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Food waste contributes to excess consumption of freshwater and fossil fuels which, along with methane and CO(2) emissions from decomposing food, impacts global climate change. Here, we calculate the energy content of nationwide food waste from the difference between the US food supply and the food consumed by the population. The latter was estimated using a validated mathematical model of metabolism relating body weight to the amount of food eaten. We found that US per capita food waste has progressively increased by approximately 50% since 1974 reaching more than 1400 kcal per person per day or 150 trillion kcal per year. Food waste now accounts for more than one quarter of the total freshwater consumption and approximately 300 million barrels of oil per year.
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The Progressive Increase of Food Waste in America and
Its Environmental Impact
Kevin D. Hall*, Juen Guo, Michael Dore, Carson C. Chow*
Laboratory of Biological Modeling, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, Maryland, United States of America
Food waste contributes to excess consumption of freshwater and fossil fuels which, along with methane and CO
from decomposing food, impacts global climate change. Here, we calculate the energy content of nationwide food waste
from the difference between the US food supply and the food consumed by the population. The latter was estimated using
a validated mathematical model of metabolism relating body weight to the amount of food eaten. We found that US per
capita food waste has progressively increased by ,50% since 1974 reaching more than 1400 kcal per person per day or 150
trillion kcal per year. Food waste now accounts for more than one quarter of the total freshwater consumption and ,300
million barrels of oil per year.
Citation: Hall KD, Guo J, Dore M, Chow CC (2009) The Progressive Increase of Food Waste in America and Its Environmental Impact. PLoS ONE 4(11): e7940.
Editor: Thorkild I. A. Sorensen, Institute of Preventive Medicine, Denmark
Received September 8, 2009; Accepted October 26, 2009; Published November 25, 2009
This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration which stipulates that, once placed in the public
domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
Funding: This research was supported by the Intramural Research Program of the National Institutes of Health, National Institute of Diabetes and Digestive and
Kidney Diseases. 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.
* E-mail: (KDH); (CCC)
Recent spikes in food prices have led to increasing concern
about global food shortages and the apparent need to increase
agricultural production [1,2]. Surprisingly little discussion has
been devoted to the issue of food waste. Quantifying food waste at
a national level is difficult because traditional methods rely on
structured interviews, measurement of plate waste, direct exam-
ination of garbage, and application of inferential methods using
waste factors measured in sample populations and applied across
the food system [3–6]. In contrast, national agricultural produc-
tion, utilization, and net external trade are tracked and codified
in detailed food balance sheets published by the Food and
Agriculture Organization of the United Nations (FAO) [7]. The
food balance sheets provide a comprehensive assessment of the
national food supply, including alcohol and beverages, adjusted for
any change of food stocks over the reference period [8]. Since
1974, there has been a progressive increase in the per capita US
food supply. Over the same period, there has also been an increase
of body weight as manifested by the US obesity epidemic. We
sought to estimate the energy content of food waste by comparing
the US food supply data with the calculated food consumed by the
US population.
Energy from ingested food supports basal metabolism and
physical activities, both of which are functions of body weight.
Surplus ingested energy is stored in the body and is reflected by a
change of body weight. Because the average body weight of the
US population has been increasing over the past 30 years, it is not
immediately clear how much of the increased food supply was
ingested by the population. Quantifying the food intake underlying
an observed change of body weight requires knowing the energy
cost of tissue deposition and the increased cost of physical activ-
ity and metabolic rate with weight gain. Here, we develop and
validate a mathematical model of human energy expenditure that
includes all of these factors and used the model to calculate the
average increase of food intake underlying the observed increase of
average adult body weight in the US since 1974 as measured by
the US National Health and Nutrition Examination Survey
(NHANES) [9].
Figure 1A shows the increase of average body weight among US
adults over the past 30 years (D). Assuming no change of physical
activity, Figure 1B shows our model predicted average food intake
(solid curve) and 95% confidence intervals (dashed curves)
underlying the observed weight gain (see Methods for model
details). Figure 1B also plots the US food supply data from the FAO
food balance sheets (#)[7] and the US Department of Agriculture
(USDA) food availability data adjusted for wastage (&)[10] over the
period 1974–2003. Figure 1C shows the progressive increase of per
capita food waste in America (solid curve) calculated by subtracting
the model predicted average food intake from the FAO per capita
food supply data. In 1974 approximately 900 kcal per person per
day was wasted whereas in 2003 Americans wasted ,1400 kcal per
person per day or ,150 trillion kcal per year. Figure 1C shows that
our estimate of the increasing energy content of US food waste is
corroborated by the parallel increase of the per capita annual mass
of municipal solid food waste (m) calculated from data supplied by
the US Environmental Protection Agency [11]. Municipal solid
food waste accounts for ,30% of the total wasted food energy
assuming that solid food from the US diet has an energy density of
1.9 kcal/g [12]. Figure 1D shows that food waste has progressively
increased from about 30% of the available food supply in 1974 to
almost 40% in recent years (solid curve) whereas the USDA
estimate of food waste (calculated by subtracting the USDA food
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availability data adjusted for spoilage and wastage from the FAO
food supply data) was an approximately constant proportion of the
total food supply (&). While the USDA estimate of food waste was
within 5% of our calculation in 1974, it was ,25% too low in 2003.
