a
Report No. SR93-03-02
Evaluation of
Methodologies to
Estimate Nonroad
Mobile Source Usage
prepared for:
Office of Mobile Sources
U.S. Environmental Protection Agency
March 19, 1993
prepared by:
Sierra Research, Inc.
1521 I Street
Sacramento, California 95814
(916)444-6666
Although the information described in this report has been
funded wholly by the United States Environmental Protection
Agency under Contract number 68-C1-0079 to Sierra Research, it
has not been subjected to the Agency's peer and administrative
review and is being released for information purposes only.
It may not necessarily reflect the views of the Agency and no
official endorsement should be inferred.
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Report No. SR93-03-02
EVALUATION OF METHODOLOGIES TO ESTIMATE
NONROAD MOBILE SOURCE USAGE
Contract No. 68-C1-0079
Work Assignment No. 08
prepared for:
Kevin Green, Work Assignment Manager
Certification Division
U.S. Environmental Protection Agency
March 19, 1993
Principal authors:
Philip Heirigs
Robert G. Dulla
Sierra Research, Inc.
1521 I Street
Sacramento, CA 95814
(916) 444-6666
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Table of Contents
Section page
1. Executive Summary 1-1
2. Introduction 2-1
3. Review of Nonroad Inventory Studies 3-1
4. Lawn and Garden Equipment 4-1
5. Airport Service Equipment 5-1
6. Recreational Equipment 6-1
7. Construction Equipment 7-1
8. Light Commercial Equipment 8-1
9. Industrial Equipment 9-1
10. Agricultural Equipment 10-1
11. Logging Equipment 11-1
Appendix A - Equipment Types Included in EPA's 1991 "Nonroad Engine
and Vehicle Emission Study"
Appendix B - Summary of Organizations Contacted to Develop Lot Size
Statistics
Appendix C - Data and Summary Statistics for Regression Analyses
Appendix D - Correspondence with U.S. Air Force Regarding Nonroad
Vehicle Usage on Military Installations
Appendix E — NEVES Inventory B Construction Equipment Activity Estimates
for the U.S., DC/MD/VA, and the San Joaquin Valley Air
Basin*
Appendix F - Summary of 1987 Construction Census Data by SIC Code*
Appendix G - Sample Copy of Dodge Construction Potentials Bulletin
for the Pacific Southwest (August 1992)
Appendix H - Crop-Specific Production Budgets for DC/MD/VA
Appendix I - Crop-Specific Projection Budgets for the San Joaquin Valley
Air Basin
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List of Tables
page
1-1 Comparison of Equipment Activity Estimates 1-6
3-1 Equipment Categories Used in EPA's 1991 Nonroad Inventory .... 3-2
3-2 Activity Indicators Used by EEA to Distribute PSR's State
Equipment Population Estimates to County Level 3-3
4—1 NEVES Inventory A Lawn and Garden Equipment
Activity Estimates 4-2
4-2 NEVES Inventory B Lawn and Garden Equipment
Activity Estimates 4-4
4-3 Types of Properties Maintained by Commercial Landscape Firms . 4-8
4-4 Associations Solicited for Information on Lawn and Garden
Equipment Populations and Usage Patterns 4-9
4-5 Metropolitan Areas Included in the American Housing Survey ... 4-12
4-6 Acres Mowed Per Hour as a Function of Implement Width
and Speed 4-16
4-7 Relative Impact of Current Activity Indicators on Total
Lawn and Garden Equipment Population in the DC/MD/VA
and SJV Areas 4-17
4-8 Lawn and Garden Equipment Distribution as a Function of
Lot Size 4-18
4-9 Calculation of Rear-Engine Riding Mower and Lawn & Garden
Tractor Populations Using Lot Size to Distribute National
Population Data 4-18
5-1 NEVES Airport Service Equipment Activity Estimates 5-2
5-2 Equipment Requirements by Aircraft Type, Ontario International
and Sacramento Metro Airports 5-4
5-3 Regression Model Using Airport Service Equipment as the
Dependent Variable and Total Passengers and Cargo Tonnage
as the Independent Variables 5-6
5-4 Regression Model Using Airport Service Equipment as the
Dependent Variable and Total Passengers and Cargo Tonnage
as the Independent Variables 5-6
5-5 Summary of Airport Service Equipment Population Calculation .. 5-7
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List of Tables continued ...
page
5-6 Airport Service Equipment Activity Utilizing Enplaned
Passengers and Cargo (Tons) as Activity Indicators 5-7
5-7 Bottom-Up Estimate of Airport Service Equipment Activity,
San Joaquin Valley Air Basin 5-8
5-8 Comparison of Airport Service Equipment Activity Estimates
for the San Joaquin Valley 5-8
6-1 NEVES Inventory A Recreational Equipment Activity Estimates .. 6-2
6-2 NEVES Inventory B Recreational Equipment Activity Estimates .. 6-3
6-3 Distribution of Off-Highway Motorcycles Based on MIC's
Definition and the Burke Survey 6-5
6—4 Comparison of Off-Road Motorcycle and ATV Populations
California DMV and MIC Estimates 6-6
6-5 Off-Highway Vehicle Travel on BLM Lands in California,
Fiscal Year 1991 6-7
6-6 Regression Model Using MIC Off-Highway Motorcycle Population
as the Dependent Variable and Rural Population as the
Independent Variable 6-11
6-7 Regression Model Using MIC Off-Highway Motorcycle Population
as the Dependent Variable and Rural Population as the
Independent Variable (Excluding California) 6-11
6-8 Regression Model Using MIC Off-Highway Motorcycle Population
as the Dependent Variable and Rural Population as the
Independent Variable (Excluding California and Utah) 6-12
6-9 Comparison of Nonroad Recreational Equipment Populations
for the SJVAB 6-13
7-1 NEVES Inventory A Construction Equipment Activity Estimates
for the United States 7-2
7-2 NEVES Inventory A Construction Equipment Activity Estimates
for the DC/MD/VA Area 7-3
7-3 NEVES Inventory A Construction Equipment Activity Estimates
for the San Joaquin Valley Air Basin 7-4
7-4 Off-Highway Fuel Cost Per Million Dollars Valuation by
SIC Code Based on the 1987 Census of Construction . 7-6
7-5 Industry Associations Solicited for Information Related
to Construction Equipment Activity 7-8
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List of Tables continued ...
page
7-6 Hours Worked by Construction Workers by Quarter for
Selected States 7-9
7-7 Regression Model Using Construction Equipment Population
as the Dependent Variable and Total Construction Valuation
as the Independent Variable 7-11
7-8 Construction Valuation Reported in DCPB for Communities
in the San Joaquin Valley Air Basin 7-12
7-9 Construction Equipment Population Estimates Utilizing DCPB
Construction Valuation (in Million $) as the Activity
Indicator 7-13
7-10 Comparison of Equipment Population Estimates for DC/MD/VA
and the SJVAB NEVES Inventory A and Inventory B vs.
This Study 7-13
7-11 Distribution of Construction Equipment by Vocation as
Reported in Construction Equipment Magazine 7-14
8-1 NEVES Inventory A Light Commercial Equipment Activity
Estimates 8-2
8—2 Regression Analysis for Light Commercial Equipment
Utilizing Total Construction Evaluation as the Independent
Variable 8-4
8-3 Comparison of Light Commercial Equipment Populations for
the DC/MD/VA Nonattainment Area and the SJVAB 8-5
8-4 Regression Analysis for Light Commercial Equipment
Utilizing Total Construction Evaluation and Oil Production
as the Independent Variable 8-6
9-1 NEVES Inventory A Industrial Equipment Activity Estimates .... 9-2
9-2 NEVES Inventory B Industrial Equipment Activity Estimates .... 9-3
9-3 Industrial Equipment Regression Results with Total
Construction Valuation and Manufacturing Employment
as Independent Variables 9-5
9-4 Comparison of Aerial Lift and Forklift Population Estimates
Using Manufacturing Employment Only vs. Manufacturing
Employment and Construction Valuation as Activity Indicators . 9-5
10-1 NEVES Inventory A Agricultural Equipment Activity Estimates .. 10-2
10-2 NEVES Inventory B Agricultural Equipment Activity Estimates ... 10-3
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List of Tables continued ...
page
10-3 Tractor Population by County for the San Joaquin Valley
Air Basin 1987 Census of Agriculture 10-5
10-4 Summary of Production Cost Estimate for Wheat 10-6
10-5 Equipment Speed as a Function of Implement Width 10-6
10-6 Corn Production Budgets for DC/MD/VA Area,
Conventional Tillage 10-9
10-7 Comparison of Diesel Fuel Usage Estimates for Corn
Production 10-10
10-8 Tractor Age Versus Annual Hourly Usage for California Farms . 10-12
10-9 Gasoline Versus Diesel Fraction for California Tractors 10-13
10-10 Summary of Bottom-Up Agricultural Activity Estimate
DC/MD/VA 10-15
10-11 Summary of Bottom-Up Agricultural Activity Estimate
San Joaquin Valley 10-16
10-12 Comparison of Top-Down and Bottom-Up Agricultural
Activity Estimates for the DC/MD/VA and SJV Areas 10-19
11-1 NEVES Logging Equipment Activity Estimates 11-2
11-2 Summary of Production and Equipment Requirements for
Standard Logging Systems 11-4
11-3 Regional Distribution of Logging Systems 11-5
11-4 Comparison of Logging Equipment Activity Estimates for
the SJVAB 11-6
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List of Figures
paee
4-1 Categorization of Lawn and Garden Equipment According
to Anticipated Use 4-6
4-2 Lot Size Distribution for Selected Areas, Single
Family Housing Units 4-13
6-1 Recreational Vehicle Usage by Month 6-9
6-2 Motorcycle Usage by Day of Week 6-9
10-1 Annual Hourly Usage vs. Tractor Age, 1990 California
Tractor Survey 10-12
10-2 Agricultural Temporal Activity Distribution, DC/MD/VA 10-18
10-3 Agricultural Temporal Activity Distribution, SJV 10-18
11-1 Logging Regions in the U.S 11-6
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See Disclaimer on Cover
1. EXECUTIVE SUMMARY
The Clean Air Act Amendments of 1990 directed EPA to evaluate the
contribution of nonroad engines and vehicles to air pollution in
nonattainment communities. The result of that directive was the
"Nonroad Engine and Vehicle Emission Study" (NEVES) which was published
by EPA in November 1991. That study quantified emissions of nonroad
vehicles in 24 ozone and/or carbon monoxide nonattainment areas.
Although the methodologies evolving from that work have significantly
improved the state-of-the-art in developing nonroad emission inventory
estimates, there may be different methodologies that could more
accurately reflect the distribution and usage of equipment that actually
occurs within those communities.
A total of 10 nonroad equipment categories were considered in NEVES,
which constitute over 80 individual equipment types. For the current
study, eight of those equipment categories were investigated:
• Lawn and Garden Equipment,
• Airport Service Equipment,
• Recreational Equipment,
• Construction Equipment,
• Light Commercial Equipment,
• Industrial Equipment,
• Agricultural Equipment, and
• Logging Equipment.
Overview
Sierra Research (Sierra) provides support to the Certification Division
of EPA's Office of Mobile Sources under a contract entitled "Analytical
and Testing Support for the Certification Division at EPA's Motor
Vehicle Emissions Laboratory." Work Assignment 1-08 of that contract
directed Sierra to investigate a variety of issues related to nonroad
mobile source usage. The scope of that effort identified three main
tasks:
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• Review of recent nonroad emission inventory studies and
investigation of alternative data sources;
• Development of alternative methodologies for estimating nonroad
activity and emissions; and
• Evaluation of methodologies.
Although each of the topics outlined above was presented as a separate
task in the Scope of Work, the evaluation of each task was not performed
independently. As individual equipment categories were analyzed, all
three issues were considered. Thus, the discussion below expands upon
elements of all three tasks as the evaluation of alternative
methodologies for each equipment category is presented.
Review of Nonroad Studies
The review of previous nonroad inventory studies focused on NEVES and
recent work performed by and for the California Air Resources Board
(GARB). In NEVES, two sets of inventories were prepared ("Inventory A"
and "Inventory B"). The first was based on information and data
compiled by an EPA contractor, while the second was based on information
submitted by industry. In this study, the activity estimates (in annual
bhp-hr) for both inventories were compiled and compared, and the
methodologies utilized to develop local-level equipment population and
usage estimates were assessed.
Because of recent and pending efforts by GARB to regulate nonroad
equipment, new efforts have been undertaken to more accurately assess
the nonroad inventory in California. Several studies to support
regulatory development have included emission estimates for nonroad
vehicles and equipment, while others have focussed exclusively on
inventory development. Among the equipment categories that have been
investigated are heavy—duty construction equipment, lawn and garden
equipment, and agricultural equipment. Although prepared for
California, some of the techniques employed in those studies were
utilized in developing new methodologies for this work (e.g.,
categorization of lawn and garden equipment by residential versus
commercial usage; development of "bottom-up" methodologies for
agricultural equipment activity estimates).
Development of Alternative Methodologies
The studies outlined above have primarily relied on "top-down"
methodologies to determine local-level equipment population and usage.
These methodologies typically scale national or state-level equipment
populations to the local level (e.g., county or air basin) using local
statistics that are related to equipment usage (e.g., the number of
households in a community may be used to allocate lawnmowers). The
rationale behind this approach is that national-level equipment
populations and usage are generally available for most equipment types,
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and these should be proportional to certain data indicative of equipment
usage (provided the so-called "activity indicators" are chosen with
care). However, a problem with this approach is that local nuances in
equipment usage patterns are often lost when relying on this
methodology. For example, NEVES Inventory A allocated 450 off-road
motorcycles to California's San Joaquin Valley, whereas California
Department of Motor Vehicles (DMV) records indicate that approximately
15,000 are registered in that area. Given the rural nature of the
San Joaquin Valley, motorcycle population and usage would be expected to
be more closely approximated by the DMV records than the estimate
prepared for NEVES.
An alternative to the "top-down" procedure is the "bottom-up" method
whereby local information forms the basis of the calculation. For
example, Sierra has developed a methodology to estimate agricultural
field emissions that relies upon farm cooperative estimates of equipment
activity (i.e., hours/acre per operation and average equipment
horsepower) for producing individual crop types. This information is
coupled with the number of acres under cultivation (by crop type) to
arrive at an estimate of equipment activity for the area of interest.
The farm cooperative data show that there are enormous differences among
crop equipment operations. Some crops, such as pasture, may require
only one or two equipment operations, whereas others, such as cotton or
tobacco, may require 20 separate equipment operations. The differences
in crop activity lead to significant temporal and spatial variations in
county emission estimates, particularly when contrasted with those that
assume the same level of activity for all crops. Clearly, the same
argument can be applied to other equipment categories, and the
evaluation of bottom-up approaches for all equipment categories is an
important step in assessing potential improvements to local-level
nonroad equipment activity estimates.
Considering that the NEVES report exclusively employed a top-down
approach for estimating local-level equipment activity, the main focus
of this task was to evaluate the potential of developing bottom-up
methodologies for determining nonroad equipment usage. A primary
component of this evaluation was to assess the availability of data
required to develop such procedures. Thus, considerable effort was
expended in contacting industry associations and government agencies
(federal, state, and local) for data related to equipment usage
patterns. For cases in which a bottom-up approach was not considered
feasible, effort was directed at identifying data sources that would
improve upon current top-down procedures.
The above evaluation was carried out for the eight equipment categories
considered in this study; a brief summary of the results follows.
Lawn and Garden Equipment — Considerable effort was expended in attempts
to locate information that could be used to develop a bottom-up
methodology for lawn and garden equipment. This consisted of extensive
contact with industry associations (e.g., Professional Grounds
Management Society), local and state Parks Departments, the National
Park Service, state Agricultural Extension services, and state highway
maintenance agencies. Although information was identified that would
allow for the development of a bottom-up procedure for certain
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specialized cases (e.g., golf courses), the information was not
comprehensive enough to form the basis of a bottom-up approach
encompassing the entire lawn and garden equipment usage regime. Thus, a
top-down procedure was developed in which equipment was stratified
according to commercial and residential use. The commercial equipment
was allocated according to employment in the horticultural service
industries, while residential equipment was allocated according to lot
size or single family housing units, depending upon equipment type.
(Lot size was considered a good indicator since equipment such as lawn
and garden tractors would not be expected to be found on small lots.)
Although Inventory A for NEVES utilized two activity indicators (single
family housing units and horticultural service employment), these
activity indicators were applied equally to residential and commercial
equipment. This may not provide reliable estimates when allocating an
equipment type that is used primarily in commercial or primarily in
residential applications.
Airport Service Equipment - A bottom-up procedure was developed that
expanded on previous airport inventory methodologies in which total
equipment requirements (e.g., minutes of belt loader operation) by
aircraft type were estimated. Because the Federal Aviation
Administration publishes data on the number of flights by aircraft type
for each commercial airport in the country, data on flights by aircraft
type are readily available. This approach has the added advantage of
being compatible with current inventory procedures for aircraft. Before
implementing this approach, however, additional information on equipment
requirements for cargo operations needs to be collected (e.g., minutes
per ton of cargo loaded/unloaded by equipment type). Finally, an
alternative top-down procedure was proposed in which total passengers
and cargo tonnage served as the activity indicators. Because equipment:
requirements are much greater for aircraft with a high passenger
capacity (although not confirmed, it is also anticipated that equipment
usage is a strong function of cargo tonnage), these indicators are
likely to provide a better indication of equipment usage than total air
carrier operations, which was the activity indicator used in NEVES.
Recreational Equipment - Again, some information was located that could
potentially be used in the development of a bottom-up methodology (i.e.,
visitor-hours in Bureau of Land Management areas), but its coverage was
not complete (e.g., ridership on other public and private lands could
not be determined). An alternative top-down approach was proposed,
however, that allocated recreational equipment according to rural
population. This is felt to provide a much better estimate of equipment
activity than the number of motorcycle dealerships as used in NEVES.
(In evaluating methodologies for allocating recreational equipment, it
was determined that motorcycle dealerships are typically located in
urban areas, whereas ridership generally occurs in more rural
environments.)
Construction Equipment — Although effort was directed at developing a
bottom-up approach for construction equipment, data necessary to perform
such a calculation were not located. However, an alternative top-down
method was proposed that utilized metropolitan area statistics on
construction valuation as the activity indicator. Because of the mobile
nature of the construction industry, there were concerns about retaining
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NEVES's use of construction employment as the activity indicator since
employment data are gathered according to where the business is located,
not where the work is actually performed.
Light Commercial Equipment — This category consists of equipment types
such as generator sets, pumps, air compressors, and welders. Because of
the variety of applications for which these can be used, development of
a bottom-up approach was not considered feasible. However, a top-down
methodology was proposed which relied on construction valuation as the
activity indicator because many of these equipment types are used in the
construction industry. This is considered an improvement over the NEVES
methodology which relied upon wholesale establishments to allocate this
equipment category to the local level.
Industrial Equipment - This category consists of equipment such as
forklifts, sweepers, and material handling equipment, and because of the
variable nature of the equipment, a bottom-up approach is not feasible.
Also, the activity indicator utilized in NEVES (i.e., manufacturing
employment) is considered an appropriate choice. The only potential
improvement offered is to also include a construction indicator when
allocating forklifts and aerial lifts,, because these equipment types are
also used in the construction industry.
Agricultural Equipment - Considerable effort was spent in determining if
the bottom-up methodology developed by Sierra for the San Joaquin Valley
could also be applied to other areas of the country. In conversations
with various state cooperatives, it was discovered that equipment usage
by crop type is available for most parts of the U.S. Further,
information was located on horsepower-time requirements for field
operations (e.g., plowing, chiseling, etc.) that could form the basis
for crop—specific equipment usage estimates in areas where the
information does not currently exist. Thus, a bottom—up method for
agricultural equipment is considered a very viable option. The top—down
procedure utilized in NEVES relied on county-level tractor population
data contained in the 1987 Census of Agriculture to scale national
equipment populations to the local level. Several improvements to that
approach were suggested in this study, also based on information
contained in the agricultural census (e.g., allocating local-level
combine population based on census data on combine population rather
than tractor population).
Logging Equipment - A bottom-up methodology was also proposed for
estimating equipment usage in logging operations. Contacts with logging
interests revealed that a few standard methods are used to harvest
timber, and the equipment requirements were obtained as a function of
board footage harvested. Since board footage appears to be readily
available (by county) from state tax agencies, this approach is
considered viable, subject to review of the equipment usage information
by the U.S. Forest Service and the logging industry. (NEVES made use of
county-specific logging employment data to allocate national equipment
populations to the local level. However, as with the construction
category, employment data by county are based on where the business is
located, not necessarily where the logging activity takes place.)
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Each of the methodologies developed above was evaluated by performing
sample calculations for two representative nonattainment areas: the
Washington, D.C. metropolitan area (DC/MD/VA) and the San Joaquin Valley
(SJV) of California. This provided not only numerical comparisons with
previous estimates, but also a reasonable assessment of the difficulties
associated with gathering the necessary data (and the availability of
data). Although complete numerical comparisons are provided in the
category-specific sections of this report, Table 1-1 outlines in general
terms the differences between the equipment activity estimates prepared
for NEVES Inventory A and the results of this study. As seen, the
differences are not consistent, with the results of this work showing
increases in activity for some equipment categories, decreases for
others, and very similar estimates for others.
Table 1-1
Comparison of Equipment Activity Estimates
This Study Versus NEVES Inventory A
(Increase [+] or Decrease [-] Relative to NEVES)
Equipment Category
Lawn and Garden
Residential Equipment
Commercial Equipment
Airport Service
Recreational
Construction
Light Commercial
Industrial (Forklifts Only)
Agricultural
Logging
Heavy-Duty Diesel
Chainsaws
DC/MD/VA
+
-
NA'
**
+
-
-
NA
NA
SJV
+
-
+
+
+
-
-
-
* NA: Not analyzed.
-: Insignificant change in activity estimate.
Recommendations
For many of the equipment categories outlined above, alternative
methodologies to estimate equipment usage have been proposed. Some of
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them can be easily implemented, while a full evaluation of others would
require additional data or resources. Some areas in which additional
effort should be considered include:
• Evaluation of lawn and garden equipment usage to determine the
difference between commercial and residential ownership and
usage characteristics;
• Evaluation of the equipment requirements for aircraft cargo
operations;
• Evaluation of the use of multiple activity indicators for
equipment in the construction category that is used in several
applications; and
• Coordination with the U.S. Forest Service to formally review the
alternative logging equipment methodology proposed in this work.
Finally, this study identified some of the difficulties associated with
developing very detailed, county-level nonroad equipment activity
estimates. However, air quality planners are more frequently utilizing
models that require emissions estimates on an even finer level of
geographical (and temporal) detail. Thus, in the long term, additional
effort should be devoted to the investigation of spatially allocating
equipment usage at the sub—county level, particularly with respect to
how this might be applied to current grid-cell level air quality
modeling approaches.
it it it
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2. INTRODUCTION
Background
In response to a mandate from the 1990 Clean Air Act Amendments, EPA
completed a detailed study of emissions from nonroad engines and
vehicles1-2*. That study employed a "top-down" approach to quantify the
emissions of nonroad vehicles in a wide range of ozone and carbon
monoxide (CO) nonattainment communities. While the methodologies
evolving from that work have significantly improved the state of the art
in developing nonroad emission inventory estimates, there is concern
that the activity levels estimated for specific nonattainment
communities may not correctly represent either the distribution or usage
of equipment that actually occurs within those communities.
Due to the magnitude of nonroad engine/vehicle emissions estimated in
the EPA study, it is imperative that the contribution of this source
category be directly related to accurate estimates of local activity.
The need for accurate inventory estimates will rise steadily in coming
years as nonattainment communities struggle to identify cost-effective
control strategies to ensure that they reach and maintain the National
Ambient Air Quality Standards.
Technical Approach
The purpose of this study was to determine whether the current "top-
down" methodologies that have been employed to develop emission
inventories for nonattainment communities accurately reflect local
activity levels. To accomplish this objective, the effort was divided
into three main tasks:
1. Review of recent nonroad emission inventory studies and
investigation of alternative data sources;
2. Development of alternative methodologies for estimating
nonroad activity and emissions; and
3. Evaluation of methodologies.
A key element of this work was concentrating data collection and
evaluation efforts on two representative nonattainment communities.
Because methodologies developed as part of this work could eventually
Superscripts denote references provided at the end of each section.
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form the basis of a generic nonroad model, it was important to evaluate
data availability and the effort required to compile the information
needed for suggested alternative methodologies. Any approach requiring
excessive data collection and compilation would likely meet with
resistance from local air quality planners who would ultimately have to
employ these methods.
The following communities were selected to evaluate alternative
methodologies:
• Washington. DC Metropolitan Area (DC/MD/VA) - This community
represents a large urban nonattainment area with a multi-state
geographical boundary. The problems of collecting data from
different state and local agencies (i.e., Virginia, Maryland and
the District of Columbia) were well represented by this
community.
• San Joaquin Valley (SJV) - This area is the focus of an ongoing
study to accurately represent 1990 emissions by source category.
Sierra is in the process of completing a detailed "bottom-up"
estimate of agricultural field emissions for the SJV. Thus, by
focusing on the SJV, it was possible to compare "top-down" and
"bottom-up" methodologies for agricultural equipment. Further,
the SJV represented a more rural area, and differences in the
availability of information between urban and rural areas could
be evaluated.
Although it was not possible to evaluate data availability for all
portions of the U.S., the communities above provided a reasonable basis
for such an assessment. Further, as data sources were identified and
evaluated, the geographic coverage of those sources was considered.
Methodologies to Determine Nonroad Equipment Activity
i
As alluded to above, methodologies to estimate nonroad equipment usage
can broadly be categorized as "top-down" or "bottom-up." Historically,
nonroad equipment activity and emissions estimates have been prepared
utilizing top-down techniques in which national or state-level equipment
populations are scaled to the local level (e.g., county or air basin)
using local statistics (e.g., number of households, employment in
particular industries, etc.). The rationale behind this approach is
that national-level equipment populations and usage are generally
available for most equipment types, and these should be proportional to
certain statistics indicative of equipment usage (provided the so-called
"activity indicators" are chosen with care). However, reliance on
national-level equipment population estimates and usage levels is akin
to assuming that all communities in the U.S. experience the same annual
growth rate in highway travel. The fact is that growth rates in highway
travel vary dramatically by community; it is believed that nonroad
activity levels exhibit significant variations as well.
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Bottom-up methodologies for estimating equipment activity rely on local
information to form the basis of the calculations. For example, Sierra
developed a methodology to estimate agricultural field emissions in the
SJV that relied upon farm cooperative estimates of equipment activity
(i.e., hours/acre per operation per crop and average horsepower by
operation per crop) and county statistics on the number of acres under
cultivation by crop. The farm cooperative data show that there are
enormous differences among crop equipment operations. Some crops, such
as pasture, may only require one or two equipment operations, whereas
others, such as cotton, may require 20 separate equipment operations.
The differences in crop activity lead to significant temporal and
spatial variations in county emission estimates, particularly when
contrasted with those that assume the same level of activity for all
crops.
There are advantages and disadvantages in utilizing either the top—down
or bottom-up methodology to estimate nonroad equipment usage. The data
requirements for top—down methodologies are generally much less severe
compared to bottom-up approaches; however, local nuances in usage
patterns are often lost when relying on national data that have been
scaled to the local level. On the other hand, while bottom-up
approaches can be tailored to include very detailed information on local
conditions, the data needed to perform the estimates are often time-
consuming to obtain and compile or are entirely unavailable. The focus
of this work, then, was to evaluate the feasibility of developing
bottom-up procedures that rely upon information that could be readily
compiled by local air quality planners. For cases in which bottom-up
methodologies were not considered feasible, means to make the current
top-down procedures more area—specific were investigated.
An additional issue that must be considered when implementing a bottom-
up methodology is the "coverage" of vehicle usage represented by the
approach. For example, agricultural tractors can be used for purposes
not directly related to crop operations (e.g., powering grain elevators,
hauling animal feed), and a methodology that only accounts for crop
operations may result in an under-estimation of equipment activity if it
is determined that other activities significantly contribute to the
overall equipment usage.* There are two ways to treat these cases:
(1) ensure that the proposed bottom-up methodology accounts for the vast
majority of equipment usage, or (2) determine the percentage of
equipment usage that is not represented by the approach and apply a
different methodology (either bottom-up or top-down) to the remaining
equipment. (In this work, it is felt that the proposed bottom—up
methodologies account for the majority of usage experienced by the
subject equipment types.)
For agricultural tractors, it is felt that "miscellaneous" activities
account for a small portion of the overall usage, particularly since the
load experienced by the engine is much greater for activities related to
land cultivation compared to miscellaneous hauling.
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Nonroad Equipment Categories
Because of the large number of individual equipment types used in the
nonroad environment, EPA has categorized equipment types according to
general function. In its 1991 nonroad study, EPA listed ten separate
equipment categories, eight of which are discussed in this report.
These include lawn and garden equipment, airport service equipment,
recreational equipment, light commercial equipment, industrial
equipment, construction equipment, agricultural equipment, and logging
equipment. Although categorization of equipment types made.allocation
of nonroad equipment populations to the local level more tractable, EPA
assumed the same equipment mix for all nonattainment communities, which
led to a lack of specificity at the local level. For example, the same
relative percentage of lawn and garden tractors would not be expected
when comparing lawn and garden equipment populations in Atlanta and
New York City. This is one example of where the current top-down
methods could be improved.
Organization of the Report
Immediately following the Introduction, Section 3 provides the reader
with a review of recently completed nonroad inventory studies. Emphasis
was placed on EPA's 1991 "Nonroad Engine and Vehicle Emission Study" and
on several studies performed under contract to GARB. The remaining
sections discuss each of the nonroad equipment categories separately.
Section 4 covers lawn and garden equipment, while airport service
equipment is discussed in Section 5. Section 6 details recreational
equipment. The construction equipment category is treated in Section 7,
light commercial equipment in Section 8, and industrial equipment in
Section 9. Finally, agricultural equipment is discussed in Section 10,
and Section 11 contains information on logging equipment. Several
appendices then provide more detailed information and data, as
referenced throughout the report.
References for Section 2
1. "Nonroad Engine and Vehicle Emission Study - Report," U.S.
Environmental Protection Agency, Office of Air and Radiation,
November 1991.
2. "Nonroad Engine and Vehicle Emission Study - Appendixes," U.S.
Environmental Protection Agency, Office of Air and Radiation,
November 1991.
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3. REVIEW OF NONROAD INVENTORY STUDIES
As outlined in the previous section, the methodologies used to determine
nonroad vehicle and engine activity (e.g., bhp-hr/yr) can be broadly
categorized as top-down or bottom-up. By far, the great majority of
previous emission estimates have relied on a top-down approach in which
national-level data on equipment population are scaled by a local
statistic such as employment. EPA and GARB have recently generated
emission estimates for nonroad equipment and have generally relied on
this methodology. It is worthwhile to review these studies to assess if
the data sources utilized in allocating activity could also be useful in
a bottom-up approach, and to assess how top-down methodologies might be
improved. Further, an understanding of existing methodologies is
important in estimating uncertainties associated with developing local
inventories.
EPA Nonroad Engine and Vehicle Study
The 1990 Clean Air Act Amendments directed EPA to determine the
emissions impact of nonroad vehicles and equipment on nonattainment
areas and recommend emission standards if these sources were found to be
significant contributors to nonattainment. The result of the first part
of that directive was the "Nonroad Engine and Vehicle Emission Study"
(NEVES) published by EPA in November 1991.'•* Because of the limited
timeframe in which emission inventories were to be developed, EPA
contracted some of this effort to outside firms. Energy and
Environmental Analysis, Inc. (EEA) was responsible for developing
population and activity estimates for nonroad equipment and engines.3
In addition to the estimates developed by EEA, industry provided
activity and usage estimates for several categories of nonroad
equipment. EPA used these data sets to calculate two separate emission
inventories (i.e., "Inventory A," which relied primarily upon data
developed by EEA; and "Inventory B," which incorporated industry-
supplied data for many equipment types). EEA's analysis and the
industry-supplied data are briefly described below.
Equipment Population and Usage Estimates Developed by EEA - Because of
the large number of equipment types used in the nonroad environment,
equipment was categorized according to general use patterns. For
example, lawn mowers, leaf blowers, and string trimmers can be broadly
categorized as lawn and garden equipment, while pavers, graders, and
cranes can be grouped as construction equipment. The equipment
categories chosen for this work are given in Table 3-1, with examples of
specific equipment types included in each category. A complete
breakdown of the 78 equipment types included in the nonroad emission
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Table 3-1
Equipment Categories Used in EPA's 1991 Nonroad Inventory
Equipment Category
Lawn and Garden
Airport Service
Recreational
Marine /Recreational
Light Commercial
Industrial
Construction
Agricultural
Logging
Marine/Commercial
Equipment Types
Lawn Mowers , Leaf Blowers , Trimmers
Airport Service Equipment
ATVs, Off-Road Motorcycles, Golf Carts
Inboard, Outboard, Sailboats
Generators, Pumps, Compressors, Welders
Aerial Lifts, Fork Lifts
Pavers , Graders , Cranes
Agricultural Tractors , Combines , Balers
Chainsaws (>4 HP) , Skidders
Ocean-Going Marine, Harbor and Fishing Vessels
study is given in Appendix A. (Population and activity estimates were
developed for each of these equipment types_.)
The first step in estimating activity was to determine equipment
population. To accomplish this, EEA utilized data supplied by Power
Systems Research (PSR). PSR develops national-level population counts
by first determining the total number of engines placed into service
each year. These data are gathered from engine sales reported by
original equipment manufacturers (OEMs), and product literature is used
to determine specific engine/equipment application. PSR then applies an
attrition rate to each model year according to expected engine life
(which is developed from surveys of end users). By summing the
remaining equipment for each model year, the total population in the
field at any given time is obtained.
Since PSR supplied equipment population on only national and state
levels, EEA developed a methodology to distribute the statewide PSR data
to the county level. This involved establishing a statistical
relationship between the statewide equipment population and specific
activity indicators for each equipment category. In most cases, a
linear relationship was established between equipment population and the
activity indicator. For example, the light commercial equipment
population was plotted against the number of wholesale trade
establishments for each state included in the study. A regression
analysis was performed which resulted in coefficients (i.e., slope and
intercept in the case of a linear model) that represented the best fit
of the data. The coefficients determined from the state data were then
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used in conjunction with the county-level activity indicator (the number
of wholesale trade establishments in the above example) to estimate
county-level equipment populations. The activity indicators used by EEA
for each equipment category are summarized in Table 3-2.
Although a regression technique results in improved local-level
equipment populations and activity estimates for most equipment
categories, imbedded in this methodology is the assumption that the
state—level equipment populations used in the regressions are valid.
Because PSR employs its own activity indicators to allocate equipment
populations to the state level, there could be instances in which the
state-level data are in error. Thus, EEA established a set of
statistical criteria to be met in its regression analyses. For cases in
which these criteria were not met, the regression technique was not
employed and a more traditional scaling of national population data by
the local-to-national activity indicator ratio was used.
Annual hourly usage, horsepower, and load factors also were obtained to
complete the activity calculation (in bhp-hr/yr); this information was
supplied by PSR. Based on survey results from over 40,000 respondents,
PSR estimated these parameters for each equipment type. Further, PSR
determined geographical differences in equipment usage for six regions
of the country: northeast, southeast, southwest, northwest,
northcentral, and southcentral. This information included an accounting
of seasonal variations in activity.
Table 3-2
Activity Indicators used by EEA3 to Distribute
PSR's State Equipment Population Estimates to County Level
Equipment Category*
Lawn and Garden
Airport Service
Recreational
Light Commercial
Industrial
Construction
Agricultural
Logging
Activity Indicator (s)
Single Family Housing Units
Landscape and Horticultural Service Employees
Air Carrier Operations
Motorcycle Dealerships
Wholesale Establishments
Total Manufacturing Employees
Total Construction Employees
Agricultural Services Employees
County-Level Logging Activity (Employees)
Since recreational and commercial marine vessels are not included in
the work assignment, they have not been included in this table.
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Equipment Population and Usage Data Supplied by Industry - Manufacturers
and manufacturer associations also provided population and usage data
which was used by EPA to construct an alternative inventory (i.e.,
Inventory B). The information supplied is summarized as follows.
• The Equipment Manufacturers Institute (EMI) provided population
data, average horsepower, annual use, and load factors on
•various types of construction and agricultural equipment.
Construction equipment population data were based on work
performed by MacKay & Company for the Associates Commercial
Corporation and Construction Equipment magazine. The national
construction equipment population determined by MacKay was
scaled to the county level based on industry sales data from
1983 to 1987. Agricultural equipment populations were based on
the 1987 agricultural census. Usage, horsepower, and load
factor data developed by EMI were based on a survey of principal
manufacturers of construction and agricultural equipment.
• The Outdoor Power Equipment Institute (OPEI) submitted
population data, average horsepower, annual use, and load
factors for various types of lawn and garden equipment.
Population estimates appeared to have been developed by summing
historical sales records over an assumed life span of the
product.
• The Portable Power Equipment Manufacturers Association (PPEMA)
provided population, usage, horsepower, and load factor
information on portable 2-stroke gasoline equipment (e.g.,
chainsaws). This information was cpmpiled by Heiden Associates,
Inc. The population data were developed from historical
shipment data to which an assumed average life was applied (for
both commercial and residential applications). Annual hourly
usage estimates were provided for commercial and residential
equipment. Finally, county-level populations were developed
with regression techniques similar to those used by EEA in the
1991 NEVES. However, Heiden's activity indicators accounted for
the differences in the urban and rural population in
nonattainment areas, arguing that some equipment (e.g.,
chainsaws) is more likely to be found in a rural setting.
• The Industrial Truck Association (ITA) supplied data on
population, annual use, and load factors for industrial
forklifts. The national and local population data were based on
1983 to 1990 shipment information from ITA member companies.
• The International Snowmobile Industry Association (ISIA)
provided information on population and annual usage for
snowmobiles.
• The Motorcycle Industry Council (MIC) provided population
estimates and survey data on the annual mileage of ATVs and off-
road motorcycles. This information was compiled from MIC's
"Motorcycle Statistical Annual."
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Recent GARB Nonroad Inventory Studies
Because of recent and pending efforts by GARB to regulate nonroad
equipment, new efforts have been undertaken to more accurately assess
the nonroad inventory in California. Several studies to support
regulatory development have included emission estimates for nonroad
vehicles and equipment, while others have focussed exclusively on
inventory development. These include:
1. "Feasibility of Controlling Emissions from Off-Road, Heavy-Duty
Construction Equipment", prepared by EEA;4
2. "Technical Support Document for California Exhaust Emission
Standards and Test' Procedures for 1994 and Subsequent Model Year
Utility and Lawn and Garden Equipment Engines", prepared by
Booz, Allen, & Hamilton (BAH);5
3. "Development of an Off-Highway Mobile Source Emissions Model"
which has been contracted to EEA;6 and
4. "SJVAQS/AUSPEX Agricultural Emissions Inventory," prepared by
Sierra Research.7
Below is a summary of these studies, with particular emphasis on the
methodologies used to develop activity estimates.
Off-Road. Heavy-Duty Construction Equipment - This report was prepared
for GARB by EEA in 1988. The primary focus.was on technology and
potential emission standards; however, baseline and future-year
inventories were developed to assess the emissions impact of regulating
this equipment category. As with other inventories of this kind, PSR
data on equipment population, horsepower, annual usage, and load factor
were used to calculate emissions. The data were stratified by northern
and southern sections of the state; thus, emission estimates could be
more area-specific than is normally the case. In addition to
construction equipment, material handling (e.g., forklifts) and
agricultural equipment were included in the population estimates.
Although EEA did not report emissions for each county in California,
GARB has used these statewide equipment data to develop county-specific
inventories for some categories of nonroad equipment.8 GARB includes
construction, mining, and logging equipment in a generalized "Heavy-Duty
Non-Farm" category, and it used the construction equipment population
and usage data to determine emissions from this category. The statewide
data were scaled by each county's construction valuation, mining
production, and logging production to determine emissions from each of
the subcategories. Agricultural equipment data were apportioned to each
county on the basis of harvested acreage. Data on material handling
equipment were not used in inventory development.
Utility and Lawn and Garden Equipment - The technical support document
for this rulemaking was prepared by Booz, Allen, & Hamilton (BAH), and
it contains a fairly detailed emission estimate for this equipment
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category. As with the EPA inventory work, BAH utilized strictly a top-
down approach in estimating localized emissions. However, rather than
relying on population, usage, horsepower, and load factor data from PSR,
BAH instead used primarily California-specific information that was
supplied by manufacturers and trade associations. Data on yearly sales
were adjusted to account for attrition using scrappage rates that were
developed by GARB in 1982,9 and county-level emission estimates were
determined by scaling the statewide inventory based on the number of
single family housing units.
Because there is a vast difference in activity patterns between
equipment used commercially and that used in consumer (or household)
use, BAH estimated equipment populations according to intended use. BAH
relied on manufacturer estimates, the 1982 GARB study, and a report on
emissions from two-stroke equipment performed by Heiden Associates for
the Portable Power Equipment Manufacturers Association (PPEMA)10 to
determine this split.
Nonroad Inventory Computer Model - EEA and its subcontractor, Systems
Applications, International (SAI), were recently retained by GARB to
develop an off-highway mobile source inventory model. Although this
effort is in the initial phases, a review of the proposed work scope is
worthwhile. The project is to be divided into three main tasks:
(1) review of current off-highway methodologies, (2) compilation of off-
highway emission parameters, and (3) development of emission algorithms.
In Task 1, EEA plans to review various nonroad reports and analyses that
have been performed by and for GARB and EPA over the last several years.
These include many of the studies summarized here, as well as additional
new data and methodologies that are anticipated to be submitted by
industry groups in response to potential regulatory action by EPA. Task
2 is devoted to compiling engine emission parameters to be used in the
computer model. This would include choosing the more important
emission-related parameters by which engine types would be stratified,
e.g., Diesel versus spark ignition, 2-stroke versus 4-stroke, valve
train design, etc. Additionally, EEA will investigate duty cycles, in-
use adjustments, temperature corrections, and scrappage rates. Finally,
emission algorithms and computer code (FORTRAN) will be developed in
Task 3.
San Joaquin Valley Agricultural Emissions Study - As part of a detailed
assessment of emissions in California's San Joaquin Valley, Sierra was
contracted by GARB to estimate emissions from agricultural field
operations in the eight-county San Joaquin Valley Air Basin (SJVAB) and
in 33 additional counties that could impact air quality in the SJVAB.
In that work, Sierra deviated considerably in methodology from previous
agricultural emission estimates. Rather than utilize a top-down
approach in which state-level equipment population and usage data are
scaled by harvested acreage, a bottom-up methodology was developed in
which local farming practices and equipment types served as the basis
for the calculations. Annual and temporal emissions were estimated as a
function of crop type, acreage, operation, and equipment type. Crucial
to the emission calculations, however, were sample production cost
estimates for each crop and area of interest; these are described below.
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In California, sample production cost estimates are prepared by county
Farm Advisors in conjunction with the University of California
Cooperative Extension. These reports are used by farmers to estimate
operating costs and provide a basis for farm loans. Hence, they contain
a detailed summary of the operations required to produce a given crop,
along with the month in which the operation is performed and the
required equipment (including horsepower) and time (hours/acre). This
information, coupled with load factor estimates, makes it possible to
calculate the power requirements per acre (i.e., bhp-hr/acre) associated
with cultivating a particular crop. Thus, emission estimates specific
to crop type can be made. This is^important in cases where a county has
a large proportion of machine-intensive crops that would otherwise be
misrepresented when using a top-down approach with total harvested
acreage as the scaling factor. For example, cotton has over 20
operations associated with its production, whereas certain tree crops
such as walnuts may have only five or six.
Additional GARB Studies - In addition to the studies described above,
two other studies related to nonroad equipment are being performed for
GARB. First, BAH has been contracted to develop a nonroad emissions
inventory for California, and second, EEA has been contracted to
investigate regulatory strategies for lower horsepower nonroad
equipment. Neither study has been released for public review, so a
review was not possible.
Summary
The vast majority of recent emission inventory studies have relied
exclusively on a top-down methodology to allocate state or national
equipment populations to the county level. Although EPA's NEVES
advanced the state of the art in developing local inventories from
national and state data, the fact remains that local specificity is
often lost when top-down methodologies are utilized. As an example,
Section 6 compares California off-road motorcycle registrations and the
population generated in NEVES for the SJV air basin. California
Department of Motor Vehicles records indicate that approximately 15,000
off-road motorcycles are registered in the eight-county SJV. On the
other hand, the methodology developed by EEA3 for NEVES results in a
total of 450 off-road motorcycles being allocated to the SJV. Clearly,
the accuracy of methodologies used to develop nonroad equipment
emissions inventories must be improved if local air quality districts
are to efficiently implement control strategies necessary to attain
ambient air quality standards.
References for Section 3
1. "Nonroad Engine and Vehicle Emission Study - Report," U.S.
Environmental Protection Agency, Office of Air and Radiation,
November 1991.
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2. "Nonroad Engine and Vehicle Emission Study - Appendixes," U.S.
Environmental Protection Agency, Office of Air and Radiation,
November 1991.
3. "Methodology to Estimate Nonroad Equipment Populations-by
Nonattainment Areas," Energy and Environmental Analysis, September
1990.
4. "Feasibility of Controlling Emissions from Off-Road, Heavy-Duty
Construction Equipment," Energy and Environmental Analysis,
December 1988.
5. "Technical Support Document for California Exhaust Emission
Standards and Test Procedures for 1994 and Subsequent Model Year
Utility and Lawn and Garden Equipment Engines," Booz, Allen, and
Hamilton, October 1990.
6. "Response to RFP 91-19: Development of an Off-Highway Mobile Source
Emissions Model," Energy and Environmental Analysis, February 1992.
7. "SJVAQS/AUSPEX Agricultural Emissions Inventory (DRAFT)," Sierra
Research, November 1992.
8. "Methods for Assessing Area Source Emissions in California,"
California Air Resources Board, Technical Support Division,
September 1991.
9. "Status Report: Emissions Inventory on Non-Farm (MS-1), Farm (MS-
2), and Lawn and Garden (Utility) (MS-3) Equipment," California Air
Resources Board, Mobile Source Control Division, July 1983.
10. "A 1989 California Baseline Emissions Inventory for Total
Hydrocarbon & Carbon Monoxide Emissions from Portable Two-Stroke
Power Equipment," Heiden Associates, July 1990.
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4. LAWN AND GARDEN EQUIPMENT
The lawn and garden equipment category encompasses a wide range of
equipment types, and because of its diversity, not all equipment types
are likely to be evenly distributed across the country. Although this
is true for most of the equipment categories included in NEVES, it seems
to be especially valid for lawn and garden equipment. For example,
snowblowers would not be found in Baton Rouge, and the relative
percentage of lawn and garden tractors is likely to be quite different
between Atlanta and the South Coast Air Basin. Thus, procedures to
adequately represent the local mix of equipment types are needed to
provide reliable estimates of lawn and garden equipment activity.
Specific equipment types included in the lawn and garden category are
trimmers/edgers/brush cutters, lawn mowers, leaf blowers/vacuums, rear
engine riding mowers, front mowers, chainsaws (< 4 HP), shredders
(< 5 HP), tillers (< 5 HP), lawn and garden tractors, wood splitters,
snowblowers", chippers/stump grinders, commercial turf equipment, and
"other" lawn and garden equipment.
NEVES Methodology
NEVES Inventory A relied primarily upon PSR population data to develop
activity estimates for lawn and garden equipment. In allocating
equipment to the local level, two variables were used in EEA's1
regression analysis to establish a relationship between local indicators
and equipment population. First, the number of single family housing
units (SFHU) was chosen because there is a logical link between lawn and
garden equipment usage and the number of SFHUs in an area, and this
indicator has been used successfully in the past to allocate lawn and
garden equipment. The second indicator utilized in the modeling
analysis was the number of employees in Standard Industrial
Classification (SIC) code 078 - Landscape and Horticultural Services.
This indicator was included to account for an increasing number of
landscaping firms that care for both commercial and residential
properties.
A summary of lawn and garden equipment activity estimates based on data
developed for NEVES Inventory A is given in Table 4-1 on a national
basis, for the DC/MD/VA area, and for the SJV. On a bhp-hr/yr basis,
the largest contributors to overall lawn and garden equipment activity
are lawn mowers, lawn and garden tractors, chippers/stump grinders, and
commercial turf equipment. The sheer number of lawn mowers makes their
contribution significant, while higher horsepower and annual hourly
usage result in a large influence from commercial turf equipment and
chippers/stump grinders. Although trimmers/edgers/brush cutters and
. 4-1
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Table 4-1
NEVES Inventory A Lawn and Garden Equipment Activity Estimates
National Estimates
Equipment Type
Trmrs/Edgrs/Brsh Ctrs
Lawn Mowers
Leaf Blowers/Vacuums
Rear Eng. Riding Mowers
Front Mowers
Chainsaws < 4 HP
Shredders < 5 HP
Tillers < 5 HP
Lawn & Garden Tractors
Wood Splitters
Snowbtowers
Chippers/Stump Grinders
Commercial Turf Eqpmt
Other L&G Equipment
Total
Population
Diesel
0
0
0
8,713
0
0
0
0
211,631
79
0
17,087
0
180
237.690
Gas
18,172,282
35,764,096
2,025,786
1,575,407
257,880
16,124,970
107,322
3,812,000
5,903,369
502,181
4,782,000
16,791
568,732
396,454
90.009.270
Activity (1000 Bhp-hr/yr)
Diesel
0
0
0
2,026
0
0
0
0
441,886
186
0
273,517
0
24
717.639
Gas
98,130
2,523,515
28,361
193,964
55,702
258,000
618
128,083
1.771,011
28,875
130,549
186,357
2,521,189
8,920
7.933.274
Total
98,130
2,523,515
28,361
195,990
55,702
258,000
618
128,083
2,212,896
29,061
130,549
459,874
2,521,189
8,945
8.650.912
% Total
Activity
1.1
29.2
0.3
2.3
0.6
3.0
0.0
1.5
25.6
0.3
1.5
5.3
29.1
0.1
100.0
DC/MD/VA Estimates
Equipment Type
Trmrs/Edgrs/Brsh Ctrs
Lawn Mowers
Leaf Blowers/Vacuums
Rear Eng. Riding Mowers
Front Mowers
Chainsaws < 4 HP
Shredders < 5 HP
Tillers < 5 HP
Lawn & Garden Tractors
Wood Splitters
Snowbbwers
Chippers/Stump Grinders
Commercial Turf Eqpmt
Other L&G Equipment
Total
Population
Diesel
0
0
0
132
0
0
0
0
3,191
1
0
229
0
3
3.556
Gas
275,497
542,246
30,711
23,921
3,905
199,579
1.627
58,048
89,026
7,614
18,191
255
8,622
6,011
1.265.253
Activity (1 000 Bhp- hr/yr)
Diesel
0
0
0
31
0
0
0
0
6,663
2
0
3,666
0
3
10.364
Gas
1,488
38,261
430
2,945
843
3,193
9
1,950
26,708
438
497
2,830
38,221
135
117,949
Total
1,488
38,261
430
2,976
843
3,193
9
1,950
33,371
440
497
6,496
38,221
138
128.313
% Total
Activity
1.2
29.8
0.3
2.3
0.7
2.5
0.0
1.5
26.0
0.3
0.4
5.1
29.8
0.1
100.0
San JoaoulnVallev Air Basin Estimates
Equipment Type
Trmrs/Edgrs/Brsh Ctrs
Lawn Mowers
Leaf Blowers/Vacuums
Rear Eng. Riding Mowers
Front Mowers
Chainsaws < 4 HP
Shredders < 5 HP
Tillers < 5 HP
Lawn & Garden Tractors
Wood Splitters
Snowbbwers
Chippers/Stump Grinders
Commercial Turf Eqpmt
Other L&G Equipment
Total
Population
Diesel
0
0
0
65
0
0
0
0
1,563
1
0
127
0
1
1.757
Gas
134,924
265,563
15,041
11,715
1,912
175.627
797
28,429
43,600
3,729
0
125
4,223
2,944
688,629
Activity (1000 Bhp-hr/yr)
Diesel
0
0
0
20
0
0
0
0
3,964
2
0
2,400
0
2
6.388
Gas
874
27,534
286
1,923
505
3,337
6
1,137
15,958
186
0
1,638
23,332
124
76.839
Total
874
27,534
286
1,943
505
3,337
6
1,137
19,921
189
0
4,039
23,332
125
83.228
% Total
Activity
1.1
33.1
0.3
2.3
0.6
4.0
0.0
1.4
23.9
0.2
0.0
4.9
28.0
0.2
100.0
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chainsaws are responsible for a relatively small percentage of the
bhp-hr/yr activity, their impact on inventory calculations can be
significant due to the relatively higher emissions of their gasoline
2-stroke engines.
Because the regression technique employed for Inventory A utilized total
equipment category population as the dependent variable (i.e.,
regressions were not performed for each equipment type individually),
the national equipment distribution was uniformly applied to the
calculated county (or nonattainment area) category population when
developing population estimates for each individual equipment type.
This approach was taken since it was not possible within the timeframe
of NEVES to develop regressions for each separate type of equipment
(78 different equipment types were considered in NEVES). Although there
was some attempt to account for local conditions in lawn and garden
equipment populations (e.g., snowblowers were not allocated to
Baton Rouge; PPEMA's local-level chainsaw distribution, based on an
alternative regression model described below, was used in conjunction
with the PSR national population to establish the Inventory A local-
level chainsaw populations), the national equipment mix was generally
applied to all areas. As alluded to above, this approach is cause for
concern, especially for some equipment types (e.g., lawn and garden
tractors) that would obviously be more prevalent in areas with a higher
percentage of larger lot sizes.
As discussed in Section 3 of this report, industry associations provided
EPA with alternative population and usage estimates for some categories
of nonroad equipment. For lawn and garden equipment, both OPEI and
PPEMA submitted data for some equipment types included in the lawn and
garden category. OPEI provided information on population (by
metropolitan area), annual usage, horsepower, and load factor for lawn
mowers, rear engine riding mowers, lawn and garden tractors, and
tillers. (Additionally, OPEI submitted horsepower data for commercial
turf equipment.) PPEMA submitted population, usage, horsepower, and
load factor data for 2-stroke equipment, including trimmers/edgers/brush
cutters, leaf blowers, and chainsaws. (PPEMA's data were taken from two
reports prepared by Heiden Associates under contract to PPEMA.2'3)
To determine local equipment populations, OPEI relied on historical
sales information coupled with assumptions on equipment life. The PPEMA
population data were also developed from shipment data (state-level) to
which an assumed average life was applied (for both commercial and
residential applications). County-level populations were then
determined with a regression technique similar to the approach used in
the development of NEVES Inventory A. This regression approach utilized
activity indicators that accounted for differences in rural and urban
population in nonattainment areas (e.g., one of the models proposed by
PPEMA/Heiden included urban SFHU, rural SFHU, and SIC 078 employment as
the activity indicators), with the thought that some equipment types are
more likely to be found in a rural environment.
