Journal of Cleaner Production 225 (2019) 913e927
lable at ScienceDirectContents lists avaiJournal of Cleaner Production
journal homepage: www.elsevier .com/locate/ jc leproManaging Crop tradeoffs: A methodology for comparing the water
footprint and nutrient density of crops for food system sustainability
Jessica Sokolow a, b, *, Gina Kennedy a, Simon Attwood a, c
a Bioversity International, Via dei Tre Denari, 472/a, 00054, Maccarese (Fiumicino), Italy
b School of Human Ecology, Cornell University, 294 Caldwell Hall, Ithaca, NY, 14853-2602, USA
c WWF-Singapore, 354, Tanglin Road, Tanglin International Centre, 247672, Singaporea r t i c l e i n f o
Article history:
Received 21 February 2018
Received in revised form
28 February 2019
Accepted 5 March 2019
Available online 9 March 2019
Keywords:
Sustainable development goals (SDGs)
Nutrient density
Water footprint
Sustainable diets
Freshwater
Sustainable agriculture* Corresponding author. Bioversity International,
00054, Maccarese (Fiumicino), Italy.
E-mail address: js3263@cornell.edu (J. Sokolow).
https://doi.org/10.1016/j.jclepro.2019.03.056
0959-6526/© 2019 The Authors. Published by Elseviera b s t r a c t
The relationship between human nutrition and the use of available resources to feed the planet's
growing population demands greater attention from decision makers at all levels of governance. In-
dicators with dual environmental sustainability and food and nutrition security goals can encourage and
measure progress towards a more sustainable food system. This article proposes a methodology that
supports the development of an approach to assess the water footprint of nutrient-dense foods [m3/kg].
It provides a clear explanation of the methodology, and the use of water footprint benchmark data and
corresponding United States Department of Agriculture (USDA) nutrient composition data to apply the
process. The study analyzed data for 17 grains, roots and tubers, 9 pulses, 10 nuts and seeds, 17 vege-
tables, and 27 fruits. Of these, fruits and vegetables are 85% of the bottom quartile for water footprint (i.e.,
highly water efficient) and 100% of the top quartile for nutrient-density (i.e., very nutrient dense).
Spinach is a clear winner, with a very high nutrient-density and low water footprint. The article proposes
that this approach can help to establish broad typologies to guide decision makers in distinguishing
between win-win, win-lose, and lose-lose scenarios of natural resource use and nutrition security. This
resource, if considered along with contributing social, environmental, and economic factors (e.g., local
tastes, available water resources, soil fertility, local economies) can promote a food system that offers a
diverse range of nutrient-dense foods more sustainably.
© 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND
license (http://creativecommons.org/licenses/by-nc-nd/4.0/).1. Introduction
The global community needs to urgently find ways to produce
and consume healthier diets, while ensuring that food is produced
in an environmentally and socially sustainable manner. Worldwide,
over 795 million people are undernourished and many people face
hidden hunger or micronutrient deficiencies (FAO, 2015). Further,
2.1 billion people are estimated to be overweight or obese (Ng et al.,
2014). Re-thinking food systems becomes ever more important as
the global population continues to grow in size and wealth, and by
2050 is expected to grow by over a third, or 2.3 billion people, thus
further stressing the ecosystem services upon which agriculture
relies (United Nations, 2015a). Of these ecosystem services, main-
taining both quantity and quality of freshwater is an essential piece
of the equation.Via dei Tre Denari, 472/a,
Ltd. This is an open access article uRockstro€m et al. (2009) define planetary boundaries, or
thresholds, within which humans can operate safely, including
global freshwater use. This research proposes a planetary boundary
of less than 4000 km3 yr
1 for the consumptive use of freshwater.
As of 2010, the FAO estimated that the global water withdrawal,
which includes use by agriculture, industries, and municipalities,
already reached the planetary boundary of 4000 km3 yr
1 (FAO,
2016). Agriculture is responsible for 70% of all water withdrawals
from aquifers, streams, and lakes globally, even though most agri-
cultural systems are rain-fed (FAO, 2011). Furthermore, agriculture
accounts for approximately 85% of ground water and surface water
consumption (Mekonnen and Hoekstra, 2011). Increasing food
demands and dietary changes are raising global pressures on
freshwater resources (Rockstro€m et al., 2009). Globally, cereals, and
meat are the agricultural products contributing the most to water
footprint use at 27% and 22% (Hoekstra and Mekonnen, 2012). Due
in part to these global pressures, the remaining safe operating space
for freshwater may be already committed by this agricultural de-
mand (Rockstro€m et al., 2009). The demand for freshwaternder the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
914 J. Sokolow et al. / Journal of Cleaner Production 225 (2019) 913e927resources is expected to increase by 55% between 2000 and 2050,
not considering rain-fed agriculture, and much of this increase will
be from industries outside of the agricultural sector, creating
competition for water resources (Whitmee et al., 2015). This future
climate trajectory makes the case for understanding the water
footprint of food grown even more essential. Much of the world is
expected to experience increasingly arid soils, and some regions,
such as California, will face persistent droughts (Dai, 2011). The
adequate and timely supply of water resources is important for
ensuring crop yields and agricultural efficiency, particularly in the
face of future climate uncertainties (Gustafson et al., 2016).
The Sustainable Development Goals (SDGs) have put the sus-
tainability of food systems for human and environmental health on
the international agenda. The SDGs recognize the contribution of
nutrition and freshwater resources to sustainable development,
and their role in improving the wellbeing of individuals around the
world (United Nations, 2015b). Yet, the SDGs fail to more closely
and explicitly link health and environmental initiatives, such as
food security and water resource sustainability, by establishing
common goals and targets. The difficulties around ‘generating
simple yet representative indicators that combine elements of so-
cial and economic development with metrics of environmental
health and sustainability’were highlighted by Oldekop et al. (2016)
in a paper that identified 100 key research questions for the post-
2015 development agenda. There is limited interaction among
nutrition and water use goals (and their associated targets and
indicators; Table 1). Targets 2.1 and 2.2 concern ending hunger and
malnutrition, with limited consideration of the environmental
impacts required to do so. Target 2.4 addresses the need to ensure a
more sustainable food system, including resilient agriculture
practices. The associated indicator tracks the proportion of agri-
cultural area under productive and sustainable agriculture. In
considering the complexity of the relationship between human
nutrition and environmental sustainability, indicators that take into
consideration interactions between the two factors can help map
the tradeoffs and synergies between goals and targets (Nilsson
et al., 2016; Stafford-Smith et al., 2016).
While there is international recognition for an integrated
approach to addressing health and environmental issues, the pol-
icy, scientific, and business communities still lack a consensus andTable 1
Relevant SDG goals and their associated targets and indicators.
Target
Nutrition SDG 2. End hunger, achieve food security and improved nutrition an
2.1 By 2030, end hunger and ensure access by all people, in particular
the poor and people in vulnerable situations, including infants, to safe,
nutritious and sufficient food all year round
2.2 By 2030, end all forms of malnutrition, including achieving, by 2025,
the internationally agreed targets on stunting and wasting in children
under 5 years of age, and address the nutritional needs of adolescent
girls, pregnant and lactating women and older persons
2.4 By 2030, ensure sustainable food production systems and
implement resilient agricultural practices that increase productivity and
production, that help maintain ecosystems, that strengthen capacity for
adaptation to climate change, extreme weather, drought, flooding and
other disasters and that progressively improve land and soil quality
Water Use SDG 6. Ensure availability and sustainable management of water and
6.4 By 2030, substantially increase water-use efficiency across all
sectors and ensure sustainable withdrawals and supply of freshwater to
address water scarcity and substantially reduce the number of people
suffering from water scarcityan in-depth understanding of the metrics required to assess mul-
tiple dimensions of sustainable diets and food systems (Gustafson
et al., 2016). Allen et al. (2014) gathered expert knowledge to
determine key indicators that jointly assess major interactions
relevant to both environmental sustainability and food and nutri-
tion security (Allen et al., 2014). One such interaction is the rela-
tionship between water depletion (environmental sustainability)
and the nutritional quality of food (food and nutrition security). To
assess these issues, experts proposed an indicator which measures
the water footprint of nutrient-dense foods [m3/kg]. This study
proposes an initial framework to develop and test this approach.
This paper offers a method to measure and compare the water
footprint of crops relative to their potential nutrient contribution to
the human diet. This metric aims to compare information that is
disparate from two distinct, but intersectional, fields. Some crops
may better contribute to a location's environmental health, as well
as human health, based on their water footprint and nutrient
density. Scientific investigations have been made regarding trade-
offs between multidimensional indicators (Stoessel et al., 2012;
Tilman and Clark, 2017). Previous work includes assessments of the
water footprint effects of different dietary patterns, such as
comparing vegetarian diets to those diets that incorporate animal
products (e.g., Ruini et al., 2015; Tilman and Clark, 2014; Tom et al.,
2015). Yet little work has been done to compare the water footprint
of individual foods. This research considers how to design and
implement an approach to help guide program and policy decisions
to support sustainable food systems. This analysis can serve as a
resource, in tandem with knowledge of the local environmental,
social, and economic factors, to help facilitate feasible and im-
pactful changes in food systems that promote sustainable diets.
