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Global Food Security 26 (2020) 100454
Available online 3 November 2020
2211-9124/© 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Nudging children toward healthier food choices: An experiment combining
school and home gardens
Pepijn Schreinemachers
a
,
*
, Ghassan Baliki
b
, Rachana Manandhar Shrestha
c
,
Dhruba Raj Bhattarai
d
, Ishwori P. Gautam
e
, Puspa Lal Ghimire
f
, Bhishma P. Subedi
f
,
Tilman Brück
b
,
g
,
h
a
World Vegetable Center, Bangkok, Thailand
b
Leibniz Institute of Vegetable and Ornamental Crops (IGZ), Großbeeren, Germany
c
Department of Community and Global Health, Graduate School of Medicine, The University of Tokyo, Japan
d
Outreach Research Division, Nepal Agricultural Research Council, Khumaltar, Lalitpur, Nepal
e
National Horticulture Research Centre, Nepal Agricultural Research Council, Khumaltar, Lalitpur, Nepal
f
Asia Network for Sustainable Agriculture and Bioresources (ANSAB), Baneshwor, Kathmandu, Nepal
g
Natural Resources Institute (NRI), University of Greenwich, Chatham Maritime, UK
h
ISDC – International Security and Development Center, Berlin, Germany
ARTICLE INFO
Keywords:
Healthy eating
Impact evaluation
Nepal
Nutrition-sensitive agriculture
Randomized control trial
School garden
ABSTRACT
School gardens have become a widely used approach to inuence children’s food knowledge, preferences and
choices in low- and high-income countries alike. However, evidence indicates that such programs are more
effective at inuencing food knowledge and preferences than actual food choices. Such nding may occur
because school gardens insufciently inuence the food behavior of parents and because healthy food items are
not always available in children’s homes. We tested this hypothesis using a one-year cluster randomized
controlled trial in Nepal with 15 treatment and 15 control schools and a matched sample of 779 schoolchildren
(aged 8–12) and their caregivers. Data were collected before and after the intervention during the 2018–2019
school year. In addition, children’s food consumption was monitored using a monthly food logbook. Average
treatment effects were quantied with a double-difference estimator. For caregivers, the intervention led to a
26% increase in their food and nutrition knowledge (p <0.001), a 5% increase in their agricultural knowledge
(p =0.022), a 10% increase in their liking for vegetables (p <0.001), and a 15% increase in home garden
productivity (p =0.073). For children, the intervention had no discernible effect on food and nutrition
knowledge (p =0.666) but led to a 6% increase in their liking for vegetables (p =0.070), healthy food practices
(p <0.001), and vegetable consumption (October–December +15%; p =0.084; January–March +26%; p =
0.017; April–June +26%; p =0.088). The results therefore indicate both schools and parents matter for nudging
children toward healthier food choices.
1. Introduction
Evidence indicates that it is critically important to develop healthy
food preferences and eating habits in children because these can persist
into adolescence and adulthood (Birch et al., 2007; Cooke, 2007; Kelder
et al., 1994; Wadhera et al., 2015). Many interventions therefore aim to
nudge children toward healthier eating habits to obtain long-term and
even lifetime improvements in nutrition and health. School garden
programs are one such intervention trying to instill healthier eating
habits in children, and are increasingly common in high- and
low-income countries alike (Benkowitz et al., 2019; Christian et al.,
2014; FAO, 2005; Hunter et al., 2020; Hutchinson et al., 2015; Huys
et al., 2019; Nury et al., 2017; Ozer, 2007; Parmer et al., 2009; Triador
et al., 2015). Through a combination of hands-on experience with
gardening and nutritional education, children learn how to grow,
appreciate and like healthy foods such as fruit and vegetables, which
* Corresponding author.
E-mail addresses: pepijn.schreinemachers@worldveg.org (P. Schreinemachers), baliki@igzev.de (G. Baliki), rach.manandhar@gmail.com (R.M. Shrestha),
raj01dhruba@gmail.com (D.R. Bhattarai), ishworigautam@gmail.com (I.P. Gautam), puspaghimire@ansab.org.np (P.L. Ghimire), bhishmasubedi@ansab.org.np
(B.P. Subedi), brueck@igzev.de (T. Brück).
Contents lists available at ScienceDirect
Global Food Security
journal homepage: www.elsevier.com/locate/gfs
https://doi.org/10.1016/j.gfs.2020.100454
Received 22 January 2020; Received in revised form 17 October 2020; Accepted 19 October 2020
Global Food Security 26 (2020) 100454
2
tend to be under-consumed.
Compelling as the concept may be, evidence for the nutritional
impact of school gardens remains limited. The current evidence basis
largely relies on studies for high-income countries. A review of 12
quantitative studies in the United States found positive outcomes in the
area of science achievement (knowledge) for 9 schools but increased
fruit and vegetable consumption for only 1 school (Blair, 2009). Another
review of studies for Australia, the United States and Europe found
signicant effects on healthier food preferences in 8 out of 13 studies,
improvements in food knowledge and attitudes in 7 out of 10 studies,
but a signicant increase in children’s fruit and vegetable consumption
in only 2 of the 13 studies (Ohly et al., 2016). More recently, an eval-
uation of a school garden program in Belgium found small but signi-
cant effects on knowledge and awareness, but no signicant effect on
vegetable consumption (Huys et al., 2019). These studies therefore show
that school garden programs tend to be more effective in improving
children’s knowledge, attitudes and preferences than at changing actual
food behavior.
