pone.0075530 1..9

Understanding the Sustainability of Retail Food

Recovery

Caleb Phillips

*, Rhonda Hoenigman

, Becky Higbee

, Tom Reed

1 Department of Computer Science, University of Colorado, Boulder, Colorado, United States of America, 2 School of Medicine, Stanford, Palo Alto, California, United

States of America, 3 Community Food Share, Inc., Niwot, Colorado, United States of America

Abstract

In this paper we study the simultaneous problems of food waste and hunger in the context of food (waste) rescue and

redistribution as a means for mitigating hunger. To this end, we develop an empirical model that can be used in Monte

Carlo simulations to study the dynamics of the underlying problem. Our model’s parameters are derived from a data set

provided by a large food bank and food rescue organization in north central Colorado. We find that food supply is a non-

parametric heavy-tailed process that is well modeled with an extreme value peaks over threshold model. Although the

underlying process is stochastic, the basic approach of food rescue and redistribution to meet hunger demand appears to

be feasible. The ultimate sustainability of this model is intimately tied to the rate at which food expires and hence the ability

to preserve and quickly transport and redistribute food. The cost of the redistribution is related to the number and density

of participating suppliers. The results show that costs can be reduced (and supply increased) simply by recruiting additional

donors to participate. With sufficient funding and manpower, a significant amount of food can be rescued from the waste

stream and used to feed the hungry.

Citation: Phillips C, Hoenigman R, Higbee B, Reed T (2013) Understanding the Sustainability of Retail Food Recovery. PLoS ONE 8(10): e75530. doi:10.1371/

journal.pone.0075530

Editor: Rodrigo Huerta-Quintanilla, Cinvestav-Merida, Mexico

Received June 3, 2013; Accepted August 14, 2013; Published October 10, 2013

Copyright: ß 2013 Phillips et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits

unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The authors have no support or funding to report.

Competing Interests: The authors have declared that no competing interests exist.

* E-mail: caleb.phillips@colorado.edu

Introduction

There is a contradiction present in the United States (US) today:

up to 50% of food produced for consumption is wasted in some

stage of production, distribution, or preparation [1–3]. Mean-

while, 14.7% of americans (1 in 7) have low food security and and

5.7% have very low food security. A clear question arises when

studying these statistics: is it possible to recover food from the

waste stream and redistribute it to those who are hungry in a way

that reduces both waste and hunger?

The idea of food recovery is not new — there are dozens of non-

profit food rescue and gleaning organizations (e.g., [4–6]) that

have been recovering and redistributing food for more than 30

years. These organizations receive food donations from grocery

stores, farms, retailers, and restaurants that are overstock or close

to the ‘‘best by’’ date and would otherwise be discarded. Recently,

a coalition of major grocers and retailers organized under the

Feeding America project with the goal of large scale food rescue,

redistribution, and documentation [7]. Yet, to our knowledge

there has been no prior effort to quantify the cost and practicality

of expanding the current food rescue and redistribution efforts to

address hunger on a large scale.

In this paper, we investigate food recovery as a time-sensitive,

spatial distribution problem involving food supply and demand

and the energy cost of redistribution. Using data provided by a

large food bank and food rescue organization in north central

Colorado, we build an empirical model for the food waste process,

and develop an optimization framework for finding low-cost

solutions to food recovery and redistribution. Through simulation

we study the average, best case, and worst case bounds on both the

amount of food available and recovery costs. By investigating the

sensitivity of the model to its parameters, such as food expiry rate

and location and density of participating donors, we can also

determine the most important components that affect a compre-

hensive and sustainable food rescue system.

The data we use in this study was supplied by Community Food

Share (CFS), the sole food bank for Boulder and Broomfield

counties in north central Colorado [8]. This data includes the

pounds of food received from 90 distinct donors on each day for

one year, July 1, 2010 to August 31, 2011, comprising 20,270

donations and 2,328,821 lbs of food. This food was distributed to

304 unique agencies, which are predominantly homeless shelters,

soup kitchens, smaller food pantries, and other organizations that

serve at-risk populations.

