United States 

Department of 

Agriculture

Economic

Research

Service

Technical

Bulletin

Number 1927

January 2011

Consumer-Level Food Loss 

Estimates and Their Use in 

the ERS Loss-Adjusted Food 

Availability Data

Mary K. Muth, Shawn A. Karns, 

Samara Joy Nielsen, Jean C. Buzby, 

and Hodan Farah Wells
www.ers.usda.gov

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A Report from the Economic Research Service

Abstract

The Food Availability (per capita) Data System developed by USDA’s Economic 

Research Service tracks annual food and nutrient availability for many commodities. 

The Food Availability data series in this system overstates actual consumption, so ERS 

has included an additional series, the Loss-Adjusted Food Availability data, to adjust 

the Food Availability data for nonedible food parts and food losses, including losses 

from farm to retail, at retail, and at the consumer level. In this report, we propose new 

consumer-level loss estimates for “cooking loss and uneaten food” of the edible share 

to replace those currently used in the Loss-Adjusted Food Availability data and propose 

their adoption for the entire data span (1970 to the most recent year in the series). The 

proposed loss percentages are calculated by subtracting food consumption estimates from 

food purchase or availability estimates for each food. These calculations are adjusted 

with information from an expert panel experienced in analyzing food consumption data. 

In general, the proposed food loss estimates for individual foods indicate substantial 

differences from the currently used estimates. Although some estimates indicate smaller 

loss percentages than the currently used estimates, many are larger. Overall, if the 

proposed loss estimates are used in the ERS loss-adjusted series, the average American 

would consume 17.3 pounds less each year, or 41.9 fewer calories per day, than 

suggested by the currently used loss estimates.

Keywords: Food consumption, food availability, consumer-level food loss, plate waste, 

NHANES, The Nielsen Company, The Perishables Group, Inc., Homescan

Mary K. Muth, Shawn A. Karns, Samara Joy Nielsen, 

Jean C. Buzby, and Hodan Farah Wells

Consumer-Level Food Loss 

Estimates and Their Use in 

the ERS Loss-Adjusted Food 

Availability Data

Technical

Bulletin

Number 1927

January 2011
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Acknowledgments

This research was conducted under USDA, Economic Research Service 

Agreement No. 59-4000-6-0121. The authors thank Christine Bruhn 

(University of California at Davis), Thomas Fungwe (USDA’s Center for 

Nutrition Policy and Promotion), Helen Jensen (Iowa State University), 

Chery Smith (University of Minnesota), and Parke Wilde (Tufts 

University) for participating in the expert panel; Ephraim Leibtag and 

Andrea Carlson (both ERS) for their insights and recommendations; Eric 

Hengtes (International Life Science Institute), Alanna Moshfegh (USDA’s 

Agricultural Research Service), and Greg Pompelli (ERS) for their reviews; 

and Michaela Coglaiti (RTI International) for assisting with data collection. 

Thanks also to John Weber and Cynthia Ray of ERS for editorial and design 

assistance. All views expressed here are those of the authors and cannot be 

attributed to RTI International, ERS, or USDA.

Recommended citation format for this publication: 

Muth, Mary K., Shawn A. Karns, Samara Joy Nielsen, Jean C. Buzby, 

and Hodan Farah Wells. Consumer-Level Food Loss Estimates and Their 

Use in the ERS Loss-Adjusted Food Availability Data, TB-1927, U.S. 

Dept. of Agr., Econ. Res. Serv. January 2011.
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Contents

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Need for Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Data Sources and Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Comparison of Demographics for Homescan and NHANES . . . . . . . . . 7

Supplemental Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Expert Panel Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Overview of the Methodology for Calculating

 Consumer-Level Loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Proposed Consumer-Level Loss Estimates . . . . . . . . . . . . . . . . . . . . . . . 13

Meat, Poultry, Fish, Eggs, and Nuts . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Dairy Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Added Fats and Oils (Excluding Dairy) . . . . . . . . . . . . . . . . . . . . . . . . . 17

Fruits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Vegetables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Grain Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Added Sugars and Sweeteners . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Analysis Using RTI’s Proposed Estimates in the ERS 

Loss-Adjusted Food Availability Data . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Meat, Poultry, Fish, Eggs, and Nuts . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Dairy Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Added Fats and Oils (Excluding Dairy) . . . . . . . . . . . . . . . . . . . . . . . . . 29

Fruits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Vegetables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Grain Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Added Sugars and Sweeteners . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Total Annual Pounds and Total Daily Calories . . . . . . . . . . . . . . . . . . . 37

Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Major Changes in Consumer-Level Food Loss Estimates . . . . . . . . . . . 38

Strengths and Weaknesses of the Research Approach . . . . . . . . . . . . . . 39

Recommendations for Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Appendixes

A: Food Descriptions and Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

B: Detailed Food Loss Calculations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

C: Materials for Consumer-Level Food Loss Expert Panel . . . . . . . . . . . . 96 
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Figures

1. Loss Adjustments Applied to the Commodity Food Supply to 

Estimate Calories and MyPyramid Equivalents Consumed . . . . . . . . . . 1

2. Steps in the Process for Calculating Consumer-Level Food Loss 

Conversion Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Tables

1. Food Categories in the Food Availabilty Data Series . . . . . . . . . . . . . . 5

2. Comparison of Race/Ethnicity and Income for Homescan 

Households and NHANES Respondents (Weighted) . . . . . . . . . . . . . . 8

3. Consumer Loss Estimates for Meat, Poultry, Fish, Eggs, and Nuts . . . 14

4. Consumer Loss Estimates for Dairy Products . . . . . . . . . . . . . . . . . . . 16

5. Consumer Loss Estimates for Added Fats and Oils . . . . . . . . . . . . . . . 17

6. Consumer Loss Estimates for Fruits . . . . . . . . . . . . . . . . . . . . . . . . . . 19

7. Consumer Loss Estimates for Vegetables . . . . . . . . . . . . . . . . . . . . . . 22

8. Consumer Loss Estimates for Grain Products . . . . . . . . . . . . . . . . . . . 24

9. Consumer Loss Estimates for Added Sugars and Sweeteners . . . . . . . 25

10. Comparison of ERS and RTI Estimates of Meat, Poultry, Fish, 

Eggs, and Nuts Loss at the Consumer Level (Per Capita) . . . . . . . . . . 27

11. Comparison of ERS and RTI Estimates of Dairy Products 

Loss at the Consumer Level (Per Capita) . . . . . . . . . . . . . . . . . . . . . . . 28

12. Comparison of ERS and RTI Estimates of Added Fats and Oils 

Loss (excluding dairy) at the Consumer Level (Per Capita) . . . . . . . . 30

13. Comparison of ERS and RTI Estimates of Fruit Loss at the 

Consumer Level (Per Capita) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

14. Comparison of ERS and RTI Estimates of Vegetable Loss 

at the Consumer Level (Per Capita) . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

15. Comparison of ERS and RTI Estimates of Grain Products Loss 

at the Consumer Level (Per Capita) . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

16. Comparison of ERS and RTI Estimates of Added Sugars and Sweeteners 

Loss at the Consumer Level (Per Capita) . . . . . . . . . . . . . . . . . . . . . . . 36

17. Summary of Inclusion of RTI Best Estimates into the ERS 

Loss-Adjusted Food Availability Data Series (Per Capita) . . . . . . . . . 37
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Summary

What Is the Issue?

The Food Availability (per capita) Data System developed by USDA’s 

Economic Research Service (ERS) tracks annual food and nutrient availability (a proxy for consumption) in the United States since 1909 for several 

hundred commodities. Because the core Food Availability data series in the 

system overstates actual consumption, ERS has added another series to the 

system—the Loss-Adjusted Food Availability data—which adjusts the Food 

Availability data for nonedible food parts and food losses, including losses 

from farm to retail, at retail, and at the consumer level. This second data 

series more closely estimates per capita consumption.

The current Loss-Adjusted Food Availability data are incomplete and need 

updating. Under an agreement with ERS, RTI International has proposed new 

estimates for the data series’ loss of the edible share of food at the consumer 

level. These proposed estimates cover food loss both at home and away 

from home for most of the commodities included in the series. These losses 

include losses during cooking and preparation (e.g., frying fats); discards due 

to preparation of too much food; expired use-by/open dates; spoilage; and 

plate waste. ERS then examined how adoption of RTI’s proposed estimates 

in this data series would affect ERS’s per capita estimates of daily calories 

and pounds available for consumption per year for each commodity. Higher 

loss estimates relative to current ERS loss estimates equate to decreased 

consumption; lower estimates equate to increased consumption. The purpose 

of this report is to provide documentation about the proposed estimates and 

to make these estimates available for public comment. We propose to adopt 

the new estimates for the entire data span (1970 to the most recent year in the 

series).

What Did the Study Find?

Proposed loss estimates. Consumer-level food loss varies greatly among 

individual foods based on a number of factors, such as a food’s perishability or shelf life, the likelihood of a food being used as an ingredient or 

eaten without further preparation, and the degree to which a food is typically 

consumed by children or adults (because of differences in food consumption 

patterns across age groups). Based on RTI’s proposed estimates, foods with 

the largest annual increase (more than 35 percentage points) in estimated 

consumer-level loss as compared with the currently used ERS estimates 

include fresh pumpkin, dry buttermilk, dry whole and nonfat milk, Swiss 

cheese, edible beef tallow, and lard. Foods with the largest decrease (more 

than 15 percentage points) include chicken, lamb, nonfat cottage cheese, 

frozen potatoes, and veal. Changes in consumer-level food loss estimates 

could stem from changes in food preparation habits and the increase in food 

consumed away from home or simply from RTI’s use of a different methodology for calculating losses than that used currently by ERS. 

Effects of proposed loss estimates on ERS food availability estimates. If 

RTI’s proposed food loss estimates are adopted for use in ERS’s data series, 

changes in estimates of per capita availability of individual foods relative to 

current ERS estimates would vary. Changes over entire food groups, however, 
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would tend to be small. The most affected group would be meat, poultry, fish, 

eggs, and nuts, with an annual increase in food available for consumption 

of 22.3 pounds per person, or 15 percent. The food group with the smallest 

change would be grain products, with an annual decrease in availability of 2.1 

pounds per person, or 1.5 percent, though RTI could calculate estimates for 

only three grain products due to data limitations, such as when the grain was 

used almost exclusively as an ingredient (e.g., various types of flours). Overall, 

use of RTI’s proposed estimates in the data series would result in a reduction in 

estimated per capita availability of 17.3 pounds of food per year, or 41.9 fewer 

calories per day, for the average American. 

How Was the Study Conducted?

RTI conducted the first of two phases in this study by comparing estimates 

of total U.S. retail household purchases with total U.S. at-home consumption 

for each food in ERS’s Loss-Adjusted Food Availability series. The main 

data sources included The Nielsen Company’s Homescan® data for 2004 

(food purchases from retail outlets) and the National Health and Nutrition 

Examination Survey (NHANES) for 2003-04 (food consumption). RTI also 

calculated alternative estimates of food loss by comparing the total quantity 

available at the consumer level in the Loss-Adjusted Food Availability series 

with total reported consumption in NHANES. RTI relied on several supplemental data sources to adjust the purchase data to facilitate comparisons with 

the consumption data. In addition, RTI took direct measurements of count 

data (e.g., produce sold by count rather than weight), inedible percentages of 

food, and moisture gains for foods if data were not available from one of the 

data sources.

Comparison of daily calories using current ERS and RTI’s

proposed estimates of consumer-level food loss

Vegetables

Meat/poultry/

fish/eggs/nuts

Grains

Fruit

Dairy

Added sugars

Added fats and

oils 

(2,614.6)

Daily calories

(2,656.5) Total calories/day

Source: Calorie estimates are for 2006 as computed by authors.

Current ERS estimates RTI best estimates

0

500

1,000

1,500

2,000

2,500

3,000

638.6

468.8

259.8

91.2

617

452

129.3

586.9

448.1

236.2

84.5

607.6

528

123.3
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RTI also developed and conducted an expert panel to provide additional data 

for the analysis, including estimates of food loss to validate the RTI estimates 

(or provide an estimate for foods for which estimates could not be calculated) 

and estimates of the percentage of each food typically used as an ingredient. 

Based on the resulting data, RTI provided one recommended or proposed 

estimate for each food for which an updated estimate could be calculated for 

use in ERS’s Loss-Adjusted Food Availability data. 

In the second phase of this study, ERS applied the consumer-level loss estimates proposed by RTI for each commodity to ERS’s Loss-Adjusted Food 

Availability data. Results revealed changes in ERS estimates of the pounds 

of food available for consumption per capita per year and changes in the 

number of calories available for consumption per capita per day.
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Introduction

In September 2007, USDA’s Economic Research Service established an 

agreement with RTI International (henceforth, RTI) to propose new conversion factors for loss of the edible share of food at the consumer level for 

each of the hundreds of commodities covered in ERS’s Loss-Adjusted Food 

Availability data series. The conversion factors include losses for food 

consumed at home and away from home. The approach to calculating new 

conversion factors is based on exploratory research conducted by RTI during 

an earlier stage of the agreement (Muth et al., July 2007). The purpose of this 

report is to provide documentation about the proposed estimates and to make 

these estimates available for public comment. We propose to adopt the new 

estimates for the entire data span (1970 to the most recent year in the series).

Need for Project

ERS’s Food Availability (per capita) Data System provides statistical indicators that track food and nutrient availability since 1909 for many commodities. The data can facilitate policymaking and regulatory decisions about 

nutrition education, public health programs, vitamin and mineral fortification, 

and food labeling. Currently, the Food Availability data (previously known 

as the food supply, or food disappearance, data) are the premiere source of 

time-series data in the Food Availability Data System. However, the data 

overstate actual consumption, so ERS has included a second series in the 

system, the Loss-Adjusted Food Availability (LAFA) data, which adjusts the 

Food Availability data for nonedible food parts and food losses. Three types 

of loss adjustments are applied to estimates of the food supply to derive lossadjusted estimates of calories and MyPyramid equivalents consumed per day 

by individuals (fig. 1). In particular, these three loss adjustments are for (1) 

loss from primary weight to retail weight, (2) loss from retail to consumer 

level, and (3) loss at the consumer level.

Figure 1

Loss adjustments applied to the commodity food supply to estimate calories and MyPyramid equivalents 

consumed

Commodity

food supplya

Calories and

MyPyramid

equivalents

consumed 

 At home

 Away from home

Loss from 

primary

 weight to 

retail weight 

Loss from 

retail to 

consumer level

Loss at 

consumer levelb

a

Available commodity food supply is generally calculated as (production + imports + beginning stocks) – (nonfood uses + exports + ending 

stocks).

b

Loss at the consumer level accounts for the inedible portion.

Source: RTI International.

This study focuses on 

food loss that occurs at 

the consumer level (other 

than the inedible portion), 

which is one of three types 

of food losses estimated in 

the ERS Food Availability 

Data System.
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The focus of this study is on food losses at the consumer level (other than 

losses of the inedible share of food, such as apple cores and chicken bones). 

In particular, these losses include:

•	Losses during cooking and preparation (e.g., frying fats)

•	Discards due to preparation of too much food, expired

use-by/open dates, or spoilage

•	Plate waste or loss

These losses occur in the following settings:

•	At home: includes foods consumed at home from purchases at grocery

stores, warehouse stores, specialty grocery stores, farmers’ markets, and 

other retail food outlets.

•	Away from home: includes foods consumed from restaurants, school and

company cafeterias, hospitals, nursing homes, catered events, and other 

foodservice outlets.

In addition to providing estimates closer to actual per capita consumption 

(e.g., pounds per year), the LAFA data series provides estimates of daily per 

capita MyPyramid equivalents, or daily allowance as defined by the 2005 

Dietary Guidelines for Americans (USDHHS and USDA, 2005). These estimates can be compared with Federal dietary recommendations for specific 

food groups of the U.S. population. The LAFA data also include average 

daily calories (per capita) available in the U.S. food supply by major food 

group and individual component foods. 

The LAFA data series has incomplete documentation on its several hundred 

food loss estimates from farm to table, and many of these estimates need to 

be updated. ERS has several initiatives underway to update and document the 

loss estimates at all stages, including this current study.1 The loss estimates 

currently used by ERS were based on limited information, as described in 

Kantor (1998) and Kantor et al. (1997). Estimates of consumer-level loss 

are among the least documented in the series, yet they play a critical role 

in estimating overall consumption: consumer-level updates are needed for 

each food/commodity covered in the database. This task of updating the 

consumer-level losses includes overcoming the following challenges:

•	Research, data, and literature on food loss at the consumer level are

extremely limited.2 This was a finding from an earlier study by RTI, 

which conducted a thorough literature review (see Muth et al., July 2007). 

•	The consumer-level food loss estimates are complicated in that they

include losses for food consumed at home and away from home. 

•	Hundreds of commodities need updated food loss estimates. Additionally,

most fruits and vegetables have up to five types of processing (e.g., fresh, 

frozen, dried, canned, and juice), each of which needs updated loss estimates. Furthermore, the commodity group for “fresh apples” has a sixth 

category called “other,” which consists mostly of sliced apples.

•	Loss factors for foods primarily used as ingredients (e.g., certain fats and

oils, like shortening, and grains) are more difficult to estimate and require 

a different estimation method than that used for other foods. 

1See documentation for the LossAdjusted Food Availability data series 

(USDA, Economic Research Service, 

2010). www.ers.usda.gov./data/foodconsumption/foodguidedoc.htm

2Other previous publications on 

consumer-level food loss include Adams et al. (2005); Buzby and Guthrie 

(2002); Engstrom and Carlsson-Kanyama (2004); Gallo (1980); Marlette et 

al. (2005); Reger et al. (1996), and van 

Garde and Woodburn (1987).
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•	Researchers run the risk of double counting the nonedible share of food

when updating food loss estimates.

•	Although the loss estimates go back through 1970 for each food (and

type of processing in the case of fruits and vegetables), it is more difficult 

to update loss estimates for earlier years. To the extent possible, future 

ERS research may focus on determining whether or how consumer-level 

food loss estimates have varied over this time period. 

The intent of the study was to make the best use of existing data to quantitatively estimate consumer-level food loss while addressing these issues to the 

extent practicable.

Objectives

The first goal of this project was to propose new conversion factors for loss 

of the edible share of food at the consumer level, both at home and away 

from home, for each covered commodity for the most recent full year of 

complete data in the Loss-Adjusted Food Availability series. The conversion 

factors currently used by ERS for the following seven groups of commodities 

are accessible through Excel files posted on the ERS Web site 

(www.ers.usda.gov/data/foodconsumption/foodguideindex.htm): 

1. Meats, poultry, fish, eggs, and nuts 

2. Dairy products 

3. Added fats and oils 

4. Fruits 

5. Vegetables 

6. Grains 

7. Added sugars and sweeteners

Among the seven Excel files, there are a few hundred covered commodities 

(e.g., wheat, corn, rye, etc., in the grains file), each with its own spreadsheet. Within the individual spreadsheets, the consumer-level loss factors 

are provided in the column titled “Other (cooking loss and uneaten food).” 

Henceforth, these particular loss conversion factors are referred to as the 

“conversion factor.” The specific objective was to propose a conversion 

factor estimate for each covered commodity for the most recent year of data 

available. In this first phase of the analysis, RTI also investigated qualitatively why foods have different consumer-level conversion factors.

The second goal of this project was to determine the degree to which adoption of the proposed conversion factor estimates for each food commodity 

would impact per capita estimates of the annual amount of that food available 

for consumption and the daily calories. ERS performed this second phase of 

the analysis. The new estimates proposed here are specifically designed for 

use in the LAFA data series, so they may not be applicable to other categorizations of foods or estimates of food availability or consumption in other 

analyses. 

The goal of this project 

is to update the conversion factors for loss of 

the edible share of food at 

the consumer level, both 

at home and away from 

home, for each covered 

commodity.
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Data Sources and Methodology

In developing the methodology to calculate the proposed consumer-level 

loss conversion factors, RTI relied on two main data sources for consumerlevel food purchase estimates and food consumption estimates. In addition to 

these main data sources, RTI also relied on several supplemental data sources 

to adjust the purchase data to align with the consumption data. RTI also 

conducted an expert panel to obtain input on the estimation process and additional data needed for the consumer-level loss estimation process. 

Data Sources

The data used in this study include publicly available data from several 

USDA sources and propriety data from The Nielsen Company and the 

Perishables Group, Inc. 

Main Data Sources for Food Purchases and Food Consumption

The main sources of data in this study are The Nielsen Company’s 

Homescan® data for 2004 (food purchases) and the National Health and 

Nutrition Examination Survey (NHANES) for 2003-04 (food consumption) 

(see CDC, 2007). RTI examined the detailed foods included in Homescan 

and NHANES to develop a definition for each food category that corresponded as closely as possible to the LAFA data descriptions contained 

in the footnotes of each commodity table. Table 1 lists the food categories 

included in the analysis (detailed descriptions are provided in appendix A, 

table A-1). Many of the food categories contain only Universal Product Code 

(UPC) foods (i.e., with a bar code on the package), while others include both 

UPC and random-weight foods. Random-weight foods are sold by weight 

and include some fresh fruits and vegetables, bakery products produced and 

packaged in the store, and meat products cut and packaged in the store.

The Nielsen Company’s Homescan Data for 2004

As described in Muth et al. (February 2007), the Homescan Core panel 

currently includes 125,000 households in 52 markets and 9 remaining areas 

in the continental United States. On a weekly basis, the Homescan panel 

households record purchases of all UPC food products using a handheld 

scanner once household members return home from food shopping. In past 

years, a subset of approximately 15,000 households in the core panel also 

recorded all purchases of random-weight foods. This subset of households is 

referred to as the Fresh Foods panel. 

Homescan households that provide purchase data for at least 10 of the 12 

months during a year are included in the “static” sample of households. 

In 2004, approximately 40,000 of the households in the core panel were 

included in the static dataset for UPC foods, and approximately 7,500 of 

the 15,000 households in the Fresh Foods panel were included in the static 

dataset for random-weight foods. For households in the static datasets 

that reported data for fewer than 12 months of the year, RTI scaled up the 

purchase estimates to account for missing months using the methodology 

described in Zhen et al. (2008); this adjustment increased purchase estimates 

by 1.5 percent per year on average. Furthermore, RTI applied Nielsen’s 
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Table 1

Food categories in the Food Availability data series

Category Food

Meat, poultry, fish •	 Beef

•	 Veal

•	 Pork

•	 Lamb

•	 Chicken

•	 Turkey

•	 Fresh and frozen fish

•	 Fresh and frozen shellfish

•	 Canned salmon

•	 Canned sardines

•	 Canned tuna

•	 Canned shellfish

•	 Other canned fish

•	 Cured fish

Eggs •	 Eggs

Nuts •	 Peanuts

•	 Peanut butter

•	 Snack peanuts

•	 Other peanuts

•	 Almonds

•	 Hazelnuts (filberts)

•	 Pecans

•	 Walnuts

•	 Macadamia nuts

•	 Pistachio nuts

•	 Other tree nuts

•	 Coconut

Dairy—Beverages •	 Plain whole milk

•	 Plain 2% milk

•	 Plain 1% milk

•	 Skim milk

•	 Whole flavored milk

•	 Low-fat flavored milk

•	 Buttermilk

•	 Half and Half1

•	 Cream (light cream, heavy 

cream, and half and half)

•	 Eggnog

Dairy—Other •	 Sour cream

•	 Cream cheese

•	 Cheddar cheese

•	 Other American 

cheese

•	 Provolone cheese

•	 Romano cheese

•	 Parmesan cheese

•	 Mozzarella cheese

•	 Ricotta cheese

•	 Other Italian cheese

•	 Swiss cheese

•	 Brick cheese

•	 Muenster cheese

•	 Blue cheese

•	 Other miscellaneous cheese

•	 Processed cheese

•	 Processed cheese foods 

and spreads

•	 Regular cottage cheese

•	 Low-fat cottage cheese

•	 Regular ice cream

•	 Low-fat ice cream (ice milk)

•	 Frozen yogurt and other miscellaneous frozen products

•	 Refrigerated yogurt

•	 Total evaporated and condensed canned whole and 

skim milk

•	 Dry whole and nonfat milk

•	 Dry buttermilk

Fats and oils •	 Butter

•	 Margarine

•	 Lard

•	 Edible beef tallow

•	 Shortening

•	 Salad and cooking oils

•	 Other edible fats and oils

Fruits—Fresh •	 Fresh oranges

•	 Fresh tangerines

•	 Fresh grapefruit

•	 Fresh lemons

•	 Fresh limes

•	 Fresh apples

•	 Fresh apricots

•	 Fresh avocados

•	 Fresh bananas

•	 Fresh blueberries

•	 Fresh cantaloupe

•	 Fresh cherries

•	 Fresh cranberries

•	 Fresh grapes

•	 Fresh honeydew

•	 Fresh kiwi

•	 Fresh mangoes

•	 Fresh peaches

•	 Fresh pears

•	 Fresh pineapple

•	 Fresh papaya

•	 Fresh plums

•	 Fresh strawberries

•	 Fresh watermelon

Fruits—Canned •	 Canned apples and 

applesauce 

•	 Canned apricots

•	 Canned cherries

•	 Canned peaches

•	 Canned pears

•	 Canned pineapple

•	 Canned plums

•	 Canned olives

Fruits—Frozen •	 Frozen blackberries

•	 Frozen blueberries

•	 Frozen cherries

•	 Frozen raspberries

•	 Frozen strawberries

•	 Other frozen berries

•	 Frozen apples

•	 Frozen apricots

•	 Frozen peaches

•	 Frozen plums

•	 Other frozen fruit

Fruits—Dried •	 Dried apples

•	 Dried apricots

•	 Dried dates

•	 Dried figs

•	 Dried peaches

•	 Dried pears1

•	 Dried plums

•	 Raisins

Continued—
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projection factors (weights) in the dataset to obtain national purchase 

estimates.

The Homescan data collection process is designed to collect information on 

all food purchases made by households over the course of a year. However, 

for a variety of reasons, households might not report all their purchase information. For example, a household may skip reporting purchases for a week 

or two because of illness or vacation, it may not scan packages for foods that 

were consumed “on the go,” or it may forget to scan minor purchases made 

at convenience stores. In addition, a household might not report all fresh 

purchases made at farmers’ markets, butcher shops, and bakeries because of 

the additional burden associated with recording this information. 

Table 1

Food categories—continued

Category Food

Fruits—Juices •	 Grapefruit juice

•	 Lemon juice

•	 Lime juice

•	 Orange juice

•	 Apple juice

•	 Cranberry juice

•	 Grape juice

•	 Pineapple juice

•	 Prune juice

Vegetables—Fresh •	 Fresh artichokes

•	 Fresh asparagus

•	 Fresh bell peppers

•	 Fresh broccoli

•	 Fresh brussels 

sprouts

•	 Fresh cabbage

•	 Fresh carrots

•	 Fresh cauliflower

•	 Fresh celery

•	 Fresh collard greens

•	 Fresh sweet corn

•	 Fresh cucumbers

•	 Fresh eggplant

•	 Fresh escarole and endive

•	 Fresh garlic

•	 Fresh kale

•	 Fresh head lettuce

•	 Fresh romaine and leaf lettuce

•	 Fresh lima beans

•	 Fresh mushrooms

•	 Fresh mustard greens

•	 Fresh okra

•	 Fresh onions

•	 Fresh potatoes

•	 Fresh pumpkin

•	 Fresh radishes

•	 Fresh snap beans

•	 Fresh spinach

•	 Fresh squash

•	 Fresh sweet potatoes

•	 Fresh tomatoes

•	 Fresh turnip greens

Vegetables—

Canned

•	 Canned asparagus

•	 Canned snap beans

•	 Canned cabbage 

(sauerkraut)

•	 Canned carrots

•	 Canned sweet corn

•	 Canned cucumbers (pickles)

•	 Canned green peas

•	 Canned chile peppers

•	 Canned tomatoes

•	 Canned mushrooms

•	 Canned potatoes

•	 Other canned vegetables

Vegetables—

Frozen

•	 Frozen asparagus

•	 Frozen snap beans

•	 Frozen broccoli

•	 Frozen carrots

•	 Frozen cauliflower

•	 Frozen sweet corn

•	 Frozen green peas

•	 Frozen lima beans

•	 Frozen spinach

•	 Frozen potatoes

•	 Other frozen vegetables

Vegetables—Dried •	 Dehydrated onions

•	 Dehydrated potatoes

•	 Potato chips and shoestring 

potatoes

•	 Dry edible beans

Grains •	 White and whole 

wheat flour

•	 Durum flour

•	 Rice

•	 Rye flour

•	 Corn flour and meal

•	 Corn hominy and grits

•	 Corn starch

•	 Barley products

•	 Oat products

Added sugars and 

sweeteners

•	 Cane and beet sugar

•	 High-fructose corn 

syrup

•	 Glucose

•	 Dextrose

•	 Honey

•	 Edible syrups

1Half and half and dried pears were only included in the first phase of the analysis because the Loss-Adjusted Food Availability 

data for these commodities were not available for 2006.

Source: RTI International.
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National Health and Nutrition Examination Survey (NHANES) 

for 2003-04 

As part of an ongoing program of studies designed to assess the health and 

nutritional status of individuals in the United States, NHANES participants 

report their food consumption for two 24-hour recall periods. The 2003-04 

survey captures information from approximately 10,000 respondents from 

counties across the United States using two interview formats: in person 

for the first recall period and via telephone for the second recall period. 

Respondents reported the quantity of food consumed and the place at which 

the food was eaten (at home versus away from home). The data are intended 

to represent the weight of the food consumed and thus exclude the inedible 

(or refuse) portion. For comparability with The Nielsen Homescan data, 

researchers used the quantity of food consumed at home for the food-loss 

calculations.

For fruits and vegetables, NHANES classifies each item consumed based 

on whether the food was prepared from fresh, canned, or frozen products. If 

respondents are unsure of how the food was prepared, the item is classified in 

the Not Further Specified (NFS) category. RTI calculated the total consumption estimate in the NFS categories and then apportioned the estimate into the 

different forms of preparation based on the percentages of use in the LAFA 

data. Where applicable, the assumed percentages are documented in the food 

category descriptions in appendix table A-1.

To calculate total consumption for each food category, RTI used data from 

the first day of 24-hour recall interviews because (1) individuals likely 

have similar consumption patterns for both days and it would be difficult to 

create an average daily consumption value, and (2) some individuals did not 

complete the second-day interview; thus, the data would not be comparable 

for individuals that complete 1 versus 2 days. For each category, RTI applied 

the weights in the dataset to obtain average national daily estimates of 

consumption and then multiplied this amount by 365 days and the 2004 U.S. 

population to obtain a national annual estimate of consumption for each food 

for that year. RTI then converted grams to pounds for comparability with the 

purchase data.

Comparison of Demographics for Homescan and NHANES

Table 2 provides a comparison of weighted percentages by ethnicity and 

household income categories for respondents in the static Homescan and 

NHANES datasets. The weighted percentage of non-Hispanic Whites was 

higher for Homescan than for NHANES, with offsetting higher weighted 

percentages of non-Hispanic Blacks, Hispanics, and other/multiracial individuals in NHANES. The weighted percentages for household income 

indicate that NHANES respondents typically have higher incomes than 

Homescan respondents. In general, the percentages based on ethnicity and 

household income are similar enough to provide some confidence that 

comparisons between Homescan purchases and NHANES consumption are 

valid. However, some differences may occur if certain types of ethnic populations or income groups purchase and/or consume certain foods more than 

an average household. For example, if lower income households purchase 
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Table 2

Comparison of race/ethnicity and income for Homescan households and NHANES respondents (weighted)

Homescan, 2004 NHANES, 2003-04

Percent

Race/Ethnicity

Non-Hispanic White 75.6 68.7

Non-Hispanic Black 11.0 12.2

Other/Multiracial 3.5 5.8

Hispanic 9.8 13.2

Total 100.0 100.0

Household income

Under $5,000 1.1 1.6

$5,000–$9,999 3.7 3.5

$10,000–$14,999 6.7 6.6

$15,000–$19,999 7.6 6.1

$20,000–$24,999 10.3 7.1

$25,000–$34,999 15.3 12.3

$35,000–$44,999 13.6 11.5

$45,000–$59,999 15.1 —

$45,000–$64,999 — 17.1

$60,000–$69,999 7.3 —

$65,000–$74,999 — 6.4

$70,000 and over 19.3 —

$75,000 and over — 26.8

Don’t know/refused — 1.0

Total 100.0 100.0

Note: — means the data range was not available for the series. 

Source: RTI International.

fewer fresh fruits and vegetables than higher income households, the data 

from Homescan households might underestimate total fresh fruit and vegetable purchases relative to that for NHANES participants. However, the 

survey weights provided in each of the datasets should compensate to some 

extent for the differences in the characteristics of the households.

Supplemental Data Sources

RTI used the following supplemental data sources to adjust the purchase data 

to facilitate comparisons with the consumption data: 

•	Perishables Group, Inc. purchase estimates for some categories of

foods that include random-weight purchases broken out by food 

category (e.g., meat and poultry, fresh fruits, and fresh vegetables).

Perishables Group compiled these data using a method that appears to 

capture a larger portion of random-weight purchases than the Homescan 

method. For categories that match the food categories listed in table 1, 

RTI replaced the random-weight estimates from Homescan with the 

estimates from Perishables Group. Because ERS purchased these data 

beginning in July 2004, RTI used the total annual purchase estimates for 

the period July 2004 through June 2005 based on the assumption that 
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purchases in January 2005 through June 2005 should be comparable to 

the same time period as 2004. 

•	USDA National Nutrient Database for Standard Reference (2007).

RTI used this source to convert count data for fresh fruits and vegetables 

to edible weights and to obtain estimates of inedible percentages (called 

refuse percentages in the database) for fresh fruits and vegetables sold by 

weight. The database was also used to convert liquid volumes to weights 

for such products as milk and juice. Detailed information and estimates 

are provided in appendix table A-2.

•	United Nations Food and Agriculture Organization, “Food

Composition for International Use” (UN/FAO, 1953). This source 

was used to obtain estimates of inedible portions for fish and shellfish, 

which are not contained in the National Nutrient Database for Standard 

Reference.

•	USDA, ARS Agriculture Handbook No. 102 (USDA/ARS, 1975). This 

source was used to convert purchase weights to prepared weights for 

foods, such as rice and oatmeal, and to obtain estimates of liquid syrup or 

brine percentages for canned fruits and vegetables. Detailed estimates are 

provided in appendix table A-3.

•	Direct measurements of count data, inedible percentages, and moisture gains for foods that did not have data available from the sources 

listed earlier. For categories of foods not covered in the previous 

sources, RTI counted numbers of fruits or vegetables in a bunch (e.g., 

carrots), measured inedible (refuse) percentages for canned fruits and 

vegetables, and measured weight increases for prepared foods (e.g., rice) 

versus weights of the same foods at the time of purchase. Detailed estimates are provided in appendix table A-3.

In addition to providing detailed product descriptions, appendix table A-1 

provides the final set of assumptions for each food regarding conversion of 

counts (e.g., one fresh cucumber) and fluid ounces (e.g., for fresh orange 

juice) to weights, solids in canned foods (e.g., canned potatoes), and inedible percentages. For foods that are often part of mixtures (e.g., fruit juices or 

canned vegetables), the table indicates if RTI divided mixtures of up to two 

foods into each of the respective categories.

The main and supplemental sources provide most of the data required to 

calculate consumer-level food loss with the exception of the percentage 

of each food used as an ingredient. The quantities of foods purchased as 

indicated in Homescan include the quantities consumed directly and the 

quantities used in recipes. Fresh apples, for example, are consumed directly 

and also used as an ingredient to make apple pie or other baked foods. In 

calculating consumption quantities from NHANES, RTI focused on the 

detailed food categories that could be compared directly with Homescan 

purchase quantities. However, RTI needed to adjust the Homescan purchase 

quantities to exclude the percentage of each food used as an ingredient. For 

the purposes of the current study, an expert panel estimated the ingredient 

percentages using the methodology described in the following section. An 

alternative approach for estimating the ingredient percentages might use the 

recipe files in the USDA National Nutrient Database for Standard Reference.
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Expert Panel Methodology

In addition to using the data sources described earlier, RTI also developed 

and conducted an expert panel to provide additional data for the analysis. The 

primary purposes of the expert panel were to:

•	Review the methodology to determine if alternative data sources or 

approaches might be used and to better understand the characteristics of 

the data sources that were used.

•	Obtain expert estimates of food loss that can be used to validate the estimates or provide an estimate for foods for which estimates could not be 

calculated.

•	Obtain expert estimates of the percentage of each food typically used as 

an ingredient.

RTI convened the following panel of experts on May 13, 2008, at RTI 

International in Research Triangle Park, NC:

•	Dr. Jean Buzby, Economist, ERS, U.S. Department of Agriculture

•	Dr. Christine Bruhn, Consumer Food Marketing Specialist, University of 

California at Davis

•	Dr. Thomas Fungwe, Nutrition Policy Analyst, Center for Nutrition 

Policy and Promotion, U.S. Department of Agriculture 

•	Dr. Helen Jensen, Professor, Department of Economics, Iowa State 

University

•	Dr. Chery Smith, Associate Professor, Department of Food Science and 

Nutrition, University of Minnesota

•	Dr. Parke Wilde, Associate Professor, Friedman School of Nutrition 

Science and Policy, Tufts University

Although the panel members were selected for their general knowledge of the 

food industry, most indicated particular familiarity with specific food groups. 

For example, Dr. Bruhn indicated dairy products, fruits, and vegetables; Dr. 

Fungwe cited fruits and vegetables; Dr. Jensen indicated meat, poultry, and 

fish; dairy products; grain-based products, and added sugars and sweeteners; 

and Dr. Smith cited meat, poultry, and fish; fruits; and vegetables.