Three randomized controlled trials conducted in low-income coun-
tries broadly conrm these observations (summarized in Schreine-
machers et al., 2020). All studies come from our own research group. To
our knowledge, there are no other rigorous studies that have evaluated
school garden programs in low-income countries. The study for Nepal
showed a positive effect on children’s awareness of vegetables, their
knowledge of agriculture and of food and nutrition, and their stated
preferences for vegetables, but no signicant effect on fruit or vegetable
consumption (Schreinemachers et al., 2017a). For Burkina Faso, there
were no signicant effects except for food and nutrition knowledge
(Schreinemachers et al., 2019). For Bhutan, there were positive effects
on awareness, knowledge, and preferences and an increase in the
probability of children consuming vegetables, with a positive associa-
tion between vegetable consumption and children having a vegetable
garden at home (Schreinemachers et al., 2017b).
A review of factors inuencing children’s food behavior (Scaglioni
et al., 2018) identied parental food habits as the most important factor.
Personal habits provide a possible explanation for the weak effect of
school gardens on children’s food behavior: children eat most of their
meals at home rather than at school and parents (often mothers)
generally decide what meals are served. Another factor limiting chil-
dren’s food choice may be that healthy food items such as fruit and
vegetables are not always available at home, especially in poor rural
households or year-round. Low availability may explain why the Bhutan
study found a positive association between home gardens and children’s
vegetable consumption. Other studies also demonstrated that home
garden interventions can increase household vegetable production and
consumption in the South Asian context (Baliki et al., 2019; Bird et al.,
2019; Osei et al., 2016; Schreinemachers et al., 2016).
These two explanations lead to the hypothesis that school gardens
can nudge children toward healthier food choices if such programs
simultaneously inuence the food behavior of parents and increase the
availability of healthy food items within the household. The study tests
this hypothesis with data from a randomized controlled trial of a novel
school garden project in Nepal that supported 15 (out of a total sample
of 30) schools to implement school gardens and provided home garden
training and nutrition education to the children’s caregivers.
The hypothesis is important to improve the design of school garden
programs as evidence to date, reviewed above, shows only weak impact
on children’s fruit and vegetable intake. Several studies have pointed at
the importance of multi-component school-based interventions. For
instance, a structured literature review of school-based interventions
concluded that combinations of classroom curriculum, parent and food
service components show the greatest promise for increased fruit and
vegetable consumption among children (Blanchette and Brug, 2006). In
addition, reviews by Rasmussen et al. (2006) and Scaglioni et al. (2018)
showed that increasing children’s access to fruit and vegetables at home
and greater parental intake are both associated with increased fruit and
vegetable consumption among children. Our study also contributes to
deepening our understanding of factors driving healthy food choices
among children in low-income countries, which is important in the
context of dietary trends toward increased consumption of
highly-processed foods and beverages and rising prevalence of over-
weight and obesity among children and adolescents (Abarca-G´
omez
et al., 2017; Ng et al., 2014; Popkin et al., 2012).
2. Methods and data
2.1. Choice of study location
Nepal was selected for the study in order to build on a previous
school garden project that had designed and tested a school garden
model (Schreinemachers et al., 2017a; Shrestha et al., 2020). Further-
more, the government of Nepal has showed much interest in school
gardens as it ts the country’s Multi-sector Nutrition Plan, which em-
phasizes the need for combining health, education, agriculture and so-
cial welfare for addressing malnutrition in the country (Government of
Nepal, 2017). Nepal has made good progress reducing malnutrition
(Headey and Hoddinott, 2015), but stunting continues to affect 32% of
adolescent boys and girls and anemia prevalence is 21% for adolescent
girls (Ministry of Health and Population et al., 2012). Unbalanced diets
are identied as one of the causes of malnutrition. For instance, it has
been reported that children under the age of two in Kathmandu Valley
are getting a quarter of their calories from unhealthy snack foods and
beverages (Pries et al., 2019), which is indicative of a wider problem of
unhealthy eating habits. Another study reported that only 1.1% of
Nepal’s adult population consumes 400 g of fruits and vegetables a
day—the amount recommended by the World Health Organization
(Frank et al., 2019).
Within Nepal, the study was conducted in Sindhupalchok District,
located between Kathmandu and the border with China. The district’s
area is 2542 km
2
and had about 290,000 residents in 2011, the year of
the last census (CBS, 2018). About 25% of the district population lived
below the national poverty line in 2011, which was about the same as
the national average (World Bank, 2011). It has extreme altitude dif-
ferences, ranging from 850 to 7000 m above sea level. The district was
severely affected by the Nepal Ghorka Earthquake of April 25, 2015.
2.2. Program theory and intervention design
The program theory posits that hands-on gardening experience and
complementary lessons at school strengthen children’s knowledge about
the importance of good nutrition. As a result, children are expected to
develop a more positive attitude toward eating vegetables (and healthy
eating more generally). These changes would be reinforced and sup-
ported at home as parents gain better skills in gardening and feel
motivated to grow vegetables after receiving seed packs, garden training
and a better understanding of how vegetables contribute to family
health. It is not expected that school gardens supply substantial quan-
tities of vegetables; the purpose of a school garden is as an educational
tool while home gardens or local markets would be the main source of
increased vegetable supplies. A stronger interest of children and parents
in vegetables combined with their increased availability and accessi-
bility is expected to have a positive effect on children’s vegetable
consumption.