The calculated progressive increase of food waste suggests that
the US obesity epidemic has been the result of a ‘‘push effect’’ of
increased food availability and marketing with Americans being
unable to match their food intake with the increased supply of
cheap, readily available food. Thus, addressing the oversupply of
food energy in the US may help curb the obesity epidemic as well
as decrease food waste, which has profound environmental
Assuming that agriculture utilizes about 70% of the freshwater
supply [13], our calculations imply that more than one quarter of
total freshwater use is accounted for by wasted food. Furthermore,
given that the average farm requires 3 kcal of fossil fuel energy to
produce 1 kcal of food (before accounting for energy requirements
of food processing and transportation) [14], wasted food accounts
for ,300 million barrels of oil per year representing ,4% of the
total US oil consumption in 2003 [15]. In addition to this wasteful
consumption of fossil fuels and their direct impact on climate
change, food waste rotting in landfills produces substantial
quantities of methane [16] – a gas with 25 fold more potent
global warming potential than CO
[17] which would have been
the primary end product had the food been eaten and metabolized
by humans.
Our food waste estimate resulted from subtracting the
calculated average food energy intake from the food supply of
the US population. Thus, there are two potential sources of error
in our food waste estimate. First, the FAO food balance sheets
were the source of our estimate of the US food supply [7]. The
accuracy of food balance sheets has been questioned, especially for
developing nations with a relatively high reliance on subsistence
Figure 1. Food Supply, Intake, and Waste in America. (A) The average adult body weight (D) as measured by the National Health and Nutrition
Examination Survey. (B). Per capita U.S. food availability unadjusted (#) and adjusted for wastage (&) according to the United States Department of
Agriculture (USDA). The solid curve represents the mathematical model prediction of average food intake change (dashed curves indicate695%
confidence intervals). (C) Energy content of per capita U.S. food waste predicted using our mathematical model (solid curve, left axis). The right axis
plots the per capita annual mass of municipal solid food waste (m). (D) Percentage of available food energy wasted as calculated by previous USDA
estimates (&) and predicted using our mathematical model (solid curve).
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farming whose products rarely enter the marketplace and are
therefore are difficult to account [8,18]. While such issues are
certainly less problematic for affluent nations like the US, there
remain significant uncertainties regarding the absolute energy
content of the food supply [8,18]. However, our results rely
primarily on the observed progressive increase of the food supply
rather than its absolute level. Thus, unless the uncertainties of the
US food supply data are systematically biased to progressively
overestimate food supply at later dates, then our conclusions about
the progressive increase of food waste remain valid.
The second source of error in our calculation of food waste
results from our mathematical model estimates of average food
intake. The fact that average body weight of the US population
has increased in parallel with the increasing food supply raises the
question of how much of the additional available food was actually
ingested by the population. Without an accurate mathematical
model of human metabolism, we could not determine how
increasing food intake quantitatively translates into a change of
body weight. Figure 2A demonstrates that our model accurately
calculated the energy intake changes underlying the observed
weight gain in two controlled over-feeding experiments [19,20]
and Figure 2B shows that our model accurately predicted the
relationship between weight change and energy expenditure in
longitudinal data from a cohort of Pima Indians after a 3.6 year
follow-up [21]. Compared to the 30 year time course of the
NHANES data, we acknowledge that our model validation results
are somewhat limited. Nevertheless, our model includes all of
the main contributors to how food intake impacts body weight
and we tested the robustness of our conclusions to uncertainties
of the assumed parameter values by Monte Carlo sampling over
parameter sets (see Methods) to generate the 95% confidence
intervals (dashed curves in Figure 1). Furthermore, our estimate of
a,50% increase of per capita food waste over the past 30 years is
paralleled by a similar increase of per capita municipal solid food
waste as depicted in Figure 1C thereby providing independent
corroboration of our findings.
Our estimates of food waste likely represent lower bounds since
we did not simulate the effects of a progressive decrease of physical
activity that may have contributed to the US obesity epidemic [22].
However, some investigators contend that physical activity has not
declined in the past few decades [23] which is in accordance with
our model assumption. We have also not corrected the per capita
adult food availability given that children consume less food than
adults on an absolute basis. Accounting for both of these factors
would increase the calculated food waste and therefore our
estimates are conservative.
Our calculation of food wasted by the US population does not
rely on direct measurements of waste produced by small groups
that often know they are being investigated [6] nor individual
assessments of food intake which are known to significantly
underestimate actual food consumption [24]. Furthermore, infer-
ential methods are prone to cumulative errors when using assumed
food waste factors applied to various stages along the food system
[3–5]. Previous estimates of food waste using these traditional
methods have typically concluded that about one third of food mass
is wasted [4,5]. The USDA estimate that 27% of food is wasted is
acknowledged to be an underestimate [4]. Therefore, the USDA
food availability data is known to overstate the amount of food that
people actually ingest [25]. Our results imply that the assumption of
a roughly constant proportion of food waste calculated by the
USDA has become progressively worse over time.