The goal of our simulations is to reproduce and understand the

dynamics of the food recovery problem: how much energy (cost)

must be expended to meet the average and worst-case demand,

can demand be met reliably, how frequent are underruns

(supplyvdemand) and overages (excess pickup), and how the rate

of food expiry limits the amount of recoverable food. We also use

the CFS data to extrapolate the supply that could be available

from stores in Boulder and Broomfield counties that are not

currently donating. In future work, we hope to include restuarants

and cafes as well, but have excluded them in the present study

because CFS does not pick up from them.

The approach we take for simulation is classic repeated

measures, where the average behavior of a complex system is

studied through repeated Monte Carlo simulation and ex post facto

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analysis. Each simulation is run for one year (365 days) using the

same random seed (so that results can be compared). We use a

model parameter, E(Dt) is [½0,1, to capture food expiration. An E

of 0.5, indicates that 50% of ‘‘waste’’ food is expected to expire in

24 hours (or, put another way, half of the food will remain on day

tz1, and one quarter on day tz2, and so on). Using the same set

of suppliers, we can evaluate how food availability changes as a

function of cost (driving distance) and E.

Materials and Methods

Figure 1 provides an overview of the modeling and simulation

process used in this work. First, using the provided data, we build

empirical statistical models for food supply (waste) and food

demand (hunger). Food supply is a random process where the

available recoverable waste at each donor is a function of many

factors. However, we find that this quantity can be modeled

statistically using a Peaks over Threshold (POT) approach derived

from Extreme Value Theory, traditionally used in weather

modeling. This model uses the donor category and size (square

footage) as inputs. Food demand is less dynamic and is simply a

function of the distribution of hunger in the region studied. We

find that demand is Gaussian (normally distributed), and use

statistics from hunger surveys (e.g., [9]) to derive a set of demand

goals.

Next, Geographical Information Systems (GIS) data is used to

develop a spatial distribution model. We locate potential donors

and compute shortest-path driving routes between all pairs of

donors. This information is used to determine which donors are

near one another, so that food rescue can be efficiently batched

and routed. The driving distance from the central warehousing

point to each donor (or cluster of donors) is used as the cost of

performing that pick up.

Using the supply, demand, and spatial distribution models, we

then simulate the system using a Monte Carlo method. On each

iteration (day), an optimal food rescue ‘‘schedule’’ is determined

that attempts to meet the demand goal (when possible), and

minimize cost (kilometers driven). In order to find the optimal

schedule, we formulate the food rescue problem as a linear

program, where a food recovery schedule is chosen to minimize

cost while meeting demand. This linear program simulates work

done by food procurement managers at food banks like CFS to

produce a permissable schedule.

Finally, using a repeated measures approach, many successive

days are simulated and the emergent behavior can be studied and

plotted ex post facto. We track the amount of excess food recovered,

the prevalence of shortages on days when demand cannot be met,

and the cost required on each day. This simulation framework can

be used to vary model parameters (i.e., location and storage

capacity of warehouse, rate that food expires, etc.) to understand

how various choices affect the sustainability (i.e., cost versus value)

of the system.

Statistical Modeling

We first build a generative statistical model for the food waste

available at a given donor as a function of their type (e.g., grocer,

manufacturer, or farm), and size. This data has a distinct heavy

Figure 1. Overview of modeling and simulation process. Food rescue data is used to fit generative statistical models for supply (waste) and

demand (hunger). A spatial distribution model uses the supply models and geographic information system (GIS) data to determine the cost of

picking up food at donors.

doi:10.1371/journal.pone.0075530.g001

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tail, where distribution is skewed to the left, indicating that small

values are most common, but that with small probability, large

and sometimes extremely large values can be observed. There is

not a clear enough autocorrelation in the data to permit using

time-series models. Instead, we find that this process is described

extremely well using a peaks over threshold (POT) model where

events greater than zero are modeled using a Generalized Pareto

distribution with Maximum Likelihood Estimator (MLE) fitted

parameters provided for each donor category in table 1. Figure 2

provides a comparison of the fitted model to the observations for

the ‘grocers’ category. This model has an illustrative analogy in

weather modeling. In Colorado, for example, on many days it does

not rain, on some days it rains a little, and on a few days it rains

heavily. This pattern corresponds to what we observe in the food

rescue data: at a given donor the amount of food that can be

rescued is often small, but can occassionally be very large.