The school garden intervention consisted of a physical garden for
hands-on experience in vegetable growing and nutrition education
following a booklet with 23 weekly learning modules (Bhattarai et al.,
2016). It involved children in grades 4 and 5 (aged 8–12 years old). We
selected these early grades assuming that the food behavior of young
children can be inuenced more easily, while they are old enough to do
physical work in the garden and do the nutrition learning modules. Two
teachers per school were trained in running the school garden, of whom
one was designated as school garden focal teacher and was responsible
P. Schreinemachers et al.
Global Food Security 26 (2020) 100454
3
for the implementation. Schools were given three periodic cash in-
stallments of (US$ 440, 220 and 220). The money had to be spent on
land preparation, a water tank, garden tools, plastic sheets for making a
nursery and fencing materials as specied in contracts signed between
each school and the implementing agency. The garden was usually
established on the school ground, but a few schools needed to rent land.
The average garden was 90 m
2
in size (the range was 32–240 m
2
). Seed
of nine local vegetable varieties was distributed for the winter season
(cauliower, radish, carrot, pea, broad leaf mustard, turnip, broccoli,
fenugreek, spinach) and seed of another ten varieties was distributed for
the summer season (soybean, swiss chard, capsicum, coriander, bitter
gourd, eggplant, okra, pumpkin, yard long bean, tomato). Each school
received at least two technical support visits by a trained staff.
As part of the intervention, children’s caregivers additionally
received support to improve their home gardens. The term “caregivers”
here refers to the main person in the household taking care of a child. It
is usually the mother, but sometimes it is the father or grandmother. In
some households, for example, parents were working in Kathmandu or
abroad and the grandmother was the caregiver. The home garden
training consisted of three periodic sessions on gardening and nutrition.
The training used a bi-modular agricultural and nutrition manual
developed specically for the project. The garden-based training
included topics such as garden establishment, crop rotation, compost
making, pest management and seed saving. Nutrition training included
topics such as the role of vegetables for family health, the nutritional
content of different food items, and cooking methods to preserve the
nutritional quality of vegetables. About 80% of the caregivers partici-
pated in the nutrition training. In addition to the training, each caregiver
received 155 g of seed of 9 different vegetables for the winter season and
again 116 g of 10 different vegetables for the summer season. Caregivers
and schools were supplied with the same varieties. Caregivers also
received Effective Microorganism (EM) during the winter season for
preparing quality compost and biopesticides to deal with red ants and
aphids for the summer season (as project staff noticed that these were a
key problem). School garden focal teachers provided technical back-
stopping to the caregivers and visited their home gardens on Saturdays.
The teachers were paid by the project to do this. The visits of school
teachers to parents’ home to observe their garden was expected to create
an additional nudge to motivate children and caregivers to implement
their garden well.
The Nepal Agricultural Research Council (NARC) implemented the
school-garden component while the Asia Network for Sustainable
Agriculture and Bioresources (ANSAB) implemented the home garden
component. Senior project staff of these organizations conducted two
monitoring visits to oversee progress in the project implementation. The
research team conducted one monitoring visit in December 2018,
including focus group discussions with teachers, parents and children in
ve treatment schools to learn about the implementation process and
challenges encountered.
2.3. Outcome variables
The primary outcome variable is the proportion of children’s meals
that included vegetables recorded using a 24-h recall method. The data
were self-reported by the children using food logbooks. Children were
asked to list all food items they ate or drank for breakfast, lunch, af-
ternoon snack, evening snack, and dinner—as based on the common
meal pattern in Nepal. During the data entry, each meal was coded as
0 (no vegetable consumed) or 1 (some vegetable consumed). This in-
formation was used to calculate the proportion of meals that included
vegetables with the denominator being the total number of meals a child
had consumed on a particular day. These data were recorded for the
baseline and endline surveys and for every month in between. The
baseline and endline were monitored by enumerators while school
teachers monitored the data entry for the other months.
Secondary outcome variables were selected along the pathway from
knowledge creation to behavior change. These can be considered as
intermediary outcomes and help to understand the critical stages in the
program’s theory of change.
Food and nutrition knowledge were measured using 15 multiple choice
questions with four answer options each of which exactly one was
factually correct. The questions probed about the association between
food and body functions (e.g. “Which food is good for your eyes? 1.
Cucumber; 2. Beans; 3. Carrots; 4. Chicken meat”), about nutrients
lodged in food (e.g. “Which food has lots of Vitamin C? 1. Carrots; 2.
Chicken meat; 3. Lemons; 4. Rice”), and about healthy diets (e.g. “Which
food is not part of a healthy diet? 1. Vegetables; 2. Carbonated drinks; 3.
Meat; 4. Fruit”). The variable was expressed as the proportion of correct
answers and was recorded for children and caregivers separately.
Agricultural knowledge was measured using 14 photos of common
garden pests (e.g. snail, caterpillar) and benecial insects (e.g. bee,
ladybug). Children and caregivers were asked to tick all photos of insects
that are potentially harmful to plants. The variable was expressed as the
proportion of correct answers, ranging from 0 to 1.