The principle of energy conservation implies that the energy
content of food is closely related to the energy requirements for
agricultural production as well as the methane and CO
produced by decomposition of wasted food. Thus, the energy
content of wasted food may be a more important environmental
index than the mass of wasted food as determined by more
traditional methods. Nevertheless, traditional methods of measur-
ing food waste provide important information about the types of
foods wasted and the relative contribution of waste along various
points of the supply chain from farm to fork. Because our
methodology calculates food intake by the population and tracks
food energy and not food types, we cannot address such issues.
Nevertheless, the progressive deviation of our calculated wasted
food energy compared with the USDA adjustment for wastage
suggests that traditional methods are increasingly missing large
quantities of food waste in America.
The basis of our mathematical model is the energy balance
equation where the rate of change in stored body energy is given
by the difference between the metabolizable energy intake rate I
and the energy expenditure rate E
Figure 2. Mathematical model validation. (A) The experimentally
imposed increases of food intake during controlled over-feeding
experiments (black bars) along with model predicted values (white
bars) calculated using the measured body weight changes. (B) Model
predicted relationship between changes of 24 hour energy expenditure
and body weight change after 3.6 years of over- and under-feeding (¤)
along with the best-fit regression line determined from longitudinal
measurements in a cohort of Pima Indians followed for the same
average time interval. (mean6SD).
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dt ~I{Eð1Þ
where BW is the body weight, and ris the energy density of the
change in the body weight. We can express the energy expenditure
rate as
dt zgFFM
dt ð2Þ
is a constant, c
= 22 kcal/kg/day and c
= 3.2 kcal/
kg/day are the regression coefficients relating resting metabolic rate
versus fat-free mass (FFM) and fat mass (F), respectively [26].
Physical activity energy expenditure is proportional to body weight
for most activities [27] and drepresents the level of physical activity.
The parameter baccounts for the adaptation of energy expenditure
during a diet perturbation DIand gFis 180 kcal/kg and gFFM is
230 kcal/kg account for the biochemical cost of tissue deposition
[28,29] assuming that the change of FFM is primarily accounted
for by body protein and its associated water [30]. We note that
FFM,F,I,BW,Tand dare all functions of time.
Consider a population where each individual’s weight change
obeys equation (1) with their own individual intake and
expenditure functions. We take a sample sum over (1) to obtain
Ii{Ki{cFFM FFMi{cFFi{diBWi{biDIi{gF
dt zgFFM
where each subject is indexed by i,
is the number of subjects in
the population, and R~drBWðÞ
=dt is the rate of change of
energy stored in the body. Dividing both sides of equation (3) by
, gives us the sample mean of the population for all terms of the
energy balance equation (1):
dt {gFFM
Since FFM =BWF, we can rewrite equation (4) as
RR~I{(cF{cFFM )F{
dt {gFFM
For the first NHANES phase from 1971–1974, we assumed that
people were approximately weight-stable corresponding to a state of
energy balance. Using the fact that d|BW ~d|BW zCov d,BWðÞ
and b|DI~b|DIzCov b,DIðÞ, energy balance implies that the
following equation must hold when the system is in an initial state of
energy balance such that
II0{(cF{cFFM )F0{cFFM zd0
{Cov(d,BW ){Cov(b,DI)ð6Þ
Therefore, assuming that the covariance of physical activity and
body weight and the covariance of the parameter bwith changes
of food intake are approximately constant, substitution of equation
6 into equation 5 gives:
DI{(cF{cFFM )F{F0
dt {gFFM
where the average value of the parameter b= 0.24 was determined
using under-feeding studies [29].
To estimate the rate of change of stored energy we consider fat
and fat-free mass changes separately:
dt zrFFM
dt ð8Þ
where rF= 9400 kcal/kg and rFFM = 1800 kcal/kg are the energy
densities for changes in fat and fat-free masses, respectively [30].
The relative change of FFM and Fcan be described by the Forbes
theory of body composition change:
where C= 10.4 kg is a parameter describing how body composi-
tion changes as a function of the initial body fat mass, F
[31]. To
calculate the value of the parameter awe required an estimate of
the initial average body fat mass which was not directly measured
in NHANES. We therefore estimated initial body fat mass from
the body mass index (BMI) via the equations published by Jackson
et al. [32] for men and women:
where the mean values are taken over the men and women
populations respectively. The population mean for the fat mass is
then given by a weighted average of that of the men and women,
ðÞFM~19:1kg, where pW~0:525 is the
proportion of women in the NHANES population. This initial
average fat mass is then used in equation (9) to calculate a= 0.54.