The equipment activity (in bhp-hr/yr) estimated for Inventory B is
summarized in Table 4-2 on a national basis, for the DC/MD/VA area, and
for the SJV. In comparing Tables 4-1 and 4-2, a net decrease of roughly
4-3
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Table 4-2
NEVES Inventory B Lawn and Garden Equipment Activity Estimates
National Estimates
Equipment Type
Trmrs/Edgrs/Brsh Ctrs
Lawn Mowers
Leaf Blowers/Vacuums
Rear Eng. Riding Mowers
Front Mowers
Chalnsaws < 4 HP
Shredders < 5 HP
Tillers < 5 HP
Lawn & Garden Tractors
Wood Splitters
Snowbtowers
Chippers/Stump Grinders
Commercial Turf Eqpmt
Other L&G Equipment
Total
Population
Diesel
0
0
0
9,460
0
0
0
0
184,567
79
0
17,087
0
180
211.373
Gas
13,583,335
32,000,000
2,871,164
1,710,540
280,000
7,895,502
107,322
2,737,564
5,148,433
502,181
4,782,000
16,791
568,732
396,454
72.600.018
Activity (1000 Bhp-hr/yr)
Diesel
0
0
0
1,283
0
0
0
0
37,312
186
0
273,517
0
168
312.466
Gas
167,754
1,532,160
71,779
232,052
65.170
290,554
9,582
145,967
1,040,807
28,875
110,966
186,357
2,210,889
8.920
6.101.833
Total
167.754
1,532,160
71,779
233,335
65.170
290,554
9,582
145,967
1,078,119
29,061
110,966
459,874
2,210,889
9,089
6.414.299
% Total
Activity
2.6
23.9
1.1
3.6
1.0
4.5
0.1
2.3
16.8
0.5
1.7
7.2
34.5
0.1
100.0
DC/MD/VA
Equipment Type
Trmrs/Edgrs/Brsh Ctrs
Lawn Mowers
Leaf Blowers/Vacuums
Rear Eng. Riding Mowers
Front Mowers
Chalnsaws < 4 HP
Shredders < 5 HP
Tillers < 5 HP
Lawn & Garden Tractors*
Wood Splitters
Snowbtowers
Chippers/Stump Grinders
Commercial Turf Eqpmt
Other L&G Equipment
Total
Population
Diesel
0
0
0
66
0
0
0
0
4,107
1
0
259
0
3
4.436
Gas
114,061
608,000
50,512
11,979
1,955
97,410
1,627
41,482
- 114.555
7,614
18,191
255
8,622
6.011
1.082.274
Estimates
Activity (1000 Bhp-hr/yr)
Diesel
0
0
0
9
0
0
0
0
830
2
0
4,146
0
3
4.990
Gas
1,409
29,111
1,263
1,625
455
3,585
145
2,212
23,158
438
422
2,830
33,517
135
100.305
Total
1,409
29,111
1,263
1,634
455
3,585
145
2,212
23,989
440
422
6,976
33,517
138
105.296
* The lawn and garden tractor population reported here differs from the value used in develop
% Total
Activity
1.3
27.6
1.2
1.6
0.4
3.4
0.1
2.1
22.8
0.4
0.4
6.6
31.8
0.1
100.0
ngthe
NEVES inventory because of a data entry error.
s;l;ji^^
Equipment Type
Trmrs/Edgrs/Brsh Ctrs
Lawn Mowers
Leaf Blowers/Vacuums
Rear Eng. Riding Mowers
Front Mowers
Chalnsaws < 4 HP
Shredders < 5 HP
Tillers < 5 HP
Lawn & Garden Tractors
Wood Splitters
Snowbtowers
Chippers/Stump Grinders
Commercial Turf Eqpmt
Other L&G Equipment
Total
Population
Diesel
0
0
0
65
0
0
0
0
1,563
1
0
127
0
1
1.757
Gas
97,092
265,563
26,386
11,715
1,912
85,720
797
28,429
43,600
3,729
0
125
4,223
2.944
572.235
Activity (1000 Bhp-hr/yr)
Diesel
0
0
0
10
0
0
0
0
2,504
2
0
2,400
0
2
4.918
Gas
1,199
10,596
660
1,725
483
3,154
71
1,320
13,442
186
0
1,638
20,460
124
55.060
Total
1,199
10,596
660
1,735
483
3,154
71
1,320
15.946
189
0
4,039
20,460
125
59.978
% Total
Activity
2.0
17.7
1.1
2.9
0.8
5.3
0.1
2.2
26.6
0.3
0.0
6.7
34.1
0.2
100.0
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25 percent in activity is observed for the national estimates. (The
decrease is 18 percent for DC/MD/VA and 28 percent for the SJV.) This
is the result of decreased equipment population predicted in the
industry submittals used for Inventory B, as well as lower annual usage
for some equipment types.
Approach
Considerable effort was expended in attempting to identify alternative
data sources of population and usage patterns for lawn and garden
equipment. However, since this category is comprised of large numbers
of small, relatively inexpensive equipment types, detailed records are
generally unavailable. Nonetheless, an attempt was made to locate
information that might be useful in improving top-down methodologies and
in developing a bottom-up approach to estimate lawn and garden equipment
activity. Presented below is a discussion of potential methodologies
that might be employed to accomplish that task.
Top-Down - As noted above, the methodology developed for NEVES
Inventory A utilized the national-level equipment distribution to
calculate populations of individual equipment types in each
nonattainment community, which results in a lack of specificity at the
local level. One means of making a top-down methodology more area-
specific would be to run regressions on each type of equipment
separately, carefully choosing activity indicators to be closely matched
with particular equipment types. For example, utilization of SFHU
multiplied by yearly snowfall would likely provide a better indicator of
localized snowblower population than current-estimates. The drawback in
doing this, of course, is the increased effort that would have to be
devoted to nonroad equipment inventory development. (Although end-users
would obviously not be expected to develop locality-specific models,
there would be increased data gathering requirements if the number of
activity indicators was expanded.)
An alternative to treating each piece of equipment individually is to
categorize lawn and garden equipment according to a limited number of
usage regimes. It appears a logical way to do this is to first
distinguish equipment according to residential or commercial ownership.
(This distinction has been made in several previous analyses of lawn and
garden equipment.3'5) This is important from a'number of perspectives.
First, some equipment types (e.g., commercial turf mowers) are not used
for residential applications, and utilizing SFHUs as an activity
indicator to distribute equipment that is used solely by commercial
landscape firms likely results in inaccuracies at the local level.
Second, the equipment age distributions and use patterns are
substantially different when comparing commercially and residentially
owned equipment. This difference would have to be accounted for in
models used to forecast emissions, particularly models used to estimate
the impact of proposed regulations.
Figure 4-1 illustrates equipment types included in the lawn and garden
category according to their primary ownership patterns. Sierra has
categorized equipment into four basic regimes: residential equipment
4-5
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Figure 4-1
Categorization of Lawn and Garden Equipment
According to Anticipated Use
Lawn & Garden
Equipment
Residential
Ownership
Residential
Eqpt Used ONLY
Residentially
Rear-Eng Riders
Lawn & Garden Trctrs
Snowblowers
General
Eqpt Used
Residentially
Trmrs/Edgrs/Brsh Ctrs
Lawn Mowers
Leaf Blowers/Vacuums
Chainsaws
Shredders
Tillers
Commercial
Ownership
General
Eqpt Used
Commercially
Trmrs/Edgrs/Brsh Ctrs
Lawn Mowers
Leaf Blowers/Vacuums
Chainsaws
Shredders
Tillers
Equipment Types Common
to Both Residential and
Commercial Ownership
Commercial
Eqpt Used ONLY
Commercially
Front Mowers
Wood Splitters
Chlppers/Stmp Grndrs
Commercial Turf Eqpmt
Other L&G Eqpmt
used only residentially, general equipment used residentially, general
equipment used commercially, and commercial equipment used only
commercially.* This categorization would allow more appropriate
activity indicators to be applied in generating top-down methodologies,
and it would aid in allocating equipment usage when considering
potential bottom—up approaches.
* The distribution of equipment types according to ownership regime was
based on assumed usage patterns. Some equipment types included in the
Residential Only regime (e.g., snowblowers) are also likely to be used
commercially, whereas woodsplitters are likely to be owned also by
private individuals. Survey data indicating the exact nature of lawn
and garden equipment ownership and usage patterns would clearly improve
any methodology developed according to ownership regime. Nonetheless,
Figure 4-1 and the following discussion provides a basis for an
alternative top-down methodology.
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Sierra's efforts to develop improved methodologies for distributing
equipment according to the top-down approach focused on identifying
appropriate activity indicators for the four equipment subcategories
listed in Figure 4-1. Lot size appears to be a good indicator for
equipment distribution for residentially owned equipment; thus,
considerable effort was expended in identifying sources of data for lot
size and consumer buying habits as a function of lot size. Although
SIC 078 -is a logical indicator of commercial equipment usage, several
other indicators were analyzed for potential use.
Categorization of lawn and garden equipment according to residential and
commercial ownership may not adequately describe population and usage
for all equipment types. For example, a significant fraction of
chainsaw usage may be attributed to areas outside of nonattainment
communities. This was recognized by EPA in the development of NEVES,
which (through the use of PPEMA's equipment distribution) relied on
rural population as one of the activity indicators to allocate equipment
populations for this equipment type. The use of lot size, however, can
be considered a surrogate for rural activity (i.e., larger lots are
expected to be found in more rural settings). Thus, the use of rural
population as an activity indicator was not investigated in this study.
(See Reference 2 for details of the methodology recommended by PPEMA
which utilizes a multivariate regression model that includes rural
population, urban population, and SIC 078 employment as activity
indicators.)
The above discussion has focused on equipment populations, but activity
estimates are also directly proportional to annual hourly usage.
Because annual equipment usage is significantly different between
residential and commercial equipment, it is important to account for
this difference in activity estimates. EPA recognized this and included
a correction for commercially owned equipment when the annual hourly use
figures were developed for NEVES. However, this correction was based on
relatively limited data. Thus, some effort was expended in this work
attempting to identify alternative data sources for usage estimates.
Bottom—Up - Developing a bottom-up methodology for estimating lawn and
garden equipment activity at first appears to be a difficult, if not
impossible, task. However, part of this study was devoted to evaluating
the feasibility of alternative methodologies for calculating equipment
usage at the local level. Thus, Sierra investigated the data (and
availability of those data) that would be required to prepare a bottom-
up estimate of lawn and garden equipment activity. In addition, it was
felt that information obtained as part of this effort might also be
useful in refining top-down approaches.
Similar to the aforementioned top-down approach, the first aspect of a
bottom-up approach would be to distinguish between residential and
commercial properties. The number and size of residential lots in a
community appear to be a good indicator of residential activity, and if
estimates of landscaped area by lot size can be obtained, the total
residential maintained acreage in an area could be estimated.
Estimating the total acreage of commercially maintained properties is
likely to be a much more difficult task because of the large number of
individual properties, as well as the different types of properties,
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that are maintained. As seen in Table 4-3, there are many different
classifications of property types, and each one may have differing
levels of maintenance requirements. For example, an acre of golf course
would have much different equipment requirements than an acre of urban
park. Although this list is not all-inclusive, it does give an
indication of the level of effort that would be involved in compiling
the commercially maintained acreage in an area.
If the total number of maintained acres in an area were to be compiled,
the next step in a bottom-up approach would be to apply equipment-
specific hour/acre estimates and maintenance intervals to obtain an
estimate of lawn and garden equipment activity. Thus, some effort was
devoted to identifying sources of information for equipment—time
requirements (i.e., hours/acre or hours/1000 ft2) and regional
differences in maintenance practices.
Table 4-3
Types of Properties Maintained by Commercial Landscape Firms*
Residential Homes
Apartments, Condos, Planned Communities
Hotels and Resorts
Golf Courses
Urban Parks
Schools, Colleges, Universities
Athletic Fields
Industrial/Office Parks
Shopping Malls
Hospitals
Cemeteries
Amusement Parks
Roadways, Rest Areas
Based in part on information received from the
Professional Grounds Management Society.
Potential Data Sources
As outlined above, the primary information needed to develop alternative
top-down methodologies consists of commercial versus residential
equipment ownership distribution, activity indicators to allocate
residential and commercial equipment, and equipment usage patterns. A
bottom-up approach would require local statistics on the number and size
of residential lots (to develop residential landscape acreage),
commercial landscape acreage, and information on time-equipment
requirements for maintaining different types of landscape designs.
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A variety of different organizations were contacted to determine if
information relating to the above subjects is available. Information on
commercial equipment usage was solicited from professional and trade
associations as listed in Table 4-4. Inquiries for lot size information
were directed to federal, state, and local planning agencies, while
commercial landscape acreage and maintenance requirements were sought
from federal, state, and county maintenance agencies. The following
discussion details the effort to obtain the above information and
summarizes the type of information that is available.
Top-Down Information Sources
Commercial Versus Residential Equipment Ownership — The methodology
developed for NEVES accounts for commercially owned equipment by
including the number of employees in landscape and horticultural
services as an activity indicator in the regression analysis.
Additionally, a "commercial" correction was made to annual hourly usage
estimates for equipment considered to be owned by both residences and
commercial firms. Other lawn and garden equipment activity and
emissions estimates have more explicitly differentiated between
commercial and residential equipment populations and usage, choosing to
treat commercial and residential equipment entirely separately in terms
of annual sales, expected life, population, and annual hours of
operation. These studies include a report prepared by BAH for GARB to
Table 4-4 -
Associations Solicited for Information on Lawn and Garden Equipment
Populations and Usage Patterns
American Society of Agricultural Engineers
Lawn and Garden Dealers Association
National Gardening Association
National Lawn and Garden Distributors Association
American Sod Producers Association
Landscape Nursery Council
National Golf Foundation
Golf Course Superintendents Association of America
Associated Landscape Contractors of America
Professional Grounds Management Society
American Society of Agronomy
National Landscape Association
National Recreation and Parks Association
National Institute on Park and Grounds Management
Professional Lawn Care Association of America
University of Georgia - Agricultural Extension
Grounds Maintenance Magazine
Landscape Management Magazine
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support CARB's rulemaking on lawn and garden equipment4 and a report
prepared by Heiden Associates for PPEMA in response to CARB's rulemaking
on lawn and garden equipment.3 Although these estimates exist, they
generally have been developed at the state level. County-level activity
and emissions estimates were based on applying the county-to-state ratio
of SFHU to the state-level results. As part of this work, information
was sought that could be used to develop estimates of commercial and
residential equipment populations at the county level. Unfortunately,
none of the organizations listed in Table 4-4 could provide additional
information on populations, usage, or ownership patterns for lawn and
garden equipment.
Potential Activity Indicators for Commercial Equipment - Several
methodologies to distribute commercial lawn and garden equipment
populations to the local level were considered. First, the associations
listed in Table 4-4 were contacted to determine if information was
available on equipment ownership or usage patterns. However, as it
became apparent that little data existed on commercial equipment usage
and local populations, other approaches to allocate commercial equipment
to the local level were investigated. Some of these are discussed
below.
Licensing requirements for landscape firms were investigated as a
possible means of distributing national-level commercial equipment
populations to the local level. Several calls were placed to state
agencies to determine if there were any general licensing requirements
for landscape maintenance firms. It was discovered that in California,
landscape contractors must be licensed, but there are no state
requirements for licensing of firms performing only maintenance
services. Although many counties and cities require business licenses,
it was felt that the effort to compile such information would exceed its
usefulness.
Another potential indicator of commercial lawn and garden activity that
was considered is the number of EPA-certified pesticide applicators in
an area. Initially it was felt that this information might be available
from EPA on a national and state basis. Unfortunately, discussions with
Region IX staff revealed that although there is a federal requirement
for states to keep records of certified applicators, there is no
requirement that they submit this information to EPA. Thus, this
approach would require a substantial effort to compile the needed
information from individual states. In addition, the level of detail
that could be provided by each state is likely to be highly variable.
Finally, land use information was sought on a national and local level.
It was felt that if acreage data were available for various land use
categories (e.g., residential, commercial/industrial, bare ground,
agriculture, etc.) at national and local levels, this information could
be used to allocate commercial lawn and garden equipment. Although this
information has been compiled for Maryland based on detailed aerial
photography, identification of other sources proved unsuccessful. It
appears that digitized land use maps available from the U.S. Geological
Survey might be used to develop this information, but such an effort is
well beyond the scope of this project. In addition to the purchase
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See Disclaimer on Cover
price, the time required to compile the data into a usable form would be
excessive.
Potential Activity Indicators for Residential Equipment - One approach
to make top-down methodologies more area-specific that deserves
consideration is to distribute national equipment populations by lot
size. This makes intuitive sense because certain equipment types would
not be expected to be found at residences with small lots. For example,
it is doubtful that many lawn and garden tractors are located in highly
urbanized areas where lot sizes are generally well below 1/4 acre in
size. Conversely, although not considered in great detail in this
report, the relative percentage of electric lawn and garden equipment is
likely to be greater for smaller lots which generally have more
accessible electrical outlets.'
Because lot size would appear to have a bearing on lawn and garden
equipment distribution and usage in an area, Sierra investigated
potential sources of this information. The search focused on federal
agencies (e.g., Department of Housing and Urban Development (HUD),
Department of Commerce/Census Bureau), state agencies, and local
agencies to determine if lot size information is readily available.
Contact with state and local planning agencies was limited to the
DC/MD/VA and SJV areas, while a broader perspective was obtained in
discussions with federal agencies.
The most complete data on lot size information identified in this work
are published by HUD and the Census Bureau.6 Every two years, the
American Housing Survey is conducted in which information on housing
statistics throughout the U.S. is compiled. Included in the survey
results is information on lot size of detached SFHUs. In addition to
the national survey, approximately 10 to 12 different metropolitan areas
are surveyed each year, with the area-specific surveys conducted in a
four-year cycle. Thus, lot size information is readily available for 40
to 50 metropolitan areas in the U.S. The metropolitan areas included in
the survey are listed in Table 4-5.
Lot size distributions are shown in Figure 4-2 for the DC/MD/VA area,7
Los Angeles/Long Beach area,? and for the U.S. as a whole.6 It is
apparent from this figure that lot size.is a strong function of
geographic area and the degree of urbanization. As seen, roughly 50
percent of the detached SFHD lots are between 1/4 and 1 acre in the
DC/MD/VA area, whereas over 75 percent of the lots in the highly urban
Los Angeles/Long Beach area are less than 1/4 acre.
Although lot size information is available from the American Housing
Survey for many metropolitan areas of the U.S., not all nonattainment
communities are represented. For example, the SJV contains no
metropolitan areas that are individually treated in the survey. Thus,
some effort was expended in determining other sources of lot size
Several calls were, however, placed to electric utilities to determine
if information was available on usage patterns or purchasing profiles
for electric lawn and garden equipment. Unfortunately, no information
was identified.
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Table 4-5
Metropolitan Areas Included in the American Housing Survey6
Anaheim-Santa Ana, CA
Atlanta, GA
Baltimore, MD
Boston, MA
Birmingham, AL
Buffalo, NY
Chicago, IL
Cincinnati, OH
Cleveland, OH
Columbus, OH
Dallas, TX
Denver, CO
Detroit, MI
Fort Worth-Arlington, TX
Hartford, CT
Houston, TX
Indianapolis, IN
Kansas City, MO-KS
Los Angeles-Long Beach, CA
Memphis, TN
Miami-Fort Lauderdale, FL
Milwaukee, WI
Minneapolis-St. Paul, MN
New Orleans, LA
New York-Nassau-Suffolk, NY
Norfolk-Virginia Beach-Newport News, VA
Northern NJ
Oklahoma City, OK
Philadelphia, PA
Phoenix, AZ
Pittsburgh, PA
Portland, OR
Providence-Pawtucket-Warwick, RI-MA
Riverside-San Bernardino-Ontario, CA
Rochester, NY
St. Louis, MO
Salt Lake City, UT
San Antonio, TX
San Diego, CA
San Francisco-Oakland, CA
San Jose, CA
Seattle-Tacoma, WA
Tampa-St. Petersburg, FL
Washington, DC-MD-VA
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Figure 4-2
Lot Size Distribution for Selected Areas
Single Family Housing Units
<1/8 1/8-1/4 1/4-1/2 1/2-1 1-4
Lot Size (Acres)
5-9
10 +
information. State and local planning agencies were contacted both in
the SJV and in the DC/MD/VA areas to determine the availability of data
and the level of effort required to compile the information. In
general, reasonable success was achieved in the DC/MD/VA area, whereas
this information was often unavailable or of limited detail in the
smaller, more rural communities making up the SJV air basin.
Although it appears possible to compile enough information to establish
a reasonable approximation of lot size distribution in areas not covered
by the American Housing Survey, the cost and level of effort required to
gather the data from local sources may exceed many air quality planners'
resources. For example, 31 separate city and county planning agencies
were contacted within the SJV to establish a lot size distribution for
that area. Generally, the information was received in the form of total
acreage, number of improved parcels, and minimum lot size by zoning
category. Thus, a considerable amount of work was required to develop
the lot size distribution for the entire area. In addition, lot size
information from the SJV's largest county, Fresno, would require a
computer sort at a cost of either $2,500 (through the Fresno County
Planning Office) or $11,000 (through the Fresno County Assessor's
Office). An alternative methodology for areas in which lot size
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information is not readily available may be to use a community included
in the American Housing Survey that has similar characteristics in terms
of population, urban versus rural split, etc. A complete summary of the
organizations contacted to develop lot size statistics for this study is
included as Appendix B.
Equipment Distribution as a Function of Lot Size - Once information on
lot size is obtained, it is necessary to determine the distribution of
equipment as a function of lot size. This information was provided for
rear-engine riding mowers and lawn and garden tractors by OPEI in its
comments on NEVES.9 Further, it appears that this information is also
available for several lawn and garden equipment types (e.g., string
trimmers, riding mowers and tractors, walk-behind mowers, yard blowers,
and chain saws) from Irwin Broh & Associates (IB&A). IB&A is a
marketing research firm that specializes in the leisure time industry,
including outdoor power equipment. Drawbacks to the IB&A data include
its high cost ($9,500 for each equipment type) and the fact that the
surveys are based on units purchased in the past year only.
If lot size were not chosen to allocate lawn and garden equipment types
to the local level, there does appear to be some survey information that
establishes regional differences in residential equipment distributions.
Each year the National Gardening Association contracts the Gallup
Organization to conduct a survey of U.S. consumer gardening practices.10
Included in this survey is information on product purchases by region,
size of community, type of community, and income. Also included in the
survey results are 5-year trendlines for purchases of 12 different
equipment types (e.g., walk-behind mowers, chainsaws, string trimmers,
etc.). The cost of the survey is $350. (EPA could purchase it for the
nonprofit/government rate of $125.) Although this information provides
regional differences in equipment distributions, it is anticipated that
this information would be best utilized as a cross-check of PSR and
manufacturer-supplied data.
Bottom-Up Information Sources
Commercial Equipment Usage Patterns - As noted above, the primary
contacts for information on commercial lawn and garden equipment
populations and usage were the professional and trade associations
listed in Table 4-4. For the most part, little information is available
on the usage of equipment by commercial landscape firms. Although
estimates of annual equipment usage are contained in NEVES (based on PSR
survey data), it was hoped that alternative data could be obtained to
compare with those figures.
One area in which fairly detailed information on commercial maintenance
practices is available, however, is golf courses. The Golf Course
Superintendents Association of America publishes the "Mower and
Maintenance Equipment Report" which lists detailed usage patterns for
equipment such as riding greensmowers, walking greensmowers, riding
rotary mowers (under and over-72 inch deck), riding reel mowers (four
categories), tractors, blowers, sweepers, boom sprayers, and powered
sand trap rakes.11 Information in the report includes the distribution
4-14
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See Disclaimer on Cover
of manufacturers and the mean hours used per week by season and type of
golf course. Further, it appears that the National Golf Foundation
could supply information on the number of golf courses in a region.
Thus, detailed estimates of equipment activity and emissions could be
generated for golf courses using a bottom-up approach. The obvious
drawback, however, is that golf courses represent only a portion of the
commercially maintained acreage in any given area, and the maintenance
requirements of golf courses are generally much more severe than other
commercial properties. Thus, information from these sources would not
be generally applicable to all commercial lawn and garden equipment.
Additionally, the cost of the maintenance report is $1500, while the
National Golf Foundation also charges fees for computer sorts of its
data base.
Other sources were queried for information relating to commercial
landscape maintenance equipment and usage. Several highway departments
were contacted in the DC/MD/VA region to determine if records were kept
on roadside maintenance. It was initially felt that if the miles of
maintained roadway and general maintenance requirements were available,
a bottom-up methodology could be developed. In conversations with
maintenance engineers, however, it became clear that maintenance
practices are highly variable. For example, the frequency of mowing in
the DC/MD/VA region can vary from two to five times per season,
depending upon rainfall and budget constraints. In addition, the effort
to compile the information on maintained roadways in an area can be
considerable. Although it was a fairly straightforward process to
obtain landscaped miles for state roads (at least for Maryland) ,
gathering the same information for county and city roads requires
numerous individual requests. Such an effort would likely be
impractical for most local air quality districts.
Maintenance requirements for urban parks were also investigated. As
with highways, the total maintained acreage for urban parks could
probably be reasonably estimated, but the effort to do so would be
overwhelming because of the large number of state, county, and city
agencies that would have to be contacted. Nonetheless, some effort was
expended in determining if any organizations keep records of park
acreage or maintenance practices. Several associations listed in
Table 4-4 were solicited for this information without success.
Additionally, Parks Departments in the DC/MD/VA and SJV areas were
contacted. In this effort, it was discovered that the National Park
Service keeps records of maintained acreage for its parks. This
information was received for the National Capital Region and for
California. Again, however, this information is only for a small
component of the total commercially maintained acreage.
If total acreage and mowing schedules could be compiled for an area,
machine-hours per acre would be needed to complete an activity
calculation. Some information was collected on hour/acre requirements
by equipment type. In general, these estimates are based on the width
of cut of the implement, an assumed average speed, and an assumed
efficiency. OPEI submitted information from two published articles to
EPA in conjunction with its review of NEVES.9 These articles estimated
hour/acre for walk-behind mowers, riding mowers, and lawn and garden
tractors. A study funded by the National Park Service and the
4-15
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See Disclaimer on Cover
Department of Energy12 lists hour/acre requirements for a variety of
mowers, ranging from walk-behind to large self-propelled or tractor-
drawn units. Finally, a January 1992 issue of Landscape Management
magazine lists the square feet per hour for deck sizes ranging from
21 to 60 inches.13 As an example, Table 4-6 lists hour/acre estimates
as a function of implement width and speed with an assumed efficiency of
80 percent.
Table 4-6
Acres Mowed Per Hour as a Function of Implement Width and Speed12
(80 Percent Efficiency Assumed)
Speed
(mph)
1.5
2.0
3.0
4.0
5.0
Implement Width (Inches)
18
0.23
0.30
0.45
0.60
0.75
25
0.30
0.40
0.60
0.80
1.00
36
0.44
0.58
0.87
1.16
1.45
60
0.72
0.96
1.45
1.92
2.43
84
1.02
1.40
2.04
2.72
3.40
Sample Calculations
Although considerable effort was expended in determining the viability
of a bottom-up approach for estimating lawn and garden equipment
activity, it appears that developing such a methodology is not feasible
because of the labor-intensive nature of compiling the needed
information. Therefore, the discussion that follows is focused on
improvements to the top-down approach that was developed for NEVES.
Commercial Equipment Used Only Commercially - One possible improvement
to the lawn and garden equipment activity estimates developed for NEVES
would be to distinguish between commercial and residential equipment.
As illustrated previously in Figure 4-1, it is anticipated that certain
equipment types are used primarily in either commercial or residential
applications. Thus, a top-down model that uses both SFHUs and SIC 078
would not necessarily be appropriate for allocating commercial lawn and
garden equipment to the local level. For example, Table 4-7 summarizes
the results of rerunning the regression analysis performed for NEVES
using only SFHU, SFHU and SIC 078, and only SIC 078. (The summary
statistics and the data used for the regression analysis are contained
in Appendix C.) As seen, the choice of indicators has a considerable
impact on the results for the DC/MD/VA region. Using only SIC 078 as
the activity indicator increases the lawn and garden equipment
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Table 4-7
Relative Impact of Current Activity Indicators on Total
Lawn and Garden Equipment Population in the DC/MD/VA and SJV Areas
' Area
DC/MD/VA
SJV
Activity Indicator(s) Used in Regression
SFHU
1,278,500
792,000
SFHU & SIC078*
1,578,500
765,000
SIC078
1,965,000
744,600
* These indicators were used for NEVES. Note that the total equipment
populations shown here do not match those reported in NEVES (i.e.,
Table 4-1) because of special treatment given to chainsaws and other
adjustments made to the PSR lawn and garden equipment population
estimates.
population over 50 percent compared to the results of just using SFHUs.
Obviously, by accounting for commercial and residential equipment
separately, the results obtained in a top-down approach would be
improved. (PSR state-level population data on individual equipment
types were not available for this study, therefore a regression analysis
performed for equipment regimes outlined in Figure 4-1 could not be
performed.)
Residential Equipment Used Only Residentially - For rear-engine riding
mowers and lawn and garden tractors, an analysis can be performed to
determine the population in the DC/MD/VA area using lot size to
distribute the equipment. OPEI provided information on the lot size
distribution of riding mowers, lawn tractors, and garden
tractors, which is summarized in Table 4-8.9 (Because PSR reports
population data for lawn and garden tractors combined, the data from
OPEI were combined by the sales split prior to developing population
estimates.) This information was combined with the lot size data
obtained from the American Housing Survey to calculate the population of
rear-engine riding mowers and lawn and garden tractors in the DC/MD/VA
area. The spreadsheets used to perform these calculations are included
in Table 4—9. The national equipment populations used in the
calculations were those provided by PSR for NEVES. (Note that the
equipment distributions reported in Table 4-8 have been adjusted in
Table 4-9 to be consistent with the lot size ranges reported in the
American Housing Survey.)
When comparing these results with the NEVES populations reported in
Table 4-1 for the DC/MD/VA region, an approximate 30 percent reduction
in population is observed when using the lot size approach. One
explanation for this difference is that it was assumed here that these
equipment types are used primarily for residential purposes because of
their bulk and limited maneuverability. (The same opinion was offered
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Table 4-8
Lawn and Garden Equipment Distribution as a Function of Lot Size
(From OPEI Retail Sales Data)*
Lot Size
(Acres)"
1/4
1/2
1
1-1/2
2
Percent by Equipment Type
Rear-Eng Rider
13
21
26
10
20
Lawn Tractor
8
20
27
11
29
Garden Tractor
7
14
21
12
41
Note that columns do not sum to 100. Remainder of equipment was
assumed to be allocated to lots greater than 2 acres.
Represents average lot size.
Table 4-9
Calculation of Rear-Engine Riding Mower and Lawn & Garden Tractor
Populations Using Lot Size to Distribute National Population Data
Rear-^Enaine Rfdina Mowers
Lot Size
0-1/8
1/8 - 1/4
1/4 - 1/2
1/2-1
1 -4
5-9
10+
National Statistics
Detached SFHUs*
Percent
12.0
24.8
19.1
13.1
19.9
3.2
7.9
Total
7,638,600
15,786,440
12,158,105
8,338,805
12,667,345
2,036,960
5.028,745
Totals 63.655,000
Rear- Engine Riders
Percent
0
6.5
17.0
23.5
53.0
Total
0
102,968
269,300
372£68
839,584
1,584,120
DC/MD/VA Statistics
Detached SFHUs*
Percent
5.4
22.9
36.7
17.3
13.1
1.9
2.7
Total
39,026
165,498
265,231
125,027
94,674
13,731
19,513
722,700
Rear-Eng
Riders
0
1,079
5,875
5,582
5,443
17.978
Includes mobile homes.
Lawn and Garden Tractors
Lot Size
0-1/8
1/8 - 1/4
1/4 - 1/2
1/2-1
1 -4
5-9
10+
National Statistics
Detached SFHUs*
Percent
12.0
24.8
19.1
13.1
19.9
3.2
7.9
Total
7,638,600
15,786,440
12,158,105
8,338,805
12,667,345
2,036,960
5.028.745
Totals 63.655,000
Rear- Engine Riders
Percent
0
4.0
13.5
22.5
60.0
Total
0
244,600
825,525
1,375,875
3,669,000
DC/MD/VA Statistics
Detached SFHUs*
Percent
5.4
22.9
36.7
17.3
13.1
1.9
2.7
Total
39,026
165,498
265,231
125,027
94,674
13,731
19.513
6.115,00011 722.700
Rear-Eng
Riders
0
2,564
18,009
20,629
23,784
64.986
' Includes mobile homes.
4-18
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See Disclaimer on Cover
by BAH in its study of lawn and garden equipment prepared for GARB.4)
As seen in Table 4-7, including SIC 078 as an indicator to allocate lawn
and garden equipment in the DC/MD/VA area significantly increases the
population counts. If these equipment types were allocated according
only to SFHUs using the NEVES methodology, the agreement between the two
estimates would be closer. (Also note, however, that both estimates for
DC/MD/VA are below the population figures submitted to EPA by OPEI [see
Table 4-2]. This reinforces the point that lawn and garden equipment
population and usage estimates are subject to considerable uncertainty
because of limited availability of field data.)
Recommendations
It is obvious that the limiting factor in improving the accuracy of lawn
and garden equipment activity and emissions estimates is the limited
availability of data. This is apparent for both top-down and bottom-up
approaches. Because the data requirements for bottom-up methodologies
are far more extensive, the possibility of developing a generalized
bottom—up approach to estimate lawn and garden activity at the local
level is bleak. However, with some additional effort to develop data on
lawn and garden equipment usage, top-down approaches can be improved.
It is recommended that a survey of manufacturers and end users be
conducted to determine the distribution and usage patterns of commercial
and residential equipment. Although such estimates exist to a limited
extent, there has not been a comprehensive survey conducted in a
consistent manner to establish the validity of existing data. Among the
data needs are:
• Equipment population and sales by residential and commercial
ownership;
• Annual hourly usage by residential ownership, commercial
ownership, and region; and
• Equipment distributions as a function of lot size.
References for Section 4
1. "Methodology to Estimate Nonroad Equipment Populations by
Nonattainment Areas," Energy and Environmental Analysis, September
1991.
2. "Estimates of 24 Nonattainment Area Portable Two-Stroke Power
Equipment Populations Based on Actual Industry Shipments Data and
Four Alternative Activity Models," Heiden Associates, October 1991.
3. "A 1989 California Baseline Emissions Inventory for Total
Hydrocarbon & Carbon Monoxide Emissions from Portable Two-Stroke
Power Equipment," Heiden Associates, July 1990.
4-19
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See Disclaimer on Cover
4. "Technical Support Document for California Exhaust Emissions
Standards and Test Procedures for 1994 and Subsequent Model Year
Utility and Lawn and Garden Equipment Engines," Booz, Allen, and
Hamilton, October 1990.
5. "Status Report: Emissions Inventory on Non-Farm (MS-1), Farm (MS-
2), and Lawn and Garden (Utility) (MS-3) Equipment," California Air
Resources Board, July 1983.
6. "American Housing Survey for the United States in 1989," U.S.
Department of Commerce and U.S. Department of Housing and Urban
Development, July 1991.
7. "American Housing Survey for the Washington Metropolitan Area in
1989," U.S. Department of Commerce and U.S. Department of Housing
and Urban Development, July 1991.
8. "American Housing Survey for the Los Angeles-Long Beach
Metropolitan Area in 1985," U.S. Department of Commerce and U.S.
Department of Housing and Urban Development, August 1989.
9. Letter of August 6, 1991. John Liskey (Outdoor Power Equipment
Institute) to Clare Ryan (U.S. Environmental Protection Agency).
10. "National Gardening Survey 1991-1992," National Gardening
Association, 1992.
11. "1991 Mower and Maintenance Equipment Report," The Center for Golf
Course Management, Golf Course Superintendents Association of
America, 1991.
12. "Energy Conservation Concepts in Managing Urban Parks," National
Park Service and U.S. Department of Energy (No Date).
13. "Calculating Mowing Costs," in Landscape Management. January 1992.
4-20
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See Disclaimer on Cover
5. AIRPORT SERVICE EQUIPMENT
Airport service equipment has been stratified into just two equipment
types: aircraft support equipment and terminal tractors. Aircraft
support equipment includes aircraft load lifters, de-icing equipment,
start units, ground power units, utility service equipment, baggage
conveyors, and airport service vehicles. Terminal tractors include
push-back tractors, tow tractors (i.e., baggage tugs), and yard
spotters. Although it would be desirable to consider each equipment
type individually, national and state-level population, horsepower, and
usage data are not available to this level of detail. Unfortunately,
this reduces confidence in local activity estimates developed according
to top-down and, to a lesser extent, bottom-up methodologies. (Military
installations could account for a significant fraction of the airport
service equipment activity in some communities, and contact with U.S.
Air Force personnel was initiated. However, resource constraints did
not allow for a thorough investigation of this source. See Appendix D
for correspondence.)
NEVES Methodology
In the top-down methodology developed for NEVES, EEA utilized air
carrier operations as the activity indicator in its regression analysis
performed on the PSR state-level equipment populations.1 This makes
intuitive sense in that the number of take-offs and landings would be
expected to correlate with equipment populations at individual airports,
and this indicator met EEA's statistical criteria for an acceptable
model. Airport service equipment population and activity derived from
this approach are summarized in Table 5-1 fox the U.S., the DC/MD/VA
area, and the SJV. (Note that Inventory A and Inventory B are
equivalent for this equipment category.) As seen, the bulk of the
activity is attributed to terminal tractors used to pull baggage carts
and tow planes. This is somewhat surprising given the variety of
equipment types included by PSR under the heading of "aircraft support
equipment." (This apparent anomaly is discussed in further detail
below.)
As a cross-check of the EEA/PSR methodology and data, it is interesting
to calculate the number of equipment-hours required to service each air
carrier operation. For example, using the data from NEVES for the
DC/MD/VA region, the total annual equipment-hours sum to 2,273,250.
Dividing this figure by the total air carrier operations at Dulles and
National Airports (318,302 in 1989) results in an average of 7.1
equipment-hours per operation (or 14.2 equipment-hours per landing and
take-off (LTD) cycle). The same calculation results in an average of
9.4 equipment-hours for each air carrier operation in the SJV
. 5-1
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See Disclaimer on Cover
Table 5-1
NEVES Airport Service Equipment Activity Estimates
National Estimates
Equipment Type
Aircraft Support
Terminal Tractors
Total
Population
Diesel
9,529
64,598
74.127
Gas
2,767
6,516
9,283
Activity (1000 Bhp-hr/yr)
Diesel
487,359
6,102,185
6.589,545
Gas I Total
50,651
329,243
379.894
538,010
6,431,429
6.969.438
% Total
Activity
7.7
92.3
100.0
DC/MD/VA Estimates
Equipment Type
Aircraft Support
Terminal Tractors
Total
Population
Diesel
237
1,604
1.841
Gas
69
162
231
Activity (1000 Bhp-hr/yr)
Diesel
12,121
151,520
163.642
Gas
1,263
8,186
9.449
Total
13,384
159,706
173.090
% Total
Activity
7.7
92.3
100.0
Sari JoaqufnValfev Air Basin Estimates
Equipment Type
Aircraft Support
Terminal Tractors
Total
Population
Diesel
13
86
99
Gas
4
9
13
Activity (1000 Bhp-hr/yr)
Diesel
765
9,532
10.297
Gas
84
533
617
Total
849
10,065
10.914
% Total
Activity
7.8
92.2
100.0
(18.8 equipment-hours per LTO). Clearly, these values appear to be
unreasonably high, particularly for the SJV in which the bulk of the
flights are performed by smaller aircraft which generally have lower
servicing requirements.
The equipment-hours calculated above are the result of compil-ing a
number of variables, and errors in any one directly impact the overall
calculation. It is very difficult to assess potential problem areas
such as overestimates of annual usage or state equipment populations,
and the statistical validity of any top-down model based on inaccurate
data is meaningless. Nonetheless, the discussion that follows outlines
potential improvements to the top-down approach and builds upon previous
efforts to develop a bottom-up methodology for estimating activity from
airport service equipment.
Approach
Sierra contacted a number of airport and air cargo industry
representatives to determine the type of information that is readily
available and might be used in estimating activity from airport service
equipment. Among those contacted included the Federal Aviation
Administration (FAA), selected airports (e.g., Sacramento, Dulles,
Washington National, Los Angeles, and several in the San Joaquin
Valley), United Parcel Service (UPS), the U.S. Postal Service, Emery
Worldwide Air, United Airlines, and several industry associations. This
5-2
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See Disclaimer on Cover
effort led to the conclusion that very good records are available on the
number and type of flights from airports in the U.S., while equipment
and fuel usage data are generally not available. However, three
documents were identified that contained support equipment usage data by
type of aircraft; thus, these data formed the foundation of a bottom-up
methodology as described below. Additionally, effort was expended to
examine alternative activity indicators that might be used in a top-down
approach to allocate PSR equipment data to the local level.
Methodology and Data Sources
Top-Down - Because the methodology developed for NEVES appeared to
overestimate airport service equipment activity, additional indicators
were investigated that might provide a more representative allocation of
equipment using the top-down approach. The number of air carrier
operations was used in NEVES to allocate equipment to the local level,
and this initially appears to be a good choice. However, there are
dramatically different service equipment requirements depending upon the
type of aircraft. For example, a 400-passenger Boeing 747 has greater
equipment requirements than a 150-passenger Boeing 727. Additionally,
cargo planes have different equipment requirements than passenger
planes. Thus, two activity indicators that may provide a better basis
for a top-down model are total passengers and tons of cargo. This
information is readily available and is published yearly by the FAA in
its "Airport Activity Statistics of Certified Route Air Carriers."2 Use
of these data to develop a model patterned after that proposed in NEVES
resulted in quite different equipment populations for the two study
areas included in this work.
Bottom-Up - In investigating potential bottom-up methodologies for
airport service equipment, Sierra took two approaches: one based oh
fuel usage, and another based on servicing requirements. First, fuel
usage information was considered. Since most airports have a limited
number of fueling locations, it was initially felt that fuel records
would be readily available. From this information, it would be possible
to estimate service equipment activity. After placing calls to a number
of airports and fuel suppliers, however, it became apparent that the
fuel supply systems at airports are highly variable. For example,
Sacramento Metro has a single fueling location (managed by the county),
and it was possible to obtain data on fuel usage by month and air
carrier. On the other hand, other airports had a number of private
firms supplying fuel, and those firms generally declined requests for
fuel sales records. In addition, even if fuel records are available, it
is often impossible to determine how much fuel is used in nonroad
support equipment versus other uses without contacting individual air
carriers directly. A methodology based on extensive polling of air
carriers at each individual airport was not considered viable and would
likely meet with resistance from local air quality planners responsible
for preparing inventories.
The second approach to developing a bottom-up methodology was based on
the servicing requirements of different types of aircraft. Since the
FAA publishes the number of flights by each type of aircraft at all
5-3
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See Disclaimer on Cover
airports supporting commercial operations, a bottom-up approach is
possible if information can be compiled on the equipment needs of each
aircraft type. This approach has been utilized in the past, and three
documents were identified that contain this information. The first,
published in 1973, was based on ground support equipment activity at
Chicago's O'Hare airport.3 Subsequent analyses used a similar
methodology to estimate support equipment activity. Sacramento Metro
published aircraft support equipment activity estimates in its "Air
Quality Program Report,"4 and an Environmental Impact Report prepared to
support an expansion at Ontario International Airport also contains
information on service equipment requirements by aircraft type.5 This
information is summarized in Table 5-2 for Ontario International and
Sacramento Metropolitan airports. As can be seen, the time required for
individual aircraft types is a strong function of passenger capacity.
Table 5-2
Equipment Requirements by Aircraft Type
Ontario International and Sacramento Metro Airports
Aircraft Typ*
Ontario
B-747
B-767
L-1011
DC- 10
B-757
DC-8
B-7Z7
B-737
OC-9
BAE-146
CNASOO
DHC7
Sacfamtnto
B-7Z7
MO- SO
B-737
BAE-146
JETSTREAM
SHORTS 300
EM BRAS*
Apprex.
Capacity
400-420
290
290
270
224
188
120-150
110-190
100
75
SO
50
120-150
150
110-150
75
20
36
30
Equipment Type and Tim* (Mm.)
Tractor
77.5
74
74
74
33
48
33
42.5
24
24
2O
0
40
40
40
40
0
0
0
Bait
La«d*r
66
80
80
80
56
60
56
60
30
30
0
0
30
30
30
30
0
0
0
Container
Loader
92
80
80
80
6
0
6
0
0
0
0
0
0
0
0
0
0
0
0
Cabin
Sarvfee
24
25
25
25
12
15
12
15
0
0
0
0
16
16
16
16
16
16
16
Lvtiy
Truck
24
18
18
18
15
18
15
15
15
15
10
0
10
10
10
10
10
10
to
Water
Truck
12
10
10
10
0
0
0
0
10
10
10
0
0
0
0
0
0
0
0
Food
Truck
55
20
20
20
17
30
17
20
17
17
0
0
17
17
17
17
0
0
0
Fuel
Truck
SO
45
45
45
20
40
20
15
15
15
10
10
15
15
15
15
15
15
15
Tow
Tractor
10
10
10
10
10
5
10
5
5
5
0
0
2.5
2.5
2.5
2.5
0
0
0
Cond-
itioner
0
0
0
0
0
30
0
0
0
0
0
0
0
0
0
0
0
0
0
Air
Start
3
0
0
0
0
5
0
0
0
0
0
0
0
0
0
0
0
0
0
Ground
Power UnH
60
40
40
40
15
X
15
0
0
0
0
0
18
18
18
18
18
18
18
Trane—
porter
19
0
0
0
3
0
3
0
0
0
0
0
0
0
0
0
0
0
0
Auxifiaiy
Power Unit
20
20
20
20
25
20
25
40
40
40
X
0
20
20
20
20
0
0
0
Total
Eqpmt-hr»
9.0
7.0
7.0
7.0
3.5
5.0
3.5
3.5
2.6
ZS
1.3
0.2
2.8
2.8
2.8
2.8
1.0
1.0
1,0
Because the above-referenced reports supply information only on
passenger planes, several cargo carriers were contacted to determine the
availability of data on equipment usage per ton of cargo. The U.S.
Postal Service at first appeared to be a likely source of information,
but it was discovered that all of their air operations are contracted to
private firms. Thus, Emery Worldwide Air (a Postal Service contractor)
and UPS were contacted, and written requests for information were
submitted to them. Unfortunately, neither company responded to these
requests.
Sample Calculations
Top-Down - The top-down regression model developed by EEA for NEVES was
rerun utilizing total passengers and tons of cargo as the activity
indicators (independent variables), with the state-level PSR equipment
5-4
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See Disclaimer on Cover
populations serving as the dependent variable. The regression results
are presented in Table 5-3, while Table 5-4 lists the results for the
case in which a zero intercept is assumed. (Data and regression results
are also contained in Appendix C.)
Sierra's regression analysis results in an R-square of 0.82 (Table 5-3);
thus, this model meets the R-square criterion established by EEA.1
Further,- the summary statistics in Table 5-3 indicate that collinearity
is not a problem with this multivariate model. However, the statistics
also indicate that the passenger variable is only valid at the
90 percent confidence interval, and the criterion for the intercept not
to be significantly different from zero at the 95 percent confidence
level is not met (although this latter criterion is only marginally
exceeded). Nonetheless, this model was used to determine airport
service equipment populations for the DC/MD/VA and the SJV areas, which
provided a comparison to the current NEVES approach.
The results of the above regression model (assuming a zero intercept,
i.e., Table 5-4) were used in conjunction with data on enplaned
passengers and enplaned cargo for the two study areas to arrive at a
total equipment population for each area. This calculation is
summarized in Table 5-5. The total population determined above was then
used with PSR's assumed mix of equipment and fuel types, horsepower, and
annual hourly usage to arrive at the activity (in Bhp-hr/year) for the
DC/MD/VA and SJV regions. This calculation is summarized in Table 5-6.
As indicated in Table 5-6, the equipment population estimated for
DC/MD/VA and the SJV is significantly lower using total passenger and
cargo enplanement as activity indicators. For the SJV, this was
anticipated because of the lower average passenger carrying capacity of
the planes serving the area. However, for the DC/MD/VA region, the
result was somewhat surprising.
Bottom-Up - Using the data summarized in Table 5-2 in conjunction with
information from FAA's "Airport Activity Statistics of Certified Route
Carriers" on the type of flight, it was possible to develop bottom-up
activity estimates for passenger aircraft. This was done for the SJV,
and the results are given in Table 5-7. The total equipment-hrs
(47,654) was multiplied by an average of 76 bhp-hr per equipment-hour
(based on PSR data) to obtain the results in bhp-hr/year. (The same
analysis could also be performed for the DC/MD/VA metropolitan area, but
the SJV analysis adequately demonstrates the methodology.) Because
airports in the SJV are similar in structure to Sacramento Metro,
equipment-time data from Sacramento were used in the calculations to
arrive at a total equipment-hour value for airport service equipment in
the SJV.
A comparison of results from the three different estimates (i.e., NEVES,
the alternative top-down approach outlined above, and the bottom-up
estimate) is shown in Table 5-8. The different methodologies give
vastly different results. The alternative top-down method resulted in a
42 percent decrease in annual bhp-hr compared to NEVES, while the
bottom-up method resulted in a 67 percent decrease from NEVES. It
5-5
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Table 5-3
Regression Model Using Airport Service Equipment it the Dependent Variable
and Total Passengers and Cargo Tonnage •» the Independent Variable*
OEP VARIABLE: AP POP
ANALYSIS OF VARIANCE
SOURCE
MODEL
ERROR
C TOTAL
OF
SUM OF
SQUARES
MEAN
SQUARE
' 2 93621472.78 46810734.39
20 20675541.66 1033777.08
22 114297014.43
F VALUE
45.281
PR08>F
0.0001
ROOT MSE
OEP MEAN
C.V.
1016.748
2669.739
38.08418
R-SOUARE
ADJ R-SO
0.8191
0.8010
PARAMETER ESTIMATES
VARIABLE OF
PARAMETER
ESTIMATE
STANDARD
ERROR
INTERCEP 1 622.28967864 306.59441114
PASS 1 .00006553352 .00003816396
CARGO 1 0.005678711 0.002716053
T FOR HO:
PARAMETERS
2.030
1.717
2.091
PROS > |T|
0.0559
0.1014
0.0495
COLLINEARITY DIAGNOSTICS
NUMBER EIGENVALUE
2.587314
0.372742
0.039943
CONDITION
NUMBER
1.000000
2.634633
8.048298
VAR PROP
INTERCEP
0.0524
0.9155
0.0321
VAR PROP
PASS
0.0103
0.0221
0.9676
VAR PROP
CARGO
0.0110
0.0376
0.9514
MODEL: AP POP • a'(PASS) * b'(CARCO) » e
AP POP - AIRPORT SERVICE EQUIPMENT POPULATION
PASS • ENPLANED PASSENGERS
CARGO • ENPLANED CARGO (TONS)
a « 0.00006553
b • 0.005679
c > 622
Table 5-4
Regression Model Using Airport Service Equipment a* the Dependent Variable
and Total Passengers and Cargo Tonnage as the Independent Variables
(Forced Zero Intercept)
DEP VARIABLE: AP POP
ANALYSIS OF VARIANCE
SOURCE
OF
SUM OF
SQUARES
MEAN
SQUARE
MODEL 2 253295369.81 126647684.91
ERROR 21 24934306.19 1187347.91
U TOTAL 23 278229676.00
F VALUE
106.664
PROB>F
0.0001
ROOT MSE 1089.655 R-SQUARE 0.9104
OEP MEAN 2669.739 ADJ R-SQ 0.9018
C.V. 40.81503
NOTE: NO INTERCEPT TERN IS USED. R-SOUARE IS REDEFINED.
PARAMETER ESTIMATES
VARIABLE OF
PASS
CARGO
PARAMETER
ESTIMATE
1 .00008840719
1 0.005606375
STANDARD
ERROR
.00003907664
0.002910559
T FOR HO:
PARAMETERS
2.262
1.926
PROS > |T|
0.0344
0.0677
COLIIHEARITY DIAGNOSTICS
NUMBER EIGENVALUE
1.958873
0.0411265
CONDITION VAR PROP VAR PROP
NUMBER PASS CARGO
1.000000 0.0206 0.0206
6.901477 0.9794 0.9794
ZERO INTERCEPT MODEL
MODEL: AP POP » a*(PASS) * b*(CARGO>
AP POP • AIRPORT SERVICE EQUIPMENT POPULATION
PASS > ENPLANED PASSENGERS
CARGO > ENPLANED CARGO (TONS)
a > 0.00008841
b > 0.005606
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Table 5-5
Summary of Airport Service Equipment Population Calculation
Nonattainment
Community
DC/MD/VA
SJVAB
Enplaned
Passengers
1 1 ,477,384
558,272
Enplaned
Cargo (Tons)
116,771
2,300
Estimated
Eqpmt. Pop.
1,669
62
PSR/Est.
Ratio*
0.79
1.03
Final
Eqpmt. Pop.
1,319
64
* This represents the ratio of the PSR state-level equipment population to the estimated
state-level population based on the regression coefficients. EEA recommends mul-
tiplying the estimated local-area population (i.e., that estimated with the regression
results) by this ratio to ensure PSR's total state-level populations are maintained. See
reference 1 for a more thorough discussion of this approach.
Table 5-6
Airport Service Equipment Activity Utilizing Enplaned Passengers and Cargo (Tons) as Activity Indicators
DG/MD/VA Estimate*
Equipment Type
Aircraft Support
Terminal Tractors
Total
Population
Diesel
152
1,028
1.180
Gas
36
103
139
Horsepower
Diesel
137
96
Qaa
48
82
Load Factor
Diesel
0.51
0.82
Gas
0.56
0.78
Annual Hours
Diesel
791
1282
Gas
735
844
Activity (1000 Bhp-hr/yr)
Diesel
8.401
103,745
112.145
Gas
711
5,560
6.271
Total
9.112
109.305
118.417
% Total
Activity
7.7
92.3
100.0
San Joaauin Valev Air Basin Estimates
Equipment Type
Aircraft Support
Terminal Tractors
Total
Population
Diesel
7
50
57
Gas
2
5
7
Horsepower
Diesel
137
96
Gas
48
82
Load Factor
Diesel
0.51
0.82
Gas
0.56
0.78
Annual Hours
Diesel
842
1408
Gas
783
926
Activity (1000 Bhp-hr/yr)
Diesel
412
5,542
5,954
Gas
42
296
338
Total
454
5,838
6.292
% Total
Activity
7.2
92.8
100.0
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Table 5-7
Bottom-Up Estimate of Airport Service Equipment Activity
San Joaquin Valley Air Basin*
Aircraft Tvpe
Passenger •
DC -9- 80
B-727-100
B-727-200
B-737-300
B-737- 100/200
EMBRAER
EMBRAER120
JETSTREAM 31
B-747
B-737-400
SHORTS 360
BAE- 146- 200/300
DCH-8
Total Passenger
Cargo
BEECH 18
C-208
B-727-200
Total Carao:
total Departures:
Approx.
Caoadtv
150
112
158
136
120
30
30
19
400
146
36
75
37
Approx.
