/subsistence farmers increased from 23
percent in 2012/13 to 36 percent in the 2016/2017 Uganda
National Household Survey (UNHS) round. Descriptive
results also suggest a very high level of reported food
insecurity with only 15 percent of the households reporting
to be food secure. The results also suggest good access
to clean water at about 67 percent of the households
compared to the national average of 80 percent of the
households (UBOS, 2018). The household univariate
statistics are fairly stable across the three waves of data.
Table 1: Socio-economic characteristics of the sample
Pooled 2012 2014 2016
Household size 6.58 (2.49) 6.0 (2.58) 6.74 (2.31) 7.09 (2.43)
Caregiver’s years of schooling 4.06 (3.09) 4.08 (3.10) 3.99 (3.10) 4.10 (3.06)
Caregiver had at least 4 antenatal care visits (%) 58.33 52.39 61.52 61.80
Household grew at least 1 bio-fortified crop (%) 18.68 27.61 13.78 13.70
Household agricultural income (‘000 shs) 725 (1,195) 692 (1,072) 594 (1,069) 897 (1,410)
Household was Food secure (%) 15.26 17.85 15.23 12.36
Household accessed clean water source (%) 66.25 64.25 66.00 68.77
Number of observations 10,095 3,597 3,302 3,196
Source: Calculated based on Innovation Laboratory for Nutrition panel data 2012, 2014 and 2016.
Note: Values are means (with SDs in parentheses) or percentages.
Table 2 presents summary statistics of key variables of
children aged 0-59 months. The results show that the
sample is divided equally between male and female
children with majority in the 6-23 months age bracket. The
computed level of stunting is 20 percent of the sample
children which is slightly less than the national average
stunting rate of 29 percent reported by the 2016 UDHS
(UBOS and ICF 2018). Descriptive statistics of possible
stunting confounding factors are also noted here. Results
suggest a high rate of breast feeding at close to 90 percent
and children born with normal birth weight at over 95
percent. About 50 percent of the sample children received
all basic vaccinations.
Table 2: Characteristics of children aged 0-59 months
Pooled 2012 2014 2016
Sex of index child (%) Male child* 50.02 50.18 52.77 47.47
Age of index child (%) 0-5 months 21.33 23.62 25.69 14.60
6-23 months 67.34 74.28 72.35 53.98
23-59 months 11.33 2.10 1.96 31.42
Stunted (%) 21.78 20.15 20.23 25.26
Child is currently breast feeding (%) 88.76 88.03 89.47 89.24
Minimum dietary diversity for complementary feeding (%) 37.67 37.39 38.07 37.66
Weight at birth (>=2.5 kg) (%) 95.10 95.77 95.00 94.06
Received basic vaccinations (%) 52.92 49.15 56.04 55.81
Number of observations 4,572 1,901 1,226 1,445
Source: Calculated based on Innovation Laboratory for Nutrition panel data 2012, 2014 and 2016. *
balance is for female child

Regression results
Regression results presented in Table 3 show that all other
variables constant, compared to producing none (the
comparison category), producing at least one bio-fortified
food crop variety significantly reduced the probability of a
child stunting by 5.2 percentage points. The results also
show that compared to producing no bio-fortified food crop
at all, children living in households that produced at least
two bio-fortified food crops had a reduced (although not
significant) chance of stunting. The results appear to
suggest that what is important for child nutrition is for a
household to produce at least one bio-fortified food crop
variety implying that the intensity of production of bio-
fortified food crops by a household did not seem to be that
important for child nutrition. However, the results remained
robust as the number of bio-fortified crop varieties grown
by a household increase.
Table 3: Panel Logistic regression results of the association between child stunting and adoption of bio-fortified
crops
Variable description Stunted % Marginal effects (dy/dx) P-value
Adopted bio-fortified crops 0 crop 23.15 - -
1 crop 16.15 -0.052 0.002
2 crops 16.34 -0.031 0.343
Child’s sex Female 28.67
Male 14.91 -0.138 0.000
Child’s age in months 0-5 6.77 - -
6-23 23.90 0.126 0.000
24-59 37.45 0.391 0.000
Child received 2 basic No 13.68
vaccinations Yes 26.51 0.051 0.000
Weight at birth <2.5 kg 30.00
=>2.5 kg 20.07 -0.116 0.000
Child’s diet is below minimum diet diversity No 12.21
Yes 24.72 0.044 0.012
Child is breast feeding No 30.24
Yes 19.10 -0.033 0.111
Years of schooling completed by caregiver -0.006 0.008
Caregiver had at least 4 No 24.37
antenatal care visits Yes 20.16 -0.037 0.004
Household size -0.005 0.055
Access to improved water source No 25.78
Yes 19.62 -0.039 0.004
Livestock Diversity Score -0.027 0.000
Log likelihood -1646.37
Wald chi2 255.38
Prob > chi2 0.0000
Source: Estimated based on Innovation Laboratory for Nutrition panel data 2012, 2014 and 2016.