The values in table 1 also reveal the donation behavior of each

category. Grocers are fairly consistent donors with a rate of 0.302,

indicating that a typical grocery store donates some food on about

30% of days, with a mean weight of 369 lbs. Farms, on the other

hand, donate infrequently with a rate of 0.023 — they donate on

about 2% of days. However, when they donate the mean quantity

Figure 2. Comparison of observations to model. QQ-plot and histogram comparing observations to model predictions for the ‘Grocers’

category.

doi:10.1371/journal.pone.0075530.g002

Table 1. GPD fit parameters for daily supply and demand distributions in pounds. Standard Error values for the fitted parameters

are given in parentheses.

Data Threshold (h) Rate (r) Location (m) Scale (s) Shape (f) Mean

All 0 0.121 0 275.947 (5.382) 0.439 (0.016) 491.884

Grocers 0 0.302 0 293.139 (6.130) 0.205 (0.016) 368.728

Manufacturers 0 0.038 0 562.549 (42.979) 0.107 (0.051) 629.954

Individuals 0 0.029 0 141.755 (18.374) 0.905 (0.126) 1492.042

Farms 0 0.023 0 918.811 (188.314) 0.867 (0.200) 6908.353

doi:10.1371/journal.pone.0075530.t001

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is much larger than it is for grocers—around 6,000 lbs. Using the

fit parameters in table 1, Pareto-distributed daily supply values can

be generated for each category:

i,t

s  (U

{1)

v~r

h o:w:

ð1Þ

where U

and U

are uniformly distributed random numbers in

½0,1. We use this function in our experiments to generate random,

correctly distributed supply values that can be used in Monte

Carlo-style simulations. Because there is substantially less data

available for farms and individual donors, in the remaining

analysis we focus on retail donors (grocers, manufacturers, and

bakeries). In future work, we hope to leverage other data sources to

model the spatiotemporal dynamics of recoverable farm waste.

The 90 donors included in the CFS data do not represent the

complete set of potential donors in the CFS service area. We

estimate the list of potential donors in Boulder and Broomfield

counties to be 156. In this experiment, farms and individual

donors have been excluded, and instead the focus has been an

exhaustive set of retail food establishments: grocers, manufactur-

ers, and bakeries. We cannot claim that this list is exhaustive, but

we think it captures the bulk of the potential donors. To determine

potential supply from these current non-donors, we first use the

CFS donor data to correlate other variables with mean daily

supply: store size (building square footage), municipal zoning

category, and store distance from the CFS warehouse. The result

of the ANOVA shows that the most important correlating

variables are size and zoning category. The relationship between

donor size and mean daily supply appears to follow a power law.

An ANOVA gives F-values of 69.042 and 27.841 for log

of store

square footage and donor category respectively, and 29.548 when

used together. The F-value is a statistic that describes the ratio

between explained variance and unexplained variance—or, put

differently, the ratio of between-group variability to within-group

variability. A Pearson’s product-moment correlation test confirms

this relationship with a statistically significant correlation between

the log of donor size and the log of mean daily supply for both

grocers alone (p-value ~0:009 and r~0:342), and all suppliers

together (p-value ~0:097 and r~0:413). Given this, we can say

that the mean daily supply (waste) and variability is independently

correlated with the size of the donor. Figure 3 shows this

relationship as a scatterplot. This is an important result because it

allows us to predict the supply (waste) distribution for a given

donor based simply on publicly available information: square

footage and municipal zoning category.

A final modeling task is predicting demand. Due to privacy

concerns for some agencies, it is not possible to determine the per-

agency demand from any defining characteristics, such as agency

size or surrounding population density. Instead, we use the

aggregate daily demand, which corresponds to the amount of food

delivered by CFS or picked up at the warehouse directly by the

agencies. During the period for which we have data, CFS

distributed food on 294 of 427 days, totaling 4,445,071 lbs. On

average, CFS distributes 10,410 lbs of food per day, or 15,119 lbs

per day if weekends, holidays, and other closures are excluded.