Eopmt— hrs
2.8
2.8
2.8
2.8
2.8
1
1
1
9
2.8
1
2.8
1
Departure* Performed
Bakersfield
722
0
0
0
0
1.669
1.626
39S
0
0
0
0
1.024
5.436
3
259
3
265
5.701
Fresno
3
460
1.244
1.008
1.794
4.424
1.623
5.965
1
0
753
1.932
1.066
20.271
2
606
0
608
20.679
Merced
0
0
0
0
0
1.788
70
300
0
0
0
0
0
2.158
0
0
0
0
2.158
Modesto
0
0
0
0
0
1.190
0
509
0
0
0
0
0
1.699
0
0
0
0
1.699
Stockton
1
0
0
2
0
1.106
1.083
526
0
1
2
880
0
3.601
0
0
0
0
3.601
VIsalia
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
192
0
192
192
Total
726
460
1.244
1,008
1.794
10.177
4.402
7.695
1
1
755
2.812
2.090
33.165
5
1.057
3
1.065
34.230
Total
Eopmt-hrs
2.033
1.288
3.483
2.822
5,023
10.177
4.402
7.695
9
3
755
7.874
2.090
47.654
* Activity - (47,654 Equipment-hr) • (76 Bhp-hr/Equipment-hr) « 3,621.700 Bhp-hr/yr
Table 5-8
Comparison of Airport Service Equipment
Activity Estimates for the SJV
Method
NEVES
Alternative Top-Down
Bottom-Up
Equipment-Hours
143,500
82,700
47,654
1000 bhp-hr/Year
10,914
6,292
3,622
should be noted, however, that the 1067 cargo flights (accounting for
2,300 tons of enplaned cargo) would increase the overall bottom—up
activity estimate for the SJV.
Additional Considerations
Although the data compiled on airport service equipment are useful in
making rough estimates of equipment activity using a bottom-up approach,
there are a number of problems in using this information to develop a
5-8
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generalized bottom-up methodology. First, no information on cargo
operations was obtained, and this could be an important contribution to
service equipment activity in some areas because cargo, unlike
passengers, does not unload itself. Second, the time estimates for some
pieces of equipment are highly airport specific, and would vary
according to an airport's physical layout. Third, it is not clear that
the data compiled on equipment usage by aircraft type include all of the
applications for which the equipment is used. Finally, fuel type,
horsepower, and load factor data were not obtained on an equipment-
specific basis, which necessitated the use of an average
bhp-hr/equipment-hr value based on PSR data. All of these factors
reduce the confidence of any estimates developed with this methodology.
On the other hand, another point to be considered is that the bottom-up
approach outlined above is consistent with the current recommended
procedures for developing emissions inventories for aircraft.6 Because
information on number of flights by aircraft type must be compiled to
prepare aircraft inventories, much of the data needed in a bottom-up
approach for estimating airport service equipment activity would already
be available to local authorities responsible for inventory preparation.
Although there are deficiencies in the bottom-up method outlined above,
there appear also to be problems with the estimates performed for NEVES.
When considering the total equipment-hours per LTD, the NEVES estimates
seem unreasonably high. This could be the result of an overestimate of
population, annual usage, or both. In addition, inspection of Table 5-2
indicates that terminal tractor usage generally accounts for less than
25 percent of the equipment time to service an aircraft. However, NEVES
data suggest that 90 percent of the overall airport service equipment
activity is attributable to terminal tractors. Given the large number
of equipment types included in "aircraft support equipment," their
contributing only 10 percent of the total equipment hours might be
questioned.
Recommendations
It appears that additional effort is needed to make either top-down or
bottom-up methodologies adequate for estimating airport service
equipment activity. There is a data gap that must be filled before
estimates can be made with any confidence. For top-down estimates,
serious consideration should be given to alternative activity
indicators, while population and usage estimates provided by PSR need
careful review. For bottom-up estimates, details on cargo operations
are needed, and more information on equipment fuel and horsepower
ratings would be helpful. Nonetheless, an adequate bottom—up method
could be developed with little additional effort, provided industry was
willing to share data related to equipment usage by type of flight
(e.g., passenger versus cargo). Finally, the bottom-up methodology
discussed above would mesh well with the approach that is recommended by
EPA for developing emission estimates from aircraft.6
5-9
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References for Section 5
1. "Methodology to Estimate Nonroad Equipment Populations by
Nonattainment Areas," Energy and Environmental Analysis, September
1990.
2. "Airport Activity Statistics of Certified Route Air Carriers," U.S.
Department of Transportation, 1990.
3. "An Air Pollution Impact Methodology for Airports," Argonne
National Laboratory, 1973.
4. "Sacramento Metropolitan Airport Air Quality Program Report,"
County of Sacramento, Department of Airports, 1992.
5. "Ontario International Airport Final Environmental Impact Report
for Terminals, Other Facilities and Operations to Support 12
Million Annual Passengers," City of Los Angeles, Department of
Airports, November 1991.
6. "Procedures for Emission Inventory Preparation. Volume IV: Mobile
Sources," U.S. Environmental Protection Agency, July 1989.
H II II
ii It it
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6. RECREATIONAL EQUIPMENT
The recreational equipment category consists of all-terrain vehicles
(ATVs), minibikes, off-road motorcycles, snowmobiles, golf carts, and
specialty vehicles/carts (e.g., snow-grooming equipment, ice maintenance
equipment, go-carts, personnel carriers, and industrial ATVs). On a
national basis, recreational equipment accounts for the smallest
percentage of bhp-hr/yr activity of all the nonroad equipment
categories. Furthermore, it is likely that much of this activity occurs
outside of nonattainment areas. Thus, any alternative methodology
developed to improve local estimates of recreational equipment activity
must be easy to apply to justify its use.
NEVES Methodology
The local population estimates developed for Inventory A in NEVES were
based on a regression analysis of state-level PSR data using SIC 557
(Motorcycle Dealers - Establishments) as the activity indicator. In a
few areas, however, data for SIC 557 were unavailable. Thus, SIC 55
(Automotive Dealers and Service Stations - Employees) was used as an
alternative indicator for the Baton Rouge, El Paso, Provo-Orem, and
Spokane metropolitan areas. The statistical criteria established by
EEA1 were satisfied for both models.
The population and usage estimates for ATVs, minibikes, and off-road
motorcycles were modified in Inventory B based on information submitted
to EPA by the Motorcycle Industry Council (MIC). MIC's national
population estimates (which were much higher than those developed by EEA
for Inventory A) were allocated to the local level using the same local-
to-national equipment population ratio established in Inventory A. MIC
also provided estimates for the average number of miles ridden annually
for ATVs and off-road motorcycles (263 and 313 miles, respectively).
These estimates were divided by an assumed average speed to arrive at an
annual hourly usage. Similarly, the International Snowmobile Industry
Association (ISIA) provided EPA with an independent estimate of the
national snowmobile population and average annual usage. This national
population estimate was also scaled to the local level by using the
Inventory A local-to-national population ratios.
Tables 6—1 and 6-2 show the results of the above analyses for the
nation, the DC/MD/VA metropolitan area., and the SJVAB for Inventories A
and B, respectively. Note that only annual hourly usage is compiled in
these tables because EPA chose to use emission factors in units of
grams/hour when generating the emissions inventories for this equipment
category (except snowmobiles, for which the use of bhp-hr/yr to
represent activity was retained).
6-1
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Table 6-1
NEVES Inventory A Recreational Equipment Activity Estimates
National: Estimates
Eauipment Type
All Terrain Vehicles
Minibikes
Off- Road Motorcycles
Golf Carts
Snowmobiles
Specialty Vehicles & Carts
Total
Population
Diesel
0
0
0
0
0
3,334
3,334
Gas
1,312,981
48,990
201,125
122,670
776,559
266,096
2.728.421
Activity (1000hr/yr)
Diesel
0
0
0
0
0
1,400
1.400
Gas
152,306
2,156
20,314
116,537
103,282
16,764
411.358
Total
152,306
2,156
20,314
116,537
103,282
18,164
412.758
% Total
Activity
36.9
0.5
4.9
28.2
25.0
4.4
100.0
DC/MD/VA Estimates
Equipment Type
All Terrain Vehicles
Minibikes
Off- Road Motorcycles
Golf Carts
Snowmobiles
Specialty Vehicles & Carts
Total
Population
Diesel
0
0
0
0
0
19
19
Gas
7,219
269
1,106
674
1,067
1,464
11.799
Activity (1000 hr/yr)
Diesel
0
0
0
0
0
8
8
Gas
837
12
112
640
142
92
1.835
Total
837
12
112
640
142
100
1.843
% Total
Activity^
45.4
0.6
6.1
34.7
7.7
5.4
100.0
San Jbaquin Valtev Air -Basin Estimates
Equipment Type
All Terrain Vehicles
Minibikes
Off- Road Motorcycles
Golf Carts
Snowmobiles
Specialty Vehicles & Carts
Total
Population
Diesel
0
0
0
0
0
7
7
Gas
2,941
110
451
275
0
596
4JJ73
Activity (1000 hr/yr)
Diesel
- 0
0
0
0
0
3
3
Gas
397
7
62
315
0
44
824
Total
397
7
62
315
0
47
828
% Total
Activity
48.0
0.9
7.5
38.0
0.0
5.7
100.0
A potential deficiency in the NEVES methodology to estimate emissions
from nonroad recreational equipment is the choice of activity indicators
used to distribute the national equipment population to the local level.
Because these equipment types are generally used outside of urban areas,
activity indicators that are expected to be based within urban areas
(such as motorcycle dealerships) will not provide the best estimate of
equipment distribution and activity, regardless of the favorable
statistics resulting from such a choice. For example, according to
EEA's estimates (which were based on the Department of Commerce's
"County Business Patterns"), there are 11 motorcycle dealerships in the
entire SJVAB, while Los Angeles County alone has 112. Given the rural
nature of the SJVAB and the highly urban characteristics of Los Angeles
County, nonroad recreational vehicle usage in Los Angeles County would
not be expected to be 10 times than that in the SJVAB. Furthermore,
6-2
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Table 6-2
NEVES Inventory B Recreational Equipment Activity Estimates
National Estimates
Equipment Type
All Terrain Vehicles
Minibikes
Off- Road Motorcycles
Golf Carts
Snowmobiles
Specialty Vehicles & Carts
Total
Population
Diesel
0
0
0
0
0
3,334
3.334
Gas
2,240,000
48,990
750,000
122,670
1,200,000
266,096
4.627.756
Activity (1000 hr/yr)
Diesel
0
0
0
0
0
1,400
1.400
Gas
33,600
735
11,250
116,537
108,000
16,764
286,885
Total
33,600
735
11,250
116,537
108,000
18,164
288.286
% Total
Activity
11.7
0.3
3.9
40.4
37.5
6.3
100.0
DC/MD/VA Estimates
Equipment Type
All Terrain Vehicles
Minibikes
Off- Road Motorcycles
Golf Carts
Snowmobiles
Specialty Vehicles & Carts
Total
Population
Diesel
0
0
0
0
0
19
19
Gas
12,317
269
4,124
674
1,650
1,463
20.497
Activity (1000 hr/yr)
Diesel
0
0
0
0
0
8
8
Gas
185
4
62
640
149
92
1.132
Total
185
4
62
640
149
100
1.140
% Total
Activity^
16.2
0.4
5.4
56.2
13.0
8.8
100.0
_ _ San: JoaquirvValleylAir Basin: Estimates
Equipment Type
All Terrain Vehicles
Minibikes
Off- Road Motorcycles
Golf Carts
Snowmobiles
Specialty Vehicles & Carts
Total
Population
Diesel
0
0
0
0
0
7
7
Gas
5,017
110
1,680
275
0
596
7,678
Activity (1000 hr/yr)
Diesel
0
0
0
0
0
3
3
Gas
75
2
25
261
0
38
401
Total
75
2
25
261
0
40
404
% Total
Activity
18.6
0.4
6.2
64.7
0.0
10.0
100.0
California Department of Motor Vehicles (DMV) records indicate that
there are roughly 14,000 off-road motorcycles registered in the eight-
county SJVAB2, whereas Inventories A and B in NEVES estimated 450 and
1680, respectively. Finally, using motorcycle dealerships to allocate
golf carts and specialty vehicles/carts makes little intuitive sense.
Approach
The challenge in allocating nonroad recreational vehicle usage to
individual counties is to develop a methodology that distributes
activity to the areas where that activity actually occurs. Identifying
6-3
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a consistently published and all-encompassing data source that provides
such information, however, proved difficult. A number of agencies were
contacted to determine the availability of information on nonroad
recreational vehicle usage. At a national level, the Bureau of Land
Management and the National Forest Service were contacted. State DMVs
were contacted in California, Maryland, and Virginia to determine the
registration requirements for off-highway recreational vehicles. The
California Department of Parks and Recreation and several off-highway
vehicle recreation areas in California were solicited for information on
vehicle usage. Finally, manufacturer associations (i.e., MIC, ISIA)
were also asked for data concerning nonroad recreational equipment
populations and usage.
Methodology and Data Sources
Equipment Population - The most obvious source of equipment population
information at first appeared to be state DMVs. It was felt that if
registration records were kept at the county level, distributing
equipment according to registrations would be an improvement over using
motorcycle dealerships (although it was recognized that the equipment is
not necessarily used in the county in which it is registered). After
contacting several states, however, it became clear that the
registration requirements for nonroad recreational equipment are highly
variable, as is the level of detail available from each state. For
example, California requires registration of off-highway recreational
vehicles and keeps independent records of these vehicles. Maryland also
requires registration of off-road motorcycles, but combines both off-
road and on-road in its registration records. On the other hand,
Virginia and North Carolina require no registration of off-highway
recreational equipment. (Because detailed data were not available for
DC/MD/VA, North Carolina was contacted because it could potentially
serve as a surrogate for this area.)
Besides those from state DMVs and PSR, the only other estimates of off-
highway recreational equipment populations that were identified came
from industry associations. MIC annually publishes population estimates
for motorcycles and ATVs in its "Motorcycle Statistical Annual."3 In
that document, national and state-level population estimates are
provided for on-highway motorcycles (motorcycles certified by the
manufacturer to be in compliance with Federal Motor Vehicle Safety
Standards (FMVSS)), off-highway motorcycles (motorcycles not certified
to be in compliance with FMVSS, including ATVs), and dual-purpose
motorcycles (motorcycles complying with FMVSS and designed for use on
public roads or off-highway recreational use). Because of their
different use characteristics, however, a means to separate the total
off-highway motorcycle population into motorcycles and ATVs is
necessary. Additionally, methodologies to allocate the state-level
populations to the county level (i.e., to the county in which the
equipment is used) are also needed.
To determine the number of off-road motorcycles and ATVs from the state-
level total off-highway populations, results of a 1990 survey conducted
for MIC by Burke Marketing Research4 can be used. That survey compiled
6-4
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See Disclaimer on Cover
information on motorcycle type for 2,595 motorcycles throughout the
United States. The motorcycle types of interest included scooters, on-
highway motorcycles, dual-purpose motorcycles, off-highway motorcycles,
competition motorcycles, and ATVs. These survey results, summarized in
Table 6-3, can be used in conjunction with the state population figures
reported by MIC to arrive at ATV and off-road motorcycle populations.
The results for "off-highway" and "competition" motorcycles were
combined to determine the appropriate fraction of off-road motorcycles
for this work.
Table 6-3
Distribution of Off—Highway Motorcycles
Based on MIC's Definition and the Burke Survey4
Location
Total U.S.
Western U.S.
California
Off-Road Motorcycle Distribution
Off-Highway MC
143 (15.1%)
49 (18.3%)
16 (15.8%)
Competition MC
198 (20.8%)
80 (29.9%)
39 (38.6%)
ATV
609 (64.1%)
139 (51.9%)
46 (45.6%)
Table 6-4 compares California DMV estimates and MIC estimates of off-
road motorcycle (including dual-purpose motorcycles) and ATV population
for the eight SJVAB counties and the entire state of California. The
DMV estimates include only "active" off-road registrations, and the
county-specific industry estimates are based on the same ratio of county
to state off-highway populations reported by the DMV. It is recognized
that such a procedure could not be applied on a national basis, but the
analysis is included here to provide a cross-check of the MIC data.
As seen in Table 6-4, the off-road motorcycle population appears to be
under-represented by the California DMV compared to the MIC estimates,
while the ATV populations are reasonably similar. A report prepared by
Tyler and Associates for the California Departments of Transportation
and Parks and Recreation indicated that a significant number of off-
highway vehicles used in California are not registered with the State
DMV.5 That study, which was based on surveys conducted in California,
determined that almost 5 times more unregistered off-road motorcycles
were ridden off-highway compared to the number of registered off-road
motorcycles. Applying this factor to the California DMV off-highway
motorcycle registrations results in an estimated California population
of roughly 950,000 vehicles. Similarly, Tyler determined that the
"true" population of ATVs in California should be 2.5 times the number
of registered ATVs. Although these numbers appear to be unreasonably
6-5
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Table 6-4
Comparison of Off-Road Motorcycle and ATV Populations
California DMV and MIC Estimates
County
Fresno
Kern
Kings
Made r a
Merced
San Joaquin
Stanislaus
Tulare
SJVAB
California
California DMV
Off-Rd MC"
2,289
3,808
437
506
746
2,156
2,435
1,467
13,844
159,849
ATV
4,326
3,788
948
870
1,124
2,474
3,079
2,737
19,346
161,335
MIC*
Off-Rd MC**
4,058
6,750
775
897
1,322
3,822
4,317
2,601
24,541
283,365
ATV
3,964
3,471
869
797
1,030
2,267
2,821
2,508
17,727
147,835
MIC only reports state-level populations; thus, the county-level
estimates shown here are based on scaling the MIC state-level data by
the same county-to-state ratios provided by the California DMV.
Includes dual-purpose motorcycles. The California DMV requires dual-
registration for motorcycles that are used both on- and off-highway.
high, they do explain the higher population reported by MIC for
motorcycles.
In addition to off-highway motorcycles and ATVs, alternative sources of
equipment population data for the remaining equipment types included in
the recreational equipment category were also sought; however, the only
equipment type for which additional information was obtained was
snowmobiles, and this had previously been supplied to EPA by ISIA. In
response to Sierra's requests for information, ISIA provided comments it
had prepared on CARB's proposed regulations for off-highway vehicles.6
In this submittal, ISIA estimated the California snowmobile population
based on California DMV registration records. In support of NEVES, ISIA
also provided an estimate for the national snowmobile population.
Annual Usage — A number of organizations were contacted to determine
usage patterns for off-highway recreational equipment. On a national
level, the U.S. Forest Service was contacted to determine if studies are
available on off-highway vehicle usage in the National Forest System.
Although the number of trail-miles on the National Forest System are
available for each state,7 county-level numbers do not appear to be
6-6
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See Disclaimer on Cover
available. (This was at first considered a potential activity indicator
for a modified top-down approach.) Furthermore, in speaking with Forest
Service personnel, it appears that information related to off-highway
vehicle usage is generally compiled on a case-by-case basis. For
example, off-highway vehicle usage was being investigated in
California's Mojave Desert and Imperial Sand Dunes, but the focus of
that research was on the impacts these vehicles have on the desert
tortoise.8 Thus, standardized, regularly updated information on off-
highway vehicle usage is not available from the U.S. Forest Service.
The Department of Interior's Bureau of Land Management (BLM) was also
contacted for information related to off-highway vehicle usage. BLM
keeps good records of trail-miles and visitor hours by type of activity
for each area and state in which BLM has jurisdiction, and Sierra was
able to obtain a computerized printout of the above information for
fiscal year 1991.9-10 The states for which information was obtained
included Alaska, Arizona, California, Colorado, Idaho, Montana,
New Mexico, Nevada, Oregon, Utah, and Wyoming. Although the information
received provides a good assessment of vehicle usage on BLM lands, it
does not allow for the quantitative estimate of vehicle usage for an
entire state or county because vehicle usage is obviously not confined
to BLM lands. It is interesting, however, to look at the reported usage
in California BLM areas, which is summarized in Table 6-5, This table
indicates that the California Desert region, which is a vast tract of
essentially uninhabited land southeast of Los Angeles, receives the
majority of off-highway vehicle usage in California's BLM areas. Again,
this points out the importance of choosing an activity indicator that
represents the area in which the equipment is used.
Two reports cited earlier, the Burke4 and Tyler5 studies, contain
information related to annual off-highway recreational vehicle usage.
Both studies were based on surveys; however, the Burke study contains
results for the entire U.S., while the Tyler study is specific to
Table 6-5
Off-Highway Vehicle Travel on BLM Lands in California
Fiscal Year 1991
BLM District
Bakersfield
Susanville
Ukiah
California Desert
Total Calif. BLM
Number of
Participants
297,000
333,700
255,800
5,075,804
5,962,304
Total
Visitor-hrs
1,858,500
991,000
1,214,600
31,021,325
35,085,425
Percent of
Visitor-hrs
5.3
2.8
3.5
88.4
100
6-7
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California. Burke compiled the average number of miles ridden annually
for off-road motorcycles and ATVs by region of the country. On average,
off-highway motorcycles are ridden 313 miles and ATVs are ridden 263
miles annually. In addition, the average number of days ridden each
year was determined in the Burke survey. On the other hand, Tyler
estimated the annual fuel consumption for off-road motorcycles (in
California) to be 43 gallons and the annual fuel consumption for ATVs to
be 28 gallons. (The purpose of the Tyler report was to develop a
methodology to estimate the monthly fuel usage by recreational off-
highway vehicles. This information was then used to determine how much
fuel tax refund monies were to be placed into an off-highway vehicle
account.)
Unfortunately, a direct comparison between the results presented by
Burke and Tyler is not possible because of the different units in which
the activities were reported. However, dividing the average 313 miles
per year reported by Burke by the average 43 gallons per year reported
by Tyler, the fuel economy for off-highway motorcycles is calculated as
7.3 miles/gallon. This value appears rather low, but detailed fuel
economy data for off-highway motorcycles could not be obtained in order
to validate the above estimate. (Based on conversations with various
experts, Tyler reported a range of 10 to 65 miles/gallon, depending on
engine size, type of riding, and 2- or 4-stroke.) Assuming a value of
7.3 miles/gallon is low, this indicates that the Burke annual mileage
estimates are low, the Tyler annual fuel usage estimates are high, or
both.
Temporal Usage - Burke4 and Tyler5 also reported information that can be
used to determine seasonal and weekend use for off-highway recreational
equipment. This information is available directly from Burke's summary
statistics (e.g., percent of riding by season, percent of riding done on
weekends), while monthly vehicle usage can be inferred from the monthly
fuel usage data reported by Tyler.
In addition to the above studies, off-road vehicle parks in California
were contacted to determine if information on vehicle usage was
available. Although the yearly attendance for each park is not
particularly useful, the distribution of attendance by month and by day
of week can be important from an emissions modeling perspective.
Figure 6-1 shows the monthly distribution of attendance for the
Hollister Hills State Vehicle Recreation Area (SVRA). As seen,
attendance peaks in the November to April period. (An exception is a
spike in attendance for June 1989. This apparent anomaly may have been
the result of competition events.) This pattern confirms discussions
held with a number of park officials who indicated that summer ridership
is low because of higher temperatures and low soil moisture content,
which results in poor traction and dusty conditions.
Information from the Burke survey indicated that roughly two-thirds of
off-road motorcycle and ATV usage occurs on weekends. Attendance data
from the Carnegie SVRA (which is located in the SJVAB) confirmed this
assessment. Figure 6-2 shows the daily motorcycle attendance for a
number of months in 1991. This figure indicates that weekend usage for
this park averages 70 to 80 percent of the total usage. (Also note the
increased attendance for the fall months compared to the summer months.)
6-8
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14,000
12,000
Q>
10,000
8,000
6,000
4,000
See Disclaimer on Cover
Rgure 6-1
Recreational Vehicle Usage by Month
(Hollister Hills SVRA)
1988/89
1990/91
Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun
Month
600
500
o
£400
£300
I
P
o 200
100
Rgure 6-2 -
Motorcycle Usage by Day of Week
(Carnegie SVRA)
*T i i i i i i i i i i i i
MTWTFSSMTWTFSSMTWTFSSMTWTFSS
Day of Week
6-9
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See Disclaimer on Cover
Sample Calculations
After reviewing all available data, it became clear that developing a
bottom-up approach to estimating nonroad recreational vehicle usage is
nearly impossible. Although Sierra was able to compile information on
vehicle usage for certain areas (e.g., BLM land, California SRVAs),
there are no data available from which a comprehensive bottom-up
methodology can be developed. Therefore, the focus of the area-specific
calculations was on an improved top-down approach. Furthermore, the
estimates that follow are specific to the SJVAB, although the
methodology could obviously be extended to other areas.
As discussed above, when investigating activity indicators for off-
highway recreational vehicle usage, it is important to choose an
indicator that is representative of where the vehicles are operated.
This proved difficult, especially since these vehicles are often
operated in remote areas where commonly used statistics (e.g.,
employment by SIC code) are not applicable.
One of the more obvious activity indicators, which was evaluated in a
regression model, is the number of acres of public land in each state.*
The statistical validity of this model, however, was highly questionable
(e.g., R-square of 0.28), and no further attempts to base equipment
distribution on land ownership characteristics were made. It appears
that this indicator provided poor results because a large percentage of
the public land is in the western U.S., while a significant fraction of
off-road motorcycles and ATVs are located in eastern states.
Because off-highway recreational equipment is used primarily in rural
areas (MIC reports that roughly 80 percent of the off-highway motorcycle
and ATV usage occurs in rural areas11), another activity indicator that
was investigated is rural population. A regression model was developed
in which the state-level off-highway motorcycle populations reported by
MIC3 served as the dependent variable, while the state-level rural
population was used as the independent variable. (The population
statistics were obtained from the 1990 Census of Population and
Housing.12) The 23 states used in the NEVES analysis were included in
this analysis as well.
The results of the above model are summarized in Table 6-6, which shows
a very poor correlation between rural population and off-highway
motorcycle population (R-square of 0.42). In investigating the reason
for the poor correlation, however, it became clear that California had a
profound effect on the overall regression results. By taking California
out of the model, the statistical significance of the regression
improved substantially, as seen in Table 6-7 (R-square of 0.84), and
removing Utah from the model resulted in still further improvement
(R-square of 0.87).
Data and regression statistics are summarized in Appendix C for the
models developed in this section.
6-10
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Table 6-6
Regression Model Using MIC Off-Highway Motorcycle Population as the Dependent
Variable and Rural Population as the Independent Variable
DEP VARIABLE: MC POP
ANALYSIS OF VARIANCE
SOURCE
MODEL
ERROR
C TOTAL
DF
ROOT MSE
DEP MEAN
C.V.
SUM OF
SQUARES
MEAN
SQUARE
1 40208213665 40208213665
21 55193745465 2628273594
22 95401959130
VARIABLE DF
INTERCEP 1
R POP 1
51266.69
81021.74
63.27523
R-SQUARE
ADJ R-SQ
PARAMETER
ESTIMATE
10442.78781
0.04436638
PARAMETER ESTIMATES
STANDARD
ERROR
20973.53352
0.0113431
F VALUE
15.298
0.4215
0.3939
T FOR HO:
PARAMETERS
0.498
3.911
PROB>F
0.0008
PROS > |T|
0.6237
0.0008
MODEL: MC POP = a*(R POP) +b
MC POP » MOTORCYCLE POPULATION
R POP = RURAL POPULATION
a~= 0.0444
b = 10,443
Table 6-7
Regression Model Using MIC Off-Highway Motorcycle Population as the Dependent
Variable and Rural Population as the Independent Variable
(Excluding California)
DEP VARIABLE: MC POP
ANALYSIS OF VARIANCE
SOURCE
MODEL
ERROR
C TOTAL
DF
ROOT MSE
DEP MEAN
C.V.
SUM OF
SQUARES
MEAN
SQUARE
1 28370921916 28370921916
20 5207385811 260369290.56
21 33578307727
16135.96
69968.18
23.06186
R-SQUARE
ADJ R-SQ
F VALUE
108.964
0.8449
0.8372
PROB>F
0.0001
VARIABLE DF
INTERCEP 1
R POP 1
PARAMETER
ESTIMATE
11154.18142
0.03761341
PARAMETER ESTIMATES
STANDARD
ERROR
6601.52593
0.003603304
T FOR HO:
PARAMETERS
1.690
10.439
PROB > |T|
0.1066
0.0001
MODEL: MC POP « a*(R POP) +b
MC POP = MOTORCYCLE POPULATION
R POP = RURAL POPULATION
a~» 0.0376
b = 11,154
6-11
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The above results can be explained by considering where off-highway
recreational vehicles are ridden. In the southwestern U.S., there is
considerable access to open areas that have no significant human
population, and these are areas in which off-highway vehicles are often
used by individuals living in urban areas. For example, Table 6-5
indicates a considerable amount of off-highway vehicle ridership in the
California Desert region, although there is no discernable human
population inhabiting this area. It is likely that many off-road
enthusiasts making use of this area are from the Los Angeles
metropolitan area, which is only a couple of hours drive from the
desert. On the other hand, the eastern U.S. has much less public land
available for off-road recreational purposes. (Only 2 percent of the
land in the Northeast is federally owned, whereas 47 percent of the land
in the West is federally owned.13) Therefore, vehicle usage in the
eastern U.S. is likely to be concentrated in areas where the rural
population is significant.
Because the assessment of nonroad equipment usage and emissions is
focused on nonattainment communities (which are not likely to contain
significant areas of uninhabited land), the following approach was taken
in estimating local equipment populations for the SJVAB:
1. A regression model was developed that correlated state off-
highway motorcycle populations (from MIC3) with rural population
(from the 1990 Census12). However, California and Utah were not
included in the analysis. Summary statistics for this
regression are given in Table 6-8.
Table 6-8
Regression Model Using MIC Off-Highway Motorcycle Population as the Dependent
Variable and Rural Population as the Independent Variable
(Excluding California and Utah)
DEP VARIABLE: MC POP
ANALYSIS Of VARIANCE
SOURCE
MODEL
ERROR
C TOTAL
DF
SUM OF
SQUARES
MEAN
SQUARE
1 28498709451 28498709451
19 4442101977 233794840.91
20 32940811429
ROOT MSE
DEP MEAN
C.V.
VARIABLE DF
INTERCEP 1
R POP 1
15290.35
71142.86
21.49246
R-SQUARE
ADJ R-SQ
F VALUE
121.896
0.8651
0.8581
PARAMETER ESTIMATES
PARAMETER
ESTIMATE
6694.67763
0.03960076
STANDARD
ERROR
6723.66782
0.00358681
T FOR HO:
PARAMETERS
0.996
11.041
PROB>F
0.0001
PROB > |T|
0.3319
0.0001
MODEL: MC_POP = a*(R_POP) +b
MC POP = MOTORCYCLE POPULATION
R POP s RURAL POPULATION
a~= 0.0396
b « 6,695
6-12
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- - - See Disclaimer on Cover
2. Because the value of the t-statistic in Table 6-8 indicates the
intercept for this model is not significantly different from
zero, the regression was re-evaluated such that the intercept
was forced through the origin. The new value of the slope
resulting from this calculation is 0.0427.
3. The total rural population for the SJVAB was used with the slope
-determined above to arrive at a local equipment population.
4. The local equipment population derived above was not adjusted by
the actual-to-predicted MIC state population ratio. Doing this
for California would raise the equipment population by 3.5
times. By not making this adjustment (which was recommended by
EEA in its analysis of local-level equipment populations1), a
portion of the California off-highway population is inherently
being allocated to areas with no human population (e.g., the
desert).
5. The off-road population determined above (which, because MIC
state data were used, includes both off-highway motorcycles and
ATVs) was stratified according to off-highway motorcycles and
ATVs based on the information contained in Table 6-3.
The results of the above analysis are summarized in Table 6-9.
Table 6-9
Comparison of Nonroad Recreational Equipment Populations
for the SJVAB
Equipment
Type
ATVs
Minibikes
Off-Road MC
Golf Carts
Specialty Vehicles
NEVES
Inventory A
2,941
110
450
275
596
NEVES
Inventory B
5,017
110
1,680
275
596
This
Study
10,330
371
12,320
686
2,016
In addition to off-highway motorcycles and ATVs, other equipment types
are included in the nonroad recreational vehicle category. For
snowmobiles, a similar approach as taken above would likely result in
reasonably accurate estimates of equipment usage. Although Sierra was
not able to obtain state-level snowmobile population estimates for this
study, a recent report by JFA14 indicates that such information may be
available from ISIA. The only change to the methodology would be to
6-13
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See Disclaimer on Cover
account for the rural population located in areas that receive
sufficient snowfall to allow snowmobile operation. As an alternative,
the state equipment population could simply be scaled by the local-to-
state rural population. Since snowmobiles would not be operated in the
non-mountainous portions of the counties comprising the SJVAB, their
usage was assumed to be zero and is therefore not included in Table 6-9.
To determine the local golf cart population, the most obvious activity
indicator is golf courses. Because state-level golf course statistics
were not available, a regression analysis was not performed. A simple
scaling of the national equipment population (from the PSR data base) by
the local-to-national golf course population ratio served as the basis
of the calculation. Although this admittedly is not a rigorous approach
to determine golf cart populations, it likely provides a more accurate
assessment of local-level equipment populations than the activity
indicator used in NEVES (i.e., motorcycle dealerships). The National
Golf Foundation provided information on the total number of golf courses
in the U.S. (14.136),15 while county road maps were utilized to
determine the number of golf courses in the SJVAB (79).16"20 Applying
this ratio to the national golf cart population resulted in a total of
686 golf carts in the SJVAB. This is also shown in Table 6-9.
(Clearly, this does not account for golf carts used for utility
purposes. However, it may be impossible to identify an appropriate
activity indicator that would properly apportion golf carts used for
utility purposes.)
Because minibikes are likely to be used in the same areas as off-highway
motorcycles, the local minibike population was determined by simply
scaling the PSR national population by the local-to-national ratio of
off-highway motorcycle population. The derivation of the SJVAB
population is described above, and the MIC national population (from the
"Motorcycle Statistical Annual"3) served as the denominator of the
scaling factor. For utility vehicles/carts, it is difficult to
determine an appropriate activity indicator to distribute equipment to
the local level; however, because many of the equipment types included
in this "catch-all" category are used in a more rural environment (e.g.,
snow-grooming equipment, go-carts), this equipment was also allocated
using the local-to-national ratio of off-highway motorcycle population.
Not included in Table 6-9 are the overall annual hourly usage estimates.
For ATVs, motorcycles, and minibikes, information from the Burke survey
should be used to arrive at annual usage figures, while ISIA estimates
of usage should be used for snowmobiles. (Although PSR reports usage
information for recreational equipment, the quality and quantity of data
used to develop those estimates is unclear.) For the remaining
equipment types in this category, PSR usage estimates appear to be the
only ones available.
Additional Considerations
For ATVs and off-highway motorcycles, the state-level population figures
reported by MIC are the best available, but a means of incorporating
dual-purpose motorcycles into the analysis would improve its accuracy.
6-14
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See Disclaimer on Cover
This could be accomplished by simply summing the off-highway and dual-
purpose state populations before performing the regression analysis.
Annual usage estimates, however, are likely to differ between off-
highway and dual-purpose motorcycles, and care would have to be
exercised to ensure that the dual-purpose usage was not included in both
on-highway and off-highway inventory estimates.
The remaining equipment types are relatively minor contributors to an
area's overall inventory, but improvements in methodologies could be
made. For golf carts, the number of golf courses in each state is
available from the National Golf Foundation (which could be used in
conjunction with PSR state-level population data in a regression
analysis), but data from this organization typically is purchased.
Similarly, state snowmobile populations, coupled with rural population
in areas with sufficient snowfall, would provide a better distribution
of snowmobile population.
R ecommendations
In investigating alternative methodologies, Sierra found that a bottom-
up approach for this equipment category would be nearly impossible to
develop. Alternative top-down methodologies, however, were considered
that appear to offer a significant improvement over the current
methodology. Although additional variables could be factored into the
regression analysis, rural population is a reasonably easy parameter for
local air quality planners to obtain, and it is more likely to place the
vehicles in the areas where they are used. Furthermore, the use of MIC
state population data is recommended,3 as are the usage estimates
developed by Burke.4
References for Section 6
1. "Methodology to Estimate Nonroad Equipment Populations by
Nonattainment Areas," Energy and Environmental Analysis, September
1991.
2. "Off-Highway Vehicles Currently Registered as of 31 May 1991,"
California Department of Motor Vehicles, June 1991.
3. "1991 Motorcycle Statistical Annual," Motorcycle Industry Council,
1991.
4. "1990 Survey of Motorcycle Ownership and Usage," Burke Marketing
Research, May 1991.
5. "A Study to Determine Fuel Tax Attributable to Off-Highway and
Street Licensed Vehicles Used for Recreation Off-Highway," Tyler
and Associates, November 1990.
6. "Comments on the California Air Resources Board Mail Out #90-70, 'A
Proposal to Establish Exhaust Emission Standards and Test
6-15
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See Disclaimer on Cover
Procedures for Off-Highway Light-Duty Vehicles and Recreational
Vehicles'," International Snowmobile Industry Council, June 1991.
7. "Report of the Forest Service, Fiscal Year 1991," U.S. Forest
Service, June 1992.
8. John Baas, U.S. Forest Service, Personal Communication, August
1992.
9. "Trails - Number and Miles for FY 91," U.S. Department of the
Interior, Bureau of Land Management, September 1992.
10. "Annual Visitor Use in Recreation Management Areas for FY 91," U.S.
Department of the Interior, Bureau of Land Management, September
1992.
11. "EPA Proposal to Control Emissions from Off-Highway Motorcycles and
All-Terrain Vehicles (ATVs)," Letter from J.C. DeLaney, Motorcycle
Industry Council, to J. German, U.S. Environmental Protection
Agency, June 1991.
12. "1990 Census of Population. General Population Characteristics,"
U.S. Department of Commerce, Bureau of the Census, July 1992.
13. "1991 Statistical Abstract of the U.S.," U.S. Department of
Commerce, 1991.
14. "Nonroad Mobile Source Sales and Attrition Study," Jack Faucett
Associates, September 1992.
15. National Golf Foundation, Personal Communication, November 1992.
16. "Fresno and Kings Counties," California State Automobile
Association, April 1992.
17. "Kern County," Automobile Club of Southern California, August 1991.
18. "Madera, Mariposa, and Merced Counties," California State
Automobile Association, May 1991.
19. "San Joaquin and Stanislaus Counties," California State Automobile
Association, May 1991.
20. "Tulare County," Automobile Club of Southern California, June 1991.
6-16
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7. CONSTRUCTION EQUIPMENT
Construction equipment comprises the largest single category of nonroad
equipment investigated in NEVES, both in terms of number of individual
equipment types and overall energy output (i.e., bhp-hr/year). Twenty-
seven specific equipment types are included in this category, ranging
from lower horsepower machines, such as compactors, signal boards, and
cement mixers, to large, high-horsepower equipment such as scrapers,
crawler tractors, and off-highway trucks.
NEVES Methodology
NEVES Inventory A estimates relied on state-level PSR data to develop -
local—level construction equipment populations, and the activity
indicator used by EEA in its regression analysis was total construction
employment.1 Initially, EEA attempted to develop separate estimates for
road construction equipment and general construction equipment; however,
the models developed for these subcategories showed the best statistical
fits using total construction employment as the indicator. In addition,
the Equipment Manufacturers Institute (EMI) objected to disaggregating
equipment according to application because many specific equipment types
are used in a number of different industries. Thus, EEA's final
analysis grouped the entire construction equipment category together
when developing the regression coefficients.
The Inventory B estimates prepared for NEVES were based primarily on
recommendations from EMI. For equipment populations, EMI relied on
national population estimates that were developed by MacKay & Company
under contract to Construction Equipment magazine and the Associates
Commercial Corporation.2 These data were published in a series of
articles that appeared in Construction Equipment in 1987, which included
information on 16 specific equipment types. EMI's county—level
equipment populations were determined by scaling the national
populations by known county-to-national sales for each equipment type
over a five-year period (1983-1987). EMI developed usage, horsepower,
and load factor information from a survey of its member companies that
was conducted specifically in support of NEVES. (A total of 33
companies responded to EMI's survey.)
A summary of the Inventory A construction equipment population and
activity for the U.S., DC/MD/VA, and the SJVAB is shown in Tables 7-1,
7-2 and 7-3, respectively (Inventory B estimates are listed in
Appendix E.) Because of their large numbers and relatively high
horsepower, crawler tractors, rubber-tired loaders, and
tractors/loaders/backhoes are responsible for roughly one-half of the
total activity from the construction equipment category. Other
7-1
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See Disclaimer on Cover
Table 7-1
NEVES Inventory A Construction Equipment Activity Estimates
for the United States
National Estimates
Equipment Type
Asphalt Pavers
Tampers/Rammers
Plate Compactors
Concrete Pavers
Rollers
Scrapers
Paving Equipment
Surfacing Equipment
Signal Boards
Trenchers
Bore/Drill Rigs
Excavators
Concrete/Industrial Saws
Cement & Mortar Mixers
Cranes
Graders
Off-Highway Trucks
Crushing/Proc Eqpt
Rough Terrain Forkl'rfts
Rubber Tired Loaders
Rubber Tired Dozers
Trctrs/Ldrs/Backhoes
Crawler Tractors
Skid Steer Loaders
Off-Highway Tractors
Dumpers/Tenders
Other Construction Eqpt
Total
Population
Diesel
15,536
0
2,322
5,511
36,300
26,700
43,615
0
20,384
50,510
7,761
61,336
135
4,016
98,357
70,045
16,529
7,207
53,853
209,454
7,757
299,255
285,923
150,054
38,921
194
11,867
1.523.552
Gas
16,824
29,419
145,233
0
21,999
0
230,810
30,833
1,559
27,170
8,501
18
36,900
232,152
2,541
0
0
1,007
2,217
3,433
0
1,365
0
27,805
16,023
26,136
7,506
869.451
Activity (1000 Bhp-hr/yr)
Diesel
597,301
0
3,383
323,975
1 ,259,405
4,918,049
1,160,099
0
71,110
1,186,116
473,367
4,781,386
2,689
5,476
5,753,469
5,038,080
6,917,672
599,983
1,710,805
12,919,561
1 ,332,898
12.7X.588
22,054,701
2,069,835
4,652,038
797
592,223
91.155.005
L Gas
111,995
9,216
58,012
0
120,952
0
135,957
44,827
1,987
171,280
32,401
251
191,723
64,833
23,716
0
0
10,892
43,655
60,414
0
31,782
0
120,434
0
10,166
165,413
1.409.905
Total
709,297
9,216
61,394
323,975
1 ,380,357
4,918,049
1 ,296,055
44,827
73,098
1,357,396
505,768
4.781,637
194,411
70,310
5,777,184
5,038,080
6,917,672
610,875
1 ,754,460
12,979,975
1 ,332,898
12,762,370
22,054,701
2,190,269
4,652,038
10,962
757.635
92.564.910
% Total
Activity
0.8
0.0
0.1
0.3
1.5
5.3
1.4
0.0
0.1
1.5
0.5
52
0.2
0.1
6.2
5.4
7.5
0.7
1.9
14.0
1.4
13.8
23.8
2.4
5.0
0.0
0.8
100.0
equipment types that are large contributors to overall construction
equipment activity include large earthmoving equipment (e.g., scrapers,
excavators) and multi-purpose equipment that is used in many different
industries, such as skid steer loaders.
Approach
When investigating activity indicators for a top-down methodology, data
on county-level employment or the number of establishments by SIC code
have generally been the most obvious choices. Detailed employment data
(by SIC code) are readily available from the Department of Commerce, and
this information is updated yearly in "County Business Patterns." For
industries in which nonroad equipment is likely to be used in the same
county that establishments (and employees) are based, this approach will
likely result in a reasonably good distribution of national or state
equipment populations to the local level. For industries that are very
7-2
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Table 7-2
NEVES Inventory A Construction Equipment Activity Estimates
for the DC/MD/VA Area
DC/MD/VA Estimates
Equipment Type
Asphalt Pavers
Tampers/Rammers
Plate Compactors
Concrete Pavers
Rollers
Scrapers
Paving Equipment
Surfacing Equipment
Signal Boards
Trenchers
Bore/Drill Rigs
Excavators
Concrete/Industrial Saws
Cement & Mortar Mixers
Cranes
Graders
Off-Highway Trucks
Crushing/Proc Eqpt
Rough Terrain Forklifts
Rubber Tired Loaders
Rubber Tired Dozers
Trctrs/Ldrs/Backhoes
Crawler Tractors
Skid Steer Loaders
Off-Highway Tractors
Dumpers/Tenders
Other Construction Eqpt
Total
Population
Diesel
271
0
40
96
635
464
760
0
355
880
135
1,068
2
70
1,713
1,220
288
126
938
3,648
135
5,213
4,980
2,614
678
3
207
26.540
Gas
53
411
2,530
0
383
0
4,020
537
27
473
148
0
643
4,044
44
0
0
18
39
60
0
24
0
484
0
423
19
14.380
Activity (1000 Bhp-hr/yr)
Diesel
10,404
0
59
5,643
22,035
85,549
20,207
0
1,239
20,660
8,245
83,283
47
95
100,215
87,755
120,494
10,451
29,799
225,036
23,217
221,745
384,155
36,053
81, OX
14
10,315
1 .587.747
Gas
350
129
1,010
0
2,107
0
2,368
781
35
2,983
564
4
3,339
1,129
413
0
0
190
760
1,052
0
554
0
2,098
0
165
423
20.456
Total
10,754
129
1,069
5,643
24,142
85,549
22,575
781
1,273
23,643
8,810
83,288
3,386
1,225
100,629
87,755
120,494
10,640
30,560
226,089
23,217
222,298
384,155
38,151
81,030
179
10,739
1 .608.203
% Total
Activity
0.7
0.0
0.1
0.4
1.5
5.3
1.4
0.0
0.1
1.5
0.5
5.2
0.2
0.1
6.3
5.5
7.5
0.7
1.9
14.1
1.4
13.8
23.9
2.4
5.0
0.0
0.7
100.0
mobile (such as construction), however, reliance on county employment
statistics will not necessarily distribute the equipment to the area
where it is actually used. Thus, a shortcoming of the NEVES methodology
to allocate equipment to the local level is its reliance on an activity
indicator that may not represent the area where the equipment is used.
In investigating alternative methodologies for estimating construction
equipment usage at the local level, both top-down and bottom-up
approaches were considered. A primary concern in this effort was
developing a methodology that would provide a better representation of
where construction equipment is actually used. Thus, considerable
effort was expended in attempts to identify sources of information that
might offer a means of improving local estimates of equipment usage.
This search primarily focused on fuel usage and equipment usage. For
fuel usage information, a number of federal and state agencies were
contacted. As discussed below, these included the Department of Energy
(DOE), the Department of Transportation (DOT), the Department of
Commerce (DOC), the California Energy Commission, and the California
Department of Transportation (CalTrans). It was felt that if detailed
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Table 7-3
NEVES Inventory A Construction Equipment Activity Estimates
for the San Joaquin Valley Air Basin
San Joaauin Valley Air Basin Estimates
Equipment Type
Asphalt Pavers
Tampers/Rammers
Plate Compactors
Concrete Pavers
Rollers
Scrapers
Paving Equipment
Surfacing Equipment
Signal Boards
Trenchers
Bore/Drill Rigs
Excavators
Concrete/Industrial Saws
Cement & Mortar Mixers
Cranes
Graders
Off-Highway Trucks
Crushing/Proc Eqpt
Rough Terrain Forklifts
Rubber Tired Loaders
Rubber Tired Dozers
Trctrs/Ldrs/Backhoes
Crawler Tractors
Skid Steer Loaders
Off-Highway Tractors
Dumpers/Tenders
Other Construction Eqpt
Total
Population
Diesel Gas
141
0
21
50
332
243
397
0
185
459
71
558
1
37
895
637
150
66
490
1,905
71
2,722
2,600
1,365
354
2
108
13.857
27
215
1,321
0
200
0
2,099
280
14
247
77
0
336
2,111
23
0
0
9
20
31
0
12
0
253
0
221
10
7.508
Activity (1000 Bhp-hr/yr)
Diesel
6,610
0
44
3,710
13,697
54,594
14,758
0
877
13,239
5,981
51,982
30
67
59,543
54,873
77,009
7,441
20,806
142,234
15,053
132,132
248.230
26,560
47,992
10
6,594
1 .004.065
Gas
223
86
748
0
1,310
0
1,730
553
25
1,912
409
3
2,117
798
245
0
0
135
531
665
0
330
0
1,545
0
121
271
13.756
Total
6,833
86
791
3,710
15,006
54,594
16,487
553
902
15,151
6,390
51,984
2,147
866
59,788
54,873
77,009
7,576
21,337
142,900
15,053
132,462
248,230
28,106
47,992
131
6,864
1.017.821
% Total
Activity
0.7
0.0
0.1
0.4
1.5
5.4
1.6
0.1
0.1
1.5
0.6
5.1
0.2
0.1
5.9
5.4
7.6
0.7
2.1
14.0
1.5
13.0
24.4
2.8
4.7
0.0
0.7
100.0!
construction industry fuel records were available on a county basis,
local-level equipment usage and emissions could be estimated.
In addition to fuel usage information, a number of construction industry
representatives were also asked about data related to equipment usage by
type of activity or dollar value of construction work. Because a
comprehensive listing of construction project valuation is published by
McGraw-Hill for every urban area in the U.S. ,3 it was felt that a
bottom-up methodology could be developed if information on equipment
usage as a function of construction valuation could also be obtained.
Among those contacted were a variety of professional associations (e.g.,
American Institute of Constructors, Associated General Contractors,
International Union of Operating Engineers), private contractors, Data
Resources Institute, and firms specializing in construction-estimating
software.
The above contacts were made with the intention of developing a bottom-
up methodology. Information found in this search, however, could also
7-4
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be used in developing alternative top-down procedures. The most obvious
example of this is McGraw-Hill's "Dodge Construction Potentials
Bulletin" (DCPB) cited above.3 As detailed below, this information
provided an alternative to construction employment in allocating
national and state equipment populations to the local level.
Methodology and Data Sources
Sources of Fuel Use Information - Fuel usage was initially considered a
parameter that should be available in reasonably detailed form. The
most likely source of fuel usage information, at least at the national
level, appeared to be DOE. Numerous calls were placed to various
divisions of DOE, including the Energy Information Administration.
Although state-level statistics on fuel consumption are available in
several DOE publications,4"6 none of these documents contain information
in sufficient detail to develop equipment usage estimates from the data.
Furthermore, conversations with DOE staff did not reveal any unpublished
information that might be used in such estimates.
In addition to DOE, DOT was contacted to determine the availability of
fuel consumption data. Although state-level statistics on gasoline
consumption in the construction industry are published in DOT's "Highway
Statistics" series,7 there are no sources of these data at the county
level. Furthermore, conversations with DOT staff indicated that these
data are compiled from state tax refund information, which is likely not
to include all of the fuel used off-highway (e.g., if applications for
refunds are not submitted for all qualifying fuel purchases, the state
tax refund records would underestimate the quantity of fuel sold for
off-highway purposes).
In conversations with the above agencies, it was discovered that the
Commerce Department may have developed fuel use estimates through its
various census activities; thus, DOC was also contacted. This led to
two documents published by the Census Bureau: "Census of Mineral
Industries"8 and "Census of Construction Industries"9, both of which
were based on 1987 surveys of these industries. Although the mineral
industries census contains gasoline and fuel oil purchases by state and
by two-digit SIC code, it is impossible to distinguish between on-
highway and off-highway use. Additionally, the fuel oil purchases do
not distinguish between Diesel fuel that could be used to power nonroad
equipment and other grades of distillate fuel oil. Thus, this
information cannot be used in estimating equipment usage in the mining
industries (where some equipment types in the "Construction" category
are used).
The "Census of Construction Industries" provides a considerable amount
of information about the construction industry. There are two basic
components of this census: a geographic area series that lists
information for each state (and selected metropolitan areas within each
state) , and an industry series that compiles information for specific
industries (by 4-digit SIC code). Included in the geographic area
series is a category entitled "Cost of materials, components, supplies,
and fuels." For each state (and for selected metropolitan areas within
7-5
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each state), data for this category are compiled by 4-digit SIC code.
If a means of separating nonroad equipment fuel usage from this more
generic materials category could be developed, it would be possible to
arrive at an estimate of fuel cost (and fuel usage) for certain
metropolitan areas, at least for the year in which the census was
conducted (which is every five years for the Census of Construction
Industries).
Within each SIC code, the industry series contains a detailed breakdown
of the materials, components, supplies, and fuels category, including an
estimate of gasoline and Diesel fuel used on- and off-highway. This
information, however, is not area-specific; it is compiled for the
nation as a whole. Nonetheless, it is interesting to look at a summary
of the off-highway fuel component for some selected SIC codes as shown
in Table 7-4. (A more complete compilation of materials, components,
supplies, and fuels for all 27 SIC codes included in the construction
census is contained in Appendix F.) The trends shown in the table
indicate that for the more equipment-intensive activities, fuel costs
comprise a larger share of the overall cost of construction. This makes
intuitive sense, and was not unexpected.
Table 7-4
Off-Highway Fuel Cost Per Million Dollars Valuation by SIC Code
Based on the 1987 Census of Construction
SIC Code
1521 - Single Family
Housing (SFH)
1522 - Residential,
Non-SFH
1541 - Industrial Bldgs ,
Warehouses
1542 - Nonresidential
Buildings
1611 - Highway & Street
Construction
1622 - Bridges, Tunnels,
Elevated Hwys
1629 - Hvy Construction,
Other
1794 - Excavation Work
Average Over All
Construction SIC Codes
Off-Hwy Fuel "Cost/
$Million Valuation
$1,200
$880
$2,030
$1,690
$17,450
$8,030
$14,110
$25,210
$4,370
Off-Hwy Fuel Cost
Normalized to SFH
1.0
0.7
1.7
1.4
14.5
6.7
11.7
20.9
3.6
7-6
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The information contained in Appendix F (from which Table 7-4 was
derived) could be used in conjunction with data from the geographic area
series to estimate total off-highway fuel costs for a state or
metropolitan area. Making assumptions about fuel costs and the
Diesel/gasoline sales split would then allow an estimate of the overall
fuel usage related to construction activity for 1987. Because this
approach would not be valid for the years between censuses, however, and
because -DOC staff indicated that less confidence is placed in the fuel
usage data than many of the other figures compiled in the census,10 such
an approach is not recommended for developing a general methodology to
estimate construction equipment activity.
In addition to federal agencies, several California state agencies were
contacted to determine the availability of fuel consumption information
for construction equipment by activity or at a county level. Because
CalTrans is responsible for maintaining state highways in California, it
was felt that it might keep information on fuel usage for highway and
bridge construction; however, conversations with several staff members
did not reveal any information of this kind. The California Board of
Equalization and the State Controller's Office were contacted to
determine the availability of fuel tax refund records. Although records
are available on nonroad gasoline refunds, the same concerns expressed
by DOT that are discussed above would apply in this case as well.
Finally, the California Energy Commission was contacted, but it also had
no information on construction equipment fuel usage.
Finally, several industry associations and private firms were solicited
for fuel consumption information. In several cases""13, the
representatives contacted were of the opinion that developing a
generalized methodology to estimate fuel usage (or equipment usage)
based on permit valuation (or other suitable parameter, such as lane-
miles or square footage) would be impossible due to the variability
associated with different construction projects. Furthermore, even
within a particular type of construction, fuel usage can vary widely
depending upon the site preparation requirements. Another potential
source for fuel use information that was considered was construction
project estimation software packages. Two firms were consulted for
details on their products,13-14 and fuel usage is estimated in one
software package. Inputs to this model, however, are very specific in
terms of type of equipment and number of hours required. Obviously,
this type of information would be impossible to compile for an entire
urban area.*
Sources of Equipment Use Information - Many of the industry associations
contacted for fuel consumption information were also asked about
guidelines for equipment usage. In general, the response was the same
as that received above (i.e., because of the highly variable nature of
In a study performed for GARB,15 KVB developed a methodology that
related cubic yards of earth moved to fuel usage. (The data on earth
moved came from construction permits.) However, there appear to be
significant data gaps (i.e., not all areas have data on earth moved)
when applying this methodology and extrapolations are necessary. Thus,
no effort was directed at refining this approach.
7-7
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See Disclaimer on Cover
construction projects, it is not possible to predict equipment usage
based on a general parameter of construction activity). A listing of
the associations contacted is contained in Table 7-5.