Similarly, being a male child significantly reduced the
probability of the child being stunted by 13.8 percentage
points when compared to the child being a female. This
result points to a possibility of households giving better
attention including nutrition to male children as compared
to female children. Shively and Sununtnasuk (2015)
obtained similar results. The authors observed that
although sex of the child appeared to play no role in HAZ
outcomes of children under two in Nepal, the probability of
stunting significantly increased for female children over 24
months old.
Age of child in months also turned out to be a significant
predictor of child stunting in our study. Results show that
children in the age brackets 6-23 months and 24-59
months are at a higher risk of stunting compared to
younger children aged 0-5 months. The risk of stunting
increases with age of the child. All other variables held
constant, relative to a child being in the age bracket 0-5
months (the omitted category) a child being in the age
group 6-23 months increased the probability of that child
being stunted by 12.6 percentage points and for a child in
the age bracket 24-59 months the probability of that child
being stunted jumped to 39.1 percentage points. Our
results agree with Muldiasman et al., (2018) who through
interpreting odds ratios, found the most dominant risk
factor to stunting to be age group 24-29 months with a risk
factor of 2.5 times higher than age group of 6-11 months.
Our results suggest that something seriously goes wrong
with child nutrition immediately after weaning and/or during
the later period of breast feeding.

In the current study, child vaccinations had an unexpected
positive and significant relationship with stunting
contrasting Shively and Sununtnasuk (2015) who found
the number of vaccinations to be negatively and
significantly associated with the probability of stunting. The
authors further noted that vaccinations, per se, may not be
the causal mechanism at play but rather the number of
vaccinations received may be a proxy for other
unobserved factors including the intensity or quality of
health services and other interventions in the child’s
location or general level of parental care.
Low birth weight (LBW) (defined as <2.5kg) among
newborn infants (which includes those born preterm (<37
weeks gestation), with intrauterine growth restriction
(IUGR), or both), is a significant predictor of neonatal
mortality and morbidity as well as future health and
nutritional status (Dube et. al., 2012 and Fall, 2013). We
investigated the association between birth weight and child
stunting. The results suggest that, all else constant, a child
being born with normal birth weight (≧2.5kg) significantly
reduced the probability of stunting growth later in life by
11.6 percentage points compared to a child born
underweight. Our results are consistent with the findings of
Muldiasman et al., (2018) who found that children with a
low birth weight were 2.0 times at a higher risk of stunting
compared to children with normal birth weight. This calls
for public policy to increase attention and focus on caring
for the pregnant mothers to increase the chances of them
having normal weight babies at birth.
Both household diet and by implication child diet are
important factors for child nutrition. We investigated the
association between a child’s diet and child nutrition
outcome of stunting. The results suggest that all other
variables held constant, infants whose diet is below
minimum diet diversity have an increased probability of 4.4
percent to be stunted compared to children with diets at
and above minimum diet diversity.
Our analysis shows that a child being breast fed also
negatively (but not significantly) reduced the probability of
the child stunting later in life. It is less clear why our results
for breast feeding are not significant. Other studies in
developing countries have found a strong association
between breast feeding and reduced risk of stunting (e.g.
Muldiasman et al., 2018; WHO, 2014 and Black et al.,
2008). Muldiasman et al., (2018) showed that children who
do not get an early initiation to breast feeding are 1.3 times
more likely to be stunted than those who are breastfed
early. Their findings however, focused on the timing of
breastfeeding rather than whether a child is breastfed at all
or not which is the focus of our current investigation.
The education level of the caregiver in terms of number of
years of formal schooling had the expected negative
relationship with child stunting. The results suggest that,
all else constant, a one year increase in formal education
by the caretaker significantly reduced the probability of
child stunting by 0.6 percentage points. Maternal
education is known to be a strong predictor of stunting
(Shively and Sununtnasuk, 2015 and Desai and Alva,
1998). Maternal education has also been found to work
through the effect of education on improved access to
health and nutrition information (Thomas, Strauss and
Henriques, 1991).
