Productivity, resource efficiency and financial savings: An investigation of the current capabilities and potential of South Australian home food gardens

Abstract

As the dominant form of urban agriculture (UA) in Australia, existing home food gardens potentially represent a significant resource in the context of future urban food security and sustainability. However, a severe lack of in-field data has hindered our understanding of the form and function of home food gardens which in turn may hinder innovation and improvement. We investigated the productivity, resource efficiency and potential financial savings of home food gardens in South Australia. A group of 34 citizen science participants measured and recorded inputs and outputs from their gardens. Inputs included time spent on various gardening activities, financial costs, and water use. Outputs included crop yields, from which retail value and nutritional content were then derived. The paper outlines a field-demonstrated, comprehensive methodology for continued and consistent data collection for all forms of UA. We found smaller gardens to be more intensive than larger gardens, requiring higher inputs, but also returning higher outputs per unit area. Both productivity and resource efficiency varied among the gardens, and labour requirements were significantly lower than previously estimated. Water use efficiency of the gardens were calculated and found to have comparable water use efficiency to commercial horticulture. Of the gardens involved, we calculated that 65% should break even in five or less years and save money. After applying a minimum wage almost one in five gardens were financially viable. The results represent the most comprehensive measurements on home food gardens to date, and allow practical, evidence-based recommendations for diversification, time saving and smart irrigation practices to improve garden productivity and enhance the viability of UA.

Full text

RESEARCH ARTICLE
Productivity, resource efficiency and financial
savings: An investigation of the current
capabilities and potential of South Australian
home food gardens
Georgia CsortanID*, James Ward, Philip Roetman
School of Natural & Built Environments, University of South Australia, Adelaide, South Australia, Australia
*Georgia.Pollard@unisa.edu.au
Abstract
As the dominant form of urban agriculture (UA) in Australia, existing home food gardens
potentially represent a significant resource in the context of future urban food security and
sustainability. However, a severe lack of in-field data has hindered our understanding of the
form and function of home food gardens which in turn may hinder innovation and improve-
ment. We investigated the productivity, resource efficiency and potential financial savings of
home food gardens in South Australia. A group of 34 citizen science participants measured
and recorded inputs and outputs from their gardens. Inputs included time spent on various
gardening activities, financial costs, and water use. Outputs included crop yields, from
which retail value and nutritional content were then derived. The paper outlines a field-dem-
onstrated, comprehensive methodology for continued and consistent data collection for all
forms of UA. We found smaller gardens to be more intensive than larger gardens, requiring
higher inputs, but also returning higher outputs per unit area. Both productivity and resource
efficiency varied among the gardens, and labour requirements were significantly lower than
previously estimated. Water use efficiency of the gardens were calculated and found to
have comparable water use efficiency to commercial horticulture. Of the gardens involved,
we calculated that 65% should break even in five or less years and save money. After apply-
ing a minimum wage almost one in five gardens were financially viable. The results repre-
sent the most comprehensive measurements on home food gardens to date, and allow
practical, evidence-based recommendations for diversification, time saving and smart irriga-
tion practices to improve garden productivity and enhance the viability of UA.
Introduction
Anticipation in the potential of urban agriculture is intensifying as the global human popula-
tion continues to grow, and we face the challenges of feeding more people with limited natural
resources. Urban agriculture (UA) is an integral element in our collective vision of a sustain-
able urban future [1–4], yet its potential contribution to sustainability and food security is
poorly quantified. Early research into the economic value of home vegetable gardens in the
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OPEN ACCESS
Citation: Csortan G, Ward J, Roetman P (2020)
Productivity, resource efficiency and financial
savings: An investigation of the current capabilities
and potential of South Australian home food
gardens. PLoS ONE 15(4): e0230232. https://doi.
org/10.1371/journal.pone.0230232
Editor: James D. Englehardt, University of Miami,
UNITED STATES
Received: December 24, 2018
Accepted: February 25, 2020
Published: April 14, 2020
Copyright: ©2020 Csortan et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
available in University of South Australia’s
Research Access Portal: (https://doi.org/10.25954/
5e7c58f52d025).
Funding: General supporting organisations were
the University of South Australia, the City of
Marion, the City of Salisbury, the Adelaide and
Mount Lofty Ranges Natural Resources
Management Board and the Department for
Environment and Water. This research was also
support in part by an Australia Government

USA involved small, detailed studies on one or two purpose built, experimental gardens [5–8].
More recent UA research has expanded its focus to typically investigate the yields and retail
value of food produced by larger numbers of home and community food gardens [9–13]. Yet
this expansion of garden numbers and types also appears to have resulted in less practical gar-
den detail being collected. Of these more recent studies, labour and costs (two key consider-
ations in the potential of UA) were only measured by Codyre et al. [10]. Measurements of
water use in urban food gardens, in particular, have been severely overlooked and understud-
ied [14,15], to the point where our recent PRISMA systematic review found, “. . .no recorded
attempt to collect any ‘water use efficiency’ data on existing urban food gardens,such as those
created and managed by home gardeners”–pg. 7 [16].
Thus, there remains a considerable lack of knowledge regarding many of the practicalities
of UA, and several demands for an increase in field-based data collection have been made [17–
21]. Without such field-based data on all the key inputs and outputs of urban food gardens,
there is no consistent, comparable knowledge on which to base sustainable improvements or
to help upscale UA for a greater future contribution to urban food security. At the local level,
both households and local government are interested in whether growing food can help people
to save money–thereby reducing their cost of living. Home food gardens are the most common
form of urban food production in Australia [19] and yet their productivity, resource efficiency
and potential financial savings are still relatively unknown. We have a clear need to better
understand UA as it currently exists in our urban areas.
The work presented here is the culmination of a large study on the productivity, resource effi-
ciency (including water use and financial sustainability) and social value of urban food gardens in
South Australia. Previously published papers drawing on project data include a review of historic
and current approaches to measuring UA productivity [22]; a PRISMA systematic review into the
water use and water use efficiency of UA [16]; and papers on home food garden diversity [23]; and
on the motivations, values, social value, food preservation and food distribution of gardeners [4].
This paper presents the field-collected data from “Edible Gardens”, a state-wide citizen sci-
ence project of the University of South Australia. The project incorporated a citizen science
approach as an effective way to overcome many of the challenges inherent in practical UA
research, thus allowing the focus to be on the performance of real-world, existing food gardens
and gardeners [11,12,22]. This paper documents a new and replicable research methodology
for collecting input and output data on existing urban food gardens, with an emphasis on
home food gardens. Diverse forms of food garden activity, in addition to vegetable and herb
production, are considered by these methods, including orchard production and the keeping
of urban livestock. The aim of this research was to provide deeper insight into the productivity,
resource efficiency and potential financial savings of home food gardens, using South Australia
as a case study. Productivity and resource efficiency results are reported, in addition to esti-
mates of return on labour and financial investment, and water use efficiency results for existing
home food gardens. We were also able to examine the influence of scale in home food gardens
and its potential relationship with intensity of production and resource requirements. Implica-
tions of these findings include new ways for gardeners to choose suitable gardening setups,
and a practical, evidence-based rationale for improving and optimising UA towards broader
goals of urban sustainability, accessibility and food security.
Methods
Study location
This research was conducted in the Australian state of South Australia (pop. 1.7 million people
[24]), with the capital city of Adelaide (latitude: -34.928, longitude: 138.599). Along the
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Research Training Program (RTP) Scholarship.
The funders had no role in study design, data
collection and analysis, decision to publish, or
preparation of the manuscript.
Competing interests: The authors have declared
that no competing interests exist.

southern coast where most of the population live, South Australia has a mixture of warm to
hot dry summers and cold, wet winters [25]. Adelaide itself receives an average of 545 mm of
rainfall per annum [26]. The availability of water is a persistent issue, particularly during sum-
mer when the evapotranspiration rates are higher than the low rainfall [27].
The Edible Gardens project ran from the end of 2016 until mid-2018. Ethical approval was
granted by the University of South Australia–Human Research Ethics Committee in January
2015 (Protocol number 0000034940). The project had two parts, the first an online survey and
the second a period of field-based garden data collection by selected “citizen scientists” (mem-
bers of the public who volunteered and received guidance on how to collect input and output
data from their own food gardens). All forms of urban food production were considered, from
growing fresh produce in different ways to the keeping of urban livestock such as poultry, bees
or fish. The project was open and promoted to home, community and school gardens across
South Australia using a combination of online channels and print materials. The online survey
was open to South Australian residents over the age of 18. The in-depth survey collected a vari-
ety of base garden data, estimates of resource use and insights into food garden experiences [4,
22,23]. From September 2016 until April 2018, 402 South Australian residents completed the
survey.
As part of the online survey, 232 respondents (58%) volunteered to conduct field-based gar-
den data collection. A sampling framework was designed to select participants to ensure the
full range of available garden types to those surveyed. Primary selection criteria included: gar-
den size, production methods, estimated inputs (time and expenses), water sources and irriga-
tion methods. Secondary criteria related to gardener details, including gardening experience,
gardening consistency, age, education and gender. Following the selection process, 105 gar-
deners were selected to register their gardens and collect data on their own food gardens. It
should be noted that, while the Edible Gardens project results are potentially representative of
the different types of food gardens prevalent in South Australia, there are limitations in extrap-
olating the results to the wider population.