This total distribution amount is approximately twice that

donated, since CFS purchases approximately 50% of the food

they distribute.

Several reports have detailed food insecurity in the US, reaching

different conclusions about the extent of the problem. According

to [7], which describes the efforts of the Feeding America

program, 5.7 million unique individuals (or 1.661% of the US

population in 2009)) are served each week by the approximately

37,000 agencies aligned with their program. There are 348,017

individuals in the service area of CFS, meaning that, based on

national-level statistics, there are approximately 5,781 unique

individuals in this region per week who need food assistance. In

[10], Nord et al. show thatin 2009, 14.7% of households nationally

were food insecure at some point during the year, 9% had low

food security, and 5.7% had very low food security. Low food

security is defined by the USDA as ‘‘Reports of reduced quality,

variety, or desirability of diet. Little or no indication of reduced

food intake’’ and very low food security is defined as ‘‘Reports of

multiple indications of disrupted eating patterns and reduced food

intake’’ [11]. Using the 5.7% figure would suggest that 20,490

individuals have very low food security in the CFS service region.

A local study conducted by Feeding America in conjunction with

CFS found that approximately 10,800 unique individuals seek

food assistance per week in the CFS service region [9]. Using

USDA statistics for average consumption of food, a typical

American in 2010 consumes approximately 2.85 lbs of food per

day, of which the majority is meat and protein (0.41 lbs) and grain

(0.48 lbs) [12]. This approximation assumes that the weight of

dairy products is equivalent to the same volume weight of water

and the weight of vegetables and fruit is equivalent to half the

weight of the same volume of water. Given this, if we assume that

each individual who has very low food security acquires a third of

their daily intake via CFS, the mean daily demand would be

between 5,491 lbs (using national Feeding America levels),

10,260 lbs (using local CFS Feeding America service statistics,

19,465 lbs (using USDA very low food security percentile), and

48,600 lbs (using USDA low food security perentile). This is a

staggeringly large range that serves to highlight the fact that

consensus on hunger and food demand does not exist. For the

purposes of this study we will focus on the 10,260 number because

it is based on carefully collected regional data and agrees with the

10,410 lbs distributed on average observed in the data. Recover-

ing this quantity of food from the waste stream would approxi-

mately double the amount currently rescued by CFS, perhaps

allowing them to avoid purchasing any food at all.

Scheduling

The CFS pickup schedule involves visiting a subset of donors

each day. Pickups at donors in close proximity to each other are

batched together for efficiency. Food is taken to a central

warehouse, where it is sorted, weighed, and then re-distributed

as needed. Our scheduling strategy searches for a pickup schedule

that emulates this hub-and-spoke distribution system. We use a

linear program (LP) to find the pickup schedule for each day that

meets the demand and minimizes the cost in kilometers traveled.

To establish a multi-day schedule, we repeat the linear program

for each day. Although this does not guarantee a globally optimal

solution, it effectively mimics the problem faced by CFS, where the

quantity and location of food cannot typically be known far in

advance.

Setting up the linear program starts with N donors with food

supply, and M agencies with food demand. The aggregate

available food supply (

ss) on a given day (t) in arbitrary units is the

sum of the supply from each individual donor. Similarly, aggregate

demand (

dd) on a given day (t) in arbitrary units is the sum of

demand at each individual agency.

The multi-day pickup schedule is controlled by a boolean

matrix (r

i,t

), which identifies which suppliers have pickups

scheduled on which days:

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i,t

1 pickup at supplier i on day t

0 o:w:



ð2Þ

Each donor is associated with a constant cost of making a

pickup (c

). The total cost (

) on day (t) is the cost to visit the

selected donors on that day:

 r

i,t

ð3Þ

The total supply for that pickup schedule (

) is then:

i,t

 r

i,t

ð4Þ

A multi-day pickup schedule for food presents a unique

challenge in that food can go bad. Some of the food not picked

up on day t will expire by day tz1, but the other food will remain.