Appendix C includes the materials used to recruit the panel members and 

conduct the expert elicitation. RTI began the elicitation with a presentation that provided an overview of consumer-level food loss sources and 

background information; previous literature on estimates of consumer-level 

food loss; the methodology for estimating purchase quantities, consumption quantities, and consumer-level food loss percentages; and the exercise 

to be completed for the expert panel. RTI also reviewed the definitions for 

each food category. During the presentation, RTI discussed many of the 

challenges in implementing the methodology and the reasons why Nielsen 

purchase estimates and NHANES consumption estimates may be imperfect 

measures.3 Finally, after reviewing the initial calculated estimates of food 

loss for each food, the experts provided estimates of the following based on 

their prior experience with food purchase and consumption practices: 

3In the discussion section on page 

38 of this report, we describe issues 

concerning the data as discussed during 

the expert panel.
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•	Percentages of each food typically used as an ingredient

•	Percentages of consumer-level food loss for each food

After the expert panel concluded, RTI entered the estimates into a worksheet 

and calculated the mean, mediametern, minimum, and maximum estimates of 

ingredient use and consumer-level food loss percentages for each food. RTI 

then used these estimates in further calculations.

Overview of the Methodology for Calculating 

Consumer-Level Loss

RTI calculated consumer-level conversion factors using the steps outlined in 

figure 2, which indicates the data source for each calculation.4 Food-specific 

adjustments made during the calculations are detailed in appendix table A-1. 

Note that for foods sold with UPCs and as random weight, RTI summed the 

purchase quantities prior to comparing the estimate with the consumption 

quantity. After calculating the consumer-level food loss estimates in steps 

1 through 4, RTI compared the estimates with the loss estimates currently 

used by ERS, the average expert panel loss estimates, and loss estimates 

calculated by comparing ERS’s estimate of the per capita quantity available 

to consumers (multiplied by the population) with NHANES consumption 

estimates. This latter estimate includes consumer-level losses that occur at 

home and at restaurants and other foodservice operations. The amount of 

consumer-level food loss at home differs from that away from home, and 

it is uncertain as to which might be greater. Households may have greater 

amounts of food loss due to cooking loss and spoilage loss of perishables 

foods, but restaurants and other foodservice operations are likely to have 

greater amounts of food loss due to discarded cooking fats and oils and plate 

waste (Muth et al., July 2007). For the purposes of this study, RTI assumed 

that the differences offset each other so that losses for food consumed at 

home are similar to losses for foods consumed away from home.

The following example using pistachio nuts illustrates RTI’s process for 

calculating consumer-level loss. Based on the Nielsen purchase data, 63.9 

million pounds of pistachio nuts were purchased in the United States in 2004, 

of which 10.2 million pounds were random weight. Supplementary data on 

random-weight purchases from the Perishables Group were not available 

for pistachio nuts, so the analysis relied exclusively on Nielsen purchase 

data to estimate purchase volumes. RTI adjusted the Nielsen purchase estimates to account for the estimated 47-percent inedible portion for pistachio 

nuts purchased in the shell. Based on the NHANES consumption data, 26.0 

million pounds of pistachio nuts were consumed in the United States in 

2004, of which 24.9 million pounds were consumed from store purchases. 

A comparison of Nielsen purchases (63.9 million pounds) with NHANES 

consumption from store purchases (26.0 million pounds) indicates an unadjusted loss estimate of 61 percent. After subtracting the portion used as an 

ingredient (43 percent), the resulting loss estimate is 19 percent.

RTI then compared these estimates to other possible estimates. Specifically, 

a comparison of ERS LAFA data at the consumer level (58.5 million pounds) 

with total NHANES consumption (26.0 million pounds) indicates an unadjusted loss estimate of 56 percent. After subtracting the portion used as an 

4An underlying assumption in the 

calculations depicted in figure 2 is that 

consumer-level loss of foods consumed 

directly as an ingredient is similar to 

consumer-level loss of foods consumed 

as an ingredient in prepared foods (e.g., 

loss of apples consumed directly is 

similar to loss of apples in apple pie).
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ingredient (43 percent), the resulting loss estimate is 13 percent. Both of 

the calculated estimates of 19 percent and 13 percent are similar to the loss 

estimate of 10 percent currently used by ERS and the expert panel average 

estimate of 12 percent. The final proposed loss estimate of 16 percent is the 

average of the two estimates.

Figure 2

Steps in the process for calculating consumer-level food loss 

conversion factorsa

a

For foods sold with UPCs and as random weight, purchase quantities were added together in 

step 1.

Source: RTI International.

Step 1. Estimated national store purchases for each food 

using Nielsen Homescan and Perishables Group data 

 Foods with Universal Product Codes (UPC)

 Random weight foods without UPCs

Step 2. Applied adjustments to national store purchases

 Converted count data (e.g., ears of corn) to edible weight 

using data from the USDA National Nutrient Database 

for Standard Reference and direct measurements 

 Converted liquid volumes to weights using densities in the 

USDA National Nutrient Database for Standard Reference 

 Converted purchase weights to prepared weights using food 

yields in USDA, ARS Agriculture Handbook No. 102 and direct 

measurements

Step 3. Estimated national consumption of each food using 

NHANES 24-hour dietary recall data

 Food at home

 Food away from home

Step 4. Calculated consumer-level loss percentages for food 

at home for each food

where the % inedible was obtained from the USDA National 

Nutrient Database for Standard Reference; USDA, ARS Agriculture 

Handbook No. 102 (1975); or direct measurements and % ingredient 

use was the median value from the expert panel estimates.

% loss = - % ingredient use [Total purchases (1-% inedible)

 - total consumption]

Total purchases

Step 5. Conducted comparisons with other data sources

 Comparison with previous ERS consumer-level loss value

 Comparison with estimates obtained from the expert panel

 Direct comparison of NHANES consumption estimates with the 

current Food Availability Data estimates at the consumer level
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Proposed Consumer-Level Loss Estimates

Using the methodology described in the previous section, RTI calculated 

estimates of consumer-level food loss conversion factors for the foods in 

ERS’s LAFA data series. Because RTI has multiple estimates for most foods, 

it provided ERS with one recommendation or proposed estimate for the value 

to be used for each food. If an alternative method of estimating percentages 

of ingredient use is developed, many of the estimates proposed here may 

need to be revised.

In some cases for which a plausible consumer-level food loss conversion 

factor was not available based on the calculations, RTI proposed using estimates from other similar foods (from either a single food or an average of 

several foods).5 In other cases (e.g., milk), RTI was unable to propose an 

updated estimate of consumer-level food loss because, in part, consumers 

appear to overstate consumption in NHANES and no other foods were 

similar enough to use for the estimate. Estimates for these foods may need to 

be updated using an alternative method, if developed, or the average of the 

experts’ estimates. 

It is important to note that the estimates calculated using the Nielsen (or 

Nielsen + Perishables) data provide an estimate for at-home food consumption, while the estimates using LAFA data provide an estimate for all 

consumption (at home and away from home). Based on interviews with 

restaurant and foodservice operators, spoilage and cooking losses are likely 

higher at home than away from home, but plate waste is likely lower (Muth 

et al., July 2007). Thus, RTI assumed these effects balance out such that, in 

theory, the consumer-level food loss is similar for both. However, in practice, 

differences in the quality and representativeness of the data result in different 

estimates that might or might not be plausible for each food.

Meat, Poultry, Fish, Eggs, and Nuts

Table 3 provides the estimate currently used by ERS and the proposed estimate of consumer-level food loss conversion factors for meat, poultry, fish, 

eggs, and nuts. These foods are grouped together as featured or illustrated 

in USDA’s Food Guide Appendix A-2 of the 2005 Dietary Guidelines for 

Americans. Details of the calculations and an explanation of the proposed 

estimates are provided in appendix table B-1. Most of the proposed estimates of consumer-level food loss conversion factors for meat, poultry, and 

fish were obtained using Nielsen or Nielsen+Perishables Group purchase 

estimates. RTI assumed most meat, poultry, and fish would be reported by 

NHANES respondents as the consumed item; thus, the selected estimates 

were not adjusted for ingredient use. However, consumers may purchase 

raw meat and poultry and prepare it for consumption in mixed dishes. To 

the extent that NHANES respondents report consumption of mixed dishes 

prepared with meat and poultry that are not separately reported, the loss estimates may be somewhat overstated.

RTI used the estimate for beef for veal and lamb because the estimates for 

veal and lamb were not plausible (most likely because of measurement error 

associated with low per capita consumption of these foods). Likewise, RTI 

5In this report, we use the terms 

“plausible,” “implausible,” and “somewhat reasonable” based on our subjective assessment from working on the 

food consumption data. For example, a 

calculated loss estimate of 85 percent 

for fresh mustard greens (see appendix 

table B-5 for calculation steps) was 

considered too high to be plausible 

because consumers would not likely 

buy fresh mustard greens and discard 

the inedible share and 85 percent of 

the edible share. Therefore, the more 

reasonable loss estimate of 24 percent 

for fresh lettuce was applied to fresh 

mustard greens. In general, negative 

loss estimates and loss estimates above 

50 percent were considered implausible 

with very few exceptions, such as fresh 

pumpkin, which has other nonfood 

uses.

Because we have multiple 

estimates of the value of the 

consumer-level loss conversion factor for each food, we 

provide a recommendation 

for the value to use in each 

case for which we have plausible values.

Each of the data sources 

used has advantages 

and disadvantages in 

terms of data quality and 

comprehensiveness.
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used the same estimate for fresh and frozen fish as for fresh and frozen shellfish, and the same estimate for canned tuna, canned shellfish, other canned 

fish, and cured fish as for canned salmon. Finally, for eggs, RTI used the 

LAFA estimate at the consumer level adjusted for ingredient use because 

eggs are frequently used as an ingredient and thus would be reported as 

consumed in other foods.

In general, the estimates for red meats and chicken are substantially lower 

than the estimates currently used by ERS. This result is expected because 

meat and poultry products are trimmed closer, many more products are 

sold boneless, and ground products tend to have lower fat percentages than 

in earlier time periods. Thus, smaller portions of these foods are likely 

discarded during preparation or consumption. For meats and poultry, only 

the loss estimate for turkey (35 percent) is higher than the current estimate 

Table 3

Consumer loss estimates for meat, poultry, fish, eggs, and nuts

Category Food

Previous

consumer 

loss 

estimate

Proposed 

consumer 

loss 

estimate

Percent

Meat, poultry, and fish Beef 32 20

Veal 35 20

Pork 39 29

Lamb 36 20

Chicken 40 15

Turkey 32 35

Fresh and frozen fish 33 40

Fresh and frozen shellfish 33 40

Canned salmon 10 17

Canned sardinesa 10 36

Canned tuna 10 17

Canned shellfisha 10 17

Other canned fisha 10 17

Cured fish 10 17

Eggs Eggs 15 23

Nuts

Peanuts, snack peanuts, other 

peanuts 10 4

Peanut butter 10 14

Almonds 10 21

Hazelnuts (filberts)a 10 20

Pecans 10 14

Walnuts 10 18

Macadamia nutsa 10 8

Pistachio nuts 10 16

Other tree nuts 10 18

Coconuta 10 10

aFood has 10 or fewer consumption observations in NHANES (National Health and Nutrition 

Examination Survey); thus, the total consumption estimate may not be reliable.

Source: RTI International.
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(32 percent), but the difference is relatively small. The estimate for turkey is 

higher than for chicken (15 percent), possibly because turkey is more often 

eaten during holidays when consumers may tend to discard relatively more 

uneaten food than on other days. Estimates for fresh, frozen, and canned 

seafood and for eggs are somewhat higher than the current estimates. 

Most of the consumer-level estimates for nuts are based on the LAFA data 

adjusted for ingredient use. Estimates based on Nielsen data may be slightly 

less reliable because a portion of nuts are purchased as random weight, and 

Perishables Group data for this category were not available. However, for 

peanuts, snack peanuts, and other peanuts, use of Nielsen data resulted in 

a somewhat reasonable estimate of 4 percent. RTI also used an average of 

the estimates calculated using Nielsen and the LAFA data for peanut butter, 

pistachio nuts, and coconuts because the estimates were similar. Because a 

reasonable estimate could not be obtained directly for walnuts, RTI took an 

average of other tree nuts (almonds, hazelnuts, and pecans); RTI also applied 

this average estimate to “other tree nuts.” 

The proposed estimates of consumer-level loss for nuts are somewhat 

higher than current estimates with the exception of that for macadamia nuts, 

which is slightly lower, and coconuts, which is the same. The estimates for 

almonds, hazelnuts, walnuts, and other tree nuts increased by the largest 

amount, but all of the loss estimates are 21 percent or less.

Dairy Products

Table 4 provides the current ERS estimate and the proposed estimate of 

consumer-level food loss conversion factors for liquid dairy products (i.e., 

milk) and for other types of dairy products, including cheese, yogurt, and ice 

cream. Details of the calculations and an explanation of the proposed estimates are provided in appendix table B-2.

Calculating estimates for liquid dairy products is particularly challenging for 

two reasons. First, some milk purchases may be unrecorded in Homescan 

because panelists may not report midweek purchases (purchases at times 

other than the primary shopping trip), which likely include milk. Second, 

NHANES respondents may overstate their milk consumption, or that of their 

children, because they know that milk is desirable from a health perspective. 

Thus, several estimates of consumer-level food loss in the dairy category are 

considered to be “unreliable.” 

Based on the LAFA data, RTI was able to provide an estimate for low-fat 

flavored milk, buttermilk, cream, and eggnog, but the estimates are highly 

variable. Furthermore, the loss estimates indicate substantially higher 

percentages of loss than current estimates. In particular, the loss estimates 

indicate that a substantial portion of eggnog and low-fat flavored milk are 

discarded or spoil before consumption. Given the seasonal nature of eggnog 

and that flavored milks are most often consumed by children, these estimates 

may be plausible. Because RTI was not able to calculate a plausible estimate 

for whole flavored milk, it used the estimate for low-fat flavored milk. 

In the case of plain whole milk, it may be possible to derive a plausible 

estimate of consumer-level loss if a more accurate estimate for ingredient 
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Table 4

Consumer loss estimates for dairy products

Category Food

Previous

consumer 

loss estimate

Proposed 

consumer 

loss estimate

Percent

Dairy—

Beverages Plain whole milk 20 TBD

Plain 1% and 2% milk 20 NA

Skim milk 20 NA

Whole flavored milk 20 45

Low-fat flavored milk 20 45

Buttermilk 20 18

Light cream, heavy cream, half & half 20 12

Eggnog 20 51

Dairy—

Other

Sour cream 20 8

Cream cheese 20 13

Cheddar cheese 13 11

Other American cheese 13 28

Provolone cheese 13 14

Parmesan and Romano cheese 13 8

Mozzarella cheese 13 31

Ricotta cheesea 13 12

Other Italian cheesea 13 16

Swiss cheese 13 50

Brick cheese 13 40

Muenster cheese 13 35

Blue cheese 13 43

Other miscellaneous cheesea 13 42

Processed cheese 13 8

Processed cheese foods and spreads 13 8

Regular cottage cheese 20 31

Low-fat cottage cheese 20 4

Regular ice cream 20 24

Low-fat ice cream (ice milk) 20 24

Frozen yogurt and other miscellaneous 

frozen products

20 33

Refrigerated yogurt 20 21

Total evaporated and condensed 

canned whole and skim milk

20 15

Dry whole and nonfat milk 1 41

Dry buttermilka 1 41

NA = not available; TBD = to be decided; reasonable estimate might be calculable if ingredient 

percentage is revised.

a Food has 10 or fewer consumption observations in NHANES (National Health and Nutrition 

Examination Survey); thus, the total consumption estimate may not be reliable.

Source: RTI International.
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use can be derived. This estimate might also be a reasonable approximation 

for skim milk and plain 1 percent and 2 percent milk, but RTI expects the 

estimate for whole milk to be higher because it is frequently consumed by 

children.

Most of the estimates for other dairy products are based on the LAFA data, 

but a few are based on Nielsen data. Some are calculated based on estimates 

for other categories. In particular, “other” Italian cheese is calculated as an 

average of provolone, parmesan and Romano, mozzarella, and ricotta; and 

“other” miscellaneous cheese is calculated as an average of Swiss, brick, 

Muenster, and blue cheese. Furthermore, in place of implausible estimates, 

RTI used the estimate for processed cheese for processed cheese foods and 

spreads; the estimate for regular ice cream for low-fat ice cream; and the 

estimate for dry whole and nonfat milk for dry buttermilk. The implausible 

estimates may be due to a low number of respondents for those foods in 

NHANES. 

Although some of the estimates for other dairy products are higher than the 

estimates currently used by ERS, many are somewhat lower. In particular, 

estimates for sour cream, cream cheese, processed cheese, and low-fat 

cottage cheese decreased substantially, while estimates for cheeses, such 

as mozzarella, Swiss, brick, Muenster, and blue, increased substantially. 

Estimates for cheddar cheese, ricotta cheese, regular and low-fat ice cream, 

and refrigerated yogurt are similar to the current estimates. Estimates for dry 

whole and nonfat milk are much larger, but the difference may be due to estimation of the loss in its liquid equivalent in contrast to the dry equivalent for 

the current estimate.

Added Fats and Oils (Excluding Dairy)

Table 5 provides the current ERS estimate and RTI’s proposed estimate of 

consumer-level food loss conversion factors for added fats and oils, including 

butter, margarine, shortening, and cooking oils. Details of the calculations 

and an explanation of the proposed estimates are provided in appendix table 

B-3. Estimates for butter and margarine are identical at 35 percent, with the 

estimates also being the same for margarine for both the Nielsen and LAFA 

data. Thus, RTI has a high degree of confidence that this value is correct, 

Table 5

Consumer loss estimates for added fats and oils

Category Food

Previous

consumer 

loss estimate

Proposed 

consumer 

loss estimate

Percent

Fats and oils Butter 15 35

Margarine 15 35

Lard 0 35

Edible beef tallow 0 35

Shortening 15 35

Salad and cooking oils 20 15

Other edible fats and oils 0 25

Source: RTI International.
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although it is substantially higher than the current estimate of 15 percent. 

In contrast, the loss estimate for salad and cooking oils declined from the 

current estimate of 20 percent to 15 percent using the estimate based on the 

LAFA data. RTI was unable to calculate an estimate for lard, edible beef 

tallow, and shortening because direct consumption of these foods is not 

reflected in NHANES and it was impractical to estimate consumption as an 

ingredient in thousands of foods. Thus, RTI proposes applying the estimate 

for margarine to these foods. For “other” edible fats and oils, RTI proposes 

using an average of the estimates for margarine and salad and cooking oils, 

which results in an estimate of 25 percent (current estimate was 0 percent).

Fruits

Table 6 provides the current ERS estimate and the RTI’s proposed estimate of consumer-level food loss conversion factors for fruits in all forms, 

including fresh, canned, frozen, dried, and juices. Details of the calculations 

and an explanation of the proposed estimates are provided in appendix table 

B-4.

Estimates for fresh fruits were obtained using Nielsen, Perishables Group, 

and the LAFA data or by applying estimates from other categories. Plausible 

estimates were initially chosen from those based on the Nielsen and 

Perishables Group data or the LAFA data. In cases where the initial estimates 

were implausible, RTI applied an estimate from a similar fresh fruit. 

For example, RTI used the estimate for fresh cantaloupe for fresh honeydew. 

In two cases—fresh apples and fresh bananas—RTI believes it would be 

possible to obtain a plausible estimate of consumer-level loss with a more 

reliable estimate of ingredient use. If a plausible estimate can be calculated 

for fresh apples, RTI proposes also applying the same value to fresh pears.

Many of the estimates, such as those for fresh apricots, fresh blueberries, 

fresh mangoes, and fresh watermelon, decreased relative to the current estimates. Most of these fruits have somewhat longer shelf lives than other fruits, 

so lower values are expected for these fruits. Estimates for other fruits, such 

as fresh avocados, fresh cherries, fresh honeydew, fresh kiwi, fresh peaches, 

and fresh strawberries, increased substantially. Fruits that are more perishable (e.g., avocados, peaches, and strawberries) are expected to have higher 

consumer-level loss estimates because they are more likely to spoil prior to 

consumption.

Most of the estimates for canned fruits are based on Nielsen data because 

Nielsen estimates purchases for UPC-only foods more reliably than for foods 

that are also sold as random weight. However, the estimate for canned apricots is based on the LAFA data because the estimate based on the Nielsen 

data was not plausible. RTI proposes applying the value for canned pineapple 

for canned peaches and canned pears because estimates using either the 

Nielsen or LAFA data are not plausible. Although the estimates for canned 

apples and applesauce, canned peaches, canned pears, and canned pineapple are similar to current estimates, the estimates for other canned fruits 

increased substantially. The other canned fruits with higher estimates are 

not typically packaged in single-serve containers, which may contribute to 
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Table 6

Consumer loss estimates for fruits

Category Food

Previous

consumer 

loss estimate

Proposed 

consumer 

loss estimate

Percent

Fruits—Fresh Fresh oranges 20 36

Fresh tangerines 20 52

Fresh grapefruit 20 54

Fresh lemons 20 44

Fresh limesa 20 44

Fresh apples 20 TBD

Fresh apricotsa 20 10

Fresh avocados 20 32

Fresh bananas 20 TBD

Fresh blueberries 20 8

Fresh cantaloupe 20 43

Fresh cherries 20 51

Fresh cranberriesa 20 26

Fresh grapes 20 33

Fresh honeydew 20 43

Fresh kiwi 20 45

Fresh mangoes 20 13

Fresh peaches 35 42

Fresh pears 20 TBD

Fresh pineapple 20 37

Fresh papayaa 20 20

Fresh plums 20 27

Fresh strawberries 20 35

Fresh watermelon 20 13

Fruits—Canned Canned apples and applesauce 10 8

Canned apricotsa 10 27

Canned cherriesa 10 32

Canned peaches 10 9

Canned pears 10 9

Canned pineapple 10 9

Canned plumsa 10 26

Canned olives 10 25

Fruits—Frozen Frozen blackberries 10 40

Frozen blueberriesa 10 29

Frozen cherriesa 10 29

Frozen raspberriesa 10 24

Frozen strawberriesa 10 24

Other frozen berriesa 10 30

Frozen applesa 10 35

Frozen apricotsa 10 35

Frozen peachesa 10 35

Other frozen fruita 10 35

Continued—
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greater consumer-level food loss due to discards of uneaten food or spoilage 

of opened containers.

Estimates for frozen fruits are based primarily on the LAFA data or assumed 

values from other categories. Only the estimate for frozen raspberries is plausible when based on the Nielsen data. RTI proposes using the estimate for 

frozen blueberries for frozen cherries and the estimate for frozen peaches for 

frozen apples, frozen apricots, and other frozen fruits because the calculated 

estimates were implausible. All of the estimates for frozen fruits increased 

substantially from current estimates. However, for this category as a whole, 

few NHANES respondents reported consuming each food. Thus, the estimates may be less reliable than for foods consumed more frequently. 

As with frozen fruits, few NHANES respondents reported consuming each 

type of dried fruit, but the estimates are generally closer to current estimates. The estimates for dried dates, dried plums, and raisins are based on 

the Nielsen data. RTI proposes using the estimate for dried plums for dried 

apples, dried apricots, dried peaches, and dried pears and the estimate for 

dried dates for dried figs. The estimates increased relative to the current estimates for dried dates, dried figs, and raisins but are very similar for all other 

dried fruits.

Table 6

Consumer loss estimates for fruits—continued

Category Food

Previous

consumer 

loss estimate

Proposed 

consumer 

loss estimate

Percent

Fruits—Dried Dried applesa 10 11

Dried apricotsa 10 11

Dried datesa 10 25

Dried figsa 10 25

Dried peachesa 10 11

Dried pearsa 10 11

Dried plums 10 11

Raisins 10 26

Fruits—Juices Grapefruit juice 10 NA

Lemon juice 10 NA

Lime juice 10 NA

Orange juice 10 NA

Apple juice 10 NA

Cranberry juicea 10 NA

Grape juice 10 NA

Pineapple juice 10 NA

Prune juice 10 32

NA = not available; TBD = to be decided; reasonable estimate might be calculable if ingredient 

percentage is revised.

a Food has 10 or fewer consumption observations in NHANES (National Health and Nutrition 

Examination Survey); thus, the total consumption estimate may not be reliable.

Source: RTI International.
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The final category in table 6 is fruit juices, but a plausible estimate could 

be calculated only for prune juice.6 Adjustments were required to compare 

the juice purchase data with the juice consumption data. Specifically, RTI 

adjusted the purchase data from fluid ounces to weight ounces based on the 

density of each juice type and calculated the total reconstituted juice volume 

for concentrates. Furthermore, RTI scrutinized product categories in both 

the purchase and consumption data to exclude juice drinks (i.e., flavored 

fruit drinks and other products that are not 100 percent juice). However, 

these adjustments and the selection of products did not result in comparable 

purchase and consumption estimates. For most of the categories (grapefruit 

juice, lemon juice, lime juice, orange juice, and apple juice), NHANES 

respondents appear to be overstating consumption possibly because fruit 

juices are viewed as healthful foods, or they may be unable to accurately 

estimate the amount of liquid consumed. Thus, using either the Nielsen or 

LAFA data results in estimates with the incorrect sign in appendix table B-2. 

In contrast, consumption of grape juice and cranberry juice by NHANES 

respondents appears to be so small compared with that reflected in Nielsen or 

LAFA data that the estimates are implausible. The only plausible fruit juice 

estimate of 32 percent for prune juice is based on the Nielsen data; this estimate suggests a much greater percentage of loss than the 10-percent estimate 

currently used by ERS. 

Vegetables

Table 7 provides the current ERS estimate and the proposed estimate of 

consumer-level food loss conversion factors for vegetables in various forms, 

including fresh, canned, frozen, and dry (or dehydrated). Details of the 

calculations and an explanation of the proposed estimates are provided in 

appendix table B-5.

Estimates for fresh vegetables were obtained using Nielsen, Perishables 

Group, and LAFA data or by applying estimates from other categories. 

Plausible estimates were initially chosen from those based on the Nielsen and 

Perishables Group data or the LAFA data. In cases where the initial estimates 

were implausible, RTI applied an estimate from a similar fresh vegetable. 

In particular, RTI used the estimate for fresh artichokes for fresh asparagus; 

fresh broccoli for fresh brussels sprouts and fresh cauliflower; fresh lettuce 

(includes leaf lettuce, romaine, escarole, and endive) for fresh cabbage, fresh 

kale for fresh collard greens, fresh mustard greens, and fresh turnip greens; 

and fresh onions for fresh garlic. In a few cases, RTI used the estimate for a 

different form of the vegetable because no other fresh vegetables are similar; 

these substitutions include frozen lima beans for fresh lima beans, and frozen 

snap beans (also called green or string beans) for fresh snap beans. However, 

spoilage is likely higher for the fresh form versus the frozen form. In two 

cases—fresh sweet corn and fresh okra—RTI believes it would be possible to 

obtain a plausible estimate of consumer-level loss with a more accurate estimate of ingredient use; however, RTI was not able to recommend a consumer 

loss estimate based on the available data.

As with other food categories, many of the consumer loss estimates for 

fresh vegetables increased somewhat from the estimates currently used by 

ERS. The largest increases in the estimates are for fresh bell peppers, fresh 

6The detailed estimates for juice, 

which were not proposed for use by 

RTI, are provided in appendix table 

B-4.
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Table 7

Consumer loss estimates for vegetables

Category Food

Previous

consumer 

loss estimate

Proposed 

consumer 

loss estimate

Percent

Vegetables—

Fresh Fresh artichokesa 20 18

Fresh asparagusa 20 18

Fresh bell peppers 20 39

Fresh broccoli 20 12

Fresh brussels sprouts 20 12

Fresh cabbage 20 24

Fresh carrots 20 34

Fresh cauliflower 20 9

Fresh celery 20 39

Fresh collard greens 20 38

Fresh sweet corn 32 TBD

Fresh cucumbers 20 32

Fresh eggplanta 27 26

Fresh garlica 20 43

Fresh kalea 20 38

Fresh romaine and leaf lettuce and 

escarole and endivea 20 24

Fresh lima beansa 20 27

Fresh mushrooms 20 21

Fresh mustard greensa 20 38

Fresh okra 20 TBD

Fresh onions 35 43

Fresh potatoes 30 16

Fresh pumpkina 20 69

Fresh radishes 20 47

Fresh snap beans 22 24

Fresh spinach 20 9

Fresh squash 20 25

Fresh sweet potatoes 31 44

Fresh tomatoes 20 7

Fresh turnip greensa 20 38

Vegetables—

Canned Canned asparagusa 10 2

Canned snap beans 10 24

Canned cabbage (sauerkraut) 10 16

Canned carrotsa 10 31

Canned sweet corn 10 7

Canned cucumbers (pickles) 10 3

Canned green peasa 10 24

Canned chile peppers 10 4

Canned tomatoes 10 28

Canned mushroomsa 10 9

Canned potatoesa 10 28

Other canned vegetablesa 10 16

continued—
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celery, fresh collard greens, fresh garlic, fresh kale, fresh mustard greens, 

fresh pumpkin, fresh radishes, and fresh turnip greens. Larger estimates are 

likely plausible for foods that are often used for flavoring or garnishes and 

for seasonal foods, such as fresh greens and fresh pumpkins. The largest 

decreases in the estimates are for fresh broccoli (and thus fresh cauliflower), 

fresh brussels sprouts, fresh potatoes, fresh spinach, and fresh tomatoes. 

Changes in packaging and shelf life may play a factor in lower estimates for 

these foods across periods.

Several of the estimates for canned vegetables are based on Nielsen data 

because Nielsen estimates purchases of UPC-only foods more reliably than 

foods that are also sold random weight; a few of the estimates for canned 

vegetables are based on the LAFA data. The estimate for canned snap beans 

is used for canned green peas. Furthermore, the estimate for canned cabbage 

(i.e., sauerkraut) is based on an average of all canned vegetables for lack of 

a plausible estimate, and the estimate for “other” canned vegetables is also 

based on an average of all canned vegetables. While some of the estimates 

increased from current estimates (e.g., canned carrots, canned green peas, 

canned tomatoes, and canned potatoes), several decreased (canned asparagus, 

canned sweet corn, pickles, canned chili peppers, and canned mushrooms).

Table 7

Consumer loss estimates for vegetables—continued

Category Food

Previous

consumer 

loss estimate

Proposed 

consumer 

loss estimate

Percent

Vegetables—

Frozen Frozen asparagus 30 26

Frozen snap beans 20 24

Frozen broccoli 16 12

Frozen carrotsa 12 34

Frozen cauliflowera 17 27

Frozen sweet corn 14 36

Frozen green peasa 17 24

Frozen lima beans 32 27

Frozen spinach 23 34

Frozen potatoes 32 16

Other frozen vegetablesa 23 26

Vegetables—

Dried Dehydrated onions 10 4

Dehydrated potatoes 10 4

Potato chips and shoestring potatoes 10 4

Dry edible beans 10 NA

Dry edible peas and lentils 10 NA

NA = not available; TBD = to be decided; reasonable estimate might be calculable if ingredient 

percentage is revised.

a Food has 10 or fewer consumption observations in NHANES (National Health and Nutrition 

Examination Survey); thus, the total consumption estimate may not be reliable.

Source: RTI International.
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As with loss estimates for canned vegetables, several of the estimates for 

frozen vegetables are based on Nielsen data, and a few are based on the 

LAFA data. Because calculated values for frozen carrots, frozen green beans, 

and frozen potatoes are implausible, RTI proposed other values to use. In 

particular, RTI proposes using the value for fresh carrots for frozen carrots, 

frozen snap peas for frozen green peas, and fresh potatoes for frozen potatoes. However, spoilage may be higher for fresh forms of carrots and potatoes; thus, the estimates may overstate actual consumer-level food loss. The 

estimate for “other” frozen vegetables is calculated as the average of all other 

frozen vegetables with the exception of frozen asparagus. For frozen asparagus, RTI also proposes using the average of all other frozen vegetables in 

the absence of plausible values from other sources. Most of the estimates for 

frozen vegetables are similar to current estimates with the exception of a few 

that increased more than others (e.g., frozen carrots, frozen cauliflower, and 

frozen spinach). In addition, the loss estimate for frozen potatoes decreased 

substantially across periods, so RTI used the estimate for fresh potatoes, 

which should be a high estimate. 

Finally, for dehydrated or dry vegetables, only the estimate for dehydrated 

potatoes is plausible. Given the similarities in the products, RTI proposes 

using the same estimate for dehydrated onions and potato chips and shoestring potatoes. This estimate is somewhat lower than the current estimate 

but reasonable given the low perishability of these foods. RTI was unable to 

calculate a reasonable estimate for dry edible beans or dry edible peas and 

lentils given the current data.

Grain Products

Table 8 provides the current ERS estimate and the RTI’s proposed estimate 

of consumer-level food loss conversion factors for grain products, which are 

primarily used as ingredients. Details of the calculations and an explanation 

of the proposed estimates are provided in appendix table B-6.

Table 8

Consumer loss estimates for grain products

Category Food

Previous

consumer 

loss estimate

Proposed 

consumer 

loss estimate

Percent

Grains White and whole wheat flour 20 NA

Durum flour 20 NA

Rice 20 33

Rye flour 20 NA

Corn flour and meal 20 NA

Corn hominy and grits 20 NA

Corn starch 20 NA

Barley productsa 20 14

Oat products 20 14

NA = not available; TBD = to be decided; reasonable estimate might be calculable if ingredient 

percentage is revised.

a Food has 10 or fewer consumption observations in NHANES (National Health and Nutrition 

Examination Survey); thus, the total consumption estimate may not be reliable.

Source: RTI International.
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RTI was unable to calculate an estimate for most foods in this group because 

they are used almost exclusively as ingredients (e.g., various types of flours). 

The exceptions are rice, barley products, and oat products. For rice, the estimate based on the LAFA data is 33 percent, which is substantially higher 

than the current estimate of 20 percent but is plausible given that a large 

amount of plate waste likely occurs for rice. The LAFA-based estimate for 

barley products is 14 percent, which is somewhat lower than the current 

estimate of 20 percent. Based on the similarity of products, RTI proposes 

applying this barley estimate for oat products.

Added Sugars and Sweeteners

Table 9 provides the current ERS estimate and the proposed estimate of 

consumer-level food loss conversion factors for added sugars and sweeteners. 

Details of the calculations and an explanation of the proposed estimates are 

provided in appendix table B-7.

RTI derived estimates for cane and beet sugar and for honey using the 

LAFA data. While the estimate for cane and beet sugar increased substantially from the current estimate, the estimate for honey decreased somewhat. 

Consumption of high-fructose corn syrup (HFCS), glucose, and dextrose is 

not reflected in the consumption data because these items are used only as 

ingredients; RTI proposes using the estimate calculated for honey. For the 

final category of edible syrups, the estimate unadjusted for ingredient use is a 

somewhat plausible 8 percent. But because the expert panel members believe 

the items in this category are frequently used as an ingredient, a plausible 

estimate is not feasible; thus, RTI proposes using the estimate for honey for 

this category also.

Table 9

Consumer loss estimates for added sugars and sweeteners

Category Food

Previous

consumer 

loss estimate

Proposed 

consumer 

loss estimate

Percent

Added sugars and 

sweeteners Cane and beet sugar 20 34

High-fructose corn syrup 20 15

Glucose 20 15

Dextrose 20 15

Honey 20 15

Edible syrups 20 15

Source: RTI International.
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Analysis Using RTI’s Proposed Estimates in the 

ERS Loss-Adjusted Food Availability Data

For each commodity, ERS used the consumer-level loss estimate proposed 

by RTI in the Loss-Adjusted Food Availability data to determine the effects 

on ERS’s per capita estimates of the pounds of food available annually for 

consumption and the number of calories available per day for consumption. Only those foods for which ERS had an estimate proposed by RTI are 

included in the calculations.

Meat, Poultry, Fish, Eggs, and Nuts

Table 10 presents a comparison of ERS and RTI loss estimates for each 

commodity in the meat, poultry, fish, eggs, and nuts group from the edible 

share of loss at the consumer level, per capita. If RTI’s proposed estimates 

are adopted in the data series, this food group would have the largest change 

of all food groups: an increase in availability of 22.25 pounds per year (15 

percent). This is equivalent to a 76-calorie increase per capita per day, which 

is almost a 17-percent increase from the baseline.

The largest change for an individual food commodity in this group is 

for chicken. The current ERS estimate for consumer loss is 40 percent, 

whereas RTI’s proposed estimate is much lower at 15 percent. This lower 

loss estimate results in an increase of 14.63 pounds of chicken available 

for consumption per year and an increase of 42.3 calories per day (a 41.7-

percent increase for both). Adoption of the proposed loss estimates in the 

data series would mean that for the first time since the data series began in 

1909, consumers would now eat more chicken than beef in terms of pounds 

per year.

The largest decrease in terms of availability in this food group is for eggs, 

with a 2.4-pound-per-year decrease. However, over the course of a year, this 

has little effect in terms of calories (4.3 fewer calories per day). The largest 

decrease in terms of percent is for canned sardines (a 28.9-percent decrease 

in daily calories), but sardines are not a major component of the total food 

available for consumption so the impact on total calories from all foods is 

negligible. The impact on tree nut consumption is minimal, though peanuts 

changed the most with a 6.7-percent increase per year.