Equation 9 implies that:
dt zrFFM zgFFM
~gFzrFza(gFFM zrFFM )
Therefore, substituting equations 11 and 12 into equation 7
gives the change of energy intake underlying the observed increase
of average body weight:
gFzrFzagFFM zarFFM
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Using the nominal parameter values and assuming no change of
physical activity, equation 13 can be represented as:
dt |9100 kcal=kgzBW{BW0
|22 kcal=kg=dð13Þ
The first term of equation 14 evaluates to ,10 kcal/d for the
NHANES data since the rate of change of average body weight
was only ,9.5610
kg/d. The second term evaluates to
,220 kcal/d for the NHANES data since the change of average
body weight was ,10 kg between 1974 and 2003.
Our mathematical model was previously demonstrated to give
accurate results in situations of underfeeding and body weight loss
[29]. In the context of weight gain, we validated our model by
predicting the changes of energy intake in the controlled feeding
experiments of Diaz et al. [20] and Bouchard et al. [19] who
overfed subjects by 15006400 kcal/d and 840 kcal/d for 42 and
100 days, respectively. Figure 2A shows that using the observed
weight changes, our model predicted that energy intake was
increased by 17006370 kcal/d and 7506230 kcal/d for the Diaz
and Bouchard studies, respectively, which corresponds well with
their actual intakes.
While these results give us some confidence in the validity of our
model in response to weight gain, we note that the controlled over-
feeding experiments were conducted in a small number subjects
over a relatively short period of time. We could only find one study
that measured longitudinal changes of energy expenditure with
weight gain over extended time periods [21]. In that study, Weyer et
al. investigated a cohort of Pima Indian subjects with an average 3.6
year follow-up and Figure 2B plots the best-fit regression line to the
measured changes of energy expenditure (via indirect calorimetry)
versus weight change [21]. Figure 2B also shows our model
predictions (¤) of energy expenditure change as a function of body
weight change in response to 3.6 years of over- and under-feeding to
various degrees. While the model results correspond well with the
regression line fit to the indirect calorimetry data, it is apparent that
the model predicts a slightly greater slope than was indicated in the
best-fit regression line. We hypothesize that the discrepancy is due
to the limited physical activity of the study subjects during the
measurement period inside the indirect calorimetry chamber. Since
physical activity energy expenditure is proportional to body weight,
decreased physical activity would result in a decreased slope of the
relationship between energy expenditure versus weight change.
To calculate the absolute level of energy intake corresponding to
the NHANES data, we assumed that the average initial energy
intake was I0= 2100 kcal/d calculated using the energy require-
ment equations of the Institute of Medicine of the National
Academies [33] for a sedentary population corresponding to the
average demographics of the initial adult NHANES population.
The initial value for I0also closely matched the USDA estimated
per capita food availability in 1974 adjusted for spoilage and
wastage [10]. Our estimate of the food waste was given by:
 ð14Þ
where FA is the per capita food energy availability as estimated
from US food balance sheets provided by the Food and
Agriculture Organization [7]. To investigate how our calculation
of food waste compares to current USDA estimates, we compared
our estimated energy intake, I0zDI, with the USDA per capita
food availability corrected for spoilage and wastage.
To calculate the confidence intervals of our calculations, each
model parameter value was randomly selected from a normal
distribution with mean and standard deviations given in Table 1.
The parameter ranges were estimated using the reported uncer-
tainties of the measured parameter values, where available. In the
case of the Forbes constant, C, and the physical activity parameter,
d, we chose a 50% uncertainty to reflect the potential for high
variability of these parameters across the population. We performed
simulations and report the mean and 95% confidence intervals
for the predicted food intake and waste calculations.
Author Contributions
Conceived and designed the experiments: KDH CCC. Performed the
experiments: KDH CCC. Analyzed the data: KDH JG MD CCC.
Contributed reagents/materials/analysis tools: KDH CCC. Wrote the
paper: KDH CCC.
1. von Braun J, Ahmed A, Asenso-Okyere K, Fan S, Gulati A, et al. (2008) High
food prices: The what, who, and how of proposed policy actions. Washington
D.C.: International Food Policy Research Institute.
2. Wiggins S, Levy S (2008) Rising food prices: Cause for concern. London:
Overseas Development Institute.
3. Griffin M, Sobal J, Lyson TA (200 9) An analysis of a community food waste
stream. Agric Hum Values 26: 67–81.
4. Kantor LS, Lipton K, Manchester A, Oliveira V (1997) Estimating and
addressing America’s food losses. Food Review 20: 2–12.
5. Muth MK, Kosa KM, Nielsen SM, Karns SA (2007) Exploratory research on
estimation of consumer-level food loss conversion factors, Agreement No. 58-4000-
6-0121, Final Report.
6. Ventour L (2008) The food we waste. WRAP and Exodus market research.
7. FAOSTAT. Food and Agriculture Organization. Available: http: //faostat.fao.
8. (2001) Food balance sheets: A handbook. Rome: Food and Agriculture
Organization of the United Nations.
9. National Health and Nutrition Examination Survey. Centers for Disea se
Control and Prevention. Available:
10. Food Availability (Per Capita) Data System. United States Department of
Agriculture Economic Research Service. Available:
11. (2007) Municipal solid waste in the United States: 2007 facts and figures (Table 9).
United States Environmental Protection Agency, Office of Solid Waste.