Liking for vegetables was measured by showing respondents 15 photos
of vegetables and recording their liking as 4 (like it a lot), 3 (like it), 2
(neutral), 1 (don’t like it). If the person didn’t know the vegetable then
the answer was recorded as 5 and excluded from the analysis. The mean
liking for vegetables was calculated and brought into the range [0,1] by
using unity-based normalization ([value-min]/[max-min]). Answers
were recorded for children and caregivers separately. In addition,
caregivers were similarly asked to record their perception of their chil-
dren’s liking of vegetables.
Snack choices were recorded for children through 10 questions. Each
question presented photos of three common snack items, including one
healthier item and two less healthy items. Children had to choose the
item he or she liked most to eat as a snack. The variable was expressed as
the proportion of healthier snack choices, ranging from 0 to 1.
Food practices were measured using 8 statements that were read out
to the caregivers. Examples are “Children in my household buy junk
food” and “Children in my household eat a meal before going to school”.
Possible answers included: Never (1), Rarely (2), Often (3), and Very
often (4). The mean liking for vegetables was calculated and brought
into the range [0,1] by using unity-based normalization.
With regard to the home gardens, the interviews with caregivers
recorded the names of different vegetables produced in different sea-
sons, garden practices used in the home garden (e.g. compost making,
raised planting beds), and challenges encountered in the home garden.
2.4. Study design and sample size
The study used a cluster randomized controlled trial design in which
villages (and their schools) were randomly assigned to either a control
group or a treatment group. The treatment group received school gar-
dens and complementary home gardens while the control group
received neither intervention during the study period. The consort ow
diagram in Fig. 1 describes the sample selection process.
Small sample size is a common challenge in the evaluation of school-
based programs. The unit of intervention is the school and it is usually
impractical to include many schools at the pilot stage. Previous evalu-
ations of school garden programs in high-income countries used be-
tween one and ve schools and collected data for no more than 500
children (Blair, 2009). One recent study for the UK used two treatment
arms and a total sample of 23 schools and 1391 children (Christian et al.,
2014). The previous study in Nepal is the most extensive study to date
and included 30 schools and 1570 children (Schreinemachers et al.,
2017a).
Power calculations were used to decide on the sampling strategy. We
derived the minimum detectable difference (DD =0.20) and intra-
cluster correlation coefcient (ICC =0.025) from the previous study
on school gardens in Nepal (Schreinemachers et al., 2017a). These
values were based on related outcome variables, including the share of
P. Schreinemachers et al.
Global Food Security 26 (2020) 100454
4
children who ate fruit and vegetables, the number of fruits and vege-
tables consumed, and share of correct answers on knowledge tests for
nutrition and sustainable agriculture. Holding the statistical power
threshold constant at 0.8 and using a 95% condence interval, we ran
simulations varying the number of schools and sample of children per
school. The simulations indicated that the study would be sufciently
powered using 30 schools and 30 matched children-households per
school.
A problem with a lack of balance in outcome variables may arise
given the small sample of schools (clusters). Two strategies were applied
to deal with this. First, eligibility criteria were used to reduce the vari-
ation between clusters. We selected non-boarding government-run
schools with access to a source of water for irrigation. The use of eligi-
bility criteria increases the internal validity of the study by making
schools more comparable, but there is a trade-off in external validity as
the results cannot be generalized to all schools. Limiting the selection
criteria is necessary and justied when experimenting with a novel
intervention. Second, we used sample stratication to increase the
likelihood of balance. Bruhn and McKenzie (2009) showed that strati-
cation performs particularly well in small sample experiments. Alti-
tude (as a proxy of the agroclimatic conditions) and the teacher-student
ratio (as a proxy of school quality) were used as stratication variables
to ensure balance between the two groups.
Schools were selected from six rural municipalities (palikas) of
Sindhupalchok District that were relatively easy to access (Chautra,
Indrawati, Melamchi, Sunkoshi, Lisankhu Pakhar, Barabisha). A list of
52 schools that met the eligibility criteria in these locations was created.
The list was completed together with the local district education ofce.
These 52 schools represent about 10% of all primary schools in the
district, but may not be representative for all schools. Thirty schools
were randomly selected from this list for inclusion in the study. Sec-
ondary data were collected on the above-mentioned stratication
variables. From each stratum, we randomly assigned half of the schools
to the treatment and the other half to the control.
Project participation of children and caregivers in the treatment
group was complete, but not all caregivers participated in every training
event. Sample attrition between baseline and endline was 5.1% for the
sample of children, but for the sample of caregivers it was 11.7% for the
treatment and 10.2% for the control. A comparison between attrited and
non-attrited households showed no signicant differences in means (p <
0.05), which suggests that sample attrition is not a source of bias in this
study.
2.5. Research ethics
The study was approved by the Nepal Health Research Council
(NHRC) Ethical Review Board on May 30, 2018 (Reg. No. 222/2018). It
was also approved by the Institutional Biosafety and Research Ethics
Board of the World Vegetable Center (Approval No. 23). Study partici-
pation was voluntary for all children and caregivers. School principals
and caregivers signed a written consent form for themselves and for
their children. Participation in the project bore no risk for parents and
children while the potential benets in terms of improved nutrition as a
result of the school and home garden intervention were potentially
substantial. The project supported the control schools to establish a
school garden after the completion of the endline data collection, which
was an important incentive for control schools to participate in the
project. The trial is included in the Registry for International Develop-
ment Impact Evaluations (RIDIE; Study ID 5cd93ec673096).