Dairy Products

For reasons described previously, RTI was unable to provide proposed 

consumer loss estimates for the plain beverage milks, skim milk, and half and 

half. For the dairy product group as a whole, adopting RTI’s proposed estimates in the data series results in 8.96 fewer pounds per capita per year (-4.8 

percent), or 23.6 fewer calories per day (-9.1 percent) (table 11).7

There would be no notable increases in terms of pounds per year or calories per day for any dairy product from adopting RTI’s proposed estimates 

for consumer loss of the edible share of food. Percentage-wise, a dozen 

commodities show more than a 20-percent decrease, but only one would 

result in major changes in pounds per year or calories per day because the 

7Note that all fluid dairy products 

were converted to pounds.
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Table 10

Comparison of ERS and RTI estimates of meat, poultry, fish, eggs, and nuts loss at the consumer level 

(per capita)1

Commodity

Consumer loss 

estimates

Difference 

between 

ERS and RTI 

estimates

Quantity of food 

consumed after 

adjusting for all 

losses

Difference in 

quantity 

consumed 

between ERS 

and RTI

 estimates

Calories

Difference in 

calories between 

ERS and RTI 

estimates ERS 

estimate

RTI 

estimate

ERS 

estimate

RTI 

estimate

ERS 

estimate

RTI 

estimate

Percent Pounds/year

Pounds/

year Percent Number/day

Number/

day Percent

Beef 32.0 20.0 -60.0 40.86 48.07 7.21 17.6 154.0 181.2 27.2 17.6

Veal 35.0 20.0 -75.0 0.17 0.21 0.04 23.1 0.5 0.6 0.1 23.1

Pork 39.0 29.0 -34.5 26.85 31.25 4.40 16.4 90.6 105.5 14.9 16.4

Lamb 36.0 20.0 -80.0 0.44 0.55 0.11 25.0 1.7 2.1 0.4 25.0

Chicken 40.0 15.0 -166.7 35.10 49.73 14.63 41.7 101.6 143.9 42.3 41.7

Turkey 32.0 35.0 8.6 8.75 8.37 -0.39 -4.4 22.3 21.3 -1.0 -4.4

Fresh and frozen fish 33.0 40.0 17.5 3.97 3.55 -0.41 -10.4 7.1 6.4 -0.7 -10.4

Fresh and frozen 

shellfish 33.0 40.0 17.5 3.54 3.17 -0.37 -10.4 3.9 3.5 -0.4 -10.4

Canned salmon 10.0 17.0 41.2 0.16 0.15 -0.01 -7.8 0.3 0.3 -0.0 -7.8

Canned sardines 10.0 36.0 72.2 0.16 0.12 -0.05 -28.9 0.4 0.3 -0.1 -28.9

Canned tuna 10.0 17.0 41.2 2.43 2.24 -0.19 -7.8 3.5 3.2 -0.3 -7.8

Canned shellfish 10.0 17.0 41.2 0.34 0.31 -0.03 -7.8 0.5 0.4 -0.0 -7.8

Other canned fish 10.0 17.0 41.2 0.20 0.19 -0.02 -7.8 0.3 0.3 -0.0 -7.8

Cured fish 10.0 17.0 41.2 0.27 0.25 -0.02 -7.8 0.4 0.4 -0.0 -7.8

Total meat -- -- -- 123.25 148.16 24.91 20.2 387.0 469.3 82.3 21.3

Eggs 15.0 23.0 34.8 21.99 19.58 -2.41 -11.0 39.3 35.0 -4.3 -11.0

Peanuts 10.0 4.0 -150.0 5.51 5.87 0.37 6.7 38.6 41.2 2.6 6.7

Almonds 10.0 21.0 52.4 0.86 0.75 -0.10 -12.2 6.2 5.4 -0.8 -12.2

Hazelnuts 10.0 20.0 50.0 0.06 0.06 -0.01 -11.1 0.5 0.4 -0.1 -11.1

Pecans 10.0 14.0 28.6 0.38 0.36 -0.02 -4.4 3.2 3.1 -0.1 -4.4

Walnuts 10.0 18.0 44.4 0.45 0.41 -0.04 -8.9 3.7 3.3 -0.3 -8.9

Macadamia nuts 10.0 8.0 -25.0 0.11 0.11 0.00 2.2 1.0 1.0 0.0 2.2

Pistachio nuts 10.0 16.0 37.5 0.11 0.10 -0.01 -6.7 0.8 0.7 -0.1 -6.7

Other tree nuts 10.0 18.0 44.4 0.81 0.74 -0.07 -8.9 6.4 5.8 -0.6 -8.9

Total tree nuts -- -- -- 2.78 2.53 -0.25 -8.8 21.7 19.8 -1.9 -8.6

Coconut 10.0 10.0 0.0 0.48 0.48 0.00 0.0 3.9 3.9 0.0 0.0

Total meat group -- -- -- 148.49 170.74 22.25 15.0 452.0 528.0 76.1 16.8

1RTI estimate is the RTI “best estimate.”

Source: USDA, Economic Research Service using RTI “best estimate” data and ERS Loss-Adjusted Food Availability data for 2006.
28

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table 11

Comparison of ERS and RTI estimates of dairy products loss at the consumer level (per capita)1

Commodity

Consumer loss 

estimates

Difference 

between 

ERS and RTI 

estimates

Quantity of food 

consumed after 

adjusting for all 

losses

Difference in 

quantity 

consumed 

between ERS

 and RTI

 estimates

Calories

Difference in 

calories between 

ERS and RTI 

estimates ERS 

estimate

RTI 

estimate

ERS 

estimate

RTI 

estimate

ERS 

estimate

RTI 

estimate

Percent Pounds/year

Pounds/

year Percent Number/day

Number/

day Percent

Plain whole milk 20.0 NA NA 39.16 NA NA NA 29.1 NA NA NA

Plain 2 percent milk 20.0 NA NA 41.92 NA NA NA 26.0 NA NA NA

Plain 1 percent milk 20.0 NA NA 15.35 NA NA NA 8.0 NA NA NA

Skim milk 20.0 NA NA 19.17 NA NA NA 8.1 NA NA NA

Whole flavored milk 20.0 45.0 55.6 1.70 1.17 -0.53 -31.3 1.8 1.2 -0.5 -31.3

Low-fat flavored milk 20.0 45.0 55.6 8.81 6.06 -2.75 -31.3 6.9 4.8 -2.2 -31.3

Buttermilk 20.0 18.0 -11.1 1.18 0.81 -0.37 -31.3 0.6 0.4 -0.2 -31.3

Refrigerated yogurt 20.0 21.0 4.8 7.79 7.69 -0.10 -1.3 5.9 5.8 -0.1 -1.3

Cheddar cheese 13.0 11.0 -18.2 8.48 8.67 0.19 2.3 42.4 43.4 1.0 2.3

Other American cheese 13.0 28.0 53.6 2.21 1.83 -0.38 -17.2 10.9 9.0 -1.9 -17.2

Provolone cheese 13.0 14.0 7.1 0.88 0.87 -0.01 -1.1 3.8 3.8 0.0 -1.1

Romano cheese 13.0 8.0 -62.5 0.20 0.21 0.01 5.7 1.0 1.0 0.1 5.7

Parmesan cheese 13.0 8.0 -62.5 0.53 0.56 0.03 5.7 2.6 2.7 0.1 5.7

Mozzarella cheese 13.0 31.0 58.1 8.61 6.83 -1.78 -20.7 32.2 25.6 -6.7 -20.7

Ricotta cheese 13.0 12.0 -8.3 0.67 0.68 0.01 1.1 1.3 1.3 0.0 1.1

Other Italian cheese 13.0 16.0 18.8 0.35 0.34 -0.01 -3.4 1.7 1.6 -0.1 -3.4

Swiss cheese 13.0 50.0 74.0 1.04 0.60 -0.44 -42.5 4.9 2.8 -2.1 -42.5

Brick cheese 13.0 40.0 67.5 0.02 0.02 -0.01 -31.0 0.11 0.1 0.0 -31.0

Muenster cheese 13.0 35.0 62.9 0.26 0.20 -0.07 -25.3 1.2 0.9 -0.3 -25.3

Blue cheese 13.0 43.0 69.8 0.16 0.11 -0.06 -34.5 0.7 0.5 -0.2 -34.5

Other miscellaneous 

cheese 13.0 42.0 69.0 1.15 0.77 -0.38 -33.3 5.3 3.5 -1.8 -33.3

Regular cottage cheese 20.0 31.0 35.5 0.87 0.75 -0.12 -13.8 1.0 0.8 -0.1 -13.8

Low-fat cottage cheese 20.0 4.0 -400.0 0.96 1.16 0.19 20.0 0.9 1.0 0.2 20.0

Regular ice cream 20.0 24.0 16.7 10.35 9.83 -0.52 -5.0 25.8 24.5 -1.3 -5.0

Low-fat ice cream 20.0 24.0 16.7 4.88 4.64 -0.24 -5.0 10.6 10.0 -0.5 -5.0

Frozen yogurt and other 

frozen miscellaneous 

product 20.0 33.0 39.4 3.07 2.57 -0.50 -16.3 6.2 5.2 -1.0 -16.3

Evaporated and 

condensed canned 

whole milk 20.0 15.0 -33.3 1.09 1.16 0.07 6.3 1.8 1.9 0.1 6.3

Evaporated and condensed bulk whole milk 20.0 15.0 -33.3 0.44 0.46 0.03 6.3 0.7 0.8 0.0 6.3

Evaporated and condensed bulk and canned 

skim milk 20.0 15.0 -33.3 2.98 3.16 0.19 6.3 2.9 3.1 0.2 6.3

Continued—
29

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

baseline amounts are low. The exception is low-fat flavored milk, which 

decreases by 2.75 pounds per capita per year. However, this translates to only 

2.2 fewer calories per capita per day. The impact on total dairy is the cumulative impact from a large number of individual foods with small changes. 

Added Fats and Oils (Excluding Dairy)

Table 12 compares the ERS and RTI loss estimates of the edible share of 

annual added fats and oils at the consumer level, per capita. Adopting the 

RTI estimates for added fats and oils as a group decreases the annual amount 

available for consumption by 4.28 pounds per capita (a 6.3-percent decrease). 

This translates into 51.7 fewer calories per day (an 8.1-percent decrease). 

This change is important as Americans have historically consumed too much 

fat, on average, relative to Federal dietary guidelines. If the RTI estimates are 

used for added fats and oils, the average American would consume 62 grams 

of added fats and oils on a daily basis.8 This is still high, particularly when 

considering that the amount does not include naturally occurring fats in food. 

Although many individual added fats and oils had more than a 20-percent 

decrease in annual pounds per capita per year, only shortening had a notable 

decrease in pounds per year. Per capita shortening availability decreased 

almost 4 pounds per year, or 43.9 calories per capita per day. Shortening was 

responsible for most of the change in the added fats and oils group.

Fruits

Table 13 presents a comparison of ERS consumer-level loss estimates and 

RTI’s proposed estimates, per capita, for each fresh and processed type of 

fruit in the LAFA data. Adopting RTI’s proposed estimates would decrease 

the annual amount of fruit consumed by the average American by 9.75 

8To put this into context, 62 grams of 

added fats and oils would account for 

28 percent of the total proposed calories 

for a 2,000-calorie-per-day diet. The 

2005 Dietary Guidelines for Americans

recommend that fats and oils, both 

naturally occurring and added, contribute 20-35 percent of total calories for 

adults; 25-35 percent for children ages 

4-18; and 30-35 percent for children 

ages 2-3.

Table 11

Comparison of ERS and RTI estimates of dairy products loss at the consumer level (per capita)1—continued

Commodity

Consumer loss 

estimates

Difference 

between 

ERS and RTI 

estimates

Quantity of food 

consumed after 

adjusting for all 

losses

Difference in 

quantity 

consumed 

between ERS 

and RTI

 estimates

Calories

Difference in 

calories between 

ERS and RTI 

estimates ERS 

estimate

RTI 

estimate

ERS 

estimate

RTI 

estimate

ERS 

estimate

RTI 

estimate

Percent Pounds/year

Pounds/

year Percent Number/day

Number/

day Percent

Dry whole milk 1.0 41.0 97.6 0.03 0.02 -0.01 -40.4 0.16 0.1 -0.1 -40.4

Nonfat dry milk 1.0 41.0 97.6 3.12 1.86 -1.26 -40.4 14.1 8.4 -5.7 -40.4

Dry buttermilk 1.0 41.0 97.6 0.24 0.14 -0.10 -40.4 1.2 0.7 -0.5 -40.4

Half and half2 20.0 12.0 -66.7 NA NA NA NA NA NA NA NA

Eggnog 20.0 51.0 60.8 0.10 0.06 -0.04 -38.8 0.1 0.0 0.0 -38.8

Total dairy -- -- -- 187.75 178.80 -8.96 -4.8 259.8 236.2 -23.6 -9.1

NA = RTI “best estimate” was not available and therefore by default, the ERS estimate was used.

1RTI estimate is the RTI “best estimate.” 

2Loss-Adjusted Food Availability data for half and half was not available for 2006.

Source: USDA, Economic Research Service using RTI “best estimate” data and ERS Loss-Adjusted Food Availability data for 2006.
30

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

pounds (roughly a 7-percent decrease). This translates into only 7 fewer 

calories per day. Americans are already under-consuming fruits, on average 

(Wells and Buzby, 2008), so adopting RTI’s proposed estimates means 

that the shortfall from the dietary recommendations for fruit would be even 

greater.

Although many of the loss estimates for canned, frozen, and dried fruit 

increased significantly, their impact on per capita consumption estimates for 

individual fruits were small because these fruits were not among the most 

popularly consumed. Most of the change in the loss estimate for total fruits 

was due to the changes in fresh fruit. 

In terms of pounds per year, the largest changes were for fresh oranges, 

which decreased by 1.4 pounds, and fresh cantaloupe, which decreased by 

1.7 pounds. 

Table 12

Comparison of ERS and RTI estimates of added fats and oils loss at the consumer level (per capita)1

Commodity

Consumer loss 

estimates

Difference 

between ERS 

and RTI 

estimates

Quantity of food 

consumed after 

adjusting for all 

losses

Difference in 

quantity 

consumed 

between ERS 

and RTI

 estimates

Calories

Difference in 

calories between 

ERS and RTI 

estimates ERS 

estimate

RTI 

estimate

ERS 

estimate

RTI 

estimate

ERS 

estimate

RTI 

estimate

Percent Pounds/year

Pounds/

year Percent Number/day

Number/

day Percent

Added fats and oils

Butter 15.0 35.0 57.1 3.73 2.85 -0.88 -23.5 33.4 25.5 -7.9 -23.5

Margarine 15.0 35.0 57.1 3.62 2.77 -0.85 -23.5 24.3 18.6 -5.7 -23.5

Lard 0.0 35.0 100.0 0.84 0.54 -0.29 -35.0 9.3 6.1 -3.3 -35.0

Edible beef tallow 0.0 35.0 100.0 1.94 1.26 -0.68 -35.0 21.7 14.1 -7.6 -35.0

Shortening 15.0 35.0 57.1 16.69 12.76 -3.93 -23.5 186.7 142.8 -43.9 -23.5

Salad and cooking 

oils 20.0 15.0 -33.3 28.18 29.94 1.76 6.3 315.2 334.9 19.7 6.2

Other edible fats 

and oils 0.0 25.0 100.0 2.04 1.53 -0.51 -25.0 22.8 17.1 -5.7 -25.0

Total added fats 

and oils -- -- -- 57.04 51.66 -5.38 -9.4 613.4 559.0 -54.4 -8.9

Dairy share of fats

Cream (light, heavy, 

and half & half) 20.0 12.0 -66.7 5.80 6.38 0.58 10.00 12.5 13.8 1.3 10.0

Eggnog 20.0 51.0 60.8 0.21 0.13 -0.08 -38.8 0.2 0.1 -0.1 -38.8

Sour cream 20.0 8.0 -150.0 2.96 3.41 0.44 15.0 6.2 7.1 0.9 15.0

Cream cheese 20.0 13.0 -53.8 1.78 1.94 0.16 8.8 6.2 6.8 0.5 8.7

Total dairy fats -- -- -- 10.76 11.86 1.10 10.2 25.1 27.8 2.7 10.6

Total added and 

dairy fats -- -- -- 67.79 63.52 -4.28 -6.3 638.6 586.9 -51.7 -8.1

1RTI estimate is the RTI “best estimate.” 

Source: USDA, Economic Research Service using RTI “best estimate” data and ERS Loss-Adjusted Food Availability data for 2006.
31

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table 13

Comparison of ERS and RTI estimates of fruit loss at the consumer level (per capita)1

Commodity

Consumer loss 

estimates

Difference 

between ERS 

and RTI 

estimates

Quantity of food 

consumed after 

adjusting for all 

losses

Difference in 

quantity 

consumed 

between ERS

 and RTI

 estimates

Calories

Difference in 

calories between 

ERS and RTI 

estimates ERS 

estimate

RTI 

estimate

ERS 

estimate

RTI 

estimate

ERS 

estimate

RTI 

estimate

Percent Pounds/year

Pounds/

year Percent Number/day

Number/

day Percent

Fresh oranges 20.0 36.0 44.4 4.66 3.26 -1.41 -30.19 2.71 1.89 -0.8 -30.2

Fresh tangerines 20.0 52.0 61.5 1.10 0.45 -0.65 -59.26 0.72 0.29 -0.4 -59.3

Fresh grapefruit2 20.0 NA NA 0.59 NA NA NA 0.23 NA NA NA

Fresh lemons 20.0 44.0 54.5 1.22 0.33 -0.89 -72.73 0.44 0.12 -0.3 -72.7

Fresh limes 20.0 44.0 54.5 1.26 0.79 -0.47 -37.50 0.47 0.29 -0.2 -37.5

Fresh apples 20.0 NA NA 10.90 NA NA NA 7.07 NA NA NA

Fresh apricots 20.0 10.0 -100.0 0.04 0.04 0.00 13.70 0.20 0.22 0.0 13.7

Fresh avocados 20.0 32.0 37.5 1.65 1.29 -0.37 -22.22 3.29 2.56 -0.7 -22.2

Fresh bananas 20.0 NA NA 10.17 NA NA NA 11.27 NA NA NA

Fresh blueberries 20.0 8.0 -150.0 0.37 0.43 0.06 16.00 0.26 0.30 0.0 16.0

Fresh cantaloupe 20.0 43.0 53.5 2.32 0.60 -1.72 -74.19 0.98 0.25 -0.7 -74.2

Fresh cherries 20.0 51.0 60.8 0.67 0.38 -0.29 -43.66 0.46 0.26 -0.2 -43.7

Fresh cranberries 20.0 26.0 23.1 0.06 0.06 0.00 -7.69 0.04 0.03 0.0 -7.7

Fresh grapes 20.0 33.0 39.4 4.86 4.03 -0.83 -17.11 4.16 3.45 -0.7 -17.1

Fresh honeydew 20.0 43.0 53.5 0.35 0.04 -0.31 -88.46 0.16 0.02 -0.1 -88.5

Fresh kiwifruit 20.0 45.0 55.6 0.25 0.15 -0.09 -37.88 0.19 0.12 -0.1 -37.9

Fresh mangoes 20.0 13.0 -53.8 0.84 0.96 0.12 14.29 0.67 0.77 0.1 14.3

Fresh papaya 20.0 20.0 0.0 0.21 0.21 0.00 0.00 0.10 0.10 0.0 0.0

Fresh peaches 35.0 42.0 16.7 2.23 1.96 -0.27 -12.07 1.14 1.01 -0.1 -12.1

Fresh pears 20.0 NA NA 1.75 NA NA NA 1.26 NA NA NA

Fresh pineapple 20.0 37.0 45.9 1.31 0.59 -0.72 -54.84 0.81 0.37 -0.4 -54.8

Fresh plums 20.0 27.0 25.9 0.59 0.54 -0.06 -9.46 0.34 0.31 0.0 -9.5

Fresh raspberries 20.0 NA NA 0.24 NA NA NA 0.15 NA NA NA

Fresh strawberries 20.0 35.0 42.9 3.77 3.01 -0.77 -20.27 1.55 1.23 -0.3 -20.3

Fresh watermelon 20.0 13.0 -53.8 3.63 4.42 0.79 21.88 1.35 1.65 0.3 21.9

Total fruit—fresh -- -- -- 55.02 47.15 -7.87 -14.30 40.01 35.22 -4.8 -12.0

Canned apples and 

applesauce 10.0 8.0 -25.0 2.85 2.91 0.06 2.22 1.48 1.51 0.0 2.2

Canned apricots 10.0 27.0 63.0 0.12 0.10 -0.02 -18.89 0.03 0.03 0.0 -18.9

Canned sweet cherries 10.0 32.0 68.8 0.01 0.01 0.00 -24.44 0.01 0.01 0.0 -24.4

Canned tart cherries 10.0 32.0 68.8 0.10 0.08 -0.02 -24.44 0.05 0.04 0.0 -24.4

Canned peaches 10.0 9.0 -11.1 2.94 2.97 0.03 1.11 0.88 0.89 0.0 1.1

Canned pears 10.0 9.0 -11.1 2.03 2.05 0.02 1.11 0.73 0.74 0.0 1.1

Canned pineapple 10.0 9.0 -11.1 2.37 2.40 0.03 1.11 0.95 0.96 0.0 1.1

Canned plums 10.0 26.0 61.5 0.03 0.03 -0.01 -17.78 0.02 0.01 0.0 -17.8

Canned olives 10.0 25.0 60.0 0.66 0.55 -0.11 -16.67 0.97 0.80 -0.2 -16.7

Continued—
32

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table 13

Comparison of ERS and RTI estimates of fruit loss at the consumer level (per capita)1—continued

Commodity Consumer loss 

estimates

Difference 

between 

ERS and RTI 

estimates

Quantity of food 

consumed after 

adjusting for all 

losses

Difference in 

quantity 

consumed 

between ERS 

and RTI

estimates

Calories

Difference in 

calories between 

ERS and RTI 

estimates ERS 

estimate

RTI 

estimate

ERS 

estimate

RTI 

estimate

ERS 

estimate

RTI 

estimate

Percent Pounds/year

Pounds/

year Percent Number/day

Number/

day Percent

Total fruit—canned -- -- -- 11.11 11.09 -0.02 -0.20 5.11 4.99 -0.1 -2.4

Frozen blackberries 10.0 40.0 75.0 0.06 0.04 -0.02 -33.33 0.05 0.03 0.0 -33.3

Frozen blueberries 10.0 29.0 65.5 1.54 1.21 -0.32 -21.11 0.97 0.77 -0.2 -21.1

Frozen raspberries 10.0 24.0 58.3 0.29 0.25 -0.05 -15.56 0.23 0.20 0.0 -15.6

Frozen strawberries 10.0 24.0 58.3 0.39 0.33 -0.06 -15.56 0.17 0.14 0.0 -15.6

Other frozen berries 10.0 30.0 66.7 0.05 0.04 -0.01 -22.22 0.03 0.03 0.0 -22.2

Frozen apples 10.0 35.0 71.4 0.02 0.01 0.00 -27.78 0.01 0.01 0.0 -27.8

Frozen apricots 10.0 35.0 71.4 0.58 0.42 -0.16 -27.78 0.35 0.25 -0.1 -27.8

Frozen sweet cherries 10.0 29.0 65.5 0.18 0.14 -0.04 -21.11 0.10 0.08 0.0 -21.1

Frozen tart cherries 10.0 29.0 65.5 0.39 0.30 -0.08 -21.11 0.22 0.17 0.0 -21.1

Frozen peaches 10.0 35.0 71.4 0.40 0.29 -0.11 -27.78 0.24 0.17 -0.1 -27.8

Frozen plums and prunes 10.0 NA NA 0.01 NA NA NA 0.01 NA NA NA

Total fruit—frozen -- -- -- 3.91 3.05 -0.86 -22.01 2.39 1.86 -0.5 -22.1

Dried apples 10.0 11.0 9.1 0.10 0.10 0.00 -1.11 0.31 0.30 0.0 -1.1

Dried apricots 10.0 11.0 9.1 0.11 0.11 0.00 -1.11 0.32 0.32 0.0 -1.1

Dried dates 10.0 25.0 60.0 0.12 0.10 -0.02 -18.75 0.43 0.35 -0.1 -18.8

Dried figs 10.0 25.0 60.0 0.07 0.06 -0.01 -16.67 0.23 0.19 0.0 -16.7

Dried peaches 10.0 11.0 9.1 0.02 0.02 0.00 -1.11 0.06 0.06 0.0 -1.1

Dried pears3 10.0 11.0 9.1 NA NA NA NA NA NA NA NA

Dried plums 10.0 11.0 9.1 0.25 0.25 0.00 -1.11 0.75 0.74 0.0 -1.1

Raisins 10.0 26.0 61.5 1.29 1.06 -0.23 -17.78 6.01 4.94 -1.1 -17.8

Total fruit—dried -- -- -- 1.97 1.70 -0.27 -13.73 8.10 6.90 -1.2 -14.8

Grapefruit juice 10.0 NA NA 1.49 NA NA NA 0.70 NA NA NA

Lemon juice 10.0 NA NA 1.06 NA NA NA 0.28 NA NA NA

Lime juice 10.0 NA NA 0.23 NA NA NA 0.06 NA NA NA

Orange juice 10.0 NA NA 34.11 NA NA NA 19.92 NA NA NA

Apple juice 10.0 NA NA 16.37 NA NA NA 9.35 NA NA NA

Cranberry juice 10.0 NA NA 1.83 NA NA NA 1.04 NA NA NA

Grape juice 10.0 NA NA 3.31 NA NA NA 2.47 NA NA NA

Pineapple juice 10.0 NA NA 1.99 NA NA NA 1.30 NA NA NA

Prune juice 10.0 32.0 68.8 0.26 0.20 -0.06 -24.44 0.23 0.17 -0.1 -24.4

Total fruit—juice -- -- -- 60.65 60.59 -0.06 -0.10 35.36 35.30 -0.1 -0.2

Total fruit -- -- -- 133.07 123.99 -9.08 -6.83 91.23 84.54 -6.7 -7.3

NA = RTI “best estimate” was not available and therefore by default, the ERS estimate was used.

1RTI estimate is the RTI “best estimate.” 

2The RTI “best estimate” for grapefruit at 54 percent was unrealistic given the nonedible share (refuse) at 50 percent. Therefore, the ERS estimate was used.

3Loss-Adjusted Food Availability data for dried pears was not available for 2006.

Source: USDA, Economic Research Service using RTI “best estimate” data and ERS Loss-Adjusted Food Availability data for 2006.
33

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Vegetables

Table 14 presents a comparison of ERS and RTI consumer-level loss estimates, per capita, for each fresh and processed type of vegetable in the 

LAFA data. Overall, adopting all of RTI’s best estimates for vegetables 

results in an almost 11-pound drop in annual vegetable consumption per 

capita (a 6.2-percent decrease), which translates into roughly 6 fewer calories 

per day (a 4.6-percent drop). This is important because Americans already 

under-consume vegetables, according to Federal dietary guidelines (Wells 

and Buzby, 2008).

As for fruit, the bulk of the change occurs for the estimates for the fresh 

form. For fresh vegetables, fresh potatoes increase the most (4.85 pounds per 

year, or 20 percent). This is due to the combined effect of the consumer-level 

food loss dropping almost by half and the importance of potatoes in total 

vegetable consumption. Similar reasoning helps explain the almost 2-poundsper-year increase in fresh tomatoes. Meanwhile, five fresh vegetables had 

over a 1-pound decrease in annual consumption: fresh bell peppers, carrots, 

celery, onions, and pumpkin. The change in fresh pumpkins is due to the 

more than tripling of the consumer-level loss estimate. The change in the 

other four fresh vegetables is due to the combination of noticeable increases 

in their loss estimates and their importance to total annual consumption of 

vegetables.

Grain Products

Because of the previously stated data limitations, RTI was unable to propose 

consumer-level food loss estimates for six of the nine types of grain products 

in the LAFA data series. If ERS adopts RTI’s three proposed estimates for 

grains, the availability per capita per year would fall by roughly 2 pounds, or 

9.4 calories per day (1.5 percent) (table 15).

If adopted, RTI’s proposed estimates for barley and oat products would result 

in minimal per capita changes in pounds per year or calories per day. Most of 

the change in this food group is for rice, which decreases by 2.36 pounds per 

capita per year, or 10.7 calories per day (16.3 percent).

Added Sugars and Sweeteners

Table 16 compares how ERS consumer-level loss estimates and RTI’s 

proposed estimates for annual per capita added sugars and sweeteners translate into total pounds per capita per year and daily calories in the LAFA data. 

Adopting the RTI estimates for this category decreases the amount available 

for consumption by 4.36 pounds per capita per year, or 20.7 calories per day 

(a 4.4-percent decrease for both). 

Refined sugar has the largest change in this food group when RTI’s proposed 

loss estimates are adopted: a decrease in per capita availability of almost 7.8 

pounds per year, or 36.8 calories per day, a 17.5-percent drop per person. 

Meanwhile, per capita availability of high-fructose corn syrup (loss-adjusted) 

increases by 2.6 pounds per year, or 12.3 calories per day.
34

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Table 14

Comparison of ERS and RTI estimates of vegetable loss at the consumer level (per capita)1

Commodity

Consumer loss 

estimates

Difference 

between 

ERS and RTI 

estimates

Quantity of food 

consumed after 

adjusting for all 

losses

Difference in 

quantity 

consumed 

between ERS 

and RTI

estimates

Calories

Difference in 

calories between 

ERS and RTI 

estimates ERS 

estimate

RTI 

estimate

ERS 

estimate

RTI 

estimate

ERS 

estimate

RTI 

estimate

Percent Pounds/year

Pounds/

year Percent Number/day

Number/

day Percent

Fresh artichokes 20.0 18.0 -11.1 0.25 0.28 0.03 10.0 0.15 0.16 0.0 10.0

Fresh asparagus 20.0 18.0 -11.1 0.31 0.33 0.02 6.1 0.08 0.08 0.0 6.1

Fresh bell peppers 20.0 39.0 48.7 4.98 3.45 -1.53 -30.6 1.25 0.86 -0.4 -30.6

Fresh broccoli 20.0 12.0 -66.7 1.91 2.29 0.37 19.5 0.81 0.97 0.2 19.5

Fresh brussels sprouts 20.0 12.0 -66.7 0.15 0.16 0.02 11.4 0.08 0.09 0.0 11.4

Fresh cabbage 20.0 24.0 16.7 3.72 3.47 -0.25 -6.7 1.09 1.02 -0.1 -6.7

Fresh carrots 20.0 34.0 41.2 5.15 4.11 -1.05 -20.3 2.60 2.07 -0.5 -20.3

Fresh cauliflower 20.0 9.0 -122.2 0.26 0.40 0.15 57.9 0.08 0.13 0.0 57.9

Fresh celery 20.0 39.0 48.7 3.67 2.66 -1.01 -27.5 0.63 0.46 -0.2 -27.5

Fresh collard greens 20.0 38.0 47.4 0.13 0.07 -0.06 -48.6 0.05 0.03 0.0 -48.6

Fresh sweet corn 32.0 NA NA 0.30 NA NA NA 0.32 NA NA NA

Fresh cucumbers 20.0 32.0 37.5 2.81 2.18 -0.64 -22.6 0.41 0.32 -0.1 -22.6

Fresh eggplant 27.0 26.0 -3.8 0.33 0.33 0.01 1.9 0.10 0.10 0.0 1.9

Fresh escarole and endive 20.0 24.0 16.7 0.08 0.07 0.00 -6.1 0.02 0.01 0.0 -6.1

Fresh garlic 20.0 43.0 53.5 1.35 0.89 -0.47 -34.3 2.51 1.65 -0.9 -34.3

Fresh kale 20.0 38.0 47.4 0.06 0.04 -0.03 -43.9 0.04 0.02 0.0 -43.9

Fresh head lettuce 20.0 24.0 16.7 10.90 10.22 -0.68 -6.2 1.72 1.62 -0.1 -6.3

Fresh romaine and leaf 

lettuce 20.0 24.0 16.7 5.70 5.31 -0.38 -6.7 1.19 1.11 -0.1 -6.7

Fresh lima beans 20.0 27.0 25.9 0.01 0.00 0.00 -29.2 0.01 0.01 0.0 -29.2

Fresh mushrooms 20.0 21.0 4.8 1.62 1.60 -0.02 -1.3 0.44 0.43 0.0 -1.3

Fresh mustard greens 20.0 38.0 47.4 0.14 0.10 -0.03 -24.7 0.05 0.03 0.0 -24.7

Fresh okra 20.0 NA NA 0.18 NA NA NA 0.07 NA NA NA

Fresh onions 35.0 43.0 18.6 9.30 7.95 -1.35 -14.5 4.84 4.14 -0.7 -14.5

Fresh potatoes 30.0 16.0 -87.5 24.23 29.07 4.85 20.0 21.07 25.29 4.2 20.0

Fresh pumpkin 20.0 69.0 71.0 1.95 0.04 -1.91 -98.0 0.63 0.01 -0.6 -98.0

Fresh radishes 20.0 47.0 57.4 0.28 0.17 -0.11 -38.6 0.06 0.04 0.0 -38.6

Fresh snap beans 22.0 24.0 8.3 1.05 1.02 -0.03 -3.0 0.40 0.39 0.0 -3.0

Fresh spinach 20.0 9.0 -122.2 0.77 0.93 0.16 21.2 0.22 0.27 0.0 21.2

Fresh squash 20.0 25.0 20.0 2.29 2.11 -0.18 -7.9 0.45 0.42 0.0 -7.9

Fresh sweet potatoes 31.0 44.0 29.5 1.46 1.00 -0.46 -31.7 2.15 1.47 -0.7 -31.7

Fresh tomatoes 20.0 7.0 -185.7 10.37 12.26 1.90 18.3 2.29 2.71 0.4 18.3

Fresh turnip greens 20.0 38.0 47.4 0.10 0.06 -0.04 -36.0 0.04 0.03 0.0 -36.0

Total vegetables—fresh -- -- -- 95.81 92.58 -3.23 -3.4 45.85 45.93 0.1 0.2

Canned asparagus 10.0 2.0 -400.0 0.12 0.13 0.01 8.9 0.03 0.03 0.0 8.9

Canned snap beans 10.0 24.0 58.3 1.97 1.66 -0.31 -15.6 0.49 0.41 -0.1 -15.6

Continued—
Table 14

Comparison of ERS and RTI estimates of vegetable loss at the consumer level (per capita)1—continued

Commodity

Consumer loss 

estimates

Difference 

between

ERS and RTI 

estimates

Quantity of food 

consumed after 

adjusting for all 

losses

Difference in 

quantity 

consumed 

between ERS 

and RTI

estimates

Calories

Difference in 

calories between 

ERS and RTI 

estimates ERS 

estimate

RTI 

estimate

ERS 

estimate

RTI 

estimate

ERS 

estimate

RTI 

estimate

Percent Pounds/year

Pounds/

year Percent Number/day

Number/

day Percent

Canned cabbage 

(sauerkraut) 10.0 16.0 37.5 0.45 0.42 -0.03 -6.7 0.11 0.10 0.0 -6.7

Canned carrots 10.0 31.0 67.7 0.62 0.47 -0.14 -23.3 0.19 0.14 0.0 -23.3

Canned sweet corn 10.0 7.0 -42.9 5.16 5.33 0.17 3.3 5.20 5.38 0.2 3.3

Canned cucumbers 

(pickles) 10.0 3.0 -233.3 1.01 1.09 0.08 7.8 1.47 1.59 0.1 7.8

Canned green peas 10.0 24.0 58.3 0.62 0.53 -0.10 -15.6 0.53 0.45 -0.1 -15.6

Canned mushrooms 10.0 9.0 -11.1 0.81 0.82 0.01 1.1 0.25 0.25 0.0 1.1

Canned chile peppers 10.0 4.0 -150.0 3.93 4.19 0.26 6.7 1.04 1.11 0.1 6.7

Canned potatoes 10.0 28.0 64.3 0.47 0.37 -0.09 -20.0 0.35 0.28 -0.1 -20.0

Canned tomatoes 10.0 28.0 64.3 22.41 17.93 -4.48 -20.0 5.34 4.27 -1.1 -20.0

Other canned vegetables 10.0 16.0 37.5 1.66 1.55 -0.11 -6.7 0.55 0.51 0.0 -6.7

Total vegetables—

canned -- -- -- 39.23 34.50 -4.73 -12.1 15.55 14.52 -1.0 -6.6

Frozen asparagus 30.0 26.0 -15.4 0.02 0.02 0.00 5.7 0.00 0.00 0.0 5.7

Frozen snap beans 20.0 24.0 16.7 1.15 1.09 -0.06 -5.0 0.40 0.38 0.0 -5.0

Frozen broccoli 16.0 12.0 -33.3 1.61 1.68 0.08 4.8 0.56 0.59 0.0 4.8

Frozen carrots 12.0 34.0 64.7 0.90 0.68 -0.23 -25.0 0.41 0.31 -0.1 -25.0

Frozen cauliflower 17.0 27.0 37.0 0.20 0.18 -0.02 -12.0 0.05 0.04 0.0 -12.0

Frozen sweet corn 14.0 36.0 61.1 2.07 1.54 -0.53 -25.6 2.08 1.55 -0.5 -25.6

Frozen green peas 17.0 24.0 29.2 1.16 1.06 -0.10 -8.4 1.13 1.03 -0.1 -8.4

Frozen lima beans 32.0 27.0 -18.5 0.18 0.19 0.01 7.4 0.23 0.25 0.0 7.4

Frozen potatoes 23.0 16.0 -43.8 17.41 21.51 4.10 23.5 16.88 20.86 4.0 23.5

Frozen spinach 32.0 34.0 5.9 0.34 0.29 -0.05 -14.3 0.13 0.11 0.0 -14.3

Miscellaneous frozen 

vegetables 23.0 26.0 11.5 1.52 1.46 -0.06 -3.9 0.73 0.71 0.0 -3.9

Total vegetables—

frozen -- -- -- 26.55 29.70 3.15 11.9 22.62 25.83 3.2 14.2

Dehydrated onions 10.0 4.0 -150.0 0.16 0.17 0.01 6.7 0.71 0.76 0.0 6.7

Dehydrated potatoes 10.0 4.0 -150.0 1.47 1.57 0.10 6.7 6.49 6.92 0.4 6.7

Potato chips and 

shoestring potatoes 10.0 4.0 -150.0 3.97 4.23 0.26 6.7 27.47 29.30 1.8 6.7

Total vegetables–

dehydrated -- -- -- 5.60 5.98 0.37 6.7 34.67 36.98 2.3 6.7

Dry edible beans 10.0 NA NA 5.35 NA NA NA 9.06 NA NA NA

Dry peas and lentils 10.0 NA NA 1.03 NA NA NA 1.50 NA NA NA

Total vegetables -- -- -- 173.57 162.75 -10.82 -6.2 129.26 123.27 -5.99 -4.6

NA = RTI “best estimate” was not available and therefore by default, the ERS estimate was used.