2. Methods
2.1. Water footprint analysis
For the purpose of this paper, the measure ‘water footprint’ de-
scribes the water demand of foods and the appropriation of the
world's freshwater (MekonnenandHoekstra, 2011). According to the
Water Footprint Network, water footprint is defined as the “measure
of humanity's appropriation of fresh water in volumes of waterIndicator
d promote sustainable agriculture
2.1.1 Prevalence of undernourishment
2.1.2 Prevalence of moderate or severe food insecurity in the population, based
on the Food Insecurity Experience Scale
2.2.1 Prevalence of stunting (height for age< -2 standard deviation from the
median of the World Health Organization (WHO) Child Growth Standards)
among children under 5 years of age
2.2.2 Prevalence of malnutrition (weight for height >þ2 or < -2 standard
deviation from the median of the WHO Child Growth Standards) among
children under 5 years of age, by type (wasting and overweight)
2.4.1 Proportion of agricultural area under productive and sustainable
agriculture
sanitation for all
6.4.1 Change in water-use efficiency over time
6.4.2 Level of water stress: freshwater withdrawal as a proportion of available
freshwater resources
J. Sokolow et al. / Journal of Cleaner Production 225 (2019) 913e927 915consumed and/or polluted”.1 The analysis focused on blue and green
water footprints, which reflect the freshwater planetary boundary as
defined by Rockstrӧ;m et al. (2009). The Water Footprint Network
defines green water footprint as “water from precipitation that is
stored in the root zone of the soil and evaporated, transpired or
incorporated by plants” and blue water footprint as “water that has
been sourced from surface or groundwater resources and is either
evaporated, incorporated into a product or taken from one body of
water and returned to another, or returned at a different time”.
While food value chains include many uses of water, this
research focuses on crop water requirements, particularly as irri-
gation is generally the greatest use of water in the system
(Gustafson et al., 2016). Additionally, there are metrics that give
greater weight to water consumed in areas where there is compe-
tition for water resources, but these methods have not received
universal scientific consensus to date and thus were not incorpo-
rated into this research and analysis effort (Gustafson et al., 2016).
This analysis used the water footprint benchmark data pub-
lished by Mekonnen and Hoekstra (2014). Mekonnen and Hoekstra
established a set of global water footprint benchmark values for
selected crops grown worldwide. The study used a spatial model,
taking into consideration soil water balance to calculate crop water
requirements, actual crop water use (both green and blue), and
actual yields. The water footprints were then calculated taking into
consideration evapotranspiration as well as the growing period and
crop yield. Green-blue water footprints, which are considered
simply ‘water footprints’ for the purposes of this paper, are calcu-
lated by dividing the evapotranspiration of green and blue water
over the growing period by crop yield. Green-blue water footprints
sum both rain and irrigated water consumption.
For this study, the water footprint benchmarks established in
Mekonnen and Hoekstra (2014) were used for the comparison in
part because of the ease of the data availability, unlike national
water use accounts. For national water use accounts, data is often
limited and, when available, is constrained to statistics on water
withdrawals in the different sectors of the economy. Furthermore,
these accounts often exclude green and grey water data (Bulsink
et al., 2010). Whilst there are also limitations with benchmark ap-
proaches to ‘virtual’ water accounting (e.g., in policy setting e see
Wichelns, 2010), the benchmarks established in Mekonnen and
Hoekstra (2014) serve as a useful tool for establishing an
approach and methodology to obtain a high-level understanding of
the comparison between water footprint and nutrient density.
2.2. Nutrient density analysis
Previously, the focus of food security efforts was to ensure
people gain an adequate number of calories. This approach does not
consider the full complexity of food security, which, as defined by
the 1996World Food Summit, is “when all people, at all times, have
physical and economic access to sufficient, safe, and nutritious food
to meet their dietary needs and food preferences for an active and
healthy life” (FAO, 1996). This definition not only calls for the suf-
ficiency of food (or an adequate supply of calories), but ‘safe’ and
‘nutritious’ food as well, thus requiring more complex approaches
and measurements that address all forms of malnutrition
(Development Initiatives, 2017). Poor quality diets, characterized by
low intakes of fruit, vegetables, nuts and seeds, and whole grains
and high intakes of processed meat, sodium, and sugar-sweetened
beverages are the number one risk factor in the global burden of
disease and affect about three billion people worldwide1 Water Footprint Network. Available at: http://waterfootprint.org/en/water-
footprint/what-is-water-footprint/. Accessed July 16, 2017.(Forouzanfar et al., 2015). Focusing on the nutrient density of plant
crops helps to assess the contribution of these foods to diet quality.
For this analysis, all selected crops were analyzed in their raw
form, as preparation methods can alter the nutrient content of the
food. Additionally, crops chosen had established water footprint
baselines and associated data present in the USDA Food Composi-
tion Database (Mekonnen and Hoekstra, 2011; USDA, 2015). This
database was selected, as it contains the most comprehensive food
composition data to date. As both datasets used different crop
coding systems, an effort was made (by use of averages and ex-
clusions, as needed) to match food items in both databases. Ulti-
mately, the study analyzed data for 17 grains, roots and tubers, 9
pulses,10 nuts and seeds,17 vegetables, and 27 fruits (see Appendix
B). Culinary, rather than botanical definitions, were used to assign a
food crop to a food group. Based on the selected crops, the nutrient
information for 10 key nutrients, along with the energy density,
was gathered based on 100 g of food.
The study used nutrient profiling to quantify and compare the
nutrient density of plant crops with an important role in improving
diet quality. Nutrient profiling is defined as “the science of classi-
fying or ranking foods according to their nutritional composition
for reasons related to preventing disease and promoting health”
(WHO, 2010). While many current nutrient profiling methods
consider nutrients to limit in a diet, such as saturated fat, added
sugars, and sodium (Arambepola et al., 2008; Darmon et al., 2009;
Fulgoni et al., 2009), because these limiting factors were not rele-
vant to the crops analyzed the analysis only considered beneficial
nutrients. This analysis used the nutrient density score (NDS)
methodology defined in Darmon et al. (2005), which established a
nutrient density standard for fruits and vegetables. The study built
on Darmon et al. (2009) by weighting the NDSs according to the
bioavailability of the nutrients Di Noia (2014).
The nutrient profiling model used nutrient requirements
established by theWorld Health Organization (WHO) and Food and
Agriculture Organization (FAO) (WHO and FAO, 2004). The analysis
uses recommended nutrient intakes (RNIs) for males between the
ages of 19 and 65 years (unless otherwise noted in Table 2; WHO
and FAO, 2004). Micronutrients chosen are essential nutrients
important for public health nutrition internationally. Food groups
not analyzed in this study, such as animal source foods, contribute
different components to the diet that are not reflected in the nu-
trients selected for this analysis.
The nutrient profiling calculations for each crop occurred in the
following stages (Di Noia, 2014; Darmon et al., 2005):
1. The nutrient adequacy score was calculated. This score is the
mean of percent RNIs (or mean requirement, in the case of
vitamin A) per day for the nutrients listed in Table 2 per 100 g of
the selected crop (based on the USDA data). The RNI/mean
requirement established by WHO/FAO takes into account
bioavailability, where applicable. The nutrient adequacy score ¼
(S [nutrienti/RNIi] 100)/10, where: nutrienti¼ amount of one
of the 10 nutrients in 100 g of food (based on USDA data),
RNIi¼ the recommended nutrient intake per day for the same
nutrient for adult males (see Table 2 to see specific details
regarding the RNI).
2. For each nutrient, percent RNIs were capped at 100% daily value,
as some foods are excellent sources of a particular nutrient but
contain few other nutrients. This approach prevents against
inflated score values for crops containing high levels of a single
nutrient (Di Noia, 2014; Gustafson et al., 2016).
3. The nutrient adequacy score was then divided by the energy
density of the food (kilocalories per 100 g): The nutrient density
score (expressed per 100 kcal) ¼ (nutrient adequacy score/en-
ergy density) x 100.
916 J. Sokolow et al. / Journal of Cleaner Production 225 (2019) 913e927
Table 2
Recommended nutrient intake (RNI) per day for selected nutrients.
Nutrient Units (Per Day) RNI Per Daya Notes on Applicability of the RNI
Vitamin A mg RE (Retinol Equivalents) 300 Vitamin A value is the ‘mean requirement’ instead of the RNI.
Vitamin C mg 45 RNI reflects the amount required to half saturate body tissue with vitamin C in 97.5% of the population.
Thiamin mg 1.2 Applies to males 19 years and older
Riboflavin mg 1.3 Applies to males 19 years and older
Niacin mgNEs (Niacin Equivalents) 16 Applies to males 19 years and older
Vitamin B6 mg 1.5 RNI is an average of the following:
- Males 19e50 years - 1.3mg/day
- Males 51 þ years - 1.7 mg/day
Folate mg 400 Applies to all adults
Calcium mg 1000 e
Zinc mg 7 Considers moderate bioavailability
Iron mg 19.4 Based on the average of 5% bioavailability (27.4mg) and 12% bioavailability (11.4mg)
a Unless otherwise noted, RNI is based on the WHO/FAO recommendation for males between 19 and 65 years old.The final nutrient density score (NDS) represents the mean of
the percent RNIs per 100 kcal of food. The nutrient profiling values
calculated can then be compared to the water footprint bench-
marks, as established in Mekonnen and Hoekstra (2014). See
Appendix B for a table with results from the nutrient density
calculation.3. Results
The analysis sheds light on the tradeoffs and synergies between
a crop's water footprint and its nutrient density. See Appendix A for
a table containing a side-by-side comparison between the NDSs
and water footprint benchmarks, which permits a comparison of
these tradeoffs and synergies.
Fig. 1 through 6, below, provide a visual comparison of the water
footprint and nutrient density for the selected crops, where Fig. 1
represents all crops and Fig. 2 through 6 represent crops by food
group. The figures include lines to demarcate the median water
footprints and NDSs to facilitate the consideration of tradeoffs (see
Table 3 for medians). The top left quadrant includes crops that are
relatively water inefficient and nutrient poor and the bottom right
quadrant represents crops that are water efficient and nutrient
dense. These results demonstrate how this new interdisciplinary
approach can be analyzed. The figures use different scales to better
demonstrate distinctions among crops within each food group. The
median water footprints and NDSs help to compare between the
food groups analyzed (Table 3).