Home gardeners showed the most interest in the project. Of the gardens selected, 71 home
gardeners and 2 school gardeners proceeded to register with the project and request data
collection toolkits. Although 20 community gardeners completed the online survey and 13 vol-
unteered for field-based garden data collection, none registered to collect data (time commit-
ments and the need to form working relationships with the managing groups of each
community garden were problematic).
Registration for field-based garden data collection was done online and involved nominat-
ing the number of garden “areas” for which the gardener was willing to collect data (maximum
of four areas per garden), uploading a photo and describing the food garden. For each nomi-
nated garden area, participants reported its size (area under production), cultivation tech-
nique, typical crop/s, water source/s, irrigation method/s, and position on the land block. As a
way to distinguish between different individual gardens areas within the registered gardens,
“method-crop” categories were developed. The method-crop categories were a combination of
the type of production method (or garden area) and the type of typical crop. These categories
were developed as a compromise between aggregating data at the level of garden area (not suf-
ficiently detailed), and differentiating data by individual crop (too impractical and time-con-
suming for participants).
Data collection
Registered gardeners were posted a data collection toolkit (as reported in [22]) which was cus-
tomised to suit their garden. The water sources and irrigation methods used dictated what
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type of, and how many, water meters were supplied to the participant. Two types of water
meters were available, positive displacement Elster Model v100 meters (suitable for lower flow
and pressure systems e.g. rainwater tanks, and which were kindly donated by SA Water) and
small digital impeller meters (suitable for consistent pressure and flow systems such as reticu-
lated mains water) [7]. The toolkits included instructions, data sheets and measurement tools
(spring balance scales and water meters). The instructions and data sheets were based upon
the Harvest Count section of the Farming Concrete project developed by Gittleman et al. [12].
The participants were asked to measure five variables on an ongoing basis:
1. Time spent on all food-garden related activities (hours & minutes);
2. Money spent on food-garden related purchases ($AUS);
3. Irrigation applied (mains, rainwater, bore/grey water, using water meters) (litres);
4. Harvested crop yields (using spring balance scales or digital scales) (kilograms); and
5. Food shared with others outside of the household (or school) (kilograms).
With regards to time spent irrigating, participants were instructed to log time spent physi-
cally irrigating, turning on or moving watering systems but not the length of time an irrigation
system was left running (if the gardener was not gardening for the duration). For areas with
completely automated irrigation systems, participants were able to log zero minutes for irriga-
tion. Participants were permitted to continue collecting data for as long as they wished,
although longer participation was encouraged. Participants entered their data into the Edible
Gardens project online system which contained webpages for garden registration, data entry
and data visualisation and interaction [22]. As participants entered data, the data visualisation
page automatically generated colourful and interactive graphs of the input and yield results for
each garden. Each participant could access, download and save their own data at any time dur-
ing the project.
Three forms of supplementary data were collected by the research team. First, we tracked
the retail value of different fresh foods over the course of the project using data collected from
two supermarkets, one national retailer and one state-based retailer. The average retail price
from the two supermarkets at the time of reported harvest were applied. Although many of the
participants reported using organic growing approaches, certified organic retail prices for food
products were considered inappropriate due to the high certification costs paid by certified
growers. Second, nutritional information, including energy, protein and percentages of edible
parts of each crop were collected from the Food Standards Australia New Zealand (FSANZ)
online database—Nutrient Tables for Use in Australia (NUTTAB) [28]. Third, natural rainfall
data were collected from the Australian Bureau of Meteorology’s Online Climate Database
[29], using rainfall data from the recording stations closest to each garden. The natural rainfall
data was added to the measured irrigation data, as recommended by Pollard et al. [7] to calcu-
late total water received by plants, enabling greater comparison of the water use results from
this study with future studies in wetter or drier climates.
Statistical analysis
The dataset was cleaned and managed by the research team (the authors). Registered gardens
who lacked record of any of the three inputs (time, money and water) or yield measurements,
or had less than 30 data entries, were removed from the dataset. Statistical analyses were con-
ducted using SPSS software (version 25. IBM Corp) and Microsoft Excel. The statistical tests
used were: Spearman correlations (r
s
); Kruskal-Wallis tests (H) with post hoc Dunn’s test,
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strength rankings from Rea and Parker [30] and Bonferroni corrections; Wilcoxon-signed
rank tests (T); and two-way Chi-square tests (X
2
).
Owing to the variability in garden size and duration of participation, most of the values in
this paper are presented as the value per square metre, per 30 days. Presenting results as values
per square metre (or square yard) is typical of UA research. The duration of data collection of
past studies has varied considerably yet studies tend not to normalise to a standard length of
time. For example, Stall [6] reported a yield of 6.79 kg/m
2
(from 8 months), Cleveland et al. [5]
reported a yield of 1.24 kg/m
2
(from 2.5 years) and 2.31 kg/m
2
(from 3 years), Codyre et al. [10]
reported a yield of 1.43 kg/m
2
(from 1 year) and Zainuddin and Mercer [31] reported a yield of
0.35 kg/m
2
(from 3 months). This variability makes comparisons among results challenging.
Introducing time as an additional denominator, while also considering the seasonal time of year,
allows for clearer comparisons than currently possible, among studies of different durations.
Results and discussion
Data were collected across South Australia on 34 home gardens, containing 93 individual gar-
den areas. Data collection occurred during the period from November 2016 to June 2018, and
within all four seasons, although not all gardens collected data for the entire period. Across all
34 participating gardens, the median duration of data collection was 176 days (just under 6
months), while the total data collection period amounted to 7,565 individual “garden-days” or
252 “garden-months”. The total combined area under production was 3,161 m
2
and the total
harvested yield was 3,479kg of fresh vegetables, fruits, herbs, eggs, fish and honey.
Of the 11 method-crop categories involved in the Edible Gardens project, five had sufficient
data (>500 entries) or sufficient numbers of individual areas (>10 across the 34 participating
gardens) to form comparable samples. For a summary of all the project data please refer to S1
Table.S2 Table presents an overview of the five dominant method-crop categories, while S3
Table compares the time per activity per method-crop category. Results from all garden areas
(and all 11 method-crop categories) are included in the results pertaining to whole gardens.
This paper also presents individual results on the five dominant method-crop categories,
which were:
1. Bed-orch: In-ground orchard (fruit trees)
2. Bed-mixed: In-ground garden beds producing vegetables, herbs and ‘other’ crops
3. Chkn-egg: Chickens kept for egg production
4. Raised-mixed: Raised garden beds producing vegetables, herbs and ‘other’ crops
5. Wick-mixed: Wicking beds producing vegetables, herbs and ‘other’ crops
There were also two aquaponics systems, totalling more than 430 data entries. However, as
there were only two systems, these results were not comparable with the other more dominant
method-crop categories and so were not included in the individual method-crop analysis. The
method-crop categories support comparisons both between areas within individual gardens
and comparisons with other studies. Descriptions on garden types and growing areas have
until now had no categorisation system which adequately defined the diversity of existing
urban food production. This level of detail is valuable as each of the five main method-crop
categories present different input requirements and output returns, which in turn represent
various trade-offs.
Of the citizen scientists responsible for the garden data collection, 24 were women and 10
were men, 33 owned (or had mortgages on) their own homes while only one rented. The
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majority (27) of the participants shared at least some of the food they grew. Hereafter, the Edi-
ble Gardens project will be referred to as “EG”.
Combined with the results, the following discussion is split into three parts: (1) new find-
ings on the state of existing urban food gardens (including productivity and resource effi-
ciency); (2) the potential financial sustainability of home food gardens; and (3) implications of
these findings, including a new way for gardeners to select the most suitable garden setups for
themselves.
New findings on the state of existing urban food gardens
Productivity. The EG gardens produced a wide variety of vegetables, fruits, herbs and ani-
mal products from a range of different cultivation methods. Examples of the most common
crops/products were tomatoes, strawberries, parsley and chicken eggs, while some of the more
unusual crops included doughnut peaches, Armenian cucumbers, shisho leaves and trombon-
cinos. For the full list of harvested crops please refer to S4 Table. The total harvested yield of
3,479kg had an estimated supermarket retail value of AUS$28,076. Yields varied considerably
among the EG gardens, ranging from 0.02 kg/m
2
/30 days to 1.42 kg/m
2
/30 days. The median
yield was 0.21kg/m
2
/30 days. Exactly 50% of the EG gardens produced yields of less than 0.20
kg/m
2
/30 days, while 24% managed to produce higher yields of more than 0.50 kg/m
2
/30 days
(Fig 1).
Previous research has also noted the wide variability of yields among individual gardens
[10,11,13,20,31,32]. Table 1 compares the EG yield results with the yield results of those pre-
vious studies which explicitly reported area under production and their duration of data col-
lection. When measuring the year-round inputs and outputs of urban food gardens, time
becomes an important consideration to account for natural cycles of more and less productive
times of the year. Table 1 also presents the results of this study, both for the combined yield
(for all five dominant method-crops) and also for only the vegetable-growing categories, as the
previous studies only considered mostly vegetable cultivation.
While the volume and types of food produced by the different method-crop categories var-
ied, no statistical difference was found among their yields; although the most productive cate-
gories (median kg/m
2
/30 days) were raised-mixed, wick-mixed and chkn-egg. Raised-mixed
and wick-mixed were also the two method-crop categories with the best median retail value
Fig 1. The EG gardens and their yields per square metre per 30 days.