Table 7-5
Industry Associations Solicited for Information
Related to Construction Equipment Activity
American Society of Professional Estimators
Building Research Board
Construction Industry Research Board
American Institute of Constructors
International Union of Operating Engineers
Associated General Contractors
National Joint Heavy and Highway Construction Committee/
Construction Industry Information Network
Alternative Activity Indicators - As outlined above, because
construction work is often performed outside the county in which a firm
is based for a top-down approach, it is important to choose an activity
indicator that places the equipment in the area where it is used.
Therefore, rather than using county-level construction employment, the
DCPB cited above appears to be a more appropriate choice. This document
is published monthly and contains various statistics on contracts for
new, addition, and major alteration projects. Each state (and all urban
areas within each state) is represented.* The statistics reported in
this document include (1) the number of projects, the square footage,
and the value of non-residential and residential building; and (2) the
number of projects and the value of non-building construction projects
(e.g., streets and highways, dams and reservoirs, bridges, water supply
systems, etc.), although the non-building projects are not reported by
urban area. The data are compiled for the current month, the current
year, and the previous year, and two-year trends on a regional basis are
included.
Although the DCPB is copyrighted, Sierra obtained permission to include
a sample copy in this report, which is contained in Appendix G. The
cost of the DCPB is variable, and depends upon the following:
• Whether each community purchases data individually or EPA
purchases the data and disseminates the information to local
areas. Volume discounts are available for purchases on a
* Although every county of every state is not represented in DCPB, the
coverage is reasonably complete. For example, summing the non- "
residential building valuation for the individual metropolitan areas in
California resulted in 97 percent of the total California non-
residential valuation listed in the DCPB.
7-8
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See Disclaimer on Cover
national level, and these discounts would likely apply if EPA
served as a central data base manager for this information.
Many federal, state, and local agencies already purchase this
information. The extent to which this information is currently
available to EPA and/or local communities (including licensing
limits under existing contracts) would also influence the cost
.of the data.
The media upon which the data are supplied (i.e., paper or
electronic form) also impacts the cost of the data. If EPA
purchased this data, the electronic data base form is highly
recommended.
Once the above parameters are defined, EPA can request the preparation
of a formal price quotation.
Temporal Usage Patterns - Appendix L of NEVES16 included a discussion of
seasonal adjustments to the annual emissions inventories calculated in
that work. For construction equipment, the seasonal adjustments were
based on assumptions reported by Hare and Springer17 in their 1973 study
of nonroad vehicle emissions. As an alternative to the information
cited above, the 1987 Census of Construction9 contains data on the hours
worked by construction workers by quarter. This information is
summarized in Table 7-6 for the U.S. as a whole and for several selected
states. As seen, there is a slight decrease in activity in the colder
months for the more northern states, but the decrease is not as
significant as might be expected. The same information was also
compiled for heavy construction only (i.e.,"SIC 16), which showed a more
substantial decrease in wintertime activity.
Table 7-6
Hours Worked by Construction Workers by Quarter for
Selected States9
Area
U.S.
Maryland
Virginia
California
Florida
Alaska
Hours Worked by Construction Workers (Percent)
Jan-Mar
22
22
22
23
23
20
Apr-Jun
25
25
25
25
25
26
Jul-Sep
27
27
27
27
26
31
Oct-Dec
25
26
25
25
26
23
7-9
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Sample Calculations
Bottom-Up - Although considerable effort was expended in attempting to
identify sources of information that could be used to develop a bottom-
up methodology, no suitable data were obtained. Nonetheless, the data
reported in Table 7-4 were used in conjunction with local-level
statistics on total construction valuation to arrive at an average
expenditure for off-highway gasoline and Diesel fuel for the SJVAB.
From the DCPB,3 the total construction valuation for the SJVAB (for the
year ending August 1992) is $1,921.8 million. From Table 7-4, the
average off-highway fuel expenditures for all SIC codes included in the
construction census9 is $4370 per $1 million valuation. Multiplying
these figures results in a total expenditure of $8.4 million, or roughly
15 million gallons of fuel (assuming an average 1987 pre-tax price
(refiner to end user) of $0.55 per gallon for No. 2 Diesel fuel, as
reported by DOE4).
For comparison purposes, the Inventory A estimates of bhp—hr/yr
summarized in Table 7-3 can be converted to gallons per year using
conversion factors recommended by Southwest Research Institute in a
recent report prepared for EPA.18 (Using the values of 7.1 Ib/gal and
0.4 Ib/bhp-hr results in a conversion factor of 0.056 gal/bhp-hr for
Diesel-fueled engines.) This analysis resulted in a total of 57 million
gallons of fuel being used for nonroad construction equipment in the
SJVAB, which represents a four-fold increase over the estimate prepared
with fuel expenditure data from the construction census. Although some
of the equipment types included in the NEVES definition of construction
equipment are used in other applications (e.g., mining, agriculture),
clearly such a large difference in estimates cannot be explained by
equipment usage not included in construction activities. Because DOC
staff indicated that less confidence is placed in the off-highway fuel
expenditure data than other data reported in the census, and because
there is no way to separate gasoline and Diesel usage, no further
attempts were made to use these data to estimate construction equipment
activity.
Top-Down - As discussed previously, it is important for activity
indicators used in allocating equipment populations to the local level
to be representative of where the equipment is used. For construction
equipment, the best source of such information identified in this work
is the DCPB.3 Thus, the sample calculations presented below have made
use of this information.
Another consideration to be made in constructing a top-down methodology
is whether to use a regression technique or the more simple scaling of
national-level equipment population data (accomplished by applying the
following ratio to the national equipment population: local-level value
of the chosen activity indicator/national value of the activity
indicator). As discussed previously, for cases in which little
confidence is placed in the state-level population data (which form the
basis of the regression analysis), it is more appropriate to use the
scaling approach. For equipment that is inherently mobile and can move
freely from state to state (such as construction equipment), it can also
7-10
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be argued that a scaling approach is more appropriate.
approaches are presented below.
Thus, both
A regression model was formulated in which state-level total
construction valuation data reported in DCPB (sum of non—residential,
residential, and non—building construction) for the 23 states included
in NEVES served as the independent variable, and PSR state-level
equipment populations served as the dependent variable. The results of
this regression analysis are summarized in Table 7-7. (Although
regressions were attempted with non-residential, residential, and non-
building construction valuation independently, the best statistical
results were obtained when using total construction valuation.)
As seen in Table 7-7, the statistics resulting from the use of
construction valuation as the independent variable indicate a good
correlation with equipment population, with an R-square value of 0.89.
These results were used with the local-level construction valuation data
to calculate local-level equipment populations for the DC/MD/VA
nonattainment area and the SJVAB. (Because the t-statistic indicates
that the intercept term is not significantly different from zero, the
model was rerun with no intercept prior to calculating populations.
This resulted in a change in slope to 15.525 which was used in the
Table 7-7
Regression Model Using Construction Equipment Population as the Dependent Variable
and Total Construction Valuation as the Independent Variable
DEP VARIABLE: COM POP
ANALYSIS OF VARIANCE
SOURCE
MODEL
ERROR
C TOTAL
DF
SUM OF
SQUARES
MEAN
SQUARE
1 101237642787 101237642787
21 12684715174 604034055.90
22 113922357961
ROOT MSE
DEP MEAN
C.V.
24577.1
76879.83
31.96821
R-SQUARE
ADJ R-SQ
PARAMETER ESTIMATES
F VALUE
167.603
0.8887
0.8834
VARIABLE DF
INTERCEP 1
TOT CON 1
PARAMETER
ESTIMATE
-10514.5
16.80812764
STANDARD
ERROR
8475.44193
1.29831190
T FOR HO:
PARAMETERS
-1.241
12.946
PROB>F
0.0001
PROS > |T|
0.2284
0.0001
MODEL: CON POP = a*(TOT CON) + b
CON POP = CONSTRUCTION EQUIPMENT POPULATION
TOT~CON - CONSTRUCTION VALUATION (MILLION $)
a =~16.808
b = -10,515
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ensuing calculations; the summary statistics are contained in Appendix
C.) Since the DCPB does not report the non-building valuation (e.g.,
highways, bridges, etc.) for each metropolitan area, it was first
necessary to estimate the local-level non-building valuation from the
state-level statistics. This was accomplished by assuming the same
relative percentage of non-building valuation (compared to the sum of
non-residential and residential construction) for the metropolitan area
and the "state. Data used in this calculation for the SJVAB are
summarized in Table 7-8.
Table 7-8
Construction Valuation Reported in DCPB3 for Communities
in the San Joaquin Valley Air Basin*
Metropolitan Area
Bakersfield
Fresno
Merced
Modesto
Stockton
Visalia-Tulare
SJVAB
California
Construction Valuation (Million $)
Non-Residential
178.8
135.0
10.7
59.3
75.9
40.9
500.6
6,691.8
Residential
256.2
274.9
78.0
141.8
168.7
111.1
1,030.7
8,120.7
Non-Building
NR"
NR
NR
NR
NR
NR
390.5
3,777.2
Shaded cell represents estimated parameter.
NR: Not reported.
After the total construction valuation was determined for the DC/MD/VA
area and the SJVAB, construction equipment populations were estimated
utilizing the regression model results. Table 7-9 shows the resulting
estimates, and Table 7-10 compares these results with the equipment
populations estimated for Inventories A and B from NEVES.* As seen,
the results from Inventory A and this study are very similar for the
Note that a simple scaling approach utilizing the DCPB data and
Inventory A equipment populations results in an equipment population of
46,760 for DC/MD/VA and 27,800 for the SJV. These estimates are both
higher than Inventory A and Inventory B equipment populations, which are
shown in Table 7-10.
7-12
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Table 7-9
Construction Equipment Population Estimates Utilizing DCPB Construction
Valuation (in Million $) as the Activity Indicator
Nonattainment
Community
DC/MD/VA
SJVAB
Const.
Valuation
3231.8
1921.8
Estimated
Eqp . Pop .
50,174
29,836
PSR/Est.
Pop.'
0.815
1.07
Final
Eqp . Pop .
40,892
31,924
See footnote on Table 5-5.
Table 7-10
Comparison of Equipment Population Estimates for DC/MD/VA and the SJVAB
NEVES Inventory A and Inventory B vs. This Study
Nonattainment
Area
DC/MD/VA
SJVAB
Total Construction Equipment Populations
NEVES
Inventory A
40,920
21,365
NEVES
Inventory B
31,800
16,272
This Study
40,892
31,924
DC/MD/VA area, while there is considerable deviation for the SJVAB.
(Significantly lower populations are observed in the Inventory B
estimates for both communities.) The increase in equipment population
estimates for the SJVAB determined in this work is probably due to the
fact that the activity indicator used for Inventory A is based on county
construction industry employment. whereas the DCPB lists construction
valuation in the metropolitan area where the work is being performed.
In the case of the SJVAB, its southern section borders Los Angeles
County and its northern section is in reasonable proximity to the
San Francisco area, both economic hubs for California. It is quite
likely that much of the construction work performed in the SJVAB is
actually performed by firms that are located outside of the basin.
Thus, the "County Business Patterns" employment data would tend to over-
predict construction equipment activity in areas that "export"
construction labor and equipment, while areas that "import" these
services (such as the SJVAB) would be under-represented if county
employment figures were used as the activity indicator.
Another factor that would be useful to include in a top-down methodology
is the use of some equipment types classified in the construction
category in other fields. For example, off-highway heavy-duty trucks
are also used in the mining industries and skid steer loaders are used
7-13
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See Disclaimer on Cover
for numerous different applications (e.g., agriculture, material
handling). Although data are not available that indicate the
applications for which all equipment types included in the construction
category are used, the MacKay study cited earlier2 contains its
estimates of usage by vocation for 16 individual equipment types. These
data are summarized in Table 7-11.
Table 7-11
Distribution of Construction Equipment by Vocation M Reported In Construction Equipment Magazine
Equipment Type
Crawler Loaders
Wheel Loaders
Crawler Dozers
Crawler Excavators
Telescoping Excavators
Motor Graders
Rigid
Articulated
Wheel Excavators
Off-Hwy Haulers
Rigid Frame
Articulated
Tractor-Trailer
Scrapers
Conventional
Elevated
Skid-Steer Loaders*
Trenchers
Chain
Wheel
Backhoe Loaders
Asphalt Pavers
Rollers/Compactors
Drum
Rubber
Planers/Profilers
Concrete Pavers
Slab
Slipform
Combination
National
Population
64,000
130,000
115,000
46,000
8,000
47,000
27,000
8,800
16,600
1,100
2,300
10,100
6,300
57.000
38,000
12,500
189,000
12,000
37,000
58,000
1.800
4,800
3,600
1.100
Distribution by Vocation (Percent)
Building
Contractors
24
14
21
21
24
19
15
17
4
9
10
12
19
10
14
15
29
16
22
15
2
1
2
5
Highway
Contractors
14
14
13
17
26
31
37
15
11
32
4
22
24
10
10
13
29
48
33
30
59
49
52
31
Heavy
Const
47
28
28
43
22
17
22
32
6
10
15
39
38
18
19
26
10
17
23
24
16
44
41
63
Materials
Producers
3
14
11
5
0
7
5
0
14
12
24
7
5
»*
7
8
7
**
7
8
6
**
**
**
Utilities
3
10
10
7
2
6
4
4
2
0
0
4
5
**
37
26
4
««
2
8
5
**
**
**
Mining
2
9
7
3
4
7
3
3
60
34
46
7
3
*•
4
2
7
**
3
2
1
**
•*
»•
Qovt
4
6
4
2
15
9
13
24
2
3
1
7
4
**
2
5
7
12
4
11
9
4
4
1
Other
2
6
6
2
7
4
1
5
1
0
0
2
2
**
7
5
7
**
6
2
2
*•
**
**
* MacKay estimates that 59 percent of the skid-steer loader population is owned by non-construction Industrie*. The
total U.S. machine population was reported as 140,000.
** The remaining equipment population is distributed among these vocations.
7-14
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There are two approaches that could be taken to account for multi-use
characteristics of certain equipment types. First, additional
indicators could be included in the regression analysis (i.e., multiple
independent variables). This approach, however, is most valid when all
equipment types included in the category populations are related to the
activity indicators. This could be accomplished for the construction
category by using individual equipment type state-level populations (or
smaller .groups of construction equipment) in the regression analysis.
Unfortunately, Sierra did not have access to the PSR data base from
which the NEVES category populations were derived, so regressions by
individual equipment type could not be attempted.
Although using multiple independent variables in a regression analysis
offers a refinement for cases in which an equipment type is used in more
than one application, inaccuracies could result if the equipment type is
not used to a similar extent in the applications represented by the
activity indicators. In those cases, a more traditional scaling of the
national-level equipment populations could be performed based on
expected usage in various industries. An example of this approach
follows.
Table 7-11 lists the distribution by vocation of off-highway haulers as
roughly 57 percent mining, 28 percent construction, and 15 percent
materials producers (the small percentages in the remaining vocations
were combined with construction). Therefore, if statistics on national
and county-level mining activity, construction activity, and materials
production activity are available, then county-level equipment
populations can be estimated. This was done for the SJVAB in which
employment data for SIC codes 10 (metal mining) , 12 (coal mining) , and
14 (nonmetallic minerals) were used to represent equipment engaged in
mining;19 construction valuation from DCPB3 was used to represent
construction; and employment data from SIC code 32 (stone, clay, glass
products) were used to represent materials production.20 The analysis
resulted in a total of 93 off-highway trucks being allocated to the
SJVAB when the PSR national equipment population was used, which
represents a 40 percent reduction compared to the NEVES Inventory A
estimates. (This decrease in population is primarily related to the
small number of employees engaged in the mining industries represented
by SICs 10, 12, and 14. Total mining employment was not used in this
analysis because most of California's mining employment is related to
oil and gas extraction (SIC 13) which would not be expected to have a
significant population of heavy-duty off-highway trucks.)
Additional Considerations
Because the DCPB data used for allocating construction equipment to the
local level do not contain local statistics on non-building
construction, this parameter had to be estimated from the state-level
data. Although some level of uncertainty was introduced into the
calculation by taking this approach, it was felt that this method was
superior to simply ignoring the non-building construction. Furthermore,
basing the local-level non-building estimate on the sum of residential
and non—residential construction valuation has merit since the non-
7-15
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building sector (particularly highways and bridges) is likely to be
related to other types of construction in an area.
Although little attention was given to annual hourly usage in this
section, it is obviously an important parameter in estimating total
equipment activity. There appear to be three sources for this
information: PSR, a recent update to MacKay's 1987 study of
construction equipment populations20 (although the level of detail of
the annual use estimates is unclear*), and EMI. It is difficult to
determine which data source is most reliable without considerable
background on how the estimates were derived. It appears, however, that
the PSR and MacKay data were developed based on surveys of end users,
while the EMI data came from a survey of its members (i.e., equipment
manufacturers).
Recommendations
Although development of a generalized bottom-up methodology to estimate
local-level construction equipment activity appears to be infeasible,
various improvements to the NEVES top-down approach can be easily
implemented. The primary change that would result in improved local-
level population estimates is the use of construction valuation data in
McGraw-Hill's "Dodge Construction Potentials Bulletin" for the activity
indicator. This information is more accurate in assigning equipment to
the area in which it is used than are the county employment statistics,
and it is updated monthly.
In the long term, consideration should be given to using multiple
activity indicators for equipment that is used for several applications.
A missing element of this approach, however, is data indicating the
fields in which many of the equipment types are used. Although MacKay
has compiled some information of this kind, their data are by no means
complete. Any future proposed survey work related to nonroad equipment
usage should include questions related to the industry in which the
equipment is used.
References for Section 7
1. "Methodology to Estimate Nonroad Equipment Populations by
Nonattainment Areas," Energy and Environmental Analysis, September
1991.
2. "Machines at Work in America," in Construction Equipment. MacKay &
Company, March, April, and June 1987.
3. "Dodge Construction Potentials Bulletin," McGraw-Hill, August 1992.
The update of MacKay's 1987 study was not purchased for this work (its
cost is $6,000). Thus, details of the study's exact contents were not
obtained.
7-16
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See Disclaimer on Cover
4. "Petroleum Marketing Monthly," U.S. Department of Energy, Energy
Information Administration, June 1992.
5. "State Energy Data Report - Consumption Estimates 1960-1990," U.S.
Department of Energy, Energy Information Administration, May 1992.
6. "Fuel Oil and Kerosene Sales," U.S. Department of Energy, Energy
Information Administration.
7. "Highway Statistics 1989," U.S. Department of Transportation,
September 1990.
8. "1987 Census of Mineral Industries," U.S. Department of Commerce,
Bureau of the Census, December 1990.
9. "1987 Census of Construction Industries," U.S. Department of
Commerce, Bureau of the Census, July 1990.
10. Julie Van Burkum, U.S. Department of Commerce, Personal
Communication, August 1992.
11. J.A. Dunyon, R and D Steel, Inc., Personal Communication, August
1992.
12. Bob Komarek, American Institute of Constructors, Personal
Communication, November 1992.
13. Jim Loucks, Bid Pro, Personal Communication, November 1992.
14. Lance Ward, The Construction Link, Personal Communication, November
1992.
15. "Nonroad Engine and Vehicle Emission Study - Appendixes," U.S.
Environmental Protection Agency, November 1991.
16. "Exhaust Emissions from Uncontrolled Vehicles and Related Equipment
Using Internal Combustion Engines. Part 5: Farm, Construction, and
Industrial Engines," C.T. Hare andK.J. Springer, Southwest
Research Institute, October 1973.
17. "Nonroad Emission Factors," M.N. Ingalls, Southwest Research
Institute, February 1991.
18. "County Business Patterns 1988," U.S. Department of Commerce,
December 1990.
19. "The Universe of Construction Equipment," MacKay & Company, 1991.
7-17
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8. LIGHT COMMERCIAL EQUIPMENT
The light commercial equipment category consists of equipment powered by
engines less than 50 horsepower. Specific equipment types in this
category include generator sets, pumps, air compressors, gas
compressors, welders, and pressure washers. Although this category
consists of equipment powered by smaller engines, the methodologies
discussed below can also be utilized to develop local-level equipment
usage estimates for similar, higher-horsepower machines.
NEVES Methodology
NEVES Inventory A estimates relied on state-level PSR data to develop
local-level construction equipment populations. The activity indicator
employed by EEA in its regression analysis was wholesale activity
(number of establishments).1 This indicator was chosen by EEA because
of the many different applications for which these equipment types are
used. Although this model did not initially meet the statistical
criteria established by EEA for an acceptable model (i.e., the R-square
was below 0.8), removal of two outliers (New York and Texas) resulted in
an R-square of 0.9. Thus, the number of wholesale establishments was
retained as the activity indicator. (For this equipment category,
Inventory B was the same as Inventory A because industry associations
did not provide EPA with alternative population and usage estimates.)
The Inventory A population and usage estimates for light commercial
equipment are given in Table 8-1 for the U.S., DC/MD/VA, and the SJVAB.
As seen, generator sets make up the majority of this equipment category,
followed by welders and pumps. Although the equipment population is
substantial, second only to lawn and garden equipment in total numbers,
the lower horsepower of this equipment category results in it accounting
for only 3.4 percent of the total (gas plus Diesel) bhp-hr/yr attributed
to the entire nonroad equipment category.
As with construction equipment, it is unclear if the activity indicator
used in NEVES to allocate equipment population to the local level
adequately reflects the area in which the equipment is ultimately used.
For example, Sierra has recently investigated the light commercial
equipment populations for Anchorage, Alaska.2 In that work, it was
discovered that although Anchorage had a fair number of wholesale
establishments, much of the light commercial equipment (notably,
generator sets) purchased through these businesses ultimately was
transported to the North Slope or the "bush." Thus, such equipment
would be improperly allocated to the Anchorage area if wholesale
establishments were to serve as the activity indicator for distributing
the state-level equipment populations to the local level.
8-1
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See Disclaimer on Cover
Table 8-1
NEVES Inventory A Light Commercial Equipment Activity Estimates
National Estimates !
Equipment Type
Generator Sets
Pumps
Air Compressors
Gas Compressors
Welders
Pressure Washers
Total
Population
Diesel
198,391
61,810
15,713
0
100,490
3,943
380,347
Gas
2,943,286
651,687
176,124
436
350,545
290,959
4,413,037
Activity (1000 Bhp— hr/yr)
Diesel
1 ,359,748
415,542
208,181
0
845,171
3,478
2,832.120
Gas
3,148,256
683,040
393,236
66,708
587,643
192,164
5,071 ,047
Total
4,508,005
1,098,582
601,416
66,708
1,432,814
195,642
7,903,167
% Total
Activity
57.0
13.9
7.6
0.8
18.1
2.5
100.01
DC/MD/VA Estimates-
Equipment Type
Generator Sets
Pumps
Air Compressors
Gas Compressors
Welders
Pressure Washers
Total
Population
Diesel
1,502
468
119
0
761
30
2.880
Gas
22,279
4,933
1,333
3
2,653
2,202
33.403
Activity (1000 Bhp-hr/yr)
Diesel
10,295
3,146
1,577
0
6,400
26
21.444
Gas
23,831
5,170
2,976
459
4,447
1,454
38,338
Total
34,125
8,317
4,553
459
10,848
1.481
59,782
% Total
Activity '
57.1
13.9
7.6
0.8
18.1
2.5 i
100.01
San-Joaquin-ValIev:;Air-Basin-Estimates»K:*«;^ i
Equipment Type
Generator Sets
Pumps
Air Compressors
Gas Compressors
Welders
Pressure Washers
Total
Population
Diesel
1,253
390
99
0
635
25
2402
Gas
18,586
4,115
1,112
3
2,214
1.837
27867
Activity (1000 Bhp-hr/yr)
Diesel
7,650
3,186
1,647
0
7,461
26
19.971
Gas
17,795
5,227
3,122
459
5,170
1,454
33,227
Total
25,445
8,413
4,769
459
12,631
1.480
53.198
% Total
Activity ••
47.8:
15.8;
9.0!
0.9!
23.7 ;
2.8'
100.0!
Approach
Based on contacts with Departments of Motor Vehicles and local air
pollution control agencies, it was quickly determined that this
equipment is generally not registered or permitted in any way. In
addition, these equipment types have highly variable use patterns.
example, generator sets may range from emergency back-up to primary
power sources. Thus, developing a bottom—up strategy to determine
local—level populations and usage was not considered possible.
Therefore, efforts were directed at improving the current top-down
methodologies for equipment allocation.
For
8-2
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See Disclaimer on Cover
Data Sources and Sample Calculations
As discussed in previous sections, NEVES Inventory A relied on
regressions by equipment category rather than by individual equipment
type when determining local-level equipment populations. Not only does
this approach apply the same national-level equipment mix to each
nonattainment area, it also assumes that the same activity indicators
are applicable to each equipment type in a given category. Although the
equipment categories defined by EEA1 were chosen so that equipment with
similar engines, uses, and operating characteristics were grouped
together, there are cases in which the same activity indicator(s) are
not equally relevant to each equipment type included in a category.
This is apparent for several of the equipment types included in the
light commercial equipment category (e.g., pressure washers are likely
to be used primarily in construction activities, whereas generator sets
are used in many different applications), and it will likely result in a
less-than-perfect regression analysis, regardless of the activity
indicator chosen to distribute the equipment populations to the local
level.
Only one equipment type in the light commercial category appears to be
used in just a single application: gas compressors. Although the
equipment definitions provided in NEVES are not explicit, it is assumed
that this equipment type is used in oil and gas field operations to
compress natural gas for storage. The most obvious indicator to
allocate this equipment type to the local level is oil and gas
production. This information can be obtained at the state level from
the Energy Information Administration3; however, the availability of
county-level data for all states is uncertain.4 (County-level
production figures are available for California and the SJVAB from the
California Department of Conservation.5) Because the population of this
equipment type is relatively insignificant (only 436 in the entire U.S.,
according to PSR1) , the effort to allocate this equipment type should be
minimal. A simple scaling of the national population by the ratio of
local to national oil production is adequate.
For the remaining equipment types, there does not appear to be a single
activity indicator that entirely describes equipment usage.
Furthermore, as discussed above, data on wholesale trade establishments
do not necessarily indicate the location in which light commercial
equipment is likely to be used. Because most of these equipment types
are used at least partially in the construction industry, an alternative
activity indicator that appeared logical was total construction
valuation. Thus, statistics from the Dodge Construction Potentials
Bulletin (DCPB)6 (described in Section 7) were used in conjunction with
PSR state-level equipment population data to develop an alternative
regression model. These results are summarized in Table 8-2.
In reviewing the regression results presented in Table 8-2, it is
apparent that the R-square statistic is below the 0.8 value recommended
by EEA.1 The poor correlation is being driven, however, primarily by
two outliers: Louisiana and Texas. By removing these states from the
model and retaining total construction valuation as the independent
8-3
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See Disclaimer on Cover
Table 8-2
Regression Analysis for Light Commercial Equipment Utilizing
Total Construction Valuation as the Independent Variable
DEP VARIABLE: COMN POP
ANALYSIS OF VARIANCE
SOURCE
MODEL
ERROR
C TOTAL
DF
SUM OF
SQUARES
MEAN
SQUARE
1 256621047782 256621047782
21 132274749379 6298797589
22 388895797162
ROOT MSE
OEP MEAN
C.V.
79364.96
126128.5
62.92391
R-SQUARE
ADJ R-SQ
PARAMETER ESTIMATES
F VALUE
40.741
0.6599
0.6437
VARIABLE DF
INTERCEP 1
TOT CON 1
PARAMETER
ESTIMATE
-13013.6
26.76051470
STANDARD
ERROR
27369.09684
4.19253939
T FOR HO:
PARAMETERS)
-0.475
6.383
PROB>F
0.0001
PROS > |T|
0.6393
0.0001
MODEL: COMN POP = a*(TOT CON) + b
COMM POP a COMMERCIAL EQUIPMENT POPULATION
TOT CON a CONSTRUCTION VALUATION (MILLION $)
a »~26.761
b » -13,013
variable, the R-square value improves to 0.89. Based on the statistical
criteria followed for NEVES Inventory A, this model is acceptable for
determining the local equipment populations.
The above regression model was used to estimate equipment populations
for the DC/MD/VA nonattainment area and the SJVAB; however, the slope
was first recalculated with the assumption of a zero intercept, which
resulted in a slope of 25.173. (The data and regression statistics for
this model are in Appendix C.) The resulting equipment populations are
summarized in Table 8-3. (The gas/Diesel split and the annual hourly
usage are the same as reported in NEVES because there are no other known
sources of data for these parameters; thus, only total equipment
populations are reported in Table 8-3.) As seen in the table, the
estimates for the DC/MD/VA area are reasonably similar when comparing
the NEVES estimates to those calculated in this study. On the other
hand, the equipment populations generated with construction valuation as
the activity indicator resulted in a 50 percent increase over the NEVES
estimates which relied on wholesale establishments. This can be
explained by considering that the northern and southern portions of the
SJVAB are fairly close to the San Francisco and Los Angeles metropolitan
areas, respectively. It is likely that a certain percentage of the
equipment used in the SJVAB is actually purchased from wholesale
establishments in the larger metropolitan areas. Again, this points out
the need to choose activity indicators carefully so that equipment is
allocated to the area where it is actually used.
8-4
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Table 8-3
Comparison of Light Commercial Equipment Populations for the
DC/MD/VA Nonattainment Area and the SJVAB
Equipment Type
Generator Sets
Pumps
Air Compressors
Gas Compressors
Welders
Pressure Washers
Total
DC/MD/VA
NEVES
23,781
5,401
1,452
3
3,414
2,232
36,283
This Study
28,260
6,420
1,725
0
4,057
2,652
43 , 114
SJVAB
NEVES
19,839
4,505
1,211
3
2,849
1,862
30,269
This Study
30,122
6,843
1,838
39
4,325
2,826
45,993
Additional Considerations
Although a model based solely on total construction valuation makes
intuitive sense and meets the aforementioned statistical criteria for
acceptability, there was concern about the underprediction of equipment
populations for Louisiana and Texas (88,000 and 303,000 units,
respectively). It was initially felt that some of the unaccounted
equipment population for these states might be related to the oil
industry. Therefore, an additional activity indicator that was
attempted in a regression model was oil production. (Oil production by
state is available from the Energy Information Administration.3) The
results, summarized in Table 8-4, indicate that the model exhibits very
good statistical validity. A subsequent review of oil field operations,
however, indicated that small generator sets are not likely to be used,
and this regression model is not recommended for allocating light
commercial equipment. This analysis reinforces the need to consider how
applicable an activity indicator is to equipment usage rather than
relying too heavily on favorable regression statistics.
In exploring other potential explanations for the apparent
underprediction of state-level equipment populations for Louisiana and
Texas, product literature for generator sets and water pumps was
consulted to determine the end users that are targeted by
manufacturers.7-8 For smaller generator sets with engines about five
horsepower and below (at least for Honda products), the primary focus of
the product literature is on recreational uses (e.g., fishing, hunting,
camping). On the other hand, construction was cited as a primary
application for Honda's larger, heavy-duty models (8 to 20 horsepower).
8-5
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Table 8-4
Regression Analysis for Light Commercial Equipment Utilizing
Total Construction Valuation and Oil Production as the Independent Variables
OEP VARIABLE: COW POP
SOURCE OF
ANALYSIS OF VARIANCE
SUM OF
SQUARES
MODEL 2 370534210391
ERROR 20 18361586771
C TOTAL 22 388895797162
ROOT MSE
DEP MEAN
C.V.
30299.82
126128.5
24.02298
MEAN
SQUARE
185267105195
918079338.54
R-SQUARE
ADJ R-SQ
F VALUE
201.799
0.9528
0.9481
PR08>F
O.OOOt
PARAMETER ESTIMATES
VARIABLE
INTERCEP
TOT CON
OIL'PROD
OF
1
1
1
PARAMETER
ESTIMATE
16335.76794
15.31559258
0.55632647
STANDARD
ERROR
10776.01149
1.90201628
0.04994395
T FOR HO:
PARAMETERS
1.516
8.052
11.139
PROS > |T|
0.1452
0.0001
0.0001
MODEL: COMM POP « a*(TOT CON) * b*(OIL PROD) + c
COMM POP a COMMERCIAL EQUIPMENT POPULATION
TOT CON = CONSTRUCTION VALUATION (MILLION S)
OIL PROD > OIL PROOICTION (1000 BARRELS)
a »~15.316
b - 0.556
c - 16.336
Water pump applications cited by Honda include agriculture (i.e.,
irrigation), construction, emergency flood situations, boating, and
personal home use.
Because all of the equipment types included in this equipment category
are used at least partially in the construction industry (with the
exception of gas compressors, which have a negligible population and
should be allocated using natural gas or oil production), it is
important to use construction valuation as an activity indicator to
distribute state or national populations to the local level. However,
for multi-purpose equipment types, more accurate local-level population
estimates could be obtained by determining the fraction of the equipment
population that is used in various other activities. Ideally, this
would be accomplished through a survey of end users; however, a rough
estimate for generator sets is possible by assuming usage by horsepower
range. An example of this follows.
Based on the product literature cited above, generator sets could be
stratified into two groups: above and below 5 horsepower. Although
such an analysis was not performed in this work (because Sierra did not
have access to the FSR data base purchased for NEVES), it is possible to
obtain population by horsepower rating through PSR's data base.
Generator sets below 5 horsepower would be allocated to the local level
using an appropriate activity indicator that represents recreational
8-6
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See Disclaimer on Cover
usage, while those above 5 horsepower would be allocated to the local
level using construction activity. Although this approach is not as
accurate as using survey results to determine typical usage, it would be
an improvement over a method that relies only on construction activity
for all sizes of generator sets.
Recommendations
Because it is unlikely that a reliable bottom-up methodology could be
developed for this equipment category, recommendations for improvements
to inventory techniques are limited to top-down methodologies. In the
short term, Sierra recommends that construction valuation (based on
DCPB6) be used as the activity indicator in a regression model rather
than wholesale establishments as utilized in NEVES. Construction
valuation will allow a better representation of where the equipment is
used, although some of the equipment types in the light commercial
category are used in more than just the construction industry.
In the long term, several additional improvements can be made in
allocating light commercial equipment to the local level. First,
equipment types should be considered independently when performing
regression analyses with state—level data. If EPA plans future
purchases of PSR data bases, state-level equipment populations can be
compiled for each equipment type. With these data, a mix of activity
indicators can be used for each equipment type. For example, generator
sets should be allocated using a construction indicator and a recreation
indicator, while pumps should be allocated using construction and
agriculture activity indicators.
Data related to the mix of industries (or activities) in which
individual equipment types are used could also be utilized to improve
local-level equipment populations, provided activity indicators linking
equipment population to the industry or activity can be acquired.
Because information on equipment usage patterns is not currently
available, the compilation of such data would likely require a survey of
end users.
References for Section 8
1. "Methodology to Estimate Nonroad Equipment Populations by
Nonattainment Areas," Energy and Environmental Analysis, September
1991.
2. "Review of 'Nonroad Engine Emission Inventories for CO and Ozone
Nonattainment Boundaries. Anchorage Area'," Memo from Sierra
Research to Alaska Department of Environmental Conservation,
November 1992.
3. "Petroleum Supply Annual 1990," Energy Information Administration,
U.S. Department of Energy, May 1991.
8-7
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See Disclaimer on Cover
4. Personal Communication, Rob Houser, California Department of
Conservation, November 1990.
5. "77th Annual Report of the State Oil and Gas Supervisor. 1991,"
California Department of Conservation, 1992.
6. "Dodge Construction Potentials Bulletin," McGraw-Hill, August 1992.
7. "Honda Generators," American Honda Motor Co., Inc., 1991.
8. "Honda Water Pumps," American Honda Motor Co., Inc., 1991.
8-8
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See Disclaimer on Cover
9. INDUSTRIAL EQUIPMENT
The industrial equipment category includes primarily material handling
equipment used in manufacturing and construction activities. Specific
equipment types included in this category are aerial lifts, forklifts,
sweepers/scrubbers, "other" general industrial equipment (e.g., abrasive
blasting equipment, industrial blowers/vacuums), and "other" material
handling equipment (e.g., conveyors).
NEVES Methodology
NEVES Inventory A estimates utilized state-level PSR data in a
regression analysis to develop local-level industrial equipment
populations, and the activity indicator used by EEA in the model was the
number of employees engaged in manufacturing.1 The regression
statistics were very favorable, with an R-square value of 0.93, and the
use of manufacturing employment as the activity indicator makes
intuitive sense.
Data from only two of the five equipment types in this category were
revised for Inventory B as a result of input from industry associations.
First, the annual usage, the horsepower, and the load factor for aerial
lifts were modified based on data received from the Equipment
Manufacturers Institute (EMI), although EMI did not provide alternative
population estimates. Second, the forklift population was significantly
revised, particularly the Diesel/gasoline split (LPG and CNG equipment
are included in the gasoline population), based on input from the
Industrial Truck Association (ITA). ITA also provided alternative
estimates for forklift annual hourly usage, while the horsepower and
load factor remained unchanged from Inventory A.
The activity estimates (in bhp-hr/yr) developed in Inventories A and B
are summarized in Tables 9-1 and 9-2, respectively, for the U.S., the
DC/MD/VA metropolitan area, and the SJVAB. As seen in the tables,
including industry estimates in the calculations caused a net decrease
in total activity for this category by roughly 30 percent. This
decrease is attributed to the substantial decrease in forklift activity
that resulted from a lower annual usage (about one-half the Inventory A
estimate) and a higher percentage of gasoline machines (which have a
corresponding lower horsepower rating). Also of interest in the tables
is the net increase in relative activity for aerial lifts when using
industry data. This change is caused by a much higher annual hourly
usage reported by EMI compared to PSR's estimate (a seven-fold
increase), while the equipment population remained the same between the
two inventories.
9-1
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See Disclaimer on Cover
Table 9-1
NEVES Inventory A Industrial Equipment Activity Estimates
National Estimates
Equipment Type
Aerial Lifts
Forklifts
Sweepers/Scrubbers
Other General Industrial
Other Material Handling
Total
Population
Diesel
12,310
160,583
36,977
18,366
5,258
233.494
Gas
28,388
182,482
25,892
23,724
2,036
262.522
Activity (1000 Bhp-hr/yr)
Diesel
81,326
6.425,616
3,034,120
813,813
139,805
10.494.680
Gas
147,613
5,773,475
377,115
160,649
20,472
6.479.325
Total
228,939
12.199,091
3,411,235
974,462
160,277
16.974.005
% Total
Activity
1.3
71.9
20.1
5.7
0.9
100.0
DC/MD/VA Estimates
Equipment Type
Aerial Lifts
Forklifts
Sweepers/Scrubbers
Other General Industrial
Other Material Handling
Total
Population
Diesel
60
782
180
90
26
1.138
Gas
138
887
126
116
10
1.277
Activity (1000 Bhp-hr/yr)
Diesel
396
31,291
14,770
3,988
691
51.137
Gas
718
28,063
1,835
786
101
31.502
Total
1,114
59,355
16,605
4,773
792
82.639
% Total
Activity
1.3
71.8
20.1
5.8
1.0
100.0
SansJoaquin Valley Air Basin EsA^a^am^^M^m^mi^a^m^;-^
Equipment Type
Aerial Lifts
Forklifts
Sweepers/Scrubbers
Other General Industrial
Other Material Handling
Total
Population
Diesel
67
868
200
99
28
1,262
Gas
154
986
140
128
11
1.419
Activity (1000 Bhp-hr/yr)
Diesel
529
37,110
17,057
5,786
834
61.316
Gas
956
33,341
2,121
1,143
124
37.685
Total
1,485
70,451
19,178
6,929
958
99.001
% Total
Activity
1.5
71.2
19.4
7.0
1.0
100.0
Approach
As with the light commercial equipment category, developing a bottom-up
methodology to estimate local-level activity from industrial equipment
appears to be an impossible task. The equipment types included in this
category are not registered or permitted in any way, and because they
are used in a multitude of industries, it is difficult to develop
generalized usage guidelines upon which to base a bottom—up approach.
Therefore, the bulk of the effort for this section was devoted to
investigating potential improvements to the top-down methodology
developed for NEVES.
Data Sources and Sample Calculations
Several of the equipment types included in this category at first
appeared to be associated with warehouse activity. Conversations with
9-2
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See Disclaimer on Cover
Table 9-2
NEVES Inventory B Industrial Equipment Activity Estimates
National Estimates !
Equipment Type
Aerial Lifts
Forklifts
Sweepers/Scrubbers
Other General Industrial
Other Material Handling
Total
Population
Diesel
12,310
47,068
36,977
18,366
5,258
119.979
Gas
28,388
311,884
25,892
23,724
2,036
391.924
Activity (1000 Bhp-hr/yr)
Diesel I Gas
535,784
996,194
3,034,120
813,813
139,805
5.519.715
1,062,908
4,930,886
377,115
160,649
20,472
6.552.031
Total
1,598,692
5,927,080
3,411,235
974,462
160.277
12.071.746
% Total
Activity
13.2!
49.1
28.3
8.1
1.3
100.0!
DC/MD/VA Estimates
Equipment Type
Aerial Lifts
Forklifts
Sweepers/Scrubbers
Other General Industrial
Other Material Handling
Total
Population
Diesel
60
338
180
90
26
694
Gas
138
2,342
126
116
10
2.732
Activity (1000 Bhp-hr/yr)
Diesel
2,611
7,154
14,770
3,988
691
29.214
Gas
5,167
37,027
1.835
786
101
44.915
Total
7,778
44,181
16,605
4,773
792
74.130
% Total
Activity
10.5
59.6
22.4
6.4
1.1
100.0
pssss^
Equipment Type
Aerial Lifts
Forklifts
Sweepers/Scrubbers
Other General Industrial
Other Material Handling
Total
Population
Diesel
67
886
200
99
28
1.280
Gas
154
948
140
128
11
1.381
Activity (1000 Bhp-hr/yr)
Diesel
2,658
18,752
17,057
5,786
834
45,088
Gas
5,256
14,988
2,121
1,143
124
23.631
Total
7,914
33,740
19,178
6,929
958
68.719
% Total
Activity
11.5
49.1
27.9
10.1
1.4
100.0
warehouse managers, however, indicated that most facilities are enclosed
and therefore do not use equipment with internal combustion engines
inside because of problems with potential OSHA violations.2 Many of
these facilities rely on electrically driven equipment with internal
combustion equipment backups when necessary. Thus, it is reasonable to
assume that most of this equipment is used in construction activity
(e.g., large job sites, delivery of materials to jobsites, etc.),
manufacturing, and open warehouses.
The activity indicator chosen by EEA in its regression analysis (i.e.,
manufacturing employment) resulted in very favorable statistics and
makes intuitive sense when allocating this equipment to the local level.
As alluded to above, however, not all of the equipment in this category
would be expected to be operated solely in the manufacturing industries.
Additionally, as discussed in Section 8, inaccuracies in allocating
equipment can be introduced if some of the equipment types within a
category are used in industries that are not associated with the
activity indicator. For example, aerial lifts and forklifts are used in
construction as well as manufacturing. Thus, a model that includes both
9-3
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See Disclaimer on Cover
manufacturing and construction indicators would likely provide more
accurate local-level estimates for these two equipment types.
The above discussion points out the desirability of allocating equipment
types individually (or in smaller groups) to local areas rather than
relying on larger equipment groupings. However, local air quality
planners would have to balance a more detailed approach with the
available resources needed to perform the analysis. In the case of
industrial equipment, the inclusion of a construction indicator for some
of the equipment types would not result in much additional effort,
particularly since construction statistics are necessary for the
construction equipment category. With this in mind, the regression
analysis performed for NEVES was reevaluated using both manufacturing
employment and construction valuation as activity indicators.
Table 9-3 summarizes the regression results utilizing PSR state-level
equipment populations as the dependent variable, with manufacturing
employment (from the Department of Commerce's "County Business
Patterns"3) and construction valuation (from McGraw-Hill's "Dodge
Construction Potentials Bulletin"4) serving as independent variables.
As seen in the table, the R—square value is favorable at 0.94 and the
condition numbers indicate that collinearity is not a problem; however,
the t-statistic for construction valuation indicates that the
coefficient is significant at only about an 80 percent confidence level.
It is possible that the use of total industrial equipment populations,
which include a portion of equipment not related to construction (i.e.,
sweepers/scrubbers, "other" general industrial equipment, and "other"
material handling equipment), may be influencing the poor t-statistic
for the construction coefficient.
Although poor statistics resulted from the use of both construction
valuation and manufacturing employment in the regression analysis, the
aerial lift and forklift populations for the DC/MD/VA nonattainment area
and the SJVAB were estimated using the results of this model. These
populations are shown in Table 9-4. As seen, the results for the
DC/MD/VA area are essentially the same for both models (i.e.,
manufacturing employment alone versus manufacturing employment and
construction valuation), while the SJVAB equipment population estimates
decrease by about 10 percent when construction valuation is included in
the regression model. Also shown in the table are results for NEVES
Inventory B, which indicate a substantially higher forklift population
for the DC/MD/VA region and an estimate similar to Inventory A for the
SJVAB.
Additional Considerations
As discussed above, combining equipment types into a single category
prior to performing regressions and allocating equipment to the local
level introduces uncertainty when a portion of the equipment is also
used for applications that are not associated with the chosen indicator.
9-4
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See Disclaimer on Cover
Table 9-3
Industrial Equipment Regression Results with Total Construction Valuation and
Manufacturing Employment as Independent Variables
OEP VARIABLE: IND POP
ANALYSIS OF VARIANCE
SUM OF MEAN
SOURCE DF SQUARES SQUARE F VALUE
MODEL 2 2223666886 1111833443 156.074
ERROR 20 142474751.15 7123737.56
C TOTAL 22 2366141637
PROB>F
0.0001
ROOT MSE 2669.033 R-SQUARE 0.9398
OEP MEAN 12189.17 ADJ R-SQ 0.9338
C.V. 21.89675
PARAMETER ESTIMATES
PARAMETER STANDARD T FOR HO:
VARIABLE DF ESTIMATE ERROR PARAMETERS
INTERCEP
TOT CON
MFR^EMP
NUMBER
1
2
3
1 -687.942 930.38319149 -0.739
1 0.44009757 0.30738663 1.432
1 0.01754764 0.002578607 6.805
COLL I NEAR I TY DIAGNOSTICS
CONDITION VAR PROP VAR PROP
EIGENVALUE NUMBER INTERCEP TOT_CON
2.698238 1.000000 0.0402 0.0100
0.260361 3.219233 0.9568 0.0414
0.0414013 8.072964 0.0029 0.9486
MODEL: IND POP = a*(TOT CON) + b*(MFR EMP) * C
IND POP a INDUSTRIAL EQUIPMENT POPULATION
TOT CON * CONSTRUCTION VALUATION (MILLION $)
MFR EMP = MANUFACTURING EMPLOYMENT
a =~0.440
b = 0.0175
C = -688
PROS > |T|
0.4682
0.1677
0.0001
VAR PROP
MFR_EHP
0.0102
0.0500
0.9398
Table 9-4
Comparison of Aerial Lift and Forklift Population Estimates
Using Manufacturing Employment Only vs. Manufacturing Employment
and Construction Valuation as Activity Indicators
Equipment
Aerial Lifts
Forklifts
Manuf ac tur ing
Employment*
DC/MD/VA
198
1,669
SJVAB
221
1,854
Mfr. Emp. &
Const. Valuation
DC/MD/VA
201
1,690
SJVAB
197
1,659
Inventory B
DC/MD/VA
198
2,680
SJVAB
221
1,834
Represents Inventory A population.
9-5
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See Disclaimer on Cover
Although an attempt was made to account for aerial lifts and forklifts
used in construction as well as the manufacturing industries, the state-
level data upon which the analysis was based included equipment that is
not used in construction. It is unclear if this contributed to the
unfavorable statistics resulting from this model, and it was not
possible to develop state-level populations only for aerial lifts and
forklifts because Sierra does not have access to the PSR data base used
in the development of NEVES Inventory A.
Although the regression technique developed by EEA for NEVES results in
improved local-level equipment activity estimates for most equipment
categories (provided the activity indicator(s) are chosen with care),
imbedded in this methodology is the assumption that the state-level
equipment populations used in the regressions are valid. Because PSR
employs its own chosen activity indicators to allocate national
equipment populations to the state level for each equipment type, there
are likely to be instances in which the state—level populations are in
error. Such an occurrence could result in a statistically insignificant
relationship between the state population and the activity indicator.
EEA recognized that this could occur, and in such cases recommended an
alternative methodology to allocate national populations to the county
level based on a simple scaling of the national equipment population
(i.e., the national population is multiplied by the ratio of the county
to national activity indicator). This alternative methodology was used
in the allocation of logging equipment for NEVES Inventory A.
Because inaccuracies in the state-level industrial equipment populations
could also be driving the poor statistical validity of the regression
model utilizing manufacturing employment and construction valuation,
utilization of the above alternative allocation methodology should be
considered. To apply this approach, however, the fraction of this
equipment used in manufacturing versus construction would have to be
known. Unfortunately, no sources of such information were identified in
this work. Alternatively, consideration should be given to the use of
industry-supplied data to apportion forklifts in the communities for
which data are available.
Recommendations
Although the regression analysis including both manufacturing employment
and construction valuation resulted in statistics that indicated the
construction valuation coefficient to be significant only at an 80
percent level, some means to account for construction usage of aerial
lifts and forklifts should be considered. Such an approach adds a
moderate amount of complexity, but would result in improved equipment
population estimates. In the short-term, EPA should consider either:
(1) ignoring the poor statistics and proceeding with use of the model
for developing local-level population estimates for aerial lifts and
forklifts, or (2) utilizing industry information in the communities
where data are available.
In the long-term, the above model should be rerun with only the state-
level aerial lift and forklift populations, while the remaining
9-6
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See Disclaimer on Cover
equipment types in this category should retain the use of manufacturing
employment as the activity indicator. Additionally, survey data to
quantify the fractional usage of these equipment types in various
applications and industries should be collected. This would allow for a
more accurate allocation of equipment to the local level.
References for Section 9
1. "Methodology to Estimate Nonroad Equipment Populations by
Nonattainment Areas," Energy and Environmental Analysis, September
1991.
2. Robert Davis, Warehouse Manager, Lionel Toys, Personal
Communication, July 1992.
3. "County Business Patterns," U.S. Department of Commerce.
4. "Dodge Construction Potentials Bulletin," McGraw-Hill, August 1992.
9-7
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10. AGRICULTURAL EQUIPMENT
Specific nonroad equipment types included in the agricultural equipment
category include 2-wheel tractors, agricultural tractors, agricultural
mowers, combines, sprayers, balers, larger walk-behind tillers (> 5 HP),
swathers, hydro power units, and "other agricultural equipment." Among
those items in the "other" category are harvesters, detasslers, and
cotton pickers. By far, the great majority of the work performed on a
farm is by agricultural tractors. Because they can be equipped with a
variety of attachments, agricultural tractors often negate the need for
some of the single-purpose, self-propelled equipment listed above such
as mowers, sprayers, and swathers.
NEVES Methodology
NEVES Inventory A utilized national PSR data on population and annual
hourly usage to develop activity estimates for agricultural equipment.
However, EPA chose to deviate from the regression technique suggested by
EEA in allocating national equipment populations to the local level.
Instead, national equipment populations were scaled by the county to
national equipment population ratios contained in the 1987 Census of
Agriculture.1 This approach was taken by EPA because the equipment
populations developed from the EEA regression methodology resulted in
questionable local-level equipment populations.
The Inventory B estimates developed by EPA are very similar to the
Inventory A estimates. The primary difference between the two
inventories is that the number of tillers was reduced in Inventory B by
about 30 percent because industry felt that many of the higher
horsepower tillers are used for lawn and garden applications. In
addition to tillers, a slight difference in swather population is
observed in the local area inventories.
The results of the analysis are summarized in Tables 10-1 and 10-2 for
Inventory A and Inventory B, respectively, on a national basis, for
DC/MD/VA, and for the SJV. As mentioned above, agricultural tractors
are responsible for a great majority of the activity on farms,
accounting for 90 percent of the total bhp-hr. Thus, in any methodology
used to develop agricultural activity estimates, it is important to
focus on agricultural tractors.
Approach
Because of Sierra's previous experience in developing agricultural
emission inventories, it was felt at the outset that a bottom-up
10-1
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See Disclaimer on Cover
Table 10-1
NEVES Inventory A Agricultural Equipment Activity Estimates
National Estimates
Equipment Type
2-Wheel Tractors
Agricultural Tractors
Agricultural Mowers
Combines
Sprayers
Balers
Tillers > 5 HP
Swathers
Hydro Power Units
Other Ag Equipment
Total
Population
Diesel
0
2,519,295
0
284,854
9,691
4,260
78
50,032
2,366
18,042
2.888,618
Gas
13,802
5,808
16,023
1,835
72,721
0
783,102
32,857
15,042
6,403
947.593
Activity (1000 Bhp-hr/yr)
Diesel
0
71,030,515
0
3,758,250
39,229
17,004
103
193,476
27,625
173,079
75.239.281
Gas
15,874
149,123
14,213
18,322
68,067
0
233,521
137,642
46,700
20,725
704.187
Total
15,874
71,179,638
14,213
3,776,572
107,296
17,004
233,624
331,118
74,325
193,804
75.943.467
% Total
Activity
0.0
93.7
0.0
5.0
0.1
0.0
0.3
0.4
0.1
0.3
100.0
DC/MDWA Estimates
Equipment Type
2-Wheel Tractors
Agricultural Tractors
Agricultural Mowers
Combines
Sprayers
Balers
Tillers > 5 HP
Swathers
Hydro Power Units
Other Ag Equipment
Total
Population
Diesel
0
6,385
0
722
25
11
0
127
6
46
7.322
Gas
35
15
41
5
184
0
1,985
83
38
16
2.402
Activity (1000 Bhp-hr/yr)
Diesel
0
180,023
0
9,526
117
44
0
491
70
441
190.712
Gas
40
385
36
50
224
0
592
348
118
52
1.845
Total
40
180,408
36
9,576
341
44
592
839
188
493
192.557
% Total
Activity
0.0
93.7
0.0
5.0
0.2
0.0
0.3
0.4
0.1
0.3
100.0
San Joaquin Valley Air Basin Estimates
Equipment Type
2-Wheel Tractors
Agricultural Tractors
Agricultural Mowers
Combines
Sprayers
Balers
Tillers > 5 HP
Swathers
Hydro Power Units
Other Ag Equipment
Total
Population
Diesel
0
34,454
0
3,896
133
58
1
684
32
247
39.505
Gas
189
79
219
25
995
0
10,710
449
206
88
12.960
Activity (1000 Bhp-hr/yr)
Diesel
0
1.257,406
0
75,860
781
346
2
4,042
438
3,202
1 .342,076
Gas
272
2,625
208
371
1,521
0
3,779
2,920
749
386
12,832
Total
272
1,260,031
208
76,231
2,302
346
3,781
6,962
1,187
3,588
1 .354.908
% Total
Activity
0.0
93.0
0.0
5.6
0.2
0.0
0.3
0.5
0.1
0.3
100.0
10-2
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See Disclaimer on Cover
Table 10-2
NEVES Inventory B Agricultural Equipment Activity Estimates
National Estimates
Equipment Type
2-Wheel Tractors
Agricultural Tractors
Agricultural Mowers
Combines
Sprayers
Balers
Tillers > 5 HP
Swathers
Hydro Power Units
Other Ag Equipment
Total
Population
Diesel
0
2,519,295
0
284,854
9,692
4,260
29
50,032
2,366
18,042
2.888.569
Gas
13,802
5,808
16,023
1,835
72,721
0
562,407
32,857
15,042
6,405
726.900
Activity (1000 Bhp-hr/yr)
Diesel
0
71,008,919
0
3,750,675
39,321
74,579
5
254,362
27,630
172,849
75.328.341
Gas
15,847
149,038
14,213
18,342
68,416
0
38,383
181,109
46,701
20,782
552.831
Total
15,847
71,157,957
14,213
3,769,018
107,738
74,579
38,389
435,471
74,331
193,632
75.881.173
% Total
Activity
0.0
93.8
0.0
5.0
0.1
0.1
0.1
0.6
0.1
0.3
100.0
DG/MD/VA Estimates
Equipment Type
2-Wheel Tractors
Agricultural Tractors
Agricultural Mowers
Combines
Sprayers
Balers
Tillers > 5 HP
Swathers
Hydro Power Units
Other Ag Equipment
Total
Population
Diesel
0
6,385
0
722
25
11
0
678
6
46
7.873
Gas
35
15
41
5
184
0
1,418
445
38
16
2.197
Activity (1000 Bhp-hr/yr)
Diesel
0
180,023
0
9,526
101
193
- 0
3,447
70
441
193.800
Gas
40
385
36
50
172
0
98
2,453
118
52
3.405
Total
40
180,408
36
9,576
273
193
98
5,900
188
493
197.205
% Total
Activity
0.0
91.5
0.0
4.9
0.1
0.1
0.0
3.0
0.1
0.3
100.0
San Joaquin Vallev Air Basin Estimates
Equipment Type
2-Wheel Tractors
Agricultural Tractors
Agricultural Mowers
Combines
Sprayers
Balers
Tillers > 5 HP
Swathers
Hydro Power Units
Other Ag Equipment
Total
Population
Diesel
0
34,454
0
3,896
133
58
1
2,166
32
4,155
44.895
Gas
189
79
219
25
995
0
10,710
1,422
206
88
13,933
Activity (1000 Bhp-hr/yr)
Diesel
0
1 ,257,406
0
75,860
673
1,015
0
59,354
438
48,556
1,443.302
Gas
272
2,625
208
371
1,170
0
647
9,249
749
386
15,677
I Total
272
1,260,031
208
76,231
1,843
1,015
647
68,603
1,187
48,942
1 .458.979
% Total
Activity
0.0
86.4
0.0
5.2
0.1
0.1
0.0
4.7
0.1
3.4
100.0
10-3
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See Disclaimer on Cover
approach is clearly superior to current top-down methodologies. Top-
down methodologies do not account for regional differences in farming
practices or crop types, and thus do not account for local nuances.