Fig. 1 includes all 80 individual crops included in the analysis,Fig. 1. Water footprint and nutrientwhere the medianwater footprint is approximately 818m3/ton and
the median NDS is 9.00. Crops in the most water efficient quartile
have a water footprint of less than or equal to 345m3/ton, of which
85% are fruits and vegetables. Crops in the most nutrient dense
quartile have a NDS of greater than or equal to 21.4, of which 100%
are fruits and vegetables. This does not come as a surprise, due to
the overwhelming evidence in the literature of the importance of
fruit and vegetable consumption, and the harms of low consump-
tion. According to the 2013 Global Burden of Disease Study, low
fruit and vegetable consumption is among the top risk factors for
mortality worldwide (Forouzanfar et al., 2015). Additionally, as
demonstrated by the density of crops in the bottom left portion of
the graph, most crops are not nutrient dense but relatively water
efficient.
Fig. 2 represents the 17 vegetables analyzed, where the median
water footprint is approximately 249m3/ton and themedian NDS is
36.94. One crop that stands out is spinach, which has the highest
possible NDS of 100 and a low water footprint of 132m3/ton,
though green-leafed lettuce and cauliflower are also highly nutri-
tious and water efficient. Spinach has high levels of Vitamin A
(RAE), 156% of RNI, which was capped at 100% when calculating the
NDS as to not inflate the score, and 49% of RNI for folate (DFE) and
62% of RNI for Vitamin C (including total ascorbic acid), which
contributes to the vegetable's high NDS. Spinach's energy density
value is 23 kcal per 100 g, which is relatively low in comparison to
grains, which often have energy density values above 300 kcal per
100 g.
Fig. 3 represents 27 fruits analyzed, where the median waterdensity comparison: All crops.
J. Sokolow et al. / Journal of Cleaner Production 225 (2019) 913e927 917
Fig. 2. Water footprint and nutrient density comparison: Vegetables.
Fig. 3. Water footprint and nutrient density comparison: Fruits.
Fig. 4. Water footprint and nutrient density comparison: Nuts and seeds.footprint is 597m3/ton and median NDS is 13.31. Citrus fruits, such
as oranges, lemons, limes, grapefruits, and pomelos, are both
nutrient dense and water efficient. Some fruits, such as coconuts
and avocados, are energy dense with high levels of healthy dietary
fats. They are valuable additions to the diet, but do not contribute asignificant proportion of micronutrients identified as critical for
this analysis. Figs, coconuts, and dates have a low nutrient density
and high water footprint among fruits.
Fig. 4 represents 10 nuts and seeds analyzed, where the median
water footprint is 5721.5m3/ton and median NDS is 5.24. Overall,
918 J. Sokolow et al. / Journal of Cleaner Production 225 (2019) 913e927
Fig. 5. Water footprint and nutrient density comparison: Pulses.
Fig. 6. Water footprint and nutrient density comparison: Grains/roots/tubers/plantains.
Table 3
Median water footprint and nutrient density score (NDS) by food group.
Food Group (Including All Crops) Median NDS Median Water
Footprint (m3/ton)
All Crops 9.00 818
Nuts/Seeds 5.24 5721.5
Grains/Roots/Tubers/Plantains 5.18 1292
Pulses 8.49 3196
Vegetables 36.94 249
Fruits 13.31 597nutrient density is low for nuts and seeds, as compared to other
food groups, with the highest NDS at 9.31 for chestnuts. The
selected nuts and seeds are more energy dense, many having high
concentrations of important macronutrients (protein and fat). A
consideration for interpretation is the relative lower energy density
of chestnuts (197 kcal per 100 g on average), compared to other
nuts and seeds (greater than 440 kcal per 100 g). This food group is
also generally water inefficient, which is seen clearly in Fig. 1 that
compares all crops analyzed, where most nuts and seeds fall in the
upper left quadrant, representing crops with low nutrient density
and water efficiency.
Fig. 5 represents 9 pulses analyzed, where the median water
footprint is 3196m3/ton and median NDS is 8.49. Like the nuts and
seeds food group, the NDSs for pulses are generally low, ranging
from 6 to 10, as compared to vegetables with a median NDS of
36.94. Cowpeas and soybeans are both relatively nutrient dense, asthey fall well above the median when comparing the pulses
analyzed. This figure demonstrates that some tradeoffs are difficult
to judge, where the water footprint and nutrient density measures
are disparate. For example, cowpeas have a higher NDS than broad
beans, 10.21 versus 9.03. Cowpeas have a more significant impact
on the water resources than broad beans, 6850m3/ton versus
1521m3/ton. For this reason, criteria must be established based on
the needs of the local population and environment to make de-
cisions based on these tradeoffs.
Fig. 6 represents 17 grains, roots, tubers, and plantains analyzed,
where the median water footprint is 1292m3/ton and median NDS
is 5.18. These crops are generally clustered with NDSs falling be-
tween 1 and 10. Demonstrated most clearly by Fig. 1, these crops,
which fall in the bottom left portion of the graph, generally have
lower water footprints and NDSs as compared to other food groups
analyzed. When comparing these grains, roots, tubers, and plan-
tains, sweet potato stands out as being both nutrient dense and
having a low water footprint.
Lessons learned from these tradeoffs, along with careful
consideration of the social, environmental, and economic contexts,
can help decision makers build a sustainable food system meeting
human and environmental health needs.4. Discussion
This paper assesses the nexus between water footprint and
nutrient density from a bird's eye view. This perspective aims to
foster an understanding of how these two areas can be
J. Sokolow et al. / Journal of Cleaner Production 225 (2019) 913e927 919
2 FAO/INFOODS website is available at: http://www.fao.org/infoods/infoods/
tables-and-databases/faoinfoods-databases/en/.conceptualized and compared to contribute to designing a more
effective, more sustainable food systemdin essence, a food system
that produces nutritious foods and has a limited water footprint
impact that aims to help keep freshwater appropriation by humans
within planetary boundaries. Measuring the water footprint of
nutrient-dense foods can serve as a framework to develop broad
typologies. Using global precipitation and soil maps, along with
nutrition surveys, decision makers responsible for agriculture, food
policy, natural resource management, and environmental conser-
vation can develop broad typologies for water footprint and
nutrient density situations. The aim of developing typologies is that
areas and regions can be classified and grouped according to a
particular combination of characteristics that pertain directly to
food production (e.g., soil type and land capability), nutritional/
dietary requirements (e.g., human health characteristics), and wa-
ter use (e.g., precipitation patterns, water body cover, soil type,
aquifer presence, and population densities). By doing this, areas
with similar characteristics can be grouped on the basis of likely
similar water availability issues and similar nutrition issues. This
can help guide policy and decision makers in terms of giving due
consideration to both water use and nutritional attributes of
different crop types. The groupings may also allow similar strate-
gies to be adopted in terms of the crops that are a) required on a
nutritional basis, b) able to flourish given the water availability in a
given region, and c) suited to the agroecological conditions (e.g. soil
type, altitude, climatic variables) in a region. This means that
general principles of response can be prescribed (although there
will likely always be a need to tailor responses to each location to
some extent), without the necessity of treating each region as
exhibiting a completely unique set of threats, issues, and potential
responses.
This analysis is an early and unique attempt to combine two
distinct but cross-disciplinary indicators related to the food system.
There are limitations to the indicators and methodology used, as
discussed in the following sections. Thus, further research and
analysis (particularly where this dual approach is trialed in actual
locations and communities) will be required to test and refine
metrics for this methodology.
4.1. Nutrient density score
The NDS serves as an effective indicator to measure the
contribution of a selected crop to the human diet. When designing
this indicator, critical micronutrients were chosenwith the nutrient
composition of fruits and vegetables in mind as the analysis was
limited to plant crops. There could be a different set of water
footprint/nutrient density tradeoffs if looking, for example, at fish
and different types of aquaculture. For example, small indigenous
fish have higher nutrient density than many larger species of
farmed fish such as carp and tilapia (Thilsted et al., 2016). Therefore,
this analysis is most robust in its understanding of the water
footprint and nutrient density tradeoffs regarding fruits and veg-
etables, and plant foods more broadly. While this paper presents
data for other food groups, additional work is required at a more
localized level to tailor the NDS to the important nutritional con-
tributions of these food groups to the diet based on typical dietary
patterns and socio-economic conditions. Future region and
country-specific analysis using the specific staple foods (e.g., maize,
rice, or cassava) and the most commonly consumed fruits and
vegetables is needed. These further analyses could include protein
and amino acid, and fat and fatty acid profile of crops as well as an
attempt to include neglected and underutilized species to ensure
the approach reflects the full complexity of nutrient density/water
trade-offs at a more granular level of analysis.
In addition to modifications to the scoring methodology,additional analyses can be performed on different foods, including
different methods of preparation. Because this study analyzed
crops in their raw form, additional analysis is required to assess
crops in their commonly consumed form(s) to more accurately
reflect their contribution to diets. Furthermore, additional research
may include animal source foods (e.g., aquaculture), which typi-
cally have a larger water footprint than crop products (Mekonnen
and Hoekstra, 2010). There may be options to better integrate
crop production and animal source foods, such as aquaculture,
through use of pond water to irrigate and fertilize crops (Attwood
et al., 2015), that would help manage the overall water footprint
of the production system.
A huge challenge for developing food composition datasets,
upon which to base policies and programs, is that the nutrient
content of foods can vary significantly among different ecological
and climatic conditions, and among the many different varieties
that exist within a species. In a study by Barikmo et al. (2007) of the
differences in micronutrient contents found in cereals in different
ecological zones of Mali, the micronutrient content of the same
cereal species varied among different regions (Barikmo et al., 2007).
Large variations in nutrient content within varieties of the same
crop have also been demonstrated (Kennedy and Burlingame,
2003). Scientists around the world are working to develop
comprehensive food composition databases in an effort to better
contribute to the food security of regions, with much of this data
stored in the FAO/INFOODS Composition Databases.2 This data can
then be applied to the proposed methodology to better understand
the water footprint and nutrient density dichotomy across various
regions, including which crop varieties better contribute to high
nutrient density and low water footprint values.
