Local setting influences the quantity of household food waste in mid-sized South African towns
Local setting influences the quantity of household food waste in mid-sized South African towns
Gamuchirai Chakona 0 1
Charlie M. Shackleton 0 1
0 Department of Environmental Science, Rhodes University , Grahamstown , South Africa
1 Editor: Robert Nerenberg, University of Notre Dame , UNITED STATES
The world faces a food security challenge with approximately 868 million people undernourished and about two billion people suffering from the negative health consequences of micronutrient deficiencies. Yet, it is believed that at least 33% of food produced for human consumption is lost or wasted along the food chain. As food waste has a negative effect on food security, the present study sought to quantify household food waste along the ruralurban continuum in three South African mid-sized towns situated along an agro-ecological gradient. We quantified the types of foods and drinks that households threw away in the previous 48 hours and identified the causes of household food waste in the three sites. More households wasted prepared food (27%) than unprepared food (15%) and drinks (8%). However, households threw away greater quantities of unprepared food in the 48-hour recall period (268.6±610.1 g, 90% confidence interval: 175.5 to 361.7 g) compared to prepared food (121.0±132.4 g, 90% confidence interval: 100.8 to 141.3 g) and drinks (77.0±192.5 ml, 90% confidence interval: 47.7 to 106.4 ml). The estimated per capita food waste (5±10 kg of unprepared food waste, 3±4 kg of prepared food waste and 1±3 litres of drinks waste per person per year) overlaps with that estimated for other developing countries, but lower than most developed countries. However, the estimated average amount of food waste per person per year for this study (12.35 kg) was higher relative to that estimated for developing countries (8.5 kg per person per year). Household food waste was mainly a result of consumer behavior concerning food preparation and storage. Integrated approaches are required to address this developmental issue affecting South African societies, which include promoting sound food management to decrease household food waste. Also, increased awareness and educational campaigns for household food waste reduction interventions are discussed.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
Funding: The research was funded by VW
foundation under the Livelihoods Urbanisation and
Natural Resources in Africa (LUNA) project
through Freiburg University, Germany. The funders
had no role in study design, data collection and
analysis, decision to publish, or preparation of this
Competing interests: The authors have declared
that no competing interests exist.
The world faces a food security challenge with approximately 868 million people
undernourished and about two billion people suffering from the negative health consequences of
micronutrient deficiencies [
]. Yet, at least one-third of food produced for human
consumption is lost or wasted along the food chain between farm and fork [2±7]. Food waste refers to
wholesome edible material intended for human consumption, arising at any point in the food
supply chain that is instead discarded, lost, degraded or consumed by pests . Food loss is
defined as the decrease in food quantity or quality which makes it unfit for human
]. According to the European Commission [
], food waste is composed of raw or
cooked food materials such as vegetable peelings, meat trimmings and spoiled or excess
ingredients or prepared food as well as bones, carcasses and organs. However, food waste can be
measured only for edible products that are directed to human consumption [
]. Food losses
take place at production, postharvest and processing stages in the food supply chain and the
food losses that occur at the end of the food chain (retail and final consumption) are called
ªfood wasteº [
]. In our study food waste refers to food losses that occur at the end of the
food chain (final consumption at household or consumer level) which include edible products
that are directed to human consumption and are discarded when not consumed for various
reasons. It is related to consumers' behaviour [
] and it includes food loss before, during or
after meal preparation in the household.
According to Gustavsson et al. [
], the annual value of wasted food along the whole food
chain is approximately US$ 680 billion in industrialised and US$ 310 billion in developing
countries. About 31±40% of all the food produced in the United States is never eaten [
and in the United Kingdom, consumers discard about one-third of the food they purchase
even though more than 60% is still suitable for human consumption . Despite considerable
efforts to help in reducing household food waste over the last three years in the United
Kingdom, household food waste has increased to 7.3 million tonnes in 2015 compared to 7.0
million tonnes in 2012 . The amount of avoidable household food waste (i.e. the food that
could have been eaten) also increased by 5.1% in 2015 . It has been estimated that globally
the amount of food wasted is four times the amount needed to eliminate world hunger .
Thus, strategies to avoid and reduce food waste could go a long way towards achieving world
food security [
]. Reducing food waste would also contribute positively to biodiversity and
ecosystem services conservation through reduced land transformation and use of chemicals,
reduced greenhouse gas emissions, and the costs of waste disposal and processing could be
saved and redirected [
The types and quantities of food wasted vary between and within countries, as well as
between households. At the macro-scale, the type and quantity of food waste differs between
developed and developing countries. In developed countries, most (approx. 60%) food waste is
generated after it has been purchased by consumers [
]. In contrast, in developing
countries most food is lost before it reaches the final consumer, i.e. in the growth, storage and
distribution phases [
], resulting from financial, managerial and technical constraints in harvesting
techniques as well as insufficient storage and cooling facilities. The quantities also differ, with
means of 95±115 kg/year/person in North America and Western Europe, compared to 6±11
kg/year/person in sub-Saharan Africa and South/Southeast Asia [
]. Other macro-scale factors
may include location, such as rural sites compared to urban ones, and global or national food
prices. Typically, urban households waste more food than their rural counterparts [
of higher wealth and their need to store food at home (after purchase) rather than harvesting it
on demand as occurs during the growing season in rural settings. This pattern may also result
in differences between rural areas, based on the degree of suitability for agricultural
production, and the length of the growing season. Urbanisation is a primary driver of changes in
dietary transition, consumption patterns, and hence also food waste patterns [
]. Lundqvist et al.