Prenatal care, also known as antenatal care (ANC), is a
type of preventive healthcare. Its goal is to provide regular
check-ups that allow doctors or midwives to treat and
prevent potential health problems throughout the course of
the pregnancy and to promote healthy lifestyles that
benefit both mother and child. All the other variables held
constant, the caregiver having at least 4 antenatal care
visits decreased the probability of child stunting by 3.7
percentage points. According to Kawungezi et. al., (2015),
women in rural areas of Uganda are two times less likely
to attend ANC than the urban women. Most women in
Uganda have registered late ANC attendance, averagely
at 5.5 months of pregnancy and do not complete the
required four visits. The inadequate utilization of ANC is
greatly contributing to persisting high rates of maternal and
neonatal mortality in Uganda. This study found the effect
spilling over to long term child health outcomes as well.
The impact of household size on long term child nutrition
outcomes has not been well studied. Our results suggest
that it is an important factor as far as child stunting is
concerned. All other variables held constant; infants born
in large homes had a reduced probability of 0.5 percentage
points of being stunted. It is less clear why an increase in
the number of household members would buttress children
from the problem of under nutrition. The contrary would be
more conceivable on account of household level
competition for resources including food. While our result
warrants further investigation, it could point to the benefit
of accumulated experience of raising and feeding children
in larger homesteads. This advantage would be expected
to hit a plateau as household size increases.
Household access to an improved water source also
significantly reduces the probability of the child being
stunted by 3.9 percentage points when compared to
infants in households with unimproved water source.
Shively and Sununtnasuk (2015) controlled for piped and
well source of water but yet obtained a positive and
insignificant relationship with stunting. The authors cite
potential shortcomings with the potability of water from
covered sources. Analysis by Muldiasman et al., (2018)
also showed a significant association between both water
source and birth weight and child stunting.
Many empirical studies have consistently demonstrated
the association between livestock and household poverty
and nutrition. Research by Ellis and Bahiigwa (2003)
showed that rural poverty in Uganda is strongly associated
with lack of land and livestock, as well as the inability to
secure nonfarm alternatives to diminishing farm

opportunities. A study by Mosites et al., (2015), found that
a tenfold increase in household livestock ownership had a
significant association with lower stunting prevalence in
Ethiopia and Uganda but not in Kenya. The same study
found out that the weighted livestock score was only
marginally associated with stunting status and suggested
a slightly beneficial effect of household livestock
ownership and child stunting prevalence. The positive
benefits of animal-source foods (ASF) to food and nutrition
security have also been empirically demonstrated by
among others Knueppel et al., (2009). This study found
that food security was positively associated with animal –
source food consumption, among other factors. We
investigated the possible association between stunting
and livestock by computing the household livestock
diversity score. Following Mosites et al., (2015), Livestock
Diversity Score was constructed as a sum of the number
of livestock families kept by a household. Accordingly, the
following categories of livestock were included in the
score: cattle, sheep, goat, pig, poultry, donkey, rabbits, fish
farm, bee keeping and other. Our results suggest that all
other variables held constant, children born in households
with more livestock had a 2.7 percent reduced probability
of stunting compared to children born in households with
fewer or no livestock at all. It is noteworthy that the impact
of livestock on child stunting is about half of the 5.2 percent
reduction earlier noted for adoption of at least one bio-
fortified crop variety.
CONCLUSION
Adoption and production of bio-fortified crops by
households can be associated with preventing stunting
among children aged 0-59 months after controlling for all
other confounding factors.
Apparently, all other variables held constant, what is
important for child nutrition is for a household to produce
at least one bio-fortified food crop variety implying that the
intensity of production of bio-fortified food crops by a
household does not seem to be that important for child
nutrition. Bio-fortified food crops are now readily available
in most developing countries and their adoption should be
encouraged to achieve positive nutrition outcomes for
children.
Authors’ Contributions: BB, PW, SG, and EA had a
significant role in the design and implementation of the
panel study and/or panel data cleaning and processing.
RN and RIE jointly analyzed the data under the guidance
of BB. BB wrote the paper and had primary responsibility
for the final content. All authors have read and approve the
final manuscript.
Study funding was provided by the Feed the Future
Innovation Lab for Nutrition at Tufts University, supported
by the United States Agency for International Development
(award number AID-OAA-L-10-00006), and by the
National Institutes of Health (CD, grant numbers
K24DK104676, 2P30 DK040561). The funding sources
had no role in the design, analysis, or writing of this article.
Conflicts of Interest: No conflicts of interest.
Funding Disclosures: All authors
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Accepted 9 May 2020
Citation: Bashaasha B, Namulondo R, Emegu RI, Webb
P, Ghosh S, Agaba E (2020). Association Between Bio-
fortification and Child Nutrition Among Smallholder
Households in Uganda. Journal of Agricultural Economics
and Rural Development, 6(2): 752-759.
Copyright: © 2020: Bashaasha et al. This is an open-
access article distributed under the terms of the Creative
Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium,
provided the original author and source are cited.

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