We have made the simplifying assumption that all food expires at

the same rate regardless of the way the food is stored, state of the

Figure 3. Correlation between donor size and food available. Log/Log-linear correlation between mean daily scale and square footage of

participating donors.

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food, or type of food. The food supply available on day tz1 is the

new supply for that day, plus the previous day’s supply that was

not picked up and did not expire:

i,tz1

zE(1)  s

i,t

 (1{r

i,t

) ð5Þ

where E(Dt) is [½0,1 and quantifies the fraction of food not

expected to expire over Dt nights. 1{r

i,t

is the logical inverse of

the boolean scheduling matrix (and hence is 1 when a pickup does

not occur and 0 otherwise). This recurrence can be rewritten as a

summation of the previous t days:

i,t

t{1

u~1

 s

i,t{u

a~1

(1{r

i,a

)



ð6Þ

Another important component to the model is a central

warehouse where excess food can be stored after pickup to allow

for overages in recovery to be used the following day. The

warehouse supply on a given day t is the amount of food picked up

on day t minus the day t demand plus the warehouse supply from

the previous day that did not expire:

{

t{1

 E(1) ð7Þ

A sub-optimal schedule for multiple days can be calculated by

applying the linear program iteratively for T days:

C~min E

s:t:

§(

{

) ð8Þ

To calculate distance (cost), we use the MapQuest Driving API

directions [13] to retrieve driving directions (which presumably use

an optimized shortest path, taking into account the actual

constraints of the roads) for each supplier. We use the total

driving length of the first offered route, in kilometers, as the cost of

visiting that supplier.

Pickups at nearby suppliers are batched using k-means with a

radius of 10 km for each cluster. This behavior mimics the

scheduling performed by food bank food procurement managers

who often build a schedule around the largest and most

constrained donors. Although it is possible to compute an optimal

route through the selected donors using a combinatorial algorithm

(this problem is itself NP-complete, as it is an instance of the classic

Traveling Salesman problem), this is extremely computationally

costly and would not improve the realism of the simulator. By

using a suboptimal route here we have opted for simplicity and

realism in design, while erring on the side of conservativity in

terms of the sustainability analysis. In future work we hope to

explore ways to improve the scheduling systems used by food

rescue programs. When a pickup is done at any cluster member,

all other cluster members are also visited. The cost of visiting a

cluster is the average driving distance between all cluster members

and the central warehouse multiplied by two (to count the return

trip), plus the minimum sum of the distances between each node.

Hence, the cost for visiting a given cluster k is:

w,i

zmin

i,j

s:t: i, j[cluster

ð9Þ

where N

is the number of members in cluster k, c

i,j

is the cost of

driving from i to j, and w is the index of the warehouse. This cost

function replicates the batching of pickups performed by CFS

drivers. Although this formulation does not explicitly model all the

factors that may contribute to the cost of food recovery, we have

chosen to model those key variables that clearly contribute the

greatest challenge to the problem, principally: spatial distribution,

uncertain (stochastic) supply, and perishability. As we will see, this

construction allows the computation of low-cost multiday solutions

that provide a reasonable facsimile for those schedules computed

by food recovery organizations.

Results and Discussion

Simulation results show that when we set the daily demand to

5,454 lbs, the mean recieved by CFS in our data set, the demand

is met on the majority of days when we use E~0:5. In fact, there is

a mean excess of 436.88 lbs a day, indicating that the recovered

food from donors is generally higher than the demand. This mean

excess value is driven up by spikes of food rescue, which occur at

several times during the year. These spikes correspond to

extremely large random food rescue events, which are also

observed from farms and manufacturers in the real data. This food

is recovered for a mean daily cost of 301.72 (kilometers driven).

CFS estimates their daily driving distance (a sum of three vehicles,

without optimizing paths), to be 212 km. The difference here

stems from the fact that our model counts the cost of picking up

food at distant farms and manufacturers, which generally deliver

the food directly to CFS. If we exclude donors farther away than

100 km, the mean cost drops to 198.35 km, which is within 10%

of the value provided by CFS, without producing any additional

underruns.