This is particularly troublesome from the standpoint of crop type. The
type of crop grown has a significant impact on the amount of activity
that a farmer must provide. For example, if an area has most of its
acreage devoted to pasture, its activity would be quite different from
an area -with a large fraction of machine-intensive crops such as cotton
or tobacco. The bulk of this effort, therefore, was devoted to
developing a bottom-up procedure that could be used by local air quality
planning agencies to estimate emissions from agricultural operations.
Nonetheless, some effort was devoted to assessing current top-down
methodologies and providing recommendations for potential improvements,
as detailed below.
Top-Down - In EEA's development of local-level agricultural equipment
populations for NEVES, many of the more obvious activity indicators
(e.g., number of farms, total farmed acreage, average farm revenue,
etc.) were attempted but failed to meet EEA's criteria for an
"acceptable" model. The model ultimately developed by EEA utilized the
number of employees in agricultural services (excluding Landscaping and
Horticultural Services) as the activity indicator. However, because it
appeared that this model allocated too much equipment to nonattainment
communities, EPA chose to use a simplified approach that scaled the
national equipment population (from PSR) by the county to national
tractor population ratio reported in the 1987 Census of Agriculture.1
(The equipment populations reported in the census were not used directly
because it was felt that a large proportion of inoperable machines are
included in these data.)
The above approach has merit, in that the distribution of tractors (and
other equipment, e.g., combines, cotton pickers, mower conditioners, and
pickup balers) among counties is very well represented by the census
data. Potential improvements that could be considered, however, include
the following:
• The Agricultural Census contains data on total equipment
population as well as data on equipment manufactured in the 1983
to 1987 timeframe. Thus, utilization of data on equipment
manufactured from 1983 to 1987 to develop the equipment
distribution might be considered.
• Because the census contains population data specifically for
combines, a separate county-level distribution for combines
could be prepared.
The first of these potential changes to the EPA methodology is being
presented because newer equipment is generally used more, and it is
unlikely that never- and seldom-used equipment form a substantial
percentage of this group. However, if economic conditions in certain
regions of the country influenced purchasing decisions in the 1983 to
1987 timeframe, it would be more appropriate to maintain the current
methodology and utilize the total tractor population to establish the
distribution. As seen in Table 10-3, shifting the basis for developing
the equipment distribution has a profound effect on the percentage of
10-4
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See Disclaimer on Cover
Table 10-3
Tractor Population by County for the San Joaquin Valley Air Basin
1987 Census of Agriculture
County
Fresno
Kem
Kings
Madera
Merced
San Joaquin
Stanislaus
Tulare
Total Air Basin
National
Tractors
Total Population
Number
18,738
7,123
3,465
4,467
7,204
8,995
8,538
12,335
70.865
4.609.388
Percent
0.41%
0.15%
0.08%
0.10%
0.16%
0.20%
0.19%
0.27%
1.54%
1983-1987
Number
2,726
1,319
433
640
818
1,113
1,087
1,061
9.197
426.837
Percent
0.64%
0.31%
0.10%
0.15%
0.19%
0.26%
0.25%
0.25%
2.15%
Combines
Total Po
Number
158
103
181
89
89
270
199
165
1.254
667.128
pulation
Percent
0.02%
0.02%
0.03%
0.01%
0.01%
0.04%
0.03%
0.02%
0.19%
1983-1987
Number I Percent
25
14
32
14
19
37
21
31
193
67.192
0.04%
0.02%
0.05%
0.02%
0.03%
0.06%
0.03%
0.05%
0.29%
tractors in the SJVAB. This obviously would also translate into a
change in the overall inventory when utilizing a top-down approach.
Because combines are second only to tractors in activity (see Tables
10-1 and 10-2), it is important to consider developing a separate
distribution for this equipment type. As seen in Table 10-3, the
combine population distribution for the SJVAB does not match its tractor
distribution; the SJVAB accounts for 1.5 percent of the national tractor
population but only 0.2 percent of the combine population. Clearly, the
inventory estimates could be improved by assigning combine population
according to the combine data contained in the Agricultural Census.
Bottom—Up - The remaining discussion in this section focuses on a
bottom-up approach for estimating emissions from agricultural
operations. As discussed above, it is believed that this methodology
results in much better local estimates of agricultural equipment
activity.
To implement a bottom-up strategy, it was necessary to identify specific
operations performed in the production of crops grown in a particular
area. In an analysis performed for CARB2, it was discovered that very
detailed estimates of farming operations by crop type are available
through the University of California Cooperative Extension. These so-
called "Production Budgets" or "Sample Production Cost Estimates" list
the operations performed, equipment used (including horsepower) and time
per acre, and often indicate the month in which the operation is
performed. Production budgets are generally used by farmers to estimate
operating costs and provide a basis for farm loans. However, it is also
possible to use this information, coupled with data on the number of
acres under cultivation by crop type, to develop activity estimates for
agricultural equipment. A typical production cost estimate for
irrigated wheat in California is shown in Table 10-4.
To cross-check the data contained in the production budgets, the
hour/acre values listed in the sample production cost estimates were
analyzed to determine the corresponding equipment speed. If speeds
10-5
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See Disclaimer on Cover
Table 10-4
Summary of Production Cost Estimate for Wheat
Wheat: Glenn County, California
Operation
Disc
Chisel
Disk Offset
Triplane
Fertilize
Spiketooth
Drill & Pert
Border
Ditch
Harvest
- Equipment
- Tractor Crawler
Ag Tractor
Ag Tractor
Tractor Crawler
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Tractor Crawler
SP Combine 24'
Horse-
Power
125
200
200
125
135
135
135
100
125
65
Hours/
Acre
0.25
0.13
0.13
0.2
0.1
0.1
0.2
0.1
0.1
0.33
Relative Activity b
Dec
Jan
Feb
Mar
1
Apr
May
Jun
1
/ Month
Jul
Aug
Sep
Oct
1
1
1
1
Ncv
1
1
1
1
Table 10-5
Equipment Speed as a Function of Implement Width (mph)*
Wheat: Glenn County. California
Operation
Disc
Chisel
Disk Offset
Triplane
Fertilize
Spiketooth
Drill & Fert
Border
Ditch
Harvest
Equipment
Tractor Crawler
Ag Tractor
Ag Tractor
Tractor Crawler
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Tractor Crawler
SP Combine 24'
Horse-
Power
125
200
200
125
135
135
135
100
125
65
Hours/
Acre
0.25
0.13
0.13
0.2
0.1
0.1
0.2
0.1
0.1
0.33
Implement Width (feet)
6
5.5
10.6
10fi
R9
13.8
13.8
6.9
13.8
13.8
4.2
8
4.1
7.9
79
5?
10.3
10.3
5.2
10.3
10.3
3.1
10
3.3
6.3
63
4.1
8.3
8.3
4.1
8.3
8.3
2.5
12
2.8
5.3
53
34
6.9
6.9
3.4
6.9
6.9
2.1
14
2.4
4.5
45
?9
5.9
5.9
2.9
5.9
5.9
1.8
16
2.1
4.0
40
?fi
5.2
5.2
2.6
5.2
5.2
1.6
18
1.8
3.5
35
?3
4.6
4.6
2.3
4.6
4.6
1.4
20
1.7
3.2
3?
?1
4.1
4.1
2.1
4.1
4.1
1.3
22
1.5
2.9
?9
1 9
3.8
3.8
1.9
•3.8
3.8
1.1
24
1.4
2.6
?fi
1 7
3.4
3.4
1.7
3.4
3.4
1.0
26
1.3
2.4
?4
1 fi
3.2
3.2
1.6
3.2
3.2
1.0
28
1.2
2.3
?3
1 5
2.9
2.9
1.5
2.9
2.9
0.9
* These mph estimates assume 100 percent efficiency. The steady-state speeds would be sllghty higher than
shown here when accounting for turns and overlap.
outside a. reasonable range were obtained, the utility of using these
data might be questioned. Using wheat as an example, the equipment
speeds have been calculated as a function of implement width. This
calculation is summarized in Table 10-5. As seen, the speeds range from
2 to 8 mph for tractors pulling implements between 10 and 20 feet in
width (assuming 100 percent efficiency). The steady-state speeds would
be slightly higher than shown in the table when accounting for turns and
overlap. These speeds are within values expected for farm operations
and correspond to speeds listed by Caterpillar for its line of general-
purpose agricultural tractors.3
10-6
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See Disclaimer on Cover
Although detailed production budgets are available from the Cooperative
Extension in California, it was unclear if similar information existed
in other states. Availability of this information from states (or
areas) throughout the U.S. is crucial to the development of a
generalized bottom-up methodology to estimate agricultural equipment
activity. Thus, considerable effort was expended to determine the
extent to which information on crop operations exists on a national
basis. -Fortunately, the information appears to be readily available,
and production budgets specific to the mid-atlantic region were obtained
and used to develop estimates of agricultural activity in the DC/MD/VA
area, in addition to the estimates developed for the SJV. A discussion
of those estimates follows.
Methodology and Data Sources
Estimation of agricultural activity (in Bhp-hr) can be represented by
the following equation:
Activityjjk = Acreage^ x Hours/Acrejk X HPkl X Load Factor,
where:
i = County
j = Crop
k = Operation
1 - Equipment Type.
Thus, information on acreage by county and crop, hours/acre by crop and
operation, horsepower by operation and equipment type, and load factor
by equipment type is needed to complete the calculation. This equation
can be further disaggregated to include fuel type and temporal activity
for a more detailed estimate of activity.
Acreage by Crop Type - The number of acres under cultivation by county
and crop type is generally available from a state's Cooperative
Extension or Department of Agriculture. For the DC/MD/VA area, acreage
data were obtained from "Maryland Agricultural Statistics, Summary for
1990"4 and "Virginia Agricultural Statistics 1990."5 For the SJV,
acreage data were compiled from the "1990 Agricultural Commissioners'
Data."6 These documents are published annually and contain acreage data
by county for major crop types. (The California document was more
detailed than those published by Maryland and Virginia; however,
anticipated funding cuts may result in reporting of only major crops in
the future.) Additionally, the Virginia document contained information
on planting and harvesting times, which proved valuable in estimating
temporal activity. It is believed that this information is reasonably
reliable because of the close contact and cooperation between local
farmers and farm advisors.
Crop Operations - As discussed above, the availability of production
budgets was critical in developing a bottom-up methodology for
agricultural operations. For the DC/MD/VA region, several state
10-7
-------
- - - See Disclaimer on Cover
Cooperative Extension services were contacted for this information.
Although Maryland did not publish production budgets, it was their
opinion that any information obtained from Virginia, North Carolina,
Pennsylvania, and New Jersey would be generally applicable to the entire
mid-atlantic region.7 Thus, Cooperative Extensions and Departments of
Agriculture were contacted in those states with some success. For
example, the Virginia Cooperative Extension publishes the "Virginia Farm
Management Crop and Livestock Budgets,"8 and the Pennsylvania State
University College of Agriculture publishes the "Farm Management
Handbook."9 Both documents contain production budgets for the major
crops grown in these states. Although not in a single document, the
North Carolina Cooperative Extension Service was able to provide
production budgets for selected crops. Overall, the production budgets
for the various states were reasonably similar, as shown in Table 10-6
for corn. However, production budgets from Virginia were the primary
source of information in developing activity estimates for the DC/MD/VA
area. The budgets used in this analysis were compiled into spreadsheets
for 12 separate crops and are contained in Appendix H.
For the SJV, production budgets for individual crops (or several similar
crops) were obtained through the University of California Cooperative
Extension and by contacting individual county Farm Advisors. For
California as a whole, over 100 production budgets were identified.
These were compiled into spreadsheets for 34 crop types. Because of the
agricultural diversity in the SJV, it was impossible to develop (or
obtain) production budgets for each individual crop. Thus, several
specific crops were consolidated into crop types for the purpose of
calculation. The production budgets and a summary of crops included in
each crop type for the SJV are included as Appendix I.
Although it appears that production budgets are available for most areas
of the U.S., there could be cases in which not all crops are covered, or
the information is incomplete. Thus, some effort was devoted to
identifying a generic source of information related to machinery usage
for various field operations. For areas in which detailed production
budgets are not readily available, Doane's Agricultural Report10 lists
data on agricultural machinery usage, including tractor horsepower
requirements, acres per hour, and fuel usage per hour for a variety of
field operations. The operations included in this report are: plowing,
chiseling, discing, planting, cultivating, and harvesting. Thus, if a
local air pollution control agency can obtain information on the
operations required for each crop (e.g., through a survey of local
cooperatives), an estimate can be made regarding equipment usage.
Temporal Activity - For the SJV, the temporal patterns outlined in the
production budgets were used to establish monthly activity patterns.
However, temporal activity patterns were not presented in the crop
budgets prepared by Virginia. Because utilization of the Virginia
budgets was desired, Virginia's temporal patterns had to be estimated.
This was achieved as follows. "Virginia Agricultural Statistics 1990"3
contained detailed information on planting and harvesting times for
specific crops. .These data were coupled with the production budgets
from North Carolina and Pennsylvania which contain temporal activity
information for each operation. By overlaying the planting and
harvesting operations outlined in the North Carolina and Pennsylvania
10-8
-------
See Disclaimer on Cover
Table 10-6
Com Production Budgets for DC/MD/VA Area
Conventional Tillage
Com; ProtiuctionSVirainia
Operation/
Implement
Bush Hog
Plow
Disc
Harrow
Plant
Sprayer
Harvest
Equipment
AQ Tractor
AG Tractor
AQ Tractor
AG Tractor
AG Tractor
AG Tractor
Combine
Horse -
Power
110
110
110
50
70
50
152
Total
Hours/
Acre
0.17
0.44
0.15
0.20
0.31
0.11
0.53
1.91
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
0.7
Bhp-hr/
Acre
13.1
33.9
11.6
7.0
15.2
3.9
56.4
141.0
Diesel
Gal/Acre
0.73
1.90
0.65
0.39
0.85
0.22
3.16
7.89
Com ProductibniSPennsVlvanla
Operation/
Implement
Plow
Cultivate
Herbicide
Plant
Fertilize
Harvest
Haul
Equipment
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
Combine
AG Tractor
Horse-
Power
150
120
95
150
95
152
120
Total
Hours/
Acre
0.25
0.52
0.13
0.18
0.13
0.33
0.12
1.66
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
0.7
Bhp-hr/
Acre
26.3
43.7
8.6
18.9
8.6
35.1
10.1
151.3
Diesel
Gal/Acre
1.47
2.45
0.48
1.06
0.48
1.97
0.56
8.47
Com; PrckiuctibnMsNbrtttiiGaroliha
Operation/
Implement
Bush Hog
Tandom Disc
Ripper- Bedder
Plant
Sprayer
Roll Cultivator
Harvest
Equipment
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
Combine
Horse-
Power
45
130
130
70
45
70
152
Total
Hours/
Acre
0.33
0.18
0.22
0.37
0.44
0.28
0.40
2.22
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
0.7
Bhp-hr/
Acre
10.4
16.4
20.0
18.1
13.9
13.7
42.6
135.1
Diesel
Gal/Acre
0.58
0.92
1.12
1.02
0.78
0.77
2.38
7.56
production budgets on the established planting and harvesting times for
Virginia, the timing of the remaining Virginia operations was estimated.
For example, 25 percent of Virginia's corn is planted in April. Because
information from Pennsylvania suggests that ground preparation (e.g.,
plowing, discing) is performed one month prior to planting, 25 percent
of the ground preparation related to corn production in Virginia was
assumed to occur in March.
10-9
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See Disclaimer on Cover
Load Factors - Load factors are an important part of the activity
calculation and have a direct bearing on the ultimate outcome of any
estimates. In actual operation, load factors likely vary as a function
of the type of operation the tractor is performing (e.g., plowing versus
spraying), horsepower rating for a particular operation, soil
conditions, etc. Unfortunately, however, there are little data relating
load factor to these types of variables. Thus, an average load factor
is generally applied in activity estimates. This approach was followed
here, and the load factors from NEVES were used throughout.
Fuel Usage — Although not required for the calculational methodology
outlined above, fuel usage is another parameter that can be used as the
basis of an equipment activity or emissions estimate, and several means
to estimate fuel usage (by crop type) were identified. First, Table
10-6 contains an estimate which was determined by simply assuming all
equipment was Diesel-fueled, and applying a conversion factor of 0.056
gal/bhp-hr11 to the bhp-hr/acre activity estimates. Second, information
related to fuel use is typically contained in the crop production
budgets (although it is generally listed in terms of $/acre rather than
gallons/acre). Finally, Doane's Agricultural Report10 is a source of
fuel use by operation.
A comparison of the three approaches outlined above, based on corn
production in Virginia, is contained in Table 10-7. As seen in the
table, the estimates are highly variable, particularly the value
determined from the cost data in the production budget. However,
comparing the values from this work (i.e., Table 10-6) and those
calculated from the Doane's data gives reasonable agreement. Further,
the Doane's information would be expected to give somewhat optimistic
values because the data are based on new machinery.
Table 10-7
Comparison of Diesel Fuel Usage Estimates for Com Production
Com Productions Virginia
Operation/
Implement
Bush Hog
Plow
Disc
Harrow
Plant
Sprayer
Harvest
Equipment
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
Combine
Horse-
Power
110
110
110
50
70
50
152
Total
Fable 10-6
(Gal/Acre)^
0.73
1.90
0.65
0.39
0.85
0.22
3.16
7.89
VA Production Budget*
($/Acre)
$1.20
$3.04
$1.05
$0.53
$1.34
$0.29
$3.43
(Gal/Acre)
1.41
3.58
1.24
0.62
1.58
0.34
4.04
12.80
Doane's**
(Gal/Acre)
0.70
1.82
0.55
0.40
0.60
0.15
ZOO
6.22
* Cost of Diesel fuel was assumed to be $0.85 per gallon based on 1992 Doane's Report
** 'Bush Hog* fuel use was not reported in Doane's. The value listed was calculated as 40 percent
of the 'Plow* value (based on the difference reported in the Virginia crop budget for com).
10-10
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See Disclaimer on Cover
Gasoline Versus Diesel Fraction - Although the focus of this study is on
equipment usage, another parameter that enters into an emissions
calculation is the type of fuel used in the equipment. Estimates of the
gasoline versus Diesel fraction for agricultural equipment exist, but
the numbers cited in various studies often do not agree. For example,
NEVES estimates the U.S. agricultural tractor population to consist of
over 2.5 million Diesel machines and only 5,800 gasoline machines. On
the other hand, a 1988 EEA report for GARB12 contains estimates of
29,193 Diesel and 14,617 gasoline agricultural tractors for northern
California in 1990.
In speaking with county Farm Advisors, industry representatives, and
other experts, it became clear that a large number of gasoline tractors
are still being used on farms even though the vast majority of new
tractors sold since the late-1960s and early-1970s have been Diesel-
fueled. It is also our understanding, however, that while there is a
significant population of gasoline tractors, the annual hourly usage is
much lower when compared to Diesel—fueled machines. This view has been
substantiated through an analysis of data from a survey of tractor use
on California farms, which is described below.
The Department of Agricultural Engineering at the University of
California, Davis recently conducted a survey of California farms for
the 1990 crop year to determine tractor usage patterns13. Although the
primary focus of the survey was on safety issues, data collected can be
used to estimate the fraction of gasoline- versus Diesel-fueled tractors
in California. As part of the survey, participants were asked to list
tractor age and annual hourly field use. The results are summarized in
Table 10-8. From this information, it was possible to estimate the
annual hours for each age group by combining the midpoint of the annual
hourly usage category with the number of tractors in each bin. This
calculation indicates that older tractors are, in fact, used far less
than newer tractors, with the annual hourly usage of machines 30 years
old being one-third that of machines under five years of age. The usage
versus age distribution is also shown graphically in Figure 10-1.
Although the survey did not differentiate between gasoline and Diesel
tractors, an estimate can be made by combining information contained in
the survey with data published in the 1973 SwRI emissions study14. In
that work, the market penetration of Diesel farm tractors was given for
the 1950 to 1971 timeframe. The Diesel fraction remained below 20
percent until 1958, grew to 40 percent in 1960, 60 percent in 1965, and
approached 80 percent in 1970. Based on this information, the following
Diesel fractions were applied to the age groupings given in Table 10-8.
0-19 Years — 100 percent Diesel penetration was assumed. Although
SwRI estimated the Diesel fraction at 75 percent for
1971, there are no data beyond that point. According
to industry organizations, however, the majority of
agricultural tractors were Diesel-fueled beyond 197015.
20-24 Years - 70 percent Diesel penetration was assumed based on
interpolating the 1965 and 1970 Diesel penetration
rates.
10-11
-------
See Disclaimer on Cover
Table 10-8
Tractor Age Versus Annual Hourly Usage for California Farms
Tractor
Age
<1
1-4
5-9
10-14
15-19
20-24
25-29
>30
Total
Number of Tractors in Usage Category
<104 hrs
0
34
41
61
62
86
32
230
546
104-519
hrs
0
89
114
172
104
82
47
114
722
520-1999
hrs
0
61
59
72
40
29
11
13
285
>2000
hrs
0
3
3
22
2
1
1
2
34
Total
Tractors
0
187
217
327
208-
198
91
359
1587
Annual
Hours
n/a
601
544
586
433
346
354
189
429
700
600
JO 500
3
o
15400
c
< 300
200
100
1-4
Figure 10-1
Annual Hourly Usage vs. Tractor Age
1990 California Tractor Survey
5-9
10-14 15-19 20-24
Tractor Age (Years)
25-29
30+
10-12
-------
See Disclaimer on Cover
25-29 Years - 50 percent Diesel penetration was assumed based on
interpolating the 1960 and 1965 Diesel penetration
rates.
30+ Years - 10 percent Diesel penetration was assumed. This figure
was arrived at by distributing the 30+ tractor
population equally among four age groups: 1940-1945,
1945-1950, 1950-1955, and 1955-1960. Based on the SwRI
data, 25 percent Diesel penetration was assumed for the
1955-1960 group, and 10 percent for the 1950-1955 group;
the remaining groups were assumed to be entirely
gasoline-fueled.
These percentages were applied to the data in Table 10-8 to arrive at an
hourly weighted gasoline/Diesel split in the field. This calculation is
summarized in Table 10-9. As seen in the table, the above methodology
results in a Diesel usage fraction of 86 percent for California
tractors.
Although the above analysis is specific to California, it points out
that gasoline tractor use is not insignificant. Thus, a similar
calculation should be carried out on a national basis, provided
information on tractor age distribution can be obtained.
Table 10-9
Gasoline Versus Diesel Fraction for California Tractors
Tractor
Age
<1
1-4
5-9
10-14
15-19
20-24
25-29
>30
Total
Number of Tractors
Diesel
0
187
217
327
208
139
46
36
1159
Gasoline
0
0
0
0
0
59
45
.323
427
Annual
Hours*
n/a
601
544
586
433
346
354
189
n/a
Percent Usage
Diesel
0
16.5
17.3
28.2
13.2
7.1
2.4
1.0
85.7
Gasoline
0
0
0
0
0
3.0
2.3
9.0
14.3
Annual hours were assumed to be the same for gasoline and Diesel
within each age group; however, the overall average would be higher
for Diesel because of the greater percentage of newer tractors.
10-13
-------
See Disclaimer on Cover
Sample Calculations
The bottom-up methodology discussed above was applied to the DC/MD/VA
and SJV areas, and a summary of the results is given in Tables 10-10 and
10—11, respectively. These tables list the number of acres by crop type
in each area, the relative monthly activity (on a Bhp-hr/acre basis and
1000 Bhp-hr), total Bhp-hr for each crop (per acre and total for crop),
and Diesel fuel usage (per acre and total for crop). The Diesel
estimates were arrived at by assuming all equipment was Diesel-fueled*
and applying the conversion of 0.056 gal/Bhp-hr.'' Totals (and totals
by month) are also included in the tables for each area.
It is interesting to compare the temporal activity patterns for the two
areas under study, which are shown in Figures 10-2 and 10-3 for DC/MD/VA
and the SJV, respectively. As expected, the most activity is observed
from late-spring to early fall, with relatively more activity in the
winter months in the SJV. The DC/MD/VA area experiences its highest
activity in September, primarily the result of corn harvesting and
continued hay production. The large spike in activity for the SJV in
November can be attributed to harvest and post-harvest operations
associated with cotton production.
It is also of interest to compare the results of this bottom-up
methodology to those obtained in NEVES as shown in Table 10-12. NEVES
has predicted almost four times higher activity for the DC/MD/VA area,
while the results for the SJV are surprisingly close. One possible
explanation for this large difference in area-specific estimates relates
back to the issue of crop-specific differences in equipment
requirements. In Sierra's previous effort to develop agricultural
emissions estimates for the SJV,2 it was noted that farming practices in
the eight-county SJV were much more machine-intensive than the remaining
counties in California. It is probably also true that farming in the
SJV is more machine-intensive than most other areas of the United
States. Thus, close agreement between NEVES and a bottom-up methodology
for the SJV may be indicative of overestimates of equipment population
and usage for the remainder of the U.S. Certainly the results for the
DC/MD/VA area indicate that local conditions play an important factor in
determining local activity.
Additional Considerations
Although the analysis above is fairly complete, a number of additional
variables could be included to improve its accuracy. For example,
Extension specialists in the DC/MD/VA area indicated that no-till
practices are popular among local farmers for corn and soybean
Although an estimate of gasoline versus Diesel equipment usage is
presented above, the sample calculation prepared here assumed 100
percent Diesel. This was done to be on the same basis as NEVES, which
allowed for a more consistent comparison between methodologies.
10-14
-------
Table 10-10
Summary of Bottom-Up Agricultural Activity Estimate
DC/MD/VA
o
Crop
Com (Grain)
Com (Silage)
Wheat
Soybean
Barley
Tobacco
Oats
Alfalfa Establishment
AHaHa Production
Hay Establishment
Hay Production
Fresh Vegetables
[Total DC/VA/MO
Acres
92.000
34.300
36.500
55.000
11.000
4.000
2.800
2.750
11.010
16.730
100.410
2.777
369.277
" *
Bhp-hr/acre:
1000 Bhp-hr:
Bhp-hr/acre:
1000 Bhp-hr:
Bhp-hr/acre:
1000 Bhp-hr:
Bhp-hr/acre:
1000 Bhp-hr:
Bhp-hr/acre:
1000 Bhp-.hr:
Bhp-hr/acre:
1000 Bhp-hr:
Bhp-hr/acre:
1000 Bhp-hr:
Bhp-hr/acre:
1000 Bhp-hr:
Bhp-hr/acre:
1000 Bhp-hr:
Bhp-hr/acre:
1000 Bhp-hr:
Bhp-hr/acre:
1000 Bhp-hr:
Bhp-hr/acre:
1000 Bhp-hr:
1000 Bhp-hr:
DC/MD/VA Crop SummarY
Relative Monthly Activity
Dec Jan Feb Mar Apr May Jun Jul Aua Sep Oct Nov
0.0 0.0 0.0 14.6 37.7 24.9 6.6 0.8 0.0 22.6 19.7 14.1
000 1.346 3.471 2,288 603 71 0 2.075 1,816 1.297
0.0 0.0 0.0 11.4 30.5 22.3 6.6 0.8 19.1 66.9 9.6 0.0
0 0 0 390 1.047 763 225 26 655 2.294 328 0
0.0 0.0 1.9 1.9 0.0 0.0 13.8 13.8 0.0 0.0 25.1 25.1
0 0 70 70 0 0 505 505 0 0 916 916
16.9 0.0 0.0 0.0 13.6 27.1 22.5 7.3 1.0 0.0 11.3 28.2
930 0 0 0 750 1.491 1.237 400 S3 0 620 1.551
0.0 0.0 1.9 1.9 0.0 0.0 24.9 2.8 0.0 12.5 25.1 12.5
0 0 21 21 0 0 274 30 0 138 276 138
0.0 0.0 19.8 19.8 10.7 55.2 57.4 24.6 22.2 28.8 26.8 8.5
0 0 79 79 43 221 230 98 89 115 107 34
0.0 0.0 1.9 1.9 0.0 0.0 24.9 21.1 18.4 12.5 25.1 12.5
0 0 5 5 0 0 70 59 51 35 70 35
0.0 0.0 28.2 44.3 16.1 1.0 42.2 42.2 42.2 41.2 0.0 0.0
0 0 77 122 44 3 116 116 116 113 0 0
0.0 0.0 0.0 1.3 1.3 57.7 59.0 59.0 59.0 59.0 41.2 0.0
0 0 0 14 14 635 649 649 649 649 454 0
0.0 0.0 28.2 41.0 12.8 0.0 0.0 24.3 24.3 24.3 0.0 0.0
0 0 471 666 214 0 0 406 406 406 0 0
0.0 0.0 0.0 0.0 0.0 0.0 35.2 35.2 35.2 35.2 0.0 0.0
000000 3.532 3.532 3.532 3.532 0 0
0.0 11.4 11.4 16.9 18.4 7.1 7.1 1.S 1.5 0.0 0.0 0.0
0 32 32 47 51 20 20 4 4 0 0 0
930 32 757 2.780 5.635 5.420 7.460 5.898 5.556 9.358 4.587 3.971
Bhp-hr
Total
141.0
12,968
167.0
5.729
81.7
2.982
127.9
7,032
81.7
899
273.9
1.096
118.5
332
257.3
707
337.3
3.713
154.8
2.589
140.7
14.128
75.3
209
52,364
Diesel
Total*
7.89
726
9.35
321
4.58
167
7.16
394
4.58
50
15.34
61
6.63
19
14.41
40
18.89
208
8.67
145
7.88
791
4.22
12
2.933
Units: Gallons/Acre and 1000 Gallons, respectively.
-------
Table 10-11
Summary of Bottom-Up Agricultural Activity Estimate
San Joaquin Valley
Crop
AHalfa
Almond*
ApricoU/Cherrie*
Asparagus
Bean*
Citru*
Clover Seed
Field Com
Cotton
Orape
Kiwi
Lettuce
Melon
Oat Hay
Olive
Onion
Irrigated Pasture
Peach
Pear/Apple
Pistachio
Acrea
603,477
327.97S
21.805
20.161
129.981
163.294
22.874
269.247
1,217.135
507.736
3.628
25.236
73.035
126,975
19.996
29.394
211.600
81,077
12.250
48.722
li( San Joaquin Valtev 'Crop Summarv
Bhp-hr/Acre:
1000 Bhp-hr.
Bhp-hr/Acre:
1000 Bhp-hr:
Bhp-hr/Acre:
1000 Bhp-hr:
Bhp-hr/Acre:
1000 Bhp-hr:
Bhp-hr/Acre:
1000 Bhp-hr:
Bhp-hr/Aore:
1000 Bhp-hr:
Bhp-hr/Acre:
1000 Bhp-hr:
Bhp-hr/Acre:
1000 Bhp-hr:
Bhp-hr/Acre:
1000 Bhp-hr:
Bhp -hr/Acre:
1000 Bhp-hr:
Bhp-hr/Acre:
1000 Bhp-hr:
Bhp-hr/Acre:
1000 Bhp-hr:
Bhp-hr/Acre:
1000 Bhp-hr:
Bhp-hr/Acre:
1000 Bhp-hr:
Bhp-hr/Acre:
1000 Bhp-hr:
Bhp-hr/Acre:
1000 Bhp-hr:
Bhp-hr/Acre:
1000 Bhp-hr:
Bhp-hr/Acre:
1000 Bhp-hr:
Bhp-hr/Acre:
1000 Bhp-hr:
Bhp-hr/Acre:
1000 Bhp-hr:
Relative Monthly Activity
Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov
0.0 0.0 0.0 0.0 27.0 27.0 27.0 27.0 45.2 41.0 35.6 0.0
0000 16.288 16,288 16,288 16,288 27.257 24.737 21.470 0
0.0 63.4 13.0 15.9 4.2 15.9 8.4 24.7 49.0 2.1 8.4 0.0
0 20.777 4.247 S.223 1.377 5.223 2.755 8,093 16.063 689 2.755 0
54.4 54.4 20.3 54.8 36.6 54.8 98.3 80.1 3S.9 7.7 7.7 7.7
1.187 1,187 443 1.194 798 1.194 2,144 1,747 782 168 168 168
62.7 0.0 0.0 9.5 9.5 14.8 5.3 0.0 0.0 4.9 67.6 67.6
1.263 0 0 192 192 299 107 0 0 99 1.363 1.363
0.0 59.5 59.5 85.6 31.5 19.3 14.0 14.0 3.5 3.5 3.5 0.0
0 7.734 7.734 11.146 4.094 2.502 1.820 1.820 455 455 455 0
0.0 0.0 0.0 30.8 15.2 6.0 16.9 27.0 20.4 23.8 0.0 0.0
000 5.029 2.477 972 2.762 4.401 3.334 3,886 0 0
3.8 2.0 8.8 2.8 2.0 2.0 2.9 2.9 1.0 13.8 16.0 15.7
88 45 202 63 45 45 67 67 22 315 366 360
29.4 0.0 0.0 112.1 39.2 7.3 0.0 0.0 0.0 0.0 0.0 13.7
7.916 0 0 30.175 10.554 1.960 00000 3.675
0.0 38.1 24.5 0.0 49.9 38.7 43.6 39.2 0.0 1.4 0.0 134.9
0 46.349 29.820 0 60.776 47.144 53.108 47.712 0 1.704 0 164.143
0.0 0.0 0.0 21.0 126.0 126.0 126.0 126.0 15.8 26.3 10.5 0.0
000 10.662 63.975 63.975 63,975 63.975 7.997 13.328 S.331 0
0.0 38.5 74.7 17.5 36.2 24.5 24.5 24.5 0.0 0.0 0.0 11.7
0 140 271 63 131 89 89 89 0 0 0 42
4.7 25.0 26.6 85.0 92.2 127.2 25.0 26.6 95.0 144.7 74.7 4.7
118 630 672 2.397 2.326 3.209 630 672 2.397 3.651 1.884 118
97.1 112.9 42.0 B9.0 73.3 62.9 0.0 0.0 0.0 0.0 0.0 0.0
7.094 8.244 3.067 6.503 5.353 4.594 000000
28.2 0.0 2.0 0.9 0.0 0.0 15.9 0.0 0.0 0.0 0.0 77.0
3.582 0 258 116 0 0 2.022 0000 9.777
' 0.0. 0.0 0.0 0.0 7.7 0.0 0.0 7.7 0.0 0.0 259.7 0.0
0 0 0 0 154 0 0 154 0 0 5.193 0
0.0 0.0 0.0 171.1 171.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0 5,029 5,029 0000000
0.0 12.9 12.9 0.0 0.0 0.0 0.0 25.7 0.0 0.0 0.0 0.0
0 2.722 2.722 0000 5.443 0000
0.0 41.0 38.7 18.0 11.4 42.3 44.7 17.4 15.9 52.3 21.0 20.0
0 3.320 3.136 1.461 922 3.429 3.628 1.414 1.291 4.242 1.703 1.617
12.6 12.6 9.0 38.0 25.5 25.5 38.1 46.9 27.3 0.0 24.1 0.0
154 154 110 466 313 313 467 575 334 0 295 0
0.0 22.6 0.0 22.8 0.0 45.5 0.0 0.0 113.8 183.6 0.0 68.3
0 1.108 0 1.108 0 2.217 0 0 5.542 8.945 0 3.325
Bhp-hr
Total
229.7
138,615
204.9
67,202
512.7
11.179
241.9
4.876
294.0
38.214
140.0
22.661
73.6
1,684
201.6
54.280
370.3
450.754
577.5
293.218
252.0
914
741.2
18.704
477.2
34,854
124.1
15,754
275.1
5.501
342.2
10,057
51.5
10.887
322.7
26.164
259.7
3,181
456.6
22.246
Diesel
Total*
12.66
7,762
11.5
3.763
28.7
626
8.47
171
16.46
2.140
7.84
1.280
4.12
94
11.29
3.040
20.74
25.242
32.34
16.420
14.11
51
41.50
1.047
26.72
1.952
6.95
682
15.41
308
19.16
563
2.88
610
16.07
1.465
14.54
178
25.57
1.246
Unit*: Gallons/Aero and 1000 Gallon*, respectively.
-------
Table 10-11, Continued
o
i
San Joaquin Valley Crop Summary
Crop
Plum
Potato
Prune
Rica
Saffiower
Silage (Com)
Squa*h
Sugarbeet
Sunflower Seed Total
Tomato Total
Vegetable* (Seasonal)
Vegetable* (Year -Long)
Walnut
Wheat
Total
Acre*
40.641
34.371
8.023
19.464
57.503
150.346
4.280
82.634
2.020
189.269
166,232
12.084
95.013
427.795
5.205.273
Bhp-hr/Acre:
1000 Bhp-hr:
Bhp-hr/Acre:
1000 Bhp-hr:
Bhp-hr/Acre:
1000 Bhp-hr:
Bhp-hr/Acre:
1000 Bhp-hr:
Bhp-hr/Acre:
1000 Bhp-hr:
Bhp-hr/Acre:
1000 Bhp-hr:
Bhp-hr/Acre:
1000 Bhp-hr:
Bhp-hr/Acre:
1000 Bhp-hr:
Bhp -hr/Acre:
1000 Bhp-hr:
Bhp-hr/Acre:
1000 Bhp-hr:
Bhp-hr/Acre:
1000 Bhp-hr:
Bhp-hr/Acre:
1000 Bhp-hr:
Bhp-hr/Acre:
1000 Bhp-hr:
Bhp-hr/Acre:
1000 Bhp-hr:
1000 Bho-hr:
Relative M on thl y Activity
Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov
0.0 146.1 4.1 29.9 4.1 58.7 187.7 87.4 4.1 32.8 33.0 4.1
0 5.938 168 1.216 168 2.387 7.630 3.552 168 1.333 1,339 168
13.5 13.5 13.5 13.5 174.9 174.9 174.9 0.0 0.0 13.5 13.5 13.5
464 464 464 464 6.013 6.013 6.013 0 0 464 464 464
11.6 46.2 37.2 85.6 12.8 36.9 35.6 6.4 220.5 0.0 0.0 0.0
93 371 298 687 103 296 286 51 1,769 000
0.0 0.0 0.0 15.4 44.8 60.2 7.5 0.0 0.0 39.2 39.2 0.0
0 0 0 300 872 1.172 145 0 0 762 762 0
15.0 41.6 50.2 8.6 8.6 0.0 0.0 0.0 19.7 37.3 17.6 17.6
860 2.394 2.889 495 495 0 0 0 1.132 2.146 1.014 1.014
0.0 0.0 0.0 0.0 0.0 0.0 110.7 31.2 0.0 0.0 3.6 0.0
000000 16.639 4.683 0 0 547 0
0.0 0.0 52.5 166.3 245.0 192.5 131.3 52.5 52.5 0.0 0.0 0.0
0 0 225 712 1.049 824 562 225 225 0 0 0
1 44.0 65.3 70.5 31.8 33.0 33.0 23.0 22.5 0.0 0.0 0.0 0.0
3.636 5.398 5.827 2.629 2,729 2.729 1.900 1.861 0000
0.0 0.0 0.0 33.6 27.7 17.6 0.0 0.0 13.9 13.9 109.9 16.8
0 0 0685636 0 0282822234
0.0 0.0 15.5 45.3 45.3 45.3 88.6 88.6 105.3 105.3 46.4 46.4
0 0 2.938 8.566 8.568 8.566 16.777 16.777 19.936 19.936 8.787 8.787
0.0 44.2 44.2 44.2 44.2 44.2 44.2 0.0 0.0 0.0 0.0 0.0
0 7.350 7.350 7.350 7,350 7.350 7,350 00000
22.1 22.1 22.1 22.1 22.1 22.1 22.1 22.1 22.1 22.1 22.1 22.1
267 267 267 267 267 267 267 267 267 267 267 267
0.0 46.2 30.8 3.9 53.9 42.4 42.4 15.4 42.4 11.6 11.6 0.0
0 4.390 2.926 366 5,121 4.024 4,024 1.463 4.024 1.097 1.097 0
0.0 0.0 0.0 8.8 0.0 0.0 15.0 0.0 0.0 0.0 75.8 44.8
000 3.743 0 0 6.423 000 32.416 19.165
26.721 118.980 76.034 107.690 207.592 187.114 217.877 181.330 93.023 88.253 87.899 214.487
Bhp-hr
Total
592.2
24,067
619.3
21.288
492.8
3,953
206.2
4.013
216.3
12,438
145.5
21.869
692.5
3.820
323.2
26.709
233.4
471
632.1
119.637
265.3
44,101
265.3
3,206
300.3
28.532
144.3
61.748
1.607.000
Die»el
Total*
33.16
1.348
34.68
1.192
27.59
221
11.55
225
12.11
697
8.15
1.225
49.98
214
18.10
1.496
13.07
26
35.40
6.700
14.86
2.470
14.86
180
16.82
1,598
8.08
3.458
89.890
' Unita: Gallons/Acre and 1000 Gallon*, respectively.
-------
12,000
10,000
£ 8,000
dj.
CD 6,000
4,000
2,000
See Disclaimer on Cover
Rgure10-2
Agricultural Temporal Activity Distribution
DC/MD/VA
830
9.358
7,400
5,688
2,780
757
6,420
S,SS8
5,560
4.587
8,971
Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov
Month
200,000 -
150,000 -
CD
100,000 -
50,000 -
Rgure10-3
Agricultural Temporal Activity Distribution
San Joaquln Valley
-
-
-
-
26,271
118,980
76,034
107,690
207,503
187,114
217,871
101,930
93,023
88,253
87,899
214,487
Dec Jan Feb Mar Apr May Jun
Month
Jut Aug Sep Oct Nov
10-18
-------
See Disclaimer on Cover
Table 10-12
Comparison of Top-Down (NEVES Inventory A) and Bottom-Up (This Study)
Agricultural Activity Estimates for the DC/MD/VA and SJV Areas
(1000 Bhp-hr)
Area
DC/MD/VA
SJV
NEVES
200,700
1,606,000
This Study
52,400
1,607,000
production (i.e., seed is planted directly into the stubble from the
previous year). Developing an estimate of the percentage of no-till
operations and including this in the analysis would reduce the overall
activity in the area.
The analysis above focused on activity associated with field crops
because that accounts for the majority of equipment usage. However,
there is some nonroad equipment used in livestock operations (e.g.,
moving hay bales, transporting animal wastes), and accounting for this
would also increase the accuracy of the activity estimates. However,
detailed data on fuel or equipment usage for livestock operations were
not identified. General information is contained in the livestock
production budgets prepared by Pennsylvania State University9 which list
gasoline, fuel, and oil costs (combined) for livestock operations (e.g.,
dairy cows). However, these cost estimates are not detailed enough to
develop equipment usage estimates, and since the fuel costs comprise
only 0.5 percent of the overall dairy cow budget, the accuracy of these
estimates is uncertain.
Recommendations
The results of this analysis indicate that developing a bottom-up
methodology for estimating agricultural equipment usage deserves serious
consideration. It appears that information relating to cropping
practices exists and is available through state Cooperative Extension
services and Departments of Agriculture. If Bhp-hr/acre estimates were
developed by crop and region, it would be reasonably easy for local air
quality planners to determine the corresponding acreage and crop
distributions for the counties of interest.
References for Section 10
1. "1987 Census of Agriculture," U.S. Department of Commerce, 1989.
2. "SJVAQS/AUSPEX Agricultural Emissions Inventory (Draft)," Sierra
Research, November 1992.
.10-19
-------
See Disclaimer on Cover
3. "Caterpillar Performance Handbook, Edition 20," Caterpillar, Inc.
October 1989.
4. "Maryland Agricultural Statistics, Summary for 1990," Maryland
Department of Agriculture, 1991.
5. "Virginia Agricultural Statistics 1990," Virginia Agricultural
Statistics Service and Department of Agriculture and Consumer
Services, September 1991.
6. "1990 Agricultural Commissioners' Data," California Agricultural
Statistics Service and California Department of Agriculture, 1991.
7. Jim Hanson, University of Maryland Cooperative Extension, Personal
Communication, July 1992.
8. "Virginia Farm Management Crop and Livestock Enterprise Budgets,"
Virginia Cooperative Extension, 1991.
9. "The Farm Management Handbook," Penn State College of Agriculture,
1991.
10. "Doane's Agricultural Report," April 1992.
11. "Nonroad Emission Factors," Southwest Research Institute, February
1991.
12. "Feasibility of Controlling Emissions from Off-Road, Heavy-Duty
Construction Equipment," Energy and Environmental Analysis,
December 1988.
13. "Tractor Horsepower Versus Age," Unpublished Data, W. Steinke and
J. Knutson, Agricultural Engineering Extension, University of
California, Davis, 1992.
14. "Exhaust Emissions from Uncontrolled Vehicles and Related Equipment
Using Internal Combustion Engines. Part 5: Farm, Construction, and
Industrial Engines," Southwest Research Institute, October 1973.
15. Personal Communication. John Gifford, Deere & Company, April 1992.
10-20
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See Disclaimer on Cover
11. LOGGING EQUIPMENT
Specific nonroad equipment types included in the logging equipment
category are larger chainsaws and shredders (>4 and >5 HP,
respectively), skidders, and fellers/bunchers. This category represents
a very minor portion of the nonroad equipment inventory in nonattainment
areas, primarily because logging activities are generally not conducted
within metropolitan areas. In fact, only 5 of the 24 nonattainment
areas considered in NEVES had any logging activity attributed to them.
Thus, an alternative methodology developed for this equipment category
must be easily applied to justify its use.
NEVES Methodology
NEVES again utilized PSR data on population and annual hourly usage to
develop activity estimates for logging equipment. However, the state-
level PSR data upon which a regression analysis would be based appeared
flawed, according to EEA. This conclusion was arrived at after the most
obvious indicators of logging activity (e.g., SIC 241 - Logging
Establishments) failed to meet the statistical criteria established by
EEA for an acceptable model. Thus, national-level equipment populations
were scaled to the local level by simply applying the county to national
ratio of SIC 241 Employees. Number of employees was used as the
indicator because it was felt that this was a better representation of
activity than number of establishments. (Note that data on logging
equipment were not supplied by industry for NEVES; thus, an alternative
inventory was not developed.)
Data generated with the above methodology were used to calculate the
activity (bhp-hr) attributable to logging equipment on a national basis
and for the SJV as shown in Table 11-1. As seen, the bulk of the
activity (on a bhp-hr basis) is from the heavy-duty Diesel equipment;
however, the high emission rates of 2-stroke chainsaw engines warrant
their inclusion in this category.
A fundamental flaw in the top-down methodology (in particular, the
choice of activity indicators for this equipment category), becomes
apparent when examining the SIC 241 data. As with the construction
industry, the logging industry is relatively mobile. Although a company
(or companies) may be based in a specific county, there are no
assurances that it does the majority of its work in that county. For
example, all of the SIC 241 employees in the SJV are located in Fresno
County, which contains the largest metropolitan area in the air basin.
However, Fresno County accounts for only 40 percent of the timber
harvested in the eight-county area.1 It is likely that these firms do
business in counties within the SJV that are not represented by any
11-1
-------
See Disclaimer on Cover
Table 11-1
NEVES Logging Equipment Activity Estimates
National: Estimates
Equipment Type
Chainsaws > 4 HP
Shredders > 5 HP
Skldders
Fellers/Bunchers
Total
Population
Diesel
0
0
30,911
15.581
46.492
Gas
51,775
100,271
0
0
152.046
Activity (1000 Bhp-hr/yr)
Diesel I Gas
0
0
3,973,238
2.247.128
6.220.366
52,015
133,481
0
0
185,496
Total
52,015
133,481
3,973,238
2.247,128
6.405.862
% Total
Activity
0.8
2.1
62.0
35.1
100.0
San JoaquinValJev Air Basin Estimates
Equipment Type
Chainsaws > 4 HP
Shredders > 5 HP
Skldders
Fellers/Bunchers
Total
Population
Diesel
0
0
47
23
70
Gas
562
151
0
0
713
Activity (1000 Bhp-hr/yr)
Diesel
0
0
7,231
4,309
11.540
Gas
639
234
0
0
873
Total
639
234
7,231
4,309
12.413
% Total
Activity
5.1
1.9
58.3
34.7
100.0
employees in SIC 241, and it is just as likely that they harvest timber
outside the SJV. Conversely, firms located outside the SJV also are
likely to harvest timber inside the SJV. This problem is even more
apparent when investigating data for the South Coast Air Basin (SCAB).
Although there are half the number of SIC 241 employees in -the SCAB
compared to the SJV, the timber harvest is 0.2 percent of that recorded
in the SJV (223,600,000 board feet compared to 400,000 board feet).1
Obviously, the firms located in the SCAB perform the majority of their
work outside the SCAB.
Approach
Because the choice of activity indicators appeared questionable in the
distribution of logging equipment performed for NEVES, some effort was
devoted to investigating alternative indicators. The most obvious
indicator of logging activity is board feet of timber harvested. This
information is available on a national and regional basis from the U.S.
Forest Service2 (for National Forest lands), but it does not include
timber harvested on private lands. (However, it does appear that
detailed, local-level timber harvest data are available from state
agencies as described below.)
As discussions with logging interests continued, it became apparent that
there are relatively few logging systems (or methods) employed by timber
harvesters in the U.S. Generally, these can be categorized as cable,
mechanized, tractor, and helicopter methods. As outlined below,
equipment requirements and production from each system were obtained
from industry experts, and a bottom-up method was developed using
thousand board feet (MBF) as the activity variable.
11-2
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See Disclaimer on Cover
Methodology and Data Sources
Representatives of private firms, logging industry associations, and the
U.S. Forest Service were contacted for information relating to equipment
usage for timber harvesting operations." This information was compiled
and formed the basis of a bottom-up methodology for determining logging
equipment activity. Details on each of the timber harvesting methods
outlined by the logging industry are contained in Table 11-2, and a
brief description of each follows.
Cable Systems - Also known as a "high lead side," this methodology
utilizes a Diesel engine to power a cable that is strung between two
points in the harvest area. Trees in the harvest area are felled,
attached to the cable, and dragged to a loading area. A tractor is used
to move the felled logs into position for attachment to the cable, and a
hydraulic log loader is used to load the logging trucks. Chainsaws are
used to fell the trees and trim branches prior to loading. Generally,
this method is used in rough terrain.
Mechanical Systems - Mechanical systems rely on feller/bunchers to fell
trees rather than chainsaws. Thus, only one saw is listed in Table 11-2
which would be used for general cutting purposes. Also shown in
Table 11-2 for this system is a Harvester/Processor. These are used to
trim the logs prior to transport. Mechanical systems are generally
limited to smooth terrain.
Tractor Systems - Tractor systems remain the most common method of
timber harvesting.3 This approach relies on manual felling of timber
with chainsaws and a tractor or skidder to drag the tree to the loading
area.
Helicopter Systems - Helicopter systems are not used to a great extent,
but they allow for high production rates when they are utilized. The
helicopter essentially takes the place of the cable or skidders in
moving the log from the harvest area to the loading zone.
Horsepower and Load Factor Estimates - In addition to equipment and
daily usage patterns for each system, industry provided horsepower
estimates for the corresponding equipment types. These estimates were
used as received for equipment not included in NEVES (e.g.,
harvester/processor, log loader). However, it was felt that horsepower
estimates provided by PSR were more representative of the industry as a
whole and were used for chainsaws, feller/bunchers, and skidders. Load
factor data were applied in the same fashion. Although actual load
factor estimates were not provided by industry, percent of time at idle,
part throttle, and full throttle was submitted. Composite load factors
were developed from these data by assuming 10 percent load at idle,
50 percent load at part throttle, and 90 percent load at full throttle.
Horsepower and load factor information was combined with the daily
equipment usage and production rates to develop bhp-hr/MBF estimates for
each logging system. Results of this analysis are given in Table 11-2.
11-3
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See Disclaimer on Cover
Table 11-2
Summary of Production and Equipment Requirements
for Standard Logging Systems
Cable (High Lead) Side - 45 MBF/Day Average Production
Equipment Type
1 - Skagit Tower
1 - Log Loader
1 - Cat Tractor
HP
275
250
230
Load
Factor
0.6
0.8
0.7
Daily
Hours
6
9
1
Total Diesel 66 Bhp-hr/MBF
2.5 - Feller Saws
1 - Chaser Saw
6
6
0.92
0.92
5
4
Total 2 -Stroke Gasoline 2.02 Bhp-hr/MBF
Bhp-hr
990
1800
161
2951
69
22
91
Mechanical Side - 80 MBF/Dav Average Production
Equipment Type
1 - Feller/Buncher
2 - Skidders
1 - Harvester/Processor
1 - Log Loader
HP
183
150
200
250
Load
Factor
0.71
0.74
0.8
0.7
Daily
Hours
9
6
9
10
Total Diesel 71 Bhplhr/MBF
1 - Chaser Saw
6
0.92
8
Total 2 -Stroke Gasoline 0.55 Bhplhf/MBF
Bhp-hr
1169
1332
1440
1750
5691
44
44
Tractor Side - 80 MBF/Day Average Production
Equipment Type
2 - Tractors/Skidders
1 - Log Loader
HP
150
250
Load
Factor
0.74
0.7
Daily
Hours
6
10
Total Diesel 39BhWn1/MBF
4 - Feller Saws
1 - Chaser Saw
6
6
0.92
0.92
8
8
Total 2 -Stroke Gasoline 2.76 BhD-hr/MBF
Bhp-hr
1332
1750
3082
177
44
221
Helicopter Side - 250 MBF/Dav Average Production
Equipment Type
1 - Front-End Loader
1 - Log Loader
HP
216
250
Load
Factor
0.7
0.7
Daily
Hours
10
10
Totaf Diesel 19 Bhplhr/MBF
8 - Feller Saws
4 - Chaser Saw
6
6
0.92
0.92
8
8
Total 2 -Stroke Gasoline 2.12 Bhp-hr/MBF
Bhp-hr
3024
1750
4774
353
177
530
11-4
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See Disclaimer on Cover
Regional Distribution of Logging Systems - In order to apply the
information above to develop activity estimates for specific areas, it
was necessary to determine the type of system(s) used in various regions
of the U.S. This information is summarized in Table 11-3,3 and
Figure 11-1 illustrates the boundaries for each region.