] reported that dietary transition typically leads to increased consumption of food that has a
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short shelf-life, such as dairy, fruit and vegetables, which may result in greater food waste in
the absence of efficient storage options in the home.
At the household scale, the quantities of food waste depend on a range of factors such as
household size, composition, income, demographics and culture [
]. For example, work in
developed countries indicates that larger households waste less food per capita than smaller
households [8,18±19]. This relationship varies between households with children and those
without, as generally, young people waste more food than older people [20±21]. Low income
households are presumed to waste less food than wealthier households [
], although Parfitt
et al. [
] note that some studies report little or no correlation between income and food
wastage. At times, this may also be a covariate with culture or ethnicity. For example, Hispanic
households in the USA waste approximately 25% less food than non-Hispanics, but this may
also be related to wealth [
], whilst Rathje and Murphy [
] suggest it is also a reflection of
cooking styles as Hispanic cuisine involves a lot of mixed dishes to which it is easy to add
leftovers. These household level influences on food waste have rarely been properly disaggregated
in developing countries, including South Africa.
Indeed, there is very little knowledge or empirical quantification of household food waste
in sub-Saharan African countries, including South Africa [23±24]. This lack of empirical
information led Nahman et al. [
] to estimate household waste patterns from secondary data of
waste stream analyses in landfills of large South African cities characterising the relative
contribution of food waste to the overall waste stream. However, this approach has several
inaccuracies. On one hand, food waste production was underestimated as not all wasted food goes to
landfills, but rather may be dumped on compost heaps or fed to livestock or domestic animals.
On the other hand, the organic component on landfills also includes garden waste
(accumulated plant matter from gardening activities such as cutting the lawn, weed removal, hedge
trimming or pruning consisting of lawn clippings, leaf matter, wood and soil), which is not
always easy to separate from food waste [
]. Using similar secondary data sets Nahman and
De Lange [
] considered the entire food chain, whilst Oelofse and Nahman [
the food value chain based on assumptions from surveys in other countries [
], thus none are
based on empirical measures at the household level, and none considered rural settings.
Although, some studies in South Africa have shown that consumer food waste contributes
significantly to the waste stream [27±28], no national data on food waste is available for the
]. Indeed, little is known about the quantities of food waste generated by households in
South Africa, or how it varies by location (such as urban or rural) or household wealth. Food
waste research is neglected yet it is an important aspect of the food system [
] as it negatively
affects food security [
]. South Africa has high rates of under- and mal-nourishment [
hence integrated approaches are required to address this developmental issue affecting South
African societies, which include promoting sound food management to decrease food waste.
As food waste has a negative effect on food security, and because there is little empirical
information on household food waste patterns in sub-Saharan African countries, including
South Africa, this study sought to quantify and characterise household food waste in different
settings. We did so by surveying rural and urban households at three sites along a macro-scale
gradient of agro-ecological potential (geographical areas exhibiting climatic conditions that
determine their ability to support rain-fed agriculture). This allowed analysis of differences
between sites along the agro-ecological gradient, between households along the rural-urban
continuum and in relation to household attributes such as household wealth, size, food
expenditure and household food insecurity access scale (HFIAS). We hypothesised that: (1) The
quantities of household food waste would decrease with declining potential for agriculture
because in rain-fed agricultural areas, weather conditions as well as constraints in harvesting
techniques, insufficient storage and cooling facilities may promote the quantities of food
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waste, (2) Households in rural areas would waste less food than those in the urbanised settings
because urban households have been reported to waste more food than their rural
] because of higher wealth and their need to store food at home (after purchase) rather
than harvesting it on demand as occurs during the growing season in rural settings, (3)
Wealthy households would throw away more food than poor households because poor
households may not have ªenoughº food to spare hence are compelled to cut down on the food they
waste. This can also follow reports by [
] that low income households are presumed to waste
less food than wealthier households, (4) Most food waste would be from unprepared or raw
food which could be attributed to poor storage facilities in homes or behavioural condition of
hording food when being sold at low prices, (5) Households with limited access (having
difficulties in getting food) to food would waste less food than those with good access to food
because having limited access to food may create an environment where consumers are
compelled to prepare what is enough for the meal and may not have more to spare and discard,
and (6) Single households would waste more food on a per capita basis. Smaller households
would throw away more food per capita than larger households as the amount of food waste
generated per person decreases with increasing households size and this has also been reported
in developed countries [11,18±19, 31].
Materials and methods
This study was conducted in three medium-sized (35 000±50 000 people) towns in South
Africa; Richards Bay and Dundee in KwaZulu-Natal province and Harrismith in the Free State
province (Fig 1).