2.6. Data collection and analysis
The study administered a baseline survey at the start of the school
year in June 2018 and an endline survey in June 2019. The surveys were
Fig. 1. Consort ow diagram for the study.
P. Schreinemachers et al.
Global Food Security 26 (2020) 100454
5
done in the same month to control for seasonal variations in the supply
of fresh food. Data were collected from the schoolchildren and their
respective caregivers. We randomly selected 15 children each from
grades 4 and 5 of each school if there were more than 15 children in a
grade. The data set is publicly available on Harvard Dataverse (Schrei-
nemachers, 2020).
We quantied the average treatment effect (ATE), which is the
change in outcomes for the treatment group minus the change in out-
comes for the control group, using a difference-in-difference estimator.
The method eliminates the effect of selection bias, if present. The key
assumption is that the average change in the control group represents
the counterfactual change in the treatment group in the absence of the
project. The so-called “parallel paths” assumption is likely to hold
because the treatment was randomly assigned and the intervention
period is short (see also below for empirical evidence supporting this
assumption). A cluster effect was added to all regression models because
schools are the unit of intervention but children and households are the
unit of observation. Means, standard deviations and t-values were also
cluster-adjusted.
3. Results
The mean age of schoolchildren in the sample is 10 years and 55%
are girls (Table 1). On average children walk about 25 min to school,
though many children walk much longer as shown by the high standard
deviation. For 81% of the children, the caregiver is their mother, but for
5% it is their grandmother, and for 5% it is their father. For the
remaining children, the caregiver may be an aunt or older sister. Most of
the caregivers are engaged in farming (74%). About 38% of the care-
givers are able to read and write.
A comparison of means for general characteristics of the children,
caregivers and households included in the study indicates that the
sample is balanced at baseline (Table 1). Most importantly, a compari-
son of mean outcomes at baseline does not show any differences sig-
nicant at a 95% condence interval (Tables 2–4). This nding gives
condence that the stratied random assignment created a balanced
sample.
The results in Table 2 show a positive effect of the intervention on the
food and nutritional knowledge of caregivers (p <0.001). The effect size
is 0.14 percentage-points, which is a 26.4% increase over mean baseline
levels. There was no effect on the food and nutritional knowledge chil-
dren (p =0.666) as the food and nutrition knowledge of the control and
the treatment increased in parallel. This suggests that the nutrition ed-
ucation included in the school garden program did not add to the
existing curriculum.
In terms of agricultural knowledge, children were able to correctly
tell if a photo of an insect was that of an insect pest or of a benecial
insect for 52% of the photos shown on average. For caregivers this value
was 59%. Considering that the questions were binary, the answers were
only a little better than blind guesses. It thus indicates poor knowledge
about insect pests and benecial insects. The results show that the
intervention had a small effect on the agricultural knowledge of the
caregivers (+5.1% over baseline levels; p =0.022), but not of the
children (p =0.119).
The results show that caregivers had a slightly stronger liking for
vegetables than children, but the difference was small (about 4.5
percentage-points at baseline). There is a slightly wider gap between
children’s liking for vegetables and their caregivers’ perception of
children’s liking (8.5 percentage-points), which suggests that children
like vegetables more than their parents think they do. In terms of impact,
the endline shows a stronger liking for vegetables in the treatment group
than in the control group and the average increase is 6.1% for children
(p =0.070) and 10.2% for caregivers (p <0.001). However, it is noted
that the effect is not because average liking increased in the treatment
group, but because average liking decreased in the control group.
Caregivers’ perception of how much their children like vegetables
increased 10.5% (p <0.001).
In the baseline, for 62% of the choice questions children stated to
prefer healthier snacks over less healthy ones. There is an increase in the
preferences for healthier snacks between baseline and endline with the
treatment group showing a stronger increase. Overall, the ATE shows a 5
percentage-point increase in children’s preferences for healthier snacks
(p =0.042), which is equivalent to an 8.1% improvement over baseline
levels.
Turning to caregivers’ home gardens, we found the treatment group
adopted practices such as own seed saving, pruning and removal of sick
plants, but there was no effect on any of the other practices trained –
though some were already used widely at the baseline (Table 3). The
intervention had a positive effect on the number of different vegetables
harvested from the home garden during the summer season (p =0.037)
while the effect during the rainy season was weaker (p =0.101) and
there was no effect during the winter season (the main season for leafy
vegetables and brassicas) (p =0.395). Altogether for the whole year, the
treatment group increased the number of vegetables harvested by 1.4
species, which is a 15.4% increase over baseline levels (p =0.073).
Caregivers reported improvements in terms of household food
practices, including children buying less junk food (−11.7%), children
eating before school (+2.1%), greater encouragement for children to eat
vegetables (+8.2%), and children washing hands before eating (+7.7%)
as shown in Table 4. There was no effect on the consumption of milk or
meat, which is perhaps not surprising because these were not part of the
home garden intervention, but also no increase in the inclusion of veg-
etables in meals, which was already high at the baseline and therefore
had little room for improvement. The overall effect of the intervention
on the adoption of healthy food practices was +5.2% over baseline
conditions (p <0.001).