1RTI estimate is the RTI “best estimate.” 

Source: USDA, Economic Research Service using RTI “best estimate” data and ERS Loss-Adjusted Food Availability data for 2006.

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Table 15

Comparison of ERS and RTI estimates of grain products loss at the consumer level (per capita)1

Commodity

Consumer loss 

estimates

Difference 

between ERS 

and RTI 

estimates

Quantity of food 

consumed after 

adjusting for all 

losses

Difference in 

quantity 

consumed 

between ERS 

and RTI

 estimates

Calories

Difference in 

calories between 

ERS and RTI 

estimates ERS 

estimate

RTI 

estimate

ERS 

estimate

RTI 

estimate

ERS 

estimate

RTI 

estimate

Percent Pounds/year

Pounds/

year Percent Number/day

Number/

day Percent

White and whole 

wheat flour 20.0 NA NA 86.90 NA NA NA 393.9 NA NA NA

Durum flour 20.0 NA NA 8.59 NA NA NA 12.2 NA NA NA

Rice 20.0 33.0 39.4 14.54 12.18 -2.36 -16.3 65.7 55.0 -10.7 -16.3

Rye flour 20.0 NA NA 0.35 NA NA NA 1.5 1.5 NA NA

Corn flour and meal 20.0 NA NA 13.38 NA NA NA 60.6 60.6 NA NA

Corn hominy and grits 20.0 NA NA 5.98 NA NA NA 27.5 27.5 NA NA

Corn starch 20.0 NA NA 3.10 NA NA NA 15.4 15.4 NA NA

Barley products 20.0 14.0 -42.9 0.35 0.39 0.04 10.0 1.5 1.7 0.2 10.0

Oat products 20.0 14.0 -42.9 2.45 2.69 0.24 10.0 11.3 12.4 1.1 10.0

Total grain products -- -- -- 135.64 133.56 -2.08 -1.5 617.0 607.6 -9.4 -1.5

NA = RTI “best estimate” was not available and therefore by default, the ERS estimate was used. 

1RTI estimate is the RTI “best estimate.” 

Source: USDA, Economic Research Service using RTI “best estimate” data and ERS Loss-Adjusted Food Availability data for 2006.

Table 16

Comparison of ERS and RTI estimates of added sugars and sweeteners loss at the consumer level 

(per capita)1

Commodity

Consumer loss 

estimates

Difference 

between ERS 

and RTI 

estimates

Quantity of food 

consumed after 

adjusting for all 

losses

Difference in 

quantity 

consumed 

between ERS

and RTI

estimates

Calories

Difference in 

calories between 

ERS and RTI 

estimates ERS 

estimate

RTI 

estimate

ERS 

estimate

RTI 

estimate

ERS 

estimate

RTI 

estimate

Percent Pounds/year

Pounds/

year Percent Number/day

Number/

day Percent

Refined sugar 20.0 34.0 41.2 44.43 36.66 -7.78 -17.5 210.4 173.5 -36.8 -17.5

High-fructose corn 

syrup (HFCS) 20.0 15.0 -33.3 41.48 44.07 2.59 6.2 196.4 208.6 12.3 6.2

Glucose 20.0 15.0 -33.3 9.79 10.40 0.61 6.3 46.3 49.2 2.9 6.3

Dextrose 20.0 15.0 -33.3 2.21 2.35 0.14 6.3 10.5 11.1 0.7 6.3

Honey 20.0 15.0 -33.3 0.80 0.84 0.05 6.2 3.8 4.0 0.2 6.3

Edible syrups 20.0 15.0 -33.3 0.31 0.33 0.02 6.3 1.5 1.6 0.1 6.3

Total added sugars 

and sweeteners -- -- -- 99.01 94.65 -4.36 -4.4 468.8 448.1 -20.7 -4.4

NA = RTI “best estimate” was not available and therefore by default, the ERS estimate was used.

1RTI estimate is the RTI “best estimate.” 

Source: USDA, Economic Research Service using RTI “best estimate” data and ERS Loss-Adjusted Food Availability data for 2006.
37

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Total Annual Pounds and Total Daily Calories

Table 17 presents a summary of the phase 2 findings for all food groups. In 

essence, if all of RTI’s proposed estimates are incorporated in the LAFA 

data, the total impact on per capita availability of all foods available drops by 

1.8 percent per year (17.3 pounds). This translates into 41.9 fewer calories 

per day for the average American. 

Table 17

Summary of inclusion of RTI best estimates into the ERS 

Loss-Adjusted Food Availability data series (per capita)1

Commodity

Quantity of food 

consumed after 

adjusting for all 

losses

Difference in 

quantity 

consumed 

between ERS 

and RTI

 estimates

Calories

Difference in 

calories between 

ERS and RTI 

estimates ERS 

estimate

RTI 

estimate

ERS 

estimate

RTI 

estimate

Pounds/year

Pounds/

year Percent Number/day

Number/

day Percent

Added fats 

and oils 67.8 63.5 -4.3 -6.3 638.6 586.9 -51.7 -8.1

Added 

sugars and 

sweeteners 99.0 94.6 -4.4 -4.4 468.8 448.1 -20.7 -4.4

Dairy 187.8 178.8 -9.0 -4.8 259.8 236.2 -23.6 -9.1

Fruit 133.1 124.0 -9.1 -6.8 91.2 84.5 -6.7 -7.3

Grain 

products 135.6 133.6 -2.1 -1.5 617.0 607.6 -9.4 -1.5

Meat, 

poultry, fish, 

eggs, and 

nuts 148.5 170.7 22.3 15.0 452.0 528.0 76.1 16.8

Vegetables 173.6 162.7 -10.8 -6.2 129.3 123.3 -6.0 -4.6

Total 945.3 928.0 -17.3 -1.8 2,656.5 2,614.6 -41.9 -1.6

1RTI estimate is the RTI “best estimate.” 

Source: USDA, Economic Research Service using RTI “best estimate” data and ERS 

Loss-Adjusted Food Availability data for 2006.
38

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Discussion

If ERS adopts all of RTI’s proposed estimates of consumer-level food loss 

(i.e., of the edible share) in the LAFA data, per capita estimates for some 

individual foods or food groups would change substantially relative to 

current estimates. Still, the estimated total amount of food consumed annually would change by less than 2 percent. 

The proposed estimates of consumer-level food loss conversion factors documented here clearly allow for more accurate estimates of average consumption of foods in the United States. In the current ERS data system, there is 

relatively little variation in many of the estimates for consumer-level food 

loss used in the LAFA data. For example, almost all fresh vegetables have 

a loss estimate of 20 percent; however, if RTI’s proposed estimates are 

adopted, each fresh vegetable would have its own tailored loss estimate based 

on real data.9

Major Changes in Consumer-Level Food Loss Estimates

Major changes in the estimated consumer-level food loss conversion factors 

were described for each food category in the previous section. In summary, 

the estimates for the following foods indicate the largest (percentage-point) 

annual increases over the estimates currently used by ERS:

•	Fresh pumpkin (49 percent)

•	Dry whole and nonfat milk (40 percent)

•	Dry buttermilk (40 percent)

•	Swiss cheese (37 percent)

•	Lard (35 percent)

•	Edible beef tallow (35 percent)

•	Fresh grapefruit (34 percent)

•	Fresh tangerines (32 percent)

•	Fresh cherries (31 percent)

•	Eggnog (31 percent)

All of these foods have relatively low per capita consumption estimates. In 

contrast, the estimates for the following foods, which tend to have higher per 

capita consumption estimates, indicate the largest (percentage-point) annual 

decreases:

•	Chicken (25 percent)

•	Lamb (16 percent)

•	Low-fat cottage cheese (16 percent)

•	Frozen potatoes (16 percent)

•	Veal (15 percent)

•	Fresh potatoes (14 percent)

•	Fresh tomatoes (13 percent)

9There are a few exceptions for some 

fresh vegetables for which an estimate 

could not be made.
39

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

•	Fresh blueberries (12 percent)

•	Beef (12 percent)

•	Sour cream (12 percent)

•	Fresh cauliflower (11 percent)

•	Fresh spinach (11 percent)

Across all food categories, the estimates appear to indicate that consumerlevel food loss is substantially higher than in current estimates; thus, the 

estimated average calories consumed by Americans are lower than in current 

estimates. The change in estimates could reflect changes in food preparation 

habits and the increase in food consumed away from home or simply the use 

of a different methodology than for the current estimates. 

Strengths and Weaknesses of the Research Approach

A major strength of the approach used in this analysis is that the consumerlevel food loss conversion factors are based on an approach using wellknown data sources for food purchases, consumer-level food availability, and 

food consumption. The methods are described and documented to a greater 

extent here than for the current estimates used in the ERS LAFA data series. 

Furthermore, in cases where expert panel estimates were used, the conversion 

factors are based on estimates from a clearly identified panel of experts. The 

estimates in this report can be updated easily as new information or better 

data sources become available.

The limitations of the approach occur primarily because of limitations in the 

underlying data sources or the unavailability of certain types of data. Many 

of these limitations were described earlier but are summarized here. With 

regard to the Nielsen Homescan data used in the analysis, some of the limitations are as follows:

•	Certain types of purchases are likely missing because the method of data 

collection makes it difficult to enter these purchases (e.g., Homescan 

panelist purchases at farmers’ markets and through community-supported 

agriculture). It would be difficult, if not impossible, for households to 

enter such purchases because the data collection method is set up to 

capture primary store purchases for foods with UPC codes. The result is 

that purchases of these foods may be underestimated.

•	Foods that are self-produced (e.g., vegetables grown or fish caught) are 

not represented in the Homescan data because no purchase transaction 

occurred. Therefore, these foods are not represented in foods purchased 

but could be represented in foods consumed.

•	Homescan data appear to underestimate all types of random-weight 

purchases, especially fresh fruits and vegetables. This underestimation likely derives from the use of a smaller panel that may not be as 

representative as the larger panel and the additional burden of entering 

random-weight foods in the data collection process. Although RTI used 

Perishables Group, Inc. data where available, the data were not available 

for all food categories and, in many cases, still did not result in plausible 

estimates of consumer-level food loss.

Major strengths of the 

approach in this study are 

that it uses well-known 

data sources and that 

it describes and documents the methodology 

to a greater extent than in 

previous studies.
40

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

•	Certain types of purchases that occur at times other than the primary 

shopping trip (e.g., milk purchased at a convenience story) may not be 

entered because of the additional burden. Households might be much less 

likely to record these purchases.

With regard to the NHANES consumption data, some of the limitations are 

as follows:

•	Seasonality of data collection varies over the course of the year and 

might affect total consumption estimates. In particular, during the warm 

weather months, NHANES data are collected in northern States, and 

during the cold weather months, NHANES data are collected in southern 

States. This may affect estimates of consumption of seasonal foods (e.g., 

fresh fruit and vegetable consumption is higher when foods are in season 

and lower priced).

•	Estimates of quantities consumed are approximations because they are 

based on respondents identifying which food model on a card is closest 

to the amount they consumed over the past 24 hours. Based on the 

consumption estimates for juice, NHANES respondents may have particular difficulty in estimating the amount of liquids consumed. A more 

precise method would be to weigh each food consumed, but this would 

be impractical from a data collection standpoint and would have a large 

influence on consumption choices.

•	Certain foods might be misclassified during the data collection process 

(e.g., juices versus juice-containing drinks). However, the data collection 

methods are designed to overcome possible misclassification.

•	The data collection process may induce respondents to overreport 

consumption of some foods and underreport consumption of others. In 

particular, respondents may overreport consumption of more healthful 

foods, such as milk, and underreport consumption of less healthful foods, 

such as butter.

•	For some foods, few NHANES respondents reported consuming the 

food during the 24-hour recall period (see the footnotes in tables 3-8). 

Estimates of consumer-level food loss for these foods may be less reliable than those of foods with a larger number of respondents. 

Finally, with regard to certain types of data that are unavailable: 

•	Data on purchases of foods from restaurants, cafeterias, and other awayfrom-home sources that could be used for detailed comparisons by food 

category are not available. Thus, RTI assumed that losses for food at 

home and away from home are similar. This assumption is reasonable 

given that spoilage loss is greater for food at home but plate waste is 

greater for food away from home; thus, the types of losses are offsetting.

•	The estimation of consumption of foods in recipes is extremely data 

intensive and would be cost and time prohibitive. Furthermore, the use 

of data from recipes adds another layer of measurement error that might 

lead to greater imprecision in the estimates. Thus, RTI assumed that loss 

of food consumed directly is similar to loss of food as consumed in a 

recipe (e.g., fresh apples vs. apples in baked apple pie).
41

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

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•	Data are not available to disaggregate the source of loss, such as preparation/cooking loss, spoilage, or plate waste. Furthermore, it is not feasible 

to determine the degree to which other factors affect food loss, such as 

purchases of larger package sizes. 

Recommendations for Future Work

The following recommendations for future work will be considered independently of ERS’s decision on whether or not to adopt RTI’s proposed estimates described here after the public comment period. In order of priority, 

RTI recommends the following additional work to further refine and develop 

estimates of consumer-level food loss:

•	After ERS completes the separate but ongoing project to update food 

loss from primary weight to retail weight and adopts the updated loss 

estimates at this level, the estimated weights of food available at the 

consumer level will have been updated. Therefore, these updated estimates should be used to recalculate the consumer-level food loss conversion factors that were based on the LAFA data in the previous section. 

Then, the resulting estimates should be reviewed and compared with the 

Homescan-based estimates to make a final determination regarding which 

to use.

•	For certain foods, the percentage of ingredient use should be further 

investigated to develop a more accurate estimate. All of the percentages 

of ingredient use were based on expert panel data. For most foods, these 

percentages appear to result in plausible estimates of consumer-level 

food loss. However, for the following foods, plausible loss estimates may 

be able to be calculated with more accurate ingredient percentages:

– Plain whole milk

– Fresh apples

– Fresh bananas

– Fresh pears

– Fresh sweet corn

– Fresh okra

Initial exploratory analyses using data provided by USDA’s Center for 

Nutrition Policy and Promotion (see Carlson et al., 2008) suggest that 

ingredient percentages could be calculated for these foods. If estimates 

using these foods are plausible, an expanded list of foods could be 

analyzed.

•	For certain foods, RTI was unable to provide an estimate of consumerlevel food loss because of limitations in the data used for the analysis. 

The foods affected include: 

– Plain 1 percent and 2 percent milk

– Grapefruit juice

– Lemon juice

– Lime juice

Additional research could 

help improve the initial 

consumer-level food 

loss conversion factors 

provided in this report or 

provide estimates in cases 

for which they could not be 

derived.
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Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

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– Orange juice

– Apple juice

– Cranberry juice

– Grape juice

– Pineapple juice

– Dry edible beans

– Dry edible peas and lentils

– White and whole wheat flour

– Durum flour

– Rye flour

– Corn flour and meal

– Corn hominy and grits

– Corn starch

For these foods, RTI recommends exploring additional data sources for estimating consumption or that ERS base the loss estimates on an average of the 

expert panel estimates.

Additionally, for each food (and type of processing in the case of fruits and 

vegetables) in the LAFA data series, loss estimates go back through 1970. 

Therefore, to the extent possible in future research, ERS may focus on determining whether, how, and why consumer-level food loss estimates for each 

food in the LAFA data may have varied over this time period. Also, with 

the growing abundance of multi-ingredient processed foods, future research 

could assess research methods to incorporate production, imports, and 

exports of processed foods.
43

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

References

Adams, M.A., R.L. Pelletier, M.M. Zive, and J.F. Sallis. 2005. “Salad 

Bars and Fruit and Vegetable Consumption in Elementary Schools: 

A Plate Waste Study.” Journal of the American Dietetic Association

105(11):1789-92.

Buzby, J.C., and J.F. Guthrie. March 2002. Plate Waste in School Nutrition 

Programs: Final Report to Congress. E FAN-02-009. U.S. Department 

of Agriculture, Economic Research Service. www.ers.usda.gov/

publications/efan02009/

Carlson, A., M. Lino, W.Y. Juan, K. Marcoe, L. Bente, H.A.B. Hiza, 

P.M. Guenther, and E. Leibtag. May 2008. Development of the CNPP 

Prices Database. CNPP-22. U.S. Department of Agriculture, Center for 

Nutrition Policy and Promotion.

Engstrom, R., and A. Carlsson-Kanyama. 2004. “Food Losses in Food 

Service Institutions Examples from Sweden.” Food Policy 29:203-13.

Gallo, A.E. 1980. “Consumer Food Waste in the U.S.” Consumer Research, 

Fall:13-16.

Kantor, L.S. 1998. A Dietary Assessment of the U.S. Food Supply: 

Comparing Per Capita Food Consumption With the Food Guide Pyramid 

Serving Recommendations. Agricultural Economic Report No. 772. U.S. 

Department of Agriculture, Economic Research Service. www.ers.usda.

gov/publications/aer772/ 

Kantor, L.S., K. Lipton, A. Manchester, and V. Oliveira. 1997. 

“Estimating and Addressing America’s Food Losses.” Food Review, 

January-April:2-12.

Marlette, M., S.B. Templeton, and M. Panemangalore. 2005. “Food Type, 

Food Preparation, and Competitive Food Purchases Impact School Lunch 

Plate Waste by Sixth Grade Students.” Journal of the American Dietetic 

Association 105(11):1779-82.

Muth, M.K., K.M. Kosa, S.J. Nielsen, and S.A. Karns. July 2007. 

“Exploratory Research on Estimation of Consumer-Level Food Loss 

Conversion Factors.” Report prepared for the U.S. Department of 

Agriculture, Economic Research Service. Research Triangle Park, NC: 

RTI International.

Muth, M.K., P.H. Siegel, and C. Zhen. February 2007. “ERS Data Quality 

Study Design.” Report prepared for the U.S. Department of Agriculture, 

Economic Research Service. Research Triangle Park, NC: RTI 

International.

Reger, C., C.E. O’Neil, T.A. Nicklas, L. Myers, and G.S. Berenson. 

1996. “Plate Waste of School Lunches Served to Children in a 

Low-Socioeconomic Elementary School in South Louisiana.” School 

Food Service Research Review 20(suppl):13-19.
44

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

United Nations Food and Agriculture Organization (UN/FAO). 1953. “Food 

Composition Tables for International Use: Fish and Shellfish.” www.fao.

org/docrep/X5557E/x5557e0o.htm.

U.S. Department of Agriculture, Agricultural Research Service (USDA/

ARS). 1975. Food Yields Summarized by Different Stages of Preparation 

Handbook. Agriculture Handbook No. 102. 

U.S. Department of Agriculture, Agricultural Research Service. 2007. 

“USDA National Nutrient Database for Standard Reference, Release 20. 

www.ars.usda.gov/ba/bhnrc/ndl.

U.S. Department of Agriculture, Economic Research Service (USDA/ERS). 

2008. Loss-Adjusted Food Availability.” www.ers.usda.gov/data/foodconsumption/foodguideindex.htm.

U.S. Department of Health and Human Services and U.S. Department of 

Agriculture (USDHHS and USDA). January 2005. Dietary Guidelines for 

Americans, 2005. 6th Edition.

U.S. Department of Health and Human Services, Centers for Disease 

Control and Prevention (CDC), National Center for Health Statistics 

(NCHS). 2007. National Health and Nutrition Examination Survey Data. 

2003-2004. www.cdc.gov/nchs/about/major/nhanes/nhanes2003-2004/

exam03_04.htm. 

Van Garde, S.J., and M.J. Woodburn. 1987. “Food Discard Practices 

of Householders.” Journal of the American Dietetic Association

87(3):322-29.

Wells, H.F., and J.C. Buzby. March 2008. Dietary Assessment of Major 

Trends in U.S. Food Consumption, 1970-2005. Economic Information 

Bulletin No. 33. U.S. Department of Agriculture, Economic Research 

Service. www.ers.usda.gov/publications/eib33/

Zhen, C., J. Taylor, M.K. Muth, and P.H. Siegel. 2008. Comparing Foodat-Home Expenditures Across Datasets. Report prepared for the U.S. 

Department of Agriculture, Economic Research Service. Research 

Triangle Park, NC: RTI International.
45

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Appendix A:

Food Descriptions and Assumptions
46

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table A-1

Food Category Descriptions and Values Used for Count Data Conversion, Inedible and Solids Percentages, 

and Density Estimates 

Category Food Description Food-specific adjustment values 

(and NDB Number If available)a

Meat, poultry, 

fish

Beef [For all meats: included random-weight and 

UPC cuts and ground beef. Excluded microwave dinners, lunch meats, and sausage.] For 

beef: included cuts (roasts, steaks, etc.), ground 

beef and beef patties; fresh and frozen; random 

weight and UPC. Excluded organ meats. Excluded meatloaf (combination beef and pork). 

Inedible = 29% (13011—Beef, composite of 

retail cuts, lean, trimmed to 1/4 fat, all grades, 

raw) 

For count data, assumed 3-oz patties

Meat, poultry, 

fish

Veal Included steaks, chops, other cuts, and ground 

veal; fresh and frozen; random weight and UPC. 

Excluded organs. 

Inedible = 31% (17088—Veal, composite of 

trimmed retail cuts, separable lean and fat, 

raw)

Meat, poultry, 

fish

Pork Included roasts, chops, ribs, other cuts, and 

ground pork; fresh and frozen; random weight 

and UPC. Included ham except for deli cut ham. 

Excluded bacon, chitlings, belly, tail, feet, hock, 

jowl, ear, and organs. Excluded pork BBQ because not reflected in consumption data. 

Inedible = 30% (10002—Pork, fresh, composite of trimmed retail cuts [leg, loin, shoulder], 

separable lean only, raw)

For count data, assumed ends and pieces = 

48 oz; half boneless ham = 60 oz.

Meat, poultry, 

fish

Lamb Included chops, roasts, other cuts, and ground 

lamb; fresh and frozen; random weight and UPC. 

Inedible = 26.5% (average of 17001—Lamb, 

domestic, composite of trimmed retail cuts, 

separable lean and fat, trimmed to 1/4′′ fat, 

choice, raw, and 17062 Lamb, New Zealand, 

imported, frozen, composite of retail cuts, 

separable lean and fat, raw)

Meat, 

poultry, fish

Chicken Included whole chickens, parts, cuts, and ground 

chicken; fresh and frozen; random weight and 

UPC. Excluded chicken nuggets and patties 

(breaded products) and organs. Excluded Cornish hen. 

Inedible = 29% (average of 05001—Chicken, 

broilers or fryers, meat and skin and giblets 

and neck, raw, and 05109—Chicken, roasting, 

meat and skin and giblets and neck, raw)

For count data, assumed drumsticks = 1 

pound.

Meat, poultry, 

fish

Turkey Included whole turkeys, parts, cuts, and ground 

turkey; fresh and frozen; random weight and 

UPC. Excluded meat balls, breaded products, 

and organs. 

Inedible = 21% (05163—Turkey, all classes, 

meat and skin and giblets and neck, raw)

Meat, poultry, 

fish

Fresh and 

frozen 

fish

Included all varieties of raw, random-weight fish 

(e.g., catfish, cod, tuna, salmon, whiting, and 

flounder). Included all varieties of UPC breaded 

and unbreaded frozen fish. 

Inedible = 53% for round fish (FAO)

Meat, poultry, 

fish

Fresh and 

frozen 

shellfish

Included all varieties of raw, random-weight 

shellfish (e.g., shrimp, scallops, crab, oysters, 

clams, and lobster). Included all varieties of UPC 

breaded and unbreaded frozen shellfish. 

Inedible = 63% (crustaceans in the shell 

[FAO]) 

Inedible = 75% (mollusks in the shell [FAO])

Meat, poultry, 

fish

Canned 

salmon

Included canned salmon, including Chinook. Reduced purchase weight based on liquid percentage because it is discarded (differs for packets 

versus cans). Included cakes, patties, loafs, and 

salads in consumption data (uses of canned 

salmon). Excluded salmon spreads and pates. 

Solids = 81% (Canned: “Food Yields,” Table 1, 

Item 2242, Salmon: Canned: Contents of can: 

All samples), 

Solids = 99% (Pouch: Direct measurement)

Meat, poultry, 

fish

Canned 

sardines

Included canned sardines. Reduced purchase 

weight based on liquid percentage because it is 

discarded (differs for packets versus cans). Excluded smoked because included in cured fish.

Solids = 87% (Oil: “Food Yields,” Table 1, 

Item 2258, Sardines: Canned: Atlantic), 

Solids = 75% (Water: Direct measurement)

Continued—
47

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table A-1

Food Category Descriptions and Values Used for Count Data Conversion, Inedible and Solids Percentages, 

and Density Estimates—continued

Category Food Description Food-specific adjustment values 

(and NDB number if available)a

Meat, poultry, 

fish

Canned 

tuna

Included canned tuna and tuna sandwich 

spreads. Reduced purchase weight based on 

liquid percentage because it is discarded (differs 

for packets versus cans). Included tuna salad 

spreads. Excluded smoked because included in 

cured fish. Included cakes, patties, loafs, and 

salads in consumption data (uses of canned 

tuna).

Solids = 82% (Oil: “Food Yields,” Table 1, 

Item 2575, Tuna: Canned, contents of can: 

Solid pack, in oil), 

Solids = 79% (Water: “Food Yields,” Table 1, 

Item 2571, Tuna: Canned, contents of can: 

Chunks, in brine), 

Solids = 94% (Pouch: “Food Yields,” Table 1, 

Item 2573, Tuna: Canned, contents of can: 

Flakes, in oil)

Meat, poultry, 

fish

Canned 

shellfish

Included canned clams, crab, oysters, lobster, 

anchovies, and other canned shellfish varieties. 

Excluded canned seafood dips.

Solids = 45% (Clams: “Food Yields,” Table 1, 

Item 880, Clams: Canned, minced or chopped, 

contents of can), 

Solids = 77% (Crabs: “Food Yields,” Table 1, 

Item 962, Crab: Meat, canned), 

Solids = 54% (Oysters: Direct measurement),

Solids = 64% (Shrimp: Direct measurement),

Solids = 63% (All other shellfish)

Meat, poultry, 

fish

Other 

canned 

fish

Skipped because infeasible to align purchase 

and consumption categories.

Meat, poultry, 

fish

Cured fish Skipped because infeasible to align purchase 

and consumption categories.

Eggs Eggs Included only chicken eggs in various forms. 

Included pickled eggs. Excluded prepared egg 

sandwiches. 

Inedible = 12% for raw eggs (01123—Egg, 

whole, raw, fresh) 

For dried eggs, used shell egg equivalent of 

5.326 oz dried = 1 dozen eggs. 

For frozen eggs, converted 20.282 oz = 1 

dozen eggs. 

Converted dozen eggs to ounces based on 

size of eggs (e.g., large = 24 oz). Included 

frozen/refrigerated breakfasts that are primarily 

egg (e.g., egg pattie and egg mix).

Nuts Peanuts Included peanuts in cans, jars, and bags. 

Grouped peanuts and snack peanuts for estimating loss (not possible to differentiate in the 

purchase or consumption data). Excluded trail 

mixes and snack mixes with peanuts.

Unshelled converted to shelled using conversion factor (0.64).

Nuts Peanut 

butter

Excluded other nut butters from the peanut butter 

category in the purchase data. Included half of 

peanut butter and jelly combinations.

Nuts Snack 

peanuts

Combined with “Peanuts” category.

Nuts Other 

peanuts

Combined with “Peanuts” category.

Nuts Almonds Included whole and chopped almonds in cans, 

jars, bags, and in the shell (random weight and 

UPC). Excluded almond butter, almond meal, 

and trail mixes. 

Inedible = 60% for unshelled almonds (12061—

Nuts, almonds)

Inedible = 0% for shelled almonds

Continued—
48

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table A-1

Food Category Descriptions and Values Used for Count Data Conversion, Inedible and Solids Percentages, 

and Density Estimates—continued

Category Food Description Food-specific adjustment values 

(and NDB number If available)a

Nuts Hazelnuts 

(filberts)

Included whole and chopped hazelnuts in cans, 

jars, bags, and in the shell (random weight and 

UPC).

Inedible = 54% for unshelled hazelnuts 

(12120—Nuts, hazelnuts, or filberts)

Inedible = 0% for shelled hazelnuts

Nuts Pecans Included whole and chopped pecans in cans, 

jars, bags, and in the shell (random weight and 

UPC). 

Inedible = 47% for unshelled pecans (12142—

Nuts, pecans)

Inedible = 0% for shelled pecans

Counts for bags of unshelled pecans—assumed 1-pound bags.

Nuts Pecans Included whole and chopped pecans in cans, 

jars, bags, and in the shell (random weight and 

UPC). 

Inedible = 47% for unshelled pecans (12142—

Nuts, pecans)

Inedible = 0% for shelled pecans

Counts for bags of unshelled pecans—assumed 1-pound bags.

Nuts Walnuts Included whole and chopped walnuts in cans, 

bags, and in the shell (random weight and UPC). 

Inedible = 55% for unshelled walnuts 

(12155—Nuts, walnuts, English)

Inedible = 0% for shelled walnuts

Counts for bags of unshelled walnuts—assumed 1-pound bags.

Nuts Macadamia nutsIncluded roasted and unroasted macadamia 

nuts in cans, bags, jars, and in the shell (random 

weight and UPC). 

Inedible = 69% for unshelled macadamia nuts 

(12131—Nuts, macadamia nuts, raw)

Inedible = 0% for shelled macadamia nuts

Counts for bags of unshelled macadamias—

assumed 1-pound bags.

Nuts Pistachio 

nuts

Included pistachios in cans, bags, jars, and in the 

shell (random weight and UPC). 

Inedible = 47% for unshelled pistachio nuts 

(12151—Nuts, pistachio nuts, raw)

Inedible = 0% for shelled pistachio nuts

Counts for bags of unshelled pistachios—assumed 1-pound bags.

Nuts Other tree 

nuts

Included cashews, brazil nuts, mixed nuts, and 

pine nuts. Included cans, bags, jars, and in the 

shell (random weight and UPC). Cashews not 

sold in shell. 

Inedible = 49% for unshelled brazil nuts 

(12078—Nuts, brazil nuts, dried, unblanched)

Inedible = 23% for unshelled pine nuts 

(12147—Nuts, pine nuts, dried)

Inedible = 50% for unshelled mixed nuts (average of all nuts)

Inedible = 0% for shelled tree nuts

Counts for bags of unshelled nuts—assumed 

1-pound bags.

Nuts Coconut Included coconut chips, flakes, chunks, and 

strings. Included grated, shredded, and ground 

coconut.

Dairy—

Beverages

Plain 

whole 

milk

Included only cow milk in all milk categories. 

Included whole milk with or without vitamins A, 

C, and D, in refrigerated and shelf-stable forms. 

Included powdered milk in reconstituted equivalent. Included kosher and lactose-free varieties. 

Density = 244 g/8 fluid oz = 1.0759 weight oz/

fluid oz (01077—Milk, whole, 3.25% milk fat)

Dairy—

Beverages

Plain 2% 

milk

Grouped 0.5%, 1%, 1.5%, and 2% milk into 

one low-fat milk category because consumption 

data have one reduced-fat milk category. Also 

included acidophilus and lactose-reduced varieties. Included shelf-stable milk and powdered milk 

in reconstituted equivalent. 

Density = 244 g/8 fluid oz = 1.0759 weight oz/

fluid oz (01079—Milk, reduced fat, fluid, 2% 

milk fat, with added vitamin A)

Density = 244 g/8 oz = 1.0759 weight oz/fluid 

oz (01082—Milk, low-fat, fluid, 1% milk fat, 

with added vitamin A)

Continued—
49

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table A-1

Food Category Descriptions and Values Used for Count Data Conversion, Inedible and Solids Percentages, 

and Density Estimates—continued

Category Food Description Food-specific adjustment values 

(and NDB number If available)a

Dairy—

Beverages

Plain 1% 

milk

Combined with “Plain 2% milk” category.

Dairy—

Beverages

Skim milk Included skim and nonfat milk, with or without 

vitamins A, C, and D. Also included lactose free, 

raw, and kosher. Included shelf-stable milk and 

powdered milk in reconstituted equivalent. 

Density = 245 g/8 fluid oz = 1.0803 weight oz/

fluid oz (01085—Milk, nonfat, fluid, with added 

vitamin A [fat free or skim])

Dairy—

Beverages

Buttermilk Included nonfat, 1%, and 2% buttermilk. Density = 245 g/8 fluid oz = 1.0803 weight 

oz/fluid oz (01088—Milk, buttermilk, fluid, 

cultured, low fat)

Dairy—

Beverages

Whole 

flavored 

milk

Included only chocolate flavored whole milk (excluded banana, blue raspberry, strawberry, etc.) 

because only chocolate is included in consumption data. 

Density = 250 g/8 fluid oz = 1.1023 weight oz/

fluid oz (01102—Milk, chocolate, fluid, commercial, whole)

Dairy—

Beverages

Low-fat 

flavored 

milk

Included only chocolate flavored low-fat milk 

(e.g., banana, strawberry, and blue raspberry) 

because only chocolate is included in consumption data. Included nonfat, 0.5%, 1%, 1.5%, and 

2% fat. 

Density = 250 g/8 fluid oz = 1.1023 weight oz/

fluid oz (01103—Milk, chocolate, fluid, commercial, reduced fat)

Density = 250 g/8 fluid oz = 1.1023 weight oz/

fluid oz (01104—Milk, chocolate, fluid, commercial, low fat)

Dairy—

Beverages

Light 

cream

Combined heavy cream, light cream, and half 

and half. Included whipping cream with light 

and heavy cream. Products may be refrigerated, frozen, or canned. Included canned cream. 

Excluded nondairy creamer.

Table, light, heavy cream used average density = 239 g/8 fluid oz = 1.0538 weight oz/fluid 

oz (240,239,238 g) (01050—Cream, fluid, 

light (coffee cream or table cream), 01052—

Cream, fluid, light whipping, 01053—Cream, 

fluid, heavy whipping)

Density = 60 g/8 fluid oz = 0.2646 weight oz/

fluid oz (01054—Cream, whipped, cream topping, pressurized)

Density = 242 g/8 fluid oz = 1.0670 weight oz/

fluid oz (01049—Cream, fluid, half and half)

Dairy—

Beverages

Heavy 

cream

Combined with “Light cream” category.

Dairy—

Beverages

Eggnog Consumption data include reduced and whole fat 

milk eggnog. 

Density = 254 g/8 fluid oz = 1.1199 weight oz/

fluid oz (01057—Eggnog)

Dairy Sour 

cream

Included sour cream in regular, light, and fat-free 

forms. Excluded imitation (nondairy) sour cream. 

Excluded sour cream dips.

Dairy Cream 

cheese

Included cream cheese in regular or lite forms. 

Included flavored cream cheese. Included 

Neufchatel. Excluded imitation (nondairy) cream 

cheese. Excluded cream cheese dips. 

Dairy Cheddar 

cheese

Included random-weight and UPC mild, medium, 

sharp, and extra sharp cheddar cheese.

Dairy Other 

American 

cheese

Included random-weight and UPC Colby, 

Monterey jack, and pepper jack.

Continued—
50

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table A-1

Food Category Descriptions and Values Used for Count Data Conversion, Inedible and Solids Percentages, 

and Density Estimates—continued

Category Food Description Food-specific adjustment values 

(and NDB number If available)a

Dairy Provolone 

cheese

Included random-weight and UPC provolone 

cheese.

Dairy Romano 

cheese

Included random-weight and UPC Romano and 

parmesan cheese. Also included grated and 

shredded forms because it is included in the 

consumption data.

Dairy Parmesan 

cheese

Combined with “Romano cheese” category.

Dairy Mozzarella 

cheese

Included random-weight and UPC nonfat, low-fat, 

and whole mozzarella, including pizza cheese.

Dairy Ricotta 

cheese

Included random-weight and UPC low-fat and 

regular ricotta cheese.

Dairy Other Italian 

cheese

Skipped because infeasible to align purchase 

and consumption categories.

Dairy Swiss 

cheese

Included random-weight and UPC Swiss, baby 

Swiss, and gruyere cheese.

Dairy Brick 

cheese

Included random-weight and UPC plain brick and 

salami-flavored brick cheese.

Dairy Muenster 

cheese

Included random-weight and UPC low-fat and 

regular Muenster cheese.

Dairy Blue cheese Included random-weight and UPC blue Roquefort, gorgonzola, and Stillton cheese, in crumbles 

or bricks.

Dairy Other miscellaneous 

cheese

Included random-weight and UPC edam, 

gouda, and limburger (ERS definition). Excluded 

cheese balls and cheese logs from consumption 

estimate (unless these were included under a 

specific cheese type). Excluded goat cheese.

Dairy Processed 

cheese

Included random-weight and UPC American 

cheese. Included processed cheese slices, 

snacks, and loaves.