Table 11-3
Regional Distribution of Logging Systems
Region
Alaska
Northwest
Northeast
Intermountain
South
Logging System
Cable
100%
35%
20%
5%
Mechanical
10%
10%
10%
25%
Tractor
50%
90%
70%
70%
Helicopter
5%
Figure 11-1
Logging Regions in the U.S.
Pacific
Southwest
(PSW)
11-5
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See Disclaimer on Cover
Timber Harvested - The final variable needed to complete the activity
calculation for logging equipment is MBF of timber harvested by county.
This information appears to be readily available from state Departments
of Forestry, Natural Resources, or Tax Agencies. This was confirmed for
Washington,4 Oregon,5 and California3. In addition to total MBF, Oregon
and Washington report MBF according to land ownership (i.e., federal
versus private) for each county, while California reports MBF by land
ownership for the whole state. (This distinction could be important if
it was found that harvesting methods differ by land ownership.)
Sample Calculations
The above methodology was applied to the SJV air basin using the
"Northwest" logging system distribution given in Table 11-3. This
resulted in an average Diesel equipment activity of 51 bhp-hr/MBF and a
2-stroke gasoline (i.e., chainsaw) activity of 2.2 bhp-hr/MBF. Applying
an annual timber harvest total of 223,6000 MBF for the SJV32 gives a
total logging activity of 11,404,000 bhp-hr for heavy-duty Diesel
equipment and 492,200 bhp-hr for chainsaws. (These results are
summarized in Table 11-4.) Both the heavy-duty Diesel and the 2-stroke
gasoline figures compare reasonably well with the PSR estimates. The
close agreement between estimates is somewhat curious, however,
particularly since the methodology used in NEVES appears only to account
for mechanical systems. (Note the 2 to 1 ratio of skidders to
feller/bunchers - consistent with the mechanical system outlined in
Table 11-2.)
Table 11-4
Comparison of Logging Equipment Activity Estimates for the SJVAB
(1000 bhp-hr)
Methodology
NEVES
This Work
Heavy-Duty Diesel
11,540
11,404
Chainsaws
639
492
Additional Considerations
Shredders over 5 HP were categorized as logging equipment in NEVES.
When discussing the use of shredders with U.S. Forest Service
personnel,37 however, it was discovered that they are only used in
limited applications, such as clearing brush along road sides. There
are generally no requirements for loggers to shred slash after timber
harvesting. Slash treatment normally consists of "lopping and
scattering", which is simply spreading out limbs to a maximum depth of
18 inches.
11-6
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See Disclaimer on Cover
On the other hand, chippers are used in the field in some instances, and
the chips produced in this fashion are sent to pulp mills. An example
of this type of operation is found in lodge pole stands in eastern
Oregon where chippers may process whole trees at one time.37 Detailed
equipment requirements for chip production in the field were not
obtained, but including this method of timber harvesting/processing
would improve the overall accuracy of the activity estimates. However,
given the small contribution of logging equipment to the nonroad
inventory in most metropolitan areas, the benefits of doing so may be
questionable.
The above points out an inherent problem in classifying equipment types
in specific categories. Although chippers are included under lawn and
garden equipment, a portion of them should be allocated to logging
operations. Conversely, even the larger shredders (which average 8 HP,
according to PSR) would more appropriately be placed in the lawn and
garden category because they are likely used primarily in commercial
landscape maintenance operations.
Although the above information on equipment usage by logging system is
based on the opinions and expertise of a limited number of industry
representatives, the bottom-up approach developed in this work likely
provides a better estimate of logging equipment activity than the
current top—down methodology. However, a number of issues would need to
be considered prior to utilizing the bottom-up approach for official
inventory purposes. These include:
• a more thorough review of the logging systems outlined above by
the timber industry,
• a more thorough assessment of the types of systems used by
logging region, and
• an evaluation of how often logging systems and equipment
requirements are modified.
These issues would be best resolved through close contact with the U.S.
Forest Service.
Recommendations
The methodology developed above provides a sound basis for estimating
logging equipment activity. Additionally, the data requirements are
minimal, easing the burden on local air quality planners. Therefore,
use of this method is recommended contingent upon additional industry
review of the equipment requirements and daily production estimates
outlined in Table 11-2.
11-7
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See Disclaimer on Cover
References for Section 11
1. "California Timber Harvest by County - January 1, 1989 to December
31, 1989," Timber Tax Division, California Board of Equalization,
May 1990.
2. "Report of the Forest Service, Fiscal Year 1991," U.S. Department
of "the Interior, Forest Service, June 1992.
3. Don Studier, U.S. Forest Service, Personal Communication, August
1992.
4. "Timber Harvest Summary - All Ownerships 1991," Washington State
Department of Natural Resources, Timber Sales Division, 1992.
5. "1991 Oregon Timber Tax Profile," Oregon Department of Revenue,
October 1991.
6. Ron Lewis, U.S. Forest Service, Personal Communication, August
1992.
11-8
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APPENDIX A
Equipment Types Included in EPA's 1991
"Nonroad Engine and Vehicle Emission Study"
-------
APPENDIX A
Equipment Types Included in EPA's 1991
"Nonroad Engine and Vehicle Emission Study"
Equipment Category
Lawn and Garden
Airport Service
Recreational
Light Commercial
Industrial
Equipment Types
Trimmers/Edgers/Brush Cutters
Lawn Mowers
Leaf Blowers/Vacuums
Rear Engine Riding Mowers
Front Mowers
Chainsaws <4 HP
Shredders <5 HP
Tillers <5 HP
Lawn and Garden Tractors
Wood Splitters
Snowblowers
Chippers/Stump Grinders
Commercial Turf Equipment
Other Lawn and Garden
Aircraft Support Equipment
Terminal Tractors
All Terrain Vehicles (ATVs)
Minibikes
Off-Road Motorcycles
Golf Carts
Snowmobiles
Specialty Vehicles/Carts
Generator Sets <50 HP
Pumps <50 HP
Air Compressors <50 HP
Gas Compressors <50 HP
Welders <50 HP
Pressure Washers <50 HP
Aerial Lifts
Forklifts
Sweepers/Scrubbers
Other General Industrial Equipment
Other Material Handling Equipment
A-l
-------
Equipment Category
Construction
Agricultural
Logging
Equipment Types
Asphalt Pavers
Tampers/Rammers
Plate Compactors
Concrete Pavers
Rollers
Scrapers
Paving Equipment
Surfacing Equipment
Signal Boards
Trenchers
Bore/Drill Rigs
Excavators
Concrete/Industrial Saws
Cement and Mortar Mixers
Cranes
Graders
Off-Highway Trucks
Crushing/Processing Equipment
Rough Terrain Forklifts
Rubber Tired Loaders
Rubber Tired Dozers
Tractors/Loaders/Backhoes
Crawler Tractors
Skid Steer Loaders
Off-Highway Tractors
Dumpers/Tenders
Other Construction Equipment
2-Wheel Tractors
Agricultural Tractors
Agricultural Mowers
Combines
Sprayers
Balers
Irrigation Sets k
Tillers >5 HP
Swathers
Hydro Power Units
Other Agricultural Equipment
Chainsaws >4 HP
Shredders >5 HP
Skidders
Fellers/Bunchers
A-2
-------
APPENDIX B
Summary of Organizations Contacted to Develop Lot Size Statistics
-------
APPENDIX B
Summary of Organizations Contacted to Develop Lot Size Statistics
Significant efforts were made to collect information relating to lot
size distributions. The following is a summary of the organizations
contacted to acquire this information.
Federal Agencies:
Department of Housing and Urban Development - Duane McGough
(202) 708-1060.
Mr. McGough provided further information on the methodology
involved in compiling HUD's American Housing Survey which we had
previously obtained from the State Library. The survey includes a
distribution of lot sizes within selected metropolitan areas.
Bureau of Land Management - (916) 322-7777
Requested any regional land use information or maps that they might
have. We were informed that they have data for BLM land only.
U.S. Geological Survey - Todd (703) 648-6045
Digital land use and land cover data are available at a cost of
well over $100.00 for the San Joaquin Valley alone. It is also
questionable whether our software is compatible with their format.
USGS referred us to the California Division of Mines and Geology.
State of California:
California Division of Mines and Geology - (916) 324-7380
We were informed that they have mining reclamation and geology
information only.
California Department of Finance - (916) 445-3878
Obtained population and housing estimates for each of the eight
counties in the San Joaquin Valley. These reports list the number
of single family, multi family, and mobile housing units, updated
annually.
San Joaquin Valley:
Sacramento County Planning '- Melinda Grosh (916) 440-5917
Sent 1988 acreage/zone and minimum lot size/zone information for
unincorporated county.
Sacramento City Planning - Gary Ziggenfootz (916) 264-5381
B-l
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Was supposed to send acreage/zone information. Still waiting.
Fresno County Planning - Rick Hoover (209) 453-5010
Wanted $2,500 to do a data sort. Referred us to County Assessor
(Barry Kondo) who wanted $11,000 to do a data sort.
Fresno City Planning - Cathy Chung (209) 498-2715
Verbally gave estimates based on "looking at the lot size
distributions in a few key areas of the city," and applying the
percentages to data from the 1992 Population and Housing Estimates
report.
Kern County Planning Commission - Dave Mitchell (805) 861-2615
Sent the 1988 Land Use Inventory report that lists the number of
DU's by zone and average lot size/zone.
Bakersfield City Planning - Jim Eggert (805) 326-3733
Sent total acreage/zone and minimum lot size/zone information.
Calculated number of DU's differed from Population and Housing
Estimates report data by several orders of magnitude. Follow-up
phone call to Jim Eggert revealed that the acreage/zone information
included a lot of undeveloped land, but he didn't know exactly how
much.
Taft City Planning - Marilyn Beardslee (805) 763-3144
Never called back.
Delano City Planning Commission - Jeremy Tobias (805) 721-3303
Verbally gave us total acreage/zone and DU density/zone data. Poor
distribution.
McFarland City Planning - Mike 0'Haver (805) 792-3091
Sent total acreage/zone and minimum lot size/zone information.
Poor distribution.
Shafter City Planning - Lawrence Tomasello (805) 746-6361
Verbally gave us total acreage/zone and DU density/zone
information. Poor distribution.
Arvin City Planning - Howard Phillips (805) 854-3134
Never called back.
Tehachappi City Planning - Christopher Grimes (805) 822-2200
Had nothing but Population and Housing Estimates data that we had
already obtained from the California Department of Finance.
Kings County Planning - Bill Zumwald (209) 582-3211
Sent fairly good acreage/zone data for unincorporated county.
Corcoran City Planning - No information available.
Hanford City Planning - Greg Shindler (209) 585-2578
Sent total developed acreage/zone and minimum lot size/zone
information.
B-2
-------
Merced County Planning - No information available.
Referred us to Robert Beckler at the Merced County Area Government
who wanted $500.00 to do a data search.
Merced City Planning - No information available.
Madera County Planning - No information available.
Stanislaus County Planning - Julie Larson (209) 525-6330
Sent acreage/zone and minimum lot size/zone information. Poor
distribution.
Stanislaus County Assessors Office - Jan Perroti (209) 525-6461
No information available.
Ceres City Planning - Bill Carlson (209) 538-5774
Verbally gave us acreage/zone and minimum lot size/zone
information. Poor distribution.
Hughson City Planning - (209) 883-4054
Never called back.
Modesto City Planning - Steve Mitchell (209) 577-5267
Sent acreage/zone and minimum lot size/zone data. Very poor
distribution.
Newman City Planning - (209) 862-3725
Never called back.
Oakdale City Planning - John Thayer (209) 847-4245
Sent acreage/zone and minimum lot size/zone information. Poor
distribution.
Patterson City Planning - Jenny (209) 892-2041
No information available.
Riverbank City Planning - Glory (209) 869-6193
Never called back.
Turlock City Planning - Ernie Rubi (209) 668-5565
Verbally gave us lot size/zone information. Poor distribution.
Waterford City Planning - Harder Bruch (209) 874-2328
Never called back.
San Joaquin County Planning - Bill Factor (209) 468-2200
No information available. Referred us to Ron Sugimoto at County
Assessors Office who wanted $450 to do a data search.
Tulare County Planning - Andrew Pacheco (209) 733-6254
No information available.
Tulare City Planning - Mark Kelty (209) 685-2300
No acreage/zone information available.
B-3 .
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Washington D.C. Area:
Washington D.C. Planning - Anita Royster (202) 727-6492
Sent acreage/zone and minimum lot size/zone information.
State of Virginia:
Northern Virginia Planning District and Commission -
Mr. Billingsly (703) 642-0700.
No lot size information available.
Arlington County Planning - Margaret Simkovsky (703) 358-3525
Sent acreage/zone and lot size/zone information.
Fairfax City Planning - Sue Cotellessa (703) 385-7930
Only very general lot size data were available. 2600 out of 4000
total city acres are zoned for single family and duplex lots.
Single family lots are all between 9,500 and 20,000 square feet.
Fairfax County Planning - Fatima Khaja (703) 324-3820
Sent detailed breakdown of acreage/zone and minimum lot size/zone
information for both incorporated and unincorporated areas of
county.
Falls Church City Planning - Gary Fuller (703) 241-5040
Verbally gave acreage/zone and minimum lot size/zone information.
Poor distribution, but the city is comprised of only two square
miles so we wouldn't expect a wide lot size distribution.
Loudoun County Planning - Cynthia Richmond (703) 777-0296
Verbally gave us very good estimates of DU's across a distribution
of lot sizes. This information was from a report compiled by the
Economic Development and Demographics Department using 1990 census
and county assessment records. Both incorporated and
unincorporated Loudoun County is included in this report.
Manassas City Planning - Liz Weiler (703) 257-8200
Sent developed and undeveloped acres/zone information.
Manassas Park City Planning - Troy Taylor (703) 335-8800
Verbally gave us acreage/zone and minimum lot size/zone
information. Poor distribution.
Prince William County Planning - Dan Eurich (703) 792-6830
Sent some zoning data which was not very helpful. We sent.the
required letter of request for additional lot size data. Received
detailed acreage/lot size and minimum lot size information.
Stafford County Planning - Jeff Harvey (703) 659-8668
No information available.
B-4
-------
State of Maryland:
Calvert County Planning - Randi Vott (410) 535-2348
Had nothing except estimated of SFU's in Calvert County. Referred
us to Maryland Office of Planning.
Maryland Office of Planning - John Kozarski (410) 225-4500
Sent a publication that contains land use/acre by county
information as well as census data for each of the five counties in
the nonattainment area.
B-5
-------
APPENDIX C
Data and Summary Statistics for Regression Analyses
-------
Data Used in Regression Analyses for
Lawn and Garden Equipment
STATE
Nation
California
Colorado
Connecticut
Delaware
Florida
Georgia
Illinois
Indiana
Louisiana
Maryland
Massachusetts
Minnesota
Missouri
New Hampshire
New Jersey
New York
Ohio
Pennsylvania
Texas
Utah
Virginia
Washington
Wisconsin
SFHU
57,130,570
5,753,543
766,655
700,160
160,615
2,498,179
1,412,873
2,542,992
1,549,401
1,105,431
1,055,018
1,117,154
1,073,613
1,429,688
218,958
1,564,273
2,786,819
2,866,325
2,898,363
3,863,693
342,290
1,437,060
1,145,385
1,213.055
SIC 078
Employment
230,059
43,819
3,326
4,552
802
18,897
7,179
8,452
4,597
1,470
6,913
5,949
2,362
3,604
954
8,695
10,790
10,702
10,716
14,172
663
6,728
4,179
2.435
PSR
Population
14,565,558
1,898,121
1,512,989
279,525
5,590,445
2,286,651
4,153,123
1,855,420
1,677,623
2,035,273
2,351,447
1,582,153
1,899,011
455,364
3,290,457
5,872,506
4,310,529
4,099,958
7,994,691
557,840
2,292,295
2,177,032
1.677.327
C-l
-------
LAWN AND GARDEN EQUIPMENT - REGRESSION WITH SFU 8:40 TUESDAY, MARCH 9, 1993 1
OEP VARIABLE: LG POP
ANALYSIS OF VARIANCE
SOURCE DF
MODEL 1
ERROR 21
C TOTAL 22
ROOT MSE
DEP MEAN
C.V.
SUM OF
SQUARES
1.95099E+14
1.59726E+13
2.11072E+14
872124.5
3235449
26.95528
MEAN
SQUARE
1.95099E+14
760601122205
R- SQUARE
ADJ R-SQ
F VALUE
256.507
0.9243
0.9207
PROB>F
0.0001
PARAMETER ESTIMATES
PARAMETER STANDARD T FOR HO:
VARIABLE DF ESTIMATE ERROR PARAMETERS PROS > |T|
INTERCEP 1 -707243 306058.26742 -2.311 0.0311
SFU 1 2.29565527 0.14333671 16.016 0.0001
C-2
-------
LAWN AND GARDEN EQUIPMENT - REGRESSION WITH SFU - FORCED ZERO INTERCEPT 2
8:40 TUESDAY, MARCH 9, 1993
£P VARIABLE: LG POP
ANALYSIS OF VARIANCE
SUM OF MEAN
SOURCE DF SQUARES SQUARE F VALUE PROB>F
MODEL 1 4.31805E+14 4.31805E+14 474.176 0.0001
ERROR 22 2.00341E+13 910641766303
U TOTAL 23 4.51839E+14
ROOT MSE 954275.5 R-SQUARE 0.9557
DEP MEAN 3235449 ADJ R-SQ 0.9536
C.V. 29.49437
OTE: NO INTERCEPT TERM IS USED. R-SQUARE IS REDEFINED.
PARAMETER ESTIMATES
PARAMETER STANDARD T FOR HO:
VARIABLE DF ESTIMATE ERROR PARAMETERS PROB > |T|
SFU 1 2.02923856 0.09318868 21.776 0.0001
C-3
-------
LAWN AND GARDEN EQUIPMENT - REGRESSION WITH SIC078 8:40 TUESDAY, MARCH 9, 1993 3
DEP VARIABLE: LG_POP
ANALYSIS OF VARIANCE
SOURCE DF
MODEL 1
ERROR 21
C TOTAL 22
ROOT MSE
DEP MEAN
C.V.
SUM OF
SQUARES
1.95227E+14
1.58444E+13
2.11072E+14
868616.6
3235449
26.84686
MEAN
SQUARE
1.95227E+14
754494758144
R-SQUARE
ADJ R-SQ
F VALUE
258.753
0.9249
0.9214
PROB>F
0.0001
PARAMETER ESTIMATES
PARAMETER STANDARD T FOR HO:
VARIABLE DF ESTIMATE ERROR PARAMETERS PROS > |T|
INTERCEP 1 637563.09721 242666.46080 2.627 0.0157
SIC078 1 328.38371235 20.41452694 16.086 0.0001
C-4
-------
)EP VARIABLE: LG POP
LAWN AND GARDEN EQUIPMENT - REGRESSION WITH SIC078 - FORCED ZERO INTERCEPT
ANALYSIS OF VARIANCE
SOURCE
MODEL
ERROR
U TOTAL
DF
SUM OF
SQUARES
MEAN
SQUARE
1 4.30786E+14 4.30786E+14
22 2.10525E+13 956933400614
23 4.51839E+14
ROOT MSE 978229.7
DEP MEAN 3235449
C.V. 30.23474
JOTE: NO INTERCEPT TERM IS USED. R-SQUARE IS REDEFINED.
R-SQUARE
ADJ R-SQ
f VALUE
450.174
0.9534
0.9513
8:40 TUESDAY, MARCH 9, 1993
PROB>F
0.0001
VARIABLE DF
SIC078 1
PARAMETER
ESTIMATE
364.07981859
PARAMETER ESTIMATES
STANDARD
ERROR
17.15957404
T FOR HO:
PARAMETERS
21.217
PROB > |T|
0.0001
C-5
-------
LAWN AND GARDEN EQUIPMENT - REGRESSION WITH SFU AND SIC078
8:40 TUESDAY, MARCH 9, 1993 5
DEP VARIABLE: LG POP
ANALYSIS OF VARIANCE
SOURCE
OF
SUM OF
SQUARES
MEAN
SQUARE
MODEL 2 2.05558E+14 1.02779E+14
ERROR 20 5.51403E+12 275701693591
C TOTAL 22 2.11072E+14
INTERCEP
SFU
SIC078
ROOT MSE
DEP MEAN
C.V.
VARIABLE DF
525073
3235449
16.22875
R-SQUARE
ADJ R-SQ
PARAMETER
ESTIMATE
-206946
1.20543012
173.44157757
PARAMETER ESTIMATES
STANDARD
ERROR
201375.61924
0.19692648
28.16024114
f VALUE
372.790
0.9739
0.9713
T FOR HO:
PARAMETERS
-1.028
6.121
6.159
PROB>F
0.0001
PROB > |T|
0.3164
0.0001
0.0001
COLLINEARITY DIAGNOSTICS
NUMBER EIGENVALUE
2.607524
0.351239
0.0412371
CONDITION
NUMBER
1.000000
2.724665
7.951884
VAR PROP
INTERCEP
0.0335
0.5431
0.4234
VAR PROP
SFU
0.0096
0.0061
0.9843
VAR PROP
SIC078
0.0137
0.0984
0.8878
C-6
-------
LAWN AND GARDEN EQUIPMENT - REGRESSION WITH SFU AND SIC078- FORCED ZERO INTERCEPT
EP VARIABLE: LG POP
SOURCE
MODEL
ERROR
U TOTAL
ANALYSIS OF VARIANCE
DF
SUM OF
SQUARES
MEAN
SQUARE
1 4.31805E+14 4.31805E+14
22 2.00341E+13 910641766303
23 4.51839E+14
ROOT MSE 954275.5
DEP MEAN 3235449
C.V. 29.49437
OTE: NO INTERCEPT TERM IS USED. R-SQUARE IS REDEFINED.
R-SQUARE
ADJ R-SQ
F VALUE
474.176
0.9557
0.9536
8:40 TUESDAY, MARCH 9, 1993
PR08>F
0.0001
VARIABLE DF
SFU 1
PARAMETER
ESTIMATE
2.02923856
PARAMETER ESTIMATES
STANDARD
ERROR
0.09318868
T FOR HO:
PARAMETERS
21.776
PROB > |T|
0.0001
COLLINEARITY DIAGNOSTICS
NUMBER EIGENVALUE
1 1.000000
CONDITION
NUMBER
1.000000
VAR PROP
SFU
1.0000
C-7
-------
Data Used in Regression Analyses for
Airport Service Equipment
State
California
Colorado
Connecticut
Delaware
Florida
Georgia
Illinois
Indiana
Louisiana
Maryland
Massachusetts
Minnesota
Missouri
New Hampshire
New Jersey
New York
Ohio
Pennsylvania
Texas
Utah
Virginia/DC
Washington
Wisconsin
PSR
Population
9950
1473
1547
219
3355
1646
4636
1745
1406
1571
2201
1685
1826
326
2726
4938
4525
4003
6739
469
1570
1230
1618
Enplaned
Passengers
53,556,596
12,896,777
2,312,455
0
34,081 ,249
23,385,836
29,593,824
3,160,635
4,164,618
4,420,547
9,654,220
9,090,931
12,775,950
267,963
9,979,900
26,263,054
11,719,192
15,960,382
46,435,641
5,388,178
13,991,121
8,457,229
2,779.015
Enplaned Cargo (Tons)
Total
881,441.17
107,754.34
28,758.02
3,449.54
373,889.13
261,276.15
452,863.48
124,392.59
31,997.17
38,113.76
158,505.94
111,425.60
122,828.80
7,724.80
198,040.31
456,300.91
116,144.31
150,203.32
372,104.12
54,094.25
, 134,609.08
151,155.94
27.996.24
Freight
713,222.60
68,740.08
14,432.08
3,449.54
290,246.86
167,448.17
307,692.38
113,379.40
22,504.93
18,390.83
128,002.89
68,429.16
68,139.79
7,092.79
163,974.49
334,645.80
72,389.78
81,550.94
247,401.15
35,305.65
75,287.58
113,697.71
18.616.36
Mail
168,218.57
39,014.26
14,325.94
0.00
83,642.27
93,827.98
145,171.10
11,013.19
9,492.24
19,722.93
30,503.05
42,996.44
54,689.01
632.01
34,065.82
121,655.11
43,754.53
68,652.38
124,702.97
18,788.60
59,321.50
37,458.23
9.379.88
C-8
-------
AIRPORT EQUIPMENT - REGRESSION WITH PASSENGERS AND CARGO 13:25 WEDNESDAY, MARCH 10, 1993 1
EP VARIABLE: AP POP
ANALYSIS OF VARIANCE
SOURCE
MODEL
ERROR
C TOTAL
DF
SUM OF
SQUARES
2 93621472.78
20 20675541.66
22 114297014.43
INTERCEP
PASS
CARGO
ROOT MSE
DEP MEAN
C.V.
VARIABLE OF
1016.748
2669.739
38.08418
MEAN
SQUARE
46810736.39
1033777.08
R-SQUARE
ADJ R-SQ
PARAMETER
ESTIMATE
622.28967864
.00006553352
0.005678711
PARAMETER ESTIMATES
STANDARD
ERROR
306.59441114
.00003816396
0.002716053
F VALUE
45.281
0.8191
0.8010
PROB>F
0.0001
T FOR HO:
PARAMETERS
2.030
1.717
2.091
PROB > |T|
0.0559
0.1014
0.0495
COLLINEAR1TY DIAGNOSTICS
NUMBER EIGENVALUE
1
2
3
2.587314
0.372742
0.039943
CONDITION
NUMBER
1.000000
2.634633
8.048298
VAR PROP
INTERCEP
0.0524
0.9155
0.0321
VAR PROP
PASS
0.0103
0.0221
0.9676
VAR PROP
CARGO
0.0110
0.0376
0.9514
C-9
-------
AIRPORT EQUIPMENT - REGRESSION WITH PASSENGERS 13:25 WEDNESDAY, MARCH 10, 1993 2
•:P VARIABLE: AP POP
ANALYSIS OF VARIANCE
SOURCE OF
MODEL 1
ERROR 21
C TOTAL 22
ROOT MSE
DEP MEAN
C.V.
SUM OF
SQUARES
89102394.43
25194620.01
114297014.43
1095.328
2669.739
41.02754
MEAN
SQUARE
89102394.43
1199743.81
R- SQUARE
ADJ R-SQ
F VALUE
74.268
0.7796
0.7691
PROB>F
0.0001
PARAMETER ESTIMATES
PARAMETER STANDARD T FOR HO:
VARIABLE DF ESTIMATE ERROR PARAMETERS PROS > |T|
INTERCEP 1 613.87832009 330.26127347 1.859 0.0771
PASS 1 0.0001389359 .00001612182 8.618 0.0001
C-10
-------
:P VARIABLE: AP POP
AIRPORT EQUIPMENT - REGRESSION WITH CARGO
ANALYSIS OF VARIANCE
SOURCE
DP
SUM OF
SQUARES
MEAN
SQUARE
MODEL 1 90573247.67 90573247.67
ERROR 21 23723766.76 1129703.18
C TOTAL 22 114297014.43
ROOT MSE
OEP MEAN
C.V.
1062.875
2669.739
39.81194
R-SQUARE
ADJ R-SQ
F VALUE
80.174
0.7924
0.7826
VARIABLE OF
INTERCEP 1
CARGO 1
PARAMETER
ESTIMATE
777.75309211
0.00996907
PARAMETER ESTIMATES
STANDARD
ERROR
306.21128357
0.001113363
T FOR HO:
PARAMETERS
2.540
8.954
13:25 WEDNESDAY, MARCH 10, 1993 3
PROB>F
0.0001
PROB > |T|
0.0191
0.0001
C-ll
-------
DEP,
BLE: AP POP
AIRPORT EQUIPMENT - REGRESSION WITH PASSENGERS AND CARGO - FORCED ZERO INTERCEPT 4
13:25 WEDNESDAY, MARCH 10, 1993
ANALYSIS OF VARIANCE
SOURCE
MODEL
ERROR
U TOTAL
DF
SUM OF
SQUARES
MEAN
SQUARE
2 253295369.81 126647684.91
21 24934306.19 1187347.91
23 278229676.00
ROOT MSE 1089.655
DEP MEAN 2669.739
C.V. 40.81503
NOTE: NO INTERCEPT TERM IS USED. R-SQUARE IS REDEFINED.
R-SQUARE
ADJ R-SQ
F VALUE
106.664
0.9104
0.9018
PROB>F
0.0001
PARAMETER ESTIMATES
VARIABLE OF
PASS 1
CARGO 1
PARAMETER
ESTIMATE
.00008840719
0.005606375
STANDARD
ERROR
.00003907664
0.002910559
T FOR HO:
PARAMETERS
2.262
1.926
PROB > |T |
0.0344
0.0677
COLLINEAR ITY DIAGNOSTICS
NUMBER EIGENVALUE
1 1.958873
2 0.0411265
CONDITION
NUMBER
1.000000
6.901477
VAR PROP
PASS
0.0206
0.9794
VAR PROP
CARGO
0.0206
0.9794
C-12
-------
Data Used in Regression Analyses for
Recreational Equipment
State
Nation
California
Colorado
Connecticut
Delaware
Florida
Georgia
Illinois
Indiana
Louisiana
Maryland
Massachusetts
Minnesota
Missouri
New Hampshire
New Jersey
New York
Ohio
Pennsylvania
Texas
Utah
Virginia
Washington
Wisconsin
Rural
Population
2,188,700
578,887
685,568
178,830
1,771,304
2,380,887
1,762,050
1,946,060
1,348,214
893,039
946,822
1,318,625
1,601,064
543,582
819,547
2,826,408
2,807,706
3,693,348
3,351,993
223,769
1,893,915
1,148,744
1.679.813
U.S. Acreage (1000s)
Total
2,271,342
100,207
66,486
3,135
1,266
34,721
37,295
35,795
23,158
28,868
6,319
5,035
51,206
44,248
5,769
4,813
30,681
26,222
28,804
168,218
52,697
25,496
42,694
35.011
Fed. Owned
724,068
46,465
24,045
14
30
4,261
2,030
500
437
1,142
198
83
3,460
2,069
740
160
1,555
318
641
3,270
33,535
2,465
12,480
1.830
State Owned
10,817
1,277
287
181
12
341
62
372
57
38
283
267
3,441
109
31
298
258
208
276
225
116
54
234
120
Total Public
734,885
47,742
24,332
195
42
4,602
2,092
872
494
1,180
481
350
6,901
2,178
771
458
1,813
526
917
3,495
33,651
2,519
12,714
1.950
MIC
Population
324,200
39,100
23,900
6,800
102,300
104,500
65,700
65,200
80,300
33,700
39,700
68,000
77,700
21,800
52,100
113,900
110,200
136,800
171,300
45,300
59,600
64,000
57.400
C-13
-------
RECREATIONAL EQUIPMENT - REGRESSION UITH RURAL POPULATION - INCLUDING CALIFORNIA 1
13:37 WEDNESDAY, MARCH 10, 1993
DEP JUilABLE: HC POP
ANALYSIS OF VARIANCE
•P^^AB
SOURCE DF
MODEL 1
ERROR 21
C TOTAL 22
ROOT MSE
DEP MEAN
C.V.
SUM OF
SQUARES
40208213665
55193745465
95401959130
51266.69
81021.74
63.27523
MEAN
SQUARE
40208213665
2628273594
R- SQUARE
ADJ R-SQ
F VALUE
15.298
0.4215
0.3939
PROB>F
0.0008
PARAMETER ESTIMATES
PARAMETER STANDARD T FOR HO:
VARIABLE DF ESTIMATE ERROR PARAMETERS PROB > |T|
INTERCEP 1 10442.78781 20973.53352 0.498 0.6237
R POP 1 0.04436638 0.0113431 3.911 0.0008
C-14.
-------
EP VARIABLE: MC POP
RECREATIONAL EQUIPMENT - REGRESSION WITH RURAL POPULATION - EXCLUDING CALIFORNIA 2
13:37 WEDNESDAY, MARCH 10, 1993
ANALYSIS OF VARIANCE
SOURCE DF
MODEL 1
ERROR 20
C TOTAL 21
ROOT MSE
DEP MEAN
C.V.
SUM OF
SQUARES
28370921916
5207385811
33578307727
16135.96
69968.18
23.06186
MEAN
SQUARE
28370921916
260369290.56
R- SQUARE
ADJ R-SQ
F VALUE
108.964
0.8449
0.8372
PROB>F
0.0001
VARIABLE DF
INTERCEP 1
R POP 1
PARAMETER
ESTIMATE
11154.18142
0.03761341
PARAMETER ESTIMATES
STANDARD
ERROR
6601.52593
0.003603304
T FOR HO:
PARAMETERS
1.690
10.439
PROB > |T|
0.1066
0.0001
C-15
-------
RECREATIONAL EQUIPMENT - REGRESSION WITH RURAL POPULATION
EP^^AI
ABLE: HC POP
EXCLUDING CALIFORNIA AND UTAH 3
13:37 WEDNESDAY, MARCH 10, 1993
ANALYSIS OF VARIANCE
SOURCE DF
MODEL 1
ERROR 19
C TOTAL 20
ROOT MSE
DEP MEAN
C.V.
SUM OF
SQUARES
28498709451
4442101977
32940811429
15290.35
71142.86
21 .49246
MEAN
SQUARE
28498709451
233794840.91
R- SQUARE
ADJ R-SQ
F VALUE
121.896
0.8651
0.8581
PR08>F
0.0001
VARIABLE DF
INTERCEP
R POP
PARAMETER
ESTIMATE
6694.67763
0.03960076
PARAMETER ESTIMATES
STANDARD
ERROR
6723.66782
0.00358681
T FOR HO:
PARAMETERS
0.996
11.041
PROB > |T |
0.3319
0.0001
C-16
-------
RECREATIONAL EQUIPMENT - REGRESSION WITH RURAL POPULATION - INCLUDING CALIFORNIA - FORCED ZERO INTERCEPT 4
13:37 WEDNESDAY, MARCH 10, 1993
JEP VARIABLE: MC POP
ANALYSIS OF VARIANCE
SUM OF MEAN
SOURCE DF SQUARES SQUARE F VALUE PROB>F
MODEL 1 190540655751 190540655751 75.063 0.0001
ERROR 22 55845314249 2538423375
U TOTAL 23 246385970000
ROOT MSE 50382.77 R-SOUARE 0.7733
DEP MEAN 81021.74 ADJ R-SQ 0.7630
C.V. 62.18426
IOTE: NO INTERCEPT TERM IS USED. R-SQUARE IS REDEFINED.
PARAMETER ESTIMATES
PARAMETER STANDARD T FOR HO:
VARIABLE DF ESTIMATE ERROR PARAMETERS PROS > |T|
R POP 1 0.04922551 0.005681701 8.664 0.0001
C-17
-------
RECREATIONAL EQUIPMENT - REGRESSION WITH RURAL POPULATION - EXCLUDING CALIFORNIA - FORCED ZERO INTERCEPT 5
13:37 WEDNESDAY, MARCH 10. 1993
DEP VARIABLE: MC POP
ANALYSIS OF VARIANCE
SOURCE
MODEL
ERROR
U TOTAL
DF
SUM OF
SQUARES
MEAN
SQUARE
1 135329623022 135329623022
21 5950706978 283366998.93
22 141280330000
ROOT MSE 16833.51
DEP MEAN 69968.18
C.V. 24.0588
NOTE: NO INTERCEPT TERM IS USED. R-SQUARE IS REDEFINED.
R-SQUARE
ADJ R-SQ
F VALUE
477.577
0.9579
0.9559
PROB>F
0.0001
VARIABLE DF
R POP 1
PARAMETER
ESTIMATE
0.04280965
PARAMETER ESTIMATES
STANDARD
ERROR
0.001958934
T FOR HO:
PARAMETERS
21.854
PROS > |T|
0.0001
C-18
-------
RECREATIONAL EQUIPMENT - REGRESSION WITH RURAL POPULATION - EXCLUDING CALIFORNIA AND UTAH - FORCED ZERO INTERCEPT 6
13:37 WEDNESDAY, MARCH 10, 1993
IP VARIABLE: MC_POP
ANALYSIS OF VARIANCE
SUM OF MEAN
SOURCE DF SQUARES SQUARE F VALUE PROB>F
MODEL 1 134554354925 134554354925 575.771 0.0001
ERROR 20 4673885075 233694253.75
U TOTAL 21 139228240000
ROOT MSE 15287.06 R-SQUARE 0.9664
DEP MEAN 71142.86 ADJ R-SQ 0.9648
C.V. 21.48784
)TE: NO INTERCEPT TERM IS USED. R-SOUARE IS REDEFINED.
PARAMETER ESTIMATES
PARAMETER STANDARD T FOR HO:
VARIABLE DF ESTIMATE ERROR PARAMETERS PROS > |l|
R POP 1 0.04270133 0.001779576 23.995 0.0001
C-19
-------
Data Used in Regression Analyses for
Construction Equipment
State
Nation
CA
CO
CT
DE
FL
GA
IL
IN
LA
MD
MA
MN
MO
NH
NJ
NY
OH
PN
TX
UT
VA
WA
Wl
Dodge Statistics (Value, $Millions)
Non-
Residential
55,753.0
6,691.8
1,169.2
823.0
162.5
2,634.9
1,528.0
2,331.5
1,480.5
586.9
1,042.6
1,184.8
1,062.8
892.3
184.5
1,283.1
3,676.8
2,686.6
2,948.8
3,613.3
425.4
1,109.9
1,889.8
1,045.5
Residential
74,368.0
8,120.7
1,516.5
647.7
198.3
5,843.4
3,055.1
2,985.9
2,086.3
677.2
2,036.2
1,261.9
1,815.3
1,385.7
313.8
1,071.2
2,613.3
3,098.5
2,570.4
4,817.3
660.4
2,427.1
2,529.5
1,555.8
Non-
Buildinq
35,279.0
3,777.2
1,259.9
342.3
135.3
1,418.8
538.7
1,718.2
623.9
759.8
548.3
1,122.9
748.1
644.4
108.3
1,258.8
2,936.9
1,482.9
1,331.8
2,081.0
186.7
1,265.1
932.3
625.6
Total
Construction
165,400.0
18,589.7
3,945.6
1,813.0
496.1
9,897.1
5,121.8
7,035.6
4,190.7
2,023.9
3,627.1
3,569.6
3,626.2
2,922.4
606.6
3,613.1
9,227.0
7,268.0
6,851.0
10,511.6
1,272.5
4,802.1
5,351.6
3,226.9
PSR
Population
309,940
53,587
34,536
7,664
115,151
51,304
107,700
40,068
53,258
51,648
53,500
42,174
45,658
9,546
71,940
140,785
89,420
103,140
242,129
15,650
52,952
42,185
34,301
C-20
-------
>EP VARIABLE: CON POP
CONSTRUCTION EQUIPMENT - REGRESSION WITH TOTAL CONSTRUCTION VALUATION
ANALYSIS OF VARIANCE
1
13:39 WEDNESDAY, MARCH 10, 1993
SUM OF MEAN
SOURCE DF SQUARES SQUARE
MODEL 1 101237642787 101237642787
ERROR 21 12684715174 604034055.90
C TOTAL 22 113922357961
ROOT MSE 24577.1 R- SQUARE
DEP MEAN 76879.83 ADJ R-SQ
C.V. 31.96821
F VALUE
167.603
0.8887
0.8834
PROB>F
0.0001
VARIABLE DF
INTERCEP 1
TOT CON 1
PARAMETER
ESTIMATE
-10514.5
16.80812764
PARAMETER ESTIMATES
STANDARD
ERROR
8475.44193
1.29831190
T FOR HO:
PARAMETERS
-1.241
12.946
PROB > |T|
0.2284
0.0001
C-21
-------
CONSTRUCTION EQUIPMENT - REGRESSION WITH TOTAL CONSTRUCTION VALUATION - FORCED ZERO INTERCEPT 2
13:39 WEDNESDAY, MARCH 10, 1993
CON POP
ANALYSIS OF VARIANCE
SUM OF MEAN
SOURCE DF SQUARES SQUARE F VALUE PROB>F
MODEL 1 236249672745 236249672745 381.765 0.0001
ERROR 22 13614361377 618834608.02
U TOTAL 23 249864034122
ROOT MSE 24876.39 R-SQUARE 0.9455
DEP MEAN 76879.83 ADJ R-SQ 0.9430
C.V. 32.3575
NOTE: NO INTERCEPT TERM IS USED. R-SQUARE IS REDEFINED.
PARAMETER ESTIMATES
PARAMETER STANDARD T FOR HO:
VARIABLE DF ESTIMATE ERROR - PARAMETERS PROS > |T|
TOT CON 1 15.52524188 0.79458445 19.539 0.0001
C-22
-------
Data Used in Regression Analyses for
Light Commercial Equipment
State
Nation
CA
CO
CT
DE
FL
GA
IL
IN
LA
MD
MA
MN
MO
NH
NJ
NY
OH
PN
TX
UT
VA
WA
Wl
Dodge Statistics (Value, $Millions)
Non-
Residential
55,753.0
6,691.8
1,169.2
823.0
162.5
2,634.9
1,528.0
2,331.5
1,480.5
586.9
1,042.6
1,184.8
1,062.8
892.3
184.5
1,283.1
3,676.8
2,686.6
2,948.8
3,613.3
425.4
1,109.9
1,889.8
1.045.5
Residential
74,368.0
8,120.7
1,516.5
647.7
198.3
5,843.4
3,055.1
2,985.9
2,086.3
677.2
2,036.2
1,261.9
1,815.3
1,385.7
313.8
1,071.2
2,613.3
3,098.5
2,570.4
4,817.3
660.4
2,427.1
2,529.5
1.555.8
Non-
Building
35,279.0
3,777.2
1,259.9
342.3
135.3
1,418.8
538.7
1,718.2
623.9
759.8
548.3
1,122.9
748.1
644.4
108.3
1 ,258.8
2,936.9
1,482.9
1,331.8
2,081.0
186.7
1,265.1
932.3
625.6
Total
Construction
165,400.0
18,589.7
3,945.6
1,813.0
496.1
9,897.1
5,121.8
7,035.6
4,190.7
2,023.9
3,627.1
3,569.6
3,626.2
2,922.4
606.6
3,613.1
9,227.0
7,268.0
6,851.0
10,511.6
1,272.5
4,802.1
5,351.6
3.226.9
Oil
Production
(1000BBL)
2,684,687
320,868
30,454
0
0
5,674
0
19,954
3,000
147,583
0
0
0
146
0
0
416
10,008
2,643
678,480
27,604
15
0
0
PSR
Population
444,051
94,743
51,276
8,511
124,741
69,850
186,748
62,894
128,868
55,539
95,626
69,189
64,153
14,390
109,978
217,406
1 58,436
161,093
571,015
27,465
68,948
59,106
56.929
C-23
-------
COMMERCIAL EQUIPMENT - REGRESSION WITH TOTAL CONSTRUCTION VALUATION 1
10:50 TUESDAY. MARCH 9, 1993
PEPJMR1 ABLE: COMM POP
ANALYSIS OF VARIANCE
DEP^ttRI/
SUM OF MEAN
SOURCE DF SQUARES SQUARE
MODEL 1 256621047782 256621047782
ERROR 21 132274749379 6298797589
C TOTAL 22 388895797162
ROOT MSE 79364.96 R- SQUARE
DEP MEAN 126128.5 ADJ R-SQ
C.V. 62.92391
F VALUE
40.741
0.6599
0.6437
PROB>F
0.0001
PARAMETER ESTIMATES
VARIABLE DF
INTERCEP 1
TOT CON 1
PARAMETER
ESTIMATE
-13013.6
26.76051470
STANDARD
ERROR
27369.09684
4.19253939
T FOR HO:
PARAMETERS
-0.475
6.383
PROB > |T |
0.6393
0.0001
C-24
-------
COMMERCIAL EQUIPMENT
DEP VARIABLE: COW POP
SOURCE
MODEL
ERROR
U TOTAL
REGRESSION WITH TOTAL CONSTRUCTION VALUATION - FORCED ZERO INTERCEPT 2
10:50 TUESDAY, MARCH 9, 1993
ANALYSIS OF VARIANCE
DF
SUM OF
SQUARES
MEAN
SQUARE
1 621090009404 621090009404
22 133698827411 6077219428
23 754788836815
ROOT MSE 77956.52
DEP MEAN 126128.5
C.V. 61.80723
NOTE: NO INTERCEPT TERM IS USED. R-SQUARE IS REDEFINED.
R-SQUARE
ADJ R-SQ
F VALUE
102.200
0.8229
0.8148
PROB>F
0.0001
VARIABLE DF
TOT CON 1
PARAMETER
ESTIMATE
25.17271385
PARAMETER ESTIMATES
STANDARD
ERROR
2.49003371
T FOR HO:
PARAMETERS
10.109
PROS > |T|
0.0001
C-25
-------
COMMERCIAL EQUIPMENT - REGRESSION WITH TOTAL CONSTRUCTION VALUATION - LA AND TX REMOVED 3
10:50 TUESDAY, MARCH 9, 1993
ABLE: COMM_POP
ANALYSIS OF VARIANCE
SOURCE Of
MODEL 1
ERROR 19
C TOTAL 20
ROOT MSE
DEP MEAN
C.V.
SUM OF
SQUARES
161612415752
19810474691
181422890443
32290.19
104813
30.80745
MEAN
SQUARE
161612415752
1042656563
R- SQUARE
ADJ R-SQ
F VALUE
155.001
0.8908
0.8851
PROB>F
0.0001
PARAMETER ESTIMATES
VARIABLE DF
INTERCEP 1
TOT CON 1
PARAMETER
ESTIMATE
-9781.2
22.47916001
STANDARD T FOR HO:
ERROR PARAMETERS
11591.86758
1 .80556596
-0.844
12.450
PROS > |T |
0.4093
0.0001
C-26
-------
COMMERCIAL EQUIPMENT - REGRESSION WITH TOTAL CONSTRUCTION VALUATION - FORCED ZERO INTERCEPT - LA AND TX REMOVED 4
10:50 TUESDAY, MARCH 9. 1993
DEP VARIABLE: COMM POP
ANALYSIS OF VARIANCE
SUM OF MEAN
SOURCE DF SQUARES SQUARE F VALUE PROB>F
MODEL 1 391570902966 391570902966 381.038 0.0001
ERROR 20 20552842200 1027642110
U TOTAL 21 412123745166
ROOT MSE 32056.86 R-SQUARE 0.9501
DEP MEAN 104813 ADJ R-SQ 0.9476
C.V. 30.58482
NOTE: NO INTERCEPT TERM IS USED. R-SQUARE IS REDEFINED.
PARAMETER ESTIMATES
PARAMETER STANDARD T FOR HO:
VARIABLE DF ESTIMATE ERROR PARAMETERS PROS > |T|
TOT CON 1 21.26941308 1.08961041 19.520 0.0001
C-27
-------
COMMERCIAL EQUIPMENT • REGRESSION WITH TOTAL CONSTRUCTION AND OIL PRODUCTION
IABLE: COMM POP
ANALYSIS OF VARIANCE
SOURCE
MODEL
ERROR
C TOTAL
DF
SUM OF
SQUARES
MEAN
SQUARE
2 370534210391 185267105195
20 18361586771 918079338.54
22 388895797162
ROOT MSE
DEP MEAN
C.V.
30299.82
126128.5
24.02298
R-SQUARE
ADJ R-SQ
VARIABLE DF
INTERCEP 1
TOT COM 1
OIL~PROD 1
PARAMETER
ESTIMATE
16335.76794
15.31559258
0.55632647
PARAMETER ESTIMATES
STANDARD
ERROR
10776.01149
1.90201628
0.04994395
F VALUE
201.799
0.9528
0.9481
T FOR HO:
PARAMETERS
1.516
8.052
11.139
10:50 TUESDAY, MARCH 9, 1993
PROB>F
0.0001
PROB > |T|
0.1452
0.0001
0.0001
COLLINEARITY DIAGNOSTICS
NUMBER EIGENVALUE
2.162498
0.682194
0.155308
CONDITION
NUMBER
1.000000
1.780427
3.731479
VAR PROP
INTERCEP
0.0545
0.1502
0.7954
VAR PROP
TOT_CON
0.0498
0.0069
0.9433
VAR PROP
OIL_PROD
0.0699
0.6282
0.3018
C-28
-------
COMMERCIAL EQUIPMENT - REGRESSION WITH TOT CONST AND OIL PROD - FORCED ZERO INTERCEPT 6
10:50 TUESDAY, MARCH 9, 1993
OEP VARIABLE: COMM POP
ANALYSIS OF VARIANCE
SOURCE
MODEL
ERROR
U TOTAL
DF
SUM OF
SQUARES
MEAN
SQUARE
2 734317440595 367158720298
21 20471396220 974828391.42
23 754788836815
ROOT MSE 31222.24
DEP MEAN 126128.5
C.V. 24.75432
NOTE: NO INTERCEPT TERM IS USED. R-SQUARE IS REDEFINED.
R-SQUARE
ADJ R-SQ
F VALUE
376.639
0.9729
0.9703
PROB>F
0.0001
PARAMETER ESTIMATES
VARIABLE DF
TOT CON 1
OILJ>ROD 1
PARAMETER
ESTIMATE
17.57040980
0.53781424
STANDARD
ERROR
1.22153628
0.0499023
T FOR HO:
PARAMETERS
14.384
10.777
PROB > |T |
0.0001
0.0001
COLL I NEARITY DIAGNOSTICS
NUMBER EIGENVALUE
1 1.577467
2 0.422533
CONDITION
NUMBER
1.000000
1.932190
VAR PROP
TOT_CON
0.2113
0.7887
VAR PROP
OIL_PROD
0.2113
0.7887
C-29
-------
Data Used in Regression Analyses for
Industrial Equipment
State
Nation
CA
CO
CT
DE
FL
GA
IL
IN
LA
MD
MA
MN
MO
NH
NJ
NY
OH
PN
TX
UT
VA
WA
Wl
Dodge Statistics (Value, $Millions)
Non-
Residential
55,753.0
6,691.8
1,169.2
823.0
162.5
2,634.9
1,528.0
2,331.5
1,480.5
586.9
1,042.6
1,184.8
1,062.8
892.3
184.5
1,283.1
3,676.8
2,686.6
2,948.8
3,613.3
425.4
1,109.9
1,889.8
1,045.5
Residential
74,368.0
8,120.7
1,516.5
647.7
198.3
5,843.4
3,055.1
2,985.9
2,086.3
677.2
2,036.2
1,261.9
1,815.3
1,385.7
313.8
1,071.2
2,613.3
3,098.5
2,570.4
4,817.3
660.4
2,427.1
2,529.5
1.555.8
Non-
Building
35,279.0
3,777.2
1,259.9
342.3
135.3
1,418.8
538.7
1,718.2
623.9
759.8
548.3
1,122.9
748.1
644.4
108.3
1,258.8
2,936.9
1,482.9
1,331.8
2,081.0
186.7
1,265.1
932.3
625.6
Total
Construction
165,400.0
18,589.7
3,945.6
1,813.0
496.1
9,897.1
5,121.8
7,035.6
4,190.7
2,023.9
3,627.1
3,569.6
3,626.2
2,922.4
606.6
3,613.1
9,227.0
7,268.0
6,851.0
10,511.6
1,272.5
4,802.1
5,351.6
3.226.9
CBP
MfrEmp
2,140,959
184,893
383,455
67,621
517,930
580,809
1,033,272
620,193
1 63,435
231,375
600,730
387,642
432,073
110,611
684,408
1,249,626
1,119,170
1,051,180
938,491
94,934
429,930
326,080
530.128
PSR
Population
43,110
5,372
7,993
799
11,866
7,049
24,322
8,871
5,204
5,468
1 1 ,464
8,437
8,634
1,588
13,573
23,924
25,036
18,910
27,475
1,883
5,614
4,810
8.949
C-30
-------
OEP VARIABLE: INO POP
INDUSTRIAL EQUIPMENT - REG U/MANUFACTURING EMPLOYMENT
ANALYSIS OF VARIANCE
10:52 TUESDAY, MARCH 9. 1993 1
SOURCE DF
MODEL 1
ERROR 21
C TOTAL 22
ROOT MSE
DEP MEAN
C.V.
SUM OF
SQUARES
2209064094
157077543.01
2366141637
2734.937
12189.17
22.43743
MEAN
SQUARE
2209064094
7479883.00
R- SQUARE
ADJ R-SQ
F VALUE
295.334
0.9336
0.9305
PROB>F
0.0001
VARIABLE DF
INTERCEP 1
MFR EMP 1
PARAMETER
ESTIMATE
-379.266
0.02082825
PARAMETER ESTIMATES
STANDARD
ERROR
927.40687683
0.001211982
T FOR HO:
PARAMETERS
-0.409
17.185
PROS > |T|
0.6867
0.0001
C-31
-------
INDUSTRIAL EQUIPMENT - REG U/TOTAL CONSTRUCTION 10:52 TUESDAY, MARCH 9, 1993 2
DEP VARIABLE: IND POP
ANALYSIS OF VARIANCE
SOURCE DF
MODEL 1
ERROR 21
C TOTAL 22
ROOT MSE
DEP MEAN
C.V.
SUM OF
SQUARES
1893772515
472369122.77
2366141637
4742.76
12189.17
38.90961
MEAN
SQUARE
1893772515
22493767.75
R- SQUARE
ADJ R-SQ
F VALUE
84.191
0.8004
0.7909
PROB>F
0.0001
PARAMETER ESTIMATES
PARAMETER STANDARD T FOR HO:
VARIABLE DF ESTIMATE ERROR PARAMETERS PROB > |T|
INTERCEP 1 236.18946674 1635.54592 0.144 0.8866
TOT CON 1 2.29885844 0.25054136 9.176 0.0001
C-32
-------
INDUSTRIAL EQUIPMENT - REG U/TOT CONST AND MFR EMPLOYMENT
10:52 TUESDAY, MARCH 9, 1993 3
DEP VARIABLE: INO POP
ANALYSIS OF VARIANCE
SOURCE
DF
SUM OF
SQUARES
MODEL 2 2223666886
ERROR 20 142474751.15
C TOTAL 22 2366141637
INTERCEP
TOT CON
MFR'EMP
ROOT MSE
DEP MEAN
C.V.