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return ($/m
2
/30 days). There was, however, a statistical difference in the retail value returns of
the method-crop categories (Kruskal-Wallis test H(4) = 13.04, p= 0.0011, ε
2
= 0.17 (relatively
strong)), while the Dunn’s test with Bonferroni correction found that bed-orch had a signifi-
cantly lower retail value return (Mdn. = $0.80) than raised-mixed areas (Mdn. = $5.32,
p= 0.017) and wick-mixed areas (Mdn. = $3.48, p= 0.044).
Frequency of harvesting was found to differ among the method-crop categories (Two-way
Chi-square test X
220
= 33.60, N = 2646, p= 0.029) and by season (X
215
= 85.80, N = 2646,
p= 0.001). Looking deeper, there was also a difference when comparing the seasonal harvest-
ing consistency (by number of harvesting events) of each method-crop category (X
212
=
337.01, N = 4963, p= 0.001). Chkn-egg and raised-mixed areas appeared to have the most con-
sistent harvesting event frequency, although this did not translate directly to quantity of food
harvested each season (Fig 2).
Resource efficiency: Time. Participants tracked all time spent on various food-garden
related activities. Table 2 displays the total and percentage times of the different activities. The
main activities were: harvesting (31%), irrigating (all kinds combined) (20%), and then live-
stock care and weeding/pruning (both 8%). ‘Other’ was used as a catch-all for irregular activi-
ties including pollination, seed saving, aquaponics system care and repair work. Greater time
invested (hours/m
2
/30 days) was positively correlated with greater irrigation (Spearman’s rho
r
s
= 0.570, N = 33, p= 0.001) and total water (W) (r
s
= 0.531, N = 33, p= 0.001), but greater
time also returned higher yields (r
s
= 0.637, N = 34, p= 0.001) and retail value return (r
s
=
0.738, N = 34, p= 0.001). With regards to the dominant method-crop categories, no difference
was found among the method-crops and associated time spent on shared activities (two-way
Chi-square test X
2
). As reported previously by Pollard et al. [16] the EG participants used a
variety of different irrigation methods to water their gardens. Cleveland et al. [5] estimated
spending over 50% of their total time watering their two gardens by hand. We compared this
with those EG gardeners who applied water via hand-watering and watering via drippers or
sprayers (N = 25) (but not those who utilised automatic irrigation systems). Thus, time spent
watering ranged from 13% to 79% of all time recorded, with a median of 37%.
Table 1. Comparison of average yields (kg/m
2
) and (kg/m
2
/30 days) among previous research and the EG results. These results were calculated for the previous
studies.
Year Project name /
Authors
No. garden
areas (& type)
Location Reported yield
(kg/m
2
)
Average yield
(kg/m
2
/30 days)
Further Details
1979 Stall [6] 1 (created) Florida, USA 6.79 0.850 Duration: 8 months; Area: 13.9 m
2
; Measured: costs (not
include. water), yield & retail value.
1980 Stephens et al. [7] 2 (created) Florida, USA 2.70 0.450 Duration: 3 months; Area: 189.27 m
2
; Measured: labour, costs,
yield & retail value.
1985 Cleveland et al. [5] 2 (created) Arizona, USA 1.20 0.020 Duration: 2.5 & 3 years; Area: 135.7 m
2
; Measured: labour,
costs, water use, yield & retail value.
2014 CoDyre et al. [10] 50 (existing) Ontario, CAN 1.43 0.006 Duration: 4–5 months; Area: (approx.) 627.5 m
2
; Measured:
labour, costs, estimated yield weights & retail value.
2014 Zainuddin & Mercer
[31]
15 gardens
(existing)
Melbourne,
AUS
0.35 0.020 Duration: 3 months by each participant within a 1 year span;
Area: 1096 m
2
; Measured: yield & food sharing.
2016 Algert et al. [9] 16 in 8 gardens
(created)
California,
USA
6.00 0.188 Duration: 17 weeks; Area: 48 m
2
; Measured: yield, retail value
& food sharing.
This
study
all five main method-
crop areas
82 in 34
gardens
(existing)
South
Australia, AUS
2.70 0.370 Duration: 1–19 months by each participant within a 19-month
span; Area: 3023.3 m
2
; Measured: labour, costs, water use,
yield, retail value & food sharing.
This
study
only veg/vegh/ herb/
other (mixed) areas
47 in 34
gardens
(existing)
South
Australia, AUS
3.40 0.470 Duration: 1–19 months by each participant within a 19-month
span; Area: 922.3 m
2
; Measured: labour, costs, water use, yield,
retail value & food sharing.
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One surprising result was our gardeners’ perceptions that they spent considerably more
time tending their food gardens (according to the online survey results [23]) than they
reported when physically monitoring their time. Participants’ records of garden labour from
in-field garden data collection was compared with their estimation of garden labour (provided
for the online survey) using a Wilcoxon signed-rank test (T). The weekly time the EG garden-
ers estimated in the online survey was significantly higher (Mdn = 3.6 hours) than their
reported typical weekly hours spent (Mdn = 1.3 hours), (T = 22.5, p= 0.001, r = 0.8, N = 34).
This perception corresponds with findings by Wise [19], who suggested that the estimated
time from their surveyed gardeners was considerably higher than the time requirements sug-
gested by professional gardeners. One reason for this difference is that time spent thinking,
planning or deciding what to grow, may not have been fully accounted for in the EG in-field
measurements.
Fig 2. The seasonal proportions of the total harvest quantities from each of the method-crop categories.
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Table 2. All monitored garden-related activities and time spent on each
1
.
Activity Total Labour–all gardens (hours) Average activity duration (mins) Median activity duration (mins) N = % of total (rank)
Harvesting 751.4 9 5 34 31% 1
Combined Irrigation 480.8 - - - 20% (2)
(Mains) 246.4 14 10 27 10% 2
(Rainwater) 214.9 20 12 19 9% 4
(Bore/Grey) 19.5 9 5 5 1% 13
‘Other’ 223.3 28 5 14 9% 3
Livestock Care 189.3 18 10 18 8% 5
Weeding/Pruning 207.4 50 24 28 8% 6
Plant/Sowing 173.5 54 30 31 7% 7
Soil Prep/Mulch 166.7 70 45 27 7% 8
Building 147.9 123 60 18 6% 9
Sharing Produce 56.4 10 5 27 2% 10
Fertilizing 36.3 29 10 19 1% 11
Pest Control 24.4 50 20 9 1% 12
Total 2,457 hours
1
Not all the 34 gardens reported all the activities. The ‘Other’ category was used as a catch-all for activities which did not fall easily into the prescribed activities, for
example, pollinating plants, repairing storm damage, working on aquaponics systems (including feeding fish), and seed saving.
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This finding is particularly interesting when considering that ‘not having enough time’ was
both the top initial and the top ongoing challenge faced by the EG survey respondents [23].
Bed-orch areas had the lowest median time requirements (4 mins/m
2
/30 days), followed by
chkn-egg areas (8 mins/m
2
/30 days) and bed-mixed areas (9 mins/m
2
/30 days). Overall, the
EG gardens had an average labour input of 14 mins/m
2
/30 days. This result is considerably
lower than the reported average labour input of the 50 urban food gardens involved in the
study by Codyre et al. [10], which equated to 41 mins/m
2
/30 days. This variation could be par-
tially due to the different durations of data collection, with some of EG gardens collecting data
for more than a full year, compared with the 4–5 months of growing season suitable for data
collection in the study by Codyre et al. [10].
Resource efficiency: Area. Many of the results presented here relate to scale. Of the 34
gardens involved, their total area under production ranged from 4 m
2
(smallest) through to
731 m
2
(largest), with a median size of 49 m
2
. Lack of space did not necessarily equal less pro-
ductive potential. In fact, as the area under production increases, both the inputs and outputs
per unit area decrease–i.e. the intensity of the gardening operation effectively decreases with
scale. These negative correlations (Spearman’s rho) are displayed in Fig 3, where the lines dis-
play an approximate upper envelope as dictated by available data.
This finding corroborates the theoretical findings of a linear programming model by Ward
and Symons [33]. They found that per person, smaller gardens of between 10–20 m
2
were opti-
mal in their ability to produce both high-value and low-water use crops [33]. These results also
support approaches recommended by popular gardening literature which try to maximise the
use of small spaces close to home, from compact crop varieties, intensive planting, vertical
growing structures [34].
Garden size categories were applied to the 34 EG gardens to test for differences: <20 m
2
(n = 9), 20–50 m
2
(n = 8), 50–100 m
2
(n = 12), 100–800 m
2
(n = 5). No statistical difference
among the garden size categories was found in relation to applied irrigation (kL/m
2
/30 days),
total water (kL/m
2
/30 days), gardener experience or gardening consistency. Significant differ-
ences were found among the ongoing costs, hours spent, yield, retail value and the number of
social learning sources utilised by each of the gardeners (Table 3). Ongoing costs were any and
all food garden related costs (except setup costs), paid by the EG participants during their data
collection periods. The statistically significant results from the Kruskal-Wallis tests and post
hoc Dunn’s test with Bonferroni correction are outlined below (Table 3).
Consideration of scale also appears to complement the findings on garden diversification.
Having a higher number of combinations of method-crop areas was associated with seasonal
distribution of harvests (X
29
= 415.43, N = 4532, p= 0.001) and the diversity of harvested yield
categories (fruits, herbs, animal products and vegetables) (X
29
= 603.82, N = 4521, p= 0.001).
The EG gardens with four different method-crop areas (the maximum allowed in the study,
N = 12, with the median no. garden areas = 3), returned the most evenly proportioned seasonal
spread of their harvests. And those gardens with three or four method-crop areas appeared to
harvest the most even proportions of fruits, herbs, animal products and vegetables. This con-
cept of using a combination of method-crop areas, smaller and more intensive ones—together
with larger less intensive method-crop areas with lower input requirements and concomitantly
lower yields, in part validates the concept of ‘concentric zoning’ in permaculture literature
[35].