Dairy Processed 

cheese 

foods and 

spreads

Included cheese spreads and sauces, plain and 

with flavorings such as bacon, nacho, jalapeno, 

and ham. Excluded imitation cheese products. 

Density = 63 g/2 fluid oz = 1.1111 weight oz/

fluid oz (06930—Sauce, cheese, ready-toserve)

Dairy Regular 

cottage 

cheese

Included regular plain and flavored cottage 

cheese, including with fruit or vegetables. 

Excluded farmer’s cheese and cottage cheese 

gelatin desserts.

Continued—
51

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table A-1

Food Category Descriptions and Values Used for Count Data Conversion, Inedible and Solids Percentages, 

and Density Estimates—continued

Category Food Description Food-specific adjustment values 

(and NDB number If available)a

Dairy Low-fat 

cottage 

cheese

Included nonfat, 1%, and 2% plain and flavored 

cottage cheese, including with fruit or vegetables.

Dairy Regular ice 

cream

Included regular, rich, and softserve ice cream 

in all flavors. (Deleted 5 count records, could not 

determine size.)

Density = 66 g/4 fluid oz = 0.5820 weight oz/

fluid oz (19095—Ice creams, vanilla)

Dairy Low-fat ice 

cream (ice 

milk)

Included sherbet, ice milk, and light or fat-free 

ice cream. 

Density of ice cream = 68 g/4 fluid oz = 

0.5997 weight oz/fluid oz (19260—Ice creams, 

vanilla, light, no sugar added)

Dairy Frozen 

yogurt and 

other miscellaneous 

frozen 

products

Included frozen yogurt in all flavors. (In the purchase data, frozen novelties are in counts only; 

therefore, total weight is not calculable.)

Dairy Refrigerated 

yogurt

Included plain and flavored yogurt in nonfat, lowfat, and whole milk varieties. Includes sweetened 

with sugar or low calorie sweeteners. Excluded 

yogurt shakes, dips, drinks, and smoothies.

Dairy Total evaporated and 

condensed 

canned 

whole and 

skim milk

Included whole, 2%, and skim evaporated and 

condensed (sweetened) milk. Included diluted 

and undiluted forms in consumption data. Included “filled” types. 

Density = 306 g/8 fluid oz = 1.3492 weight oz/

fluid oz (01095—Milk, canned, condensed, 

sweetened)

Density = 256 g/8 fluid oz = 1.1288 weight oz/

fluid oz (01097—Milk, canned, evaporated, 

nonfat)

Density = 244 g/8 fluid oz = 1.0759 weight oz/

fluid oz (01075—Milk substitutes, fluid with 

hydrogenated vegetable oils)

Dairy Dry whole 

and nonfat 

milk

Included nonfat, low-fat, and whole dry milk. In 

consumption data, included both reconstituted 

and nonreconstituted. Excluded goat and soy 

types.

Density = 244 g/8 fluid oz = 1.0759 weight oz/

fluid oz (01077—Milk, whole, 3.25% milk fat)

Dairy Dry buttermilkIncluded dry (powdered) buttermilk. In consumption data, included both reconstituted and nonreconstituted forms.Density = 245 g/8 fluid oz = 1.0803 weight 

oz/fluid oz (01088—Milk, buttermilk, fluid, 

cultured, low-fat)

Fats and oils Butter Included tubs, sticks, and whipped butter, in salted and unsalted. Split butter-margarine tubs and 

sticks between butter and margarine categories. 

For count data, assumed 1 pound of butter.

Fats and oils Margarine Included tubs, sticks, and whipped margarine 

and spreads with margarine (e.g., veg oil-butter 

spread, margarine-like spread). Included nonfat, 

reduced fat, and regular types. Split buttermargarine tubs and sticks between butter and 

margarine categories.

Fats and oils Lard Skipped because consumption data are not 

available.

Fats and oils Edible beef 

tallow

Skipped because consumption and purchase 

data are not available.

Fats and oils Shortening Skipped because consumption data are not 

available.

Continued—
52

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table A-1

Food Category Descriptions and Values Used for Count Data Conversion, Inedible and Solids Percentages, 

and Density Estimates—continued

Category Food Description Food-specific adjustment values 

(and NDB number If available)a

Fats and oils Salad and 

cooking oils

Included salad and cooking oil (corn, olive, 

peanut, canola, soybean, and other types) and 

cooking spray. Included all types of salad dressings and mayonnaise. 

Density = 218 g/8 fluid oz = 0.9612 weight 

oz/fluid oz (04582—Oil, vegetable, canola, 

04670—USDA Commodity Food, oil, vegetable, 

low saturated fat)

Density = 245 g/8 fluid oz = 1.0803 weight 

oz/fluid oz (04015—Salad dressing, Russian 

dressing)

Fats and oils Other edible 

fats and oils

Skipped because infeasible to align purchase 

and consumption categories.

Fruits—Fresh Fresh 

oranges

Included random-weight and UPC oranges and 

refrigerated cut oranges. Included temples with 

oranges. 

Inedible = 27% (09200—Oranges, raw, all commercial varieties [fruit (2 5/8 diameter)])

For count data, assumed 1 orange = 131 g.

Fruits—Fresh Fresh 

tangerines

Included random-weight and UPC tangerines, 

tangelos, and mandarin oranges with tangerines (NHANES groups mandarins with tangerines) and refrigerated cut tangerines. 

Inedible = 26% (09218—Tangerines [mandarin 

oranges], raw [medium (21⁄2 ̋ diameter)])

For count data, assumed 1 tangerine = 88 g.

Fruits—Fresh Fresh 

grapefruit

Included random-weight and UPC grapefruit 

and refrigerated cut grapefruit. 

Inedible = 50% (09111—Grapefruit, raw, pink 

and red and white, all areas [medium (approx 4 ̋ 

diameter)])

For count data, assumed 1 grapefruit = 256 g.

Fruits—Fresh Fresh 

lemons

Included random-weight and UPC lemons. Split 

bags of lemons and limes between the two 

product categories. 

Inedible = 47% (09150—Lemons, raw, without 

peel)

For count data, assumed 1 lemon = 71 g.

Fruits—Fresh Fresh limes Included random-weight and UPC limes. Split 

bags of lemons and limes between the two 

product categories. 

Inedible = 16% (09159—Limes, raw)

For count data, assumed 1 lime = 67 g.

Fruits—Fresh Fresh 

apples

Included random-weight and UPC apples of 

all varieties and refrigerated cut apple chunks. 

Excluded candy kits, candy or caramel apples, 

and prepared apple salads. 

Inedible = 10% (09003—Apples, raw, with skin 

[medium (3 ̋ diameter)])

For count data, assumed 1 apple = 182 g.

Fruits—Fresh Fresh 

apricots

Included random-weight and UPC apricots. Inedible = 7% (09021—Apricots, raw)

For count data, assumed 1 apricot = 35 g.

Fruits—Fresh Fresh avocadosIncluded random-weight and count avocados. Inedible = 26% (09037—Avocados, raw, all 

commercial varieties)

For count data, assumed 1 avocado = 201 g.

Fruits—Fresh Fresh 

bananas

Included random-weight and UPC bananas. Inedible = 36% (09040—Bananas, raw [medium 

(7 ̋ to 7 7/8 ̋ long)])

For count data, assumed 1 banana = 118 g and 

1 bunch = 7 bananas.

Fruits—Fresh Fresh 

blueberries

Included random-weight and UPC blueberries. Inedible = 5% (09050—Blueberries, raw)

Fruits—Fresh Fresh 

cantaloupe

Included random-weight and UPC cantaloupe 

and refrigerated cut cantaloupe chunks. 

Inedible = 49% (09181—Melons, cantaloupe, 

raw [melon, medium (about 5 ̋ diameter)])

For count data, assumed 1 cantaloupe = 552 g.

Fruits—Fresh Fresh 

cherries

Included random-weight and UPC cherries. Inedible = 8% (09070—Cherries, sweet, raw)

Continued—
53

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table A-1

Food Category Descriptions and Values Used for Count Data Conversion, Inedible and Solids Percentages, 

and Density Estimates—continued

Category Food Description Food-specific adjustment values 

(and NDB number if available)a

Fruits—Fresh Fresh 

cranberries

Included fresh, frozen, and canned cranberries 

(no other cranberry categories in the data system). Excluded shelf-stable cranberry sauces. 

Inedible = 2% (09078—Cranberries, raw)

Fruits—Fresh Fresh 

grapes

Included random-weight and UPC grapes. Inedible = 4% (Skin not considered inedible) 

(09132—Grapes, red or green [European type, 

such as Thompson seedless], raw)

Fruits—Fresh Fresh 

honeydew

Included random-weight and UPC honeydew 

and refrigerated cut honeydew chunks. 

Inedible = 54% (09184—Melons, honeydew, raw)

For count data, assumed 1 honeydew = 1140 g.

Fruits—Fresh Fresh kiwi Included random-weight and UPC kiwi and 

refrigerated cut kiwi. 

Inedible = 14% (09148—Kiwi fruit [Chinese 

gooseberries], fresh, raw)

For count data, assumed 1 kiwi = 83.5 g.

Fruits—Fresh Fresh 

mangoes

Included random-weight and UPC mangoes 

and refrigerated cut mango chunks. 

Inedible = 31% (09176—Mangoes, raw)

For count data, assumed 1 mango = 207 g.

Fruits—Fresh Fresh 

peaches

Included random-weight and UPC peaches and 

refrigerated cut peaches. 

Inedible = 4% (09236—Peaches, raw [medium (2 

2/3 ̋ diameter)])

For count data, assumed 1 peach = 150 g.

Fruits—Fresh Fresh pears Included random-weight and UPC pears and 

refrigerated cut pear chunks. 

Inedible = 10% (09252—Pears, raw [medium])

For count data, assumed 1 pear = 178 g.

Fruits—Fresh Fresh 

pineapple

Included random-weight and UPC pineapples 

and refrigerated cut pineapple chunks. Split 

cut mixtures with one other fruit. Excluded fruit 

medleys with pineapple. 

Inedible = 49% (09266—Pineapple, raw, all 

varieties)

For count data, assumed 1 pineapple = 905 g.

Fruits—Fresh Fresh 

papaya

Included random-weight and UPC papayas and 

refrigerated cut papaya. Split cut mixtures with 

one other fruit. 

Inedible = 33% (09226—Papayas, raw [medium 

(5 1/8 ̋ long x 3 ̋ diameter)])

For count data, assumed 1 papaya = 304 g.

Fruits—Fresh Fresh plums Included random-weight and UPC plums. Inedible = 6% (09279—Plums, raw [fruit (2 1/8 ̋ 

diameter)])

For count data, assumed 1 plum = 66 g.

Fruits—Fresh Fresh 

strawberries

Included random-weight and UPC strawberries 

and refrigerated cut strawberries. 

Inedible = 6% (09316—Strawberries, raw [medium (11⁄4 ̋ diameter)])

For count data, assumed 1 strawberry = 12 g.

Fruits—Fresh Fresh 

watermelon

Included random-weight and UPC watermelon 

and refrigerated cut watermelon. 

Inedible = 48% (09326—Watermelon, raw (melon 

[15 ̋ long x 71⁄2 ̋ diameter)])

For count data, assumed 1 watermelon = 4518 g.

Fruits—

Canned

Canned 

apples and 

applesauce 

Included applesauce in cans and jars, sweetened and unsweetened. Included readyto-serve fruit cups. Included flavored apple 

sauces. Excluded pie and pastry fillings (ingredient), glazes, butters, jams, jellies, preserves, 

spreads, and relishes. Excluded baby foods. 

Solids = 87% (Apples: “Food Yields,” Table 1, 

Item 52, Apples: Canned, contents of can: Sliced, 

unspecified)

Fruits—

Canned

Canned 

apricots

Included canned apricots including readyto-serve fruit cups packed in water, juice, or 

syrup. Included pickled apricots. Excluded pie 

and pastry fillings (ingredient), relishes, jams, 

marmalade, preserves, spreads, and butters. 

Excluded baby foods.

Solids = 59% (“Food Yields,” Table 1, Item 74, 

Apricots: Canned, contents of can: Halves: All 

samples in syrup or water)

Continued—
54

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table A-1

Food Category Descriptions and Values Used for Count Data Conversion, Inedible and Solids Percentages, 

and Density Estimates—continued

Category Food Description Food-specific adjustment values 

(and NDB number If available)a

Fruits—

Canned

Canned 

cherries

Included canned cherries packed in water, 

juice, or syrup. Included maraschino cherries. 

Excluded pie and pastry fillings, jams, jellies, 

fruit spreads, and fruit salads.

Solids = 69% (“Food Yields,” Table 1, Item 533, 

Cherries: Canned, contents of can: Sour, red 

pitted: All samples)

Fruits—

Canned

Canned 

peaches

Included canned peaches packed in water, 

juice, or syrup. Split canned peach and pear 

mixtures. Excluded pie and pastry fillings, 

glazes, preserves, fruit salads, and gelatin desserts. Excluded baby food.

Solids = 60% (“Food Yields,” Table 1, Item 1666, 

Peaches: Canned, contents of can: All samples)

Fruits—

Canned

Canned 

pears

Included canned pears packed in water, juice, 

or syrup. Split pear and peach mixtures. Excluded preserves and gelatin desserts. Excluded baby food.

Solids = 59% (“Food Yields,” Table 1, Item 1735, 

Pears: Canned, contents of can [halves]: All 

samples)

Fruits—

Canned

Canned 

pineapple

Included canned pineapple packed in water, 

juice, or syrup. Split canned pineapple mixtures 

with one other fruit. Excluded pie and pastry fillings, gelatin desserts, and fruit salad desserts.

Solids = 65% (“Food Yields,” Table 1, Item 

1843, Pineapple: Canned, contents of can: All 

samples, all styles)

Fruits—

Canned

Canned 

plums

Included canned plums packed in water, juice, 

or syrup. Included pickled plums. Excluded 

preserves and baby food.

Solids = 56% (“Food Yields,” Table 1, Item 1886, 

Plums: Canned, contents of can: All samples)

Fruits—

Canned

Canned 

olives

Included green, black, and stuffed olives. 

Excluded ready-made olive salads.

Solids = 63% (Unpitted: “Food Yields,” Table 

1, Item 1560, Olives: Green, contents of can, 

unspecified size: Plain: Unpitted)

Solids = 64% (Stuffed: “Food Yields,” Table 1, 

Item 1562, Olives: Green, contents of can, 

unspecified size: Plain: Stuffed)

Fruits—

Frozen

Frozen 

blackberries

Included whole frozen blackberries.

Fruits—

Frozen

Frozen 

blueberries

Included whole frozen blueberries.

Fruits—

Frozen

Frozen 

cherries

Included whole frozen cherries.

Fruits—

Frozen

Frozen 

raspberries

Included whole frozen raspberries.

Fruits—

Frozen

Frozen 

strawberries

Included whole and sliced frozen strawberries.

Fruits—

Frozen

Other frozen 

berries

Skipped because infeasible to align purchase 

and consumption categories.

Fruits—

Frozen

Frozen 

apples

Skipped because consumption data are not 

available.

Fruits—

Frozen

Frozen 

apricots

Skipped because purchase data are 

insufficient.

Fruits—

Frozen

Frozen 

peaches

Included sliced frozen peaches.

Fruits—

Frozen

Frozen 

plums

Skipped because consumption data are not 

available.

Fruits—

Frozen

Other frozen 

fruit

Purchase data includes mango, rhubarb, 

pineapple, papaya, passion fruit, guava, etc.

Continued—
55

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table A-1

Food Category Descriptions and Values Used for Count Data Conversion, Inedible and Solids Percentages, 

and Density Estimates—continued

Category Food Description Food-specific adjustment values 

(and NDB number If available)a

Fruits—Dried Dried apples Included dried apple chunks, rings, and fruit 

leathers. Excluded apple chips.

Fruits—Dried Dried 

apricots

Included dried apricots and apricot fruit rolls 

and leathers. Excluded trail mixes with apricots.

Fruits—Dried Dried dates Whole and chopped dates. Reduced weight of dates with pits by 15%.

Fruits—Dried Dried figs Included all varieties of dried figs.

Fruits—Dried Dried 

peaches

Included dried whole, sliced, and chunk peaches and peach fruit rolls and leathers.

Fruits—Dried Dried pears Included dried whole and slices pears.

Fruits—Dried Dried plums Included dried whole plums and pieces. Reduced weight of plums with pits by 13%.

Fruits—Dried Raisins Included raisins in canisters, boxes, and bags. 

Split raisin and dried fruit mixtures with at most 

one other fruit (of any type). 

Fruits—

Juices

Grapefruit 

juice

[For all juices: Included juice concentrate (converted to reconstituted equivalent by multiplying 

by 4). Included juice mixtures if only two juices 

(split between the juices). Excluded powders, 

juice flavored “drinks,” syrups, extracts, and 

nectars. In purchase data, included product if 

description indicated JC for juice and excluded 

product if description indicated DR for juice 

drink.] Included refrigerated, frozen, and shelfstable grapefruit juice. 

Density = 247 g/8 fluid oz = 1.0891 weight oz/

fluid oz (09123—Grapefruit juice, white, canned, 

unsweetened)

Fruits—

Juices

Lemon juice Included refrigerated, frozen concentrate, and 

shelf-stable lemon juice. Excluded lemonade. 

Density = 244 g/8 fluid oz = 1.0759 weight 

oz/fluid oz (09153—Lemon juice, canned or 

bottled)

Fruits—

Juices

Lime juice Included refrigerated, frozen concentrate, and 

shelf-stable lime juice. Excluded limeade. 

Density = 246 g/8 fluid oz = 1.0847 weight oz/

fluid oz (09161—Lime juice, canned or bottled, 

unsweetened)

Fruits—

Juices

Orange 

juice

Included refrigerated, frozen concentrate, and 

shelf-stable orange juice. Split orange juice 

mixtures with up to one other juice. 

Density = 249 g/8 fluid oz = 1.0979 weight oz/

fluid oz (09207—Orange juice, canned, unsweetened)

Fruits—

Juices

Apple juice Included apple juice and cider in cans and 

bottles. Included frozen concentrate. Included 

nonalcoholic sparkling apple juices. Split 

apple juice mixtures with up to one other juice. 

Excluded apple-flavored fruit drinks and sodas. 

Excluded baby juices. 

Density = 248 g/8 fluid oz = 1.0935 weight oz/

fluid oz (09016—Apple juice, canned or bottled, 

unsweetened, without added ascorbic acid)

Fruits—

Juices

Cranberry 

juice

Included refrigerated, frozen concentrate, and 

shelf-stable cranberry juice and juice cocktail. 

Split cranberry juice mixtures with up to one 

other juice. 

Density = 253 g/8 fluid oz = 1.1155 weight oz/

fluid oz (43382—Cranberry juice, unsweetened)

Fruits—

Juices

Grape juice Included refrigerated, frozen concentrate, 

and shelf-stable grape juice. Split grape juice 

mixtures with up to one other juice. Excluded 

baby juices. 

Density = 253 g/8 fluid oz = 1.1155 weight oz/

fluid oz (09135—Grape juice, canned or bottled, 

unsweetened, without added vitamin C)

Continued—
56

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table A-1

Food Category Descriptions and Values Used for Count Data Conversion, Inedible and Solids Percentages, 

and Density Estimates—continued

Category Food Description Food-specific adjustment values 

(and NDB number If available)a

Fruits—

Juices

Pineapple 

juice

Included refrigerated, frozen concentrate, and 

shelf-stable pineapple juice. Split pineapple 

juice mixtures with up to one other juice. 

Density = 250 g/8 fluid oz = 1.1023 weight oz/

fluid oz (09273—Pineapple juice, canned, unsweetened, without added ascorbic acid)

Fruits—

Juices

Prune juice Included refrigerated and shelf-stable prune 

juice. 

Density = 256 g/8 fluid oz = 1.1288 weight oz/

fluid oz (09294—Prune juice, canned)

Vegetables—

Fresh

Fresh 

artichokes

Included canned (including pickled), fresh, and 

frozen to correspond to the food availability 

definition. Included random-weight and UPC 

artichokes. Excluded artichoke salads. 

Inedible = 60% (11007—Artichokes [globe or 

French], raw [artichoke, medium])

For count data, assumed 1 artichoke = 128 g.

Assumed 100% of NFS consumption is fresh.

Vegetables—

Fresh

Fresh 

asparagus

Included random-weight and UPC fresh 

asparagus. 

Inedible = 47% (11011—Asparagus, raw [spear, 

medium (51⁄4 ̋ to 7 ̋ long)])

For count data, assumed 1 spear = 16 g and 1 

bunch = 36 spears.

Assumed 81% of NFS consumption is fresh.

Vegetables—

Fresh

Fresh bell 

peppers

Included random-weight and UPC fresh 

peppers and refrigerated cut bell peppers. 

Included fresh cut mixtures with up to one other 

vegetable (split). 

Inedible = 18% (11333—Peppers, sweet, green, 

raw (medium [approx 23⁄4 ̋ long, 21⁄2 ̋ diameter)])

For count data, assumed 1 pepper = 119 g.

Vegetables—

Fresh

Fresh 

broccoli

Included random-weight and UPC broccoli and 

refrigerated cut broccoli. Included refrigerated 

cut mixtures with up to one other vegetable 

(split). Included broccoli. Excluded broccoli 

sprouts, broccoli rabe, and broccoflower. 

Excluded dip trays. 

Inedible = 39% (11090—Broccoli, raw)

For count data, assumed 1 bunch = 608 g.

Assumed 69% of NFS consumption is fresh.

Vegetables—

Fresh

Fresh 

brussels 

sprouts

Included random-weight and UPC brussels 

sprouts. 

Inedible = 10% (11098—brussels sprouts, raw)

For count data, assumed 1 sprout = 19 g.

Assumed 100% of NFS consumption is fresh.

Vegetables—

Fresh

Fresh

cabbage

Included random-weight and UPC cabbage 

heads, Chinese cabbage, and refrigerated 

shredded cabbage. Excluded cabbage sprouts. 

Inedible = 20% (11109—Cabbage, raw (head, 

medium [about 53⁄4 ̋ diameter)])

For count data, assumed 1 cabbage = 908 g.

Vegetables—

Fresh

Fresh 

carrots

Included random-weight and UPC carrots and 

refrigerated cut carrots. Included fresh cut 

carrot mixtures with up to one other vegetable 

(split). Excluded dip trays. 

Inedible = 11% (11124—Carrots, raw [medium])

For count data, assumed 1 carrot = 61 g and 1 

bunch = 5 carrots.

Assumed 74% of NFS consumption is fresh.

Vegetables—

Fresh

Fresh 

cauliflower

Included random-weight and UPC cauliflower 

and refrigerated cut cauliflower. Included fresh 

cut cauliflower mixtures with up to one other 

vegetable (split). 

Inedible = 61% (11135—Cauliflower, raw [head, 

medium (5 ̋ to 6 ̋ diameter)])

For count data, assumed 1 cauliflower = 575 g.

Assumed 80% of NFS consumption is fresh.

Vegetables—

Fresh

Fresh celery Included random-weight and UPC celery and 

refrigerated cut celery. Included fresh cut celery 

mixtures with up to one other vegetable (split). 

Excluded dip trays. 

Inedible = 11% (11143—Celery, raw [stalk, 

medium (71⁄2 ̋ to 8 ̋ long)])

For count data, assumed 1 stalk = 40 g and 1 

bunch = 8 stalks.

Vegetables—

Fresh

Fresh

collard 

greens

Included random-weight and UPC collard 

greens. Assumed 100% of NFS consumption 

is fresh. 

Inedible = 43% (11161—Collards, raw)

For count data, assumed 1 pound of greens.

Vegetables—

Fresh

Fresh sweet 

corn

Included random-weight and UPC corn ears 

and cut corn. Included fresh cut mixtures with 

up to one other vegetable (split). 

Inedible for corn on the cob = 64% (11167—

Corn, sweet, yellow, raw [ear, medium (63⁄4 ̋ to 

71⁄2 ̋ long) yields])

For count data, assumed 1 ear = 90 g.

Assumed 35% of NFS consumption is fresh.

Continued—
57

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table A-1

Food Category Descriptions and Values Used for Count Data Conversion, Inedible and Solids Percentages, 

and Density Estimates—continued

Category Food Description Food-specific adjustment values 

(and NDB number If available)a

Vegetables—

Fresh

Fresh 

cucumbers

Included random-weight and UPC cucumbers. Inedible = 27% (11206—Cucumber, peeled, raw 

[medium])

For count data, assumed 1 cucumber = 201 g.

Vegetables—

Fresh

Fresh 

eggplant

Included random-weight and UPC eggplant. Inedible = 19% (11209—Eggplant, raw [eggplant, 

unpeeled (approx 11⁄4 lb)])

For count data, assumed 1 eggplant = 548 g.

Vegetables—

Fresh

Fresh 

escarole 

and endive

Combined with “Fresh head lettuce” category. Inedible = 14% (11213—Endive, raw)

For count data, endive = 513 g and lettuce = 

539 g.

Vegetables—

Fresh

Fresh garlic Included random-weight and UPC garlic. 

Excluded garlic sprouts. 

Inedible = 13% (11215—Garlic, raw)

For count data, assumed 1 clove = 3 g and 1 

head = 13 cloves.

Vegetables—

Fresh

Fresh kale Included random-weight and UPC kale. 

Included fresh cut mixtures with up to one 

other vegetable (split). 

Inedible = 39% (11233—Kale, raw)

For count data, assumed 1 pound of greens.

Vegetables—

Fresh

Fresh head 

lettuce

Included random-weight and UPC lettuce. 

Combined category for head lettuce, leaf lettuce, escarole, endive, and chicory. Included 

bunches, heads, and lettuce mixes. Excluded 

kits with dressing because most of weight is 

dressing. 

Inedible = 26% (11250—Lettuce, butterhead 

[includes Boston and bibb types], raw)

Inedible = 5% (11252—Lettuce, iceberg [includes 

crisphead types], raw)

Inedible = 6% (11251—Lettuce, cos or romaine, 

raw)

Inedible = 28% (11253—Lettuce, green leaf, raw)

Inedible = 28% (11257—Lettuce, red leaf, raw)

Inedible = 18% (All other lettuce—avg)

For count data, assumed 1 head = 539 g.

Vegetables—

Fresh

Fresh 

romaine and 

leaf lettuce

Combined with “Fresh head lettuce” category. 

Vegetables—

Fresh

Fresh lima 

beans

Included random-weight and UPC lima beans. For count data, assumed 1 pound of beans.

Vegetables—

Fresh

Fresh 

mushrooms

Included all varieties of random-weight and 

UPC mushrooms. 

Inedible = 3% (11260—Mushrooms, white, raw 

[medium])

Inedible = 3% (11265—Mushrooms, portabella, 

raw)

For count data, assumed white mushrooms = 

18 g and portabella = 84 g.

Vegetables—

Fresh

Fresh 

mustard 

greens

Included random-weight and UPC mustard 

greens. Included fresh cut mixtures with up to 

one other vegetable (split). 

Inedible = 7% (11270—Mustard greens, raw)

For count data, assumed 1 pound of greens.

Vegetables—

Fresh

Fresh okra Included random-weight and UPC okra. 

Included fresh cut mixtures with up to one 

other vegetable (split). 

Inedible = 14% (11278—Okra, raw)

Assumed 100% of NFS consumption is fresh.

Vegetables—

Fresh

Fresh 

onions

Included random-weight and UPC fresh 

onions, green onions, and canned onions. 

Split fresh cut mixtures with up to one other 

vegetable (split). 

Inedible = 10% (11282—Onions, raw [medium 

(21⁄2 ̋ diameter)])

For count data, assumed 1 onion = 110 g.

Assumed 94% of NFS consumption is fresh.

Vegetables—

Fresh

Fresh 

potatoes

Included random-weight and bagged potatoes. Inedible = 25% (11352—Potato, flesh and skin, 

raw [Potato medium (21⁄4 ̋ to 31⁄4 ̋ diameter)])

For count data, assume 1 medium potato = 

213 g.

Continued—
58

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table A-1

Food Category Descriptions and Values Used for Count Data Conversion, Inedible and Solids Percentages, 

and Density Estimates—continued

Category Food Description Food-specific adjustment values 

(and NDB number If available)a

Vegetables—

Fresh

Fresh 

pumpkin

Included random-weight and UPC pumpkins. 

Excluded pie filling. 

Inedible = 30% (11422—Pumpkin, raw)

For count data, assumed 1 pumpkin = 10 

pounds.

Assumed 100% of NFS consumption is fresh.

Vegetables—

Fresh

Fresh 

radishes

Included random-weight and UPC radish 

bunches and refrigerated cut radishes. 

Excluded radish sprouts. 

Inedible = 10% (11429—Radishes, raw [medium 

(3⁄4 ̋ to 1 ̋ diameter)])

For count data, assumed 1 radish = 4.5 g.

Vegetables—

Fresh

Fresh snap 

beans

Included random-weight and UPC green 

beans, wax beans, and French beans. 

Included fresh cut mixtures with up to one 

other vegetable (split). 

Inedible = 12% (11052—Beans, snap, green, 

raw)

Assumed 25% of NFS consumption is fresh.

Vegetables—

Fresh

Fresh 

spinach

Included random-weight and UPC spinach 

bunches and packaged salads. Excluded 

salad kits with dressing because majority of 

weight is due to dressing. 

Inedible = 28% (11457—Spinach, raw)

For count data, assumed 1 bunch = 340 g.

Assumed 67% of NFS consumption is fresh.

Vegetables—

Fresh

Fresh 

squash

Included random-weight and UPC squash. 

Included fresh, canned (including pickled), and 

frozen. Included fresh cut mixtures with up to 

one other vegetable (split). 

Inedible = 5% (11641—Squash, summer, all 

varieties, raw [medium]) 

Inedible = 21% (All other winter varieties)

Solids = 61% (“Food Yields,” Table 1, Item 2453, 

Squash, summer: Canned, yellow, cut, contents 

of can, all samples)

For count data, assumed 1 squash = 196 g.

Vegetables—

Fresh

Fresh sweet 

potatoes

Included fresh, canned, and frozen sweet 

potatoes and yams (ERS groups all in the 

fresh sweet potato category). Included canned 

sweet potatoes and yams in syrup. Excluded 

sweet potato pie filling. 

Inedible = 28% (11507—Sweet potato, raw, 

unprepared [sweet potato, 5 ̋ long])

Solids = 65% (“Food Yields,” Table 1, Item 2507, 

Sweet potatoes: Canned, contents of can: Syrup 

pack: All samples)

For count data, assumed 1 medium potato = 

130 g.

Vegetables—

Fresh

Fresh 

tomatoes

Included random-weight and UPC tomatoes. 

Included cherry tomatoes. Included fresh cut 

mixtures with up to one other vegetable (split). 

Inedible = 9% (11529—Tomatoes, red, ripe, 

raw, year round average [medium whole (2 3/5 ̋ 

diameter)])

For count data, assumed 1 tomato = 123 g.

Assumed 22% of NFS consumption is fresh.

Vegetables—

Fresh

Fresh turnip 

greens

Included random-weight and UPC turnip 

greens. Included fresh cut mixtures with up to 

one other vegetable (split). 

Inedible = 30% (11568—Turnip greens, raw)

For count data, assumed 1 pound of greens.

Vegetables—

Canned

Canned 

asparagus

Included canned and pickled asparagus. 

Excluded canned soups. 

Solids = 60% (“Food Yields,” Table 1, Item 103, 

Asparagus: Canned, contents of can: All samples, cut spears, spears, or tips, all can sizes)

Assumed 15% of NFS consumption is canned.

Vegetables—

Canned

Canned 

snap beans

Included canned pole beans, green beans, 

string beans, and wax beans. Included half of 

green bean mixtures with one other vegetable. 

Excluded bean salad (multiple beans) and 

baby food. 

Solids = 58% (“Food Yields,” Table 1, Item 206, 

Beans: Snap, green, and wax: Canned, contents 

of can: All samples, including unspecified)

Assumed 49% of NFS consumption is canned.

Vegetables—

Canned

Canned 

cabbage 

(sauerkraut)

Included red, snow, and regular cabbage. 

Included kim chee, sauerkraut, and other pickled cabbage. Included refrigerated sauerkraut. 

Excluded cabbage relishes. Excluded canned 

soups.

Solids = 88% (“Food Yields,” Table 1, Item 

2272, Sauerkraut: Canned, contents of can: All 

samples)

Continued—
59

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table A-1

Food Category Descriptions and Values Used for Count Data Conversion, Inedible and Solids Percentages, 

and Density Estimates—continued

Category Food Description Food-specific adjustment values 

(and NDB number If available)a

Vegetables—

Canned

Canned 

carrots

Included canned and pickled carrots. Included 

half of carrot mixtures with one other vegetable (e.g., peas and carrots). In consumption 

data, included creamed and glazed carrots 

prepared from canned. Excluded baby food. 

Solids = 66% (“Food Yields,” Table 1, Item 487, 

Carrots: Contents of can: Wet pack: All samples)

Assumed 15% of NFS consumption is canned.

Vegetables—

Canned

Canned 

sweet corn

Included regular, cream style, and fiesta style 

canned corn. Excluded baby corn. Included 

half of corn mixtures with one other vegetable. 

In consumption data, included creamed corn. 

Excluded baby food. 

Solids = 68% (“Food Yields,” Table 1, Item 922, 

Corn: Canned, whole grain, contents of can: Wet 

pack: All samples)

Assumed 15% of NFS consumption is canned.

Vegetables—

Canned

Canned 

cucumbers 

(pickles)

Included all varieties of cucumber pickles. 

Excluded cucumber salad. Included half of 

cucumber mixtures with one other vegetable. 

Solids = 54% (Average of 3 products)

Solids = 62% (Pickles: Midgets: Direct measurement)

Solids = 47% (Pickles: Slices: Direct measurement)

Solids = 53% (Pickles: Baby dills: Direct measurement)

For count data, assumed 1 pickle = 65 g.

Vegetables—

Canned

Canned 

green peas

Included regular and creamed canned peas. 

Included half of green pea mixtures with one 

other vegetable. Excluded snow peas, sugar 

snap peas, blackeye peas, and pea soup. 

Excluded canned soup and baby food. 

Solids = 64% (“Food Yields,” Table 1, Item 1756, 

Peas, Green: Canned, contents of can: Wet pack: 

No. 303)

Assumed 42% of NFS consumption is canned.

Vegetables—

Canned

Canned 

chile 

peppers

Included regular and pickled chile peppers. Solids = 45% (Direct measurement)

Assumed 100% of NFS consumption is canned.

Vegetables—

Canned

Canned 

tomatoes

Included chopped and whole canned tomatoes, puree, paste, and sauce (with and without meat). Included pickled tomatoes. Included 

half of tomato mixtures with one other vegetable. Excluded tomato soup. 

Solids = 66% (“Food Yields,” Table 1, Item 2545, 

Tomatoes: Canned, contents of can: No. 303)

Assumed 78% of NFS consumption is canned.

Vegetables—

Canned

Canned 

mushrooms

Included canned regular and pickled mushrooms. Excluded canned mushroom salads, 

dried mushrooms, and frozen mushroom hor 

d’oeuvres. Excluded mushroom soup.

Solids = 58% (“Food Yields,” Table 1, Item 

1512, Mushrooms: Canned, contents of can, all 

samples)

Vegetables—

Canned

Canned 

potatoes

Included all varieties of canned potatoes. 

Split canned mixed vegetables with potatoes 

and one other primary ingredient. Excluded 

shelf-stable potato side dishes, canned potato 

salad, and canned potato soup. 

Solids = 68% (“Food Yields,” Table 1, Item 2091, 

Potato and potato products: Canned, contents of 

can: All sizes)

Vegetables—

Canned

Other 

canned 

vegetables

Skipped because infeasible to align purchase 

and consumption categories.

Vegetables—

Frozen

Frozen 

asparagus

[For all frozen vegetables, excluded blends 

and medleys because other contents are 

not known.] Included cut and whole spears. 

Included stir-fry asparagus. Excluded mixtures. 

Vegetables—

Frozen

Frozen snap 

beans

Included green beans, wax beans, and French 

beans. Included mixtures with one vegetable 

(split). 

Assumed 26% of NFS consumption is frozen.

Continued—
60

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table A-1

Food Category Descriptions and Values Used for Count Data Conversion, Inedible and Solids Percentages, 

and Density Estimates—continued

Category Food Description Food-specific adjustment values 

(and NDB number If available)a

Vegetables—

Frozen

Frozen 

broccoli

Included cut plain broccoli and broccoli, with or 

without sauce. Split broccoli mixtures with one 

other vegetable. Excluded broccoli rabe. 

Assumed 31% of NFS consumption is frozen.

Vegetables—

Frozen

Frozen 

carrots

Included cut and whole carrots, with or without 

sauce. Split carrot mixtures with one other 

vegetable. 

Assumed 11% of NFS consumption is frozen.

Vegetables—

Frozen

Frozen 

cauliflower

Included cut plain cauliflower, with or without 

sauce. Split cauliflower mixtures with one other 

vegetable. 

Assumed 20% of NFS consumption is frozen.

Vegetables—

Frozen

Frozen 

sweet corn

Included corn on the cob and cut corn. Split 

corn mixtures with one other vegetable. 

Included fiesta corn (mixture of corn and 

sweet peppers). 

For corn on the cob, calculated edible percentage of 55%.

Assumed 34% of NFS consumption is frozen.

Vegetables—

Frozen

Frozen 

green peas

Included green peas, cream peas, early June 

peas, garden peas, green and yellow split pea, 

purple hull pea, all with or without sauce. 

Excluded pea pods. Split pea mixtures with 

one other vegetable. 

Assumed 58% of NFS consumption is frozen.

Vegetables—

Frozen

Frozen lima 

beans

Included lima beans, with or without sauce. 

Split lima bean mixtures with one other 

vegetable.