4.2. Water footprint
Mekonnen and Hoekstra's spatial model serves as an effective
way to compare water footprints for selected crops grown world-
wide. When comparing crop water footprint data from Mekonnen
and Hoekstra (2014), it is important to keep in mind the impact of
crop yields on water footprint. Because of the role of yields in
determining water footprints, these values are influenced by the
various factors that impact yields apart from water availability,
including nutrient supply, crop varieties, farmer access to agricul-
tural inputs (e.g., agrochemicals, labor), seasonality and severity of
rainfall, soil type and condition, pest and disease incidence, and
pollinator densities and activity. Mekonnen and Hoekstra explain,
for example, that green-blue water footprint is typically lower for
irrigated as opposed to rain-fed crops because irrigated yields are
larger than rain-fed yields. Higher water productivity is not just
determined by whether irrigation is used or not, or due to climatic
and soil factors determining evapotranspiration. Instead, water
footprint is largely determined by agricultural management prac-
tices (Mekonnen and Hoekstra, 2014). In fact, farmers can achieve a
large increase in their yields without impacting evapotranspiration
with effective nutrient, water, and soil management (Mueller et al.,
2012). For example, agricultural management approaches such as
mulching (Leaky et al., 2009), adding manure (Tilman et al., 2002),
or conservation tillage (Chartzoulakis and Bertaki, 2015; Leaky
et al., 2009; Tilman et al., 2002) can retain or increase the soil's
organic matter, thus increasing its capacity to hold water and
decrease evapotranspiration (Leaky et al., 2009). Technological
tools, such as the Commonwealth Scientific and Industrial Research
Organization's (CSIRO) Agricultural Production Systems Simulator
920 J. Sokolow et al. / Journal of Cleaner Production 225 (2019) 913e927(APSIM™) offers crop management strategies for farmers,
including optimal plant density, sowing period, and nitrogen
quantities to maximize crop water use efficiency and improve crop
production (Botwright Acun~a et al., 2015; Moore et al., 2011).
In determining sustainable crops for a region (i.e., crops that
reduce natural resource use and increase nutrition security), deci-
sion makers must not only consider water-use efficiency, but trade-
offs between a whole host of environmental, social, and economic
factors (Rohmer et al., 2018). They should consider the supply chain
configuration in a common framework with dietary consumption
(Rohmer et al., 2018), including where and how land-use change
and related impacts will occur as a result of modifications to the
food system (Chaplin-Kramer et al., 2017). Many well-known
dryland crops (e.g., sorghum, millet, dates, and figs) that are often
sustainable crop solutions in arid regions have higher water foot-
print values in this analysis, likely in part due to their lower crop
yields (and the inclusion of yield in the calculations). Sorghum and
millet are such crops widely known to be dryland cereals that
provide nutritious and resilient sources of food for communities
prone to low or erratic rainfall (ICRISAT and ICARDA, 2012). Several
factors impact crop resilience (e.g., chlorophyll stability and leaf
mass), where water use is just one component (Gholami et al.,
2012). Certain varieties of crops are more drought-tolerant than
others, and thus are more sustainable options in dryland regions, as
seen in the case of Kalanchoe claigremontiana for dates (Djibril et al.,
2005) and Deyme Ahvaz and Sabz Estahban fig varieties (Gholami
et al., 2012). For millets, barnyard millet is known as a drought-
tolerant variety due in part to its short growing period, using less
water and escaping the seasonal drought period (Tadele, 2016;
Zegada-Lizarazu and Iijima, 2005).
Millet and sorghum are characterized as neglected and
underutilized species (NUS), which are species that often have not
been sufficiently studied to understand their potential, particularly
under changing climate conditions (Padulosi et al., 2013). NUS
crops offer an opportunity to improve diets, strengthen income
generation across the supply chain, and empower indigenous
communities (particularly women, who are often custodians of
traditional knowledge) (Padulosi et al., 2013). Millet varieties may
offer climate-smart crops, as their adaptations to challenging en-
vironments are better than the current major crops of the world.
Millet is often grown in low fertility, sandy upland soils, where poor
environmental, management, and plant-related factors are applied
(Sadras et al., no date). These factors contribute to the low water
productivity of millet, particularly in arid regions such as the Sahel
(Sadras et al., no date; Tadele, 2016). Agronomy and inputs, not
limited to water, are often the principal factors limiting the pro-
ductivity of millet (Sadras et al., no date). Adjusting these factors,
such as applying more fertilizer to millet crops, must be considered
in relation to its costs and benefits in the region of interest. Further
research can help to understand the benefits conventional and
modern improvement techniques can have on the productivity of
these varieties under varying conditions (Tadele, 2016). Enhancing
the productivity of NUS crops, such as millets, requires the collab-
orative efforts of diverse stakeholders, including breeders, agron-
omists, policy makers, and donors and their associated institutions
(Tadele, 2016).
When considering tradeoffs for water use efficiency and
nutrient density, it is important to consider the multitude of factors
impacting the sustainability of a crop for a region. These factors
include not only water use and nutrient profile, but crop hardiness,
food access in drought-prone regions, income generation, and
gender empowerment. Additional analysis of crops, and their
associated cultivars, is required to better understand the water
footprint of different crops based on the agricultural management
practices applied. Further research must also consider social andeconomic factors that impact this interaction; for example, rain-
water collection systems can help grow thirstier crops like aspar-
agus. To support sustainably-focused, evidence-based food and
agriculture policies, researchers must work to collect, manage, and
evaluate all available quantitative and qualitative data on the
regional or national food system conditions, and assess which
particular management interventions may be suitable, effective,
and feasible.
When designing tailored guidelines and recommendations at
regional, national, or local levels, researchers and policy makers
will likely want to take into account the unique agroecological
factors of their region when performing analyses. There currently
are barriers for conducting this analysis at regional and national
levels due to the limited availability of water footprint and nutrient
density data at those scales. Water footprint can differ for the same
crop species based on the unique local conditions, such as climate,
soil characteristics, local to catchment scale hydrology, and water
management practices (Mekonnen and Hoekstra, 2014). When
analyzing water footprint in field or modelling studies, it is also
important to take into consideration constraining factors, such as
pests, diseases, and weeds, which could affect the water footprint
analysis (Grassini et al., 2009). Field studies and modelling studies
can have limitations based on the location or the quality of the data
set. The water footprint data from Mekonnen and Hoekstra (2014)
represents spatial variations of the water productivity using remote
sensing studies. This method allows researchers to analyze large
land areas, may lose the fine-grain detail of specific ecological and
management conditions. Thus, when implementing this analysis
for a nation, researchersmust carefully consider thewater footprint
data available, as well as the surrounding conditions, to determine
what water footprint data should be considered for analysis.
A final factor to keep in mind is that this analysis includes data
from two distinct datasets, where crops have different names,
codes, and groupings across each dataset. The study addresses
these differences by aggregating and excluding food groups, as
necessary. Further work must be done to improve these compari-
sons. As more scientists and practitioners recognize the need to
work on intersectional, interdisciplinary issues, improved data
management approaches and techniques will be necessary to align
data across subject matters and geographic regions.
4.3. Implications for future users
Decision makers must take into account social, environmental,
and economic factors and the trade-offs and synergies among them
when designing and implementing policies or developing pro-
grams to deliver on policy intent. Decision makers should also
consider historical, present, and future scenarios, including mar-
ginal changes, in order to build dynamic, prospective, and inno-
vative solutions aligned with the on-the-ground reality (Yang and
Campbell, 2017). In addition to broad directives and guiding prin-
ciples, it is importantdif possibledfor policy makers to develop
these initiatives through participatory approaches, rather than top-
down efforts, to fully understand the challenges and constraints
communities face, and opportunities available, in their particular
context. Policy makers may determine synergies that benefit both
environmental and nutritional outcomes, or identify and seek to
address tradeoffs between outcomes where they are likely to occur.
Whilst the aim of this paper was to provide a broad overview of
water use and nutritional attributes, context-specific, on-the-
ground circumstances must also be considered when developing
policies or guidelines that are informed by general, broad scale
principles. For example, the communitymay have: limited access to
clean drinking water impacting the choice of crops that can be
safely prepared (e.g., leafy greens that required more water for
J. Sokolow et al. / Journal of Cleaner Production 225 (2019) 913e927 921washing as compared to other vegetables); well-established and
hard-to-change dietary patterns; or financial constraints that make
producing or consuming a particular product difficult.
Some nations have already taken actions to incorporate features
of sustainable diets into their dietary guidelines, for example sug-
gesting food choices that limit the use of freshwater resources. A
study by Fischer and Garnett (2015) reviews and assesses the role of
sustainability in national food-based dietary guidelines (Fischer
and Garnett, 2015). Brazil and the United Kingdom suggest
reducing the consumption and production of animal-based foods,
in part due to the water use associated with animal production
systems (Fischer and Garnett, 2015). The Brazil guidelines also
highlight the high levels of water use required by intensive pro-
duction systems, such as soybean and corn monocultures (Fischer
and Garnett, 2015). Swedish guidelines provide more specific
guidance on specific types of vegetables, suggesting high fiber
vegetables (e.g., root vegetables) over salad greens due to their
environmental impact (Fischer and Garnett, 2015). These examples
are small ways considerations regarding the water footprints of
nutrient-dense foods can be incorporated into national guidance.
Overall, this analysis serves as a starting point to understand
how multi-disciplinary concepts relating to sustainable food sys-
tems can be constructed and tested. This research provides a useful
entry point for researchers, programmers, and policy makers
interested in trade-offs and synergies between planetary and hu-
man health. The initiative is also in line with ambitions of the SDGs,
as it demonstrates one way to analyze potential win-win options to
achieve multiple SDG targets. A more comprehensive under-
standing of the environmental, health, social, and economic areas
of a nation and its regions and communities is required to develop
effectives policies and interventions, and to ensure the policies or
programs are meeting the needs of the intended audience.
5. Conclusions
Global sustainability challenges, including challenges facing hu-
man and environmental health, are highly intertwined in a complex
system. A holistic, systems integration approach involving diverse
disciplines can help to integrate human and environmental issues in
an attempt to identify efficient, effective solutions to these chal-
lenges. Researchers and policy makers must engage in this process
through the analysis of a diverse range of food system metrics that
monitor and track both environmental and human health issues.