Resource efficiency: Water. While water was the most challenging input to measure, the
data obtained has justified the effort. The median volume of irrigation applied to the EG food
gardens was 20L/m
2
/30 days. For those areas only producing vegetables, herbs and other
(mixed), the median volume was over double that, 52L/m
2
/30 days. Of the total 690.8kL of irri-
gation applied, 71% was reticulated mains water, 30% was collected rainwater and 5% was
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Fig 3. Scatter plots of the negative correlation between area under productionand the inputs and outputs. All
values are presented as per square metre per 30 days. The lines represent the upper envelope of the results.
https://doi.org/10.1371/journal.pone.0230232.g003
Table 3. Significant results from comparisons among the garden size categories.
Variable (m
2
/30 days) Kruskal-Wallis test Dunn’s test with Bonferroni correction
H (df) pStrength ranking ε
2
<20 m
2
20–50 m
2
50–100 m
2
100–800 m
2
Mdn.Diff.Mdn.p Mdn.p Mdn.p
Ongoing costs AUS$ 11.38 (3) 0.010 0.34 (relatively strong) $1.20 >-- -- $0.11 0.011
Labour (hours) 19.41 (3) 0.001 0.59 (strong) 0.46 >0.10 0.006 0.11 0.003 0.02 0.001
Yield (kg) 9.19 (3) 0.020 0.30 (relatively strong) 0.55 >-- -- 0.06 0.022
Retail value return AUS$ 13.35 (3) 0.004 0.41 (strong) $4.47 >-- $1.46 0.03 $0.29 0.005
No. of social learning sources 12.31 (3) 0.006 0.39 (strong) 1 <3.5 0.005 -- --
The strength rankings of the Kruskal-Wallis tests are based on Rea and Parker [17].
In the Dunn’s test, “Diff.” indicates whether the difference between the garden area size >20 m
2
was greater than or less than the other garden sizes.
A visible pvalue indicates a significance level <α= 0.5
“--” indicates no significant difference.
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bore or grey water. Bore water is pumped shallow well water. Natural rainfall on the garden
areas constituted between 4% - 94% (Mdn. 54%) of the total water (combined applied irriga-
tion and rainfall) for each garden. As one of the only previous studies to measure their water
use, Cleveland et al. [5] reported the net return for each dollar spent on water as USD$8.80
and USD$7.75 for their two gardens. Our net return per dollar spent on irrigation (assuming
the cost of mains water was applied to all irrigation including rainwater and bore/grey water)
was between $-41.53 (with some gardens running at a loss) to $169.95 with a median of $13.46
(N = 32 gardens with irrigation data). Cleveland et al. [5] also reported water to be their single
largest expense at almost 30% of their total costs. This differed slightly from the EG results,
with irrigation contributing between 3% to 90% of total costs, with a median of 23% (N = 27
gardens with cost data). Surprisingly, the cost of mains water was relatively small in the scheme
of the total ongoing garden costs. The cost of the water applied to UA can be difficult to mea-
sure, as discussed in detail by [16], since although mains water has a set cost there remains dis-
agreement over the levelized costs of alternative water sources. While no water costs for
(collected) rainwater, bore or grey water were recorded, it should be noted that these water
sources are not free. Statistically, greater total water (W) was positively correlated with greater
yields (r
s
= 0.514, N = 33, p= 0.002) and retail value return (r
s
= 0.535, N = 333, p= 0.001).
Water use efficiency (WUE) is defined as, “a measure of how efficiently production systems
convert water (rainfall and/or irrigation) into a harvestable yield or into money” [16]. Three of
the four WUE equations proposed by Pollard et al. [16], were applied to the EG results. The
values for simple gross (WUE
gross
), nutritional (WUE
nut
) and financial (WUE
fin
) WUE were
calculated for each participating garden (and later for each method-crop category). Fig 4 charts
each of the WUE values of the garden size categories. For comparison, Fig 4 also includes the
WUE values of Cleveland et al. [5] as previously calculated [16], and the average WUE
gross
of
related crops from Mekonnen and Hoekstra [36]. All the EG whole garden WUE scores either
matched or surpassed the comparable scores of the two Cleveland gardens.
No statistical difference was found in any of the WUE values and the different garden size
categories. However, WUE
fin
was positively correlated with labour (hours/m
2
/30 days) (r
s
=
0.479, N = 33, p= 0.005) and negatively correlated with total area under production (r
s
=
-0.414, N = 33, p= 0.017). WUE
nut
was negatively correlated with total water (kL/m
2
/30 days)
(r
s
= -0.438, N = 33, p= 0.011).
The same three WUE equations were also applied to the method-crop categories (Fig 5)
with WUE
gross
compared against global average water footprint of related crops as reported by
Mekonnen and Hoekstra [36]. Natural rainfall (also known as “green water”) was not
accounted for in the EG calculation of the WUE values for chkn-egg areas, as we assumed that
this was not being consumed by the poultry. Comparison with the chicken egg average water
footprint reported by Mekonnen and Hoekstra [37] was attempted, however, their blue water
figures also account for the irrigation applied to the chicken’s feed. As this study did not con-
sider the embodied water of the feed inputs, chkn-egg areas outcompeted all other method-
crop categories in all WUE scores. Although the embodied water in chicken feed is a consider-
ation for broader-scale sustainability, these results are still relevant from an urban water per-
spective—where keeping chickens for eggs in a home garden is a highly water-efficient option.
Thus the WUE
gross
results for chkn-egg areas in Fig 5 are instead compared to a range of esti-
mated WUE
gross
based on a brief review of online poultry keeping websites (assuming between
0.5 – 1L of irrigation per chicken per day, with average of 250 eggs per year at 60 grams per
egg, which equates to a range of 15 – 30kg/kL). Wick-mixed presented the lowest variability in
both WUE
gross
and WUE
nut
, while bed-orch presented the lowest variability in WUE
fin
. Bed-
orch also presented reasonably strong WUE
nut
.
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Interestingly, while the lower bounds of the EG method-crop WUE scores matched the
average global WUE
gross
values of related crop types from Mekonnen and Hoekstra [36], the
majority of the method-crop WUE scores ranged considerably higher. This suggests that the
blue and green water footprint of home-produced food (not accounting for chkn-egg areas)
can be comparable in water-efficiency to conventional farming, and that the EG participants
were generally not substantially over- or under-watering. Chkn-egg areas performed well in all
the water-related metrics. They had the lowest median total water (W) requirements (10L/m
2
/
30 days), followed by bed-orch areas (51L/m
2
/30 days).
Despite the apparent difference in WUE values, the only statistically significant differences
among the method-crop categories were between chkn-egg and the other four main method-
crop categories (Table 4). There were differences in WUE
gross
(H(3) = 31.71, p= 0.001, ε
2
=
0.41 (strong)), WUE
nut
(H(3) = 36.40, p= 0.001, ε
2
= 0.47 (strong)) and WUE
fin
(H(3) = 34.69,
p= 0.001, ε
2
= 0.45 (strong)).
Another interesting finding was that wick-mixed areas did not outperform the other areas
in terms of their applied irrigation or total water (W) requirements. A wicking bed is defined
as, “. . . a plant driven system where plants receive water through capillary rise from a self-
Fig 4. The WUE
gross
, WUE
nut
and WUE
fin
results for the garden size categories. The blue circles represent the
WUE
gross
and WUE
nut
values of the two gardens from the Cleveland et al. [5] study as previously calculated in [16],
while WUE
fin
results have been adjusted to the 2018 AUS$ value. The pale grey band represents the global average
WUE
gross
scores for ‘vegetables’, ‘roots and tubers’ and ‘fruits’ from Mekonnen and Hoekstra [36].
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contained coarse material-filled subsoil reservoir” [29, pg. 1]. Neither did wick-mixed areas
present significantly better WUE scores. Indeed, the only metric where the wick-mixed areas
out-performed the other categories was with their frequency of irrigation. There was an
Fig 5. A comparison of the WUE
gross
, WUE
nut
and WUE
fin
scores for each of the five main method-crop
categories. Outliers are identified by the arrows, and the chkn-egg WUE
nut
scores required its own axis. The
additional yellow lines on the WUE
gross
chart represent comparative WUE values of related foods from Mekonnen and
Hoekstra [36]. The grey band represents the estimated WUE
gross
range for chicken eggs (irrigation only) as based on a
brief review of online poultry keeping websites.
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Table 4. Significant results from WUE comparisons among the method-crop categories.
Variable Dunn’s test with Bonferroni correction
Chkn-egg Bed-orch Bed-mixed Raised-mixed Wick-mixed
Mdn.Diff.Mdn.p Mdn.p Mdn.p Mdn.p
WUE
gross
(kg/kL) 22.04 >2.57 0.001 1.10 0.001 1.25 0.001 2.51 0.004
WUE
nut
(kJ/kL) 123,121 >4,347 0.001 1,813 0.001 1,607 0.001 2,134 0.004
WUE
fin
($/kL) 192.35 >10.42 0.001 21.18 0.001 27.52 0.001 38.16 0.046
“Diff.” indicates whether the difference between the method-crop category “chkn-egg” was greater than or less than the other categories.