12. Kant AK, Graubard BI (2005) Energy density of diets reported by American
adults: association with food group intake, nutrient intake, and body weight.
Int J Obes (Lond) 29: 950–956.
13. Postel SL, Daily GC, Ehrlich PR (1996) Human appropriatio n of renewable
fresh water. Science 271: 785–788.
14. Horrigan L, Lawrence RS, Walker P (2002) How sustainable agriculture can
address the environmental and human health harms of industrial agriculture.
Environmental Health Perspectives 110: 445–456.
15. U.S. product supplied for crude oil and petroleum products. Washington D.C.:
Energy Information Administration, U.S. Department of Energy.
Table 1. Mathematical model parameters.
Parameter Value (mean6SD) Description
2264 kcal/kg/d Resting metabolic rate coefficient for FFM
3.662 kcal/kg/d Resting metabolic rate coefficient for F
d764 kcal/kg/d Physical activity coefficient
b0.2460.1 Adaptive thermogenesis parameter
2306100 kcal/kg Energy cost for FFM deposition
180620 kcal/kg Energy cost for F deposition
C10.465 kg Forbes body composition parameter
II021006100 kcal/d Average energy intake in 1974
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16. Agency USEP (2009) U.S. Emissions Inventory 2009: Invento ry of U.S.
Greenhouse Gas Emissions and Sinks: 1990–2007.
17. Forster P, Ramaswamy V, Artaxo P, Berntsen T, Betts R, et al. (2007) Changes in
Atmospheric Constituents and in Radiative Forcing. In: Solomon S, Qin D,
Manning M, Chen Z, Marquis M, et al, eds. Climate Change 2007: The Physical
Science Basis Contribution of Working Group I to the Fourth Assessment Report
of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge
University Press.
18. Smil V (2000) Feeding the world: A challenge for the twenty-first century.
Cambridge: The MIT Press.
19. Bouchard C, Tremblay A, Despres JP, Nadeau A, Lupien PJ, et al. (1990) The
response to long-term overfeeding in identical twins. N Engl J Med 322:
20. Diaz EO, Prentice AM, Goldberg GR, Murgatroyd PR, Coward WA (1992)
Metabolic response to experimental overfeeding in lean and overweight healthy
volunteers. Am J Clin Nutr 56: 641–655.
21. Weyer C, Pratley RE, Salbe AD, Bogardus C, Ravussin E, et al. (2000) Energy
expenditure, fat oxidation, and body weight regulation: a study of metabolic
adaptation to long-term weight change. J Clin Endocrinol Metab 85:
22. Hill JO, Wyatt HR, Reed GW, Peters JC (2003) Obesity and the environment:
where do we go from here? Science 299: 853–855.
23. Swinburn BA, Sacks G, Lo SK, Westerterp KR, Rush EC, et al. (2009)
Estimating the changes in energy flux that characterize the rise in obesity
prevalence. Am J Clin Nutr.
24. Schoeller DA (1990) How accurate is self-reported dietary energy intake? Nutr
Rev 48: 373–379.
25. Putnam J, Allshouse J, Kantor LS (2002) U.S. per capita food supply trends:
More calories, refined carbohydrates, and fats. Food Review 25: 2–15.
26. Nelson KM, Weinsier RL, Long CL, Schutz Y (1992) Prediction of resting
energy expenditure from fat-free mass and fat mass. Am J Clin Nutr 56:
27. Blaxter K (1989) Energy metabolis m in animals and man. Cambridge:
Cambridge University Press.
28. Hall KD (2009) Mathematical Modeling of Energy Expenditure during Tissue
Deposition. Br J Nutr: submitted.
29. Hall KD, Jordan PN (2008) Modeling weight-loss maintenance to help prevent
body weight regain. Am J Clin Nutr 88: 1495–1503.
30. Hall KD (2008) What is the required energy deficit per unit weight loss?
Int J Obes (Lond) 32: 573–576.
31. Forbes GB (1987) Lean body mass-body fat interrelationships in humans. Nutr
Rev 45: 225–231.
32. Jackson AS, Stanforth PR, Gagnon J, Rankinen T, Leon AS, et al. (2002) The
effect of sex, age and race on estimating percentage body fat from body mass
index: The Heritage Family Study. Int J Obes Relat Metab Disord 26: 789–796.
33. (2002) Dietary reference intakes for energy, carbohydrate, fiber, fat, fatty acids,
cholesterol, protein, and amino acids. Washington DC: Institute of Medicine of
the National Academies, The National Academies Press.
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... Selain itu, berdasarkan perkiraan dari Layanan Riset Ekonomi USDA tentang kehilangan makanan 31 persen di tingkat ritel dan konsumen, food waste di Amerika yang diperkirakan berada di kisaran 30-40% dari pasokan makanan ini setara dengan 133 miliar pound dan hampir 161 miliar dollar pada tahun 2010. Dalam sebuah studi, ditemukan juga bahwa food waste per kapita AS telah meningkat secara progresif sebesar 50% sejak tahun 1974 mencapai lebih dari 1400 kkal per orang per hari atau sekitar 150 triliun kkal per tahun (Kevin D. Hall, et al., 2009). ...