VARIABLE DF
2669.033
12189.17
21.89675
MEAN
SQUARE
1111833443
7123737.56
R-SQUARE
ADJ R-SQ
PARAMETER
ESTIMATE
-687.942
0.44009757
0.01754764
PARAMETER ESTIMATES
STANDARD
ERROR
930.38319149
0.30738663
0.002578607
F VALUE
156.074
0.9398
0.9338
T FOR HO:
PARAMETERS
-0.739
1.432
6.805
PROB>F
0.0001
PROB > |T|
0.4682
0.1677
0.0001
COLL I NEARITY DIAGNOSTICS
NUMBER EIGENVALUE
1
2
3
2.698238
0.260361
0.0414013
CONDITION
NUMBER
1.000000
3.219233
8.072964
VAR PROP
INTERCEP
0.0402
0.9568
0.0029
VAR PROP
TOT_CON
0.0100
0.0414
0.9486
VAR PROP
MFR_EMP
0.0102
0.0500
0.9398
C-33
-------
INDUSTRIAL EQUIPMENT - REG W/TOT CONST AND MFR EMPLOYMENT - FORCED ZERO INTERCEPT
IABLE: INO POP
ANALYSIS OF VARIANCE
C.V. 21.65915
NOTE: NO INTERCEPT TERM IS USED. R-SQUARE IS REDEFINED.
10:52 TUESDAY, MARCH 9, 1993
SOURCE DF
MODEL 2
ERROR 21
U TOTAL 23
ROOT MSE
DEP MEAN
SUM OF
SQUARES
5637019162
U6369571.26
5783388733
2640.072
12189.17
MEAN
SQUARE
2818509581
6969979.58
R- SQUARE
ADJ R-SQ
F VALUE
404.378
0.9747
0.9723
PROB>F
0.0001
VARIABLE DF
TOT CON 1
MFR~EMP 1
PARAMETER
ESTIMATE
0.38742900
0.01726934
PARAMETER ESTIMATES
STANDARD
ERROR
0.29577522
0.002523311
T FOR HO:
PARAMETERS
1.310
6.844
PROB > |T|
0.2044
0.0001
COLL1NEARITY DIAGNOSTICS
NUMBER EIGENVALUE
1 1.958496
2 0.0415041
CONDITION
NUMBER
1.000000
6.869355
VAR PROP
TOT_CON
0.0208
0.9792
VAR PROP
MFR_EMP
0.0208
0.9792
C-34
-------
APPENDIX D
Correspondence With U.S. Air Force Regarding Nonroad
Vehicle Usage on Military Installations
-------
September 17, 1992
research
15211 Street
Sacramento, CA 95814
(916)444-6666
Mr. David Carillo Fax: (916) 444-8373
Compliance Program Manager
Directorate of Environmental Quality
HQ USAF CEVC
Boiling Air Force Base
Washington, D.C. 20332
Dear Mr. Carillo:
As we have previously discussed, Sierra Research, Inc. has been
contracted by the U.S. Environmental Protection Agency (EPA) to develop
methodologies to estimate nonroad vehicle and equipment usage. Because
military installations could contribute a significant fraction of the
nonroad inventory in some nonattainment areas, it is important for local
air quality planners to have a proper assessment of that contribution.
Thus, Sierra is interested in obtaining recommendations for the most
efficient means of developing estimates of nonroad vehicle and equipment
usage at military installations.
Attached is a brief description of the project as it relates to military
installations. It would be most helpful if you could distribute this to
the Air Programs Technical Committee at, or prior to, its meeting
scheduled for September 23, 1992. Any information you can provide
relating to nonroad equipment usage and recommendations for retrieval of
this information from individual 'bases would be very helpful in
developing guidance for estimating nonroad equipment usage at military
installations.
Thank you for your assistance in this matter. If you have any questions
about the study, I can be reached at (916) 444-6666. The EPA project
manager is Kevin Green. Questions can also be directed to him at (313)
668-4510.
Sincerely,
y
Philip Heirigs
Attachment
cc: Kevin Green, EPA
D-l
-------
ATTACHMENT
Nonroad Equipment Usage on Military Installations
Background
The 1990 Clean Air Act Amendments required the U.S. Environmental
Protection Agency (EPA) to determine the emissions impact of nonroad
vehicles and equipment on certain metropolitan areas that do not meet
the National Ambient Air Quality Standards (NAAQS) for ozone and/or
carbon monoxide. The result of this mandate was the "Nonroad Engine and
Vehicle Emissions Study" (NEVES) published by EPA in November 1991.
Although this study improved the state-of-the-art in developing nonroad
emissions estimates, there is concern that the activity levels developed
for specific nonattainment areas may not accurately reflect the
distribution or usage of equipment that actually occurs within those
communities. Hence, Sierra Research has been contracted by the EPA to
investigate alternative data sources and methodologies that may be used
to develop estimates of nonroad equipment activity at the local level.
The approach utilized in NEVES to determine county-level equipment
populations and usage can be categorized as a "top—down" methodology in
which national estimates of equipment population were scaled to the
local level using activity indicators specific to different equipment
categories (e.g., the number of employees in the construction industry
was used to allocate construction equipment). This approach, however,
neglected to account for nonroad equipment that is used on military
installations. Because military installations could potentially
represent a significant portion of the nonroad equipment usage in
certain nonattainment areas, it is important to account for them in
developing local inventories.
Nonroad Equipment and Vehicle Types
Eight different equipment categories are being considered in Sierra's
analysis of nonroad vehicle usage. These include lawn and garden
equipment, aircraft support equipment, recreational equipment, light
commercial equipment, industrial equipment, construction equipment,
agricultural equipment, and logging equipment. The specific equipment
types falling within these categories are listed in Table 1. Obviously,
not all of these categories are applicable to military installations,
however, some may be used to a fair degree. Thus, Sierra is interested
in obtaining recommendations on how local air quality planners could
obtain the necessary information from military installations to develop
an accurate estimate of nonroad equipment usage.
Data Requirements
Because of the large number of nonroad equipment types (and potentially
large number of individual pieces of equipment), an assessment of
D-2
-------
equipment usage may, at first glance, appear to be infeasible. However,
surrogates for equipment usage may provide reliable estimates of actual
equipment activity without having to track each piece of equipment
separately. One such surrogate that has been used with some success in
the past is fuel usage. For example, if the total amount of fuel used
for landscape maintenance is available and a rough approximation of the
mix of lawn and garden equipment can be made, then this information can
be used to obtain a reasonably accurate estimate of emissions from lawn
and garden equipment.
The biggest challenge in developing methodologies to estimate nonroad
equipment usage on military installations appears to be identifying the
kind of information that could be easily compiled by individual bases
for use by local air quality planners. This is an area in which the
Technical Committee might be able to provide considerable input. Thus,
Sierra is soliciting recommendations for potential indicators of nonroad
equipment activity and the most efficient means of obtaining the
information needed to develop nonroad equipment usage estimates on
military installations.
-2-
D-3
-------
Table 1
Equipment Categories Used by EPA in the 1991
"Nonroad Engine and Vehicle Emissions Study"
Equipment Category
Lawn and Garden
Airport Service
Recreational
Light Commercial
Industrial
Equipment Types
Trimmers/Edgers/Brush Cutters
Lawn Mowers
Leaf Blowers/Vacuums
Rear Engine Riding Mowers
Front Mowers
Chains aws <4 HP
Shredders <5 HP
Tillers <5 HP
Lawn and Garden Tractors
Wood Splitters
Snowblowers
Chippers/Stump Grinders
Commercial Turf Equipment
Other Lawn and Garden
Aircraft Support Equipment
Terminal Tractors
All Terrain Vehicles (ATVs)
Minibikes
Off-Road Motorcycles
Golf Carts
Snowmobiles
Specialty Vehicles/Carts
Generator Sets
Pumps
Air Compressors
Gas Compressors
Welders
Pressure Washers
Aerial Lifts
Forklifts
Sweepers/Scrubbers
Other General Industrial Equipment
Other Material Handling Equipment
-3-
D-A
-------
Equipment Category
Construction
Agricultural
Logging
Equipment Types
Asphalt Pavers
Tampers/Rammers
Plate Compactors
Concrete Pavers
Rollers
Scrapers
Paving Equipment
Surfacing Equipment
Signal Boards
Trenchers
Bore/Drill Rigs
Excavators
Concrete/Industrial Saws
Cement and Mortar Mixers
Cranes
Graders
Off-Highway Trucks
Crushing/Processing Equipment
Rough Terrain Forklifts
Rubber Tired Loaders
Rubber Tired Dozers
Tractors/Loaders/Backhoes
Crawler Tractors
Skid Steer Loaders
Off-Highway Tractors
Dumpers/Tenders
Other Construction Equipment
2-Wheel Tractors
Agricultural Tractors
Agricultural Mowers
Combines
Sprayers
Balers
Irrigation Sets
Tillers >5 HP
Swathers
Hydro Power Units
Other Agricultural Equipment
Chainsaws >4 HP
Shredders >5 HP
Skidders
Fellers/Bunchers
-4-
D-5
-------
APPENDIX E
NEVES Inventory B Construction Equipment Activity Estimates
for the U.S., DC/MC/VA, and the San Joaquin Valley Air Basin
-------
NEVES Inventory B Construction Equipment Activity Estimates
National Estimates
Equipment Type
Asphalt Pavers
Tampers/Rammers
Plate Compactors
Concrete Pavers
Rollers
Scrapers
Paving Equipment
Surfacing Equipment
Signal Boards
Trenchers
Bore/Drill Rigs
Excavators
Concrete/Industrial Saws
Cement & Mortar Mixers
Cranes
Graders
Off-Highway Trucks
Crushing/Proc Eqpt
Rough Terrain Forkl'rfts
Rubber Tired Loaders
Rubber Tired Dozers
Trctrs/Ldrs/Backhoes
Crawler Tractors
Skid Steer Loaders
Off- Highway Tractors
Dumpers/Tenders
Other Construction Eqpt
Total
Population
Diesel
12,000
0
2,322
8,400
42,800
16,400
43,615
0
20,384
53,390
7,761
52,295
61,336
4,016
98,357
64,000
19,400
7,207
25,132
130,000
7,757
189,000
159,050
140,000
38,921
194
11,867
1,215.604
Gas
0
23,611
145,233
0
0
0
230,810
30,833
1,559
27,170
8,501
0
36,900
232,152
2,541
0
0
1,007
2,217
0
0
0
0
0
0
24,301
1,103
767.938
Activity (1000 Bhp-hr/yr)
Diesel
421,196
0
3,383
294,837
1,704,965
3,952,236
1,160,099
0
71,110
270,316
473,367
5,250,434
1,221,613
5,476
5,753,469
4,694,216
10,508,951
599,983
645,043
17,174,430
1,332,898
3,569,454
12,403,352
1,850,957
4,652,038
797
592,223
78.606.841
Gas
0
7,397
58,012
0
0
0
135,957
44,827
1,987
0
32,401
0
191,723
64,833
23,716
0
0
10,892
0
0
0
0
0
0
0
9,452
24,307
605.503
Total
421,196
7,397
61,394
294,837
1,704,965
3,952,236
1,296,055
44,827
73,098
270,316
505,768
5,250,434
1,413,336
70,310
5,777,184
4,694,216
10,508,951
610,875
645,043
17,174,430
1,332,898
3,569,454
12,403,352
1,850,957
4,652,038
10,249
616,530
79.212.344
% Total
Activity
0.5
0.0
0.1
0.4
2.2
5.0
1.6
0.1
0.1
0.3
0.6
6.6
1.8
0.1
7.3
5.9
13.3
0.8
0.8
21.7
1.7
4.5
15.7
2.3
5.9
0.0
0.8
100.0
E-l
-------
NEVES Inventory B Construction Equipment Activity Estimates
DC/MD/VA Estimates
Equipment Type
Asphalt Pavers
Tampers/Rammers
Plate Compactors
Concrete Pavers
Rollers
Scrapers
Paving Equipment
Surfacing Equipment
Signal Boards
Trenchers
Bore/Drill Rigs
Excavators
Concrete/Industrial Saws
Cement & Mortar Mixers
Cranes
Graders
Off-Highway Trucks
Crushing/Proc Eqpt
Rough Terrain Forklifts
Rubber Tired Loaders
Rubber Tired Dozers
Trctrs/Ldrs/Backhoes
Crawler Tractors
Skid Steer Loaders
Off-Highway Tractors
Dumpers/Tenders
Other Construction Eqpt
Total
Population
Diesel
28
0
40
20
171
234
760
0
355
880
135
678
2
70
1,713
278
201
126
938
910
135
3,024
3,898
2,940
678
3
207
18.424
Gas
0
411
2,530
0
0
0
4,020
537
27
473
148
0
643
4,044
44
0
0
18
39
0
0
0
0
0
0
423
19
13.376
Activity (1000 Bhp-hr/yr)
Diesel
994
0
59
691
6,812
56,392
20,207
0
1,239
4,454
8,245
68,071
47
95
100,215
20,390
108,881
10,451
24,076
120,221
23,217
57,111
303,982
38,870
81, OX
14
10,315
1.066.080
Gas
0
129
1,010
0
0
0
2,368
781
35
0
564
0
3,339
1,129
413
0
0
190
0
0
0
0
0
0
0
165
423
10.547
Total
994
129
1,069
691
6,812
56,392
22,575
781
1,273
4,454
8,810
68,071
3,386
1,225
100,629
20,390
108,881
10,640
24,076
120,221
23,217
57,111
303,982
38,870
81,030
179
10,739
1.076.627
% Total
Activity
0.1
0.0
0.1
0.1
0.6
5.2
2.1
0.1
0.1
0.4
0.8
6.3
0.3
0.1
9.3
1.9
10.1
1.0
2.2
11.2
2.2
5.3
28.2
3.6
7.5
0.0
1.0
100.0
E-2
-------
NEVES Inventory B Construction Equipment Activity Estimates
San Joaquin Valley Air Basin Estimates
Equipment Type
Asphalt Pavers
Tampers/Rammers
Plate Compactors
Concrete Pavers
Rollers
Scrapers
Paving Equipment
Surfacing Equipment
Signal Boards
Trenchers
Bore/Drill Rigs
Excavators
Concrete/Industrial Saws
Cement & Mortar Mixers
Cranes
Graders
Off- Highway Trucks
Crushing/Proc Eqpt
Rough Terrain Forklifts
Rubber Tired Loaders
Rubber Tired Dozers
Trctrs/Ldrs/Backhoes
Crawler Tractors
Skid Steer Loaders
Off- Highway Tractors
Dumpers/Tenders
Other Construction Eqpt
Total
Population
Diesel
57
0
21
39
342
94
397
0
185
459
71
144
1
37
895
442
51
66
490
1,040
71
1,701
900
1,323
354
2
108
9.288
Gas
0
215
1,321
0
0
0
2,099
280
14
247
77
0
336
2,111
23
0
0
9
20
0
0
0
0
0
0
221
10
6.984
Activity (1000 Bhp-hr/yr)
Diesel
2,450
0
44
1,702
13,963
24,812
14,758
0
877
2,437
5,981
15,563
30
67
59,543
36,349
30,387
7,441
13,852
144,275
15,053
36,164
83,728
18,581
47,992
10
6,594
582.652
Gas
0
86
748
0
0
0
1,730
553
25
0
409
0
2,117
798
245
0
0
135
0
0
0
0
0
0
0
121
271
7.238
Total
2,450
86
791
1,702
13,963
24,812
16,487
553
902
2,437
6,390
15,563
2,147
866
59,788
36,349
30,387
7,576
13,852
144,275
15,053
36,164
83,728
18,581
47,992
131
6,864
589.889
% Total
Activity
0.4
0.0
0.1
0.3
2.4
4.2
2.8
0.1
0.2
0.4
1.1
2.6
0.4
0.1
10.1
6.2
5.2
1.3
2.3
24.5
2.6
6.1
14.2
3.1
8.1
0.0
1.2
100.0
E-3
-------
APPENDIX F
Summary of 1987 Construction Census Data by SIC Code
-------
Item
Net Value of Construction Work
Work Subbed IN from Others
Work Subbed OUT to Other*
Materials, Components, Supples
Power, Fuels, Lubes
Becticfty
Natural Gas
Gasoline and Diesel Fuel
On -Highway
Off-Highway
Other, Incl. Oil and Qrease
Mails, Cmpnts, Spla, Fuels (MCSF)
Fraction Off-Hwy Fuel Use
Fraction Off-Hwy Fuel Use
Normalized to SFH MCSF
Net Value of Construction Work
Fraction Off-Hwy Fuel Use
Fraction Off-Hwy Fuel Us*
Normalized to SFH Net Value
SIC 152 - Qnl Cntrs, Res
SIC 1521
Single Family
Housing
27,319,239
3.035,374
11,778,907
12,863,522
493,123
89,445
16,792
356,584
323,666
32.917
30,300
13,356,645
0.0025
1.00
27,319,239
0.00120
1.00
SIC 1522
Residential,
Non-SFH
6,257.443
707,613
7,058,100
2,796,294
80,036
19,832
2.867
52.123
46,602
5,521
5,213
2,876,330
0.0019
0.78
6.257,443
0.00088
0.73
SIC 1531
Operative
Builders
26,837,792
559,465
22,122,017
12.773,237
379,023
143,007
30.464
187.447
168,907
18,539
18,104
13,152.260
0.0014
0.57
26.837,792
0.00069
0.57
SIC 154 - Qnl Cntrs, Nonres
SIC 1541
Ind Bldgs &
Warehouses
11,094,502
1,143,085
10,367,065
4,897,508
170,543
29.785
9.366
119,340
96.819
22,520
12,051
5,068,051
0.0044
1.80
11,094,502
0.00203
1.68
SIC 1542
Nonresident! al
Buildings
39,510,241
3,024,631
50,283,190
15.984,990
600,125
123,906
34.593
404.289
337,666
66,622
37,337
16,585,115
0.0040
1.63
39,510,241
0.00169
1.40
SIC 1611
Highway &
Street Const
27.983,839
7,065,089
6,177,587
11,067.102
1.163.712
103,582
76,196
886.197
397.964
488,233
97,736
12.230,814
0.0399
16.20
27,983,839
0.01745
14.48
SIC 162 - Heavy Const, Except Highway
SIC 1622
Bridge, Tunnel
Elev Hwy
4,186,846
745,097
1,294,090
1,767.054
87,835
12,623
2.711
65.599
31.998
33,600
6,901
1,854,889
0.0181
7.35
4.186,846
0.00803
6.66
SIC 1623
Water, Sewer
Utility Lines
15.055.297
3.419.333
1.954.721
4.734,144
476,371
32.128
7.666
400,312
209,814
190,498
36,264
5.210,515
0.0366
14.83
15.055,297
0.01265
10.50
SIC 1629
Hvy Const,
Other
21,209,274
3,294,752
4.423,695
6,949.590
636.206
65.774
5.350
512.577
213,346
299.231
52,503
7,585.798
0.0394
16.01
21.209,274
0.01411
11.71
-------
N)
Item
Net Value of Construction Work
Work Subbed IN from Other*
Work Subbed OUT to Other*
Material*, Component*, Supple*
Power, Fuels, Lubes
BectJctty
Natural Qas
Gasoline and Diesel Fuel
On-Hl9hway
Off-Highway
Other, Incl. Oil and Grease
Mats, Cmpnts, Sola, Fuels (MCSF)
Fraction Off-Hwy Fuel Use
Fraction Off-Hwy Fuel Use
Normalized to SFH MCSF
Net Value of Construction Work
Fraction Off-Hwy Fuel Use
Fraction Off-Hwy Fuel Use
Normalized to SFH Net Value
SIC 1711
Plumbing,
HVAC
44,517,739
21,987,082
4.985.584
18,556,072
766.206
119,454
32,481
567,556
519,004
48.551
46,713
19,322.278
0.0025
1.02
44,517,739
0.00109
0.91
SIC 1721
Painting,
Paper Hanginc
7,445,552
3.405.709
507.770
1,641,607
162,121
19,761
4,543
129,315
114,005
15.309
6.501
1,803.728
0.0085
3.44
7,445,552
0.00206
1.71
SIC 1731
Electrical
Work
34.657,764
16,067,963
1,180.462
12.788,495
489.713
81.132
16.935
362,957
337.671
25.286
28,688
13.278.208
0.0019
0.77
34,657,764
0.00073
0.61
SIC 1741
Masonry & Oth
Stone Work
8,269,188
5,477.793
444.973
2.715.354
142,508
13,033
3,173
116,154
100.806
15.348
10,147
2,857,862
0.0054
2.16
8,269,188
0.00186
1.54
SIC 1742
Plastering,
Drvwall. Insl
15,137.323
11,653,461
1,289,527
5,521,016
192,618
23,317
6,686
151,494
139,341
12,153
11,120
5,713,634
0.0021
0.86
15.137,323
0.00080
0.67
SIC 1743
Tile. Marble,
Mosaic
2.181,972
1,317,188
89.620
871.938
36,522
5,156
1,129
27,720
25,953
1.766
2,516
908,460
0.0019
0.79
2,181,972
0.00081
0.67
SIC 1751
Carpentry
10,038.947
4.964.753
1.204,915
3.997.519
172.121
26,963
5.481
129.077
118,997
10.080
10.599
4,169,640
0.0024
0.98
10,038.947
0.00100
0.83
SIC 1752
Floor Laying
& Other Fir
3,371,200
1,626,503
280,234
1,548,341
63,085
11,249
2,204
46,453
43.785
2.667
3.178
1.611.426
0.0017
0.67
3,371.200
0.00079
0.66
SIC 1761
Roofing, Sdg
Sheet Metal
14.182,802
5.685.007
845.003
5.637.184
252.272
39,938
12,525
184.020
170.647
13,373
15.786
5,889.456
0.0023
0.92
14.182,802
0.00094
0.78
-------
U)
Net Value of Construction Work
Work Subbed IN from Others
Work Subbed OUT to Other*
Material*. Component*, Supple*
Power, Fuel*, Lube*
Bectictty
Natural Qaa
Gasoline and Diesel Fuel
On-Hlghway
Off-Highway
Other, Incl. Oil and Grease
Mads, CmpnU, Spls, Fuel* (MCSF)
Fraction OH-Hwy Fuel Use
Fraction OH-Hwy Fuel Use
Normalized to SFH MCSF
Net Value of Construction Work
Fraction Orf-Hwy Fuel Use
Fraction Off-Hwy Fuel Use
Normalized to SFH Net Value
SIC 1771
Concrete
Work
13,853,510
9,045,744
1,202,160
5,242,978
323,906
28,072
10,223
263,008
215,662
47,345
22,602
5,566,884
0.0085
3.45
13,853,510
0.00342
2.84
SIC 1781
Water Well
Drillina
1,299,288
213,779
30,768
495.741
62,217
5,169
883
51,794
37,230
14,564
4,369
557,958
0.0261
10.59
1,299,288
0.01121
9.30
SIC 1791
Structural
Steel
4,510,231
2,854,127
352,424
1,318.112
80,001
12,184
3,260
60,043
50,011
10,031
4,513
1,398,113
0.0072
£91
4,510,231
0.00222
1.85
SIC 1793
Glass &
Glazing
3,142,354
1,767,757
80,118
1,599.680
52,933
12,799
3,600
34.614
32,604
2,210
1,917
1.652,613
0.0013
0.54
3,142,354
0.00070
0.58
SIC 1794
Excavation
Work
7,490,988
4,281,940
753,409
1,582,800
408,444
23,079
3,696
348,871
160.059
188,812
32,796
1,991,244
0.0948
38.48
7,490,988
0.02521
20.92
SIC 1795
Wrecking &
Demolition
844,714
364,834
67,769
79,210
30,000
2,285
501
24,766
15.431
9,334
2,446
109.210
0.0855
34.68
844,714
0.01105
9.17
SIC 1796
Install Bldg
Equipment
5,009,764
1,489,221
350.061
1,363.562
66.236
11,696
3,388
47,437
42,714
4,722
3,714
1,429,798
0.0033
1.34
5.009.764
0.00094
0.78
SIC 1799
Special Trade
Contractors
9,832,759
3,523.255
981,554
3,439.643
250,775
33.931
6.745
196.900
174,695
22.205
13.198
3,690,418
0.0060
2.44
9.832.759
0.00226
1.87
SIC 6552
Land Subdvdr
& Developers
2.505,153
84.450
2.130.768
606.656
67.440
23.497
2,665
34.952
31,028
3.924
6,325
674.096
0.0058
2.36
2.505.153
0.00157
1.30
-------
APPENDIX G
Sample Copy of Dodge Construction Potentials Bulletin
for the Pacific Southwest (August 1992)
-------
DODGE ^ /
Construction * 1
Potentials
Bulletin
' | — >j. -/> Pacific Southwest
^^^BB ? " >-B Rcrjion 9 Nevada
^^•* y~ s ) Arizona New Mexico
^^•W ( \ ( California Hawaii
^\(^rJ^\\ Colorado Utah
August 1992
New York, N.Y. 10020
Contracts for New, Addition and Major Alteration Projects
Square Feet in Thousands
Value in SMillions
Current Month
Total Construction
Total Building
Non-Residential
Residential
Non-Building
Number of
Projects
15,905
14.727
2,185
12,542
1,178
Square
Feet
42,261
42,261
17,646
24,615
—
Value
4,331.2
2,980.8
1 ,388.0
1 ,592.8
1,350.3
Last Year
Value
4,538.0
3,651.0
1 ',986.2
1 ,664.8
, 887.0
Cumulative to Date*
Non-Residential
Commercial
Stores & Food Service
Warehouses (Ex. Mfr. Own.)
Office & Bank Buildings
Hotels & Motels
Garages & Service Stations
Manufacturing
Manufacturing Plants
Warehouses (Mfr. Owned)
Laboratories (Mfr. Owned)
Education & Science
Schools & Colleges
Laboratories (Ex. Mfr. Own.)
Libraries, Museums, etc.
Dormitories
Hospital & Health Treatment
Public Buildings
Government Administration
Other Government Service
Religious
Amusement
Miscellaneous Non-Res.
1,431
432
145
753
28
73
98
78
20
0
235
179
18
38
9
115
46
17
29
62
132
57
11,457
2,966
3,490
2,061
72
2,868
445
194
251
0
2,084
1,540
334
211
56
584
499
26
473
230
549
1,743
706.4
214.6
133.0
270.4
11.2
77.2
37.1
18.3
18.8
0.0
273.9
184.7
51.7
37.5
4.7
86.2
70.2
10.8
59.4
23.1
76.5
109.9
778.1
21 Z3
116.3
231.0
131,3
,87.2
154:2
' 91. n
' 3.7
;s9.s
275.1
'193.2
68.0
14.0
9.7
. 171.4
,149.4
13.4
135.9
39.4
96.2
312.8
Residential
One-Family Houses
Two-Family Houses
Apartment Buildings
Non-Building
12,198
44
300
21,917
153
2,545
1.440.1 1,423.3
8.2 69.6
144.6 171.9
Streets & Highways
Bridges
Dams & Reservoirs
River & Harbor Development
Sewerage & Waste Disposal
Water Supply Systems
Elec. Power & Heating Sys.
Gas Systems
Communication Systems
Airport & Space Facilities
Miscellaneous Non-Bldg.
329
31
5
87
107
138
26
0
11
11
433
- - - 289.2
65.7
1.8
- - 72.7
- - 46.8
- - 72.3
- - 413.0
- - 0.0
1.6
- - 55.1
- - 332.0
>272.1
- 51.4
- 4.2
71.0
58.0
109.3
'44.2
1.6
0;7
33.5
241.1
Number of Projects
This Year Last Year
137,348
126,340
18,038
108,302
11,008
122,863
115,914
'14.704
101,210
Square Feet
This Year Last Year
336,730
336,730
124,067
212,663
371,460
This Year
33,154.6
371,460 25,972.4
1 39,81 8M 1,853.0
231,642.14,119.5
6,949' - - - '---
7,182.2
Value
Last Year '
32,671.7
26,454.1
12,182.5
14,271.6
6,217.6
> c.-..
-1
-2
•3
+ 15
11,796
3,374
973
6,404
256
789
751
610
134
7
1,868
1,499-
165
204
80
1,059
355
110
245
544
1,137
448
9,772 76,275
3,052 26,225
- 1,267- 16,145
4,633s 12,224
'• 182, 8,346
' ' 638 13,334
669
557
93
, 19
1,568
'1,266
178
124-
71
76Z
340
86
, 254
5,360
4,444
913
3
15,628
11,462
1,849
2,317
974
5,415
4,872
1,087
3,784
377' 3,094
853
292
6,132
6,317
83,205 5,948.8
29,191 !: 1,806.6
23,983; 683.2
... 15,2531 1.853.0
4;196? 1.080.1
10,583:;: 525.8
11,196? 481.5
7.628J 388.3
2,307s 86.9
1.2601 6.3"
16,970- 2,130.7
12,838; 1,522.1
3,083: 264.7
1,049; 344.0
1,097
6,130
7,224
: 735
6,489
2,498
4,714
..:;--. •>_;
6,785
96.4
866.4
653.3
201.7
451.6
; 282.7
i 950.7
442.5
5,716.8
. 1 ,825.9
:917;8
2,050.1
461 .3
461.7
v 731.6
'.';•'• 5o^6
ss:i
143.9
2,120.8
1,573.9^
375.2^
171.7
103.3
970.8
1,038.9
121.4
917.5
260.4
640.5
599.5
- 1
•25
-10
+ 14
-34
-23
+2
-96
I
-7
-11
-37
+55
.51
+9
*48
-25
• -
105,353
884
2,065
:95,009
3.483
2.718
' 193,040
2,634
16,990
189,384 12,876.6
11,805. 149.0
30.453- 1,093.9
11,735.2
592.6
1,943.8
.» 1 r.
2,688
235 -
68
606
682
949
166
2
58
103
5,451
2,316
243-
62
603
518
868-
176
20
52 --- •
' 134
1.957 ---
.'.
.
.
.
..
'.
.
.
•..
.'.
-•
-. 1,994.0
517.3
81.2
492.3
826.0
799.2
569.6
0.7
21.9
268.0
; 1,612.1
2,121.9 -5
274.8 -33
59.8 -25
441.1 -12
854.9 -3
797.5 -C
466.7 -22
25.0 -9T
20.1 -9
151.1 -7~
1.004.7 •-.!
This report li confidential. Reproduction or dissemination of any Information
contained herein is granted only by contract or prior written permission from
McGraw-Hill, Inc. CopyrightO (1992) McGnrw-HII. Inc.
G-l
'"Cumulative to Date* figure* Include delayed entries and adjustments attectino proitcu
in previously reported months. In the S Change column, increases of 100% or mwe are
by ».
-------
DQDGE
itruction
fntials
Bulletin
Pacific Southwest
August 1992
Non-Residential Building
Non-Residential Construction Value
Sep OS Nov Doc Jan Feb Mar Apr May Jun Jul Aug
Last 12 Months: Sep. 91 - Aug. 92
Previous Year: Sep. 90 • Aug. 91
Metropolitan Areas/Counties
Square Feet in Thousands, Value in SMiiiions
Current Month
Albuquerque, NM
Anaheim-Santa Ana, CA
Bakersfield, CA
Boulder-Longmont, CO
Chico, CA
Colorado Springs, CO
Denver, CO
Adams, CO
Arapahoe, CO
Denver, CO
Douglas, CO
Jefferson, CO
Fort Collins-Loveland, CO
Fresno, CA
Greeley, CO
Honolulu, HI
Las Cruces, NM
Las Vegas, NV
Los Angeles-Long Beach,
Merced, CA
Modesto, CA
Oakland, CA
Alameda, CA
ConttaCosta, CA
O^^Bfentura, CA
P^WAZ
Number of
Projects
31
113
25
10
8
26
81
2
18
46
2
13
9
10
3
164
7
65
391
1
20
111
68
43
31
116
Square
Feet
240
426
107
26
34
500
2,845
13
106
2,563
9
155
65
51
26
1,128
14
268
1,683
5
138
540
289
250
55
1,893
Value
22.5
51.9
5.4
2.4
2.0
38.8
121.0
1.4
8.5
101.1
0.5
9.5
4.8
3.2
1.6
103.7
5.7
22.3
183.2
0.6
7.9
78.0
51.2
26.8
8.9
87.2
Cumulative to Date
Number of Projects
This Year Last Year
218
1,017
223:,
107
67 ,
149 ''/,;'
1,653
60^- '"
181
1,226
19
167
64
77
20
777
44
503
2,694 2
24
120
974 - ' ,
627
347
216
865
109
961
247
94
22
143
409
46
77
200
16
70
47
213
20
739
31
386
,529
36
135
800
488
312
223
791
Square Feet
This Year Last Year
947
4,084
2,892
1,209
248
1,485
7,929
246
597
5,819
230
1,038
498
1,629
272
4,880
172
11,106
13,433
222
752
4,685
2.800
1,885
1,240
10,145
1,049
8,875
1,818
1,284
139
-.:-'< 931-
6,326
- 182
- 706
, 4,716
253
469
,587
1,880
90
6,785
265
, 6,427
,21,705
391
768
5,093
3,307
1,786
1,707
5,984
This Year
99.3
486.6
178.8
81.6
22.6
134.4
618.5
36.7
61.0
433.7
9.2
77.8
41.7
135.0
18.5
553.6
19.1
1,214.3
1,493.7
10.7
59.3
612.3
375.7
236.6
147.0
681.5
Value
Last Year
\\::, ;;92.7<
,677.9
;vp|32,1^
'-"',' "''76.7:
'->'4y>;;l6:is
^0.^81:9?
, '* 590^8
,- •-'- 25.0
' , - -70.4
-- - "436:7'
" ,16.1
- ;, -; 42.6-,
-;/'?v*5£pt-
"V :139.S!
;'V,'S' 8.0T
-, ' 73t:4?
v:V,26lt;
456.4
•;\'2;155.4
, 'V -23.1
/ - 65.1
' >-496.8
-.- ' V,306.9
- • ,-189.8,
, „ In 6,0
s , , 523.7
SCh.
+7
-28
+35
+6
*
+64
+5
+47
-13
-1
-43
+83
-20
-3
*
-24
-27
*
-31
-54
-9
+23
+22
+25
+27
+30
This report Is confidential. Reproduction or dissemination of any information contained herein'» granted
only by contract or prior written permission from McGraw-Hill. Inc. Copyright O (1992) McGnm-HiO, Inc.
G-2
-------
DODGE
Construction
Potentials
Bulletin
Pacific Southwest
August 1992
Non-Residential Building
Metropolitan Areas/Counties
Provo-Orem, UT
Pueblo, CO
Redding, CA
Reno, NV
Riverside-San Bernardino,
CA
Riverside, CA
San Bernardino, CA
Sacramento, CA
El Dorado, CA
Placer, CA
Sacramento, CA
Yolo, CA
Salinas-Seaside-Monterey,
A
Salt Lake Clty-Ogden, UT
Davis, UT
Salt Lake, UT
Weber. UT
San Diego, CA
San Francisco, CA
Marin, CA
San Francisco, CA
San Mateo, CA
San Jose, CA
Santa Barbara-Santa Maria-
Lompoc, CA
Santa Cruz, CA
Santa Fe, NM
Los Alamos, NM
Santa Fe, NM
Santa Rosa-Petaluma, CA
Stockton, CA
Tucson, AZ
Vallejo-Fairfield-Napa, CA
Napa, CA
Solano, CA
Visalia-Tulare-Porterville,
CA
Yuba City, CA
Sutler, CA
Yuba, CA
Yuma, AZ
Current Month
Number of Square
Projects Feet Value
9 4 0.9
0 0 0.0
24 77 5.6
12 23 1.4
69 778 64.6
37 713 57.4
32 65 7.2
65 212 24.2
8 51 4.0
11 70 6.4
43 84 12.9
3 6 0.8
15 62 7.3
69 1,034 43.0
8 40 2.3
47 373 18.5
14 622 22.2
99 616 64.2
126 1,057 114.2
4 568 58.3
90 287 43.2
32 202 12.7
100 199 40.7
17 11 2.0
3 2 0.6
8 89 5.4
0 0 0.0
8 89 5.4
16 78 7.0
13 140 4.6
35 135 13.1
18 409 48.4
3 15 1.0
15 394 47.4
9 115 4.5
4 192 8.2
4 192 8.2
0 0 0.0
5 25 4.0
Square Feet in Thousands, Value in $Millions
Cumulative to Date
Number of Projects
This Yew Last Yew
138 61
22 29
76 '" 50
105 , 145
1,026 959
555 = " , 483,
471 , - 476;
524 427
48 11
66 75
376.:. 296
34/ ,45
101 105
673 322
56 * 29
539,- ,- 251
78 -/ „ ,,42-
1,112 ; 986
1,214 1,150
64 40
906, , 956
244 ,, 154
624;, 495
138 121
38 ;;; - ', 58
46 ~37
6-, ,-, ,11
40: -, ''26
125 136
111 110
316 , , 149
131 " 89
36 ' 25
95 64
80 88
38 31
31 '19
7 12
29 " 23
Squwe Feet
This Yew Last Yew
539 ,: 830
63 , t 309
261 '-"' 415
1,208 \ 735
10,300 V" 16,803
5.643 ,- 7.984
4,657 ' 8.820
4.580 5,710
192 149
945 823
3,121 - 4,281
322 458
718; : 1,457
3,772 3,113
641 ; 299
2.150 - <,2.437
980 , , .377
5,036 , 9,022
3,753 , 2,828
821 , > 391
1,712 - ', „ 937
1,220 - *:1,501
2,593 , - -4,749
f '%/"
527 570
628 ;"-„ ,798
248 , 291
45 , 74
202 ; 217
750 1,072
1,046 1,835
1,670 „ 1,165
2,063 V ,1,868
220 236
1,843 , 1.632
630 2,247
674 536
648 77
26 459
160 101
Value
This Yew Last Yew % Ch.
44.7 51.0 -12
7.2 19.4 -63
22.5 23.4 -4
62.7 60.5 +4
743.3 „ 989.7 -25
401.1 503.8 -20
342.2 M85.9 -30
464.7 492.7 -6
16.1 180 -11
78.7 56.3 +40
353.4 356.2 -1
16.5 62.3 -73
56.5 »' >82.0 -31
316.8 r.202.7 +56
95.8 20.8 *
181.0 '1545 +17
40.0 ,. , . 274 +46
530.2 840.0 ^m
650.5 575.5 ^H
95.5 328 ^H
445.6 ,,352.5 ^?5
109.4,,;'-- 190.2 -43
352.0 v - 538.0 -35
50.0; 53.3 -6
89.9, (;, 69.8 +29
19.7 . v.r, 34.9 -SA
4.8 * . •• 15.9 -70
14.9 , 19.1 -22
49.9 76.2 -34
75.9 108.8 -30
166.1 „ - 139.2 -19
151.3 " 129.5 -17
15.9 20.3 22
135.4 109.1 -24
40.9 105.4 -61
37.2 30.4 -23
26.6 7.5 *
10.6 22.9 -54
17.4 11.6 -50
This report b confidential. Reproduction or dissemination of any information contained herein i» grani
only by contract or prior written permission from McGraw-Hill, Inc. Copyright O (1992) McGraw-Hill,
G-3
ited
,iB, Inc.
-------
DODGE
Construction
PAtials
BHRin
Pacific Southwest
August 1992
Residential Building
Residential Construction Value
Dwelling Units
Total Dwelling Units
One-Family Houses
Two-Family Houses
Apartment Buildings
Current Month
This Yea/ Last Yew
14,966 15,441
12,188 12.007
88 744
2,690 2.690
Cumulative to Date
This Year Last Yew % Ch.
124,590 130,012, -4
105,260' .94,986 +11
1,758 '- 6,958 > -75
17,572 28.068 -37
Sop Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug
Last 12 Months: Sep. 91 - Aug. 92
Previous Yean Sep. 90 - Aug. 91
Metropolitan Areas/Counties
Square Feet in Thousands, Value in $Millions
Current Month
Alt
An
Bakersfield, CA
Boulder-Lo
Chico, CA
Denver, CO
Adams, CO
Arapahoe, CO
Denver, CO
Douglas, CO
Jefferson, CO
Fresno, CA
Greeley, CO
Honolulu, HI
Las Cruces, NM
Las Vegas, NV
Los Angeles
U* A
Merced, CA
Modesto, CA
Oakland, CA
•.lameda, CA
e, NM
nta Ana, CA
CA
igmont, CO
irings, CO
:o
)
o
Loveland, CO
NM
MV
-Long Beach,
^
\
a, CA
tura, CA
Dwelling
Units
168
446
301
152
88
110
713
77
236
79
154
167
131
404
36
268
74
1,181
1,161
105
225
511
159
352
62
1,522
Square
Feet
310
718
528
243
169
235
984
138
251
77
243
274
233
727
49
331
109
1,660
1,726
186
371
875
329
545
120
2,588
Value
16.8
64.3
29.0
18.5
9.5
16.9
75.0
7.4
23.0
5.2
21.7
17.8
12.2
38.4
2.7
23.5
4.9
69.8
139.5
10.4
19.0
63.8
28.4
35.3
11.8
159.3
Cumulative to Date
Dwelling
This Year
1,642 „
4,336 ,
2,725
1,303
677
1,551
6,632
665
1,597.
271
1,591
2,508
1,218
2,910
296;
2,847 ;
611
9,554 ;
7,266 ;r
783?:
1,4864
4,154 ;
1 .739 :'
2,415
799 :
12,306
Units
Last Year
1,007
5,671
2,337
852
904
816
4,279
' 420
760
366
:1,344
1,389
987
3,051
239
3,681
. 445
11.235
11,105
618
1,486
4,479
1.598
2,881
1,336
9,453
Square Feet
This Year Last Year
2,981
7,493
4,646 "-
2,281
1,321 ,
2,280 J ,
10,246 " -
1.161 ,, -
2.172 : ,
409 '
2,845
3,659
1,909
5,209
463 -
3,599
957
14,335
11,945
1,430
2,771
8,082
3,099
4,983
1,503
22,077
1,871
9,569
4,222
1,579
1,720;
: 8,005
„ 837
1,436
408
2.927
2,397
1,617
5,307
395
5,451
614
15,916
18,849
1,080
2,326
7,155
2.900
4,256
2,158
18,268
This Year
161.6
658.8
256.2
176.8
73.8
161.1
761.3
64.5
178.4
36.1
253.5
228.6
98.7
274.9
24.9
360.6
42.8
603.0
1,058.7
78.0
141.8
616.7
277.4
339.2
137.7
1,392.9
Value
Last Year
,-. ;v ljoo.6
<\" "7:69.3;
- "V 225.6;
* |*'J;tJ[9.8:
;{^f&&
^••'IVIOBiS;
"; ' ° ,26.3$
236.4
,77.2
-274.4
-' ,-;i9.2:
\ ;-491.7
.-/-';4^26.5-
: 652.5
1,401.3
, , .58.5
115.5
503.7
,;;, 224.9
- 278.8
175J
1,045.3
SCh.
+61
-14
+14
+59
-8
+63
+37
+70
+65
+37
+7
+58
+28
0
+30
-27
+62
-8
-24
+33
+23
+22
+23
+22
-22
+33
This report is confidential. Reproduction or dissemination of any information contained herein is granted
only by contract or prior written permission from McGraw-Hill. Inc. Copyright C (1992) McGraw-Hil, Inc.
G-A
-------
DODGE
Construction
Potentials
Bulletin
Pacific Southwest
August 1992
Residential Buildin
Metropolitan Areas/Counties
Square Feet in Thousands, Value in SMillions
Provo-Orem, UT
Pueblo, CO
Redding, CA
Reno, NV
Riverside-San Bernardino,
CA
Riverside, CA
San Bernardino, CA
Sacramento, CA
El Dorado, CA
Placer, CA
Sacramento, CA
Yolo, CA
§allnas-Seaslde-Monterey,
A
Salt Lake City-Ogden, UT
Davis, UT
Salt Lake, UT
Weber, UT
San Diego, CA
San Francisco, CA
Marin, CA
San Francisco. CA
San Mateo, CA
San Jose, CA
Santa Barbara-Santa Maria-
Lorn poc, CA
Santa Cruz, CA
Santa Fe, NM
Los Alamos, NM
Santa Fe, NM
Santa Rosa-Petaluma, CA
Stockton, CA
Tucson, AZ
Vallejo-Fairfield-Napa, CA
Napa, CA
Solano, CA
Visalla-Tulare-Portervllle,
CA
Yuba City, CA
Sutler, CA
Yuba, CA
Yuma, AZ
Current Month
Dwelling Square
Unto Feet Value
170 284 17.0
16 23 0.9
81 141 8.4
187 334 20.6
1,496 2,316 157.8
882 1,278 90.5
614 1,038 67.4
610 1,137 80.5
127 229 15.8
103 210 17.0
342 630 43.2
38 69 4.4
48 85 6.5
420 680 38.0
106 177 11.3
271 432 23.1
43 71 3.5
371 703 64.7
173 310 28.5
28 58 5.5
78 141 10.7
67 111 12.3
253 344 27.7
75 139 13.8
31 54 4.8
32 67 5.2
4 8 0.7
28 59 4.5
118 259 18.4
85 175 11.7
213 383 20.0
127 233 19.0
20 35 3.3
107 198 15.7
208 344 18.4
86 149 9.6
76 132 8.7
10 17 0.9
39 66 3.4
Cumulative to Date
Dwelling Unto
This Yew Last Year
1,579 f- ,v«3i?
i49ix/,;-;j>i,i8:
Square Feet
This Year Last Year
2,224 f/YL X686
332 'f; t»^18i:
766 „ '' '-"'-'912s 1 ,404 *; \ -,< 1 J72
1,333 , 1,233
2,406 **'* <\2,5S9
. -.i-X, -; -
Value
This Year Last Year % Ch.
126.7;-;-^?2.2> +37
12.4->;^);ti.7 +43
83.7 '*"*y*<98.5' -15
147.9 > 145.3 +2
10,813 11,140= 1 8,303 ; ,20,222 1,262.0 " -->1;289.6 -2
5,990 6,584; 9,610 f ,riO,922 685.0 s 746.0 -8
4,823 , , ' - 4',556- 8,693 " -; V'f9;300; 577.0 ; ',- ,-: 504.4 ' -',- -400.4 +26
219, , ,- 391- 479 ,: ,V -897, 31.0 , -™>|50.9 -39
546 - 522- 947 - 1,006; 69.4 ; v*^' ,|ril ' .4
3,707"' 3,142; 6,474 *51,339> 96.1 - -^80.3 +20
2.420 ',- 2.072S 4,237 " -4,214 228.2' : '-186.1 +23
403' -326= 723,- X 664 36.4" '"33.1 +j|
4,412 6,678: 7,601 ' 12>09 697.3 1,032.3 ^
1,059 1,892, 1,964 - , 2,908
150', 288f 324 - , ,,'',462
533 ' , 1 ,0711 939 ",-~, , , 1 ,473
376;' ' 533J 702 ''',/> 974
1 ,680 i ,. 2,99$ 2,61 3 , / ( 4,304
463 .<';, 660
296 _ 392
282 . 276
49 14
233 262
1,329 - 1,554
1,482 1,628
2,422 1,928
1,467 1,181
278 300
1,189- , 881
1,252 1,259
633 601
475 473
158 128
434 336
191.1 ,272.9 -3DT
31.7'' ,;-r:-'-40.1 -21
90.7 146.2 -38
68.7 •'•;;: ;:86.7 -21
21 5.8 ,"^'"333.1 -35
898 :; (,5,522 88.9 , ; ^123.2. -28
5251^'-; -'611- 46.0 ,----:;',5 '48.2 -5
527' ','/ 533 40.5 '*-'-' ^34.3- +18
83\: 32 6.3-'-;,' 2.5 *
444 502 34.2 - -31.8 +8
2,420 2,783
2,51 9 >;---, 2,539
4,899 >^ : 3,904
2,835 2^543
550 ,-- - 692
2,285 -'--1,852
2,077 t , ,2,323
1,085 1,002
826 ;' 764
259 , 238
700 549
170.3 -189.1 -10
168.7 ; ;:,170.1 -1
246.3 „ 185.5 +33
233.7 .199.3 +17
53.3 „ 62.4 -15
180.4-,, 137.0 *32
111.1 113.4 -2
68.0- ,> -61.4 +11
54.2 48.6 *12
13.7 12.8 *8
35.2 ' -25.9 +36
This rvpon is conlldentiaL Repraduedon or dissemlnaboo o( any Moonatton contained herein a grar
only by contract or prior written permlaaion tram MoGraw-Hil, Inc. Copyrigrn O (1892) McOraw-Hil,
G-5
•anted
Inc.
-------
DODGE
Construction
*tials
in
Pacific Southwest
August 1992
Construction by State
Square Feet in Thousands, Value in SMillions
Current Month
Non-Residential
Arizona
California
Colorado
Hawaii
Nevada
New Mexico
Utah
Number al
Projects
2,185
201
1,334
156
232
88
73
101
Dwelling
Units
...
...
SST
17,646
2,610
7,182
4,304
1.661
334
434
1,122
Value
1,388.0
145.2
750.7
200.6
169,7
28.7
42.9
50.2
Residential
Arizona
California
Colorado
Hawaii
Nevada
New Mexico
Utah^^
12,542
2,055
6.301
1,239
370
1,245
518
814
14,966
2,252
7.704
1.472
575
1,511
- 543
909
24,615
3,899
12,982
2.313
859
2,239
899
1.424
1,592.8
223.8
923.9
157.6
54.4
104.1
47.7
81.3
Non-Building
Arizona
California
Colorado
Hawaii
Nevada
New Mexico
Utah
1,178
90
712
127
46
67
49
87
1,350.3
65.6
595.5
491.8
86.3
55.7
33.5
21.8
Cumulative to Date
Number of Projects
This Year Last Year
18,038
1,491
11,084
2,215
1,077
698
511
962
14,704
1,175
Dwelling Units
This Year Last Year
... . J_
10,211*,
909' ••- - ---
1.001-
574
350
484
...
...
Square Feet
This Year Last Year
124,067
15,610
65,589
14.580
7,203
13,513
2.304
5.268
139,818
9,891
This Year
11,853.0
1,105.3
94,023' 6.691.8
Value
Last Year
12,182.5
.890.5-
8,105.4,
->11;483^ 1,169.2 1!^19.4
,8,1 35
7I686
,3.520
5.080
849.4
1.387.1
224.8
425.4
,-879.4
568.0
287.5
332.3
SCh.
-3
+24
-17
+4
-3
*
-22
+28
108,302
18,303
55,102
12,428
3,302
9,093
3,660
6,414
ior,2io
^15#56!
56,362:
8,898=
3,228,
- 9,881'
. -2,584-
5,20t'
124,590
19,251
63,195
14,064
5.058
12.028
3,781
7,213
130,012
15,'945
75',081 '
9,760,
'7,238,"
13,578^
2,779?
5,63t>
212,663
34,742
111.155
22.453
7,434
18,717
6,462
11,702
231 ;642M 4,1 19.5 14|271:6'
- 31,173^
' 134',622-;
17,877;
,12}244f
20,398'
::4j599j
10,728^
2.010.6
8.120.7
1.516.5
589.6
866.5
355.1
660.4
1-.636.6
V9;007.6
' 1,094:1 '
-;"857.4;
7901:7"
-/236.5J
537.5-
-1
+23
•10
+39
-31
-4
+50
+23
11 008
1,640
6,484
938
488
568
524
6 Q4g
609
4,185-
,779
; .47QX
< ~212;
-,,"344';
•--' 517.9
3,777.2
•"- .- 1,259.9
',..,., 614.5
' - -- .-- -" - . J 559.0
-- - --- '.. ~; 2S/.0
... --'-.<•..' 186.7
e olTft"
556.7
3,510.4
' 943.0
381.2
28516
264.7
+ 4C
-7
+8
+34
+61
+96
-29
fhis report Is confidential. Reproduction or Dissemination ol any Information contained herein is granted
;jnly by contract or prior written permission from McGraw-Hig. Inc. Copyright O (1992) McGraw-Hill. Inc.
-------
DODGE
Construction
Potentials
Bulletin
Pacific Southwest
August 1992
Largest Entries
Square Feet in Thousands, Value in SMillions
State County
CO Weld
CA Los Angeles
HI Honolulu
AZ Maricopa
CO Denver
CA Marin
CO Denver
CO Denver
CA Solano
CO Morgan
CO Morgan
UT Weber
CA Los Angeles
CA Los Angeles
CA Riverside
CA Orange
CA Marin
CA Alameda
CA Fresno
CA Fresno
CA San Diego
CA Alameda
HI Honolulu
NM San Juan
CA San Francisco
CO El Paso
CA San Diego
CA Los Angeles
NM Bemalillo
CA Alameda
AZ Mohave
CO Adams
NV Clark
CO Mesa
HI Honolulu
HI Maul
CA Santa Clara
CA Santa Clara
CA Los Angeles
CA Alameda
NV Clark
HI Honolulu
CA Los Angeles
CO El Paso
CA Orange
CA Sutler
Adjustments and
Project Type Dwelling Units Square Feet
A Elec. Power 4 Heating Sys.
• Miscellaneous Non-Bldg.
* Streets 4 Highways
Warehouses (Ex. Mfr. Own.)
• Miscellaneous Non-Res.
Office 4 Bank Buildings
• Airport 4 Space Facilities
*A Garages 4 Service Stations
A Hospital 4 Health Treatment
Miscellaneous Non-Res.
Elec. Power 4 Heating Sys.
Warehouses (Ex. Mfr. Own.)
A Stores 4 Food Service
* Water Supply Systems
* Amusement, Social 4 Rec.
*A Streets 4 Highways
* Other Government Service
River 4 Harbor Development
•A Streets 4 Highways
*A Bridges
• Laboratories (Ex. Mfr. Own.)
* Schools 4 Colleges
* Garages 4 Service Stations
*A Elec. Power 4 Heating Sys.
• Schools 4 Colleges
Office 4 Bank Buildings
* Schools 4 Colleges
* Libraries, Museums, etc.
* Laboratories (Ex. Mfr. Own.)
* Miscellaneous Non-Bldg.
* Schools 4 Colleges
*A Airport 4 Space Facilities
...
—
1.500
500
455
—
1,820
152
785
—
610
323
—
119
...
109
—
. . .
134
100
485
—
200
200
107
90
94
—
135
*A Streets 4 Highways
* River 4 Harbor Development —
Miscellaneous Non-Bldg. —
*A Other Government Service — 90
* Streets 4 Highways —
* Bridges —
* Miscellaneous Non-Bldg. —
Hospital 4 Health Treatment — ' 80
Apartment Buildings 296 187
Office 4 Bank Buildings — 76
Apartment Buildings 153 187
Amusement, Social 4 Rec. — 99
* Schools 4 Colleges — 60
Stores 4 Food Service — 1 90
Value
350.0
172.1
58.0
50.0
44.4
40.0
32.6
32.6
28.0
25.5
21.0
21.0
20.0
18.7
18.3
18.1
17.9
17.8
17.6
17.6
17.5
17.2
17.0
16.2
15.3
15.0
14.2
13.9
13.7
13.5
12.7
11.6
11.5
11.4
11.2
10.4
10.4
10.4
10.2
9.3
9.2
9.1
8.5
8.1
8.0
8.0
Delayed Entries Square Feet in Thousands, Value in {Millions
State County
Project Type
Date of Original Entry Dwelling Units Square Feet
Value
CA
CA
CA
CA
CA
CA
NV
NM
Los Angeles
Ventura
Orange
Alameda
Riverside
Sacramento
Clark
McKlnley
*A Amusement, Social 4 Rec.
* Government Administration
Apartment Buildings
Miscellaneous Non-Bldg.
Sewerage 4 Waste Disposal
*A Sewerage 4 Waste Disposal
Stores 4 Food Service
A Sewerage 4 Waste Disposal
July
July
April
July
June
June
February
February
1992
1992
1992 3i
1992
1992
1992
1992
1992
317
200
2 400
(200)
72.4
44.5
20.0
(17.9)
(10.0)
8.9
(8.0)
8.0
•Public Ownership A: Alterations or Additions
This report la confidential. Reproduction or dissemination o» any Information contained herein Is granted
only by contract or prior written pemttuion Irom McGraw-Hil, Inc. Copyright C(1992) McGraw-HH, Inc.