A visible pvalue indicates a significance level <α= 0.5
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association between the method-crop categories and the frequency of irrigation (X
212
=
155.76, N = 1146, p= 0.001). The dominant frequencies were weekly and then every three
weeks for wick-mix (43%; 30%) and similarly for bed-orch (40%; 33%). For chkn-egg areas the
most dominant two frequencies were weekly and then daily (34%; 31%). Whereas the most
dominant two frequencies were daily and then weekly for bed-mixed (46%; 31%) and for
raised-mixed (50%; 40%). This finding supports the only other published scientific research on
wicking beds, by Semananda et al. [38], who found wicking beds to be significantly more
labour efficient than precision surface irrigation. While growing seedlings, their experimental
wicking beds could go up to 4 weeks without irrigation, and with mature tomato plants could
go 1–2 weeks without irrigation [38].
The overall inputs and outputs of each of the main method-crop categories are displayed in
Table 5. The method-crop categories with the lowest median input requirements were bed-
orch (labour), chkn-egg (total water) and bed-orch (ongoing-costs). The highest median input
requirements were wick-mixed (labour), bed-mixed (total water) and raised-mixed (costs).
The method-crop categories with the greatest median returns were raised-mixed (yield and
retail value), while the lowest median returns were from bed-mixed (yield) and bed-orch (retail
value).
Financial sustainability
Setup costs and ongoing costs. While the online survey did collect information on
respondents’ entire food garden setup costs, information was not collected on the setup costs
of individual growing areas. The setup costs for the method-crop areas were therefore calcu-
lated based on a combination of additional investigation into the typical retail costs of compo-
nent parts, together with detailed costing obtained (in follow-up communications) from at
least five participants per method-crop category. Time requirements and cost of labour were
not included in these calculations. Some differences were found among the setup costs for the
five main method-crop categories. Bed-mixed had the lowest average setup cost (AUS$18.91/
m
2
), followed by bed-orch areas (AUS$22.73/m
2
). Table 6 presents an overview of the various
costs, value and the calculated time to break even for each dominant method-crop. For a
breakdown of the calculated setup cost details and assumptions, please refer to S5 Table. It
should be noted that these costs include the retail costs of purchasing all the separate parts, and
these figures could change with the use of second-hand or salvaged materials. Economies of
scale have also not been fully considered, and the total setup costs of different areas may
Table 5. A comparison of the median input, output and WUE results of each of the five main method-crop categories.
Method-crop Category bed-orch bed-mixed chkn-egg raised-mixed wick-mixed
Median labour (mins) /per m
2
/ 30 days 49 8 16 18
Median total water (W) (L) / per m
2
/ 30 days 51 99 993 95
Median irrigation (L) / per m
2
/ 30 days 521 9 52 52
Median costs (+ mains water)/ per m
2
/ 30 days $0.02 $0.51 $0.77 $0.82 $0.60
Median yield (kg) / per m
2
/ 30 days 0.19 0.17 0.25 0.39 0.27
Median retail value AUS$ / per m
2
/ 30 days $0.85 $2.25 $2.19 $5.32 $3.48
Median net position AUS$ / per m
2
/ 30 days $0.80 $1.61 $1.04 $2.18 $1.86
Median WUE
gross
(kg/kL) 2.57 1.10 22.04 1.25 2.51
Median WUE
nut
(kJ/kL) 4,347 1,813 123,121 1,607 2,134
Median WUE
fin
(AUS$/kL) $10.42 $21.18 $192.35 $27.52 $38.16
Median size (m
2
) 10.0 15.0 10.0 12.0 5.0
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change according to total size and intensity of use. This is clearly an area that warrants future
research.
A series of Kruskal-Wallis tests were used to look for statistical differences among the
method-crop categories. The median break-even time for all the categories was less than five
years, and although the initial Kruskal-Wallis test returned a significant result with differences
among the method-crop categories (H(4) = 11.10, p= 0.025, ε
2
= 0.14, N = 80), the follow on
post hoc Dunn’s test with the Bonferroni correction did not identify any significant pairwise
comparisons.
Interestingly, unlike the misperception on time spent, when comparing the estimated aver-
age monthly garden costs from the initial survey and the average reported monthly garden
costs, no significant difference was found. The EG participants were far more accurate estimat-
ing their costs than their time. Individually, bed-orch areas again had the lowest median ongo-
ing costs (including the cost of mains water) (AUS$0.02/m
2
/30 days), followed by bed-mixed
(AUS$0.51/m
2
/30 days) and wick-mixed (AUS$0.60/ m
2
/30 days). There were significant dif-
ferences in the ongoing costs and the labour of the method-crop categories, with bed-orch
areas having much lower ongoing costs and labour requirements than the other four method-
crop categories. The differences in ongoing costs AUS$/m
2
/30 days were (H(4) = 27.24,
p= 0.001, ε
2
= 0.347 (relatively strong)) and the differences in labour hours/m
2
/30 days were
(H(4) = 21.53, p= 0.001, ε
2
= 0.271 (relatively strong)), with post hoc test results presented in
Table 7.
The EG survey respondents reported contributing similar proportions of home-grown food
to their households as South Australian households did 40 years ago, according to a compara-
ble question from a 1975 survey by Halkett [39] (Table 8). The main difference between these
two sets of results lies in the category ‘Less than 5%’. We expect that this is due to our having
two additional response categories, ‘Unsure’ and ‘break even’, both of which are similar to the
‘Less than 5%’ category. Interestingly, the total proportion of respondents confident that they
offset a reasonable proportion (>5%) of their fruit and vegetable bill remains similar between
Table 6. A comparison of the related costs and time required to break even for each of the five main method-crop categories.
Method-
crop
Average setup cost ($
per m
2
)
Median monthly costs (+ mains
water) ($ per m
2
)
Median monthly retail
value ($ per m
2
)
Min–Max
(years)
# “never” break
even (%)
Median time until break
even (years)
Bed-orch $22.73 $0.02 $0.85 0.42–62.1 3 (15%) 2.0
Bed-mixed $18.91 $0.51 $2.25 0.04–11.70 4 (17%) 0.5
Chkn-egg $121.46 $0.62 $1.87 0.39–38.00 3 (19%) 4.6
Raised-
mixed
$106.09 $0.74 $5.27 0.91–7.90 5 (28%) 1.1
Wick-
mixed
$222.82 $0.60 $3.48 0.76–58.00 1 (11%) 2.0
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Table 7. Results from comparisons in ongoing costs and labour requirements among the method-crop categories.
Variable (m
2
/30 days) Dunn’s test with Bonferroni correction
Bed-orch Chkn-egg Bed-mixed Raised-mixed Wick-mixed
Mdn.Diff.Mdn.p Mdn.p Mdn.p Mdn.p
Ongoing costs ($) 0.02 <0.62 0.003 0.51 0.004 0.74 0.001 0.60 0.004
Labour (mins) 4 <8 0.021 9 0.018 16 0.001 18 0.007
“Diff.” indicates whether the difference between the method-crop category “bed-orch” was greater than or less than the other categories.
A visible pvalue indicates a significance level <α= 0.5
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the current cohort (38%) and that of the 1975 study (34%). Therein lies a potential opportunity
for new approaches or guidance to help increase the productivity and resource efficiency of
home food gardens, and thus increase the contribution of home-grown food in South
Australia.
“Breaking-even” and returns per hour. By considering each garden’s reported setup
cost, average monthly cost and average monthly retail value of the food produced, it was possi-
ble to calculate the length of time until each entire garden re-couped associated costs and
would theoretically “break even”. Note that the setup costs for each garden were based on the
gardener’s estimated online survey response. Table 9 displays the length of time to break even
for the EG gardens, while Fig 6 displays the setup, one-year and five-year net positions of the
four garden size categories.
On a more day-to-day level, the net returns (retail value minus costs including the cost of
mains water) per hour invested by the EG participants ranged from AUS-$3.73 to AUS$87.76
with a median of AUS$9.91 (N = 34). Fig 7 displays the full range of net returns and lines for
comparison at AUS$5 per hour and AUS$30 per hour net returns. A line indicating the cur-
rent Australian minimum wage for full-time work (AUS$18.93/hr) has also been included
[40].
The EG net return and the area under production can also be compared with the net return
and garden area per person as modelled by Ward and Symons [33] (Fig 8). Ward and Symons
[33] used a two-stage linear programming model to optimise the theoretical net return of UA
as based on home food gardens contributing to the dietary requirements of a household. It
should be noted that the only inputs costed by Ward and Symons (25) was fertiliser and water,
therefore their modelled results may be lower if taking all ongoing costs into account.
Wise [19] hypothesised that approximately 16% of Australian food gardeners were produc-
ing yields worth more than AUS$250 per year and thus achieving reasonable financial savings.
Table 8. Comparison of the Edible Gardens survey question on households saving money and a similar question from the 1975 Adelaide study by Halkett [39] on
household fruit and vegetable requirements.
Edible Gardens Project Study by Halket (22)
Percentage of weekly household fruit and vegetable
budget
Count % of total
(n = 374)
% of total
(n = 430)
Count Percentage of household fruit and vegetable
requirements
None 79 21% 22% 96 None
Less than 5% 52 14% 46% 198 Less than 5%
6–25% 62 17% 21% 91 6–25%
26–75% 64 17% 8% 33 26–75%
Over 75% 14 4% 5% 12 Over 75%
Unsure 55 15% - - No comparable category
Break even 48 13% - - No comparable category
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Table 9. Percentage distribution of the calculated time to break even.