Agro-ecological zones (AEZs) are geographical areas exhibiting similar climatic conditions
that determine their ability to support rain-fed agriculture. These are influenced by latitude,
elevation, and temperature, as well as seasonality, rainfall amounts and distribution during the
growing season [
]. The towns were selected along a gradient of agro-ecological suitability,
with Richard's Bay being a warm (mean annual temperature is 21.5ÊC) coastal and high
rainfall site (approximately 970 mm per annum (p.a.)), while Harrismith is central and relatively
high altitude (1 650 metres above sea level (m.a.s.l)), temperate (mean annual temperature is
18.6ÊC) with low rainfall (approximately 622 mm p.a.) and Dundee being intermediate
(inland, 1 260 m.a.s.l; 14.2ÊC and 683 mm p.a.). The seasonality of the rainfall increases along
this gradient, along with the severity of winter temperatures. Thus, the gradient also reflects
one of declining suitability for rain-fed agriculture, from high in Richards Bay to low in
Harrismith where rural farms mostly practice cattle ranging. The agricultural regions of South
Africa are shown in Fig 2 where Richards Bay falls in the region that specialises mostly in
sugarcane production whilst cattle ranching is mostly suitable in Dundee and Harrismith. Each
site included the rural, peri-urban and urban complex and data were collected along a
ruralurban continuum. Unemployment is high (>30%) in all sites, but higher in the rural zones
than the urban ones [
Food waste data was obtained through administering questionnaires to randomly selected
households at each site. Within each town, 200 households were randomly selected,
comprising of 60 rural households, 80 peri-urban households and 60 urban households. Random
cluster sampling using ArcGIS was used with five randomly selected households per cluster. There
were twelve clusters in each of the urban and rural areas and 16 clusters in the peri-urban area.
GPS coordinates for the households were generated within each selected cluster. A woman of
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Fig 1. Location of study towns in South Africa.
reproductive age (15±49 years old) and the person in the household who prepares most of the
meals was interviewed as she was regarded as having the knowledge of all the food that was
consumed and not consumed within the household. All interviews were conducted
face-toface after the researcher had gained consent from the interviewee and it took an average of 1hr
45 minutes to finish the interview. However, not all selected households agreed to participate
in the interviews, therefore those which refused were left out, leaving a total of 554 households
with 183 individuals interviewed in Richards Bay; 173 in Dundee and 198 in Harrismith. The
three towns were regarded as having equal weight during analysis in this study as it was
difficult to get participants.
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Fig 2. South Africa's agricultural regions.
Data were collected between October and November 2014. A questionnaire was used to
collect information on households' behaviour regarding the meals they consume at home (S1
File). That is, general information on where household members usually eat their meals, if
most household members usually ate the same meal, how often some food was not eaten and
what households did with the left-overs. In the present study, food waste was classified into
three main classes which were prepared food waste, unprepared food waste and drinks waste.
Participants were asked to report on the food waste that they had generated over the past 48
hours in their homes, including classifying the type of food waste, naming the type of food, the
quantity (weight or volume) and reason for disposal. Participants were also asked to measure
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and record their food waste as they created it during the 48-hour period before the interview
using estimated standard household measures such as cups, tablespoons or teaspoons.
Prepared and unprepared foods were measured in kilograms (kg) and grams (g) whilst drinks
were measured in litres (l) and millilitres (ml). The study also used a conversion factor of 1
litre = 1 kg considering that 1 cubic metre is equal to 1000 litres or 1000 kg. However, it was
not possible for households to separate some of the prepared foods and weigh them separately
according to food type and therefore these were measured as mixed dishes.
Households were also given food waste diaries where they were asked to record the food
they threw away, specifying the type of food, quantity and their reasons for throwing that food
away. Upon every reason, they were asked to write their feelings when throwing away the food
and their perceptions about food waste. Unfortunately, this method did not yield any data as
none of the households returned the diaries upon collection. However, these measurements
need to be taken with caution as it has been found that the above-mentioned methods can
yield data that are not representative of habitual household behavior on wasting food. For
example, Quested et al. [
] found that quantities of waste recorded in diaries are
approximately 40% lower than those obtained from analysis of waste streams and Hanson et al. [
also reported that household food waste diary approaches systematically underestimates food
loss and waste levels.
Additionally, the internationally applied Household Food Insecurity Access Scale (HFIAS)
was performed. The HFIAS score is a continuous measure of the degree of food insecurity
mostly related to access in the household in the past four weeks (30 days). The HFIAS tool is
composed of nine questions that ask about any possible modifications households made in
their diet or food consumption patterns due to limited resources to acquire food [
nine questions are subdivided into three themes of food insecurity which are: 1) experiencing
anxiety and uncertainty about the household food supply; 2) insufficient quality of diet which
includes variety and preferences of the type of food and 3) insufficient food intake or reducing
quantity of food consumed [
]. The questions address the situation of all household members
and do not distinguish adults from children or men from women or adolescents.
The nine questions represent a generally increasing level of severity of food insecurity and
nine ªfrequency-of-occurrenceº questions were asked as a follow-up to each occurrence
question to determine how often any condition occurred. For each frequency-of-occurrence
question, a score was assigned to each household: 1 if the response was rarely (condition having
happened once or twice in the past four weeks); 2 if it occurred sometimes (three to ten times
in the past four weeks) or 3 if the answer was often (occurred for more than ten times in the
past four weeks).
Households were assigned a score that ranged from 0 to 27 at the end of the nine questions
which was based on their response to the nine questions (yes or no) and
frequency-of-occurrence (rarely, sometimes and often). A household was assigned a score of zero if the household
responded ªnoº to all occurrence questions. The maximum score of 27 was given to a
household if the response to all nine frequency-of-occurrence questions was ªoftenº, and scores
were added together. A high HFIAS score indicates household's poor access to food and
significant household food insecurity [
Information on household characteristics such as the household size, age, gender of
household head, sources of food, income, land acquisition, wealth (assets acquired by household)
and the cost of food purchases per week were also asked. An index of wealth was created by
combining information obtained on the household's possessions and this included car/truck,
motorbike, tractor, bicycle, fridge, television, radio, cattle/goats, chickens, cell phone, house
and electricity. For each household, the number of each asset was normalised (by dividing
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with the highest number obtained in each category for all households) then all summed to get
a wealth index per household, which could range from zero to 12.