Researchers should also continue to develop high quality datasets
and effective, clear analysis methods to track issues pertaining to
sustainable food systems. These metrics and methods should sup-
port multi-sector initiatives that cross environmental, agriculture,
and nutrition sectors to best address these unique challenges.
This study frames a cross-disciplinary analytical approach to
measure water footprint of nutrient-dense foods. This methodol-
ogy is an important contribution to a decision makers' ‘sustainable
food system dashboard’ to track progress towards the SDGs. Further
research and analysis can refine the metrics and methods for this
approach based on unique and timely information relevant to local
conditions. This method can help to establish broad typologies to
guide decision makers in distinguishing between win-win, win-
lose, and lose-lose scenarios of natural resource use and nutrition
security.
Policies should continue to recognize and consider the unique
socio-cultural and socio-economic conditions which impact the
sustainability of food systems within context. Of these conditions, a
community's unique tastes and preferences for different foods and
cuisines may inhibit efforts to promote specific food tradeoffs that
benefit humans and their environment. Programs promoting pos-
itive behavior change, including school programs on nutrition andsustainability, can be an effective bottom-up approach contributing
to the sustainability of food systems. Due to the time it takes for the
impact of these behavior changes to be seen, more immediate
measures must be taken through the work of front-line technical
assistance. Agricultural extensionworkers should be equippedwith
the best materials and resources as they are able, including seeds
for crop species that better contribute to a nutritious diet and
sustainable ecosystem and training resources on effective water
management and nutrient education. These tools can better sup-
port the agricultural management of farmers, giving them the re-
sources needed to be effective stewards of the land and
contributors to a more sustainable food system.
The concept of a sustainable food system is one of the most
complex issues of the time, as the global community must work to
feed the world's growing population in a time of rapid environ-
mental change. Now, more than ever, it is important for researchers
and policy makers to holistically consider integrated food system
metrics that help to conceptualize and improve upon the current
system. This is an effort that will require enormous support from
diverse stakeholders worldwide, but with the 2030 SDGs in sight,
now is the time to work collectively to meet the global goals, and to
improve the wellbeing of individuals worldwide and the health of
the planet.
6. Significance statement
The water footprint of nutrient-dense foods can help decision
makers concurrently track progress towards Sustainable Develop-
ment Goals (SDGs) focused on food and nutrition security, human
health, natural resource use, and environmental sustainability. The
methodology proposed in this article supports the development of
an approach that tracks interactions between multiple, interdisci-
plinary SDGs and their Targets, and supports innovative solutions
and collaborations across disciplines. This analysis has the potential
to encourage more sustainable and healthier diets across the globe.
Further research can build off this methodology to create typol-
ogies for water footprint and nutrient density situations regionally
to help guide food system decision makers.
Funding sources
We acknowledge the support of the CGIAR Research Program on
Agriculture for Nutrition and Health, the CGIAR Research Program
on Roots, Tubers, and Bananas, and the World Wide Fund for Na-
ture, Singapore.
Acknowledgements
We are grateful to Stefano Padulosi for providing insight and
guidance on millet and other important dryland crops.
Appendix A. Nutrient Density and Water Footprint Tradeoffs
of Selected Crop
The following table organizes the crop(s), using the FAO naming
conventions used in Mekonnen and Hoekstra (2014), into food
groups. For the purpose of this table, vitamin A-rich fruits and
vegetables and vitamin-A rich dark leafy greens were combined
under the fruit and vegetable categories, as few of these crops were
available in the Mekonnen and Hoekstra (2014) dataset. Within
each category, crops were filtered first by nutrient density score, or
NDS, (with scores ranked high to low) to bring to light the most
nutrient-dense crops, then by water footprint (with scores ranked
low to high). The reference numbers by food group are not intended
to be a ranking system but aim to facilitate the citation of crops.
922 J. Sokolow et al. / Journal of Cleaner Production 225 (2019) 913e927
Reference Number Crop Name Nutrient Density Score (NDS) Green-Blue Global Water Notes on Averages and
(By Food Group) Footprint Average (m3/ton) Food Groupings
Grains, white roots and tubers, and plantains
1 Sweet potatoes 17.11 330
2 Potatoes 10.37 224 NDS is an average of varieties
3 Plantains 8.46 1597
4 Maize 8.43 1028
5 Bananas 7.43 756
6 Yams 6.96 342 NDS is an average of varieties
7 Taro (coco yam) 5.33 591
8 Wheat 5.31 1620 NDS is an average of varieties
9 Cassava 5.18 550
10 Rice, paddy 5.02 1486 NDS is an average of varieties
11 Oats 4.85 1660
12 Rye 4.60 1445
13 Millet 4.60 4363
14 Buckwheat 4.49 2913
15 Barley 4.46 1292 NDS is an average of varieties
16 Triticale 4.39 866
17 Sorghum 4.11 2960
Pulses (beans, peas, and lentils)
1 Soybeans 10.33 2107
1 Cowpeas, dry 10.21 6850 NDS is an average of varieties
2 Beans, dry 8.98 4070 NDS is an average of varieties
3 Broad beans, dry 9.03 1521
4 Lupins 8.49 1371
5 Pigeon peas 8.31 4811
6 Lentils 8.08 4814 NDS is an average of varieties
7 Peas, dry 7.41 1486
8 Chickpeas 7.37 3196
Nuts and Seeds
1 Chestnuts 9.31 2606 NDS is an average of varieties
2 Sesame seed 8.07 8969
3 Sunflower seed 7.47 3165
4 Safflower seed 7.10 6938
5 Groundnuts 5.32 2618
6 Pistachios 5.16 10697
7 Almonds 4.11 6540
8 Cashew nuts 3.66 13774
9 Hazelnuts (filberts) 3.57 4903
10 Walnuts 2.74 4105
Vegetables (Including Vitamin-A Rich and Dark Leafy Greens)
1 Spinach 100.00 132 Considered dark leafy green
2 Lettuce 81.14 161 NDS is an average of varieties
3 Sugar beets 14.36 108
4 Cauliflower 58.68 211
5 Chilies and peppers, green 56.56 282 NDS is an average of varieties
6 Cabbages 48.25 208
7 Asparagus 47.10 1643
8 Okra 42.32 511
9 Tomatoes 37.00 171