In the nal part of the analysis, we address our main hypothesis
regarding children’s food choices. The proportion of meals that included
vegetables was calculated from children’s food logbook data. The results
were averaged by quarter, because there were missing observations for
some months as a result of school breaks or illness (Table 5). The results
show no effect for the rst quarter of the year-long study period (p =
0.620), which is the period before the intervention got started in
September. This result is important because it supports the parallel paths
Table 1
Mean baseline characteristics for children, caregivers and households for control
and treatment, 2018.
Characteristic Control (n =
392)
Treatment (n
=387)
p-
value
1
Mean SD Mean SD
Schoolchildren:
Age (years) 10.43 1.55 10.33 1.49 0.677
Female (prop.) 0.56 0.50 0.55 0.50 0.836
Grade 4 (prop.) 0.48 0.50 0.51 0.50 0.523
Distance to school (minutes) 26.27 24.34 24.13 23.03 0.487
Caregivers:
Age (years) 35.51 9.02 35.35 10.02 0.863
Female (prop.) 0.93 0.25 0.93 0.25 0.973
Mother (prop.) 0.83 0.37 0.79 0.41 0.290
Father (prop.) 0.05 0.22 0.06 0.25 0.500
Grandmother (prop.) 0.05 0.22 0.05 0.21 0.922
Able to read and write (prop.) 0.38 0.48 0.39 0.49 0.830
Main occupation farming
(prop.)
0.74 0.44 0.74 0.44 0.976
Household:
Household size (persons) 5.41 1.97 5.55 1.84 0.368
Includes a grandmother (prop.) 0.24 0.43 0.28 0.45 0.271
Sells vegetables (prop.) 0.15 0.36 0.18 0.38 0.503
Mother prepares meals (prop.) 0.84 0.37 0.81 0.39 0.461
Grandmother prepares meals
(prop.)
0.06 0.24 0.07 0.26 0.585
Note:
1
Welch two sample t-test with unequal and clustered variance. Prop. =
proportion.
P. Schreinemachers et al.
Global Food Security 26 (2020) 100454
6
assumption discussed above. The ATE turns positive (p =0.084 for Q2,
p =0.017 for Q3, p =0.088 for Q4) for the subsequent three quarters.
The effect sizes appear small, but represent a 15.1% increase over
baseline levels for Q2, a 25.9% increase for Q3, and a 25.5% increase for
Q4, which is substantial. It does therefore show that the intervention
increased the frequency of vegetable consumption in the sample of
school children. We note that the effect on production had a high p-value
for the period from March to May (Table 3), while the effect on con-
sumption has a high p-value for the period from January to March,
which only partly overlaps, while there was a weak effect for the period
from April to June (p =0.088).
Table 2
Baseline and endline means and average treatment effects on children’s and caregivers’ knowledge and preferences.
Outcome (proportions) Baseline Endline Impact
C T p-value C T p-value ATE p-value
Food and nutrition knowledge:
Children 0.48 0.49 0.431 0.54 0.57 0.213 0.01 0.666
(0.15) (0.14) (0.15) (0.15) (0.03)
Caregivers 0.53 0.53 0.919 0.57 0.70 <0.001 0.14 <0.001
(0.17) (0.17) (0.17) (0.16) (0.03)
Agricultural knowledge:
Children 0.52 0.53 0.466 0.53 0.57 0.003 0.03 0.119
(0.11) (0.12) (0.12) (0.12) (0.02)
Caregivers 0.59 0.59 0.654 0.58 0.61 <0.001 0.03 0.022
(0.12) (0.12) (0.11) (0.10) (0.01)
Liking for vegetables:
Children 0.63 0.64 0.700 0.58 0.63 0.021 0.04 0.070
(0.18) (0.18) (0.18) (0.17) (0.02)
Caregivers 0.59 0.59 0.943 0.53 0.60 <0.001 0.06 <0.001
(0.17) (0.17) (0.15) (0.18) (0.02)
Caregivers’ perception of children’s liking 0.56 0.58 0.191 0.54 0.63 <0.001 0.06 <0.001
(0.18) (0.18) (0.17) (0.16) (0.02)
Children’s healthy snack preferences 0.63 0.61 0.541 0.66 0.69 0.257 0.05 0.042
(0.22) (0.22) (0.23) (0.22) (0.03)
Notes: C=Control; T =Treatment. The numbers in parentheses indicate standard deviations for the means and standard errors for the average treatment effect (ATE).
Table 3
Baseline and endline means and average treatment effects for home garden management practices and vegetable production.