In the next set of simulations, we use a demand goal of

10,260 lbs/day and E~0:5, which is the amount that CFS delivers

each day. CFS purchases approximately 50% of their food to

make up the difference between the mean donations received of

5,454 lbs/day and the mean demand of 10,260 lbs/day. In these

simulations, there is a mean shortage of 286 lbs/day, for a mean

cost of 1,544 km. If we exclude donors greater than 100 km away,

the mean cost drops to 703.1 km, and the average daily shortage

also decreases to 262.16 lbs.

There are two takeaways from these results. First, although

there is a slight shortage in meeting demand, the amount of food

available through food rescue still represents a significant increase

over what CFS currently picks up. Next, the observation that in

some cases excluding the furthest away donors can substantially

reduce cost (by 54% in this case) while obtaining approximately

the same amount of food suggests that there may be some

fundamental density of donors required for efficient food rescue.

In scenarios where donors are sparsely distributed relative to

recipients, the cost of rescue may be very high for the same

amount of food. On the other hand, in denser environments the

model can capitalize on the random supply from nearby (and

clustered) donors to drive down cost. Understanding the effect of

spatial distribution of donors (and density) is an interesting

question for future work.

An important feature of the model is the E parameter, which

controls how quickly food expires. Repeating the 10,250 lbs/day

simulations using an E~0:8, indicating that food expires at a rate

of 20%, instead of 50% as above, then demand is met on most

days with a mean excess of 182.15 lbs and a mean cost of

215.1 km. This is nearly an eight-fold reduction in cost for the

same amount of food, simply by changing the rate at which food

expires. Figure 4 shows this relationship explicitly by plotting the

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number of days in a 365 day simulation where supply failed to

meet demand as a function of the E value. For small E values, the

number of underruns is very high; as E is increased, meeting the

demand becomes obtainable and the shortages are mitigated.

Clearly, the effect of E cannot be underestimated. In a practical

sense, this highlights the importance of utilizing proper food

storage in the recovery process to minimize supply fluctuations

and mitigate shortages.

Next, we consider the rest of the suppliers in Boulder and

Broomfield Counties that are not currently donating food, but

could be, and increase the total set of suppliers from 90 to 156. In

this experiment we let E~0:5 as before. For both the original and

this expanded sets of suppliers, there is a saturation point where no

additional recoverable food is available. For the set of 90 suppliers

this maximum is around 13,000 lbs daily. For the larger set of

suppliers, the maximum is closer to 19,000 lbs. There are two

conclusions that can be drawn from this result. First, despite the

complexity of the underlying model, the relationship between the

amount of food available for rescue and the number of donors

participating appears to be linear. Second, and perhaps more

importantly, with sufficient resources and more participating

donors, CFS may be able to comfortably meet their current

demand without purchasing food. To meet this goal, they would

only need to drive approximately 500 km a day, which is slightly

more than two times their current expenditure. This indicates that,

provided sufficient funding is available, and a large number of

businesses are participating as donors, the food rescue model can

successfully feed the area’s hungry using only food that would

otherwise be wasted. Admittedly, the demand of 10,260 lbs is well

below the gold standard of 48,600 based on USDA food security

estimates for the area. For that to be met, according to our model,

CFS would need to have sufficient resources to drive at least

3,000 km per day.

Figure 5 shows this relationship between cost and the number of

donors explicitly. To generate this graph, we take successively

large random samples of the 156 supplier set and run a simulation

for a fixed demand goal. In the plot, each line corresponds to the

cost required for some fixed demand. Each line exhibits roughly

Figure 4. Number of underruns for a 365-day simulation. Number of days when the supply does not meet demand as a function of the

epsilon value, using the set of 90 suppliers with a central warehouse.

doi:10.1371/journal.pone.0075530.g004

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the same behavior: as the number of suppliers increases the cost

goes up until a point is reached where the suppliers are able to

meet demand (and hence we can optimize solutions to drive

down cost). After this point, whi ch is differ ent for each demand

goal, the cost required decreases linearly (or superlinea rly in

some cases) as a function of the fraction of par ticipating

suppliers. This indicates that the cost of the food redistribution

problem can be reduced simply by increasing the number of

participating donors.