All interviews were conducted in the respondent's preferred language of isiZulu in Richards
Bay and Dundee, and Sesotho in Harrismith or English. Different enumerators were used in
each town and in all study sites, they were trained on how to conduct interviews using the
questionnaire so as to provide full understanding of the administered questions. Ethics
approval was obtained from the Rhodes University Ethics Committee, and all the respondents
signed informed consent forms after the researcher had explained the details of the project to
them (S2 File).
Data were entered and cleaned using Microsoft Excel and all statistical analyses were
performed using Statistica version 13 (StatSoft Inc.). A 2-way ANOVA was conducted to measure
if there were any significant differences in the amounts of food waste generated by households
between and within towns and clusters as interviewers had different clusters. No Significant
differences were observed and therefore data from all interviewers was used for analysis.
Descriptive data is presented as means and standard deviations (SDs) (mean ± SD), standard
error of mean (SEM), confidence intervals (CI) as lower confidence limit (LCL) and upper
confidence limit (UCL) and percentages. Considering the random cluster sampling used in
this study, all household observations for food quantities within a cluster were simply reduced
to a single summary measure, which was the cluster mean and then standard statistical
methods were used to analyze these cluster means as if they were the primary observations. The
mean amount of food wasted (including prepared food, unprepared food and drinks) by
households within 48 hrs was calculated from the amounts reported by households. This was
used to calculate the amount which was wasted by households over a year and the amount
wasted per household over a year was divided by the mean household size of the sample, to get
the estimated amount which was wasted per person per household over a year period. The
differences in amount of food wasted per household between towns and locations were tested
using 2-way ANOVA and differences within each town were tested using 1-way ANOVA.
Post-hoc followed in case a significant effect was detected. Post-hoc tests (the Bonferroni
correction) was performed when the equality of variances' assumption holds and this also
provided specific information on which means were significantly different from each other. The
associations of the food waste quantities with food expenditure, household size, wealth
variables and HFIAS were examined through Spearman correlation tests and the amount of food
waste was used as a response variable. Statistical significance was set at p < 0.05 for all tests.
The South African Rand to US dollar exchange rate was approximately 11:1 at the time.
The full sample consisted of 554 women of reproductive age with a mean age of 31.5±10.0
years (90% CI: 30.9 to 32.3 years). The household size for the full sample was 6.9±3.89 persons
(90% CI: 6.7 to 7.2 persons) and almost 60% of the households were female-headed. More than
80% of households in Dundee and Harrismith received some form of cash income whilst only
59% of household in Richards Bay did so. Households in Richards Bay were spending less cash
per week on purchasing food (R196±180) than in Dundee and Harrismith with R333±253 and
R323±271 per week, respectively, on food. The wealth index was almost similarly low for all
towns, ranging between 2.3±1.0 in Dundee and 2.6±0.6 in Richards Bay. About 73% of
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1Percentage of households eating meals together.
2Percentage of households that always eat at home.
3Percentage of households that eat mainly at home.
4Percentage of households that eat at home and partly elsewhere.
5Percentage of households that eat mainly elsewhere.
6Percentage of households that always eat elsewhere.
Partly at home and partly
households in Richards Bay had land available for their own production whilst only 57% of
households in Dundee and 27% in Harrismith did so.
Household behaviour regarding meals at home
In all three towns, the majority of households reported that household members were always
eating their meals at home and were eating the same meal at the same time (Table 1).
Household members in all three towns rarely had their meals apart from each other and not at home.
For all meals in all towns, households rarely left any food after the meal (Table 2). Only a
very small percentage of the households has reported to have left food uneaten at each meal.
When the food was not consumed, households in all towns reported that they rarely throw
it away but rather keep the leftovers and consume the food within a day or two (Table 3). Very
rarely did households give the food to other people, feed animals or throw it away.
Types of food waste along the agro-ecological gradient
Overall, 191 households out of 554 sampled households (35% of the sample) had wasted food
(discarded food) in the past 48 hours and 67 households (12%) were from Richards Bay, 55
households (10%) from Dundee and 69 households (13%) from Harrismith (data not reported
in the tables). Overall all households, about 27% threw away prepared food, 15% threw away
unprepared food whilst 8% of the households in all towns wasted drinks (Table 4). There were
no significant differences in the percentages of households wasting food and drinks between
the towns (prepared food (F2, 538 = 1.38, p = 0.253), unprepared (F2, 538 = 2.66, p = .071) and
drinks (F2, 538 = 0.54, p = 0.58)).
Considering the rural-urban continuum, more urban residents threw away all classes of
food waste relative to their peri-urban and rural counterparts (Table 4). However, the
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differences were significant only for Harrismith. Pair-wise comparisons (Bonferroni
correction) for prepared food (F2, 195 = 4.22, p = 0.02) showed that urban households threw away
prepared food significantly more than the peri-urban households (p < 0.05) and rural households
(p < 0.05) and showed that drinks (F2, 195 = 12.7, p = 0.000001) were wasted significantly more
in urban households than the peri-urban (p < 0.00001) and rural households (p < 0.00001).