10 Carrots 36.94 134 Vitamin-A rich
11 Pumpkins, squash, gourds 28.41 252 NDS is an average of varieties; Orange or
dark yellowed-flesh, vitamin-A rich
12 Beans, green 25.50 374 Assumed to be eaten as pods;
Average of varieties
13 Peas, green 24.97 446 Assumed to be eaten as pods
14 Artichokes 18.44 720
15 Cucumbers and gherkins 15.74 249
16 Eggplants 12.34 267
17 Onions and shallots, green 10.25 221 NDS is an average of onions and shallots
Fruits (Including Vitamin-A Rich)
1 Strawberries 37.90 311
2 Lemons and limes 33.79 584 NDS is an average of lemons and limes
3 Grapefruit and pomelos 33.08 453
4 Cantaloupes and other melons 29.90 154 NDS is an average of varieties; Cantaloupe (ripe)
and musk melon considered vitamin-A rich
5 Oranges 28.25 510
6 Pineapples 25.93 224
7 Tangerines, mandarins, 22.72 597 NDS is an average of tangerines and
clementines, satsumas clementines only
8 Mangoes 21.70 1676 Vitamin-A rich
9 Currants 21.30 477 NDS is an average of varieties
10 Kiwi Fruit 20.51 475
11 Gooseberries 19.71 495
12 Raspberries 17.15 346
J. Sokolow et al. / Journal of Cleaner Production 225 (2019) 913e927 923
(continued )
13 Apricots 15.74 1195 Vitamin-A rich
14 Watermelons 13.31 175
15 Sour cherries 12.25 1312
16 Grapes 10.45 522 NDS is an average of varieties
17 Dates 1.90 2180 NDS is an average of varieties
18 Cranberries 9.26 199
19 Peaches and nectarines 8.87 770 NDS is an average of peaches and nectarines
20 Plums 8.65 1758
21 Blueberries 7.14 675
22 Avocados 6.35 1087
23 Coconuts 6.32 2671 NDS includes coconut meat and water
24 Cherries 8.56 1493
25 Figs 4.69 3049
26 Apples 3.94 695
27 Pears 3.66 739Appendix B. Nutrient Density Scores
The following nutrient composition data includes correspond-
ing nutrient adequacy and nutrient density scores and energyCrop Crop Description Calcium, Iron, Zinc, Vitamin
Code Ca Fe Zn C, total
ascorbic
acid
20071 WHEAT, HARD RED SPRING 25.00 3.60 2.78 0.00
20072 WHEAT, HARD RED WINTER 29.00 3.19 2.65 0.00
20073 WHEAT, SOFT RED WINTER 27.00 3.21 2.63 0.00
20074 WHEAT, HARD WHITE 32.00 4.56 3.33 0.00
20075 WHEAT, SOFT WHITE 34.00 5.37 3.46 0.00
20076 WHEAT, DURUM 34.00 3.52 4.16 0.00
AVERAGE Wheat (Average) 31.20 3.97 3.25 0.00
20088 WILD RICE,RAW 21.00 1.96 5.96 0.00
20036 RICE, BROWN,LONG-GRAIN,RAW 9.00 1.29 2.13 0.00
20040 RICE, BROWN,MEDIUM-GRAIN,RAW 33.00 1.80 2.02 0.00
20044 RICE, WHITE,LONG-GRAIN,REG,RAW,ENR 28.00 4.31 1.09 0.00
20050 RICE, WHITE,MEDIUM-GRAIN,RAW,ENR 9.00 4.36 1.16 0.00
AVERAGE Rice (Average) 20.00 2.74 2.47 0.00
20004 BARLEY, HULLED 33.00 3.60 2.77 0.00
20005 BARLEY, PEARLED, 29.00 2.50 2.13 0.00
RAW
AVERAGE Barley (Average) 31.00 3.05 2.45 0.00
20062 RYE GRAIN 24.00 2.63 2.65 0.00
20038 OATS 54.00 4.72 3.97 0.00
20031 MILLET,RAW 8.00 3.01 1.68 0.00
20067 SORGHUM GRAIN 13.00 3.36 1.67 0.00
20008 BUCKWHEAT 18.00 2.20 2.40 0.00
20069 TRITICALE 37.00 2.57 3.45 0.00
11362 POTATOES,RAW,SKIN 30.00 3.24 0.35 11.40
11352 POTATOES, FLESH & SKN,RAW 12.00 0.81 0.30 19.70
11353 POTATOES, RUSSET,FLESH & SKN,RAW 13.00 0.86 0.29 5.70
11354 POTATOES, WHITE,FLESH & SKN,RAW 9.00 0.52 0.29 9.10
11355 POTATOES,RED,FLESH & SKN,RAW 10.00 0.73 0.33 8.60
AVERAGE Potatoes (Average) 14.80 1.23 0.31 10.90
11507 SWEET POTATO,RAW,UNPREP 30.00 0.61 0.30 2.40
11134 CASSAVA,RAW 16.00 0.27 0.34 20.60
11518 TARO,RAW 43.00 0.55 0.23 4.50
11601 YAM,RAW 17.00 0.54 0.24 17.10
11258 MOUNTAIN YAM,HAWAII,RAW 26.00 0.44 0.27 2.60
AVERAGE Yams, not including Jicama (Average) 21.50 0.49 0.26 9.85
11080 BEETS,RAW 16.00 0.80 0.35 4.90
16001 BEANS, ADZUKI, 66.00 4.98 5.04 0.00
MATURE SEEDS,RAW
16014 BEANS, BLACK, 123.00 5.02 3.65 0.00
MATURE SEEDS,RAW
16016 BEANS, BLACK TURTLE, MATURE 160.00 8.70 2.20 0.00
SEEDS,RAW
16019 BEANS, CRANBERRY (ROMAN),MATURE 127.00 5.00 3.63 0.00
SEEDS,RAW
16022 BEANS, FRENCH,MATURE SEEDS,RAW 186.00 3.40 1.90 4.60density for selected crops. The selected crops were chosen based on
the availability of the water footprint benchmark. Calculated aver-
ages match calculated nutrient density scores (NDSs) to water
footprint benchmark data.Thiamin Riboflavin Niacin Vitamin Folate, Vitamin NDS Energy NDS
B-6 DFE A, RAE (RNIs Density (RNIs
per (kcal per per
100 g) 100g) 100 kcal)
0.50 0.11 5.71 0.34 43.00 0.00 18.01 329.00 5.47
0.38 0.12 5.46 0.30 38.00 0.00 16.16 327.00 4.94
0.39 0.10 4.80 0.27 41.00 0.00 15.54 331.00 4.70
0.39 0.11 4.38 0.37 38.00 0.00 17.62 342.00 5.15
0.41 0.11 4.77 0.38 41.00 0.00 18.81 340.00 5.53
0.42 0.12 6.74 0.42 43.00 0.00 20.60 339.00 6.08
0.40 0.11 5.23 0.35 40.20 0.00 17.79 335.80 5.31
0.12 0.26 6.73 0.39 95.00 1.00 21.93 357.00 6.14
0.54 0.10 6.49 0.48 23.00 0.00 16.85 367.00 4.59
0.41 0.04 4.31 0.51 20.00 0.00 14.17 362.00 3.91
0.58 0.05 4.19 0.16 231.00 0.00 18.72 365.00 5.13
0.58 0.05 5.09 0.15 231.00 0.00 19.11 360.00 5.31
0.44 0.10 5.36 0.34 120.00 0.20 18.16 362.20 5.02
0.65 0.29 4.60 0.32 19.00 1.00 19.22 354.00 5.43
0.19 0.11 4.60 0.26 23.00 1.00 12.31 352.00 3.50
0.42 0.20 4.60 0.29 21.00 1.00 15.77 353.00 4.46
0.32 0.25 4.27 0.29 38.00 1.00 15.56 338.00 4.60
0.76 0.14 0.96 0.12 56.00 0.00 18.87 389.00 4.85
0.42 0.29 4.72 0.38 85.00 0.00 17.41 378.00 4.60
0.33 0.10 3.69 0.44 20.00 0.00 13.51 329.00 4.11
0.10 0.43 7.02 0.21 30.00 0.00 15.39 343.00 4.49
0.42 0.13 1.43 0.14 73.00 0.00 14.76 336.00 4.39
0.02 0.04 1.03 0.24 17.00 0.00 8.13 58.00 14.03
0.08 0.03 1.06 0.30 15.00 0.00 9.29 77.00 12.06
0.08 0.03 1.04 0.35 14.00 0.00 6.49 79.00 8.21
0.07 0.03 1.07 0.20 18.00 0.00 6.12 69.00 8.87
0.08 0.03 1.15 0.17 18.00 0.00 6.07 70.00 8.68
0.07 0.03 1.07 0.25 16.40 0.00 7.22 70.60 10.37
0.08 0.06 0.56 0.21 11.00 709.00 14.71 86.00 17.11
0.09 0.05 0.85 0.09 27.00 1.00 8.29 160.00 5.18
0.10 0.03 0.60 0.28 22.00 4.00 5.97 112.00 5.33
0.11 0.03 0.55 0.29 23.00 7.00 8.88 118.00 7.52
0.10 0.02 0.48 0.18 14.00 0.00 4.29 67.00 6.40
0.11 0.03 0.52 0.24 18.50 3.50 6.58 92.50 6.96
0.03 0.04 0.33 0.07 109.00 2.00 6.17 43.00 14.36
0.46 0.22 2.63 0.35 622.00 1.00 29.93 329.00 9.10
0.90 0.19 1.96 0.29 444.00 0.00 31.15 341.00 9.13
0.90 0.19 1.96 0.29 444.00 0.00 31.34 339.00 9.25
0.75 0.21 1.46 0.31 604.00 0.00 29.87 335.00 8.92
0.54 0.22 2.08 0.40 399.00 0.00 27.46 343.00 8.01
(continued on next page)
924 J. Sokolow et al. / Journal of Cleaner Production 225 (2019) 913e927
(continued )
Crop Crop Description Calcium, Iron, Zinc, Vitamin Thiamin Riboflavin Niacin Vitamin Folate, Vitamin NDS Energy NDS
Code Ca Fe Zn C, total B-6 DFE A, RAE (RNIs Density (RNIs
ascorbic per (kcal per per
acid 100 g) 100g) 100 kcal)