... "Americans are unable to match their food intake with the increased supply of cheap, readily available food" (Kevin D. Hall, et al., 2009). ...
Full-text available
One of the major global challenges that are still circling today is the problem of food waste. Food waste is food that is ready for consumption but is simply wasted so that it eventually accumulates in the landfill (due to retailers, food serving services, and consumers). According to The Economist Intelligence, the United States is among the countries classified as the country with the largest amount of food waste in the world. America earns about 277 kilograms per year for food waste and food loss. This has caused the United States to be in third place according to FAO. This behavior or food waste behavior is often carried out by people with low social interaction in America. These adversely threatening impacts on the environment and living things of food waste have led the U.S. to initiate a range of actions to address this issue by launching activities such as the campaign, the U.S. Food Waste Challenge, the U.S. Food Loss and Waste 2030 Champions, collaborations with related international organizations and institutions, and much more. This article was compiled to find out the condition and development of the food waste issue in America, the impact on the environment, and what actions or regulations were taken as an effort to resolve the issue by the Government and Americans. Keywords: Food waste, America, Food waste and recovery challenge
... According to USDA data, the disposable income to buy food to eat at home has decreased from 10 percent in1970 to around 6 percent in 2009 (USDA,2016). In the same period, food waste increased by 50 percent (Hall et al., 2009). It is important to point out that in the same period, food eaten away from home rose only from 3.5 to 4 percent (USDA, 2016). ...
... Besides being morally questionable, food waste uses resources to produce and transport extra food such as land, energy, water, and fertilizers with the consequent emission of greenhouse gases. At the end of the cycle, wasted food needs to be transported and disposed of with subsequent land use, fuel use, and emission of green house gases from trucks, machinery, and decomposing food (Hall et al., 2009) ...
Blue economy refers to the economic activities geared towards advanced sustainable management and conservation of maritime resources and coastal resources and sustainable development in order to foster economic growth. The challenges of meeting the food demand of the world's rising populations require sustainable food supply chains anchored on coastal communities and sustainable food production. Moreover, marine resources are vital to ensuring food security, accounting for two-thirds of the world's fishery production, 80% of the world's aquaculture production, and per capita supply of fish is 65% higher than the world average. As the world population grows, the volume of food needed in the future will depend on these intrinsic factors and human choices. The chapter explores the current status of sea resources and proposed some ways forward based on existing opportunities and challenges using secondary data to accelerate the sustainable use of the sea resources and analyzes some of the human actions that may affect the sustainable future of the food supply chain, food waste.
... Ignoring source reduction efforts leads to a "prevention paradox" because management efforts cannot keep up with the pace of waste production (Messner et al 2020), which has been increasing steadily through the years (Hall et al 2009;Hic et al 2015). Studies have found that individual drivers of food waste require solutions that slowly nudge consumers to shift behaviors (Dobernig and Schanes 2018), from reducing plate size in hotels and restaurants (Reynolds et al 2019) to selling "ugly" produce that would otherwise be discarded before making it to the market (Collart and Interis 2018; Hingston et al 2020). ...
Landfilled organics waste both natural and financial resources by discarding usable materials that could bolster food security programs and composting efforts. According to the Drawdown Project, one-third of the food we produce in the United States goes to the landfill without ever reaching someone’s plate, contributing to leachate at disposal sites and accounting for more greenhouse gasses than the entire airline industry. As communities across the state struggle to support the 1 in 6 Mainers experiencing food insecurity with dwindling financial resources and limited personnel, food waste diversion provides a local solution that bolsters resilience at low cost. The absence of bold food waste diversion policy in Maine is not due to a lack of successful examples nearby, as Vermont’s recent universal organics recycling policy has seen tremendous success both in diverting more than 53,000 tons of food waste per year and in yielding a 40% increase in food donations. However, Maine faces distinct logistical challenges that complicate efforts to scale up current local food waste diversion efforts such as regional population sparsity and staffing resource constraints. This thesis project examines how Vermont’s Universal Recycling Policy could inspire a path forward to a food waste diversion policy that would work for Maine. The analysis draws upon professional interviews, surveys sent to municipalities, and organizational reports to examine the barriers and assets at play in Maine’s journey toward a bold food waste diversion policy, culminating in suggestions that will work for Mainers.
... Sustainable agriculture aims at the efficient use of arable land and water with improved agricultural practices while mitigating methane release from the sector, promoting target 12.2 (Meena et al., 2019). Reducing food waste minimizes resources used in food production while also reducing emissions from this waste (with respect to target 12.3) (Hall et al., 2009;Ryzhko and Aliksievich, 2014). Practicing prevention, reduction, recycling, and reuse can reduce waste generation and the resulting methane emissions (Target 12.5) (Ari and Şentürk, 2020). ...