Years to break even No. of gardens %
<1 year 10 29
1–5 years 12 35
5–10 years 3 9
>10 years 3 9
Never (costs greater than retail value return) 6 18
Total: 34 100
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A more recent study by Algert et al. [9] investigated the potential cost savings and nutritional
improvements of home grown food for low-income households. Algert et al. [9] found that
their eight home gardeners saved an average of USD$339 over the 4-month summer season.
Comparing results is challenging as their gardeners’ ongoing costs were estimated but not
explicitly reported, nor was any labour data recorded. Whereas Codyre et al. [10] reported an
average cost of USD$10.82/kg, compared with the AUS$8.07/kg of the EG gardens. The EG
garden data reveals slightly different results on how many gardeners were set to save money.
Even when taking the ongoing costs of each garden into account (but not the initial setup
costs), 79% of the 34 EG gardens were calculated to save an excess of more than AUS$250 per
year. The numbers were verified by a subset of long-term participants who had collected more
Fig 6. A progression of the setup, 1-year and 5-year net position of the different garden size categories (N = 34).
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Fig 7. Net return (m
2
/30 days) plotted against labour (m
2
/30 days). Additional lines represent the lower (AUS$5/
hour), Australian minimum wage (AUS$18.93) and upper (AUS$30/hour) bounds of the returns for labour.
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than 240 days of data (N = 14), with 71% of these saving an excess of more than AUS$250 per
year. While this finding is quite positive for the large numbers of households looking to save
money, the full cost of setting up a food garden has not yet been considered.
According to combined participant and author calculations of setup costs, 65% of the EG
gardens would overcome both their initial setup and ongoing costs and break even within five
or less years. Assuming garden assets are relatively long-lived (much longer than five years),
after the setup costs have been paid off, it should be easier to gain reasonable savings for a con-
siderable time. This calculation was based on the average retail value of the food produced by
the EG gardens, according to prices from two supermarket retailers. The time to break even
would be considerably shortened if the calculation was instead based upon the retail value of
certified organic produce as sold at supermarkets. Indeed, a brief comparison of 58 fruits and
vegetables from two supermarket retailers (prices collected August 2019) found the certified
organic prices to be on average 93% higher. In Australia, the price of organic foods is reported
as the main barrier to purchasing [41]. Thus, for those households desiring to eat organic food
but struggling to afford the higher prices, producing their own food as organically as possible
is one way to access a cheaper alternative.
The median return rate on labour invested in the gardens was AUS$9.91 per hour. If gar-
dening is taken as a simple hobby, with time spent as discretionary then the median hourly
return could be considered reasonable. Alternatively, for people who classify themselves as
‘under employed’ (defined as those who wish to work more, and are available to work more
paid hours but who currently work less than 35 hours per week [42]), the labour data on the
time requirements of different food garden related activities can help to account for the oppor-
tunity cost for someone considering whether to grow food as a form of alternative or supple-
mentary employment. As of late 2018, South Australia had an unemployment rate of 5.2%, an
underemployment rate of 9.6% and a youth unemployment rate of 16.5% [43], thus this is
clearly an area which would benefit from closer study.
Impact of applying a wage rate. Comparatively, applying a fixed wage rate (such as mini-
mum wage) to the labour required to produce food, and adding it to the input costs, substan-
tially alters the financial viability of the EG gardens. If gardens can only reliably deliver
Fig 8. Comparison between the net return curve modelled by Ward & Symons (2017) and the upper net return
envelope of those EG gardens with more than 3 months of data (N = 28).
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produce by valuing labour at less than minimum wage, then this needs to be considered when
evaluating any widespread contribution to future food security. Moreover, the cost of labour
becomes a critical factor when considering the viability of potential commercial UA opportu-
nities. One of the early studies from 1980 by Stephens et al. [7] found that even when the mini-
mum wage of the time (USD$3.10/hr) was applied, the two gardens in their study both still
returned net profits. It is interesting to note, however, that even then the labour costs con-
sumed 19–51% of the retail value of the harvested crops and made up between 50–72% of the
total garden costs, and the cost of urban water–for example–has risen considerably since 1980.
In a more recent study, even before accounting for labour, the gardens involved in CoDyre
et al.’s [10] study were already calculated to run at a loss. The retail value of their harvests was
based on the average price of 1kg of mixed vegetables with the same crop proportions as the
average harvest. This resulted in an average value of USD$4.58/kg. Once the minimum wage
rate of the area was applied, labour costs constituted 70% of their total costs and were more
than double the other ongoing costs of the gardens [15].
The EG gardens median return rate of AUS$9.91 per hour is approximately half of the cur-
rent Australian minimum wage for full time work (AUS$18.93 per hour) [40]. When applying
AUS$18.93 to the labour invested by the EG gardeners, the majority (82%) of gardens were in
a net loss position (Fig 9) on an ongoing basis, even without taking the initial setup cost into
account.
However, even once the minimum wage rate was applied, 18% of the EG gardens did pro-
duce enough food to effectively pay for their time at a legal wage rate. A further 12% of the EG
gardens were out of pocket, but by less than AUS$100, thus it could be argued that it would
not take much of either an increase in productivity or a reduction in labour, to shift those gar-
dens into a positive net position. It is important to note that these results are based on our
monitoring and assessment of the relatively simple forms of urban food production involved
in the EG project, i.e. methods commonly utilising hand tools and manual irrigation. A com-
mercial urban food business may be organised differently and could potentially use a combina-
tion of labour-saving techniques and/or tools. Yet, the wage rate is likely become one of, if not
the greatest, expense for commercial UA–even without considering the possibility of casual
labour rates (an additional loading of 25% in Australia [40]), or whether a UA business is able
to sell their produce at retail or wholesale prices. This is an important consideration as new
Fig 9. The impact of applying a wage rate to invested labour and how this impacts the net position of the EG
gardens.
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UA businesses are more likely to arise from serious home food gardens, rather than from any
downscaling by commercial horticulturalists or farmers.
Implications of the findings
To frame these results in a practical way the following section presents a series of recommen-
dations, questions and comparative scenarios. To begin with, we recommend current and
future food gardeners ask themselves two basic questions. The first is, “What do you want to
get out of growing food?” This question relates to previously presented results from the online
survey questions asking people about their key motivations [4]. The top five motivations of
home gardeners were: 1) Producing fresh tasty produce, 2) Enjoyment, 3) Health reasons, 4)
Natural connection and 5) To save money [4]. Indeed, saving money was a motivation for 59%
of the EG survey respondents. The second question is, “What are your biggest challenges or
limitations?” This question relates to our previously presented challenges [23]. The top six ini-
tial challenges for home gardeners when they first began producing food were: 1) Lack of time,
2) Unsuitable conditions, 3) Lack of knowledge, 4) Lack of space, 5) Cost and 6) Water issues
[23]. Both the motivations and challenges of an individual gardener (or household) can be
used to help guide their choices in determining which method-crop category (or combination
of categories) would best suit them.
A similar perspective was investigated in a Tasmanian study by Kirkpatrick and Davison
[21], who surveyed whether garden setups, gardener practices and gardener motivations were
statistically related to each other. However, they found only two sets of “garden”, “practice”
and “motivation” to have positive associations (5). The approach presented here differs by our
attempt to purposefully better relate gardener motivations and challenges to their garden set-
ups from the outset (via the selection of suitable method-crop categories). Fig 10 summarises
and ranks the input and output results of the five dominant method-crop categories on a per
square metre per 30-day basis.
No single method-crop category presented well across all inputs and outputs. There were
different trade-offs for each. It is also important to note that the median size of each category is
different–thus changing the results for entire or larger method-crop areas.
Recommendations to improve garden results. Gardener experience (as typically mea-
sured in years), is an interesting variable. Years of experience has been found to have a positive
relationship with gardening efficiency [10], and both a positive [8] and no measurable relation-
ship, with garden productivity [9]. We would argue that both knowledge and skills–and not
just the number of years–are needed to apply and learn from food gardening experiences.
Using the results from the EG project, we make a series of recommendations which may con-
tribute to food gardener knowledge and thus potentially improve productivity and resource
efficiency. This section includes insights into diversification, time saving and smart irrigation
practices.
As discussed briefly above, some diversification of cultivation techniques may help to even
out the input requirements and outputs of a single garden, provide more consistent year-
round harvests and produce the most even (and diverse) combination of vegetables, fruits,
herbs, and animal products (if desired). This combination of food types can also help balance
the nutritional energy content and retail value return of an entire food garden (Fig 11).
In some cases, different techniques can co-exist in the same area (such as chickens under
fruit trees). And yet, too many cultivation techniques and gardening approaches in a single
garden was found to have a negative relationship with labour, yield and retail value per unit
area. Potentially, utilising too many different techniques and approaches results in gardeners
spreading themselves too thinly; at least some degree of specialisation is helpful. It has also
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been previously suggested that some diversification of food production can be more resilient
overall in the face of natural disasters or climate-related challenges [44–46]. Diversification
can also be implemented via crop type or crop variety choices. One way to both save additional
Fig 10. Comparison and ranking of the five main method-crop categories by their inputs and outputsper square
metre per 30 days. The number of stars represent the best performers in that category.
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Fig 11. The nutritional content and retail value of the four food types.
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money and to help extend the harvest period is to produce early- and/or late-season crops.
Producing crops before or after their peak period, when they are at their most expensive to
purchase from supermarkets can provide additional savings. For households who do not want
to spend, or do not have the money to spend significant amounts of money setting up new
food garden areas, several ways to reduce the initial outlay were reported by the EG survey
respondents. Materials or parts can be purchased second-hand, plants can be grown from seed
instead of being bought as seedlings, and materials can be salvaged or found for free.