16024 BEANS, GREAT NORTHERN, MATURE 175.00 5.47 2.31 5.30 0.65 0.24 1.96 0.45 482.00 0.00 30.51 339.00 9.00
SEEDS,RAW
16027 BEANS, KIDNEY,ALL TYPES, MATURE 143.00 8.20 2.79 4.50 0.53 0.22 2.06 0.40 394.00 0.00 30.52 333.00 9.17
SEEDS,RAW
16030 BEANS, KIDNEY,CALIFORNIA RED,MATURE 195.00 9.35 2.55 4.50 0.53 0.22 2.06 0.40 394.00 0.00 31.29 330.00 9.48
SEEDS,RAW
16032 BEANS, KIDNEY,RED,MATURE SEEDS,RAW 83.00 6.69 2.79 4.50 0.61 0.22 2.11 0.40 394.00 0.00 29.80 337.00 8.84
16035 BEANS, KIDNEY,ROYAL RED,MATURE 131.00 8.70 2.66 4.50 0.39 0.24 2.11 0.40 393.00 0.00 29.47 329.00 8.96
SEEDS,RAW
16037 BEANS, NAVY,MATURE SEEDS,RAW 147.00 5.49 3.65 0.00 0.78 0.16 2.19 0.43 364.00 0.00 30.55 337.00 9.07
16040 BEANS, PINK,MATURE SEEDS,RAW 130.00 6.77 2.55 0.00 0.77 0.19 1.89 0.53 463.00 0.00 31.04 343.00 9.05
16042 BEANS, PINTO,MATURE SEEDS,RAW 113.00 5.07 2.28 6.30 0.71 0.21 1.17 0.47 525.00 0.00 29.87 347.00 8.61
16045 BEANS,SML WHITE, MATURE SEEDS,RAW 173.00 7.73 2.81 0.00 0.74 0.21 1.34 0.44 386.00 0.00 30.93 336.00 9.20
16047 BEANS,YEL,MATURE SEEDS,RAW 166.00 7.01 2.83 0.00 0.69 0.33 2.43 0.44 389.00 0.00 31.80 345.00 9.22
16049 BEANS, WHITE,MATURE SEEDS,RAW 240.00 10.44 3.67 0.00 0.44 0.15 0.48 0.32 388.00 0.00 29.91 333.00 8.98
16078 MOTHBEANS, MATURE SEEDS,RAW 150.00 10.85 1.92 4.00 0.56 0.09 2.80 0.37 649.00 2.00 30.36 343.00 8.85
16080 MUNG BNS,MATURE SEEDS,RAW 132.00 6.74 2.68 4.80 0.62 0.23 2.25 0.38 625.00 6.00 30.81 347.00 8.88
AVERAGE Beans, Only Mature Seeds Not Including 146.67 6.98 2.88 2.39 0.64 0.21 1.94 0.39 464.39 0.50 30.37 338.11 8.98
Broadbeans (Average)
16052 BROADBEANS (FAVA BEANS),MATURE 103.00 6.70 3.14 1.40 0.56 0.33 2.83 0.37 423.00 3.00 30.78 341.00 9.03
SEEDS,RAW
16085 PEAS,GRN,SPLIT, 37.00 4.82 3.55 1.80 0.73 0.22 2.89 0.17 274.00 7.00 26.08 352.00 7.41
MATURE SEEDS,RAW
16056 CHICKPEAS (GARBANZO BNS,BENGAL 57.00 4.31 2.76 4.00 0.48 0.21 1.54 0.54 557.00 3.00 27.86 378.00 7.37
GM),MATURE SEEDS,RAW
16060 COWPEAS, CATJANG,MATURE SEEDS,RAW 85.00 9.95 6.11 1.50 0.68 0.17 2.80 0.36 639.00 2.00 36.24 343.00 10.56
16062 COWPEAS, COMMON (BLACKEYES, 110.00 8.27 3.37 1.50 0.85 0.23 2.08 0.36 633.00 3.00 33.13 336.00 9.86
CROWDER,SOUTHERN),MATURE
SEEDS,RAW
AVERAGE Cowpeas (Average) 97.50 9.11 4.74 1.50 0.77 0.20 2.44 0.36 636.00 2.50 34.68 339.50 10.21
16101 PIGEON PEAS (RED GM),MATURE 130.00 5.23 2.76 0.00 0.64 0.19 2.97 0.28 456.00 1.00 28.51 343.00 8.31
SEEDS,RAW
16069 LENTILS,RAW 35.00 6.51 3.27 4.50 0.87 0.21 2.61 0.54 479.00 2.00 33.57 352.00 9.54
16144 LENTILS, PINK OR RED,RAW 48.00 7.39 3.60 1.70 0.51 0.11 1.50 0.40 204.00 3.00 23.70 358.00 6.62
AVERAGE Lentils (Average) 41.50 6.95 3.44 3.10 0.69 0.16 2.05 0.47 341.50 2.50 28.63 355.00 8.08
16076 LUPINS, MATURE SEEDS,RAW 176.00 4.36 4.75 4.80 0.64 0.22 2.19 0.36 355.00 0.00 31.51 371.00 8.49
12087 NUTS, CASHEW NUTS,RAW 37.00 6.68 5.78 0.50 0.42 0.06 1.06 0.42 25.00 0.00 20.22 553.00 3.66
12093 CHESTNUTS, CHINESE, 18.00 1.41 0.87 36.00 0.16 0.18 0.80 0.41 68.00 10.00 18.13 224.00 8.10
RAW
12097 CHESTNUTS, 27.00 1.01 0.52 43.00 0.24 0.17 1.18 0.38 62.00 1.00 19.19 213.00 9.01
EUROPEAN,RAW,
UNPEELED
12202 CHESTNUTS, JAPANESE,RAW 31.00 1.45 1.10 26.30 0.34 0.16 1.50 0.28 47.00 2.00 16.66 154.00 10.82
AVERAGE Chestnuts (Average) 25.33 1.29 0.83 35.10 0.25 0.17 1.16 0.36 59.00 4.33 18.00 197.00 9.31
12061 ALMONDS 269.00 3.71 3.12 0.00 0.21 1.14 3.62 0.14 44.00 0.00 23.80 579.00 4.11
12155 WALNUTS, ENGLISH 98.00 2.91 3.09 1.30 0.34 0.15 1.13 0.54 98.00 1.00 17.95 654.00 2.74
12151 PISTACHIO NUTS,RAW 105.00 3.92 2.20 5.60 0.87 0.16 1.30 1.70 51.00 26.00 28.89 560.00 5.16
12120 HAZELNUTS OR FILBERTS 114.00 4.70 2.45 6.30 0.64 0.11 1.80 0.56 113.00 1.00 22.43 628.00 3.57
16108 SOYBEANS, MATURE SEEDS,RAW 277.00 15.70 4.89 6.00 0.87 0.87 1.62 0.38 375.00 1.00 46.09 446.00 10.33
16087 PEANUTS,ALL TYPES,RAW 92.00 4.58 3.27 0.00 0.64 0.14 12.07 0.35 240.00 0.00 30.19 567.00 5.32
12036 SUNFLOWER SD KRNLS, DRIED 78.00 5.25 5.00 1.40 1.48 0.36 8.34 1.35 227.00 3.00 43.62 584.00 7.47
12021 SAFFLOWER SD KRNLS, DRIED 78.00 4.90 5.05 0.00 1.16 0.42 2.28 1.17 160.00 3.00 36.73 517.00 7.10
12023 SESAME SEEDS, WHOLE,DRIED 975.00 14.55 7.75 0.00 0.79 0.25 4.52 0.79 97.00 0.00 46.26 573.00 8.07
11109 CABBAGE,RAW 40.00 0.47 0.18 36.60 0.06 0.04 0.23 0.12 43.00 5.00 12.06 25.00 48.25
11007 ARTICHOKES, (GLOBE OR FRENCH),RAW 44.00 1.28 0.49 11.70 0.07 0.07 1.05 0.12 68.00 1.00 8.67 47.00 18.44
11011 ASPARAGUS,RAW 24.00 2.14 0.54 5.60 0.14 0.14 0.98 0.09 52.00 38.00 9.42 20.00 47.10
11250 LETTUCE, BUTTERHEAD (INCL 35.00 1.24 0.20 3.70 0.06 0.06 0.36 0.08 73.00 166.00 11.18 13.00 85.98
BOSTON&BIBB TYPES),RAW
11251 LETTUCE,COS OR ROMAINE,RAW 33.00 0.97 0.23 4.00 0.07 0.07 0.31 0.07 136.00 436.00 17.25 17.00 100.00
11252 LETTUCE, ICEBERG (INCL CRISPHEAD 18.00 0.41 0.15 2.80 0.04 0.03 0.12 0.04 29.00 25.00 3.68 14.00 26.26
TYPES),RAW
11253 LETTUCE,GRN LEAF,RAW 36.00 0.86 0.18 9.20 0.07 0.08 0.38 0.09 38.00 370.00 16.09 15.00 100.00
11257 LETTUCE,RED LEAF,RAW 33.00 1.20 0.20 3.70 0.06 0.08 0.32 0.10 36.00 375.00 14.95 16.00 93.43
AVERAGE Lettuce (Average) 31.00 0.94 0.19 4.68 0.06 0.06 0.30 0.08 62.40 274.40 12.63 15.00 81.14
11457 SPINACH,RAW 99.00 2.71 0.53 28.10 0.08 0.19 0.72 0.20 194.0 469.00 28.09 23.00 100.00
11529 TOMATOES,RED,RIPE,RAW,YEAR RND 10.00 0.27 0.17 13.70 0.04 0.02 0.59 0.08 15.00 42.00 6.66 18.00 37.00
AVERAGE
11135 CAULIFLOWER,RAW 22.00 0.42 0.27 48.20 0.05 0.06 0.51 0.18 57.00 0.00 14.67 25.00 58.68
11167 CORN,SWT,YEL,RAW 2.00 0.52 0.46 6.80 0.16 0.06 1.77 0.09 42.00 9.00 7.25 86.00 8.43
11422 PUMPKIN,RAW 21.00 0.80 0.32 9.00 0.05 0.11 0.60 0.06 16.00 426.00 15.52 26.00 59.71
11641 SQUASH, SMMR,ALL VAR,RAW 15.00 0.35 0.29 17.00 0.05 0.14 0.49 0.22 29.00 10.00 8.83 16.00 55.19
11643 SQUASH, WNTR,ALL VAR,RAW 28.00 0.58 0.21 12.30 0.03 0.06 0.50 0.16 24.00 68.00 8.56 34.00 25.17
J. Sokolow et al. / Journal of Cleaner Production 225 (2019) 913e927 925
(continued )
Crop Crop Description Calcium, Iron, Zinc, Vitamin Thiamin Riboflavin Niacin Vitamin Folate, Vitamin NDS Energy NDS
Code Ca Fe Zn C, total B-6 DFE A, RAE (RNIs Density (RNIs
ascorbic per (kcal per per
acid 100 g) 100g) 100 kcal)
11218 GOURD,WHITE-FLOWERED 26.00 0.20 0.70 10.10 0.03 0.02 0.32 0.04 6.00 0.00 4.64 14.00 33.11
(CALABASH),RAW
11220 GOURD, DISHCLOTH (TOWELGOURD),RAW 20.00 0.36 0.07 12.00 0.05 0.06 0.40 0.04 7.00 0.00 4.74 20.00 23.71
AVERAGE Pumpkin, Squash, Gourd (Average) 23.00 0.28 0.39 11.05 0.04 0.04 0.36 0.04 6.50 0.00 4.69 17.00 28.41
11205 CUCUMBER, WITH PEEL,RAW 16.00 0.28 0.20 2.80 0.03 0.03 0.10 0.04 7.00 5.00 2.36 15.00 15.74