With respect to unprepared food, there were no significant differences observed in the
percentage of households wasting unprepared food although Harrismith had the highest
percentage of households who were throwing away unprepared food whilst Richards Bay and Dundee
had the lowest percentages (Table 4). However, the percentage of households throwing away
unprepared food followed the rural-urban continuum with households in the urban locations
throwing away more unprepared food than their peri-urban and rural counterparts, although
not significantly so, other than in Harrismith (F2, 195 = 3.7, p = 0.03). Pair-wise comparisons
(Bonferroni correction) for unprepared food in Harrismith showed that urban households
threw away unprepared food significantly more than the peri-urban households (p < 0.05)
although this was not the case for rural households.
Prepared food waste
Unprepared food waste
Note: All values in the table are expressed as % of households. Unlike superscripts show significant differences in the percentage of households wasting
different food waste types between and within towns. Data was analysed using two-way ANOVA, prepared food (F2, 538 = 1.38, p = 0.253), unprepared (F2,
538 = 2.66, p = .071) and drinks (F2, 538 = 0.54, p = 0.58) (n = 554). No significant differences were observed. For prepared food in Harrismith town: pairwise
comparisons by post hoc Bonferroni indicated: 1. prepared food (F2, 195 = 4.22, p = 0.02) in urban households (n = 55) thrown away more (p<0.05) than the
peri-urban households (n = 85) and rural households (n = 58), although it was similar in latter. 2. Drinks (F2, 195 = 12.7, p = 0.000001) were wasted more in
urban households (n = 55) than the peri-urban (p < 0.00001) (n = 85) and rural households (p < 0.00001) (n = 58). For unprepared food in Harrismith town:
pairwise comparisons indicated: 1. Unprepared food in urban households (F2, 195 = 3.7, p = 0.03) was thrown away more (p<0.05) than the peri-urban and
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The types of food waste were similar across the three sites. In general, the prepared foods
that were most commonly wasted were pap (corn flour porridge), meat, vegetables and rice.
Pap and meat were thrown away by greater than 20% of the households in all the towns
(Table 5). In Richards Bay, pap and meat were mostly discarded in the rural areas, in Dundee
these were mostly thrown away in the peri-urban and rural areas whilst in Harrismith it was
the urban households who threw away these food items. Rice was frequently wasted by
households in Richards Bay and it was mostly in the rural location, whilst vegetables were mostly
wasted by households in the peri-urban locations of Dundee and Harrismith. Potatoes were
mostly wasted in Harrismith whilst samp (coarsely crushed corn) was mostly wasted in
Richards Bay and Dundee and beans mostly in Dundee. Bread and fish were among the least
wasted prepared foods in all the towns.
The unprepared foods that were wasted by households in all towns were potatoes, fish and
to larger extent vegetables (mostly tomatoes and cabbage) which were discarded by greater
than 14% of the households in all towns (Table 6). Vegetables were mostly wasted in the rural
location in Richards Bay (6%), whilst in Dundee (13%) and Harrismith (13%) it was the
periurban dwellers who wasted more vegetables. Fish and meat were mostly wasted in Harrismith
urban with 10% of households throwing away these unprepared foods. The other unprepared
foods that were wasted were town specific. For example, samp, maize meal and flour were
wasted in Richards Bay only (mostly peri- urban and urban locations), whilst fruits were
thrown away in Dundee only (more in the urban location). Unprepared waste from beans,
bread and meat were recorded in Harrismith and Dundee only whilst that from rice was found
in Richards Bay and Dundee only.
Drinks were rarely wasted in all the towns. Milk was the mostly wasted drink in Richards
Bay rural location and Dundee's peri-urban location whist soft drink and juice were the most
wasted drinks in Harrismith urban location (Table 7). The percentage of households wasting
milk also followed the agro-ecological gradient although the differences in the percentages in
Richards Bay and Dundee were minimal.
Quantities of food waste along the agro-ecological gradient
The overall mean quantity of food waste during the previous 48 hours was 121.0±132.4 g of
prepared food (90% CI: 100.8 to 141.3 g), 268.6±610.1 g of unprepared food (90% CI: 175.5 to
361.7 g) and 77.0±192.5 ml of drinks (90% CI: 47.7 to 106.4 ml) per household (Table 8).
Within sites, households in the urban locations generated a mean amount of prepared food
waste of 99.2 g to 192.0 g in the last 48 hours, 89.5 g to 141.6 g in the peri-urban locations and
66.7 g to 141.1 g in the rural locations (90% CI) (Table 8). There were no significant differences
in the amount of prepared food waste that was produced between towns (F2, 113 = 1.35,
p = 0.26) nor between locations (F2, 113 = 0.90, p = 0.41).
Considering unprepared food, although the greatest percentage of households throwing
away unprepared food was in Harrismith, with Richards Bay having the least, the mean
amount of unprepared food waste was higher in Richards Bay with the mean amount of
unprepared food waste being 493.2±965.1 g per household in the previous 48 hours (90% CI: 232.6 g
to 753.7 g). There was a significant difference in the amount of unprepared food waste
* Values for prepared food, unprepared food and drinks are expressed as means ± SD (n = given in 2nd column).
LCL = Lower confidence limit; UCL = Upper confidence limit; SEM = Standard error of mean. Unlike superscripts indicate significant differences between
towns (a,b) and between locations (*,**).