Food gardening does not appear to take as much time as was initially perceived by the par-
ticipants. But for gardeners who consider themselves “time-poor”, the two main activities to
focus on for time-saving are harvesting (31% of all time spent) and irrigating (20% of all time
spent). Developing quicker harvesting methods and planning different crops with rapid har-
vesting in mind can help to reduce labour requirements. It should also be noted that although
the bed-orch areas required the lowest amount of ongoing labour, newly planted trees do
require time to establish their roots before any reasonable harvest can be expected.
In addition to harvesting, irrigation is a core component of food production. Irrigation
took 20% of the total labour of the EG gardens. This time did not account for the durations
that irrigation systems were running, only physical time spent watering. Half of that time
(246.4 hours) was spent irrigating with mains water. If the minimum wage of AUS$18.93
was applied (as it would be in a commercial situation), 246.4 hours represents AUS$4,664.45
worth of labour being spent irrigating with mains water. With the total reported volume of
mains irrigation being 464kL, the labour cost of that water is on average, AUS$9.40 per kL.
Therefore, although the cost of mains water was (for the most part) low relative to the retail
value of the produce, the average cost of mains water (already considerably higher than that
paid by commercial farmers in South Australia [47]) becomes multiplied by four once the
cost of labour for irrigation is taken into account. Alternative water sources such as rainwater,
bore or greywater may have lower levelized costs than mains water [16], but are subject to the
same potential labour costs for irrigation. Implementing automatic irrigation systems can sig-
nificantly reduce the amount of time spent irrigating, and thus also reduce potential labour
costs.
We recommend gardeners use water flow meters to increase their understanding of the vol-
umes of water being applied to different areas of their gardens. And finally, we encourage
those interested in making the most of their irrigation, particularly collected rainwater, to
learn about their soil’s capacity to store water [48,49]. For gardeners wanting to reduce the
worry or inconvenience of managing a consistent watering regime, they can use either an auto-
matic irrigation system (preferably one with flow meters built-in), install wicking beds or plant
in-ground fruit trees, both of which are method-crop categories that require less frequent
watering. If WUE does become a key metric for measuring the success of UA–this will help
shift the focus from pure productivity, to a more inherently sustainable focus of food, water
and land.
Limitations. This project required considerable time and supporting infrastructure, par-
ticularly creating the online web interface and the development, customisation and posting of
the data collection toolkits. If additional resources were available, we would recommend stron-
ger engagement of community gardeners in the garden data collection phase, targeted data
collection on the less common method-crop categories, and specific reporting of setup costs
for all individual areas. Due to the non-random selection of in-field garden data collection par-
ticipants it is also possible that these results are not representative of gardens in South Austra-
lia. And finally, although the sample size of 34 gardens is relatively small, the multiple areas
measured per garden resulted in an effective sample size of 93 garden areas.
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Conclusion and future directions
This study provides a detailed insight into the form and function of existing home food gar-
dens. The overall contribution of this research is threefold. Firstly, this research contributes a
rigorous methodology for collecting data on existing urban food gardens through the applica-
tion of citizen science, replicable measurement methods, and consistent units of comparable
values, garden area categories and WUE efficiency equations. Secondly, the in-depth range of
key input and output data reported in this paper form part of a baseline dataset, which will be
available open-source for external use, analysis, comparisons and theoretical modelling. And
finally, the analysis and comparisons presented in this paper provide detailed insights and
practical implications for urban food gardeners, local government and those interested in the
larger future potential of UA within the vision of urban sustainability.
We strongly encourage further research into the productivity, resource efficiency and finan-
cial viability of urban food gardens, including the prospects of households saving money by
growing food (and implications for food security if they do not). Continued collection of input
and output data will further contribute to the development of consistent and comparable base-
line data on whole gardens and the method-crop categories. Investigation into the less com-
mon method-crop categories, for example, the keeping of bees, producing food in pots and
planters, vertical gardens and assessment of multi-purpose or layered garden areas is also rec-
ommended–with a mind to embrace, rather than over-simplify, the rich diversity of existing
urban food production. Appropriate use of scale and diversification may have implications for
planning and optimising effective UA design. The water-use and water-use efficiency of urban
food gardens remain key areas of interest. The development of simple technology, such as flex-
ible water meters suitable for different water sources (with varying flow rates and pressures)
and more automatic irrigation systems with built-in flow meters, would help increase the
understanding and awareness of water use in urban food gardens.
Supporting information
S1 Table. A summary of the total combined results from the EG project.
(PDF)
S2 Table. An overview of the five main method-crop garden area categories of the EG project.
(PDF)
S3 Table. The labour invested in each garden activity per square metre per 30 days for the
five main method-crop categories.
(PDF)
S4 Table. A complete list of reported crops and their harvest weights.
(PDF)
S5 Table. A breakdown of the calculated setup costs for each method-crop category,
including details and assumptions.
(PDF)
S1 Fig. A comparison of the percentages of total time spent on different garden-related activities.
(PDF)
Acknowledgments
We are grateful to all of the gardeners for their participation and dedication to this study.
Thanks go to Dr Hayley Tindle for project support and to Andrew Royal for design and
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support of the Edible Gardens interactive web interface. Supporting organisations were the
University of South Australia, the City of Marion, the City of Salisbury, the Adelaide and
Mount Lofty Ranges Natural Resources Management Board and the Department for Environ-
ment and Water.
Author Contributions
Conceptualization: Georgia Csortan, James Ward, Philip Roetman.
Formal analysis: Georgia Csortan, James Ward, Philip Roetman.
Funding acquisition: Philip Roetman.
Investigation: Georgia Csortan, James Ward, Philip Roetman.
Methodology: Georgia Csortan, James Ward, Philip Roetman.
Project administration: Georgia Csortan.
Software: James Ward.
Supervision: James Ward, Philip Roetman.
Visualization: Georgia Csortan.
Writing – original draft: Georgia Csortan.
Writing – review & editing: Georgia Csortan, James Ward, Philip Roetman.
References
1. Burton P, Lyons K, Richards C, Amati M, Rose N, Desfours L, et al. Urban food security, urban resil-
ience and climate change. Gold Coast, Australia: National Climate Change Adaption Research Facil-
ity, 2013 17 July 2015. Report No.; https://www.nccarf.edu.au/sites/default/files/attached_files_
publications/Burton_2013_Urban_food_security.pdf.
2. Deelstra T, Girardet H. Urban agriculture and sustainable cities. In: Bakker N, Dubbeling M, Gu¨ndel S,
Sabel-Koshella U, de Zeeuw H, editors. Growing cities, growing food: Urban agriculture on the policy
agenda. Feldafing, Germany: Sentralstelle fu¨r Erna
¨hrung und Landwirtschaft (ZEL); 2000. p. 43–66.
3. Farrell B. A Place for Food Within Sustainable Urban Masterplanning. In: Bishop J, editor. Building Sus-
tainable Cities of the Future. Cham, Switzerland: Springer; 2017. p. 117–40.
4. Pollard G, Roetman P, Ward J, Chiera B, Mantzioris E. Beyond Productivity: Considering the Health,
Social Value and Happiness of Home and Community Food Gardens. Urban Science. 2018; 2(4):97.
https://doi.org/10.3390/urbansci2040097.
5. Cleveland D, Orum T, Ferguson N. Economic value of home vegetable gardens in an urban desert envi-
ronment. Horticultural Science. 1985; 20(4):694–6. Retrieved from http://es.ucsb.edu/faculty/cleveland/
CV/1985gdnec.pdf.
6. Stall WM. Economic value of a home vegetable garden in South Florida. Proceedings of the Florida
State Horticultural Society. 1979; 92:213–4. Retrieved from http://fshs.org/proceedings-o/1979-vol-92/
213-214%20(STALL).pdf.
7. Stephens JM, Carter L, Van Gundy C. Economic value of vegetables grown in North Florida gardens.
Proceedings of the Florida State Horticultural Society. 1980; 93:70–2. Retrieved from http://fshs.org/
proceedings-o/1980-vol-93/70-72%20(STEPHENS).pdf.
8. Utzinger J, Connolly H. Economic value of a home vegetable garden. HortScience. 1978; 13(2):148–9.
Retrieved from: http://agris.fao.org/agris-search/search.do?recordID=US7875666.
9. Algert SJ, Baameur A, Diekmann LO, Gray L, Ortiz D. Vegetable Output, Cost Savings, and Nutritional
Value of Low-Income Families’ Home Gardens in San Jose, CA. Journal of Hunger & Environmental
Nutrition. 2016; 11(3):328–36. https://doi.org/10.1080/19320248.2015.1128866.
10. Codyre M, Fraser E, Landman K. How does your garden grow? An empirical evaluation of the costs and
potential of urban gardening. Urban Forestry & Urban Greening. 2014; 14(1):72–9. https://doi.org/10.
1016/j.ufug.2014.11.001.
PLOS ONE
The productivity, resource efficiency and financial savings of home food gardens
PLOS ONE | https://doi.org/10.1371/journal.pone.0230232 April 14, 2020 24 / 26

11. Conk SJ, Porter CM. Food Gardeners’ Productivity in Laramie, Wyoming: More Than a Hobby. Ameri-
can journal of public health. 2016; 106(5):854. https://doi.org/10.2105/AJPH.2016.303108 PMID:
26985621
12. Gittleman M, Jordan K, Brelsford E. Using citizen science to quantify community garden crop yields. Cit-
ies and the Environment (CATE). 2012; 5(1):4. Retrieved from http://digitalcommons.lmu.edu/cate/
vol5/iss1/4/.