Vegetables—

Frozen

Frozen 

spinach

Included spinach leaf, with or without sauce. 

Split spinach mixtures with one other 

vegetable. 

Assumed 31% of NFS consumption is frozen.

Vegetables—

Frozen

Frozen 

potatoes

Included frozen hash browns, fries, and wedges. Excluded whole frozen baked potatoes 

because not included in consumption data and 

other frozen potato side dishes. 

For count data, assumed 1 lb. boxes.

Vegetables—

Frozen

Other frozen 

vegetables

Skipped because difficult to align consumption 

categories with purchase categories.

Vegetables—

Dried

Dehydrated 

onions

Skipped because consumption data are not 

available.

Vegetables—

Dried

Dehydrated 

potatoes

Consumption data includes reconstituted 

mashed potatoes made with milk, fat, and/or 

egg. 

Prepared weight conversion = 6.28 (71501090—

White potato, from dry, mashed, made with milk, 

no fat)

Vegetables—

Dried

Potato 

chips and 

shoestring 

potatoes

Included potato chips, potato crisps, and 

potato snacks (e.g., crunchies, fries, sticks). 

Includes fat free, reduced fat, regular, unsalted, and salted. Excluded sweet potato chips. 

Excluded combination variety packs. 

Continued—
61

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table A-1

Food Category Descriptions and Values Used for Count Data Conversion, Inedible and Solids Percentages, 

and Density Estimates—continued

Category Food Description Food-specific adjustment values 

(and NDB number If available)a

Vegetables—

Dried

Dry edible 

beans

Included all dry bean categories (e.g., black, 

pinto, pink, lima, navy, white, cowpeas, chickpeas, kidney). Converted to prepared weight. 

Prepared with or without fat. 

Prepared weight conversion = 2.35 (average of 

black, lima, navy, chickpea, kidney, pinto) 

Prepared weight conversion = 2.29 (41102020—

Black, brown, or Bayo beans, dry, cooked, fat not 

added in cooking) 

Prepared weight conversion = 2.50 (41103020—

Lima beans, dry, cooked, fat not added in cooking) 

Prepared weight conversion = 2.40 (41101120—

White beans, dry, cooked, fat not added in cooking) 

Prepared weight conversion = 2.12 (41302020—

Chickpeas, dry, cooked, fat not added in cooking) 

Prepared weight conversion = 2.40 (41106020—

Red kidney beans, dry, cooked, fat not added in 

cooking) 

Prepared weight conversion = 2.40 (41104020—

Pinto, calico, or red Mexican beans, dry, cooked, 

fat not added in cooking)

Grains White and 

whole wheat 

flour

Skipped because consumption data are not 

available.

Grains Durum flour Skipped because consumption data are not 

available.

Grains Rice Included white, brown, basmati, jasmine, and 

other types. Included plain and mixes. Included 

wild rice because difficult to separate out in 

many mixes. Converted regular rice and 

instant rice to prepared weight. Prepared with 

or without fat. Excluded rice cakes, rice cereals, and rice flour.

Prepared weight conversion = 3.07 (56205010—

Rice, white, cooked, regular, fat not added in 

cooking)

Grains Rye flour Skipped because consumption data are not 

available.

Grains Corn flour 

and meal

Skipped because consumption data are not 

available.

Grains Corn 

hominy and 

grits

Included dry hominy grits. Prepared with or 

without fat or cheese.

Prepared weight conversion = 6.56 (56201010—

Grits, cooked, corn or hominy, regular, fat not 

added in cooking)

Grains Corn starch Skipped because consumption data are not 

available.

Grains Barley 

products

Included dry barley and hot barley cereals 

(including barley bran). Included half of hot 

cereals with barley and oats. Excluded barley 

flour and ready to eat cereals with barley. 

Prepared with or without fat.

Prepared weight conversion = 3.77 (56200400—

Barley, cooked, fat not added in cooking)

Grains Oat 

products

Included hot cereals with oats, oat flakes, 

oatmeal, and oat bran including regular, quick, 

and instant. Split hot cereals with oats and one 

other primary ingredients. Excluded ready-toeat cereals with oats (many other ingredients) 

and groats unless oat groats. Prepared with or 

without fat.

Prepared weight conversion = 5.78 (56203000—

Oatmeal, cooked, NS as to regular, quick or 

instant, fat not added in cooking)

Continued—
62

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table A-1

Food Category Descriptions and Values Used for Count Data Conversion, Inedible and Solids Percentages, 

and Density Estimates—continued

Category Food Description Food-specific adjustment values 

(and NDB number If available)a

Added 

sugars and 

sweeteners

Cane and 

beet sugar

Included brown, powdered, raw, and granulated sugars. Excluded sugar syrups. (Deleted 

5 count records because could not determine 

size.)

Added 

sugars and 

sweeteners

Highfructose 

corn syrup

Skipped because consumption data are not 

available.

Added 

sugars and 

sweeteners

Glucose Skipped because consumption data are not 

available.

Added 

sugars and 

sweeteners

Dextrose Skipped because consumption data are not 

available.

Added 

sugars and 

sweeteners

Honey Included all products in the honey product 

module of the purchase data, including plain 

and flavored varieties and honey spreads.

Added 

sugars and 

sweeteners

Edible 

syrups

Included sugar, maple, pancake, sorghum, 

and corn syrups, and molasses. Excluded toppings (e.g., chocolate, butterscotch) and berry/

fruit syrups. Deleted 2 count records; could not 

determine size.

Density = 330 g/8 fluid oz = 1.4551 weight oz/

fluid oz (19355—Syrups, sorghum)

Density = 315 g/8 fluid oz = 1.3889 weight oz/

fluid oz (19360—Syrups, table blends, pancake, 

with 2% maple)

Density = 337.16 g/8 fluid oz = 1.4866 weight oz/

fluid oz (19304—Molasses)

Notes:

UPC: universal product code on prepackaged foods.

Random weight: foods packaged and weighed at the store.

NFS = not further specified.

NDB=Nutrient Databank.

NS = not specified.

aNote that in cases where percentage of solids is indicated, the inedible percentage is calculated as 100% minus the solids percentage.

Source: Compiled by RTI International.
63

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table A-2

Weight and Inedible Percentage Assumptions for Fruit and Vegetable Count Data

Food description NDB_No Amount Additional description Weight 

(g) Inedible description Inedible (%)

Vegetables and Vegetable Products

Artichokes, (globe or 

french), raw

11007 1 Artichoke, medium 128 Stem and inedible parts 

of bracts and flowers

60

Asparagus, raw 11011 1 Spear, medium (51⁄4 ̋ to 7 ̋ 

long) (assumed 36 spears 

in a bunch)

16 Butt ends 47

Broccoli, raw 11090 1 Bunch 608 Leaves and tough stalks 

with trimmings

39

brussels sprouts, raw 11098 1 Sprout (assumed 37 

sprouts in a pound)

19 Outer leaves 10

Cabbage, raw 11109 1 Head, medium 

(about 53⁄4 ̋ diameter)

908 Outer leaves and core 20

Carrots, raw 11124 1 Medium (assumed 5 

carrots in a bunch)

61 Crown, tops and 

scrapings

11

Cauliflower, raw 11135 1 Head, medium 

(5 ̋ to 6 ̋ diameter)

575 Leaf stalks, cores and 

trimmings

61

Celery, raw 11143 1 Stalk, medium (71⁄2 ̋ to 8 ̋ 

long) (assumed 8 stalks in 

a bunch)

40 Roots and trimmings 11

Corn, sweet, yellow, 

raw

11167 1 Ear, medium (63⁄4 ̋ to 71⁄2 ̋ 

long) yields

90 35% husk, silk, trimmings; 29% cob64

Cucumber, peeled, raw 11206 1 Medium 201 Parings, ends and 

bruised spots

27

Eggplant, raw 11209 1 Eggplant, unpeeled 

(approx 11⁄4 lb)

548 Ends, parings and 

trimmings

19

Endive, raw 11213 1 Head 513 Outer leaves and core 14

Garlic, raw 11215 1 Clove (assumed 13 cloves 

in a head)

3 Knob and skin 13

Lettuce, iceberg 

(includes crisp head 

types), raw

11252 1 Head, medium (6 ̋ 

diameter)

539 Core 5

Mushrooms, portabella, 

raw

11265 1 Piece whole 84 Trimmings 3

Mushrooms, white, raw 11260 1 Medium 18 Trimmings 3

Onions, raw 11282 1 Medium (21⁄2 ̋ diameter) 110 Stem ends, sprouts and 

defects

10

Peppers, sweet, green, 

raw

11333 1 Medium (approx 23⁄4 ̋ long, 

21⁄2 ̋ diameter)

119 Stem ends, seeds and 

core

18

Potato, flesh and skin, 

raw

11352 1 Potato medium (21⁄4 ̋ to 

31⁄4 ̋ diameter)

213 Parings and trimmings 25

Radishes, raw 11429 1 Medium (3⁄4 ̋ to 1 ̋ diameter) (assumed 10 radishes 

in a bunch)

4.5 Stem ends, rootlets and 

trimmings

10

Continued—
64

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table A-2

Weight and Inedible Percentage Assumptions for Fruit and Vegetable Count Data—continued

Food description NDB_No Amount Additional description Weight 

(g) Inedible description Inedible (%)

Vegetables and Vegetable Products—continued

Spinach, raw 11457 1 Bunch 340 Large stems and roots 28

Squash, summer, all 

varieties, raw

11641 1 Medium 196 Ends 5

Sweet potato, raw, 

unprepared

11507 1 Sweet potato, 5 ̋ long 130 Parings and trimmings 28

Tomatoes, red, ripe, 

raw, year round 

average

11529 1 Medium whole (2 3/5 ̋ 

diameter)

123 Core and stem ends 9

Fruits and Fruit Juices

Apples, raw, with skin 09003 1 Medium (3 ̋ diameter) 182 Core and stem 10

Apricots, raw 09021 1 Apricot 35 Pits 7

Avocados, raw, all 

commercial varieties

09037 1 Avocado, NS as to Florida 

or California

201 Seed and skin 26

Bananas, raw 09040 1 Medium (7 ̋ to 7 7/8 ̋ long) 

(assumed 7 bananas in a 

bunch)

118 Skin 36

Figs, raw 09089 1 Medium (21⁄4 ̋ diameter) 50 Stems 1

Grapefruit, raw, pink 

and red and white, all 

areas

09111 0.5 Medium (approx 4 ̋ 

diameter)

128 Peel, seeds, core, and 

membrane

50

Kiwifruit, (Chinese 

gooseberries), held in 

storage, raw

09405 1 Fruit without skin, large 91 Skin 14

Kiwifruit, (Chinese 

gooseberries), held in 

storage, raw

09405 1 Fruit without skin, medium 76 Skin 14

Kiwifruit, (Chinese 

gooseberries), held in 

storage, raw

09405 1 Average 83.5 Skin 14

Lemons, raw, without 

peel

09150 1 Fruit (2 1/8 ̋ diameter) 58 45% peel, 2% seeds 47

Lemons, raw, without 

peel

09150 1 Fruit (2 3/8 ̋ diameter) 84 45% peel, 2% seeds 47

Lemons, raw, without 

peel

09150 1 Average 71 45% peel, 2% seeds 47

Limes, raw 09159 1 Fruit (2 ̋ diameter) 67 Peel and seeds 16

Mangos, raw 09176 1 Fruit without refuse 207 Seeds and skin 31

Melons, cantaloupe, 

raw

09181 1 Melon, medium 

(about 5 ̋ diameter)

552 9% cavity contents, 1% 

cutting loss, 39% rind

49

Melons, honeydew, raw 09184 1 Melon (51⁄4 ̋ diameter) 1000 5% cavity contents, rind 

49%

54

Continued—
65

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table A-2

Weight and Inedible Percentage Assumptions for Fruit and Vegetable Count Data—continued

Food description NDB_No Amount Additional description Weight 

(g) Inedible description Inedible (%)

Fruits and Fruit Juices —continued

Melons, honeydew, raw 09184 1 Melon (6 ̋ to 7 ̋ diameter) 1280 5% cavity contents, rind 

49% 54

Melons, honeydew, raw 09184 1 Average 1140 5% cavity contents, rind 

49%

54

Oranges, raw, all 

commercial varieties

09200 1 Fruit (2 5/8 ̋ diameter) 131 Peel and seeds 27

Papayas, raw 09226 1 Medium 

(51/8 ̋ long x 3 ̋ diameter)

304 Seeds and skin 33

Peaches, raw 09236 1 Medium (2 5/8 ̋ diameter) 150 Pit 4

Pears, raw 09252 1 Medium 178 Stem, core and seeds 10

Pineapple, raw, all 

varieties

09266 1 Fruit 905 8% core, 16% crown, 

26% parings

49

Plums, raw 09279 1 Fruit (2 1/8 ̋ diameter) 66 Pits 6

Strawberries, raw 09316 1 Medium (11⁄4 ̋ diameter) 

(assumed 27 strawberries 

in a basket)

12 Caps and stems 6

Tangerines (mandarin 

oranges), raw

09218 1 Medium (21⁄2 ̋ diameter) 88 Peel and seeds 26

Watermelon, raw 09326 1 Melon (15 ̋ long x 71⁄2 ̋ 

diameter)

4518 Rind, seeds, and cutting 

loss

48

NS = not specified.

Source: U.S. Department of Agriculture, Agricultural Research Service. 2007. USDA National Nutrient Database for Standard Reference, 

Release 20. www.ars.usda.gov/ba/bhnrc/ndl.
66

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table A-3

Edible Solids Percentages for Canned Foods and Moisture Gain Percentages for Dry Foods

Direct measurements USDA/ARS (1975)

Food Beginning 

weight

Ending 

weight

% 

Solids

% 

Gain

%

Solids

1-oz dry, 

yield (g)

% 

Gain

Item 

no.

Item 

Description

Canned apples and 

applesauce 

417 309 74 87 52 Apples—Canned, 

contents of can: 

Sliced, unspecified

Canned apricots—

Syrup

432 245 57 59 74 Apricots: Canned, 

contents of can: 

Halves: All samples 

in syrup or water

Canned cherries—

Syrup

437 305 70 69 533 Cherries: Canned, 

contents of can: 

Sour, red pitted: All 

samples

Canned cherries—

Water

365 270 74

Average for 

canned cherries

72

Canned peaches—

Syrup

432 301 70 60 1666 Peaches: Canned, 

contents of can: All 

samples

Canned peaches—

Juice

417 282 68

Average for 

canned peaches

69

Canned pears—

Syrup

425 190 45 59 1735 Pears: Canned, contents of can (halves): 

All samples

Canned pears—

Juice

424 237 56

Average for 

canned pears

50

Canned pineapple—

Syrup

452 284 63 65 1843 Pineapple: Canned, 

contents of can: All 

samples, all styles

Canned pineapple—

Juice

564 374 66

Average for 

canned pineapple

65

Canned plums—

Syrup

440 282 64 56 1886 Plums: Canned, 

contents of can: All 

samples

Canned olives—Pits 361 218 48 63 1560 Olives: Green, contents of can, 

unspecified size: 

Plain: Unpitted

Canned olives—

Stuffed

266 165 62 64 1562 Olives: Green, 

contents of can, 

unspecified size: 

Plain: Stuffed

Average for 

canned olives

55 57

Continued—
67

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table A-3

Edible Solids Percentages for Canned Foods and Moisture Gain Percentages for Dry Foods—continued

Direct measurements USDA/ARS (1975)

Food Beginning 

weight

Ending 

weight

% 

Solids

% 

Gain

%

Solids

1-oz dry, 

yield (g)

% 

Gain

Item 

no.

Item 

Description

Canned asparagus 428 245 57 60 103 Asparagus: Canned, 

contents of can: All 

samples, cut spears, 

spears, or tips, all 

can sizes

Canned snap beans 

(green beans)

421 230 55 58 206 Beans: Snap, green, 

and wax: Canned, 

contents of can: All 

samples, including 

unspecified

Canned cabbage 

(sauerkraut)

435 273 63 88 2272 Sauerkraut: Canned, 

contents of can: All 

samples

Canned carrots 435 261 60 66 487 Carrots: Canned, 

contents of can: Wet 

pack: All samples

Canned chile 

peppers

216 97 45

Canned cucumbers 

(pickles—midgets)

409 253 62

Canned cucumbers 

(pickles—slices)

492 232 47

Canned cucumbers 

(pickles—baby dills)

744 396 53

Average for 

canned cucumbers

54

Canned green peas 421 264 63 64 1756 Peas, Green: 

Canned, contents of 

can: Wet pack: No. 

303

Canned mushrooms 383 206 54 58 1512 Mushrooms: Canned, 

contents of can, all 

samples

Canned potatoes 431 238 55 68 2091 Potato and Potato 

Products: Canned, 

contents of can: All 

sizes

Canned sweet corn 429 265 62 68 922 Corn: Canned, whole 

grain, contents of 

can: Wet pack: All 

samples

Canned sweet 

potatoes

455 269 59 65 2507 Sweet Potatoes: 

Canned, contents of 

can: Syrup pack: All 

samples

Canned tomatoes 412 295 72 66 2545 Tomatoes: Canned, 

contents of can: No. 

303

Continued—
68

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table A-3

Edible Solids Percentages for Canned Foods and Moisture Gain Percentages for Dry Foods—continued

Direct measurements USDA/ARS (1975)

Food Beginning 

weight

Ending 

weight

% 

Solids

% 

Gain

%

Solids

1-oz dry, 

yield (g)

% 

Gain

Item 

no.

Item 

Description

Canned zucchini 392 277 71 61 2453 Squash, Summer: 

Canned, yellow, cut, 

contents of can, all 

samples

Canned clams 185 63 34 45 880 Clams: Canned, 

minced or chopped, 

contents of can

Canned salmon 411 328 80 81 2242 Salmon: Canned: 

Contents of can: All 

samples

Canned tuna—Oil 164 132 80 82 2575 Tuna: Canned, 

contents of can: Solid 

pack, in oil

Canned tuna—

Water

169 135 80 79 2571 Tuna: Canned, contents of can: Chunks: 

In brine

Crabmeat 188 118 63 77 962 Crab: Meat, canned

Oysters 247 134 54 Not contained in 

“Food Yields” document (used direct 

measurement)

Salmon in pouch 199 197 99

Sardines—Oil 130 111 85 87 2258 Sardines: Canned: 

Atlantic

Sardines—

Water

115 86 75

Shrimp 202 129 64 Not contained in 

“Food Yields” document (used direct 

measurement)

Smoked salmon in 

pouch

98 87 89

Tuna in pouch 82 79 96 94 2573 Tuna: Canned, contents of can: Flakes, 

in oil

Barley products 210 618 294 107 377 56200400 Barley, cooked, fat 

not added in cooking

Dehydrated sweet 

potatoes 

98 373 381

Dehydrated white 

potatoes 

77 377 490 178 628 71501090 White potato, from 

dry, mashed, made 

with milk, no fat

Dehydrated white 

potatoes—Pouch

108 504 467

Continued—
69

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table A-3

Edible Solids Percentages for Canned Foods and Moisture Gain Percentages for Dry Foods—continued

Direct measurements USDA/ARS (1975)

Food Beginning 

weight

Ending 

weight

% 

Solids

% 

Gain

%

Solids

1-oz dry, 

yield (g)

% 

Gain

Item 

no.

Item 

Description

Dry edible beans:

Black 195 383 196 65 229 41102020 Black, brown, or 

Bayo beans, dry, 

cooked, fat not 

added in cooking

Lima 205 432 211 71 250 41103020 Lima beans, dry, 

cooked, fat not 

added in cooking

Navy 184 354 192 68 240 41101120 White beans, dry, 

cooked, fat not 

added in cooking

Chickpeas 190 395 208 60 212 41302020 Chickpeas, dry, 

cooked, fat not 

added in cooking

Kidney 185 388 210 68 240 41106020 Red kidney beans, 

dry, cooked, fat not 

added in cooking

Pinto 191 391 205 68 240 41104020 Pinto, calico, or red 

Mexican beans, 

dry, cooked, fat not 

added in cooking

Average for dry 

edible beans

204 235

Grits 47 184 391 186 656 56201010 Grits, cooked, corn 

or hominy, regular, fat 

not added in cooking

Oat products 45 187 416 164 578 56203000 Oatmeal, cooked, NS 

as to regular, quick or 

instant, fat not added 

in cooking

Rice 210 1,080 514 87 307 56205010 Rice, white, cooked, 

regular, fat not added 

in cooking

NS = not specified.

Source: U.S. Department of Agriculture, Agricultural Research Service. 1975. Food Yields Summarized by Different Stages of Preparation Handbook. Agriculture Handbook No. 102. 
70

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Appendix B:

Detailed Food Loss Calculations

The tables in this appendix provide details of the food loss calculations and 

proposed food loss estimates for each food. The fields in each table are as 

follows:

•	 ERS Availability: Total pounds of each food available for consumption 

at the consumer level in 2004. Average values were obtained from the 

column “Loss at consumer level: Other (cooking loss and uneaten food)” 

of the Loss-Adjusted Food Availability tables (USDA/ERS, 2008) and 

multiplied by the 2004 U.S. population (292,303,000).

•	 NHANES Consumption

– Total: Total annual pounds of each food consumed by individuals 

at home or at away-from-home locations in 2003–2004. Average 

weighted daily consumption was obtained from NHANES and multiplied by the 2004 population and 365 days.

– Store-only: Total annual pounds of each food consumed by individuals from food purchased in stores (at-home consumption) in 2003–

2004.

•	 Nielsen Purchases

– All Purchases: Total pounds of each food purchased in 2004 including UPC and random-weight items.

– Random Weight Only: Total pounds of each random-weight item 

purchased in 2004.

•	 Nielsen + Perishables

– Perishables Group—Non-UPC: Total pounds of each randomweight food purchased in 2004, if available from Perishables Group, 

Inc.

– All Purchases—Nielsen UPC + Perishables Non-UPC: Total 

pounds of each food purchased in 2004 using Nielsen UPC data and 

Perishables non-UPC data. If estimates are not available from Perishables Group, Inc., the column was left blank.

•	 Previous Consumer Loss: Consumer-level loss conversion factor estimate available in the Loss-Adjusted Food Availability tables in early 

2008 (USDA/ERS, 2008).

•	 Expert Average Consumer Loss: Average of six experts’ (five external 

experts and one ERS expert) consumer-level loss conversion factor 

subjective estimates.

•	 Consumer Loss (Unadjusted for Ingredient Use): Calculated 

consumer-level food loss conversion factors, without adjustments for use 

of the food as an ingredient, using the following:
71

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

– Nielsen Data: Percentage loss when comparing Nielsen UPC and 

non-UPC estimates to NHANES store-only estimates.

– Nielsen + Perishables Data: Percentage loss when comparing Nielsen UPC and Perishables non-UPC (when available) to 

NHANES store-only estimates.

– Availability Data: Percentage loss when comparing ERS availability 

data to NHANES total estimates.

•	 Ingredient Use: Expert Average: Average of six experts’ (five external 

experts and one ERS expert) ingredient use percentage subjective 

estimates.

•	 Consumer Loss (Adjusted for Ingredient Use):1 Calculated consumerlevel food loss conversion factors, subtracting adjustments for use of the 

food as an ingredient, using

– Nielsen Data: Percentage loss when comparing Nielsen UPC and 

non-UPC estimates to NHANES store-only estimates (ingredient 

percentage subtracted);

– Nielsen + Perishables Data: Percentage loss when comparing Nielsen UPC and Perishables non-UPC (when available) to 

NHANES store-only estimates (ingredient percentage subtracted); 

and

– Availability Data: Percentage loss when comparing ERS availability 

data to NHANES total estimates (ingredient percentage subtracted).

•	 Proposed Consumer Loss Estimate: Proposed estimate to be used as 

the revised consumer-level food loss conversion factor.

•	 Explanation: Source of the proposed estimate or explanation for missing 

values.

1In making the adjustment for 

ingredient use, we are assuming that 

consumer-level food loss for each food 

is similar regardless of whether the 

food is consumed directly or consumed 

as an ingredient of another food.
72

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table B-1

Food Purchases, Consumption, and Consumer Loss Estimates: Meat, Poultry, Fish, Eggs, and Nuts

NHANES consumption Nielsen purchases Nielsen + Perishables

Category Food

ERS 

availabilitya

Total 

(lbs)

Store only

(lbs)

All purchases

(lbs)

Randomweight only 

(lbs)

Perishables 

Group: NonUPC (lbs)

All purchases: 

Nielsen UPC 

+ Perishables 

non-UPC (lbs)

Meat, poultry, 

and fish

Beef 17,099,725,500 5,151,042,898 2,840,731,654 3,558,386,959 3,069,885,425 2,491,657,873 2,980,159,407

Meat, poultry, 

and fish

Veal 116,921,200 89,442,139 53,748,878 18,388,988 18,150,438 15,102,272 15,340,821

Meat, poultry, 

and fish

Pork 13,007,483,500 1,880,355,501 1,349,752,566 1,905,387,472 1,860,337,502 969,439,793 1,014,489,764

Meat, poultry, 

and fish

Lamb 233,842,400 117,626,006 52,513,542 39,615,056 39,608,731 34,268,281 34,274,615

Meat, poultry, 

and fish

Chicken 16,105,895,300 4,512,754,037 2,344,695,077 2,748,180,707 2,077,261,524 2,102,899,441 2,773,818,624

Meat, poultry, 

and fish

Turkey 3,653,787,500 678,946,988 490,114,867 841,938,540 724,389,447 641,777,488 759,326,581

Meat, poultry, 

and fish

Fresh and 

frozen fish

1,490,745,300 1,921,188,390 1,056,529,846 437,326,432 149,665,050 211,845,974 499,507,356

Meat, poultry, 

and fish

Fresh and 

frozen 

shellfish

1,695,357,400 596,108,910 226,507,282 222,953,799 55,329,928 211,350,220 378,974,091

Meat, poultry, 

and fish

Canned 

salmon

87,690,900 70,922,683 67,358,979 81,556,870

Meat, poultry, 

and fish

Canned 

sardinesb

29,230,300 12,005,716 12,005,716 18,876,934

Meat, poultry, 

and fish

Canned 

tuna

906,139,300 959,062,962 795,661,781 425,009,882

Meat, poultry, 

and fish

Canned 

shellfishb

116,921,200 2,591,336 2,591,336 18,928,152

Meat, poultry, 

and fish

Other 

canned 

fishb

116,921,200 9,371,572 9,155,176 19,051,627

Meat, poultry, 

and fish

Cured fish 87,690,900 21,383,403 16,213,668

Eggs Eggs 8,568,131,688 4,456,117,455 3,278,097,296 2,733,603,127

Nuts Peanuts, 

snack 

peanuts, 

other 

peanuts

2,250,733,100 393,270,826 288,997,598 337,869,743 32,049,359

Nuts Peanut 

butter

876,909,000 591,376,670 570,875,078 676,671,639

Nuts Almonds 233,842,400 68,929,022 65,981,574 69,045,570 7,725,652

Nuts Hazelnuts 

(filberts)b

29,230,300 2,285,560 0 4,995,791 4,351,336

Nuts Pecans 146,151,500 20,007,410 13,965,742 50,840,798 13,523,607

Nuts Walnuts 146,151,500 45,614,995 42,534,181 66,844,110 7,793,549

Nuts Macadamia nutsb29,230,300 7,200,325 7,200,325 4,578,727 551,573

Nuts Pistachio 

nuts

58,460,600 25,957,110 24,881,719 63,890,630 10,244,546

Nuts Other tree 

nuts

292,303,000 212,319,970 190,146,018 248,768,298 7,429,995

Nuts Coconutb 146,151,500 1,598,269 1,497,363 18,699,543

Continued—
73

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table B-1

Food Purchases, Consumption, and Consumer Loss Estimates: Meat, Poultry, Fish, Eggs, and Nuts—

continued

Consumer loss 

(unadjusted for ingredient 

percentage)

Consumer loss (adjusted 

for ingredient percentage)

Category Food

Previous 

consumer loss 

estimate 

Expert 

average 

consumer loss 

Nielsen 

data

Nielsen 

+ 

Perishables 

data

Availability 

data

Ingredient 

use: 

Expert 

average

Nielsen 

data

Nielsen 

+ 

Perishables 

data

Availability 

data

Proposed 

consumer 

loss 

estimate

Explanation

Meat, 

poultry, 

and fish

Beef 32% 12% 20% 5% 70% 20% 0% −15% 50% 20% Estimate based on 

Nielsen (assumed 

use as an ingredient reflected in 

NHANES)

Meat, 

poultry, 

and fish

Veal 35% 13% −192% −250% 24% 15% −207% −265% 9% 20% Assumed same 

value as beef

Meat, 

poultry, 

and fish

Pork 39% 21% 29% −33% 86% 20% 9% −53% 66% 29% Estimate based on 

Nielsen (assume 

duse as an ingredient reflected in 

NHANES)

Meat, 

poultry, 

and fish

Lamb 36% 26% −33% −53% 50% 10% −43% −63% 40% 20% Assumed same 

value as beef

Meat, 

poultry, 

and fish

Chicken 40% 16% 15% 15% 72% 25% −14% −10% 47% 15% Estimate based 

on Nielsen or 

Nielsen+Perishables 

(assumed use as an 

ingredient reflected 

in NHANES)

Meat, 

poultry, 

and fish

Turkey 32% 28% 42% 35% 81% 20% 22% 15% 61% 35% Estimate based on 

Nielsen+Perishables 

(assumed use as an 

ingredient reflected 

in NHANES)

Meat, 

poultry, 

and fish

Fresh and 

frozen 

fish

33% 33% −142% −112% −29% 20% −162% −132% −49% 40% Assumed same 

value as fresh and 

frozen shellfish

Meat, 

poultry, 

and fish

Fresh and 

frozen 

shellfish

33% 35% −2% 40% 65% 30% −32% 10% 35% 40% Estimate based on 

Nielsen+Perishables 

(assumed use as an 

ingredient reflected 

in NHANES)

Meat, 

poultry, 

and fish

Canned 

salmon

10% 12% 17% 19% 45% −28% −26% 17% Estimate based on 

Nielsen (assumed 

used as an ingredient reflected in 

NHANES)

Meat, 

poultry, 

and fish

Canned 

sardinesb

10% 9% 36% 59% 10% 26% 49% 36% Estimate based on 

Nielsen (assumed 

used as an ingredient reflected in 

NHANES)

Meat, 

poultry, 

and fish

Canned 

tuna

10% 9% −87% −6% 45% −132% −51% 17% Assumed same 

value as canned 

salmon

Continued—
74

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table B-1

Food Purchases, Consumption, and Consumer Loss Estimates: Meat, Poultry, Fish, Eggs, and Nuts—

continued

Consumer loss 

(unadjusted for ingredient 

percentage)

Consumer loss (adjusted 

for ingredient percentage)

Category Food

Previous 

consumer loss 

estimate 

Expert 

average 

consumer loss 

Nielsen 

data

Nielsen 

+ 

Perishables 

data

Availability 

data

Ingredient 

use: 

expert 

average

Nielsen 

data

Nielsen 

+ 

Perishables 

data

Availability 

data

Proposed 

consumer 

loss 

estimate

Explanation

Meat, 

poultry, 

and fish

Canned 

shellfishb

10% 14% 86% 98% 10% 76% 88% 17% Assumed same 

value as canned 

salmon

Meat, 

poultry, 

and fish

Other 

canned 

fishb

10% 14% 52% 92% 45% 7% 47% 17% Assumed same 

value as canned 

salmon

Meat, 

poultry, 

and fish

Cured 

fish

10% 13% NC 76% 15% NC 61% 17% Assumed same 

value as canned 

salmon

Eggs Eggs 15% 12% −20% 48% 25% −45% 23% 23% Estimate based on 

Availability adjusted 

for ingredient use

Nuts Peanuts, 

snack 

peanuts, 

other 

peanuts

10% 8% 14% 83% 10% 4% 73% 4% Estimate based on 

Nielsen data

Nuts Peanut 

butter

10% 12% 16% 33% 10% 6% 23% 14% Estimate based on 

average of Nielsen 

and Availability data

Nuts Almonds 10% 9% 4% 71% 50% −46% 21% 21% Estimate based on 

Availability data

Nuts Hazelnuts 

(filberts)b

10% 11% NC 92% 72% NC 20% 20% Estimate based on 

Availability data

Nuts Pecans 10% 9% 73% 86% 73% 0% 14% 14% Estimate based on 

Availability data

Nuts Walnuts 10% 9% 36% 69% 69% −32% 0% 18% Estimate based on 

average of other 

tree nuts (almonds, 

hazelnuts, and 

pecans)

Nuts Macadamia nutsb10% 9% −57% 75% 68% −125% 8% 8% Estimate based on 

Availability data

Nuts Pistachio 

nuts

10% 10% 61% 56% 43% 19% 13% 16% Estimate based on 

average of Nielsen 

and Availability data

Nuts Other 

tree nuts

10% 12% 24% 27% 53% −29% −25% 18% Estimate based on 

average of tree nuts 

(almonds, hazelnuts, 

and pecans)

Nuts Coconutb 10% 13% 92% 99% 85% 7% 14% 10% Estimate based on 

average of Nielsen 

and Availability data

Note: Consumer-level loss estimates should be positive. Negative consumer-level loss estimates indicate reported consumption exceeds food purchases.

NC = not calculated.