Outcome Baseline Endline Impact
C T p-value C T p-value ATE p-value
Technology adoption (prop. of households):
Seed packs 0.95 (0.23) 0.85 (0.36) <0.001 0.89 (0.32) 0.92 (0.28) 0.411 0.12 (0.04) 0.002
Own seed saving 0.50 (0.50) 0.43 (0.50) 0.138 0.48 (0.50) 0.54 (0.50) 0.238 0.13 (0.06) 0.033
Pruning 0.09 (0.29) 0.12 (0.33) 0.369 0.15 (0.35) 0.29 (0.45) 0.013 0.11 (0.07) 0.096
Sick plant removal 0.46 (0.50) 0.40 (0.49) 0.255 0.65 (0.48) 0.71 (0.46) 0.171 0.12 (0.07) 0.082
Compost making 0.22 (0.41) 0.24 (0.43) 0.588 0.22 (0.41) 0.25 (0.43) 0.434 0.01 (0.05) 0.835
Raised beds 0.35 (0.48) 0.36 (0.48) 0.838 0.54 (0.50) 0.63 (0.48) 0.134 0.07 (0.09) 0.425
Seedling nursery 0.28 (0.45) 0.30 (0.46) 0.664 0.26 (0.44) 0.35 (0.48) 0.147 0.06 (0.07) 0.391
Mulching 0.37 (0.48) 0.35 (0.48) 0.695 0.31 (0.46) 0.30 (0.46) 0.923 0.01 (0.08) 0.910
Strong fences 0.16 (0.37) 0.20 (0.40) 0.317 0.35 (0.48) 0.42 (0.49) 0.177 0.04 (0.07) 0.567
Vegetable species harvested:
Whole year 9.19 (5.22) 9.31 (4.42) 0.831 10.78 (5.90) 12.33 (4.47) 0.049 1.42 (0.79) 0.073
Summer 3.66 (2.25) 3.52 (1.99) 0.587 3.28 (2.29) 3.84 (1.99) 0.033 0.70 (0.33) 0.037
Rainy 2.36 (2.00) 2.40 (1.69) 0.859 3.46 (1.97) 3.90 (1.57) 0.064 0.42 (0.25) 0.101
Winter 3.16 (2.23) 3.39 (2.11) 0.432 4.04 (2.35) 4.58 (1.89) 0.117 0.31 (0.36) 0.395
Notes: C=Control; T =Treatment. The numbers in parentheses indicate standard deviations for the means and standard errors for the average treatment effect (ATE).
Summer (roughly from March to May), rainy season (roughly from June to September), and winter season (roughly from October to February).
Table 4
Baseline and endline means and average treatment effects for food practices as reported by caregivers.
Outcome (proportion of total) Baseline Endline Impact
C T p-value C T p-value ATE p-value
Include vegetables in meals 0.94 (0.14) 0.92 (0.16) 0.557 0.94 (0.15) 0.95 (0.14) 0.759 0.02 (0.03) 0.515
Children buying junk food 0.68 (0.25) 0.69 (0.25) 0.585 0.75 (0.22) 0.68 (0.24) 0.003 −0.08 (0.03) <0.001
Children eat before school 0.97 (0.10) 0.97 (0.12) 0.861 0.97 (0.13) 0.99 (0.06) 0.106 0.02 (0.01) 0.103
Encourage children to eat vegetables 0.87 (0.20) 0.84 (0.23) 0.143 0.88 (0.18) 0.92 (0.16) 0.048 0.07 (0.03) 0.012
Eat dinner together 0.97 (0.11) 0.97 (0.12) 0.644 0.98 (0.08) 0.98 (0.09) 0.706 −0.01 (0.01) 0.577
Provide milk to children 0.65 (0.33) 0.65 (0.32) 0.855 0.69 (0.28) 0.68 (0.27) 0.575 −0.02 (0.03) 0.485
Cook meat for children 0.76 (0.22) 0.75 (0.21) 0.534 0.77 (0.21) 0.78 (0.21) 0.570 0.03 (0.03) 0.264
Children wash hands before eating 0.92 (0.17) 0.90 (0.20) 0.202 0.90 (0.17) 0.95 (0.14) 0.008 0.07 (0.03) 0.008
Average 0.77 (0.10) 0.76 (0.11) 0.331 0.77 (0.09) 0.80 (0.09) 0.008 0.04 (0.01) <0.001
Notes: C=Control; T =Treatment. The numbers in parentheses indicate standard deviations for the means and standard errors for the average treatment effect (ATE).
P. Schreinemachers et al.
Global Food Security 26 (2020) 100454
7
4. Discussion
4.1. Implications of the results
This study demonstrates that the combined school and home garden
intervention improved children’s liking for vegetables, their food prac-
tices, and the proportion of meals that included vegetables. We posit that
this increase in vegetable consumption resulted from concurrently tar-
geting children and caregivers and from the enhanced the availability of
vegetables in children’s homes. We found an increase in caregivers’
knowledge of food and nutrition and in their knowledge of agriculture,
and also an increase in the number vegetable species harvested from the
home garden. This suggests that a more conducive food environment
was created that enabled children to turn knowledge into healthy food
practices. Changes in food practices at home—including caregivers
encouraging their children to eat vegetables, increased handwashing,
and reduced junk food consumption—are evidence of this.
Unfortunately, the study could not disentangle the separate contri-
butions of the school and home garden interventions and the synergies
between them. Doing this would have required a trial with four treat-
ment arms. However, Schreinemachers et al. (2017a) evaluated the
impact of a school garden intervention that did not include a home
garden component and did not nd a signicant effect on vegetable
consumption. This comparison therefore suggests that school gardens
alone could not have created the positive effect on children’s vegetable
consumption. Still, we cannot rule out if the increase in children’s
vegetable consumption could have been achieved by a stand-alone home
garden intervention. To our knowledge there are no studies of home
garden interventions that have quantied the impact on children’s
vegetable consumption. Benkowitz et al. (2019) found a positive asso-
ciation between children having experience in growing vegetables at
home and their intake of vegetables in a small and non-representative
sample of German school children. Further studies will be needed to
analyze the effect of home gardens on children’s vegetable intake.