11209 EGGPLANT,RAW 9.00 0.23 0.16 2.20 0.04 0.04 0.65 0.08 22.00 1.00 3.08 25.00 12.34
11333 PEPPERS,SWT,GRN, 10.00 0.34 0.13 80.40 0.06 0.03 0.48 0.22 10.00 18.00 13.79 20.00 68.97
RAW
11670 PEPPERS,HOT CHILI,GRN,RAW 18.00 1.20 0.30 242.50 0.09 0.09 0.95 0.28 23.00 59.00 17.66 40.00 44.15
AVERAGE Peppers, Sweet and Hot, Green (Average) 14.00 0.77 0.22 161.45 0.07 0.06 0.72 0.25 16.50 38.50 15.73 30.00 56.56
11282 ONIONS,RAW 23.00 0.21 0.17 7.40 0.05 0.03 0.12 0.12 19.00 0.00 4.16 40.00 10.41
11677 SHALLOTS,RAW 37.00 1.20 0.40 8.00 0.06 0.02 0.20 0.35 34.00 0.00 7.27 72.00 10.09
AVERAGE Onions and Shallots (Average) 30.00 0.71 0.29 7.70 0.05 0.02 0.16 0.23 26.50 0.00 5.72 56.00 10.25
11199 YARDLONG BEAN,RAW 50.00 0.47 0.37 18.80 0.11 0.11 0.41 0.02 62.00 43.00 10.59 47.00 22.52
11052 BEANS, SNAP,GREEN, 37.00 1.03 0.24 12.20 0.08 0.10 0.73 0.14 33.00 35.00 8.83 31.00 28.48
RAW
AVERAGE Beans, Green (Average) 43.50 0.75 0.31 15.50 0.09 0.11 0.57 0.08 47.50 39.00 9.71 39.00 25.50
11304 PEAS, GREEN,RAW 25.00 1.47 1.24 40.00 0.27 0.13 2.09 0.17 65.00 38.00 20.22 81.00 24.97
11124 CARROTS,RAW 33.00 0.30 0.24 5.90 0.07 0.06 0.98 0.14 19.00 835.00 15.14 41.00 36.94
11278 OKRA,RAW 82.00 0.62 0.58 23.00 0.20 0.06 1.00 0.22 60.00 36.00 13.97 33.00 42.32
9040 BANANAS,RAW 5.00 0.26 0.15 8.70 0.03 0.07 0.67 0.37 20.00 3.00 6.61 89.00 7.43
9277 PLANTAINS,RAW 3.00 0.60 0.14 18.40 0.05 0.05 0.69 0.30 22.00 56.00 10.32 122.00 8.46
9200 ORANGES,RAW,ALL COMM VAR 40.00 0.10 0.07 53.20 0.09 0.04 0.28 0.06 30.00 11.00 13.28 47.00 28.25
9218 TANGERINES, (MANDARIN 37.00 0.15 0.07 26.70 0.06 0.04 0.38 0.08 16.00 34.00 9.53 53.00 17.98
ORANGES),RAW
9433 CLEMENTINES,RAW 30.00 0.14 0.06 48.80 0.09 0.03 0.64 0.08 24.00 0.00 12.90 47.00 27.45
AVERAGE Tangerine and Clementine (Average) 33.50 0.15 0.07 37.75 0.07 0.03 0.51 0.08 20.00 17.00 11.22 50.00 22.72
9150 LEMONS,RAW, 26.00 0.60 0.06 53.00 0.04 0.02 0.10 0.08 11.00 1.00 12.05 29.00 41.54
WITHOUT PEEL
9156 LEMON PEEL,RAW 134.00 0.80 0.25 129.00 0.06 0.08 0.40 0.17 13.00 3.00 15.05 47.00 32.01
9159 LIMES,RAW 33.00 0.60 0.11 29.10 0.03 0.02 0.20 0.04 8.00 2.00 8.35 30.00 27.82
AVERAGE Lemons and Limes (Average) 64.33 0.67 0.14 70.37 0.04 0.04 0.23 0.10 10.67 2.00 11.81 35.33 33.79
9111 GRAPEFRUIT,RAW,PINK&RED&WHITE,ALL 12.00 0.09 0.07 34.40 0.04 0.02 0.25 0.04 10.00 46.00 10.58 32.00 33.08
AREAS
9003 APPLES,RAW,WITH SKIN 6.00 0.12 0.04 4.60 0.02 0.03 0.09 0.04 3.00 3.00 2.05 52.00 3.94
9252 PEARS,RAW 9.00 0.18 0.10 4.30 0.01 0.03 0.16 0.03 7.00 1.00 2.08 57.00 3.66
9021 APRICOTS,RAW 13.00 0.39 0.20 10.00 0.03 0.04 0.60 0.05 9.00 96.00 7.56 48.00 15.74
9063 CHERRIES, SOUR,RED, 16.00 0.32 0.10 10.00 0.03 0.04 0.40 0.04 8.00 64.00 6.12 50.00 12.25
RAW
9070 CHERRIES, SWEET,RAW 13.00 0.36 0.07 7.00 0.03 0.03 0.15 0.05 4.00 3.00 3.07 63.00 4.88
AVERAGE Cherries (Average) 14.50 0.34 0.09 8.50 0.03 0.04 0.28 0.05 6.00 33.50 4.60 56.50 8.56
9236 PEACHES,YEL,RAW 6.00 0.25 0.17 6.60 0.02 0.03 0.81 0.03 4.00 16.00 3.64 39.00 9.33
9191 NECTARINES,RAW 6.00 0.28 0.17 5.40 0.03 0.03 1.13 0.03 5.00 17.00 3.70 44.00 8.41
AVERAGE Peaches and Nectarines (Average) 6.00 0.27 0.17 6.00 0.03 0.03 0.97 0.03 4.50 16.50 3.67 41.50 8.87
9279 PLUMS,RAW 6.00 0.17 0.10 9.50 0.03 0.03 0.42 0.03 5.00 17.00 3.98 46.00 8.65
9316 STRAWBERRIES,RAW 16.00 0.41 0.14 58.80 0.02 0.02 0.39 0.05 24.00 1.00 12.13 32.00 37.90
9302 RASPBERRIES,RAW 25.00 0.69 0.42 26.20 0.03 0.04 0.60 0.06 21.00 2.00 8.92 52.00 17.15
9107 GOOSEBERRIES,RAW 25.00 0.31 0.12 27.70 0.04 0.03 0.30 0.08 6.00 15.00 8.67 44.00 19.71
9083 CURRANTS, EUROPEAN BLACK,RAW 55.00 1.54 0.27 181.00 0.05 0.05 0.30 0.07 0.00 12.00 13.56 63.00 21.52
9084 CURRANTS,RED& 33.00 1.00 0.23 41.00 0.04 0.05 0.10 0.07 8.00 2.00 11.80 56.00 21.07
WHITE,RAW
AVERAGE Currants (Average) 44.00 1.27 0.25 111.00 0.05 0.05 0.20 0.07 4.00 7.00 12.68 59.50 21.30
9050 BLUEBERRIES,RAW 6.00 0.28 0.16 9.70 0.04 0.04 0.42 0.05 6.00 3.00 4.07 57.00 7.14
9078 CRANBERRIES,RAW 8.00 0.23 0.09 14.00 0.01 0.02 0.10 0.06 1.00 3.00 4.26 46.00 9.26
9129 Grapes, muscadine, raw 37.00 0.26 0.11 6.50 0.00 1.50 0.00 0.00 2.00 3.00 12.26 57.00 21.50
9131 GRAPES, AMERICAN TYPE (SLIP SKN),RAW 14.00 0.29 0.04 4.00 0.09 0.06 0.30 0.11 4.00 5.00 3.63 67.00 5.42
9132 GRAPES,RED OR GRN (EURO TYPE, SUCH AS 10.00 0.36 0.07 3.20 0.07 0.07 0.19 0.09 2.00 3.00 3.05 69.00 4.42
THOMPSON SEEDLESS),RAW
AVERAGE Grapes (Average) 20.33 0.30 0.07 4.57 0.05 0.54 0.16 0.07 2.67 3.67 6.31 64.33 10.45
9326 WATERMELON,RAW 7.00 0.24 0.10 8.10 0.03 0.02 0.18 0.05 3.00 28.00 3.99 30.00 13.31
9181 MELONS, CANTALOUPE, RAW 9.00 0.21 0.18 36.70 0.04 0.02 0.73 0.07 21.00 169.00 16.20 34.00 47.63
9183 MELONS, CASABA,RAW 11.00 0.34 0.07 21.80 0.02 0.03 0.23 0.16 8.00 0.00 7.02 28.00 25.09
9184 MELONS, HONEYDEW, 6.00 0.17 0.09 18.00 0.04 0.01 0.42 0.09 19.00 3.00 6.11 36.00 16.97
RAW
AVERAGE Melons (Average) 8.67 0.24 0.11 25.50 0.03 0.02 0.46 0.11 16.00 57.33 9.78 32.67 29.90
9089 FIGS,RAW 35.00 0.37 0.15 2.00 0.06 0.05 0.40 0.11 6.00 7.00 3.47 74.00 4.69
9176 MANGOS,RAW 11.00 0.16 0.09 36.40 0.03 0.04 0.67 0.12 43.00 54.00 13.02 60.00 21.70
9037 AVOCADOS,RAW,ALL COMM VAR 12.00 0.55 0.64 10.00 0.07 0.13 1.74 0.26 81.00 7.00 10.16 160.00 6.35
9266 PINEAPPLE,RAW,ALL VAR 13.00 0.29 0.12 47.80 0.08 0.03 0.50 0.11 18.00 3.00 12.96 50.00 25.93
9421 DATES, MEDJOOL 64.00 0.90 0.44 0.00 0.05 0.06 1.61 0.25 15.00 7.00 5.89 277.00 2.12
9087 DATES, DEGLET NOOR 39.00 1.02 0.29 0.40 0.05 0.07 1.27 0.17 19.00 0.00 4.73 282.00 1.68
(continued on next page)
926 J. Sokolow et al. / Journal of Cleaner Production 225 (2019) 913e927
(continued )
Crop Crop Description Calcium, Iron, Zinc, Vitamin Thiamin Riboflavin Niacin Vitamin Folate, Vitamin NDS Energy NDS
Code Ca Fe Zn C, total B-6 DFE A, RAE (RNIs Density (RNIs
ascorbic per (kcal per per
acid 100 g) 100g) 100 kcal)
AVERAGE Dates (Average) 38.67 0.74 0.28 16.07 0.06 0.05 1.13 0.18 17.33 3.33 5.31 203.00 1.90
9148 KIWIFRUIT,GRN,RAW 34.00 0.31 0.14 92.70 0.03 0.03 0.34 0.06 25.00 4.00 12.51 61.00 20.51
12104 COCONUT MEAT,RAW 14.00 2.43 1.10 3.30 0.07 0.02 0.54 0.05 26.00 0.00 5.75 354.00 1.62
12119 COCONUT H2O (LIQ FROM COCONUTS) 24.00 0.29 0.10 2.40 0.03 0.06 0.08 0.03 3.00 0.00 2.09 19.00 11.01
AVERAGE Coconuts, Meat and H20 (Average) 19.00 1.36 0.60 2.85 0.05 0.04 0.31 0.04 14.50 0.00 3.92 186.50 6.32Appendix C. Supplementary data
Supplementary data to this article can be found online at
https://doi.org/10.1016/j.jclepro.2019.03.056.References
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