TBD = to be decided; reasonable estimate might be calculable if ingredient percentage is revised.

a“ERS Availability” refers to the volume of each food available at the consumer level in the Food Availability data series. The definition of these foods may differ 

somewhat from how we defined them when obtaining consumption data from NHANES.

bFood has 10 or fewer consumption observations in NHANES; thus, the total consumption estimate may not be reliable.
75

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table B-2

Food Purchases, Consumption, and Consumer Loss Estimates: Dairy Products

NHANES consumption Nielsen purchases Nielsen + Perishables

Category Food

ERS 

availabilitya

Total 

(lbs)

Store only

(lbs)

All purchases

(lbs)

Randomweight only 

(lbs)

Perishables 

Group: nonUPC (lbs)

All purchases: 

Nielsen UPC 

+ Perishables 

non-UPC (lbs)

Dairy—

Beverages

Plain 

whole milk

15,375,137,800 13,855,521,430 12,605,738,409 6,316,577,578 3,069,885,425 2,491,657,873 2,980,159,407

Dairy—

Beverages

Plain 1% 

and 2% 

milk

20,695,052,400 21,708,995,268 20,484,626,252 15,162,626,951 18,150,438 15,102,272 15,340,821

Dairy—

Beverages

Skim milk 6,839,890,200 6,672,780,489 6,484,848,527 6,155,687,797 1,860,337,502 969,439,793 1,014,489,764

Dairy—

Beverages

Whole 

flavored 

milk

759,987,800 1,632,080,243 748,523,315 166,717,539 39,608,731 34,268,281 34,274,615

Dairy—

Beverages

Low-fat 

flavored 

milk

3,010,720,900 1,646,289,194 720,388,372 648,883,677 2,077,261,524 2,102,899,441 2,773,818,624

Dairy—

Beverages

Buttermilk 467,684,800 120,280,615 98,971,538 202,653,811 724,389,447 641,777,488 759,326,581

Dairy—

Beverages

Light 

cream, 

heavy 

cream, 

half & half

2,016,890,700 594,545,923 449,178,249 533,050,634 149,665,050 211,845,974 499,507,356

Dairy—

Beverages

Eggnog 29,230,300 93,232,353 83,427,050 171,276,676

Dairy—

Other

Sour 

cream

1,081,521,100 276,990,470 143,582,176 383,172,700

Dairy—

Other

Cream 

cheese

584,606,000 153,022,194 124,761,883 292,626,749 1,262,353

Dairy—

Other

Cheddar 

cheese

2,806,108,800 875,907,112 603,385,679 422,110,568 62,264,074

Dairy—

Other

Other 

American 

cheese

730,757,500 149,021,230 140,478,312 160,513,973 44,880,389

Dairy—

Other

Provolone 

cheese

277,687,850 92,282,801 30,474,131 49,123,091 30,979,964

Dairy—

Other

Parmesan and 

Romano 

cheese

198,766,040 50,116,492 34,078,292 84,355,790 12,770,892

Dairy—

Other

Mozzarella cheese2,727,186,990 234,487,337 178,029,046 194,297,701 13,455,390

Dairy—

Other

Ricotta 

cheeseb

227,996,340 18,314,351 18,314,351 83,195,210 831,345

Dairy—

Other

Other 

Italian 

cheeseb

108,152,110

Dairy—

Other

Swiss 

cheese

3,799,939,000 262,519,962 195,951,533 114,940,444 63,506,395

Dairy—

Other

Brick 

cheese

8,769,090 3,466,327 3,466,327 247,013

Dairy—

Other

Muenster 

cheese

67,229,690 23,218,766 22,899,818 30,757,518 18,680,763

Dairy—

Other

Blue 

cheese

55,537,570 12,896,701 9,233,502 3,377,589 2,352,092

Continued—
76

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table B-2

Food Purchases, Consumption, and Consumer Loss Estimates: Dairy Products

NHANES consumption Nielsen purchases Nielsen + Perishables

Category Food

ERS 

availabilitya

Total 

(lbs)

Store only

(lbs)

All purchases

(lbs)

Randomweight only 

(lbs)

Perishables 

Group: 

non-UPC 

(lbs)

All purchases: Nielsen 

UPC + 

Perishables 

non-UPC 

(lbs)

Dairy—

Other

Other miscellaneous cheeseb371,224,810 10,011,350 8,222,088 9,843,604 6,713,728

Dairy—

Other

Processed 

cheese

1,134,135,640 929,762,071 733,399,387 793,224,636 87,417,481

Dairy—

Other

Processed 

cheese foods 

and spreads

976,292,020 152,424,049 124,185,142 95,664,454

Dairy—

Other

Regular cottage 

cheese

689,835,080 248,494,241 185,962,999 262,194,078

Dairy—

Other

Low-fat cottage 

cheese

347,840,570 288,989,365 288,989,365 304,405,574

Dairy—

Other

Regular ice 

cream

3,858,399,600 2,903,504,460 2,403,740,705 2,250,676,568

Dairy—

Other

Low-fat ice 

cream (ice milk)

1,870,739,200 628,540,715 275,494,989 428,747,273

Dairy—

Other

Frozen yogurt 

and other 

miscellaneous 

frozen products

789,218,100 351,439,695 255,817,112 408,168,371

Dairy—

Other

Refrigerated 

yogurt

2,367,654,300 1,722,897,199 1,573,497,042 1,698,933,938

Dairy—

Other

Total evaporated 

and condensed 

canned whole 

and skim milk

1,227,672,600 57,815,702 57,760,662 307,410,859

Dairy—

Other

Dry whole & 

nonfat milk

1,286,133,200 156,472,291 98,547,921 341,822,030

Dairy—

Other

Dry buttermilkb 58,460,600 8,276,981

Continued—
77

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table B-2

Food Purchases, Consumption, and Consumer Loss Estimates: Dairy Products—continued

Consumer loss 

(unadjusted for ingredient 

percentage)

Consumer loss 

(adjusted for ingredient 

percentage)

Category Food

Previous 

consumer loss 

estimate 

Expert 

average 

consumer 

loss 

Nielsen 

data

Nielsen 

+ 

Perishables 

data

Availability 

data

Ingredient 

use: 

expert 

average

Nielsen 

data

Nielsen 

+ 

Perishables 

data

Availability 

data

Proposed 

consumer 

loss 

estimate

Explanation

Dairy—

Beverages

Plain 

whole 

milk

20% 18% −100% 10% 23% −123% −13% TBD Revised ingredient percentage to 

apply to Availability-based estimate

Dairy—

Beverages

Plain 1% 

and 2% 

milk

20% 17% −35% −5% 22% −57% −27% NA

Dairy—

Beverages

Skim milk 20% 14% −5% 2% 22% −27% −19% NA

Dairy—

Beverages

Whole 

flavored 

milk

20% 14% −349% −115% 0% −349% −115% 45% Used estimate for 

whole flavored 

milk

Dairy—

Beverages

Low-fat 

flavored 

milk

20% 14% −11% 45% 0% −11% 45% 45% Estimate based 

on Availability 

data (no ingredient use assumed)

Dairy—

Beverages

Buttermilk 20% 20% 51% 74% 57% −6% 18% 18% Estimate based 

on Availability 

data

Dairy—

Beverages

Light 

cream, 

heavy 

cream, 

half & 

half

20% 19% 16% 71% 58% −43% 12% 12% Estimate based 

on Availability 

data

Dairy—

Beverages

Eggnog 20% 23% 51% −219% 0% 51% −219% 51% Estimate based 

on Nielsen data

Dairy—

Other

Sour 

cream

20% 19% 63% 74% 66% −4% 8% 8% Estimate based 

on Availability 

data (could be 

based on Nielsen)

Dairy—

Other

Cream 

cheese

20% 19% 57% 74% 61% −4% 13% 13% Estimate based 

on Availability 

data

Dairy—

Other

Cheddar 

cheese

13% 12% −43% 69% 58% −100% 11% 11% Estimate based 

on Availability 

data 

Dairy—

Other

Other 

American 

cheese

13% 12% 12% 80% 51% −39% 28% 28% Estimate based 

on Availability 

data

Dairy—

Other

Provolone 

cheese

13% 12% 38% 67% 53% −15% 14% 14% Estimate based 

on Availability 

data

Dairy—

Other

Parmesan and 

Romano 

cheese

13% 12% 60% 75% 67% −7% 8% 8% Estimate based 

on Availability 

data

Continued—
78

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table B-2

Food Purchases, Consumption, and Consumer Loss Estimates: Dairy Products—continued

Consumer loss 

(unadjusted for ingredient 

percentage)

Consumer loss 

(adjusted for ingredient 

percentage)

Category Food

Previous 

consumer loss 

estimate 

Expert 

average 

consumer 

loss 

Nielsen 

data

Nielsen 

+ 

Perishables 

data

Availability 

data

Ingredient 

use: 

expert 

average

Nielsen 

data

Nielsen 

+ 

Perishables 

data

Availability 

data

Proposed 

consumer 

loss 

estimate

Explanation

Dairy—

Other

Mozzarella cheese13% 11% 8% 91% 60% −52% 31% 31% Estimate based 

on Availability 

data

Dairy—

Other

Ricotta 

cheeseb

13% 12% 78% 92% 80% −2% 12% 12% Estimate based 

on Availability 

data

Dairy—

Other

Other 

Italian 

cheeseb

13% 9% NC NC 20% NC NC 16% Average of 

provolone, 

parmesan and 

Romano, mozzarella, and 

ricotta

Dairy—

Other

Swiss 

cheese

13% 12% −70% 93% 43% −114% 50% 50% Estimate based 

on Availability 

data

Dairy—

Other

Brick 

cheese

13% 12% −1303% 60% 20% −1,323% 40% 40% Estimate based 

on Availability 

data

Dairy—

Other

Muenster 

cheese

13% 12% 26% 65% 30% −4% 35% 35% Estimate based 

on Availability 

data

Dairy—

Other

Blue 

cheese

13% 12% −173% 77% 33% −207% 43% 43% Estimate based 

on Availability 

data

Dairy—

Other

Other miscellaneous 

cheeseb

13% 12% 16% 97% 20% −4% 77% 42% Average of 

Swiss, brick, 

Muenster, and 

blue

Dairy—

Other

Processed 

cheese

13% 12% 8% 18% 10% −2% 8% 8% Estimate based 

on Availability 

data

Dairy—

Other

Processed 

cheese 

foods and 

spreads

13% 12% −30% 84% 25% −55% 59% 8% Assumed same 

value as processed cheese

Dairy—

Other

Regular 

cottage 

cheese

20% 18% 29% 64% 33% −3% 31% 31% Estimate based 

on Availability 

data

Dairy—

Other

Low-fat 

cottage 

cheese

20% 17% 5% 17% 13% −8% 4% 4% Estimate based 

on Availability 

data

Dairy—

Other

Regular 

ice cream

20% 13% −7% 25% 1% −7% 24% 24% Estimate based 

on Availability 

data

Dairy—

Other

Low-fat ice 

cream 

(ice milk)

20% 16% 36% 66% 1% 35% 66% 24% Assumed same 

value as regular ice cream

Continued—
79

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table B-2

Food Purchases, Consumption, and Consumer Loss Estimates: Dairy Products—continued

Consumer loss 

(unadjusted for ingredient 

percentage)

Consumer loss 

(adjusted for ingredient 

percentage)

Category Food

Previous 

consumer 

loss 

estimate 

Expert 

average 

consumer 

loss 

Nielsen 

data

Nielsen 

+ 

Perishables 

data

Availability 

data

Ingredient 

use: 

expert 

average

Nielsen 

data

Nielsen 

+ 

Perishables 

data

Availability 

data

Proposed 

consumer 

loss 

estimate

Explanation

Dairy—

Other

Frozen 

yogurt and 

other miscellaneous 

frozen 

products

20% 15% 37% 55% 4% 33% 51% 33% Estimate 

based on 

Nielsen data

Dairy—

Other

Refrigerated yogurt20% 13% 7% 27% 7% 1% 21% 21% Estimate 

based on 

Availability 

data

Dairy—

Other

Total evaporated and 

condensed 

canned 

whole and 

skim milk

20% 15% 81% 95% 81% 1% 15% 15% Estimate 

based on 

Availability 

data

Dairy—

Other

Dry whole 

& nonfat 

milk

1% 17% 71% 88% 30% 41% 58% 41% Estimate 

based on 

Nielsen data

Dairy—

Other

Dry 

buttermilkb

1% 9% NC NC 95% NC NC 41% Assumed 

same value 

as dry whole 

and nonfat 

milk

Note: Consumer-level loss estimates should be positive. Negative consumer-level loss estimates indicate reported consumption exceeds food 

purchases.

NA = not available.

NC = not calculated.

TBD = to be decided; reasonable estimate might be calculable if ingredient percentage is revised.

a“ERS Availability” refers to the volume of each food available at the consumer level in the Food Availability data series. The definition of these 

foods may differ somewhat from how we defined them when obtaining consumption data from NHANES.

bFood has 10 or fewer consumption observations in NHANES; thus, the total consumption estimate may not be reliable.
Table B-3

Food Purchases, Consumption, and Consumer Loss Estimates: Fats and Oils—continued

Consumer loss 

(unadjusted for ingredient 

percentage)

Consumer loss 

(adjusted for ingredient 

percentage)

Category Food

Previous 

consumer loss 

estimate 

Expert 

average 

consumer 

loss 

Nielsen 

data

Nielsen 

+ 

Perishables 

data

Availability 

data

Ingredient use: 

expert 

average

Nielsen 

data

Nielsen 

+ 

Perishables 

data

Availability 

data

Proposed 

consumer 

loss 

estimate

Explanation

Fats and 

oils

Butter 15% 10% 55% 75% 40% 15% 35% 35% Estimate based 

on Availability data (could 

be based on 

Nielsen)

Fats and 

oils

Margarine 15% 11% 75% 75% 40% 35% 35% 35% Estimate based 

on Nielsen and 

Availability data

Fats and 

oils

Lard 0% 25% NC NC 100% NC NC 35% Assumed same 

value as 

margarine

Fats and 

oils

Edible beef 

tallow

0% 25% NC NC 100% NC NC 35% Assumed same 

value as 

margarine

Fats and 

oils

Shortening 15% 15% NC NC 100% NC NC 35% Assumed same 

value as 

margarine

Fats and 

oils

Salad and 

cooking 

oils

20% 19% 41% 78% 63% −22% 15% 15% Estimate based 

on Availability 

data

Fats and 

oils

Other 

edible fats 

and oils

0% 20% NC 75% 73% NC 1% 25% Average of butter/margarine 

and salad and 

cooking oils

Note: Consumer-level loss estimates should be positive. Negative consumer-level loss estimates indicate reported consumption exceeds food 

purchases.

NC = not calculated.

a“ERS Availability” refers to the volume of each food available at the consumer level in the Food Availability data series. The definition of these 

foods may differ somewhat from how we defined them when obtaining consumption data from NHANES.

Table B-3

Food Purchases, Consumption, and Consumer Loss Estimates: Fats and Oils

Category Food ERS 

availabilitya

NHANES consumption Nielsen purchases Nielsen + Perishables

Total 

(lbs)

Store only

(lbs)

All purchases

(lbs)

Randomweight only 

(lbs)

Perishables 

Group: nonUPC (lbs)

All purchases: 

Nielsen UPC 

+ Perishables 

non-UPC (lbs)

Fats and oils Butter 1,227,672,600 305,720,265 224,299,108 494,907,161

Fats and oils Margarine 1,432,284,700 362,886,075 315,500,636 1,258,601,500

Fats and oils Lard 116,921,200

Fats and oils Edible beef 

tallow 578,759,940

Fats and oils Shortening 7,512,187,100

Fats and oils Salad and 

cooking oils 9,207,544,500 2,049,615,355 1,074,474,443 1,824,782,952

Fats and oils Other edible 

fats and oils 409,224,200 103,886,539 58,565,324

Continued—

80

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA
81

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table B-4

Food Purchases, Consumption, and Consumer Loss Estimates: Fruits

Category

Food ERS 

availabilitya

NHANES consumption Nielsen purchases Nielsen + Perishables

Total 

(lbs)

Store only

(lbs)

All purchases

(lbs)

Randomweight only 

(lbs)

Perishables 

Group: nonUPC (lbs)

All purchases: 

Nielsen UPC 

+ Perishables 

non-UPC (lbs)

Fruits—

fresh

Fresh 

oranges 1,963,106,948 1,222,015,950 1,032,993,307 770,682,897 347,750,852

Fruits Fresh 

tangerines 497,499,706 95,932,593 81,864,769 176,592,455 56,589,141

Fruits Fresh 

grapefruit 511,530,250 225,765,893 205,416,954 158,044,581 77,452,533

Fruits Fresh 

lemons 402,793,534 35,393,836 26,339,801 79,793,295 61,878,350

Fruits Fresh limesb 392,855,232 5,474,806 4,421,995 62,387,114 54,761,974

Fruits Fresh apples 4,329,592,036 3,213,079,388 2,842,975,346 1,629,372,912 1,008,355,474 1,120,992,389 1,742,009,828

Fruits Fresh 

apricotsb 27,184,179 22,533,828 22,533,828 13,845,048 13,392,044

Fruits Fresh 

avocados 577,590,728 169,181,057 119,493,123 165,985,459 153,504,763 37,754,601 50,235,298

Fruits Fresh 

bananas 4,312,930,765 3,475,879,017 3,287,736,304 2,152,293,657 2,045,381,639 2,332,122,851 2,439,034,869

Fruits Fresh 

blueberries 114,582,776 84,845,617 76,922,249 116,915,076 11,932,307

Fruits Fresh 

cantaloupe 1,207,503,693 665,932,916 469,233,878 282,309,408 275,645,656

Fruits Fresh 

cherries 210,458,160 3,373,418 1,956,498 131,627,918 108,172,765 74,701,131 98,156,284

Fruits Fresh 

cranberriesb 27,768,785 338,000 241,328 16,111,010 1,291,015

Fruits Fresh grapes 1,767,848,544 1,165,449,932 1,035,772,108 955,064,534 862,909,348 589,069,468 681,224,654

Fruits Fresh 

honeydew 242,026,884 66,742,016 15,160,386 37,597,678 35,590,356

Fruits Fresh kiwi 100,552,232 54,531,195 47,232,316 36,669,728 33,900,915

Fruits Fresh 

mangoes 342,871,419 228,941,497 208,200,459 68,263,040 59,059,406

Fruits Fresh 

peaches 1,092,628,614 469,887,063 447,333,713 307,636,161 298,671,292

Fruits Fresh pears 672,296,900 640,785,877 569,646,798 188,781,986 161,407,516 184,955,059 212,329,529

Fruits Fresh 

pineapple 562,390,972 138,936,964 73,371,716 153,595,450 30,812,764 222,281,773 345,064,459

Fruits Fresh 

papayab 176,258,709 16,786,410 14,827,325 24,690,845 24,163,692

Fruits Fresh plums 247,288,338 162,107,512 158,692,697 113,229,817 100,607,598

Fruits Fresh 

strawberries 1,208,965,208 667,834,374 562,781,651 532,226,307 106,111,973

Fruits Fresh 

watermelon 1,565,574,868 1,320,088,742 1,026,792,871 738,047,693 530,043,107

Fruits—

canned

Canned 

apples and 

applesauce 993,830,200 499,420,344 331,928,583 498,482,916

Fruits Canned 

apricotsb 29,230,300 16,173,917 11,969,258 10,871,094

Fruits Canned 

cherriesb 29,230,300 2,602,861 2,318,254 21,139,596

Continued—
82

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table B-4

Food Purchases, Consumption, and Consumer Loss Estimates: Fruits—continued

Category

Food ERS 

availabilitya

NHANES consumption Nielsen purchases Nielsen + Perishables

Total 

(lbs)

Store only

(lbs)

All purchases

(lbs)

Randomweight only 

(lbs)

Perishables 

Group: nonUPC (lbs)

All purchases: 

Nielsen UPC 

+ Perishables 

non-UPC (lbs)

Fruits Canned 

peaches 292,303,000 350,002,311 262,886,658 219,927,529

Fruits Canned 

pears 993,830,200 116,114,531 76,868,620 78,206,618

Fruits Canned 

pineapple 409,224,200 207,571,736 166,251,486 223,074,295

Fruits Canned 

plumsb 818,448,400 13,737,821 1,155,599 2,064,557

Fruits Canned 

olives 643,066,600 93,469,918 52,644,320 80,821,972

Fruits—

frozen

Frozen 

blackberries 29,230,300 2,261,098 2,261,098 4,589,555

Fruits Frozen 

blueberriesb 58,460,600 14,701,957 13,997,025 23,748,864

Fruits Frozen 

cherriesb 3,142,092

Fruits Frozen 

raspberriesb 58,460,600 1,719,168 1,719,168 8,281,772

Fruits Frozen 

strawberriesb 467,684,800 52,269,862 49,752,383 66,882,421

Fruits Other frozen 

berriesb 8,184,484 487,125 487,125 59,961

Fruits Frozen 

applesb 146,151,500 2,534,306

Fruits Frozen 

apricotsb 11,692,120

Fruits Frozen 

peachesb 116,921,200 19,755,733 4,611,341 6,924,664

Fruits Other frozen 

fruitb

Fruits Dried 

applesb 23,384,240 2,431,029

Fruits Dried 

apricotsb 26,307,270 279,432 279,432 17,757,489

Fruits Dried datesb 35,076,360 5,022,022 3,985,676 9,542,288

Fruits Dried figsb 35,076,360 2,599,160

Fruits Dried 

peachesb 11,692,120 561,225

Fruits Dried pearsb 1,169,212 1,924,979 1,924,979 23,805

Fruits Dried plums 99,383,020 30,108,376 25,108,934 48,795,230

Fruits Raisins 409,224,200 61,982,962 52,196,711 123,816,423

Fruits—

Juices

Grapefruit 

juice 509,347,976 599,331,439 568,152,023 276,387,729

Fruits—

Juices Lemon juice 251,580,782 20,462,076 11,737,339 94,731,845

Fruits—

Juices Lime juice 30,437,150 11,818,487 5,966,400 8,272,716

Continued—
83

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table B-4

Food Purchases, Consumption, and Consumer Loss Estimates: Fruits—continued

Category

Food ERS 

availabilitya

NHANES consumption Nielsen purchases Nielsen + Perishables

Total 

(lbs)

Store only

(lbs)

All purchases

(lbs)

Randomweight only 

(lbs)

Perishables 

Group: nonUPC (lbs)

All purchases: 

Nielsen UPC 

+ Perishables 

non-UPC (lbs)

Fruits—

Juices

Orange juice

8,985,764,397 12,733,302,026 10,986,041,987 6,470,320,254

Fruits—

Juices

Apple juice

4,928,508,761 5,023,276,999 4,231,237,843 2,051,347,616

Fruits—

Juices

Cranberry 

juiceb 521,720,802 2,876,531 2,876,531 178,936,640

Fruits—

Juices

Grape juice

1,043,441,603 25,184,202 22,742,226 801,246,906

Fruits—

Juices

Pineapple 

juice 773,301,584 173,356,667 163,380,482 143,933,120

Fruits—

Juices

Prune juice

257,767,195 78,135,004 78,135,004 114,617,417

Continued—

Table B-4

Food Purchases, Consumption, and Consumer Loss Estimates: Fruits—continued

Consumer loss 

(unadjusted for ingredient 

percentage)

Consumer loss 

(adjusted for ingredient 

percentage)

Category Food

Previous 

consumer loss 

estimate 

Expert 

average 

consumer 

loss 

Nielsen 

data

Nielsen 

+ 

Perishables 

data

Availability 

data

Ingredient use: 

expert 

average

Nielsen 

data

Nielsen 

+ 

Perishables 

data

Availability 

data

Proposed 

consumer 

loss 

estimate

Explanation

Fruits Fresh 

oranges 20% 23% −34% 38% 2% −36% 36% 36%

Estimate based 

on Availability 

data

Fruits Fresh 

tangerines 20% 23% 54% 81% 2% 52% 79% 52%

Estimate based 

on Nielsen data

Fruits Fresh 

grapefruit 20% 23% −30% 56% 2% −32% 54% 54%

Estimate based 

on Availability 

data

Fruits Fresh 

lemons 20% 24% 67% 91% 23% 44% 69% 44%

Estimate based 

on Nielsen data

Fruits Fresh 

limesb

20% 26% 93% 99% 18% 75% 80% 44%

Assumed same 

value as fresh 

lemons

Fruits Fresh 

apples

20% 18% −74% −63% 26% 23% −98% −87% 2% TBD

Revised ingredient percentage to apply 

to Availabilitybased estimate

Fruits Fresh 

apricotsb

20% 20% −63% 17% 8% −70% 10% 10%

Estimate based 

on Availability 

data

Fruits Fresh 

avocados

20% 33% 28% −138% 71% 18% 11% −155% 53% 32%

Estimate based 

on average of 

Availability and 

Nielsen

Continued—
84

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table B-4

Food Purchases, Consumption, and Consumer Loss Estimates: Fruits—continued

Consumer loss 

(unadjusted for ingredient 

percentage)

Consumer loss 

(adjusted for ingredient 

percentage)

Category

Food

Previous 

consumer loss 

estimate 

Expert 

average 

consumer loss 

Nielsen 

data

Nielsen 

+ 

Perishables 

data

Availability 

data

Ingredient use: 

expert 

average

Nielsen 

data

Nielsen 

+ 

Perishables 

data

Availability 

data

Proposed 

consumer 

loss 

estimate

Explanation

Fruits

Fresh 

bananas 20% 22% −53% −35% 19% 20% −73% −55% −1% TBD

Revised ingredient 

percentage to apply 

to Availability-based 

estimate

Fruits

Fresh 

blueberries 20% 19% 34% 26% 23% 12% 3% 8%

Estimate based on 

average of Availability and Nielsen

Fruits

Fresh 

cantaloupe 20% 26% −66% 45% 2% −68% 43% 43%

Estimate based on 

Availability data

Fruits

Fresh 

cherries 20% 17% 99% 98% 98% 47% 52% 51% 51% 51%

Estimate based on 

Nielsen+Perishables 

or Availability data

Fruits

Fresh 

cranberriesb 20% 14% 99% 99% 73% 26% 26% 26%

Estimate based on 

Nielsen or 

Availability data

Fruits

Fresh 

grapes 20% 20% −8% −52% 34% 1% −9% −53% 33% 33%

Estimate based on 

Availability data

Fruits

Fresh 

honeydew 20% 21% 60% 72% 1% 59% 71% 43%

Assumed same 

value as fresh cantaloupe

Fruits Fresh kiwi 20% 22% −29% 46% 1% −30% 45% 45%

Estimate based on 

Availability data

Fruits

Fresh 

mangoes 20% 21% −205% 33% 20% −225% 13% 13%

Estimate based on 

Availability data

Fruits

Fresh 

peaches 35% 22% −45% 57% 15% −60% 42% 42%

Estimate based on 

Availability data

Fruits Fresh pears 20% 20% −202% −168% 5% 8% −209% −176% −3% TBD

Used same value as 

fresh apples

Fruits

Fresh 

pineapple 20% 19% 52% 79% 75% 15% 37% 64% 60% 37%

Estimate based on 

Nielsen data

Fruits

Fresh 

papayab 20% 24% 40% 90% 20% 20% 70% 20%

Estimate based on 

Nielsen data

Fruits Fresh plums 20% 19% −40% 34% 8% −48% 27% 27%

Estimate based on 

Availability data

Fruits

Fresh 

strawberries 20% 24% −6% 45% 10% −16% 35% 35%

Estimate based on 

Availability data

Fruits

Fresh

watermelon 20% 22% −39% 16% 3% −42% 13% 13%

Estimate based on 

Availability data

Fruits

Canned 

apples and 

applesauce 10% 14% 33% 50% 25% 8% 25% 8%

Estimate based on 

Nielsen data

Fruits

Canned 

apricotsb 10% 13% −10% 45% 18% −28% 27% 27%

Estimate based on 

Availability data

Fruits

Canned 

cherriesb 10% 12% 89% 91% 58% 32% 34% 32%

Estimate based on 

Nielsen data

Fruits

Canned 

peaches 10% 13% −20% −20% 19% −38% −38% 9%

Assumed same 

value as canned 

pineapple

Continued—
85

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table B-4

Food Purchases, Consumption, and Consumer Loss Estimates: Fruits—continued

Consumer loss 

(unadjusted for ingredient 

percentage)

Consumer loss 

(adjusted for ingredient 

percentage)

Category

Food

Previous 

consumer loss 

estimate 

Expert 

average 

consumer 

loss 

Nielsen 

data

Nielsen 

+ 

Perishables 

data

Availability 

data

Ingredient use: 

expert 

average

Nielsen 

data

Nielsen 

+ 

Perishables 

data

Availability 

data

Proposed 

consumer 

loss 

estimate

Explanation

Fruits

Canned 

pears 10% 13% 2% 88% 18% −16% 71% 9%

Assumed same 

value as canned 

pineapple

Fruits

Canned 

pineapple 10% 17% 25% 49% 16% 9% 33% 9%

Estimate based on 

Nielsen data

Fruits

Canned 

plumsb 10% 18% 44% 98% 18% 26% 81% 26%

Estimate based on 

Nielsen data

Fruits

Canned 

olives 10% 15% 35% 85% 10% 25% 75% 25%

Estimate based on 

Nielsen data

Fruits

Frozen 

blackberries 10% 11% 51% 92% 53% −2% 40% 40%

Estimate based on 

Availability data

Fruits

Frozen 

blueberriesb 10% 12% 41% 75% 46% −5% 29% 29%

Estimate based on 

Availability data

Fruits

Frozen 

cherriesb 10% 13% NC NC 78% NC NC 29%

Assumed same 

value as frozen 

blueberries

Fruits

Frozen 

raspberriesb 10% 13% 79% 97% 55% 24% 42% 24%

Estimate based on 

Nielsen data

Fruits

Frozen 

strawberriesb 10% 15% 26% 89% 65% −39% 24% 24%Estimate based on 

Availability data

Fruits

Other frozen berriesb 10% 13% −712% 94% 64% −776% 30% 30%Estimate based on 

Availability data

Fruits

Frozen 

applesb 10% 12% NC NC 78% NC NC 35%

Assumed same 

value as frozen 

peaches

Fruits

Frozen 

apricotsb 10% 12% NC NC 64% NC NC 35%

Assumed same 

value as frozen 

peaches

Fruits

Frozen 

peachesb 10% 15% 33% 83% 48% −15% 35% 35%

Estimate based on 

Availability data

Fruits

Other 

frozen fruitb 10% 12% NC NC 53% NC NC 35%

Assumed same 

value as frozen 

peaches

Fruits

Dried 

applesb 10% 15% NC NC 34% NC NC 11%

Assumed same 

value as dried plums

Fruits

Dried 

apricotsb 10% 15% 98% 99% 20% 78% 79% 11%

Assumed same 

value as dried plums

Fruits

Dried 

datesb 10% 13% 58% 86% 33% 25% 52% 25%

Estimate based on 

Nielsen data

Fruits Dried figsb 10% 13% NC NC 22% NC NC 25%

Assumed same 

value as dried dates

Fruits

Dried 

peachesb 10% 16% NC NC 19% NC NC 11%

Assumed same 

value as dried plums

Fruits

Dried 

pearsb 10% 16% −7,986% −65% 16% −8,003% −81% 11%

Assumed same 

value as dried plums

Fruits Dried plums 10% 18% 49% 70% 38% 11% 32% 11%

Estimate based on 

Nielsen data

Continued—
86

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table B-4

Food Purchases, Consumption, and Consumer Loss Estimates: Fruits—continued

Consumer loss 

(unadjusted for ingredient 

percentage)

Consumer loss 

(adjusted for ingredient 

percentage))

Category Food

Previous 

consumer loss 

estimate 

Expert 

average 

consumer 

loss 

Nielsen 

data

Nielsen 

+ 

Perishables 

data

Availability 

data

Ingredient use: 

expert 

average

Nielsen 

data

Nielsen 

+ 

Perishables 

data

Availability 

Data

Proposed 

consumer 

loss 

estimate

Explanation

Fruits Raisins 10% 13% 58% 85% 32% 26% 53% 26%

Estimate 

based on 

Nielsen 

data

Fruits—

Juices

Grapefruit 

juice 10% 18% −106% −18% 0% −106% −18% NA

Fruits—

Juices Lemon juice 10% 17% 88% 92% 95% −7% −3% NA

Fruits—

Juices Lime juice 10% 17% 28% 61% 95% −67% −34% NA

Fruits—

Juices

Orange 

juice 10% 15% −70% −42% 5% −75% −47% NA

Fruits—

Juices Apple juice 10% 17% −106% −2% 5% −111% −7% NA

Fruits—

Juices

Cranberry 

juiceb 10% 14% 98% 99% 5% 93% 94% NA

Fruits—

Juices Grape juice 10% 15% 97% 98% 0% 97% 98% NA

Fruits—

Juices

Pineapple 

juice 10% 13% −14% 78% 10% −24% 68% NA

Fruits—

Juices Prune juice 10% 20% 32% 70% 0% 32% 70% 32%

Estimate 

based on 

Nielsen 

data

Note: Consumer-level loss estimates should be positive. Negative consumer-level loss estimates indicate reported consumption exceeds food 

purchases.

NA = not available.

NC = not calculated.

TBD = to be decided; reasonable estimate might be calculable if ingredient percentage is revised.

a“ERS Availability” refers to the volume of each food available at the consumer level in the Food Availability data series. The definition of these 

foods may differ somewhat from how we defined them when obtaining consumption data from NHANES.

bFood has 10 or fewer consumption observations in NHANES; thus, the total consumption estimate may not be reliable.
87

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table B-5

Food Purchases, Consumption, and Consumer Loss Estimates: Vegetables

Category Food ERS 

availabilitya

NHANES consumption Nielsen purchases Nielsen + Perishables

Total 

(lbs)

Store only

(lbs)

All 

purchases

(lbs)

Randomweight only 

(lbs)

Perishables 

Group: nonUPC (lbs)

All purchases: 

Nielsen UPC 

+ Perishables 

non-UPC (lbs)

Vegetables Fresh 

artichokesb 58,460,600 30,113,786 27,433,069 32,501,663 9,086,677

Vegetables Fresh 

asparagusb 139,428,531 165,981,716 129,966,733 61,708,802 49,189,486

Vegetables Fresh 

bell peppers 1,351,842,914 483,944,303 248,500,592 312,026,050 292,772,133 255,791,319 275,045,236

Vegetables Fresh

broccoli 846,947,943 635,345,598 419,047,533 219,073,767 165,763,792

Vegetables Fresh brussels sprouts 52,614,540 73,263,518 49,721,335 17,523,794 14,699,349

Vegetables Fresh 

cabbage 1,592,466,744 427,547,167 362,664,505 314,180,911 305,248,541

Vegetables Fresh carrots 1,958,927,015 820,575,282 560,037,733 786,952,339 88,326,077 505,991,531 1,204,617,793

Vegetables Fresh 

cauliflower 143,637,694 172,108,841 101,287,864 125,202,930 33,509,078

Vegetables Fresh celery 1,324,161,820 125,328,745 95,495,803 289,417,477 144,734,440 118,182,687 262,865,724

Vegetables Fresh collard 

greens 76,641,847 83,493,615 61,314,958 31,697,928

Vegetables Fresh sweet 

corn 783,956,646 659,196,877 436,433,131 88,686,523 82,637,835 74,606,280 80,654,968

Vegetables Fresh 

cucumbers 1,113,849,812 648,842,611 473,883,088 293,201,085 272,499,750 65,115,425 85,816,759

Vegetables Fresh 

eggplantb 153,897,530 24,602,286 15,223,658 31,109,645 31,053,997

Vegetables Fresh garlicb 465,375,606 155,711 155,711 9,886,424 1,279,499

Vegetables Fresh kaleb 44,576,208 20,746,683 10,007,351 2,227,050

Vegetables Fresh 

romaine and 

leaf lettuce 

and escarole 

and endiveb 6,475,329,898 3,420,619,369 1,934,493,656 1,722,372,910 315,597,246 121,037,691 1,527,813,355

Vegetables Fresh lima 

beansb 6,430,666 27,518,216 633,192 59,996

Vegetables Fresh 

mushrooms 609,597,907 178,495,160 114,328,863 167,163,581 33,484,841 120,440,023 254,118,763

Vegetables Fresh mustard greensb 106,018,298 4,830,559 4,830,559 3,611,017

Vegetables Fresh okra 80,441,786 9,234,913 4,287,924 399,859

Vegetables Fresh onions 4,709,001,330 744,377,347 320,067,216 1,234,388,808 674,299,334 901,274,704 1,461,364,178

Vegetables Fresh 

potatoes 11,312,126,100 5,616,699,046 3,132,932,340 3,176,725,667 423,170,606 2,439,661,035 5,267,819,033

Vegetables Fresh 

pumpkinb 791,848,827 4,557,477 4,557,477 5,605,511

Vegetables Fresh 

radishes 110,490,534 27,411,664 18,502,991 82,823,640 60,899,431 38,925,131 60,849,340

Vegetables Fresh snap 

beans 401,273,558 392,563,424 191,057,172 89,318,559 86,637,033

Vegetables Fresh 

spinach 317,791,822 258,151,655 170,797,226 79,471,371 24,301,184

Continued—
88

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Table B-5

Food Purchases, Consumption, and Consumer Loss Estimates: Vegetables—continued

Category Food ERS 

availabilitya

NHANES consumption Nielsen purchases Nielsen + Perishables

Total 

(lbs)

Store only

(lbs)

All 

purchases

(lbs)

Randomweight only 

(lbs)

Perishables 

Group: nonUPC (lbs)

All purchases: 

Nielsen UPC 

+ Perishables 

non-UPC (lbs)

Vegetables Fresh 

squash 702,930,254 395,970,595 220,237,929 237,123,847 219,354,833

Vegetables Fresh sweet 

potatoes 977,870,456 332,693,259 210,806,848 270,603,347 196,000,410

Vegetables Fresh 

tomatoes 3,989,935,950 2,961,778,511 1,705,582,182 991,270,793 728,088,794 626,221,431 889,403,430

Vegetables Fresh turnip 

greensb 77,752,598 15,611,759 11,380,757 2,482,777

Vegetables Canned 

asparagusb 46,768,480 29,273,654 28,918,753 30,992,370

Vegetables Canned 

snap beans 613,836,300 831,624,243 564,402,348 495,483,037

Vegetables Canned 

cabbage 

(sauerkraut) 134,459,380 17,608,243 17,166,749 104,439,678

Vegetables Canned 

carrotsb 362,455,720 43,235,691 33,416,463 56,480,786

Vegetables Canned 

sweet corn 1,645,665,890 503,469,137 411,771,928 515,700,446

Vegetables Canned 

cucumbers 

(pickles) 534,914,490 489,551,298 333,075,949 329,697,999

Vegetables Canned 

green peasb 222,150,280 135,790,955 111,408,860 208,852,542

Vegetables Canned chile 

peppers 1,227,672,600 42,787,270 24,581,588 7,839,071

Vegetables Canned 

tomatoes 7,950,641,600 453,220,338 317,598,111 845,835,971

Vegetables Canned 

mushroomsb 277,687,850 17,930,797 9,731,381 58,534,234

Vegetables Canned 

potatoesb 236,765,430 25,190,042 4,066,154 57,820,519

Vegetables Other 

canned 

vegetablesb

Vegetables Frozen 

asparagus 8,769,090 3,993,442

Vegetables Frozen snap 

beans 453,069,650 221,967,681 181,979,181 103,221,194

Vegetables Frozen

broccoli 549,529,640 227,004,571 177,853,102 213,237,795

Vegetables Frozen

carrotsb 192,919,980 46,154,085 37,226,482 29,558,075

Vegetables Frozen

cauliflowerb 73,075,750 14,175,222 13,532,057 29,636,211

Vegetables Frozen sweet 

corn 672,296,900 196,198,214 129,549,535 219,807,108

Vegetables Frozen green 

peasb 137,382,410 168,972,231 155,132,060 145,406,425

Continued—
89

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Table B-5

Food Purchases, Consumption, and Consumer Loss Estimates: Vegetables—continued

Category Food ERS 

availabilitya

NHANES consumption Nielsen purchases Nielsen + Perishables

Total 

(lbs)

Store only

(lbs)

All 

purchases

(lbs)

Randomweight only 

(lbs)

Perishables 

Group: nonUPC (lbs)

All purchases: 

Nielsen UPC 

+ Perishables 

non-UPC (lbs)

Vegetables Frozen lima 

beans 35,076,360 35,173,677 24,954,164 41,398,509

Vegetables Frozen 

spinach 178,304,830 72,893,823 60,403,581 75,602,746

Vegetables Frozen 

potatoes 7,862,950,700 3,697,625,051 564,447,979 269,514,333

Vegetables Other 

frozen 

vegetablesb

Vegetables Dehydrated 

onions

Vegetables Dehydrated 

potatoes 529,068,430 244,580,775 215,035,499 692,014,443

Vegetables Potato 

chips and 

shoestring 

potatoes 1,137,058,670 1,168,276,951 1,013,767,038 971,582,640

Vegetables Dry edible 

beans 1,639,819,830 1,378,351,818 1,045,069,823 311,951,763

Vegetables Dry edible 

peas and 

lentils 70,868,584 69,753,912 32,093,741

Continued—
90

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

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Table B-5

Food Purchases, Consumption, and Consumer Loss Estimates: Vegetables—continued

Consumer loss 

(unadjusted for ingredient 

percentage)

Consumer loss 

(adjusted for ingredient 

percentage)