13. Vitiello D, Nairn M, Planning P. Community Gardening in Philadelphia: 2008 Harvest Report. Pennsyl-
vania, USA: Penn Planning and Urban Studies, University of Pennsylvania, 2009; Retrieved from:
http://www.farmlandinfo.org/sites/default/files/Philadelphia_Harvest_1.pdf.
14. Mok H-F, Williamson VG, Grove JR, Burry K, Barker SF, Hamilton AJ. Strawberry fields forever? Urban
agriculture in developed countries: a review. Agronomy for Sustainable Development. 2014; 34(1):21–
43. http://dx.doi.org/10.1007/s13593-013-0156-7.
15. Wortman S, Lovell S. Environmental Challenges Threatening the Growth of Urban Agriculture in the
United States. Journal Of Environmental Quality. 2013; 42(5):1283–94. https://doi.org/10.2134/
jeq2013.01.0031 PMID: 24216408
16. Pollard G, Ward J, Roetman P. Water Use Efficiency in Urban Food Gardens: Insights from A System-
atic Review and Case Study. Horticulturae. 2018; 4(3):27. https://doi.org/10.3390/
horticulturae4030027.
17. Taylor J, Lovell S. Urban home food gardens in the Global North: research traditions and future direc-
tions. Agriculture and Human Values. 2013; 31(2):285–305. https://doi.org/10.1007/s10460-013-9475-
1.
18. Ward J, Ward P, Mantzioris E, Saint C. Optimising diet decisions and urban agriculture using linear pro-
gramming. Food Security. 2014; 6(5):701–18. https://doi.org/10.1007/s12571-014-0374-0.
19. Wise P. Grow your own: the potential value and impacts of residential and community food gardening.
Canberra, ACT, Australia: The Australia Institute, 2014 59; Retrieved from: http://www.tai.org.au/sites/
defualt/files/PB%2059%20Grow%20Your%20Own.pdf.
20. Pourias J, Duchemin E, Aubry C. Products from urban collective gardens: food for thought or for con-
sumption? Insights from Paris and Montreal. Journal of Agriculture, Food Systems and Community
Development. 2015; 5(2):1–25. http://dx.doi.org/10.5304/jafscd.2015.052.005.
21. Kirkpatrick JB, Davison A. Home-grown: Gardens, practices and motivations in urban domestic vegeta-
ble production. Landscape and Urban Planning. 2018; 170:24–33. https://doi.org/10.1016/j.
landurbplan.2017.09.023.
22. Pollard G, Roetman P, Ward J. The case for citizen science in urban agriculture research. Future of
Food: Journal on Food, Agriculture and Society. 2017; 5(3):9–20. Retrieved from: http://www.fofj.org/
index.php/FOFJ/article/view/9/18.
23. Pollard G, Ward J, Roetman P. Typically Diverse: The Nature of Urban Agriculture in South Australia.
Sustainability. 2018;10(4). https://doi.org/10.3390/su10040945.
24. Australian Bureau of Statistics (ABS). Australian Demographic Statistics, Sep 2018, cat no. 3101.0. In:
Australian Bureau of Statistics (ABS), editor. Canberra, ACT, Australia: Commonwealth of Australia;
2019.
25. Bureau of Meteorology. Climate Classification Maps [Internet]. Commonwealth of Australia. 2016 [cited
12th April 2019]. Available from: http://www.bom.gov.au/jsp/ncc/climate_averages/climate-
classifications/index.jsp.
26. Bureau of Meteorology. Adelaide (Kent Town) 1977–2016: Climate Statistics for Australian Locations
Adelaide, SA: Australian Government.; 2016 [cited 2016 12 November 2016]. Available from: http://
www.bom.gov.au/climate/averages/tables/cw_023090_All.shtml.
27. Ward J, Ward P, Saint C, Mantzioris E. The Urban Agriculture Revolution: Implications for Water Use in
Cities. Water: Journal of the Australian Water Association. 2014; 41:Retrieved from http://search.
informit.com.au/documentSummary;dn=604047792664059;res=IELAPA.
28. Food Standards Australia New Zealand. NUTTAB 2010 Online Searchable Database [Internet]. 2017
[cited 1 August 2015]. Available from: http://www.foodstandards.gov.au/science/monitoringnutrients/
nutrientables/nuttab/pages/default.aspx.
29. Bureau of Meteorology. Climate Data Online [Internet]. Commonwealth of Australia. 2018 [cited 14th
June 2018]. Available from: http://www.bom.gov.au/climate/data/index.shtml.
30. Rea L, Parker R. Designing and Conducting Survey Research: A Comphrehensive Guide. 4th ed.
United States: John Wiley & Sons Inc; 2014. 360 p.
31. Zainuddin Z, Mercer D. Domestic Residential Garden Food Production in Melbourne, Australia: a fine-
grained analysis and pilot study. Australian Geographer. 2014; 45(4):465–84. http://dx.doi.org/10.1080/
00049182.2014.954299.
PLOS ONE
The productivity, resource efficiency and financial savings of home food gardens
PLOS ONE | https://doi.org/10.1371/journal.pone.0230232 April 14, 2020 25 / 26

32. Algert SJ, Baameur A, Renvall MJ. Vegetable Output and Cost Savings of Community Gardens in San
Jose, California. Journal of the Academy of Nutrition and Dietetics. 2014; 114(7):1072–6. https://doi.
org/10.1016/j.jand.2014.02.030 PMID: 24751664
33. Ward J, Symons J. Optimising crop selection for small urban food gardens in dry climates. Horticulturae.
2017; 3(2):33. https://doi.org/10.3390/horticulturae3020033.
34. Dorling Kindersley Australasia. Small Space, Big Harvest: Inventive ideas for growing your own food.
Warren P, Adams R, King N, editors. Melbourne, Victoria, Australia: Dorling Kindersley Australasia;
2015. 255 p.
35. Morrow R, Allsop R. Earth User’s guide to Permaculture. 2nd ed. Pymble, New South Wales, Australia:
Kangaroo Press; 2006. 264 p.
36. Mekonnen M, Hoekstra A. The green, blue and grey water footprint of crops and derived crop products.
Hydrology and Earth System Sciences. 2011; 15(5):1577–600. https://doi.org/10.5194/hess-15-1577-
2011.
37. Mekonnen M, Hoekstra A. A Global Assessment of the Water Footprint of Farm Animal Products. Eco-
systems. 2012; 15(3):401–15. https://doi.org/10.1007/s10021-011-9517-8.
38. Semananda NP, Ward JD, Myers BR. Evaluating the Efficiency of Wicking Bed IrrigationSystems for
Small-Scale Urban Agriculture. Horticulturae. 2016; 2(4):13. http://dx.doi.org/10.3390/
horticulturae2040013.
39. Halkett IPB. The Quarter-acre block: the use of suburban gardens. Canberra City: Australian Institute
of Urban Studies; 1976.
40. Fair Work Ombudsman. Minimum Wages. In: Fair Work Ombudsman, editor. Canberra, ACT, Austra-
lia. Retrieved from: <file:///C:/Users/User/Downloads/Minimum-wages.pdf>: Australian Government;
2018. p. 1.
41. Andrew Lawson, Cosby A, Bez N, Williams J. Australian Organic Market Report. Nundah, QLD, Austra-
lia: Research by the Australian Centre for Agriculture and Law and the Mobium Group, 2017 ISSN
2207–5135; https://d3n8a8pro7vhmx.cloudfront.net/ofa/pages/74/attachments/original/1492724931/
AOMR-2017-REPORT.pdf?1492724931.
42. Sabto M. Can Urban Farms Feed City and Soul? ECOS Magazine. 2013 (183). Retrieved from: http://
www.ecosmagazine.com/?paper=EC13118.
43. Australian Bureau of Statistics (ABS). Labour Force, Australia, Sep 2018 cat no 6202.0. In: Australian
Bureau of Statistics (ABS), editor. Canberra, ACT: Commonwealth of Australia; 2018.
44. Deppe C. The Resilient Gardener: Food production and self-reliance in uncertain times. White River
Junction, Vermont, USA: Chelsea Green Publishing; 2010.
45. Lin BB. Resilience in Agriculture through Crop Diversification: Adaptive Management for Environmental
Change. BioScience. 2011; 61(3):183–93. https://doi.org/10.1525/bio.2011.61.3.4.
46. Reidsma P, Ewert F. Regional Farm Diversity Can Reduce Vulnerability of Food Production to Climate
Change. Ecology and Society. 2008;13(1). https://doi.org/10.5751/es-02476-130138.
47. Australian Bureau of Statistics (ABS). Water Account Australia 2014–2015, cat no 4610.0. In: Australian
Bureau of Statistics (ABS), editor. Canberra, ACT: Commonwealth of Australia; 2016.
48. Berardy A, Chester MV. Climate change vulnerability in the food, energy, and water nexus: Concerns
for agricultural production in Arizona and its urban export supply. Environmental Research Letters.
2017;12(3). https://doi.org/10.1088/1748-9326/aa5e6d.
49. Stirzaker R. Out of the scientist’s garden: A story of water and food. Melbourne, AUS: CSIRO Publish-
ing; 2010.
PLOS ONE
The productivity, resource efficiency and financial savings of home food gardens
PLOS ONE | https://doi.org/10.1371/journal.pone.0230232 April 14, 2020 26 / 26