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The agriculture sector contributes to ∼40% of methane emissions globally. Methane is also 28 times (Assessment Report 5) more potent greenhouse gas than CO2. In this study, we assess the impact of measures for mitigating methane emissions from the agricultural sector on the achievement of all the 17 United Nations’ Sustainable Development Goals (SDGs). A keyword literature review was employed that focused on finding the synergies and trade-offs with non-fossil methane emissions from the agricultural sector and respective SDGs’ targets. The results were in broad consensus with the literature aimed at finding the relationship between SDGs and measures targeting climate change. There is a total of 88 synergies against eight trade-offs from the 126 SDGs’ targets that were assessed. It clearly shows that measures to mitigate methane emissions from the agricultural sector will significantly help in achieving the SDGs. Since agriculture is the primary occupation and the source of income in developing countries, it can further be inferred that methane mitigation measures in developing countries will play a larger role in achieving SDGs. Measures to mitigate methane emissions reduce poverty; diversify the source of income; promote health, equality, education, sanitation, and sustainable development while providing energy and resource security to the future generations.
... Furthermore, recent studies also concluded that EAFH was associated with obesity [11,12]. Although other mechanisms such as decreased energy expenditure evidently contribute, obesity results from a positive energy balance secondary to excessive food consumption, and specifically, the consumption of large portion sizes of energy-dense foods [23][24][25]. ...
Background/objectives: We investigated the associations between eating away from home (EAFH) and overweight and obesity among Ugandan adults using the 2014 Uganda non-communicable disease risk factor survey. Subjects/methods: In total, 3,025 participants aged 18-69 years were included in the analysis. The frequency of EAFH was assessed by asking participants the number of meals eaten per week that were not prepared at a home. EAFH frequency was categorized as; less than once/week, 1-2 times/week, or ≥ 3 times/week. Logistic regression was used to evaluate the associations between overweight, obesity, and EAFH. We also tested whether sex and age modified these associations. Results: Participants that ate away from home ≥ 3 times/week were 2.13 times more likely to be obese than those that ate away from home less than once/week (odds ratio [OR], 2.13; 95% confidence interval [CI], 1.28-3.54). However, when the analysis was stratified by sex, women that ate away from home ≥ 3 times/week were 42% less likely to be overweight than those that ate away from home less than once/week (OR, 0.58; 95% CI, 0.36-0.94). Men that ate away from home ≥ 3 times a week were 3.89 times and 2.23 times more likely to be obese and overweight, respectively, than those that ate away from home less than once/week (obesity: OR, 3.89; 95% CI, 1.50-10.09; overweight: OR, 2.23; 95% CI, 1.42-3.51). Age-stratified analysis showed that among participants aged 31-50 years, those that ate away from home ≥ 3 times a week were 3.53 times more likely to be obese than those that ate away from home less than once/week (OR, 3.53; 95% CI, 1.69-7.37). Conclusions: Frequent EAFH was positively associated with overweight and obesity among men, and obesity among young/middle-aged adults, but negatively associated with overweight in women. Nutritional interventions for obesity reduction in Uganda should include strategies aimed at reducing the frequency of eating meals prepared away from home, and specifically target men and young/middle-aged adults.
... First, our ECA imputations are based on data for calorie availability, not calorie intake. Country-level estimates for calorie intake are not available, and FAO Food Balance Sheet data are not without criticism (Hall et al., 2009;Svedberg, 1999). FAO's methodology tends to underestimate calorie availability in emerging economies, particularly in rural areas where unreported subsistence production represents an important share of the food intake (Hawkesworth et al., 2010). ...
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Current trends in adult obesity threaten global health. Although the implications of changes in diets, lifestyles, and food environments have been examined, the specific role of excess calorie availability (ECA)—understood as calorie availability in excess of human requirements for a healthy life—and the cohort mechanisms that underlie trends in adult body mass index (BMI) are poorly understood. We examine these relationships for 156 countries over the past century using an age-, sex-, and cohort-specific approach. We measure the association between increases in food energy supply and changes in BMI across countries and time. We find positive and significant associations between ECA and adult BMI for both males and females, and between ECA during early childhood and BMI at adulthood for males. We also find a strengthening of these correlations over successive generations. These cohort mechanisms are boosted by age effects, leading individuals in each successive cohort to reach unhealthy BMI levels at younger ages. Individuals in more recent cohorts are overweight or obese earlier and for larger proportions of their lifespan than those in earlier cohorts. Even after controlling for development dynamics, the pattern is consistent across countries and appears to be driven, in part, by availability of calories in excess of underlying requirements. Our findings provide novel insights into the role of ECA, and potential unintended health consequences of agricultural and trade policies directed at increasing calorie supplies.
This study investigates how framing in relation to environmental consequences directs consumers to prioritize among gain, hedonic, and normative goals when accepting suboptimal food to reduce food waste. A random sample of 1,704 United States consumers completed a three-wave survey instrument, including repeated discrete choice experiments. Goals related to gains deteriorated substantially over time. Goals for reducing environmental impact by purchasing sub-optimal food were stronger and more time-invariant. There was no increase in goal strength for reduce environmental impact due to the type information provided. Furthermore, there was no support for a lower decrease across time in normative goal strength due to exposure to positive framing. There were combined effects of information and time, respectively. Five latent groups were identified. These results are relevant for actions to increase the acceptance of suboptimal food, finding that differences in consumer preferences are attributable to goal type and goal strength.