Category Food

Previous 

consumer loss 

estimate 

Expert 

average 

consumer 

loss 

Nielsen 

data

Nielsen 

+ 

Perishables 

data

Availability 

data

Ingredient use: 

expert 

average

Nielsen 

data

Nielsen 

+ 

Perishables 

data

Availability 

Data

Proposed 

consumer 

loss 

estimate

Explanation

Vegetables

Fresh 

artichokesb 20% 20% 16% 48% 30% −14% 18% 18%

Estimate based 

on Availability 

data

Vegetables

Fresh 

asparagusb 20% 19% −111% −19% 13% −124% −32% 18%

Used same 

value as fresh 

artichokes

Vegetables

Fresh bell 

peppers 20% 17% 20% 10% 64% 25% −5% −15% 39% 39%

Estimate based 

on Availability 

data

Vegetables

Fresh 

broccoli 20% 20% −91% 25% 13% −105% 12% 12%

Estimate based 

on Availability 

data

Vegetables

Fresh brussels sprouts 20% 23% −184% −39% 10% −194% −49% 12%Used same value 

as fresh broccoli

Vegetables

Fresh 

cabbage 20% 21% −15% 73% 15% −30% 58% 24%

Used same value 

as fresh lettuce

Vegetables

Fresh 

carrots 20% 15% 29% 54% 58% 20% 9% 34% 39% 34%

Estimate based 

on Nielsen+ 

Perishables

Vegetables

Fresh 

cauliflower 20% 23% 19% −20% 10% 9% −30% 9%

Estimate based 

on Nielsen

Vegetables

Fresh 

celery 20% 21% 67% 64% 91% 25% 42% 39% 66% 39%

Estimate based 

on Nielsen+ 

Perishables

Vegetables

Fresh collard greens 20% 18% −93% −9% 10% −103% −19% 38%Used same value 

as fresh kale

Vegetables

Fresh 

sweet corn 32% 20% −392% −441% 16% 15% −407% −456% 1% TBD

Revise ingredient 

percentage to apply to Availabilitybased estimate

Vegetables

Fresh 

cucumbers 20% 18% −62% −452% 42% 10% −72% −462% 32% 32%

Estimate based 

on Availability 

data

Vegetables

Fresh 

eggplantb 27% 25% 51% 84% 25% 26% 59% 26%

Estimate based 

on Nielsen

Vegetables

Fresh 

garlicb 20% 16% 98% 100% 32% 67% 68% 43%

Used same value 

as onions

Vegetables Fresh kaleb 20% 19% −349% 53% 15% −364% 38% 38%

Estimate based 

on Availability 

data

Vegetables

Fresh 

romaine 

and leaf 

lettuce and 

escarole 

and endiveb 20% 27% −12% −27% 47% 23% −36% −50% 24% 24%

Estimate based 

on Availability 

data

Vegetables

Fresh lima 

beansb 20% 24% −955% −328% 23% −978% −350% 27%

Used same value 

as frozen lima 

beans

Continued—
91

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Table B-5

Food Purchases, Consumption, and Consumer Loss Estimates: Vegetables—continued

Consumer loss 

(unadjusted for ingredient 

percentage)

Consumer loss 

(adjusted for ingredient 

percentage)

Category Food

Previous 

consumer loss 

estimate 

Expert 

average 

consumer 

loss

Nielsen 

data

Nielsen 

+ 

Perishables 

data

Availability 

data

Ingredient use: 

expert 

average

Nielsen 

data

Nielsen 

+ 

Perishables 

data

Availability 

data

Proposed 

consumer 

loss 

estimate

Explanation

Vegetables

Fresh 

mushrooms 20% 23% 32% 55% 71% 34% −2% 21% 37% 21%

Estimate based 

on Nielsen+ 

Perishables

Vegetables

Fresh 

mustard 

greensb 20% 20% −34% 95% 10% −44% 85% 38%

Used same value 

as fresh kale

Vegetables Fresh okra 20% 23% −972% 89% 17% −989% 72% TBD

Revised ingredient percentage 

to apply to Availability−based 

estimate

Vegetables

Fresh 

onions 35% 20% 74% 78% 84% 35% 39% 43% 49% 43%

Estimate based 

on Nielsen+ 

Perishables

Vegetables

Fresh 

potatoes 30% 20% 1% 41% 50% 25% −24% 16% 25% 16%

Estimate based 

on Nielsen+ 

Perishables

Vegetables

Fresh 

pumpkinb 20% 18% 19% 99% 30% −11% 69% 69%

Estimate based 

on Availability 

data

Vegetables

Fresh 

radishes 20% 23% 78% 70% 75% 23% 55% 47% 52% 47%

Estimate based 

on Nielsen+ 

Perishables

Vegetables

Fresh snap 

beans 22% 21% −114% 2% 18% −132% −16% 24%

Used same value 

as frozen snap 

beans

Vegetables

Fresh 

spinach 20% 27% −115% 19% 10% −125% 9% 9%

Estimate based 

on Availability 

data

Vegetables

Fresh 

squash 20% 22% 7% 44% 18% −11% 25% 25%

Estimate based 

on Availability 

data

Vegetables

Fresh 

sweet 

potatoes 31% 17% 22% 66% 22% 0% 44% 44%

Estimate based 

on Availability 

data

Vegetables

Fresh 

tomatoes 20% 20% −72% −92% 26% 18% −90% −110% 7% 7%

Estimate based 

on Availability 

data

Vegetables

Fresh turnip 

greensb 20% 20% −358% 80% 15% −373% 65% 38%

Used same value 

as fresh kale

Vegetables

Canned 

asparagusb 10% 10% 7% 37% 5% 2% 32% 2%

Estimate based 

on Nielsen data

Vegetables

Canned 

snap beans 10% 10% −14% −35% 5% −19% −40% 24%

Used same value 

as canned green 

peas

Vegetables

Canned 

cabbage 

(sauerkraut) 10% 15% 84% 87% 5% 79% 82% 16%

Used average 

of all canned 

vegetables

Continued—
92

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

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Table B-5

Food Purchases, Consumption, and Consumer Loss Estimates: Vegetables—continued

Consumer loss 

(unadjusted for ingredient 

percentage)

Consumer loss 

(adjusted for ingredient 

percentage)

Category Food

Previous 

consumer loss 

estimate 

Expert 

average 

consumer 

loss 

Nielsen 

data

Nielsen 

+ 

Perishables 

data

Availability 

data

Ingredient use: 

expert 

average

Nielsen 

data

Nielsen 

+ 

Perishables 

data

Availability 

data

Proposed 

consumer 

loss 

estimate

Explanation

Vegetables

Canned 

carrotsb 10% 13% 41% 88% 10% 31% 78% 31%

Estimate based 

on Nielsen data

Vegetables

Canned 

sweet corn 10% 11% 20% 69% 14% 7% 56% 7%

Estimate based 

on Nielsen data

Vegetables

Canned 

cucumbers 

(pickles) 10% 9% −1% 8% 5% −6% 3% 3%

Estimate based 

on Availability 

data

Vegetables

Canned 

green 

peasb 10% 17% 47% 39% 23% 24% 16% 24%

Estimate based 

on Nielsen data

Vegetables

Canned 

chile 

peppers 10% 11% −214% 97% 93% −306% 4% 4%

Estimate based 

on Availability 

data

Vegetables

Canned 

tomatoes 10% 13% 62% 94% 66% −4% 28% 28%

Estimate based 

on Availability 

data

Vegetables

Canned 

mushroomsb 10% 14% 83% 94% 74% 9% 20% 9%

Estimate based 

on Nielsen data

Vegetables

Canned 

potatoesb 10% 13% 93% 89% 65% 28% 24% 28%

Estimate based 

on Nielsen data

Vegetables

Other 

canned 

vegetablesb 10% 13% NC NC 20% NC NC 16%

Used average 

of all canned 

vegetables

Vegetables

Frozen 

asparagus 30% 18% NC NC 5% NC NC 26%

Used average 

for all frozen 

vegetables

Vegetables

Frozen 

snap beans 20% 17% −76% 51% 28% −104% 24% 24%

Estimate based 

on Availability 

data

Vegetables

Frozen 

broccoli 16% 14% 17% 59% 5% 12% 54% 12%

Estimate based 

on Nielsen data

Vegetables

Frozen 

carrotsb 12% 15% −26% 76% 5% −31% 71% 34%

Used same 

value as fresh 

carrots

Vegetables

Frozen 

cauliflowerb 17% 14% 54% 81% 28% 27% 53% 27%

Estimate based 

on Nielsen data

Vegetables

Frozen 

sweet corn 14% 13% 41% 71% 5% 36% 66% 36%

Estimate based 

on Nielsen data

Vegetables

Frozen 

green 

peasb 17% 14% −7% −23% 10% −17% −33% 24%

Used same 

value as frozen 

snap beans

Vegetables

Frozen lima 

beans 32% 21% 40% 0% 13% 27% −13% 27%

Estimate based 

on Nielsen data

Vegetables

Frozen 

spinach 23% 16% 20% 59% 25% −5% 34% 34%

Estimate based 

on Availability 

data

Continued—
93

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Table B-5

Food Purchases, Consumption, and Consumer Loss Estimates: Vegetables—continued

Consumer loss 

(unadjusted for ingredient 

percentage)

Consumer loss 

(adjusted for ingredient 

percentage)

Category Food

Previous 

consumer loss 

estimate 

Expert 

average 

consumer 

loss 

Nielsen 

data

Nielsen 

+ 

Perishables 

data

Availability 

data

IIngredient use: 

expert 

average

Nielsen 

dta

Nielsen 

+ 

Perishables 

data

Availability 

data

Proposed 

consumer 

loss 

estimate

Explanation

Vegetables

Frozen 

potatoes 32% 22% −109% 53% 52% −161% 1% 16%

Used same 

value as fresh 

potatoes

Vegetables

Other 

frozen 

vegetablesb 23% 15% NC NC 5% NC NC 26%

Used average 

for all frozen 

vegetables

Vegetables

Dehydrated 

onions 10% 5% NC NC 100% NC NC 4%

Used same 

estimate as 

Dehydrated 

potatoes

Vegetables

Dehydrated 

potatoes 10% 10% 69% 54% 65% 4% −11% 4%

Estimate 

based on 

Nielsen data

Vegetables

Potato 

chips and 

shoestring 

potatoes 10% 8% −4% −3% 2% −6% −5% 4%

Used same 

estimate as 

dehydrated 

potatoes

Vegetables

Dry edible 

beans 10% 15% −235% 16% 75% −310% −59% NA

Vegetables

Dry edible 

peas and 

lentils 10% 15% −117% NC 75% −192% NC NA

Note: Consumer-level loss estimates should be positive. Negative consumer-level loss estimates indicate reported consumption exceeds food purchases.

NA = not available.

NC = not calculated.

TBD = to be decided; reasonable estimate might be calculable if ingredient percentage is revised.

a“ERS Availability” refers to the volume of each food available at the consumer level in the Food Availability data series. The definition of these foods 

may differ somewhat from how we defined them when obtaining consumption data from NHANES.

bFood has 10 or fewer consumption observations in NHANES; thus, the total consumption estimate may not be reliable.
94

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

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Table B-6

Food Purchases, Consumption, and Consumer Loss Estimates: Grains

Category Food ERS 

availabilitya

NHANES consumption Nielsen purchases Nielsen + Perishables

Total 

(lbs)

Store only

(lbs)

All 

purchases

(lbs)

Randomweight only 

(lbs)

Perishables 

Group: nonUPC (lbs)

All purchases: 

Nielsen UPC 

+ Perishables 

non-UPC (lbs)

Grains White and 

whole wheat 

flour

Grains Durum flour

Grains Rice 5,466,066,100 3,687,468,888 2,791,629,634 1,023,791,318

Grains Rye flour

Grains Corn flour 

and meal

Grains Corn hominy 

and grits 2,016,890,700 609,351,609 496,644,366 458,411,629

Grains Corn starch

Grains Barley 

productsb 140,305,440 4,701,898 4,701,898 20,159,389

Grains Oat products 958,753,840 2,430,070,157 2,338,894,031 2,639,557,557

Continued—

Table B-6

Food Purchases, Consumption, and Consumer Loss Estimates: Grains—continued

Consumer loss 

(unadjusted for ingredient 

percentage)

Consumer loss 

(adjusted for ingredient 

percentage)

Category Food

Previous 

consumer loss 

estimate 

Expert 

average 

consumer 

loss 

Nielsen 

data

Nielsen 

+ 

Perishables 

data

Availability 

data

Ingredient use: 

expert 

average

Nielsen 

data

Nielsen 

+ 

Perishables 

data

Availability 

data

Proposed 

consumer 

loss 

estimate

Explanation

Grains

White and 

whole wheat 

flour 20% 20% NC NC 100% NC NC NA

Grains Durum flour 20% 18% NC NC 100% NC NC NA

Grains Rice 20% 20% −173% 33% 100% −273% −67% 33%

Estimate based 

on Availability 

data

Grains Rye flour 20% 22% NC NC 100% NC NC NA

Grains

Corn flour 

and meal 20% 16% NC NC 100% NC NC NA

Grains

Corn hominy 

and grits 20% 16% −8% 70% 100% −108% −30% NA

Grains Corn starch 20% 20% NC NC 100% NC NC NA

Grains

Barley 

productsb 20% 19% 77% 97% 83% −6% 14% 14%

Estimate based 

on Availability 

data

Grains Oat products 20% 18% 11% −153% none 14%

Used same 

estimate as 

barley products

Note: Consumer-level loss estimates should be positive. Negative consumer-level loss estimates indicate reported consumption exceeds food purchases.

NA = not available.

NC = not calculated.

a“ERS Availability” refers to the volume of each food available at the consumer level in the Food Availability data series. The definition of these foods 

may differ somewhat from how we defined them when obtaining consumption data from NHANES.

bFood has 10 or fewer consumption observations in NHANES; thus, the total consumption estimate may not be reliable.
95

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

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Table B-7

Food Purchases, Consumption, and Consumer Loss Estimates: Added Sugars and Sweeteners

Category Food ERS 

availabilitya

NHANES consumption Nielsen purchases Nielsen + Perishables

Total 

(lbs)

Store only

(lbs)

All 

purchases

(lbs)

Randomweight only 

(lbs)

Perishables 

Group: nonUPC (lbs)

All purchases: 

Nielsen UPC 

+ Perishables 

non-UPC 

(lbs))

Added 

sugars and 

sweeteners

Cane and 

beet sugar

16,018,204,400 607,918,929 509,788,981 2,800,394,593

Added 

sugars and 

sweeteners

High-fructose corn 

syrup

Added 

sugars and 

sweeteners

Glucose

Added 

sugars and 

sweeteners

Dextrose

Added 

sugars and 

sweeteners

Honey

263,072,700 99,294,956 79,317,182 77,914,937

Added 

sugars and 

sweeteners

Edible 

syrups

87,690,900 670,246,242 446,622,665 485,100,527

Continued—

Table B-7

Food Purchases, Consumption, and Consumer Loss Estimates: Added Sugars and Sweeteners—continued

Consumer loss 

(unadjusted for ingredient 

percentage)

Consumer loss 

(adjusted for ingredient 

percentage)

Category Food

Previous 

consumer loss 

estimate 

Expert 

average 

consumer 

loss 

Nielsen 

data

Nielsen 

+ 

Perishables 

data

Availability 

data

Ingredient use: 

expert 

average

Nielsen 

data

Nielsen 

+ 

Perishables 

data

Availability 

data

Proposed 

consumer 

loss 

estimate

Explanation

Added 

sugars and 

sweeteners

Cane and 

beet sugar 20% 17% 82% 96% 63% 19% 34% 34%

Estimate 

based on 

Availability 

data

Added 

sugars and 

sweeteners

High-fructose corn 

syrup 20% 15% NC NC 100% NC NC 15%

Used same 

value as 

honey

Added 

sugars and 

sweeteners Glucose 20% 20% NC NC 100% NC NC 15%

Used same 

value as 

honey

Added 

sugars and 

sweeteners Dextrose 20% 20% NC NC 100% NC NC 15%

Used same 

value as 

honey

Added 

sugars and 

sweeteners Honey 20% 21% −2% 62% 48% −49% 15% 15%

Estimate 

based on 

Availability 

data

Added 

sugars and 

sweeteners

Edible 

syrups 20% 16% 8% −664% 88% −80% −752% 15%

Used same 

value as 

honey

Note: Consumer-level loss estimates should be positive. Negative consumer-level loss estimates indicate reported consumption exceeds food 

purchases.

NC = not calculated.

a“ERS Availability” refers to the volume of each food available at the consumer level in the Food Availability data series. The definition of these 

foods may differ somewhat from how we defined them when obtaining consumption data from NHANES.
96

Consumer-Level Food Loss Estimates and Their Use in the ERS Loss-Adjusted Food Availability Data/ TB-1927

Economic Research Service/USDA

Appendix C:

Materials for Consumer-Level Food Loss 

Expert Panel

Consumer-Level Food Loss Study RTI International

Background Information

Under a grant with USDA’s Economic Research Service, RTI International 

is developing updated estimates of consumer-level food loss that occurs at 

home and away from home. These estimates will be incorporated into ERS’s 

Food Availability (per capita) Data System1, which provides important statistical indicators that track food and nutrient availability since 1909 for many 

commodities. The data facilitate policymaking and regulatory decisions about 

nutrition education, public health programs, vitamin and mineral fortification, 

and food labeling. Currently, the Food Availability data series (also known 

as food supply or food disappearance) are the premiere source of time-series 

data in the Food Availability Data System. However, the data overstate actual 

consumption, so ERS has also included an additional series in the system, the 

Loss-Adjusted Food Availability data, which adjusts the Food Availability 

data for nonedible food parts and food losses, including losses from farm to 

retail, at retail, and at the consumer level.

Purpose of Project

The focus of this project is on updating the consumer-level loss estimates 

for “cooking loss and uneaten food” of the edible share currently used in the 

Loss-Adjusted Food Availability data. The goal is to update the consumerlevel loss factors, both at home and away from home, for approximately 200 

foods. These foods are classified into seven broad categories: meats, poultry, 

and fish; dairy products; added fats and oils; fruits; vegetables; grain products; and added sugars and sweeteners.

What We Would Need from You

If you agree to participate in the panel, you would need to travel to Research 

Triangle Park, NC, for a 1-day meeting in May 2008. During the meeting, 

you will participate in general group discussions and complete worksheets 

with your estimates of food loss for individual product categories. Preparation 

prior to that date will include reviewing the product category definitions and 

description of the process we will use for the meeting.

Potential dates of the panel are listed on Panelist Information Form. If you 

are selected and able to participate, we will pay you an honorarium and travel 

expenses. 

1See http://www.ers.usda.gov/data/

foodconsumption/foodguideindex.htm.
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Activities for the Panel Meeting (specific schedule to be 

determined):

1. Presentation and overview of the research goals and methods

2. Review and discussion of RTI’s calculated estimates of the percentages 

of foods consumed from purchases at grocery stores versus at restaurants 

and other foodservice establishments

•	 Panelists will be asked to provide revised estimates based on their

knowledge of at-home versus away-from-home food consumption 

choices

3. Review of RTI’s calculated estimates of consumer-level food loss to 

validate whether they are reasonable and whether the relative values for 

the different food groups are appropriate

•	 Panelists will be asked to provide revised estimates based on their

knowledge of food purchase, preparation, and consumption practices

4. Discussion of estimates of loss factors for “ingredients” based on the 

foods in which these ingredients are typically used (focus particularly on 

frying fats)

•	 Panelists will be asked to provide original estimates based on their

knowledge of food purchase, preparation, and consumption practices

Qualitative discussion of trends in food purchase and consumption behavior 

that have likely influenced food loss over time since 1970

RTI International

RTI is an independent, nonprofit research institute based in Research 

Triangle Park, NC. Established in 1958 as the Research Triangle Institute, 

RTI has a distinguished history of scientific achievement in the areas of 

health and pharmaceuticals, education and training, surveys and statistics, 

advanced technology, democratic governance, economic and social development, energy, and the environment. RTI has ongoing projects in more 

than 40 countries and a staff of more than 2,600. The Food and Agricultural 

Policy Research Program at RTI has been conducting analyses of the 

economic effects of policies affecting the food and agricultural industries for 

over 20 years. 
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If you are interested in participating in the panel, please fill out the attached 

sheet and send it with your resume or CV to:

Michaela Coglaiti

Food and Agricultural Policy Research Program

RTI International

3040 Cornwallis Road

Research Triangle Park, NC 27709-2194

E-mail: coglaiti@rti.org

Phone: 919-990-8498

If you have any questions on the purpose and design of the project, you may 

contact:

Mary Muth, Ph.D.

Director, Food and Agricultural Policy Research Program

RTI International

3040 Cornwallis Road

Research Triangle Park, NC 27709-2194

E-mail: muth@rti.org

Phone: 919-541-7289

If you have any questions for ERS regarding the project, you may contact:

Jean Buzby, Ph.D.

USDA/Economic Research Service

1800 M Street, NW

Room S2080

Washington DC, 20036-5831

E-mail: jbuzby@ers.usda.gov

Phone: 202-694-5370
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Consumer-Level Food Loss Study: Panelist Information

Name: Phone Number(s):

Affiliation: Preferred E-mail Address:

Please indicate which dates you would be available for a full day meeting (8:30 AM-4:30 

PM):

Monday, May 12

Tuesday, May 13

Wednesday, May 14

Thursday, May 15

Friday, May 16

Monday, May 19

Tuesday, May 20

Wednesday, May 21

With which aspects of food consumption issues are you most familiar based on your 

research? (Check all that apply.)

food purchase behavior

food preparation practices at home

food preparation practices in restaurants and cafeterias

food consumption practices at home

food consumption practices away from home 

food spoilage

cooking losses

plate loss (or waste)

other (please list): ______________________________________________

With which product types are you most familiar based on your research? 

(Check all that apply.)

meat, poultry, and fish

dairy products

added fats and oils

fruits (fresh and processed)

vegetables (fresh and processed)

grain-based products

added sugars and sweeteners

Please return to Michaela Coglaiti (email to coglaiti@rti.org or fax to 919-541-6683)

After we have received responses from all of the potential panelists, we will schedule the panel 

event. If you have relevant expertise and are available for the scheduled date, we will contact you 

to set up a Panel Participant Consulting Contract and assist you with travel arrangements.
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Consumer-Level Food Loss Study Expert Panel Members

May 2008

Christine Bruhn, PhD

UC Davis

Food Science & Tech

109 Food Sci & Tech

Davis, CA 95616

530-752-2774 

cmbruhn@ucdavis.edu

Thomas Fungwe, PhD

USDA/Center for Nutrition Policy and Promotion

3101 Park Center Drive

Alexandria, VA 22302

703-305-0101

thomas.fungwe@cnpp.usda.gov

Helen H. Jensen, PhD

Iowa State University

Department of Economics

578E Heady Hall

Ames, IA 50011

515-294-6253

hhjensen@iastate.edu

Chery F. Smith, PhD, MPH

University of Minnesota

Dept. of Food Science and Nutrition

161 FScN

St. Paul, MN 55108

612-624-221

csmith@umn.edu

Parke E. Wilde, PhD

Tufts University

Friedman School of Science and Policy

150 Harrison Ave.

Boston, MA 02111

617-636-3495

parke.wilde@tufts.edu

ERS Representative: Jean Buzby, Ph.D.

 USDA/Economic Research Service

 1800 M Street, NW, Room S2080

 Washington DC, 20036-5831

 202-694-5370

 jbuzby@ers.usda.gov
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Consumer-Level Food Loss Study Expert Panel Meeting

Tuesday, May 13, 2008, 8:30-4:30

Hobbs Ground Floor Conference Room, RTI International

8:00-8:30 Arrival and refreshments

8:30-9:15 Introduction and overview presentation

 Discussion of product categories within each of the

 following groups:

-Meat, Poultry, and Fish (including Eggs)

-Nuts

-Dairy

-Dairy Beverages

-Fats and Oils

-Fruits

-Fruit Juices

-Vegetables

-Grain Products

-Added Sugars and Sweeteners

9:15-10:30 Estimates of purchases, consumption, and consumer loss;

 meat, poultry, fish, and eggs

10:30-10:45 Break

10:45-12:15 Estimates of purchases, consumption, and consumer loss—

 nuts, dairy beverages, dairy, fats & oils

12:15-1:00 Lunch

1:00-2:45 Estimates of purchases, consumption, and consumer loss—

 fruits, fruit juices, vegetables, grains, sugars & sweeteners

2:45-3:00 Break

3:00-3:30 Estimates of consumer-level loss for ingredient products

3:30-4:00 Estimates of food consumed at-home vs. away-from-home

4:00-4:30 Discussion of trends in consumer-level food loss over time

 and wrap-up

4:30 Adjourn
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Consumer-L l F d L St d Level Food Loss Study

Expert Panel Meeting

RTI International

Research Triangle Park, NC

May 13, 2008

RTI International is a trade name of 

Research Triangle Institute

3040 Cornwallis Road ■ P.O. Box 12194 ■ Research Triangle Park, NC 27709

Phone 919-541-7289 Fax 919-541-6683 E-mail muth@rti.org

Presentation Outline

 Loss adjustments and definition of consumer-level losses

 Review of literature on consumer Review of literature on consumer-level food loss level food loss

 Findings from interviews on losses in restaurants and 

foodservice

 Method of estimating food loss using existing data

z Consumer food purchase estimates

z Consumer food consumption estimates

2

z Resulting loss estimates

 Next steps for today
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Loss Adjustments for USDA’s Food 

Availability Data

Agricultural 

Production 

and

Net Imports

Loss from 

Primary 

Weight to 

Retail 

Weight

Loss from 

Retail to 

Consumer 

Level

Loss at 

Consumer 

Level

Calories 

Consumed

At h

3

Source: Based on Kantor et al.,“Estimating and Addressing America’s Food Losses,” 1997

• At-home

• Away-from-home

Definition of Consumer-Level Food Loss

 Loss that occurs in at-home and away-from-home settings:

z At-home includes purchases at grocery stores, warehouse 

stores, specialty grocery stores, farmers’ markets

z Away-from-home includes restaurants, school and company 

cafeterias, hospitals, nursing homes, catered events

 Sources of consumer-level losses:

z Inedible share (e.g., apples cores and chicken bones)

z Other consumer-level loss—focus of this study

 Cooking and preparation

4

Cooking and preparation

 Discarded because of expired use-by or open dates or 

overpreparation of foods

 Spoilage

 Plate waste
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Literature Review: Methods of Estimating 

Consumer-Level Food Loss

Dietary recall Individuals keep diaries or are interviewed on their 

food discards food discards

Archeological Trained observers examine garbage and then 

estimate or measure food discards

Plate 

examination

Researchers examine and then estimate or 

measure plate waste

5

Inferential Calculations are made using secondary data on 

food purchases and food consumption

Sources: Gallo, 1980; Buzby and Guthrie, 2002; Adams et al., 2005

Literature Review: 

Drawbacks of Estimation Methods

Dietary recall  Method is reactive—participants will alter their 

behavior because they are being observed

Archeological  Captures only plate waste and not other losses

 Misses liquids, foods fed to pets, and foods 

disposed in garbage disposal

 Costly and time consuming

Plate  Captures only plate waste and not other losses

6

examination

 Costly and time consuming

Inferential  Few datasets are available and their accuracy 

may be a concern

Sources: Gallo, 1980; Buzby and Guthrie, 2002; Adams et al., 2005
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Literature Review: 

Estimates of Consumer-Level Food Loss (1)

 Kantor et al. (1997 and 1998)

z 26% of edible food is lost

z Based on limited published studies and discussions with 

commodity experts

 Gallo (1980)

z Stated that previous studies found losses in the range of 7% 

(dietary recall & archeological methods) to 35% (inferential 

method)

 van Guarde and Woodburn (1987)

7

van Guarde and Woodburn (1987)

z 6% of food lost due to poor quality, spoilage, plate waste, etc.

z Based on 7-day diary and 3-day measurement for 243 

households

Literature Review: 

Estimates of Consumer-Level Food Loss (2)

 Engstrom & Carlsson-Kanyama (2004)

z 20% of food in foodservice settings is lost (> 50% due to 

pl t l ) late loss)

z Based on visual examination and interviews in restaurants 

and schools in Sweden

 Buzby & Guthrie (2002)

z 12% of calories served in school cafeterias lost due to plate 

waste

z Based on estimate available from a large, national 

representative study conducted in 1991-92

8

representative study conducted in 1991 92

 Marlette et al. (2005)

z 14-36% food loss in schools due to plate waste

z Based on photographing lunches before and after eating
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Literature Review: 

Factors Affecting Consumer-Level Food Loss

Seasonality More waste occurs in summer months

Age of 

children

Younger children waste more than older children

Gender Females waste more than males

Income Higher income individuals waste more than lower 

income individuals

Setting More waste occurs in hospitals and military mess

9

More waste occurs in hospitals and military mess

halls than in school and company cafeterias

Size of 

household

Larger households waste more than smaller 

households (likely due to more children)

Sources: Gallo, 1980; Buzby and Guthrie, 2002; Adams et al., 2005; Engstrom and Carlsson-Kanyama, 2004; and van 

Guarde and Woodburn, 1987)

Results of Earlier Interviews on Relative 

Losses in Households versus Foodservice

 Spoilage loss of dairy products, fresh fruits, fresh vegetables, 

and meat, poultry, and fish is likely greater in households.

z Restaurants/foodservice purchase more frequently and Restaurants/foodservice purchase more frequently and

monitor inventories more carefully.

 Cooking and preparation losses for meat, poultry, and fish is 

likely greater in households.

z Restaurants/foodservice use more pre-portioned and pretrimmed products.

 Cooking and preparation losses for fats and cooking oils is 

likely greater in restaurants/foodservice due to more frequent 

f f i ki h d

10

use of frying as a cooking method.

 Plate loss is likely greater for restaurants and foodservice.

z Portion sizes are much greater than for households and 

individuals have less control over portion sizes.
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Estimating Food Loss Using Existing Data

 Compare consumption data to retail purchase data for 

approximately 200 foods in the Food Availability Data System

z Method addresses all types of Method addresses all types of at-home losses including losses including spoilage, cooking loss, and plate waste

 Data sources:

z The Nielsen Company (2004)—retail food purchases from 

panel of households (does not include foodservice)

 Subtract inedible portion to determine edible portion of 

food purchases

z National Health and Nutrition Examination Survey (NHANES) 

(2003 2004) t l ti b d ll ith

11

- —actual consumption based on recall with

separate estimates for at-home and away-from-home

z Both data sources have weights or projection factors to 

calculate national estimates

Estimating Food Loss Using Existing Data 

(continued)

 Total purchases (TP) of food from retail (edible portion):

TP = NP ● (1 – P I) _

where

NP = estimate of purchases from Nielsen Homescan (or 

possibly, Perishables Group)

P_I = percentage of food that is inedible

 Note that some purchases might be used as ingredient

12

 Percentage of consumer-level food loss (P_CFL):

P_CFL = (TP – NC)/TP

where NC = estimate of at-home consumption from NHANES
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Examples of Issues in Applying Numerical 

Method

 Matching detailed foods in NHANES consumption data with 

UPC-level data in Homescan purchase data is challenging.

 Foods used only or predominately as ingredients are not readily 

identifiable in NHANES consumption data.

 Consumption estimates for some foods in NHANES are based 

on a small number of observations.

 Estimates of the inedible portion of foods needed to be revised 

and estimated for some categories.

13

 Fresh foods sold as random weight appear to be under 

represented in Nielsen Homescan purchase data (e.g., apples).

 Some foods in Nielsen Homescan purchase data are only in 

counts, not weight of product, or are in unprepared weight. 

Process for Estimating Purchase Quantities 

(1)

 Examined product modules and product descriptions in 

Homescan for each food category in the Food Availability Data

z Id tifi d l t d t d l d UPC d f Identified relevant product modules and UPC codes for 

foods with UPCs

 Excluded baby foods because not reflected in 

consumption data

 Combination products with up to 2 foods were split (e.g., 

frozen peas and carrots)

 Excluded medleys and combinations with more than 2 

foods

z Identified relevant product modules for random weight

14

Identified relevant product modules for random weight

foods

 Focused on ensuring consistency between purchase and 

consumption data while adhering to Food Availability Data 

definitions to the extent possible
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Process for Estimating Purchase Quantities 

(2)

 Types of adjustments to the data

z Converted count data to weights using average weights in USDA 

N ti l N t i t D t b f St d d R f ( l ) National Nutrient Database for Standard Reference (e.g., apples)

z Converted liquid volumes to weights = density of item 

(mass/volume) * volume of item

z Reduced purchase quantities by inedible (refuse) percentages:

 USDA National Nutrient Database for fresh fruits & vegetables

 Food & Agriculture Organization for fish & shellfish

 Direct measurements for canned products

z Converted purchase weights to cooked weights for products such 

15

as rice, dried beans, and dehydrated potatoes

z Converted nuts in shell to shelled weight

z Converted dozens of eggs to weight using average weight for 

each size egg times 12

z Converted juice concentrates to equivalent reconstituted weight

Process for Estimating Purchase Quantities 

(3)

 Types of adjustments to the data (continued)

z Scaled up purchase quantities for households that had 

nonparti i ti th i H ticipating months in Homescan

 Nielsen’s practice is to include households in the data set if 

they participated in 10 or more months of the year

 For households that participated in fewer than 12 months, 

adjusted purchases upward to account for missing months

z Used Perishables Group data as a supplement to estimate 

random weight purchase volumes

 Perishables Group data provide an alternative estimate of 

random weight purchase volumes for some categories of

16

random weight purchase volumes for some categories of

meat, poultry, fish, fruits, and vegetables

z In the future, may scale up total purchase quantities based on 

degree of “under-estimation” identified in a related study 

comparing Homescan to BLS Consumer Expenditures Survey
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Process for Estimating Consumption 

Quantities (1)

 Identified food categories and descriptions in NHANES for each 

food category in the Food Availability Data, focusing on 

consistency with products identified in the Homescan data

 Obtained mean grams of consumption in the population using 

the first day of the dietary recall data

z Food purchased in stores

z Food purchased in restaurants and foodservice

 Multiplied mean grams by the 2004 population: 292,303,000

17

 Multiplied by 365 days to obtain an annual estimate

 Multiplied by 0.0022046226 to convert from grams to pounds

Process for Estimating Consumption 

Quantities (2)

 Special Case 1: peanut butter

z Used mean weight of all the peanut butter products and half Used mean weight of all the peanut butter products and half

the mean weight of all the peanut butter sandwiches; 

summed both weights

 Special Case 2: fruit juices

z For combination fruit juices with two juices, calculated half 

the mean weight (e.g., apple-grape, pineapple-orange) and 

added it to mean weight for the respective single type juices

 Special Case 3: butter and margarine

18

Special Case 3: butter and margarine

z For butter-margarine products, calculated half the mean 

weight and added it to the mean weight for butter and for 

margarine.
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Process for Estimating Consumption 

Quantities (3)

 Special Case 4: fresh, canned, and frozen vegetables

z NHANES identifies whether vegetables were NHANES identifies whether vegetables were

consumed from fresh, canned, or frozen and thus 

consumption quantities can be estimated by type

z Apportioned “not further specified (NFS)” 

quantities using percentages by use in the Food 

Availability worksheets (percentages based on 

fresh weight)

19

z Some categories include both apportioned NFS 

weights and split weights from combination 

products (e.g., peas and carrots)

Process for Estimating Losses

 Calculated percentage difference between purchase 

estimate and consumption estimate for each food estimate and consumption estimate for each food

z Some categories such as “other” categories and 

ingredient foods were infeasible using this method

 Expert panel review of current and new estimates; 

provide informed guesstimates of actual losses

 Based on available information sources and expert

20

Based on available information sources and expert

guesstimates, prepare revised estimated for all foods
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Next Steps for Today

 Review food categories and definitions

 Review estimates of purchases consumption and percentage Review estimates of purchases, consumption, and percentage

consumer-level losses (excluding the inedible percentage)

z Also consider adjustments for portion of food used as an 

ingredient

 Develop estimates of percentage losses for ingredients

 Review estimates of food consumed at-home versus away from 

home

21

 Discuss trends in consumer-level loss since 1970

Discussion: Food Categories and Definitions

 Groupings to be considered:

z Meat, Poultry, Fish, & Eggs

z N t u s

z Dairy

z Dairy Beverages

z Fats & Oils

z Fruits

z Fruit Juices

z Vegetables

22

z Grain Products

z Added Sugars and Sweeteners

 Are the definitions suitable for understanding what is contained 

in each category, or do they need further clarification?
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Discussion: Estimates of Consumer-Level 

Food Loss (and Portion Used as Ingredient)

 What portion of each food would typically be used as an 

ingredient and therefore not counted in the NHANES 

consumption estimate? consumption estimate?

 For foods with positive consumer-level loss estimates, are the 

estimates reasonable for what would be expected?

z Also, consider the revised estimates using Perishables 

Group data, if available

 What foods are similar enough that we can transfer a reasonable 

consumer-level loss estimate?

23

 For foods with negative consumer-level loss estimates, how 

should we revise the estimate?

 For the remaining foods, what is your best guess for the 

consumer-level loss estimate?

Discussion: Estimating Loss for Ingredientonly Foods

 What are the most typical foods that each ingredient 

f d i d i (t h l thi k b t ibl l food is used in (to help us think about possible loss 

estimate)?

 Can we use a loss estimate from another food 

category for any of the ingredient foods?

 If a loss estimate is not available, what is your best 

guess for consumer-level loss across both at-home

24

guess for consumer-level loss across both at-home

and away-from-home uses?
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Discussion: Estimates of Food-at-Home 

versus Food Away-from-Home

 Are the estimates of food-at-home percentages using 

NHANES data reasonable? NHANES data reasonable?

 If not, how do we revise the estimates to make them 

more reasonable?

 Instead of using individual estimates for each food, 

should we determine a general estimate for similar 

foods based on the data from NHANES?

25

 How should we estimate values for foods that could 

not be estimated using NHANES?

Discussion: Trends in Consumer-Level Food 

Loss Over Time

 What trends have likely affected the percentages of 

consumer-level loss since the 1970? level loss since the 1970?

 Would you say consumer-level losses are generally 

increasing or decreasing since 1970?

 Are the trends likely similar across foods?

 What are your suggestions for estimating trends in 

consumer level loss?

26

-level loss?