Data was analysed using two-way ANOVA, no significant differences were observed in the amount of prepared food waste between towns (F2, 113 = 1.35,
p = 0.26) nor between locations (F2, 113 = 0.90, p = 0.41). For unprepared food: pairwise comparisons by post hoc Bonferroni indicated: 1. Amount of
unprepared food waste (F2, 113 = 4.13, p = 0.019) in Richards Bay households (n = 39) was more (p<0.05) than that in Dundee households (n = 39) and in
Harrismith households (n = 40), although it was similar in latter. 2. For Richard Bay town: Amount of unprepared food waste (F2, 36 = 3.74, p = 0.033) was
more in rural households (n = 12) than the peri-urban (p < 0.05) (n = 16) and urban households (p < 0.05) (n = 11). For drinks waste in Harrismith town:
pairwise comparisons indicated: 1. Amount of drinks waste (F2, 37 = 4.22, p = 0.022) in urban households (n = 13) was more (p<0.05) than in the peri-urban
(p = 0.01, n = 16) and rural households (p = 0.02, n = 11).
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observed between the towns, (F2, 113 = 4.13, p = 0.019) and pair-wise comparisons (Bonferroni
correction) for unprepared food waste have shown that Richards Bay households threw away
unprepared food significantly more than Dundee households (p<0.05) and Harrismith
households (p<0.05) (Table 8). No significant differences were observed in the mean amount of
unprepared food waste between the locations (F2, 113 = 1.89, p = 0.156), being 179.6±258.1 g
(90% CI; 105.9 g to 253.4 g) per household in the previous 48 hours in the urban locations,
212.9±401.0 g (90% CI: 115.8 g to 310.0 g) per household in the peri-urban location and 433.9
±975.5 g (90% CI: 155.1 g to 712.7 g) per household in the rural locations (Table 8). However,
significant differences were observed in Richards Bay between the amount of unprepared food
wasted (F2, 36 = 3.74, p = 0.033), with rural households throwing away significantly more
unprepared food than the urban households (p<0.05) and peri-urban households (p<0.05).
The mean amount of drinks wasted per household in the previous 48 hours was 77.0±192.5
ml (90% CI: 47.7 ml to 106.4 ml) and the amount of drinks wasted was relatively similar
between towns (F2, 113 = 0.429, p = 0.653) (Table 8). Within sites, the mean amount of drink
waste was 107.3±233.2 ml (90% CI: 40.6 ml to 173.9 ml) per household in the previous 48
hours in the urban locations, 62.2±158.5 ml (90% CI: 23.8 ml to 100.6 ml) in the peri-urban
location and 67.1±192.4 ml (90% CI: 12.2 ml to 122.1 ml) in the rural locations (F2, 113 = 0.568,
p = 0.569) (Table 8). However, significant differences were observed in Harrismith between
the amount of drinks wasted (F2,37 = 4.22, p = 0.022) and post hoc tests showed that urban
households were throwing away significantly more drinks than both the peri-urban
households (p = 0.01) and rural households (p = 0.02). Furthermore, the estimated amount of food
waste per household per year as well as the amount wasted per person per year in the study
sites was extrapolated from the 48 hrs mean quantities as given in Table 9. No correlations
were observed between different food waste types and household socio-economic
characteristics, including household size, wealth, household food expenditure and HFIAS for most
situations (S1 Table). However, there was a negative one between prepared food waste and
household size, and a positive one between unprepared food waste and HFIAS in Richards Bay
only (S1 Table).
Reasons for household food waste
The reasons given by the respondents regarding why they throw away the different foods in
their households are enumerated in Table 10. The most cited reasons for the prepared food
waste were that the food looked or smelt bad and that households had prepared too much and
Values denote the amount of food wasted per household and/ per person in a given time frame.
* Amount in 48hrs x 365/2.
** amount wasted per household/ mean household size (7 persons per household).
ml* were converted to kg using conversion factor of 1 litre:1 kg.
13 / 20
it was not possible for them to save leftovers. In Richards Bay, the highest reasons were because
households had prepared too much and it was not possible for them to save leftovers and
because the food had gone off/bad (Table 10). In Dundee, the two most common reasons for
throwing away prepared food were that the food was off/bad and that they had prepared too
much and it was not possible to save leftovers. In Harrismith, prepared food was wasted mostly
because the food was off/bad or the respondent had served too much and could not finish all
the food (Table 10).
For unprepared food, households in all towns threw away food mostly because the food had
passed its best before date or the food was bad i.e. rotten, sour or moldy (Table 10). A smaller
percentage of households, especially in Dundee had bought too much and has ended up
throwing away the unprepared food. In general, the greatest percentage of households wasted
drinks when they had passed the best before date or by accident. Drinks were mostly wasted in
Dundee and Harrismith when these had passed the best before date whilst in Richards Bay it
was mainly due to accidents although a greater percentage of households had thrown away the
drinks that had passed the best before date (Table 10). Also, a greater percentage of the
households in Dundee had wasted drinks by accident (Table 10).
Results from this study suggest that households in all three towns were showing signs of
minimising the amount of food they threw away as more than 80% of the households in all towns
consume their meals at home with all household members, and rarely left any food uneaten. If
they happened not to finish the food, they usually kept it and consumed it in the next day or
two. This corroborates other studies reporting that minimal food waste occurs when
household members eat together at home (Lebersorger (cited in [
])) rather than eating out. Only
35% of the sampled households had thrown away any food in the previous 48 hours and the
greatest percentage had thrown away prepared food, especially pap, meat and vegetables
(mostly cabbage) and, to a lesser extent, drinks. The type of food that was wasted depended on
the food types that were being consumed by households as more than 50% of households
across all towns and locations consumed starchy cereals (mostly maize meal), vegetables
(mostly cabbage and onion) and meat [
]. This is slightly different from the types of food that
were reported to have been wasted in the developed world where ThoÈnissen [
] found a high
14 / 20
proportion of dairy products being wasted in the Netherlands and Pekcan et al. [
the highest proportion of food waste in Turkey consisted of fresh fruits and vegetables.