Conclusions

In this paper we provide the first formal investigation of the

fundamental sustainabiliy of food recovery. We develop a novel

model that can be used for Monte Carlo style simulation using

fitted empirical parameters, and we show that this model

reproduces the dynamics observed by CFS. While we believe that

this model can represent a large class of similar regions with a mix

of rural and urban environments, we are careful to remind the

reader that our conclusions may not apply in disparite environ-

ments (i.e., dense urban or sparse rural). In future work, we hope

Figure 5. Cost and participating donors. Relationship between cost and percentage of participating donors in the complete supplier set. Each

line corresponds to the cost curve associated with a different demand goal between 2,000 lbs (bottom-most line) and 48,500 lbs (line along main

diagonal).

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to integrate additional data in order to broaden our conclusions

and perform a more rigorous validation of our model.

Our chief experimental findings in this work are:

Food supply (waste) events are heavy-tailed and can be well

described with extreme value theory ‘‘peaks over threshold’’

models and the generalized Pareto distribution.

The efficacy of food recovery hinges on the ability to keep

rescued food from perishing.

Despite the underlying heavy-tailed process and complexity of

the model, the supply appears to be a linear function of the

number of participating donors. Hence, doubling the number

of participating donors is likely to double the amount of food

available.

The cost of food recovery can be reduced substantially simply

by increasing the number of participating donors (and

therefore creating more opportunity for food supply events

to occur, when they are required by demand).

In the scenario we studied in north central Colorado, we have

shown that there is substantially more food available than what

is currently being recovered. Increasing recovery efforts could

reduce both hunger and waste in the region.

In future work, we will expand this investigation to address

additional questions. We are interested in whether this model can

be scaled up to a state or national level. It stands to reason that

dense urban and sparse rural environments will produce

substantially different cost and supply dynamics. However,

understanding how these dynamics affect the efficacy of food

recovery is an open question. There are approximately 85,000

grocery stores and 566,000 food service organizations in the US

[14,15]. In [16], Bloom suggests that the typical food waste

associated with a restuarant is on average 3,000 lbs per employee,

per year (or 123 lbs/day for a 15 employee restaurant). Clearly,

there is no shortage of potential donors; the important question is

whether they are well positioned for recovery and whether the cost

of rescue is acceptable.

An additional question is one of nutrition. In our current study,

we looked at bulk pounds of food without concern for the type.

This is a simplification that has bearing on both the economics of

the problem (supply and demand) and the basic expiry of the food.

Currently, 88% of grocery stores nationally donate some dry

goods, 51% donate produce, and 31% donate prepped food and

meat [16]. Fresh and healthful foods are the hardest for food banks

to acquire since they have a limited shelf life (small E), which is

negatively affected by transportation, time, stocking time, and

pickup limitations (how many pickups per week are possible).

Optimizing pickup strategies to capitalize on small food waste

events, and sufficiently funding food rescue organizations so that

they have the resources to pickup food when it is available might

mitigate this problem. A complete solution might require recovery

efforts at multiple scales and with varying technologies.

Although preliminary, our work here is an important first step

towards understanding the dynamics and limitations of food

recovery to mitigate hunger. In the end, we can present the

positive result that, despite its underlying complexity, food

recovery can be considered a stable process where obtaining

additional food is simply a function of having sufficient partic-

ipating donors and funding to perform pickups. We hope that this

work will help to spur interest in this area, equally among

researchers who might be able to bring additional insight into the

problem, businesses who can agree to donate their food waste, and

policy makers who posess the ability to procure needed funding

and resources for food rescue organizations to succeed.

Author Contributions

Conceived and designed the experiments: CP RH. Performed th e

experiments: CP RH. A nalyzed the da ta: B H TR. Contributed

reagents/materials/analysis tools: TR. Wrote the paper: CP RH.

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The Sustainability of Retail Food Recovery

PLOS ONE | www.plosone.org 9 October 2013 | Volume 8 | Issue 10 | e75530