Our results show that school garden interventions need to be
designed in such way that they do not only stimulate children’s
knowledge of and preferences for vegetables, but also increase chil-
dren’s access to vegetables at home as well as stimulate parents to
prepare and eat more vegetables. This nding supports the Blanchette
and Brug (2006) who concluded that multi-component school-based
interventions have the greatest promise for increased fruit and vegetable
promotion among children. It also supports Rasmussen et al. (2006) and
Scaglioni et al. (2018) who showed that children will eat more fruit and
vegetables if they have better access to fruit and vegetables at home and
if their parents also eat more of them.
The need for comprehensive intervention designs is increasingly
recognized in the agriculture-nutrition literature. Several authors have
pointed at the need for multi-sectoral programming, combining or
aligning agriculture, nutrition, education and health interventions to
optimize impact (Burchi et al., 2011; Cunningham et al., 2017;
McDermott et al., 2013). Nepal’s multi-sectoral nutrition strategy like-
wise emphasizes the value of such multi-sectoral approach (Government
of Nepal, 2017). An integrated home and school garden program ts to
such strategy.
4.2. Strengths and weaknesses of the study
Given the lack of rigorous studies, as discussed above, we make a
signicant contribution to strengthening the quality of the existing ev-
idence. Another contribution is that the study measured a range of
outcome variables along the intervention’s impact pathway from
knowledge and preferences to changes in food behavior. For instance,
the positive effect on the proportion of meals that included vegetables
was supported by a positive effect on the number of species harvested
from home gardens.
We originally planned to use the individual dietary diversity score as
an outcome variable, but baseline data showed that most children
already consumed vegetables on a daily basis and the measure was
therefore not sensitive enough to pick up improvements in the quantity
or frequency of vegetable consumption. Estimating quantities of vege-
tables consumed does not seem realistic to accomplish for children aged
8–12 years. Depending on study site circumstances, future studies in this
area are therefore advised to use food frequency measures and not rely
on dietary diversity scores.
The present study has certain limitations. First, this was a two-year
research project and we therefore could only study the immediate,
one-year effect of the intervention. It would have been valuable to do a
longer-term study as behavior change is generally understood as a slow
process and the small effect sizes found for some of the impact indicators
may become larger (or smaller) over time. It is also important to study
the intervention’s sustainability. Second, self-reported data in non-
blinded trials have a risk of social desirability bias (van de Mortel,
2008). We think that this risk is low in our data. Enumerators observed
that children gave honest answers and were not trying to give “correct”
answers. Still, we could have explored this type of potential bias more.
Third, we were only able to include two treatment arms in the trial while
it would have required four treatment arms to disentangle the separate
and combined effects of the home and school garden interventions.
5. Conclusion
A school garden intervention in Nepal was coupled to a comple-
mentary home garden intervention targeting children’s caregivers and
aimed at increasing household availability of vegetables and at pro-
moting caregivers’ preferences for vegetables. This study showed that
such comprehensive intervention design was able to increase children’s
vegetable consumption by 15–26%, measured in terms the proportion of
meals that included vegetables. These results point at the importance of
comprehensive intervention designs (as opposed to school gardens as a
standalone intervention) that aim to affect food behavior not just at the
individual level, but at the household and community levels. The policy
implication is that school gardens in low-income countries must not only
try to inuence children’s food preferences and food behavior but it is
important that they also address the availability of nutritious food in
households and the caregivers’ corresponding preferences and behavior.
Declaration of competing interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Table 5
Average treatment effect on the proportion of meals eaten that included at least
one vegetable.
Period Control
mean
(SD)
Treatment
mean (SD)
p-
value
Impact
(ATE)
p-
value
% change
over
baseline
Baseline
(June)
*
0.32 0.29 0.235
(0.22) (0.21)
Jul–Sep
(Q1) *
0.26 0.24 0.387 0.01 0.620 +4.0
(0.15) (0.14) (0.03)
Oct–Dec
(Q2)
0.26 0.27 0.545 0.04 0.084 +15.1
(0.14) (0.14) (0.02)
Jan–Mar
(Q3)
0.25 0.29 0.053 0.07 0.017 +25.9
(0.12) (0.15) (0.03)
Apr–Jun
(Q4)
0.22 0.25 0.212 0.06 0.088 +25.5
(0.14) (0.14) (0.03)
Notes: * Refers to the outcome indicator before the intervention was imple-
mented. The numbers in parentheses indicate standard deviations for the means
and standard errors for the average treatment effect (ATE). The ATE is relative to
the baseline.
P. Schreinemachers et al.
Global Food Security 26 (2020) 100454
8
Acknowledgment
This research was supported by the Drivers of Food Choice (DFC)
Competitive Grants Programs, which is funded by the UK Government’s
Foreign, Commonwealth and Development Ofce (FCDO) and the Bill &
Melinda Gates Foundation, and managed by the University of South
Carolina, Arnold School of Public Health, USA; however, the views
expressed do not necessarily reect the UK Government’s ofcial pol-
icies. We also acknowledge long-term strategic donors to the World
Vegetable Center: Taiwan-, FCDO, United States Agency for Interna-
tional Development (USAID), Australian Centre for International Agri-
cultural Research (ACIAR), Germany, Thailand, Philippines, Korea, and
Japan.
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