However, the wasting of meat is consistent with other studies, especially in the developed world as
meat has also been reported as one of the products contributing to food waste in the UK ,
the Netherlands [
], Austria (Lechner and Schneider (cited in [
])), USA [
] and in Turkey
]. In South Africa, meat and fish are widely consumed [
], therefore, it was easier for
households in the present study to generate food waste from meat as it is readily available in
the households. Globally, fruits and vegetables, starchy cereals, fish, meat and dairy contribute
more than 20% of food waste per annum [6±7].
The percentages of households who were throwing away food and drinks did not differ
along the agro-ecological gradient. However, location along the rural-urban continuum did
correlate with the percentage of households throwing away drinks as was shown by the
significantly higher prevalence of wasting drinks in urban locations relative to both peri-urban and
rural locations. Most drinks thrown away in the urban locations were soft drinks and juice,
whilst in the peri-urban and rural locations it was more commonly milk. This could be
attributed to households in urban areas having good access to cheap and affordable goods which
can encourage bulk buying, some of which may end up expiring before being used. Milk was
mostly wasted in rural and peri-urban areas because it is a perishable product and requires
proper storage and cooling, which can be lacking in poor rural households.
In general, households generated greater quantities of unprepared food waste (268.6±610.1
g) than prepared food waste (121.0±132.4 g); t(234) p = 0.011 as was hypothesised in the study
(hypothesis four). No significant differences were found in the amounts of prepared food
waste and drinks waste between and within the towns. Although there was no significance
difference across towns for prepared food waste, the amount of unprepared food waste followed
the agro-ecological gradient as per the study hypothesis one with households in Richards Bay
throwing away greater quantities (493.2±965.1 g per household per 48 hours) of unprepared
food than the other two towns. When extrapolating the findings from the 48 hour-period to
over a year; households wasted approximately 32.0±66.0 kg (90% CI) of unprepared food per
year, equating to approximately 4.6±9.6 kg per person per year of unprepared food waste
(when using mean sample household size). Households in the study sites also waste
approximately 18.4±25.8 kg (90% CI) of prepared food per household per year and 2.7±3.7 kg per
person per year and 8.7±19.4 litres (90% CI) of drinks per household per year with each
household member wasting approximately 1.3±2.8 litres of drinks annually. The estimated per
capita food waste by consumers in this study (8.6±16.1 kg per person per year), including
unprepared, prepared and drinks waste, overlaps with that which is estimated in the
developing countries (6±11 kg per person per year in sub-Saharan Africa and South/Southeast Asia)
and lower than that in the developed countries (95±115 kg per person per year in Europe and
North America) as reported by Gustavsson et al. [
]. Although there is an overlap between the
estimated per person per year food waste quantities for this study and that for developing
countries, the average per capita food waste by consumers for the present study which is 12.35
kg ((8.6+16.1)/2) is higher than that for developing countries which is 8.5 kg ((6+11)/2),
reflecting South Africa's middle-income status. However, further studies need to be done in
these areas to measure the quantities of food waste before concluding on the actual figures of
the quantities of food that is being discarded per annum.
The amount of unprepared food waste differed between towns, being higher in Richards
Bay than the other two. This could be because of high levels of food access in Richards Bay
] which could be attributed to wetter and warmer climatic conditions and a longer growing
season which favours agriculture. The same weather conditions can also affect the processing
and storage of the produce which could promote rotting as this was one of the reasons why
15 / 20
about 36% of the households in Richards Bay threw away unprepared food. Lack of
infrastructure and associated technical and managerial skills in food production and post-harvest
processing have been reported as the main driver promoting food waste in developing countries,
although this might apply on a large scale [
]. Also, food waste in developing countries has
been linked to poor financial status, storage and cooling facilities [
] and most food was wasted
in the rural areas in Richards Bay where most households fall into low income status [
limited market and knowledge on how to preserve their farm produce [
] and could have
poor storage and cooling facilities.
A significant negative correlation between the amount of prepared food waste and
household size was observed, i.e. smaller households were wasting more prepared food than larger
households, as was hypothesised in the study (hypothesis six). This could be because small
households prepare large portions of food which they failed to eat as they generously spend
their resources which may appear as more than enough. In larger households, resources may
appear as insufficient, therefore, members may be sparing their resources through exercising
portion measurements and can only prepare what would be enough for the meal. These results
are consistent with studies from developed countries which have shown that larger households
waste less food per person than smaller households [11,18±19]. Also, a significant positive
correlation in the amount of unprepared food waste and HFIAS was observed in Richards Bay
meaning that households with poor food access were discarding greater amounts of
unprepared food than those who had good access to food, which was opposite to what had been
hypothesised in the study (hypothesis five). Households with poor food access could have been
bulk buying when food was being sold at lower prices when they had the resources to acquire
food and the food passed the expiry date before being used and for perishable food, the quality
could have been poor and the food spoilt before being used. In other studies in the developed
countries, the availability of cheap food (which may increase household access to food) has
been noted to encourage overbuying and hoarding behaviours that result in food waste [
That is, impulse buying as a result of retail promotions, poor storage practices which results in
food becoming moldy or `off' and poor food management in homes where food is not used
before going past `use by' or `best before' date has also been reported in the UK . This also
applies to prepared food where a large percentage of households in all towns discarded the
food because it had gone bad and more drinks were discarded because they had passed the
best before date. Households also prepared large portions of food which they ended up not
eating and although they could have served leftovers, they could not use them on time. This is
consistent with Exodus  who reported poor portion control as households in UK prepared
meal portions that were too large resulting in an inability to finish all the food.