Food systems literature has shifted towards interdisciplinarity and the use of systems lenses but can still be disjointed and unconnected. To bring together disciplinary knowledge and establish a common understanding of food systems, we conducted a systematic review to inventory sustainability outcomes of the U.S. food system. The literature search returned 2,866 articles, which was reduced to 49, reviewed here. A qualitative content analysis process identified 93 outcomes. These were split across three main themes of environmental, socio-economic, and health outcomes. This review also identified several trends in food systems literature, such as an underrepresentation of socio-economic outcomes and a lack of inclusion of social outcomes in natural science journals. The sustainability outcomes inventoried here may help to facilitate greater communication and collaboration in food systems research and situate current and future food systems studies within this inventory.
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Background: While there is still hunger in the world, a significant amount of food is wasted, which harms the environment. This study focuses on food waste at the consumer level and investigates the role of personality traits on food-waste-aversion. Focus of the Article: Segmentation with personality traits for food waste reduction campaigns. Research Question: Is there a link between personality traits and food-waste-aversion? Are there any associations among consumers’ levels of food-waste-aversion, frugality, conscientiousness, and religiosity? Importance to the Social Marketing Field: Segmentation is a neglected marketing tool in designing campaigns against food waste. This study identifies possible market segments of social marketing campaigns against food waste. In addition, associations among food-waste-aversion and personality traits of conscientiousness, frugality, and religiosity are shown in this study. Methods: This study adopts a cross-sectional research design. A convenience sample of 301 consumers in Turkey is surveyed via an online questionnaire. Results: Results of Chi-Square Automatic Interaction Detector (CHAID) analysis indicate five distinct consumer segments, namely frugal believers, frugal seculars, conscientious individuals, casual females, and casual males. Moreover, positive associations (p < 0.001) among food-waste-aversion and personality traits of conscientiousness, frugality, and religiosity are shown. Recommendations for Research or Practice: This study provides a segmentation procedure with the trait perspective. Frugality, conscientiousness, and religiosity traits can play an essential role in food waste reduction. Targeting individuals with communications fit with their personality is likely to increase the success of food waste reduction interventions. Limitations: Due to a lack of behavioral data, this study investigates food waste at the attitudinal level. Further study could use behavioral measures. In addition, the majority of participants in the survey are Muslim. In order to validate research findings across different cultures, it should be carried out in other countries.
Overweight and obesity are a major threat to global healthcare, leading to a number of preventable diseases. While the mainstay of management is based on nutrition, exercise provides a significant benefit in ameliorating the harmful effects of excess adipose tissue, and assisting in maintenance of healthy weight. Overweight and obesity will continue to be a serious threat to human health for years to come, and must be tackled using a system-based approach—both at the individual level through lifestyle interventions, as discussed throughout this chapter, and at the government level through evidence-based policy action shaping the obesogenic environment.
he average American dietary style at the beginning of the 21st century resembles an hour glass rather than the Federal Gov- ernment's Food Guide Pyramid. We gobble huge amounts of added fats and sugars from the top tier of the Pyramid (marked "Use sparingly") and heaping plates of pasta and other refined grains from the bot- tom tier, but we are sorely lacking in the vegetables, fruits, low-fat milk products, and other nutritious foods in the middle of the Pyramid. A big jump in average calorie intake between 1985 and 2000 without a corresponding increase in the level of physical activity (calorie expenditure) is the prime factor behind America's soaring rates of obesity and Type 2 dia- betes. ERS's loss-adjusted annual per capita food supply series (ad- justed for spoilage, cooking losses, plate waste, and other food losses accumulated throughout the mar- keting system and the home) sug- gests that average daily calorie consumption in 2000 was 12 per-
Humanity now uses 26 percent of total terrestrial evapotranspiration and 54 percent of runoff that is geographically and temporally accessible. Increased use of evapotranspiration will confer minimal benefits globally because most land suitable for rain-fed agriculture is already in production. New dam construction could increase accessible runoff by about 10 percent over the next 30 years, whereas population is projected to increase by more than 45 percent during that period.
Lean and fat compartments of the body are companions. Dietary alterations induce changes in both compartments.
Food waste comprises a significant portion of the waste stream in industrialized countries, contributing to ecological damages and nutritional losses. Guided by a systems approach, this study quantified food waste in one U.S. County in 1998–1999. Publications and personal interviews were used to quantify waste from food production, processing, distribution, and consumption. Approximately 10,205tons of food waste was generated annually in this community food system. Of all food waste, production waste comprised 20%, processing 1%, distribution 19%, and 60% of food waste was generated by consumers. Less than one-third (28%) of total food waste was recovered via composting (25%) and food donations (3%), and over 7,000tons (72%) were landfilled. More than 8.8billion kilocalories of food were wasted, enough to feed county residents for 1.5months. This case study offers an example of procedures to quantify and compare food waste across a whole community food system.