G-7
-------
APPENDIX H
Crop-Specific Production Budgets for DC/MD/VA
-------
Com (Grain
Operation
Bush Hog
Plow
Disc
Harrow
Plant
Spray/Fert
Harvest
Equipment
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
Combine
Horse-
Power
110
110
110
50
70
50
152
Hours/
Acre
0.17
0.44
0.15
0.2
0.31
0.11
0.53
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
0.7
Com (Grain) Total
Dec
0.0
Production Budget -Virginia
Relative Monthly Activity - Bhp-hr/Acre by Month Ap
Jan
0.0
Feb
0.0
Mar
0.25
0.25
0.25
14.6
Apr
0.55
0.55
0.55
0.25
0.25
37.7
May
0.2
0.2
0.2
0.55
0.55
0.25
24.9
Jun
0.2
0.2
0.55
6.6
Jul
0.2
0.8
Auq
O.C
»ars at Bottom
Sep
0.4
22.6
Oct
0.35
19.7
Nov
0.25
14.1
Bhp-hr)
Acre
13.1
33.9
11.6
7.0
15.2
3.9
56.4
141.0
Diesel
Gal/Acre
0.73
1.90
0.65
0.39
0.85
0.22
3.16
7.89
Com (Silage) Production Budget - Virginia
Operation
Plow
Disc
Harrow
Plant
Spray/Fert
Harvest
Haul
Equipment
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
Horse-
Power
110
110
50
70
50
110
70
Hours/
Acre
0.44
0.15
0.20
0.31
0.11
0.70
0.85
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
0.7
Com (Silaqe) Total
Relative Monthly Activity - Bhp-hr/Acre by Month Appears at Bottom
Dec
0.0
Jan
0.0
Feb
0.0
Mar
0.25
0.25
11.4
Apr
0.55
0.55
0.25
0.25
30.5
May
0.2
0.2
0.55
0.55
0.25
22.3
Jun
0.2
0.2
0.55
6.6
Jul
0.2
0.8
Aug
0.2
0.2
19.1
Sep
0.7
0.7
66.9
Oct
0.1
0.1
9.6
Nov
0.0
Bhp-hr/
Acre
33.9
11.6
7.0
15.2
3.9
53.9
41.7
167.0
Diesel
Gal/Acre
1.90
0.65
0.39
0.85
0.22
3.02
2.33
9.35
Wheat Production -Virginia
Operation
Offset
Disc
Plant
Spray/Fert
Harvest
Equipment
AG Tractor
AG Tractor
AG Tractor
AG Tractor
Combine
Horse-
Power
110
110
70
50
152
Hours/
Acre
0.26
0.15
0.38
0.11
0.26
Load
Factor
0.7
0.7
0.7
0.7
0.7
Wheat Total
Relative Monthly Activity - Bhp-hr/Acre by Month API
Dec
0.0
Jan
0.0
Feb
0.5
1.9
Mar
0.5
1.9
Apr
0.0
May
0.0
Jun
0.5
13.8
Jul
0.5
13.8
Aug
0.0
»ars at Bottom
Sep
0.0
Oct
0.5
0.5
0.5
25.1
Nov
0.5
0.5
0.5
25.1
Bhp-hfl
Acre
20.0
11.6
18.6
3.9
27.7
81.7
Diesel
Gal/Acre
1.12
0.65
1.04
0.22
1.55
4.58
Soy Bean Production - Virginia
Conventional Tillage
Operation
Plow
Disc
Harrow
Plant
Spray/Fert
Harvest
Equipment
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
Combine
Horse-
Power
110
110
50
70
50
152
Hours/
Acre
0.44
0.15
0.20
0.31
0.11
0.53
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
Soybean Total
Relative Monthly Activity - Bhp-hr/Acre by Month Ap
Dec
0.3
16.9
Jan
0.0
Feb
0.0
Mar
0.0
Apr
0.3
0.3
V
13.6
May
0.45
0.45
0.3
0.3
27 '.1
Jun
0.25
0.25
0.45
0.45
0.3
22.5
Jul
0.25
0.25
0.45
7.3
Aug
0.25
1.0
Dears at Bottom
Sep
0.0
Oct
0.2
11.3
Nov
0.5
28!2
Bhp-hr/
Acre
33.9
11.6
7.0
15.2
3.9
56.4
127.9
Diesel
Gal/Acre
1.90
0.65
0.39
0.85
0.22
3.16
7.16
-------
Barley Production - Virginia
Conventional Tillage
Operation
Offset
Disc
Plant
Spray/Fert
Harvest
Equipment
AG Tractor
AG Tractor
AG Tractor
AG Tractor
Combine
Horse-
Power
110
110
70
50
152
Hours/
Acre
0.26
0.15
0.38
0.11
0.26
Load
Factor
0.7
0.7
0.7
0.7
0.7
Bailey Total
Relative Monthly Activity - Bhp-hr/Acre by Month Appears at Bottom
Dec
0.0
Jan
0.0
Feb
0.5
1.9
Mar
0.5
1.9
Apr
0.0
May
0.0
Jun
0.9
24.9
Jul
0.1
2.8
Auq
0.0
Sep
0.25
0.25
0.25
12.5
Oct
0.5
0.5
0.5
25.1
Nov
0.25
0.25
0.25
12.5
Bhp-hr)
Acre
20.0
11.6
18.6
3.9
27.7
81.7
Diesel
Gal/Acre
1.12
0.65
1.04
0.22
1.55
4.58
Tobiiccd Productidnl'l: North Carolina Opesratibns/VirginiaTernDoral Pattern
Operation
Tandem Disc
Grain Drill
Bottom Plow
TandomDisc
Sprayer
Tandem Disc
Harrow
Lime
Disc-Hiller
Transplant
Cultivate
Fertilize
Cultivate
Sprayer
Tobacco Trailer
Bottom Plow
BushHoci
Equipment
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
Horse-
Power
110
45
80
110
45
110
80
45
55
55
45
45
45
45
25
80
45
Hours/
Acre
0.13
0.22
0.53
0.13
0.01
0.18
0.13
0.32
0.31
1.74
0.35
0.47
0.35
0.39
1.20
0.52
0.24
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
Tobacco Total
Relative Monthly Activity - Bhp-hr/Acre by Month Appears at Bottom
Dec
0.0
Jan
0.0
Feb
0.5
0.5
v
19.8
Mar
0.5
0.5
19.8
Apr
0.5
0.5
0.5
10.7
May
0.5
0.5
0.5
0.5
0.5
0.5
55.2
Jun
0.5
0.5
0.5
0.5
0.5
57.4
Jul
0.5
0.5
0.5
0.5
24.6
Aug
0.5
0.5
0.5
22.2
Sep
0.5
0.5
0.5
28.8
Oct
0.5
0.5
0.5
0.5
26.8
Nov
0.5
0.5
8.5
Bhp-hr)
Acre
10.0
6.9
29.7
10.0
0.3
13.9
7.3
10.1
11.9
67.0
11.0
14.8
11.0
12.3
21.0
29.1
7.6
273.9
Diesel
Gal/Acre
0.56
0.39
1.66
0.56
0.02
0.78
0.41
0.56
0.67
3.75
0.62
0.83
0.62
0.69
1.18
1.63
0.42
15.34
K)
Oats Production - Virginia
Conventional Tillage (Based on Barlev with Added Hay Out and Haul)
Operation
Offset
Disc
Plant
Spray/Fert
Harvest
Hay Harvest
HayJHaul
Equipment
AG Tractor
AG Tractor
AG Tractor
AG Tractor
Combine
AG Tractor
AG Tractor
Horse-
Power
110
110
70
50
152
70
70
Hours/
Acre
0.26
0.15
0.38
0.11
0.26
0.50
0.25
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
0.7
Relative Monthly Activity - Bhp-hr/Acre by Month Appears at Bottom
Dec
Uats Total II 0.0
Jan
0.0
Feb
0.5
1.9
Mar
0.5
1.9
Apr
0.0
May
0.0
Jun
0.9
24.9
Jul
0.1
0.5
0.5
21.1
Auq
0.5
0.5
18.4
Sep
0.25
0.25
0.25
12.5
Oct
0.5
0.5
0.5
25.1
Nov
0.25
0.25
0.25
12.5
Bhp-hr)
Acre
20.0
11.6
18.6
3.9
27.7
24.5
12.3
118.5
Diesel
Gal/Acre
1.12
0.65
1.04
0.22
1.55
1.37
0.69
6.63
-------
Alfalfa Establishment - Virginia
Conventional Tillage (Assume 2 Cuttinas)
Operation
Establish:
Plow
Disc
Harrow
Plant
Cuttipack
Sprayer
Production:
Cut Hay
Rake Hay
Bale Hay
Haul
Cut Hay
Rake Hay
Bale Hay
Haul
Equipment
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
Horse-
Power
70
70
50
70
50
50
70
50
70
50
70
50
70
50
Hours/
Acre
0.63
0.52
0.20
0.38
0.19
0.11
0.4
0.33
0.61
0.61
0.4
0.33
0.61
0.61
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
Alfalfa (Establishment) Total
Relative Monthly Activity - Bhp-hr/Acre by Month API
Dec
0.0
Jan
0.0
Feb
0.5
0.5
28.2
Mar
0.5
0.5
0.5
0.5
0.5
44.3
Apr
0.5
0.5
0.5
16.1
May
0.25
1.0
Jun
0.25
0.5
0.5
0.5
0.5
^2.2
Jul
0.25
0.5
0.5
0.5
0.5
42.2
Auq
0.25
0.5
0.5
0.5
0.5
42.2
Mars at Bottom
Sep
0.5
0.5
0.5
0.5
41.2
Oct
0.0
Nov
0.0
Bhp-hry
Acre
30.9
25.5
7.0
18.6
6.7
3.9
19.6
11.6
29.9
21.4
19.6
11.6
29.9
21.4
257.3
Diesel
Gal/Acre
1.73
1.43
0.39
1.04
0.37
0.22
1.10
0.65
1.67
1.20
1.10
0.65
1.67
1.20
14.41
Alfalfa Production
5 Tons/Acre (4 Cuttinas)
Operation
Sprayer (2)
Cut Hay (4)
Rake Hay (4)
Bale Hay (4)
Haul (4L
Equipment
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
Horse -
Power
50
70
50
70
50
Hours/
Acre
0.22
1.60
1.32
2.44
2.44
Load
Factor
0.7
0.7
0.7
0.7
0.7
Alfalfa Production Total
Relative Monthly Activity - Bhp-hr/Acre by Month Ap
Dec
0.0
Jan
0.0
Feb
0.0
Mar
0.33
1.3
Apr
0.33
1.3
May
0.7
0.7
0.7
0.7
57.7
Jun
0.33
0.7
0.7
0.7
0.7
59.0
Jul
0.33
0.7
0.7
0.7
0.7
59.0
Aug
0.33
0.7
0.7
0.7
0.7
59.0
Dears at Bottom
Sep
0.33
0.7
0.7
0.7
0.7
59.0
Oct
0.5
0.5
0.5
0.5
41.2
Nov
0.0
Bhp-ho
Acre
7.7
78.4
46.2
119.6
85.4
33T3
Diesel
Gal/Acre
0.43
4.39
2.59
6.70
4.78
18.89
-------
Hay (Orchard Grass) Establishment - Virginia
Conventional Tillage (Assume 1 Cutting)
Operation
Establish:
Plow
Disc
Harrow
Plant
Production:
Cut Hay
Rake Hay
Bate Hay
Haul
Equipment
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
Horse -
Power
70
70
50
70
70
50
70
70
Hours/
Acre
0.63
0.52
0.20
0.38
0.40
0.33
0.40
0.45
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
Hav Establishment Total
Relative Monthly Activity - Bhp-hr/Acre by Month API
Dec
0.0
Jan
0.0
Feb
0.5
0.5
28.2
Mar
0.5
0.5
0.5
0.5
41.0
Apr
0.5
0.5
12.8
May
0.0
Jun
0.0
Jul
0.33
0.33
0.33
0.33
24.3
Aufl
0.33
0.33
0.33
0.33
24.3
aears at Bottom
Sep
0.33
0.33
0.33
0.33
24.3
Oct
0.0
Nov
0.0
Bhp-hry
Acre
30.9
25.5
7.0
18.6
19.6
11.6
19.6
22.1
154.8
Diesel
Gal/Acre
1.73
1.43
0.39
1.04
1.10
0.65
1.10
1.23
8.67
Hay (Orchard Grass) Production - Virginia
2.5 Tons/Acre (2 Cuttinas)
Operation
Cut Hay (2)
Rake Hay (2)
Bale Hay (2)
Haul (2)
Spray/Fert (2)
Hay Production Tola
Equipment
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
Horse -
Power
70
50
70
50
50
Hours/
Acre
0.80
0.66
0.80
0.90
0.22
Load
Factor
0.7
0.7
0.7
0.7
0.7
Relative Monthly Activity - Bhp-hr/Acre by Month Ap
Dec
0.0
Jan
0.0
Feb
0.0
Mar
0.0
Apr
0.0
May
0.0
Jun
0.5
0.5
0.5
0.5
0.5
35.2
Jul
0.5
0.5
0.5
0.5
0.5
35.2
Aug
0.5
0.5
0.5
0.5
0.5
35.2
Dears at Bottom
Sep
0.5
0.5
0.5
0.5
0.5
35.2
Oct
0.0
Nov
0.0
Bhp-hr)
Acre
39.2
23.1
39.2
31.5
7.7
140.7
Diesel
Gal/Acre
2.20
1.29
2.20
1.76
0.43
7.88
f
-p-
Fresh Vegetables - Pennsylvania Operations/Virginia Equipment
(Based on Fresh Market Com)
Operation
Plow
Disk
Harrow
Plant
Spray/Insect
Spray/Herb
Equipment
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
AG Tractor
Horse-
Power
110
110
50
70
50
50
Hours/
Acre
0.44
0.15
0.20
0.31
0.11
0.11
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
Fresh Vegetable Total
Relative Monthly Activity - Bhp-hr/Acre by Month Appears at Bottom
Dec
0.0
Jan
0.25
0.25
11.4
Feb
0.25
0.25
11.4
Mar
0.25
0.25
0.25
0.25
16.9
Apr
0.25
0.25
0.25
0.25
0.2
0.2
18.4
May
0.25
0.25
0.2
0.2
7.1
Jun
0.25
0.25
0.2
0.2
7.1
Jul
0.2
0.2
1.5
Aug
0.2
0.2
1.5
Sep
0.0
Oct
0.0
Nov
0.0
Bhp-hry
Acre
33.9
11.6
7.0
15.2
3.9
3.9
75.3
Diesel
Gal/Acre
1.90
0.65
0.39
0.85
0.22
0.22
L 4.22
-------
APPENDIX I
Crop-Specific Production Budgets for the San Joaquin Valley Air Basin
-------
Asparaaus Production Budqet -SJV ;
Operation
Year!:*
Disc Stubble
Make Levees
Remove Levees
Plow
Disc
Rip
Landplane
Disc
Cutt./cvr weeds
Cultivate
Year 2:*
Winter Disc
Cultivate
Cult./cvr weeds
Chop Fern
Furrow
Chisel
Years 3- 10:*
Disc
Ridge & Rdl
Work Centers
Furrow
Cultivate
Split Ridge
Chop Fem
Equipment
Cramer
Ag Tractor 2wc
Ag Tractor 2wc
Crawler
Cramer
Crawler
Crawler
Crawler
Ag Tractor 2wc
Ag Tractor 2wc
Crawler
Ag Tractor 2wc
Ag Tractor 2wc
Ag Tractor 2wc
Ag Tractor 2wc
Crawler
Crawler
Ag Tractor 2wc
Ag Tractor 2wc
Ag Tractor 2wc
Ag Tractor 2wc
Ag Tractor 2wc
Aq Tractor 2wc
norse—
Power
90
60
60
90
90
90
90
90
60
60
90
60
60
60
60
90
90
60
60
60
60
60
60
Hours/
Acre
0.66
0.5
0.33
0.66
0.33
1
1
0.33
1.2
1.33
0.66
1.33
1.2
0.4
0.66
0.33
0.66
0.25
1.6
1
0.66
0.5
04
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
'
, 0.7
, 0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
Asparaqus Total
Relative Monthly Activity - Bhp-hr/Acre by Month Appears at Bottom
Dec
1
1
62.7
Jan
0.0
Feb
0.0
Mar
1
1
1
1
1
1
1
1
9.5
Apr
1
1
1
1
1
1
1
1
9.5
May
1
1
1
1
1
14.8
Jun
1
1
5.3
Jul
0.0
Augl Sep
1
1
1
0.0 1 4.9
Oct
1
1
1
1
1
1
1
1
1
1
1
1
1
67.6
Nov
1
1
1
1
1
1
1
1
1
1
1
1
1
67.6
Bhp-hr/
Acre
4.2
2.1
1.4
4.2
2.1
6.3
6.3
2.1
5.0
5.6
4.2
5.6
5.0
1.7
2.8
2.1
33.3
8.4
53.8
33.6
22.2
16.8
13.4
241.9
Diesel
Gal/Acre
0.02
0.01
0.01
0.02
0.01
0.04
0.04
0.01
0.03
0.03
0.02
0.03
0.03
0.01
0.02
0.01
1.49
0.38
2.41
1.51
0.99
0.75
0.60
8.47
* Asparagus is grown on a 10-year cycle. Thus, hour/acre estimates were adjusted accordingy before calculating emissions.
- -/ : 'Beans Production Budqet -»• SJV •-:.•"• , •• ; ]M^^^^-.:^-f:'':' • • ..^P-' .-: . .••,-A:--^:h-: .
Operation
Disc
Subsoil
Landplane
Springtooth
Listing
Ditching
Close Ditches
Harrow
Planting
Cultivate
Cut Beans
Equipment
Ag Tractor
Crawler
Ag Tractor
Crawler
Crawler
Crawler
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Aq Tractor
Horse -
Power
90
60
90
60
60
60
60
60
60
60
60
Hours/
Acre
1
1
0.5
0.5
0.5
0.25
0.5
0.5
0.25
1
0.25
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
Beans total
Relative Monthly Activity - Bhp-hr/Acre by Month Ap
Dec
0.0
Jan
1
1
1
1
1
59.5
Feb
1
1
1
1
1
59.5
Mar
1
1
1
85.8
Apr
1
1
1
1
31.5
May
1
1
19.3
Jun
1
14.0
Jul
1
14.0
Aug
1
3.5
Mars at Bottom
Sep
1
3.5
Oct
1
3.5
Nov
070
Bhp-hr^
Acre
63.0
42.0
31.5
21.0
21.0
10.5
21.0
21.0
10.5
42.0
10.5
294.0
Diesel
Gal/Acre
3.53
2.35
1.76
1.18
1.18
0.59
1.18
1.18
0.59
2.35
0.59
16.46
-------
Operation
Pest Control
Growth Regulator
Prune & Shred
Fertilize
Soil Amendments
Weed Control
Citrus Production Budget - SJV
Equipment
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Horse-
Power
50
50
50
50
50
50
Hours/
Acre
0.7
0.7
0.2
0.5
0.2
1.7
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
Citrus Total
Relative Monthly Activity - Bhp-hr/Acre by Month App
Dec
0.0
Jan
0.0
Fob
L 0.0
Mar
1
1
1
30.8
Apr
1
1
1
15.2
May
0.25
6.0
Jun
1
1
16.9
Jul
1
1
0.25
27.0
Aufl
1
1
20.4
Bars at Bottom
Sep
1
23.8
Oct
0.0
Nov
0.0
Bhp-hr/
Acre
24.5
24.5
7.0
17.5
7.0
59.5
140.0
Diesel
Gal/Acre
1.37
1.37
0.39
0.98
0.39
3.33
7.84
Clover Seed Production Budget — SJV
Operation
Chisel *
Disc*
Land Plane*
Make Levees
Herbicide
Spot spray
Rodentidde
Herbicide
Roll Fields
Insecticide
Defoliate
Equipment
Ag Tractor 4wo
Ag Tractor 4wo
Ag Tractor 4wc
Ag Tractor 2wc
Ag Tractor 2wo
Ag Tractor 2wo
Ag Tractor 2wc
Ag Tractor 2wc
Ag Tractor 2wc
Ag Tractor 2wc
Ad Tractor 2wd
Horse-
Power
200
200
200
70
70
70
70
70
70
70
70
Hours/
Acre
0.16
0.33
0.3
0.05
0.04
0.2
0.2
0.06
0.14
0.04
0.02
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
Clover Seed Total
Relative Monthly Activity - Bhp-hr/Acre by Month Apf
Dec
1
1
7
3.8
Jan
1
1
2.0
Feb
1
1
1
8.8
Mar
1
1
3
2.8
Apr
1
1
2.0
May
1
1
2.0
Jun
1
1
1
2.9
Jul
1
1
1
2.9
Aug
1
1.0
»ars at Bottom
Sep
1
1
1
1
1
13.8
Oct
1
1
16.0
Nov
15.7
Bhp-hr/
Acre
7.5
15.4
14.0
2.5
2.0
9.8
9.8
2.9
6.9
2.0
1.0
73.6
Diesel
Gal/Acre
0.42
0.86
0.78
0.14
0.11
0.55
0.55
0.16
0.38
0.11
0.05
4.12
I
N)
* Established every 3 years, thus, hour/acre estimates were divided by 3 before calculating monthly and yearly Bhp-hr values.
Field Corn Production Budget - SJV
Operation
Chisel Deep
Disc Stubble
Chisel light
Disc light
Pro plant Herb.
Tri plane
List & Fort
Mulch beds
Roll beds
Open Ditch
Close Ditch
Disc over Ditch
Rant
Cultivate
Apply Manure
Fertilize
Apply Herbacide
Apply Mmcide
( Field CbmTotal
Equipment
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Horee-
Power
200
200
130
130
80
130
130
130
130
130
130
130
80
80
130
130
80
80
Hours/
Acre
0.21
0.14
0.11
0.12
0.13
0.14
0.15
0.18
0.1
0.02
0.02
0.02
0.14
0.31
0.15
0.15
0.13
0 13
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
07
Relative Monthly Activity - Bhp-hr/Acre by Month API
Dec
1
Jan
II 29.4 1 0.0
Feb
Mar
1
1
1
1
1
0.0|112.1
Apr
1
1
1
1
39.2
May
1
7.3
Jun
0.0
Jul
0.0
Aug
jears at Bottom
Sep
0.0 1 0.0
Oct
0.0
Nov
1
13.7
Bhp-hr/
Acre
29.4
19.6
10.0
10.9
7.3
12.7
13.7
16.4
9.1
1.8
1.8
1.8
7.8
17.4
13.7
13.7
7.3
7.3
201.6
Diesel
Gal/Acre
1.65
1.10
0.56
0.61
0.41
0.71
0.76
0.92
0.51
0.10
0.10
0.10
0.44
0.97
0.76
0.76
0.41
0.41
11.29
-------
Cotton Production Budget - SJV
Operation
Deep Rip
Primary Discing
Apply Herbaclde
Incrp Herb w/dsc
Make Beds
Make Ditch
Close Ditch
Plant
Uncap Beds
Cultivate
Harvest
Build Module
Misc. Harvest
Cut Stalks
Cross Disc
Preplant NH3
Mitlcide
Insect
Herbicide
Growth Regulator
Sidedress Pert
Equipment
Crawler tractor
Crawler tractor
AQ Tractor
AQ Tractor
AQ Tractor
AQ Tractor
AQ Tractor
AQ Tractor
AQ Tractor
AQ Tractor
Harvester
AQ Tractor
AQ Tractor
AQ Tractor
AQ Tractor
AQ Tractor
AQ Tractor
AQ Tractor
AQ Tractor
AQ Tractor
AQ Tractor
Horse—
Power
170
170
100
100
100
100
100
100
100
100
170
55
100
100
100
100
100
100
100
100
100
Hours/
Acre
0.08
0.14
0.12
0.1
0.15
0.06
0.06
0.18
0.15
1.15
0.65
0.33
0.65
0.1
0.19
0.17
0.17
0.17
0.17
0.17
0.17
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.51
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
Relative Monthly Activity - Bhp-hr/Acre by Month Appears at Bottom
Dec
Cotton Total 1 0.0
Jan
1
1
1
38.1
Feb
1
1
1
1
1
24.5
Mar
0.0
Apr
1
1
1
49.9
May
1
1
38.7
Jun
1
1
1
1
43.6
Jul
1
1
1
1
39.2
Aug
Sep
1
0.0 1 1.4
Oct
Nov
1
1
1
1
1
0.0 1 134.9
Bhp-hr;
Acre
9.5
16.7
8.4
7.0
10.5
4.2
4.2
12.6
10.5
80.5
56.4
12.7
45.5
7.0
13.3
11.9
11.9
11.9
11.9
11.9
11.9
Diesel
Gal/Acre
0.53
0.93
0.47
0.39
0.59
0.24
0.24
0.71
0.59
4.51
3.16
0.71
2.55
0.39
0.74
0.67
0.67
0.67
0.67
0.67
0.67
370.3 1 20.74
Grape Production Budget - SJV
Operation
Raisin & Wine:
Chisel
Disc
Cultivation
Trellis Rpr
Raisin:
Terracing
Box & Shake
Equipment
AQ Tractor
AQ Tractor
AQ Tractor
AQ Tractor
AQ Tractor
AQ Tractor
Horse—
Power
60
60
60
30
30
30
Hours/
Acre
4
4
4
1
1.5
1
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
Relative Monthly Activity - Bhp-hr/Acre by Month Appears at Bottom
Dec
Jan
Grape Total . II 0.0 1 0.0
Feb
Mar
1
Apr
1
1
1
May
1
1
1
0.01 21.01126.01126.0
Jun
1
1
1
Jul
1
1
1
126.01126.0
Aug
1
15.8
Sep
1
1
26.3
Oct
1
10.5
Nov
Bhp-hry
Acre
168.0
168.0
168.0
21.0
31.5
21.0
Diesel
Gal/Acre
9.41
9.41
9.41
1.18
1.76
1.18
O.Oll 577.5 1 32.34
Kiwi Production Budqefs-s SJV
Operation
Strip Weed Cntrl
Fertilize
Brush Rmvl
Mow
Kiwi Total
Equipment
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Horse -
Power
50
50
50
50
Hours/
Acre
1
1
2.2
3
Load
Factor
0.7
0.7
0.7
0.7
Relative Monthly Activity - Bhp-hr/Acre by Month Appears at Bottom
Dec
r Jan
1
Feb
1
1
1
1
II 0.0 1 38.51 74.7
Mar
1
17.5
Apr
1
1
1
May
1
1
Jun
1
1
Jul
1
1
Aug
Sep
Oct
Nov
1
Bhp-hr/
Acre
35.0
35.0
77.0
105.0
Diesel
Gal/Acre
1.96
1.96
4.31
5.88
36.21 24.51 24.51 24.51 0.01 0.01 0.01 11.711 252.01 14.11
-------
Lettuce Production Budoet — SJV
Operation
Land Prep
Plant
Cultivate
Misc.
Harvest/Haul
Herbacide
Fertilize
Pest Control
Equipment
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Aq Tractor
Horse -
Power
100
100
60
40
60
60
60
60
Hours/
Acre
6
0.5
2.5
2
2.5
0.16
0.16
0.16
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
Lettuce Total
Relative Monthly Activity - Bhp-hr/Acre by Month Apf
Dec
1
4.7
Jan
1
1
1
1
25.0
Feb
1
1
1
1
1
26.6
Mar
1
1
1
1
1
95.0
Apr
1
1
92.2
May
1
1
127.2
Jun
1
1
1
1
25.0
Jul
1
1
1
1
1
26.6
Aug
1
1
1
1
95.0
Dears at Bottom
Sep
1
1
1
144.7
Oct
1
1
74.7
Ncv
1
4.7
Bhp-ho
Acre
420.0
35.0
105.0
56.0
105.0
6.7
6.7
6.7
741.2
Diesel
Gal/Acre
23.52
1.96
5.88
3.14
5.88
0.38
0.38
0.38
41.50
Melon Production Budoet - SJV
Operation
Land Prep
Land Prep
Fumigation
Fertilizer
Plant
Herbadde
Cultivate
Cut Ditch
Misc
Equipment
Crawler
Ag Tractor
Crawler
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Crawler
Ag Tractor
Horse-
Power
135
90
135
90
90
90
90
135
90
Hours/
Acre
1
0.5
0.5
0.5
0.5
0.33
2
0.33
1
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
Melon Total
Relative Monthly Activity - Bhp-hr/Acre by Month Appears at Bottom
Dec
1
1
1
1
97.1
Jan
1
1
1
1
1
112.9
Feb
1
1
1
42.0
Mar
1
1
1
1
1
89.0
Apr
1
1
1
1
73.3
May
1
1
1
62.9
Jun
0.0
Jul
0.0
Aug
0.0
Sep
0.0
Oct
0.0
Ncv
0.0
Bhp-hr)
Acre
94.5
31.5
47.3
31.5
31.5
20.8
126.0
31.2
63.0
477.2
Diesel
Gal/Acre
5.29
1.76
2.65
1.76
1.76
1.16
7.06
1.75
3.53
26.72
Oat Hav Production Budpet - SJV
Operation
Disc
Chisel
Ttl plane
Finish Disc
Border
Plant
Open Ditch
Close Ditch
Preplant Pert.
Fertilize
Herbacide
Harvest
Equipment
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Sp Combine
Horse-
Power
200
200
200
130
130
80
130
130
130
130
80
65
Hours/
Acre
0.14
0.27
0.14
0.12
0.02
0.26
0.01
0.01
0.01
0.01
0.02
0.33
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
Oat Hay Total
Relative Monthly Activity - Bhp-hr/Acre by Month Ap
Dec
1
1
1
1
28.2
Jan
0.0
Feb
1
1
2.0
Mar
1
0.9
Apr
0.0
May
0.0
Jun
1
1
15.9
Jul
0.0
Aug
0.0
wars at Bottom
Sep
0.0
Oct
^_ o.o
Nov
1
1
1
fl.G
Bhp-hry
Acre
19.6
37.8
19.6
10.9
1.8
14.6
0.9
0.9
0.9
0.9
1.1
15.0
124.T
Diesel
Gat/Acre
1.10
2.12
1.10
0.61
0.10
0.82
0.05
0.05
0.05
0.05
0.06
0.84
6.95
-------
Olive; Production Budoet - SJV
Operation
Pre emerg Herb.
Contact Herb.
Harvest shaker
Equipment
Ag Tractor
Ag Tractor
Shaker Catche
Horse-
Power
55
55
120
Hours/
Acre
0.2
0.4
3
Load
Factor
0.7
0.7
0.7
Olive Total
Relative Monthly Activity - Bhp-hr/Acre by Month Appears at Bottom
Dec
0.0
Jan
0.0
Feb
0.0
Mar
0.0
Apr
1
7,7
May
0.0
Jun
0.0
Jul
1
7.7
Auq
0.0
Sep
0.0
Oct
1
1
259.7
Nov
0.0
Bhp-hr/
Acre
7.7
15.4
252.0
275.1
Diesel
Gal/Acre
0.43
0.86
14.11
15.41
Onion Production BudgetlHiiSJV
Operation
Subsoil
Disc & Roll
Chisel
Level
Shape Beds &Rdl
Plant
Cultivate
Equipment
Crawler
Crawler
Crawler
Crawler
Ag Tractor
Ag Tractor
Aa Tractor
Horse-
Power
80
80
80
80
60
60
60
Hours/
Acre
1.24
0.69
0.66
0.52
0.5
0.5
3
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
0.7
Orion Total
Relative Monthly Activity - Bhp-hr/Acre by Month API
Dec
0.0
Jan
0.0
Feb
0.0
Mar
1
1
1
1
1
1
1
171.1
Apr
1
1
1
1
1
1
1
171.1
May
0.0
Jun
0.0
Jul
0.0
Aug
0.0
Dears at Bottom
Sep
0.0
Oct
0.0
Nov
0.0
Bhp-hry
Acre
69.4
38.6
37.0
29.1
21.0
21.0
126.0
342.2
Diesel
Gal/Acre
3.89
2.16
2.07
1.63
1.18
1.18
7.06
19.16
Irrigated Pasture Production Budget - SJV
Operation
Mow & Fertilize
Equipment
AG Tractor
Horse-
Power
98
Hours/
Acre
0.75
Load
Factor
0.7
Irrigated Pasture Total
Relative Monthly Activity - Bhp-hr/Acre by Month Ap
Dec
0.0
Jan
1
12.9
Feb
1
12.9
Mar
0.0
Apr
0.0
May
0.0
Jun
0.0
Jul
2
25.7
Aug
0.0
Dears at Bottom
Sep
0.0
Oct
0.0
Nov
0.0
Bhp-hry
Acre
51.5
51.5
Diesel
Gal/Acre
2.88
2.88
• Hour/acre estimate was halved to reflect assessment of county Farm Advisor.
Peach Production Budoet - SJV
Operation
Shred Brush
Dormant Spray
Bloom Spray
OFM
Brown rot
WeedCntrl
Fertilize
Potasium 1/4
Mow or Disc
Backhoe
Remove Tree
Replant Tree
Bin Hancflinq
Peach Total
Equipment
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Aq Tractor
Horse-
Power
65
65
65
65
65
65
30
30
65
65
65
30
30
Hours/
Acre
1.8
0.3
0.7
1
0.3
0.2
0.2
0.1
1.5
0.3
0.3
0.2
1
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
Relative Monthly Activity - Bhp-hr/Acre by Month Ap
Dec
0.0
Jan
1
1
41.0
Feb
1
1
U8.7
Mar
1
1
18.0
Apr
1
1
11.4
May
1
1
1
1
42.3
Jun
1
1
1
44.7
Jul
1
1
17.4^
Aug
1
1
15.9
Dears at Bottom
Sep
1
1
1
52.3
Oct
1
21.0
Nov
0.25
1
1
20.0
Bhp-hry
Acre
81.9
13.7
31.9
45.5
13.7
9.1
4.2
2.1
68.3
13.7
13.7
4.2
21.0
322.7
Diesel
Gal/Acre
4.59
0.76
1.78
2.55
0.76
0.51
0.24
0.12
3.82
0.76
0.76
0.24
1.18
18.07
-------
Pear
Operation
Brush Rmvl
Rodent Control
Fertilize
Chop Weeds
Herbadde&
Pest & Disease
Harvest
Equipment
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Aa Tractor
Horse -
Power
30
50
50
50
50
50
50
50
50
50
50
50
50
50
30
Hours/
Acre
0.6
0.3
0.5
1.8
0.36
0.36
0.36
0.12
0.4
0.4
0.4
0.4
0.4
0.36
1.5
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
Pear/Apple Total
f Apple Production Budget— SJV
Relative Monthly Activity - Bhp-hr/Acre by Month API
Dec
1
12.6
Jan
1
12.6
Feb
1
9.0
Mar
1
1
1
1
38.0
Apr
1
1
25.5
May
1
1
1
25.5
Jun
1
1
1
1
38.1
Jul
1
2
1
1
46.9
Auq
1
1
27.3
Dears at Bottom
Sep
0.0
Oct
1
1
1
24.1
Nov
0.0
Bhp-ho
Acre
12.6
10.5
17.5
63.0
12.6
12.6
12.6
4.2
14.0
14.0
14.0
14.0
14.0
12.6
31.5
259.7
Diesel
Gal/Acre
0.71
0.59
0.98
3.53
0.71
0.71
0.71
0.24
0.78
0.78
0.78
0.78
0.78
0.71
1.76
14.54
Pistachio Production Budget - SJV
Operation
Brush Shredding
Disc
Weed Control
Weed Control
Fertilize
Pest Control
Harvest shaker
Equipment
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Shaker Catche
Horse -
Power
65
65
65
65
65
65
120
Hours/
Acre
0.5
1.5
1.5
0.5
1
1
3
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
0.51
Pistachio Total
Relative Monthly Activity - Bhp-hr/Acre by Month Ap
Dec
0.0
Jan
1
22.8
Feb
0.0
Mar
1
22.8
Apr
0.0
May
1
1
45.5
Jun
0.0
Jul
0.0
Aufl
1
1
1
113.8
Dears at Bottom
Sep
1
183.6
Oct
0.0
Nov
1
68.3
Bhp-hr)
Acre
22.8
68.3
68.3
22.8
45.5
45.5
183.6
456.6
Diesel
Gal/Acre
1.27
3.82
3.82
1.27
2.55
2.55
10.28
25.57
M
I
Plum Production Budb£t« SJV
Operation
Brush Disposal
Furrowing
Disc
Misc.
Spray
Fertilize
Harvest shaker
Equipment
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Shaker Catche
Horse-
Power
65
65
65
65
65
65
120
Hours/
Acre
0.6
1.7
1.9
1
3.15
0.63
3
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
0.51
Plum Total
Relative Monthly Activity - Bhp-hr/Acre by Month Ap
Dec
/
0.0
Jan
1
1
4
146.1
Feb
1
4.1
Mar
1
1
29.9
Apr
1
4.1
May
1
1
1
58.7
Jun
1
1
187.7
Jul
1
1
1
1
87.4
Aufl
1
4.1
Dears at Bottom
Sep
1
1
32.8
Oct
1
1
33.0
Nov
1
4.1
Bhp-hr)
Acre
27.3
77.4
86.5
45.5
143.3
28.7
183.6
592.2
Diesel
Gal/Acre
1.53
4.33
4.84
2.55
8.03
1.61
10.28
33.16
-------
Potato Production Budoet - SJV
Operation
Chisel
Disc
Firtsh Level
Ust
Herbacide
Cultivate
Misc
Vine Rolling
Equipment
Crawler
Crawler
Crawler
Crawler
Ag Tractor
Ag Tractor
Ag Tractor
Aq Tractor
Horse-
Power
135
135
135
135
90
90
90
90
Hours/
Acre
1
1
0.5
0.5
0.33
1.5
2
1.5
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
Potato Total
Relative Monthly Activity - Bhp-hr/Acre by Month Apt
Dec
1
^f3.5
Jan
1
13.5
Feb
1
13.5
Mar
1
13.5
Apr
174.9
May
1
1
1
1
1
1
174.9
Jun
1
1
1
1
1
1
1
174.9
Jul
0.0
Auq
0.0
Dears at Bottom
Sep
1
13.5
Oct
1
13.5
Ncv
1
13.5
Bhp-hr;
Acre
94.5
94.5
47.3
47.3
20.8
94.5
126.0
94.5
619.3
Diesel
Gal/Acre
5.29
5.29
2.65
2.65
1.16
5.29
7.06
5.29
34.68
Prune Production Budoet - SJV
Operation
Brush Disposal
Tillage
Fertilize
Spray: Weed Cntrl
Mow
Misc.
Dormant Spray
Bloom Spray
Zinc Spray
Harvest shaker
Equipment
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Shaker Catche
Horse-
Power
55
55
55
55
55
55
55
55
55
120
Hours/
Acre
1.2
1.33
0.5
0.6
2.5
1
0.3
0.3
0.3
3
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.51
Prune Total
Relative Monthly Activity - Bhp-hr/Acre by Month API
Dec
1
H1.6
Jan
1
46.2
Feb
1
1
37.2
Mar
1
1
1
1
1
85.6
Apr
2
12.8
May
1
1
36.9
Jun
1
1
35.6
Jul
1
6.4
Aug
1
1
1
220.5
xars at Bottom
Sep
0.0
Oct
0.0
Ncv
0.0
Bhp-hr;
Acre
46.2
51.2
19.3
23.1
96.3
38.5
11.6
11.6
11.6
183.6
492.8
Diesel
Gal/Acre
2.59
2.87
1.08
1.29
5.39
2.16
0.65
0.65
0.65
10.28
27.59
Rice Production Budget - SJV
Operation
Chisel
Stubble Disc
Harrow Disc
3 Wheel Plane
Laser Level*
Roll
Mtn. Drains
Mtn. Roads
Harvest
Harvest
Equipment
Crawler
Ag Tractor 4wc
Ag Tractor 4wc
Ag Tractor 4wc
Ag Tractor 4wc
Ag Tractor
Ag Tractor 4wc
Ag Tractor
SP Combine
Bankout
Horse-
Power
125
200
200
200
200
40
200
40
120
60
Hours/
Acre
0.16
0.16
0.3
0.16
0.16
0.1
0.1
0.1
0.75
0.5
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.51
Rice Total
Relative Monthly Activity - Bhp-hr/Acre by Month Ap
Dec
0.0
Jan
0.0
Feb
0.0
Mar
1
1
15.4
Apr
1
1
1
44.8
May
1
1
1
1
60.2
Jun
0.33
7.5
Jul
0.0
Aug
0.0
»ars at Bottom
Sep
1
1
39.2
Oct
1
1
39.2
Ncv
0.0
Bhp-hr;
Acre
14.0
22.4
42.0
22.4
7.5
2.8
14.0
2.8
63.0
15.3
206.2
Diesel
Gal/Acre
0.78
1.25
2.35
1.25
0.42
0.16
0.78
0.16
3.53
0.86
11.55
-------
Safflbwer Production Budget — SJV
Operation
Stubble Disc
Disc
Fertilize
List up Beds
Incorp. Herbcd
Rolling Cultvtr
Plant
Ditch
Misc.
Harvest
Ban tout Wagon
Chop Stubble
Stubble disc
Equipment
Crawler
Crawler
Crawler
Crawler
Ag Tractor
Ag Tractor
Ag Tractor
Crawler
Ag Tractor
Combine
Ag Tractor
Ag Tractor
Crawler
Horse-
Power
190
120
120
120
135
90
90
120
90
135
90
135
190
Hours/
Acre
0.11
0.26
0.1
0.26
0.2
0.2
0.33
0.03
0.04
0.25
0.25
0.25
0.22
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
Safflower Total
Relative Monthly Activity - Bhp-hr/Acre by Month Apj
Dec
1
1
1
15.0
Jan
1
1
1
1
1
1
41.6
Feb
1
1
1
1
1
1
1
1
1
50.2
Mar
1
1
1
8.6
Apr
1
1
1
8.6
May
0.0
Jun
0.0
Jul
0.0
Auq
1
1
19.7
»ars at Bottom
Sep
1
1
1
1
37.3
Oct
1
1
17.6
Ncv
1
1
17.6
Bhp-hr/
Acre
14.6
21.8
8.4
21.8
18.9
12.6
20.8
2.5
2.5
23.6
15.8
23.6
29.3
216.3
Diesel
Gal/Acre
0.82
1.22
0.47
1.22
1.06
0.71
1.16
0.14
0.14
1.32
0.88
1.32
1.64
12.11
Silabe (ComV Production Budoet - SJV
Operation
Disc Stubble
Disc finish
Land Plane Field
List & Fed
Mulch beds
Roll Beds
Open Ditch
Close Ditch
Disc over Ditch
Plant
Cultivate
Apply Manure
Fertilize
Apply Herbaclde
Apdv Miticide
Equipment
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Aq Tractor
Horse-
Power
200
200
200
80
130
130
130
130
130
80
80
80
80
80
80
Hours/
Acre
0.14
0.12
0.14
0.15
0.18
0.1
0.02
0.04
0.04
0.14
0.31
0.15
0.15
0.04
0.04
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0)7
0.7
0.7
0.7
0.7
0.7
0.7
Silaqe (Com) Total
Relative Monthly Activity - Bhp-hr/Acre by Month API
Dec
0.0
Jan
0.0
Feb
0.0
Mar
0.0
Apr
0.0
May
0.0
Jun
1
1
1
1
1
110.7
Jul
1
1
1
1
1
31.2
Aug
0.0
»ars at Bottom
Sep
0.0
Oct
1
1
3.6
Nov
o.o
Bhp-hrJ
Acre
19.6
16.8
19.6
8.4
16.4
9.1
1.8
3.6
3.6
7.8
17.4
8.4
8.4
2.2
2.2
145.5
Diesel
Gal/Acre
1.10
0.94
1.10
0.47
0.92
0.51
0.10
0.20
0.20
0.44
0.97
0.47
0.47
0.13
0.13
8.15
M
I
00
-------
Squash Production Budget — SJV
Operation
Land Prep
Plastic Mulch
Fertilize
Planting
Tunnel Constrctn
Plastic Remvl
Piste Mulch rmvl
Equipment
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Aq Tractor
Horse-
Power
75
75
75
75
75
75
75
Hours/
Acre
3
1.5
5
1.5
3
1
2
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
0.7
Squash Total
Relative Monthly Activity - Bhp-hr/Acre by Month API
Dec
0.0
Jan
0.0
Feb
1
52.5
Mar
1
1
1
166.3
Apr
1
1
1
1
245.0
May
1
1
1
1
192.5
Jun
1
1
1
1
131.3
Jul
1
1
52.5
Auq
1
1
52.5
Dears at Bottom
Sep
0.0
Oct
0.0
Nov
0.0
Bhp-hry
Acre
157.5
78.8
262.5
78.8
157.5
52.5
105.0
892.5
Diesel
Gal/Acre
8.82
4.41
14.70
4.41
8.82
2.94
5.88
49.98
Sudarbeet Production Budget — SJV
Operation
Stubble Disc
Subsoil
Trl plane
List
WntrWeed
lilliston
Herbadde
Incorporate
Plant
Reactivate
Replant
Cultivate
Flat Roll
Thin Elect.
Fertilize
Layby Herb
Rolling Curtvtr
V Ditch
Equipment
Crawler
Crawler
Crawler
Crawler
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Crawler
Horse -
Power
190
190
120
120
90
90
90
135
90
90
90
90
90
90
90
90
90
120
Hours/
Acre
0.22
0.4
0.34
0.25
0.2
0.2
0.08
0.27
0.33
0.03
0.03
0.33
0.12
0.33
0.33
0.25
0.2
0.15
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
Sugarbeet Total
Relative Monthly Activity - Bhp-hr/Acre by Month Appears at Bottom
Dec
1
1
1
1
44.0
Jan
1
1
1
1
1
1
1
1
65.3
Feb
1
1
1
1
1
1
1
1
1
70.5
Mar
1
31.8
Apr
1
1
1
1
1
1
1
1
1
1
33.0
May
1
1
1
1
33.0
Jun
1
1
1
1
1
1
1
23.0
Jul
1
1
1
1
1
1
22.5
Aug
Sep
0.0 1 0.0
Oct
0.0
Nov
0.0
Bhp-hr;
Acre
29.3
53.2
28.6
21.0
12.6
12.6
5.0
25.5
20.8
1.9
1.9
20.8
7.6
20.8
20.8
15.8
12.6
12.6
323.2
Diesel
Gal/Acre
1.64
2.98
1.60
1.18
0.71
0.71
0.28
1.43
1.16
0.11
0.11
1.16
0.42
1.16
1.16
0.88
0.71
0.71
18.10
-------
Sunflower Seed Production Budget -« SJV
Operation
Stubble Diac
Disc
Chisel
Land Plane
List Beds
Weed Control
Cult. & Pert
Plant & Herb.
Cultivate
Cult. & Apply N
Harvest
Stubble Disc
Equipment
Crawler
Crawler
Ag Tractor 4wc
Ag Tractor 4wc
Ag Tractor 2wc
Ag Tractor 2wc
Ag Tractor 2wc
Ag Tractor 2wc
Ag Tractor 2wc
Ag Tractor 2wc
Combine
Crawler
Horse-
Power
100
100
200
200
120
120
120
120
120
120
120
100
Hours/
Acre
0.25
0.2
0.16
0.3
0.2
0.2
0.2
0.33
0.01
0.2
0.33
0.2
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
Relative Monthly Activity - Bhp-hr/Acre by Month Appears at Bottom
Dec
Jan
Feb
Mar
1
1
Apr
1
Sunflower Seed Total II 0.01 0.01 0.01 33.61 27.7
May
1
1
Jun
Jul
Aug
1
Sep
1
Oct
1
1
1
1
1
Nov
1
Bhp-hr/
Acre
17.5
14.0
22.4
42.0
16.8
16.8
16.8
27.7
0.8
16.8
27.7
14.0
Diesel
Gal/Acre
0.98
0.78
1.25
2.35
0.94
0.94
0.94
1.55
0.05
0.94
1.55
0.78
17.61 0.01 0.01 13.91 13.91109.91 16.811 233.41 13.07
Tomato Production Budaet - SJV
' Operation
Row
Subsoil
Diac Roller
Triplane
w/o Nemacide
Nemacide
Flat Roll
Fall Appld Herb
Herbacide
Cultivate
Herbacide
Planting
Planting
Remove Soil Cap
Break Crust
Replant
Cultivate
Thin
Pert
V Ditch
Layby Herbacide
Cultivate
Cult. Hi-Crop
Vine Trainer
Harvest 80% AC
Harvest 20% AC
Dollies 80% AC
Dollies 20% AC
Avenue Opener 7%
Equipment
AQ Tractor
Tracklayer
Tracklayer
Tracklayer
AQ Tractor
AQ Tractor
AQ Tractor
AQ Tractor
AQ Tractor
AQ Tractor
AQ Tractor
LghtWTTrackl
AQ Tractor
AQ Tractor
Tracklayer
LghtWTTrackl
AQ Tractor
AQ Tractor
AQ Tractor
Tracklayer
AQ Tractor
AQ Tractor
AQ Tractor
AQ Tractor
AQ Tractor
AQ Tractor
AQ Tractor
AQ Tractor
AG Tractor
Horse—
Power
165
190
120
120
90
90
90
90
90
90
90
65
90
90
120
65
90
90
90
120
90
90
90
135
90
90
90
90
90
Hours/
Acre
0.4
0.4
0.26
0.34
0.23
0.04
0.12
0.03
0.15
0.13
0.24
0.07
0.26
0.07
0.16
0.03
0.4
0.11
0.33
0.15
0.4
0.29
0.33
0.2
1
0.32
2
0.32
0 1
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
07
Relative Monthly Activity - Bhp-hr/Acre by Month Appears at Bottom
Dec
Jan
Feb
1
1
1
1
1
1
1
,
Tomato Total 11 0.0| 0.0 1 15.5
Mar
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
45.3
Apr
May
1
Jun
1
1
Jul
Aug
1
1
1
1
1
1
1
1
1
Sep
1
1
1
1
1
1
1
Oct
Nov
Bhp-hr/
Acre
46.2
53.2
21.8
28.6
14.5
2.5
7.6
1.9
9.5
8.2
15.1
3.2
16.4
4.4
13.4
1.4
25.2
6.9
20.8
12.6
25.2
18.3
20.8
18.9
63.0
20.2
126.0
20.2
6.3
Diesel
Qal/Acre
2.59
2.98
1.22
1.60
0.81
0.14
0.42
0.11
0.53
0.46
0.85
0.18
0.92
0.25
0.75
0.08
1.41
0.39
1.16
0.71
1.41
1.02
1.16
1.06
3.53
1.13
7.06
1.13
0.35
45.31 45.31 88.61 88.6 1 1 05.3 1 105.3 1 46. 4 1 46.4II 632.11 35.40
-------
Vec
Operation
Subsoil
Disc & Roll
Chisel
Level
Shape Beds&Roll
Plant
Cultivate
Equipment
Crawler
Crawler
Crawler
Crawler
Ag Tractor
Ag Tractor
Ag Tractor
Horse-
Power
80
80
80
80
60
60
60
Hours/
Acre
1.24
0.69
0.66
0.52
0.25
0.42
1.5
letables ( Seasbna
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
0.7
Relative
Dec
Vegetables (Seasonal) Total II 0.0
Jan
1
1
1
1
1
1
1
44.2
It Production Budget - SJV
Monthly Activity - Bhp-hr/Acre by Month Appears at Bottom
Feb
1
1
1
1
1
1
1
Mar
Apr
1
1
1
1
1
1
1
May
1
1
1
1
1
1
1
44.2 1 44.2 1 44.2 1 44.2
Jun
1
1
1
1
1
1
1
Jul
Aug
Sep
44.2 1 0.0 1 0.0 1 0.0
Oct
Nov
Bhp-hr/
Acre
69.4
38.6
37.0
29.1
10.5
17.6
63.0
0.0 1 0.0 II 265.3
Diesel
Qal/Acre
3.89
2.16
2.07
1.63
0.59
0.99
3.53
14.86
VeaetablesfYear-Lonq) Production Budqet - SJV .
Operation
Subsol
Disc & Roll
Chisel
Level
Shape Beds&Roll
Plant
Cultivate
Equipment
Crawler
Crawler
Crawler
Crawler
Ag Tractor
Ag Tractor
Ag Tractor
Horse-
power
80
80
80
80
60
60
60
Hours/
Acre
1.24
0.69
0.66
0.52
025
0.42
1.5
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
0.7
Relative Monthly Activity - Bhp-hr/Acra by Month Appears at Bottom || Bhp-hr/
Dec
1
—
Vegetables (Year-Long) Total II 22.
Jan
1
1
1
1
1
1
1
22.1
Feb
22.
Mar
1
1
1
1
1
1
1
22.1
Apr
1
1
1
1
1
1
1
22.1
May
1
22.1
Jun
1
22.1
Jul
1
1
1
1
1
1
1
Aug
22.1 1 22.
Sep
1
1
1
1
1
1
1
22.1
Oct
1
22.1
Nov II Acre
! f 69.4
'. I 38.6
1 1 37.0
1 1 29.1
1 1 10.5
1| 17.6
1 1I 63.0
Diesel
Qal/Acre
3.89
2.16
2.07
1.63
0.59
0.99
3.53
22.111 265.31 14.86
H
Wf?:-':'AWalhut Production- Budinete*:SJV
Operation
Brush Dspsl
Fertilize 200*
Tillage
MowxS
Weed Spray
Equipment
AQ Tractor
AO Tractor
AQ Tractor
AQ Tractor
AQ Tractor
Horse-
Power
55
55
55
55
55
Hours/
Acre
1.2
0.2
1.8
4
0.6
Load
Factor
0.7
0.7
0.7
0.7
0.7
Relative Monthly Activity - Bhp-hr/Acre by Month Appears at Bottom
Dec
Walnut Total II 0.0
Jan
1
46.2
Feb
1
Mar
1
30.8 1 3.9
Apr
1
1
1
53.9
May
1
1
42.4
Jun
1
1
42.4
Jul
1
1
15.4
Aug
1
1
42.4
Sep
1
11.6
Oct
1
11.6
Nov
0.0
Bhp-hr/
Acre
46.2
7.7
69.3
154.6
23.1
300.3
Diesel
Qal/Acre
2.59
0.43
3.88
8.62
1.29
16.82
Wheat Production Budqet - SJV
Operation
Disc
Chisel
Disk Offset
Triplane
Fertilize
Spiketoolh
Drill & Pert
Border
Ditch
Harvest
Equipment
Tractor Crawler
Ag Tractor
Ag Tractor
Tractor Crawler
Ag Tractor
Ag Tractor
Ag Tractor
Ag Tractor
Tractor Crawtei
SP Combine
Horee-
Power
125
200
200
125
135
135
135
100
125
65
Hours/
Acre
0.25
0.13
0.13
0.2
0.1
0.1
0.2
0.1
0.1
0.33
Load
Factor
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
Relative Monthly Activity - Bhp-hr/Acre by Month Appears at Bottom
Dec
Jan
Feb
Mar
1
Apr
Wheat Total 'II 0.0 1 0.0 1 0.0 1 8.8 1 0.0
May
0.0
Jun
1
Jul
15.01 0.0
Aug
Sep
Oct
1
1
1
1
Nov
1
1
1
1
Bhp-hr/
Acre
21.9
18.2
18.2
17.5
9.5
9.5
18.9
7.0
8.8
15.0
O.OJ 0.0 1 75.8L44.8TI 144.3
Diesel
Qal/Acre
1.23
1.02
1.02
0.98
0.53
0.53
1.06
0.39
0.49
0.84
8.08
------- |