There have been reports that low-income households throw away less food than
highincome households [
]. However, the present study showed no significant associations
between the quantities of food waste (prepared, unprepared and drinks) with household
socioeconomic status indices (food expenditure and wealth index) as was also reported Parfitt et al.
]. This could be because households which were sampled had a narrow difference in the
wealth. However, further research need to be done to fully support this finding. In general,
urban households in Harrismith wasted more food than the rural and peri-urban households
which could be because they had more access to food. Also, greater quantities of drinks were
wasted in urban locations than in the peri-urban and rural locations, which can also point to
the issue of affordability, i.e. urban households have a higher socio-economic status and can
afford to buy drinks in larger quantities than peri-urban or rural households. However, this
was not consistent in the other towns as more food was wasted in the rural locations in
Richards Bay and in the peri-urban locations in Dundee.
16 / 20
More households in this study were discarding prepared food than unprepared food and
drinks but the quantities of unprepared food discarded were significantly higher than prepared
food. Quantities of unprepared food waste followed the agro-ecological gradient with residents
in Richards Bay discarding greater quantities than the other towns. Households in the study
sites waste approximately 32±66 kg of unprepared food per year with each member wasting
approximately 5±10 kg per year. The average estimated per capita food waste by consumers in
this study, including all food waste types, is higher than that which is estimated in the
developing countries (average 8.5 kg/person/year in sub-Saharan Africa and South/Southeast Asia)
and lower than that in the developed countries (average 105 kg/person/year in Europe and
North America). Household food waste in the study sites was mainly a result of household
behavior concerning food preparation (as the majority of the households threw away food
because they could have prepared too much and not possible to save left overs) and storage (as
food became visibly bad and smelly bad) as was noted in the developed countries [
many households in this study were preparing too much food which they ended up discarding,
integrated approaches are required to address this issue affecting South African societies,
which include promoting sound food management to decrease household food waste.
Nonetheless, further studies need to be conducted to fully understand the reasons why households
prepare too much food and yet they do not like to consume leftovers. In this case, one can
conclude if there is need for increased awareness on measuring ingredients when preparing food
so that households cook portion sizes which can all be eaten and can also make use of leftovers
to make new meals. Also, education campaigns focusing on raising awareness on consumer
food purchasing skills, meal planning, using leftovers into new meals, interpreting sell-by,
useby and best before dates as well as food management and storage skills so that food can have a
longer life even on the shelves [46±47], should not be ignored. This may also apply to South
Africa as some of the food in the study sites was discarded because it had passed best before
date, had gone bad (rotten, sour or moldy), and some households thought it was not possible
to save left overs. One of the biggest gaps in South Africa lies in the awareness and knowledge
of food waste in the food system . In areas like Richards Bay where households practice
agriculture, campaigns should focus on supporting households on how to process their
produce, especially drying vegetables after harvesting, which they can use in the future. This may
decrease reports on the cases where food may go bad/become rotten hence reducing the
amount of food being thrown away. All the above-mentioned recommendations need to be
tested so as to understand if it can help households to minimise the quantities of food waste
they generate. In the South Africa, costs associated with disposal of household food waste to
landfill are estimated at R505 million per annum [
]. Considering the rising food prices and
global food shortages, reducing food waste significantly increases water and food security in
many parts of the world as well as reducing greenhouse gas emissions, conserving energy,
protecting soil from degradation and decreasing pressure for land conversion into agriculture
S1 Table. Spearman correlations between HFIAS, household size, food expenditure and
wealth status of households with the amount of food wasted by households in the previous
48 hours. The correlations between different food waste types (prepared and unprepared food
waste and drinks waste) and household socio-economic characteristics, including household
size, wealth, household food expenditure and HFIAS. The Significant correlations at p<0.05
17 / 20
are shown in bold.
section D of the questionnaire.
S1 File. Food and Nutrition questionnaire. The questionnaire that was used to capture
information on household food waste in South Africa. The specific questions on food waste are on
S2 File. Information to participants. All the information about the project that was presented
to prior to data collection is in the information to participants file. Participants were first
informed about the project and were asked for their willingness to participate. Once they had
agreed to participate, they would sign consent forms.
We would want to extend our gratitude to Rhodes University's Department of Environmental
Science as well as Richards Bay, Dundee and Harrismith communities for their willingness to
participate in this study.
Conceptualization: Gamuchirai Chakona.
Formal analysis: Gamuchirai Chakona.
Funding acquisition: Charlie M. Shackleton.
Investigation: Gamuchirai Chakona.
Methodology: Gamuchirai Chakona, Charlie M. Shackleton.
Project administration: Gamuchirai Chakona.
Resources: Charlie M. Shackleton.
Supervision: Charlie M. Shackleton.
Writing ± original draft: Gamuchirai Chakona.
Writing ± review & editing: Gamuchirai Chakona, Charlie M. Shackleton.
18 / 20
19 / 20
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